45 - Samuel Albanie on DeepMind's AGI Safety Approach
AXRP - the AI X-risk Research Podcast
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: Ko-fi: Transcript: 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...
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44 - Peter Salib on AI Rights for Human Safety
AXRP - the AI X-risk Research Podcast
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: Ko-fi: Transcript: 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...
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43 - David Lindner on Myopic Optimization with Non-myopic Approval
AXRP - the AI X-risk Research Podcast
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: Ko-fi: Transcript: 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...
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42 - Owain Evans on LLM Psychology
AXRP - the AI X-risk Research Podcast
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: Ko-fi: Transcript: Topics we discuss, and timestamps: 0:00:37 Why introspection? 0:06:24 Experiments in "Looking Inward" 0:15:11 Why fine-tune for...
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41 - Lee Sharkey on Attribution-based Parameter Decomposition
AXRP - the AI X-risk Research Podcast
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: Ko-fi: Transcript: 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...
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40 - Jason Gross on Compact Proofs and Interpretability
AXRP - the AI X-risk Research Podcast
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: Ko-fi: Transcript: 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...
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38.8 - David Duvenaud on Sabotage Evaluations and the Post-AGI Future
AXRP - the AI X-risk Research Podcast
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: Ko-fi: Transcript: FAR.AI: FAR.AI on X (aka Twitter): FAR.AI on YouTube: The Alignment Workshop: Topics we discuss, and timestamps: 01:42 - The difficulty of sabotage evaluations 05:23 - Types of sabotage...
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38.7 - Anthony Aguirre on the Future of Life Institute
AXRP - the AI X-risk Research Podcast
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: Ko-fi: Transcript: FAR.AI: FAR.AI on X (aka Twitter): FAR.AI on YouTube: The Alignment Workshop: Topics we discuss, and timestamps: 00:33 - Anthony, FLI, and Metaculus 06:46 - The Alignment Workshop 07:15 - FLI's...
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38.6 - Joel Lehman on Positive Visions of AI
AXRP - the AI X-risk Research Podcast
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: Ko-fi: Transcript: FAR.AI: FAR.AI on X (aka Twitter): FAR.AI on YouTube: The Alignment Workshop: 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...
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38.5 - Adrià Garriga-Alonso on Detecting AI Scheming
AXRP - the AI X-risk Research Podcast
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: Ko-fi: Transcript: FAR.AI: FAR.AI on X (aka Twitter): FAR.AI on YouTube: The Alignment Workshop: 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...
info_outlineEarlier 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