Failure to move forward is often not due to a lack of problem-solving capacity, but to failure of properly articulating goals, formulating relevant questions, and following appropriate measures of success.
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If the only successes of AI have been in developing narrow task-specific systems, it is because only within a highly specialized and grounded context are we able to *define the goal* sufficiently precisely, and to *measure progress*. "General AI" is far too vague to be actionable
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The solution must be co-evolved with the problem definition.
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I guess there will not be an "all" intelligence system. Just like there are no "all" intelligent individuals. Intelligence is specialized and so will AI systems.
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You are correct of course, but a number of gradient descent fanatics would disagree.
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@fchollet Are you familiar with@Numenta,@JeffCHawkins and the theory of#HTM? If so, what are your thoughts regarding their approach to machine intelligence?Thanks. Twitter will use this to make your timeline better. UndoUndo
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We also need more work that attempts to build fully-functional agents rather than just I/O functions. I think we would learn more than we do working on narrow tasks. But as you say, measuring performance is difficult.
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I have proposed using multiple concrete definitions simultaneously https://twitter.com/Abel_TorresM/status/1140528797739102209 … Building "fully-functional agents" goes against the existing mindset of Intelligence = Optimality. An agent capable of flexibly adapting to several tasks will not be optimal in all of them
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Completely agree but can we define intelligence? The standard IQ scores are sometimes irrelevant.
Thanks. Twitter will use this to make your timeline better. UndoUndo
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Are cells, mitochondria, viruses intelligent? Such broad word.
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Some say choosing your next action is decision making and the process is involved/part of intelligence. So the stochasticity in cell is/may also be part of it.https://www.amazon.com/Wetware-Computer-Every-Living-Cell/dp/0300167849 …
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