This sponsored episode features mathematician Ohad Asor discussing logical approaches to AI, focusing on the limitations of machine learning and introducing the Tau language for software development and blockchain tech. Asor argues that machine learning cannot guarantee correctness. Tau allows logical specification of software requirements, automatically creating provably correct implementations with potential to revolutionize distributed systems. The discussion highlights program synthesis, software updates, and applications in finance and governance.SPONSOR MESSAGES:***Tufa AI Labs is a brand new research lab in Zurich started by Benjamin Crouzier focussed on o-series style reasoning and AGI. They are hiring a Chief Engineer and ML engineers. Events in Zurich. Goto https://tufalabs.ai/***TRANSCRIPT + RESEARCH:https://www.dropbox.com/scl/fi/t849j6v1juk3gc15g4rsy/TAU.pdf?rlkey=hh11h2mhog3ncdbeapbzpzctc&dl=0Tau:https://tau.net/Tau Language:https://tau.ai/tau-language/Research:https://tau.net/Theories-and-Applications-of-Boolean-Algebras-0.29.pdfTOC:1. Machine Learning Foundations and Limitations [00:00:00] 1.1 Fundamental Limitations of Machine Learning and PAC Learning Theory [00:04:50] 1.2 Transductive Learning and the Three Curses of Machine Learning [00:08:57] 1.3 Language, Reality, and AI System Design [00:12:58] 1.4 Program Synthesis and Formal Verification Approaches2. Logical Programming Architecture [00:31:55] 2.1 Safe AI Development Requirements [00:32:05] 2.2 Self-Referential Language Architecture [00:32:50] 2.3 Boolean Algebra and Logical Foundations [00:37:52] 2.4 SAT Solvers and Complexity Challenges [00:44:30] 2.5 Program Synthesis and Specification [00:47:39] 2.6 Overcoming Tarski's Undefinability with Boolean Algebra [00:56:05] 2.7 Tau Language Implementation and User Control3. Blockchain-Based Software Governance [01:09:10] 3.1 User Control and Software Governance Mechanisms [01:18:27] 3.2 Tau's Blockchain Architecture and Meta-Programming Capabilities [01:21:43] 3.3 Development Status and Token Implementation [01:24:52] 3.4 Consensus Building and Opinion Mapping System [01:35:29] 3.5 Automation and Financial ApplicationsCORE REFS (more in pinned comment):[00:03:45] PAC (Probably Approximately Correct) Learning framework, Leslie Valianthttps://en.wikipedia.org/wiki/Probably_approximately_correct_learning[00:06:10] Boolean Satisfiability Problem (SAT), Varioushttps://en.wikipedia.org/wiki/Boolean_satisfiability_problem[00:13:55] Knowledge as Justified True Belief (JTB), Matthias Steuphttps://plato.stanford.edu/entries/epistemology/[00:17:50] Wittgenstein's concept of the limits of language, Ludwig Wittgensteinhttps://plato.stanford.edu/entries/wittgenstein/[00:21:25] Boolean algebras, Ohad Osorhttps://tau.net/tau-language-research/[00:26:10] The Halting Problemhttps://plato.stanford.edu/entries/turing-machine/#HaltProb[00:30:25] Alfred Tarski (1901-1983), Mario Gómez-Torrentehttps://plato.stanford.edu/entries/tarski/[00:41:50] DPLLhttps://www.cs.princeton.edu/~zkincaid/courses/fall18/readings/SATHandbook-CDCL.pdf[00:49:50] Tarski's undefinability theorem (1936), Alfred Tarskihttps://plato.stanford.edu/entries/tarski-truth/[00:51:45] Boolean Algebra mathematical foundations, J. Donald Monkhttps://plato.stanford.edu/entries/boolalg-math/[01:02:35] Belief Revision Theory and AGM Postulates, Sven Ove Hanssonhttps://plato.stanford.edu/entries/logic-belief-revision/[01:05:35] Quantifier elimination in atomless boolean algebra, H. Jerome Keislerhttps://people.math.wisc.edu/~hkeisler/random.pdf[01:08:35] Quantifier elimination in Tau language specification, Ohad Asorhttps://tau.ai/Theories-and-Applications-of-Boolean-Algebras-0.29.pdf[01:11:50] Tau Net blockchain platformhttps://tau.net/[01:19:20] Tau blockchain's innovative approach treating blockchain code itself as a contracthttps://tau.net/Whitepaper.pdf

John Palazza from CentML joins us in this sponsored interview to discuss the critical importance of infrastructure optimization in the age of Large Language Models and Generative AI. We explore how enterprises can transition from the innovation phase to production and scale, highlighting the significance of efficient GPU utilization and cost management. The conversation covers the open-source versus proprietary model debate, the rise of AI agents, and the need for platform independence to avoid vendor lock-in, as well as emerging trends in AI infrastructure and the pivotal role of strategic partnerships.

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. Check out their super fast DeepSeek R1 hosting!

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. They are hiring a Chief Engineer and ML engineers. Events in Zurich.

Goto https://tufalabs.ai/

***

TRANSCRIPT:

https://www.dropbox.com/scl/fi/dnjsygrgdgq5ng5fdlfjg/JOHNPALAZZA.pdf?rlkey=hl9wyydi9mj077rbg5acdmo3a&dl=0

John Palazza:

Vice President of Global Sales @ CentML

https://www.linkedin.com/in/john-p-b34655/

TOC:

1. Enterprise AI Organization and Strategy

[00:00:00] 1.1 Organizational Structure and ML Ownership

[00:02:59] 1.2 Infrastructure Efficiency and GPU Utilization

[00:07:59] 1.3 Platform Centralization vs Team Autonomy

[00:11:32] 1.4 Enterprise AI Adoption Strategy and Leadership

2. MLOps Infrastructure and Resource Management

[00:15:08] 2.1 Technology Evolution and Enterprise Integration

[00:19:10] 2.2 Enterprise MLOps Platform Development

[00:22:15] 2.3 AI Interface Evolution and Agent-Based Solutions

[00:25:47] 2.4 CentML's Infrastructure Solutions

[00:30:00] 2.5 Workload Abstraction and Resource Allocation

3. LLM Infrastructure Optimization and Independence

[00:33:10] 3.1 GPU Optimization and Cost Efficiency

[00:36:47] 3.2 AI Efficiency and Innovation Challenges

[00:41:40] 3.3 Cloud Provider Strategy and Infrastructure Control

[00:46:52] 3.4 Platform Independence and Vendor Lock-in

[00:50:53] 3.5 Technical Innovation and Growth Strategy

REFS:

[00:01:25] Apple Acquires GraphLab, Apple Inc.

https://techcrunch.com/2016/08/05/apple-acquires-turi-a-machine-learning-company/

[00:03:50] Bain Tech Report 2024, Gartner

https://www.bain.com/insights/topics/technology-report/

[00:04:50] PaaS vs IaaS Efficiency, Gartner

https://www.gartner.com/en/newsroom/press-releases/2024-11-19-gartner-forecasts-worldwide-public-cloud-end-user-spending-to-total-723-billion-dollars-in-2025

[00:14:55] Fashion Quote, Oscar Wilde

https://www.amazon.com/Complete-Works-Oscar-Wilde-Collins/dp/0007144369

[00:15:30] PointCast Network, PointCast Inc.

https://en.wikipedia.org/wiki/Push_technology

[00:18:05] AI Bain Report, Bain & Company

https://www.bain.com/insights/how-generative-ai-changes-the-game-in-tech-services-tech-report-2024/

[00:20:40] Uber Michelangelo, Uber Engineering Team

https://www.uber.com/en-SE/blog/michelangelo-machine-learning-platform/

[00:20:50] Algorithmia Acquisition, DataRobot

https://www.datarobot.com/newsroom/press/datarobot-is-acquiring-algorithmia-enhancing-leading-mlops-architecture-for-the-enterprise/

[00:22:55] Fine Tuning vs RAG, Heydar Soudani, Evangelos Kanoulas & Faegheh Hasibi.

https://arxiv.org/html/2403.01432v2

[00:24:40] LLM Agent Survey, Lei Wang et al.

https://arxiv.org/abs/2308.11432

[00:26:30] CentML CServe, CentML

https://docs.centml.ai/apps/llm

[00:29:15] CentML Snowflake, Snowflake

https://www.snowflake.com/en/engineering-blog/optimize-llms-with-llama-snowflake-ai-stack/

[00:30:15] NVIDIA H100 GPU, NVIDIA

https://www.nvidia.com/en-us/data-center/h100/

[00:33:25] CentML\'s 60% savings, CentML

https://centml.ai/platform/

Federico Barbero (DeepMind/Oxford) is the lead author of "Transformers Need Glasses!".

Have you ever wondered why LLMs struggle with seemingly simple tasks like counting or copying long strings of text? We break down the theoretical reasons behind these failures, revealing architectural bottlenecks and the challenges of maintaining information fidelity across extended contexts.

Federico explains how these issues are rooted in the transformer's design, drawing parallels to over-squashing in graph neural networks and detailing how the softmax function limits sharp decision-making.

But it's not all bad news! Discover practical "glasses" that can help transformers see more clearly, from simple input modifications to architectural tweaks.

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. Check out their super fast DeepSeek R1 hosting!

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. They are hiring a Chief Engineer and ML engineers. Events in Zurich.

Goto https://tufalabs.ai/

***

https://federicobarbero.com/

TRANSCRIPT + RESEARCH:

https://www.dropbox.com/s/h7ys83ztwktqjje/Federico.pdf?dl=0

TOC:

1. Transformer Limitations: Token Detection & Representation

[00:00:00] 1.1 Transformers fail at single token detection

[00:02:45] 1.2 Representation collapse in transformers

[00:03:21] 1.3 Experiment: LLMs fail at copying last tokens

[00:18:00] 1.4 Attention sharpness limitations in transformers

2. Transformer Limitations: Information Flow & Quantization

[00:18:50] 2.1 Unidirectional information mixing

[00:18:50] 2.2 Unidirectional information flow towards sequence beginning in transformers

[00:21:50] 2.3 Diagonal attention heads as expensive no-ops in LAMA/Gemma

[00:27:14] 2.4 Sequence entropy affects transformer model distinguishability

[00:30:36] 2.5 Quantization limitations lead to information loss & representational collapse

[00:38:34] 2.6 LLMs use subitizing as opposed to counting algorithms

3. Transformers and the Nature of Reasoning

[00:40:30] 3.1 Turing completeness conditions in transformers

[00:43:23] 3.2 Transformers struggle with sequential tasks

[00:45:50] 3.3 Windowed attention as solution to information compression

[00:51:04] 3.4 Chess engines: mechanical computation vs creative reasoning

[01:00:35] 3.5 Epistemic foraging introduced

REFS:

[00:01:05] Transformers Need Glasses!, Barbero et al.

https://proceedings.neurips.cc/paper_files/paper/2024/file/b1d35561c4a4a0e0b6012b2af531e149-Paper-Conference.pdf

[00:05:30] Softmax is Not Enough, Veličković et al.

https://arxiv.org/abs/2410.01104

[00:11:30] Adv Alg Lecture 15, Chawla

https://pages.cs.wisc.edu/~shuchi/courses/787-F09/scribe-notes/lec15.pdf

[00:15:05] Graph Attention Networks, Veličković

https://arxiv.org/abs/1710.10903

[00:19:15] Extract Training Data, Carlini et al.

https://arxiv.org/pdf/2311.17035

[00:31:30] 1-bit LLMs, Ma et al.

https://arxiv.org/abs/2402.17764

[00:38:35] LLMs Solve Math, Nikankin et al.

https://arxiv.org/html/2410.21272v1

[00:38:45] Subitizing, Railo

https://link.springer.com/10.1007/978-1-4419-1428-6_578

[00:43:25] NN & Chomsky Hierarchy, Delétang et al.

https://arxiv.org/abs/2207.02098

[00:51:05] Measure of Intelligence, Chollet

https://arxiv.org/abs/1911.01547

[00:52:10] AlphaZero, Silver et al.

https://pubmed.ncbi.nlm.nih.gov/30523106/

[00:55:10] Golden Gate Claude, Anthropic

https://www.anthropic.com/news/golden-gate-claude

[00:56:40] Chess Positions, Chase & Simon

https://www.sciencedirect.com/science/article/abs/pii/0010028573900042

[01:00:35] Epistemic Foraging, Friston

https://www.frontiersin.org/journals/computational-neuroscience/articles/10.3389/fncom.2016.00056/full

We speak with Sakana AI, who are building nature-inspired methods that could fundamentally transform how we develop AI systems.

The guests include Chris Lu, a researcher who recently completed his DPhil at Oxford University under Prof. Jakob Foerster's supervision, where he focused on meta-learning and multi-agent systems. Chris is the first author of the DiscoPOP paper, which demonstrates how language models can discover and design better training algorithms. Also joining is Robert Tjarko Lange, a founding member of Sakana AI who specializes in evolutionary algorithms and large language models. Robert leads research at the intersection of evolutionary computation and foundation models, and is completing his PhD at TU Berlin on evolutionary meta-learning. The discussion also features Cong Lu, currently a Research Scientist at Google DeepMind's Open-Endedness team, who previously helped develop The AI Scientist and Intelligent Go-Explore.

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. Check out their super fast DeepSeek R1 hosting!

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. They are hiring a Chief Engineer and ML engineers. Events in Zurich.

Goto https://tufalabs.ai/

***

* DiscoPOP - A framework where language models discover their own optimization algorithms

* EvoLLM - Using language models as evolution strategies for optimization

The AI Scientist - A fully automated system that conducts scientific research end-to-end

* Neural Attention Memory Models (NAMMs) - Evolved memory systems that make transformers both faster and more accurate

TRANSCRIPT + REFS:

https://www.dropbox.com/scl/fi/gflcyvnujp8cl7zlv3v9d/Sakana.pdf?rlkey=woaoo82943170jd4yyi2he71c&dl=0

Robert Tjarko Lange

https://roberttlange.com/

Chris Lu

https://chrislu.page/

Cong Lu

https://www.conglu.co.uk/

Sakana

https://sakana.ai/blog/

TOC:

1. LLMs for Algorithm Generation and Optimization

[00:00:00] 1.1 LLMs generating algorithms for training other LLMs

[00:04:00] 1.2 Evolutionary black-box optim using neural network loss parameterization

[00:11:50] 1.3 DiscoPOP: Non-convex loss function for noisy data

[00:20:45] 1.4 External entropy Injection for preventing Model collapse

[00:26:25] 1.5 LLMs for black-box optimization using abstract numerical sequences

2. Model Learning and Generalization

[00:31:05] 2.1 Fine-tuning on teacher algorithm trajectories

[00:31:30] 2.2 Transformers learning gradient descent

[00:33:00] 2.3 LLM tokenization biases towards specific numbers

[00:34:50] 2.4 LLMs as evolution strategies for black box optimization

[00:38:05] 2.5 DiscoPOP: LLMs discovering novel optimization algorithms

3. AI Agents and System Architectures

[00:51:30] 3.1 ARC challenge: Induction vs. transformer approaches

[00:54:35] 3.2 LangChain / modular agent components

[00:57:50] 3.3 Debate improves LLM truthfulness

[01:00:55] 3.4 Time limits controlling AI agent systems

[01:03:00] 3.5 Gemini: Million-token context enables flatter hierarchies

[01:04:05] 3.6 Agents follow own interest gradients

[01:09:50] 3.7 Go-Explore algorithm: archive-based exploration

[01:11:05] 3.8 Foundation models for interesting state discovery

[01:13:00] 3.9 LLMs leverage prior game knowledge

4. AI for Scientific Discovery and Human Alignment

[01:17:45] 4.1 Encoding Alignment & Aesthetics via Reward Functions

[01:20:00] 4.2 AI Scientist: Automated Open-Ended Scientific Discovery

[01:24:15] 4.3 DiscoPOP: LLM for Preference Optimization Algorithms

[01:28:30] 4.4 Balancing AI Knowledge with Human Understanding

[01:33:55] 4.5 AI-Driven Conferences and Paper Review

Clement Bonnet discusses his novel approach to the ARC (Abstraction and Reasoning Corpus) challenge. Unlike approaches that rely on fine-tuning LLMs or generating samples at inference time, Clement's method encodes input-output pairs into a latent space, optimizes this representation with a search algorithm, and decodes outputs for new inputs. This end-to-end architecture uses a VAE loss, including reconstruction and prior losses.

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. Check out their super fast DeepSeek R1 hosting!

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. They are hiring a Chief Engineer and ML engineers. Events in Zurich.

Goto https://tufalabs.ai/

***

TRANSCRIPT + RESEARCH OVERVIEW:

https://www.dropbox.com/scl/fi/j7m0gaz1126y594gswtma/CLEMMLST.pdf?rlkey=y5qvwq2er5nchbcibm07rcfpq&dl=0

Clem and Matthew-

https://www.linkedin.com/in/clement-bonnet16/

https://github.com/clement-bonnet

https://mvmacfarlane.github.io/

TOC

1. LPN Fundamentals

[00:00:00] 1.1 Introduction to ARC Benchmark and LPN Overview

[00:05:05] 1.2 Neural Networks' Challenges with ARC and Program Synthesis

[00:06:55] 1.3 Induction vs Transduction in Machine Learning

2. LPN Architecture and Latent Space

[00:11:50] 2.1 LPN Architecture and Latent Space Implementation

[00:16:25] 2.2 LPN Latent Space Encoding and VAE Architecture

[00:20:25] 2.3 Gradient-Based Search Training Strategy

[00:23:39] 2.4 LPN Model Architecture and Implementation Details

3. Implementation and Scaling

[00:27:34] 3.1 Training Data Generation and re-ARC Framework

[00:31:28] 3.2 Limitations of Latent Space and Multi-Thread Search

[00:34:43] 3.3 Program Composition and Computational Graph Architecture

4. Advanced Concepts and Future Directions

[00:45:09] 4.1 AI Creativity and Program Synthesis Approaches

[00:49:47] 4.2 Scaling and Interpretability in Latent Space Models

REFS

[00:00:05] ARC benchmark, Chollet

https://arxiv.org/abs/2412.04604

[00:02:10] Latent Program Spaces, Bonnet, Macfarlane

https://arxiv.org/abs/2411.08706

[00:07:45] Kevin Ellis work on program generation

https://www.cs.cornell.edu/~ellisk/

[00:08:45] Induction vs transduction in abstract reasoning, Li et al.

https://arxiv.org/abs/2411.02272

[00:17:40] VAEs, Kingma, Welling

https://arxiv.org/abs/1312.6114

[00:27:50] re-ARC, Hodel

https://github.com/michaelhodel/re-arc

[00:29:40] Grid size in ARC tasks, Chollet

https://github.com/fchollet/ARC-AGI

[00:33:00] Critique of deep learning, Marcus

https://arxiv.org/vc/arxiv/papers/2002/2002.06177v1.pdf

Prof. Jakob Foerster, a leading AI researcher at Oxford University and Meta, and Chris Lu, a researcher at OpenAI -- they explain how AI is moving beyond just mimicking human behaviour to creating truly intelligent agents that can learn and solve problems on their own. Foerster champions open-source AI for responsible, decentralised development. He addresses AI scaling, goal misalignment (Goodhart's Law), and the need for holistic alignment, offering a quick look at the future of AI and how to guide it.

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. Check out their super fast DeepSeek R1 hosting!

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. They are hiring a Chief Engineer and ML engineers. Events in Zurich.

Goto https://tufalabs.ai/

***

TRANSCRIPT/REFS:

https://www.dropbox.com/scl/fi/yqjszhntfr00bhjh6t565/JAKOB.pdf?rlkey=scvny4bnwj8th42fjv8zsfu2y&dl=0

Prof. Jakob Foerster

https://x.com/j_foerst

https://www.jakobfoerster.com/

University of Oxford Profile:

https://eng.ox.ac.uk/people/jakob-foerster/

Chris Lu:

https://chrislu.page/

TOC

1. GPU Acceleration and Training Infrastructure

[00:00:00] 1.1 ARC Challenge Criticism and FLAIR Lab Overview

[00:01:25] 1.2 GPU Acceleration and Hardware Lottery in RL

[00:05:50] 1.3 Data Wall Challenges and Simulation-Based Solutions

[00:08:40] 1.4 JAX Implementation and Technical Acceleration

2. Learning Frameworks and Policy Optimization

[00:14:18] 2.1 Evolution of RL Algorithms and Mirror Learning Framework

[00:15:25] 2.2 Meta-Learning and Policy Optimization Algorithms

[00:21:47] 2.3 Language Models and Benchmark Challenges

[00:28:15] 2.4 Creativity and Meta-Learning in AI Systems

3. Multi-Agent Systems and Decentralization

[00:31:24] 3.1 Multi-Agent Systems and Emergent Intelligence

[00:38:35] 3.2 Swarm Intelligence vs Monolithic AGI Systems

[00:42:44] 3.3 Democratic Control and Decentralization of AI Development

[00:46:14] 3.4 Open Source AI and Alignment Challenges

[00:49:31] 3.5 Collaborative Models for AI Development

REFS

[[00:00:05] ARC Benchmark, Chollet

https://github.com/fchollet/ARC-AGI

[00:03:05] DRL Doesn't Work, Irpan

https://www.alexirpan.com/2018/02/14/rl-hard.html

[00:05:55] AI Training Data, Data Provenance Initiative

https://www.nytimes.com/2024/07/19/technology/ai-data-restrictions.html

[00:06:10] JaxMARL, Foerster et al.

https://arxiv.org/html/2311.10090v5

[00:08:50] M-FOS, Lu et al.

https://arxiv.org/abs/2205.01447

[00:09:45] JAX Library, Google Research

https://github.com/jax-ml/jax

[00:12:10] Kinetix, Mike and Michael

https://arxiv.org/abs/2410.23208

[00:12:45] Genie 2, DeepMind

https://deepmind.google/discover/blog/genie-2-a-large-scale-foundation-world-model/

[00:14:42] Mirror Learning, Grudzien, Kuba et al.

https://arxiv.org/abs/2208.01682

[00:16:30] Discovered Policy Optimisation, Lu et al.

https://arxiv.org/abs/2210.05639

[00:24:10] Goodhart's Law, Goodhart

https://en.wikipedia.org/wiki/Goodhart%27s_law

[00:25:15] LLM ARChitect, Franzen et al.

https://github.com/da-fr/arc-prize-2024/blob/main/the_architects.pdf

[00:28:55] AlphaGo, Silver et al.

https://arxiv.org/pdf/1712.01815.pdf

[00:30:10] Meta-learning, Lu, Towers, Foerster

https://direct.mit.edu/isal/proceedings-pdf/isal2023/35/67/2354943/isal_a_00674.pdf

[00:31:30] Emergence of Pragmatics, Yuan et al.

https://arxiv.org/abs/2001.07752

[00:34:30] AI Safety, Amodei et al.

https://arxiv.org/abs/1606.06565

[00:35:45] Intentional Stance, Dennett

https://plato.stanford.edu/entries/ethics-ai/

[00:39:25] Multi-Agent RL, Zhou et al.

https://arxiv.org/pdf/2305.10091

[00:41:00] Open Source Generative AI, Foerster et al.

https://arxiv.org/abs/2405.08597

Daniel Franzen and Jan Disselhoff, the "ARChitects" are the official winners of the ARC Prize 2024. Filmed at Tufa Labs in Zurich - they revealed how they achieved a remarkable 53.5% accuracy by creatively utilising large language models (LLMs) in new ways. Discover their innovative techniques, including depth-first search for token selection, test-time training, and a novel augmentation-based validation system. Their results were extremely surprising.

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. Check out their super fast DeepSeek R1 hosting!

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. They are hiring a Chief Engineer and ML engineers. Events in Zurich.

Goto https://tufalabs.ai/

***

Jan Disselhoff

https://www.linkedin.com/in/jan-disselhoff-1423a2240/

Daniel Franzen

https://github.com/da-fr

ARC Prize: http://arcprize.org/

TRANSCRIPT AND BACKGROUND READING:

https://www.dropbox.com/scl/fi/utkn2i1ma79fn6an4yvjw/ARCHitects.pdf?rlkey=67pe38mtss7oyhjk2ad0d2aza&dl=0

TOC

1. Solution Architecture and Strategy Overview

[00:00:00] 1.1 Initial Solution Overview and Model Architecture

[00:04:25] 1.2 LLM Capabilities and Dataset Approach

[00:10:51] 1.3 Test-Time Training and Data Augmentation Strategies

[00:14:08] 1.4 Sampling Methods and Search Implementation

[00:17:52] 1.5 ARC vs Language Model Context Comparison

2. LLM Search and Model Implementation

[00:21:53] 2.1 LLM-Guided Search Approaches and Solution Validation

[00:27:04] 2.2 Symmetry Augmentation and Model Architecture

[00:30:11] 2.3 Model Intelligence Characteristics and Performance

[00:37:23] 2.4 Tokenization and Numerical Processing Challenges

3. Advanced Training and Optimization

[00:45:15] 3.1 DFS Token Selection and Probability Thresholds

[00:49:41] 3.2 Model Size and Fine-tuning Performance Trade-offs

[00:53:07] 3.3 LoRA Implementation and Catastrophic Forgetting Prevention

[00:56:10] 3.4 Training Infrastructure and Optimization Experiments

[01:02:34] 3.5 Search Tree Analysis and Entropy Distribution Patterns

REFS

[00:01:05] Winning ARC 2024 solution using 12B param model, Franzen, Disselhoff, Hartmann

https://github.com/da-fr/arc-prize-2024/blob/main/the_architects.pdf

[00:03:40] Robustness of analogical reasoning in LLMs, Melanie Mitchell

https://arxiv.org/html/2411.14215

[00:07:50] Re-ARC dataset generator for ARC task variations, Michael Hodel

https://github.com/michaelhodel/re-arc

[00:15:00] Analysis of search methods in LLMs (greedy, beam, DFS), Chen et al.

https://arxiv.org/html/2408.00724v2

[00:16:55] Language model reachability space exploration, University of Toronto

https://www.youtube.com/watch?v=Bpgloy1dDn0

[00:22:30] GPT-4 guided code solutions for ARC tasks, Ryan Greenblatt

https://redwoodresearch.substack.com/p/getting-50-sota-on-arc-agi-with-gpt

[00:41:20] GPT tokenization approach for numbers, OpenAI

https://platform.openai.com/docs/guides/text-generation/tokenizer-examples

[00:46:25] DFS in AI search strategies, Russell & Norvig

https://www.amazon.com/Artificial-Intelligence-Modern-Approach-4th/dp/0134610997

[00:53:10] Paper on catastrophic forgetting in neural networks, Kirkpatrick et al.

https://www.pnas.org/doi/10.1073/pnas.1611835114

[00:54:00] LoRA for efficient fine-tuning of LLMs, Hu et al.

https://arxiv.org/abs/2106.09685

[00:57:20] NVIDIA H100 Tensor Core GPU specs, NVIDIA

https://developer.nvidia.com/blog/nvidia-hopper-architecture-in-depth/

[01:04:55] Original MCTS in computer Go, Yifan Jin

https://stanford.edu/~rezab/classes/cme323/S15/projects/montecarlo_search_tree_report.pdf

Sepp Hochreiter, the inventor of LSTM (Long Short-Term Memory) networks – a foundational technology in AI. Sepp discusses his journey, the origins of LSTM, and why he believes his latest work, XLSTM, could be the next big thing in AI, particularly for applications like robotics and industrial simulation. He also shares his controversial perspective on Large Language Models (LLMs) and why reasoning is a critical missing piece in current AI systems.

SPONSOR MESSAGES:

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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. They are hiring a Chief Engineer and ML engineers. Events in Zurich.

Goto https://tufalabs.ai/

***

TRANSCRIPT AND BACKGROUND READING:

https://www.dropbox.com/scl/fi/n1vzm79t3uuss8xyinxzo/SEPPH.pdf?rlkey=fp7gwaopjk17uyvgjxekxrh5v&dl=0

Prof. Sepp Hochreiter

https://www.nx-ai.com/

https://x.com/hochreitersepp

https://scholar.google.at/citations?user=tvUH3WMAAAAJ&hl=en

TOC:

1. LLM Evolution and Reasoning Capabilities

[00:00:00] 1.1 LLM Capabilities and Limitations Debate

[00:03:16] 1.2 Program Generation and Reasoning in AI Systems

[00:06:30] 1.3 Human vs AI Reasoning Comparison

[00:09:59] 1.4 New Research Initiatives and Hybrid Approaches

2. LSTM Technical Architecture

[00:13:18] 2.1 LSTM Development History and Technical Background

[00:20:38] 2.2 LSTM vs RNN Architecture and Computational Complexity

[00:25:10] 2.3 xLSTM Architecture and Flash Attention Comparison

[00:30:51] 2.4 Evolution of Gating Mechanisms from Sigmoid to Exponential

3. Industrial Applications and Neuro-Symbolic AI

[00:40:35] 3.1 Industrial Applications and Fixed Memory Advantages

[00:42:31] 3.2 Neuro-Symbolic Integration and Pi AI Project

[00:46:00] 3.3 Integration of Symbolic and Neural AI Approaches

[00:51:29] 3.4 Evolution of AI Paradigms and System Thinking

[00:54:55] 3.5 AI Reasoning and Human Intelligence Comparison

[00:58:12] 3.6 NXAI Company and Industrial AI Applications

REFS:

[00:00:15] Seminal LSTM paper establishing Hochreiter's expertise (Hochreiter & Schmidhuber)

https://direct.mit.edu/neco/article-abstract/9/8/1735/6109/Long-Short-Term-Memory

[00:04:20] Kolmogorov complexity and program composition limitations (Kolmogorov)

https://link.springer.com/article/10.1007/BF02478259

[00:07:10] Limitations of LLM mathematical reasoning and symbolic integration (Various Authors)

https://www.arxiv.org/pdf/2502.03671

[00:09:05] AlphaGo’s Move 37 demonstrating creative AI (Google DeepMind)

https://deepmind.google/research/breakthroughs/alphago/

[00:10:15] New AI research lab in Zurich for fundamental LLM research (Benjamin Crouzier)

https://tufalabs.ai

[00:19:40] Introduction of xLSTM with exponential gating (Beck, Hochreiter, et al.)

https://arxiv.org/abs/2405.04517

[00:22:55] FlashAttention: fast & memory-efficient attention (Tri Dao et al.)

https://arxiv.org/abs/2205.14135

[00:31:00] Historical use of sigmoid/tanh activation in 1990s (James A. McCaffrey)

https://visualstudiomagazine.com/articles/2015/06/01/alternative-activation-functions.aspx

[00:36:10] Mamba 2 state space model architecture (Albert Gu et al.)

https://arxiv.org/abs/2312.00752

[00:46:00] Austria’s Pi AI project integrating symbolic & neural AI (Hochreiter et al.)

https://www.jku.at/en/institute-of-machine-learning/research/projects/

[00:48:10] Neuro-symbolic integration challenges in language models (Diego Calanzone et al.)

https://openreview.net/forum?id=7PGluppo4k

[00:49:30] JKU Linz’s historical and neuro-symbolic research (Sepp Hochreiter)

https://www.jku.at/en/news-events/news/detail/news/bilaterale-ki-projekt-unter-leitung-der-jku-erhaelt-fwf-cluster-of-excellence/

YT: https://www.youtube.com/watch?v=8u2pW2zZLCs

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:

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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/

***

Randall Balestriero

https://x.com/randall_balestr

https://randallbalestriero.github.io/

Show notes and transcript: https://www.dropbox.com/scl/fi/3lufge4upq5gy0ug75j4a/RANDALLSHOW.pdf?rlkey=nbemgpa0jhawt1e86rx7372e4&dl=0

TOC:

- Introduction

- 00:00:00: Introduction

- Neural Network Geometry and Spline Theory

- 00:01:41: Neural Network Geometry and Spline Theory

- 00:07:41: Deep Networks Always Grok

- 00:11:39: Grokking and Adversarial Robustness

- 00:16:09: Double Descent and Catastrophic Forgetting

- Reconstruction Learning

- 00:18:49: Reconstruction Learning

- 00:24:15: Frequency Bias in Neural Networks

- Geometric Analysis of Neural Networks

- 00:29:02: Geometric Analysis of Neural Networks

- 00:34:41: Adversarial Examples and Region Concentration

- LLM Safety and Geometric Analysis

- 00:40:05: LLM Safety and Geometric Analysis

- 00:46:11: Toxicity Detection in LLMs

- 00:52:24: Intrinsic Dimensionality and Model Control

- 00:58:07: RLHF and High-Dimensional Spaces

- Conclusion

- 01:02:13: Neural Tangent Kernel

- 01:08:07: Conclusion

REFS:

[00:01:35] 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/pdf/2407.20099

[00:44:55] Balestriero & LeCun – Limitations of reconstruction-based learning methods

https://openreview.net/forum?id=ez7w0Ss4g9

(truncated, see shownotes PDF)

Nicholas Carlini from Google DeepMind offers his view of AI security, emergent LLM capabilities, and his groundbreaking model-stealing research. He reveals how LLMs can unexpectedly excel at tasks like chess and discusses the security pitfalls of LLM-generated code.

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/

***

Transcript: https://www.dropbox.com/scl/fi/lat7sfyd4k3g5k9crjpbf/CARLINI.pdf?rlkey=b7kcqbvau17uw6rksbr8ccd8v&dl=0

TOC:

1. ML Security Fundamentals

[00:00:00] 1.1 ML Model Reasoning and Security Fundamentals

[00:03:04] 1.2 ML Security Vulnerabilities and System Design

[00:08:22] 1.3 LLM Chess Capabilities and Emergent Behavior

[00:13:20] 1.4 Model Training, RLHF, and Calibration Effects

2. Model Evaluation and Research Methods

[00:19:40] 2.1 Model Reasoning and Evaluation Metrics

[00:24:37] 2.2 Security Research Philosophy and Methodology

[00:27:50] 2.3 Security Disclosure Norms and Community Differences

3. LLM Applications and Best Practices

[00:44:29] 3.1 Practical LLM Applications and Productivity Gains

[00:49:51] 3.2 Effective LLM Usage and Prompting Strategies

[00:53:03] 3.3 Security Vulnerabilities in LLM-Generated Code

4. Advanced LLM Research and Architecture

[00:59:13] 4.1 LLM Code Generation Performance and O(1) Labs Experience

[01:03:31] 4.2 Adaptation Patterns and Benchmarking Challenges

[01:10:10] 4.3 Model Stealing Research and Production LLM Architecture Extraction

REFS:

[00:01:15] Nicholas Carlini’s personal website & research profile (Google DeepMind, ML security) - https://nicholas.carlini.com/

[00:01:50] CentML AI compute platform for language model workloads - https://centml.ai/

[00:04:30] Seminal paper on neural network robustness against adversarial examples (Carlini & Wagner, 2016) - https://arxiv.org/abs/1608.04644

[00:05:20] Computer Fraud and Abuse Act (CFAA) – primary U.S. federal law on computer hacking liability - https://www.justice.gov/jm/jm-9-48000-computer-fraud

[00:08:30] Blog post: Emergent chess capabilities in GPT-3.5-turbo-instruct (Nicholas Carlini, Sept 2023) - https://nicholas.carlini.com/writing/2023/chess-llm.html

[00:16:10] Paper: “Self-Play Preference Optimization for Language Model Alignment” (Yue Wu et al., 2024) - https://arxiv.org/abs/2405.00675

[00:18:00] GPT-4 Technical Report: development, capabilities, and calibration analysis - https://arxiv.org/abs/2303.08774

[00:22:40] Historical shift from descriptive to algebraic chess notation (FIDE) - https://en.wikipedia.org/wiki/Descriptive_notation

[00:23:55] Analysis of distribution shift in ML (Hendrycks et al.) - https://arxiv.org/abs/2006.16241

[00:27:40] Nicholas Carlini’s essay “Why I Attack” (June 2024) – motivations for security research - https://nicholas.carlini.com/writing/2024/why-i-attack.html

[00:34:05] Google Project Zero’s 90-day vulnerability disclosure policy - https://googleprojectzero.blogspot.com/p/vulnerability-disclosure-policy.html

[00:51:15] Evolution of Google search syntax & user behavior (Daniel M. Russell) - https://www.amazon.com/Joy-Search-Google-Master-Information/dp/0262042878

[01:04:05] Rust’s ownership & borrowing system for memory safety - https://doc.rust-lang.org/book/ch04-00-understanding-ownership.html

[01:10:05] Paper: “Stealing Part of a Production Language Model” (Carlini et al., March 2024) – extraction attacks on ChatGPT, PaLM-2 - https://arxiv.org/abs/2403.06634

[01:10:55] First model stealing paper (Tramèr et al., 2016) – attacking ML APIs via prediction - https://arxiv.org/abs/1609.02943