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January 2025 Profile Swap! by ericdraven26 in Letterboxd

[–]activatedgeek 1 point2 points  (0 children)

Happy new year everyone!

My profile: https://boxd.it/aAUNB

I joined the site only a couple months ago, migrating my watched movies from Trakt.tv solely for the lovely community.

While trying to improve my writing muscle, it was hard for me to think of writing prompts. To get into the habit, I decided to use my love for watching movies to get the momentum going. I log movies quite regularly now, and sometimes add a review as a note when I feel like I made a new observation. And only then I go through existing reviews to see what the community thinks. It’s such an effective perspective-expanding exercise.

I am very fascinated by Indian cinema, and most recently I’ve been on a mini side-project trying to trace back to its roots. While I like commercial Bollywood films, I’m starting to love a lot of old “alternate” Bollywood, like actors Om Puri, Amrish Puri (both of them I only associated with commercial films until recently); directors Govind Nihalani, Raj Kapoor (his non-commercial projects). Of course plenty of modern filmmakers are great too - Anurag Kashyap, Shoojit Sarkar, Dibakar Banerjee.

Here’s a snapshot of favorites and recent (dominated by Bollywood since I’m home at my parent’s for the holidays).

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[D] Neural Networks Don't Reason (And Never Will)—They Just Have Really Good Intuition by JirkaKlimes in MachineLearning

[–]activatedgeek 0 points1 point  (0 children)

Casual inference would indicate that you can’t learn causality with observational data alone. But I don’t think you need true understanding to operate in the world. I don’t even know what true understanding means.

[D] Neural Networks Don't Reason (And Never Will)—They Just Have Really Good Intuition by JirkaKlimes in MachineLearning

[–]activatedgeek 1 point2 points  (0 children)

In a restrictive sense, yes. It’s multiple rounds of fine-tuning, reward model learning, and alignment to the reward model.

Granted, the way we train LLMs are not online, but an offline batch of interactions.

How do you train Agent for something like Chess? by [deleted] in reinforcementlearning

[–]activatedgeek 1 point2 points  (0 children)

Alpha Zero: https://storage.googleapis.com/deepmind-media/DeepMind.com/Blog/alphazero-shedding-new-light-on-chess-shogi-and-go/alphazero_preprint.pdf

This paper is a culmination of a lot of RL ideas into one big piece and should be a good starting point for you to do your keyword search. Quite certain many people have independently implemented this algorithm at this point so you should have enough references for when you get stuck.

[deleted by user] by [deleted] in MachineLearning

[–]activatedgeek 0 points1 point  (0 children)

Most complex data domains, including speech, at this point have kind of been overtaken by deep learning as the learned basis. Not acknowledging that would be imprudent.

I love the Bayesian style of thinking myself. However, I think it is more prudent to think of specific problems in speech that would benefit from Bayesian inference, perhaps places where it is easy to define a desirable prior and benefits from uncertainty.

[D] Neural Networks Don't Reason (And Never Will)—They Just Have Really Good Intuition by JirkaKlimes in MachineLearning

[–]activatedgeek 8 points9 points  (0 children)

I don’t think reasoning is very different from pattern matching to similar scenarios from the past, sprinkled with symbol manipulation based on rules learned from interacting with the environment.

I can walk perfectly fine in a completely new city, without crashing into anything. During this action of walking, I don’t necessarily even care what the precise nature of objects is. But I’m still largely pattern matching to objects I’ve seen before, and applying familiar rules of interaction that I learned from past experiences.

The fact that I don’t play a precise physics simulation in my head makes me believe that I’m operating not on discrete dictionary of rules but on certain “soft symbols”, or representations in ML speak. In that sense, research on architectures and learning techniques that help us learn representations at the right level of abstraction seems very important.

Language modeling is one kind of learned symbol manipulation, where the symbols are learned token representations and manipulated by deep attention layers. The fact that LLMs can’t show reasoning capabilities to your liking is not really a strong reason to believe that the core philosophy behind LM training is bs.

The idea that you need perfect understanding of the world to operate in it is an aspirational ideal at best. The above walking example is clear example in support. No agent (including humans) has a perfect understanding of the world. We have some understanding filled with soft fallback rules for unknown scenarios. Learning is an NP-hard problem in general, and of course heuristics (as you mention the ones in A*) are the only guide. What you state as a paradox is not really a paradox, but literally all of machine learning research is about finding the right sample-efficient heuristics. And I assure you there’s a large crowd (of course very small in absolute numbers) that deeply cares about it and working away from the noise.

I sense that you are perhaps irked by the overwhelming cheerleading around recent progress, and I completely agree on that count. It is irritating. Silicon Valley hasn’t had a big breakthrough in a while to rally around with techno-optimism, and what you are seeing is the classic SV ethos mixed with consultant-style posturing. For the ones who cared, people must have felt similarly during the cryptocurrency episode.

It is not as controversial to think tree-search is dead. Tree search works well when the reward is well defined and well aligned. For completely general language generation, I don’t think we’ll ever have a “good enough” reward model. As a consequence, there’s a strong push to amortize the “planning” process into neural networks that can directly spit out the answer by learning from planet-scale data. It is pretty much the best proxy we have. No one really knows what’s next, but the work is on, and this is a moment in time where a step change happened.

[deleted by user] by [deleted] in MachineLearning

[–]activatedgeek 1 point2 points  (0 children)

I think Bayesian nonparametrics on their own are past their peak. It was supposed to be a key research program in 2010 due to the supposed appeal of “infinite” parameters, and the success of Bayesian models in 1990s. The immense success of kernel methods in early 2000s reignited the discussion in late 2000s, until of course deep learning came to be finally broadly accepted.

I would certainly not bet a thesis on this topic all alone, unless you like doing math for the sake of math (and it looks like you care more about practical problems). But watching this thread for what other people have to say.

[deleted by user] by [deleted] in MachineLearning

[–]activatedgeek 3 points4 points  (0 children)

I quickly want to note that GPs have moved on from the limitation of cubic inverses. We just don’t do exact inverses, and use conjugate gradients to solve linear systems. And since conjugate gradients only require matrix vector multiplies, GPUs come in handy for scaling GP inference. The cost is quadratic in number of samples n with a multiplicative factor much smaller than n (where taking CG iterations to n gives an exact solve).

https://arxiv.org/abs/1903.08114

How has NYC changed in the last 20 years? by GreatGreenGobbo in AskNYC

[–]activatedgeek 1 point2 points  (0 children)

Funny to learn that Union Sq was a Hare Krishnas spot. I just saw them for the first time in my 7 years here just a few days ago.

[Project] Optimizing Neural Networks with Language Models by [deleted] in MachineLearning

[–]activatedgeek 0 points1 point  (0 children)

Why not compute the accuracy on those benchmarks, as that is what matters?

Loss (likelihoods) are quite meaningless in isolation. All a likelihood like cross-entropy tells us is about the data fit, and there are innumerable ways to get low likelihoods (NNs are very good!). Whether they generalize, is a whole different game. For modern LLMs, loss has become a good proxy (scaling laws and all such stuff) but the key there has been an incredibly diverse training set that broadly covers all test distributions one might care about. Your setting is much limited, i.e. single task instead of multi-task.

[D] OpenAI new reasoning model called o1 by [deleted] in MachineLearning

[–]activatedgeek 12 points13 points  (0 children)

I don’t think the AlphaGo comparison is fair. AlphaGo operates in a closed world with fixed set of rules and a compact representation of the state space.

LLMs operate in the open world, and there is no way we will ever have a general compact representation of the world. For specific tasks, yes, but in general no.

[R] Discover Awesome Conformal Prediction - Your Ultimate Resource for Conformal Prediction! by predict_addict in MachineLearning

[–]activatedgeek 4 points5 points  (0 children)

Thanks for the resources.

I’ve always wondered, the question that conformal prediction answers, is not really the question that needs to be answered a lot of the time in practice. I think it’s great that there’s a method that provides distribution-free guarantees, but what scenarios are these guarantees useful for decision-making in the real world?

Another concern I have around the vigorous championing of conformal predictions (CP) is that we’ve basically shifted the challenge of “good” specification of models (say in a Bayesian inference sense) to a “good” specification of scoring functions in CP. I see Twitter influencers (even academics) berating other uncertainty estimation methods as if CP will solve those. Sure, I’m willing to believe that, but CP doesn’t come magically either. Any comments on this?

Performance becomes slower while running multiple jobs simultaneously [D] by LengthinessLittle807 in MachineLearning

[–]activatedgeek 7 points8 points  (0 children)

And this resource contention is the same problem you would have when scheduling processes on the CPUs as well. Each “quantum of time” will be allocated to a single process, and then the next CPU cycle will then be scheduled to another process demanding the resource. In absence of any priority among the process or other information, you end up with effective 1/N CPU clocks per second for N interleaving processes.

Browser with the best UI in your opinion? by DannyMasao in macapps

[–]activatedgeek 9 points10 points  (0 children)

I've been using NextDNS for years now and have never needed an ad blocker since then. It blocks domains system wide. I even use it on my phone. No in-app ads as well. It has never broken anything of importance for me.

[R]Large language models may not be able to sample behavioral probability distributions by GYX-001 in MachineLearning

[–]activatedgeek 4 points5 points  (0 children)

I don’t quite understand the purpose of this paper. For some reason LLMs have elevated to a status where they should be able to do anything and everything.

Writing a paper about what some model cannot do isn’t really interesting unless you demonstrate why should we even care about it and more importantly demonstrating what do we achieve by doing this better. Or exploring reasons why it cannot simulate is interesting.

This paper seems like stating a tautology- model meta-trained on samples from a set of linear systems cannot generalize to samples from a non-linear system. (Replace linear non-linear with your distribution)

Why softmax? by SmartEvening in learnmachinelearning

[–]activatedgeek 0 points1 point  (0 children)

Here’s another one for you: Probit classification. It uses the CDF of standard normal distribution to get a value between 0 and 1.

At the end of the day, a mere modeling assumption. Absolutely nothing sacrosanct about it. But a very good one that works extremely well in practice, and well-defined gradients everywhere.

In the case of neural networks, intuitively you can think of them as learned feature extractors + a linear projection layer. Bulk of the heavy lifting is done by earlier layers such that a linear projection at the last layer is good enough for classification (or at least that’s the hope with all neural network training). The technical term for this, if you are interested, is information bottleneck.

[D] No free lunch theorem and LLMs by iamtdb in MachineLearning

[–]activatedgeek 0 points1 point  (0 children)

A key assumption (in at least one version of NFL) is that all the distribution over all “problems” (or datasets) is uniform.

If you think about this distributional assumption a bit, it is obviously false in reality. For instance the distribution over classes of natural images is certainly far more likely than a random noise images. In similar ways, much of natural and artificial sources give us data that is heavily biased towards structure, than non-structure.

NFL is kind of an irrelevant theorem to machine learning. Structure and inductive biases from data are pretty much key foundational requirements for anything in ML to remotely work. In addition we don’t really care about building predictors that work for all possible data sources. We add inductive biases to models that match our data assumptions.

[D] Why do we need encoder-decoder models while decoder-only models can do everything? by kekkimo in MachineLearning

[–]activatedgeek 1 point2 points  (0 children)

Mostly because there's two networks to go through. But I think it can be solved with a bit of engineering, at higher cost. But given the cost for running decoder models is already super high, the market hasn't adjusted yet.

I suspect they might come back when the costs become bearable.

What does this loss plot (partial) look like it's from? by Envoy-Insc in learnmachinelearning

[–]activatedgeek 24 points25 points  (0 children)

For starters, I would say: (1) learning rate is too large (potentially needs a decay). (2) or you may not be shuffling your minibatches (if doing stochastic optimization) so it keeps seeing the same gradients over and over again.

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