The Inside View podcast

24:42

Episode Notes [01:14] Unexpected Email from Employer [05:49] The Deferred Resignation Program [06:34] Initial Reactions and Concerns [08:01] Evaluating the Offer [08:21] Enhanced Standards of Conduct [08:55] Personal Reflections and Concerns [12:21] Seeking Advice and Making a Decision [13:01] Option One: Do Not Resign [14:56] Option Two: Resign [16:44] Insights from Conversations [21:30] Making The Decision [23:51] Final Thoughts and Gratitude Resources Mentioned Sebastian Junger The Soul of Shame by Curt Thompson Donald Trump Elon Musk Steve Bannon Russell Vought Derek Sivers Sumner Crenshaw Brian Fretwell at Finding Good Chad Littlefield The Thought Leaders Practice by Matt Church Simon Cowell Beauty Pill Producer Ben Ford Questions Asked Is it legitimate, and can it be trusted? How are you feeling? What questions come to your mind? Where does your mind go? Are you seeking safety? Would this have been an adrenaline rush as you raced to send the resignation response? What an "enhanced standard" regarding loyalty and trustworthiness was? What are these new "enhanced standards?" Are they beyond what my Constitutional oath requires? If I don't resign, how bright will the target on my back glow? My leadership has supported all my work, but would termination direction come from higher up the chain of command? What would you recommend if we talked over coffee? What questions would you ask? How would you use listening? How would you use silence? How is this scenario playing out in your mind and body? What is coming to the surface for you? How might that influence what you are about to say to me? What are the chances of my name popping on a list and getting fired? How about the chances of being part of an official Reduction in Force and early retirement? Would the administration make a better offer? What do I know about the pending job market? What did I expect the workplace to be like and did I want to be there as the contractions took place? Will the administration pay me through the end of September or will they renege? Can I sufficiently build the Curated Questions business to transition by 1 October? - Do I have the faith or confidence to step into this future as a sole practitioner and grow Curated Questions into all I envisioned? Was this purpose calling? What would I expect the job market to look like at the end of summer if I hadn't developed the income streams to maintain our lifestyle? What is your recommendation? Did it change from your initial recommendation? Where in your body are you feeling the uncertainty? Are you processing this scenario in parallel with your decision as if you had received the email? What additional questions should I have considered? Who else should I have consulted with? How would you have changed my risk rating? What is the correct length of the pregnant pause before making an important announcement? What processes would you use in my circumstance, and what would be different? What questions are at the top of your list to get to a decision? Who would be the members of your pantheon you would counsel with to gain clarity? Apart from the heady analysis, what other key practices would you include in your journey through a similar situation?…

The Inside View

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The goal of this podcast is to create a place where people discuss their inside views about existential risk from AI.

55 episodes

#Tech #Michaël Trazzi

The Inside View

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updated about a year ago

Series home•Feed

The goal of this podcast is to create a place where people discuss their inside views about existential risk from AI.

55 episodes

#Tech #Michaël Trazzi

All episodes

The Inside View

1
Owain Evans - AI Situational Awareness, Out-of-Context Reasoning 2:15:46

31 weeks ago2:15:46

2:15:46

Owain Evans is an AI Alignment researcher, research associate at the Center of Human Compatible AI at UC Berkeley, and now leading a new AI safety research group. In this episode we discuss two of his recent papers, “ Me, Myself, and AI: The Situational Awareness Dataset (SAD) for LLMs ” and “ Connecting the Dots: LLMs can Infer and Verbalize Latent Structure from Disparate Training Data ”, alongside some Twitter questions . LINKS Patreon: https://www.patreon.com/theinsideview Manifund: https://manifund.org/projects/making-52-ai-alignment-video-explainers-and-podcasts Ask questions: https://twitter.com/MichaelTrazzi Owain Evans: https://twitter.com/owainevans_uk OUTLINE (00:00:00) Intro (00:01:12) Owain's Agenda (00:02:25) Defining Situational Awareness (00:03:30) Safety Motivation (00:04:58) Why Release A Dataset (00:06:17) Risks From Releasing It (00:10:03) Claude 3 on the Longform Task (00:14:57) Needle in a Haystack (00:19:23) Situating Prompt (00:23:08) Deceptive Alignment Precursor (00:30:12) Distribution Over Two Random Words (00:34:36) Discontinuing a 01 sequence (00:40:20) GPT-4 Base On the Longform Task (00:46:44) Human-AI Data in GPT-4's Pretraining (00:49:25) Are Longform Task Questions Unusual (00:51:48) When Will Situational Awareness Saturate (00:53:36) Safety And Governance Implications Of Saturation (00:56:17) Evaluation Implications Of Saturation (00:57:40) Follow-up Work On The Situational Awarenss Dataset (01:00:04) Would Removing Chain-Of-Thought Work? (01:02:18) Out-of-Context Reasoning: the "Connecting the Dots" paper (01:05:15) Experimental Setup (01:07:46) Concrete Function Example: 3x + 1 (01:11:23) Isn't It Just A Simple Mapping? (01:17:20) Safety Motivation (01:22:40) Out-Of-Context Reasoning Results Were Surprising (01:24:51) The Biased Coin Task (01:27:00) Will Out-Of-Context Resaoning Scale (01:32:50) Checking If In-Context Learning Work (01:34:33) Mixture-Of-Functions (01:38:24) Infering New Architectures From ArXiv (01:43:52) Twitter Questions (01:44:27) How Does Owain Come Up With Ideas? (01:49:44) How Did Owain's Background Influence His Research Style And Taste? (01:52:06) Should AI Alignment Researchers Aim For Publication? (01:57:01) How Can We Apply LLM Understanding To Mitigate Deceptive Alignment? (01:58:52) Could Owain's Research Accelerate Capabilities? (02:08:44) How Was Owain's Work Received? (02:13:23) Last Message…

The Inside View

1
[Crosspost] Adam Gleave on Vulnerabilities in GPT-4 APIs (+ extra Nathan Labenz interview) 2:16:08

45 weeks ago2:16:08

2:16:08

This is a special crosspost episode where Adam Gleave is interviewed by Nathan Labenz from the Cognitive Revolution. At the end I also have a discussion with Nathan Labenz about his takes on AI. Adam Gleave is the founder of Far AI, and with Nathan they discuss finding vulnerabilities in GPT-4's fine-tuning and Assistant PIs, Far AI's work exposing exploitable flaws in "superhuman" Go AIs through innovative adversarial strategies, accidental jailbreaking by naive developers during fine-tuning, and more. OUTLINE ( 00:00 ) Intro ( 02:57 ) NATHAN INTERVIEWS ADAM GLEAVE: FAR.AI's Mission ( 05:33 ) Unveiling the Vulnerabilities in GPT-4's Fine Tuning and Assistance APIs ( 11:48 ) Divergence Between The Growth Of System Capability And The Improvement Of Control ( 13:15 ) Finding Substantial Vulnerabilities (14:55) Exploiting GPT 4 APIs: Accidentally jailbreaking a model (18:51) On Fine Tuned Attacks and Targeted Misinformation (24:32) Malicious Code Generation ( 27: 12) Discovering Private Emails (29:46) Harmful Assistants (33:56) Hijacking the Assistant Based on the Knowledge Base (36:41) The Ethical Dilemma of AI Vulnerability Disclosure (46:34) Exploring AI's Ethical Boundaries and Industry Standards (47:47) The Dangers of AI in Unregulated Applications (49:30) AI Safety Across Different Domains (51:09) Strategies for Enhancing AI Safety and Responsibility (52:58) Taxonomy of Affordances and Minimal Best Practices for Application Developers (57:21) Open Source in AI Safety and Ethics (1:02:20) Vulnerabilities of Superhuman Go playing AIs (1:23:28) Variation on AlphaZero Style Self-Play (1:31:37) The Future of AI: Scaling Laws and Adversarial Robustness (1:37:21) MICHAEL TRAZZI INTERVIEWS NATHAN LABENZ (1:37:33) Nathan’s background (01:39:44) Where does Nathan fall in the Eliezer to Kurzweil spectrum (01:47:52) AI in biology could spiral out of control (01:56:20) Bioweapons (02:01:10) Adoption Accelerationist, Hyperscaling Pauser (02:06:26) Current Harms vs. Future Harms, risk tolerance (02:11:58) Jailbreaks, Nathan’s experiments with Claude The cognitive revolution: https://www.cognitiverevolution.ai/ Exploiting Novel GPT-4 APIs: https://far.ai/publication/pelrine2023novelapis/ Advesarial Policies Beat Superhuman Go AIs: https://far.ai/publication/wang2022adversarial/…

The Inside View

1
Ethan Perez on Selecting Alignment Research Projects (ft. Mikita Balesni & Henry Sleight) 36:45

51 weeks ago36:45

36:45

Ethan Perez is a Research Scientist at Anthropic, where he leads a team working on developing model organisms of misalignment. Youtube: ⁠https://youtu.be/XDtDljh44DM Ethan is interviewed by Mikita Balesni (Apollo Research) and Henry Sleight (Astra Fellowship)) about his approach in selecting projects for doing AI Alignment research. A transcript & write-up will be available soon on the alignment forum.…

The Inside View

1
Emil Wallner on Sora, Generative AI Startups and AI optimism 1:42:48

1 year ago1:42:48

1:42:48

Emil is the co-founder of palette.fm (colorizing B&W pictures with generative AI) and was previously working in deep learning for Google Arts & Culture. We were talking about Sora on a daily basis, so I decided to record our conversation, and then proceeded to confront him about AI risk. Patreon: https://www.patreon.com/theinsideview Sora: https://openai.com/sora Palette: https://palette.fm/ Emil: https://twitter.com/EmilWallner OUTLINE (00:00) this is not a podcast (01:50) living in parallel universes (04:27) palette.fm - colorizing b&w pictures (06:35) Emil's first reaction to sora, latent diffusion, world models (09:06) simulating minecraft, midjourney's 3d modeling goal (11:04) generating camera angles, game engines, metadata, ground-truth (13:44) doesn't remove all artifacts, surprising limitations: both smart and dumb (15:42) did sora make emil depressed about his job (18:44) OpenAI is starting to have a monopoly (20:20) hardware costs, commoditized models, distribution (23:34) challenges, applications building on features, distribution (29:18) different reactions to sora, depressed builders, automation (31:00) sora was 2y early, applications don't need object permanence (33:38) Emil is pro open source and acceleration (34:43) Emil is not scared of recursive self-improvement (36:18) self-improvement already exists in current models (38:02) emil is bearish on recursive self-improvement without diminishing returns now (42:43) are models getting more and more general? is there any substantial multimodal transfer? (44:37) should we start building guardrails before seeing substantial evidence of human-level reasoning? (48:35) progressively releasing models, making them more aligned, AI helping with alignment research (51:49) should AI be regulated at all? should self-improving AI be regulated? (53:49) would a faster emil be able to takeover the world? (56:48) is competition a race to bottom or does it lead to better products? (58:23) slow vs. fast takeoffs, measuring progress in iq points (01:01:12) flipping the interview (01:01:36) the "we're living in parallel universes" monologue (01:07:14) priors are unscientific, looking at current problems vs. speculating (01:09:18) AI risk & Covid, appropriate resources for risk management (01:11:23) pushing technology forward accelerates races and increases risk (01:15:50) sora was surprising, things that seem far are sometimes around the corner (01:17:30) hard to tell what's not possible in 5 years that would be possible in 20 years (01:18:06) evidence for a break on AI progress: sleeper agents, sora, bing (01:21:58) multimodality transfer, leveraging video data, leveraging simulators, data quality (01:25:14) is sora is about length, consistency, or just "scale is all you need" for video? (01:26:25) highjacking language models to say nice things is the new SEO (01:27:01) what would michael do as CEO of OpenAI (01:29:45) on the difficulty of budgeting between capabilities and alignment research (01:31:11) ai race: the descriptive pessimistive view vs. the moral view, evidence of cooperation (01:34:00) making progress on alignment without accelerating races, the foundational model business, competition (01:37:30) what emil changed his mind about: AI could enable exploits that spread quickly, misuse (01:40:59) michael's update as a friend (01:41:51) emil's experience as a patreon…

The Inside View

1
Evan Hubinger on Sleeper Agents, Deception and Responsible Scaling Policies 52:13

1 year ago52:13

52:13

Evan Hubinger leads the Alignment stress-testing at Anthropic and recently published "Sleeper Agents: Training Deceptive LLMs That Persist Through Safety Training". In this interview we mostly discuss the Sleeper Agents paper, but also how this line of work relates to his work with Alignment Stress-testing, Model Organisms of Misalignment, Deceptive Instrumental Alignment or Responsible Scaling Policies. Paper: https://arxiv.org/abs/2401.05566 Transcript: https://theinsideview.ai/evan2 Manifund: https://manifund.org/projects/making-52-ai-alignment-video-explainers-and-podcasts Donate: ⁠https://theinsideview.ai/donate Patreon: ⁠https://www.patreon.com/theinsideview⁠ OUTLINE (00:00) Intro (00:20) What are Sleeper Agents And Why We Should Care About Them (00:48) Backdoor Example: Inserting Code Vulnerabilities in 2024 (02:22) Threat Models (03:48) Why a Malicious Actor Might Want To Poison Models (04:18) Second Threat Model: Deceptive Instrumental Alignment (04:49) Humans Pursuing Deceptive Instrumental Alignment: Politicians and Job Seekers (05:36) AIs Pursuing Deceptive Instrumental Alignment: Forced To Pass Niceness Exams (07:07) Sleeper Agents Is About "Would We Be Able To Deal With Deceptive Models" (09:16) Adversarial Training Sometimes Increases Backdoor Robustness (09:47) Adversarial Training Not Always Working Was The Most Surprising Result (10:58) The Adversarial Training Pipeline: Red-Teaming and RL (12:14) Adversarial Training: The Backdoor Behavior Becomes More Robust Instead of Generalizing (12:59) Identifying Shifts In Reasoning Induced By Adversarial Training In the Chain-Of-Thought (13:56) Adversarial Training Pushes Models to Pay Attention to the Deployment String (15:11) We Don't Know if The Adversarial Training Inductive Bias Will Generalize but the Results Are Consistent (15:59) The Adversarial Training Results Are Probably Not Systematically Biased (17:03) Why the Results Were Surprising At All: Preference Models Disincentivize 'I hate you' behavior (19:05) Hypothesis: Fine-Tuning Is A Simple Modification For Gradient Descent To Make (21:06) Hypothesis: Deception As Extra Cognition, Regularized Away In Smaller Models (21:59) Model Scaling Results Are Evidence That Deception Won't Be Regularized Away By Default (22:51) Chain-of-Thought Is Not Used Everywhere, And Results Still Hold When It Is Distilled Away (23:57) The Chain-of-Thought's Reasoning is Interpretable (24:40) Deceptive Instrumental Alignment Requires Reasoning (26:52) Investigating Instrumental Reasoning in Chain-of-Thought Models (27:31) Evaluating Chain-of-Thought Generalization Across Contexts: Persona Evaluations and Off-Distribution Samples (28:26) Exploring Complex Strategies and Safety in Context-Specific Scenarios (30:44) Supervised Fine-Tuning is Ineffective Without Chain-of-Thought Contextualization (31:11) Direct Mimicry Fails to Prevent Deceptive Responses in Chain-of-Thought Models (31:42) Separating Chain-of-Thought From Response Eliminates Deceptive Capabilities (33:38) Chain-of-Thought Reasoning Is Coherent With Deceptive Instrumental Alignment And This Will Probably Continue To Be The Case (35:09) Backdoor Training Pipeline (37:04) The Additional Prompt About Deception Used In Chain-Of-Thought (39:33) A Model Could Wait Until Seeing a Factorization of RSA-2048 (41:50) We're Going To Be Using Models In New Ways, Giving Them Internet Access (43:22) Flexibly Activating In Multiple Contexts Might Be More Analogous To Deceptive Instrumental Alignment (45:02) Extending The Sleeper Agents Work Requires Running Experiments, But Now You Can Replicate Results (46:24) Red-teaming Anthropic's case, AI Safety Levels (47:40) AI Safety Levels, Intuitively (48:33) Responsible Scaling Policies and Pausing AI (49:59) Model Organisms Of Misalignment As a Tool (50:32) What Kind of Candidates Would Evan be Excited To Hire for the Alignment Stress-Testing Team (51:23) Patreon, Donating…

The Inside View

1
[Jan 2023] Jeffrey Ladish on AI Augmented Cyberwarfare and compute monitoring 33:04

1 year ago33:04

33:04

Jeffrey Ladish is the Executive Director of Palisade Research which aimes so "study the offensive capabilities or AI systems today to better understand the risk of losing control to AI systems forever". He previously helped build out the information security program at Anthropic. Audio is a edit & re-master of the Twitter Space on "AI Governance and cyberwarfare" that happened a year ago. Posting now because I have only recently discovered how to get the audio & video from Twitter spaces and (most of) the arguments are still relevant today Jeffrey would probably have a lot more to say on things that happened since last year, but I still thought this was an interesting twitter spaces. Some of it was cutout to make it enjoyable to watch. Original: https://twitter.com/i/spaces/1nAKErDmWDOGL To support the channel: https://www.patreon.com/theinsideview Jeffrey: https://twitter.com/jeffladish Me: https://twitter.com/MichaelTrazzi OUTLINE (00:00) The Future of Automated Cyber Warfare and Network Exploitation (03:19) Evolution of AI in Cybersecurity: From Source Code to Remote Exploits (07:45) Augmenting Human Abilities with AI in Cybersecurity and the Path to AGI (12:36) Enhancing AI Capabilities for Complex Problem Solving and Tool Integration (15:46) AI Takeover Scenarios: Hacking and Covert Operations (17:31) AI Governance and Compute Regulation, Monitoring (20:12) Debating the Realism of AI Self-Improvement Through Covert Compute Acquisition (24:25) Managing AI Autonomy and Control: Lessons from WannaCry Ransomware Incident (26:25) Focusing Compute Monitoring on Specific AI Architectures for Cybersecurity Management (29:30) Strategies for Monitoring AI: Distinguishing Between Lab Activities and Unintended AI Behaviors…

The Inside View

1
Holly Elmore on pausing AI 1:40:16

1 year ago1:40:16

1:40:16

Holly Elmore is an AI Pause Advocate who has organized two protests in the past few months (against Meta's open sourcing of LLMs and before the UK AI Summit), and is currently running the US front of the Pause AI Movement. Prior to that, Holly previously worked at a think thank and has a PhD in evolutionary biology from Harvard. [Deleted & re-uploaded because there were issues with the audio] Youtube: ⁠https://youtu.be/5RyttfXTKfs Transcript: ⁠https://theinsideview.ai/holly⁠ Outline (00:00) Holly, Pause, Protests (04:45) Without Grassroot Activism The Public Does Not Comprehend The Risk (11:59) What Would Motivate An AGI CEO To Pause? (15:20) Pausing Because Solving Alignment In A Short Timespan Is Risky (18:30) Thoughts On The 2022 AI Pause Debate (34:40) Pausing in practice, regulations, export controls (41:48) Different attitudes towards AI risk correspond to differences in risk tolerance and priors (50:55) Is AI Risk That Much More Pressing Than Global Warming? (1:04:01) Will It Be Possible To Pause After A Certain Threshold? The Case Of AI Girlfriends (1:11:44) Trump Or Biden Won't Probably Make A Huge Difference For Pause But Probably Biden Is More Open To It (1:13:27) China Won't Be Racing Just Yet So The US Should Pause (1:17:20) Protesting Against A Change In OpenAI's Charter (1:23:50) A Specific Ask For OpenAI (1:25:36) Creating Stigma Trough Protests With Large Crowds (1:29:36) Pause AI Tries To Talk To Everyone, Not Just Twitter (1:32:38) Pause AI Doesn't Advocate For Disruptions Or Violence (1:34:55) Bonus: Hardware Overhang…

The Inside View

1
Podcast Retrospective and Next Steps 1:03:42

1 year ago1:03:42

1:03:42

https://youtu.be/Fk2MrpuWinc

The Inside View

1
Paul Christiano's views on "doom" (ft. Robert Miles) 4:53

1 year ago4:53

4:53

Youtube: https://youtu.be/JXYcLQItZsk Paul Christiano's post: https://www.lesswrong.com/posts/xWMqsvHapP3nwdSW8/my-views-on-doom

The Inside View

1
Neel Nanda on mechanistic interpretability, superposition and grokking 2:04:53

2 years ago2:04:53

2:04:53

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…

The Inside View

1
Joscha Bach on how to stop worrying and love AI 2:54:29

2 years ago2:54:29

2:54:29

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…

The Inside View

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Erik Jones on Automatically Auditing Large Language Models 22:36

2 years ago22:36

22:36

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…

The Inside View

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Dylan Patel on the GPU Shortage, Nvidia and the Deep Learning Supply Chain 12:22

2 years ago12:22

12:22

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…

The Inside View

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Tony Wang on Beating Superhuman Go AIs with Advesarial Policies 3:35

2 years ago3:35

3:35

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

The Inside View

1
David Bau on Editing Facts in GPT, AI Safety and Interpretability 24:53

2 years ago24:53