I think you could make a strong argument that the most important technologies developed over the last decade are a set of systems that are sometimes called “collective knowledge systems”.
The most successful collective knowledge system is the combination of Google plus the web. Of course Google was originally intended to be just a search engine, and the web just a collection of interlinked documents. But together they provide a very efficient system for surfacing the smartest thoughts on almost any topic from almost any person.
The second most successful collective knowledge system is Wikipedia. Back in 2001, most people thought Wikipedia was a wacky project that would at best end up being a quirky “toy” encyclopedia. Instead it has become a remarkably comprehensive and accurate resource that most internet users access every day.
Other well-known and mostly successful collective knowledge systems include “answer” sites like Yahoo Answers, review sites like Yelp, and link sharing sites like Delicious. My own company Hunch is a collective knowledge system for recommendations, building on ideas originally developed by “collaborative filtering” pioneer Firefly and the recommendation systems built into Amazon and Netflix.
Dealing with information overload
It has been widely noted that the amount of information in the world and in digital form has been growing exponentially. One way to make sense of all this information is to try to structure it after it is created. This method has proven to be, at best, partially effective (for a state-of-the-art attempt at doing simple information classification, try Google Squared).
It turns out that imposing even minimal structure on information, especially as it is being created, goes a long way. This is what successful collective knowledge systems do. Google would be vastly less effective if the web didn’t have tags and links. Wikipedia is highly structured, with an extensive organizational hierarchy and set of rules and norms. Yahoo Answers has a reputation and voting system that allows good answers to bubble up. Flickr and Delicious encourage user to explicitly tag items instead of trying to infer tags later via image recognition and text classification.
Importance of collective knowledge systems
There are very practical, pressing needs for better collective knowledge systems. For example, noted security researcher Bruce Schneier argues that the United States’ biggest anti-terrorism intelligence challenge is to build a collective knowledge system across disconnected agencies:
What we need is an intelligence community that shares ideas and hunches and facts on their versions of Facebook, Twitter and wikis. We need the bottom-up organization that has made the Internet the greatest collection of human knowledge and ideas ever assembled.
The same could be said of every organization, large and small, formal and and informal, that wants to get maximum value from the knowledge of its members.
Collective knowledge systems also have pure academic value. When Artificial Intelligence was first being seriously developed in the 1950’s, experts optimistically predicted they’d create machines that were as intelligent as humans in the near future. In 1965, AI expert Herbert Simon predicted that “machines will be capable, within twenty years, of doing any work a man can do.”
While AI has had notable victories (e.g. chess), and produced an excellent set of tools that laid the groundwork for things like web search, it is nowhere close to achieving its goal of matching – let alone surpassing – human intelligence. If machines will ever be smart (and eventually try to destroy humanity?), collective knowledge systems are the best bet.
Should the US government just try putting up a wiki or micro-messaging service and see what happens? How should such a system be structured? Should users be assigned reputations and tagged by expertise? What is the unit of a “contribution”? How much structure should those contributions be required to have? Should there be incentives to contribute? How can the system be structured to “learn” most efficiently? How do you balance requiring up front structure with ease of use?
These are the kind of questions you might think are being researched by academic computer scientists. Unfortunately, academic computer scientists still seem to model their field after the “hard sciences” instead of what they should modeling it after — social sciences like economics or sociology. As a result, computer scientists spend a lot of time dreaming up new programming languages, operating system architectures, and encryption schemes that, for the most part, sadly, nobody will every use.
Meanwhile the really important questions related to information and computer science are mostly being ignored (there are notable exceptions, such as MIT’s Center for Collective Intelligence). Instead most of the work is being done informally and unsystematically by startups, research groups at large companies like Google, and a small group of multi-disciplinary academics like Clay Shirky and Duncan Watts.