What Artificial Intelligence isn’t!

An Italian mystic and Catholic friar, Francis of Assisi said, ‘start by doing what’s necessary; then do what’s possible; and suddenly you are doing the impossible. 

When we sat down to write about Artificial Intelligence after a long and lasting interval we felt we had to separate the grain from the chaff. So, we started defining artificial intelligence when there was practically none that could bring the entirety of what AI is or thought of, under one coherent sentence. With the help of Francis of Assisi, we worked on doing the necessary - what Artificial Intelligence isn’t - the result of which is this first blog. In its series, we should either arrive at the proper definition of it or achieve the impossible, or both. Because of the massive, often quite unintelligible publicity that it gets, Artificial Intelligence is almost completely misunderstood by individuals outside the field. Even AI’s practitioners are somewhat confused about what AI really is.

Is AI mathematics? 

A great many AI researchers believe strongly that knowledge representation used in AI programs must conform to previously established formalisms and logics, or the field is unprincipled and ad hoc. Many AI researchers believe that they know how the answer will turn out even before they have figured out what exactly the questions are. They know that some mathematical formalism or other must be the best way to express the contents of the knowledge which people have. Thus, to these researchers, AI is an exercise in the search for the proper formalisms to use in representing knowledge. 

Is AI software engineering? 

A great many AI practitioners seem to think so. If you can put knowledge into a program, then this program must be an AI program. This conception of AI, derived as it is from much of the work going on in industry in expert systems, has served to confuse AI people tremendously about what the correct focus of AI ought to be and what the fundamental issues in AI are. If AI is just so much software engineering, if building an AI program primarily means the addition of domain knowledge such that a program knows about insurance or geology, for example, then what differentiates an AI program in insurance from any other computer program which works within the field of insurance? Under this conception, it is difficult to determine where software engineering leaves off and where ai begins. 

Is AI linguistics? 

A great many AI researchers seems to think that building grammars of english and putting those grammars on a machine is AI. Of course, linguistics have never thought of their field as having much to do with AI at all. However, as money for linguistics has begun to disappear and money for AI has increased, it has become increasingly convenient to claim that work on language which had nothing to do with computers at al has some computational relevance. Suddenly, theories of language that were never considered by their creators to be process models at all are now proposed as AI models. 

Is AI psychology? 

Would building a complete model of human thought processes and putting it on a computer be considered a contribution to AI? Many AI researchers could not care less about the human mind, yet the human mind is the only kind of intelligence that we can reasonably hope to study. We have an existence proof. We know the human mind works. However, in adopting this view, one still has to worry about computer models that display intelligence but are clearly in no way related to how humans function. Are such models intelligent? Such issues inevitably force one to focus on the issue of the nature of intelligence paart from its particular physical embodiment.  

So, what should AI be? 

In the end, the question of what AI is all about probably doesn’t have just one answer. What Artificial Intelligence is, depends heavily on the goals of the researchers involved, and any definition of it is dependant upon the methods that are being employed in building its models. Last, of course, it is a question of results. These issues about what AI is exist precisely because the development of AI has not yet been completed. They will disappear entirely when a machine really is the way writers of science fiction have imagined it could be. 

Most practitioners would agree on two main goals in AI. The primary goal is to build an intelligent machine. The second goal is to find out about the nature of intelligence. Both goals have at their heart a need to define intelligence. AI people are fond of talking about intelligent machines, but when it comes down to it, there is very little agreement about what exactly constitutes intelligence. It follows that the littles agreement exists in the ai community about exactly what AI is and what it should be. We all agree that we would like to endow machines with an attribute we really can’t define. Needless to say, AI suffers from a lack of definition of its scope. 

One way to attack this problem is to attempt to list some features that we would expect an intelligent entity to have. None of those features would define intelligence, indeed a being could lack any one of them and still be considered intelligent. Nevertheless each attribute would be an integral part of intelligence in its way. We will list out the features that we consider to be critical and them briefly discuss them in the next blog. They are communication, internal knowledge, world knowledge, intentionality, and creativity.