1/ Most disciplines emerge around specific domains of knowledge (e.g. biology - living forms, physics - laws of the universe, law - legal systems and justice, etc.). AI is instead defined by a purpose: conceiving artificial systems that are "intelligent".
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2/ Historically, many forms of intelligence (or rather, methods to (re)produce appropriate behaviours) have been explored through a cycles of springs (and winters). Deep learning is just the last of a series.
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3/ Although each time the mainstream topic eclipsed all the rest, the subsequent advances were enabled only because fundamental research in the others somehow continued. [Diversity in research should be really enforced as a policy, just as in evolutionary computation.]
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4/ Each spring started in practice from 5 ingredients: societal needs, acceptable theoretical paradigms, strong advocates, initial unexpected successes, adequate computational technologies. These trigger and are reinforced by adequate funding, starting a hype cycle.
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5/ The hype cycle of raising expectations, illusions and then delusions seems to be systemic. Eacht time the "novel" methods reach a plateau. The problem of intelligence looks again more general/complex than it seemed shortly before.
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6/ Still,(most of the times, an hype cycle results in concrete achievements. They just become infrastructure: invisible, but necessary for a next wave to come.
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7/ Focusing on the current mainstream methods, in supervised ML, by construction, the model is made to satisfy the training samples. What is “right” is set during training. But what to do if what is “right” change after training? For instance, if this is specified by law?
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8/ AI/ML capitalizes too much on optimization, but the contexualization phase (e.g. settling what is "right") might be even more problematic, for instance w.r.t. the social environment, for its high variability. There is a technological gap here [see e.g. our paper on normware].
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9/ Even from a philosophical standpoint, if our aim is to pursue rationality (rational systems, rational institutions, etc.), it is rather obvious that this won't be obtained by empirical means only.
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Super interesting, thanks!
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