Nina is a software engineer at Stripe currently working with Evan Hubinger (Anthropic) on AI Deception and Mesa Optimization. I met her at a party two days ago and I found her explanation of AI Deception really clear so I thought I should have her explain it on camera.

Youtube: https://youtu.be/6ngasL054wM

Twitter: https://twitter.com/MichaelTrazzi

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

Curtis, also known on the internet as AI_WAIFU, is the head of Alignment at EleutherAI. In this episode we discuss the massive orders of H100s from different actors, why he thinks AGI is 4-5 years away, why he thinks we're 90% "toast", his comment on Eliezer Yudkwosky's Death with Dignity, and what kind of Alignment projects is currently going on at EleutherAI, especially a project with Markov chains and the Alignment test project that he is currently leading.

Youtube: https://www.youtube.com/watch?v=9s3XctQOgew

Transcript: https://theinsideview.ai/curtis

OUTLINE

(00:00) Highlights / Intro

(00:50) The Fuck That Noise Comment On Death With Dignity

(10:28) Th Probability of Doom Is 90%

(12:44) Best Counterarguments For His High P(doom)

(14:41) Compute And Model Size Required For A Dangerous Model

(17:59) Details For Curtis' Model Of Compute Required

(21:23) Why This Estimate Of Compute Required Might Be Wrong, Ajeya Cotra's Transformative AI report

(29:00) Curtis' Median For AGI Is Around 2028, Used To Be 2027

(30:50) How Curtis Approaches Life With Short Timelines And High P(Doom)

(35:27) Takeoff Speeds—The Software view vs. The Hardware View

(39:57) Nvidia's 400k H100 rolling down the assembly line, AIs soon to be unleashed on their own source code

(41:04) Could We Get A Fast Takeoff By Fuly Automating AI Research With More Compute

(46:00) The Entire World (Tech Companies, Governments, Militaries) Is Noticing New AI Capabilities That They Don't Have

(47:57) Open-source vs. Close source policies. Mundane vs. Apocalyptic considerations.

(53:25) Curtis' background, from teaching himself deep learning to EleutherAI

(55:51) Alignment Project At EleutherAI: Markov Chain and Language Models

(01:02:15) Research Philosophy at EleutherAI: Pursuing Useful Projects, Multingual, Discord, Logistics

(01:07:38) Alignment MineTest: Links To Alignmnet, Embedded Agency, Wireheading

(01:15:30) Next steps for Alignment Mine Test: focusing on model-based RL

(01:17:07) Training On Human Data & Using an Updated Gym Environment With Human APIs

(01:19:20) Model Used, Not Observing Symmetry

(01:21:58) Another goal of Alignment Mine Test: Study Corrigibility

(01:28:26) People ordering H100s Are Aware Of Other People Making These Orders, Race Dynamics, Last Message

Eric is a PhD student in the Department of Physics at MIT working with Max Tegmark on improving our scientific/theoretical understanding of deep learning -- understanding what deep neural networks do internally and why they work so well. This is part of a broader interest in the nature of intelligent systems, which previously led him to work with SETI astronomers, with Stuart Russell's AI alignment group (CHAI), and with Erik Hoel on a project related to integrated information theory.

Transcript: https://theinsideview.ai/eric

Youtube: https://youtu.be/BtHMIQs_5Nw

The Quantization Model of Neural Scaling: https://arxiv.org/abs/2303.13506
An Effective Theory of Representation Learning https://arxiv.org/abs/2205.10343

Omnigrok: Grokking Beyond Algorithmic Data: https://arxiv.org/abs/2210.01117

Jesse Hoogland is a research assistant at David Krueger's lab in Cambridge studying AI Safety. More recently, Jesse has been thinking about Singular Learning Theory and Developmental Interpretability, which we discuss in this episode. Before he came to grips with existential risk from AI, he co-founded a health-tech startup automating bariatric surgery patient journeys.

(00:00) Intro

(03:57) Jesse’s Story And Probability Of Doom

(06:21) How Jesse Got Into Singular Learning Theory

(08:50) Intuition behind SLT: the loss landscape

(12:23) Does SLT actually predict anything? Phase Transitions

(14:37) Why care about phase transition, grokking, etc

(15:56) Detecting dangerous capabilities like deception in the (devel)opment

(17:24) A concrete example: magnets

(20:06) Why Jesse Is Bullish On Interpretability

(23:57) Developmental Interpretability

(28:06) What Happens Next? Jesse’s Vision

(31:56) Toy Models of Superposition

(32:47) Singular Learning Theory Part 2

(36:22) Are Current Models Creative? Reasoning?

(38:19) Building Bridges Between Alignment And Other Disciplines

(41:08) Where To Learn More About Singular Learning Theory

Make sure I upload regularly: https://patreon.com/theinsideview

Youtube: https://youtu.be/713KyknwShA

Transcript: https://theinsideview.ai/jesse

Jesse: https://twitter.com/jesse_hoogland

Host: https://twitter.com/MichaelTrazzi

Patreon supporters:

- Vincent Weisser

- Gunnar Höglund

- Ryan Coppolo

- Edward Huff

- Emil Wallner

- Jesse Hoogland

- William Freire

- Cameron Holmes

- Jacques Thibodeau

- Max Chiswick

- Jack Seroy

- JJ Hepburn

Explainer podcast for Richard Ngo's "Clarifying and predicting AGI" post on Lesswrong, which introduces the t-AGI framework to evaluate AI progress. A system is considered t-AGI if it can outperform most human experts, given time t, on most cognitive tasks.

Youtube: https://youtu.be/JXYcLQItZsk

Clarifying and predicting AGI: https://www.alignmentforum.org/posts/BoA3agdkAzL6HQtQP/clarifying-and-predicting-agi

Max Kaufmann⁠ and Alan Chan discuss the evaluation of large language models, AI Governance and more generally the impact of the deployment of foundational models.

Alan is PhD student at Mila advised by Nicolas Le Roux, with a strong interest in AI Safety, AI Governance and coordination. He has also recently been working with David Krueger and helped me with some of the interviews that have been published recently (ML Street talk and Christoph Schuhmann).

Youtube: https://youtu.be/BOLxeR_culU

Outline

(0:00:00) Introduction
(0:01:16) LLMs Translating To Systems In The Future Is Confusing
(0:03:23) Evaluations Should Measure Actions Instead of Asking Yes or No Questions
(0:04:17) Identify Key Contexts for Dangerous Behavior to Write Concrete Evals
(0:07:29) Implicit Optimization Process Affects Evals and Benchmarks
(0:08:45) Passing Evals Doesn't Guarantee Safety
(0:09:41) Balancing Technical Evals With Social Governance
(0:11:00) Evaluations Must Be Convincing To Influence AI Development
(0:12:04) Evals Might Convince The AI Safety Community But Not People in FAccT
(0:13:21) Difficulty In Explaining AI Risk To Other Communities
(0:14:19) Both Existential Safety And Fairness Are Important
(0:15:14) Reasons Why People Don't Care About AI Existential Risk
(0:16:10) The Association Between Sillicon Valley And People in FAccT
(0:17:39) Timelines And RL Understanding Might Impact The Perception Existential Risk From AI
(0:19:01) Agentic Models And Longtermism Hinder AI Safety Awareness
(0:20:17) The Focus On Immediate AI Harms Might Be A Rejection Of Speculative Claims
(0:21:50) Is AI Safety A Pascal Mugging
(0:23:15) Believing In The Deployment Of Large Foundational Models Should Be Enough To Start Worrying
(0:25:38) AI Capabilities Becomign More Evident to the Public Might Not Be Enough
(0:27:27) Addressing Generalization and Reward Specification in AI
(0:27:59) Evals as an Additional Layer of Security in AI Safety
(0:28:41) A Portfolio Approach to AI Alignment and Safety
(0:29:03) Imagine Alignment Is Solved In 2040, What Made It Happen?
(0:33:04) AGI Timelines Are Uncertain And Anchored By Vibes
(0:35:24) What Matters Is Agency, Strategical Awareness And Planning
(0:37:15) Alignment Is A Public Good, Coordination Is Difficult
(0:06:48) Dignity As AN Useful Heuristic In The Face Of Doom
(0:42:28) What Will Society Look Like If We Actually Get Superintelligent Gods
(0:45:41) Uncertainty About Societal Dynamics Affecting Long-Term Future With AGI
(0:47:42) Biggest Frustration With The AI Safety Community
(0:48:34) AI Safety Includes Addressing Negative Consequences of AI
(0:50:41) Frustration: Lack of Bridge Building Between AI Safety and Fairness Communities
(0:53:07) Building Bridges by Attending Conferences and Understanding Different Perspectives
(0:56:02) AI Systems with Weird Instrumental Goals Pose Risks to Society
(0:58:43) Advanced AI Systems Controlling Resources Could Magnify Suffering
(1:00:24) Cooperation Is Crucial to Achieve Pareto Optimal Outcomes and Avoid Global Catastrophes
(1:01:54) Alan's Origin Story
(1:02:47) Alan's AI Safety Research Is Driven By Desire To Reduce Suffering And Improve Lives
(1:04:52) Diverse Interests And Concern For Global Problems Led To AI Safety Research
(1:08:46) The Realization Of The Potential Dangers Of AGI Motivated AI Safety Work
(1:10:39) What is Alan Chan Working On At The Moment

Breandan Considine is a PhD student at the School of Computer Science at McGill University, under the supervision of Jin Guo and Xujie Si). There, he is building tools to help developers locate and reason about software artifacts, by learning to read and write code.

Disclaimer: when talking to people in this podcast I try to sometimes invite guests who share different inside views about existential risk from AI so that everyone in the AI community can talk to each other more and coordinate more effectively. Breandan is overall much more optimistic about the potential risks from AI than a lot of people working in AI Alignement research, but I think he is quite articulate in his position, even though I disagree with many of his assumptions. I believe his point of view is important to understand what software engineers and Symbolic reasoning researchers think of deep learning progress.

Transcript: https://theinsideview.ai/breandan

Youtube: ⁠https://youtu.be/Bo6jO7MIsIU⁠

Host: https://twitter.com/MichaelTrazzi

Breandan: https://twitter.com/breandan

OUTLINE

(00:00) Introduction
(01:16) Do We Need Symbolic Reasoning to Get To AGI?
(05:41) Merging Symbolic Reasoning & Deep Learning for Powerful AI Systems
(10:57) Blending Symbolic Reasoning & Machine Learning Elegantly
(15:15) Enhancing Abstractions & Safety in Machine Learning
(21:28) AlphaTensor's Applicability May Be Overstated
(24:31) AI Safety, Alignment & Encoding Human Values in Code
(29:56) Code Research: Moral, Information & Software Aspects
(34:17) Automating Programming & Self-Improving AI
(36:25) Debunking AI "Monsters" & World Domination Complexities
(43:22) Neural Networks: Limits, Scaling Laws & Computation Challenges
(59:54) Real-world Software Development vs. Competitive Programming
(1:02:59) Measuring Programmer Productivity & Evaluating AI-generated Code
(1:06:09) Unintended Consequences, Reward Misspecification & AI-Human Symbiosis
(1:16:59) AI's Superior Intelligence: Impact, Self-Improvement & Turing Test Predictions
(1:23:52) AI Scaling, Optimization Trade-offs & Economic Viability
(1:29:02) Metrics, Misspecifications & AI's Rich Task Diversity
(1:30:48) Federated Learning & AI Agent Speed Comparisons
(1:32:56) AI Timelines, Regulation & Self-Regulating Systems

Christoph Schuhmann is the co-founder and organizational lead at LAION, the non-profit who released LAION-5B, a dataset of 5,85 billion CLIP-filtered image-text pairs, 14x bigger than LAION-400M, previously the biggest openly accessible image-text dataset in the world.

youtube: https://youtu.be/-Mzfru1r_5s

transcript: https://theinsideview.ai/christoph

OUTLINE

(01:13) How LAION Collected Billions Of Image-Text Pairs

(05:08) On Misuse: "Most People Use Technology To Do Good Things"

(09:32) Regulating Generative Models Won't Lead Anywhere

(14:36) Instead of Slowing Down, Deploy Carefully, Always Double Check

(18:23) The Solution To Societal Changes Is To Be Open And Flexible To Change

(22:16) We Should Be Honest And Face The Tsunami

(24:14) What Attitude Should We Have After Education Is Done

(30:05) Existential Risk From AI

Siméon Campos is the founder of EffiSciences and SaferAI, mostly focusing on alignment field building and AI Governance. More recently, he started the newsletter Navigating AI Risk on AI Governance, with a first post on slowing down AI.

Transcript: https://theinsideview.ai/simeon

Simeon: https://twitter.com/Simeon_Cps

Collin Burns is a second-year ML PhD at Berkeley, working with Jacob Steinhardt on making language models honest, interpretable, and aligned. In 2015 he broke the Rubik’s Cube world record, and he's now back with "Discovering latent knowledge in language models without supervision", a paper on how you can recover diverse knowledge represented in large language models without supervision.  

Transcript: https://theinsideview.ai/collin 

Paper: https://arxiv.org/abs/2212.03827

Lesswrong post: https://bit.ly/3kbyZML

Host: https://twitter.com/MichaelTrazzi

Collin: https://twitter.com/collinburns4

OUTLINE

(00:22) Intro

(01:33) Breaking The Rubik's Cube World Record

(03:03) A Permutation That Happens Maybe 2% Of The Time

(05:01) How Collin Became Convinced Of AI Alignment

(07:55)  Was Minerva Just Low Hanging Fruits On MATH From Scaling?

(12:47) IMO Gold Medal By 2026? How to update from AI Progress

(17:03) Plausibly Automating AI Research In The Next Five Years

(24:23) Making LLMs Say The Truth

(28:11) Lying Is Already Incentivized As We Have Seend With Diplomacy

(32:29) Mind Reading On 'Brain Scans' Through Logical Consistency

(35:18) Misalignment, Or Why One Does Not Simply Prompt A Model Into Being Truthful

(38:43) Classifying Hidden States, Maybe Using Truth Features Reepresented Linearly

(44:48) Building A Dataset For Using Logical Consistency

(50:16) Building A Confident And Consistent Classifier That Outputs Probabilities

(53:25) Discovering Representations Of The Truth From Just Being Confident And Consistent

(57:18) Making Models Truthful As A Sufficient Condition For Alignment

(59:02) Classifcation From Hidden States Outperforms Zero-Shot Prompting Accuracy

(01:02:27) Recovering Latent Knowledge From Hidden States Is Robust To Incorrect Answers In Few-Shot Prompts

(01:09:04) Would A Superhuman GPT-N Predict Future News Articles

(01:13:09) Asking Models To Optimize Money Without Breaking The Law

(01:20:31) Training Competitive Models From Human Feedback That We Can Evaluate

(01:27:26) Alignment Problems On Current Models Are Already Hard

(01:29:19) We Should Have More People Working On New Agendas From First Principles

(01:37:16) Towards Grounded Theoretical Work And Empirical Work Targeting Future Systems

(01:41:52) There Is No True Unsupervised: Autoregressive Models Depend On What A Human Would Say

(01:46:04) Simulating Aligned Systems And Recovering The Persona Of A Language Model

(01:51:38) The Truth Is Somewhere Inside The Model, Differentiating Between Truth And Persona Bit by Bit Through Constraints

(02:01:08) A Misaligned Model Would Have Activations Correlated With Lying

(02:05:16) Exploiting Similar Structure To Logical Consistency With Unaligned Models

(02:07:07) Aiming For Honesty, Not Truthfulness

(02:11:15) Limitations Of Collin's Paper

(02:14:12) The Paper Does Not Show The Complete Final Robust Method For This Problem

(02:17:26) Humans Will Be 50/50 On Superhuman Questions

(02:23:40) Asking Yourself "Why Am I Optimistic" and How Collin Approaches Research

(02:29:16) Message To The ML and Cubing audience