From 2060f33ed7cdea31d32ee16a21350a58a96eb6ab Mon Sep 17 00:00:00 2001 From: Tara S Pande <38411006+TaraSPande@users.noreply.github.com> Date: Fri, 6 Dec 2024 20:13:38 -0800 Subject: [PATCH] Update index.md --- index.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/index.md b/index.md index 4ec0338..5bc5e3f 100644 --- a/index.md +++ b/index.md @@ -74,7 +74,7 @@ Large language models (LLMs) have revolutionized a wide range of domains. In par | Nov 11 | **No Class - Veterans Day** | | | Nov 18 | **Open-Source and Science in the Era of Foundation Models**
Percy Liang, Stanford University
Slides [Original Recording](https://bcourses.berkeley.edu/courses/1535641/external_tools/90481) [Edited Video](https://www.youtube.com/live/f3KKx9LWntQ) | - [Cybench: A Framework for Evaluating Cybersecurity Capabilities and Risks of Language Models](https://arxiv.org/abs/2408.08926) | | Nov 25 | **Measuring Agent capabilities and Anthropic's RSP**
Ben Mann, Anthropic
Slides [Original Recording](https://bcourses.berkeley.edu/courses/1535641/external_tools/90481) [Edited Video](https://www.youtube.com/live/6y2AnWol7oo) | - [Announcing our updated Responsible Scaling Policy](https://www.anthropic.com/news/announcing-our-updated-responsible-scaling-policy)
- [Developing a computer use model](https://www.anthropic.com/news/developing-computer-use) | -| Dec 2 | **Towards Building Safe & Trustworthy AI Agents and A Path for Science‑ and Evidence‑based AI Policy**
Dawn Song, UC Berkeley
Slides posted soon. [Edited Video](https://www.youtube.com/live/QAgR4uQ15rc) | - [A Path for Science‑ and Evidence‑based AI Policy](https://understanding-ai-safety.org/)
- [DecodingTrust: A Comprehensive Assessment of Trustworthiness in GPT Models](https://arxiv.org/abs//2306.11698)
- [Representation Engineering: A Top-Down Approach to AI Transparency](https://arxiv.org/abs/2310.01405)
- [Extracting Training Data from Large Language Models](https://www.usenix.org/system/files/sec21-carlini-extracting.pdf)
- [The Secret Sharer: Evaluating and Testing Unintended Memorization in Neural Networks](https://www.usenix.org/system/files/sec19-carlini.pdf)
*All readings are optional this week.* | +| Dec 2 | **Towards Building Safe & Trustworthy AI Agents and A Path for Science‑ and Evidence‑based AI Policy**
Dawn Song, UC Berkeley
Slides [Edited Video](https://www.youtube.com/live/QAgR4uQ15rc) | - [A Path for Science‑ and Evidence‑based AI Policy](https://understanding-ai-safety.org/)
- [DecodingTrust: A Comprehensive Assessment of Trustworthiness in GPT Models](https://arxiv.org/abs//2306.11698)
- [Representation Engineering: A Top-Down Approach to AI Transparency](https://arxiv.org/abs/2310.01405)
- [Extracting Training Data from Large Language Models](https://www.usenix.org/system/files/sec21-carlini-extracting.pdf)
- [The Secret Sharer: Evaluating and Testing Unintended Memorization in Neural Networks](https://www.usenix.org/system/files/sec19-carlini.pdf)
*All readings are optional this week.* | ## Enrollment and Grading