3 ms·

Huh? We don't forget pioneering work just because it was on the wrong track. Aristotle was a great scientist despite being grossly wrong about anatomy. Lord Kel

Huh? We don't forget pioneering work just because it was on the wrong track. Aristotle was a great scientist despite being grossly wrong about anatomy. Lord Kelvin had an incorrect theory of heat and misestimated the age of the earth by an order of magnitude, but he was still a genius. Likewise, the word2vec people perhaps went down the wrong path, but it was a good start and enabled some of the earliest language ML applications.

PaulHoule 2y ago
Speaking as an engineer, not a scientist. I worked with a lot of people who failed at projects with Word2Vec and Glove even though "I told you so" over and over again. I saw upwards of $20M worth of investment disappear with my own eyes and the blast radius must have been several unicorns worth of valuation. I quit a job which I really needed because I couldn't bear to see us go down that dead end (medical literature, out-of-vocabulary, lose 50% of the critical information, you lose before you even started... [1]) On one level one of my favorite kinds of of arXiv paper is "build a classifier for some mundane text classification problem", I've collected enough of these to do a meta-analysis if I was an academic. I'm interested because I build these things for myself and I'm working towards building a reliable model trainer that can be applied to a wide range of tasks. Practically I find these papers depressing though because inevitably they try a bunch of things that predictably were going to suck, especially Word2Vec and Glove. BERT + pooling + classical ML always puts up a good showing in comparison, a good showing period for topic classification, worse if the order of the words matters. The one kind of model simpler than that which is worth looking at is Bag Of Words + classical ML because it can do a pretty good job considering we were doing it in 2000. You usually see people stumble in the dark quite a bit past that, for instance, I've yet to see a reliable procedure to fine-tune BERT models for classification, some of the reason why people have the luck they do is that they have tiny training sets (say N=500), I am just not convinced that with N>5000 these models are really able to learn anything they don't learn with N=500 (is it catastrophic forgetting? my training curves should look like it) People really should be trying to expand the 'efficient frontier' of accuracy, computational resources, and development efforts -- it's not easy but one step is to 'ignore' anything that is predictably away from that frontier. Glove and word2vec have always been that, but it's not generally known: reading is supposed to let us learn from other people's mistakes but in this field it just helps people repeat other people's mistakes. [1] LLM's did show that my 'predictive evaluation' methodology of the time was pessimistic; you can throw out critical information and a model that's sufficiently incapable of reasoning under uncertainty [2] can get the right answer frequently by the wrong path. I would have seen a 50% ceiling, a system that hallucinates might get 80% accuracy, but you can't get that last 20% [2] My "always gets voted down" opinion is that the Chomsky/Pinker "language instinct" is actually a derangement of the ability to reason with uncertainty that we share that makes languages learnable because they live on a collapsed lower-dimensional manifold.
- DiscourseFan 2y ago
  I’m not a fan of Chomsky myself really, but I think its important to distinguish cognition from computation. Not saying Chomsky’s model was correct, but he wasn’t working on machine learning per se, which certainly draws more, whether intentionally or not, from phenomenology.

PaulHoule 2y ago

Speaking as an engineer, not a scientist. I worked with a lot of people who failed at projects with Word2Vec and Glove even though "I told you so" over and over again. I saw upwards of $20M worth of investment disappear with my own eyes and the blast radius must have been several unicorns worth of valuation. I quit a job which I really needed because I couldn't bear to see us go down that dead end (medical literature, out-of-vocabulary, lose 50% of the critical information, you lose before you even started... [1]) On one level one of my favorite kinds of of arXiv paper is "build a classifier for some mundane text classification problem", I've collected enough of these to do a meta-analysis if I was an academic. I'm interested because I build these things for myself and I'm working towards building a reliable model trainer that can be applied to a wide range of tasks. Practically I find these papers depressing though because inevitably they try a bunch of things that predictably were going to suck, especially Word2Vec and Glove. BERT + pooling + classical ML always puts up a good showing in comparison, a good showing period for topic classification, worse if the order of the words matters. The one kind of model simpler than that which is worth looking at is Bag Of Words + classical ML because it can do a pretty good job considering we were doing it in 2000. You usually see people stumble in the dark quite a bit past that, for instance, I've yet to see a reliable procedure to fine-tune BERT models for classification, some of the reason why people have the luck they do is that they have tiny training sets (say N=500), I am just not convinced that with N>5000 these models are really able to learn anything they don't learn with N=500 (is it catastrophic forgetting? my training curves should look like it) People really should be trying to expand the 'efficient frontier' of accuracy, computational resources, and development efforts -- it's not easy but one step is to 'ignore' anything that is predictably away from that frontier. Glove and word2vec have always been that, but it's not generally known: reading is supposed to let us learn from other people's mistakes but in this field it just helps people repeat other people's mistakes. [1] LLM's did show that my 'predictive evaluation' methodology of the time was pessimistic; you can throw out critical information and a model that's sufficiently incapable of reasoning under uncertainty [2] can get the right answer frequently by the wrong path. I would have seen a 50% ceiling, a system that hallucinates might get 80% accuracy, but you can't get that last 20% [2] My "always gets voted down" opinion is that the Chomsky/Pinker "language instinct" is actually a derangement of the ability to reason with uncertainty that we share that makes languages learnable because they live on a collapsed lower-dimensional manifold.

DiscourseFan 2y ago
I’m not a fan of Chomsky myself really, but I think its important to distinguish cognition from computation. Not saying Chomsky’s model was correct, but he wasn’t working on machine learning per se, which certainly draws more, whether intentionally or not, from phenomenology.