AlphaGo, Locked Strategies, and Eco-Cognitive Openness

Magnani, Lorenzo

doi:10.3390/philosophies4010008

Cited by 15 publications

(2 citation statements)

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“…9 We may note that, inasmuch as prediction is a standard issue in philosophy of science, we have very good reasons to expect fruitful results from the study of prediction in human/computer games of Baduk. Magnani's inquiries on anticipation as a kind of abduction in human and machine cognition could be one of the rare precedents in that direction of research [52][53][54][55]. In that sense, the abrupt disappearance of the policy network in AlphaGo's mechanism is unfortunate.…”

Section: Conflicts Of Interestmentioning

confidence: 99%

How to Make AlphaGo’s Children Explainable

Park

2022

Philosophies

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Under the rubric of understanding the problem of explainability of AI in terms of abductive cognition, I propose to review the lessons from AlphaGo and her more powerful successors. As AI players in Baduk (Go, Weiqi) have arrived at superhuman level, there seems to be no hope for understanding the secret of their breathtakingly brilliant moves. Without making AI players explainable in some ways, both human and AI players would be less-than omniscient, if not ignorant, epistemic agents. Are we bound to have less explainable AI Baduk players as they make further progress? I shall show that the resolution of this apparent paradox depends on how we understand the crucial distinction between abduction and inference to the best explanation (IBE). Some further philosophical issues arising from explainable AI will also be discussed in connection with this distinction.

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Section: Conflicts Of Interestmentioning

confidence: 99%

How to Make AlphaGo’s Children Explainable

Park

2022

Philosophies

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“…Equivalently, measurements become data when related to a specific and pre-existing hypothesis. It follows that numerical representations of observations or experimental results cannot "speak by themselves" because their status as data is defined by their relation to a necessary context, established through an open-ended abductive process characteristic of human creativity Magnani (2019).…”

Section: Popper-miller In a Nutshellmentioning

confidence: 99%

Why Probability isn’t Magic

Rigat

2021

Found Sci

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Abstract“What data will show the truth?” is a fundamental question emerging early in any empirical investigation. From a statistical perspective, experimental design is the appropriate tool to address this question by ensuring control of the error rates of planned data analyses and of the ensuing decisions. From an epistemological standpoint, planned data analyses describe in mathematical and algorithmic terms a pre-specified mapping of observations into decisions. The value of exploratory data analyses is often less clear, resulting in confusion about what characteristics of design and analysis are necessary for decision making and what may be useful to inspire new questions. This point is addressed here by illustrating the Popper-Miller theorem in plain terms and using a graphical support. Popper and Miller proved that probability estimates cannot generate hypotheses on behalf of investigators. Consistently with Popper-Miller, we show that probability estimation can only reduce uncertainty about the truth of a merely possible hypothesis. This fact clearly identifies exploratory analysis as one of the tools supporting a dynamic process of hypothesis generation and refinement which cannot be purely analytic. A clear understanding of these facts will enable stakeholders, mathematical modellers and data analysts to better engage on a level playing field when designing experiments and when interpreting the results of planned and exploratory data analyses.

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