u/JimmyTheCrossEyedDog this is not ethical.

I think of AI as a subset of ML, focused on deep learning theory, architecture, and application.

That's cool. Apart from integrating GOFAI with ML, what other non-ML techniques are gaining popularity recently?

People tend to use AI and ML to mean similar things. But yes, in academia, we still research AI ideas that are not ML. And integrating good-old-fashioned-ai (GOFAI) with more modern ML is becoming an area of increasing research interest.

I think this is a very common belief. Symbolic systems can do many things that neural networks struggle with very sample efficiently. But they’ve failed to scale with more data as well as neural networks for most tasks, and are harder to train. If we could magically combine the reasoning ability of symbolic systems with the pattern recognition and generalization of neural networks, we would be getting very close to AGI imo. That being said idk much about recent research in symbolic reasoning so my knowledge might be outdated.

Yeah, it seems that this is an old question without an agreed upon answer. I've seen a lot of YouTube videos which claim to be AI which use this method but maybe there's a more agreed upon definition in academia or industry. It's not a big deal either way really I was just curious but I think I'll just go with "it depends who you ask".

You can see the full details here: https://beta.openai.com/docs/model-index-for-researchers

Copilot itself is the 12B Codex model, with further refinements.

You'll have a hard time finding non-ML approaches to AI, but there are still plenty of non-AI applications of ML. For example, classical topics like kernel methods, learning theory, optimization, all ML topics that are not-so AI flavored.

This group did such a distillation but didn't share the weights, they got it down to 24 MB.

https://www.reddit.com/r/MachineLearning/comments/p1o2bd/research_we_distilled_clip_model_vit_only_from/

LAION or stability.ai or huggingface might be willing to provide free compute to distill one of the openCLIP models.

Come to think of it, stability.ai should be releasing the distilled stablediffusion latter this month (week or two?) and it presumably will have a distilled clip.

Sure, but it is not considered synonymous. When people say ML, they usually mean linear regression, bayesian optimization and gradient boosting, not necessarily artificial neural networks with backpropagation and some version of gradient descent.

Expert learning is also a subset of ML, yet they are not considered synonymous.

The same way we say ML is distinct from AI because it implies learning, we hold DL to be distinct from ML because these are not exactly statistical methods and it's mostly alchemy, and we hold expert systems as distinct from ML because it's just a fancy way of saying rule-based AI and it doesn't imply there's any learning involved.

One must realize that mathematical relations do not perfectly map onto human language and communication. Similarly to how a skirt is a part of a dress, yet we consider them different things, subsets of ML are not considered ML itself in language and communication.

Does the Community edition of XetHub help address this? See here: https://xetdata.com/pricing/. Everyone today gets 20GB of storage for free.

Biggest issue with XetHub is current lack of open source. They have a way better storage design than DVC, but open source is absolutely necessary. If I can't give someone else zero-cost instructions to reproduce my research then my research becomes far less useful.

Perhaps industry will pay for this, but non-free software is a deal breaker for someone who wants to advance science.

AI is powerpoint. ML is python.

when AI.ppt becomes viable in .py, we recategorize it back to ML.

In the paper they talk about "first generation compositional systems" and I believe they would include differentiable programming in that category. It has some compositional structure, but the structure is created by the programmer.

Ideally the system would be able to create it's own arbitrarily complex structures and systems to understand abstract ideas, like humans can.

Interesting take! Thanks

Seems to be super relavant!!! Will read it now!

Yes eventually will be determined by downstream task performance!

Right!

Thanks!

Thanks! Yes active learning and curriculum learning seem to be related to this! Thanks

Are you sure? This implies otherwise: https://openai.com/blog/instruction-following/

But maybe it's only for the non-codex models

I'm not attacking you. I'm trying to prevent you from spending a good amount of your life, where you will burn many potential opportunity costs on a patently dumb idea that is not profitable.

As far as the prompt generation "Good luck with that", you are of course aware of the wide propagation of stable diffusion image generation prompt guides, right? right?

e.g., https://www.howtogeek.com/833169/how-to-write-an-awesome-stable-diffusion-prompt/

as an aside, your tone and tenor on this thread shows your egotistical fragility, lack of ability to take constructive criticism in stride, and outright hostility towards people who are trying to help you. these are not traits that any VC would want to see. They will google your domain and product name and find this.

Isn’t that the whole point of differentiable programming?

I used to be of the mind set that everything called AI is just ML, and we aren't even close to achieving AI. Well still technically true, now AI is AGI, and LLMs are AI. I've given up that fight. But yes basically synonyms in our current lexicon, just call ML AI to sound cooler to scifi fans and tech journalist.

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To me...

AI is an abstract concept that a computer can achieve cognitive abilities, emotion, and problem solving to the same level of a human.

ML is statistical and mathematical models. Basically the limit of what can be achieved on logic based hardware.

Hi, I dont know if this is the right place to ask this question, but what book should I study for learning the theoretical math part on machine learning? I’ve implemented ml as in programming so I have a good grasp of the “big picture”, but I want to start reading research papers and understand the math part. I already have a solid understanding of calculus, linear algebra, statistics and probability. As of now I am seeing

1. Probabilistic Machine Learning: An Introduction (Adaptive Computation and Machine Learning series) by Kevin P. Murphy
2. Understanding Machine Learning: From Theory to Algorithms by Shai Shalev-Shwartz and Shai Ben-David

but I’m not sure which one to get. Any recommendation on which one & why would be a great help for me!

I’ve also had some recommendations on “ Elements of Statistical Learning” by Trevor Hastie and took a look into that, but I want something a bit more advanced than that.