Ch(e)at GPT? - Computerphile
Computerphile1 HN recommender · recommended in 1 month, 2023-2026
617K views · 3 years ago · explainer · evergreen
Computerphile video in which Mike Pound explains a University of Maryland paper on watermarking LLM output. The method biases token choices so generated text can later be detected statistically.
Linked as the approachable explanation whenever HN discusses detecting or watermarking AI-generated text.
2:13:35Let's build the GPT Tokenizer
Andrej Karpathy
1.3M views • 2 years ago
25:57GPT3: An Even Bigger Language Model - Computerphile
Computerphile
446K views • 6 years ago
19:29Glitch Tokens - Computerphile
Computerphile
353K views • 3 years ago
27:14Transformers, the tech behind LLMs | Deep Learning Chapter 5
3Blue1Brown
11.6M views • 2 years ago
20:39AI Language Models & Transformers - Computerphile
Computerphile
354K views • 7 years ago
36:02ChatGPT with Rob Miles - Computerphile
Computerphile
517K views • 3 years ago
1:31:13A Hackers' Guide to Language Models
Jeremy Howard
580K views • 3 years ago
8:25Large Language Models from scratch
Graphics in 5 Minutes
375K views • 4 years ago
19:20GPT-4chan: This is the worst AI ever
Yannic Kilcher
1M views • 4 years ago
1:56:20Let's build GPT: from scratch, in code, spelled out.
Andrej Karpathy
8.2M views • 3 years ago
4:41I made an GPT-powered Linux shell that guesses your bash command (and hallucinates crazy stuff)
River's Educational Channel
39K views • 5 years ago
42:40State of GPT | BRK216HFS
Microsoft Developer
785K views • 3 years ago
3:31:24Deep Dive into LLMs like ChatGPT
Andrej Karpathy
10.2M views • 1 year ago
48:32Sparks of AGI: early experiments with GPT-4
Sebastien Bubeck
1.8M views • 3 years ago
28:48LSTM is dead. Long Live Transformers!
Seattle Applied Deep Learning
540K views • 6 years ago
29:43OpenAI Codex Live Demo
OpenAI
913K views • 5 years ago
57:21An Observation on Generalization
Simons Institute for the Theory of Computing
210K views • 3 years ago
30:10The Inside Story of ChatGPT’s Astonishing Potential | Greg Brockman | TED
TED
1.9M views • 3 years ago
59:2328c3: The Science of Insecurity
28c3
42K views • 14 years ago
34:20What do translators think about GPT?
Tom Gally
2K views • 3 years ago
6 comments from Hacker News
There's a computerphile video ( ▶ this video ) with Dr. Mark Pound explaining a paper by John Kirchenbauer, Jonas Geiping et al. ( link ) that described a method for watermarking LLM output like this. It's not directly stated anywhere in the Claude support article that this is what they're using, but the properties of the watermark described seem to point to this method.
I wonder if they have a watermark built in that allows them to detect text generated by Llama easily. ▶ this video link
There's an interesting scheme described here: ▶ this video Essentially by biasing the LLM's token choices slightly you can infer with high probability later that the text was generated by it.
I think watermarking is a better solution. There is a paper on it: Kirchenbauer, J., Geiping, J., Wen, Y., Katz, J., Miers, I., & Goldstein, T. (2023). A watermark for large language models. arXiv preprint arXiv:2301.10226. ( link ) Also, Computerphile did a video on it: ▶ this video But of course, watermarking or your proposed solution (checksums) only work while the general public cannot run a tool like ChatGPT locally on personal computers. And it's only a matter of time before that happens. So in the long run, we need to either: 1. take away control from the users over their personal computers, in the form of what I would call 'AI DRM' (I strongly oppose this option), or 2. legislate: enforce a legal obligation to disclose that a text is not created by a human, or 3. accept that we cannot know whether or not a text is written by a human, and work with that reality
Watermarking. From an outsiders perspective, the issue appears to reaching consensus on how this can be implemented (but not in the technical sense). There's a game theoretic challenge in that if models define and publish detection mechanisms, this creates a motivation for people to use other systems that don't include this. On the technical front there's a good paper here: link , and a nice very approachable video explaining it here: ▶ this video .
I've heard one idea of adding weights to words. The idea being if I someone hands in a essay with a higher frequency of certain words, there is a very good chance its come from a language model. ▶ this video I'm not sure this helps with coding though, maybe variable names?