Uploaded April 2024 | Updated September 2026, 1 week ago
Professor Chris Bishop is a Technical Fellow and Director at Microsoft Research AI4Science, in Cambridge. He is also Honorary Professor of Computer Science at the University of Edinburgh, and a Fellow of Darwin College, Cambridge. In 2004, he was elected Fellow of the Royal Academy of Engineering, in 2007 he was elected Fellow of the Royal Society of Edinburgh, and in 2017 he was elected Fellow of the Royal Society. Chris was a founding member of the UK AI Council, and in 2019 he was appointed to the Prime Minister’s Council for Science and Technology.
At Microsoft Research, Chris oversees a global portfolio of industrial research and development, with a strong focus on machine learning and the natural sciences.
Chris obtained a BA in Physics from Oxford, and a PhD in Theoretical Physics from the University of Edinburgh, with a thesis on quantum field theory.
Chris's contributions to the field of machine learning have been truly remarkable. He has authored (what is arguably) the original textbook in the field - 'Pattern Recognition and Machine Learning' (PRML) which has served as an essential reference for countless students and researchers around the world, and that was his second textbook after his highly acclaimed first textbook Neural Networks for Pattern Recognition.
Recently, Chris has co-authored a new book with his son, Hugh, titled 'Deep Learning: Foundations and Concepts.' This book aims to provide a comprehensive understanding of the key ideas and techniques underpinning the rapidly evolving field of deep learning. It covers both the foundational concepts and the latest advances, making it an invaluable resource for newcomers and experienced practitioners alike.
Buy Chris' textbook here:
amzn.to/3vvLcCh
More about Prof. Chris Bishop:
en.wikipedia.org/wiki/Christopher_Bishop
microsoft.com/en-us/research/people/cmbishop
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TOC:
00:00:00 - Intro to Chris
00:06:54 - Changing Landscape of AI
00:08:16 - Symbolism
00:09:32 - PRML
00:11:02 - Bayesian Approach
00:14:49 - Are NNs One Model or Many, Special vs General
00:20:04 - Can Language Models Be Creative
00:22:35 - Sparks of AGI
00:25:52 - Creativity Gap in LLMs
00:35:40 - New Deep Learning Book
00:39:01 - Favourite Chapters
00:44:11 - Probability Theory
00:45:42 - AI4Science
00:48:31 - Inductive Priors
00:58:52 - Drug Discovery
01:05:19 - Foundational Bias Models
01:07:46 - How Fundamental Is Our Physics Knowledge?
01:12:05 - Transformers
01:12:59 - Why Does Deep Learning Work?
01:16:59 - Inscrutability of NNs
01:18:01 - Example of Simulator
01:21:09 - Control
Professor Chris Bishop is a Technical Fellow and Director at Microsoft Research AI4Science, in Cambridge. He is also Honorary Professor of Computer Science at the University of Edinburgh, and a Fellow of Darwin College, Cambridge. In 2004, he was elected Fellow of the Royal Academy of Engineering, in 2007 he was elected Fellow of the Royal Society of Edinburgh, and in 2017 he was elected Fellow of the Royal Society. Chris was a founding member of the UK AI Council, and in 2019 he was appointed to the Prime Minister’s Council for Science and Technology.
At Microsoft Research, Chris oversees a global portfolio of industrial research and development, with a strong focus on machine learning and the natural sciences.
Chris obtained a BA in Physics from Oxford, and a PhD in Theoretical Physics from the University of Edinburgh, with a thesis on quantum field theory.
Chris's contributions to the field of machine learning have been truly remarkable. He has authored (what is arguably) the original textbook in the field - 'Pattern Recognition and Machine Learning' (PRML) which has served as an essential reference for countless students and researchers around the world, and that was his second textbook after his highly acclaimed first textbook Neural Networks for Pattern Recognition.
Recently, Chris has co-authored a new book with his son, Hugh, titled 'Deep Learning: Foundations and Concepts.' This book aims to provide a comprehensive understanding of the key ideas and techniques underpinning the rapidly evolving field of deep learning. It covers both the foundational concepts and the latest advances, making it an invaluable resource for newcomers and experienced practitioners alike.
Buy Chris' textbook here:
amzn.to/3vvLcCh
More about Prof. Chris Bishop:
en.wikipedia.org/wiki/Christopher_Bishop
microsoft.com/en-us/research/people/cmbishop
Support MLST:
Please support us on Patreon. We are entirely funded from Patreon donations right now. Patreon supports get private discord access, biweekly calls, early-access + exclusive content and lots more.
patreon.com/mlst
Donate: paypal.com/donate/?hosted_button_id=K2TYRVPBGXVNA
If you would like to sponsor us, so we can tell your story - reach out on mlstreettalk at gmail
TOC:
00:00:00 - Intro to Chris
00:06:54 - Changing Landscape of AI
00:08:16 - Symbolism
00:09:32 - PRML
00:11:02 - Bayesian Approach
00:14:49 - Are NNs One Model or Many, Special vs General
00:20:04 - Can Language Models Be Creative
00:22:35 - Sparks of AGI
00:25:52 - Creativity Gap in LLMs
00:35:40 - New Deep Learning Book
00:39:01 - Favourite Chapters
00:44:11 - Probability Theory
00:45:42 - AI4Science
00:48:31 - Inductive Priors
00:58:52 - Drug Discovery
01:05:19 - Foundational Bias Models
01:07:46 - How Fundamental Is Our Physics Knowledge?
01:12:05 - Transformers
01:12:59 - Why Does Deep Learning Work?
01:16:59 - Inscrutability of NNs
01:18:01 - Example of Simulator
01:21:09 - Control
![Neural Networks Are Elastic Origami! [Prof. Randall Balestriero]](https://i.ytimg.com/vi/l3O2J3LMxqI/mqdefault.jpg "Neural Networks Are Elastic Origami! [Prof. Randall Balestriero]
Professor Randall Balestriero joins us to discuss neural network geometry, spline theory, and emerging phenomena in deep learning, based on research presented at ICML. Topics include the delayed emergence of adversarial robustness in neural networks (grokking), geometric interpretations of neural networks via spline theory, and challenges in reconstruction learning. We also cover geometric analysis of Large Language Models (LLMs) for toxicity detection and the relationship between intrinsic dimensionality and model control in RLHF.
SPONSOR MESSAGES:
***
CentML offers competitive pricing for GenAI model deployment, with flexible options to suit a wide range of models, from small to large-scale deployments.
https://centml.ai/pricing/
Tufa AI Labs is a brand new research lab in Zurich started by Benjamin Crouzier focussed on o-series style reasoning and AGI. Are you interested in working on reasoning, or getting involved in their events?
Goto https://tufalabs.ai/
***
Show notes and transcript: https://www.dropbox.com/scl/fi/3lufge4upq5gy0ug75j4a/RANDALLSHOW.pdf?rlkey=nbemgpa0jhawt1e86rx7372e4&dl=0
TOC:
[00:00:00] Introduction
1. Neural Network Geometry and Spline Theory
[00:01:41] 1.1 Neural Network Geometry and Spline Theory
[00:07:41] 1.2 Deep Networks Always Grok
[00:11:39] 1.3 Grokking and Adversarial Robustness
[00:16:09] 1.4 Double Descent and Catastrophic Forgetting
2. Reconstruction Learning
[00:18:49] 2.1 Reconstruction Learning
[00:24:15] 2.2 Frequency Bias in Neural Networks
3. Geometric Analysis of Neural Networks
[00:29:02] 3.1 Geometric Analysis of Neural Networks
[00:34:41] 3.2 Adversarial Examples and Region Concentration
4. LLM Safety and Geometric Analysis
[00:40:05] 4.1 LLM Safety and Geometric Analysis
[00:46:11] 4.2 Toxicity Detection in LLMs
[00:52:24] 4.3 Intrinsic Dimensionality and Model Control
[00:58:07] 4.4 RLHF and High-Dimensional Spaces
5. Conclusion
[01:02:13] 5.1 Neural Tangent Kernel
[01:08:07] 5.2 Conclusion
REFS:
[00:01:35] Balestriero/Humayun – Deep network geometry & input space partitioning
https://arxiv.org/html/2408.04809v1
[00:03:55] Balestriero & Paris – Linking deep networks to adaptive spline operators
https://proceedings.mlr.press/v80/balestriero18b/balestriero18b.pdf
[00:13:55] Song et al. – Gradient-based white-box adversarial attacks
https://arxiv.org/abs/2012.14965
[00:16:05] Humayun, Balestriero & Baraniuk – Grokking phenomenon & emergent robustness
https://arxiv.org/abs/2402.15555
[00:18:25] Humayun – Training dynamics & double descent via linear region evolution
https://arxiv.org/abs/2310.12977
[00:20:15] Balestriero – Power diagram partitions in DNN decision boundaries
https://arxiv.org/abs/1905.08443
[00:23:00] Frankle & Carbin – Lottery Ticket Hypothesis for network pruning
https://arxiv.org/abs/1803.03635
[00:24:00] Belkin et al. – Double descent phenomenon in modern ML
https://arxiv.org/abs/1812.11118
[00:25:55] Balestriero et al. – Batch normalization’s regularization effects
https://arxiv.org/pdf/2209.14778
[00:29:35] EU – EU AI Act 2024 with compute restrictions
https://www.lw.com/admin/upload/SiteAttachments/EU-AI-Act-Navigating-a-Brave-New-World.pdf
[00:39:30] Humayun, Balestriero & Baraniuk – SplineCam: Visualizing deep network geometry
https://openaccess.thecvf.com/content/CVPR2023/papers/Humayun_SplineCam_Exact_Visualization_and_Characterization_of_Deep_Network_Geometry_and_CVPR_2023_paper.pdf
[00:40:40] Carlini – Trade-offs between adversarial robustness and accuracy
https://arxiv.org/abs/1902.06705
[00:44:55] Balestriero & LeCun – Limitations of reconstruction-based learning methods
https://raw.githubusercontent.com/mlresearch/v235/main/assets/balestriero24b/balestriero24b.pdf
[00:47:20] Balestriero & LeCun – Spectral analysis of neural network learning
https://proceedings.neurips.cc/paper_files/paper/2022/file/aa56c74513a5e35768a11f4e82dd7ffb-Paper-Conference.pdf
[00:49:45] He et al. – MAE: Masked Autoencoders for self-supervised learning
https://arxiv.org/abs/2111.06377
[00:54:50] Balestriero et al. – Geometric analysis of LLM layers for toxicity detection
https://arxiv.org/abs/2309.12312
[00:59:35] Balestriero et al. – Superior toxicity detection via geometric features
https://arxiv.org/html/2312.01648v2
[01:04:45] UofT ML – Self-attention control & context length effects
https://arxiv.org/abs/2310.04444
[01:11:55] Roberts – Foundations of deep learning theory
https://arxiv.org/abs/2106.10165
[01:15:40] Balestriero & Cha – Kolmogorov GAM Networks via spline partition theory
https://arxiv.org/pdf/2501.00704
[01:16:40] Various – Graph Kolmogorov-Arnold Networks (GKAN) extension
https://www.nature.com/articles/s41598-024-85083-8")
![The ARC Prize 2024 Winning Algorithm [Daniel Franzen and Jan Disselhoff]](https://i.ytimg.com/vi/mTX_sAq--zY/mqdefault.jpg "The ARC Prize 2024 Winning Algorithm [Daniel Franzen and Jan Disselhoff]
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/
Daniel Franzen and Jan Disselhoff the ARChitects won the ARC Prize 2024 with co-researcher David Hartmann, achieving a remarkable 53.5% accuracy on the ARC challenge using a 12-billion parameter language model. Filmed at Tufa Labs in Zurich, they walk through their solution architecture in detail: how they tokenized grid-based visual puzzles as text and fed them directly into an LLM, why test-time training on evaluation examples gave a major score boost, and how depth-first search over token probabilities outperformed greedy and beam search for solution generation.
The conversation gets into the surprising computational capabilities of language models on spatial reasoning tasks. The team found that LLMs could handle 2D grid reasoning without explicit positional encodings, that symmetry augmentations served as a powerful validation mechanism rather than a training aid, and that the models second-best solutions were often conceptually correct just wrong in a specific detail like rotation direction. They also discuss why their fine-tuned 12B model outperformed much larger foundation models, the role of LoRA in preventing catastrophic forgetting during test-time training, and what the entropy distribution of their search trees reveals about how the model represents uncertainty across different task types.
REFERENCES:
Paper:
[00:01:00] The ARChitects: Winning ARC Prize 2024 Solution
https://github.com/da-fr/arc-prize-2024/blob/main/the_architects.pdf
[00:03:38] Robustness of Analogical Reasoning in LLMs
https://arxiv.org/html/2411.14215
[00:14:58] Search Methods in Language Models
https://arxiv.org/html/2408.00724v2
[00:22:28] GPT-4 Code Solutions for ARC (50% SOTA)
https://redwoodresearch.substack.com/p/getting-50-sota-on-arc-agi-with-gpt
[00:53:08] Overcoming Catastrophic Forgetting
https://www.pnas.org/doi/10.1073/pnas.1611835114
[00:53:58] LoRA: Low-Rank Adaptation of Large Language Models
https://arxiv.org/abs/2106.09685
Tool:
[00:07:48] Re-ARC Dataset Generator
https://github.com/michaelhodel/re-arc
LINKS:
Full Transcript: https://app.rescript.info/share/57e5d773f2d0b195cbce7eee1f53aef2
Download PDF transcript: https://app.rescript.info/api/public/sessions/7772acbf1f11f44b/pdf
Daniel Franzen
https://github.com/da-fr
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
[00:07:50] Re-ARC dataset generator for ARC task variations, Michael Hodel
https://github.com/michaelhodel/re-arc
[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")
![Language Models are Modelling The World [Nicholas Carlini]](https://i.ytimg.com/vi/n4ipEJ6uJ44/mqdefault.jpg "Language Models are Modelling The World [Nicholas Carlini]
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")
