Machine learning’s crumbling foundations
pluralistic.net
Machine learning’s crumbling foundations
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Re: Machine learning’s crumbling foundations
#2Really depends on the domain and the engine. OpenAI code generation is staggering (https://www.youtube.com/watch?v=SGUCcjHTmGY&t=1214s), its summarization and classification is still very much a work in progress.
Re: Machine learning’s crumbling foundations
#3> The disdain for the qualitative expertise of domain experts who produce data is a well-understood guilty secret within ML circles, embodied in Frederick Jelinek’s ironic talk, "Every time I fire a linguist, the performance of the speech recognizer goes up."
This reminds me of how the chimp sign language studies got much better results from hearing evaluators than from deaf ones.
Re: Machine learning’s crumbling foundations
#4Really depends on the domain and the engine. OpenAI code generation is staggering ( https://www.youtube.com/watch?v=SGUCcjHTmGY&t=1214s ), its summarization and classification is still very much a work in progress.
I agree with this - when there's an economic incentive to get clean data, you get clean data.
For instance, there's a lot of manual clean up work put into things like training data sets for speech recognition because there has been a lot of investment there. Same with self driving I assume because so much $$$ got invested there.
Radiology scans or cough based COVID detectors or medical claims on the other hand? I wouldn't expect it. It's just researchers trying to get a quick paper without adequate funding.
Re: Machine learning’s crumbling foundations
#5This seems like a re-hashing of Michael Jordan's essay on the subject:
https://medium.com/@mijordan3/artificial-intelligence-the-re...
Re: Machine learning’s crumbling foundations
#6> Ethnic groups whose surnames were assigned in recent history for tax-collection purposes (Ashkenazi Jews, Han Chinese, Koreans, etc) have a relatively small pool of surnames and a slightly larger pool of first names.
This is... not accurate. The reason the Chinese have a small pool of surnames is that their surnames are much less recent than ours, not more recent.
And I don't think the Ashkenazi surnames are particularly more recent than the surnames of the Europeans they lived among. Rather, they have concentrated surnames mostly because they were occupationally concentrated.
Re: Machine learning’s crumbling foundations
#7“Everyone wants to do the model work, not the data work”
Re: Machine learning’s crumbling foundations
#8URL should be changed to https://pluralistic.net/2021/08/19/failure-cascades/ - same content on the author's site, without having to navigate around the Medium paywall.
Re: Machine learning’s crumbling foundations
#9Re: Machine learning’s crumbling foundations
#10It's a structural issue caused by the way wealth creation works for majority of people in tech. Job hopping, trendy frameworks in CV, "high-impact" projects done ASAP, etc.
No one wants to do boring, slow pace work with lots of planning, reflection and introspection. And why would they do it? These kind of jobs are usually worst paid. We, the practitioners, have every economic incentive to go the other route.
The problem goes far wider in tech than just ML. And unless the society collectively learns to appreciate patience and long-term thinking, as virtues above all else, it won't go away any time soon. What can be done is to discourage use of ML systems if an explainable deterministic system can be used (even one developed in a rush). For example credit scoring. Rules are good while black box artificial neural network isn't, even if the NN has some % more accuracy. Then if the rules are not good then can be amended and in special cases customer support could also override the rules based on human (hopefully unbiased) judgement.
The problem mentioned in the article of COVID-19 detection based on radiology scans is an example of a system which needs ANNs due to the nature of image processing (very difficult problem for rules AI). While techniques such as ShAP could be helpful a radiologist still needs to check because ANNs learn a lot of useless noise very often and the prediction can be nonsensical. Here it would be best to use PCR tests, serology or any more traditional and "boring" tool as it works. Luckily that is the case and shit CNN models start and end their lives in some useless paper.