Talk on Why machines will soon have phenomenal consciousness by Kevin O’Regan (CNRS, Université Paris Descartes)
1:40 p.m.
We are happy to announce the second talk of the series of webinars at IDSIA dedicated to
the foundational, epistemological and ethical aspects of AI.
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*Title: Why machines will soon have phenomenal consciousness
*Abstract: Most philosophers and scientists are convinced that there is a "hard
problem" of consciousness and an "explanatory gap" preventing us from
understanding why experiences feel the way they do and why they have "something
it's like”. My claim is that this attitude may derive from the (understandable) desire
to preserve the last remnants of human uniqueness before the onslaught of conscious
robots. My "sensorimotor" approach to understanding phenomenal consciousness
suggests that this is a mistake. If we really think deeply about what we mean by having a
phenomenal experience, then there is no reason why machines should not be conscious very
soon, i.e. in the next decades.
*Speaker: Prof. Kevin O’Regan, CNRS, Université Paris Descartes.
(personal website http://nivea.psycho.univ-paris5.fr)
*Date: Wednesday 12th May, 5pm, on Zoom.
*Zoom Link: https://supsi.zoom.us/j/62721570561?pwd=N1FyZHhwTDVSSS9Rem4zWGRrY1I1QT09
ID meeting: 627 2157 0561
Passcode: oregan
8:08 a.m.
New subject: Talk on "Analogue Models and Universal Machines. Paradigms of Epistemic Transparency in AI" by Hajo Greif (Warsaw University of Technology)
We are happy to announce the third talk of the series of webinars at IDSIA dedicated to
the foundational, epistemological and ethical aspects of AI.
(For informations and events related to the topics of the series, please subscribe to the
mailing list https://lists.usi.ch/mailman3/postorius/lists/lumi.lists.idsia.ch/ )
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*Title: Analogue Models and Universal Machines. Paradigms of Epistemic Transparency in
Artificial Intelligence
*Abstract: The problem of epistemic opacity in Artificial Intelligence (AI) is often
characterised as a problem of intransparent algorithms that gives rise to intransparent
models. However, the dichotomy in the pertinent literature between a perceived necessity
of making AI algorithms transparent and the countervailing claim that they give rise to an
'essential' epistemic opacity of AI models might be false. Instead, epistemic
transparency is primarily a function of the degrees of an epistemic agent's perceptual
or conceptual grasp of the relevant elements and relations in a model, which might vary in
kind and in accordance with the pertinent level of modelling. In order to elucidate this
hypothesis, I first contrast computer models and their claims to algorithm-based
universality with cybernetics-style analogue models and their claims to structural
isomorphism between model and target system (Black 1962). I then undertake a comparison
between contemporary AI approaches that variously align with these modelling paradigms:
Behaviour-based AI, Deep Learning and the Predictive Processing Paradigm. I conclude that
epistemic transparency of the algorithms involved in these models is not sufficient and
might not always be necessary to meet the condition of recognising their epistemically
relevant elements.
*Speaker: Hajo Greif, Research Assistant Professor, Philosophy of Computing Group, ICFO,
WAiNS, Warsaw University of Technology (personal webpage: http://hajo-greif.net)
*Date: Friday, May 28, 2021, at 17:00 (5pm), on Zoom.
*Zoom Link: https://supsi.zoom.us/j/63801449377?pwd=WEZiRzV0UWJ0a3kzSXpQSjZNNTl6Zz09
ID meeting: 638 0144 9377
Passcode: hajo
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