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giovanni sileno proslijedio/la je Tweet
if you need AI to tell the teacher when students are bored, you have a problem with teacher recruitment, not with AI.https://twitter.com/k_triquet/status/1217371390183165952 …
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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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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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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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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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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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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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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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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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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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A (slightly updated) selection of considerations on the "History of AI, Current Trends, Prospective Trajectories", I gave today
@TMCAsser for the Winter Academy on AI and International Law https://www.slideshare.net/GiovanniSileno/history-of-ai-current-trends-prospective-trajectories-222188002 …. Thanks@bereniceboutin for inviting me again! Main messages:Prikaži ovu nitHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
Maintaining non-computationally driven procedural knowledge is highly critical to prepare to any event in which technology might suddenly stop working. Do "modern" countries have any strategic backup?
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giovanni sileno proslijedio/la je Tweet
Fascinating case for many reasons, including reasons relating to trias politicahttps://twitter.com/fborgesius/status/1208335318920957952 …
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Statistical biases reflect frequencies in the data. In contrast, discriminatory biases reflect the role of the algorithms determining decision-making structures, that in turn produce social discriminations. They're rather orthogonal, even without looking at people.https://twitter.com/ylecun/status/1203211859366576128 …
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"expected utility theory implicitly assumes that individuals can interact with copies of themselves, effectively in parallel universes (the other members of the ensemble)", an ergodic view on utility can leave the individual in only one world:https://www.nature.com/articles/s41567-019-0732-0 …
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As long as a cyber-physical system eventually interacts with humans, its technical infrastructure will eventually derive its semantics from the social interactions it enables, and for this reason it can always be used for purposes unintended at design time.
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Said that, if we judge under this lens our contemporary societies, their "intelligence" with respect to global warming is rather limited. Note that whether it is or it is not caused by humans is not relevant here, but how much are we preparing to what is coming.
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Yet, this definition is particularly interesting because it does not have any technological flavour. It can be applied to evaluate the (practical) intelligence of forms of life, as well as of organizations, of societies, and of human individuals.
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At second sight, however, the problem is just postponed. Who defines what's "correct"? And for who? in which other contexts is it still correct? Does it take into account the short and the long terms? etc.
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Intelligence is one of those many words used often but not easy to define. The field of artificial intelligence started from a simple pragmatic choice: intelligence is (selecting and) performing correct behaviour.
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