Re:The Pastry A.I. That Learned to Fight Cancer
by Der_Einzige on Saturday March 27, 2021 @10:11PM
(#26607395)
Look up "machine teaching" or "active learning". The idea of humans helping computers or even humans directly writing the rules is not new. Heueristics added on top of alpha beta pruning were how the top chess engines worked right up until alphazero.
Re:The Pastry A.I. That Learned to Fight Cancer
by r00fus on Saturday March 27, 2021 @11:43PM
(#26607770)
Isn't that what constituted most 80s "AI" research [1]?
Re:The Pastry A.I. That Learned to Fight Cancer
by joe_the_user on Sunday March 28, 2021 @03:41PM
(#26613590)
Aside from the scare quotes, you're wrong, "machine teaching" and "expert systems" are not the same thing (and neither is considered the same as pre-deep nets computer vission). In my still-shallow research, machine teaching is a conceptual thread that began years ago but is still active and interacts deep learning currents. Expert systems are still around also, they just aren't considered AI anymore.
See: https://blogs.microsoft.com/ai/machine-teaching/ and so forth.
Re:The Pastry A.I. That Learned to Fight Cancer
by dumb1224 on Sunday March 28, 2021 @05:41PM
(#26614919)
It's a bit different from expert systems or knowledge bases IMHO. To my limited knowledge in the medical imaging field feature engineering is still very much a trusted tool (experts tuning systems based on their domain knowledge).
Re:The Pastry A.I. That Learned to Fight Cancer
by YeGoblynQueenne on Sunday March 28, 2021 @06:15AM
(#26609192)
I find it interesting that you put scare quotes around AI to speak of the dominant trend of AI research in the last 70 years (not expert systems, but manual coding of proposed intellectual mechanisms).
I am curious. Can you explain why you use scare quotes?
It's weird to have what seems (I repeat, seems) like pretty a big deal technically be first described in a meandering New Yorker article.
This is a more-or-less hand-tune system apparently effectively competing with deep learning systems (identifying cancer is far from easy and not solved by deep learning at this point). As the article says, by being person-created, the system has obvious advantage in both explainability and users being able to understand it's mistakes.
Of course, a broader look from experts is in order but I can't see this as a strong demonstration of an alternative paradigm. Now, the challenge is to automate the construction of systems of this sort.
And I can find little-to-no information about this, except in Japanese.