There's a nice substack I found that is precisely about this problem and wider variations of it, that is, the problem of figuring out numbers that actually tell you something about the universe:
Machine learning’s crumbling foundations
21–30 of 106 posts
Re: Machine learning’s crumbling foundations
#22Earlier quoted context omitted.
https://www.familysearch.org/wiki/en/Jewish_Personal_Names has more information about compulsory adoption of surnames amongst European Jews for taxation purposes in the 18th century.
I think the assertion was more that that is when everyone was forced to take surnames?
From https://en.wikipedia.org/wiki/Surname#History :
> By 1400, most English and some Scottish people used surnames, but many Scottish and Welsh people did not adopt surnames until the 17th century, or later.
> During the modern era, many cultures around the world adopted family names, particularly for administrative reasons, especially during the age of European expansion and particularly since 1600. Notable examples include the Netherlands (1795–1811), Japan (1870s), Thailand (1920), and Turkey (1934).
So that would put Ashkenazic surnames at healthily older than e.g. Dutch surnames.
Re: Machine learning’s crumbling foundations
#23> 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…
Also, the purpose of Republican voter purges is not, particularly, to find people who have double-registered. It is more useful to the GOP to have a ton of false positives. Having a huge headline number allows them to claim that voter fraud is rampant ("Over a million people double registered!!!"). It also allows them to challenge the registration of many voters that the GOP doesn't like. Whether they've actually succeeded or not in finding double registration, these challenges raise the bar of voting difficulty for the other side.
Re: Machine learning’s crumbling foundations
#24> 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.
Doctorow seems to be missing the meaning of that quote (which is also not the title of a talk, ironic or otherwise). It was specifically a comment on the usefulness of computer language models created manually based on linguistic theories of grammar versus ones in the same model family created automatically from real-world data -- the latter tended to work better. These days I usually hear it quoted more broadly as a warning about the danger of encoding too much possibly-wrong domain knowledge in an ML system when a more generic model and the training data are sufficient to learn the useful parts on their own.
Neither of those translates into disdain for qualitative understanding of the underlying reality behind the data set, which is one of those things that everyone knows is important. The problem is that such understanding is actually hard, and easy to mess up even when you're trying.
Re: Machine learning’s crumbling foundations
#25This seems like a re-hashing of Michael Jordan's essay on the subject: https://medium.com/@mijordan3/artificial-intelligence-the-re...
Discussed here:
Artificial Intelligence – The Revolution Hasn’t Happened Yet (2018) - https://news.ycombinator.com/item?id=25530178 - Dec 2020 (120 comments)
The AI Revolution Hasn’t Happened Yet - https://news.ycombinator.com/item?id=16873778 - April 2018 (161 comments)
Re: Machine learning’s crumbling foundations
#26URL 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.
Ok, changed from https://doctorow.medium.com/machine-learnings-crumbling-foun.... Thanks!
Re: Machine learning’s crumbling foundations
#27> One common failure mode? Treating data that was known to be of poor quality as if it was reliable because good data was not available… they also use the poor quality data to assess the resulting models.
This drives me nuts. Spend $10k getting high quality data and throw a simple model at it? Nah, let’s spend a month of time from someone making $400k/yr for less trustworthy results. And on the blogosphere it’s even worse. ‘This is the best data available so here goes’ justifies so much worse-than-worthless BS.
And don’t even get me started on the ‘better than human’ headlines that result.
Re: Machine learning’s crumbling foundations
#28Really 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.
Most ML requires collecting, cleaning, and transforming datasets into something that a model can train on for a specific domain. Codex and Copilot aren't good examples of this because they are training on terabytes of public code repos - meaning that there is no code cleaning step. It's relying on the sheer volume of data that is being processed to try and filter the 'unclean' data (think buggy code written by a human) out of the model.
These are really the exception rather than the rule when it comes to collecting data for ML/AI applications.
Re: Machine learning’s crumbling foundations
#29It 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.
Re: Machine learning’s crumbling foundations
#30It 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).