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34 - AI Evaluations with Beth Barnes

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Manage episode 431050436 series 2844728
Content provided by Daniel Filan. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Daniel Filan or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://player.fm/legal.

How can we figure out if AIs are capable enough to pose a threat to humans? When should we make a big effort to mitigate risks of catastrophic AI misbehaviour? In this episode, I chat with Beth Barnes, founder of and head of research at METR, about these questions and more.

Patreon: patreon.com/axrpodcast

Ko-fi: ko-fi.com/axrpodcast

The transcript: https://axrp.net/episode/2024/07/28/episode-34-ai-evaluations-beth-barnes.html

Topics we discuss, and timestamps:

0:00:37 - What is METR?

0:02:44 - What is an "eval"?

0:14:42 - How good are evals?

0:37:25 - Are models showing their full capabilities?

0:53:25 - Evaluating alignment

1:01:38 - Existential safety methodology

1:12:13 - Threat models and capability buffers

1:38:25 - METR's policy work

1:48:19 - METR's relationships with labs

2:04:12 - Related research

2:10:02 - Roles at METR, and following METR's work

Links for METR:

METR: https://metr.org

METR Task Development Guide - Bounty: https://taskdev.metr.org/bounty/

METR - Hiring: https://metr.org/hiring

Autonomy evaluation resources: https://metr.org/blog/2024-03-13-autonomy-evaluation-resources/

Other links:

Update on ARC's recent eval efforts (contains GPT-4 taskrabbit captcha story) https://metr.org/blog/2023-03-18-update-on-recent-evals/

Password-locked models: a stress case for capabilities evaluation: https://www.alignmentforum.org/posts/rZs6ddqNnW8LXuJqA/password-locked-models-a-stress-case-for-capabilities

Sleeper Agents: Training Deceptive LLMs that Persist Through Safety Training: https://arxiv.org/abs/2401.05566

Untrusted smart models and trusted dumb models: https://www.alignmentforum.org/posts/LhxHcASQwpNa3mRNk/untrusted-smart-models-and-trusted-dumb-models

AI companies aren't really using external evaluators: https://www.lesswrong.com/posts/WjtnvndbsHxCnFNyc/ai-companies-aren-t-really-using-external-evaluators

Nobody Knows How to Safety-Test AI (Time): https://time.com/6958868/artificial-intelligence-safety-evaluations-risks/

ChatGPT can talk, but OpenAI employees sure can’t: https://www.vox.com/future-perfect/2024/5/17/24158478/openai-departures-sam-altman-employees-chatgpt-release

Leaked OpenAI documents reveal aggressive tactics toward former employees: https://www.vox.com/future-perfect/351132/openai-vested-equity-nda-sam-altman-documents-employees

Beth on her non-disparagement agreement with OpenAI: https://www.lesswrong.com/posts/yRWv5kkDD4YhzwRLq/non-disparagement-canaries-for-openai?commentId=MrJF3tWiKYMtJepgX

Sam Altman's statement on OpenAI equity: https://x.com/sama/status/1791936857594581428

Episode art by Hamish Doodles: hamishdoodles.com

  continue reading

39 episodes

Artwork
iconShare
 
Manage episode 431050436 series 2844728
Content provided by Daniel Filan. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Daniel Filan or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://player.fm/legal.

How can we figure out if AIs are capable enough to pose a threat to humans? When should we make a big effort to mitigate risks of catastrophic AI misbehaviour? In this episode, I chat with Beth Barnes, founder of and head of research at METR, about these questions and more.

Patreon: patreon.com/axrpodcast

Ko-fi: ko-fi.com/axrpodcast

The transcript: https://axrp.net/episode/2024/07/28/episode-34-ai-evaluations-beth-barnes.html

Topics we discuss, and timestamps:

0:00:37 - What is METR?

0:02:44 - What is an "eval"?

0:14:42 - How good are evals?

0:37:25 - Are models showing their full capabilities?

0:53:25 - Evaluating alignment

1:01:38 - Existential safety methodology

1:12:13 - Threat models and capability buffers

1:38:25 - METR's policy work

1:48:19 - METR's relationships with labs

2:04:12 - Related research

2:10:02 - Roles at METR, and following METR's work

Links for METR:

METR: https://metr.org

METR Task Development Guide - Bounty: https://taskdev.metr.org/bounty/

METR - Hiring: https://metr.org/hiring

Autonomy evaluation resources: https://metr.org/blog/2024-03-13-autonomy-evaluation-resources/

Other links:

Update on ARC's recent eval efforts (contains GPT-4 taskrabbit captcha story) https://metr.org/blog/2023-03-18-update-on-recent-evals/

Password-locked models: a stress case for capabilities evaluation: https://www.alignmentforum.org/posts/rZs6ddqNnW8LXuJqA/password-locked-models-a-stress-case-for-capabilities

Sleeper Agents: Training Deceptive LLMs that Persist Through Safety Training: https://arxiv.org/abs/2401.05566

Untrusted smart models and trusted dumb models: https://www.alignmentforum.org/posts/LhxHcASQwpNa3mRNk/untrusted-smart-models-and-trusted-dumb-models

AI companies aren't really using external evaluators: https://www.lesswrong.com/posts/WjtnvndbsHxCnFNyc/ai-companies-aren-t-really-using-external-evaluators

Nobody Knows How to Safety-Test AI (Time): https://time.com/6958868/artificial-intelligence-safety-evaluations-risks/

ChatGPT can talk, but OpenAI employees sure can’t: https://www.vox.com/future-perfect/2024/5/17/24158478/openai-departures-sam-altman-employees-chatgpt-release

Leaked OpenAI documents reveal aggressive tactics toward former employees: https://www.vox.com/future-perfect/351132/openai-vested-equity-nda-sam-altman-documents-employees

Beth on her non-disparagement agreement with OpenAI: https://www.lesswrong.com/posts/yRWv5kkDD4YhzwRLq/non-disparagement-canaries-for-openai?commentId=MrJF3tWiKYMtJepgX

Sam Altman's statement on OpenAI equity: https://x.com/sama/status/1791936857594581428

Episode art by Hamish Doodles: hamishdoodles.com

  continue reading

39 episodes

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