This dimension for understanding AI refers to how a computer program reaches its conclusion. Symbolic AISymbolic vs non-symbolic AIrefers to the fact that all steps are based on “symbolic”human-readable representations of the problems which use logic and searchto solve problems. Expert Systems are a typical example of symbolic AIas the knowledge is encoded in IF-THEN rules which are understandable bypeople. NLP systems which use grammars to parse language are also symbolic AI systems. Here the symbolic representation is the grammar ofthe language.The main advantage of symbolic AI is that the reasoning process canbe understand by people, which is a very important factor for takingimportant decisions. A symbolic AI program can explain why a certainconclusion is reached and what the intermediate reasoning steps havebeen. This is key for using AI systems that give advice on medicaldiagnosis; if doctors cannot understand why an AI system comes to its conclusion, it is harder for them to accept the advice.
|Figure 2: A symbolic and non-symbolic representation of an apple (source http://web.media.mit.edu/~minsky/papers/SymbolicVs.Connectionist.html).|
The final question: Can machines think? Are humans machine?
|Figure 3: Can computers express of feel emotions?|
In my opinion, currently, there are noscientific answers to those questions, and whatever you may think aboutit, is more a belief or conviction than a commonly accepted truth or a scientific result.Maybe we have to wait until 2045, which is when Ray Kurzweil predicts technological singularityto occur: the point when machines become more intelligent than humans.While this point is still far away and many believe it will neverhappen, it is a very intriguing theme evidenced by movies such as 2001: ASpace Odyssey, A.I. (Spielberg), Ex Machina and Her, among others.