A dishwasher! You invented a dishwasher! Very nice! Now PLEASE stop suggesting household robots are imminent! https://meaningness.com/metablog/artificial-intelligence-progress …pic.twitter.com/01kiTYIaqc
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A dishwasher! You invented a dishwasher! Very nice! Now PLEASE stop suggesting household robots are imminent! https://meaningness.com/metablog/artificial-intelligence-progress …pic.twitter.com/01kiTYIaqc
David Chapman je proslijedio/a tweet korisnika/cejustsaysinmice
You found another cure for cancer IN (some) MICE! Very nice… almost everything either causes or cures cancer in (laboratory) mice, they are incredibly fragile… what did this one mean? How do you know? (Also see: omg, AI, IN MICE!)https://twitter.com/justsaysinmice/status/1219695194179682304 …
David Chapman je dodan/na,
Deeper issue in the podcast: theory testing vs research program selection. The folk theory of science, based on 1950s philosophy thereof, is that experiments falsify (or maybe support) theories. This is not how most sciences work; even molecular biology is mostly an exception.
Molbio is mostly about making molecular mechanisms visible. There’s little if any theory, in the 1950s-philosophy-of-physics sense, in the field.
The parts of psychology that are most in trouble have dutifully tried to make and test theories, because that’s the folk model of Real Science, but adequate tools were unavailable. Cog psych (like molbio) tries to make mechanisms visible; that is more feasible there.
In fields in which there is not yet a mechanical way of getting results (“normal science”), the productive activity is looking for approaches, ways of framing and thinking the material, developing and evaluating *research programs* rather than theories.
Research programs consider what technical results *mean*, not just whether they are true. That’s the most interesting and satisfying work, for me personally. And as the @fourbeerspod guys say, they went into psychology to have Big Ideas, not to test tiny truths.pic.twitter.com/OqpxG6X45q
(That text block is from my thesis book: https://www.amazon.com/Vision-Instruction-Action-Artificial-Intelligence/dp/0262031817 … )
What's enraging about the current state of AI is that everyone is treating it as "normal science," i.e. mindlessly turning a publication-generating crank, and deliberately choosing not to ask "what does this mean?" So we don't know; so probably the answer is "not much."
In addition to the replication crisis, we have a generalization crisis (per the paper of @talyarkoni which @fourbeerspod discussed).
Probably this is as pervasive across sciences, and as misleading, as "most published findings are false." https://psyarxiv.com/jqw35 pic.twitter.com/ymX5RGgvLe
This can be fixed! It will take serious rethinking of how science is done, and how and why it works. Sciences must continually reflect on whether their current approach/research program is genuinely productive, or meaningless mechanical paper-generation. https://meaningness.com/metablog/upgrade-your-cargo-cult …pic.twitter.com/Q1AERSXSu8
Research communities should take responsibility for revising their epistemic norms. Psychology has developed a scenius—a creative subcultural ferment that is collaboratively rethinking fundamental assumptions. Yay!
The @fourbeerspod is a great window into the scene.pic.twitter.com/ZiEzdzEaif
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