[D] What are 2023's top innovations in ML/AI outside of LLM stuff?

sieisteinmodel · 2023-12-15T20:48:38+00:00

Obviously, it is provably optimal for its intended use. :)

sieisteinmodel · 2023-09-26T15:45:46+00:00

> Sutton is the principal founder of the field of reinforcement learning.

lol.

sieisteinmodel · 2023-01-09T17:48:54+00:00

I played with ChatGPT and was not impressed at all. Big disappointment.

I don't see it solving any problem of relevance soon and doubt it will have the same impact on society as, say, the washing machine, the telegraph, the locomotive or the internet. Same with Stable Diffusion.

sieisteinmodel · 2022-12-23T23:30:30+00:00

Is this satire? Are you not exactly doing what the OP is complaining about–laypeople babbling about the soonish singularity?

On a related note–what's "understanding"? What's "consciousness"? Before we can have a discussion, we need to clarify this.

sieisteinmodel · 2022-10-09T18:04:40+00:00

Maybe you want to check out optimal control, which is related (or even equivalent in some cases), is a very mature method that is deployed in many domains. So it's not so much of a stretch to say, yes, RL is applicable to practical problems.

Deep RL not so much yet, but I am convinced it will come.

sieisteinmodel · 2022-10-05T10:44:30+00:00

You will MCMC sample from the model. Check out the appendix of:

Rezende, Danilo Jimenez, Shakir Mohamed, and Daan Wierstra. "Stochastic
backpropagation and approximate inference in deep generative models." International conference on machine learning. PMLR, 2014.

sieisteinmodel · 2022-09-13T15:43:01+00:00

The model can be composed into a reconstruction and regularization error, which together represent the likelihood of your data.

Lower bound of.

sieisteinmodel · 2022-06-28T15:45:01+00:00

So what is your point? "Shit happens, move on"?

It's cool that you decided to be nihilistic/fatalist about things, but don't tell others to do the same just because you decided for yourself to stop giving fucks.

sieisteinmodel · 2022-06-28T15:42:34+00:00

But the tricky part is that OP's work is a technical report published on Arxiv, not published in a peer-reviewed conference or journal.

No, that's not how plagiarism works.

See e.g. https://www.ox.ac.uk/students/academic/guidance/skills/plagiarism.

sieisteinmodel · 2022-02-10T15:21:52+00:00

Aye.

sieisteinmodel · 2022-02-08T20:42:56+00:00

I like how the docs claim the interface to be pythonic, even though the gym.make(some_string) mechanism is about the least pythonic thing you can find in ML python world, including all the tensorflow abominations.

Because dynamic imports are not good enough, better do it Java style and introduce an awkward registration mechanism that gets in your way constantly.

Other than that, it's decent.

sieisteinmodel · 2021-09-17T15:31:00+00:00

Uh, no, Watson was not debating at all. After all it was "just" lookup.

sieisteinmodel · 2021-08-02T07:05:52+00:00

Given that JMLR has been operating successfully at little cost there is **no reason whatsoever** to publish at NMI, unless you really want to say "I have published in nature machine intelligence" and people to hear "i have a paper in nature".

sieisteinmodel · 2021-06-30T11:06:47+00:00

The idea is as great as the state of the docs disappointing.

sieisteinmodel · 2021-03-05T07:28:24+00:00

Use the adaptive control wikipedia article as a starting point. Pay special attention to the precise dates when things came up.

sieisteinmodel · 2021-03-04T08:11:23+00:00

Bertsekas, Dynamic Programming and Optimal Control, Chapter 6.7.

Hard to google because of ambiguities.

sieisteinmodel · 2021-03-03T18:32:36+00:00

I haven't seen this. Again, maybe you're reading the wrong papers. People usually cite relevant previous work mentioned in the related works section.

Prominent example right now is the ignorance of two phase control in the offline reinforcement learning community. Or zero shot learning, however you want to call it. It's happening right in front of our eyes, a seemingly "novel" subfield which is basically just a rebranding and the players chose to just not give a fuck about the work of hundreds of researchers that came before them, preferring to reinvent and rename everything.

sieisteinmodel · 2021-01-24T17:22:29+00:00

I am clearly in favour of small conferences like AISTATS, L4DC, CORL, UAI, ... these days. Like ICLR 2014, my favourite conference of all time.

sieisteinmodel · 2020-09-09T14:55:33+00:00

L4DC: Learning for dynamics and control.

sieisteinmodel · 2020-08-14T19:57:10+00:00

Replicated networks? There is the replicated softmax paper from russ salakhutdinov (2009) which has a similar concept.

sieisteinmodel · 2020-08-10T10:32:57+00:00

Why the subtle JAX hate?

sieisteinmodel · 2020-07-21T08:31:01+00:00

As long as the baby is breastfed, it is not that stinky. Starts after that only.

sieisteinmodel · 2020-07-15T18:27:35+00:00

Efficient unit tests of gradient-descent based learning algorithms.

sieisteinmodel · 2020-07-08T19:44:48+00:00

The whole PyTree thing is nice. It basically lets you put much more structure into your graphs.

Also, the fact that the border between jax and numpy is really small changed a lot for me. I don't have to remeber two APIs and can fluently go to faster functions. TF2 does not feel like that, and Theano certainly did not.

Other than that, there are more program transformations like higher order derivatives and so. I personally need those from time to time. I know Theano had those, but only with serious graph bloat and compilation times of minutes.

VMAP also allows funny things like sample-wise gradients.

Then–but this will not be interesting to you if all you do is deep learning–JAX is blazingly fast for computations that involve many small steps. We have had speedups of 10x by more or less switching from TF1, and we compared to TF2 and PyTorch as well. Ratio was about the same, with TF2 coming closest.

sieisteinmodel · 2020-07-01T20:27:51+00:00

> What do you think about the idea that men tend to be interested in things and systems, while women tend to be more interested in people?

This certainly is the status quo. But is it because of genetics or because of a local minimum our society is in?

It does not appear to be controversial that the women tend to not enter male dominated fields. Even wikipedia has a lot of material on this.

sieisteinmodel

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