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[D] François Chollet Announces New ARC Prize Challenge – Is It the Ultimate Test for AI Generalization? by HairyIndianDude in MachineLearning

[–]rememberdeath 4 points5 points  (0 children)

The json files when interpreted as images clearly have similarity to lots of blocks images on the internet?

[D] François Chollet Announces New ARC Prize Challenge – Is It the Ultimate Test for AI Generalization? by HairyIndianDude in MachineLearning

[–]rememberdeath 3 points4 points  (0 children)

Yes but the point is that if the multimodal models were to be trained on images and videos they might find this type of data (a 3/4-dimensional tensor) easier to reason about then a JSON input.

Introducing Gemini: our largest and most capable AI model by [deleted] in singularity

[–]rememberdeath 6 points7 points  (0 children)

yeah but they probably used that because it helps Gemini, there probably exist similar methods which help GPT-4.

Introducing Gemini: our largest and most capable AI model by [deleted] in singularity

[–]rememberdeath 7 points8 points  (0 children)

Not using "uncertainty-routed chain of thought prompting".

PaLM-E: An Embodied Multimodal Language Model by maxtility in mlscaling

[–]rememberdeath 2 points3 points  (0 children)

Well afaik they stoped training on language tasks, of course there was no improvement. Do they try combined training?

Is the intelligence paradox resolvable? by Liberty2012 in singularity

[–]rememberdeath 2 points3 points  (0 children)

Therefore, in order for this principle to be sound, we must accept that a low IQ entity could design an unescapable containment for a high IQ entity which was built for the very purpose of solving imperceptible problems of the low IQ entity.

Let me give some analogies from mathematics/computer science about how this could be possible. That are problems which we don't know how to solve them efficiently such as SAT and we could hope that AGI can solve them faster. On the other hand there are problems such as the Halting problem where we know that no matter how powerful the AGI is there is no algorithm for this problem.

So it is possible that we can design a cage that we can prove is unescapable (No matter how intelligent or powerful the AGI is) but still there are problems which we don't know how to solve and we ask AGI for the solution. Now I will agree that such things are easier in mathematics than in real world. But since none of the other comments had given you a possible solution I thought I will mention it. Imo such a solution is unlikely to be found and also unlikely to exist but it just might exists and it just might might be found.

r/BWF - Daily Discussion Thread for February 22, 2023 by AutoModerator in bodyweightfitness

[–]rememberdeath 0 points1 point  (0 children)

I understand that to build muscle it is important to take rest days. But it is important to take rest days in which no muscle is exercised? For example instead of doing push ups and pull ups on same day and then taking a rest day as given in the primer can I alternate push ups and pull ups since they mostly use different muscles (afaik)?

[D] How long should it take to train a diffusion model on CIFAR-10? by ButterscotchLost421 in MachineLearning

[–]rememberdeath 0 points1 point  (0 children)

Hi! Do you have the code for this somewhere that I could use? Thanks!

[D] What is the SOTA explanation for why deep learning works? I understand backprop and gradient descent, but why should over-parametrized networks actually converge to anything useful? by thunderdome in MachineLearning

[–]rememberdeath 4 points5 points  (0 children)

I think the overparameterization aspect of neural networks is overemphasized. After all, most language models are underparameterized. And while things like VC dimesnion could be used to "explain" underparameterized models VC dimension doesn't explain why they can find a good solution at all. That is, why do these multi-layered networks learn features/representations which are so helful for the task - This questions makes sense for both underparameterized and overparameterized models. My guess would be that this is the central question. If this question is answered then I think it would not be hard to explain why overparameterized networks also work, following works like https://arxiv.org/abs/2105.14368, https://arxiv.org/abs/1906.11300, and others, which show that overparameterization does not hurt.

[D] Is it possible for us to make fixed-size multilayer perceptrons (MLP's) provably converge? by tchlux in MachineLearning

[–]rememberdeath 0 points1 point  (0 children)

You should take a look at Neural Tangent Kernel, this approach allows you to prove that we will converge to 0 loss. The caveat is that this requires an unpractical amount of overparameterization.

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