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I keep telling everyone that #ai is not battle ready. You cannot put these blackboxes in sensitive applications where wrong decisions adversely affect human lives #ainotreadyhttps://twitter.com/EricTopol/status/1161657580675985409 …
Biologists have built ground-up understanding. #AI cannot replace that. Best place for #AI is research+discovery, to be validated by extensive wetlab experiments. Such #AI cannot be blackbox. Probabilistic models, causal inference are critical #AInotjustdeeplearning
I think @garymarcus @yudapearl @zacharylipton @lpachter will appreciate it, among countless others. We need a new hashtag: #AInotjustdeeplearning
Given some responses, especially from young people, defending #deeplearning..your hearts will be broken unless you diversify and learn other methods like probabilistic models/causality. Don't just drink kool-aid. Aim to become scientists with critical thinking. Don't believe hype
Unethical for sure. But legally questionable too. They’re safety critical systems and regulations require them to be developed to safety critical software standards. In the meantime the best we can dob them in to the MHRA.
I do agree with you. Authors publish research papers which is amazing but the practical reality or making them product ready is another challenge. We should now seriously focus on implementing these publications in to real time application which will be a bigger challenge.
This is how the last AI winter began. Business trying to solve world hunger with AI and over promise to their customers.
Tired: AI Wired: AGI
Is this the “ruler next to a tumour” effect 2.0?
Why not? As I see it, this was a validation issue. And anyway I think it should depend on the use case. Most NN are targeted for screening purposes, so the errors are mostly false positives.
Screening with high FP rates is also bad. Eg PSA screening and recommendations changing.
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