41 - Lee Sharkey on Attribution-based Parameter Decomposition

AXRP - the AI X-risk Research Podcast

#Science #Tech #Daniel Filan #Xrisk

56:04

Growing up in a military family, Tristen Epps moved around a lot. But no matter where he was living, Friday nights were sacred. He got to dress up, go to a restaurant, not order from a kids menu, and feel like he was getting to know the place he was living — for now. At home, when his mom taught him to scramble an egg, he was mesmerized by the alchemy; one simple ingredient could transform into so many things. It's that wonder and curiosity that transformed him into the leader, visionary, and Top Chef winner he is today. He joined Tinfoil Swans at the Food & Wine Classic in Aspen to talk about his mission to “un-colonize colonized food,” the freedom he feels cooking in Air Jordans, why it's important to him to celebrate oxtails with Michelin-level finesse, and his belief that cooking has power to correct history. For more info visit: foodandwine.com/tinfoilswans Sponsor: Old Fitzgerald® Kentucky Straight Bourbon Whiskey. Bardstown, KY. 50% Alc./Vol. Think Wisely. Drink Wisely. Learn more about your ad choices. Visit podcastchoices.com/adchoices…

about a year ago 2:16:11

MP3•Episode home

Patreon: https://www.patreon.com/axrpodcast

Ko-fi: https://ko-fi.com/axrpodcast

Transcript: https://axrp.net/episode/2025/06/03/episode-41-lee-sharkey-attribution-based-parameter-decomposition.html

Topics we discuss, and timestamps:

0:00:41 APD basics

0:07:57 Faithfulness

0:11:10 Minimality

0:28:44 Simplicity

0:34:50 Concrete-ish examples of APD

0:52:00 Which parts of APD are canonical

0:58:10 Hyperparameter selection

1:06:40 APD in toy models of superposition

1:14:40 APD and compressed computation

1:25:43 Mechanisms vs representations

1:34:41 Future applications of APD?

1:44:19 How costly is APD?

1:49:14 More on minimality training

1:51:49 Follow-up work

2:05:24 APD on giant chain-of-thought models?

2:11:27 APD and "features"

2:14:11 Following Lee's work

Lee links (Leenks):

X/Twitter: https://twitter.com/leedsharkey

Alignment Forum: https://www.alignmentforum.org/users/lee_sharkey

Research we discuss:

Interpretability in Parameter Space: Minimizing Mechanistic Description Length with Attribution-Based Parameter Decomposition: https://arxiv.org/abs/2501.14926

Toy Models of Superposition: https://transformer-circuits.pub/2022/toy_model/index.html

Towards a unified and verified understanding of group-operation networks: https://arxiv.org/abs/2410.07476

Feature geometry is outside the superposition hypothesis: https://www.alignmentforum.org/posts/MFBTjb2qf3ziWmzz6/sae-feature-geometry-is-outside-the-superposition-hypothesis

Episode art by Hamish Doodles: hamishdoodles.com

60 episodes

AXRP - the AI X-risk Research Podcast

41 - Lee Sharkey on Attribution-based Parameter Decomposition

AXRP - the AI X-risk Research Podcast

34 subscribers

published about a year ago

MP3•Episode home

Patreon: https://www.patreon.com/axrpodcast

Ko-fi: https://ko-fi.com/axrpodcast

Transcript: https://axrp.net/episode/2025/06/03/episode-41-lee-sharkey-attribution-based-parameter-decomposition.html

Topics we discuss, and timestamps:

0:00:41 APD basics

0:07:57 Faithfulness

0:11:10 Minimality

0:28:44 Simplicity

0:34:50 Concrete-ish examples of APD

0:52:00 Which parts of APD are canonical

0:58:10 Hyperparameter selection

1:06:40 APD in toy models of superposition

1:14:40 APD and compressed computation

1:25:43 Mechanisms vs representations

1:34:41 Future applications of APD?

1:44:19 How costly is APD?

1:49:14 More on minimality training

1:51:49 Follow-up work

2:05:24 APD on giant chain-of-thought models?

2:11:27 APD and "features"

2:14:11 Following Lee's work

Lee links (Leenks):

X/Twitter: https://twitter.com/leedsharkey

Alignment Forum: https://www.alignmentforum.org/users/lee_sharkey

Research we discuss:

Interpretability in Parameter Space: Minimizing Mechanistic Description Length with Attribution-Based Parameter Decomposition: https://arxiv.org/abs/2501.14926

Toy Models of Superposition: https://transformer-circuits.pub/2022/toy_model/index.html

Towards a unified and verified understanding of group-operation networks: https://arxiv.org/abs/2410.07476

Feature geometry is outside the superposition hypothesis: https://www.alignmentforum.org/posts/MFBTjb2qf3ziWmzz6/sae-feature-geometry-is-outside-the-superposition-hypothesis

Episode art by Hamish Doodles: hamishdoodles.com

60 episodes

#Science #Tech #Daniel Filan #Xrisk

All episodes

AXRP - the AI X-risk Research Podcast

1
46 - Tom Davidson on AI-enabled Coups 2:05:26

4 days ago2:05:26

2:05:26

Could AI enable a small group to gain power over a large country, and lock in their power permanently? Often, people worried about catastrophic risks from AI have been concerned with misalignment risks. In this episode, Tom Davidson talks about a risk that could be comparably important: that of AI-enabled coups. Patreon: https://www.patreon.com/axrpodcast Ko-fi: https://ko-fi.com/axrpodcast Transcript: https://axrp.net/episode/2025/08/07/episode-46-tom-davidson-ai-enabled-coups.html Topics we discuss, and timestamps: 0:00:35 How to stage a coup without AI 0:16:17 Why AI might enable coups 0:33:29 How bad AI-enabled coups are 0:37:28 Executive coups with singularly loyal AIs 0:48:35 Executive coups with exclusive access to AI 0:54:41 Corporate AI-enabled coups 0:57:56 Secret loyalty and misalignment in corporate coups 1:11:39 Likelihood of different types of AI-enabled coups 1:25:52 How to prevent AI-enabled coups 1:33:43 Downsides of AIs loyal to the law 1:41:06 Cultural shifts vs individual action 1:45:53 Technical research to prevent AI-enabled coups 1:51:40 Non-technical research to prevent AI-enabled coups 1:58:17 Forethought 2:03:03 Following Tom's and Forethought's research Links for Tom and Forethought: Tom on X / Twitter: https://x.com/tomdavidsonx Tom on LessWrong: https://www.lesswrong.com/users/tom-davidson-1 Forethought Substack: https://newsletter.forethought.org/ Will MacAskill on X / Twitter: https://x.com/willmacaskill Will MacAskill on LessWrong: https://www.lesswrong.com/users/wdmacaskill Research we discuss: AI-Enabled Coups: How a Small Group Could Use AI to Seize Power: https://www.forethought.org/research/ai-enabled-coups-how-a-small-group-could-use-ai-to-seize-power Seizing Power: The Strategic Logic of Military Coups, by Naunihal Singh: https://muse.jhu.edu/book/31450 Experiment using AI-generated posts on Reddit draws fire for ethics concerns: https://retractionwatch.com/2025/04/28/experiment-using-ai-generated-posts-on-reddit-draws-fire-for-ethics-concerns/ Episode art by Hamish Doodles: hamishdoodles.com…

AXRP - the AI X-risk Research Podcast

1
45 - Samuel Albanie on DeepMind's AGI Safety Approach 1:15:42

5 weeks ago1:15:42

1:15:42

In this episode, I chat with Samuel Albanie about the Google DeepMind paper he co-authored called "An Approach to Technical AGI Safety and Security". It covers the assumptions made by the approach, as well as the types of mitigations it outlines. Patreon: https://www.patreon.com/axrpodcast Ko-fi: https://ko-fi.com/axrpodcast Transcript: https://axrp.net/episode/2025/07/06/episode-45-samuel-albanie-deepminds-agi-safety-approach.html Topics we discuss, and timestamps: 0:00:37 DeepMind's Approach to Technical AGI Safety and Security 0:04:29 Current paradigm continuation 0:19:13 No human ceiling 0:21:22 Uncertain timelines 0:23:36 Approximate continuity and the potential for accelerating capability improvement 0:34:29 Misuse and misalignment 0:39:34 Societal readiness 0:43:58 Misuse mitigations 0:52:57 Misalignment mitigations 1:05:20 Samuel's thinking about technical AGI safety 1:14:02 Following Samuel's work Samuel on Twitter/X: x.com/samuelalbanie Research we discuss: An Approach to Technical AGI Safety and Security: https://arxiv.org/abs/2504.01849 Levels of AGI for Operationalizing Progress on the Path to AGI: https://arxiv.org/abs/2311.02462 The Checklist: What Succeeding at AI Safety Will Involve: https://sleepinyourhat.github.io/checklist/ Measuring AI Ability to Complete Long Tasks: https://arxiv.org/abs/2503.14499 Episode art by Hamish Doodles: hamishdoodles.com…

AXRP - the AI X-risk Research Podcast

1
44 - Peter Salib on AI Rights for Human Safety 3:21:33

6 weeks ago3:21:33

3:21:33

In this episode, I talk with Peter Salib about his paper "AI Rights for Human Safety", arguing that giving AIs the right to contract, hold property, and sue people will reduce the risk of their trying to attack humanity and take over. He also tells me how law reviews work, in the face of my incredulity. Patreon: https://www.patreon.com/axrpodcast Ko-fi: https://ko-fi.com/axrpodcast Transcript: https://axrp.net/episode/2025/06/28/episode-44-peter-salib-ai-rights-human-safety.html Topics we discuss, and timestamps: 0:00:40 Why AI rights 0:18:34 Why not reputation 0:27:10 Do AI rights lead to AI war? 0:36:42 Scope for human-AI trade 0:44:25 Concerns with comparative advantage 0:53:42 Proxy AI wars 0:57:56 Can companies profitably make AIs with rights? 1:09:43 Can we have AI rights and AI safety measures? 1:24:31 Liability for AIs with rights 1:38:29 Which AIs get rights? 1:43:36 AI rights and stochastic gradient descent 1:54:54 Individuating "AIs" 2:03:28 Social institutions for AI safety 2:08:20 Outer misalignment and trading with AIs 2:15:27 Why statutes of limitations should exist 2:18:39 Starting AI x-risk research in legal academia 2:24:18 How law reviews and AI conferences work 2:41:49 More on Peter moving to AI x-risk research 2:45:37 Reception of the paper 2:53:24 What publishing in law reviews does 3:04:48 Which parts of legal academia focus on AI 3:18:03 Following Peter's research Links for Peter: Personal website: https://www.peternsalib.com/ Writings at Lawfare: https://www.lawfaremedia.org/contributors/psalib CLAIR: https://clair-ai.org/ Research we discuss: AI Rights for Human Safety: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4913167 Will humans and AIs go to war? https://philpapers.org/rec/GOLWAA Infrastructure for AI agents: https://arxiv.org/abs/2501.10114 Governing AI Agents: https://arxiv.org/abs/2501.07913 Episode art by Hamish Doodles: hamishdoodles.com…

AXRP - the AI X-risk Research Podcast

1
43 - David Lindner on Myopic Optimization with Non-myopic Approval 1:40:59

8 weeks ago1:40:59

1:40:59

In this episode, I talk with David Lindner about Myopic Optimization with Non-myopic Approval, or MONA, which attempts to address (multi-step) reward hacking by myopically optimizing actions against a human's sense of whether those actions are generally good. Does this work? Can we get smarter-than-human AI this way? How does this compare to approaches like conservativism? Listen to find out. Patreon: https://www.patreon.com/axrpodcast Ko-fi: https://ko-fi.com/axrpodcast Transcript: https://axrp.net/episode/2025/06/15/episode-43-david-lindner-mona.html Topics we discuss, and timestamps: 0:00:29 What MONA is 0:06:33 How MONA deals with reward hacking 0:23:15 Failure cases for MONA 0:36:25 MONA's capability 0:55:40 MONA vs other approaches 1:05:03 Follow-up work 1:10:17 Other MONA test cases 1:33:47 When increasing time horizon doesn't increase capability 1:39:04 Following David's research Links for David: Website: https://www.davidlindner.me Twitter / X: https://x.com/davlindner DeepMind Medium: https://deepmindsafetyresearch.medium.com David on the Alignment Forum: https://www.alignmentforum.org/users/david-lindner Research we discuss: MONA: Myopic Optimization with Non-myopic Approval Can Mitigate Multi-step Reward Hacking: https://arxiv.org/abs/2501.13011 Arguments Against Myopic Training: https://www.alignmentforum.org/posts/GqxuDtZvfgL2bEQ5v/arguments-against-myopic-training Episode art by Hamish Doodles: hamishdoodles.com…

AXRP - the AI X-risk Research Podcast

1
42 - Owain Evans on LLM Psychology 2:14:26

9 weeks ago2:14:26

2:14:26

Earlier this year, the paper "Emergent Misalignment" made the rounds on AI x-risk social media for seemingly showing LLMs generalizing from 'misaligned' training data of insecure code to acting comically evil in response to innocuous questions. In this episode, I chat with one of the authors of that paper, Owain Evans, about that research as well as other work he's done to understand the psychology of large language models. Patreon: https://www.patreon.com/axrpodcast Ko-fi: https://ko-fi.com/axrpodcast Transcript: https://axrp.net/episode/2025/06/06/episode-42-owain-evans-llm-psychology.html Topics we discuss, and timestamps: 0:00:37 Why introspection? 0:06:24 Experiments in "Looking Inward" 0:15:11 Why fine-tune for introspection? 0:22:32 Does "Looking Inward" test introspection, or something else? 0:34:14 Interpreting the results of "Looking Inward" 0:44:56 Limitations to introspection? 0:49:54 "Tell me about yourself", and its relation to other papers 1:05:45 Backdoor results 1:12:01 Emergent Misalignment 1:22:13 Why so hammy, and so infrequently evil? 1:36:31 Why emergent misalignment? 1:46:45 Emergent misalignment and other types of misalignment 1:53:57 Is emergent misalignment good news? 2:00:01 Follow-up work to "Emergent Misalignment" 2:03:10 Reception of "Emergent Misalignment" vs other papers 2:07:43 Evil numbers 2:12:20 Following Owain's research Links for Owain: Truthful AI: https://www.truthfulai.org Owain's website: https://owainevans.github.io/ Owain's twitter/X account: https://twitter.com/OwainEvans_UK Research we discuss: Looking Inward: Language Models Can Learn About Themselves by Introspection: https://arxiv.org/abs/2410.13787 Tell me about yourself: LLMs are aware of their learned behaviors: https://arxiv.org/abs/2501.11120 Connecting the Dots: LLMs can Infer and Verbalize Latent Structure from Disparate Training Data: https://arxiv.org/abs/2406.14546 Emergent Misalignment: Narrow fine-tuning can produce broadly misaligned LLMs: https://arxiv.org/abs/2502.17424 X/Twitter thread of GPT-4.1 emergent misalignment results: https://x.com/OwainEvans_UK/status/1912701650051190852 Taken out of context: On measuring situational awareness in LLMs: https://arxiv.org/abs/2309.00667 Episode art by Hamish Doodles: hamishdoodles.com…

AXRP - the AI X-risk Research Podcast

1
41 - Lee Sharkey on Attribution-based Parameter Decomposition 2:16:11

10 weeks ago2:16:11

2:16:11

What's the next step forward in interpretability? In this episode, I chat with Lee Sharkey about his proposal for detecting computational mechanisms within neural networks: Attribution-based Parameter Decomposition, or APD for short. Patreon: https://www.patreon.com/axrpodcast Ko-fi: https://ko-fi.com/axrpodcast Transcript: https://axrp.net/episode/2025/06/03/episode-41-lee-sharkey-attribution-based-parameter-decomposition.html Topics we discuss, and timestamps: 0:00:41 APD basics 0:07:57 Faithfulness 0:11:10 Minimality 0:28:44 Simplicity 0:34:50 Concrete-ish examples of APD 0:52:00 Which parts of APD are canonical 0:58:10 Hyperparameter selection 1:06:40 APD in toy models of superposition 1:14:40 APD and compressed computation 1:25:43 Mechanisms vs representations 1:34:41 Future applications of APD? 1:44:19 How costly is APD? 1:49:14 More on minimality training 1:51:49 Follow-up work 2:05:24 APD on giant chain-of-thought models? 2:11:27 APD and "features" 2:14:11 Following Lee's work Lee links (Leenks): X/Twitter: https://twitter.com/leedsharkey Alignment Forum: https://www.alignmentforum.org/users/lee_sharkey Research we discuss: Interpretability in Parameter Space: Minimizing Mechanistic Description Length with Attribution-Based Parameter Decomposition: https://arxiv.org/abs/2501.14926 Toy Models of Superposition: https://transformer-circuits.pub/2022/toy_model/index.html Towards a unified and verified understanding of group-operation networks: https://arxiv.org/abs/2410.07476 Feature geometry is outside the superposition hypothesis: https://www.alignmentforum.org/posts/MFBTjb2qf3ziWmzz6/sae-feature-geometry-is-outside-the-superposition-hypothesis Episode art by Hamish Doodles: hamishdoodles.com…

AXRP - the AI X-risk Research Podcast

1
40 - Jason Gross on Compact Proofs and Interpretability 2:36:05

19 weeks ago2:36:05

2:36:05

How do we figure out whether interpretability is doing its job? One way is to see if it helps us prove things about models that we care about knowing. In this episode, I speak with Jason Gross about his agenda to benchmark interpretability in this way, and his exploration of the intersection of proofs and modern machine learning. Patreon: https://www.patreon.com/axrpodcast Ko-fi: https://ko-fi.com/axrpodcast Transcript: https://axrp.net/episode/2025/03/28/episode-40-jason-gross-compact-proofs-interpretability.html Topics we discuss, and timestamps: 0:00:40 - Why compact proofs 0:07:25 - Compact Proofs of Model Performance via Mechanistic Interpretability 0:14:19 - What compact proofs look like 0:32:43 - Structureless noise, and why proofs 0:48:23 - What we've learned about compact proofs in general 0:59:02 - Generalizing 'symmetry' 1:11:24 - Grading mechanistic interpretability 1:43:34 - What helps compact proofs 1:51:08 - The limits of compact proofs 2:07:33 - Guaranteed safe AI, and AI for guaranteed safety 2:27:44 - Jason and Rajashree's start-up 2:34:19 - Following Jason's work Links to Jason: Github: https://github.com/jasongross Website: https://jasongross.github.io Alignment Forum: https://www.alignmentforum.org/users/jason-gross Links to work we discuss: Compact Proofs of Model Performance via Mechanistic Interpretability: https://arxiv.org/abs/2406.11779 Unifying and Verifying Mechanistic Interpretability: A Case Study with Group Operations: https://arxiv.org/abs/2410.07476 Modular addition without black-boxes: Compressing explanations of MLPs that compute numerical integration: https://arxiv.org/abs/2412.03773 Stage-Wise Model Diffing: https://transformer-circuits.pub/2024/model-diffing/index.html Causal Scrubbing: a method for rigorously testing interpretability hypotheses: https://www.lesswrong.com/posts/JvZhhzycHu2Yd57RN/causal-scrubbing-a-method-for-rigorously-testing Interpretability in Parameter Space: Minimizing Mechanistic Description Length with Attribution-based Parameter Decomposition (aka the Apollo paper on APD): https://arxiv.org/abs/2501.14926 Towards Guaranteed Safe AI: https://www2.eecs.berkeley.edu/Pubs/TechRpts/2024/EECS-2024-45.pdf Episode art by Hamish Doodles: hamishdoodles.com…

AXRP - the AI X-risk Research Podcast

1
38.8 - David Duvenaud on Sabotage Evaluations and the Post-AGI Future 20:42

23 weeks ago20:42

20:42

In this episode, I chat with David Duvenaud about two topics he's been thinking about: firstly, a paper he wrote about evaluating whether or not frontier models can sabotage human decision-making or monitoring of the same models; and secondly, the difficult situation humans find themselves in in a post-AGI future, even if AI is aligned with human intentions. Patreon: https://www.patreon.com/axrpodcast Ko-fi: https://ko-fi.com/axrpodcast Transcript: https://axrp.net/episode/2025/03/01/episode-38_8-david-duvenaud-sabotage-evaluations-post-agi-future.html FAR.AI: https://far.ai/ FAR.AI on X (aka Twitter): https://x.com/farairesearch FAR.AI on YouTube: @FARAIResearch The Alignment Workshop: https://www.alignment-workshop.com/ Topics we discuss, and timestamps: 01:42 - The difficulty of sabotage evaluations 05:23 - Types of sabotage evaluation 08:45 - The state of sabotage evaluations 12:26 - What happens after AGI? Links: Sabotage Evaluations for Frontier Models: https://arxiv.org/abs/2410.21514 Gradual Disempowerment: https://gradual-disempowerment.ai/ Episode art by Hamish Doodles: hamishdoodles.com…

AXRP - the AI X-risk Research Podcast

1
38.7 - Anthony Aguirre on the Future of Life Institute 22:39

26 weeks ago22:39

22:39

The Future of Life Institute is one of the oldest and most prominant organizations in the AI existential safety space, working on such topics as the AI pause open letter and how the EU AI Act can be improved. Metaculus is one of the premier forecasting sites on the internet. Behind both of them lie one man: Anthony Aguirre, who I talk with in this episode. Patreon: https://www.patreon.com/axrpodcast Ko-fi: https://ko-fi.com/axrpodcast Transcript: https://axrp.net/episode/2025/02/09/episode-38_7-anthony-aguirre-future-of-life-institute.html FAR.AI: https://far.ai/ FAR.AI on X (aka Twitter): https://x.com/farairesearch FAR.AI on YouTube: https://www.youtube.com/@FARAIResearch The Alignment Workshop: https://www.alignment-workshop.com/ Topics we discuss, and timestamps: 00:33 - Anthony, FLI, and Metaculus 06:46 - The Alignment Workshop 07:15 - FLI's current activity 11:04 - AI policy 17:09 - Work FLI funds Links: Future of Life Institute: https://futureoflife.org/ Metaculus: https://www.metaculus.com/ Future of Life Foundation: https://www.flf.org/ Episode art by Hamish Doodles: hamishdoodles.com…

AXRP - the AI X-risk Research Podcast

1
38.6 - Joel Lehman on Positive Visions of AI 15:28

28 weeks ago15:28

15:28

Typically this podcast talks about how to avert destruction from AI. But what would it take to ensure AI promotes human flourishing as well as it can? Is alignment to individuals enough, and if not, where do we go form here? In this episode, I talk with Joel Lehman about these questions. Patreon: https://www.patreon.com/axrpodcast Ko-fi: https://ko-fi.com/axrpodcast Transcript: https://axrp.net/episode/2025/01/24/episode-38_6-joel-lehman-positive-visions-of-ai.html FAR.AI: https://far.ai/ FAR.AI on X (aka Twitter): https://x.com/farairesearch FAR.AI on YouTube: https://www.youtube.com/@FARAIResearch The Alignment Workshop: https://www.alignment-workshop.com/ Topics we discuss, and timestamps: 01:12 - Why aligned AI might not be enough 04:05 - Positive visions of AI 08:27 - Improving recommendation systems Links: Why Greatness Cannot Be Planned: https://www.amazon.com/Why-Greatness-Cannot-Planned-Objective/dp/3319155237 We Need Positive Visions of AI Grounded in Wellbeing: https://thegradientpub.substack.com/p/beneficial-ai-wellbeing-lehman-ngo Machine Love: https://arxiv.org/abs/2302.09248 AI Alignment with Changing and Influenceable Reward Functions: https://arxiv.org/abs/2405.17713 Episode art by Hamish Doodles: hamishdoodles.com…

AXRP - the AI X-risk Research Podcast

1
38.5 - Adrià Garriga-Alonso on Detecting AI Scheming 27:41

29 weeks ago27:41

27:41

Suppose we're worried about AIs engaging in long-term plans that they don't tell us about. If we were to peek inside their brains, what should we look for to check whether this was happening? In this episode Adrià Garriga-Alonso talks about his work trying to answer this question. Patreon: https://www.patreon.com/axrpodcast Ko-fi: https://ko-fi.com/axrpodcast Transcript: https://axrp.net/episode/2025/01/20/episode-38_5-adria-garriga-alonso-detecting-ai-scheming.html FAR.AI: https://far.ai/ FAR.AI on X (aka Twitter): https://x.com/farairesearch FAR.AI on YouTube: https://www.youtube.com/@FARAIResearch The Alignment Workshop: https://www.alignment-workshop.com/ Topics we discuss, and timestamps: 01:04 - The Alignment Workshop 02:49 - How to detect scheming AIs 05:29 - Sokoban-solving networks taking time to think 12:18 - Model organisms of long-term planning 19:44 - How and why to study planning in networks Links: Adrià's website: https://agarri.ga/ An investigation of model-free planning: https://arxiv.org/abs/1901.03559 Model-Free Planning: https://tuphs28.github.io/projects/interpplanning/ Planning in a recurrent neural network that plays Sokoban: https://arxiv.org/abs/2407.15421 Episode art by Hamish Doodles: hamishdoodles.com…

AXRP - the AI X-risk Research Podcast

1
38.4 - Shakeel Hashim on AI Journalism 24:14

31 weeks ago24:14

24:14

AI researchers often complain about the poor coverage of their work in the news media. But why is this happening, and how can it be fixed? In this episode, I speak with Shakeel Hashim about the resource constraints facing AI journalism, the disconnect between journalists' and AI researchers' views on transformative AI, and efforts to improve the state of AI journalism, such as Tarbell and Shakeel's newsletter, Transformer. Patreon: https://www.patreon.com/axrpodcast Ko-fi: https://ko-fi.com/axrpodcast The transcript: https://axrp.net/episode/2025/01/05/episode-38_4-shakeel-hashim-ai-journalism.html FAR.AI: https://far.ai/ FAR.AI on X (aka Twitter): https://x.com/farairesearch FAR.AI on YouTube: https://www.youtube.com/@FARAIResearch The Alignment Workshop: https://www.alignment-workshop.com/ Topics we discuss, and timestamps: 01:31 - The AI media ecosystem 02:34 - Why not more AI news? 07:18 - Disconnects between journalists and the AI field 12:42 - Tarbell 18:44 - The Transformer newsletter Links: Transformer (Shakeel's substack): https://www.transformernews.ai/ Tarbell: https://www.tarbellfellowship.org/ Episode art by Hamish Doodles: hamishdoodles.com…

AXRP - the AI X-risk Research Podcast

1
38.3 - Erik Jenner on Learned Look-Ahead 23:46

35 weeks ago23:46

23:46

Lots of people in the AI safety space worry about models being able to make deliberate, multi-step plans. But can we already see this in existing neural nets? In this episode, I talk with Erik Jenner about his work looking at internal look-ahead within chess-playing neural networks. Patreon: https://www.patreon.com/axrpodcast Ko-fi: https://ko-fi.com/axrpodcast The transcript: https://axrp.net/episode/2024/12/12/episode-38_3-erik-jenner-learned-look-ahead.html FAR.AI: https://far.ai/ FAR.AI on X (aka Twitter): https://x.com/farairesearch FAR.AI on YouTube: https://www.youtube.com/@FARAIResearch The Alignment Workshop: https://www.alignment-workshop.com/ Topics we discuss, and timestamps: 00:57 - How chess neural nets look into the future 04:29 - The dataset and basic methodology 05:23 - Testing for branching futures? 07:57 - Which experiments demonstrate what 10:43 - How the ablation experiments work 12:38 - Effect sizes 15:23 - X-risk relevance 18:08 - Follow-up work 21:29 - How much planning does the network do? Research we mention: Evidence of Learned Look-Ahead in a Chess-Playing Neural Network: https://arxiv.org/abs/2406.00877 Understanding the learned look-ahead behavior of chess neural networks (a development of the follow-up research Erik mentioned): https://openreview.net/forum?id=Tl8EzmgsEp Linear Latent World Models in Simple Transformers: A Case Study on Othello-GPT: https://arxiv.org/abs/2310.07582 Episode art by Hamish Doodles: hamishdoodles.com…

AXRP - the AI X-risk Research Podcast

1
39 - Evan Hubinger on Model Organisms of Misalignment 1:45:47

36 weeks ago1:45:47

1:45:47

The 'model organisms of misalignment' line of research creates AI models that exhibit various types of misalignment, and studies them to try to understand how the misalignment occurs and whether it can be somehow removed. In this episode, Evan Hubinger talks about two papers he's worked on at Anthropic under this agenda: "Sleeper Agents" and "Sycophancy to Subterfuge". Patreon: https://www.patreon.com/axrpodcast Ko-fi: https://ko-fi.com/axrpodcast The transcript: https://axrp.net/episode/2024/12/01/episode-39-evan-hubinger-model-organisms-misalignment.html Topics we discuss, and timestamps: 0:00:36 - Model organisms and stress-testing 0:07:38 - Sleeper Agents 0:22:32 - Do 'sleeper agents' properly model deceptive alignment? 0:38:32 - Surprising results in "Sleeper Agents" 0:57:25 - Sycophancy to Subterfuge 1:09:21 - How models generalize from sycophancy to subterfuge 1:16:37 - Is the reward editing task valid? 1:21:46 - Training away sycophancy and subterfuge 1:29:22 - Model organisms, AI control, and evaluations 1:33:45 - Other model organisms research 1:35:27 - Alignment stress-testing at Anthropic 1:43:32 - Following Evan's work Main papers: Sleeper Agents: Training Deceptive LLMs that Persist Through Safety Training: https://arxiv.org/abs/2401.05566 Sycophancy to Subterfuge: Investigating Reward-Tampering in Large Language Models: https://arxiv.org/abs/2406.10162 Anthropic links: Anthropic's newsroom: https://www.anthropic.com/news Careers at Anthropic: https://www.anthropic.com/careers Other links: Model Organisms of Misalignment: The Case for a New Pillar of Alignment Research: https://www.alignmentforum.org/posts/ChDH335ckdvpxXaXX/model-organisms-of-misalignment-the-case-for-a-new-pillar-of-1 Simple probes can catch sleeper agents: https://www.anthropic.com/research/probes-catch-sleeper-agents Studying Large Language Model Generalization with Influence Functions: https://arxiv.org/abs/2308.03296 Stress-Testing Capability Elicitation With Password-Locked Models [aka model organisms of sandbagging]: https://arxiv.org/abs/2405.19550 Episode art by Hamish Doodles: hamishdoodles.com…

AXRP - the AI X-risk Research Podcast

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38.2 - Jesse Hoogland on Singular Learning Theory 18:18

37 weeks ago18:18

18:18

You may have heard of singular learning theory, and its "local learning coefficient", or LLC - but have you heard of the refined LLC? In this episode, I chat with Jesse Hoogland about his work on SLT, and using the refined LLC to find a new circuit in language models. Patreon: https://www.patreon.com/axrpodcast Ko-fi: https://ko-fi.com/axrpodcast The transcript: https://axrp.net/episode/2024/11/27/38_2-jesse-hoogland-singular-learning-theory.html FAR.AI: https://far.ai/ FAR.AI on X (aka Twitter): https://x.com/farairesearch FAR.AI on YouTube: https://www.youtube.com/@FARAIResearch The Alignment Workshop: https://www.alignment-workshop.com/ Topics we discuss, and timestamps: 00:34 - About Jesse 01:49 - The Alignment Workshop 02:31 - About Timaeus 05:25 - SLT that isn't developmental interpretability 10:41 - The refined local learning coefficient 14:06 - Finding the multigram circuit Links: Differentiation and Specialization of Attention Heads via the Refined Local Learning Coefficient: https://arxiv.org/abs/2410.02984 Investigating the learning coefficient of modular addition: hackathon project: https://www.lesswrong.com/posts/4v3hMuKfsGatLXPgt/investigating-the-learning-coefficient-of-modular-addition Episode art by Hamish Doodles: hamishdoodles.com…

AXRP - the AI X-risk Research Podcast

1
38.1 - Alan Chan on Agent Infrastructure 24:48

38 weeks ago24:48

24:48

Road lines, street lights, and licence plates are examples of infrastructure used to ensure that roads operate smoothly. In this episode, Alan Chan talks about using similar interventions to help avoid bad outcomes from the deployment of AI agents. Patreon: https://www.patreon.com/axrpodcast Ko-fi: https://ko-fi.com/axrpodcast The transcript: https://axrp.net/episode/2024/11/16/episode-38_1-alan-chan-agent-infrastructure.html FAR.AI: https://far.ai/ FAR.AI on X (aka Twitter): https://x.com/farairesearch FAR.AI on YouTube: https://www.youtube.com/@FARAIResearch The Alignment Workshop: https://www.alignment-workshop.com/ Topics we discuss, and timestamps: 01:02 - How the Alignment Workshop is 01:32 - Agent infrastructure 04:57 - Why agent infrastructure 07:54 - A trichotomy of agent infrastructure 13:59 - Agent IDs 18:17 - Agent channels 20:29 - Relation to AI control Links: Alan on Google Scholar: https://scholar.google.com/citations?user=lmQmYPgAAAAJ&hl=en&oi=ao IDs for AI Systems: https://arxiv.org/abs/2406.12137 Visibility into AI Agents: https://arxiv.org/abs/2401.13138 Episode art by Hamish Doodles: hamishdoodles.com…

AXRP - the AI X-risk Research Podcast

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38.0 - Zhijing Jin on LLMs, Causality, and Multi-Agent Systems 22:42

39 weeks ago22:42

22:42

Do language models understand the causal structure of the world, or do they merely note correlations? And what happens when you build a big AI society out of them? In this brief episode, recorded at the Bay Area Alignment Workshop, I chat with Zhijing Jin about her research on these questions. Patreon: https://www.patreon.com/axrpodcast Ko-fi: https://ko-fi.com/axrpodcast The transcript: https://axrp.net/episode/2024/11/14/episode-38_0-zhijing-jin-llms-causality-multi-agent-systems.html FAR.AI: https://far.ai/ FAR.AI on X (aka Twitter): https://x.com/farairesearch FAR.AI on YouTube: https://www.youtube.com/@FARAIResearch The Alignment Workshop: https://www.alignment-workshop.com/ Topics we discuss, and timestamps: 00:35 - How the Alignment Workshop is 00:47 - How Zhijing got interested in causality and natural language processing 03:14 - Causality and alignment 06:21 - Causality without randomness 10:07 - Causal abstraction 11:42 - Why LLM causal reasoning? 13:20 - Understanding LLM causal reasoning 16:33 - Multi-agent systems Links: Zhijing's website: https://zhijing-jin.com/fantasy/ Zhijing on X (aka Twitter): https://x.com/zhijingjin Can Large Language Models Infer Causation from Correlation?: https://arxiv.org/abs/2306.05836 Cooperate or Collapse: Emergence of Sustainable Cooperation in a Society of LLM Agents: https://arxiv.org/abs/2404.16698 Episode art by Hamish Doodles: hamishdoodles.com…

AXRP - the AI X-risk Research Podcast

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37 - Jaime Sevilla on AI Forecasting 1:44:25

44 weeks ago1:44:25

1:44:25

Epoch AI is the premier organization that tracks the trajectory of AI - how much compute is used, the role of algorithmic improvements, the growth in data used, and when the above trends might hit an end. In this episode, I speak with the director of Epoch AI, Jaime Sevilla, about how compute, data, and algorithmic improvements are impacting AI, and whether continuing to scale can get us AGI. Patreon: https://www.patreon.com/axrpodcast Ko-fi: https://ko-fi.com/axrpodcast The transcript: https://axrp.net/episode/2024/10/04/episode-37-jaime-sevilla-forecasting-ai.html Topics we discuss, and timestamps: 0:00:38 - The pace of AI progress 0:07:49 - How Epoch AI tracks AI compute 0:11:44 - Why does AI compute grow so smoothly? 0:21:46 - When will we run out of computers? 0:38:56 - Algorithmic improvement 0:44:21 - Algorithmic improvement and scaling laws 0:56:56 - Training data 1:04:56 - Can scaling produce AGI? 1:16:55 - When will AGI arrive? 1:21:20 - Epoch AI 1:27:06 - Open questions in AI forecasting 1:35:21 - Epoch AI and x-risk 1:41:34 - Following Epoch AI's research Links for Jaime and Epoch AI: Epoch AI: https://epochai.org/ Machine Learning Trends dashboard: https://epochai.org/trends Epoch AI on X / Twitter: https://x.com/EpochAIResearch Jaime on X / Twitter: https://x.com/Jsevillamol Research we discuss: Training Compute of Frontier AI Models Grows by 4-5x per Year: https://epochai.org/blog/training-compute-of-frontier-ai-models-grows-by-4-5x-per-year Optimally Allocating Compute Between Inference and Training: https://epochai.org/blog/optimally-allocating-compute-between-inference-and-training Algorithmic Progress in Language Models [blog post]: https://epochai.org/blog/algorithmic-progress-in-language-models Algorithmic progress in language models [paper]: https://arxiv.org/abs/2403.05812 Training Compute-Optimal Large Language Models [aka the Chinchilla scaling law paper]: https://arxiv.org/abs/2203.15556 Will We Run Out of Data? Limits of LLM Scaling Based on Human-Generated Data [blog post]: https://epochai.org/blog/will-we-run-out-of-data-limits-of-llm-scaling-based-on-human-generated-data Will we run out of data? Limits of LLM scaling based on human-generated data [paper]: https://arxiv.org/abs/2211.04325 The Direct Approach: https://epochai.org/blog/the-direct-approach Episode art by Hamish Doodles: hamishdoodles.com…

AXRP - the AI X-risk Research Podcast

1
36 - Adam Shai and Paul Riechers on Computational Mechanics 1:48:27

45 weeks ago1:48:27

1:48:27

Sometimes, people talk about transformers as having "world models" as a result of being trained to predict text data on the internet. But what does this even mean? In this episode, I talk with Adam Shai and Paul Riechers about their work applying computational mechanics, a sub-field of physics studying how to predict random processes, to neural networks. Patreon: https://www.patreon.com/axrpodcast Ko-fi: https://ko-fi.com/axrpodcast The transcript: https://axrp.net/episode/2024/09/29/episode-36-adam-shai-paul-riechers-computational-mechanics.html Topics we discuss, and timestamps: 0:00:42 - What computational mechanics is 0:29:49 - Computational mechanics vs other approaches 0:36:16 - What world models are 0:48:41 - Fractals 0:57:43 - How the fractals are formed 1:09:55 - Scaling computational mechanics for transformers 1:21:52 - How Adam and Paul found computational mechanics 1:36:16 - Computational mechanics for AI safety 1:46:05 - Following Adam and Paul's research Simplex AI Safety: https://www.simplexaisafety.com/ Research we discuss: Transformers represent belief state geometry in their residual stream: https://arxiv.org/abs/2405.15943 Transformers represent belief state geometry in their residual stream [LessWrong post]: https://www.lesswrong.com/posts/gTZ2SxesbHckJ3CkF/transformers-represent-belief-state-geometry-in-their Why Would Belief-States Have A Fractal Structure, And Why Would That Matter For Interpretability? An Explainer: https://www.lesswrong.com/posts/mBw7nc4ipdyeeEpWs/why-would-belief-states-have-a-fractal-structure-and-why Episode art by Hamish Doodles: hamishdoodles.com…

AXRP - the AI X-risk Research Podcast

1
New Patreon tiers + MATS applications 5:32

45 weeks ago5:32

5:32

Patreon: https://www.patreon.com/axrpodcast MATS: https://www.matsprogram.org Note: I'm employed by MATS, but they're not paying me to make this video.

AXRP - the AI X-risk Research Podcast

1
35 - Peter Hase on LLM Beliefs and Easy-to-Hard Generalization 2:17:24

50 weeks ago2:17:24

2:17:24

How do we figure out what large language models believe? In fact, do they even have beliefs? Do those beliefs have locations, and if so, can we edit those locations to change the beliefs? Also, how are we going to get AI to perform tasks so hard that we can't figure out if they succeeded at them? In this episode, I chat with Peter Hase about his research into these questions. Patreon: https://www.patreon.com/axrpodcast Ko-fi: https://ko-fi.com/axrpodcast The transcript: https://axrp.net/episode/2024/08/24/episode-35-peter-hase-llm-beliefs-easy-to-hard-generalization.html Topics we discuss, and timestamps: 0:00:36 - NLP and interpretability 0:10:20 - Interpretability lessons 0:32:22 - Belief interpretability 1:00:12 - Localizing and editing models' beliefs 1:19:18 - Beliefs beyond language models 1:27:21 - Easy-to-hard generalization 1:47:16 - What do easy-to-hard results tell us? 1:57:33 - Easy-to-hard vs weak-to-strong 2:03:50 - Different notions of hardness 2:13:01 - Easy-to-hard vs weak-to-strong, round 2 2:15:39 - Following Peter's work Peter on Twitter: https://x.com/peterbhase Peter's papers: Foundational Challenges in Assuring Alignment and Safety of Large Language Models: https://arxiv.org/abs/2404.09932 Do Language Models Have Beliefs? Methods for Detecting, Updating, and Visualizing Model Beliefs: https://arxiv.org/abs/2111.13654 Does Localization Inform Editing? Surprising Differences in Causality-Based Localization vs. Knowledge Editing in Language Models: https://arxiv.org/abs/2301.04213 Are Language Models Rational? The Case of Coherence Norms and Belief Revision: https://arxiv.org/abs/2406.03442 The Unreasonable Effectiveness of Easy Training Data for Hard Tasks: https://arxiv.org/abs/2401.06751 Other links: Toy Models of Superposition: https://transformer-circuits.pub/2022/toy_model/index.html Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV): https://arxiv.org/abs/1711.11279 Locating and Editing Factual Associations in GPT (aka the ROME paper): https://arxiv.org/abs/2202.05262 Of nonlinearity and commutativity in BERT: https://arxiv.org/abs/2101.04547 Inference-Time Intervention: Eliciting Truthful Answers from a Language Model: https://arxiv.org/abs/2306.03341 Editing a classifier by rewriting its prediction rules: https://arxiv.org/abs/2112.01008 Discovering Latent Knowledge Without Supervision (aka the Collin Burns CCS paper): https://arxiv.org/abs/2212.03827 Weak-to-Strong Generalization: Eliciting Strong Capabilities With Weak Supervision: https://arxiv.org/abs/2312.09390 Concrete problems in AI safety: https://arxiv.org/abs/1606.06565 Rissanen Data Analysis: Examining Dataset Characteristics via Description Length: https://arxiv.org/abs/2103.03872 Episode art by Hamish Doodles: hamishdoodles.com…

AXRP - the AI X-risk Research Podcast

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34 - AI Evaluations with Beth Barnes 2:14:02

1 year ago2:14:02

2:14:02

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…

AXRP - the AI X-risk Research Podcast

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33 - RLHF Problems with Scott Emmons 1:41:24

1 year ago1:41:24

1:41:24

Reinforcement Learning from Human Feedback, or RLHF, is one of the main ways that makers of large language models make them 'aligned'. But people have long noted that there are difficulties with this approach when the models are smarter than the humans providing feedback. In this episode, I talk with Scott Emmons about his work categorizing the problems that can show up in this setting. Patreon: patreon.com/axrpodcast Ko-fi: ko-fi.com/axrpodcast The transcript: https://axrp.net/episode/2024/06/12/episode-33-rlhf-problems-scott-emmons.html Topics we discuss, and timestamps: 0:00:33 - Deceptive inflation 0:17:56 - Overjustification 0:32:48 - Bounded human rationality 0:50:46 - Avoiding these problems 1:14:13 - Dimensional analysis 1:23:32 - RLHF problems, in theory and practice 1:31:29 - Scott's research program 1:39:42 - Following Scott's research Scott's website: https://www.scottemmons.com Scott's X/twitter account: https://x.com/emmons_scott When Your AIs Deceive You: Challenges With Partial Observability of Human Evaluators in Reward Learning: https://arxiv.org/abs/2402.17747 Other works we discuss: AI Deception: A Survey of Examples, Risks, and Potential Solutions: https://arxiv.org/abs/2308.14752 Uncertain decisions facilitate better preference learning: https://arxiv.org/abs/2106.10394 Invariance in Policy Optimisation and Partial Identifiability in Reward Learning: https://arxiv.org/abs/2203.07475 The Humble Gaussian Distribution (aka principal component analysis and dimensional analysis): http://www.inference.org.uk/mackay/humble.pdf Fine-tuning Aligned Language Models Compromises Safety, Even When Users Do Not Intend To!: https://arxiv.org/abs/2310.03693 Episode art by Hamish Doodles: hamishdoodles.com…

AXRP - the AI X-risk Research Podcast

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32 - Understanding Agency with Jan Kulveit 2:22:29

1 year ago2:22:29

2:22:29

What's the difference between a large language model and the human brain? And what's wrong with our theories of agency? In this episode, I chat about these questions with Jan Kulveit, who leads the Alignment of Complex Systems research group. Patreon: patreon.com/axrpodcast Ko-fi: ko-fi.com/axrpodcast The transcript: axrp.net/episode/2024/05/30/episode-32-understanding-agency-jan-kulveit.html Topics we discuss, and timestamps: 0:00:47 - What is active inference? 0:15:14 - Preferences in active inference 0:31:33 - Action vs perception in active inference 0:46:07 - Feedback loops 1:01:32 - Active inference vs LLMs 1:12:04 - Hierarchical agency 1:58:28 - The Alignment of Complex Systems group Website of the Alignment of Complex Systems group (ACS): acsresearch.org ACS on X/Twitter: x.com/acsresearchorg Jan on LessWrong: lesswrong.com/users/jan-kulveit Predictive Minds: Large Language Models as Atypical Active Inference Agents: arxiv.org/abs/2311.10215 Other works we discuss: Active Inference: The Free Energy Principle in Mind, Brain, and Behavior: https://www.goodreads.com/en/book/show/58275959 Book Review: Surfing Uncertainty: https://slatestarcodex.com/2017/09/05/book-review-surfing-uncertainty/ The self-unalignment problem: https://www.lesswrong.com/posts/9GyniEBaN3YYTqZXn/the-self-unalignment-problem Mitigating generative agent social dilemmas (aka language models writing contracts for Minecraft): https://social-dilemmas.github.io/ Episode art by Hamish Doodles: hamishdoodles.com…

AXRP - the AI X-risk Research Podcast

1
31 - Singular Learning Theory with Daniel Murfet 2:32:07

1 year ago2:32:07

2:32:07

What's going on with deep learning? What sorts of models get learned, and what are the learning dynamics? Singular learning theory is a theory of Bayesian statistics broad enough in scope to encompass deep neural networks that may help answer these questions. In this episode, I speak with Daniel Murfet about this research program and what it tells us. Patreon: patreon.com/axrpodcast Ko-fi: ko-fi.com/axrpodcast Topics we discuss, and timestamps: 0:00:26 - What is singular learning theory? 0:16:00 - Phase transitions 0:35:12 - Estimating the local learning coefficient 0:44:37 - Singular learning theory and generalization 1:00:39 - Singular learning theory vs other deep learning theory 1:17:06 - How singular learning theory hit AI alignment 1:33:12 - Payoffs of singular learning theory for AI alignment 1:59:36 - Does singular learning theory advance AI capabilities? 2:13:02 - Open problems in singular learning theory for AI alignment 2:20:53 - What is the singular fluctuation? 2:25:33 - How geometry relates to information 2:30:13 - Following Daniel Murfet's work The transcript: https://axrp.net/episode/2024/05/07/episode-31-singular-learning-theory-dan-murfet.html Daniel Murfet's twitter/X account: https://twitter.com/danielmurfet Developmental interpretability website: https://devinterp.com Developmental interpretability YouTube channel: https://www.youtube.com/@Devinterp Main research discussed in this episode: - Developmental Landscape of In-Context Learning: https://arxiv.org/abs/2402.02364 - Estimating the Local Learning Coefficient at Scale: https://arxiv.org/abs/2402.03698 - Simple versus Short: Higher-order degeneracy and error-correction: https://www.lesswrong.com/posts/nWRj6Ey8e5siAEXbK/simple-versus-short-higher-order-degeneracy-and-error-1 Other links: - Algebraic Geometry and Statistical Learning Theory (the grey book): https://www.cambridge.org/core/books/algebraic-geometry-and-statistical-learning-theory/9C8FD1BDC817E2FC79117C7F41544A3A - Mathematical Theory of Bayesian Statistics (the green book): https://www.routledge.com/Mathematical-Theory-of-Bayesian-Statistics/Watanabe/p/book/9780367734817 In-context learning and induction heads: https://transformer-circuits.pub/2022/in-context-learning-and-induction-heads/index.html - Saddle-to-Saddle Dynamics in Deep Linear Networks: Small Initialization Training, Symmetry, and Sparsity: https://arxiv.org/abs/2106.15933 - A mathematical theory of semantic development in deep neural networks: https://www.pnas.org/doi/abs/10.1073/pnas.1820226116 - Consideration on the Learning Efficiency Of Multiple-Layered Neural Networks with Linear Units: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4404877 - Neural Tangent Kernel: Convergence and Generalization in Neural Networks: https://arxiv.org/abs/1806.07572 - The Interpolating Information Criterion for Overparameterized Models: https://arxiv.org/abs/2307.07785 - Feature Learning in Infinite-Width Neural Networks: https://arxiv.org/abs/2011.14522 - A central AI alignment problem: capabilities generalization, and the sharp left turn: https://www.lesswrong.com/posts/GNhMPAWcfBCASy8e6/a-central-ai-alignment-problem-capabilities-generalization - Quantifying degeneracy in singular models via the learning coefficient: https://arxiv.org/abs/2308.12108 Episode art by Hamish Doodles: hamishdoodles.com…

AXRP - the AI X-risk Research Podcast

1
30 - AI Security with Jeffrey Ladish 2:15:44

1 year ago2:15:44

2:15:44

Top labs use various forms of "safety training" on models before their release to make sure they don't do nasty stuff - but how robust is that? How can we ensure that the weights of powerful AIs don't get leaked or stolen? And what can AI even do these days? In this episode, I speak with Jeffrey Ladish about security and AI. Patreon: patreon.com/axrpodcast Ko-fi: ko-fi.com/axrpodcast Topics we discuss, and timestamps: 0:00:38 - Fine-tuning away safety training 0:13:50 - Dangers of open LLMs vs internet search 0:19:52 - What we learn by undoing safety filters 0:27:34 - What can you do with jailbroken AI? 0:35:28 - Security of AI model weights 0:49:21 - Securing against attackers vs AI exfiltration 1:08:43 - The state of computer security 1:23:08 - How AI labs could be more secure 1:33:13 - What does Palisade do? 1:44:40 - AI phishing 1:53:32 - More on Palisade's work 1:59:56 - Red lines in AI development 2:09:56 - Making AI legible 2:14:08 - Following Jeffrey's research The transcript: axrp.net/episode/2024/04/30/episode-30-ai-security-jeffrey-ladish.html Palisade Research: palisaderesearch.org Jeffrey's Twitter/X account: twitter.com/JeffLadish Main papers we discussed: - LoRA Fine-tuning Efficiently Undoes Safety Training in Llama 2-Chat 70B: arxiv.org/abs/2310.20624 - BadLLaMa: Cheaply Removing Safety Fine-tuning From LLaMa 2-Chat 13B: arxiv.org/abs/2311.00117 - Securing Artificial Intelligence Model Weights: rand.org/pubs/working_papers/WRA2849-1.html Other links: - Llama 2: Open Foundation and Fine-Tuned Chat Models: https://arxiv.org/abs/2307.09288 - Fine-tuning Aligned Language Models Compromises Safety, Even When Users Do Not Intend To!: https://arxiv.org/abs/2310.03693 - Shadow Alignment: The Ease of Subverting Safely-Aligned Language Models: https://arxiv.org/abs/2310.02949 - On the Societal Impact of Open Foundation Models (Stanford paper on marginal harms from open-weight models): https://crfm.stanford.edu/open-fms/ - The Operational Risks of AI in Large-Scale Biological Attacks (RAND): https://www.rand.org/pubs/research_reports/RRA2977-2.html - Preventing model exfiltration with upload limits: https://www.alignmentforum.org/posts/rf66R4YsrCHgWx9RG/preventing-model-exfiltration-with-upload-limits - A deep dive into an NSO zero-click iMessage exploit: Remote Code Execution: https://googleprojectzero.blogspot.com/2021/12/a-deep-dive-into-nso-zero-click.html - In-browser transformer inference: https://aiserv.cloud/ - Anatomy of a rental phishing scam: https://jeffreyladish.com/anatomy-of-a-rental-phishing-scam/ - Causal Scrubbing: a method for rigorously testing interpretability hypotheses: https://www.alignmentforum.org/posts/JvZhhzycHu2Yd57RN/causal-scrubbing-a-method-for-rigorously-testing Episode art by Hamish Doodles: hamishdoodles.com…

AXRP - the AI X-risk Research Podcast

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29 - Science of Deep Learning with Vikrant Varma 2:13:46

1 year ago2:13:46

2:13:46

In 2022, it was announced that a fairly simple method can be used to extract the true beliefs of a language model on any given topic, without having to actually understand the topic at hand. Earlier, in 2021, it was announced that neural networks sometimes 'grok': that is, when training them on certain tasks, they initially memorize their training data (achieving their training goal in a way that doesn't generalize), but then suddenly switch to understanding the 'real' solution in a way that generalizes. What's going on with these discoveries? Are they all they're cracked up to be, and if so, how are they working? In this episode, I talk to Vikrant Varma about his research getting to the bottom of these questions. Patreon: patreon.com/axrpodcast Ko-fi: ko-fi.com/axrpodcast Topics we discuss, and timestamps: 0:00:36 - Challenges with unsupervised LLM knowledge discovery, aka contra CCS 0:00:36 - What is CCS? 0:09:54 - Consistent and contrastive features other than model beliefs 0:20:34 - Understanding the banana/shed mystery 0:41:59 - Future CCS-like approaches 0:53:29 - CCS as principal component analysis 0:56:21 - Explaining grokking through circuit efficiency 0:57:44 - Why research science of deep learning? 1:12:07 - Summary of the paper's hypothesis 1:14:05 - What are 'circuits'? 1:20:48 - The role of complexity 1:24:07 - Many kinds of circuits 1:28:10 - How circuits are learned 1:38:24 - Semi-grokking and ungrokking 1:50:53 - Generalizing the results 1:58:51 - Vikrant's research approach 2:06:36 - The DeepMind alignment team 2:09:06 - Follow-up work The transcript: axrp.net/episode/2024/04/25/episode-29-science-of-deep-learning-vikrant-varma.html Vikrant's Twitter/X account: twitter.com/vikrantvarma_ Main papers: - Challenges with unsupervised LLM knowledge discovery: arxiv.org/abs/2312.10029 - Explaining grokking through circuit efficiency: arxiv.org/abs/2309.02390 Other works discussed: - Discovering latent knowledge in language models without supervision (CCS): arxiv.org/abs/2212.03827 - Eliciting Latent Knowledge: How to Tell if your Eyes Deceive You: https://docs.google.com/document/d/1WwsnJQstPq91_Yh-Ch2XRL8H_EpsnjrC1dwZXR37PC8/edit - Discussion: Challenges with unsupervised LLM knowledge discovery: lesswrong.com/posts/wtfvbsYjNHYYBmT3k/discussion-challenges-with-unsupervised-llm-knowledge-1 - Comment thread on the banana/shed results: lesswrong.com/posts/wtfvbsYjNHYYBmT3k/discussion-challenges-with-unsupervised-llm-knowledge-1?commentId=hPZfgA3BdXieNfFuY - Fabien Roger, What discovering latent knowledge did and did not find: lesswrong.com/posts/bWxNPMy5MhPnQTzKz/what-discovering-latent-knowledge-did-and-did-not-find-4 - Scott Emmons, Contrast Pairs Drive the Performance of Contrast Consistent Search (CCS): lesswrong.com/posts/9vwekjD6xyuePX7Zr/contrast-pairs-drive-the-empirical-performance-of-contrast - Grokking: Generalizing Beyond Overfitting on Small Algorithmic Datasets: arxiv.org/abs/2201.02177 - Keeping Neural Networks Simple by Minimizing the Minimum Description Length of the Weights (Hinton 1993 L2): dl.acm.org/doi/pdf/10.1145/168304.168306 - Progress measures for grokking via mechanistic interpretability: arxiv.org/abs/2301.0521 Episode art by Hamish Doodles: hamishdoodles.com…

AXRP - the AI X-risk Research Podcast

1
28 - Suing Labs for AI Risk with Gabriel Weil 1:57:30

1 year ago1:57:30

1:57:30

How should the law govern AI? Those concerned about existential risks often push either for bans or for regulations meant to ensure that AI is developed safely - but another approach is possible. In this episode, Gabriel Weil talks about his proposal to modify tort law to enable people to sue AI companies for disasters that are "nearly catastrophic". Patreon: patreon.com/axrpodcast Ko-fi: ko-fi.com/axrpodcast Topics we discuss, and timestamps: 0:00:35 - The basic idea 0:20:36 - Tort law vs regulation 0:29:10 - Weil's proposal vs Hanson's proposal 0:37:00 - Tort law vs Pigouvian taxation 0:41:16 - Does disagreement on AI risk make this proposal less effective? 0:49:53 - Warning shots - their prevalence and character 0:59:17 - Feasibility of big changes to liability law 1:29:17 - Interactions with other areas of law 1:38:59 - How Gabriel encountered the AI x-risk field 1:42:41 - AI x-risk and the legal field 1:47:44 - Technical research to help with this proposal 1:50:47 - Decisions this proposal could influence 1:55:34 - Following Gabriel's research The transcript: axrp.net/episode/2024/04/17/episode-28-tort-law-for-ai-risk-gabriel-weil.html Links for Gabriel: - SSRN page: papers.ssrn.com/sol3/cf_dev/AbsByAuth.cfm?per_id=1648032 - Twitter/X account: twitter.com/gabriel_weil Tort Law as a Tool for Mitigating Catastrophic Risk from Artificial Intelligence: papers.ssrn.com/sol3/papers.cfm?abstract_id=4694006 Other links: - Foom liability: overcomingbias.com/p/foom-liability - Punitive Damages: An Economic Analysis: law.harvard.edu/faculty/shavell/pdf/111_Harvard_Law_Rev_869.pdf - Efficiency, Fairness, and the Externalization of Reasonable Risks: The Problem With the Learned Hand Formula: papers.ssrn.com/sol3/papers.cfm?abstract_id=4466197 - Tort Law Can Play an Important Role in Mitigating AI Risk: forum.effectivealtruism.org/posts/epKBmiyLpZWWFEYDb/tort-law-can-play-an-important-role-in-mitigating-ai-risk - How Technical AI Safety Researchers Can Help Implement Punitive Damages to Mitigate Catastrophic AI Risk: forum.effectivealtruism.org/posts/yWKaBdBygecE42hFZ/how-technical-ai-safety-researchers-can-help-implement - Can the courts save us from dangerous AI? [Vox]: vox.com/future-perfect/2024/2/7/24062374/ai-openai-anthropic-deepmind-legal-liability-gabriel-weil Episode art by Hamish Doodles: hamishdoodles.com…

AXRP - the AI X-risk Research Podcast

1
27 - AI Control with Buck Shlegeris and Ryan Greenblatt 2:56:05

1 year ago2:56:05

2:56:05

A lot of work to prevent AI existential risk takes the form of ensuring that AIs don't want to cause harm or take over the world---or in other words, ensuring that they're aligned. In this episode, I talk with Buck Shlegeris and Ryan Greenblatt about a different approach, called "AI control": ensuring that AI systems couldn't take over the world, even if they were trying to. Patreon: patreon.com/axrpodcast Ko-fi: ko-fi.com/axrpodcast Topics we discuss, and timestamps: 0:00:31 - What is AI control? 0:16:16 - Protocols for AI control 0:22:43 - Which AIs are controllable? 0:29:56 - Preventing dangerous coded AI communication 0:40:42 - Unpredictably uncontrollable AI 0:58:01 - What control looks like 1:08:45 - Is AI control evil? 1:24:42 - Can red teams match misaligned AI? 1:36:51 - How expensive is AI monitoring? 1:52:32 - AI control experiments 2:03:50 - GPT-4's aptitude at inserting backdoors 2:14:50 - How AI control relates to the AI safety field 2:39:25 - How AI control relates to previous Redwood Research work 2:49:16 - How people can work on AI control 2:54:07 - Following Buck and Ryan's research The transcript: axrp.net/episode/2024/04/11/episode-27-ai-control-buck-shlegeris-ryan-greenblatt.html Links for Buck and Ryan: - Buck's twitter/X account: twitter.com/bshlgrs - Ryan on LessWrong: lesswrong.com/users/ryan_greenblatt - You can contact both Buck and Ryan by electronic mail at [firstname] [at-sign] rdwrs.com Main research works we talk about: - The case for ensuring that powerful AIs are controlled: lesswrong.com/posts/kcKrE9mzEHrdqtDpE/the-case-for-ensuring-that-powerful-ais-are-controlled - AI Control: Improving Safety Despite Intentional Subversion: arxiv.org/abs/2312.06942 Other things we mention: - The prototypical catastrophic AI action is getting root access to its datacenter (aka "Hacking the SSH server"): lesswrong.com/posts/BAzCGCys4BkzGDCWR/the-prototypical-catastrophic-ai-action-is-getting-root - Preventing language models from hiding their reasoning: arxiv.org/abs/2310.18512 - Improving the Welfare of AIs: A Nearcasted Proposal: lesswrong.com/posts/F6HSHzKezkh6aoTr2/improving-the-welfare-of-ais-a-nearcasted-proposal - Measuring coding challenge competence with APPS: arxiv.org/abs/2105.09938 - Causal Scrubbing: a method for rigorously testing interpretability hypotheses lesswrong.com/posts/JvZhhzycHu2Yd57RN/causal-scrubbing-a-method-for-rigorously-testing Episode art by Hamish Doodles: hamishdoodles.com…

AXRP - the AI X-risk Research Podcast

1
26 - AI Governance with Elizabeth Seger 1:57:13

2 years ago1:57:13