Collective Intelligence Outsmarts Artificial Intelligence

When computers first started  to infringe on everyday life, science fiction authors and society in general had high expectations for "intelligent" systems. Isaac Asimov's "I, Robot" series from the 1940s portrayed robots with completely human intelligence and personality, and, in the 1968 movie "2001: A Space Odyssey," the onboard computer HAL (Heuristically programmed ALgorithmic computer) had a sufficiently human personality to suffer a paranoid break and attempt to murder the crew!

While the computer revolution has generally outstripped almost all expectations for the role of computers in society, in the area of artificial intelligence (AI), the predictions have, in fact, outstripped our achievements. Attempts to build truly intelligent systems have been generally disappointing.

Fully replicating human intelligence would require a comprehensive theory of consciousness which we unfortunately lack. Therefore, AI has generally attempted to focus on simulating intelligent behavior, rather than intelligence itself. In the algorithmic approach, programmers labor to construct sophisticated programs that emulate a specific intelligent behavior, such as voice recognition. In the other traditional approach - expert systems - a database of facts is collected, and logical routines applied to perform analysis and deduction. Expert systems have had some success in medical and other diagnostic applications, such as systems performance management.

Each of these approaches has shown success in limited scenarios, but neither achieves the sort of broadly intelligent system promised in the early days of computing. Attempts to emulate more human-like cognitive or learning systems-using technologies such as the neural nets, fuzzy logic, and genetic algorithms-have only slightly improved the intelligence of everyday software applications.

Most of us experience the limitations of artificial intelligence every day. Spell-checkers in applications such as Microsoft Word do an amazingly poor job of applying context to language correction. As a result, sentences such as, "Eye have a spelling checker, it came with my pea sea," pass through the Microsoft spelling and grammar checker without a hitch. While the Microsoft software can recognize spelling mistakes in individual words, it cannot understand the meaning of the sentence as a whole, and the result is a long way from intelligent judgment.  

Collective intelligence offers a powerful alternative to traditional artificial intelligence paradigms. Collective intelligence leverages the inputs of large numbers of individuals to create solutions that traditional approaches cannot achieve. Although the term "collective intelligence" is not widely recognized, most of us experience the results of collective intelligence every day. For instance, Google uses collective intelligence when auto-correcting search inputs. Google has a large enough database of search terms to be able to automatically detect when you make an error and correct that error on-the-fly.  Consequently, Google is more than able to determine that "pea sea" is almost certainly meant to be "PC." 

Collective intelligence not only allows for superior spelling and grammar correction, but also is used in an increasingly wide variety of contexts, including spam detection, diagnostic systems, retail recommendations, predictive analytics, and many other fields. Increasingly, organizations find that it is more effective to apply brute force algorithms to masses of data generated by thousands of users, than to attempt to explicitly create sophisticated algorithmic models.  

The ability of collective intelligence to solve otherwise intractable business and scientific problems is one of the driving forces behind the "big data" evolution. Organizations are increasingly realizing that the key to better decision making is not better programs but granular crowd-sourced data sets.

Collective intelligence is merely one of the techniques used to endow computer systems with more apparent intelligence and to better solve real world problems - it's not in any way a replacement for the human brain. However, in an increasingly wide range of applications, collective intelligence is clearly outsmarting traditional artificial intelligence approaches.