I agree with you, and I've also been saying this for years. We have AI that is now really good at individual tasks, but we're trying to use monolithic models to solve every task in a single model. We need to be combining models with logic using classical ai and regular software.
“We can’t trust AI systems built on deep learning alone” - terrific interview @techreview, with a headline that concisely captures the single most important point in http://rebooting.ai https://www.technologyreview.com/s/614443/we-cant-trust-ai-systems-built-on-deep-learning-alone/ …
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I think you are confusing what you find in research papers and what can be found in real installations.
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I mostly agree with your book (sans complaints AI safety got a weird take, but few care about this) - DL will likely hit a glass wall (but some chance it won’t). But when you require “trust”, do humans qualify, according to the same standards?
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I was just trying to make some similar points today at the
@PartnershipAI annual meeting. We need AI with context, and I believe we can make that safely.Thanks. Twitter will use this to make your timeline better. UndoUndo
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Great interview. I recommend to read Wynograd Flores Understanding Computers and Cognition (1986!) https://books.google.ch/books/about/Understanding_Computers_and_Cognition.html?id=2sRC8vcDYNEC&redir_esc=y … to better understand why there was a (classic) AI winter...
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It goes deeper than that too... we need to be building chips and tools that can run many smaller models in parallel. Take one frame and feed it into several models at once and analyze it from many perspectives all with global output probabilities of applicability importance.
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I haven't started on it yet.https://twitter.com/imleslahdin/status/1171938832381399040 …
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Agrée with you
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