Cognificance

Cognificance

About the significance of machine cognition

Cognificance

About the significance of machine cognition

Artificial Intelligence (AI)



Wikipedia defines cognition to encompass processes such as knowledge, attention, memory and working memory, judgment and evaluation, reasoning and "computation", problem solving and decision making, comprehension and production of language. AI can take care of some of these requirements (such as evaluation, problem solving and production of language), but should not be thought of on the same level as cognition.

This is where I am at odds with the Wikipedia definition of AI, as that article attributes too many capabilities to AI that, in my humble opinion, don't apply. If we look at the Wikipedia definition of intelligence and work from there, we get closer to what I feel AI is (and isn't): "Intelligence is … the ability to perceive information, and to retain it as knowledge to be applied towards adaptive behaviors within an environment or context."

As in an animal, intelligence allows it to recognize a predator in the bushes and to initiate a reaction (running away). The gruntwork of getting away from becoming breakfast isn't taken care of by the intelligence mechanism, but rather by older parts of the brain, hormones in the bloodstream and muscles. Similarly, artificial intelligence is able to recognize patterns in data and initiate reactions to that data, but the gruntwork is done by software - anything from simple scripts to macro collections to full-blown Robotic Process Automation.

An important point about AI is that in order for pattern recognition to work, a so-called learning set needs to be generated. There is an important distinction to be made, however: just because a software is able to do pattern recognition does not necessarily qualify it as being an AI! Take for example products developed in the early 2000's that you would pass sample forms (usually scanned paper) so that an automated differentiation might be made between form layouts. Given a sample set of about 20 forms each, these algorithms are able to differentiate between up to 400 different forms (document classes) at lightning speed and with exceptionally high recognition quality. The technology behind this capability has nothing to do with AI, but rather with statistical analysis.

The difference between an AI trained on forms and one of these statistical analizers is that given an unknown document class, the algorithm will spit it out and tell you that this document doesn't fit any of the "learned" ones. An AI will tell you that it doesn't fit as well but is able to suggest similarities and take a "guess" at what document class it may be related to. That's an oversimplification, of course, but the point is: AI recognizes unknown patterns and tries to define them. statistical Algorithms don't.

It is unfortunate that many people, including IT journalists (who should know better) confuse the workings of a statistical analysis algorithm with true AI, adding to the watering-down of AI as a term.