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OpenAI Codex

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Re: OpenAI Codex

#111

Post by 3wolf »

polyanos wrote: post #106
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.

Yeah, definitely. I guess my point was that converting natural language to source code can be even more valuable for people who don't know how to code, but want to perform actions more complicated than a simple button press. For example, I often find myself doing regex based find-and-replace-alls in text files, and even that feels inefficient while also being over the head of the vast majority of users. I'd imagine there are a lot of people out there spending many hours manually editing documents and spreadsheets.

Re: OpenAI Codex

#112

Post by cscurmudgeon »

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…

We build another system we fully understand that can process the justification and see if it is correct/makes sense.

Re: OpenAI Codex

#114

Post by wizzwizz4 »

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!

Except I could do that with a few regex substitutions, which would not be reasoning. The “intelligence” is in the templates provided by the training data. (Extracting that is impressive, but not that impressive.)

Re: OpenAI Codex

#115

Post by sbierwagen »

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...)

Re: OpenAI Codex

#116

Post by jacquesm »

bufferoverflow wrote: post #93
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…

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.

Re: OpenAI Codex

#117

Post by make3 »

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…

I work in this domain, and you can make these things say anything with a little probing, even stuff like "Hitler was right to kill all the Jews, I wish he was still alive today."

They likely don't want to have "OpenAI GPT-3" and such stuff associated to one another in such demos, would be really bad for their appearence.

Re: OpenAI Codex

#118

Post by beering »

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…

> 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 Chinese Room at this point.

[0] https://nitter.hu/kleptid/status/1284069270603866113#m

Re: OpenAI Codex

#119

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…

> Even if it could explain its answers, there would just be too many moving parts to keep in your head at once.

While this is possible in practice, consider the (universal) Turing machine principle: in principle, you can simulate any system given enough memory; we may not have it our brains, but we have pen and paper or simply digital text scratchpad (both of which we use extensively in our lives).

Re: OpenAI Codex

#120

Post by talor_a »

nightski wrote: post #76
hackinthebochs wrote: post #60

Earlier quoted context omitted.

Considering the sum total of data and computation that goes in to creating an intelligent human mind, including the forces of natural selection in creating our innate structure and dispositions, it's not obvious that any conclusions can be drawn from the fact that so much data and compute goes into training these models.

Has this transfer of knowledge from one domain to another really been demonstrated by these models/learning processes? I know transfer learning is a thing (I have a couple books on my shelf on it). But it seems far from what you are describing.

they mention in the demo video that the inspiration for codex came from GPT-3 users training it to respond to queries with code samples. I saw some pretty impressive demos of the original model creating SQL queries from plain questions. I'm not sure if that counts as switching domains, but it's something?
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