The future of eco-cognitive settings: Computationally or humanly tailored?

In 2015 Google DeepMind’s program AlphaGo (able to perform the famous Go game) beat Fan Hui, the European Go champion and a 2 dan (out of 9 dan) professional, five times out of five with no handicap on a full size 19x19 board. Later on, in March 2016, Google AlphaGo also challenged Lee Sedol, a 9 dan player who is considered the top world champion, to a five-game match. The DeepMind program defeated Lee in four of the five games. It was said that the program “invented” a new and unconventional move – never adopted by human beings – which was able to originate a new strategic framework, phenomenologically seen as simulating a proper “human” skillful ability, better than the ones of the more experienced humans. I contend that it is in the framework of abductive cognition that we can appropriately and usefully analyze the concept of strategic reasoning occurring in such computational systems to the aim of seeing the distinction between locked and unlocked strategies. Indeed 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 my presentation, as ways of shedding new light on a fundamental important subcategory of algorithmic decision-making systems: the deep learning programs that are able to generate hypotheses that lead to appropriate decisions and subsequent possible actions. I said that these 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 their eco-cognitive openness, which instead qualifies human cognizers who are performing higher kinds of abductive creative reasoning, where cognitive strategies are instead unlocked.