I don't mean applications _of Deep Learning_, I mean what are the applications of this specific theory to real life Deep Learning problems. Can this theory help a Deep Learning practitioner, or is it applied only to proving some abstract bounds on some theoretical, abstracted and simplified neural nets?

You don't need to choose, and there's definitely a market for people that are capable of both good software engineering and good machine learning. Personally I'm a big believer in being well-rounded in terms of skills.

If I had to guess, what you're saying might just mean that you have more to learn about software engineering than machine learning right now. And that'll change over time.

Limited by the 4k token max in api call?

But what about something more niche like MPS or Rocm?

Does it come with an animated assistant in the shape of a paperclip?

amazing

LLMs are our new overlords, it's crazy

Yeah, right now it’s just a thin wrapper around GPT-3, but there’s a lot that could be done to improve it, like using static code analysis to build a better prompt or even training a more specialized model (like this).

But what can Vulkan do that CUDA can’t already do?

Idk. Have a decent idea what’s being worked on for the next year but it gets fuzzy after that. Maybe we’ll have another architectural breakthrough. Alex net 2012, transformers 2017, something else 2023 or 2024 maybe.

I think what you are looking for is called "expressive TTS". There have been a ton of papers in the last couple of years on the topic. Many provide code.

I've had some success with simply preserving the hidden state of the network from one sentence to the next.

SSML may not be expressive enough for your application.

Not to take anything away from this project, but it’s just an api call to gpt3 with prompt “fix this error {error}”. I thought there was some training and fine tuning, but I guess LLMs can do it all now a days

Best way to think of it is that it scrambles your code, but right before it runs it still needs to be unscrambled, so someone dedicated enough will be able to obtain the full source code (or at least byte code) anyway.

I hope my boss sees this because this is a very common misconception. This isn't the 90s where your only option for running code was to install it on the user's machine. You have other options. You have to assume that everything you install on a user device is no longer private. The only way to protect source code is to never distribute it in the first place, and only expose your software through web APIs.

Right now, this is just a simple demo of what’s possible with AI-driven debugging. But I’d like to build it out so that instead of just explaining errors, Adrenaline provided a ChatGPT-style assistant that can answer questions about your error, and teach you during the debugging process.

This is open-source, so if anyone’s interested in contributing, here’s the GitHub repository: https://github.com/shobrook/adrenaline

Dude this is dope

Try it out here: https://useadrenaline.com

Wikipedia? The ANN page has a pretty comprehensive summary of the history and over 150 references.

https://en.wikipedia.org/wiki/Artificial_neural_network

EE here, 25 years in NLP. Working in Prolog, Datalog, Logica for formal verification. Using NLP to extract facts for verification.

This is such a fascinating book! Especially the parts about Walter Pitts. He was a genius from a young age, IIRC he was sending letters to Bertrand Russell correcting his proofs when he was just a teenager. Very tragic story though. Severe mental illness kept him from having the career he deserved. Someone should make a movie about him.

I looked up clown porn and I didn't find anything useful.

I believe so, I think you can experiment with different summarization prompts too. For me it is still trial and error when dealing with large context windows.

Data not yet collected. Why don't you try some side-by-side comparisons and report back?

This should work, thank you :)

I'm no expert indeed, that's why I was asking.
But experts in the field also think that serious concerns on AI safety is justified

https://en.wikipedia.org/wiki/Open_Letter_on_Artificial_Intelligence

Also, a lot of good arguments here:

https://www.reddit.com/r/ControlProblem/wiki/faq/

Is it good for you ? I could look for something more if needed !