Afleveringen
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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
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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
<trunc, see PDF/YT>
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Zijn er afleveringen die ontbreken?
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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
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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:
***
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 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
<truncated, see show notes/YT>
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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/
***
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)
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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
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Join Prof. Subbarao Kambhampati and host Tim Scarfe for a deep dive into OpenAI's O1 model and the future of AI reasoning systems.
* How O1 likely uses reinforcement learning similar to AlphaGo, with hidden reasoning tokens that users pay for but never see
* The evolution from traditional Large Language Models to more sophisticated reasoning systems
* The concept of "fractal intelligence" in AI - where models work brilliantly sometimes but fail unpredictably
* Why O1's improved performance comes with substantial computational costs
* The ongoing debate between single-model approaches (OpenAI) vs hybrid systems (Google)
* The critical distinction between AI as an intelligence amplifier vs autonomous decision-maker
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/
***
TOC:
1. **O1 Architecture and Reasoning Foundations**
[00:00:00] 1.1 Fractal Intelligence and Reasoning Model Limitations
[00:04:28] 1.2 LLM Evolution: From Simple Prompting to Advanced Reasoning
[00:14:28] 1.3 O1's Architecture and AlphaGo-like Reasoning Approach
[00:23:18] 1.4 Empirical Evaluation of O1's Planning Capabilities
2. **Monte Carlo Methods and Model Deep-Dive**
[00:29:30] 2.1 Monte Carlo Methods and MARCO-O1 Implementation
[00:31:30] 2.2 Reasoning vs. Retrieval in LLM Systems
[00:40:40] 2.3 Fractal Intelligence Capabilities and Limitations
[00:45:59] 2.4 Mechanistic Interpretability of Model Behavior
[00:51:41] 2.5 O1 Response Patterns and Performance Analysis
3. **System Design and Real-World Applications**
[00:59:30] 3.1 Evolution from LLMs to Language Reasoning Models
[01:06:48] 3.2 Cost-Efficiency Analysis: LLMs vs O1
[01:11:28] 3.3 Autonomous vs Human-in-the-Loop Systems
[01:16:01] 3.4 Program Generation and Fine-Tuning Approaches
[01:26:08] 3.5 Hybrid Architecture Implementation Strategies
Transcript: https://www.dropbox.com/scl/fi/d0ef4ovnfxi0lknirkvft/Subbarao.pdf?rlkey=l3rp29gs4hkut7he8u04mm1df&dl=0
REFS:
[00:02:00] Monty Python (1975)
Witch trial scene: flawed logical reasoning.
https://www.youtube.com/watch?v=zrzMhU_4m-g
[00:04:00] Cade Metz (2024)
Microsoft–OpenAI partnership evolution and control dynamics.
https://www.nytimes.com/2024/10/17/technology/microsoft-openai-partnership-deal.html
[00:07:25] Kojima et al. (2022)
Zero-shot chain-of-thought prompting ('Let's think step by step').
https://arxiv.org/pdf/2205.11916
[00:12:50] DeepMind Research Team (2023)
Multi-bot game solving with external and internal planning.
https://deepmind.google/research/publications/139455/
[00:15:10] Silver et al. (2016)
AlphaGo's Monte Carlo Tree Search and Q-learning.
https://www.nature.com/articles/nature16961
[00:16:30] Kambhampati, S. et al. (2023)
Evaluates O1's planning in "Strawberry Fields" benchmarks.
https://arxiv.org/pdf/2410.02162
[00:29:30] Alibaba AIDC-AI Team (2023)
MARCO-O1: Chain-of-Thought + MCTS for improved reasoning.
https://arxiv.org/html/2411.14405
[00:31:30] Kambhampati, S. (2024)
Explores LLM "reasoning vs retrieval" debate.
https://arxiv.org/html/2403.04121v2
[00:37:35] Wei, J. et al. (2022)
Chain-of-thought prompting (introduces last-letter concatenation).
https://arxiv.org/pdf/2201.11903
[00:42:35] Barbero, F. et al. (2024)
Transformer attention and "information over-squashing."
https://arxiv.org/html/2406.04267v2
[00:46:05] Ruis, L. et al. (2023)
Influence functions to understand procedural knowledge in LLMs.
https://arxiv.org/html/2411.12580v1
(truncated - continued in shownotes/transcript doc)
-
Laura Ruis, a PhD student at University College London and researcher at Cohere, explains her groundbreaking research into how large language models (LLMs) perform reasoning tasks, the fundamental mechanisms underlying LLM reasoning capabilities, and whether these models primarily rely on retrieval or develop procedural knowledge.
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/
***
TOC
1. LLM Foundations and Learning
1.1 Scale and Learning in Language Models [00:00:00]
1.2 Procedural Knowledge vs Fact Retrieval [00:03:40]
1.3 Influence Functions and Model Analysis [00:07:40]
1.4 Role of Code in LLM Reasoning [00:11:10]
1.5 Semantic Understanding and Physical Grounding [00:19:30]
2. Reasoning Architectures and Measurement
2.1 Measuring Understanding and Reasoning in Language Models [00:23:10]
2.2 Formal vs Approximate Reasoning and Model Creativity [00:26:40]
2.3 Symbolic vs Subsymbolic Computation Debate [00:34:10]
2.4 Neural Network Architectures and Tensor Product Representations [00:40:50]
3. AI Agency and Risk Assessment
3.1 Agency and Goal-Directed Behavior in Language Models [00:45:10]
3.2 Defining and Measuring Agency in AI Systems [00:49:50]
3.3 Core Knowledge Systems and Agency Detection [00:54:40]
3.4 Language Models as Agent Models and Simulator Theory [01:03:20]
3.5 AI Safety and Societal Control Mechanisms [01:07:10]
3.6 Evolution of AI Capabilities and Emergent Risks [01:14:20]
REFS:
[00:01:10] Procedural Knowledge in Pretraining & LLM Reasoning
Ruis et al., 2024
https://arxiv.org/abs/2411.12580
[00:03:50] EK-FAC Influence Functions in Large LMs
Grosse et al., 2023
https://arxiv.org/abs/2308.03296
[00:13:05] Surfaces and Essences: Analogy as the Core of Cognition
Hofstadter & Sander
https://www.amazon.com/Surfaces-Essences-Analogy-Fuel-Thinking/dp/0465018475
[00:13:45] Wittgenstein on Language Games
https://plato.stanford.edu/entries/wittgenstein/
[00:14:30] Montague Semantics for Natural Language
https://plato.stanford.edu/entries/montague-semantics/
[00:19:35] The Chinese Room Argument
David Cole
https://plato.stanford.edu/entries/chinese-room/
[00:19:55] ARC: Abstraction and Reasoning Corpus
François Chollet
https://arxiv.org/abs/1911.01547
[00:24:20] Systematic Generalization in Neural Nets
Lake & Baroni, 2023
https://www.nature.com/articles/s41586-023-06668-3
[00:27:40] Open-Endedness & Creativity in AI
Tim Rocktäschel
https://arxiv.org/html/2406.04268v1
[00:30:50] Fodor & Pylyshyn on Connectionism
https://www.sciencedirect.com/science/article/abs/pii/0010027788900315
[00:31:30] Tensor Product Representations
Smolensky, 1990
https://www.sciencedirect.com/science/article/abs/pii/000437029090007M
[00:35:50] DreamCoder: Wake-Sleep Program Synthesis
Kevin Ellis et al.
https://courses.cs.washington.edu/courses/cse599j1/22sp/papers/dreamcoder.pdf
[00:36:30] Compositional Generalization Benchmarks
Ruis, Lake et al., 2022
https://arxiv.org/pdf/2202.10745
[00:40:30] RNNs & Tensor Products
McCoy et al., 2018
https://arxiv.org/abs/1812.08718
[00:46:10] Formal Causal Definition of Agency
Kenton et al.
https://arxiv.org/pdf/2208.08345v2
[00:48:40] Agency in Language Models
Sumers et al.
https://arxiv.org/abs/2309.02427
[00:55:20] Heider & Simmel’s Moving Shapes Experiment
https://www.nature.com/articles/s41598-024-65532-0
[01:00:40] Language Models as Agent Models
Jacob Andreas, 2022
https://arxiv.org/abs/2212.01681
[01:13:35] Pragmatic Understanding in LLMs
Ruis et al.
https://arxiv.org/abs/2210.14986
-
Jürgen Schmidhuber, the father of generative AI, challenges current AI narratives, revealing that early deep learning work is in his opinion misattributed, where it actually originated in Ukraine and Japan. He discusses his early work on linear transformers and artificial curiosity which preceded modern developments, shares his expansive vision of AI colonising space, and explains his groundbreaking 1991 consciousness model. Schmidhuber dismisses fears of human-AI conflict, arguing that superintelligent AI scientists will be fascinated by their own origins and motivated to protect life rather than harm it, while being more interested in other superintelligent AI and in cosmic expansion than earthly matters. He offers unique insights into how humans and AI might coexist.This was the long-awaited second, unreleased part of our interview we filmed last time. 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/***Interviewer: Tim ScarfeTOC[00:00:00] The Nature and Motivations of AI [00:02:08] Influential Inventions: 20th vs. 21st Century [00:05:28] Transformer and GPT: A Reflection The revolutionary impact of modern language models, the 1991 linear transformer, linear vs. quadratic scaling, the fast weight controller, and fast weight matrix memory.[00:11:03] Pioneering Contributions to AI and Deep Learning The invention of the transformer, pre-trained networks, the first GANs, the role of predictive coding, and the emergence of artificial curiosity.[00:13:58] AI's Evolution and Achievements The role of compute, breakthroughs in handwriting recognition and computer vision, the rise of GPU-based CNNs, achieving superhuman results, and Japanese contributions to CNN development.[00:15:40] The Hardware Lottery and GPUs GPUs as a serendipitous advantage for AI, the gaming-AI parallel, and Nvidia's strategic shift towards AI.[00:19:58] AI Applications and Societal Impact AI-powered translation breaking communication barriers, AI in medicine for imaging and disease prediction, and AI's potential for human enhancement and sustainable development.[00:23:26] The Path to AGI and Current Limitations Distinguishing large language models from AGI, challenges in replacing physical world workers, and AI's difficulty in real-world versus board games.[00:25:56] AI and Consciousness Simulating consciousness through unsupervised learning, chunking and automatizing neural networks, data compression, and self-symbols in predictive world models.[00:30:50] The Future of AI and Humanity Transition from AGIs as tools to AGIs with their own goals, the role of humans in an AGI-dominated world, and the concept of Homo Ludens.[00:38:05] The AI Race: Europe, China, and the US Europe's historical contributions, current dominance of the US and East Asia, and the role of venture capital and industrial policy.[00:50:32] Addressing AI Existential Risk The obsession with AI existential risk, commercial pressure for friendly AIs, AI vs. hydrogen bombs, and the long-term future of AI.[00:58:00] The Fermi Paradox and Extraterrestrial Intelligence Expanding AI bubbles as an explanation for the Fermi paradox, dark matter and encrypted civilizations, and Earth as the first to spawn an AI bubble.[01:02:08] The Diversity of AI and AI Ecologies The unrealism of a monolithic super intelligence, diverse AIs with varying goals, and intense competition and collaboration in AI ecologies.[01:12:21] Final Thoughts and Closing Remarks REFERENCES:See pinned comment on YT: https://youtu.be/fZYUqICYCAk
-
Professor Yoshua Bengio is a pioneer in deep learning and Turing Award winner. Bengio talks about AI safety, why goal-seeking “agentic” AIs might be dangerous, and his vision for building powerful AI tools without giving them agency. Topics include reward tampering risks, instrumental convergence, global AI governance, and how non-agent AIs could revolutionize science and medicine while reducing existential threats. Perfect for anyone curious about advanced AI risks and how to manage them responsibly.
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?
They are hosting an event in Zurich on January 9th with the ARChitects, join if you can.
Goto https://tufalabs.ai/
***
Interviewer: Tim Scarfe
Yoshua Bengio:
https://x.com/Yoshua_Bengio
https://scholar.google.com/citations?user=kukA0LcAAAAJ&hl=en
https://yoshuabengio.org/
https://en.wikipedia.org/wiki/Yoshua_Bengio
TOC:
1. AI Safety Fundamentals
[00:00:00] 1.1 AI Safety Risks and International Cooperation
[00:03:20] 1.2 Fundamental Principles vs Scaling in AI Development
[00:11:25] 1.3 System 1/2 Thinking and AI Reasoning Capabilities
[00:15:15] 1.4 Reward Tampering and AI Agency Risks
[00:25:17] 1.5 Alignment Challenges and Instrumental Convergence
2. AI Architecture and Safety Design
[00:33:10] 2.1 Instrumental Goals and AI Safety Fundamentals
[00:35:02] 2.2 Separating Intelligence from Goals in AI Systems
[00:40:40] 2.3 Non-Agent AI as Scientific Tools
[00:44:25] 2.4 Oracle AI Systems and Mathematical Safety Frameworks
3. Global Governance and Security
[00:49:50] 3.1 International AI Competition and Hardware Governance
[00:51:58] 3.2 Military and Security Implications of AI Development
[00:56:07] 3.3 Personal Evolution of AI Safety Perspectives
[01:00:25] 3.4 AI Development Scaling and Global Governance Challenges
[01:12:10] 3.5 AI Regulation and Corporate Oversight
4. Technical Innovations
[01:23:00] 4.1 Evolution of Neural Architectures: From RNNs to Transformers
[01:26:02] 4.2 GFlowNets and Symbolic Computation
[01:30:47] 4.3 Neural Dynamics and Consciousness
[01:34:38] 4.4 AI Creativity and Scientific Discovery
SHOWNOTES (Transcript, references, best clips etc):
https://www.dropbox.com/scl/fi/ajucigli8n90fbxv9h94x/BENGIO_SHOW.pdf?rlkey=38hi2m19sylnr8orb76b85wkw&dl=0
CORE REFS (full list in shownotes and pinned comment):
[00:00:15] Bengio et al.: "AI Risk" Statement
https://www.safe.ai/work/statement-on-ai-risk
[00:23:10] Bengio on reward tampering & AI safety (Harvard Data Science Review)
https://hdsr.mitpress.mit.edu/pub/w974bwb0
[00:40:45] Munk Debate on AI existential risk, featuring Bengio
https://munkdebates.com/debates/artificial-intelligence
[00:44:30] "Can a Bayesian Oracle Prevent Harm from an Agent?" (Bengio et al.) on oracle-to-agent safety
https://arxiv.org/abs/2408.05284
[00:51:20] Bengio (2024) memo on hardware-based AI governance verification
https://yoshuabengio.org/wp-content/uploads/2024/08/FlexHEG-Memo_August-2024.pdf
[01:12:55] Bengio’s involvement in EU AI Act code of practice
https://digital-strategy.ec.europa.eu/en/news/meet-chairs-leading-development-first-general-purpose-ai-code-practice
[01:27:05] Complexity-based compositionality theory (Elmoznino, Jiralerspong, Bengio, Lajoie)
https://arxiv.org/abs/2410.14817
[01:29:00] GFlowNet Foundations (Bengio et al.) for probabilistic inference
https://arxiv.org/pdf/2111.09266
[01:32:10] Discrete attractor states in neural systems (Nam, Elmoznino, Bengio, Lajoie)
https://arxiv.org/pdf/2302.06403
-
François Chollet discusses the outcomes of the ARC-AGI (Abstraction and Reasoning Corpus) Prize competition in 2024, where accuracy rose from 33% to 55.5% on a private evaluation set.
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?
They are hosting an event in Zurich on January 9th with the ARChitects, join if you can.
Goto https://tufalabs.ai/
***
Read about the recent result on o3 with ARC here (Chollet knew about it at the time of the interview but wasn't allowed to say):
https://arcprize.org/blog/oai-o3-pub-breakthrough
TOC:
1. Introduction and Opening
[00:00:00] 1.1 Deep Learning vs. Symbolic Reasoning: François’s Long-Standing Hybrid View
[00:00:48] 1.2 “Why Do They Call You a Symbolist?” – Addressing Misconceptions
[00:01:31] 1.3 Defining Reasoning
3. ARC Competition 2024 Results and Evolution
[00:07:26] 3.1 ARC Prize 2024: Reflecting on the Narrative Shift Toward System 2
[00:10:29] 3.2 Comparing Private Leaderboard vs. Public Leaderboard Solutions
[00:13:17] 3.3 Two Winning Approaches: Deep Learning–Guided Program Synthesis and Test-Time Training
4. Transduction vs. Induction in ARC
[00:16:04] 4.1 Test-Time Training, Overfitting Concerns, and Developer-Aware Generalization
[00:19:35] 4.2 Gradient Descent Adaptation vs. Discrete Program Search
5. ARC-2 Development and Future Directions
[00:23:51] 5.1 Ensemble Methods, Benchmark Flaws, and the Need for ARC-2
[00:25:35] 5.2 Human-Level Performance Metrics and Private Test Sets
[00:29:44] 5.3 Task Diversity, Redundancy Issues, and Expanded Evaluation Methodology
6. Program Synthesis Approaches
[00:30:18] 6.1 Induction vs. Transduction
[00:32:11] 6.2 Challenges of Writing Algorithms for Perceptual vs. Algorithmic Tasks
[00:34:23] 6.3 Combining Induction and Transduction
[00:37:05] 6.4 Multi-View Insight and Overfitting Regulation
7. Latent Space and Graph-Based Synthesis
[00:38:17] 7.1 Clément Bonnet’s Latent Program Search Approach
[00:40:10] 7.2 Decoding to Symbolic Form and Local Discrete Search
[00:41:15] 7.3 Graph of Operators vs. Token-by-Token Code Generation
[00:45:50] 7.4 Iterative Program Graph Modifications and Reusable Functions
8. Compute Efficiency and Lifelong Learning
[00:48:05] 8.1 Symbolic Process for Architecture Generation
[00:50:33] 8.2 Logarithmic Relationship of Compute and Accuracy
[00:52:20] 8.3 Learning New Building Blocks for Future Tasks
9. AI Reasoning and Future Development
[00:53:15] 9.1 Consciousness as a Self-Consistency Mechanism in Iterative Reasoning
[00:56:30] 9.2 Reconciling Symbolic and Connectionist Views
[01:00:13] 9.3 System 2 Reasoning - Awareness and Consistency
[01:03:05] 9.4 Novel Problem Solving, Abstraction, and Reusability
10. Program Synthesis and Research Lab
[01:05:53] 10.1 François Leaving Google to Focus on Program Synthesis
[01:09:55] 10.2 Democratizing Programming and Natural Language Instruction
11. Frontier Models and O1 Architecture
[01:14:38] 11.1 Search-Based Chain of Thought vs. Standard Forward Pass
[01:16:55] 11.2 o1’s Natural Language Program Generation and Test-Time Compute Scaling
[01:19:35] 11.3 Logarithmic Gains with Deeper Search
12. ARC Evaluation and Human Intelligence
[01:22:55] 12.1 LLMs as Guessing Machines and Agent Reliability Issues
[01:25:02] 12.2 ARC-2 Human Testing and Correlation with g-Factor
[01:26:16] 12.3 Closing Remarks and Future Directions
SHOWNOTES PDF:
https://www.dropbox.com/scl/fi/ujaai0ewpdnsosc5mc30k/CholletNeurips.pdf?rlkey=s68dp432vefpj2z0dp5wmzqz6&st=hazphyx5&dl=0
-
AI professor Jeff Clune ruminates on open-ended evolutionary algorithms—systems designed to generate novel and interesting outcomes forever. Drawing inspiration from nature’s boundless creativity, Clune and his collaborators aim to build “Darwin Complete” search spaces, where any computable environment can be simulated. By harnessing the power of large language models and reinforcement learning, these AI agents continuously develop new skills, explore uncharted domains, and even cooperate with one another in complex tasks.
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 reasoning and AGI. Are you interested in working on reasoning, or getting involved in their events?
They are hosting an event in Zurich on January 9th with the ARChitects, join if you can.
Goto https://tufalabs.ai/
***
A central theme throughout Clune’s work is “interestingness”: an elusive quality that nudges AI agents toward genuinely original discoveries. Rather than rely on narrowly defined metrics—which often fail due to Goodhart’s Law—Clune employs language models to serve as proxies for human judgment. In doing so, he ensures that “interesting” always reflects authentic novelty, opening the door to unending innovation.
Yet with these extraordinary possibilities come equally significant risks. Clune says we need AI safety measures—particularly as the technology matures into powerful, open-ended forms. Potential pitfalls include agents inadvertently causing harm or malicious actors subverting AI’s capabilities for destructive ends. To mitigate this, Clune advocates for prudent governance involving democratic coalitions, regulation of cutting-edge models, and global alignment protocols.
Jeff Clune:
https://x.com/jeffclune
http://jeffclune.com/
(Interviewer: Tim Scarfe)
TOC:
1. Introduction
[00:00:00] 1.1 Overview and Opening Thoughts
2. Sponsorship
[00:03:00] 2.1 TufaAI Labs and CentML
3. Evolutionary AI Foundations
[00:04:12] 3.1 Open-Ended Algorithm Development and Abstraction Approaches
[00:07:56] 3.2 Novel Intelligence Forms and Serendipitous Discovery
[00:11:46] 3.3 Frontier Models and the 'Interestingness' Problem
[00:30:36] 3.4 Darwin Complete Systems and Evolutionary Search Spaces
4. System Architecture and Learning
[00:37:35] 4.1 Code Generation vs Neural Networks Comparison
[00:41:04] 4.2 Thought Cloning and Behavioral Learning Systems
[00:47:00] 4.3 Language Emergence in AI Systems
[00:50:23] 4.4 AI Interpretability and Safety Monitoring Techniques
5. AI Safety and Governance
[00:53:56] 5.1 Language Model Consistency and Belief Systems
[00:57:00] 5.2 AI Safety Challenges and Alignment Limitations
[01:02:07] 5.3 Open Source AI Development and Value Alignment
[01:08:19] 5.4 Global AI Governance and Development Control
6. Advanced AI Systems and Evolution
[01:16:55] 6.1 Agent Systems and Performance Evaluation
[01:22:45] 6.2 Continuous Learning Challenges and In-Context Solutions
[01:26:46] 6.3 Evolution Algorithms and Environment Generation
[01:35:36] 6.4 Evolutionary Biology Insights and Experiments
[01:48:08] 6.5 Personal Journey from Philosophy to AI Research
Shownotes:
We craft detailed show notes for each episode with high quality transcript and references and best parts bolded.
https://www.dropbox.com/scl/fi/fz43pdoc5wq5jh7vsnujl/JEFFCLUNE.pdf?rlkey=uu0e70ix9zo6g5xn6amykffpm&st=k2scxteu&dl=0
-
Neel Nanda, a senior research scientist at Google DeepMind, leads their mechanistic interpretability team. In this extensive interview, he discusses his work trying to understand how neural networks function internally. At just 25 years old, Nanda has quickly become a prominent voice in AI research after completing his pure mathematics degree at Cambridge in 2020.
Nanda reckons that machine learning is unique because we create neural networks that can perform impressive tasks (like complex reasoning and software engineering) without understanding how they work internally. He compares this to having computer programs that can do things no human programmer knows how to write. His work focuses on "mechanistic interpretability" - attempting to uncover and understand the internal structures and algorithms that emerge within these networks.
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 ARC and AGI, they just acquired MindsAI - the current winners of the ARC challenge. Are you interested in working on ARC, or getting involved in their events? Goto https://tufalabs.ai/
***
SHOWNOTES, TRANSCRIPT, ALL REFERENCES (DONT MISS!):
https://www.dropbox.com/scl/fi/36dvtfl3v3p56hbi30im7/NeelShow.pdf?rlkey=pq8t7lyv2z60knlifyy17jdtx&st=kiutudhc&dl=0
We riff on:
* How neural networks develop meaningful internal representations beyond simple pattern matching
* The effectiveness of chain-of-thought prompting and why it improves model performance
* The importance of hands-on coding over extensive paper reading for new researchers
* His journey from Cambridge to working with Chris Olah at Anthropic and eventually Google DeepMind
* The role of mechanistic interpretability in AI safety
NEEL NANDA:
https://www.neelnanda.io/
https://scholar.google.com/citations?user=GLnX3MkAAAAJ&hl=en
https://x.com/NeelNanda5
Interviewer - Tim Scarfe
TOC:
1. Part 1: Introduction
[00:00:00] 1.1 Introduction and Core Concepts Overview
2. Part 2: Outside Interview
[00:06:45] 2.1 Mechanistic Interpretability Foundations
3. Part 3: Main Interview
[00:32:52] 3.1 Mechanistic Interpretability
4. Neural Architecture and Circuits
[01:00:31] 4.1 Biological Evolution Parallels
[01:04:03] 4.2 Universal Circuit Patterns and Induction Heads
[01:11:07] 4.3 Entity Detection and Knowledge Boundaries
[01:14:26] 4.4 Mechanistic Interpretability and Activation Patching
5. Model Behavior Analysis
[01:30:00] 5.1 Golden Gate Claude Experiment and Feature Amplification
[01:33:27] 5.2 Model Personas and RLHF Behavior Modification
[01:36:28] 5.3 Steering Vectors and Linear Representations
[01:40:00] 5.4 Hallucinations and Model Uncertainty
6. Sparse Autoencoder Architecture
[01:44:54] 6.1 Architecture and Mathematical Foundations
[02:22:03] 6.2 Core Challenges and Solutions
[02:32:04] 6.3 Advanced Activation Functions and Top-k Implementations
[02:34:41] 6.4 Research Applications in Transformer Circuit Analysis
7. Feature Learning and Scaling
[02:48:02] 7.1 Autoencoder Feature Learning and Width Parameters
[03:02:46] 7.2 Scaling Laws and Training Stability
[03:11:00] 7.3 Feature Identification and Bias Correction
[03:19:52] 7.4 Training Dynamics Analysis Methods
8. Engineering Implementation
[03:23:48] 8.1 Scale and Infrastructure Requirements
[03:25:20] 8.2 Computational Requirements and Storage
[03:35:22] 8.3 Chain-of-Thought Reasoning Implementation
[03:37:15] 8.4 Latent Structure Inference in Language Models
-
Jonas Hübotter, PhD student at ETH Zurich's Institute for Machine Learning, discusses his groundbreaking research on test-time computation and local learning. He demonstrates how smaller models can outperform larger ones by 30x through strategic test-time computation and introduces a novel paradigm combining inductive and transductive learning approaches.
Using Bayesian linear regression as a surrogate model for uncertainty estimation, Jonas explains how models can efficiently adapt to specific tasks without massive pre-training. He draws an analogy to Google Earth's variable resolution system to illustrate dynamic resource allocation based on task complexity.
The conversation explores the future of AI architecture, envisioning systems that continuously learn and adapt beyond current monolithic models. Jonas concludes by proposing hybrid deployment strategies combining local and cloud computation, suggesting a future where compute resources are allocated based on task complexity rather than fixed model size.
This research represents a significant shift in machine learning, prioritizing intelligent resource allocation and adaptive learning over traditional scaling approaches.
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 ARC and AGI, they just acquired MindsAI - the current winners of the ARC challenge. Are you interested in working on ARC, or getting involved in their events? Goto https://tufalabs.ai/
Transcription, references and show notes PDF download:
https://www.dropbox.com/scl/fi/cxg80p388snwt6qbp4m52/JonasFinal.pdf?rlkey=glk9mhpzjvesanlc14rtpvk4r&st=6qwi8n3x&dl=0
Jonas Hübotter
https://jonhue.github.io/
https://scholar.google.com/citations?user=pxi_RkwAAAAJ
Transductive Active Learning: Theory and Applications (NeurIPS 2024)
https://arxiv.org/pdf/2402.15898
EFFICIENTLY LEARNING AT TEST-TIME: ACTIVE FINE-TUNING OF LLMS (SIFT)
https://arxiv.org/pdf/2410.08020
TOC:
1. Test-Time Computation Fundamentals
[00:00:00] Intro
[00:03:10] 1.1 Test-Time Computation and Model Performance Comparison
[00:05:52] 1.2 Retrieval Augmentation and Machine Teaching Strategies
[00:09:40] 1.3 In-Context Learning vs Fine-Tuning Trade-offs
2. System Architecture and Intelligence
[00:15:58] 2.1 System Architecture and Intelligence Emergence
[00:23:22] 2.2 Active Inference and Constrained Agency in AI
[00:29:52] 2.3 Evolution of Local Learning Methods
[00:32:05] 2.4 Vapnik's Contributions to Transductive Learning
3. Resource Optimization and Local Learning
[00:34:35] 3.1 Computational Resource Allocation in ML Models
[00:35:30] 3.2 Historical Context and Traditional ML Optimization
[00:37:55] 3.3 Variable Resolution Processing and Active Inference in ML
[00:43:01] 3.4 Local Learning and Base Model Capacity Trade-offs
[00:48:04] 3.5 Active Learning vs Local Learning Approaches
4. Information Retrieval and Model Interpretability
[00:51:08] 4.1 Information Retrieval and Nearest Neighbor Limitations
[01:03:07] 4.2 Model Interpretability and Surrogate Models
[01:15:03] 4.3 Bayesian Uncertainty Estimation and Surrogate Models
5. Distributed Systems and Deployment
[01:23:56] 5.1 Memory Architecture and Controller Systems
[01:28:14] 5.2 Evolution from Static to Distributed Learning Systems
[01:38:03] 5.3 Transductive Learning and Model Specialization
[01:41:58] 5.4 Hybrid Local-Cloud Deployment Strategies
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Professor Swarat Chaudhuri from the University of Texas at Austin and visiting researcher at Google DeepMind discusses breakthroughs in AI reasoning, theorem proving, and mathematical discovery. Chaudhuri explains his groundbreaking work on COPRA (a GPT-based prover agent), shares insights on neurosymbolic approaches to AI.
Professor Swarat Chaudhuri:
https://www.cs.utexas.edu/~swarat/
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 ARC and AGI, they just acquired MindsAI - the current winners of the ARC challenge. Are you interested in working on ARC, or getting involved in their events? Goto https://tufalabs.ai/
TOC:
[00:00:00] 0. Introduction / CentML ad, Tufa ad
1. AI Reasoning: From Language Models to Neurosymbolic Approaches
[00:02:27] 1.1 Defining Reasoning in AI
[00:09:51] 1.2 Limitations of Current Language Models
[00:17:22] 1.3 Neuro-symbolic Approaches and Program Synthesis
[00:24:59] 1.4 COPRA and In-Context Learning for Theorem Proving
[00:34:39] 1.5 Symbolic Regression and LLM-Guided Abstraction
2. AI in Mathematics: Theorem Proving and Concept Discovery
[00:43:37] 2.1 AI-Assisted Theorem Proving and Proof Verification
[01:01:37] 2.2 Symbolic Regression and Concept Discovery in Mathematics
[01:11:57] 2.3 Scaling and Modularizing Mathematical Proofs
[01:21:53] 2.4 COPRA: In-Context Learning for Formal Theorem-Proving
[01:28:22] 2.5 AI-driven theorem proving and mathematical discovery
3. Formal Methods and Challenges in AI Mathematics
[01:30:42] 3.1 Formal proofs, empirical predicates, and uncertainty in AI mathematics
[01:34:01] 3.2 Characteristics of good theoretical computer science research
[01:39:16] 3.3 LLMs in theorem generation and proving
[01:42:21] 3.4 Addressing contamination and concept learning in AI systems
REFS:
00:04:58 The Chinese Room Argument, https://plato.stanford.edu/entries/chinese-room/
00:11:42 Software 2.0, https://medium.com/@karpathy/software-2-0-a64152b37c35
00:11:57 Solving Olympiad Geometry Without Human Demonstrations, https://www.nature.com/articles/s41586-023-06747-5
00:13:26 Lean, https://lean-lang.org/
00:15:43 A General Reinforcement Learning Algorithm That Masters Chess, Shogi, and Go Through Self-Play, https://www.science.org/doi/10.1126/science.aar6404
00:19:24 DreamCoder (Ellis et al., PLDI 2021), https://arxiv.org/abs/2006.08381
00:24:37 The Lambda Calculus, https://plato.stanford.edu/entries/lambda-calculus/
00:26:43 Neural Sketch Learning for Conditional Program Generation, https://arxiv.org/pdf/1703.05698
00:28:08 Learning Differentiable Programs With Admissible Neural Heuristics, https://arxiv.org/abs/2007.12101
00:31:03 Symbolic Regression With a Learned Concept Library (Grayeli et al., NeurIPS 2024), https://arxiv.org/abs/2409.09359
00:41:30 Formal Verification of Parallel Programs, https://dl.acm.org/doi/10.1145/360248.360251
01:00:37 Training Compute-Optimal Large Language Models, https://arxiv.org/abs/2203.15556
01:18:19 Chain-of-Thought Prompting Elicits Reasoning in Large Language Models, https://arxiv.org/abs/2201.11903
01:18:42 Draft, Sketch, and Prove: Guiding Formal Theorem Provers With Informal Proofs, https://arxiv.org/abs/2210.12283
01:19:49 Learning Formal Mathematics From Intrinsic Motivation, https://arxiv.org/pdf/2407.00695
01:20:19 An In-Context Learning Agent for Formal Theorem-Proving (Thakur et al., CoLM 2024), https://arxiv.org/pdf/2310.04353
01:23:58 Learning to Prove Theorems via Interacting With Proof Assistants, https://arxiv.org/abs/1905.09381
01:39:58 An In-Context Learning Agent for Formal Theorem-Proving (Thakur et al., CoLM 2024), https://arxiv.org/pdf/2310.04353
01:42:24 Programmatically Interpretable Reinforcement Learning (Verma et al., ICML 2018), https://arxiv.org/abs/1804.02477
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Nora Belrose, Head of Interpretability Research at EleutherAI, discusses critical challenges in AI safety and development. The conversation begins with her technical work on concept erasure in neural networks through LEACE (LEAst-squares Concept Erasure), while highlighting how neural networks' progression from simple to complex learning patterns could have important implications for AI safety.
Many fear that advanced AI will pose an existential threat -- pursuing its own dangerous goals once it's powerful enough. But Belrose challenges this popular doomsday scenario with a fascinating breakdown of why it doesn't add up.
Belrose also provides a detailed critique of current AI alignment approaches, particularly examining "counting arguments" and their limitations when applied to AI safety. She argues that the Principle of Indifference may be insufficient for addressing existential risks from advanced AI systems. The discussion explores how emergent properties in complex AI systems could lead to unpredictable and potentially dangerous behaviors that simple reductionist approaches fail to capture.
The conversation concludes by exploring broader philosophical territory, where Belrose discusses her growing interest in Buddhism's potential relevance to a post-automation future. She connects concepts of moral anti-realism with Buddhist ideas about emptiness and non-attachment, suggesting these frameworks might help humans find meaning in a world where AI handles most practical tasks. Rather than viewing this automated future with alarm, she proposes that Zen Buddhism's emphasis on spontaneity and presence might complement a society freed from traditional labor.
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 ARC and AGI, they just acquired MindsAI - the current winners of the ARC challenge. Are you interested in working on ARC, or getting involved in their events? Goto https://tufalabs.ai/
Nora Belrose:
https://norabelrose.com/
https://scholar.google.com/citations?user=p_oBc64AAAAJ&hl=en
https://x.com/norabelrose
SHOWNOTES:
https://www.dropbox.com/scl/fi/38fhsv2zh8gnubtjaoq4a/NORA_FINAL.pdf?rlkey=0e5r8rd261821g1em4dgv0k70&st=t5c9ckfb&dl=0
TOC:
1. Neural Network Foundations
[00:00:00] 1.1 Philosophical Foundations and Neural Network Simplicity Bias
[00:02:20] 1.2 LEACE and Concept Erasure Fundamentals
[00:13:16] 1.3 LISA Technical Implementation and Applications
[00:18:50] 1.4 Practical Implementation Challenges and Data Requirements
[00:22:13] 1.5 Performance Impact and Limitations of Concept Erasure
2. Machine Learning Theory
[00:32:23] 2.1 Neural Network Learning Progression and Simplicity Bias
[00:37:10] 2.2 Optimal Transport Theory and Image Statistics Manipulation
[00:43:05] 2.3 Grokking Phenomena and Training Dynamics
[00:44:50] 2.4 Texture vs Shape Bias in Computer Vision Models
[00:45:15] 2.5 CNN Architecture and Shape Recognition Limitations
3. AI Systems and Value Learning
[00:47:10] 3.1 Meaning, Value, and Consciousness in AI Systems
[00:53:06] 3.2 Global Connectivity vs Local Culture Preservation
[00:58:18] 3.3 AI Capabilities and Future Development Trajectory
4. Consciousness Theory
[01:03:03] 4.1 4E Cognition and Extended Mind Theory
[01:09:40] 4.2 Thompson's Views on Consciousness and Simulation
[01:12:46] 4.3 Phenomenology and Consciousness Theory
[01:15:43] 4.4 Critique of Illusionism and Embodied Experience
[01:23:16] 4.5 AI Alignment and Counting Arguments Debate
(TRUNCATED, TOC embedded in MP3 file with more information)
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Prof. Gennady Pekhimenko (CEO of CentML, UofT) joins us in this *sponsored episode* to dive deep into AI system optimization and enterprise implementation. From NVIDIA's technical leadership model to the rise of open-source AI, Pekhimenko shares insights on bridging the gap between academic research and industrial applications. Learn about "dark silicon," GPU utilization challenges in ML workloads, and how modern enterprises can optimize their AI infrastructure. The conversation explores why some companies achieve only 10% GPU efficiency and practical solutions for improving AI system performance. A must-watch for anyone interested in the technical foundations of enterprise AI and hardware optimization.
CentML offers competitive pricing for GenAI model deployment, with flexible options to suit a wide range of models, from small to large-scale deployments. Cheaper, faster, no commitments, pay as you go, scale massively, simple to setup. Check it out!
https://centml.ai/pricing/
SPONSOR MESSAGES:
MLST is also sponsored by Tufa AI Labs - https://tufalabs.ai/
They are hiring cracked ML engineers/researchers to work on ARC and build AGI!
SHOWNOTES (diarised transcript, TOC, references, summary, best quotes etc)
https://www.dropbox.com/scl/fi/w9kbpso7fawtm286kkp6j/Gennady.pdf?rlkey=aqjqmncx3kjnatk2il1gbgknk&st=2a9mccj8&dl=0
TOC:
1. AI Strategy and Leadership
[00:00:00] 1.1 Technical Leadership and Corporate Structure
[00:09:55] 1.2 Open Source vs Proprietary AI Models
[00:16:04] 1.3 Hardware and System Architecture Challenges
[00:23:37] 1.4 Enterprise AI Implementation and Optimization
[00:35:30] 1.5 AI Reasoning Capabilities and Limitations
2. AI System Development
[00:38:45] 2.1 Computational and Cognitive Limitations of AI Systems
[00:42:40] 2.2 Human-LLM Communication Adaptation and Patterns
[00:46:18] 2.3 AI-Assisted Software Development Challenges
[00:47:55] 2.4 Future of Software Engineering Careers in AI Era
[00:49:49] 2.5 Enterprise AI Adoption Challenges and Implementation
3. ML Infrastructure Optimization
[00:54:41] 3.1 MLOps Evolution and Platform Centralization
[00:55:43] 3.2 Hardware Optimization and Performance Constraints
[01:05:24] 3.3 ML Compiler Optimization and Python Performance
[01:15:57] 3.4 Enterprise ML Deployment and Cloud Provider Partnerships
4. Distributed AI Architecture
[01:27:05] 4.1 Multi-Cloud ML Infrastructure and Optimization
[01:29:45] 4.2 AI Agent Systems and Production Readiness
[01:32:00] 4.3 RAG Implementation and Fine-Tuning Considerations
[01:33:45] 4.4 Distributed AI Systems Architecture and Ray Framework
5. AI Industry Standards and Research
[01:37:55] 5.1 Origins and Evolution of MLPerf Benchmarking
[01:43:15] 5.2 MLPerf Methodology and Industry Impact
[01:50:17] 5.3 Academic Research vs Industry Implementation in AI
[01:58:59] 5.4 AI Research History and Safety Concerns
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Eliezer Yudkowsky and Stephen Wolfram discuss artificial intelligence and its potential existen‑
tial risks. They traversed fundamental questions about AI safety, consciousness, computational irreducibility, and the nature of intelligence.
The discourse centered on Yudkowsky’s argument that advanced AI systems pose an existential threat to humanity, primarily due to the challenge of alignment and the potential for emergent goals that diverge from human values. Wolfram, while acknowledging potential risks, approached the topic from a his signature measured perspective, emphasizing the importance of understanding computational systems’ fundamental nature and questioning whether AI systems would necessarily develop the kind of goal‑directed behavior Yudkowsky fears.
***
MLST IS SPONSORED BY TUFA AI LABS!
The current winners of the ARC challenge, MindsAI are part of Tufa AI Labs. They are hiring ML engineers. Are you interested?! Please goto https://tufalabs.ai/
***
TOC:
1. Foundational AI Concepts and Risks
[00:00:01] 1.1 AI Optimization and System Capabilities Debate
[00:06:46] 1.2 Computational Irreducibility and Intelligence Limitations
[00:20:09] 1.3 Existential Risk and Species Succession
[00:23:28] 1.4 Consciousness and Value Preservation in AI Systems
2. Ethics and Philosophy in AI
[00:33:24] 2.1 Moral Value of Human Consciousness vs. Computation
[00:36:30] 2.2 Ethics and Moral Philosophy Debate
[00:39:58] 2.3 Existential Risks and Digital Immortality
[00:43:30] 2.4 Consciousness and Personal Identity in Brain Emulation
3. Truth and Logic in AI Systems
[00:54:39] 3.1 AI Persuasion Ethics and Truth
[01:01:48] 3.2 Mathematical Truth and Logic in AI Systems
[01:11:29] 3.3 Universal Truth vs Personal Interpretation in Ethics and Mathematics
[01:14:43] 3.4 Quantum Mechanics and Fundamental Reality Debate
4. AI Capabilities and Constraints
[01:21:21] 4.1 AI Perception and Physical Laws
[01:28:33] 4.2 AI Capabilities and Computational Constraints
[01:34:59] 4.3 AI Motivation and Anthropomorphization Debate
[01:38:09] 4.4 Prediction vs Agency in AI Systems
5. AI System Architecture and Behavior
[01:44:47] 5.1 Computational Irreducibility and Probabilistic Prediction
[01:48:10] 5.2 Teleological vs Mechanistic Explanations of AI Behavior
[02:09:41] 5.3 Machine Learning as Assembly of Computational Components
[02:29:52] 5.4 AI Safety and Predictability in Complex Systems
6. Goal Optimization and Alignment
[02:50:30] 6.1 Goal Specification and Optimization Challenges in AI Systems
[02:58:31] 6.2 Intelligence, Computation, and Goal-Directed Behavior
[03:02:18] 6.3 Optimization Goals and Human Existential Risk
[03:08:49] 6.4 Emergent Goals and AI Alignment Challenges
7. AI Evolution and Risk Assessment
[03:19:44] 7.1 Inner Optimization and Mesa-Optimization Theory
[03:34:00] 7.2 Dynamic AI Goals and Extinction Risk Debate
[03:56:05] 7.3 AI Risk and Biological System Analogies
[04:09:37] 7.4 Expert Risk Assessments and Optimism vs Reality
8. Future Implications and Economics
[04:13:01] 8.1 Economic and Proliferation Considerations
SHOWNOTES (transcription, references, summary, best quotes etc):
https://www.dropbox.com/scl/fi/3st8dts2ba7yob161dchd/EliezerWolfram.pdf?rlkey=b6va5j8upgqwl9s2muc924vtt&st=vemwqx7a&dl=0
-
Francois Chollet, a prominent AI expert and creator of ARC-AGI, discusses intelligence, consciousness, and artificial intelligence.
Chollet explains that real intelligence isn't about memorizing information or having lots of knowledge - it's about being able to handle new situations effectively. This is why he believes current large language models (LLMs) have "near-zero intelligence" despite their impressive abilities. They're more like sophisticated memory and pattern-matching systems than truly intelligent beings.
***
MLST IS SPONSORED BY TUFA AI LABS!
The current winners of the ARC challenge, MindsAI are part of Tufa AI Labs. They are hiring ML engineers. Are you interested?! Please goto https://tufalabs.ai/
***
He introduced his "Kaleidoscope Hypothesis," which suggests that while the world seems infinitely complex, it's actually made up of simpler patterns that repeat and combine in different ways. True intelligence, he argues, involves identifying these basic patterns and using them to understand new situations.
Chollet also talked about consciousness, suggesting it develops gradually in children rather than appearing all at once. He believes consciousness exists in degrees - animals have it to some extent, and even human consciousness varies with age and circumstances (like being more conscious when learning something new versus doing routine tasks).
On AI safety, Chollet takes a notably different stance from many in Silicon Valley. He views AGI development as a scientific challenge rather than a religious quest, and doesn't share the apocalyptic concerns of some AI researchers. He argues that intelligence itself isn't dangerous - it's just a tool for turning information into useful models. What matters is how we choose to use it.
ARC-AGI Prize:
https://arcprize.org/
Francois Chollet:
https://x.com/fchollet
Shownotes:
https://www.dropbox.com/scl/fi/j2068j3hlj8br96pfa7bi/CHOLLET_FINAL.pdf?rlkey=xkbr7tbnrjdl66m246w26uc8k&st=0a4ec4na&dl=0
TOC:
1. Intelligence and Model Building
[00:00:00] 1.1 Intelligence Definition and ARC Benchmark
[00:05:40] 1.2 LLMs as Program Memorization Systems
[00:09:36] 1.3 Kaleidoscope Hypothesis and Abstract Building Blocks
[00:13:39] 1.4 Deep Learning Limitations and System 2 Reasoning
[00:29:38] 1.5 Intelligence vs. Skill in LLMs and Model Building
2. ARC Benchmark and Program Synthesis
[00:37:36] 2.1 Intelligence Definition and LLM Limitations
[00:41:33] 2.2 Meta-Learning System Architecture
[00:56:21] 2.3 Program Search and Occam's Razor
[00:59:42] 2.4 Developer-Aware Generalization
[01:06:49] 2.5 Task Generation and Benchmark Design
3. Cognitive Systems and Program Generation
[01:14:38] 3.1 System 1/2 Thinking Fundamentals
[01:22:17] 3.2 Program Synthesis and Combinatorial Challenges
[01:31:18] 3.3 Test-Time Fine-Tuning Strategies
[01:36:10] 3.4 Evaluation and Leakage Problems
[01:43:22] 3.5 ARC Implementation Approaches
4. Intelligence and Language Systems
[01:50:06] 4.1 Intelligence as Tool vs Agent
[01:53:53] 4.2 Cultural Knowledge Integration
[01:58:42] 4.3 Language and Abstraction Generation
[02:02:41] 4.4 Embodiment in Cognitive Systems
[02:09:02] 4.5 Language as Cognitive Operating System
5. Consciousness and AI Safety
[02:14:05] 5.1 Consciousness and Intelligence Relationship
[02:20:25] 5.2 Development of Machine Consciousness
[02:28:40] 5.3 Consciousness Prerequisites and Indicators
[02:36:36] 5.4 AGI Safety Considerations
[02:40:29] 5.5 AI Regulation Framework
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Anil Ananthaswamy is an award-winning science writer and former staff writer and deputy news editor for the London-based New Scientist magazine.
Machine learning systems are making life-altering decisions for us: approving mortgage loans, determining whether a tumor is cancerous, or deciding if someone gets bail. They now influence developments and discoveries in chemistry, biology, and physics—the study of genomes, extrasolar planets, even the intricacies of quantum systems. And all this before large language models such as ChatGPT came on the scene.
We are living through a revolution in machine learning-powered AI that shows no signs of slowing down. This technology is based on relatively simple mathematical ideas, some of which go back centuries, including linear algebra and calculus, the stuff of seventeenth- and eighteenth-century mathematics. It took the birth and advancement of computer science and the kindling of 1990s computer chips designed for video games to ignite the explosion of AI that we see today. In this enlightening book, Anil Ananthaswamy explains the fundamental math behind machine learning, while suggesting intriguing links between artificial and natural intelligence. Might the same math underpin them both?
As Ananthaswamy resonantly concludes, to make safe and effective use of artificial intelligence, we need to understand its profound capabilities and limitations, the clues to which lie in the math that makes machine learning possible.
Why Machines Learn: The Elegant Math Behind Modern AI:
https://amzn.to/3UAWX3D
https://anilananthaswamy.com/
Sponsor message:
DO YOU WANT WORK ON ARC with the MindsAI team (current ARC winners)?
Interested? Apply for an ML research position: [email protected]
Shownotes:
https://www.dropbox.com/scl/fi/wpv22m5jxyiqr6pqfkzwz/anil.pdf?rlkey=9c233jo5armr548ctwo419n6p&st=xzhahtje&dl=0
Chapters:
1. ML Fundamentals and Prerequisites
[00:00:00] 1.1 Differences Between Human and Machine Learning
[00:00:35] 1.2 Mathematical Prerequisites and Societal Impact of ML
[00:02:20] 1.3 Author's Journey and Book Background
[00:11:30] 1.4 Mathematical Foundations and Core ML Concepts
[00:21:45] 1.5 Bias-Variance Tradeoff and Modern Deep Learning
2. Deep Learning Architecture
[00:29:05] 2.1 Double Descent and Overparameterization in Deep Learning
[00:32:40] 2.2 Mathematical Foundations and Self-Supervised Learning
[00:40:05] 2.3 High-Dimensional Spaces and Model Architecture
[00:52:55] 2.4 Historical Development of Backpropagation
3. AI Understanding and Limitations
[00:59:13] 3.1 Pattern Matching vs Human Reasoning in ML Models
[01:00:20] 3.2 Mathematical Foundations and Pattern Recognition in AI
[01:04:08] 3.3 LLM Reliability and Machine Understanding Debate
[01:12:50] 3.4 Historical Development of Deep Learning Technologies
[01:15:21] 3.5 Alternative AI Approaches and Bio-inspired Methods
4. Ethical and Neurological Perspectives
[01:24:32] 4.1 Neural Network Scaling and Mathematical Limitations
[01:31:12] 4.2 AI Ethics and Societal Impact
[01:38:30] 4.3 Consciousness and Neurological Conditions
[01:46:17] 4.4 Body Ownership and Agency in Neuroscience
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