AI Redefined Given what we have studied in this

Transcript:AI Redefined Given what we have studied in this:
AI Redefined Given what we have studied in this course, the author offers a new definition AI is the study of the mechanisms underlying intelligent behavior through the construction and evaluation of artifacts designed to enact those mechanisms There are several noteworthy things about this definition we only commit to intelligent behavior evaluation is a critical component for the definition – we are no longer relying just on the Turing Test, but instead we look to evaluate the performance of any AI system – although the definition does not tell us how to perform that evaluation the emphasis here is on artifacts – working systems AI as a Field From the definition, we see that AI is (or should be) less concerned about a central or single theory of mind it is more empirically targeted – working systems Thus, AI is an engineering pursuit the creation of working systems The need for evaluation makes AI a science while creating systems is well and good, without analyzing the systems to understand why they work or do not work, AI will be working in a void This definition denotes a paradigm shift away from philosophy of mind – we do not need to study mind to create mind, human mind might help with models but the formal studies found in philosophy have done little to help psychology of the human mind – we may gain some understanding of what to do through experimentation but again, this sort of pursuit has led AI astray PSS Hypothesis Redux Recall in our first lecture, we considered the PSS Hypothesis – a PSS has the necessary and sufficient means to exhibit intelligent action the focus on this hypothesis led AI to research use of symbols to model the world design of search strategies to apply operators on the given symbols use of heuristics to guide the search the use of an empirical approach to research – build (construct) and test to prove your point While this has helped form a basis for AI, it has also misled AI as much as earlier reliance on philosophy and psychology do we need symbolic knowledge? neural networks show otherwise do we need heuristic search strategies? model based approaches (whether structural/functional, Bayesian or HMM based seem to indicate less need for this) Why Has AI Not Succeeded? The author continues by examining the challenges of symbolic AI – lack of