The academics is rife with intentionally misleading results and findings that aren't actually innovative or informative. Worse yet, providing source code and a way to repeat the results is rare, and what good is a research paper with results that aren't repeatable? ML has a bright future, but it's very hard for me to take the academic research seriously. That's why it seems like few care about anything beyond making new models and bolstering their resume by fudging the results.
Machine learning’s crumbling foundations
81–90 of 106 posts
Re: Machine learning’s crumbling foundations
#82Earlier quoted context omitted.
> 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.
For some features, like autosaving and cloud sync, sure. Others like doing things in bulk, macros, plugins, not seeing ads, control over updates, all that stuff is vanishing.
Re: Machine learning’s crumbling foundations
#83It'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…
I had the same comment in a post about
Simple Systems Have Less Downtime (2020)
Re: Machine learning’s crumbling foundations
#84Earlier quoted context omitted.
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 litt…
> 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. That's your problem, not ilamont's. The limited syllable inventory of Mandarin Chinese is very well known. No need to stretch your imagination over it. That said, surnames are not limited by the number of syllables for the obvious reason that the spelling is…
No, the "only 400 syllables" refers to the syllables without taking into account tones. But tones are as much a part of Mandarin syllables as coda consonants; taking the tones into account, there are over 1200 distinct syllables.
Re: Machine learning’s crumbling foundations
#85I work in a company where ML has made a considerable difference to our bottom line (search component of an e-commerce site). When I joined the company, search was so bad, it was easier to just use google and include 'inurl:' to actually find products on the site. Now, years later, the builtin search actually gives you what you're looking for better than google does. (This is important because if you can't find something quickly you're more likely to shop elsewhere).
If you've seen "ML done right", you wouldn't use the word "crumbling".
That said - I won't deny ML is over-hyped. It works for very specific problems and in many cases the best solution is a non-ML one. Knowing when NOT to use ML is just as important as knowing when to use it.
Re: Machine learning’s crumbling foundations
#86Earlier quoted context omitted.
Sadly PCR tests for COVID also test positive for flu and half a dozen other causes. That's why CDC/FDA are seeking proposals for a new test that actually works! https://www.cdc.gov/csels/dls/locs/2021/07-21-2021-lab-alert...
That is simply false on a basic level. That notice by the CDC says the EXACT OPPOSITE of your comment. They're recommending that labs switch to a multiplex test that can screen for both flu and covid at the same time because PCR only detects SARS CoV 2.
Note: the "multiplex test" is most likely still a PCR test (just 'multiplex PCR' instead of 'single-probe PCR'), so where you say "PCR only detects SARS CoV 2" it should say "the currently-used PCR test only detects SARS CoV 2".
Re: Machine learning’s crumbling foundations
#87Earlier quoted context omitted.
I feel like its even worse than just resorting to bad data when good data isn't available: the field of deep learning has cultivated the perception that it's robust to bad data as one of its hallmarks. That is, you can pump relatively raw data into it and it will self-select features and then self-regulate their use so therefore most of the initial steps of data cleaning, feature selection etc are not necessary, or r…
The irony is that there are techniques for dealing with noisy/mislabeled/bad data (e.g. gold loss correction [0], errors-in-variables models [1]), but that stuff isn't "sexy" and not enough practitioners know about it. 0: https://arxiv.org/abs/1802.05300 1: https://en.m.wikipedia.org/wiki/Errors-in-variables_models
Is this any more than fancy outlier detection (genuinely asking, this is not my field).
i.e. if the majority of data fed to a system is bad, will it work?
Re: Machine learning’s crumbling foundations
#88> In the early 2000s, there was a movement to produce tools and training that would let domain experts produce their own tools – rather than delivering "requirements" to a programmer, a bookstore clerk or nurse or librarian could just make their own tools using Visual Basic. This is something interesting that I hadn't noticed. "RAD" tools like VB that I remember from when I was a teenager seem to have ceased to exist…
It's interesting that tools like VB were derided by many and the practitioners looked down on. Meanwhile they were productive tools, they did live up to their name.
Re: Machine learning’s crumbling foundations
#89Earlier quoted context omitted.
I feel like its even worse than just resorting to bad data when good data isn't available: the field of deep learning has cultivated the perception that it's robust to bad data as one of its hallmarks. That is, you can pump relatively raw data into it and it will self-select features and then self-regulate their use so therefore most of the initial steps of data cleaning, feature selection etc are not necessary, or r…
The irony is that there are techniques for dealing with noisy/mislabeled/bad data (e.g. gold loss correction [0], errors-in-variables models [1]), but that stuff isn't "sexy" and not enough practitioners know about it. 0: https://arxiv.org/abs/1802.05300 1: https://en.m.wikipedia.org/wiki/Errors-in-variables_models
This.
The thing is that some of the techniques commonly applied when training NN are often "good enough" to deal with the presence of corrupted data (e.g. using SGD to optimize a model, while applying weight decay and drop-out, adds a regularization effect that somewhat replicates the effect of assuming errors-in-variables), as long as the input data is not total trash, which deters people from applying more formalized robust approaches to it.
As long as "things kind of work", it is difficult to convince other people to adopt robust methods, particularly due to the existence of a "robustness vs. efficiency" trade-off (which can make robust methods seem additionally "unsexy").
Re: Machine learning’s crumbling foundations
#90It'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…
Exactly.
Machine learning, and "data driven" business leadership, is being treated as a get rich quick scheme like it's low hanging fruit that's easy to do.
When in fact it's been known under multiple different names for a very long time, quantitative management.
And the reason it wasn't popular before is that it's very tricky to pull off.