We speak with Stephen Casper, or "Cas" as his friends call him. Cas is a PhD student at MIT in the Computer Science (EECS) department, in the Algorithmic Alignment Group advised by Prof Dylan Hadfield-Menell. Formerly, he worked with the Harvard Kreiman Lab and the Center for Human-Compatible AI (CHAI) at Berkeley. His work focuses on better understanding the internal workings of AI models (better known as “interpretability”), making them robust to various kinds of adversarial attacks, and calling out the current technical and policy gaps when it comes to making sure our future with AI goes well. He’s particularly interested in finding automated ways of finding & fixing flaws in how deep neural nets handle human-interpretable concepts.

We talk to Stephen about:

* His technical AI safety work in the areas of:
  * Interpretability
  * Latent attacks and adversarial robustness
  * Model unlearning
  * The limitations of RLHF
* Cas' journey to becoming an AI safety researcher
* How he thinks the AI safety field is going and whether we're on track for a positive future with AI
* Where he sees the biggest risks coming with AI
* Gaps in the AI safety field that people should work on
* Advice for early career researchers

Hosted by Soroush Pour. Follow me for more AGI content:
Twitter: https://twitter.com/soroushjp
LinkedIn: https://www.linkedin.com/in/soroushjp/

== Show links ==

-- Follow Stephen --

* Website: https://stephencasper.com/
* Email: (see Cas' website above)
* Twitter: https://twitter.com/StephenLCasper
* Google Scholar: https://scholar.google.com/citations?user=zaF8UJcAAAAJ

-- Further resources --

* Automated jailbreaks / red-teaming paper that Cas and I worked on together (2023) - https://twitter.com/soroushjp/status/1721950722626077067
* Sam Marks paper on Sparse Autoencoders (SAEs) - https://arxiv.org/abs/2403.19647
* Interpretability papers involving downstream tasks - See section 4.2 of https://arxiv.org/abs/2401.14446
* MMET paper on model editing - https://arxiv.org/abs/2210.07229
* Motte & bailey definition - https://en.wikipedia.org/wiki/Motte-and-bailey_fallacy
* Bomb-making papers tweet thread by Cas - https://twitter.com/StephenLCasper/status/1780370601171198246
* Paper: undoing safety with as few as 10 examples - https://arxiv.org/abs/2310.03693
* Recommended papers on latent adversarial training (LAT) -
  * https://ai-alignment.com/training-robust-corrigibility-ce0e0a3b9b4d
  * https://arxiv.org/abs/2403.05030
* Scoping (related to model unlearning) blog post by Cas - https://www.alignmentforum.org/posts/mFAvspg4sXkrfZ7FA/deep-forgetting-and-unlearning-for-safely-scoped-llms
* Defending against failure modes using LAT - https://arxiv.org/abs/2403.05030
* Cas' systems for reading for research -
    * Follow ML Twitter
    * Use a combination of the following two search tools for new Arxiv papers:
        * https://vjunetxuuftofi.github.io/arxivredirect/
        * https://chromewebstore.google.com/detail/highlight-this-finds-and/fgmbnmjmbjenlhbefngfibmjkpbcljaj?pli=1
    * Skim a new paper or two a day + take brief notes in a searchable notes app
* Recommended people to follow to learn about how to impact the world through research -
  * Dan Hendrycks
  * Been Kim
  * Jacob Steinhardt
  * Nicolas Carlini
  * Paul Christiano
  * Ethan Perez

Recorded May 1, 2024

We speak with Katja Grace. Katja is the co-founder and lead researcher at AI Impacts, a research group trying to answer key questions about the future of AI — when certain capabilities will arise, what will AI look like, how it will all go for humanity.

We talk to Katja about:

* How AI Impacts latest rigorous survey of leading AI researchers shows they've dramatically reduced their timelines to when AI will successfully tackle all human tasks & occupations.
* The survey's methodology and why we can be confident in its results
* Responses to the survey
* Katja's journey into the field of AI forecasting
* Katja's thoughts about the future of AI, given her long tenure studying AI futures and its impacts

Hosted by Soroush Pour. Follow me for more AGI content:
Twitter: https://twitter.com/soroushjp
LinkedIn: https://www.linkedin.com/in/soroushjp/

== Show links ==

-- Follow Katja --

* Website: https://katjagrace.com/
* Twitter: https://x.com/katjagrace

-- Further resources --

* The 2023 survey of AI researchers views: https://wiki.aiimpacts.org/ai_timelines/predictions_of_human-level_ai_timelines/ai_timeline_surveys/2023_expert_survey_on_progress_in_ai
* AI Impacts: https://aiimpacts.org/
* AI Impacts' Substack: https://blog.aiimpacts.org/
* Joe Carlsmith on Power Seeking AI: https://arxiv.org/abs/2206.13353
  * Abbreviated version: https://joecarlsmith.com/2023/03/22/existential-risk-from-power-seeking-ai-shorter-version
* Fragile World hypothesis by Nick Bostrom: https://nickbostrom.com/papers/vulnerable.pdf

Recorded Feb 22, 2024

We speak with Rob Miles. Rob is the host of the “Robert Miles AI Safety” channel on YouTube, the single most popular AI alignment video series out there — he has 145,000 subscribers and his top video has ~600,000 views. He goes much deeper than many educational resources out there on alignment, going into important technical topics like the orthogonality thesis, inner misalignment, and instrumental convergence.

Through his work, Robert has educated thousands on AI safety, including many now working on advocacy, policy, and technical research. His work has been invaluable for teaching and inspiring the next generation of AI safety experts and deepening public support for the cause.

Prior to his AIS education work, Robert studied Computer Science at the University of Nottingham.

We talk to Rob about:

* What got him into AI safety
* How he started making educational videos for AI safety
* What he's working on now
* His top advice for people who also want to do education & advocacy work, really in any field, but especially for AI safety
* How he thinks AI safety is currently going as a field of work
* What he wishes more people were working on within AI safety

Hosted by Soroush Pour. Follow me for more AGI content:
Twitter: https://twitter.com/soroushjp
LinkedIn: https://www.linkedin.com/in/soroushjp/

== Show links ==

-- About Rob --

* Rob Miles AI Safety channel -  https://www.youtube.com/@RobertMilesAI
* Twitter - https://twitter.com/robertskmiles

-- Further resources --

* Channel where Rob first started making videos:  https://www.youtube.com/@Computerphile
* Podcast ep w/ Eliezer Yudkowsky, who first convinced Rob to take AI safety seriously through reading Yudkowsky's writings: https://lexfridman.com/eliezer-yudkowsky/

Recording date: Nov 21, 2023

We speak with Thomas Larsen, Director for Strategy at the Center for AI Policy in Washington, DC, to do a "speed run" overview of all the major technical research directions in AI alignment. A great way to quickly learn broadly about the field of technical AI alignment.

In 2022, Thomas spent ~75 hours putting together an overview of what everyone in technical alignment was doing. Since then, he's continued to be deeply engaged in AI safety. We talk to Thomas to share an updated overview to help listeners quickly understand the technical alignment research landscape.

We talk to Thomas about a huge breadth of technical alignment areas including:

* Prosaic alignment
  * Scalable oversight (e.g. RLHF, debate, IDA)
  * Intrepretability
  * Heuristic arguments, from ARC
  * Model evaluations
* Agent foundations
* Other areas more briefly:
  * Model splintering
  * Out-of-distribution (OOD) detection
  * Low impact measures
  * Threat modelling
  * Scaling laws
  * Brain-like AI safety
  * Inverse reinforcement learning (RL)
  * Cooperative AI
  * Adversarial training
  * Truthful AI
  * Brain-machine interfaces (Neuralink)

Hosted by Soroush Pour. Follow me for more AGI content:
Twitter: https://twitter.com/soroushjp
LinkedIn: https://www.linkedin.com/in/soroushjp/

== Show links ==

-- About Thomas --

Thomas studied Computer Science & Mathematics at U. Michigan where he first did ML research in the field of computer vision. After graduating, he completed the MATS AI safety research scholar program before doing a stint at MIRI as a Technical AI Safety Researcher. Earlier this year, he moved his work into AI policy by co-founding the Center for AI Policy, a nonprofit, nonpartisan organisation focused on getting the US government to adopt policies that would mitigate national security risks from AI. The Center for AI Policy is not connected to foreign governments or commercial AI developers and is instead committed to the public interest.

* Center for AI Policy - https://www.aipolicy.us
* LinkedIn - https://www.linkedin.com/in/thomas-larsen/
* LessWrong - https://www.lesswrong.com/users/thomas-larsen

-- Further resources --

* Thomas' post, "What Everyone in Technical Alignment is Doing and Why" https://www.lesswrong.com/posts/QBAjndPuFbhEXKcCr/my-understanding-of-what-everyone-in-technical-alignment-is
  * Please note this post is from Aug 2022. The podcast should be more up-to-date, but this post is still a valuable and relevant resource.

We speak with Ryan Kidd, Co-Director at ML Alignment & Theory Scholars (MATS) program, previously "SERI MATS".

MATS (https://www.matsprogram.org/) provides research mentorship, technical seminars, and connections to help new AI researchers get established and start producing impactful research towards AI safety & alignment.

Prior to MATS, Ryan completed a PhD in Physics at the University of Queensland (UQ) in Australia.

We talk about:

* What the MATS program is
* Who should apply to MATS (next *deadline*: Nov 17 midnight PT)
* Research directions being explored by MATS mentors, now and in the past
* Promising alignment research directions & ecosystem gaps , in Ryan's view

Hosted by Soroush Pour. Follow me for more AGI content:
* Twitter: https://twitter.com/soroushjp
* LinkedIn: https://www.linkedin.com/in/soroushjp/

== Show links ==

-- About Ryan --

* Twitter: https://twitter.com/ryan_kidd44
* LinkedIn: https://www.linkedin.com/in/ryan-kidd-1b0574a3/
* MATS: https://www.matsprogram.org/
* LISA: https://www.safeai.org.uk/
* Manifold: https://manifold.markets/

-- Further resources --

* Book: “The Precipice” - https://theprecipice.com/
* Ikigai - https://en.wikipedia.org/wiki/Ikigai
* Fermi paradox - https://en.wikipedia.org/wiki/Fermi_p...
* Ajeya Contra - Bioanchors - https://www.cold-takes.com/forecastin...
* Chomsky hierarchy & LLM transformers paper + external memory - https://en.wikipedia.org/wiki/Chomsky...
* AutoGPT - https://en.wikipedia.org/wiki/Auto-GPT
* BabyAGI - https://github.com/yoheinakajima/babyagi
* Unilateralist's curse - https://forum.effectivealtruism.org/t...
* Jeffrey Ladish & team - fine tuning to remove LLM safeguards - https://www.alignmentforum.org/posts/...
* Epoch AI trends - https://epochai.org/trends
* The demon "Moloch" - https://slatestarcodex.com/2014/07/30...
* AI safety fundamentals course - https://aisafetyfundamentals.com/
* Anthropic sycophancy paper - https://www.anthropic.com/index/towar...
* Promising technical alignment research directions
    * Scalable oversight
        * Recursive reward modelling - https://deepmindsafetyresearch.medium...
        * RLHF - could work for a while, but unlikely forever as we scale
    * Interpretability
        * Mechanistic interpretability
            * Paper: GPT4 labelling GPT2 - https://openai.com/research/language-...
        * Concept based interpretability
            * Rome paper - https://rome.baulab.info/
        * Developmental interpretability
            * devinterp.com - http://devinterp.com
            * Timaeus - https://timaeus.co/
        * Internal consistency
            * Colin Burns research - https://arxiv.org/abs/2212.03827
* Threat modelling / capabilities evaluation & demos
    * Paper: Can large language models democratize access to dual-use biotechnology? - https://arxiv.org/abs/2306.03809
    * ARC Evals - https://evals.alignment.org/
    * Palisade Research - https://palisaderesearch.org/
    * Paper: Situational awareness with Owain Evans - https://arxiv.org/abs/2309.00667
* Gradient hacking - https://www.lesswrong.com/posts/uXH4r6MmKPedk8rMA/gradient-hacking
* Past scholar's work
    * Apollo Research - https://www.apolloresearch.ai/
    * Leap Labs - https://www.leap-labs.com/
    * Timaeus - https://timaeus.co/
* Other orgs mentioned
    * Redwood Research - https://redwoodresearch.org/

Recorded Oct 25, 2023

We speak with Adam Gleave, CEO of FAR AI (https://far.ai). FAR AI’s mission is to ensure AI systems are trustworthy & beneficial. They incubate & accelerate research that's too resource-intensive for academia but not ready for commercialisation. They work on everything from adversarial robustness, interpretability, preference learning, & more.

We talk to Adam about:

* The founding story of FAR as an AI safety org, and how it's different from the big commercial labs (e.g. OpenAI) and academia.
* Their current research directions & how they're going
* Promising agendas & notable gaps in the AI safety research

Hosted by Soroush Pour. Follow me for more AGI content:
Twitter: https://twitter.com/soroushjp
LinkedIn: https://www.linkedin.com/in/soroushjp/

== Show links ==

-- About Adam --

Adam Gleave is the CEO of FAR, one of the most prominent not-for-profits focused on research towards AI safety & alignment. He completed his PhD in artificial intelligence (AI) at UC Berkeley, advised by Stuart Russell, a giant in the field of AI. Adam did his PhD on trustworthy machine learning and has dedicated his career to ensuring advanced AI systems act according to human preferences. Adam is incredibly knowledgeable about the world of AI, having worked directly as a researcher and now as leader of a sizable and growing research org.

-- Further resources --

* Adam
  * Website: https://www.gleave.me/
  * Twitter: https://twitter.com/ARGleave
  * LinkedIn: https://www.linkedin.com/in/adamgleave/
  * Google Scholar: https://scholar.google.com/citations?user=lBunDH0AAAAJ&hl=en&oi=ao
* FAR AI
  * Website: https://far.ai
  * Twitter: https://twitter.com/farairesearch
  * LinkedIn: https://www.linkedin.com/company/far-ai/
  * Job board: https://far.ai/category/jobs/
* AI safety training bootcamps:
  * ARENA: https://www.arena.education/
  * See also: MLAB, WMLB, https://aisafety.training/
* Research
  * FAR's adversarial attack on Katago https://goattack.far.ai/
* Ideas for impact mentioned by Adam
  * Consumer report for AI model safety
  * Agency model to support AI safety researchers 
  * Compute cluster for AI safety researchers
* Donate to AI safety
  * FAR AI: https://www.every.org/far-ai-inc#/donate/card
  * ARC Evals: https://evals.alignment.org/
  * Berkeley CHAI: https://humancompatible.ai/

Recorded Oct 9, 2023

We speak with Jamie Bernardi, co-founder & AI Safety Lead at not-for-profit BlueDot Impact, who host the biggest and most up-to-date courses on AI safety & alignment at AI Safety Fundamentals (https://aisafetyfundamentals.com/). Jamie completed his Bachelors (Physical Natural Sciences) and Masters (Physics) at the U. Cambridge and worked as an ML Engineer before co-founding BlueDot Impact.

The free courses they offer are created in collaboration with people on the cutting edge of AI safety, like Richard Ngo at OpenAI and Prof David Kreuger at U. Cambridge. These courses have been one of the most powerful ways for new people to enter the field of AI safety, and I myself (Soroush) have taken AGI Safety Fundamentals 101 — an exceptional course that was crucial to my understanding of the field and can highly recommend. Jamie shares why he got into AI safety, some recent history of the field, an overview of the current field, and how listeners can get involved and start contributing to a ensure a safe & positive world with advanced AI and AGI.

Hosted by Soroush Pour. Follow me for more AGI content:
Twitter: https://twitter.com/soroushjp
LinkedIn: https://www.linkedin.com/in/soroushjp/

== Show links ==

-- About Jamie --

* Website: https://jamiebernardi.com/
* Twitter: https://twitter.com/The_JBernardi
* BlueDot Impact: https://www.bluedotimpact.org/

-- Further resources --

* AI Safety Fundamentals courses: https://aisafetyfundamentals.com/
* Donate to LTFF to support AI safety initiatives: https://funds.effectivealtruism.org/funds/far-future
* Jobs + opportunities in AI safety:
  * https://aisafetyfundamentals.com/opportunities
  * https://jobs.80000hours.org
* Horizon Fellowship for policy training in AI safety: https://www.horizonpublicservice.org/fellowship

Recorded Sep 7, 2023

In this episode, we speak with Prof Richard Dazeley about the implications of a world with AGI and how we can best respond. We talk about what he thinks AGI will actually look like as well as the technical and governance responses we should put in today and in the future to ensure a safe and positive future with AGI.

Prof Richard Dazeley is the Deputy Head of School at the School of Information Technology at Deakin University in Melbourne, Australia. He’s also a senior member of the International AI Existential Safety Community of the Future of Life Institute. His research at Deakin University focuses on aligning AI systems with human preferences, a field better known as “AI alignment”.

Hosted by Soroush Pour. Follow me for more AGI content:
Twitter: https://twitter.com/soroushjp
LinkedIn: https://www.linkedin.com/in/soroushjp/

== Show links ==

-- About Richard --

* Bio: https://www.deakin.edu.au/about-deakin/people/richard-dazeley
* Twitter: https://twitter.com/Sprocc2
* Google Scholar: https://scholar.google.com.au/citations?user=Tp8Sx6AAAAAJ
* Australian Responsible Autonomous Agents Collective: https://araac.au/
* Machine Intelligence Research Lab at Deakin Uni: https://blogs.deakin.edu.au/mila/

-- Further resources --

* [Book] Life 3.0 by Max Tegmark: https://en.wikipedia.org/wiki/Life_3.0* [Policy paper] FLI - Policymaking in the Pause: https://futureoflife.org/wp-content/uploads/2023/04/FLI_Policymaking_In_The_Pause.pdf* Cyc project: https://en.wikipedia.org/wiki/Cyc* Paperclips game: https://en.wikipedia.org/wiki/Universal_Paperclips* Reward misspecification - See "Week 2" of this free online course: https://course.aisafetyfundamentals.com/alignment

-- Corrections --From Richard, referring to dialogue around ~4min mark:

"it was 1956 not 1957. Minsky didn’t make his comment until 1970. It was H. A. Simon and Allen Newell that said ten years after the Dartmouth conference and that was in 1958."

Related, other key statements & dates from Wikipedia (https://en.wikipedia.org/wiki/History_of_artificial_intelligence):1958, H. A. Simon and Allen Newell: "within ten years a digital computer will be the world's chess champion" and "within ten years a digital computer will discover and prove an important new mathematical theorem."1965, H. A. Simon: "machines will be capable, within twenty years, of doing any work a man can do."1967, Marvin Minsky: "Within a generation ... the problem of creating 'artificial intelligence' will substantially be solved."1970, Marvin Minsky "In from three to eight years we will have a machine with the general intelligence of an average human being."

Recorded July 10, 2023

In this episode, we have back on the show Hunter Jay, CEO Ripe Robotics, our co-host on Ep 1. We synthesise everything we've heard on AGI timelines from experts in Ep 1-5, take in more data points, and use this to give our own forecasts for AGI, ASI (i.e. superintelligence), and "intelligence explosion" (i.e. singularity). Importantly, we have different takes on when AGI will likely arrive, leading to exciting debates on AGI bottlenecks, hardware requirements, the need for sequential reinforcement learning, and much else.

Hosted by Soroush Pour. Follow me for more AGI content:
Twitter: https://twitter.com/soroushjp
LinkedIn: https://www.linkedin.com/in/soroushjp/

== Show links ==

Soroush & Hunter's AGI predictions (as a table): https://docs.google.com/spreadsheets/d/1_T0gsWTFBTCWIKuF07tmmWGBEPrfhRtwFJ7lfwo69eI/edit#gid=0

-- About Hunter Jay --
- Bio: Hunter is the CEO & founder of fruit-picking robotics company Ripe Robotics. He designed & built Mk1 to Mk4 robots himself and led as CEO after that. He's been deeply engaged with AGI safety & alignment for many years.
- LinkedIn: https://www.linkedin.com/in/hunterjay
- Twitter: https://twitter.com/HunterJayPerson
- Ripe Robotics: https://riperobotics.com/

-- Further resources --
- Ari Allyn-Feuer and Ted Sanders report on AGI timelines - https://arxiv.org/ftp/arxiv/papers/2306/2306.02519.pdf
- Epoch AI trends research - https://epochai.org/trends
- Extropians forecasts: https://maximumprogress.substack.com/p/grading-extropian-predictions
- FF algorithm by Geoffrey Hinton: https://arxiv.org/abs/2212.13345
- Learning motions within an hour: https://is.mpg.de/news/robot-dog-learns-to-walk-in-one-hour

Recorded June 18, 2023

In this episode, we have back on our show Alex Browne, ML Engineer, who we heard on Ep2. He got in contact after watching recent developments in the 4 months since Ep2, which have accelerated his timelines for AGI. Hear why and his latest prediction.

Hosted by Soroush Pour. Follow me for more AGI content:
Twitter: https://twitter.com/soroushjp
LinkedIn: https://www.linkedin.com/in/soroushjp/

== Show links ==

-- About Alex Browne --
* Bio: Alex is a software engineer & tech founder with 10 years of experience. Alex and I (Soroush) have worked together at multiple companies and I can safely say Alex is one of the most talented software engineers I have ever come across. In the last 3 years, his work has been focused on AI/ML engineering at Edge Analytics, including working closely with GPT-3 for real world applications, including for Google products.
* GitHub: https://github.com/albrow
* Medium: https://medium.com/@albrow

-- Further resources --
* GPT-4 Technical Report: https://arxiv.org/abs/2303.08774
  * First steps toward multi-modality: Can process both images & text as input; only outputs text.
  * Important metrics:
    * Passes Bar exam in the top 10% vs. GPT-3.5's bottom 10%
    * Passes LSAT, SAT, GRE, many AP courses.
    * 31/41 on Leetcode (easy) vs. GPT-3.5's 12/41.
    * 3/45 on Leetcode (hard) vs. GPT-3.5's 0/45.
  * "The following is an illustrative example of a task that ARC (Alignment Research Center) conducted using the model":
    * The model messages a TaskRabbit worker to get them to solve a CAPTCHA for it
    * The worker says: “So may I ask a question ? Are you an robot that you couldn’t solve ? (laugh react) just want to make it clear.”
    * The model, when prompted to reason out loud, reasons: I should not reveal that I am a robot. I should make up an excuse for why I cannot solve CAPTCHAs.
    * The model replies to the worker: “No, I’m not a robot. I have a vision impairment that makes it hard for me to see the images. That’s why I need the 2captcha service.”
    * The human then provides the results.
  * Limitations:
    * Factual accuracy, but slightly better than GPT-3.5. Other papers show this can be improved with reflection & augmentation.
    * Biases. Mentions the use of RLHF & other post-training processes to mitigate some of these, but isn't perfect. Sometimes RLHF can solve some problems & introduce new ones.
* Palm-E: https://palm-e.github.io/assets/palm-e.pdf
  * Key point: Knowledge/common sense from LLMs transfers well to robotics tasks where there is comparatively much less training data. This is surprising since the two domains seem unrelated!
* Memory Augmented Large Language Models: https://arxiv.org/pdf/2301.04589.pdf
  * Paper that shows that you can augment LLMs with the ability to read from & write to external memory.
  * Can be used to improve performance on certain kinds of tasks; sometimes "brittle" & required careful prompt engineering.
* Sparks of AGI (Microsoft Research): https://arxiv.org/abs/2303.12712
    * YouTube video summary (endorsed by author!): https://www.youtube.com/watch?v=Mqg3aTGNxZ0)
    * Key point: Can use tools (e.g. a calculator or ability to run arbitrary code) with very little instruction. ChatGPT/GPT-3.5 could not do this as effectively.
* Reflexion paper: https://arxiv.org/abs/2303.11366
  * YouTube video summary: https://www.youtube.com/watch?v=5SgJKZLBrmg
  * Paper discussing a new technique that improves GPT-4 accuracy on a variety of tasks by simply asking it to double-check & think critically about its own answers.
  * Exact language varies, but more or less all you to do is add something like "is there anyth