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Machine learning’s crumbling foundations

pluralistic.net

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Re: Machine learning’s crumbling foundations

#31

Post by notanzaiiswear »

wnoise wrote: post #29
notanzaiiswear wrote: post #13

It sounds like cherry picking bad examples to me. Likewise you could say "programming's foundations are crumbling" by citing all sorts of programming projects that use bad or faulty code. Meanwhile, speech recognition seems to work extremely well by now (I am a little bit older, so I remember when it didn't work so well). I am also not aware of any real world cases of AI being used to detect Corona, so that seems to…

> programming's foundations are crumbling That's also correct, and has been for some time (it got worse on each tech boom). This may just be a special case of that.

What do you mean? At least from the point of view of the end user, apps seem to become better over time.

Re: Machine learning’s crumbling foundations

#32

Post by notanzaiiswear »

qayxc wrote: post #30
notanzaiiswear wrote: post #13

It sounds like cherry picking bad examples to me. Likewise you could say "programming's foundations are crumbling" by citing all sorts of programming projects that use bad or faulty code. Meanwhile, speech recognition seems to work extremely well by now (I am a little bit older, so I remember when it didn't work so well). I am also not aware of any real world cases of AI being used to detect Corona, so that seems to…

> Meanwhile, speech recognition seems to work extremely well by now (I am a little bit older, so I remember when it didn't work so well). *provided you speak English or Mandarin, the former preferably of a continental US variety It's astonishing how bad things get again once you mix in an accent, local dialect (e.g. Swiss German) or a less frequently spoken language (like Croatian).

Nevertheless, the huge jump is from "does not work at all" to "it works". It seems likely that the technology that worked for English will also work for many other languages.

As for Chinese, it is also pretty amazing that you can visit a Chinese website, click "translate" in your browser's menu bar, and get a reasonably readable translated version.

I wonder if people just take too many things for granted.

Or internet search - they say quality of Google searches have been declining, nevertheless we had a pretty good run for the past 20 years or so with being able to find information on the internet. That is AI as well.

Re: Machine learning’s crumbling foundations

#33

Post by civilized »

ErikAugust wrote: post #7

“Everyone wants to do the model work, not the data work”

Which is sad because data work can lead to real domain knowledge, while fitting a grab bag of generic models teaches you nothing by itself (wooo, this thing has 0.0003 higher AUC than that thing!)

Fitting generic data science predictive models is such a rote task these days that there's a crowd of start-ups begging you to pay them to automate it for you.

Re: Machine learning’s crumbling foundations

#34

Post by dmix »

You could use this article's underlying thesis to explain why a lot of tech companies fail as well.

Google Health is a good example of failing to appreciate specialization and domain-expertise. Trying to draw value from broad generic data collection when IRL it requires vertical-focused domain-oriented collection and analysis to really draw value.

Funnelling everything into a giant pool of data only had so much value - reducing it to just a proprietary API integrations platform in exchange for valuable data.

This AI analogy extends to healthcare in real life: The job of any generalist doctor is largely just triaging you to the specialists. You reach the limits of their care pretty quickly for anything serious.

AI is much the same way, the generic multipurpose tools tend to quickly lose value after surface level stuff before requiring heavy specialization. Google's search engine is full of custom vertical categorization, where simple Pagerank wasn't enough.

This is why startups can be very useful to society as they get forced to focus on smaller issues early on, out of pure practicality, or quickly die off if they try to bite a bigger problems than they can chew.

Almost every major multi-faceted business started off with a few 'whales' on which they built their business.

Most of the biggest startup flops have been the ones that took VC really early before doing the dirty hard work of truly finding and understanding the problems they are trying to solve.

Re: Machine learning’s crumbling foundations

#35

Post by wffurr »

notanzaiiswear wrote: post #13

It sounds like cherry picking bad examples to me. Likewise you could say "programming's foundations are crumbling" by citing all sorts of programming projects that use bad or faulty code. Meanwhile, speech recognition seems to work extremely well by now (I am a little bit older, so I remember when it didn't work so well). I am also not aware of any real world cases of AI being used to detect Corona, so that seems to…

The infosec meltdown sure seems to indicate programming’s foundations are crumbled. All of the unsafe C library code underlying nearly ever modern system is unsafe at any speed.

Re: Machine learning’s crumbling foundations

#36

Post by wffurr »

data4lyfe wrote: post #19

The garbage in garbage out cascading failure generally seems to crash pretty fast. Given the U.S. is a capitalistic society the companies / institutions that do this and don't achieve their goals through data science should be apparent and then fail accordingly. Am I missing something here?

The trail of devastation left by this process, in financial and human terms, when medical systems go awry or vendors to state judicial systems wrongly convict innocent people.

Re: Machine learning’s crumbling foundations

#37

Post by mcswell »

ErikAugust wrote: post #7

“Everyone wants to do the model work, not the data work”

I don't know how it is in other fields; I'm a linguist, who made the transition to computational linguistics back when you had to be a linguist to be a computational linguist (the 1980s). Slow forward to statistical (and now neural) comp ling; I find it incredibly boring. But the data work still needs to be done, and there are still linguists. And even more than me, they find computational linguistics (of whatever type) less interesting that "real" linguistics. So they will do data work, and willingly.

Re: Machine learning’s crumbling foundations

#38

Post by mcswell »

ilamont wrote: post #17
thaumasiotes wrote: post #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 partic…

Yeah, that was an odd claim about Han Chinese surnames. Many have been around for thousands of years ( https://www.chinadaily.com.cn/ezine/2007-07/20/content_54412... ) and almost all are single-character surnames based on a limited set of possible sounds (~400 in Mandarin, IIRC)

I don't know anything about Chinese surnames, but their paucity cannot be due to a limited set of possible sounds. First, I would interpret "sounds" as phonemes (including tones), and there are far fewer of those than 400. More likely what you mean is the number of combinations of phonemes into valid Chinese Mandarin monosyllables, of which I cannot imagine there being only 400. In any case, there are (from what little I've heard) lots of bisyllabic Chinese words. Can't they be represented by single (or double) characters? There are thousands of commonly used Chinese characters, and tens of thousands more uncommonly used characters.

Re: Machine learning’s crumbling foundations

#39

Post by mcswell »

wffurr wrote: post #36
data4lyfe wrote: post #19

The garbage in garbage out cascading failure generally seems to crash pretty fast. Given the U.S. is a capitalistic society the companies / institutions that do this and don't achieve their goals through data science should be apparent and then fail accordingly. Am I missing something here?

The trail of devastation left by this process, in financial and human terms, when medical systems go awry or vendors to state judicial systems wrongly convict innocent people.

I agree about your latter example, but about your first example: isn't it the case that these faulty AI systems for medical diagnosis have been rejected? Doctors don't like them because they don't want to be replaced or one-upped, and because they just don't trust them (rightly so, as it turns out). So the systems, which were put out for use on a trial basis, don't get used.

Re: Machine learning’s crumbling foundations

#40

Post by [deleted] »

axelroze wrote: post #10

It'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 pro…

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