Agree but there are already a lot of people who fit this description. The danger is more that the next generation of researchers might not.
“We need a new generation of AI researchers who are well versed in and appreciate classical AI, machine learning, and computer science more broadly while also being informed about AI history.” @AdnanDarwiche, on why deep learning is not enough https://dl.acm.org/citation.cfm?id=3281635.3271625&coll=portal&dl=ACM …
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I’m not sure. As a litmus test: what proportion of deep learning trained reseachers have even heard of CYC?
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FYI, wrong Adnan Darwiche you have tagged there :-)
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yep realized this too late :(
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Definitely agree. We have a growing group of data scientists / machine learning engineers that only worked with deep learning. Broader view and experience is needed to progress the field.
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Hi
@GaryMarcus what about plain old statistics, epidemiology, experiment design? So many things can and do go wrong before your precious data is collected. AI in medicine is often similar to the mythical Golem. A bronze giant on clay feet :)Thanks. Twitter will use this to make your timeline better. UndoUndo
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Like any build the technical debt does creep in. I guess the current crop of current researchers are carrying a lot of baggage. I hope we won't enter another
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AI history is one failure after another. Or should I say, one AI winter after another? We only need to know it so we know what to avoid. For example, avoid symbolic AI, expert systems, CYC, deep learning, backpropagation, supervised sensory learning, etc. Throw them all out.
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That’s the scientific way of progress :) rejecting the null hypotheses and experimenting with different alternative hypotheses !
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I definitely feel that we need to combine some sort of reasoning to improve perception. I think there will be a lot of people who want to combine classical ai and ml (atleast I am interested).
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