Nowism — A Theme for the New Era?

DRAFT 1 — A Work in Progress

Introduction

Here’s an idea I’ve been thinking about: it’s a concept for a new philosophy, or perhaps just a name for a grassroots philosophy that seems to be emerging on its own. It’s called “Nowism.” The view that now is what’s most important, because now is where one’s life actually happens.

Certainly we have all heard terms like Ram Das’ famous, “Be here now” and we may be familiar with the writings of Eckhart Tolle and his “Power of Now” and others. In addition there was the “Me generation” and the more recent idea of “living in the now.” On the Web there is also now a growing shift towards real-time, what I call the Stream.

These are all examples of the emergence of this trend. But I think these are just the beginnings of this movement — a movement towards a subtle but major shift in the orientation of our civilization’s collective attention. This is a shift towards the now, in every dimension of our lives. Our personal lives, professional lives, in business, in government, in technology, and even in religion and spirituality.

I have a hypothesis that this philosophy — this worldview that the “now” is more important than the past or the future, may come to characterize this new century we are embarking on. If this is true, then it will have profound effects on the direction we go in as a civilization.

It does appear that the world is becoming increasingly now-oriented; more real-time, high-resolution, high-bandwidth. The present moment, the now, is getting increasingly flooded with fast-moving and information-rich streams of content and communication.

As this happens we are increasingly focusing our energy on keeping up with, managing, and making sense of, the now. The now is also effectively getting shorter — in that more happens in less time, making the basic clockrate of the now effectively faster. I’ve written about this elsewhere.

Given that the shift to a civilization that is obsessively focused on the now is occurring, it is not unreasonable to wonder whether this will gradually penetrate into the underlying metaphors and worldviews of coming generations, and how it might manifest as differences from our present-day mindsets.

How might people who live more in the now differ from those who paid more attention to the past, or the future? For example, I would assert that the world in and before the 19th century was focused more on the past than the now or the future. The 20th century was characterized by a shift to focus more on the future than the past or the now. The 21st century will be characterized by a shift in focus onto the now, and away from the past and the future.

How might people who live more in the now think about themselves and the world in coming decades. What are the implications for consumers, marketers, strategists, policymakers, educators?

With this in mind, I’ve attempted to write up what I believe might be the start of a summary of what this emerging worldview of “Nowism” might be like.

It has implications on several levels: social, economic, political, and spiritual.

Nowism Defined

Like Buddhism, Taoism, and other “isms,” Nowism is a view on the nature of reality, with implications for how to live one’s life and how to interpret and relate to the world and other people.

Simply put: Nowism is the philosophy that the span of experience called “now” is fundamental. In other words there is nothing other than now. Life happens in the now. The now is what matters most.

Nowism does not claim to be mutually exclusive with any other religion. It merely claims that all other religions are contained within it’s scope — they, like everything else, take place exclusively within the now, not outside it. In that respect the now, in its actual nature, is fundamentally greater than any other conceivable philosophical or religious system, including even Nowism itself.

Risks of Unawakened Nowism

Nowism is in some ways potentially short-sighted in that there is less emphasis on planning for the future and correspondingly more emphasis on living the present as fully as possible. Instead of making decisions with their effects in the future foremost in mind, the focus is on making the optimal immediate decisions in the context of the present. However, what is optimal in the present may not be optimalover longer spans of time and space.

What may be optimal in the now of a particular individual may not at all be optimal in the nows of other individuals. Nowism can therefore lead to extremely selfish behavior that actually harms others, or it can lead to extremely generous behavior on a scale that far transcends the individual, if one strives to widen their own experience of the now sufficiently.

Very few individuals will ever do the necessary work to develop themselves to the point where their actual experience of now is dramatically wider than average. It is however possible to do this, while quite rare. Such individuals are capable of living exclusively in the now while still always acting with the long-term benefit of both themselves all other beings in mind.

The vast majority of people however will tend towards a more limited and destructive form of Nowism, in which they get lost in deeper forms of consumerism, content and media immersion, hedonism, and conceptualization. Rather than being freed by the now, they will be increasingly imprisoned by it.

This lower form of Nowism — what might be called unawakened Nowism — is characterized by an intense focus on immediate self-gratification, without concern or a sense of responsibility for the consequences of one’s actions on oneself or others in the future. This kind of living in the moment, while potentially extremely fun, tends to end badly for most people. Fortunately most people outgrow this tendency towards extremely unawakened Nowism after graduating college and/or entering the workforce.

Abandoning extremely unawakened Nowist lifestyles doesn’t necessarily result in one realizing any form of awakened Nowism. One might simply remain in a kind of dormant state, sleepwalking through life, not really living fully in the present, not fully experiencing the present in all its potential. To reach this level of higher Nowism, or advanced Nowism, one must either have a direct spontaneous experience of awakening to the deeper qualities of the now, or one must study, practice and work with teachers and friends who can help them to reach such a direct experience of the now.

Benefits of Awakened Nowism: Spiritual and Metaphysical Implications of Nowist Philosophy

In the 21st Century, I believe Nowism may actually become an emerging movement. With it there will come a new conception of the self, and of the divine. The self will be realized to be simultaneously more empty and much vaster than was previously thought. The divine will be understood more directly and with less conceptualization. More people will have spiritual realization this way, because in this more direct approach there is less conceptual material to get caught up in. The experience of now is simply left as it is — as direct and unmediated, unfettered, and unadulterated as possible.

This is a new kind of spirituality perhaps. One in which there is less personification of the divine, and less use of the concept of a personified deity as an excuse or justification for various worldy actions (like wars and laws, for example).

Concepts about the nature of divinity have been used by humans for millenia as tools for various good and bad purposes. But in Nowism, these concepts are completely abandoned. This also means abandoning the notion that there is or is not a divine nature at the core of reality, and each one of us. Nowists do not get caught up in such unresolvable debates. However, at the same time, Nowists do strive for a direct realization of the now — one that is as unmediated and nonconceptual as possible — and that direct realization is considered to BE thedivine nature itself.

Nowism does not assert that nothing exists or that nothing matters. Such views are nihilism not Nowism. Nowism does not assert that what happens is caused or uncaused — such views are those of the materialists and the idealists, not Nowism. Instead Nowism asserts the principles of dependent origination, in which cause and-effect appears to take place, even though it is an illusory process and does not truly exist. On the basis of a relative-level cause-effect process, an ethical system can be founded which seeks to optimize happiness and minimize unhappiness for the greatest number of beings, by adjusting ones actions so as to create causes that lead to increasingly happy effects for oneself and others, increasingly often. Thus the view of Nowism does not lead to hedonism — in fact, anyone who makes a careful study of the now will reach the conclusion that cause and effect operates unfailingly and therefore is a key tool for optimizing happiness in the now.

Advanced Nowists don’t ignore cause-and-effect, in fact quite the contrary: they pay increasingly close attention to cuase-and-effect and their particular actions. The natural result is that they begin to live a life that is both happier and that leads to more happiness for all other beings — at least this is the goal and example of the best-case. The fact that cause-and-effect is in operation, even though it is notfundamentally real, is the root of Nowist ethics. It is precisely the same as the Buddhist conception of the identity of emptiness and dependent-origination.

Numerous principles follow from the core beliefs of Nowism. They include practical guidance for living ones life with a minimum of unnecessary suffering (of oneself as well as others), further principles concerning the nature of reality and the mind, and advanced techniques and principles for reaching greater realizations of the now.

As to the nature of what is taking place right now: from the Nowist perspective, it is beyond concepts, for all concepts, like everything else, appear and disappear like visions or mirages, without ever truly-existing. This corresponds precisely to the Buddhist conception of emptiness.

The scope of the now is unlimited, however for the uninitiated the now is usually considered to be limited to the personal present experience of the individual. Nowist adepts, on the other hand, assert that the scope of the now may be modified (narrowed or widened) through various exercises including meditation, prayer, intense physical activity, art, dance and ritual, drugs, chanting, fasting, etc.

Narrowing the scope of the now is akin to reducing the resolution of present experience. Widening the scope is akin to increasing the resolution. A narrower now is a smaller experience, with less information content. A wider now is a larger experience, with more information content.

Within the context of realizing that now is all there is, one explores carefully and discovers that now does not contain anything findable (such as a self, other, or any entity or fundamental basis for any objective or subjective phenomenon, let alone any nature that could be called “nowness” or the now itself).

In short the now is totally devoid of anything findable whatsoever, although sensory phenomena do continue to appear to arise within it unceasingly. Such phenomena, and the sensory apparatus, body, brain, mind and any conception of self that arises in reaction to them, are all merely illusion-like appearances with no objectively-findable ultimate, fundamental, or independent existence.

This state is not unlike the analogy of a dream in which oneself and all the other places and characters are all equally illusory, or of a completely immersive virtual reality experience that is so convincing one forgets it isn’t real.

Nowism does not assert a divine being or deity, although it also is not mutually exclusive with the existence of one or more such beings. However all such beings are considered to be no more real than any other illusory appearance, such as the appearances of sentient beings, planets, stars, fundamental particles, etc. Any phenomena — whether natural or supernatural — are equally empty of any independent true existince. They are all illusory in nature.

However, Nowists do assert that the nature of the now itself, while completely empty, is in fact the nature of consciousness and what we call life. It cannot be computed, simulated or modeled in an information system, program, machine, or representation of any kind. Any such attempts to represent the now are merely phenomena appearing within the now, not the now itself. The now is fundamentally transcendental in this respect.

The now is not limited to any particular region in space or time, let alone to any individual being’s mind. There is no way to assert there is a single now, or many nows, for no nows are actually findable.

The now is the gap between the past and the future, however, when searched for it cannot really be found, nor can the past or future be found. The past is gone, the future hasn’t happened yet, and the now is infinite, constantly changing, and ungraspable. The entire space-time continuum is in fact within a total all-embracing now, the cosmically extended now that is beyond the limited personalized scope of now we presently think we have. Through practice this can be gradually glimpsed and experienced to greater degrees.

As the now is explored to greater depths, one begins to find that it has astonishing implications. Simultaneously much of the Zen literature — especially the koans — starts to make sense at last.

While Nowism could be said to be a branch of Buddhism, I would actually say it might be the other way arond. Nowism is really the most fundamental, pure, philosophy — stripped of all cultural baggage and historical concepts, and retaining only what is absolutely essential.

Video: My Talk on the Evolution of the Global Brain at the Singularity Summit

If you are interested in collective intelligence, consciousness, the global brain and the evolution of artificial intelligence and superhuman intelligence, you may want to see my talk at the 2008 Singularity Summit. The videos from the Summit have just come online.

(Many thanks to Hrafn Thorisson who worked with me as my research assistant for this talk).

Peace in the Middle East: Could Alternative Energy Be the Solution?

I have been thinking about the situation in the Middle East and also the rise of oil prices, peak oil, and the problem of a world economy based on energy scarcity rather than abundance. There is, I believe, a way to solve the problems in the Middle East, and the energy problems facing the world, at the same time. But it requires thinking “outside the box.”

Middle Eastern nations must take the lead in freeing the world from dependence on their oil. This is not only their best strategy for the future of their nations and their people, but also it is what will ultimately be best for the region and the whole world.

It is inevitable that someone is going to invent a new technology that frees the world from dependence on fossil fuels. When that happens all oil empires will suddenly collapse. Far-sighted, visionary leaders in oil-producing nations must ensure that their nations are in position to lead the coming non-fossil-fuel energy revolution. This is the wisdom of “cannibalize yourself before someone else does.”

Middle Eastern nations should invest more heavily than any other nations in inventing and supplying new alternative energy technologies. For example: hydrogen, solar, biofuels, zero point energy, magnetic power, and the many new emerging alternatives to fossil fuels. This is a huge opportunity for the Middle East not only for economic reasons, but also because it may just be the key to bringing about long-term sustainable peace in the region.

There is a finite supply of oil in the Middle East — the game will and must eventually end. Are Middle Eastern nations thinking far enough ahead about this or not? There is a tremendous opportunity for them if they can take the initiative on this front and there is an equally tremendous risk if they do not. If they do not have a major stake in whatever comes after fossil fuels, they will be left with nothing when whatever is next inevitably happens (which might be very soon).

Any Middle Eastern leader who is not thinking very seriously about this issue right now is selling their people short. I sincerely advise them to make this a major focus going forward. Not only will this help them to improve quality of life for their people now and in the future, but it is the best way to help bring about world peace. The Middle East has the potential to lead a huge and lucrative global energy Renaissance. All it takes is vision and courage to push the frontier and to think outside of the box.

Continue reading

Minding The Planet — The Meaning and Future of the Semantic Web

NOTES

Prelude

Many years ago, in the late 1980s, while I was still a college student, I visited my late grandfather, Peter F. Drucker, at his home in Claremont, California. He lived near the campus of Claremont College where he was a professor emeritus. On that particular day, I handed him a manuscript of a book I was trying to write, entitled, “Minding the Planet” about how the Internet would enable the evolution of higher forms of collective intelligence.

My grandfather read my manuscript and later that afternoon we sat together on the outside back porch and he said to me, “One thing is certain: Someday, you will write this book.” We both knew that the manuscript I had handed him was not that book, a fact that was later verified when I tried to get it published. I gave up for a while and focused on college, where I was studying philosophy with a focus on artificial intelligence. And soon I started working in the fields of artificial intelligence and supercomputing at companies like Kurzweil, Thinking Machines, and Individual.

A few years later, I co-founded one of the early Web companies, EarthWeb, where among other things we built many of the first large commercial Websites and later helped to pioneer Java by creating several large knowledge-sharing communities for software developers. Along the way I continued to think about collective intelligence. EarthWeb and the first wave of the Web came and went. But this interest and vision continued to grow. In 2000 I started researching the necessary technologies to begin building a more intelligent Web. And eventually that led me to start my present company, Radar Networks, where we are now focused on enabling the next-generation of collective intelligence on the Web, using the new technologies of the Semantic Web.

But ever since that day on the porch with my grandfather, I remembered what he said: “Someday, you will write this book.” I’ve tried many times since then to write it. But it never came out the way I had hoped. So I tried again. Eventually I let go of the book form and created this weblog instead. And as many of my readers know, I’ve continued to write here about my observations and evolving understanding of this idea over the years. This article is my latest installment, and I think it’s the first one that meets my own standards for what I really wanted to communicate. And so I dedicate this article to my grandfather, who inspired me to keep writing this, and who gave me his prediction that I would one day complete it.

This is an article about a new generation of technology that is sometimes called the Semantic Web, and which could also be called the Intelligent Web, or the global mind. But what is the Semantic Web, and why does it matter, and how does it enable collective intelligence? And where is this all headed? And what is the long-term far future going to be like? Is the global mind just science-fiction? Will a world that has a global mind be good place to live in, or will it be some kind of technological nightmare?

I’ve often joked that it is ironic that a term that contains theword “semantic” has such an ambiguous meaning for most people. Mostpeople just have no idea what this means, they have no context for it,it is not connected to their experience and knowledge. This is aproblem that people who are deeply immersed in the trenches of theSemantic Web have not been able to solve adequately — they have notfound the words to communicate what they can clearly see, what they areworking on, and why it matters for everyone. In this article I havetried, and hopefully succeeded, in providing a detailed introductionand context for the Semantic Web fornon-technical people. But even technical people working in the fieldmay find something of interest here as I piece together the fragmentsinto a Big Picture and a vision for what might be called “Semantic Web2.0.”

I hope the reader will bear with me as Ibounce around across different scales of technology and time, and fromthe extremes of core technology to wild speculation in order to tellthis story. If you are looking for the cold hardscience of it all, this article will provide an understanding but willnot satisfy your need for seeing the actual code; there are otherplaceswhere you can find that level of detail and rigor. But if you want tounderstand what it all really means and what the opportunity and futurelookslike – this may be what you are looking for.

I should also note that all of this is my personal view of what I’vebeen working on,and what it really means to me. It is not necessarily the official viewof the mainstream academic Semantic Web community — although there arecertainly many places where we all agree. But I’m sure that somereaders will certainly disagree or raise objections to some of myassertions, and certainly to my many far-flung speculations about thefuture. I welcome those different perspectives; we’re all trying tomake sense of this and the more of us who do that together, the more wecan collectively start to really understand it. So please feel free towrite your own vision or response, and please let me know so I can linkto it!

So with this Prelude in mind, let’s get started…

The Semantic Web Vision

The Semantic Web is a set of technologies which are designed toenable aparticular vision for the future of the Web – a future in which allknowledge exists on the Web in a format that software applications canunderstand andreason about. By making knowledge more accessible to software, softwarewillessentially become able to understand knowledge, think about knowledge,and createnew knowledge. In other words, software will be able to be moreintelligent –not as intelligent as humans perhaps, but more intelligent than say,your wordprocessor is today.

The dream of making software more intelligent has been around almost as longas software itself. And although it is taking longer to materialize than past experts hadpredicted, progress towards this goal is being steadilymade. At the same time, the shape of this dream is changing. It is becomingmore realistic and pragmatic. The original dream of artificial intelligence wasthat we would all have personal robot assistants doing all the work we don’twant to do for us. That is not the dream of the Semantic Web. Instead, today’sSemantic Web is about facilitating what humans do – it is about helping humansdo things more intelligently. It’s not a vision in which humans do nothing andsoftware does everything.

The Semantic Web vision is not just about helping software become smarter –it is about providing new technologies that enable people, groups,organizations and communities to be smarter.

For example, by providing individuals with tools that learn about what theyknow, and what they want, search can be much more accurate and productive.

Using software that is able to understand and automatically organize largecollections of knowledge, groups, organizations and communities can reachhigher levels of collective intelligence and they can cope with volumes ofinformation that are just too great for individuals or even groups tocomprehend on their own.

Another example: more efficient marketplaces can be enabled by software thatlearns about products, services, vendors, transactions and market trends andunderstands how to connect them together in optimal ways.

In short, the Semantic Web aims to make software smarter, not just for itsown sake, but in order to help make people, and groups of people, smarter. Inthe original Semantic Web vision this fact was under-emphasized, leading to theimpression that Semantic Web was only about automating the world. In fact, it isreally about facilitating the world.

The Semantic Web Opportunity

The Semantic Web is one of the most significant things to happen since theWeb itself. But it will not appear overnight. It will take decades. It willgrow in a bottom-up, grassroots, emergent, community-driven manner just likethe Web itself. Many things have to converge for this trend to really take off.

The core open standards already exist, but the necessary development tools haveto mature, the ontologies that define human knowledge have to come into beingand mature, and most importantly we need a few real “killer apps” to prove thevalue and drive adoption of the Semantic Web paradigm. The first generation ofthe Web had its Mozilla, Netscape, Internet Explorer, and Apache – and it alsohad HTML, HTTP, a bunch of good development tools, and a few killer apps andservices such as Yahoo! and thousands of popular Web sites. The same things arenecessary for the Semantic Web to take off.

And this is where we are today – this all just about to start emerging.There are several companies racing to get this technology, or applications ofit, to market in various forms. Within a year or two you will see mass-consumerSemantic Web products and services hit the market, and within 5 years therewill be at least a few “killer apps” of the Semantic Web. Ten years from nowthe Semantic Web will have spread into many of the most popular sites andapplications on the Web. Within 20 years all content and applications on theInternet will be integrated with the Semantic Web. This is a sea-change. A bigevolutionary step for the Web.

The Semantic Web is an opportunity to redefine, or perhaps to better define,all the content and applications on the Web. That’s a big opportunity. Andwithin it there are many business opportunities and a lot of money to be made. It’snot unlike the opportunity of the first generation of the Web. There areplatform opportunities, content opportunities, commerce opportunities, searchopportunities, community and social networking opportunities, and collaborationopportunities in this space. There is room for a lot of players to compete andat this point the field is wide open.

The Semantic Web is a blue ocean waiting to be explored. And like anyunexplored ocean its also has its share of reefs, pirate islands, hidden treasure, shoals,whirlpools, sea monsters and typhoons. But there are new worlds out there to be discovered,and they exert an irresistible pull on the imagination. This is an excitingfrontier – and also one fraught with hard technical and social challenges thathave yet to be solved. For early ventures in the Semantic Web arena, it’s notgoing to be easy, but the intellectual and technological challenges, and the potentialfinancial rewards, glory, and benefit to society, are worth the effort andrisk. And this is what all great technological revolutions are made of.

Semantic Web 2.0

Some people who have heard the term “Semantic Web” thrown around too muchmay think it is a buzzword, and they are right. But it is not just a buzzword –it actually has some substance behind it. That substance hasn’t emerged yet,but it will. Early critiques of the Semantic Web were right – the early visiondid not leverage concepts such as folksonomy and user-contributed content atall. But that is largely because when the Semantic Web was originally conceivedof Web 2.0 hadn’t happened yet. The early experiments that came out of researchlabs were geeky, to put it lightly, and impractical, but they are already beingfollowed up by more pragmatic, user-friendly approaches.

Today’s Semantic Web – what we might call “Semantic Web 2.0” is a kinder,gentler, more social Semantic Web. It combines the best of the original visionwith what we have all learned about social software and community in the last10 years. Although much of this is still in the lab, it is already starting totrickle out. For example, recently Yahoo! started a pilot of the Semantic Webbehind their food vertical. Other organizations are experimenting with usingSemantic Web technology in parts of their applications, or to store or mapdata. But that’s just the beginning.

The Google Factor

Entrepreneurs, venture capitalists and technologists are increasinglystarting to see these opportunities. Who will be the “Google of the SemanticWeb?” – will it be Google itself? That’s doubtful. Like any entrenchedincumbent, Google is heavily tied to a particular technology and worldview. Andin Google’s case it is anything but semantic today. It would be easier for anupstart to take this position than for Google to port their entireinfrastructure and worldview to a Semantic Web way of thinking.

If it is goingto be Google it will most likely be by acquisition rather than by internal origination. Andthis makes more sense anyway – for Google is in a position where they can just wait and buy the winner,at almost any price, rather than competing in the playing field. One thing to note however is that Google has at least one product offering that shows some potential for becoming a key part of the Semantic Web. I am speaking of Google Base, Google’s open database which is meant to be a registry for structured data so that it can be found in Google search. But Google Base does not conform to or make use of the many open standards of the Semantic Web community. That may or may not be a good thing, depending on your perspective.

Of course the downside of Google waiting to join the mainstream Semantic Web community until after the winner is announced is very large – once there is a winner it may be too late for Google to beat them. Thewinner of the Semantic Web race could very well unseat Google. The strategistsat Google are probably not yet aware of this but as soon as they seesignificant traction around a major Semantic Web play it will become of interestto them.

In any case, I think there won’t be just one winner, there will be severalmajor Semantic Web companies in the future, focusing on different parts of theopportunity. And you can be sure that if Google gets into the game, every majorportal will need to get into this space at some point or risk becomingirrelevant. There will be demand and many acquisitions. In many ways the Semantic Web will not be controlled by just one company — it will be more like a fabric that connects them all together.

Context is King — The Nature ofKnowledge

It should be clear by now that the Semantic Web is all about enablingsoftware (and people) to work with knowledge more intelligently. But what isknowledge? Knowledge is not just information. It is meaningful information – itis information plus context. For example, if I simply say the word “sem” toyou, it is just raw information, it is not knowledge. It probably has nomeaning to you other than a particular set of letters that you recognize and asound you can pronounce, and the mere fact that this information was stated byme.

But if I tell you that “sem” it is the Tibetan word for “mind” then suddenly,“sem means mind in Tibetan” to you. If I further tell you that Tibetans have about as many words for “mind” as Eskimos have for “snow,” this is further meaning. Thisis context, in other words, knowledge, about the sound “sem.” The sound is raw information. When it is given context itbecomes a word, a word that has meaning, a word that is connected to conceptsin your mind – it becomes knowledge. By connecting raw information to context,knowledge is formed.

Once you have acquired a piece of knowledge such as “sem means mind in Tibetan,” you may then also form further knowledgeabout it. For example, you may form the memory, “Nova said that ‘sem means mind in Tibetan.’” You mightalso connect the word “sem” to networks of further concepts you have about Tibet and your understanding of what the word “mind” means.

The mind is the organ of meaning – mind is where meaning is stored,interpreted and created. Meaning is not “out there” in the world, it is purelysubjective, it is purely mental. Meaning is almost equivalent to mind in fact.For the two never occur separately. Each of our individual minds has some way of internally representing meaning — when we read or hear a word that we know, our minds connect that to a network of concepts about it and at that moment it means something to us.

Digging deeper, if you are really curious,or you happen to know Greek, you may also find that a similar sound occurs inthe Greek word, sēmantikós – which means “having meaning” and in turn is the root of the English word “semantic”which means “pertaining to or arising from meaning.” That’s an odd coincidence!“Sem” occurs in Tibetan word for mind, and the English and Greek words that allrelate to the concepts of “meaning” and “mind.” Even stranger is that not only do these words have a similar sound, they have a similar meaning.

With all this knowledge at yourdisposal, when you then see the term “Semantic Web” you may be able to inferthat it has something to do with adding “meaning” to the Web. However, if youwere a Tibetan, perhaps you might instead think the term had something to dowith adding “mind” to the Web. In either case you would be right!

Discovering New Connections

We’ve discovered a new connection — namely that there is an implicit connectionbetween “sem” in Greek, English and Tibetan: they all relate to meaning andmind. It’s not a direct, explicit connection – it’s not evident unless you digfor it. But it’s a useful tidbit of knowledge once it’s found. Unlike the direct migration of the sound “sem” from Greek to English,there may not have ever been a direct transfer of this sound from Greek toSanskrit to Tibetan. But in a strange and unexpected way, they are all connected. This connectionwasn’t necessarily explicitly stated by anyone before, but was uncovered byexploring our network of concepts and making inferences.

The sequence of thought about “sem”above is quite similar to kind of intellectual reasoning and discovery that theactual Semantic Web seeks to enable software to do automatically.  How is this kind of reasoning and discovery enabled? The Semantic Web providesa set of technologies for formally defining the context of information. Just asthe Web relies on a standard formal specification for “marking up” informationwith formatting codes that enable any applications that understand those codesto format the information in the same way, the Semantic Web relies on newstandards for “marking up” information with statements about its context – itsmeaning – that enable any applications to understand, and reason about, the meaning of those statements in the same way.

By applying semantic reasoning agents to large collections of semantically enhanced content, all sorts of new connections may be inferred, leading to new knowledge, unexpected discoveries and useful additional context around content. This kind of reasoning and discovery is already taking place in fields from drug discovery and medical research, to homeland security and intelligence. The Semantic Web is not the only way to do this — but it certainly will improve the process dramatically. And of course, with this improvement will come new questions about how to assess and explain how various inferences were made, and how to protect privacy as our inferencing capabilities begin to extend across ever more sources of public and private data. I don’t have the answers to these questions, but others are working on them and I have confidence that solutions will be arrived at over time.

Smart Data

By marking up information with metadata that formally codifies its context, we can make the data itself “smarter.” The data becomes self-describing. When you get a piece of data you also get the necessary metadata for understanding it. For example, if I sent you a document containing the word “sem” in it, I could add markup around that word indicating that it is the word for “mind” in the Tibetan language.

Similarly, a document containing mentions of “Radar Networks” could contain metadata indicating that “Radar Networks” is an Internet company, not a product or a type of radar technology. A document about a person could contain semantic markup indicating that they are residents of a certain city, experts on Italian cooking, and members of a certain profession. All of this could be encoded as metadata in a form that software could easily understand. The data carries more information about its own meaning.

The alternative to smart data would be for software to actually read and understand natural language as well as humans. But that’s really hard. To correctly interpret raw natural language, software would have to be developed that knew as much as a human being. But think about how much teaching and learning is required to raise a human being to the point where they can read at an adult level. It is likely that similar training would be necessary to build software that could do that. So far that goal has not been achieved, although some attempts have been made. While decent progress in natural language understanding has been made, most software that can do this is limited around particular vertical domains, and it’s brittle — it doesn’t do a good job of making sense of terms and forms of speech that it wasn’t trained to parse and make sense of.

Instead of trying to make software a million times smarter than it is today, it is much easier to just encode more metadata about what our information means. That turns out to be less work in the end. And there’s an added benefit to this approach — the meaning exists with the data and travels with it. It is independent of any one software program — all software can access it. And because the meaning of information is stored with the information itself, rather than in the software, the software doesn’t have to be enormous to be smart. It just has to know the basic language for interpreting the semantic metadata it finds on the information it works with.

Smart data enables relatively dumb software to be smarter with less work. That’s an immediate benefit. And in the long-term as software actually gets smarter, smart data will make it easier for it to start learning and exploring on its own. So it’s a win-win approach. Start with by adding semantic metadata to data, end up with smarter software.

Making Statements About the World

Metadata comes down to making statements about the world in a manner that machines, and perhaps even humans, can understand unambiguously. The same piece of metadata should be interpreted in the same way by different applications and readers.

There are many kinds of statementsthat can be made about information to provide it with context. For example, youcan state a definition such as “person” means “a human being or a legalentity.” You can state an assertion such as “Sue is a human being.” You canstate a rule such that “if x is a human being, then x is a person.”

From thesestatements it can then be inferred that “Sue is a person.” This inference is soobvious to you and me that it seems trivial, but most software today cannot dothis. It doesn’t know what a person is, let alone what a name is. But ifsoftware could do this, then it could for example, automatically organizedocuments by the people they are related to, or discover connections betweenpeople who were mentioned in a set of documents, or it could find documentsabout people who were related to particular topics, or it could give you a listof all the people mentioned in a set of documents, or all the documents relatedto a person.

Of course this is a very basicexample. But imagine if your software didn’t just know about people – it knewabout most of the common concepts that occur in your life. Your software wouldthen be able to help you work with your documents just about as intelligentlyas you are able to do by yourself, or perhaps even more intelligently, becauseyou are just one person and you have limited time and energy but your softwarecould work all the time, and in parallel, to help you.

Examples and Benefits

How could the existence of the Semantic Web and all the semantic metadata that defines it be really useful toeveryone in the near-term?

Well, for example, the problem of email spam would finally be cured:your software would be able to look at a message and know whether it wasmeaningful and/or relevant to you or not.

Similarly, you would never have to file anything by hand again. Your software could atuomate all filing and information organization tasks for you because it would understand your information and your interests. It would be able to figure out when to file something in a single folder, multiple folders, or new ones. It would organize everything — documents, photos, contacts, bookmarks, notes, products, music, video, data records — and it would do it even better and more consistently than you could on your own. Your software wouldn’t just organize stuff, it would turn it into knowledge by connecting it to more context. It could this not just for individuals, but for groups, organizations and entire communities.

Another example: search would bevastly better: you could search conversationally by typing in everyday naturallanguage and you would get precisely what you asked for, or even what youneeded but didn’t know how to ask for correctly, and nothing else. Your searchengine could even ask you questions to help you narrow what you want. You wouldfinally be able to converse with software in ordinary speech and it would understandyou.

The process of discovery would be easier too. You could have software agent that worked as your personal recommendation agent. It would constantly be looking in all the places you read or participate in for things that are relevant to your past, present and potential future interests and needs. It could then alert you in a contextually sensitive way, knowing how to reach you and how urgently to mark things. As you gave it feedback it could learn and do a better job over time.

Going even further with this,semantically-aware software – software that is aware of context, software thatunderstands knowledge – isn’t just for helping you with your information, itcan also help to enrich and facilitate, and even partially automate, yourcommunication and commerce (when you want it to). So for example, your software could help you with your email. It would be able to recommend responses to messages for you, or automate the process. It would be able to enrich your messaging anddiscussions by automatically cross-linking what you are speaking about withrelated messages, discussions, documents, Web sites, subject categories,people, organizations, places, events, etc.

Shopping and marketplaces wouldalso become better – you could search precisely for any kind of product, withany specific attributes, and find it anywhere on the Web, in any store. You could post classified ads and automatically get relevant matches according to your priorities, from all over the Web, or only from specific places and parties that match your criteria for who you trust. You could also easily invent a new custom datastructure for posting classified ads for a new kind of product or service and publishit to the Web in a format that other Web services and applications couldimmediately mine and index without having to necessarily integrate with yoursoftware or data schema directly.

You could publish an entiredatabase to the Web and other applications and services could immediately startto integrate your data with their data, without having to migrate your schemaor their own. You could merge data from different data sources together to create new data sources without having to ever touch or look at an actual database schema.

Bumps on the Road

The above examples illustrate thepotential of the Semantic Web today, but the reality on the ground is that the technology isstill in the early phases of evolution. Even for experienced software engineersand Web developers, it is difficult to apply in practice. The main obstaclesare twofold:

(1) The Tools Problem:

There are very few commercial-gradetools for doing anything with the Semantic Web today – Most of the tools forbuilding semantically-aware applications, or for adding semantics toinformation are still in the research phase and were designed for expertcomputer scientists who specialize in knowledge representation, artificialintelligence, and machine learning.

These tools require a largelearning curve to work with and they don’t generally support large-scaleapplications – they were designed mainly to test theories and frameworks, notto actually apply them. But if the Semantic Web is ever going to becomemainstream, it has to be made easier to apply – it has to be made moreproductive and accessible for ordinary software and content developers.

Fortunately, the tools problem isalready on the verge of being solved. Companies such as my own venture, RadarNetworks, are developing the next generation of tools for building Semantic Webapplications and Semantic Web sites. These tools will hide most of thecomplexity, enabling ordinary mortals to build applications and content thatleverage the power of semantics without needing PhD’s in knowledge representation.

(2) The Ontology Problem:

The Semantic Web providesframeworks for defining systems of formally defined concepts called “ontologies,”that can then be used to connect information to context in an unambiguous way. Withoutontologies, there really can be no semantics. The ontologies ARE the semantics,they define the meanings that are so essential for connecting information tocontext.

But there are still few widely used or standardized ontologies. Andgetting people to agree on common ontologies is not generally easy. Everyonehas their own way of describing things, their own worldview, and let’s face itnobody wants to use somebody else’s worldview instead of their own.Furthermore, the world is very complex and to adequately describe all the knowledgethat comprises what is thought of as “common sense” would require a very largeontology (and in fact, such an ontology exists – it’s called Cyc and it is solarge and complex that only experts can really use it today).

Even to describe the knowledge ofjust a single vertical domain, such as medicine, is extremely challenging. Tomake matters worse, the tools for authoring ontologies are still very hard touse – one has to understand the OWL language and difficult, buggy ontologyauthoring tools in order to use them. Domain experts who are non-technical andnot trained in formal reasoning or knowledge representation may find theprocess of designing ontologies frustrating using current tools. What is needed are commercial quality tools for buildingontologies that hide the underlying complexity so that people can just pourtheir knowledge into them as easily as they speak. That’s still a ways off, butnot far off. Perhaps ten years at the most.

Of course the difficulty ofdefining ontologies would be irrelevant if the necessary ontologies alreadyexisted. Perhaps experts could define them and then everyone else could justuse them? There are numerous ontologies already in existence, both on thegeneral level as well as about specific verticals. However in my own opinion,having looked at many of them, I still haven’t found one that has the rightbalance of coverage of the necessary concepts most applications need, andaccessibility and ease-of-use by non-experts. That kind of balance is arequirement for any ontology to really go mainstream.

Furthermore, regarding the presentcrop of ontologies, what is still lacking is standardization. Ontologists havenot agreed on which ontologies to use. As a result it’s anybody’s guess whichontology to use when writing a semantic application and thus there is a highdegree of ontology diversity today. Diversity is good, but too much diversityis chaos.

Applications that use differentontologies about the same things don’t automatically interoperate unless theirontologies have been integrated. This is similar to the problem of databaseintegration in the enterprise. In order to interoperate, different applicationsthat use different data schemas for records about the same things, have to bemapped to each other somehow – either at the application-level or the data-level.This mapping can be direct or through some form of middleware.

Ontologies canbe used as a form of semantic middleware, enabling applications to be mapped atthe data-level instead of the applications-level. Ontologies can also be usedto map applications at the applications level, by making ontologies of Webservices and capabilities, by the way. This is an area in which a lot ofresearch is presently taking place.

The OWL language can expressmappings between concepts in different ontologies. But if there are manyontologies, and many of them partially overlap, it is a non-trivial task toactually make the mappings between their concepts.

Even though concept A inontology one and concept B in ontology two may have the same names, and evensome of the same properties, in the context of the rest of the concepts intheir respective ontologies they may imply very different meanings. So simplymapping them as equivalent on the basis of their names is not adequate, theirconnections to all the other concepts in their respective ontologies have to beconsidered as well. It quickly becomes complex. There are some potential waysto automate the construction of mappings between ontologies however – but theyare still experimental. Today, integrating ontologies requires the help ofexpert ontologists, and to be honest, I’m not sure even the experts have itfigured out. It’s more of an art than a science at this point.

Darwinian Selection of Ontologies

All that is needed for mainstream adoption to begin is for a largebody of mainstream content to become semantically tagged andaccessible. This will cause whatever ontology is behind that content to become popular.

When developers see that there is significant content andtraction around aparticular ontology, they will use that ontology for their ownapplicationsabout similar concepts, or at least they will do the work of mappingtheir ownontology to it, and in this way the world will converge in a Darwinianfashionaround a few main ontologies over time.

These main ontologies will then beworth thetime and effort necessary to integrate them on a semantic level,resulting in acohesive Semantic Web. We may in fact see Darwinian natural selection take place not just at the ontology level, but at the level of pieces of ontologies.

A certain ontology may do a good job of defining what a person is, while another may do a good job of defining what a company is. These definitions may be used for a lot of content, and gradually they will become common parts of an emergent meta-ontology comprised of the most-popular pieces from thousands of ontologies. This could be great or it could be a total mess. Nobody knows yet. It’s a subject for further research.

Making Sense of Ontologies

Since ontologies are so important,it is helpful to actually understand what an ontology really is, and what itlooks like. An ontology is a system of formally defined related concepts. Forexample, a simple ontology is this set of statements such as this:

A human is a living thing.

A person is a human.

A person may have a first name.

A person may have a last name.

A person must have one and only onedate of birth.

A person must have a gender.

A person may be socially related toanother person.

A friendship is a kind of socialrelationship.

A romantic relationship is a kindof friendship.

A marriage is a kind of romanticrelationship.

A person may be in a marriage withonly one other person at a time.

A person may be employed by anemployer.

An employer may be a person or anorganization.

An organization is a group ofpeople.

An organization may have a productor a service.

A company is a type organization.

We’ve just built a simple ontologyabout a few concepts: humans, living things, persons, names, socialrelationships, marriages, employment, employers, organizations, groups,products and services. Within this system of concepts there is particular logic,some constraints, and some structure. It may or may not correspond to yourworldview, but it is a worldview that is unambiguously defined, can becommunicated, and is internally logically consistent, and that is what isimportant.

The Semantic Web approach providesan open-standard language, OWL, for defining ontologies. OWL also provides fora way to define instances of ontologies. Instances are assertions within theworldview that a given ontology provides. In other words OWL provides a meansto make statements that connect information to the ontology so that softwarecan understand its meaning unambiguously. For example, below is a set ofstatements based on the above ontology:

There exists a person x.

Person x has a first name “Sue”

Person x  has a last name “Smith”

Person x has a full name “Sue Smith”

Sue Smith was born on June 1, 2005

Sue Smith has a gender: female

Sue Smith has a friend: Jane, who isanother person.

Sue Smith is married to: Bob, anotherperson.

Sue Smith is employed by Acme, Inc, a company

Acme Inc. has a product, Widget2.0.

The set of statements above, plusthe ontology they are connected to, collectively comprise a knowledge basethat, if represented formally in the OWL markup language, could be understoodby any application that speaks OWL in the precise manner that it was intendedto be understood.

Making Metadata

The OWL language provides a way tomarkup any information such as a data record, an email message or a Web pagewith metadata in the form of statements that link particular words or phrasesto concepts in the ontology. When software applications that understand OWLencounter the information they can then reference the ontology and figure outexactly what the information means – or at least what the ontology says that itmeans.

But something has to add thesesemantic metadata statements to the information – and if it doesn’t add them or adds thewrong ones, then software applications that look at the information will getthe wrong idea. And this is another challenge – how will all this metadata getcreated and added into content? People certainly aren’t going to add it all byhand!

Fortunately there are many ways tomake this easier. The best approach is to automate it using special softwarethat goes through information, analyzes the meaning and adds semantic metadataautomatically. This works today, but the software has to be trained or providedwith rules and that takes some time. It also doesn’t scale cost-effectively tovast data-sets.

Alternatively, individuals can beprovided with ways to add semantics themselves as they author information. Whenyou post your resume in a semantically-aware job board, you could fill out aform about each of your past jobs, and the job board would connect that data toappropriate semantic concepts in an underlying employment ontology. As anend-user you would just fill out a form like you are used to doing;under-the-hood the job board would add the semantics for you.

Another approach is to leveragecommunities to get the semantics. We already see communities that are addingbasic metadata “tags” to photos, news articles and maps. Already a few simpletypes of tags are being used pseudo-semantically: subject tags and geographicaltags. These are primitive forms of semantic metadata. Although they are notexpressed in OWL or connected to formal ontologies, they are at leastsemantically typed with prefixes or by being entered into fields or specificnamespaces that define their types.

Tagging by Example

There may also be another solution to the problem of how to add semantics to content in the not to distant future. Once asuitable amount of content has been marked up with semantic metadata,it may be possible, through purely statistical forms of machinelearning, for software to begin to learn how to do a pretty good job ofmarking up new content with semantic metadata.

For example, if thestring “Nova Spivack” is often marked up with semantic metadata statingthat it indicates a person, and not just any person but a specificperson that is abstractly represented in a knowledge base somewhere,then when software applications encounter a new non-semanticallyenhanced document containing strings such as “Nova Spivack” or”Spivack, Nova” they can make a reasonably good guess that thisindicates that same specific person, and they can add the necessarysemantic metadata to that effect automatically.

As more and more semanticmetadata is added to the Web and made accessible it constitutes a statisticaltraining set that can be learned and generalized from. Although humansmay need to jump-start the process with some manually semantic tagging,it might not be long before software could assist them and eventuallydo all the tagging for them. Only in special cases would software needto ask a human for assistance — for example when totally new terms orexpressions were encountered for the first several times.

The technology for doing this learning already exists — and actually it’s not very different from how search engines like Google measure the community sentiment around web pages. Each time something is semantically tagged with a certain meaning that constitutes a “vote” for it having that meaning. The meaning that gets the most votes wins. It’s an elegant, Darwinian, emergent approach to learning how to automatically tag the Web.

One this is certain, if communities were able to tagthings with more types of tags, and these tags were connected to ontologies andknowledge bases, that would result in a lot of semantic metadata being added tocontent in a completely bottom-up, grassroots manner, and this in turn would enable this process to start to become automated or at least machine-augmented.

Getting the Process Started

But making the userexperience of semantic tagging easy (and immediately beneficial) enough that regular people will do it, is a challenge that has yet to be solved.However, it will be solved shortly. It has to be. And many companies andresearchers know this and are working on it right now. This does have to be solved to get the process of jump-starting the Semantic Web started.

I believe that the Tools Problem – the lack of commercial grade tools forbuilding semantic applications – is essentially solved already (although theproducts have not hit the market yet; they will within a few years at most).The Ontology Problem is further from being solved. I think the way this problemwill be solved is through a few “killer apps” that result in the building up ofa large amount of content around particular ontologies within particular onlineservices.

Where might we see this content initially arising? In my opinion it will most likely be within vertical communities of interest, communities of practice, and communities of purpose. Within such communities there is a need to create a common body of knowledge and to make that knowledge more accessible, connected and useful.

The Semantic Web can really improve the quality of knowledge and user-experience within these domains. Because they are communities, not just static content services, these organizations are driven by user-contributed content — users play a key role in building content and tagging it. We already see this process starting to take place in communities such as Flickr, del.icio.us, the Wikipedia and Digg. We know that communities of people do tag content, and consume tagged content, if it is easy and beneficial enough for to them to do so.

In the near future we may see miniature Semantic Webs arising around particular places, topics and subject areas, projects, and other organizations. Or perhaps, like almost every form of new media in recent times, we may see early adoption of the Semantic Web around online porn — what might be called “the sementic web.”

Whether you like it or not, it is a fact that pornography was one of the biggest drivers of early mainstream adoption of personal video technology, CD-ROMs, and also of the Internet and the Web.

But I think it probably is not necessary this time around. While, I’m sure that the so-called “sementic web” could become better from the Semantic Web, it isn’t going to be the primary driver of adoption of the Semantic Web. That’s probably a good thing — the world can just skip over that phase of development and benefit from this technology with both hands so to speak.

The World Wide Database

In some ways one could think of theSemantic Web as “the world wide database” – it does for the meaning of data records what theWeb did for the formatting documents. But that’s just the beginning. It actually turnsdocuments into richer data records. It turns unstructured data into structureddata. All data becomes structured data in fact. The structure is not merelydefined structurally, but it is defined semantically.

In other words, it’s notmerely that for example, a data record or document can be defined in such a wayas to specify that it contains a certain field of data with a certain label ata certain location – it defines what that field of data actually means in anunambiguous, machine understandable way. If all you want is a Web of data,XML is good enough. But if you want to make that data interoperable and machineunderstandable then you need RDF and OWL – the Semantic Web.

Like any database,the Semantic Web, or rather the myriad mini-semantic-webs that will comprise it,have to overcome the challenge of data integration. Ontologies provide a betterway to describe and map data, but the data still has to be described andmapped, and this does take some work. It’s not a magic bullet.

The Semantic Webmakes it easier to integrate data, but it doesn’t completely remove the dataintegration problem altogether. I think the eventual solution to this problemwill combine technology and community folksonomy oriented approaches.

The Semantic Web in HistoricalContext

Let’s transition now and zoom out to see the bigger picture. The Semantic Webprovides technologies for representing and sharing knowledge in new ways. Inparticular, it makes knowledge more accessible to software, and thus to otherpeople. Another way of saying this is that it liberates knowledge fromparticular human minds and organizations – it provides a way to make knowledgeexplicit, in a standardized format that any application can understand. This isquite significant. Let’s put this in historical perspective.

Before the invention of the printing press, there were two ways to spreadknowledge – one was orally, the other was in some symbolic form such as art orwritten manuscripts. The oral transmission of knowledge had limited range and ahigh error-rate, and the only way to learn something was to meet someone whoknew it and get them to tell you. The other option, symbolic communicationthrough art and writing, provided a means to communicate knowledgeindependently of particular people – but it was only feasible to produce a fewcopies of any given artwork or manuscript because they had to be copied byhand. So the transmission of knowledge was limited to small groups or at leastsmall audiences. Basically, the only way to get access to this knowledge was tobe one of the lucky few who could acquire one of its rare physical copies.

The invention of the printing press changed this – for the first timeknowledge could be rapidly and cost-effectively mass-produced and mass-distributed.Printing made it possible to share knowledge with ever-larger audiences. Thisenabled a huge transformation for human knowledge, society, government,technology – really every area of human life was transformed by thisinnovation.

The World Wide Web made the replication and distribution of knowledge eveneasier – With the Web you don’t even have to physically print or distributeknowledge anymore, the cost of distribution is effectively zero, and everyonehas instant access to everything from anywhere, anytime. That’s a lot betterthan having to lug around a stack of physical books. Everyone potentially haswhatever knowledge they need with no physical barriers. This has been anotherhuge transformation for humanity – and it has affected every area of humanlife. Like the printing press, the Web fundamentally changed the economics ofknowledge.

The Semantic Web is the next big step in this process – it will make all theknowledge of the human race accessible to software. For the first time,non-human things (software applications) will be able to start working withhuman knowledge to do things (for humans) on their own. This is a big leap – aleap like the emergence of a new species, or the symbiosis of two existingspecies into a new form of life.

The printing press and the Web changed the economics of replicating,distributing and accessing knowledge. The Semantic Web changes the economics ofprocessing knowledge. Unlike the printing press and the Web, the Semantic Webenables knowledge to be processed by non-human things.

In other words, humans don’t have to do all the thinking on their own, theycan be assisted by software. Of course we humans have to at least first createthe software (until we someday learn to create software that is smart enough tocreate software too), and we have to create the ontologies necessary for thesoftware to actually understand anything (until we learn to create software thatis smart enough to create ontologies too), and we have to add the semanticmetadata to our content in various ways (until our software is smart enough todo this for us, which it almost is already). But once we do the initial work ofmaking the ontologies and software, and adding semantic metadata, the systemstarts to pick up speed on its own, and over time the amount of work we humanshave to do to make it all function decreases. Eventually, once the system hasencoded enough knowledge and intelligence, it starts to function withoutneeding much help, and when it does need our help, it will simply ask us andlearn from our answers.

This may sound like science-fiction today, but in fact it a lot of this isalready built and working in the lab. The big hurdle is figuring out how to getthis technology to mass-market. That is probably as hard as inventing thetechnology in the first place. But I’m confident that someone will solve iteventually.

Once this happens the economics of processing knowledge will truly bedifferent than it is today. Instead of needing an actual real-live expert, theknowledge of that expert will be accessible to software that can act as theirproxy – and anyone will be able to access this virtual expert, anywhere,anytime. It will be like the Web – but instead of just information beingaccessible, the combined knowledge and expertise of all of humanity will alsobe accessible, and not just to people but also to software applications.

The Question of Consciousness

The Semantic Web literally enables humans to share their knowledge with eachother and with machines. It enables the virtualization of human knowledge andintelligence. With respect to machines, in doing this, it will lend machines“minds” in a certain sense – namely in that they will at least be able tocorrectly interpret the meaning of information and replicate the expertise ofexperts.

But will these machine-minds be conscious? Will they be aware of themeanings they interpret, or will they just be automatons that are simplyfollowing instructions without any awareness of the meanings they areprocessing? I doubt that software will ever be conscious, because from what Ican tell consciousness — or what might be called the sentient awareness ofawareness itself as well as other things that are sensed — is an immaterialphenomena that is as fundamental as space, time and energy — or perhaps evenmore fundamental. But this is just my personal opinion after having searchedfor consciousness through every means possible for decades. It just cannot befound to be something, yet it is definitely and undeniably taking place.

Consciousness can be exemplified through the analogy of space (but unlikespace, consciousness has this property of being aware, it’s not a mere lifelessvoid). We all agree space is there, but nobody can actually point to itsomewhere, and nobody can synthesize space. Space is immaterial andfundamental. It is primordial. So is electricity. Nobody really knows whatelectricity is ultimately, but if you build the right kind of circuit you canchannel it and we’ve learned a lot about how to do that.

Perhaps we may figure out how to channel consciousness like we channelelectricity with some sort of synthetic device someday, but I think that ishighly unlikely. I think if you really want to create consciousness it’s mucheasier and more effective to just have children. That’s something ordinarymortals can do today with the technology they were born with. Of course whenyou have children you don’t really “create” their consciousness, it seems to bethere on its own. We don’t really know what it is or where it comes from, orwhen it arises there. We know very little about consciousness today.Considering that it is the most fundamental human experience of all, it isactually surprising how little we know about it!

In any case, until we truly delve far more deeply into the nature of themind, consciousness will be barely understood or recognized, let aloneexplained or synthesized by anyone. In many eastern civilizations there aremulti-thousand year traditions that focus quite precisely on the nature ofconsciousness. The major religions have all universally concluded thatconsciousness is beyond the reach of science, beyond the reach of concepts,beyond the mind entirely. All those smart people analyzing consciousness for solong, and with such precision, and so many methods of inquiry, may have a pointworth listening to.

Whether or not machines will ever actually “know” or be capable of beingconscious of that meaning or expertise is a big debate, but at least we can allagree that they will be able to interpret the meaning of information and rulesif given the right instructions. Without having to be conscious, software willbe able to process semantics quite well — this has already been proven. It’sworking today.

While consciousness is and may always be a mystery that we cannot synthesize– the ability for software to follow instructions is an established fact. Inits most reduced form, the Semantic Web just makes it possible to providericher kinds of instructions. There’s no magic to it. Just a lot of details. Infact, to play on a famous line, “it’s semantics all the way down.”

The Semantic Web does not require that we make conscious software. It justprovides a way to make slightly more intelligent software. There’s a bigdifference. Intelligence is simply a form of information processing, for themost part. It does not require consciousness — the actual awareness of what isgoing on — which is something else altogether.

While highly intelligentsoftware may need to sense its environment and its own internal state andreason about these, it does not actually have to be conscious to do this. Theseoperations are for the most part simple procedures applied vast numbers of timeand in complex patterns. Nowhere in them is there any consciousness nor doesconsciousness suddenly emerge when suitable levels of complexity are reached.

Consciousness is something quite special and mysterious. And fortunately forhumans, it is not necessary for the creation of more intelligent software, noris it a byproduct of the creation of more intelligent software, in my opinion.

The Intelligence of the Web

So the real point of the Semantic Web is that it enables the Web to becomemore intelligent. At first this may seem like a rather outlandish statement,but in fact the Web is already becoming intelligent, even without the SemanticWeb.

Although the intelligence of the Web is not very evident at first glance,nonetheless it can be found if you look for it. This intelligence doesn’t existacross the entire Web yet, it only exists in islands that are few and farbetween compared to the vast amount of information on the Web as a whole. Butthese islands are growing, and more are appearing every year, and they arestarting to connect together. And as this happens the collective intelligenceof the Web is increasing.

Perhaps the premier example of an “island of intelligence” is theWikipedia, but there are many others: The Open Directory, portals such as Yahooand Google, vertical content providers such as CNET and WebMD, commercecommunities such as Craigslist and Amazon, content oriented communities such asLiveJournal, Slashdot, Flickr and Digg and of course the millions of discussionboards scattered around the Web, and social communities such as MySpace andFacebook. There are also large numbers of private islands of intelligence onthe Web within enterprises — for example the many online knowledge andcollaboration portals that exist within businesses, non-profits, andgovernments.

What makes these islands “intelligent” is that they are places where people(and sometimes applications as well) are able to interact with each other tohelp grow and evolve collections of knowledge. When you look at them close-upthey appear to be just like any other Web site, but when you look at what theyare doing as a whole – these services are thinking.They are learning, self-organizing, sensing their environments, interpreting,reasoning, understanding, introspecting, and building knowledge. These are theactivities of minds, of intelligent systems.

The intelligence of a system such as the Wikipedia exists on several levels– the individuals who author and edit it are intelligent, the groups that helpto manage it are intelligent, and the community as a whole – which isconstantly growing, changing, and learning – is intelligent.

Flickr and Digg also exhibit intelligence. Flickr’s growing system of tagsis the beginnings of something resembling a collective visual sense organ onthe Web. Images are perceived, stored, interpreted, and connected to conceptsand other images. This is what the human visual system does. Similarly, Digg isa community that collectively detects, focuses attention on, and interpretscurrent news. It’s not unlike a primitive collective analogue to the humanfacility for situational awareness.

There are many other examples of collective intelligence emerging on theWeb. The Semantic Web will add one more form of intelligent actor to the mix –intelligent applications. In the future, after the Wikipedia is connected tothe Semantic Web, as well as humans, it will be authored and edited by smartapplications that constantly look for new information, new connections, and newinferences to add to it.

Although the knowledge on the Web today is still mostly organized withindifferent islands of intelligence, these islands are starting to reach out andconnect together. They are forming trade-routes, connecting their economies,and learning each other’s languages and cultures. The next-step will be forthese islands of knowledge to begin to share not just content and services, butalso their knowledge — what they know about their content and services. The SemanticWeb will make this possible, by providing an open format for the representationand exchange of knowledge and expertise.

When applications integrate their content using the Semantic Web they willalso be able to integrate their context, their knowledge – this will make thecontent much more useful and the integration much deeper. For example, when anapplication imports photos from another application it will also be able toimport semantic metadata about the meaning and connections of those photos.Everything that the community and application know about the photos in theservice that provides the content (the photos) can be shared with the servicethat receives the content. Better yet, there will be no need for customapplication integration in order for this to happen: as long as both servicesconform to the open standards of the Semantic Web the knowledge is instantlyportable and reusable.

Freeing Intelligence from Silos

Today much of the real value of the Web (and in the world) is still lockedaway in the minds of individuals, the cultures of groups and organizations, andapplication-specific data-silos. The emerging Semantic Web will begin to unlockthe intelligence in these silos by making the knowledge and expertise theyrepresent more accessible and understandable.

It will free knowledge and expertise from the narrow confines of individualminds, groups and organizations, and applications, and make them not only moreinteroperable, but more portable. It will be possible for example for a personor an application to share everything they know about a subject of interest aseasily as we share documents today. In essence the Semantic Web provides acommon language (or at least a common set of languages) for sharing knowledgeand intelligence as easily as we share content today.

The Semantic Web also provides standards for searching and reasoning moreintelligently. The SPARQL query language enables any application to ask forknowledge from any other application that speaks SPARQL. Instead of merekeyword search, this enables semantic search. Applications can search forspecific types of things that have particular attributes and relationships toother things.

In addition, standards such as SWRL provide formalisms for representing andsharing axioms, or rules, as well. Rules are a particular kind of knowledge –and there is a lot of it to represent and share, for example proceduralknowledge, and logical structures about the world. An ontology provides a meansto describe the basic entities, their attributes and relations, but rulesenable you to also make logical assertions and inferences about them. Withoutgoing into a lot of detail about rules and how they work here, the importantpoint to realize is that they are also included in the framework. All forms ofknowledge can be represented by the Semantic Web.

Zooming Way, Waaaay Out

So far in this article, I’ve spenta lot of time talking about plumbing – the pipes, fluids, valves, fixtures,specifications and tools of the Semantic Web. I’ve also spent some time onillustrations of how it might be useful in the very near future to individuals,groups and organizations. But where is it heading after this? What is thelong-term potential of this and what might it mean for the human race on ahistorical time-scale?

For those of you who would prefer not to speculate, stop reading here. Forthe rest of you, I believe that the true significance of the Semantic Web, on along-term timescale is that it provides an infrastructure that will enable theevolution of increasingly sophisticated forms of collective intelligence. Ultimatelythis will result in the Web itself becoming more and more intelligent, untilone day the entire human species together with all of its software andknowledge will function as something like a single worldwide distributed mind –a global mind.

Just the like the mind of a single human individual, the global mind will bevery chaotic, yet out of that chaos will emerge cohesive patterns of thoughtand decision. Just like in an individual human mind, there will be feedbackbetween different levels of order – from individuals to groups to systems ofgroups and back down from systems of groups to groups to individuals. Becauseof these feedback loops the system will adapt to its environment, and to itsown internal state.

The coming global mind will collectively exhibit forms of cognition andbehavior that are the signs of higher-forms of intelligence. It will form andreact to concepts about its “self” – just like an individual human mind. Itwill learn and introspect and explore the universe. The thoughts it thinks maysometimes be too big for any one person to understand or even recognize them –they will be comprised of shifting patterns of millions of pieces of knowledge.

The Role of Humanity

Every person on the Internet will be a part of the global mind. Andcollectively they will function as its consciousness. I do not believe some newform of consciousness will suddenly emerge when the Web passes some thresholdof complexity. I believe that humanity IS the consciousness of the Web anduntil and unless we ever find a way to connect other lifeforms to the Web, orwe build conscious machines, humans will be the only form of consciousness ofthe Web.

When I say that humans will function as the consciousness of the Web I meanthat we will be the things in the system that know. The knowledge of theSemantic Web is what is known, but what knows that knowledge has to besomething other than knowledge. A thought is knowledge, but what knows thatthought is not knowledge, it is consciousness, whatever that is. We can figureout how to enable machines to represent and use knowledge, but we don’t knowhow to make them conscious, and we don’t have to. Because we are alreadyconscious.

As we’ve discussed earlier in this article, we don’t need conscious machines, we just need more intelligent machines.Intelligence – at least basic forms of it – does not require consciousness. It may be the case that the very highest forms of intelligence require or are capable of consciousness. This may mean that software will never achieve the highest levels of intelligence and probably guaranteesthat humans (and other conscious things) will always play a special role in theworld; a role that no computer system will be able to compete with. We providethe consciousness to the system. There may be all sorts of other intelligent,non-conscious software applications and communities on the Web; in fact therealready are, with varying degrees of intelligence. But individual humans, andgroups of humans, will be the only consciousness on the Web.

The Collective Self

Although the software of the Semantic Web will not be conscious we can say that system as a whole contains or is conscious to the extent that human consciousnesses are part of it. And like most conscious entities, it may also start to be self-conscious.

If the Web ever becomes a global mind as I am predicting, will it have a“self?” Will there be a part of the Web that functions as its central self-representation?Perhaps someone will build something like that someday, or perhaps it will evolve.Perhaps it will function by collecting reports from applications and people inreal-time – a giant collective zeitgeist.

In the early days of the Web portals such as Yahoo! provided this function — they were almost real-time maps of the Web and what was happening. Today making such a map is nearly impossible, but services such as Google Zeitgeist at least attempt to provide approximations of it. Perhaps through random sampling it can be done on a broader scale.

My guess is that the global mind will need a self-representation at somepoint. All forms of higher intelligence seem to have one. It’s necessary forunderstanding, learning and planning. It may evolve at first as a bunch ofcompeting self-representations within particular services or subsystems withinthe collective. Eventually they will converge or at least narrow down to just afew major perspectives. There may also be millions of minor perspectives thatcan be drilled down into for particular viewpoints from these top-level “portals.”

The collective self, will function much like the individual self – as amirror of sorts. Its function is simply to reflect. As soon as it exists theentire system will make a shift to a greater form of intelligence – because forthe first time it will be able to see itself, to measure itself, as a whole. Itis at this phase transition when the first truly global collective self-mirroring function evolves, that we can say that the transition from a bunch of cooperating intelligent parts toa new intelligent whole in its own right has taken place.

I think that the collective self, even if it converges on a few majorperspectives that group and summarize millions of minor perspectives, will becommunity-driven and highly decentralized. At least I hope so – because theself-concept is the most important part of any mind and it should be designedin a way that protects it from being manipulated for nefarious ends. At least Ihope that is how it is designed.

Programming the Global Mind

On the other hand, there are times when a little bit of adjustment or guidance iswarranted – just as in the case of an individual mind, the collective selfdoesn’t merely reflect, it effectively guides the interpretation of the pastand present, and planning for the future.

One way to change the direction ofthe collective mind, is to change what is appearing in the mirror of thecollective self. This is a form of programming on a vast scale – When thisprogramming is dishonest or used for negative purposes it is called “propaganda,” but there are cases whereit can be done for beneficial purposes as well. An example of this today ispublic service advertising and educational public television programming. Allforms of mass-media today are in fact collective social programming. When yourealize this it is not surprising that our present culture is violent andmessed up – just look at our mass-media!

In terms of the global mind, ideally one would hope that it would be able tolearn and improve over time. One would hope that it would not have the collective equivalent of psycho-social disorders. To facilitate this, just like any form of higherintelligence, it may need to be taught, and even parented a bit. It also mayneed a form of therapy now and then. These functions could be provided by thepeople who participate in it. Again, I believe that humans serve a vital and irreplaceablerole in this process.

How It All Might Unfold

Now how is this all going to unfold? I believe that there are a number ofkey evolutionary steps that Semantic Web will go through as the Web evolvestowards a true global mind:

1. Representing individual knowledge. The first step is to make individuals’knowledge accessible to themselves. As individuals become inundated withincreasing amounts of information, they will need better ways of managing it,keeping track of it, and re-using it. They will (or already do) need”personal knowledge management.”

2. Connecting individual knowledge. Next, once individual knowledge isrepresented, it becomes possible to start connecting it and sharing it acrossindividuals. This stage could be called “interpersonal knowledgemanagement.”

3. Representing group knowledge. Groups of individuals also need ways ofcollectively representing their knowledge, making sense of it, and growing itover time. Wikis and community portals are just the beginning. The Semantic Webwill take these “group minds” to the next level — it will make the collective knowledge ofgroups far richer and more re-usable.

4. Connecting group knowledge. This step is analogous to connectingindividual knowledge. Here, groups become able to connect their knowledge togetherto form larger collectives, and it becomes possible to more easily access andshare knowledge between different groups in very different areas of interest.

5. Representing the knowledge of the entire Web. This stage — what might becalled “the global mind” — is still in the distant future, but atthis point in the future we will begin to be able to view, search, and navigatethe knowledge of the entire Web as a whole. The distinction here is thatinstead of a collection of interoperating but separate intelligentapplications, individuals and groups, the entire Web itself will begin tofunction as one cohesive intelligent system. The crucial step that enables thisto happen is the formation of a collective self-representation. This enablesthe system to see itself as a whole for the first time.

How it May be Organized

I believe the global mind will be organized mainly in the form of bottom-up and lateral, distributed emergent computation andcommunity — but it will be facilitated by certain key top-down services thathelp to organize and make sense of it as a whole. I think this future Web willbe highly distributed, but will have certain large services within it as well– much like the human brain itself, which is organized into functionalsub-systems for processes like vision, hearing, language, planning, memory,learning, etc.

As the Web gets more complex there will come a day when nobody understandsit anymore – after that point we will probably learn more about how the Web isorganized by learning about the human mind and brain – they will be quitesimilar in my opinion. Likewise we will probably learn a tremendous amountabout the functioning of the human brain and mind by observing how the Webfunctions, grows and evolves over time, because they really are quite similarin at least an abstract sense.

The internet and its software and content is like a brain, and the state ofits software and the content is like its mind. The people on the Internet arelike its consciousness. Although these are just analogies, they are actuallyuseful, at least in helping us to envision and understand this complex system. Asthe field of general systems theory has shown us in the past, systems at verydifferent levels of scale tend to share the same basic characteristics and obeythe same basic laws of behavior. Not only that, but evolution tends to convergeon similar solutions for similar problems. So these analogies may be more thanjust rough approximations, they may be quite accurate in fact.

The future global brain will require tremendous computing and storageresources — far beyond even what Google provides today. Fortunately as Moore’s Law advances thecost of computing and storage will eventually be low enough to do thiscost-effectively. However even with much cheaper and more powerful computingresources it will still have to be a distributed system. I doubt that therewill be any central node because quite simply no central solution will be ableto keep up with all the distributed change taking place. Highly distributed problemsrequire distributed solutions and that is probably what will eventually emergeon the future Web.

Someday perhaps it will be more like a peer-to-peer network, comprised ofapplications and people who function sort of like the neurons in the human brain.Perhaps they will be connected and organized by higher-level super-peers orsuper-nodes which bring things together, make sense of what is going on andcoordinate mass collective activities. But even these higher-level serviceswill probably have to be highly distributed as well. It really will bedifficult to draw boundaries between parts of this system, they will all beconnected as an integral whole.

In fact it may look very much like a grid computing architecture – in whichall the services are dynamically distributed across all the nodes such that atany one time any node might be working on a variety of tasks for differentservices. My guess is that because this is the simplest, most fault-tolerant,and most efficient way to do mass computation, it is probably what will evolvehere on Earth.

The Ecology of Mind

Where we are today in this evolutionary process is perhaps equivalent to therise of early forms of hominids. Perhaps Austrolapithecus or Cro-Magnon, ormaybe the first Homo Sapiens. Compared to early man, the global mind is like the rise of 21stcentury mega-cities. A lot of evolution has to happen to get there. But itprobably will happen, unless humanity self-destructs first,which I sincerely hope we somehow manage to avoid. And this brings me to afinal point. This vision of the future global mind is highly technological;however I don’t think we’ll ever accomplish it without a new focus on ecology.

Ecology probably conjures up images of hippies and biologists, or maybehippies who are biologists, or at least organic farmers, for most people, but infact it is really the science of living systems and how they work. And anysystem that includes living things is a living system. This means that the Webis a living system and the global mind will be a living system too. As a living system, the Web is an ecosystem and is alsoconnected to other ecosystems. In short, ecology is absolutely essential tomaking sense of the Web, let alone helping to grow and evolve it.

In many ways the Semantic Web and the collective minds, and the global mind,that it enables, can be seen as an ecosystem of people, applications,information and knowledge. This ecosystem is very complex, much like naturalecosystems in the physical world. An ecosystem isn’t built, it’s grown, andevolved. And similarly the Semantic Web, and the coming global mind, will notreally be built, they will be grown and evolved. The people and organizationsthat end up playing a leading role in this process will be the ones thatunderstand and adapt to the ecology most effectively.

In my opinion ecology is going to be the most important science anddiscipline of the 21st century – it is the science of healthysystems. What nature teaches us about complex systems can be applied to everykind of system – and especially the systems we are evolving on the Web. Inorder to ever have a hope of evolving a global mind, and all the wonderfullevels of species-level collective intelligence that it will enable, we have tonot destroy the planet before we get there. Ecology is the science that cansave us, not the Semantic Web (although perhaps by improving collectiveintelligence, it can help).

Ecology is essentially the science of community – whether biological,technological or social. And community is a key part of the Semantic Web atevery level: communities of software, communities of people, and communities ofgroups. In the end the global mind is the ultimate human community. It is thereward we get for finally learning how to live together in peace and balancewith our environment.

The Necessity of Sustainability

The point of this discussion of the relevance of ecology to the future ofthe Web, and my vision for the global mind, is that I think that it is clearthat if the global mind ever emerges it will not be in a world that is anythinglike what we might imagine. It won’t be like the Borg in Star Trek, it won’t belike living inside of a machine. Humans won’t be relegated to the roles ofslaves or drones. Robots won’t be doing all the work. The entire world won’t becoated with silicon. We won’t all live in a virtual reality. It won’t be one ofthese technological dystopias.

In fact, I think the global mind can only come to pass in a much greener,more organic, healthier, more balanced and sustainable world. Because it willtake a long time for the global mind to emerge, if humanity doesn’t figure outhow to create that sort of a world, it will wipe itself out sooner or later,but certainly long before the global mind really happens. Not only that, butthe global mind will be smart by definition, and hopefully this intelligencewill extend to helping humanity manage its resources, civilizations andrelationships to the natural environment.

The Smart Environment

The global mind also needs a global body so to speak. It’s not going to bean isolated homunculus floating in a vat of liquid that replaces the physicalworld! It will be a smart environment that ubiquitously integrates with ourphysical world. We won’t have to sit in front of computers or deliberatelylogon to the network to interact with the global mind. It will be everywhere.

The global mind will be physically integrated into furniture, houses,vehicles, devices, artworks, and even the natural environment. It will sensethe state of the world and different ecosystems in real-time and alert humansand applications to emerging threats. It will also be able to allocateresources intelligently to compensate for natural disasters, storms, andenvironmental damage – much in the way that the air traffic control systemsallocates and manages airplane traffic. It won’t do it all on its own, humansand organizations will be a key part of the process.

Someday the global mind may even be physically integrated into our bodiesand brains, even down the level of our DNA. It may in fact learn how to curediseases and improve the design of the human body, extending our lives, sensorycapabilities, and cognitive abilities. We may be able to interact with it bythought alone. At that point it will become indistinguishable from a limitedfrom of omniscience, and everyone may have access to it. Although it will onlyextend to wherever humanity has a presence in the universe, within thatboundary it will know everything there is to know, and everyone will be able toknow any of it they are interested in.

Enabling a Better World

By enabling greater forms of collective intelligence to emerge we really arehelping to make a better world, a world that learns and hopefully understandsitself well enough to find a way to survive. We’re building something thatsomeday will be wonderful – far greater than any of us can imagine. We’re helpingto make the species and the whole planet more intelligent. We’re building thetools for the future of human community. And that future community, if it ever arrives,will be better, more self-aware, more sustainable than the one we live intoday.

I should also mention that knowledge is power, and power can be used forgood or evil. The Semantic Web makes knowledge more accessible. This puts more power in the hands of the many, not just the few. As long as we stick to this vision — we stick to making knowledge open and accessible, using open standards, in as distributed a fashion as we can devise, then the potential power of the Semantic Web will be protected against being coopted or controlled by the few at the expense of the many. This is where technologists really have to be socially responsible when making development decisions. It’s important that we build a more open world, not a less open world. It’s important that we build a world where knowledge, integration and unification are balanced with respect for privacy, individuality, diversity and freedom of opinion.

But I am not particularly worried that the Semantic Web and the future globalmind will be the ultimate evil – I don’t think it is likely that we will end upwith a system of total control dominated by evil masterminds with powerfulSemantic Web computer systems to do their dirty work. Statistically speaking, criminal empires don’t last very long because theyare run by criminals who tend to be very short-sighted and who also surroundthemselves with other criminals who eventually unseat them, or theyself-destruct. It’s possible that the Semantic Web, like any other technology,may be used by the bad guys to spy on citizens, manipulate the world, and doevil things. But only in the short-term.

In the long-term either our civilization will get tired of endlesssuccessions of criminal empires and realize that the only way to actuallysurvive as a species is to invent a form of government that is immune to beingtaken over by evil people and organizations, or it will self-destruct. Eitherway, that is a hurdle we have to cross before the global mind that I envisioncan ever come about. Many civilizations came before ours, and it is likely thatours will not be the last one on this planet. It may in fact be the case that adifferent form of civilization is necessary for the global mind to emerge, andis the natural byproduct of the emergence of the global mind.

We know that the global mind cannot emerge anytime soon, and therefore, ifit ever emerges then by definition it must be in the context of a civilizationthat has learned to become sustainable. A long-term sustainable civilization is a non-evil civilization. And that is why I think it is a safebet to be so optimistic about the long-term future of this trend.

Using DNA to Send Messages into the Distant Future

This article discusses recent research into encoding short 100 word messages into the DNA of living organisms. The error-correcting characteristics of DNA enable such messages to be passed down without degrading across generations. By embedding short messages into hardy organisms such as particular strains of bacteria, it may be possible to preserve information over longer timeframes than by using any other known storage media. This in turn can be used to intentionally send messages into the far future. I blogged about this over a year ago, here, where I suggested that because this is possible, we might want to look to see whether any such messages are already there in our own DNA or that of particularly hardy organisms. Perhaps someone put their signature there for us to see a long long time ago? Perhaps the best way to create a time capsule that can last for thousands or millions of years would be to embed messages across the DNA of a bunch of different organisms in different ecoological niches, to ensure that at least some would get through to the future. Certainly a few strains of bacteria should be included, as well as perhaps cockroaches, some types of fish, some plants, and perhaps even some volunteer humans. Since the message has to be pretty short, I would suggest that we use it to indicate the location of one or more hidden storage locations on the planet (or on the moon?) where larger volumes of information, technology, DNA libraries, etc., could be located. I view this as a kind of global "backup strategy" not unlike backing up a hard-disk. I once had some thoughts about doing this using special satellites as well, which you can read about here.

How to Save the Amazon Rainforest

I read the an article today about how Brazil is gradually losing the fight to save the Amazon. The worlds’ rainforests are a global resource — not only are they
directly important to the air we all breathe, they also harbor a huge,
still untapped, reservoir of species diversity which could be of
profound importance to science and future medical and pharma research.
The problem is that currently there is no direct benefit to Brazil, or
other rainforest nations, for the global use of their rainforest resources.

The key then is to find a way to turn rainforests into economically valuable national resources for countries that maintain them. In other words, rainforests should be to Brazil, what oil is to Saudi Arabia (or actually better, because rainforests, unlike oil, are renewable). Rainforest countries should make more money by keeping their rainforests alive and healthy,  than by chopping them down.

Continue reading

Use of Role Classes to Define Predicate Semantics: Proposal for Semantic Web Best-Practice

This article proposes a design pattern for ontologies and the Semantic Web based on the concept of formally defined Roles as a means to richly express the semantics of relationships among entities in ontologies. Roles are special types of n-ary relations, and thus the use of Roles is a subset of the Semantic Web best-practices recommendation for N-Ary Relations.

The Semantic Web relies on ontologies – formal definitions of the
meaning of various concepts. For example, an ontology could define the
formal meaning of the term "Person" — specifically, that a "Person" is
a "Human" that has a "First Name" and a "Last Name" and has "Legal
Status," "Friends" and a "Gender" and many other attributes. Each of
these attributes could be further defined specifically — for example,
"a Friend" is a different Person who is "Socially-related" to the
former Person and "Has Met" that Person at least once, and "is Liked
by" and "Trusted by" that Person. Each of these predicates, such as
"Socially-related," "Trusted by," and "Has Met" may or may not be
further defined, depending on the structure of particular ontologies.

Most simple ontologies use binary relations to express predicates that connect things together. More complex and sophisticated ontologies, such as the ones I have developed for the Radar
Platform and my work with SRI and DARPA and the University of Texas Clib ontology project, instead only cast the most basic building-block
predicates with object and data type relations (in OWL). Instead, most relations (including even those that could be expressed with simple object properties) are defined using special classes called Roles. This moves much of the weight of expressing how classes interconnect from properties to Role classes.

While using Roles instead of simple object properties introduces certain minor complexities — such as the requirement to model N-ary relations, and thus Roles, such that they can be used in place of object properties to connect instances of classes — it results in even more important benefits. In particular, a major benefit is that the use of classes to represent Role relations enables far more expressive ontologies to be developed. This method is even more expressive than the potential use of additional facets on properties. While adding special additional facets to properties is certainly one way to augment the semantics of predicates, it still is not as richly expressive as simply using Role classes instead of properties for most relations. The use of Role classes enables ontolology designers to create rich ontologies of relations, such that every relation that is modeled by a Role can be formally defined as a concept with respect to other entities and relations in the ontology. In other words, it enables a much richer semantics to be defined for the domain.

I propose that the use of Role classes to define the semantics of various types of relations among entities (including among relations themselves) should be a Semantic Web Best Practice and should be adopted in all but the most simplistic ontologies. The rest of this article explains why I believe this in more detail.

Continue reading

My "A Physics of Ideas" Manifesto has been Published!

Change This, a project that helps to promote interesting new ideas so that they get noticed above the noise level of our culture has published my article on “A Physics of Ideas” as one of their featured Manifestos. They use an innovative PDF layout for easier reading, and they also provide a means for readers to provide feedback and even measure the popularity of various Manifestos. I’m happy this paper is getting noticed finally — I do think the ideas within it have potential. Take a look.

Idea: Driving Through Virtual Soundscapes

This is an idea for a new way to navigate interactively through large audio sets, such as collections of thousands of music tracks, and to automatically or interactively learn and evolve interesting trajectories through such spaces.

Continue reading

Idea: GRASP — The Statistics Portal

If you’ve ever tried writing a business plan, you know what a chore it is to locate statistics about industries, markets and products. While there are many market research firms that charge huge sums for their reports on particular segments, one quickly realizes that the wide degree of variance in their statistics means that just getting reports from one source is not very useful — one really needs to see all the statistics normalized across all the sources that project them about a market. For example, if writing a business plan for a collaborative software application, you need stats from IDC, Gartner, Forrester, and several other sources in order to estimate the average value across them all.

The same is true not just for writing business plans, but for all kinds of research and reporting that requires the use of statistics. But nobody has the time or budget to buy or even just read all the reports that are constantly coming out all over the place. So the problem is that:
– Statistics are too hard to find
– Reports containing statistics are expensive
– Statistics are not normalized

One solution to these problems would be the creation of a new kind of search portal specifically for finding statistics: The Global Reports and Statistics Portal (“GRASP”)

Continue reading

Messages in DNA: You Saw it Here First

In August of 2003, I posted an article that suggested the SETI folks ought to look at our own DNA to see if there happens to be a hidden message from aliens in there waiting to be discovered. Putting a message in human DNA, particularly in the junk DNA regions, is guaranteed (a) not to degrade significantly over long periods of time and (b) to be found by humans when we reached a suitable level of technological development, and (d) to go with each of us wherever we went on earth and beyond. So, thinking like an alien, DNA would be a much better place to leave a message for future humans than just about anywhere else. Now, the well-known science writer, Paul Davies has come up with the exact same suggestion.

By the way, I wouldn’t be surprised if we look and actually find just such a message. Most likely it will read, “Property of Microsoft Corporation, patent-pending” or something to that effect.

How to Save the Upcoming Elections from Terrorism Alert Manipulation

There has been much recent discussion lately about alleged evidence that the Bush administration is issuing terrorist alerts for political gain. While I am not taking a position on this issue, I do have a suggestion that could eliminate any doubts, and in the process protect our upcoming elections.

In order to prevent the possibility that national terrorism alerts might be issued for political gain by an incumbent Presidential administration, the right to issue or imply terrorism alerts and the right to postpone elections, should be given to a bi-partisan committee. This policy change should be instituted immediately.

Continue reading

A New Blogging Feature: Automated "Social Syndication" Networks

Here’s an idea I’ve had recently that is related to the Meme Propagation experiment (see posts below on this blog for more about that ongoing experiment). The concept is for a new, meme-based, way to syndicate content across blogs. Here’s how it might work:

1. You join a “meme syndication network” by joining at a central site. You get an account where you can profile your blog. You also set your blog’s syndication inputs — a set of other blogs that are also in the network that you are willing to automatically syndicate content from.

2. When you complete this, you are given an automatically generated HTML element containing a script to put in your blog sidebar, or anywhere else in your layout. This script is auto-generated for you from a central site that manages the network. The script automatically displays short excerpts for blog postings (pieces of microcontent) that have been “picked up” by your site from your registered “inputs” in the network. You place this script in your layout.

3. In the area created by the script in your site, you see a listing of blog postings that have been syndicated to your site from your inputs. You can post to your network by going to your account at the central network site and posting (or copying in the URL for anything you want to post) there. Any network-member sites that treat your node in the network as an “input” will then *automatically* pickup your posting and display it on their page.

Continue reading

Proposal For A New Constitutional Amendment: A Separation of Corporation and State

by Nova Spivack
Originally published on July 28, 2004; Updated on October 10, 2011
http://novaspivack.com

Should there be a Constitutional Separation of Corporation and State?

Today our American democracy faces a new threat to its integrity, a threat even greater than terrorism in the long-term. This threat is the corporation. In this essay I propose that it may be time to introduce a new principle into our democracy and a new amendment to our Constitution – a formal “Separation of Corporation and State.”

To illustrate this point, consider an earlier “separation” that has been essential to our democracy — the Separation of Church and State. What would America be like if the Constitution did not provide for the separation of Church and State? Would it be a nation that protects and celebrates freedom, equality and pluralism? Or would it be a nation, not so unlike those presently under the sway of fundamentalism, run by religious lobbies, religious police, and fanatical extremists?

I have nothing against religion – in fact I am religious myself – but I don’t think religion should have anything to do with government, or vice-versa. This is in fact one of the key ideas in our Constitution. Many of our Founding Fathers were deeply religious, but they recognized the need to make a clear distinction between their religious ideals and their political ideals. Thus over time a Constitutional separation of Church and State was formed — a separation that would not only protect the integrity and objectivity of government, but also that of religious institutions.

However, although they were well-aware of the risks of mixing politics and religion, our nation’s early Constitutional scholars were not as concerned with the risks of mixing politics and business. And why should they have been? At the time corporations were not nearly as independent or influential as monarchies and the Church. They were not considered threats. It would not be until later in the Industrial Age that corporations became a serious political force to reckon with. But one might well wonder whether our Constitution would have included protections against corporate influence had corporations been more of a force at the time it was devised.

Today corporations are becoming the single most powerful force shaping our societies and governments. While corporations have great potential to benefit society and even governments, they are entirely selfish entities – they have no accountability to the public, and no responsibility to ensure the public good. A government that is influenced by corporations can easily become a government that caters to corporations, a government that is effectively run by corporations. Such a government is not representative of its people anymore. It is therefore not a democracy.

Corporate influence on government, if not carefully regulated, is a threat to democracy. It is a threat to the American way of life. This threat to democracy may not be as dramatic as terrorism, but in the long-term it may be far more damaging to society. In fact this threat was foreseen by some of our most visionary leaders:

“The liberty of a democracy is not safe if the people tolerate the growth of private power to a point where it becomes stronger than their democratic State itself. That, in its essence, is Fascism — ownership of government by an individual, by a group or by any controlling private power.”
— Franklin D. Roosevelt

Because this threat was impossible to envision at the time our nation was formed, our Constitution was not designed with specific countermeasures and as a result our leaders, our government, our democracy, and our citizens, are presently without protection from political influence and manipulation by corporate interests. The danger of this is that our government may be run by corporations, or at least key decisions may be based on commercial interests. But is it democratic for national decisions to be driven by corporations that are only responsible to their shareholders? Are We The People represented by the corporate decision-makers and politicians they fund?

Are we living in a true democracy when many of our highest elected officials continue to receive money from, and hold stock in, large corporations they formerly worked for, or may work for when they are out of office? Are we living in a true democracy when our leaders are able to award lucrative no-bid contracts to their former employers? Are we living in a true democracy when public policy is influenced by corporate-backed political lobbies that spend millions of dollars to influence key decisions and elections? Are we living in a true democracy when the same people who start and run our wars also benefit financially from lucrative military industrial contracts? Is this ethical? Is this what our Founding Fathers intended, or is our Shining City on the Hill starting to get a bit tarnished?

I ask you then: Is it time to modify the Constitution to specifically provide for a formal “Separation of Corporation and State” in our democracy? And if we don’t take action, can our democracy survive?

One viewpoint on the matter is that we should not enforce a specific Separation of Corporation and State but rather seek to provide ethical guidelines to corporations and politicians — in other words, we should simply trust politicians and business people to maintain ethical boundaries and act appropriately. But can we really rely on them to self-regulate? Can we trust the foxes to guard the hens? After all if politicians require corporate endorsements and funding, or at least the absence of corporate interference, to win elections and stay in power, and if corporations in turn require political influence to cut costs, increase profits and beat the competition, can we really trust them to not do deals with one another?

As America and the world enter the twenty first century there appears to be a blurring of the distinction between capitalism and democracy. Many Americans, let alone others around the world, may not even be aware that there is any distinction at all! In fact, capitalism is not a form of government – it is an economic framework while democracy is not an economic framework, it is a social system. They are not one entity, they are two complementary systems. While they are often found together and have the potential for profound symbiosis (and in fact cannot really thrive without one another), neither is a sufficient substitute for the other.

For example running a corporation exclusively according to the rules of democracy is probably not good for the bottom line, but neither is running a nation exclusively according to the rules of capitalism good for society. These two forces must be balanced appropriately. In a corporation, democracy must take second place (although I argue elsewhere that perhaps corporations should be at least more democratic than they presently are). In a society however, democracy must take first place; it must never be overwhelmed by capitalist interests.

If there was no separation of Church and State in America, both our government and religious institutions would suffer. Similarly, in the case of the tension between capitalism and democracy, the only viable, sustainable, and effective path is to maintain a very precise balance. If this balance is not maintained, neither democracy nor capitalism can function with full effectiveness and everyone loses in the long-run. Short-term thinkers may gain temporary benefits by taking advantage of imbalances of this nature, but only at the expense of the many, and ultimately even at their own expense.

From the perspective of John Stuart Mill, who advocated the philosophy of “the greatest good for the greatest number,” we must not give in to the temptation to seek short-term gain at the expense of long-term sustainability. Ensuring that this does not happen is essential to the sustainability both of democracies and free markets. Unrestrained capitalism is a cancer – ultimately it consumes everything in its path.

At the same time, unrestrained democracy can easily devolve into socialism and economic gridlock – the death of the free market economy, and stunted growth. Only a very delicate, precise, and carefully enforced balance between capitalism and democracy can ensure both long-term homeostasis AND growth – a sustainable civilization.

The issue of the Separation of Corporation and State runs deep – it is not only our problem, it is everyone’s problem because America is now leading the world. Our American democracy is the template for new democracies, an example for others to follow. And now that we are in the business of seeding new democracies it is even more important that we practice what we preach. What kind of democracies are we really making in other countries? And what kind of democracy are we ourselves really living in now? What kind of standard – what kind of a template – are we providing for others who would emulate us?

What makes this nation so great is that it stands for something – it always has. We stand for freedom, we stand for equality, we stand for justice, we stand for tolerance, we stand for opportunity, we stand for human rights, we stand for democratic principles – and in fact, we stand for balance.

Balance between opposing agendas, opposing priorities, opposing points of view, has always been the heart of our nation’s underlying philosophy. This willingness to live by, and fight for, these basic rights and principles is what has made us great, what has given us moral authority on the world stage. It is also what has made the idea of America – our cultural meme – so contagious. If we forget this balance or fail to preserve it, we may lose everything we have worked for, everything we have attained, and the whole world will lose alongside us. What a lost opportunity that would be.

Americans need to think about this issue carefully. The very heart of American democracy and capitalism is balance. To preserve this balance, we must adapt and evolve our nation in the face of change. Today that balance is threatened – some would argue it is already gone – due to corporate influence over the political process. In other words, our nation is at risk of losing its heart.

The question is not therefore, “should there be a Separation of Corporation and State” but rather “how can we realistically and practically ensure a Separation of Corporation and State?” Should we add new protections to the Constitution in some way? Should we legislate? Should we simply “let the market sort it out” or trust our leaders and corporations to self-regulate and do the right thing?

I am a dedicated capitalist; I have benefited from the free market and I believe in self-organization, creative chaos, and bottom-up emergent solutions to complex distributed problems. So I would not advocate restraining capitalism to such an extent that it loses its edge. Capitalism is a reflection of nature, of evolution itself – a basic creative process that leads to innovation, growth, optimization, and development that can benefit individuals and societies in incalculable ways.

Without capitalism democracies lack energy and cannot thrive, grow, innovate and reproduce. Yet at the same time, I believe deeply in democracy and the basic principles that America stands for. Without democracy – true democracy – capitalism becomes malignant, destructive, and cannibalistic.

I would not want to live in a non-capitalist society – how boring, how complacent, how uninspiring and uncreative that would be. But neither would I want to live in a world controlled by corporations that are solely conditioned by profit motives – that would be a world raped of every natural resource, polluted to the point of being uninhabitable, commercialized and dumbed-down to the point of total conformity and cultural decay — a world completely for sale and thus completely sold out.

Because neither of these extreme futures — democracy without capitalism, or capitalism without democracy, is acceptable, I believe it is time to really address this issue of the Separation of Corporation and State as a society, and as a marketplace. Because if we don’t find a new balance between capitalism and democracy we will lose both.

But is it too late? Is it futile to address this issue? Some would argue the Great Sell-Out happened long ago. Others might even go so far as to suggest that it is not even a meaningful question anymore — that nations are no longer the primary actors in the world, but rather that we have already begun evolving a new world order that transcends nations altogether – a world governed by interacting transnational corporations – what we might call corpocracies that are the new units of civilization. But I hope that’s not the case. I believe we still have a chance at restoring the balance we’ve lost.

It is not too late to save democracy. We can and must evolve our democratic system to adapt and survive in a world of giant global corporations. While it is impossible to prevent interactions between government and corporations, or between our political leaders and corporate entities, we may be able to find ways to protect governments and politicians from corporate influence.

What would be some concrete steps to implement this proposed separation of corporation and state? As a first step, I think there should be a serious effort to revise or eradicate the concept of corporate personhood.

Beyond that, we could perhaps require that government officials sever their financial relationships to corporations while they serve in office, and perhaps even for a year or more after their service ends (provided the government still pays them during that grace period). For example it might be considered unethical and unacceptable for a top government official to leave office and immediately go to work for a major lobbying firm, or to receive huge payments for speaking or doing other favors for corporations, at least within some period of time after they serve in office.

In the case of certain high elected or appointed officials such as presidents, vice-presidents, members of Congress and the House of Representatives, cabinet members, chief regulators, and Supreme Court justices, the rules might even be a bit stricter. For instance, in the case of Supreme Court justices for instance, it might be time to require that not only they, but even their spouses, should have no financial connections to corporate influences.

A more moderate approach would be to allow financial connections to corporations while serving in a top government role, but simultaneously to more tightly regulate and monitor them — even for some time after a person serves at a high level in government.

We could also apply stricter controls to corporations and how they fund political lobbies and campaigns, and how they promote and sell products and services to the government. What these controls might actually be, and how to police them, is a topic for further thinking and debate.

These are just a few example ideas, and I’m sure much better solutions could be proposed. Beyond merely pointing out the imperative we face, and providing some examples, I do not have the answer, I do not know the formula for the balance we need to create. This is a question for people far more qualified and knowledgeable than myself to address – a question for our political leaders, our business leaders, our political scientists and Constitutional scholars, and our community activists.

But one thing is certain: The separation of corporation and state, or lack thereof, is an issue which will have the most profound effect on our nation, our society, and the rest of the world. It is perhaps the key challenge that America must address as we enter the twenty-first century.

A Physics of Ideas: Measuring The Physical Properties of Memes

by Nova Spivack, http://www.novaspivack.com

Original: July 8, 2004

Revised: February 5, 2005; February 28, 2010

(Permission to reprint or share this article is granted, with a citation to this Web Page: http://www.novaspivack.com/science/a-physics-of-ideas-measuring-the-physical-properties-of-memes)

This paper provides an overview of a new approach to measuring the physical properties of ideas as they move in real-time through information spaces and populations such as the Internet. It has applications to information retrieval and search, information filtering, personalization, ad targeting, knowledge discovery and text-mining, knowledge management, user-interface design, market research, trend analysis, intelligence gathering, machine learning,organizational behavior and social and cultural studies.

Introduction

In this article I propose the beginning of what might be called a physics of ideas. My approach is based on applying basic concepts from classical physics to the measurement of ideas — or what are often called memes — as they move through information spaces over time.

Ideas are perhaps the single most powerful hidden forces shaping our lives and our world. Human events are really just the results of the complex interactions of myriad ideas across time, space and human minds. To the extent that we can measure ideas as they form and interact, we can gain a deeper understanding of the underlying dynamics of our organizations, markets, communities, nations, and even of ourselves. But the problem is, we are still remarkably primitive when it comes to measuring ideas. We simply don’t have the tools yet and so this layer of our world still remains hidden from us.

However, it is becoming increasingly urgent that we develop these tools. With the evolution of computers and the Internet ideas have recently become more influential and powerful than ever before in human history. Not only are they easier to create and consume, but they can now move around the world and interact more quickly, widely and freely. The result of this evolutionary leap is that our information is increasingly out of control and difficult to cope with, resulting in the growing problem of information overload.

There are many approaches to combating information overload, most of which are still quite primitive and place too much burden on humans.  In order to truly solve information overload, I believe that what is ultimately needed is a new physics of ideas — a new micro-level science that will enable us to empirically detect, measure and track ideas as they develop, interact and change over time and space in real-time, in the real-world.

In the past various thinkers have proposed methods for applying concepts from epidemiology and population biology to the study of how memes spread and evolve across human societies. We might label those past attempts as “macro-memetics” because they are chiefly focused on gaining a macroscopic understanding of how ideas move and evolve. In contrast, the science of ideas that I am proposing in this paper is focused on the micro-scale dynamics of ideas within particular individuals or groups, or within discrete information spaces such as computer desktops and online services and so we might label this new physics of ideas as a form of “micro-memetics.”

To begin developing the physics of ideas I believe that we should start by mapping existing methods in classical physics to the realm of ideas. If we can treat ideas as ideal particles in a Newtonian universe then it becomes possible to directly map the wealth of techniques that physicists have developed for analyzing the dynamics of particle systems to the dynamics of idea systems as they operate within and between individuals and groups.

The key to my approach is to empirically measure the meme momentum of each meme that is active in the world. Using these meme momenta we can then compute the document momentum of any document that contain those memes. The momentum of a meme is a measure of the force of that meme within a given space, time period, and set of human minds (a “context”). The momentum of a document is the force of that document within a given context.

Once we are able to measure meme momenta and document momenta we can then filter and compare individual memes or collections of memes, as well as documents or collections of documents, according to their relative importance or “timeliness” in any context.

Using these techniques we can empirically detect the early signs of soon-to-be-important topics, trends or issues; we can measure ideas or documents to determine how important they are at any given time for any given audience; we can track and graph ideas and documents as their relative importances change over time in various contexts; we can even begin to chart the impact that the dynamics of various ideas have on real-world events. These capabilities can be utilized in next-generation systems for knowledge discovery, search and information retrieval, knowledge management, intelligence gathering and analysis, social and cultural research, and many other purposes.

The rest of this paper describes how we might attempt to do this, some applications of these techniques, and a number of further questions for research.

Background

Before I go into the details of my proposal, a little background maybe relevant. In 1993 I worked as an analyst at Individual, Inc. Individual’s business was to provide filtered strategic business intelligence to the top decision-makers of major corporations. In that job I was part of a sophisticated information filter. Individual used artificial intelligence to automatically collect news and other content from thousands of sources in real-time. Their system then filtered this  information into news feeds tailored to the strategic interests of their customers.

It was a two-phase system. First the computers sorted incoming content into topic-oriented buckets. Next these buckets of potentially interesting articles were routed to a team of human analysts with expertise in the relevant topic areas. The analysts would go through the articles in the buckets to prioritize them, remove duplicates or items that had come through in previous articles as well as items that did not belong, and add in any items that should be included. Finally the analysts would place the most strategically relevant articles from these various buckets into newsfeeds for each customer. Thus the humans were a very important part of the algorithm — they provided the intuition, knowledge, prioritization and trend detection capabilities of the system. This combination of machine and human filtering resulted in very high-quality strategic newsfeeds for their customers.

As one of Individual’s analysts, what this meant in practical terms was that every night from about 8 PM until 1 AM I had to personally read through around 1600 news articles. My beat was emerging technology, software, broadband, online-services, multimedia and satellite applications. It was a challenge to merely read through, let alone make sense of, such a volume of information every night.Furthermore, not only did I have to figure out what was important and how to prioritize it for each of the approximately 20 global corporations that I filtered for, but I also had to remember if I had ever seen and published anything about a given subject before in the previous year. By trial and error I gradually evolved a solution to this problem and this in turn led me to formulate the ideas that are the foundation of this paper.

The human brain is incredibly adept at recognizing patterns — and in particular we are tuned to detect subtle changes in size, mass and velocity. Many examples of this can be found in nature — for example in frogs. Frogs have interesting visual systems. They are tuned to focus on things that move. They are most sensitive to size and velocity, but they also notice changes in velocity. Things that are small and that don’t move are not of particular interest to them. Things that move in erratic ways are most interesting. But human brains are far more sophisticated — we don’t merely detect the size and velocity of things, we track changes in momentum. Momentum relates the “mass” or “size” of things to the way in which they change or move over time. What is important about momentum is that a low-mass thing moving quickly can have just as large a momentum as a large-mass thing moving slowly. In other words, we can detect small but “hot” emerging trends as well as large but gradual trends. We are extremely sensitive to momentum.

What I realized at Individual back in 1993 was that the way I figured out what articles to prioritize was not so different from how a frog finds flies to eat — but more sophisticated. I realized that I filter information according to the momenta of ideas — how the various memes in the articles I was reading were growing and moving through space and time in the culture I lived in and the communities I was interested in.

Human brains are highly sophisticated momentum detectors — our brains are constantly filtering billions of inputs and patterns in real-time and computing their momenta in order to differentiate signal from noise and to attenuate to what is most important at any given time. Furthermore as trends in the world emerge,grow, peak and fade away, so do their momenta, and we are able to very sensitively detect these changes in momentum in real-time,adjusting our priorities and attention accordingly. There is nothing magical about this process: it can be modeled mathematically,  and  therefore there is good reason to believe that computers can eventually be made to do this as well.

Memes

The Physics of Ideas is the science of micro-memetics — a science of the micro-level dynamics of individual memes. It is therefore necessary to define what we mean by the term “meme” (pronounced “meem”)? — basically, a meme is any replicable idea. More formally, a decent definition of a meme is:

“/meem/ [coined on analogy with `gene’ by Richard Dawkins] n. An idea considered as a {replicator}, esp. with the connotation that memes parasitize people into propagating them much as viruses do. Used esp. in the phrase `meme complex’ denoting a group of mutually supporting memes that form an organized belief system, such as a religion. This lexicon is an (epidemiological) vector of the `hacker subculture’ meme complex; each entry might be considered a meme. However, `meme’ is often misused to mean `meme complex’. Use of the term connotes acceptance of the idea that in humans (and presumably other tool- and language-using sophonts) cultural evolution by selection of adaptive ideas has superseded biological evolution by selection of hereditary traits. Hackers find this idea congenial for tolerably obvious reasons.” (Definition from: The Hacker’s Dictionary)

Memes are essential to the way the human brain processes ideas and how it decides what is important. We are basically “meme processors” — we are “life-support systems for memes” to put it another way. To use a computer analogy, our physical bodies are like the hardware and operating system, and our minds — the dynamical activity and state of this hardware — are like the software applications and content running on the hardware. Our minds could be viewed as systems of interacting memes — complex systems of ideas that interact within us, and across our relationships.

Memes are capable of spreading across human social relationships via human interactions, and via human usage of static storage vehicles such as printed media, audio or video, and digital storage media — they are highly “communicable.” (And soon, as I have proposed in other articles, with the coming Semantic Web memes will be able to spread and interact without needing humans at all — machines will be able to process them on their own).

The Media is the Mirror

Before we can measure the physical properties of memes, we need a way to identify the memes we are interested in analyzing. We can identify memes by analyzing textual media such as document collections, wire services, and the Web.

The memes within text appear to be dormant — they are frozen digital representations. They do not move or reproduce on their own — they need help from humans (for the moment). But by inference, static textual representations of memes provide a mirror of the actual “active memes” that are taking place in the minds of the people who author and consume that media. What this indicates is that by analyzing textual media we are not merely looking at the memetic properties of text, we are looking at the memetic properties of people’s minds and of organizations, societies and cultures. In a sense, by selectively choosing the right media we can make a virtual focus group — we can see what people in this group are thinking.

The media is a mirror of our minds and cultures. By analyzing suitably selected information sources (for example, “all news articles from USA newspapers”) we can effectively focus on a reflection of the memes that are actually present within the minds of humans in a particular place, time, industry, community, demographic, etc. The more we know about the information sources, the more we can infer about the memes we find, and thus the memes taking place within the minds of the people who interact with those information sources.

The simplest approach to identifying memes in textual media is to simply pre-specify a list of memes we are interested in and to then search for any matching strings. For example we might be interested in measuring memes related to a particular trend, such as “Java technology,” so we could compile a list of terms related to Java and then use search techniques to locate all instances of those terms. We can then measure their properties.

A more sophisticated approach than specifying interesting memes in advance is to discover them empirically by analyzing text to see what’s there. To do this we might automatically identify nouns or noun-phrases and then measure their dynamics to see whether they are interesting enough to warrant further analysis. There are many existing computational liguistics techniques for isolating parts of speech and linguistic expressions.

Each of these nouns or phrases is a potential meme (we may consider them to all be actual memes or we may filter for only those memes that exhibit dynamics in space and time that meet our threshold for what constitutes “interesting” or “memelike” behavior. Another, more brute-force approach, would be to simply analyze every noun and phrase in a document or corpus for any that exhibit “memelike” dynamics in order to discover memes empirically instead of specifying them and then gathering their stats.

We can use various standard methods from text-mining and natural language processing to do a smarter job of identifying memes (for example, we can use stemming to consolidate various forms of the same word, we can use translation to consolidate expressions of the same meme in different languages, and we can use conceptual clustering and even ontologies to consolidate different memes that are equivalent to the same underlying meme). But for now, we can start by identifying memes in a simple way — the same way we might identify “topics” or “keywords” in a document. Once we can do this we can then measure the physical properties of those memes as they move through time and various spaces of interest.

(Note: We don’t necessarily have to analyze every document in a corpus to gather valid statistics for memes within it. We can use random sampling techniques for arbitrary degrees of accuracy if we wish to optimize for faster results and less computation. Instead of analyzing every occurance of each meme, we can analyze a statisically valid sample of the corpus.)

The Physics of Ideas

I suggest that the physics of ideas will be quite similar, if not equivalent to, the physics of the natural world. Everything in the universe emerges from the same underlying laws, even memes. The intellectual processes taking place within our own minds, as well as across our relationships and social organizations are similar to the dynamics of particle systems, fluid flows, gasses, and galaxies. We should therefore be able to map existing physical knowledge to the memescape, the dimension of memes.

Here are a set of basic measurements of the physical properties of memes and documents:

(Author’s Note, February 28, 2010: My latest thinking on this topic has evolved considerably from when this article was originally written in 2005. Instead of viewing memes as classical particles, I now think it is probably more accurate and useful to model them as physical waves or fields. At any given location (a media outlet, or a geographic place, or a person or document) every meme can be represented as a vector at any given time. In any case, regardless of the particular physical model we choose to map to memetics, the key point here is that it should be possible to make such a mapping from physics to memetics. This is a testable hypothesis. For example, select a certain mapping and generate some measurements about the higher order dynamics of memes, and then see if we can make testable predictions from those. Through such a process it should be possible to experimentally test and verify whatever mapping we choose, to find mappings that are most useful and accurate. Once we choose a mapping from physics to memetics that works, it could be an extraordinarily powerful tool for making sense of what is going on in the world, and particularly on the Web. I leave it to the physicists among us to come up the correct model, mappings, and experiments. In addition, since the original date of publication, social media has become an enormous playing field for memes and particularly rich source of data for measuring and mapping meme dynamics. In addition to documents we may also think of people and their lifestreams as sources of memetic data for measuring memes. Below is the original proposed mapping — which primarily was a classical physical model, focused on documents only.)

Absolute meme mass = how “large” the meme is. There are various ways to come up with a measure of mass for memes and I don’t claim to have come up with the only, or even the best, way to do so. This is still a subject for further investigation. However, to begin, one approach at least is to interpret the mass as the total number of times a meme is mentioned in the corpus since the beginning of time to the present. However, it has been pointed out that this interpretation will cause the mass to increase over time. Still, it may be a useful interpretation, and in this paper I will use it provisionally. Another and perhaps better possibility, is to quantify the relative importance of particular memes in advance (for example by having analysts rate the terms that are most important to them) and to use these values as the mass of those memes.  Note: When computing meme mass, we can choose to count repeat mentions or ignore them — doing so has slightly different effects on the algorithm. We can also, if we wish, get more fancy and look at clusters of memes (via semantic network indexing or entity extraction, for example) that relate to the same concepts in order to compute “concept-cluster momenta” but that is not required.

Absolute meme velocity = how fast the meme is moving in the corpus in the present time interval = The rate of occurrences (or “mentions”) of the meme per unit time (minutes, hours, days, etc.) in a given time interval.

Absolute meme momentum = the force or importance of the meme in the corpus = the meme’s absolute mass x the meme’s absolute velocity

Relative meme mass = the mass of a meme within a subset of documents or data in the corpus representing some set of interests. (Note: we call a subset of mutually co-relevant documents a “reference frame” or a “context.”) such as a set of interests, a particular period in time, etc. (rather than in the entire corpus).

Relative meme velocity = the velocity of a meme within a reference frame.

Relative meme momentum = the relative meme mass X the relative meme velocity.

On the basis of these we can then compute derivatives such as:

Absolute meme acceleration = how the absolute meme velocity is changing in the entire corpus = The change in absolute velocity per unit time of the meme in the corpus.

Relative meme acceleration = the change in relative velocity of a meme.

Absolute meme impulse = the change in importance per unit time = the change in a meme’s absolute momentum.

Relative meme impulse = the change of a meme’s relative momentum.

Next, we use the above concepts to look at sets of memes, for example documents:

Absolute document momentum = the force or importance of a document in the entire corpus = the sum of the absolute momenta of each meme that occurs in the document.  (Note: we may choose to count or ignore repeat occurrences of an article in different locations or at different times — this has different effects).

Relative document momentum = the force or importance of a document within a reference frame = the sum of the relative meme momenta in the document. This is a more contextually sensitive measure of document momentum — it couples momentum more tightly with a context, such as a particular query or time interval, or demographic segment.  (Note: we may choose to count or ignore repeat occurrences of an article in different locations or at different times — this has different effects).

Hybrid document momentum = a measure of momentum that combines both relative and absolute measurements = either relative mass X absolute velocity or absolute mass X relative velocity.

How To Analyze a Corpus Using These Methods

We can then apply the above measurements to entire corpora (collections of documents). This enables us to empirically rank the ideas occurring in the corpus in any interval of time. Furthermore it enables us to rank and prioritize documents in the corpus according to their momenta within any time interval — in other words, how representative they are of “important” or “timely” ideas within any time interval.

To do this, first we must create an index of stats for all memes we are interested in. We can use the above mentioned techniques for identifying memes to do this. For each meme we identify, we create a record in our index that lists the stats we find for it by source location and time. We then analyze our text sources and update the records in this table (for a historical analysis we do this all at once; for a real-time analysis we do it continuously on an ongoing basis or in batches). As new instances of memes are found we append the corresponding records in the index.

We can now use these statistics to plot memes and documents according to our measurements of meme and document mass and velocity. This enables us to segment the memes or documents according to the various possible configurations of these dimensions. Each of these configurations has a useful meaning, for example a document with low absolute mass, moderate or high relative mass, high absolute velocity and high relative velocity contains “newly emerging trends of interest to the current context” whereas a document with high absolute mass, low relative mass, high absolute velocity and low relative velocity contains “established large trends that are not very relevant to the current context.”

By looking at the impulse (the change in momentum) we can also chart the direction of these trends (increasing or decreasing). Memes that have high positive impulse are becoming more “important” than those with lower impulses. This enables us to determine whether memes are “heating up” or “cooling off” — a meme is heating up if it is important and timely and has positive impulse.

Thus documents that have high document momenta contain memes that have high meme momenta — in other words they are representative of whatever ideas happen to be most important now. Tomorrow, when the momenta of various memes may have changed, the same documents might have different document momenta.

These techniques provide a way to rank documents that is in some respects like Google’s algorithm, except that it works for all types of information — not just information that is highly interlinked with hotlinks or citations but even for flat text — and it is capable of arbitrary resolution in time and space. For example, Google is basically estimating document popularity — or effectively, endorsements implied by citations — for each query. Google determines the rank of a page in a set of results by estimating the community endorsement of that page as implied by the number of relevant pages that link to it. Using the proposed physics of ideas however we can accomplish the same thing in a different and possibly better way — we can now compute the ‘potential community value’ of a document — without actually requiring links in order to figure that out. Instead, we can determine the relative strength of the ideas (the memes) that are present in the document and compare them to the memes that are present in the community of documents that are relevant to the keywords in our query.

For example, we do a query for “space tourism” and get back 6,830,000 documents in Google. Next we compute the above stats for each of those documents. We then rank the documents returned by this query according to their relative document momenta. This has the effect of ranking the documents according to the strengths of memes that are particularly of interest to the community represented by the query results. Thus it enables us to rank the resulting documents for our “space tourism” query to favor those documents that contain the highest momentum memes relative to set of memes that matter to the community — in other words the documents that contain ideas that are most “timely for the community” would appear higher. So this is a way to figure out not just what is relevant but what is important or in other words timely at a given point in time to people with a given set of interests.

Example Applications

Using the above techniques we can use momentum to provide a more sensitive way to filter any collection of information objects for which we can gather stats representing mass and velocity. There are numerous useful applications of doing this. Below I describe some of them.

Filtering E-Mail

For example, one might filter their e-mail using meme and document momenta in order to automatically view messages, people and topics with high momentum, low momentum, growing or declining momentum, etc. One could also use these techniques to data-mine the articles in a news feed or corpus for those that contain the “hottest trends.” It could be used to automatically detect “emerging hot topics,” “people to watch,” “companies to watch,” “products or brands to watch” etc. When ever you send a message the system measures the memes in that message and updates a special meme-stats index called “my interests” which just has the meme-stats for memes in messages you send. All incoming e-mail messages you receive can then be ranked according to their document momenta with respect to the meme momenta in the “my interests” index. This e-mail filter is automatically adaptive — as you send messages it learns what your current interest priorities are and this is reflected in changing meme momenta, even as your interests shift over time. These updated momenta are then used to filter incoming mail. So your mail filter learns what is important to you as you work and adapts to focus on your current priorities and interests, without you having to teach it. It just learns and adapts to model your current interests as you work.

Media Analysis

Beyond just that, these techniques can be used to perform more precise media analysis — for example they can be applied to measure the success of an advertising or marketing campaign by correlating the campaign placements with changes in momentum of the memes for the brand or product in the media.

Predicting Changes to a Stock Price

We can also use these techniques to make predictions — for example, we can correlate meme-momenta for memes related to a company with technical properties of its financials and stock price and then make predictions about price changes by analyzing news articles to detect changing meme-momenta related to the company. We can also do pure statistical correlations between meme momentuma and stock momenta for example. The financial news media is like a mirror reflecting what is taking place in the markets — but investors also use this mirror to decide what to do in the markets. So by measuring what appears in this mirror we can predict what investors are likely to do next.

Prioritizing Search Results and Implicit Query Expansion

We can also use these techniques to prioritize Internet search results — or any search results for that matter. For example, a set of Web documents can be prioritized by their document momenta, such that those that represent the memes that are currently the hottest can score higher — in other words, documents that are currently more timely can score higher than those that are less timely, and documents that are more timely yet less relevant (on a keyword level) can be ranked higher than those that are less timely but more keyword-relevant.

For example, suppose you search for “Asian restaurant.” If the meme “Vietnamese food” is currently in vogue in the media, meaning that it has higher momentum currently, then documents about restaurants that contain “Asian” or “restaurant” and that contain “Vietnamese food” will score higher than those that only mention “Asian” or “restaurant’ and “Chinese food” (assuming that Chinese food currently has a lower momentum). But this could change later as trends change. In other words, although we searched for “Asian food” we ended up getting documents ranked not merely by the keywords “Asian food” but by what topics related to Asian food have highest momentum today. This is a form of “implicit query expansion” and “implicit filtering.” In other words the system can prioritize search results for you according to the present momenta or in other words, the timeliness, of memes that occur in them. So it can show you the documents that are likely to be most important to you NOW in light of current trends and events, versus just the documents that have the best keyword relevancy.

Market Research

To make things even more interesting, we can add additional arguments to our “Rank of item” function and our meme-stats table — for example, not just a measure of mentions but also a measure of “hits” — hits on a meme increase whenever a document containing the meme is viewed. We can also add another dimension to represent the spatial distribution of memes. This will enable us to track the vectors of memes through time and space. We can do this by associating each source (each publisher) with a geographic location. We then segment our meme-stats table by geography to break out the momentum of each meme in each geographic region. This enables us to do things like filter documents by “how important they are to people in New York.”

By adding further dimensions — such as demographic profiles gleaned for example from the reader-surveys of publishers we can also segment by demographics, so we can even filter documents by “how important they are in the last month to professional, Democratic party affiliated, college educated, women in New York City who earn a median household income of $100,000.”

By adding still one more dimension to measure “sentiment” for each mention of a meme (as a function of the positive or negative language occurring near it or better yet, about it), we can even start to rank memes according to the percent of members of a given population that support or oppose them.In other words, this system can be used to empirically measure what polls and focus groups do informally. The notion here is that by selecting media sources that are representative of the community you are interested in understanding, you can then view memes and meme data relative to that group. You can also do this in the other direction, simply look to discover what memes have interesting stats for the group your are interested in. Another use of this technology might be to analyze intellectual history by computing meme-stats from historical documents or past news articles.

We can also leverage the fact that meme dynamics can be corellated with those of other memes to determine dynamical dependencies amongst them. This enables us to determine that some memes postively or negatively reinforce others. It also enables us to discover sets of related memes — such that we can learn that stats on a given meme should be inherited by related “child memes” in an automatically or manually generated taxonomy of memes.

Measuring and Mapping Ideas in the Semantic Web

We could also reference metadata about the semantics of various memes we can even filter for various types of memes — such as “memes related to vehicles” or “memes representing people” or “memes representing products ,” etc. This enables us to start measuring ideas as they occur and interact on the emerging Semantic Web — but not just particular memes, even conceptual systems of memes that are interacting or somehow ontologically related. By linking with an ontology, for example, we can track the momentum of all memes related to “American cars” versus those for “German cars.” The ontology enables inferences that help us find all memes that represent types of cars and classify them by nationality of manufacture.

Intelligence Analysis

These techniques might even be used to detect signs of potential terrorism, and to “get inside the minds” of various people or groups of interest — simply analyze the meme-stats for memes in documents they create or view to automatically generate a profile of the main ideas currently occupying their minds. Next by tracking this over time you can start to plot trajectories and make predictions. Intelligent agents can then be trained to notice “interesting” patterns in these trajectories and alert analysts as needed.

Advertising Targeting

The same methods could be used to better target advertisements or recommendations to users. Knowing what memes are currently most important to a party enables better personalization and targeting. In this case a Web site could track what memes are hottest for a given user account — derived from what pages they view and what messages they write or respond to. This data could then be used to augment the users’ interest profile with more dimensions of detail about each interest — such as how timely it is to the user, what particular nuances are specifically interesting, what their sentiment is. This could result in less irrelevance and spam for users and better results for marketers.

Knowledge Discovery

Now what gets interesting is the above methods can be used on both directions. We can use them to ask questions about memes we are interested in and we can also use to empirically discover memes we should be interested in within any corpus. So for example we can just empirically compute meme momenta and document momenta in any collection of information and then filter for whatever dynamics we are interested in, for example, “hot new emerging trends to watch.”

A New Kind of Portal

Using these methods it is possible to build a new kind of portal that provides a window into the collective mind of the planet (or any community of interest). It would show what people within the desired segment think is important over time. We could watch an animation on it of how memes for “Jihad” have spread, or for how those for a technology like “Java” have spread versus those for “Microsoft .Net,” or how a particular war is currently viewed by the public in different states or different demographic segments. A user could “drill down” into any meme to see it’s stats, all articles where it was mentioned, and related items on the Web, and maybe even products etc.

Open Questions & Directions for Further Research

It is important to note that these simple physical concepts could be taken much further. For example, using the above approach we should be able to determine the “gravity of a meme” or of a document or any set of memes or documents. We can then start to model the shape of memetic manifolds — the shape of space-time for ideas. We can also start to look at systems of memes as fields. Perhaps there may even be applications of fluid dynamics, relativity theory, or even quantum mechanics to what is taking place in the memescape — but today we are just taking baby-steps, just as Newton and the early natural philosophers did long before us. We need to begin to simply have the ability to measure memes and their basic interactions before we can go on to higher levels of analysis. I leave it to the physicists among us to take this to the next level of formalism — would anyone like to try their hand at formalizing the above proposed equations for the physics of ideas, or perhaps proposing even better ones?

There are a number of open questions I am still thinking about that suggest opportunities to refine these techniques. In particular, should we normalize documents somehow so that large documents don’t have an unfair advantage over small documents (because large documents have more terms in them and thus have higher document momenta)?

Another question is whether or not we should rank documents first by relevance to query, and then within each “relevancy band” further rank by document momentum within that band? This has the effect of limiting the impact of momentum versus relevancy — which may be useful if relevancy is considered to be more important. For example the top 100 most relevant documents are ranked by relevancy and then within that set they are ranked by document momentum and displayed, next the second 100 most relevant documents are ranked by relevancy and then within that set they are ranked by document momentum and displayed, etc.

Another question is whether there is an ideal set of priorities for the various measurement dimensions above with which to rank documents for general searches. We can let users choose their own priorities of course, for example, by letting users set their priorities for various memetic dimensions, we can then tailor our ranking for their needs. Are they just looking for all documents that are relevant to a query, or are they really trying to find documents that are representative of the most timely issues relevant to a query? We might enable users to set their weights for the absolute and relative measurements of documents in order to view different rankings of search results. Better yet, we could simply provide them with natural language filters to apply, such as “Filter for documents that contain currently hot topics related to this query.” In other words they can set priorities for the above dimensions in order to favor one dimension over another — so they might decide that query relevance is most important, document mass is second and velocity is least important. This would translate to a constraint such that it would be more difficult for documents with low relevance to be ranked higher than documents with high relevancy just because they have higher momenta On the other hand, they might want to favor momenta — for example if they really want to find documents that mention the latest trends related to a query — in which case we would favor document mass and/or velocity above document relevancy in our ranking. I am still thinking about the best way to handle these tradeoffs. Letting the user set their priorities is one way — but it may be possible to do a good job of satisfying most people with a particular set of default priorities. What is the best set of default priorities for general use?

There is also the question of how to best represent the “footprint of a meme” in geographic space. We can detect mentions of memes and using the above methods we may be able to associate each mention with a particular geography (the geographic region of the publisher and/or the intended audience — if the source has an audited audience demographic survey — as most publications that sell advertising do — then it is easy to associate any memes that occur within its content with particular geography and demography). Now the question is suppose we are tracking a particular meme — can we determine its geographic trajectory over time? Can we determine the vector of each meme at each sector in a geographic map? And can we represent that in an animated map for exampe, perhaps with something like a fluid flow animation?

Another open area to study is to analyze the higher order distributions of memes in order to automatically detect memes that are “interesting” (ie. not “noise” according to our priorities). One easy way to do this is to automatically ignore any memes that have a random distribution. We may also want to de-emphasize memes that have regular distributions — such as memes for which the dynamics have been the same for a reasonanble period of time. In other words, we want to filter for memes that have dynamics which deviate from being predictable or stable (randomness and regularity are both predictable). My hypothesis is that the really interesting memes — the memes that represent important emerging trends or current hot issues — will exhibit high volatility. For example, imagine for a moment that we are tracking memes related to “digital music” — if we look back in time there will be a point where the word “Napster” suddenly appears — at first it is a relatively “small” meme but gradually it spreads and gains momentum. Then there is a critical point where it begins to grow exponentially. Then it probably levels off for a while or even inflects after the initial hype phase ends. Next another dramatic increase in momentum should be seen around the time of the music industry’s lawsuits against Napster. Then following the resolution of these we should see Napster fall off dramatically. Later we see momentum increase again as the new commercial version of Napster is announced. This type of pattern is what we are looking for. Can we characterize these patterns well enough that we can detect them automatically?

Perhaps one way to do this is by training a neural network to recognize the types of patterns that interest us — we could do this for example by taking historical content (such as the last 10 years of the Associate Press) and then telling a neural net what memes are most important to us. The neural net can then learn from this training data. We can then run the neural net on current or more recent news and let it guess what is important to us based on the patterns of past important trends. We can rate these guesses to provide further feedback to improve learning. This approach could be used to train intelligent agents that specialize in detecting particular types of trends — for example, we could train agents to alert us when a major new technology trend is about to erupt, or when we should invest in a technology stock, or when a company we track is experiencing a major change of some sort, or to tells us when a new competing product emerges or when an existing competing product overtakes our own product, etc. We could also potentially train agents to recognize the early signs of important cultural or political issues, significant changes in sentiment or focus for a given community we are interested in, or even signs of emerging threats.

Are There Ideal Meme Distributions?

Perhaps one of the most interesting questions I have thought about in relation to the physics of ideas is whether or not there are perhaps “ideal distributions” of memes that get the best response from humans? In other words, do the higher order distributions of memes that become major trends, or that get the most attention in noisy environments, have similar characteristics? If it turns out that this is the case then it could provide a powerful new technique for advertising, information filtering, and even for user-interface design. I believe we can analyze memes to answer this question. Here’s how we might do it:

Approach 1: We choose a representative set of memes for major trends. We analyze their higher order distributions in the media. We then attempt to figure out whether these distributions have anything in common that we can isolate. We then search the media for other memes that have distributions with similar properties and test whether they are in fact major trends. We can provide feedback by scoring the output of these trials and using an evolutionary algorithm to evolve successively better filters. Eventually through such a process we can evolve an agent that is good at discovering major trends in the media.

Approach 2: We can do a perceptual psychology experiment to discover and evolve memes that get the most attention. Create a noisy environment in any sensory modality — let’s use visual information for the moment. Put 100 human subjects in a room and show them a computer generated slideshow. Our slideshow consists of 100 images. We change slides rapidly. Each slide is shown many times in the course of the slideshow, with a frequency according to one of many different distributions we wish to test. For example, one slide is shown such that it has low mass, low velocity — a low momentum. Another is shown to have high momentum. Others are shown to vary such that their momentum inflects and is volatile. We can test a number of different momentum curves in this manner — such as linear or nonlinear momentum growth, etc. At the end of the slideshow we give each subject all the slides and ask them to prioritize them in order of most important to least important — we ask them to tell us what they think the most important slides in the slideshow were. This effectively tests the various distributions we ran in the experiment to see which ones had the strongest cognitive effect on the subjects. Two weeks or a month later we repeat this rating test to see which distributions have the strongest long-term effect as well. By doing this experiment many times with many distributions we can experimentally determine which memetic distributions have the strongest cognitive impact. The next step would be to test whether the distributions we discover are applicable across sensory modalities — for example, do the distributions we found for vision also work for the auditory system. My hypothesis is that they do hold across modalities. If this is the case then we have discovered a key underying meta-pattern in the human perceptual system — the pattern by which humans recognize what to tune their attention to.

There is another interesting and related question to the above experiments: Do certain distributions retain attention better than others? The human perceptual system attenuates to signals very quickly — we tune out anything regular or predictable and focus on identifying novelty. But what is “novelty?” Any new meme that occurs is novel at first, but whether or not it remains novel or gets tuned out is another question. Which meme distributions do NOT get tuned out as quickly, or ever? Is there an optimal way to vary the distribution of a meme such that it continues to remain novel? In thinking about this, are there any meta-patterns to the memes that have gotten your attention in the past? For example, is there something about the way that particular technology trends or celebrities have moved through the media that made them appear to be hotter and more important to you? Having high momentum at a given time is part of this, but it may in fact be the change in momentum over time — the “meme impulse” — that really makes the difference. For example in my own experience I notice that trends that exhibit exponential growth in momentum quickly get my attention — but as soon as the growth becomes predictable I lose interest. So it seems that the trends that retain my interest the best are the ones that have more variable graphs — graphs that are neither random nor regular. Is there an ideal balance between randomness and order? What patterns have this balance — can we quantify this and define it more concretely?

A better understanding of the cognitive effects of various higher order distributions of memes in various human sensory modalities could be particularly useful for advertisers, marketers, and user-interface designers. An advertiser or marketer could use this knowledge to design campaigns that get the most attention and that are not “tuned out” by people as quickly. A user-interface designer could use this information to design interfaces for manging changing information in which the signal-to-noise ratio is optimized so that users can quickly focus on just the most important changing information — for example the information display of a stock-trading terminal, executive information system, military situation room, or fighter jet cockpit user-interface could perhaps be improved using these principles.

Concluding Remarks

Given that memes are now among the most powerful “hidden” forces shaping our individual minds, our relationships, organizations and our world, wouldn’t it be great if we could really measure them and analyze them empirically?

That is what I hope the basic techniques provided above will help to catalyze. By making this hidden layer visible we can gain a much better understanding of our world. Let me know if you end up using these techniques for anything interesting (and hopefully you will make your ideas open-source too so everyone can benefit).

What these basic techniques provide is a way to measure the movement of ideas in time and space. For example, we can track the trajectories of ideas in our workspaces, our teams, enterprises, cities, nations or interest-communities. We can also track them across geography or any other set of dimensions.

Because we can compute basic physical properties of memes we can start to apply Newtonian physics to analyze them. Perhaps by doing so we can really develop a “Physics of Memetics” with which we may begin to predict the outcomes of interactions among memes, the future trajectories of memes, and the influence changes to memes have on events in the so-called “real world” and vice-versa. With this in hand we could potentially teach systems to learn to detect memetic patterns of interest to us — for example the early “fingerprints” in the media that indicate the outcome of a proposed act of legislation or a vote, or a stock price, or a political change. We could also use it to detect emerging cultural trends, and to measure and compare the dynamics of brands or competing technologies in various markets in order to predict winners.

By putting this information into the public domain I hope to see these techniques in use as widely as possible. They will provide dramatic benefits in managing large volumes of information, improving knowledge worker and team productivity, and in discovering and measuring trends in communities.

Ultimately, I would like to see this embodied in a “grand cultural project” — a real-time map of the memetic dynamics taking place around the globe. This map would be filterable in order to show relative memetic dynamics in different places, communities, etc., and to show how various memes are spreading and interacting over time around the world. The data would be open and accessible via an open API so that all services that manage information could provide information to it and query it for stats when needed.

Superdistribution is the Solution to Digital Piracy and Marketing — and a Venture Opportunity too!

Forget about DRM and legal action to prevent piracy — there is a better way: Superdistribution harnesses basic human drives to save money and make money. It’s more powerful than copy protection, more powerful than ethical arguments, and more powerful even than fear of legal prosecution.

A recent article points out that in 2003 around one third of all installed software on PC’s was pirated. Probably an even higher percentage of digital music was pirated.

Piracy comes about because people like to get things as cheaply as possible. When calculating the “cost” of getting something, we need to consider not just the pricetag but also the rest of the transaction-cost — for example the cost in time to locate something, download it, potentially pirate and crack it, etc. To combat piracy, we need to bring the total cost (including all transaction costs) of paying for digital products down to roughly equal or less than than the total cost of pirating those same items. One way to accomplish this is too keep lowering prices of goods. But there are price-points below which sellers lose their margins and thus cannot pass. The problem arises when the total transaction cost of piracy is still less than the lowest commercially-viable total transaction cost to purchase a digital product legitimately. In such a situation piracy flourishes because sellers simply cannot compete by lowering prices any further. So what is a seller to do in that case?

Fortunately there is a solution: Sellers can effectively lower the total transaction cost of purchasing versus pirating by using superdistribution. Superdistribution enables “peer-to-peer” marketing and selling. The concept is simple. I buy a product from Seller X and pay price Y for it. But I can then promote it to my friends and if one of them buys it, I get a commission that reduces my price Y for my copy. If they then further distribute the product to their friends and so on down the line to some number of levels, I get further comissions (fractional by social distance of each purchaser from me). This is sometimes called “network marketing” and is fully legal in the USA so long as no up-front fees are charged to parties before they can become resellers and start earning commissions (at least this was the law last time I checked — but do your own research to be safe if you are planning to go into business doing this!). In other words, you don’t have to buy a product before you can resell it to others and earn commissions — you can resell it and earn commissions even if you yourself don’t own it.

In any case, legal subtleties aside, the concept is what matters here. Superdistribution reduces the buyer’s total transaction cost, and even enables them to potentially get their product for free or even make a profit if enough downline sales result from their referrals. The catch is that it only works in cases where the product is easily superdistributable, and the customer has good enough connections to easily find downline buyers. Finally, it only makes sense in cases where the market is not already saturated — where there are still lots of potential buyers who haven’t bought the product yet.

Superdistribution, if done properly, will virtually eliminate piracy. The reason is simple. If you buy a product wouldn’t you rather get a lower price or get it free or even make money, if you could? Because superdistributing a product has this potential, but giving it away for free does not, parties who buy products are more likely to then superdistribute them than they are to simply give them away for free to their friends. Now what about the case where a party does not buy a product? Superdistribution wins there too because even non-buyers can act as resellers — in other words, they can make even greater profits than buyers because they didn’t even spend anything. So in short, if a suitable superdistribution mechanism is provided, people will use it to resell digital products they download and/or buy rather than giving them away to others for free. This is really the solution to the music industry’s woes — it is far more effective than any form of digital rights management or legal action. By enabling non-pirates to benefit financially compared to pirates, non-piracy can naturally be brought about for the majority of cases.

Continue reading

Can Messages Be Sent Backwards in Time?

Is it possible to send messages backwards in time? This may actually be a testable hypothesis today. Here is a possible way to test it. Let’s assume this is possible and that at some point in time in the future, humans on earth develop this technology. We can test whether or not this actually will happen in the future by constructing a suitable receiving device now, and making its presence known in the global digital landscape such that it becomes part of the historical record. In the future, if and when suitable technology is developed, the inventors at that time may discover the existence of our device in their past and then may attempt to send a message back in time to our device. Of course, if they are smart they will send us instructions to make a better receiver, and possibly even a transmitter. That would get interesting! Similar ideas to this have been proposed by others, including Jack Sarfatti. My approach is as follows:

1. Construct the receiver device. This device needs to be designed such that it could be receptive to potential messages from the future. For example, it could be a highly ordered crystal lattice, or a sensitive measuring device that could detect any changes to a local region of space (such as the temperature of something or the local physical constants or topology of a small area). The challenge is to construct a system that can do this. It would take a lot of thought to come up with the right type of receiver.

2. We then publicize the location of the Receiver in digital online media and the press. This is to ensure that it becomes part of the historical record, so that in the future, anyone searching for "faster than light communication" or "communicating backwards in time" will find our information and learn enough about the receiver to send us a message.

3. Then we just wait and see what happens. If we are lucky, we might receive a message right away. Maybe it will take a while. If we never receive a message we can conclude that either (a) Humans destroyed themselves in our future and there is nobody left to send the message, (b) Humans (or some form of suitably intelligent lie) exist in the future but never develop the technology for sending messages backwards in time, (c) Intelligent beings exist and do develop such technology in the future, but our physics was wrong and they can’t communicate without our Receiver, (d) Suitably intelligent beings do exist in our future and they do have the technology but for some reason they cannot or will not send us a message (perhaps safety, or the Prime Directive, or fear of changing their own past in uncontrolled ways).

Now the physics of this proposal could be changed — for example, perhaps there is a better way to construct a receiver. For example, use a satellite and enable it to receive optical messages from some location nearbye for example, or from Earth. But in any case, the basic design of this project could work. But only if widely publicized — otherwise what is the chance that a future civilization will find out about it and try to make contact?

Smarter Tail Lights for Cars

The “stop” lights on the back of a car should change color from red to green depending on whether the car is decelerating or accelerating. This way they can function as both “stop” lights and “go” lights. This is an idea that was also recently mentioned on Should Exist. However, I would like to add that the lights have an additional feature — they should blink with a frequency proportional to the rate of change of acceleration or deceleration of the vehicle. The faster a vehicle slows down the faster its “stop” light blinks red. The faster a vehicle speeds up, the faster its “go” light blinks green. So for example, when a vehicle slows down rapidly, drivers behind it can see how fast it is slowing down by the rate of red flashing of its tail lite. If it speeds up they can tell how quickly it is speeding up by the rate of green flashing of its tail lite.

This could be an important safety modification for automobiles that would prevent many freeway traffic accidents by enabling drivers to better guage the rate of deceleration and acceleration of cars in front of them. To make this system even smarter, there could be a similar (but smaller) light on the front bumper of cars as well, so that when looking in your rear-view mirror you could tell how fast the car behind you was speeding up or slowing down.

How to Build a Network Automaton

Here is a cool new kind of complex system I am thinking about a lot that we might call a “network-automaton” or a “graph automaton” — a system that evolves networks (graphs) over time. This rule is similar to cellular automata rules such as the famous “Life” rule discovered by John Conway, however instead of computing the states of cells on a grid, it computes the shape of a network. In a nutshell this system applies a simple local rule at each node in a network that determines what other nodes it should connect to in the next step of time as a function of the connections each of those nodes had in the previous step of time. This yields complex network structures and interesting dynamical emergent behaviors over time — networks that grow and change as time goes by, networks in which there may even be stable or cyclical topological patterns that move across the network, as well as interactions between such patterns (topological interactions) that resemble the interactions between fundamental particles.
 
Network automata of the sort I propose here may be useful for modeling the structure and dynamics of a wide range of systems from physical systems, to biological systems, to the growth and development of computer networks, to social networks, business networks, and other types of higher-order networks.
 
(By the way — I would really like an open-source application — in Java perhaps — for generating and visualizing network automata rules such as those in this article. If you are a good programmer and would like to volunteer to make some software that can simulate the dynamics of the class of systems I propose here, please email me! I think this will be a very interesting avenue of exploration, and such a tool could be extremely useful.)

See the rest of this article for a detailed description of how to build a working network automaton….

Continue reading

How to Make a Smarter Spam Filter

I have been using Earthlink’s built-in spam filter on my personal email — it works really well. It is basically a whitelist system: Any messages from pre-approved parties get through to me while anything else goes into a “suspect email” folder for me to look at and potentially approve or delete. This doesn’t really eliminate spam, but at least it gets it out of my inbox. I still have to go through the “suspect email” messages though — and this takes a bit of time every day. Fortunately there is a solution to that based on a few simple heuristics. I would like to see these features in spam-filters in the future — they would cut down the task of managing “suspect messages” by about 90%. Hopefully one of you works for a spam-filtering company and will read this and add these ideas to your feature list for an upcoming release:

1. Automatically bounce a challenge reply back to any incoming message that has an empty subject line. The reply should say “Nova does not accept messages without subjects. Your message has not been delivered. Please add a subject and resend.” This would eliminate a lot of the “(no subject)” spams that I get.

2. Any message with a subject line that contains my name in it is likely to be spam, as in “Spivack: Las Vegas Vacations on Sale” etc. Rank them lower in the list, or cluster them together, or bounce a challenge back to sender, or just delete them.

3. Messages with grammatically incorrect or meaningless subject lines such as “Octopus airframe linguine” should also result in a bounced challenge to sender, or should be clustered lower in list, or deleted.

4. Messages containing symbols such as *, *&, $, #, @ in the subject line should be ranked as highly suspect, as in messages that contain a string such as “A*D*L*T”

A New Way to Find Patterns in Distributions of Numbers

This evening I had an interesting idea for a new way to look for patterns in the distribution of numbers such as the prime numbers and the digits of Pi. In a nutshell I propose that there may be patterns in these number sequences that might not be evident to a computer but could be evident to the human eye and human intelligence, which among other things is tuned to find order in chaos, even when that order is “fuzzy.” In this article I propose a new class of rules that are similar in some respects to cellular automata, for generating visualizations of the distribution of numbers, and for leveraging distributed human intelligence to evalute those visualizations for meaningful patterns.

Continue reading

Neuromarketing and Memetic Attenuation

This article discusses new research in how the brain makes buying decisions and other choices — what is now called “neuromarketing”. Neuromarketing researchers seek to discover, and influence, the neurological forces at work inside the mind of potential customers. According to the article, most decisions are made subconsciously and are not necessarily rational at all – in fact they may be primarily governed by emotions and other more subtle cognitive factors such as identity and sense of self. For example, when studied under a functional MRI, the reward centers of brains of subjects who were given “The Pepsi Challenge” lit up when they tasted Pepsi, but Coke actually lit up the parts of the brain responsible for “sense of self” — a much deeper response. In other words, the Coke brand is somehow connected to deeper neurological structures than Pepsi.

Neuromarketing is interesting — it’s actually something I’ve been thinking about on my own in an entirely different context. What I am interested in is the question of “What makes people decide that a given meme is ‘hot’?” Each of us is immersed in a sea of memes — we are literally bombarded with thousands or even millions of ideas, brands, products and other news every day — But how do we decide which ones are “important,” “cool,” and “hot?” What causes the human brain to pick out certain of these memes at the expense of the others? In other words, how do we differentiate signal from noise, and how do we rank memetic signals in terms of their relative “importance?” Below I discuss some new ideas about how memes are perceived and ranked by the human brain.

Continue reading

Social Networks, Physics, Civilizations — Do they All Obey the Same Underlying Rules?

I am having an interesting conversation with Howard Bloom, author, memeticist, historian, scientist, and social theorist. We have been discussing network models of the universe and the underlying “metapatterns” that seem to unfold at every level of scale. Below is my reply to his recent note, followed by his note which is extremely well written and interesting…

————
From: Nova Spivack
To: Howard Bloom
Subject: Re: Graph Automata — Is the Universe Similar to a Social Network?

Howard, what a great reply!

Indeed the metapattern you point out seems to happen at all levels of scale. I am looking for the underlying Rule that generates this on abstract graphs — networks of nodes and arcs.

In thinking about this further, I think we live in a “Social Universe.” What binds the universe together, and causes all structure and dynamics at every level of scale, is communication along relationships. Communication takes place via relationships. And relationships in turn develop based on the communication that takes place across them.

Relationships and communications take place between locations in the manifold of spacetime, as well as between fundamental particles, cells, people, ideas, network devices, belief systems, organizations, economies, civilizations, ecosystems, heavenly bodies, galaxies, superclusters, or entire universes. Whether you call it “gravitation” and “repulsion” and other forces are really just emergent properties of the dynamics of relationships and communications. It’s really all very self-similar.

I believe that we can make an abstract model of this — just a graph comprised of nodes connected by arcs — where the nodes (and possibly the arcs too) have states, and information may travel across them. Then, at each moment in time, we may apply simple local rules to modify the states of nodes and arcs in this network based on their previous states and the states of their neighbors.

Continue reading

Graph Automata — What Can Social Networks Teach us About Underlying Physical Laws?

Hello all, I have been thinking about the general problems of social networks on the Internet. It occurs to me that these issues are closely related to digital physics. For more on digital physics see the work of Ed Fredkin, Stephen Wolfram, Norman Margolus, Tomasso Toffoli, and other pioneers of the field of cellular automata.

In the past I have worked informally on cellular automata at MIT in the lab of Fredkin, Margolus and Toffoli — and in particular that led me to get interested in what could be called “graph automata” — rules that operate on arbitrary graphs in a manner that is similar to the way that cellular automata operate on cells in rigidly defined neighborhood topologies. The general concept is that the structure of a graph can be optimized for various parameters in a bottom-up, iterative, emergent fashion by running local rules at each node based on the neighborhood structure around each node (taking into account the number of arcs around each node, the directionality of arcs if any, and the states of nodes if any). There is a general class of rules that we could call “graph automata” that are quite interesting to study because in many ways they are better metaphors for physics than simple CA’s, in my opinion.

In any case, that’s not the point of this note. Instead, I would like to propose that one way to discover the “general laws” of digital physics might be to study social networks. Social networks are an interesting “macro-level” phenomenon that could be considered to be useful analogs for discovering the general properties of physical information networks. They are comprised of nodes connected by arcs in which information flows. We could view all physical systems through this lens and perhaps learn quite a bit from this approach.

Continue reading

Optimization of Social Network Architectures Using Tiling Rules

Here’s an interesting follow-up thought on my suggestion of some Hypothetical Laws of Social Networks.

What if in fact there is an entirely new way to design social networks, based on the mathematics of tilings? A tiling is a method of filling a space with geometric shapes. For example, you can tile a space with squares, hexagons, quasicrystals, spheres, etc. — depending on the dimensions and topology of the space.

Continue reading

More on Auto-Caching of URL's on Weblogs: Need for a New Service and API

I blogged about this earlier, but here are some new thoughts about how it should work.

I would like my Weblog provider to auto-cache every URL I link to from my blog. When I put a URL into the content of a posting, my Weblog engine should strip it out and replace it with an intermediary URL. The intermediary URL should go to a page that provides the actual URL that I put in my content, as well as another URL that links to a cached copy of the content that I linked to originally.

The cache-link could point to Google’s cached version, or better yet to a local cached hosted by my Weblog provider (Google does not guarantee that cached copies will always be available, whereas my Weblog provider could make that guarantee; furthermore Google may not even have a cached copy of the item I link to, whereas my local Weblog provider could make that cached copy at the time I post the article).

This would ensure that all content that I refer to in my Weblog will always be available, even if the original source is taken offline. I think this is essential — without it someday a significant portion of the links in my weblog may be broken due to content going offline or moving, which would render much of my Weblog content obsolete.

There are some copyright issues to consider perhaps — but I am not sure they are obstacles. After all HTML does provide a way to designate that a page should not be cached, and Google is caching a lot of pages without any legal challenges. Another approach to this would be to use the Internet Archive as a cache — at least they are non-profit, thus perhaps the legal issue of hosting cached copies of sites may be easier to resolve: they do it already.

Perhaps my friend Brewster and his pals at the Internet Archive should create an API that bloggers can use for this purpose. When the API is pinged with a URL, the archive makes a cache of that URL and returns a new permalink to that cached copy. Blog engines can use this API to submit URLs and get cache-URLs in return, which can then be inserted into blog postings automatically for the posters. This would be a great use of the Archive and would help bloggers immensely.

A New Solution to Spam: "The Internet Member's License"

I keep hearing about various half-baked proposals for solving spam, so I couldn’t help but add my own half-baked proposal into the mix. Actually my proposal may be more than half-baked — It might be the solution we’ve all been waiting for. I call it the “Internet Member’s License” (IML — pronounced “I-mail”).

Basically what I am proposing is an Internet-equivalent of a driver’s license — only it works very differently. Unlike a driver’s licnese the Internet Member’s License is not about granting permission to use the Net — you don’t need to have one in order to get online or surf etc. — instead, its purpose is to simply encode the holder’s “identity and reputation” in a standardized manner that enables others to test and screen messages and content you create.

Continue reading

How to Make the iPod Better

Well I finally made the leap and got a 40 Gb iPod recently. Thanks to RipDigital the process of digitizing my nearly 1000 CDs took only 1 day and I got it all back on a nice new 250 Gb Maxtor external drive (as well as a stack of DVDs containing a backup of all the new MP3s). After a few days of hacking around I got everything working and all my music organized, categorized into playlists and synched with my iPod. I also downloaded a useful little utility that I found on iPod Lounge that enables me to load all my Palm Desktop contacts onto my iPod addressbook too. The iPod is a fantastic innovation that is very well designed. After only a short time using I know I can’t live without it, and I can’t imagine how I surivived for the last year without one. Not only that but iTunes and the Apple Music Store are totally addictive. I’ve already spent quite a bundle on music there. But although the iPod is great, I do have a wishlist of features that I would like to see in a future version of the device and the software. I will list some of my suggestions for improving the iPod here. I will update it from time to time. Feel free to add comments with your own feature suggestions to this posting. Maybe Apple will notice it someday and use some of these ideas.

Continue reading

Using Nanoparticles to Augment Human Brains…?

CNN posted an article today about the potential risk of nanotechnology on the human brain. Basically some research shows that nano-scale particles such as industrial waste, or even components of nanotechnologies, can migrate through the human circulatory system and eventually lodge in the brain. This could cause harmful effects. But on the other hand, maybe this “bug” is actually a “feature!”

The fact that this is possible could be used to introduce nanoscale computational devices into the human nervous system — essentially splicing a distributed computer into a living human brain. Suppose the nanoparticles could establish an ad hoc local area network amongst themselves, and suppose they lodged throughout the human nervous system, attaching to neurons. Suddenly it might be possible to do real-time sensing — and triggering — of any neuron in the human body. And all of this could be monitored by an external computer system. This could enable amazing new biofeedback systems. But that’s just the beginning — because it might also enable people to “backup” their nervous systems — including perhaps their memories and skills. It could also potentially enable software augmentation of human thinking as it happens — an external computer network could interact with your own “internal network” and as you think or sense things, it could search the entire Net or an expert system, or the brains of other people in your network, and give you suggestions, knowledge, etc.

Essentially this could be a way to network humans to computers, the Internet, and then to other humans. This could enable future “group minds” and “collective intelligences” that we cannot even imagine yet. It could also enable humans to easily interact with virtual reality environments — they could be overlaid onto their sensory experience to augment information (such as a visual scene being augmented with labels or diagrams etc.), or even to “switch channels” from this “reality” (which may also be virtual) that we experience to other synthetic realities that exist in our computer networks. It might even enable people to record their dreams, and/or enter the dreams of other people — that would be the most advanced “virtual reality” possible.

Another interesting application of this technology might be to deliver neural drugs more effectively. It could also be used to facilitate interspecies communication — for example imagine a system that could map between a human brain and a dolphin brain. You permeate both a human and dolphin’s nervous systems with nanocomputing particles. First there is “learning phase” where an external system monitors them as they do things in order to learn how their brains work. Then it starts to learn how to map between them by observing how they interact with other organisms of their species and with their environments in order to figure out their language, communicat and memory representation schemes.

Once that is known it could directly map information between them, maybe even in real-time, enabling not only communication but even memory uploading and downloading. That would be cool — imagine being able to do virtual telepresence into the nervous system of a dolphin as it swims around with its pod in the wild. You could “look through it’s sense organs” as it swims around, and maybe even observe what it thinks and feels like — sort a window into being someone else — in this case someone who is of a different species. Among the many other applications of this technology of course there would be amazing potential in the arts, education, therapy, collaboration, entertainment, science, relationships, etc. and many other enjoyable diversions that people would probably figure out they could engage in once their nervous systems are networked.

Anyway the idea of permeating a human nervous system with networkable nanocomputers is definitely something to think about, or think twice about, as the case may be (pun intended!).

Humans Should Intentionally Seed Life on Mars. Why We Must Start Now.

Here’s a wildly unexpected proposal that just popped into my brain: Humanity should intentionally contaminate Mars with Earth lifeforms — as soon as possible! The benefits vastly outweigh any concerns to the contrary. Indeed, it may be the smartest thing our species ever does.

The first obvious benefit is that it will get Earth life off of Earth, making it more likely that it will survive. Humans are wrecking Earth — but even if we don’t Nature may do it for us. All it would take is one big comet or meteor impact — or a supervolcano or ice-age and much of the living systems and civilization we currently take for granted would vanish in the blink of an eye. Our only insurance is to have a “planetary backup” — so why not use Mars? We back up our data — why not our DNA — why not also backup the amazing ecosystems and living organisms that have evolved so painstakingly over aeons on Earth? By moving at least some of them to Mars we can at least rest assured that no matter what happens on Earth, life in our solar system will continue in other places. But that’s just the beginning.

Another benefit of seeding Earth life on Mars is that we can jumpstart evolution on Mars by several million (or billion) years by seeding it with life from Earth. And then we can study how it evolves and adapts. Remember, many organisms contain in their DNA bits and pieces of lots of previous generations and species — and as they adapt on Mars they could even eventually re-evolve lifeforms we have (or had) on Earth. Perhaps life on Mars will revert to adaptations that existing on Earth when our climate was harsher. But over time that could slowly transform the Mars climate, enabling life to catch up again, and evolve to “higher” forms. Eventually that could even create and spread living systems and ecosystems that humans can live off of, or live within at least. Yes it could take a very long time to evolve higher lifeforms on Mars if we start by just sending microorganisms, insects, landcrabs, lizards, etc, but it could happen given that the selective pressures on Mars are similar to those on Earth. On the other hand, life could go in a completely unanticipated direction — that would be interesting too!

It’s actually a fascinating and important scientific question worthy of funding and long-term study: given the same precursor lifeforms and similar or identical conditions, will life evolve along the same evolutionary course as it has on Earth? Will Mars get dinosaurs eventually, or even primates? And what about flora and fauna? If the Bush Administration wanted to propose A Really Bold Initiative what could be better than seeding life on another planet?

Hey NASA, are you listening? — this idea is worth $100 billion in funding. We could learn more from seeding life on Mars and studying it as it adapts, spreads and evolves for the next several thousand years than almost anything else we could do with the space program. It will help us learn about ourselves, the cosmos, and ultimately about how species move to new worlds. It will even lay the groundwork for humans to eventually colonize Mars by starting to build a food-chain and life support web there. And seeding life on Mars would have a greater long-term benefit on humanity, and the solar system, than just about any other space or Earth-sciences research program we could embark on.

Continue reading