The Inside View

Neel Nanda is a researcher at Google DeepMind working on mechanistic interpretability. He is also known for his YouTube channel where he explains what is going on inside of neural networks to a large audience. In this conversation, we discuss what is mechanistic interpretability, how Neel got into it, his research methodology, his advice for people who want to get started, but also papers around superposition, toy models of universality and grokking, among other things. Youtube: https://youtu.be/cVBGjhN4-1g Transcript: https://theinsideview.ai/neel OUTLINE (00:00) Intro (00:57) Why Neel Started Doing Walkthroughs Of Papers On Youtube (07:59) Induction Heads, Or Why Nanda Comes After Neel (12:19) Detecting Induction Heads In Basically Every Model (14:35) How Neel Got Into Mechanistic Interpretability (16:22) Neel's Journey Into Alignment (22:09) Enjoying Mechanistic Interpretability And Being Good At It Are The Main Multipliers (24:49) How Is AI Alignment Work At DeepMind? (25:46) Scalable Oversight (28:30) Most Ambitious Degree Of Interpretability With Current Transformer Architectures (31:05) To Understand Neel's Methodology, Watch The Research Walkthroughs (32:23) Three Modes Of Research: Confirming, Red Teaming And Gaining Surface Area (34:58) You Can Be Both Hypothesis Driven And Capable Of Being Surprised (36:51) You Need To Be Able To Generate Multiple Hypothesis Before Getting Started (37:55) All the theory is bullshit without empirical evidence and it's overall dignified to make the mechanistic interpretability bet (40:11) Mechanistic interpretability is alien neuroscience for truth seeking biologists in a world of math (42:12) Actually, Othello-GPT Has A Linear Emergent World Representation (45:08) You Need To Use Simple Probes That Don't Do Any Computation To Prove The Model Actually Knows Something (47:29) The Mechanistic Interpretability Researcher Mindset (49:49) The Algorithms Learned By Models Might Or Might Not Be Universal (51:49) On The Importance Of Being Truth Seeking And Skeptical (54:18) The Linear Representation Hypothesis: Linear Representations Are The Right Abstractions (00:57:26) Superposition Is How Models Compress Information (01:00:15) The Polysemanticity Problem: Neurons Are Not Meaningful (01:05:42) Superposition and Interference are at the Frontier of the Field of Mechanistic Interpretability (01:07:33) Finding Neurons in a Haystack: Superposition Through De-Tokenization And Compound Word Detectors (01:09:03) Not Being Able to Be Both Blood Pressure and Social Security Number at the Same Time Is Prime Real Estate for Superposition (01:15:02) The Two Differences Of Superposition: Computational And Representational (01:18:07) Toy Models Of Superposition (01:25:39) How Mentoring Nine People at Once Through SERI MATS Helped Neel's Research (01:31:25) The Backstory Behind Toy Models of Universality (01:35:19) From Modular Addition To Permutation Groups (01:38:52) The Model Needs To Learn Modular Addition On A Finite Number Of Token Inputs (01:41:54) Why Is The Paper Called Toy Model Of Universality (01:46:16) Progress Measures For Grokking Via Mechanistic Interpretability, Circuit Formation (01:52:45) Getting Started In Mechanistic Interpretability And Which WalkthroughS To Start With (01:56:15) Why Does Mechanistic Interpretability Matter From an Alignment Perspective (01:58:41) How Detection Deception With Mechanistic Interpretability Compares to Collin Burns' Work (02:01:20) Final Words From Neel

Joscha Bach (who defines himself as an AI researcher/cognitive scientist) has recently been debating existential risk from AI with Connor Leahy (previous guest of the podcast), and since their conversation was quite short I wanted to continue the debate in more depth. The resulting conversation ended up being quite long (over 3h of recording), with a lot of tangents, but I think this gives a somewhat better overview of Joscha’s views on AI risk than other similar interviews. We also discussed a lot of other topics, that you can find in the outline below. A raw version of this interview was published on Patreon about three weeks ago. To support the channel and have access to early previews, you can subscribe here: https://www.patreon.com/theinsideview Youtube: ⁠https://youtu.be/YeXHQts3xYM⁠ Transcript: https://theinsideview.ai/joscha Host: https://twitter.com/MichaelTrazzi Joscha: https://twitter.com/Plinz OUTLINE (00:00) Intro (00:57) Why Barbie Is Better Than Oppenheimer (08:55) The relationship between nuclear weapons and AI x-risk (12:51) Global warming and the limits to growth (20:24) Joscha’s reaction to the AI Political compass memes (23:53) On Uploads, Identity and Death (33:06) The Endgame: Playing The Longest Possible Game Given A Superposition Of Futures (37:31) On the evidence of delaying technology leading to better outcomes (40:49) Humanity is in locust mode (44:11) Scenarios in which Joscha would delay AI (48:04) On the dangers of AI regulation (55:34) From longtermist doomer who thinks AGI is good to 6x6 political compass (01:00:08) Joscha believes in god in the same sense as he believes in personal selves (01:05:45) The transition from cyanobacterium to photosynthesis as an allegory for technological revolutions (01:17:46) What Joscha would do as Aragorn in Middle-Earth (01:25:20) The endgame of brain computer interfaces is to liberate our minds and embody thinking molecules (01:28:50) Transcending politics and aligning humanity (01:35:53) On the feasibility of starting an AGI lab in 2023 (01:43:19) Why green teaming is necessary for ethics (01:59:27) Joscha's Response to Connor Leahy on "if you don't do that, you die Joscha. You die" (02:07:54) Aligning with the agent playing the longest game (02:15:39) Joscha’s response to Connor on morality (02:19:06) Caring about mindchildren and actual children equally (02:20:54) On finding the function that generates human values (02:28:54) Twitter And Reddit Questions: Joscha’s AGI timelines and p(doom) (02:35:16) Why European AI regulations are bad for AI research (02:38:13) What regulation would Joscha Bach pass as president of the US (02:40:16) Is Open Source still beneficial today? (02:42:26) How to make sure that AI loves humanity (02:47:42) The movie Joscha would want to live in (02:50:06) Closing message for the audience

Erik is a Phd at Berkeley working with Jacob Steinhardt, interested in making generative machine learning systems more robust, reliable, and aligned, with a focus on large language models.In this interview we talk about his paper "Automatically Auditing Large Language Models via Discrete Optimization" that he presented at ICML. Youtube: https://youtu.be/bhE5Zs3Y1n8 Paper: https://arxiv.org/abs/2303.04381 Erik: https://twitter.com/ErikJones313 Host: https://twitter.com/MichaelTrazzi Patreon: https://www.patreon.com/theinsideview Outline 00:00 Highlights 00:31 Eric's background and research in Berkeley 01:19 Motivation for doing safety research on language models 02:56 Is it too easy to fool today's language models? 03:31 The goal of adversarial attacks on language models 04:57 Automatically Auditing Large Language Models via Discrete Optimization 06:01 Optimizing over a finite set of tokens rather than continuous embeddings 06:44 Goal is revealing behaviors, not necessarily breaking the AI 07:51 On the feasibility of solving adversarial attacks 09:18 Suppressing dangerous knowledge vs just bypassing safety filters 10:35 Can you really ask a language model to cook meth? 11:48 Optimizing French to English translation example 13:07 Forcing toxic celebrity outputs just to test rare behaviors 13:19 Testing the method on GPT-2 and GPT-J 14:03 Adversarial prompts transferred to GPT-3 as well 14:39 How this auditing research fits into the broader AI safety field 15:49 Need for automated tools to audit failures beyond what humans can find 17:47 Auditing to avoid unsafe deployments, not for existential risk reduction 18:41 Adaptive auditing that updates based on the model's outputs 19:54 Prospects for using these methods to detect model deception 22:26 Prefer safety via alignment over just auditing constraints, Closing thoughts Patreon supporters: Tassilo Neubauer MonikerEpsilon Alexey Malafeev Jack Seroy JJ Hepburn Max Chiswick William Freire Edward Huff Gunnar Höglund Ryan Coppolo Cameron Holmes Emil Wallner Jesse Hoogland Jacques Thibodeau Vincent Weisser

Dylan Patel is Chief Analyst at SemiAnalysis a boutique semiconductor research and consulting firm specializing in the semiconductor supply chain from chemical inputs to fabs to design IP and strategy. The SemiAnalysis substack has ~50,000 subscribers and is the second biggest tech substack in the world. In this interview we discuss the current GPU shortage, why hardware is a multi-month process, the deep learning hardware supply chain and Nvidia's strategy. Youtube: https://youtu.be/VItz2oEq5pA Transcript: https://theinsideview.ai/dylan

Tony is a PhD student at MIT, and author of "Advesarial Policies Beat Superhuman Go AIs", accepted as Oral at the International Conference on Machine Learning (ICML). Paper: https://arxiv.org/abs/2211.00241 Youtube: https://youtu.be/Tip1Ztjd-so

David Bau is an Assistant Professor studying the structure and interpretation of deep networks, and the co-author on "Locating and Editing Factual Associations in GPT" which introduced Rank-One Model Editing (ROME), a method that allows users to alter the weights of a GPT model, for instance by forcing it to output that the Eiffel Tower is in Rome. David is a leading researcher in interpretability, with an interest in how this could help AI Safety. The main thesis of David's lab is that understanding the rich internal structure of deep networks is a grand and fundamental research question with many practical implications, and they aim to lay the groundwork for human-AI collaborative software engineering, where humans and machine-learned models both teach and learn from each other. David's lab: https://baulab.info/ Patron: https://www.patreon.com/theinsideview Twitter: https://twitter.com/MichaelTrazzi Website: https://theinsideview.ai TOC [00:00] Intro [01:16] Interpretability [02:27] AI Safety, Out of Domain behavior [04:23] It's difficult to predict which AI application might become dangerous or impactful [06:00] ROME / Locating and Editing Factual Associations in GPT [13:04] Background story for the ROME paper [15:41] Twitter Q: where does key value abstraction break down in LLMs? [19:03] Twitter Q: what are the tradeoffs in studying the largest models? [20:22] Twitter Q: are there competitive and cleaner architectures than the transformer? [21:15] Twitter Q: is decoder-only a contributor to the messiness? or is time-dependence beneficial? [22:45] Twitter Q: how could ROME deal with superposition? [23:30] Twitter Q: where is the Eiffel tower actually located?

I've talked to Alexander Pan, 1st year at Berkeley working with Jacob Steinhardt about his paper "Measuring Trade-Offs Between Rewards and Ethical Behavior in the MACHIAVELLI Benchmark" accepted as oral at ICML. Youtube: https://youtu.be/MjkSETpoFlY Paper: https://arxiv.org/abs/2304.03279

Vincent is currently spending his time supporting AI alignment efforts, as well as investing across AI, semi, energy, crypto, bio and deeptech. His mission is to improve science, augment human capabilities, have a positive impact, help reduce existential risks and extend healthy human lifespan. Youtube: https://youtu.be/weRoJ8KN2f0 Outline (00:00) Why Is Vincent Excited About the ICML Conference (01:30) Vincent's Background In AI Safety (02:23) Funding AI Alignment Through Crypto, Bankless (03:35) Taxes When Donating Crypto (04:09) Alignment Efforts Vincent Is Excited About (04:39) Is AI Alignment Currently Funding Cunstrained (06:23) Bottlnecks In Evaluating Grants, Diversity Of Funding Sources (07:22) Impact Markets, Retroactive Funding (08:57) On The Difficulty Of Evaluating Uncertain AI Alignment Projects (10:05) Funding Academic Labs To Transition To Alignment Work (11:54) People Should Act On Their Beliefs And Make Stuff Happen (13:15) Vincent's Model: Don't Always Assume Someone Else Will Fund This (13:49) How To Be Agentic: Start Donating, Spread The Message, AI Safety Fundamentals (15:00) You Wouldn't Start Invest With 1M Dollars, Same With Donating (16:13) Is Vincent Acting As If Timelines Were Short And The Risk Was High (17:10) Is Vincent Optimistic When He Wakes Up In The Morning

Aran Komatsuzaki is a ML PhD student at GaTech and lead researcher at EleutherAI where he was one of the authors on GPT-J. In June 2022 we recorded an episode on scaling following up on the first Ethan Caballero episode (where we mentioned Aran as an influence on how Ethan started thinking about scaling). Note: For some reason I procrastinated on editing the podcast, then had a lot of in-person podcasts so I left this one as something to edit later, until the date was so distant from June 2022 that I thought publishing did not make sense anymore. In July 2023 I'm trying that "one video a day" challenge (well I missed some days but I'm trying to get back on track) so I thought it made sense to release it anyway, and after a second watch it's somehow interesting to see how excited Aran was about InstructGPT, which turned to be quite useful for things like ChatGPT. Outline (00:00) intro (00:53) the legend of the two AKs, Aran's arXiv reading routine (04:14) why Aran expects Alignment to be the same as some other ML problems (05:44) what Aran means when he says "AGI" (10:24) what Aran means by "human-level at doing ML research" (11:31) software improvement happening before hardware improvement (13:00) is scale all we need? (15:25) how "Scaling Laws for Neural Language Models" changed the process of doing experiments (16:22) how Aran scale-pilled Ethan (18:46) why Aran was already scale-pilled before GPT-2 (20:12) Aran's 2019 scaling paper: "One epoch is all you need" (25:43) Aran's June 2022 interest: T0 and InstructGPT (31:33) Encoder-Decoder performs better than encoder if multi-task-finetuned (33:30) Why the Scaling Law might be different for T0-like models (37:15) The Story Behind GPT-J (41:40) Hyperparameters and architecture changes in GPT-J (43:56) GPT-J's throughput (47:17) 5 weeks of training using 256 of TPU cores (50:34) did publishing GPT-J accelerate timelines? (55:39) how Aran thinks about Alignment, defining Alignment (58:19) in practice: improving benchmarks, but deception is still a problem (1:00:49) main difficulties in evaluating language models (1:05:07) how Aran sees the future: AIs aligning AIs, merging with AIs, Aran's takeoff scenario (1:10:09) what Aran thinks we should do given how he sees the next decade (1:12:34) regulating access to AGI (1:14:50) what might happen: preventing some AI authoritarian regime (1:15:42) conclusion, where to find Aran

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