Uploaded December 2025 | Updated September 2026, 2 weeks ago
Sara Saab, VP of Product at Prolific, challenges our assumptions about AI alignment by comparing it to human moral development. Just as we don't expect humans to be born with perfect predetermined morality, why should we expect it from AI? She explores building backwards from AGI and the emerging ecosystem of human-machine oversight.
Sara Saab, VP of Product at Prolific, challenges our assumptions about AI alignment by comparing it to human moral development. Just as we don't expect humans to be born with perfect predetermined morality, why should we expect it from AI? She explores building backwards from AGI and the emerging ecosystem of human-machine oversight.
![Can Outsourcing Thinking Make Us Dumber? [Prof. David Krakauer]](https://i.ytimg.com/vi/jXa8dHzgV8U/mqdefault.jpg "Can Outsourcing Thinking Make Us Dumber? [Prof. David Krakauer]
Prof. David Krakauer, President of the Santa Fe Institute argues that we are fundamentally confusing knowledge with intelligence, especially when it comes to AI.
He defines true intelligence as the ability to do more with less—to solve novel problems with limited information. This is contrasted with current AI models, which he describes as doing less with more; they require astounding amounts of data to perform tasks that dont necessarily demonstrate true understanding or adaptation. He humorously calls this really shit programming.
David challenges the popular notion of emergence in Large Language Models (LLMs). He explains that the tech communitys definition—seeing a sudden jump in a models ability to perform a task like three-digit math—is superficial. True emergence, from a complex systems perspective, involves a fundamental change in the systems internal organization, allowing for a new, simpler, and more powerful level of description. He gives the example of moving from tracking individual water molecules to using the elegant laws of fluid dynamics. For LLMs to be truly emergent, wed need to see them develop new, efficient internal representations, not just get better at memorizing patterns as they scale.
Drawing on his background in evolutionary theory, David explains that systems like brains, and later, culture, evolved to process information that changes too quickly for genetic evolution to keep up. He calls culture evolution at light speed because it allows us to store our accumulated knowledge externally (in books, tools, etc.) and build upon it without corrupting the original.
This leads to his concept of exbodiment, where we outsource our cognitive load to the world through things like maps, abacuses, or even language itself.
We create these external tools, internalize the skills they teach us, improve them, and create a feedback loop that enhances our collective intelligence.
However, he ends with a warning. While technology has historically complemented our deficient abilities, modern AI presents a new danger. Because we have an evolutionary drive to conserve energy, we will inevitably outsource our thinking to AI if we can. He fears this is already leading to a diminution and dilution of human thought and creativity. Just as our muscles atrophy without use, he argues our brains will too, and we risk becoming mentally dependent on these systems.
RESCRIPT LINK (interactive transcript):
https://app.rescript.info/public/share/nCL8fdE_m3J6fA3SHlreCADpkWpbXaEF1JQ14z6N7Y8
TOC:
[00:00:00] Intelligence: Doing more with less
[00:02:10] Why brains evolved: The limits of evolution
[00:05:18] Culture as evolution at light speed
[00:08:11] True meaning of emergence: More is Different
[00:10:41] Why LLM capabilities are not true emergence
[00:15:10] What real emergence would look like in AI
[00:19:24] Symmetry breaking: Physics vs. Life
[00:23:30] Two types of emergence: Knowledge In vs. Out
[00:26:46] Causality, agency, and coarse-graining
[00:32:24] Exbodiment: Outsourcing thought to objects
[00:35:05] Collective intelligence & the boundary of the mind
[00:39:45] Mortal vs. Immortal forms of computation
[00:42:13] The risk of AI: Atrophy of human thought
David Krakauer
President and William H. Miller Professor of Complex Systems
https://www.santafe.edu/people/profile/david-krakauer
REFS:
Large Language Models and Emergence: A Complex Systems Perspective
David C. Krakauer, John W. Krakauer, Melanie Mitchell
https://arxiv.org/abs/2506.11135
Filmed at the Diverse Intelligences Summer Institute:
https://disi.org/")
![AI HAS A BODY PROBLEM... [Dr. Maxwell Ramstead]](https://i.ytimg.com/vi/jsIt2sTB_vo/mqdefault.jpg "AI HAS A BODY PROBLEM... [Dr. Maxwell Ramstead]
This episode features Dr. Maxwell Ramstead and Jason Fox both from Noumenal discussing why current AI approaches fall short for real-world applications and whats needed for true physical AI.
The guests argue that todays AI systems, including large language models, are fundamentally stuck in data space - they only process patterns in data rather than understanding the physical world that generates that data.
Maxwell uses Platos Cave as a powerful metaphor: like prisoners seeing only shadows on a wall, LLMs interact with representations of reality (text, images) rather than reality itself.
Rather than building monolithic models, Noumenal is creating a compositional system - essentially a marketplace of models where specialized AI components can be dynamically combined and deployed to robots.
Sponsor messages:
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/
TRANSCRIPT:
http://app.rescript.info/public/share/DVaCBhYF3Y-1Q2kA2N5-YkV4kOGBNZc5KLrDwntLznU
https://www.noumenal.ai/
https://x.com/mjdramstead
https://scholar.google.ca/citations?user=ILpGOMkAAAAJ&hl=fr
https://x.com/jasongfox?lang=en-GB
TOC
Opening & Context
00:00:00 - Opening Hook: Why Create a Physical AI Company?
00:01:59 - Sponsor: Tufa AI Labs
00:02:30 - Guest Introductions: Maxwell Ramstead & Jason Fox
00:05:18 - Noumenal Background
Core Problems with Current AI
00:09:30 - The Embodiment Problem: Why Bodies Matter
00:10:15 - LLMs Lack Physical Grounding
00:12:00 - AI Stuck in Platos Cave
00:16:15 - Language as Wrong Compression for Physics
00:17:22 - The Exhaustion of Static Datasets
00:19:54 - Humans as the Grounding for LLMs
Philosophical Foundations
00:28:00 - Fractured vs. Deep Understanding
00:32:15 - Defining Real: When You Bump Into Things
00:37:00 - Emergence: Weak vs. Strong Causal Power
00:41:45 - The Free Energy Principle Explained
00:44:15 - Constraints: How the Universe Builds Things
Objects, Intelligence & Grounding
00:46:15 - What Is an Object? From Data to Physics
00:51:00 - Learning Primitives & Predictive Grip
00:55:58 - There Is No General Intelligence
01:00:15 - The Human-AI Feedback Loop
01:03:08 - The Irony of LLM Specialization
01:06:05 - LLMs as Tools vs. Autonomous Agents
01:08:45 - Hallucinating Capabilities: The Third Leg Problem
The Noumenal Solution
01:09:00 - A Marketplace of Specialized Models
01:13:45 - Dynamic Skill Loading: Phone a Friend
01:16:15 - Learning from Brain Evolution
01:18:00 - Business Model Critique: Why OpenAI Wont Work
01:22:30 - The Physical Dataset Problem
Implementation & Future
01:22:30 - Community-Driven Data Collection
01:24:45 - Jim Fans Physical Turing Test
01:26:30 - Enterprise vs. Consumer Models
01:27:22 - Docker for Robotics: The Technical Architecture
01:30:12 - Reproducibility in Learning Systems
01:32:00 - Closing Thoughts")
![Neural Networks Are Elastic Origami! [Prof. Randall Balestriero]](https://i.ytimg.com/vi/l3O2J3LMxqI/mqdefault.jpg "Neural Networks Are Elastic Origami! [Prof. Randall Balestriero]
Professor Randall Balestriero joins us to discuss neural network geometry, spline theory, and emerging phenomena in deep learning, based on research presented at ICML. Topics include the delayed emergence of adversarial robustness in neural networks (grokking), geometric interpretations of neural networks via spline theory, and challenges in reconstruction learning. We also cover geometric analysis of Large Language Models (LLMs) for toxicity detection and the relationship between intrinsic dimensionality and model control in RLHF.
SPONSOR MESSAGES:
***
CentML offers competitive pricing for GenAI model deployment, with flexible options to suit a wide range of models, from small to large-scale deployments.
https://centml.ai/pricing/
Tufa AI Labs is a brand new research lab in Zurich started by Benjamin Crouzier focussed on o-series style reasoning and AGI. Are you interested in working on reasoning, or getting involved in their events?
Goto https://tufalabs.ai/
***
Show notes and transcript: https://www.dropbox.com/scl/fi/3lufge4upq5gy0ug75j4a/RANDALLSHOW.pdf?rlkey=nbemgpa0jhawt1e86rx7372e4&dl=0
TOC:
[00:00:00] Introduction
1. Neural Network Geometry and Spline Theory
[00:01:41] 1.1 Neural Network Geometry and Spline Theory
[00:07:41] 1.2 Deep Networks Always Grok
[00:11:39] 1.3 Grokking and Adversarial Robustness
[00:16:09] 1.4 Double Descent and Catastrophic Forgetting
2. Reconstruction Learning
[00:18:49] 2.1 Reconstruction Learning
[00:24:15] 2.2 Frequency Bias in Neural Networks
3. Geometric Analysis of Neural Networks
[00:29:02] 3.1 Geometric Analysis of Neural Networks
[00:34:41] 3.2 Adversarial Examples and Region Concentration
4. LLM Safety and Geometric Analysis
[00:40:05] 4.1 LLM Safety and Geometric Analysis
[00:46:11] 4.2 Toxicity Detection in LLMs
[00:52:24] 4.3 Intrinsic Dimensionality and Model Control
[00:58:07] 4.4 RLHF and High-Dimensional Spaces
5. Conclusion
[01:02:13] 5.1 Neural Tangent Kernel
[01:08:07] 5.2 Conclusion
REFS:
[00:01:35] Balestriero/Humayun – Deep network geometry & input space partitioning
https://arxiv.org/html/2408.04809v1
[00:03:55] Balestriero & Paris – Linking deep networks to adaptive spline operators
https://proceedings.mlr.press/v80/balestriero18b/balestriero18b.pdf
[00:13:55] Song et al. – Gradient-based white-box adversarial attacks
https://arxiv.org/abs/2012.14965
[00:16:05] Humayun, Balestriero & Baraniuk – Grokking phenomenon & emergent robustness
https://arxiv.org/abs/2402.15555
[00:18:25] Humayun – Training dynamics & double descent via linear region evolution
https://arxiv.org/abs/2310.12977
[00:20:15] Balestriero – Power diagram partitions in DNN decision boundaries
https://arxiv.org/abs/1905.08443
[00:23:00] Frankle & Carbin – Lottery Ticket Hypothesis for network pruning
https://arxiv.org/abs/1803.03635
[00:24:00] Belkin et al. – Double descent phenomenon in modern ML
https://arxiv.org/abs/1812.11118
[00:25:55] Balestriero et al. – Batch normalization’s regularization effects
https://arxiv.org/pdf/2209.14778
[00:29:35] EU – EU AI Act 2024 with compute restrictions
https://www.lw.com/admin/upload/SiteAttachments/EU-AI-Act-Navigating-a-Brave-New-World.pdf
[00:39:30] Humayun, Balestriero & Baraniuk – SplineCam: Visualizing deep network geometry
https://openaccess.thecvf.com/content/CVPR2023/papers/Humayun_SplineCam_Exact_Visualization_and_Characterization_of_Deep_Network_Geometry_and_CVPR_2023_paper.pdf
[00:40:40] Carlini – Trade-offs between adversarial robustness and accuracy
https://arxiv.org/abs/1902.06705
[00:44:55] Balestriero & LeCun – Limitations of reconstruction-based learning methods
https://raw.githubusercontent.com/mlresearch/v235/main/assets/balestriero24b/balestriero24b.pdf
[00:47:20] Balestriero & LeCun – Spectral analysis of neural network learning
https://proceedings.neurips.cc/paper_files/paper/2022/file/aa56c74513a5e35768a11f4e82dd7ffb-Paper-Conference.pdf
[00:49:45] He et al. – MAE: Masked Autoencoders for self-supervised learning
https://arxiv.org/abs/2111.06377
[00:54:50] Balestriero et al. – Geometric analysis of LLM layers for toxicity detection
https://arxiv.org/abs/2309.12312
[00:59:35] Balestriero et al. – Superior toxicity detection via geometric features
https://arxiv.org/html/2312.01648v2
[01:04:45] UofT ML – Self-attention control & context length effects
https://arxiv.org/abs/2310.04444
[01:11:55] Roberts – Foundations of deep learning theory
https://arxiv.org/abs/2106.10165
[01:15:40] Balestriero & Cha – Kolmogorov GAM Networks via spline partition theory
https://arxiv.org/pdf/2501.00704
[01:16:40] Various – Graph Kolmogorov-Arnold Networks (GKAN) extension
https://www.nature.com/articles/s41598-024-85083-8")
