Uploaded August 2026 | Updated September 2026, 2 weeks ago
Tim Scarfe speaks with Ilia Shumailov and Alexander Panfilov about their paper, Stealing Reasoning Traces from Proprietary LLM APIs.
The core bug sounds deceptively simple: providers return encrypted reasoning state so conversations can be resumed or forked. But those blobs can be replayed across users and sibling models. A smaller model can ask the provider to decrypt the trace, then repeat the hidden reasoning in plain text. The discussion covers leaked private data, a broadly reusable jailbreak, poisoned agent traces, chain-of-thought monitoring, responsible disclosure, and possible defenses.
Ilia Shumailov is an AI and security researcher, formerly at Google DeepMind, who completed his Cambridge PhD under Ross Anderson. Alexander Panfilov is a PhD researcher at the ELLIS Institute Tübingen and the Max Planck Institute for Intelligent Systems, working on AI safety, adversarial machine learning, and LLM red-teaming. They close by separating the demonstrated jailbreaking threat from ordinary benign distillation, and by arguing for controlled experiments over sweeping claims.
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TIMESTAMPS:
00:00:00 Intro montage
00:01:33 Portable encrypted thought and decoded reasoning
00:24:55 How the attack works and what it means
00:39:04 Doom, defense, and scientific restraint
---
REFERENCES:
paper:
[00:00:00] Stealing Reasoning Traces from Proprietary LLM APIs
arxiv.org/abs/2608.09867
[00:09:22] Chain of Thought Monitorability: A New and Fragile Opportunity for AI Safety
arxiv.org/abs/2507.11473
[00:11:30] Reasoning Models Don’t Always Say What They Think
anthropic.com/research/reasoning-models-dont-say-think
[00:37:22] PostTrainBench: Can LLM Agents Automate LLM Post-Training?
arxiv.org/abs/2603.08640
[00:41:02] Large-scale online deanonymization with LLMs
arxiv.org/abs/2602.16800
other:
[00:09:28] OpenAI and Hugging Face partner to address security incident during model evaluation
openai.com/index/hugging-face-model-evaluation-security-incident
[00:10:22] Claude, GPT, and Gemini All Struggle to Evade Monitors
metr.org/notes/2025-08-22-claude-gpt-gemini-struggle-evade-monitors
tool:
[00:42:08] Isabelle proof assistant
isabelle.in.tum.de
---
RESCRIPT:
app.rescript.info/share/07fc38276e0823dc9b8986c32e202c7f
Tim Scarfe speaks with Ilia Shumailov and Alexander Panfilov about their paper, Stealing Reasoning Traces from Proprietary LLM APIs.
The core bug sounds deceptively simple: providers return encrypted reasoning state so conversations can be resumed or forked. But those blobs can be replayed across users and sibling models. A smaller model can ask the provider to decrypt the trace, then repeat the hidden reasoning in plain text. The discussion covers leaked private data, a broadly reusable jailbreak, poisoned agent traces, chain-of-thought monitoring, responsible disclosure, and possible defenses.
Ilia Shumailov is an AI and security researcher, formerly at Google DeepMind, who completed his Cambridge PhD under Ross Anderson. Alexander Panfilov is a PhD researcher at the ELLIS Institute Tübingen and the Max Planck Institute for Intelligent Systems, working on AI safety, adversarial machine learning, and LLM red-teaming. They close by separating the demonstrated jailbreaking threat from ordinary benign distillation, and by arguing for controlled experiments over sweeping claims.
---
TIMESTAMPS:
00:00:00 Intro montage
00:01:33 Portable encrypted thought and decoded reasoning
00:24:55 How the attack works and what it means
00:39:04 Doom, defense, and scientific restraint
---
REFERENCES:
paper:
[00:00:00] Stealing Reasoning Traces from Proprietary LLM APIs
arxiv.org/abs/2608.09867
[00:09:22] Chain of Thought Monitorability: A New and Fragile Opportunity for AI Safety
arxiv.org/abs/2507.11473
[00:11:30] Reasoning Models Don’t Always Say What They Think
anthropic.com/research/reasoning-models-dont-say-think
[00:37:22] PostTrainBench: Can LLM Agents Automate LLM Post-Training?
arxiv.org/abs/2603.08640
[00:41:02] Large-scale online deanonymization with LLMs
arxiv.org/abs/2602.16800
other:
[00:09:28] OpenAI and Hugging Face partner to address security incident during model evaluation
openai.com/index/hugging-face-model-evaluation-security-incident
[00:10:22] Claude, GPT, and Gemini All Struggle to Evade Monitors
metr.org/notes/2025-08-22-claude-gpt-gemini-struggle-evade-monitors
tool:
[00:42:08] Isabelle proof assistant
isabelle.in.tum.de
---
RESCRIPT:
app.rescript.info/share/07fc38276e0823dc9b8986c32e202c7f


![The Hidden Math Behind All Living Systems
Dr. Sanjeev Namjoshi, a machine learning engineer who recently submitted a book on Active Inference to MIT Press, discusses the theoretical foundations and practical applications of Active Inference, the Free Energy Principle (FEP), and Bayesian mechanics. He explains how these frameworks describe how biological and artificial systems maintain stability by minimizing uncertainty about their environment.
Namjoshi traces the evolution of these fields from early 2000s neuroscience research to current developments, highlighting how Active Inference provides a unified framework for perception and action through variational free energy minimization. He contrasts this with traditional machine learning approaches, emphasizing Active Inferences natural capacity for exploration and curiosity through epistemic value.
The discussion covers key technical concepts like Markov blankets,
generative models, and the distinction between continuous and discrete implementations. Namjoshi explains how Active Inference moved from continuous state-space models (2003-2013) to discrete formulations (2015-present) to better handle planning problems.
He sees Active Inference as being at a similar stage to deep learning in the early 2000s - poised for significant breakthroughs but requiring better tools and wider adoption. While acknowledging current computational challenges, he emphasizes Active Inferences potential advantages over reinforcement learning, particularly its principled approach to exploration and planning.
Namjoshi advocates for balanced oversight that enables innovation while maintaining appropriate safeguards. He expresses particular concern about the rapid pace of AI development potentially outpacing our understanding of risks and regulatory frameworks.
Dr. Sanjeev Namjoshi
https://snamjoshi.github.io/
TOC:
1. Theoretical Foundations: AI Agency and Sentience
[00:00:00] 1.1 Intro
[00:04:30] 1.2 Free Energy Principle and Active Inference Theory
[00:11:16] 1.3 Emergence and Self-Organization in Complex Systems
[00:19:11] 1.4 Agency and Representation in AI Systems
[00:29:59] 1.5 Bayesian Mechanics and Systems Modeling
2. Technical Framework: Active Inference and Free Energy
[00:38:37] 2.1 Generative Processes and Agent-Environment Modeling
[00:42:27] 2.2 Markov Blankets and System Boundaries
[00:44:30] 2.3 Bayesian Inference and Prior Distributions
[00:52:41] 2.4 Variational Free Energy Minimization Framework
[00:55:07] 2.5 VFE Optimization Techniques: Generalized Filtering vs DEM
3. Implementation and Optimization Methods
[00:58:25] 3.1 Information Theory and Free Energy Concepts
[01:05:25] 3.2 Surprise Minimization and Action in Active Inference
[01:15:58] 3.3 Evolution of Active Inference Models: Continuous to Discrete Approaches
[01:26:00] 3.4 Uncertainty Reduction and Control Systems in Active Inference
4. Safety and Regulatory Frameworks
[01:32:40] 4.1 Historical Evolution of Risk Management and Predictive Systems
[01:36:12] 4.2 Agency and Reality: Philosophical Perspectives on Models
[01:39:20] 4.3 Limitations of Symbolic AI and Current System Design
[01:46:40] 4.4 AI Safety Regulation and Corporate Governance
5. Socioeconomic Integration and Modeling
[01:52:55] 5.1 Economic Policy and Public Sentiment Modeling
[01:55:21] 5.2 Free Energy Principle: Libertarian vs Collectivist Perspectives
[01:58:53] 5.3 Regulation of Complex Socio-Technical Systems
[02:03:04] 5.4 Evolution and Current State of Active Inference Research
6. Future Directions and Applications
[02:14:26] 6.1 Active Inference Applications and Future Development
[02:22:58] 6.2 Cultural Learning and Active Inference
[02:29:19] 6.3 Hierarchical Relationship Between FEP, Active Inference, and Bayesian Mechanics
[02:33:22] 6.4 Historical Evolution of Free Energy Principle
[02:38:52] 6.5 Active Inference vs Traditional Machine Learning Approaches
Transcript and shownotes with refs and URLs:
https://www.dropbox.com/scl/fi/qj22a660cob1795ej0gbw/SanjeevShow.pdf?rlkey=w323r3e8zfsnve22caayzb17k&st=el1fdgfr&dl=0
SELECTED REFS:
[0:02:45] Fristons original Free Energy Principle paper (Nature Reviews Neuroscience, 2010) - foundational text establishing FEP
[0:35:55] Schrödingers What is Life? (1944) - pioneering work connecting physics and biology
[0:44:30] Bayes Theorem - fundamental mathematical framework underlying probabilistic inference
[0:58:25] Shannons Mathematical Theory of Communication (1948) - established information theory
[1:18:20] Parr, Pezzulo & Fristons Active Inference (MIT Press) - comprehensive synthesis of the field
[1:24:05] Kahnemans Thinking, Fast and Slow - seminal work on dual-process theory of cognition
[1:25:55] Simons concept of satisficing - fundamental contribution to bounded rationality theory
[2:23:05] Dawkins The Selfish Gene (1976) - influential evolutionary theory perspective
[2:34:15] MacKays work on information theory and machine learning - bridged information theory and modern ML The Hidden Math Behind All Living Systems](https://i.ytimg.com/vi/hf18w6CuY8o/mqdefault.jpg)


![The Universal Hierarchy of Life - Prof. Chris Kempes [SFI]
What is life? - asks Chris Kempes, a professor at the Santa Fe Institute.
Chris explains that scientists are moving beyond a purely Earth-based, biological view and are searching for a universal theory of life that could apply to anything, anywhere in the universe. He proposes that things we dont normally consider alive—like human culture, language, or even artificial intelligence; could be seen as life forms existing on different substrates.
To understand this, Chris presents a fascinating three-level framework:
- Materials: The physical stuff life is made of. He argues this could be incredibly diverse across the universe, and we shouldnt expect alien life to share our biochemistry.
- Constraints: The universal laws of physics (like gravity or diffusion) that all life must obey, regardless of what its made of. This is where different life forms start to look more similar.
- Principles: At the highest level are abstract principles like evolution and learning. Chris suggests these computational or optimization rules are what truly define a living system.
A key idea is convergence – using the example of the eye. Its such a complex organ that youd think it evolved only once. However, eyes evolved many separate times across different species. This is because the physics of light provides a clear target, and evolution found similar solutions to the problem of seeing, even with different starting materials.
**SPONSOR MESSAGES**
—
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Submit investment deck: https://cyber.fund/contact?utm_source=mlst
—
Prof. Chris Kempes:
https://www.santafe.edu/people/profile/chris-kempes
TRANSCRIPT:
https://app.rescript.info/public/share/Y2cI1i0nX iuZitvlguHvaVLQTwPX1Y_E1EHxV0i9I
TOC:
00:00:00 - Introduction to Chris Kempes and the Santa Fe Institute
00:02:28 - The Three Cultures of Science
00:05:08 - What Makes a Good Scientific Theory?
00:06:50 - The Universal Theory of Life
00:09:40 - The Role of Material in Life
00:12:50 - A Hierarchy for Understanding Life
00:13:55 - How Life Diversifies and Converges
00:17:53 - Adaptive Processes and Defining Life
00:19:28 - Functionalism, Memes, and Phylogenies
00:22:58 - Convergence at Multiple Levels
00:25:45 - The Possibility of Simulating Life
00:28:16 - Intelligence, Parasitism, and Spectrums of Life
00:32:39 - Phase Changes in Evolution
00:36:16 - The Separation of Matter and Logic
00:37:21 - Assembly Theory and Quantifying Complexity
REFS:
Developing a predictive science of the biosphere requires the integration of scientific cultures [Kempes et al]
https://www.pnas.org/doi/10.1073/pnas.2209196121
Seeing with an extra sense (“Dangerous prediction”) [Rob Phillips]
https://www.sciencedirect.com/science/article/pii/S0960982224009035
The Multiple Paths to Multiple Life [Christopher P. Kempes & David C. Krakauer]
https://link.springer.com/article/10.1007/s00239-021-10016-2
The Information Theory of Individuality [David Krakauer et al]
https://arxiv.org/abs/1412.2447
Minds, Brains and Programs [Searle]
https://home.csulb.edu/~cwallis/382/readings/482/searle.minds.brains.programs.bbs.1980.pdf
The error threshold
https://www.sciencedirect.com/science/article/abs/pii/S0168170204003843
Assembly theory and its relationship with computational complexity [Kempes et al]
https://arxiv.org/abs/2406.12176 The Universal Hierarchy of Life - Prof. Chris Kempes [SFI]](https://i.ytimg.com/vi/iwClZ-7OweY/mqdefault.jpg)
![Explosive AI Timeline Predictions [Gary Marcus, Daniel Kokotajlo, Dan Hendrycks]
What if the most powerful technology in human history is being built by people who openly admit they dont trust each other? In this explosive 2-hour debate, three AI experts pull back the curtain on the shocking psychology driving the race to Artificial General Intelligence—and why the people building it might be the biggest threat of all. Kokotajlo predicts AGI by 2028 based on compute scaling trends. Marcus argues we havent solved basic cognitive problems from his 2001 research. The stakes? If Kokotajlo is right and Marcus is wrong about safety progress, humanity may have already lost control.
Sponsor messages:
Google Gemini: Google Gemini features Veo3, a state-of-the-art AI video generation model in the Gemini app. Sign up at https://gemini.google.com
Tufa AI Labs are hiring for ML Engineers and a Chief Scientist in Zurich/SF. They are top of the ARCv2 leaderboard!
https://tufalabs.ai/
Guest Powerhouse
Gary Marcus - Cognitive scientist, author of Taming Silicon Valley, and AIs most prominent skeptic whos been warning about the same fundamental problems for 25 years (https://garymarcus.substack.com/)
Daniel Kokotajlo - Former OpenAI insider turned whistleblower who reveals the disturbing rationalizations of AI lab leaders in his viral AI 2027 scenario (https://ai-2027.com/)
Dan Hendrycks - Director of the Center for AI Safety who created the benchmarks used to measure AI progress and argues we have only years, not decades, to prevent catastrophe (https://danhendrycks.com/)
Transcript: http://app.rescript.info/public/share/tEcx4UkToi-2jwS1cN51CW70A4Eh6QulBRxDILoXOno
TOC:
Introduction: The AI Arms Race
00:00:04 - The Danger of Automated AI R&D
00:00:43 - The Rationalization: If we dont, someone else will
00:01:56 - Sponsor Reads (Tufa AI Labs & Google Gemini)
00:02:55 - Guest Introductions
The Philosophical Stakes
00:04:13 - What is the Positive Vision for AGI?
00:07:00 - The Abundance Scenario: Superintelligent Economy
00:09:06 - Differentiating AGI and Superintelligence (ASI)
00:11:41 - Sam Altman: A Decade in a Month
00:14:47 - Economic Inequality & The UBI Problem
Policy and Red Lines
00:17:13 - The Pause Letter: Stopping vs. Delaying AI
00:20:03 - Defining Three Concrete Red Lines for AI Development
00:25:24 - Racing Towards Red Lines & The Myth of Durable Advantage
00:31:15 - Transparency and Public Perception
00:35:16 - The Rationalization Cascade: Why AI Labs Race to Win
Forecasting AGI: Timelines and Methodologies
00:42:29 - The Case for Short Timelines (Median 2028)
00:47:00 - Scaling Limits: Compute, Data, and Money
00:49:36 - Forecasting Models: Bio-Anchors and Agentic Coding
00:53:15 - The 10^45 FLOP Thought Experiment
The Great Debate: Cognitive Gaps vs. Scaling
00:58:41 - Gary Marcuss Counterpoint: The Unsolved Problems of Cognition
01:00:46 - Current AI Cant Play Chess Reliably
01:08:23 - Can Tools and Neurosymbolic AI Fill the Gaps?
01:16:13 - The Multi-Dimensional Nature of Intelligence
01:24:26 - The Benchmark Debate: Data Contamination and Reliability
01:31:15 - The Superhuman Coder Milestone Debate
01:37:45 - The Driverless Car Analogy
The Alignment Problem
01:39:45 - Has Any Progress Been Made on Alignment?
01:42:43 - Fairly Reasonably Scares the Sh*t Out of Me
01:46:30 - Distinguishing Model vs. Process Alignment
Scenarios and Conclusions
01:49:26 - Garys Alternative Scenario: The Neurosymbolic Shift
01:53:35 - Will AI Become Jeff Dean?
01:58:41 - Takeoff Speeds and Exceeding Human Intelligence
02:03:19 - Final Disagreements and Closing Remarks
REFS:
Gary Marcus (2001) - The Algebraic Mind
https://mitpress.mit.edu/9780262632683/the-algebraic-mind/
00:59:00
Gary Marcus & Ernest Davis (2019) - Rebooting AI
https://www.amazon.co.uk/Rebooting-AI-Building-Artificial-Intelligence-ebook/dp/B07MYLGQLB
01:31:59
Gary Marcus (2024) - Taming Silicon Valley
https://www.amazon.co.uk/Taming-Silicon-Valley-Ensure-Works-ebook/dp/B0CQWWM94N
00:03:01
Ajeya Cotra (2020) - Forecasting TAI with Biological Anchors
https://www.alignmentforum.org/posts/KrJfoZzpSDpnrv9va/draft-report-on-ai-timelines
00:53:15
Daniel Kokotajlo (2021) - What 2026 looks like
https://www.lesswrong.com/posts/6Xgy6CAf2jqHhynHL/what-2026-looks-like
00:55:00
Dan Hendrycks et al. (2021) - Measuring Massive Multitask Language Understanding (MMLU)
https://arxiv.org/abs/2009.03300
00:03:48
Dan Hendrycks et al. (2021) - Measuring Mathematical Problem Solving With the MATH Dataset
https://arxiv.org/abs/2103.03874
00:03:48
Aitor Lewkowycz, Anders Andreassen et al. (2022) - Solving Quantitative Reasoning Problems with Language Models (Minerva)
https://arxiv.org/abs/2206.14858
01:21:45
Apple Research (2024) - GSM-Symbolic/The Illusion of Thinking
https://arxiv.org/abs/2410.05229
https://machinelearning.apple.com/research/illusion-of-thinking
00:59:15 Explosive AI Timeline Predictions [Gary Marcus, Daniel Kokotajlo, Dan Hendrycks]](https://i.ytimg.com/vi/j13ySJLvdOc/mqdefault.jpg)


![Chollets ARC Challenge + Current Winners
The ARC Challenge, created by Francois Chollet, tests how well AI systems can generalize from a few examples in a grid-based intelligence test. We interview the current winners of the ARC Challenge—Jack Cole, Mohammed Osman and their collaborator Michael Hodel. They discuss how they tackled ARC (Abstraction and Reasoning Corpus) using language models. We also discuss the new 50% public set approach announced today from Redwood Research (Ryan Greenblatt).
Jack and Mohammed explain their winning approach, which involves fine-tuning a language model on a large, specifically-generated dataset and then doing additional fine-tuning at test-time, a technique known in this context as active inference. They use various strategies to represent the data for the language model and believe that with further improvements, the accuracy could reach above 50%. Michael talks about his work on generating new ARC-like tasks to help train the models.
They also debate whether their methods stay true to the spirit of Chollets measure of intelligence. Despite some concerns, they agree that their solutions are promising and adaptable for other similar problems.
Note:
Jacks team is still the current official winner at 33% on the private set. Ryans entry is not on the private leaderboard or eligible.
Chollet invented ARC in 2019 (not 2017 as stated)
Ryans entry is not a new state of the art. We dont know exactly how well it does since it was only evaluated on 100 tasks from the evaluation set and does 50% on those, reportedly. Meanwhile Jacks team i.e. MindsAIs solution does 54% on the entire eval set and it is seemingly possible to do 60-70% with an ensemble
Jack Cole:
https://x.com/Jcole75Cole
https://lab42.global/community-interview-jack-cole/
Mohamed Osman:
Mohamed is looking to do a PhD in AI/ML, can you help him?
Email: mothman198@outlook.com
https://www.linkedin.com/in/mohamedosman1905/
Michael Hodel:
https://arxiv.org/pdf/2404.07353v1
https://www.linkedin.com/in/michael-hodel/
https://x.com/bayesilicon
https://github.com/michaelhodel
Getting 50% (SoTA) on ARC-AGI with GPT-4o - Ryan Greenblatt
https://redwoodresearch.substack.com/p/getting-50-sota-on-arc-agi-with-gpt
Neural networks for abstraction and reasoning: Towards broad generalization in machines [Mikel Bober-Irizar, Soumya Banerjee]
https://arxiv.org/pdf/2402.03507
Measure of intelligence:
https://arxiv.org/abs/1911.01547
I think the audio levelling might be a bit off on this for the intro especially, I fixed it on the audio podcast version - sorry if its annoying.
Pod version: https://podcasters.spotify.com/pod/show/machinelearningstreettalk/episodes/New-50-ARC-result-and-current-winners-interviewed-e2l1prl
TOC (autogenerated):
00:00:00 Introduction
00:03:00 Francois Chollets Intelligence Concept
00:08:00 Human Collaboration
00:15:00 ARC Tasks and Symbolic AI
00:27:00 Evaluation Techniques
00:35:23 (Main Interview) Competitors and Approaches
00:40:00 Meta Learning Challenges
00:48:00 System 1 vs System 2
01:00:00 Inductive Priors and Symbols
01:18:00 Methodologies Comparison
01:25:00 Training Data Size Impact
01:35:00 Generalization Issues
01:47:00 Techniques for AI Applications
01:56:00 Model Efficiency and Scalability
02:10:00 Task Specificity and Generalization
02:13:00 Summary Chollets ARC Challenge + Current Winners](https://i.ytimg.com/vi/jSAT_RuJ_Cg/mqdefault.jpg)
![AGI in 5 Years? Ben Goertzel on Superintelligence
Ben Goertzel discusses AGI development, transhumanism, and the potential societal impacts of superintelligent AI. He predicts human-level AGI by 2029 and argues that the transition to superintelligence could happen within a few years after. Goertzel explores the challenges of AI regulation, the limitations of current language models, and the need for neuro-symbolic approaches in AGI research. He also addresses concerns about resource allocation and cultural perspectives on transhumanism.
TOC:
[00:00:00] AGI Timeline Predictions and Development Speed
[00:00:45] Limitations of Language Models in AGI Development
[00:02:18] Current State and Trends in AI Research and Development
[00:09:02] Emergent Reasoning Capabilities and Limitations of LLMs
[00:18:15] Neuro-Symbolic Approaches and the Future of AI Systems
[00:20:00] Evolutionary Algorithms and LLMs in Creative Tasks
[00:21:25] Symbolic vs. Sub-Symbolic Approaches in AI
[00:28:05] Language as Internal Thought and External Communication
[00:30:20] AGI Development and Goal-Directed Behavior
[00:35:51] Consciousness and AI: Expanding States of Experience
[00:48:50] AI Regulation: Challenges and Approaches
[00:55:35] Challenges in AI Regulation
[00:59:20] AI Alignment and Ethical Considerations
[01:09:15] AGI Development Timeline Predictions
[01:12:40] OpenCog Hyperon and AGI Progress
[01:17:48] Transhumanism and Resource Allocation Debate
[01:20:12] Cultural Perspectives on Transhumanism
[01:23:54] AGI and Post-Scarcity Society
[01:31:35] Challenges and Implications of AGI Development
New! PDF Show notes: https://www.dropbox.com/scl/fi/fyetzwgoaf70gpovyfc4x/BenGoertzel.pdf?rlkey=pze5dt9vgf01tf2wip32p5hk5&st=svbcofm3&dl=0
Refs:
00:00:15 Ray Kurzweils AGI timeline prediction, Ray Kurzweil, https://en.wikipedia.org/wiki/Technological_singularity
00:01:45 Ben Goertzel: SingularityNET founder, Ben Goertzel, https://singularitynet.io/
00:02:35 AGI Conference series, AGI Conference Organizers, https://agi-conf.org/2024/
00:03:55 Ben Goertzels contributions to AGI, Wikipedia contributors, https://en.wikipedia.org/wiki/Ben_Goertzel
00:11:05 Chain-of-Thought prompting, Subbarao Kambhampati, https://arxiv.org/abs/2405.04776
00:11:35 Algorithmic information content, Pieter Adriaans, https://plato.stanford.edu/entries/information-entropy/
00:12:10 Turing completeness in neural networks, Various contributors, https://plato.stanford.edu/entries/turing-machine/
00:16:15 AlphaGeometry: AI for geometry problems, Trieu, Li, et al., https://www.nature.com/articles/s41586-023-06747-5
00:18:25 Shane Legg and Ben Goertzels collaboration, Shane Legg, https://en.wikipedia.org/wiki/Shane_Legg
00:20:00 Evolutionary algorithms in music generation, Yanxu Chen, https://arxiv.org/html/2409.03715v1
00:22:00 Peirces theory of semiotics, Charles Sanders Peirce, https://plato.stanford.edu/entries/peirce-semiotics/
00:28:10 Chomskys view on language, Noam Chomsky, https://chomsky.info/1983 /
00:34:05 Greg Egans Diaspora, Greg Egan, https://www.amazon.co.uk/Diaspora-post-apocalyptic-thriller-perfect-MIRROR/dp/0575082097
00:40:35 The Consciousness Explosion, Ben Goertzel & Gabriel Axel Montes, https://www.amazon.com/Consciousness-Explosion-Technological-Experiential-Singularity/dp/B0D8C7QYZD
00:41:55 Ray Kurzweils books on singularity, Ray Kurzweil, https://www.amazon.com/Singularity-Near-Humans-Transcend-Biology/dp/0143037889
00:50:50 California AI regulation bills, California State Senate, https://sd18.senate.ca.gov/news/senate-unanimously-approves-senator-padillas-artificial-intelligence-package
00:56:40 Limitations of Compute Thresholds, Sara Hooker, https://arxiv.org/abs/2407.05694
00:56:55 Taming Silicon Valley, Gary F. Marcus, https://www.penguinrandomhouse.com/books/768076/taming-silicon-valley-by-gary-f-marcus/
01:09:15 Kurzweils AGI prediction update, Ray Kurzweil, https://www.theguardian.com/technology/article/2024/jun/29/ray-kurzweil-google-ai-the-singularity-is-nearer
01:14:45 OpenCog Hyperon framework, Ben Goertzel et al., https://arxiv.org/abs/2310.18318
01:18:25 Malnutrition in Ethiopia, Abriham Shiferaw Areba, https://www.frontiersin.org/journals/nutrition/articles/10.3389/fnut.2024.1403591/full
01:18:40 Transhumanism ethical debate, Nick Bostrom, https://nickbostrom.com/papers/history.pdf AGI in 5 Years? Ben Goertzel on Superintelligence](https://i.ytimg.com/vi/jSDEsvVdL-E/mqdefault.jpg)