This is a 1 hour general-audience introduction to Large Language Models: the core technical component behind systems like ChatGPT, Claude, and Bard. What they are, where they are headed, comparisons and analogies to present-day operating systems, and some of the security-related challenges of this new computing paradigm.
As of November 2023 (this field moves fast!).
Context: This video is based on the slides of a talk I gave recently at the AI Security Summit. The talk was not recorded but a lot of people came to me after and told me they liked it. Seeing as I had already put in one long weekend of work to make the slides, I decided to just tune them a bit, record this round 2 of the talk and upload it here on YouTube. Pardon the random background, that’s my hotel room during the thanksgiving break.
- Slides as PDF: https://drive.google.com/file/d/1pxx_ZI7O-Nwl7ZLNk5hI3WzAsTL…share_link (42MB)
- Slides. as Keynote: https://drive.google.com/file/d/1FPUpFMiCkMRKPFjhi9MAhby68MH…share_link (140MB)
Chapters:
Part 1: LLMs.
00:00:00 Intro: Large Language Model (LLM) talk.
00:00:20 LLM Inference.
00:04:17 LLM Training.
00:08:58 LLM dreams.
00:11:22 How do they work?
00:14:14 Finetuning into an Assistant.
00:17:52 Summary so far.
00:21:05 Appendix: Comparisons, Labeling docs, RLHF, Synthetic data, Leaderboard.
Part 2: Future of LLMs.
00:25:43 LLM Scaling Laws.
00:27:43 Tool Use (Browser, Calculator, Interpreter, DALL-E)
00:33:32 Multimodality (Vision, Audio)
00:35:00 Thinking, System 1/2
00:38:02 Self-improvement, LLM AlphaGo.
00:40:45 LLM Customization, GPTs store.
00:42:15 LLM OS
Part 3: LLM Security.
00:45:43 LLM Security Intro.
00:46:14 Jailbreaks.
00:51:30 Prompt Injection.
00:56:23 Data poisoning.
00:58:37 LLM Security conclusions.
End.
00:59:23 Outro
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