He is singlehandedly enabling millions of people to understand what is going on, what + and * do, actually demystifying the "wires".
I just wish he start thinking of himself as more than 'collapsing weights', regardless if it turns out to be true.
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He is singlehandedly enabling millions of people to understand what is going on, what + and * do, actually demystifying the "wires".
I just wish he start thinking of himself as more than 'collapsing weights', regardless if it turns out to be true.
Earlier quoted context omitted.
He’s not just talking about agents good enough to replace workers. He’s talking about whether agents are currently useful at all. >Overall, the models are not there. I feel like the industry is making too big of a jump and is trying to pretend like this is amazing, and it’s not. It’s slop. They’re not coming to terms with it, and maybe they’re trying to fundraise or something like that. I’m not sure what’s going on,…
I don't think he is saying agents are not useful at all, just that they are not anywhere near the capability of human software developers. Karpathy later says he used agents to write the Rust translation of algorithms he wrote in Python. He also explicitly says that agents can be useful for writing boilerplate or for code that can be very commonly found online. So I don't think he is saying they are not useful at all…
They start being less useful when you start treating them as "I can send them ill-specified stuff, ignore them for 10 minutes and merge their results", as things spiral out of control. Basically "vibe-coding" as a useful concept doesn't work for projects you need to iterate on, only for things you feel OK with throwing away eventually.
Augmenting the human intellect with LLMs? Usually a increase in productivity. Replacing human coworkers with LLMs? Good luck, have fun.
Maybe I'm being too simplistic, but I think we're mixing two distinct debates. Today we have an extraordinary invention—comparable to the wheel in its time. That invention is: predictive inference over all human knowledge. Period. I don't like calling it "Artificial Intelligence" because it's not intelligence; it's a prediction system that can project responses by illuminating patterns across all human knowledge enca…
> I don't like calling it "Artificial Intelligence" because it's not intelligence A pattern I noticed in a AI[sic] discussions: Handwavily declaring what intelligence is not, while not explaining what is.
Maybe I'm being too simplistic, but I think we're mixing two distinct debates. Today we have an extraordinary invention—comparable to the wheel in its time. That invention is: predictive inference over all human knowledge. Period. I don't like calling it "Artificial Intelligence" because it's not intelligence; it's a prediction system that can project responses by illuminating patterns across all human knowledge enca…
> I don't like calling it "Artificial Intelligence" because it's not intelligence A pattern I noticed in a AI[sic] discussions: Handwavily declaring what intelligence is not, while not explaining what is.
Earlier quoted context omitted.
Can perform out of distribution tasks at least around average human level performance.
Every attempt to formally define "general intelligence" for humans has been a shitshow. IQ tests were literally designed to justify excluding immigrants and sterilizing the "feeble-minded." Modern psychometrics can't agree on whether intelligence is one thing (g factor) or many things, whether it's measurable across cultures, or whether the tests measure aptitude or just familiarity with test-taking and middle-class…
LLMs or neural nets have never been good with out of distribution tasks.
He is an absolute treasure, I have watched all his videos more than 4 times and I don't think I would've been able to have a good mental model about deep learning without them, regardless of the amount of Bengio, Goodfellow etc lectures I have seen, none of them come even close. He is singlehandedly enabling millions of people to understand what is going on, what + and * do, actually demystifying the "wires". I just…
I have massive respect for Andrej, my first encounter with "him" was following his tutorials/notes when he was a grad student/tutor for AI/ML. I was a lot disappointed when he went to work for Tesla, and I think that he had some achievement there, butnot nearly the impact I believe he potentially has. His switch (back?) to OpenAI was, in my mind, much more in keeping with where his spirit really lies. So, with that i…
How to tell if you regurgitated this comment vs being truly creative? If you can show me objectively, I’m sold.
Comparing LLMs trained on reddit comments and people who learn to speak as a byproduct of actually interacting with people and the world is nuts.
The cookie warning is fun
https://news.ycombinator.com/item?id=45622944
Must have been the flu-brain misfiring
Earlier quoted context omitted.
This world model talk is interesting, and Yann Lecunn has broached on the same topic, but the fact is there are video diffusion models that are quite good at representing the "video world" and even counterfactually and temporally coherently generating a representation of that "world" under different perturbations. In fact you can go to a SOTA LLM today, and it will do quite well at predicting the outcomes of basic co…
Photons hit a human eye and then the human came up with language to describe that and then encoded the language into the LLM. The LLM can capture some of this relationship, but the LLM is not sensing actual photons, nor experiencing actual light cone stimulation, nor generating thoughts. Its "world model" is several degrees removed from the real world. So whatever fragment of a model it gains through learning to comp…
AGI is still a decade away, and always will be.
You say that as if people had been saying "10 years away" for ages, but I don't think that's true at all. There's some information about historical predictions at https://www.openphilanthropy.org/research/what-should-we-lea... (written in 2016) from which (I am including the spreadsheet found at footnote 27) these are some I-hope-representative data points, with predictions from actual AI researchers, popularizers, p…
Meanwhile you won't find anyone on here who is an author for Attention is All You Need. You know the thing that actually is the driving force behind LLMs.