So........... you are just generating multiple samples from the LLM with the same prompt?

I conçur. Sounds like a fun idea but I can’t see any real value proposition from this.

A lot of low hanging fruits are being picked right now regarding ChatGPT and GPT in general. A lot of VCs are overwhelmed at this point by what’s on offer.

Neat idea, but

> as soon as we find VC

What do you see as the value proposition of made up search engine results? And what makes this different than every other use of LLMs?

I would caution against the idea of "finding VC" as a sure thing when everyone and their brother is throwing together a GPT-based startup right now, most of which will go nowhere.

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Have you looked into local co-working spaces?

An option is discord, I havent used it myself but had a hunch chat might be flowing on a co-working room or whatever they are called

Although nothing beats forced social interactions at an office, how I miss idle chit chat

I think it's a matter of trial and error. The best ratios I've seen were 1:25, but these concerned transformer networks, which are much more sparse than resnets.

There are some tricks, but it depends on the model. Ex. for transformers, it's not just enough to imitate the last layer. I suspect that it's the same for resnets, given they're deep residual networks just like transformers.

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Great blog post. I found the Li Diffusion-LM results very intriguing due to the seemingly better semantic capture, despite the tradeoff in fluency.

Question - do you see diffusion models as having any advantages for approaching the "long text" issue (token window size limit) that autoregressive models suffer from? Curious generally, but areas like abstractive summarization in particular come to mind.

Yeah it can be helpful even if you can't easily read the axes. I've even found it helpful for 50-class. It helped me quickly see that the model was over-predicting the top few classes, and that showed up as vertical bands.

This is what offices were great for, just being able to go to lunch with a diverse group of co-workers. I would always make an effort to go to lunch with a different crowd occasionally, such as accounting or sales. As an engineer it also helps to get an idea of what everyone else is doing and how you can be more useful.

/u/psychorameses was spot on when he said that python is a scripting language. It’s really not meant for you to be able to do this.

The correct answer for this is what /u/robertknight2 suggested. If you want to deploy a production grade application then you need to export your model (either to ONNX, or TFLite or similar). Once you have the exported model you write a GUI application any old way. You can make a QT GUI application that interacts with the ONNX model using the C++ ONNX runtime API. Or you could write an electron application that uses TFJS to run a TFLite model. Both are viable options (though idk if you’ll be able to get TFLite using the GPU easily in Electron).

This is CPU only but here is an example of a C++ windows application that uses the ONNX runtime. Packaging CUDA (TensorRT really) and getting it to work with ONNX should only be a little bit more work. A quick google search yielded this thread for packaging TensorRT into a VS project on windows. It looks like it has a working example too: https://github.com/NVIDIA/TensorRT/issues/2085

How small do you make the student, when a teacher is let's say ResNet101? How do you find a good student/teacher size ratio?

Are there any tricks to knowledge distillation? Or just standard vanilla procedure?

attention is essentially a dynamically weighted cross-product. if you haven't already seen this blog post, it's one of the more popular explanations: https://jalammar.github.io/illustrated-transformer/

i think you read that comment backwards :)

Have you read the stable diffusion paper? They discuss the motivations there. https://arxiv.org/abs/2112.10752

Become a manager, you’ll have tons of meetings with people.

Alternatively, get a social hobby. You don’t need social connections to come from work.

That begs the question, what are good ways to use a preprint server to further your academic career

Yeah they’re all like “hey check out my blog post on this thing I just learned and had to tell someone about”

Also, you might want to tell them that you thought their paper was really well executed and if they would be willing to chat and if there are internship opportunities on their team (if you are interested in that).

It's a preprint server, meant to publish preprints.

We can't afford that in Machine Learning by the time your paper has passed the review process it's going to be outdated.

Data scientist is not a very well-defined job title. It can involve more or less social interaction depending on the company.

But you could get more social interaction by doing pair programming with your coworkers or asking to be assigned more work involving social interaction, e.g. talking to clients or mentoring new coworkers.

You could eventually transition into another career path such as people management, education, project management, program management, sales, etc.

Go into product (a product manager that gets the details can be invaluable)

Data scientist is very sociable career…