We are so focused on how to improve present-day search engines. But that is a kind of mental myopia. In fact, a more interesting and fruitful question is why do people search at all? What are they trying to accomplish? And is there a better way to help them accomplish that than search?
Instead of finding more ways to get people to search, or ways to make existing search experiences better, I am starting to think about how to reduce or eliminate the need to search — by replacing it with something better.
People don’t search because they like to. They search because there is something else they are trying to accomplish. So search is in fact really just an inconvenience — a means-to-an-end that we have to struggle through to do in order to get to what we actually really want to accomplish. Search is “in the way” between intention and action. It’s an intermediary stepping stone. And perhaps there’s a better way to get to where we want to go than searching.
Searching is a boring and menial activity. Think about it. We have to cleverly invent and try pseudo-natural-language queries that don’t really express what we mean. We try many different queries until we get results that approximate what we’re looking for. We click on a bunch of results and check them out. Then we search some more. And then some more clicking. Then more searching. And we never know whether we’ve been comprehensive, or have even entered the best query, or looked at all the things we should have looked at to be thorough. It’s extremely hit or miss. And takes up a lot of time and energy. There must be a better way! And there is.
Instead of making search more bloated and more of a focus, the goal should really be get search out of the way. To minimize the need to search, and to make any search that is necessary as productive as possible. The goal should be to get consumers to what they really want with the least amount of searching and the least amount of effort, with the greatest amount of confidence that the results are accurate and comprehensive. To satisfy these constraints one must NOT simply build a slightly better search engine!
Instead, I think there’s something else we need to be building entirely. I don’t know what to call it yet. It’s not a search engine. So what is it?
Bing’s term “decision engine” is pretty good, pretty close to it. But what they’ve actually released so far still looks and feels a lot like a search engine. But at least it’s pushing the envelope beyond what Google has done with search. And this is good for competition and for consumers. Bing is heading in the right direction by leveraging natural language, semantics, and structured data. But there’s still a long way to go to really move the needle significantly beyond Google to be able to win dominant market share.
For the last decade the search wars have been fought in battles around index size, keyword search relevancy, and ad targeting — But I think the new battle is going to be fought around semantic understanding, intelligent answers, personal assistance, and commerce affiliate fees. What’s coming next after search engines are things that function more like assistants and brokers.
Wolfram Alpha is an example of one approach to this trend. The folks at Wolfram Alpha call their system a “computational knowledge engine” because they use a knowledge base to compute and synthesize answers to various questions. It does a lot of the heavy lifting for you, going through various data, computing and comparing, and then synthesizes a concise answer.
There are also other approaches to getting or generating answers for people — for example, by doing what Aardvark does: referring people to experts who can answer their questions or help them. Expert referral, or expertise search, helps reduce the need for networking and makes networking more efficient. It also reduces the need for searching online — instead of searching for an answer, just ask an expert.
There’s also the semantic search approach — perhaps exemplified by my own Twine “T2” project — which basically aims to improve the precision of search by helping you get to the right results faster, with less irrelevant noise. Other consumer facing semantic search projects of interest are Goby and Powerset (now part of Bing).
Still another approach is that of Siri, which is making an intelligent “task completion assistant” that helps you search for and accomplish things like “book a romantic dinner and a movie tonight.” In some ways Siri is a “do engine” not a “search engine.” Siri uses artificial intelligence to help you do things more productively. This is quite needed and will potentially be quite useful, especially on mobile devices.
All of these approaches and projects are promising. But I think the next frontier — the thing that is beyond search and removes the need for search is still a bit different — it is going to combine elements of all of the above approaches, with something new.
For a lack of a better term, I call this a “help engine.” A help engine proactively helps you with various kinds of needs, decisions, tasks, or goals you want to accomplish. And it does this by helping with an increasingly common and vexing problem: choice overload.
The biggest problem is that we have too many choices, and the number of choices keeps increasing exponentially. The Web and globalization have increased the number of choices that are within range for all of us, but the result has been overload. To make a good, well-researched, confident choice now requires a lot of investigation, comparisons, and thinking. It’s just becoming too much work.
For example, choosing a location for an event, or planning a trip itinerary, or choosing what medicine to take, deciding what product to buy, who to hire, what company to work for, what stock to invest in, what website to read about some topic. These kinds of activities require a lot of research, evaluations of choices, comparisons, testing, and thinking. A lot of clicking. And they also happen to be some of the most monetizable activities for search engines. Existing search engines like Google that make money from getting you to click on their pages as much as possible have no financial incentive to solve this problem — if they actually worked so well that consumers clicked less they would make less money.
I think the solution to what’s after search — the “next Google” so to speak — will come from outside the traditional search engine companies. Or at least it will be an upstart project within one of them that surprises everyone and doesn’t come from the main search teams within them. It’s really such a new direction from traditional search and will require some real thinking outside of the box.
I’ve been thinking about this a lot over the last month or two. It’s fascinating. What if there was a better way to help consumers with the activities they are trying to accomplish than search? If it existed it could actually replace search. It’s a Google-sized opportunity, and one which I don’t think Google is going to solve.
Search engines cause choice overload. That wasn’t the goal, but it is what has happened over time due to the growth of the Web and the explosion of choices that are visible, available, and accessible to us via the Web.
What we need now is not a search engine — it’s something that solves the problem created by search engines. For this reason, the next Google probably won’t be Google or a search engine at all.
I’m not advocating for artificial intelligence or anything that tries to replicate human reasoning, human understanding, or human knowledge. I’m actually thinking about something simpler. I think that it’s possible to use computers to provide consumers with extremely good, automated decision-support over the Web and the kinds of activities they engage in. Search engines are almost the most primitive form of decision support imaginable. I think we can do a lot better. And we have to.
People use search engines as a form of decision-support, because they don’t have a better alternative. And there are many places where decision support and help are needed: Shopping, travel, health, careers, personal finance, home improvement, and even across entertainment and lifestyle categories.
What if there was a way to provide this kind of personal decision-support — this kind of help — with an entirely different user experience than search engines provide today? I think there is. And I’ve got some specific thoughts about this, but it’s too early to explain them; they’re still forming.
I keep finding myself thinking about this topic, and arriving at big insights in the process. All of the different things I’ve worked on in the past seem to connect to this idea in interesting ways. Perhaps it’s going to be one of the main themes I’ll be working on and thinking about for this coming decade.
Social tagging: Artificial Intelligence > bing > decision support > Goby > google > intelligent agents > Microsoft > nova spivack > Powerset > Radar Networks > reasoning > Search > Search > search engine > semantic > semantic search > Semantic Web > Siri > task completion > Twine > Web 3.0 > Web 3.0 > Wolfram Alpha
Nova – great article. As you say, search is an activity, not a destination. Generally people want to complete a task, not “search” per se. Most approaches to “task completion” engines today revolve around a context (what time of year it is, what your interests are, …) or a task (at Goby, we're solving the free time planning problem). If you assume a context/task (travel, or finding music, etc), you can devise an experience that's much more effective than a “one size fits all” search. The big open question is, can you devise such an experience without benefit of assuming a context….and where will we get the metadata to power such an experience, in the face exponentially exploding, unstructured content?
Nova – great article. As you say, search is an activity, not a destination. Generally people want to complete a task, not “search” per se. Most approaches to “task completion” engines today revolve around a context (what time of year it is, what your interests are, …) and/or a task (at Goby, we're solving the free time planning problem). If you assume a context/task (travel, or finding music, etc), you can devise an experience that's much more effective than a “one size fits all” search. The big open question is, can you devise such an experience without benefit of assuming a context….and where will we get the metadata to power such an experience, in the face exponentially exploding, unstructured content?
http://hunch.com/ ?
Yes Hunch is a good example. In their case I think the approach is one of enumerating all possible contexts, and then playing “twenty questions” with those contexts. I find it stronger in some areas than others (it does well for restaurants but not well for books, in my experience), but its very cool.
Nova, I like the way you think. It seems, generally, there are two types of questions we are asking on the web:
1. Simple Questions
-What is a Gazebo?
-Who is Shakira?
-Where is Nike Town?
-What is Macy's URL?
-Even, what is my friend doing right now?
2. Comparative Questions:
-Which restaurant should I choose?
-What movie should I watch?
-What product should I buy?
-What book should I read next?
-What funny video should I watch?
-Who should I date?
You could add another, the “Explanatory Questions” or “Why” questions, but I will try to keep it simple here.
So, current search engines are great at Simple questions, and they are getting better. However, as you cogently pointed out, very few of our questions these days are simple – it has become, or has always been, a matter of making DECISIONS.
So, let's take a simple decision – What book should I read next?
Search Engine: I type into google, “What book should I read next?” and it gives back to me sites that have that question in their copy. They are mostly recommendation engines that ask you to enter a book you like, and it will analyze a set of data of user likes, and then suggest books. Great.
Friend: Your friend would think about all of the books s/he's read (which is limited), and knowing you, they would suggest something. Luckily, you trust your friend, so your inclined to think that whatever they suggest at least is a good read.
So, without going on forever here, and without disclosing too much, it seems to me that what is required is what I'm calling DEEP SOCIAL DATA, or data about individuals (their social roles, their cultural affinities, their credibility and social influence) and then data about what those individuals have to say about cultural artifacts (books, ideas, people, brands, etc.). Then in aggregate, you have a lot more relevant information about what people “who see the world the way I do” and who have “social credibility” think. I can then search based on keyword, but have information sorted by type of cultural artifact, personalized to me, and weighted by social credibility. Page rank does this to some degree, but it doesn't know “who” these people are, and thus it doesn't weight things appropriately.
I'd be interested to hear more about what you're thinking. This is important stuff. In fact, I would say the evolution of culture and society is dependent on it.
Nova, I like the way you think. It seems, generally, there are two types of questions we are asking on the web:
1. Simple Questions
-What is a Gazebo?
-Who is Shakira?
-Where is Nike Town?
-What is Macy's URL?
-Even, what is my friend doing right now?
2. Comparative Questions:
-Which restaurant should I choose?
-What movie should I watch?
-What product should I buy?
-What book should I read next?
-What funny video should I watch?
-Who should I date?
You could add another, the “Explanatory Questions” or “Why” questions, but I will try to keep it simple here.
So, current search engines are great at Simple questions, and they are getting better. However, as you cogently pointed out, very few of our questions these days are simple – it has become, or has always been, a matter of making DECISIONS.
So, let's take a simple decision – What book should I read next?
Search Engine: I type into google, “What book should I read next?” and it gives back to me sites that have that question in their copy. They are mostly recommendation engines that ask you to enter a book you like, and it will analyze a set of data of user likes, and then suggest books. Great.
Friend: Your friend would think about all of the books s/he's read (which is limited), and knowing you, they would suggest something. Luckily, you trust your friend, so your inclined to think that whatever they suggest at least is a good read.
So, without going on forever here, and without disclosing too much, it seems to me that what is required is what I'm calling DEEP SOCIAL DATA, or data about individuals (their social roles, their cultural affinities, their credibility and social influence) and then data about what those individuals have to say about cultural artifacts (books, ideas, people, brands, etc.). Then in aggregate, you have a lot more relevant information about what people “who see the world the way I do” and who have “social credibility” think. I can then search based on keyword, but have information sorted by type of cultural artifact, personalized to me, and weighted by social credibility. Page rank does this to some degree, but it doesn't know “who” these people are, and thus it doesn't weight things appropriately.
I'd be interested to hear more about what you're thinking. This is important stuff. In fact, I would say the evolution of culture and society is dependent on it.
Nova,
Great article – thanks! I agree: for far too long, we've all assumed that the end game for search was just faster, better, more precise search. There has to be a better way – and chances are, it won't come from a company like Google that makes most of their money from search.
Search won't be needed when computers can actually anticipate what you need before you need to find it. Or when it's incredibly obvious where you need to go to find it.
I like your approach to this problem. You're thinking about this from the user's perspective rather than from the company perspective. Put another way, you're thinking about the solution to a problem rather than a technology product alone. I write a blog about solution marketing and this is an excellent example of solution thinking.
Thanks,
Steve Robins
The Solution Marketing Blog
Nova,
Great article – thanks! I agree: for far too long, we've all assumed that the end game for search was just faster, better, more precise search. There has to be a better way – and chances are, it won't come from a company like Google that makes most of their money from search.
Search won't be needed when computers can actually anticipate what you need before you need to find it. Or when it's incredibly obvious where you need to go to find it.
I like your approach to this problem. You're thinking about this from the user's perspective rather than from the company perspective. Put another way, you're thinking about the solution to a problem rather than a technology product alone. I write a blog about solution marketing and this is an excellent example of solution thinking.
Thanks,
Steve Robins
The Solution Marketing Blog
Nova,
You practically wrote my manifesto. I could just put it on my blog word-by-word. We may have different ideas on what the specific substitute for search will be, but I think we agree on that the age of keywords, on which today an entire industry is based, has to pass.
I came up with this idea of “content mapping” that, almost entirely relying on the crowd could ensure that every piece of content is placed in (and followed by) its ideal context on the web. What we called “search” in the past 20 years, would simply turn into “looking around”.
Please read my two posts under http://collectiveweb.wordpress.com/category/web…. Now that I know you're thinking about the same problem I'd be honored to hear your opinion on content mapping.
@DanielStocker
Nova,
You practically wrote my manifesto. I could just put it on my blog word-by-word. We may have different ideas on what the specific substitute for search will be, but I think we agree on that the age of keywords, on which today an entire industry is based, has to pass.
I came up with this idea of “content mapping” that, almost entirely relying on the crowd could ensure that every piece of content is placed in (and followed by) its ideal context on the web. What we called “search” in the past 20 years, would simply turn into “looking around”.
Please read my two posts under http://collectiveweb.wordpress.com/category/web…. Now that I know you're thinking about the same problem I'd be honored to hear your opinion on content mapping.
@DanielStocker
http://hunch.com/ ?
Yes Hunch is a good example. In their case I think the approach is one of enumerating all possible contexts, and then playing “twenty questions” with those contexts. I find it stronger in some areas than others (it does well for restaurants but not well for books, in my experience), but its very cool.
What you are describing is what I call a global expert system (GES) powered by a semantic inference engine. Unlike traditional, siloed expert systems that are designed to facilitate decision making in a very specific field using proprietary data sets, the GES taps into the global web of data, becoming a personalized expert system (expert assistant) for each individual.
Querying the system would provide personalized, meaningful results, not the rote one-size fits all results that search engines currently provide. One possible appellation for this type of system could be “intuition engine.”
I have a few ideas on how this could be accomplished without resorting to AI.
What you are describing is what I call a global expert system (GES) powered by a semantic inference engine. Unlike traditional, siloed expert systems that are designed to facilitate decision making in a very specific field using proprietary data sets, the GES taps into the global web of data, becoming a personalized expert system (expert assistant) for each individual.
Querying the system would provide personalized, meaningful results, not the rote one-size fits all results that search engines currently provide. One possible appellation for this type of system could be “intuition engine.”
I have a few ideas on how this could be accomplished without resorting to AI.
The trick to solving this problem isn't in creating a better search engine, it's not even in artificial intelligence. Our best bet is to … let's say “abuse” … people to do what they're best at and that is telling computers what to do.
Except the user should not longer tell a computer what to do, they should do their work and the computer should, through observation, learn how to help them … I have no idea how to call such a technology.
The trick to solving this problem isn't in creating a better search engine, it's not even in artificial intelligence. Our best bet is to … let's say “abuse” … people to do what they're best at and that is telling computers what to do.
Except the user should not longer tell a computer what to do, they should do their work and the computer should, through observation, learn how to help them … I have no idea how to call such a technology.
When you say “What we need now is not a search engine — it’s something that solves the problem created by search engines.” I think that's key. There are different ways of interpreting solutions to the problem search engines created.
I was looking at this from the perspective of how do I put together knowledge from what I find. I see your approach as looking for results that are themselves the user's desired action (rather than pointers to it), so I really enjoyed reading your take here.
Regarding this coming from Google or outside the realm of existing search engines, I'd argue that Google is probably closer to solving this now than the others. I felt Bing was marketed as a decision engine but fell short of accomplishing anything like that, it still feels to me like it's playing catch-up to Google, which cannot be billed as much of a decision engine either.
Some of the comments on this post push for social context as aiding the process, which makes great sense. Another important thing is the individual user's history.
Based on what Google provides and tracks I think it largely has the context and the history as background information. It's in Google's search histories and various forms of social networking relationship data. Although it's spread out through the myriad services Google provides, looking at each of those you start to see that they have an awful lot they can connect to anticipate a user's tastes.
Additionally, Google foreshadowed the good point you make: “to minimize the need to search, and to make any search that is necessary as productive as possible.” Its deceptively simplistic “I'm Feeling Lucky” button, which gets equal billing to the “Google Search” button on the homepage.
Perhaps an “I'm Feeling Lucky” button doesn't sound so valuable because of its comical lable. But when it comes down to it, it functions (or could function with additional background intelligence) in service of minimizing the need to search. In fact, the “I'm Feeling Lucky” button suggests that Google's supposed disincentive to decrease search time is not an issue.
I don't mean to come off as promoting Google… just observing what direction this could go based on what is currently available.
You pointed out the idea that “The goal should be to get consumers to what they really want with the least amount of searching and the least amount of effort, with the greatest amount of confidence that the results are accurate and comprehensive.”
I'm wondering a few things about your perspective in that. First is just a point of clarification: by “consumer” do you mean people consuming the service the help engine provides or do you mean people using the service in order to find something they'll consume (like buying airplane tickets)?
I ask, because if it's the latter, then I understand you're talking about a very specific sort of service as opposed to one with a much broader purpose. In which case, as someone seeking to purchase something, it makes a lot of sense to me to get to my target purchase as directly as possible. Actually even if I'm not going to purchase something but just want to find out news about relief efforts in Haiti, for example, it probably makes sense to get me there as directly as possible.
On the other hand if it's the first sense of consume, I think it may change what the help engine is providing. It may then be useful for me to know about multiple sources of information. It may then be best to provide me with a range of results that would be relevant to the sort of activity I'm engaged in.
Determining the difference between these, I think is probably a significant challenge for any help engine (or evolved search engine) that comes along.
When you say “What we need now is not a search engine — it’s something that solves the problem created by search engines.” I think that's key. There are different ways of interpreting solutions to the problem search engines created.
I was looking at this from the perspective of how do I put together knowledge from what I find. I see your approach as looking for results that are themselves the user's desired action (rather than pointers to it), so I really enjoyed reading your take here.
Regarding this coming from Google or outside the realm of existing search engines, I'd argue that Google is probably closer to solving this now than the others. I felt Bing was marketed as a decision engine but fell short of accomplishing anything like that, it still feels to me like it's playing catch-up to Google, which cannot be billed as much of a decision engine either.
Some of the comments on this post push for social context as aiding the process, which makes great sense. Another important thing is the individual user's history.
Based on what Google provides and tracks I think it largely has the context and the history as background information. It's in Google's search histories and various forms of social networking relationship data. Although it's spread out through the myriad services Google provides, looking at each of those you start to see that they have an awful lot they can connect to anticipate a user's tastes.
Additionally, Google foreshadowed the good point you make: “to minimize the need to search, and to make any search that is necessary as productive as possible.” Its deceptively simplistic “I'm Feeling Lucky” button, which gets equal billing to the “Google Search” button on the homepage.
Perhaps an “I'm Feeling Lucky” button doesn't sound so valuable because of its comical lable. But when it comes down to it, it functions (or could function with additional background intelligence) in service of minimizing the need to search. In fact, the “I'm Feeling Lucky” button suggests that Google's supposed disincentive to decrease search time is not an issue.
I don't mean to come off as promoting Google… just observing what direction this could go based on what is currently available.
You pointed out the idea that “The goal should be to get consumers to what they really want with the least amount of searching and the least amount of effort, with the greatest amount of confidence that the results are accurate and comprehensive.”
I'm wondering a few things about your perspective in that. First is just a point of clarification: by “consumer” do you mean people consuming the service the help engine provides or do you mean people using the service in order to find something they'll consume (like buying airplane tickets)?
I ask, because if it's the latter, then I understand you're talking about a very specific sort of service as opposed to one with a much broader purpose. In which case, as someone seeking to purchase something, it makes a lot of sense to me to get to my target purchase as directly as possible. Actually even if I'm not going to purchase something but just want to find out news about relief efforts in Haiti, for example, it probably makes sense to get me there as directly as possible.
On the other hand if it's the first sense of consume, I think it may change what the help engine is providing. It may then be useful for me to know about multiple sources of information. It may then be best to provide me with a range of results that would be relevant to the sort of activity I'm engaged in.
Determining the difference between these, I think is probably a significant challenge for any help engine (or evolved search engine) that comes along.
Reading some of the comments about global experts systems and sophisticated semantic intelligence reminds me of our notion of space travel in the 1960's. Quaint, innocent and naive.
Instead of looking out for the next way to gather what you need – look in. Model the digital world after our real world. In the real world our social networks are organized and are fluid to accomplish a task at hand.
The human element, people in our social networks, will reemerge as the central pillar of technological innovation for search. It's why, for example, I love Twine – kinks and all. People make it work – not technology (in case you were wondering – I have no affiliation with Twine – honest 🙂
What will replace search technologies? Look no further than your peeps (kinda has that Wizard of Oz flavor doesn't it)
Judy Shapiro
Reading some of the comments about global experts systems and sophisticated semantic intelligence reminds me of our notion of space travel in the 1960's. Quaint, innocent and naive.
Instead of looking out for the next way to gather what you need – look in. Model the digital world after our real world. In the real world our social networks are organized and are fluid to accomplish a task at hand.
The human element, people in our social networks, will reemerge as the central pillar of technological innovation for search. It's why, for example, I love Twine – kinks and all. People make it work – not technology (in case you were wondering – I have no affiliation with Twine – honest 🙂
What will replace search technologies? Look no further than your peeps (kinda has that Wizard of Oz flavor doesn't it)
Judy Shapiro
Off course, one of Twine's powers is the “semantic intelligence” that it harnesses from users' contributions. This is done behind the scenes without users even realizing it.
Utilizing Semantic Web technologies is not a 1960's, naive pursuit. It is forward-looking thinking. It is something Twine has been doing since its inception. See these: http://www.novaspivack.com/science/minding-the-…
and
http://www.readwriteweb.com/archives/twine_firs…
i dont use search engings as much as i used to when i was in shcool, i would look for decent information on “grecko roman art” or “surrealists movement and what was there goal” and conduct my research from there, now i simply use search engings for things like “what are the 7 things you cant say on tv” becuse of the former is actully what people want to do with search engings, but cant, we are stuck searching for motinious shit on the net
i dont use search engings as much as i used to when i was in shcool, i would look for decent information on “grecko roman art” or “surrealists movement and what was there goal” and conduct my research from there, now i simply use search engings for things like “what are the 7 things you cant say on tv” becuse of the former is actully what people want to do with search engings, but cant, we are stuck searching for motinious shit on the net
Off course, one of Twine's powers is the “semantic intelligence” that it harnesses from users' contributions. This is done behind the scenes without user's even realizing it.
Utilizing Semantic Web technologies is not a 1960's, naive prusuit. It is forward-thinking. It is what will drive the Web into th
Lovely thoughts.. It reminds me of my “Wine Confusion Model in British,
Italian and Californian market vs Consume choice Report” in 2009 where
I have expressed how information overload (choices) creates confusion
leading to “information overload” and therefore confusion everywhere…
For e.g, If you have 400 brands of wines to chose from which one you'll
prefer and why ? The answer probably goes with a mixture of what Mr.
Spivack is trying to say in this Article.
Thanks for sharing !
Lovely thoughts.. It reminds me of my “Wine Confusion Model in British,
Italian and Californian market vs Consume choice Report” in 2009 where
I have expressed how information overload (choices) creates confusion
leading to “information overload” and therefore confusion everywhere…
For e.g, If you have 400 brands of wines to chose from which one you'll
prefer and why ? The answer probably goes with a mixture of what Mr.
Spivack is trying to say in this Article.
Thanks for sharing !
I am presently writing a book on this issue. My working title is “The End of Search”. Having worked in our industry for almost 30 years, I am starting to understand that normal search engines, as we use them today, are just an accident of our industry.
In the last 30 years we pumped tons of digital information on our servers and today we discover that we lost control. Most full text searches are getting so big that the results are getting every day more stupid and irrelevant. First our computers have to start understanding words or basic concepts. After that we have to create context. At the moment we a living the explosion of spacial context (maps). Only now we are discovering that information makes only sense in a context. In the past this was common sense, but the power of full text searches intrigued us all.
Whatever will follow search has to be contextual. My personal feeling is that regardless on how we call it, the process will be in the center of it. Having realized many ECM projects, I learned that not the information, but the process is in the center. In other words, I foresee that 95% of backoffice processes will be accomplished by computers. Finding the best flight, hotel or restaurant is definitely something a program or “agent” can do. We simply have to instruct the machines on what our parameters and wishes are.
We will see technology which enables us to express our personal preferences. Just think of the incredible amount of stupid tasks, which could be accomplished by a computer….
Another important issue about the end of search is the automatic classification of digital information during it's creation. When we create a content it, will be immediately logically linked to relevant processes. Therefore the need of search will dissappear.
Automatic processing will replace search. Information will come to us and we will have no need to dive for it.
I am presently writing a book on this issue. My working title is “The End of Search”. Having worked in our industry for almost 30 years, I am starting to understand that normal search engines, as we use them today, are just an accident of our industry.
In the last 30 years we pumped tons of digital information on our servers and today we discover that we lost control. Most full text searches are getting so big that the results are getting every day more stupid and irrelevant. First our computers have to start understanding words or basic concepts. After that we have to create context. At the moment we are living the explosion of spacial context (maps). Only now we are discovering that information makes only sense in a context. In the past this was obvious, but the power of full text searches intrigued us all.
Whatever will follow search has to be contextual. My personal feeling is that regardless on how we call it, the process will be in the center of it. Having realized many ECM projects, I learned that not the information, but the process is in the center. In other words, I foresee that 95% of backoffice processes will be accomplished by computers. Finding the best flight, hotel or restaurant is definitely something a program or “agent” can do. We simply have to instruct the machines on what our parameters and wishes are.
We will see technology which enables us to express our personal preferences. Just think of the incredible amount of stupid tasks, which could be accomplished by a computer….
Another important issue about the end of search is the automatic classification of digital information during it's creation. When we create a content it, will be immediately logically linked to relevant processes. Therefore the need of search will dissappear.
Automatic processing will replace search. Information will come to us and we will have no need to dive for it.
I am presently writing a book on this issue. My working title is “The End of Search”. Having worked in our IT industry for almost 30 years, I am starting to understand that normal search engines, as we use them today, are just an accident of our industry.
In the last 30 years we pumped tons of digital information on our servers and today we discover that we lost control. Most full text searches are getting so big that the results are getting every day more stupid and irrelevant. First our computers have to start understanding words or basic concepts. After that we have to create context. At the moment we a living the explosion of spacial context (maps). Only now we are discovering that information makes only sense in a context. In the past this was common sense, but the power of full text searches intrigued us all.
Whatever will follow search has to be contextual. My personal feeling is that regardless on how we call it, the process will be in the center of it. Having realized many ECM projects, I learned that not the information, but the process is in the center. In other words, I foresee that 95% of backoffice processes will be accomplished by computers. Finding the best flight, hotel or restaurant is definitely something a program or “agent” can do. We simply have to instruct the machines on what our parameters and wishes are.
We will see technology which enables us to express our personal preferences. Just think of the incredible amount of stupid tasks, which could be accomplished by a computer….
Another important issue about the end of search is the automatic classification of digital information during it's creation. When we create a content it, will be immediately logically linked to relevant processes. Therefore the need of search will dissappear.
Automatic processing will replace search. Information will come to us and we will have no need to dive for it.
ups, this was posted twice
Inversearch is an alternative to conventional search and pay-per-click advertising.
Consumers and companies can connect with multiple businesses simultaneously with a single confidential message, which increases efficiency, reduces information overload and produces relevant results. Businesses receive the queries and deliver their message in response, not in advance, which results in the lowest possible customer acquisition cost and the highest possible conversion rate.
Inversearch is an alternative to conventional search and pay-per-click advertising.
Consumers and companies can connect with multiple businesses simultaneously with a single confidential message, which increases efficiency, reduces information overload and produces relevant results. Businesses receive the queries and deliver their message in response, not in advance, which results in the lowest possible customer acquisition cost and the highest possible conversion rate.