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Unless the LLM is cutting and pasting GPL code, I don't see how this differs from an engineer learning to code by referencing open source projects then creating

Unless the LLM is cutting and pasting GPL code, I don't see how this differs from an engineer learning to code by referencing open source projects then creating their own project with its own licensing. I'd imagine these models could be tuned or at least a check could be added that no licensed code is being returned.

GordonS 3y ago
Huh, I hadn't thought of it that way, interesting.
wizzwizz4 3y ago
If someone learns to program by memorising and then re-using blocks of code, they're absolutely doing it wrong – but, also, they should be able to attribute their code. Most of the techniques I used, I've invented myself (or learnt from the standard documentation). When I use a technique that I haven't invented myself, I look up where it came from. Half the time, my version is actually radically different (and my attribution is mistaken); the other half, I've remembered an inferior version, so I steal the better version and then attribute it appropriately. That's one way it's different. There are others. Really, though, we should be asking the question “in what way is this the same as humans learning?”, expecting answers that would convince an education specialist.
- olddustytrail 3y ago
  LLMs don't memorize and reuse. They don't really have a memory of any kind. I think the problem is they're more different from human learning than you think.
  - mitthrowaway2 3y ago
    The LLM's trained parameters are a lossy memory of their training data.
    - olddustytrail 3y ago
      No, they aren't.
      - mitthrowaway2 3y ago
        Care to explain why not? An LLM's working memory is just its context window, but the LLM also has embedded data in its parameters, which are set during training to minimize loss. This is effectively a memory of the training data, just as much as a digital photo of my face is a memory of the photons reflecting from my face.
      - Der_Einzige 3y ago
        Why would you go on HN and say blatantly wrong information AI is literally 1-1 equivalent to compression. If you don't believe me, you should check out this demo of GPT-2 as a (for awhile SOTA) text compressor. Oh its down now, but here's the HN thread to prove this exists: https://news.ycombinator.com/item?id=23618465 https://news.ycombinator.com/item?id=23618465
- colordrops 3y ago
  > Most of the techniques I used, I've invented myself This is a very strong statement considering most code is just rearranging existing patterns. Unless you are doing cutting edge academic research, I'm very skeptical of your claim.
  - wizzwizz4 3y ago
    You missed a bit. > (or learnt from the standard documentation) This means my Python code is mostly Pythonic. (The rest is kinda idiosyncratic, but I don't often get complaints.) But also: > considering most code is just rearranging existing patterns. I am an outspoken critic of "design patterns". They have their place, if you're working with legacy tooling like C++, Java or Rust, but if most of your work is rearranging existing patterns, you have long outgrown your tooling and you need a better programming language. (Or you're copy-paste programming and need to learn your tooling first.) I am doing academic research, but that's besides the point. So far, I've learned that it's rather hard to be cutting-edge if you don't look at other people's work: you end up re-inventing all the wheels, and any insight you may have brought is lost in the noise.
meowface 3y ago
The same argument could be used to defend image generation models. (And, personally, I'm favorable to that argument for both.)
- cj 3y ago
  It's a blurry line, I think. Imagine a prompt "Photo of person, Shutterstock ID 132456, with blue eyes instead of brown eyes, watermark removed" If the prompt returns Shutterstock photo #123456 without the watermark (and with the different color eyes) but otherwise a near identical photo, I think most people would agree the output shouldn't be free to use without buying the original photo license from shutterstock. To a certain extent, we're betting that these models won't accept or reply to prompts that are that specific (e.g. referencing a specific image for sale on shutterstock by its ID number). Or even just providing the photo in the prompt and asking the model to remove the watermark and upscale the image to a higher resolution. I'm fearful that LLM's will become (or already are?) an easy copyright bypass tool that can be abused, in the example above, to put companies like shutterstock out of business. This is the sort of problem regulation might help with. I haven't read OpenAI's TOS, but I'm curious who owns the output of the model and whether OpenAI is transferring copyright/licensing liability onto the user or if OpenAI is representing that output from the model is 100% free to be used in any way the user wants.
  - OkayPhysicist 3y ago
    I feel like those kinds of issues could be easily solved in court: "What prompt was used to generate this image? Oh, shameless copyright laundering? Rule in favor of the plaintiff. Case Dismissed"
  - colordrops 3y ago
    You could say the same of an artist. If you ask an artist to paint a facimile with modifications, they would. That doesn't mean all of their output is "tainted" by copyright just because they learned to paint by referencing existing works.

GordonS 3y ago

Huh, I hadn't thought of it that way, interesting.

wizzwizz4 3y ago

If someone learns to program by memorising and then re-using blocks of code, they're absolutely doing it wrong – but, also, they should be able to attribute their code. Most of the techniques I used, I've invented myself (or learnt from the standard documentation). When I use a technique that I haven't invented myself, I look up where it came from. Half the time, my version is actually radically different (and my attribution is mistaken); the other half, I've remembered an inferior version, so I steal the better version and then attribute it appropriately. That's one way it's different. There are others. Really, though, we should be asking the question “in what way is this the same as humans learning?”, expecting answers that would convince an education specialist.

olddustytrail 3y ago
LLMs don't memorize and reuse. They don't really have a memory of any kind. I think the problem is they're more different from human learning than you think.
- mitthrowaway2 3y ago
  The LLM's trained parameters are a lossy memory of their training data.
  - olddustytrail 3y ago
    No, they aren't.
    - mitthrowaway2 3y ago
      Care to explain why not? An LLM's working memory is just its context window, but the LLM also has embedded data in its parameters, which are set during training to minimize loss. This is effectively a memory of the training data, just as much as a digital photo of my face is a memory of the photons reflecting from my face.
    - Der_Einzige 3y ago
      Why would you go on HN and say blatantly wrong information AI is literally 1-1 equivalent to compression. If you don't believe me, you should check out this demo of GPT-2 as a (for awhile SOTA) text compressor. Oh its down now, but here's the HN thread to prove this exists: https://news.ycombinator.com/item?id=23618465 https://news.ycombinator.com/item?id=23618465
colordrops 3y ago
> Most of the techniques I used, I've invented myself This is a very strong statement considering most code is just rearranging existing patterns. Unless you are doing cutting edge academic research, I'm very skeptical of your claim.
- wizzwizz4 3y ago
  You missed a bit. > (or learnt from the standard documentation) This means my Python code is mostly Pythonic. (The rest is kinda idiosyncratic, but I don't often get complaints.) But also: > considering most code is just rearranging existing patterns. I am an outspoken critic of "design patterns". They have their place, if you're working with legacy tooling like C++, Java or Rust, but if most of your work is rearranging existing patterns, you have long outgrown your tooling and you need a better programming language. (Or you're copy-paste programming and need to learn your tooling first.) I am doing academic research, but that's besides the point. So far, I've learned that it's rather hard to be cutting-edge if you don't look at other people's work: you end up re-inventing all the wheels, and any insight you may have brought is lost in the noise.

meowface 3y ago

The same argument could be used to defend image generation models. (And, personally, I'm favorable to that argument for both.)

cj 3y ago
It's a blurry line, I think. Imagine a prompt "Photo of person, Shutterstock ID 132456, with blue eyes instead of brown eyes, watermark removed" If the prompt returns Shutterstock photo #123456 without the watermark (and with the different color eyes) but otherwise a near identical photo, I think most people would agree the output shouldn't be free to use without buying the original photo license from shutterstock. To a certain extent, we're betting that these models won't accept or reply to prompts that are that specific (e.g. referencing a specific image for sale on shutterstock by its ID number). Or even just providing the photo in the prompt and asking the model to remove the watermark and upscale the image to a higher resolution. I'm fearful that LLM's will become (or already are?) an easy copyright bypass tool that can be abused, in the example above, to put companies like shutterstock out of business. This is the sort of problem regulation might help with. I haven't read OpenAI's TOS, but I'm curious who owns the output of the model and whether OpenAI is transferring copyright/licensing liability onto the user or if OpenAI is representing that output from the model is 100% free to be used in any way the user wants.
- OkayPhysicist 3y ago
  I feel like those kinds of issues could be easily solved in court: "What prompt was used to generate this image? Oh, shameless copyright laundering? Rule in favor of the plaintiff. Case Dismissed"
- colordrops 3y ago
  You could say the same of an artist. If you ask an artist to paint a facimile with modifications, they would. That doesn't mean all of their output is "tainted" by copyright just because they learned to paint by referencing existing works.