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> stealing “training data” in the name of profit They just show it to the AI, it doesn't store it - just data about it. The same as you when you look at someth

> stealing “training data” in the name of profit They just show it to the AI, it doesn't store it - just data about it. The same as you when you look at something, be it a painting or a chair. The only reason AI companies store the data is they want repeatability. If you want to train two copies on the same data you can't rely on the same sites being up. I commit the same crime by saving websites I've read because I don't want my sources to dry up on me. > What is the price of developing an art career over 40 years? Market price. How does it benefit the customer here and now? It's no different than being a furniture maker with years of carpentry skills. The value to the customer is still just the marginal benefit over an IKEA stool.

versteegen 4y ago
> They just show it to the AI, it doesn't store it - just data about it. The same as you when you look at something, be it a painting or a chair. In general for machine learning, yes, but deep learning models with large numbers of parameters such as transformers and stable diffusion do actually memorise part of their training data verbatim. The problem is mainly duplicate data points. E.g. https://arstechnica.com/information-technology/2023/02/researchers-extract-training-images-from-stable-diffusion-but-its-difficult/ https://arstechnica.com/information-technology/2023/02/resea... https://arxiv.org/pdf/2202.07646.pdf https://arxiv.org/pdf/2202.07646.pdf Quantifying Memorization Across Neural Language Models
- LawTalkingGuy 4y ago
  Yeah, currently many models do seem to have copied a few pieces of their source material. I was translating code with one of the code- models at OpenAI and it spit out about thirty words of someone's code of conduct from Github, almost verbatim. I found the repo by searching what it had produced. That's when I changed my prompts to remove comments, but it stuck with me. In general though, I think the pigeon-hole principle is relevant. The model is fairly small compared to the training material and couldn't contain much actual full content or it wouldn't be able to contain anything about the rest. I've heard people talking about using the model to judge similarity of new training data to what it's already seen, mainly to train more efficiently, but also to avoid this.

versteegen 4y ago

> They just show it to the AI, it doesn't store it - just data about it. The same as you when you look at something, be it a painting or a chair. In general for machine learning, yes, but deep learning models with large numbers of parameters such as transformers and stable diffusion do actually memorise part of their training data verbatim. The problem is mainly duplicate data points. E.g. https://arstechnica.com/information-technology/2023/02/researchers-extract-training-images-from-stable-diffusion-but-its-difficult/ https://arstechnica.com/information-technology/2023/02/resea... https://arxiv.org/pdf/2202.07646.pdf https://arxiv.org/pdf/2202.07646.pdf Quantifying Memorization Across Neural Language Models

LawTalkingGuy 4y ago
Yeah, currently many models do seem to have copied a few pieces of their source material. I was translating code with one of the code- models at OpenAI and it spit out about thirty words of someone's code of conduct from Github, almost verbatim. I found the repo by searching what it had produced. That's when I changed my prompts to remove comments, but it stuck with me. In general though, I think the pigeon-hole principle is relevant. The model is fairly small compared to the training material and couldn't contain much actual full content or it wouldn't be able to contain anything about the rest. I've heard people talking about using the model to judge similarity of new training data to what it's already seen, mainly to train more efficiently, but also to avoid this.