tsurba
Or would one count any communication between animals as language? In that case almost any interaction would count.
The 2GB free quota on Dropbox is plenty enough for text (and some screenshots). Or you could self-host obviously. Git while lovely for source code is a hassle for notes.
https://arxiv.org/abs/2007.00810
Without properly reading the linked article, if thats all this is, not a particularly new result. Nevertheless this direction of proofs is imo at the core of understanding neural nets.
You can show for example that siamese encoders for time-series, with MSE loss on similarity, without a decoder, will converge to the the same latent space up to orthogonal transformations (as MSE is kinda like gaussian prior which doesn’t distinguish between different rotations).
Similarly I would expect that transformers trained on the same loss function for predicting the next word, if the data is at all similar (like human language), would converge to approx the same space, up to some, likely linear, transformations. And to represent that same space probably weights are similar, too. Weights in general seem to occupy low-dimensional spaces.
All in all, I don’t think this is that surprising, and I think the theoretical angle should be (have been?) to find mathematical proofs like this paper https://openreview.net/forum?id=ONfWFluZBI
They also have a previous paper (”CEBRA”) published in Nature with similar results.
Similarly I would expect that transformers trained on the same loss function for predicting the next word, if the data is at all similar (like human language), would converge to approx the same space. And to represent that same space probably weights are similar, too. Weights in general seem to occupy low-dimensional spaces.
All in all, I don’t think this is that surprising, and I think the theoretical angle should be (have been?) to find mathematical proofs like this paper https://openreview.net/forum?id=ONfWFluZBI
But chatgpt does help me work through some really difficult mathematical equations in newest research papers by adding intermediate steps. I can easily confirm when it gets them right and when not, as I do have some idea. It’s super useful.
If you are not able to make LLMs work for you at all, and complain about them on the internet, you are an old man yelling at clouds. The blog post devolves from an insightful viewpoint into a long sad ramble.
It’s 100% fine if you don’t want to use them yourself, but complaining to others gets tired quick.
EDIT: ok apparently anywhere else than the poorest of countries, too, really.
After doing programming for a decade or two, the actual act of programming is not enough to be ”creative problem solving”, it’s the domain and set of problems you get to apply it to that need to be interesting.
>90% of programming tasks at a company are usually reimplementing things and algorithms that have been done a thousand times before by others, and you’ve done something similar a dozen times. Nothing interesting there. That is exactly what should and can now be automated (to some extent).
In fact solving problems creatively to keep yourself interested, when the problem itself is boring is how you get code that sucks to maintain for the next guy. You should usually be doing the most clear and boring implementation possible. Which is not what ”I love coding” -people usually do (I’m definitely guilty).
To be honest this is why I went back to get a PhD, ”just coding” stuff got boring after a few years of doing it for a living. Now it feels like I’m just doing hobby projects again, because I work exactly on what I think could be interesting for others.
But more seriously, in the ideal case refining a prompt based on a misunderstanding of an LLM due to ambiguity in your task description is actually doing the meaningful part of the work in software development. It is exactly about defining the edge cases, and converting into language what is it that you need for a task. Iterating on that is not gambling.
But of course if you are not doing that, but just trying to get a ”smarter” LLM with (hopefully deprecated study of) ”prompt engineering” tricks, then that is about building yourself a skill that can become useless tomorrow.
But testing via coding algos to known puzzles is problematic as the code may be in the training set. Hence you need new puzzles, which is kinda what ARC was meant to do, right? Too bad OpenAI lost credibility for that set by having access to it, but ”verbally promising” (lol) not to train on it, etc.
In approx 7 years I went through working at all the top software companies in my country, but what really fixed my problems was moving on to being a researcher at the university. I’m now paid less than half from before, but it’s still enough, and I couldn’t be happier.
Getting to work on what I think is actually important and interesting every day is what helped. I also seem happier than the younger researchers who didn’t work at companies first, who don’t know how good they have it.
My Jax models and the baseline PyTorch models were quite easy to set up there, and there was not a noticeable perf difference to 8x A100s (which I used for prototyping on our university cluster) in practice.
Of course it’s just a random anecdote, but I don’t think nvidia is actually that much ahead.
It is also excellent for writing one-off code experiments and plots, saving some time from having to write them from scratch.
I’m sorry but you are just using it wrong.
In terms of my use, most questions and answers either were low quality, the question was a bit off of what you really wanted to know, or if it was a problem that required discussion or opinions, a ”real” hobbyist forum was better.
While at some point I felt like I (tried to) look up everything from SO, I now basically never use it anymore, but thanks for all the fish!
For example, when you learn instruments you also train your ear and taste. These are things one cannot take shortcuts in.
I wouldn’t worry about it, but approach new tools (once they actually arrive and are not just advertisements like this one) with curiosity.
1) useful training data available in the internet 2) number of humans creating more training data ”manually” 3) parameter scaling 4) ”easy” algorithmic inventions 5) available+buildable compute
”Just” needing a few more algorithmic inventions to keep the graphs exponential is a cop out. It is already obvious that just scaling parameters and compute is not enough.
I personally predict that scaling LLMs for solving all physical tasks (eg cleaning robots) or intellectual pursuits (they suck at multiplication) will not work out.
We will get better specialized tools by collecting data from specific, high economic value, constrained tasks, and automating them, but scaling a (multimodal) LLM to solve everything in a single model will not be economically viable. We will get more natural interfaces for many tasks.
This is how I think right now as a ML researcher, will be interesting to see how wrong was I in 2 years.
EDIT: addition about latest algorithmic advances:
- Deepseek style GRPO requires a ladder of scored problems progressively more difficult and appropriate to get useful gradients. For open-ended problems (like most interesting ones are) we have no ladders for, and it doesn’t work. In particular, learning to generate code for leetcode problems with a good number of well made unit tests is what it is good for.
- Test-time inference is just adding an insane amount of more compute after training to brute-force double-check the sanity of answers
Neither will keep the graphs exponential.
Of course it’s not the same thing as actually building the electronics! But it can be a nice introduction.
Batch norm and others are important for faster convergence due to forcing the model to focus creating second and higher order nonlinearities, as a simple shift in mean/std is normalized out, and thus the gradient does not point in a direction that would only change those properties of the output distribution.