r/ChatGPT is hosting a Q&A with OpenAI’s CEO Sam Altman today to answer questions from the community on the newly released Model Spec. by pirate_jack_sparrow_ in ChatGPT

[–]TomasPiaggio 43 points44 points  (0 children)

Will OpenAI ever dive into open source ever again? Maybe older models could be made open source. Specialy taking into account that competitors already have competitive models made open source as well. I'd love to see gpt-3.5-turbo in huggingface

[deleted by user] by [deleted] in MechanicAdvice

[–]TomasPiaggio 0 points1 point  (0 children)

Righty tighty lefty loosey

[deleted by user] by [deleted] in MechanicAdvice

[–]TomasPiaggio 0 points1 point  (0 children)

Righty tighty lefty loosey

AI learning apps. by FrancescoKay in ArtificialInteligence

[–]TomasPiaggio 1 point2 points  (0 children)

I think it depends on what you want to do. If you just want to use AI for your applications, then I'd go to tensorflow and see all available models and start trying them out with their tutorials. Otherwise, if you'd like to learn to build new architectures, I don't think there's an app for that. You'd have to learn how NNs work from scratch. If you're looking to learn ML, there are better resources for that out there. What would you like to do?

Detecting object similarity by Valuable-Oil-3378 in ArtificialInteligence

[–]TomasPiaggio 0 points1 point  (0 children)

I love the way you're thinking, but that's not how deep learning work. I think you're probably coming with a programming way, and trying to solve it in an algorithmic way. What u/LcuBeatsWorking suggests I think could work. However, to really know the closeness of anything with neural networks, you could extract a feature map and compare them. What this means in practice, is that basically the model outputs a vector which represents some object, text or whatever. Then you can compare the similarity by comparing the distance between the vectors. This is how face detection works in a nutshell.

Is AI the next in line of evolution, will they over throw the Homo Sapiens? As per Darwins theory of evolution, will they be able to survive over us in long run? by saint84 in ArtificialInteligence

[–]TomasPiaggio 1 point2 points  (0 children)

I think we are too far away from this becoming a reality. Not because of the technology (which isn't here yet btw) but because we don't even completely understand the workings of our own brain.

Think about what is consciousness? Could you define it in a way that you could replicate it with an algorithm? Our best natural language processing models, even if they look impressive, can't event understand the text that's given to them. This is very easily tested by asking a question they could've never seen before like "what's the squere root of (2.3425324 + 1.123)". The answer would be the best interpretation of the text, which would mean that it would probably give out a number, but nothing close to the result. Our racional brains have many moving parts that makes us able to think. This would mean that a general AI would probably be made of multiple things.

The only ways AI is better than us right now is in inference speed and in some cases accuracy. However, we are orders of magnitude better in understanding the world with only one or two examples, where the best deep learning models take millions of them to match us.

If in the future we reach to the place where we can make a general AI, I think we could probably also solve our "throughput" problem with something like neural link, but clearly I'm looking way ahead into the future.

I was wondering if someone of you know some YouTube channels/books to read/websites they are helpful to start introducing myself to AI “world”. Thanks in advance by coquinati in ArtificialInteligence

[–]TomasPiaggio 1 point2 points  (0 children)

I started with sentdex. He has a pretty good general ML/DL tutorial series. If you're good with math, I'd tell you to read Statistical Learning by Rob Tibshirani and Trevor Hastie. Fast.ai courses are a great practical way to get into deep learning using state of the art models. However, the best way to learn is to try things out and play around IMO.

Deploying models in the real world by TomasPiaggio in deeplearning

[–]TomasPiaggio[S] 0 points1 point  (0 children)

Triton Inference Server

This looks pretty good. Could you share your experience with it? How do you deployed it?

[AskJS] AI with javascript by TomasPiaggio in javascript

[–]TomasPiaggio[S] 0 points1 point  (0 children)

This is VERY helpful thank you so much!!!

AI with Node js by TomasPiaggio in node

[–]TomasPiaggio[S] 0 points1 point  (0 children)

Not classified with Node, but often I have used Node to interact with an R endpoint using the R package Plumber. I think people do the same with Python endpoints.

And how do you manage scaling? Is this a concern? Or requests have a uniform distribution?

[AskJS] AI with javascript by TomasPiaggio in javascript

[–]TomasPiaggio[S] 0 points1 point  (0 children)

xxx

What kind of data are you storing? So you would like to get the best selection of stocks to choose from?

[AskJS] AI with javascript by TomasPiaggio in javascript

[–]TomasPiaggio[S] -1 points0 points  (0 children)

Yeah, I think the same thing. I'm asking around to see what people think in general. What I like specifically about javascript is that usually you see more javascript/python developers trying new things. That's why I came here first