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OpenAI Codex

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Re: OpenAI Codex

#101

Post by visarga »

jdonaldson wrote: post #56
maxwells-daemon wrote: post #45

The "language models don't really understand anything" corner is getting smaller and smaller. In the last few months we've seen pretty definitive evidence that transformers can recombine concepts ([1], [2]) and do simple logical inference using contextual information ([3], "make the score font color visible"). I see no reason that this technology couldn't smoothly scale into human-level intelligence, yet lots of peop…

I think intelligence as defined as "mapping inputs into goal states" is pretty well handled by models, and the models may be able to pick and choose states that are sufficient for achieving the goals. However, the intelligence that's created by language models is very schizophrenic, and the human-level reflective intelligence that it displays is at best a bit of Frankenstein's monster (an agglomeration of utterances…

Maybe we're also >50% Frankenstein monsters, an agglomeration of utterances from other people.

Re: OpenAI Codex

#102

Post by fpgaminer »

maxwells-daemon wrote: post #45

The "language models don't really understand anything" corner is getting smaller and smaller. In the last few months we've seen pretty definitive evidence that transformers can recombine concepts ([1], [2]) and do simple logical inference using contextual information ([3], "make the score font color visible"). I see no reason that this technology couldn't smoothly scale into human-level intelligence, yet lots of peop…

> "language models don't really understand anything"

I have a sneaking suspicion that, if blinded, the crowd of people saying variations of that quote would also identify the vast majority of human speech as regurgitated ideas as well.

> I see no reason that this technology couldn't smoothly scale into human-level intelligence

Yup, the OpenAI scaling paper makes this abundantly clear. There is currently no end in sight for the size that we can scale GPT to. We can literally just throw compute at the problem and GPT will get smarter. That's never been seen before in ML. Last time I ran the calculations I estimated that, everything else being equal, we'd reach GPT-human in 20 years (GPT with similar parameter scale as a human brain). That's everything else being equal. It is more than likely that in the next twenty years innovation will make GPT and the platforms we use to train and run models like it more efficient.

And the truly terrifying thing is that, to me, GPT-3 has about the intelligence of a bug. Yet it's a bug who's whole existence is human language. It doesn't have to dedicate brain power to spatial awareness, navigation, its body, handling sensory input, etc. GPT-human will be an intelligence with the size of a human brain, but who's sole purpose is understanding human language. And it's been to every library to read every book ever written. In every language. Whatever failings GPT may have at that point, it will be more than capable of compensating for in sheer parameter count, and leaning on the ability to combine ideas across the _entire_ human corpus.

All available through an API.

Re: OpenAI Codex

#103

Post by polyanos »

vincnetas wrote: post #80
vincnetas wrote: post #74

Will really be impressed when one could say: “here is this codebase, modify this function so that it would preduce [insert desired efect]” and also other functionality of project would not crash thumbling down… Because writing code from scratch now is i think much rearer than improoving existing codebases. Aka bugfixing.

Also curious what this ai would produce when provided with contradictory requests. Because often there are multiple requirements which on theyr own sounds reasonable but when you try to fit all requirements in one system, things get nasty.

It is only able to translate small instructions into code. I think it will take a while to get to a situation where you can just give it a list of requirements and it spits a working program.

Hell it messed up when they gave it the instruction "make every fifth line bold" in their Word api part of the demo, where it made the first line of every paragraph (which is only 4 lines long in total) bold instead of every fifth line.

Re: OpenAI Codex

#104

Post by 6gvONxR4sf7o »

sbierwagen wrote: post #89
6gvONxR4sf7o wrote: post #69

Earlier quoted context omitted.

> The "language models don't really understand anything" corner is getting smaller and smaller. In my mind, understanding a thing means you can justify an answer. Like a student showing their work and being able to defend it. An answer with a proof understands the answer with respect to the proof it provides. E.g. to understand an answer with regards to first order logic, it'll have to be able to defend a logical ded…

>In my mind, understanding a thing means you can justify an answer. Sure, but how does that work with superhuman AI? Consider some kind of math bot that proves theorems about formal systems which are just flat out too large to fit into human working memory. Even if it could explain its answers, there would just be too many moving parts to keep in your head at once. We already see something this in quant funds. The st…

It's not about us being able to interpret answer or justification, but the reasoner's ability to justify. If a superhuman AI can justify its answers in terms of first order logic, for example, it could be defined as understanding the answers with respect to FOL. Whether we as humans are able to check whether this specific bot in fact meets that definition is a separate empirical question.

If that quant algo you mentioned just says "it'll go up tomorrow" that's different than "it'll go up tomorrow" with an attached "it's positively correlated with Y, which is up today" which is different from a full causal DAG model of the world attached, which is again different from those same things expressible in english. But again, those are definitions, which are separate from our ability to check whether they're met.

Luckily, we're not in the realm of bots spitting out unfeasible to check proofs, except for a few niche areas like theorem proving (e.g. four color theorem). For language models like in the article, the best I'm aware of is finding relevant passages to an answer and classifying entailments.

> A machine can't justify something you physically don't have enough neurons to comprehend.

We can't always verify its justification, but it either can or can't justify an answer with respect to a given justification system.

Re: OpenAI Codex

#105

Post by nmca »

lstmemery wrote: post #97
maxwells-daemon wrote: post #45

The "language models don't really understand anything" corner is getting smaller and smaller. In the last few months we've seen pretty definitive evidence that transformers can recombine concepts ([1], [2]) and do simple logical inference using contextual information ([3], "make the score font color visible"). I see no reason that this technology couldn't smoothly scale into human-level intelligence, yet lots of peop…

I have to disagree with you here. In the Codex paper[1], they have two datasets that Codex got correct about 3% of the time. These are interview and code competition questions. From the paper: "Indeed, a strong student who completes an introductory computer science course is expected to be able to solve a larger fraction of problems than Codex-12B." This suggests to me that Codex really doesn't understand anything ab…

12B, though. What about 1.2T?

Re: OpenAI Codex

#106

Post by polyanos »

3wolf wrote: post #86

I think integrations like the MS Word example they show off at the end of the live demo have the potential to be even more impactful than just generating code for programmers.

That still needs work though, it messed up the "Make every fifth line bold" pretty bad. Still, it showed it could adapt to a new API pretty well.

Re: OpenAI Codex

#107

Post by fpgaminer »

Vermeulen wrote: post #10

A warning to devs building on OpenAI APIs: We spent months developing a chatbot using GPT3 for our game and released a video showcasing it: https://www.youtube.com/watch?v=nnuSQvoroJo&t=264s Afterwards OpenAI then added GPT3 chatbot guidelines disallowing basically anything like this. We were in communication with them beforehand, but they decided later that any sort of free form chatbot was dangerous. What they allo…

It sucks that OpenAI has no competition right now. They have every right to control their technology however they like. But it's a shame that they're being so stifling with that right, killing really fun stuff like you demonstrated.

But that monopoly won't last, and I think it's more than likely that competition will crop up within the next year. There's definitely a lot of secret sauce to getting a 175B parameter model trained and working the way OpenAI has. The people working there are geniuses. But it can still be reproduced, and will. Once competition arrives I'm hoping we'll see these shackles disappear and see the price drop as well. Meanwhile the open source alternatives will get better. We already have open source 6B models. A 60B model shouldn't be far off, and is likely to give us 90% of GPT-3.

Re: OpenAI Codex

#108

Post by lstmemery »

nmca wrote: post #105
lstmemery wrote: post #97

Earlier quoted context omitted.

I have to disagree with you here. In the Codex paper[1], they have two datasets that Codex got correct about 3% of the time. These are interview and code competition questions. From the paper: "Indeed, a strong student who completes an introductory computer science course is expected to be able to solve a larger fraction of problems than Codex-12B." This suggests to me that Codex really doesn't understand anything ab…

12B, though. What about 1.2T?

You need to scale the amount of data to take advantage of the increase in parameters. I'm not sure where we would find another 100 GitHubs worth of data.

Re: OpenAI Codex

#109

Post by spullara »

Vermeulen wrote: post #34
Miraste wrote: post #31

Earlier quoted context omitted.

OpenAI cloaks themselves in false "open" terminology to hide how proprietary and incredibly restrictive they've made their tech. That's a very cool demo; have you considered trying to make it run on GPT-J instead? It's an open source alternative you can run yourself or pay an independent api provider without supporting OpenAI.

Haven't been able to find a GPT-J service with good latency - though we haven't tried hosting ourselves

I have gotten it running on AWS in a container if you want the Dockerfile/scripts I can send it to you. Email is in my profile.

Re: OpenAI Codex

#110

Post by ruuda »

lstmemery wrote: post #97
maxwells-daemon wrote: post #45

The "language models don't really understand anything" corner is getting smaller and smaller. In the last few months we've seen pretty definitive evidence that transformers can recombine concepts ([1], [2]) and do simple logical inference using contextual information ([3], "make the score font color visible"). I see no reason that this technology couldn't smoothly scale into human-level intelligence, yet lots of peop…

I have to disagree with you here. In the Codex paper[1], they have two datasets that Codex got correct about 3% of the time. These are interview and code competition questions. From the paper: "Indeed, a strong student who completes an introductory computer science course is expected to be able to solve a larger fraction of problems than Codex-12B." This suggests to me that Codex really doesn't understand anything ab…

> but they will likely take advantage of the AST

In the end, a more general approach with more compute, always wins over applying domain knowledge like taking advantage of the AST. This is called “the bitter lesson”. http://www.incompleteideas.net/IncIdeas/BitterLesson.html

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