5/20/2021 11:54:25 AM
Artificial Intelligence, modernity and interdisciplinary research
Artificial intelligence (AI) and Interdisciplinary Research (IDR) :
When applying knowledge from other fields to AI the relationship between AI and IDR must be considered as a two-way street.While one direction may be more well known (applying AI to other fields ), here we consider both directions; from AI to other fields and from other fields to AI. Then we argue that development is equally important to move forward and to achieve the full potential of the AI revolution.
What is AI?
We have claimed that AI is exciting, but we have not said what it is. Definitions of artificial intelligence according to eight textbooks are shown in this context. These definitions vary along two main dimensions; roughly, the ones on the top are concerned with thought processes and reasoning; whereas the ones on the bottom address behavior. The definitions on the left measure success in terms of fidelity to human performance, whereas the ones on the right measure against an ideal concept of intelligence, which we call rationality. A system is rational if it does the right thing, given what it knows.
Some definitions of artificial intelligence:
Firstly, systems that think like humans :
The exciting new effort to make computers think …machines with minds, in the full and literal sense. [The automation of ] activities that we associate with human thinking, activities such as decision-making, problem-solving, learning, etc
Systems that act like humans :
The art of creating machines that perform functions that require intelligence when performed by people. The study of how to make computers do things at which, at the moment, people are better.
Secondly, systems that think rationally :
The study of mental faculties through the use of which, at the moment, people are better and the study of the computations that make it possible to perceive, reason, and act.
Thirdly, systems that act rationally:
Computational Intelligence is the study of the design of intelligent agents. AI is concerned with intelligent behavior in artifacts.
Forthcoming, systems that think human-like:
If we are going to say that a given program thinks like a human, we must have a perspective of how humans think. We need to get inside the actual functions of the human mind. This action to do through introspection-trying to catch our thoughts as they go by and through psychological experiments. If the program's input /output and timing behaviors match corresponding human behaviors, reflects that some of the mechanisms of the program could also be operating in humans. The interdisciplinary field of cognitive science brings together computer models from AI and experimental techniques and psychology to try to construct precise and testable theories of the human mind functions. Both AI and cognitive sciences are developing rapidly, especially in the areas of vision and natural language; Vision in particular has recently made advances via an integrated approach that considers neurophysiological evidence and computational models.
Fifthly, systems that think rationally :
The Greek philosopher Aristotle was one of the first to attempt to codify right thinking, that is, irrefutable reasoning processes. His syllogisms provided patterns for argument structures that always yielded correct conclusions when given correct premises-for example, Socrates is a man, all men are mortal, therefore, Socrates is mortal. There are two main obstacles to this approach; first, it is not easy to take informal knowledge and state it in the formal terms required by logical notation, particularly when the knowledge is less than 100 % certain. Second, there is a big difference between being able to solve a problem in theory and doing so in practice.
Sixthly, systems that act rationally :
An agent is just something that acts ( agents come from the Latin agree, to do ). But computer agents are expected to have other attributes that distinguish them from mere programs. A rational agent acts to achieve the
best outcome or when there is uncertainty, the best expected outcome. In the law of thought approaches to AI, the emphasis was on correct inferences. Making correct inferences is sometimes a part of being a rational agent because one way to act rationally is to reason logically to the conclusion that a given action will achieve one's goals and then act on that conclusion. Considering AI in rational agents design has at least two advantages; First, it is more general than the laws of thought approach, because the correct inference is just one of several possible mechanisms for achieving rationality; Second, it is more amenable to scientific
development than the approaches based on human behavior or human thought because the standard of rationality is clearly defined and completely general. Human behavior, on the other hand, is well-adapted for one specific environment and is the product, in part, of a complicated and largely unknown evolutionary process that still is far from producing perfection.
By: Motahareh Sadat kollolemad, Rania Rajabi
Members of the Systems Artificial Intelligence Network (SAIN) interest group
The book of :
Artificial Intelligence, A Modern Approach From Stuart Russell and Peter Norvig
Kusters R, Misevic D, Berry H, Cully A, Le Cunff Y, Dandoy L, Díaz-Rodríguez N, Ficher M, Grizou J, Othmani A, Palpanas T, Komorowski M, Loiseau P, Moulin Frier C, Nanini S, Quercia D, Sebag M, Soulié Fogelman F, Taleb S, Tupikina L, Sahu V, Vie J-J and Wehbi F (2020) Interdisciplinary Research in Artificial Intelligence: Challenges and Opportunities. Front. Big Data 3:577974. doi:10.3389/fdata.2020.577974