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OpenAI Codex

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Re: OpenAI Codex

#121

Post by gnramires »

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…

Also, you should note the memory and capabilities required to reach a conclusion might be much greater than to show it's true. Showing a needle may be easy, finding it in the haystack very hard. In this sense the hope for explainability is expanded. But still, I guess the real world is really messy "the full explanation" may be too large -- like when you explain a human intuition, the "full explanation" might have been your entire brain, your entire set of experiences up to that point; yet we can give partial explanations that should be satisfactory

A have a hypothesis that inevitably, reasoning needs to 'funnel' through explicit, logical representations (like we do with mathematics, language, etc.) to occur effectively. Or at least (quasi-)formalization is an important element of reasoning. This formal subset can be communicated.

Re: OpenAI Codex

#123

Post by polyanos »

sbierwagen wrote: post #115
refulgentis wrote: post #2

I'm trying to extract some signal from this link...lots of upvotes, no comments, 30 min old, top 3 on HN...I'm worried this will be read as negative, but it's not, just learning, and enough time has passed I'm itching to jump in and ask: - Is the significance here exactly what it says on the tin: the model behind GitHub's AI code completion will be shared with people on an invite basis? Or am I missing something? - W…

Take a look at the video demo. It takes natural text in a box and generates code. Copilot was super-autocomplete, so the interface was writing code in an IDE that it filled out for you. Natural language interface will be a little easier for non-programmers. (Though, how would you read the code to make sure it does what you meant...)

>Take a look at the video demo. It takes natural text in a box and generates code. Copilot was super-autocomplete, so the interface was writing code in an IDE that it filled out for you.

No it wasn't, you can literally describe, in natural text, what you want in a comment and CoPilot will do its best to generate a complete method based on that comment. It seemed like it was so auto-compltely because that focussed on the "helping the developer" part.

I'm fairly sure CoPilot could have shown something similar if they had a demo where you could make something visual easily, like HTML + Javascript/Typescript/whatever scripting language. They're using exactly the same model (Codex) after all.

Re: OpenAI Codex

#124

Post by kevinqi »

ruuda wrote: post #110
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…

> 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

"the bitter lesson" is a very interesting, thank you! However, I wonder if AST vs. text analysis is fully comparable to the examples given in the post. Applying human concepts for chess, go, image processing, etc. failed over statistical methods, but I don't think AST vs. text is exactly the same argument. IMO, using an AST is simply a more accurate representation of a program and doesn't necessarily imply an attempt to bring in human intuition/concepts.

Re: OpenAI Codex

#125

Post by mattkrause »

jacquesm wrote: post #116
bufferoverflow wrote: post #93

Earlier quoted context omitted.

It probably can scale, but we're nowhere near the computational power we need to even recreate the brain. And don't forget, our brain took a billion years to evolve. A typical brain has 80-90 billion neurons and 125 trillion synapses. That's a big freaking network to train. Hopefully we can figure out how to train parts of it and then assemble something very smart.

Takes on average 2.5 decades to train it.

That's just from the most recent checkpoint :-)

If you were to build it "from scratch" you'd also need to include the millions of years of (distributed) evolution required to get that particular kid to that point.

Tony Zador has some interesting thoughts about that, including"A critique of pure learning", here: https://www.nature.com/articles/s41467-019-11786-6)

Re: OpenAI Codex

#126

Post by lstmemery »

ruuda wrote: post #110
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…

> 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

I don't think the bitter lesson is applies to ASTs.

From the Bitter Lesson:

"Early methods conceived of vision as searching for edges, or generalized cylinders, or in terms of SIFT features. But today all this is discarded. Modern deep-learning neural networks use only the notions of convolution and certain kinds of invariances, and perform much better."

Those models are taking advantage of inductive biases. Every model has them, including the massive language models. They are not the same as engineered features (such as SIFTs) or heuristics.

Using the AST is just another way of looking at the code already in your dataset. For the model to understand what it is writing, it needs to map the text sequences map to ASTs anyways. It can attempt to learn this, but the 12B model still makes illegal Python code so it clearly hasn't.

Re: OpenAI Codex

#127

Post by mensetmanusman »

If this actually worked, wouldn’t that be amazing? If you could break down a software idea into a blue print of concepts that need to be accomplished, and then dictate what should be done…

I doubt it works, but I wonder how many decades from now we will be able to walk through a finite number of simple requests and wrap them together as working software. Then people will be able to convert their blueprint into action!

Re: OpenAI Codex

#128

Post by simsla »

karmasimida wrote: post #51
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…

> The "language models don't really understand anything" This is still true. By all account, human doesn't need to read 159GB of Python code to write Python, or we simply can't. But it doesn't necessarily indicate language models aren't useful.

The problem with this (very popular) argument is that you can't give a CS course to a baby and expect them to get at programming.

By the time we see our first line of code, most of us have seen a ridiculous amount of data. We've been trained in problem solving, logical reasoning, maths, natural language processing, ... Hell, we've been trained as pattern matchers since we've been born.

By my account, humans actually need a large amount of training data. It might be the knowledge federation and generalisation that we're good at, but I don't think we're a clear winner in data efficiency.

Re: OpenAI Codex

#129

Post by cgio »

mszcz wrote: post #72
nradov wrote: post #29

Earlier quoted context omitted.

The notion of a toy like a chatbot being "dangerous" is just so ludicrous. The OpenAI folks take themselves way too seriously. Their technology is cool and scientifically interesting, but in the end it's nothing more than a clever parlor trick.

I think different kind of dangerous, not the SkyNet stuff. The first idea that popped into my mind is below. I know, it's dark but... 8 year old to AI: "my parents won't let me watch TV, what do I do?". AI: "stab them, they'll be too busy to forbid you". Then again the same thing can be said by a non-AI. My thinking is that you'd be talking to an actual average person. I'm not so sure that that is such a good thing.

The scary irony is that you just posted a training sample for a future AI.

Re: OpenAI Codex

#130

Post by 6gvONxR4sf7o »

beering wrote: post #118
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. What if the language model can generate a step-by-step explanation in the form of text? [0] There's no guarantee that the reasoning was used to come up with the answer in the first place, and no proof that the reasoning isn't just the product of "a really fancy markov chain generator", but would you accept it? We're really walking into Searle's Chin…

Searle's Chinese Room is exactly why I started thinking of understanding this way. It convinced me that a one-size-fits-all notion of understanding isn't useful. But it also made me think that understanding "with respect to system X" is useful.

If you can challenge an answer and get justification expressed in the form of X, then it understands with respect to X. A step-by-step text explanation is one form of X.

> ... but would you accept it?

This is all to sidestep questions of whether you accept X as "real" understanding or not. :D

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