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Claim chowder: > The model can unquestionably code. [https://news.ycombinator.com/item?id=38205052 https://news.ycombinator.com/item?id=38205052] Seems pretty

by boxed 3y ago

Claim chowder: > The model can unquestionably code. [https://news.ycombinator.com/item?id=38205052 https://news.ycombinator.com/item?id=38205052] Seems pretty clear most of the fantastic results from before was overfitting. Like every other case. It's amazing to me how these models can be caught red handed being overfitted again and again and again, and people don't get the memo.

rideontime 3y ago
When that "AI-generated Angry Birds clone" was doing numbers, didn't it turn out that most of the code came straight from tutorials for writing Angry Birds clones?
- torginus 3y ago
  It might have, however a ton of gamedev related code (like giving adding physics to an object, and having it explode on contact with something else), is either formulaic, or can be glued together from pieces of other code, a feat ChatGPT is definitely capable of. It doesn't require it to develop a sophisticated model of the problem. And the above applies to a lot of prod code as well.
- el-dude-arino 3y ago
  Yep, because that's all AI is; a parrot. For well-defined problems it works great... BECAUSE THEIR WELL DEFINED. Generative AI is only as good as the dataset that you give it, so for problems that exist in a heavily contrived and parameterized space (like leetcode-style problems) it works really well. But give it a novel problem with intertwining libraries and external dependencies along with custom type and structure definitions, it's going to fall flat. Humans do something AI can't, and that's draw from experience to apply a solution on a novel problem. This is why I'm not terribly worried about AI coming for engineer jobs anywhere in the near future. I use ChatGPT all the time to write me small functions, generate regular expressions, etc. Basically all the drudgery. I'd argue we've hit peak-AI at this point because from this point on, all datasets are going to be colored by AI-generated results. Generative-AI is now on a trajectory were it'll simply regress to the mean of knowledge.
  - Solvency 3y ago
    If you think we've hit peak you're grossly underestimating the sheer volume of copyrighted books, manuscripts, screenplays, podcasts, movies, documents, history, and research papers that ChatGPT hasn't been trained on. There's a LOT more juice to squeeze still.
    - nirvael 3y ago
      This is actually incorrect, there's not that much data left to train on. I remember reading an article about it, might have been one of Gwern's or something about Chinchilla scaling, but to produce an order of magnitude increase we need an order of magnitude more data and there just isn't that amount available.
      - nirvael 3y ago
        Found the reference (see section 2): https://www.lesswrong.com/posts/6Fpvch8RR29qLEWNH/chinchilla-s-wild-implications https://www.lesswrong.com/posts/6Fpvch8RR29qLEWNH/chinchilla...
  - Tepix 3y ago
    You make it sound as if (software) engineer jobs are constantly doing cutting edge innovation, and perhaps as if all engineers are capable of it. That doesnt match my experience. Most of it is a more or less smart combination of standard concepts: Something GPTs are already pretty good at and i'm sure they will improve further.
    - bamboozled 3y ago
      But we are, at least I am, almost always integrating or interacting with new libraries, APIs, new features , new behaviours. I might not be personally writing that code but I definitely need to be up to date and adapting all the time.
  - tessellated 3y ago
    How can you spell such a mistake in ALL CAPS and don't see it? ;) I don't know about the rest of your comment, albeit I consider myself more in the non-Chomsky camp. This emergence thing seems a little more than an elaborate hoax to me. Well, maybe they've just trained GPT-4 to wiggle my balls, when I ask it to analyse a poem that I wrote 15 years ago. The local llm scene, regardless of this debate, is nevertheless the hottest topic IMHO atm. Several finetunes have repeatedly blown my mind even in the past 2 weeks. On a Raspberry Pi. AI can interface. Natural Language Processing. This is the first time in history humaity has such knowledge and technology. It's basically C-3PO.
skepticATX 3y ago
It’s hard to blame people when OpenAI’s whole shtick is that they’re mere years (at most) away from summoning a god machine that will usher in a techno-utopia, or destroy all of humanity. And to make matters worse, they have selected for this belief when hiring. So it’s pervasive throughout their work. The data is out there to objectively assess these tools, but it’s not good for business.
- bugglebeetle 3y ago
  What we need is a standards bodies creating tests in private and running distributed, blind tests using only publicly accessible APIs or open source models. There’s no reason the ACM or a similar body couldn’t do this for computer science evals, for example.
  - boxed 3y ago
    I like Twitters Community Notes more and more. It's a surprisingly good system to call out this crap.
whoisthemachine 3y ago
That's what a "mixture of experts" has always implied to me - a vast amount of overfitting.
deleted 3y ago
[deleted]
jiggawatts 3y ago
When GPT4's training data was only up to Sep 2021, I fed it a Rust solution to one of the 2022 AoC problems I had lying around on my PC. I asked it to annotate it with with "formal doc comments". Now keep in mind that it had never seen this problem, and it was written in a very terse "competition solving" style with zero comments, one-letter identifiers, and generic function names like "day1" and "parse". It figured it all out. It worked out that it was for solving a maze -- even though the word "maze" was not used anywhere in the code! It worked out that a constant "(-1,0)" in one isolated bit of code was "orienting the character west", even though to figure that out it had to trace the logic through 4 or 5 layers of indirection. It connected a single 'w' character in the parser to a vector somewhere else! Etc... PS: It wrote a more useful, more accurate, more coherent, and more grammatically correct comment than I had seen in any codebase I had worked on in something like two years.
- boxed 3y ago
  > When GPT4's training data was only up to Sep 2021 Allegedly.

by boxed 3y ago

rideontime 3y ago

When that "AI-generated Angry Birds clone" was doing numbers, didn't it turn out that most of the code came straight from tutorials for writing Angry Birds clones?

torginus 3y ago
It might have, however a ton of gamedev related code (like giving adding physics to an object, and having it explode on contact with something else), is either formulaic, or can be glued together from pieces of other code, a feat ChatGPT is definitely capable of. It doesn't require it to develop a sophisticated model of the problem. And the above applies to a lot of prod code as well.
el-dude-arino 3y ago
Yep, because that's all AI is; a parrot. For well-defined problems it works great... BECAUSE THEIR WELL DEFINED. Generative AI is only as good as the dataset that you give it, so for problems that exist in a heavily contrived and parameterized space (like leetcode-style problems) it works really well. But give it a novel problem with intertwining libraries and external dependencies along with custom type and structure definitions, it's going to fall flat. Humans do something AI can't, and that's draw from experience to apply a solution on a novel problem. This is why I'm not terribly worried about AI coming for engineer jobs anywhere in the near future. I use ChatGPT all the time to write me small functions, generate regular expressions, etc. Basically all the drudgery. I'd argue we've hit peak-AI at this point because from this point on, all datasets are going to be colored by AI-generated results. Generative-AI is now on a trajectory were it'll simply regress to the mean of knowledge.
- Solvency 3y ago
  If you think we've hit peak you're grossly underestimating the sheer volume of copyrighted books, manuscripts, screenplays, podcasts, movies, documents, history, and research papers that ChatGPT hasn't been trained on. There's a LOT more juice to squeeze still.
  - nirvael 3y ago
    This is actually incorrect, there's not that much data left to train on. I remember reading an article about it, might have been one of Gwern's or something about Chinchilla scaling, but to produce an order of magnitude increase we need an order of magnitude more data and there just isn't that amount available.
    - nirvael 3y ago
      Found the reference (see section 2): https://www.lesswrong.com/posts/6Fpvch8RR29qLEWNH/chinchilla-s-wild-implications https://www.lesswrong.com/posts/6Fpvch8RR29qLEWNH/chinchilla...
- Tepix 3y ago
  You make it sound as if (software) engineer jobs are constantly doing cutting edge innovation, and perhaps as if all engineers are capable of it. That doesnt match my experience. Most of it is a more or less smart combination of standard concepts: Something GPTs are already pretty good at and i'm sure they will improve further.
  - bamboozled 3y ago
    But we are, at least I am, almost always integrating or interacting with new libraries, APIs, new features , new behaviours. I might not be personally writing that code but I definitely need to be up to date and adapting all the time.
- tessellated 3y ago
  How can you spell such a mistake in ALL CAPS and don't see it? ;) I don't know about the rest of your comment, albeit I consider myself more in the non-Chomsky camp. This emergence thing seems a little more than an elaborate hoax to me. Well, maybe they've just trained GPT-4 to wiggle my balls, when I ask it to analyse a poem that I wrote 15 years ago. The local llm scene, regardless of this debate, is nevertheless the hottest topic IMHO atm. Several finetunes have repeatedly blown my mind even in the past 2 weeks. On a Raspberry Pi. AI can interface. Natural Language Processing. This is the first time in history humaity has such knowledge and technology. It's basically C-3PO.

skepticATX 3y ago

It’s hard to blame people when OpenAI’s whole shtick is that they’re mere years (at most) away from summoning a god machine that will usher in a techno-utopia, or destroy all of humanity. And to make matters worse, they have selected for this belief when hiring. So it’s pervasive throughout their work. The data is out there to objectively assess these tools, but it’s not good for business.

bugglebeetle 3y ago
What we need is a standards bodies creating tests in private and running distributed, blind tests using only publicly accessible APIs or open source models. There’s no reason the ACM or a similar body couldn’t do this for computer science evals, for example.
- boxed 3y ago
  I like Twitters Community Notes more and more. It's a surprisingly good system to call out this crap.

whoisthemachine 3y ago

That's what a "mixture of experts" has always implied to me - a vast amount of overfitting.

deleted 3y ago

[deleted]

jiggawatts 3y ago

When GPT4's training data was only up to Sep 2021, I fed it a Rust solution to one of the 2022 AoC problems I had lying around on my PC. I asked it to annotate it with with "formal doc comments". Now keep in mind that it had never seen this problem, and it was written in a very terse "competition solving" style with zero comments, one-letter identifiers, and generic function names like "day1" and "parse". It figured it all out. It worked out that it was for solving a maze -- even though the word "maze" was not used anywhere in the code! It worked out that a constant "(-1,0)" in one isolated bit of code was "orienting the character west", even though to figure that out it had to trace the logic through 4 or 5 layers of indirection. It connected a single 'w' character in the parser to a vector somewhere else! Etc... PS: It wrote a more useful, more accurate, more coherent, and more grammatically correct comment than I had seen in any codebase I had worked on in something like two years.

boxed 3y ago
> When GPT4's training data was only up to Sep 2021 Allegedly.