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OpenAI Codex

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Re: OpenAI Codex

#131

Post by riku_iki »

maxwells-daemon wrote: post #81
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…

Look at the "math test" video. Given the question: "Jane has 9 balloons. 6 are green and the rest are blue. How many balloons are blue?" The model outputs: "jane_balloons = 9; green_balloons = 6; blue_balloons = jane_balloons - green_balloons; print(blue_balloons)" That seems like a good justification of a (very simple) step-by-step reasoning process!

chances are high that something similar was in training set, and model approximated it.

Re: OpenAI Codex

#132

Post by wrinkl3 »

karmasimida wrote: post #59
f0e4c2f7 wrote: post #11

They just finished a demo on twitch. Pretty crazy! https://www.twitch.tv/videos/1114111652 Starts at 15:45.

It is simultaneously impressive and underwhelming for me. I mean yes this is a super impressive demo, but it didn't go beyond my expectation. I really want to see whether this model can write a correct binary search method without seeing one before. Or even correctly using the binary search, does it understand concept like index boundaries?

> I really want to see whether this model can write a correct binary search method without seeing one before.

It has almost definitely seen a lot of coding problems so I would expect "write a function to binary search a sorted array" to output the intended result. I don't think anybody expects it to come up with algorithms it hasn't encountered.

Re: OpenAI Codex

#133

Post by ssalka »

cgio wrote: post #129
mszcz wrote: post #72

Earlier quoted context omitted.

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.

Hope there's a human around to label that one as "ignore".

Re: OpenAI Codex

#134

Post by krisoft »

maxwells-daemon wrote: post #81
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…

Look at the "math test" video. Given the question: "Jane has 9 balloons. 6 are green and the rest are blue. How many balloons are blue?" The model outputs: "jane_balloons = 9; green_balloons = 6; blue_balloons = jane_balloons - green_balloons; print(blue_balloons)" That seems like a good justification of a (very simple) step-by-step reasoning process!

I wonder what would it have outputted if we would remove the “ and the rest are blue” part from the question.

Would not surprise me if an innatentive human student would answer that with the same code. After all school “trains” people to expect such challenges to be solveable. A more attenive human might say “we can’t know” or provide an upper limit to the number of potential blue balloons.

Re: OpenAI Codex

#135

Post by wrinkl3 »

GistNoesis wrote: post #50

Can I use this to write solidity contracts ?

That's about as advisable as asking it to write firmware for a pacemaker. Smart contracts are some of the most delicate codebases - even a tiny bug can cause you to lose a lot of money. With a model like this bugs are very likely, especially in such a niche domain.

Re: OpenAI Codex

#136

Post by hakuseki »

6gvONxR4sf7o wrote: post #69
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" 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…

What about GPT-f? It's a language model that proved theorems in the metamath formal system.

Re: OpenAI Codex

#137

Post by marcellus23 »

aeternum wrote: post #39
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.

It's pretty easy to get GPT-3 to say things that are incredibly sexist and racist. I think OpenAI is more concerned about the bad press associated with that than AI-safety.

It’s also pretty easy to get `echo` to say things that are incredibly racist and sexist.

Re: OpenAI Codex

#138

Post by 6gvONxR4sf7o »

hakuseki wrote: post #136
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…

What about GPT-f? It's a language model that proved theorems in the metamath formal system.

I'd definitely say it understands those theorems with respect to the metamath formal system then. The next question is what it understands the proofs with respect to.

Re: OpenAI Codex

#139

Post by krisoft »

riku_iki wrote: post #131
maxwells-daemon wrote: post #81

Earlier quoted context omitted.

Look at the "math test" video. Given the question: "Jane has 9 balloons. 6 are green and the rest are blue. How many balloons are blue?" The model outputs: "jane_balloons = 9; green_balloons = 6; blue_balloons = jane_balloons - green_balloons; print(blue_balloons)" That seems like a good justification of a (very simple) step-by-step reasoning process!

chances are high that something similar was in training set, and model approximated it.

You are very likely right. The question is how far the approximation can generalise? One way to test that would be to quizz the model with slightly varied prompts. Any human who can “solve” this world problem should be reasonably expected to solve the same problem if we change the subject’s name. ( From Jane to Bob, or Sanj, or even to Xcfg.) Or the name of the object (From balloon to token, or even to embobler). Or the attributes used to segment them. (From red/blue to heavy/light for example)

Or we can try to rewrite the challenge sentences with different wording. As long as the new sentences convey the same problem you would expect that a system who can “understand” them would generate the same or similar solution.

Curiously this kind of thought experiment also shows a weakness of the Turing-test as originally formulated. A machine correctly solving these word puzzle variations could “prove” that it “understands” the sentences, but it would also reveal that it is not a human. Since i would expect a real human to protest against the inanity of the challenges quite fast. ;)

Re: OpenAI Codex

#140

Post by [deleted] »

krisoft wrote: post #139
riku_iki wrote: post #131

Earlier quoted context omitted.

chances are high that something similar was in training set, and model approximated it.

You are very likely right. The question is how far the approximation can generalise? One way to test that would be to quizz the model with slightly varied prompts. Any human who can “solve” this world problem should be reasonably expected to solve the same problem if we change the subject’s name. ( From Jane to Bob, or Sanj, or even to Xcfg.) Or the name of the object (From balloon to token, or even to embobler). Or…

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