AXRP - The AI X-risk Research podcast

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Innhold levert av Daniel Filan. Alt podcastinnhold, inkludert episoder, grafikk og podcastbeskrivelser, lastes opp og leveres direkte av Daniel Filan eller deres podcastplattformpartner. Hvis du tror at noen bruker det opphavsrettsbeskyttede verket ditt uten din tillatelse, kan du følge prosessen skissert her https://no.player.fm/legal.

Silver Linings with The Old Gays

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Our Chosen Family 33:19

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33:19

What’s the secret to lasting friendships? How does queer community show up through the ebbs and flows of life? And what’s the REAL story behind the “YMCA” song? In the first episode of Silver Linings, The Old Gays dive into an essential part of queer life: chosen family. They discuss the vital love, support, and sense of belonging that community provides, especially during life's toughest moments. They open up about what “queer” means to them, how chosen family has impacted their lives, and how to maintain close bonds over time–including their love for each other! “We’ve come a long way, baby.” Family isn’t just what you’re born with; it’s the people who show up, shape you, and stick around. See omnystudio.com/listener for privacy information.…

AXRP - the AI X-risk Research Podcast

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AXRP (pronounced axe-urp) is the AI X-risk Research Podcast where I, Daniel Filan, have conversations with researchers about their papers. We discuss the paper, and hopefully get a sense of why it's been written and how it might reduce the risk of AI causing an existential catastrophe: that is, permanently and drastically curtailing humanity's future potential. You can visit the website and read transcripts at axrp.net.

54 episoder

#Science #Tech #Daniel Filan #Xrisk

AXRP - the AI X-risk Research Podcast

34 subscribers

updated for ca. et år siden

Del

Serier hjem•Feed

54 episoder

#Science #Tech #Daniel Filan #Xrisk

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

for 9 weeks siden2:36:05

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…

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

for 13 weeks siden20:42

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…

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

for 16 weeks siden22:39

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…

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

for 18 weeks siden15:28

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…

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

for 18 weeks siden27:41

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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…

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38.4 - Shakeel Hashim on AI Journalism 24:14

for 21 weeks siden24:14

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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…

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

for 24 weeks siden23:46

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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…

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

for 26 weeks siden1:45:47

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…

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38.2 - Jesse Hoogland on Singular Learning Theory 18:18

for 26 weeks siden18:18

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…

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38.1 - Alan Chan on Agent Infrastructure 24:48

for 28 weeks siden24:48

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…

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

for 28 weeks siden22:42

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…

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

for 34 weeks siden1:44:25

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…

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

for 35 weeks siden1:48:27

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…

for 4 years siden2:23:17

2:23:17

How should we think about the technical problem of building smarter-than-human AI that does what we want? When and how should AI systems defer to us? Should they have their own goals, and how should those goals be managed? In this episode, Dylan Hadfield-Menell talks about his work on assistance games that formalizes these questions. The first couple years of my PhD program included many long conversations with Dylan that helped shape how I view AI x-risk research, so it was great to have another one in the form of a recorded interview. Link to the transcript: axrp.net/episode/2021/06/08/episode-8-assistance-games-dylan-hadfield-menell.html Link to the paper "Cooperative Inverse Reinforcement Learning": arxiv.org/abs/1606.03137 Link to the paper "The Off-Switch Game": arxiv.org/abs/1611.08219 Link to the paper "Inverse Reward Design": arxiv.org/abs/1711.02827 Dylan's twitter account: twitter.com/dhadfieldmenell Link to apply to the MIT EECS graduate program: gradapply.mit.edu/eecs/apply/login/?next=/eecs/ Other work mentioned in the discussion: - The original paper on inverse optimal control: asmedigitalcollection.asme.org/fluidsengineering/article-abstract/86/1/51/392203/When-Is-a-Linear-Control-System-Optimal - Justin Fu's research on, among other things, adversarial IRL: scholar.google.com/citations?user=T9To2C0AAAAJ&hl=en&oi=ao - Preferences implicit in the state of the world: arxiv.org/abs/1902.04198 - What are you optimizing for? Aligning recommender systems with human values: participatoryml.github.io/papers/2020/42.pdf - The Assistive Multi-Armed Bandit: arxiv.org/abs/1901.08654 - Soares et al. on Corrigibility: openreview.net/forum?id=H1bIT1buWH - Should Robots be Obedient?: arxiv.org/abs/1705.09990 - Rodney Brooks on the Seven Deadly Sins of Predicting the Future of AI: rodneybrooks.com/the-seven-deadly-sins-of-predicting-the-future-of-ai/ - Products in category theory: en.wikipedia.org/wiki/Product_(category_theory) - AXRP Episode 7 - Side Effects with Victoria Krakovna: axrp.net/episode/2021/05/14/episode-7-side-effects-victoria-krakovna.html - Attainable Utility Preservation: arxiv.org/abs/1902.09725 - Penalizing side effects using stepwise relative reachability: arxiv.org/abs/1806.01186 - Simplifying Reward Design through Divide-and-Conquer: arxiv.org/abs/1806.02501 - Active Inverse Reward Design: arxiv.org/abs/1809.03060 - An Efficient, Generalized Bellman Update For Cooperative Inverse Reinforcement Learning: proceedings.mlr.press/v80/malik18a.html - Incomplete Contracting and AI Alignment: arxiv.org/abs/1804.04268 - Multi-Principal Assistance Games: arxiv.org/abs/2007.09540 - Consequences of Misaligned AI: arxiv.org/abs/2102.03896…

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