A reason behind this is probably because a lot of reaserch has been put into this field and companies don't invest time there as they do not expect to find anything new.

The main way diffusion uses to predict added noise is with deep convolutional neural networks,
Furthermore, stable diffusion specifically uses even more deep CNNs to downscale,upscale the image.

Am I looking at your comment right now, or is it just some number of voltages over the neurons of my visual cortex?

Trees/forests are still state of the art for structured data... So not only did they not give up on them, but traditional methods are seen as better in some domains. Not to mention the ease of use, and the quick training.

Also explainable AI is much more promising with traditional methods, especially trees.

I think the best thing for you is find investors for your project. Some like Angel investors or open a go find me You need definitely more than 25k Cause you don't want to get stuck halfway with either out of money or a minor quality result.

It looks at the screen. Your question indicate you're not well versed in AI. I'd advise you to read up more on fundamental deep learning techniques if you don't know what a CNN does.

The AI agent is a computer program. It does not have eyes or a physical body. Therefore, it only works with things that exist inside a computer, i.e. numbers.

Another addendum to this fantastic answer: lots of work in uplift modelling also uses traditional ML methods (related to your counterfactual point) and will likely continue to do so.

Oh, yes, gladly. This "open"AI paper says it:

> Larger models are significantly more sample efficient, such that optimally compute efficient training involves training very large models on a relatively modest amount of data and stopping significantly before convergence.

https://arxiv.org/abs/2001.08361

You can improve outcomes from small datasets by making the model larger.

> wait bro the key benefit is the the hierarchical description

agreed

> I think that the improvements your suggesting pretty much describe the paper itself

Allow users to work in actual unstructured language, or an extant programming language, and I'd agree.

I don't really unserstand the question What do you mean "looking at a screen"? Or "looking at numbers and finding a pattern"?

The model takes in multidimensional array as input. That array is all the rgb values at a given instant. Take that to mean whatever suits you.

right my confusion is how it views the rgb pixel input, would you summarize it as it's looking at a screen, a whole image like a human player would, like the little ai is in it's own vr head set. or is it more just looking at numbers and finding a pattern

I still do, but the points about complexity and roi remain the same. I get that you like this form of data and that's okay (actually, that's great!) , but not everybody has to adopt it because you find it exciting.

Good to hear! Do you know what the space of hybrid models looks like? Specifically using deep learning for input signal to data and classical machine learning algorithms (e.g. gradient boosted trees) for data processing.

My intuition says that hybrid models definitely have a role in general problem solving machines. I've tried searching this topic and the space is muddy at best.

It is out and it's 3x more expensive than it's A100 equivalent was 2 years ago. The prices are not going down for a very long time, probably not until the next generation is out.

Hey man, thanks a lot. I actually wanted to join this discord but the site I looked at didn't have a valid link. Have a nice day :)

The customers I build models for insist on interpretability and robustness, which deep learning just doesn't give them right now. Actually just got a conference paper out of a modification to a classical method, which was kinda fun.

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Thank you, I'll go through that. If I may ask, how did you get there? I only seem to get clickbait articles and videos no matter what keywords I google. Is there any kind of special "prompt" you've to google to get the results you want like with Stable Diffusion? :D

Glad it helped! The first thing I tried was just to re-initialize just like at the beginning of training, but I don't remember how much I dug into trying to modify it before moving on. That's great your seeing some improvements though! Would love to hear how the rest of your experiment goes!! =)

I guess there will always be room for interpretation on what intelligence is since after all is just a label we put on things. What I was thinking though was something like intelligence in animals and how that's considered intelligence without necessarily comparing animals to human beings.

Huggingface just finished a sprint where they fine-tuned whisper on 100s of languages, going to their discord and following the guides is going to be by far the easiest way.

Check under ML-4-AUDIO channels "sprint-announcement", "discussions", and "whisper-model-playground"

Most likely as a post-processor, along the lines of guided generation; pretty much the GeDi proposed by Salesforce in 2020.

GitLab is an open-source and free alternative to GitHub for hosting ML projects and code. It's used by many organizations for software development, data analysis, and machine learning. It offers a wide range of features, including an integrated CI/CD pipeline, version control, issue tracking, and project management. Additionally, GitLab also supports Jupyter Notebooks and data science projects.

Absolutely. OPs question was about research, so I did not include this, but it's absolutely true. It also makes sense - everyone has relational DBs, they are cheap and scalable, so chances are a business already has a quite reasonable data for ML just waiting in their tabular database. This, of course, means money, which means money for research, even in-company research, which may not be even published, but is research nonetheless.