Abstract
Locked and unlocked strategies are at the center of this article, as ways of shedding new light on the cognitive aspects of deep learning machines. The character and the role of these cognitive strategies, which are occurring both in humans and in computational machines, is indeed strictly related to the generation of cognitive outputs, which range from weak to strong level of knowledge creativity. I maintain that these differences lead to important consequences when we analyze computational AI programs, such as AlphaGo, which aim at performing various kinds of abductive hypothetical reasoning. In these cases, the programs are characterized by locked abductive strategies: they deal with weak (even if sometimes amazing) kinds of hypothetical creative reasoning, because they are limited in what I call eco-cognitive openness, which instead qualifies human cognizers who are performing higher kinds of abductive creative reasoning, where cognitive strategies are instead.
Highlights
I will contend that in AlphaGo only locked strategies are at play, and this fact affects the type of creativity which is in general performed by deep learning machines
Locked and unlocked strategies are at the center of this article, as ways of shedding new light on the cognitive aspects of deep learning machines
With the help of the concepts of locked and unlocked strategies, abduction, and optimization of eco-cognitive openness, I have described some central aspects of the cognitive character of reasoning strategies and related heuristics, to the aim of shedding new light on the cognitive aspects of deep learning machines
Summary
The concept of abduction has been involved in AI at least since the beginnings of this young discipline. Already in 1988 Paul Thagard [6] described four types of abduction implemented in PI, a computational program devoted to perform some of the main cognitive abilities illustrated by philosophy of science: scientific discovery, explanation, evaluation, etc.. Fundamental cognitive processes (such as abduction, disregarded by the mainstream deductive logic tradition), when implemented in a computer, become AI programs: the abstract theories and the more concrete computational programs become two different ways of expressing the same thing. In this sense, theories of reasoning are about rules for reasoning and these are rules that teach us to do certain things in certain situations. What about the new perspectives on hypothetical abductive reasoning offered by deep learning programs such as AlphaGo? As I have anticipated, to clarify the cognitive character of this program, the examination of the kinds of strategies that are at play is in my opinion central
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