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The Pastry A.I. That Learned to Fight Cancer
- wizzwizz4 6y agoI like the traditional approach. Training a brain to solve a problem is all well and good, but with a BakeryScan-like system, you can see why, and change it if necessary. It's engineering, rather than statistics.
- nick__m 6y agoAnd I liked the complementarity at the end, were they used deeplearning to virtually remove the cellophane so BakeryScan could continue to identify the pastries.
- joe_the_user 6y agoWow, 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.
- Der_Einzige 6y agoLook 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.
- r00fus 6y agoIsn't that what constituted most 80s "AI" research [1]? [1] https://en.wikipedia.org/wiki/Expert_system https://en.wikipedia.org/wiki/Expert_system
- YeGoblynQueenne 6y agoI 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?
- wizzwizz4 6y agoBecause all AI is ML, obviously. All that stuff before massive GPU farms? Not real AI. (I'm not sure why this is a common perception.)
- joe_the_user 6y agoAside 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/ https://blogs.microsoft.com/ai/machine-teaching/ and so forth.
- dumb1224 6y agoIt'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). https://en.wikipedia.org/wiki/Feature_engineering https://en.wikipedia.org/wiki/Feature_engineering
- avibhu 6y agoAre there any good resources for learning more classical computer vision? How would someone approach a problem like this without using machine learning?
- joe_the_user 6y agoI did a small amount of work in pre-deep learning computer vision. It was only about six months exposure but as far as I could tell, the field was a mess. There weren't general methods, just a list of specific filters, approaches and so-forth that "worked in some cases" - and a lot of leaned on a knowledge of the particular setup you were modeling - optics in some instances, Fourier analysis in others and ad-hoc processes for others. Deep learning is general purpose, doesn't require area-experts and is more or less guaranteed to mostly work if you have sufficient data describing the task at hand.
- chestervonwinch 6y agoHave you read any computer vision textbooks? Szeliski's book for example is quite popular and sort-of free: https://szeliski.org/Book/ https://szeliski.org/Book/
- vector_spaces 6y agoNot specific to computer vision, but my understanding is that the two famous AI texts by Norvig -- Principles of Artificial Intelligence Programming and Artificial Intelligence: A Modern Approach -- are introductions to the "symbolic" approach to AI (as opposed to the "computational intelligence" approaches given by the modern regimes of machine learning, deep learning, and similar), and I would imagine that methods from the symbolic approach are primarily what are used here (but I'm not an expert). PAIP was recently made available for free download by the author [1] [1] https://github.com/norvig/paip-lisp/releases/tag/v1.0 https://github.com/norvig/paip-lisp/releases/tag/v1.0
- abecedarius 6y agoThough PAIP is essentially all symbolic "good old-fashioned AI" (and focuses on programming rather than AI theory), AIMA never was -- that's why they emphasized "a modern approach" in the title. I guess it's reasonable though to expect AIMA's coverage of computer vision would be good background for understanding this pastry AI.
- sleepy_keita 6y agoThe local bakery had this system put in a year or so ago, and I was hopeful they'd finally use it to accept payments other than just cash... Unfortunately not.
- phenkdo 6y agoThis doesn't describe what traditional CV approach this uses, presumably descriptor based ones: A-KAZE, SIFT etc. do work well for such scenarios. So both Deep learning and traditional machine vision have their place.
- joe_the_user 6y agoYeah, it's a fascinating article but thin on technical details, which makes it frustrating.