Machine Learning Street Talk (MLST)
Machine Learning Street Talk (MLST)
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Top 10 Machine Learning Street Talk (MLST) Episodes
Goodpods has curated a list of the 10 best Machine Learning Street Talk (MLST) episodes, ranked by the number of listens and likes each episode have garnered from our listeners. If you are listening to Machine Learning Street Talk (MLST) for the first time, there's no better place to start than with one of these standout episodes. If you are a fan of the show, vote for your favorite Machine Learning Street Talk (MLST) episode by adding your comments to the episode page.
Joscha Bach and Connor Leahy on AI risk
Machine Learning Street Talk (MLST)
06/20/23 • 91 min
Support us! https://www.patreon.com/mlst MLST Discord: https://discord.gg/aNPkGUQtc5 Twitter: https://twitter.com/MLStreetTalk The first 10 mins of audio from Joscha isn't great, it improves after.
Transcript and longer summary: https://docs.google.com/document/d/1TUJhlSVbrHf2vWoe6p7xL5tlTK_BGZ140QqqTudF8UI/edit?usp=sharing Dr. Joscha Bach argued that general intelligence emerges from civilization, not individuals. Given our biological constraints, humans cannot achieve a high level of general intelligence on our own. Bach believes AGI may become integrated into all parts of the world, including human minds and bodies. He thinks a future where humans and AGI harmoniously coexist is possible if we develop a shared purpose and incentive to align. However, Bach is uncertain about how AI progress will unfold or which scenarios are most likely. Bach argued that global control and regulation of AI is unrealistic. While regulation may address some concerns, it cannot stop continued progress in AI. He believes individuals determine their own values, so "human values" cannot be formally specified and aligned across humanity. For Bach, the possibility of building beneficial AGI is exciting but much work is still needed to ensure a positive outcome. Connor Leahy believes we have more control over the future than the default outcome might suggest. With sufficient time and effort, humanity could develop the technology and coordination to build a beneficial AGI. However, the default outcome likely leads to an undesirable scenario if we do not actively work to build a better future. Leahy thinks finding values and priorities most humans endorse could help align AI, even if individuals disagree on some values. Leahy argued a future where humans and AGI harmoniously coexist is ideal but will require substantial work to achieve. While regulation faces challenges, it remains worth exploring. Leahy believes limits to progress in AI exist but we are unlikely to reach them before humanity is at risk. He worries even modestly superhuman intelligence could disrupt the status quo if misaligned with human values and priorities. Overall, Bach and Leahy expressed optimism about the possibility of building beneficial AGI but believe we must address risks and challenges proactively. They agreed substantial uncertainty remains around how AI will progress and what scenarios are most plausible. But developing a shared purpose between humans and AI, improving coordination and control, and finding human values to help guide progress could all improve the odds of a beneficial outcome. With openness to new ideas and willingness to consider multiple perspectives, continued discussions like this one could help ensure the future of AI is one that benefits and inspires humanity. TOC: 00:00:00 - Introduction and Background 00:02:54 - Different Perspectives on AGI 00:13:59 - The Importance of AGI 00:23:24 - Existential Risks and the Future of Humanity 00:36:21 - Coherence and Coordination in Society 00:40:53 - Possibilities and Future of AGI 00:44:08 - Coherence and alignment 01:08:32 - The role of values in AI alignment 01:18:33 - The future of AGI and merging with AI 01:22:14 - The limits of AI alignment 01:23:06 - The scalability of intelligence 01:26:15 - Closing statements and future prospects
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"AI should NOT be regulated at all!" - Prof. Pedro Domingos
Machine Learning Street Talk (MLST)
08/25/24 • 132 min
Professor Pedro Domingos, is an AI researcher and professor of computer science. He expresses skepticism about current AI regulation efforts and argues for faster AI development rather than slowing it down. He also discusses the need for new innovations to fulfil the promises of current AI techniques.
MLST is sponsored by Brave:
The Brave Search API covers over 20 billion webpages, built from scratch without Big Tech biases or the recent extortionate price hikes on search API access. Perfect for AI model training and retrieval augmented generation. Try it now - get 2,000 free queries monthly at http://brave.com/api.
Show notes:
Domingos' views on AI regulation and why he believes it's misguided
His thoughts on the current state of AI technology and its limitations
Discussion of his novel "2040", a satirical take on AI and tech culture
Explanation of his work on "tensor logic", which aims to unify neural networks and symbolic AI
Critiques of other approaches in AI, including those of OpenAI and Gary Marcus
Thoughts on the AI "bubble" and potential future developments in the field
Prof. Pedro Domingos:
https://x.com/pmddomingos
2040: A Silicon Valley Satire [Pedro's new book]
https://amzn.to/3T51ISd
TOC:
00:00:00 Intro
00:06:31 Bio
00:08:40 Filmmaking skit
00:10:35 AI and the wisdom of crowds
00:19:49 Social Media
00:27:48 Master algorithm
00:30:48 Neurosymbolic AI / abstraction
00:39:01 Language
00:45:38 Chomsky
01:00:49 2040 Book
01:18:03 Satire as a shield for criticism?
01:29:12 AI Regulation
01:35:15 Gary Marcus
01:52:37 Copyright
01:56:11 Stochastic parrots come home to roost
02:00:03 Privacy
02:01:55 LLM ecosystem
02:05:06 Tensor logic
Refs:
The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World [Pedro Domingos]
https://amzn.to/3MiWs9B
Rebooting AI: Building Artificial Intelligence We Can Trust [Gary Marcus]
https://amzn.to/3AAywvL
Flash Boys [Michael Lewis]
https://amzn.to/4dUGm1M
Prof. Melanie Mitchell 2.0 - AI Benchmarks are Broken!
Machine Learning Street Talk (MLST)
09/10/23 • 61 min
Patreon: https://www.patreon.com/mlst Discord: https://discord.gg/ESrGqhf5CB Prof. Melanie Mitchell argues that the concept of "understanding" in AI is ill-defined and multidimensional - we can't simply say an AI system does or doesn't understand. She advocates for rigorously testing AI systems' capabilities using proper experimental methods from cognitive science. Popular benchmarks for intelligence often rely on the assumption that if a human can perform a task, an AI that performs the task must have human-like general intelligence. But benchmarks should evolve as capabilities improve. Large language models show surprising skill on many human tasks but lack common sense and fail at simple things young children can do. Their knowledge comes from statistical relationships in text, not grounded concepts about the world. We don't know if their internal representations actually align with human-like concepts. More granular testing focused on generalization is needed. There are open questions around whether large models' abilities constitute a fundamentally different non-human form of intelligence based on vast statistical correlations across text. Mitchell argues intelligence is situated, domain-specific and grounded in physical experience and evolution. The brain computes but in a specialized way honed by evolution for controlling the body. Extracting "pure" intelligence may not work. Other key points: - Need more focus on proper experimental method in AI research. Developmental psychology offers examples for rigorous testing of cognition. - Reporting instance-level failures rather than just aggregate accuracy can provide insights. - Scaling laws and complex systems science are an interesting area of complexity theory, with applications to understanding cities. - Concepts like "understanding" and "intelligence" in AI force refinement of fuzzy definitions. - Human intelligence may be more collective and social than we realize. AI forces us to rethink concepts we apply anthropomorphically. The overall emphasis is on rigorously building the science of machine cognition through proper experimentation and benchmarking as we assess emerging capabilities. TOC: [00:00:00] Introduction and Munk AI Risk Debate Highlights [05:00:00] Douglas Hofstadter on AI Risk [00:06:56] The Complexity of Defining Intelligence [00:11:20] Examining Understanding in AI Models [00:16:48] Melanie's Insights on AI Understanding Debate [00:22:23] Unveiling the Concept Arc [00:27:57] AI Goals: A Human vs Machine Perspective [00:31:10] Addressing the Extrapolation Challenge in AI [00:36:05] Brain Computation: The Human-AI Parallel [00:38:20] The Arc Challenge: Implications and Insights [00:43:20] The Need for Detailed AI Performance Reporting [00:44:31] Exploring Scaling in Complexity Theory Eratta: Note Tim said around 39 mins that a recent Stanford/DM paper modelling ARC “on GPT-4 got around 60%”. This is not correct and he misremembered. It was actually davinci3, and around 10%, which is still extremely good for a blank slate approach with an LLM and no ARC specific knowledge. Folks on our forum couldn’t reproduce the result. See paper linked below. Books (MUST READ): Artificial Intelligence: A Guide for Thinking Humans (Melanie Mitchell) https://www.amazon.co.uk/Artificial-Intelligence-Guide-Thinking-Humans/dp/B07YBHNM1C/?&_encoding=UTF8&tag=mlst00-21&linkCode=ur2&linkId=44ccac78973f47e59d745e94967c0f30&camp=1634&creative=6738 Complexity: A Guided Tour (Melanie Mitchell) https://www.amazon.co.uk/Audible-Complexity-A-Guided-Tour?&_encoding=UTF8&tag=mlst00-21&linkCode=ur2&linkId=3f8bd505d86865c50c02dd7f10b27c05&camp=1634&creative=6738
Show notes (transcript, full references etc)
https://atlantic-papyrus-d68.notion.site/Melanie-Mitchell-2-0-15e212560e8e445d8b0131712bad3000?pvs=25
YT version: https://youtu.be/29gkDpR2orc
Harri Valpola: System 2 AI and Planning in Model-Based Reinforcement Learning
Machine Learning Street Talk (MLST)
05/25/20 • 98 min
In this episode of Machine Learning Street Talk, Tim Scarfe, Yannic Kilcher and Connor Shorten interviewed Harri Valpola, CEO and Founder of Curious AI. We continued our discussion of System 1 and System 2 thinking in Deep Learning, as well as miscellaneous topics around Model-based Reinforcement Learning. Dr. Valpola describes some of the challenges of modelling industrial control processes such as water sewage filters and paper mills with the use of model-based RL. Dr. Valpola and his collaborators recently published “Regularizing Trajectory Optimization with Denoising Autoencoders” that addresses some of the concerns of planning algorithms that exploit inaccuracies in their world models!
00:00:00 Intro to Harri and Curious AI System1/System 2
00:04:50 Background on model-based RL challenges from Tim
00:06:26 Other interesting research papers on model-based RL from Connor
00:08:36 Intro to Curious AI recent NeurIPS paper on model-based RL and denoising autoencoders from Yannic
00:21:00 Main show kick off, system 1/2
00:31:50 Where does the simulator come from?
00:33:59 Evolutionary priors
00:37:17 Consciousness
00:40:37 How does one build a company like Curious AI?
00:46:42 Deep Q Networks
00:49:04 Planning and Model based RL
00:53:04 Learning good representations
00:55:55 Typical problem Curious AI might solve in industry
01:00:56 Exploration
01:08:00 Their paper - regularizing trajectory optimization with denoising
01:13:47 What is Epistemic uncertainty
01:16:44 How would Curious develop these models
01:18:00 Explainability and simulations
01:22:33 How system 2 works in humans
01:26:11 Planning
01:27:04 Advice for starting an AI company
01:31:31 Real world implementation of planning models
01:33:49 Publishing research and openness
We really hope you enjoy this episode, please subscribe!
Regularizing Trajectory Optimization with Denoising Autoencoders: https://papers.nips.cc/paper/8552-regularizing-trajectory-optimization-with-denoising-autoencoders.pdf
Pulp, Paper & Packaging: A Future Transformed through Deep Learning: https://thecuriousaicompany.com/pulp-paper-packaging-a-future-transformed-through-deep-learning/
Curious AI: https://thecuriousaicompany.com/
Harri Valpola Publications: https://scholar.google.com/citations?user=1uT7-84AAAAJ&hl=en&oi=ao
Some interesting papers around Model-Based RL:
GameGAN: https://cdn.arstechnica.net/wp-content/uploads/2020/05/Nvidia_GameGAN_Research.pdf
Plan2Explore: https://ramanans1.github.io/plan2explore/
World Models: https://worldmodels.github.io/
MuZero: https://arxiv.org/pdf/1911.08265.pdf
PlaNet: A Deep Planning Network for RL: https://ai.googleblog.com/2019/02/introducing-planet-deep-planning.html
Dreamer: Scalable RL using World Models: https://ai.googleblog.com/2020/03/introducing-dreamer-scalable.html
Model Based RL for Atari: https://arxiv.org/pdf/1903.00374.pdf
The Lottery Ticket Hypothesis with Jonathan Frankle
Machine Learning Street Talk (MLST)
05/19/20 • 86 min
In this episode of Machine Learning Street Talk, we chat with Jonathan Frankle, author of The Lottery Ticket Hypothesis. Frankle has continued researching Sparse Neural Networks, Pruning, and Lottery Tickets leading to some really exciting follow-on papers! This chat discusses some of these papers such as Linear Mode Connectivity, Comparing and Rewinding and Fine-tuning in Neural Network Pruning, and more (full list of papers linked below). We also chat about how Jonathan got into Deep Learning research, his Information Diet, and work on developing Technology Policy for Artificial Intelligence!
This was a really fun chat, I hope you enjoy listening to it and learn something from it!
Thanks for watching and please subscribe!
Huge thanks to everyone on r/MachineLearning who asked questions!
Paper Links discussed in the chat:
The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks: https://arxiv.org/abs/1803.03635
Linear Mode Connectivity and the Lottery Ticket Hypothesis: https://arxiv.org/abs/1912.05671
Dissecting Pruned Neural Networks: https://arxiv.org/abs/1907.00262
Training BatchNorm and Only BatchNorm: On the Expressive Power of Random Features in CNNs: https://arxiv.org/abs/2003.00152
What is the State of Neural Network Pruning? https://arxiv.org/abs/2003.03033
The Early Phase of Neural Network Training: https://arxiv.org/abs/2002.10365
Comparing Rewinding and Fine-tuning in Neural Network Pruning: https://arxiv.org/abs/2003.02389
(Also Mentioned)
Block-Sparse GPU Kernels: https://openai.com/blog/block-sparse-gpu-kernels/
Balanced Sparsity for Efficient DNN Inference on GPU: https://arxiv.org/pdf/1811.00206.pdf
Playing the Lottery with Rewards and Multiple Languages: Lottery Tickets in RL and NLP: https://arxiv.org/pdf/1906.02768.pdf
r/MachineLearning question list: https://www.reddit.com/r/MachineLearning/comments/g9jqe0/d_lottery_ticket_hypothesis_ask_the_author_a/ (edited)
#machinelearning #deeplearning
Connor Leahy - e/acc, AGI and the future.
Machine Learning Street Talk (MLST)
04/21/24 • 79 min
Connor is the CEO of Conjecture and one of the most famous names in the AI alignment movement. This is the "behind the scenes footage" and bonus Patreon interviews from the day of the Beff Jezos debate, including an interview with Daniel Clothiaux. It's a great insight into Connor's philosophy. At the end there is an unreleased additional interview with Beff.
Support MLST:
Please support us on Patreon. We are entirely funded from Patreon donations right now. Patreon supports get private discord access, biweekly calls, very early-access + exclusive content and lots more.
https://patreon.com/mlst
Donate: https://www.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
Topics:
Externalized cognition and the role of society and culture in human intelligence
The potential for AI systems to develop agency and autonomy
The future of AGI as a complex mixture of various components
The concept of agency and its relationship to power
The importance of coherence in AI systems
The balance between coherence and variance in exploring potential upsides
The role of dynamic, competent, and incorruptible institutions in handling risks and developing technology
Concerns about AI widening the gap between the haves and have-nots
The concept of equal access to opportunity and maintaining dynamism in the system
Leahy's perspective on life as a process that "rides entropy"
The importance of distinguishing between epistemological, decision-theoretic, and aesthetic aspects of morality (inc ref to Hume's Guillotine)
The concept of continuous agency and the idea that the first AGI will be a messy admixture of various components
The potential for AI systems to become more physically embedded in the future
The challenges of aligning AI systems and the societal impacts of AI technologies like ChatGPT and Bing
The importance of humility in the face of complexity when considering the future of AI and its societal implications
Disclaimer: this video is not an endorsement of e/acc or AGI agential existential risk from us - the hosts of MLST consider both of these views to be quite extreme. We seek diverse views on the channel.
00:00:00 Intro
00:00:56 Connor's Philosophy
00:03:53 Office Skit
00:05:08 Connor on e/acc and Beff
00:07:28 Intro to Daniel's Philosophy
00:08:35 Connor on Entropy, Life, and Morality
00:19:10 Connor on London
00:20:21 Connor Office Interview
00:20:46 Friston Patreon Preview
00:21:48 Why Are We So Dumb?
00:23:52 The Voice of the People, the Voice of God / Populism
00:26:35 Mimetics
00:30:03 Governance
00:33:19 Agency
00:40:25 Daniel Interview - Externalised Cognition, Bing GPT, AGI
00:56:29 Beff + Connor Bonus Patreons Interview
DR. JEFF BECK - THE BAYESIAN BRAIN
Machine Learning Street Talk (MLST)
10/16/23 • 70 min
Support us! https://www.patreon.com/mlst
MLST Discord: https://discord.gg/aNPkGUQtc5
YT version: https://www.youtube.com/watch?v=c4praCiy9qU
Dr. Jeff Beck is a computational neuroscientist studying probabilistic reasoning (decision making under uncertainty) in humans and animals with emphasis on neural representations of uncertainty and cortical implementations of probabilistic inference and learning. His line of research incorporates information theoretic and hierarchical statistical analysis of neural and behavioural data as well as reinforcement learning and active inference.
https://www.linkedin.com/in/jeff-beck...
https://scholar.google.com/citations?...
Interviewer: Dr. Tim Scarfe
TOC
00:00:00 Intro
00:00:51 Bayesian / Knowledge
00:14:57 Active inference
00:18:58 Mediation
00:23:44 Philosophy of mind / science
00:29:25 Optimisation
00:42:54 Emergence
00:56:38 Steering emergent systems
01:04:31 Work plan
01:06:06 Representations/Core knowledge
#activeinference
Dr. MAXWELL RAMSTEAD - The Physics of Survival
Machine Learning Street Talk (MLST)
07/16/23 • 125 min
Patreon: https://www.patreon.com/mlst Discord: https://discord.gg/ESrGqhf5CB Join us for a fascinating discussion of the free energy principle with Dr. Maxwell Ramsted, a leading thinker exploring the intersection of math, physics, and philosophy and Director of Research at VERSES. The FEP was proposed by renowned neuroscientist Karl Friston, this principle offers a unifying theory explaining how systems maintain order and their identity. The free energy principle inverts traditional survival logic. Rather than asking what behaviors promote survival, it queries - given things exist, what must they do? The answer: minimizing free energy, or "surprise." Systems persist by constantly ensuring their internal states match anticipated states based on a model of the world. Failure to minimize surprise leads to chaos as systems dissolve into disorder. Thus, the free energy principle elucidates why lifeforms relentlessly model and predict their surroundings. It is an existential imperative counterbalancing entropy. Essentially, this principle describes the mind's pursuit of harmony between expectations and reality. Its relevance spans from cells to societies, underlying order wherever longevity is found. Our discussion explores the technical details and philosophical implications of this paradigm-shifting theory. How does it further our understanding of cognition and intelligence? What insights does it offer about the fundamental patterns and properties of existence? Can it precipitate breakthroughs in disciplines like neuroscience and artificial intelligence? Dr. Ramstead completed his Ph.D. at McGill University in Montreal, Canada in 2019, with frequent research visits to UCL in London, under the supervision of the world’s most cited neuroscientist, Professor Karl Friston (UCL).
YT version: https://youtu.be/8qb28P7ksyE https://scholar.google.ca/citations?user=ILpGOMkAAAAJ&hl=frhttps://spatialwebfoundation.org/team/maxwell-ramstead/https://www.linkedin.com/in/maxwell-ramstead-43a1991b7/https://twitter.com/mjdramstead VERSES AI: https://www.verses.ai/ Intro: Tim Scarfe (Ph.D) Interviewer: Keith Duggar (Ph.D MIT) TOC: 0:00:00 - Tim Intro 0:08:10 - Intro and philosophy 0:14:26 - Intro to Maxwell 0:18:00 - FEP 0:29:08 - Markov Blankets 0:51:15 - Verses AI / Applications of FEP 1:05:55 - Potential issues with deploying FEP 1:10:50 - Shared knowledge graphs 1:14:29 - XRisk / Ethics 1:24:57 - Strength of Verses 1:28:30 - Misconceptions about FEP, Physics vs philosophy/criticism 1:44:41 - Emergence / consciousness References: Principia Mathematica https://www.abebooks.co.uk/servlet/BookDetailsPL?bi=30567249049 Andy Clark's paper "Whatever Next? Predictive Brains, Situated Agents, and the Future of Cognitive Science" (Behavioral and Brain Sciences, 2013) https://pubmed.ncbi.nlm.nih.gov/23663408/ "Math Does Not Represent" by Erik Curiel https://www.youtube.com/watch?v=aA_T20HAzyY A free energy principle for generic quantum systems (Chris Fields et al) https://arxiv.org/pdf/2112.15242.pdf Designing explainable artificial intelligence with active inference https://arxiv.org/abs/2306.04025 Am I Self-Conscious? (Friston) https://www.frontiersin.org/articles/10.3389/fpsyg.2018.00579/full The Meta-Problem of Consciousness https://philarchive.org/archive/CHATMO-32v1 The Map-Territory Fallacy Fallacy https://arxiv.org/abs/2208.06924 A Technical Critique of Some Parts of the Free Energy Principle - Martin Biehl et al https://arxiv.org/abs/2001.06408 WEAK MARKOV BLANKETS IN HIGH-DIMENSIONAL, SPARSELY-COUPLED RANDOM DYNAMICAL SYSTEMS - DALTON A R SAKTHIVADIVEL https://arxiv.org/pdf/2207.07620.pdf
![Machine Learning Street Talk (MLST) - Understanding Deep Learning - Prof. SIMON PRINCE [STAFF FAVOURITE]](https://storage.googleapis.com/goodpods-images-bucket/episode_images/18d161ed777f23a11ab4c3735fc5eaa7241649b65a8afe518b8619664c265b69.avif)
Understanding Deep Learning - Prof. SIMON PRINCE [STAFF FAVOURITE]
Machine Learning Street Talk (MLST)
12/26/23 • 126 min
Watch behind the scenes, get early access and join private Discord by supporting us on Patreon: https://patreon.com/mlst
https://discord.gg/aNPkGUQtc5
https://twitter.com/MLStreetTalk
In this comprehensive exploration of the field of deep learning with Professor Simon Prince who has just authored an entire text book on Deep Learning, we investigate the technical underpinnings that contribute to the field's unexpected success and confront the enduring conundrums that still perplex AI researchers.
Key points discussed include the surprising efficiency of deep learning models, where high-dimensional loss functions are optimized in ways which defy traditional statistical expectations. Professor Prince provides an exposition on the choice of activation functions, architecture design considerations, and overparameterization. We scrutinize the generalization capabilities of neural networks, addressing the seeming paradox of well-performing overparameterized models. Professor Prince challenges popular misconceptions, shedding light on the manifold hypothesis and the role of data geometry in informing the training process. Professor Prince speaks about how layers within neural networks collaborate, recursively reconfiguring instance representations that contribute to both the stability of learning and the emergence of hierarchical feature representations. In addition to the primary discussion on technical elements and learning dynamics, the conversation briefly diverts to audit the implications of AI advancements with ethical concerns.
Follow Prof. Prince:
https://twitter.com/SimonPrinceAI
https://www.linkedin.com/in/simon-prince-615bb9165/
Get the book now!
https://mitpress.mit.edu/9780262048644/understanding-deep-learning/
https://udlbook.github.io/udlbook/
Panel: Dr. Tim Scarfe -
https://www.linkedin.com/in/ecsquizor/
https://twitter.com/ecsquendor
TOC:
[00:00:00] Introduction
[00:11:03] General Book Discussion
[00:15:30] The Neural Metaphor
[00:17:56] Back to Book Discussion
[00:18:33] Emergence and the Mind
[00:29:10] Computation in Transformers
[00:31:12] Studio Interview with Prof. Simon Prince
[00:31:46] Why Deep Neural Networks Work: Spline Theory
[00:40:29] Overparameterization in Deep Learning
[00:43:42] Inductive Priors and the Manifold Hypothesis
[00:49:31] Universal Function Approximation and Deep Networks
[00:59:25] Training vs Inference: Model Bias
[01:03:43] Model Generalization Challenges
[01:11:47] Purple Segment: Unknown Topic
[01:12:45] Visualizations in Deep Learning
[01:18:03] Deep Learning Theories Overview
[01:24:29] Tricks in Neural Networks
[01:30:37] Critiques of ChatGPT
[01:42:45] Ethical Considerations in AI
References on YT version VD: https://youtu.be/sJXn4Cl4oww
Open-Ended AI: The Key to Superhuman Intelligence? - Prof. Tim Rocktäschel
Machine Learning Street Talk (MLST)
10/04/24 • 55 min
Prof. Tim Rocktäschel, AI researcher at UCL and Google DeepMind, talks about open-ended AI systems. These systems aim to keep learning and improving on their own, like evolution does in nature.
Ad: Are you a hardcore ML engineer who wants to work for Daniel Cahn at SlingshotAI building AI for mental health? Give him an email! - danielc@slingshot.xyz
TOC:
00:00:00 Introduction to Open-Ended AI and Key Concepts
00:01:37 Tim Rocktäschel's Background and Research Focus
00:06:25 Defining Open-Endedness in AI Systems
00:10:39 Subjective Nature of Interestingness and Learnability
00:16:22 Open-Endedness in Practice: Examples and Limitations
00:17:50 Assessing Novelty in Open-ended AI Systems
00:20:05 Adversarial Attacks and AI Robustness
00:24:05 Rainbow Teaming and LLM Safety
00:25:48 Open-ended Research Approaches in AI
00:29:05 Balancing Long-term Vision and Exploration in AI Research
00:37:25 LLMs in Program Synthesis and Open-Ended Learning
00:37:55 Transition from Human-Based to Novel AI Strategies
00:39:00 Expanding Context Windows and Prompt Evolution
00:40:17 AI Intelligibility and Human-AI Interfaces
00:46:04 Self-Improvement and Evolution in AI Systems
Show notes (New!) https://www.dropbox.com/scl/fi/5avpsyz8jbn4j1az7kevs/TimR.pdf?rlkey=pqjlcqbtm3undp4udtgfmie8n&st=x50u1d1m&dl=0
REFS:
00:01:47 - UCL DARK Lab (Rocktäschel) - AI research lab focusing on RL and open-ended learning - https://ucldark.com/
00:02:31 - GENIE (Bruce) - Generative interactive environment from unlabelled videos - https://arxiv.org/abs/2402.15391
00:02:42 - Promptbreeder (Fernando) - Self-referential LLM prompt evolution - https://arxiv.org/abs/2309.16797
00:03:05 - Picbreeder (Secretan) - Collaborative online image evolution - https://dl.acm.org/doi/10.1145/1357054.1357328
00:03:14 - Why Greatness Cannot Be Planned (Stanley) - Book on open-ended exploration - https://www.amazon.com/Why-Greatness-Cannot-Planned-Objective/dp/3319155237
00:04:36 - NetHack Learning Environment (Küttler) - RL research in procedurally generated game - https://arxiv.org/abs/2006.13760
00:07:35 - Open-ended learning (Clune) - AI systems for continual learning and adaptation - https://arxiv.org/abs/1905.10985
00:07:35 - OMNI (Zhang) - LLMs modeling human interestingness for exploration - https://arxiv.org/abs/2306.01711
00:10:42 - Observer theory (Wolfram) - Computationally bounded observers in complex systems - https://writings.stephenwolfram.com/2023/12/observer-theory/
00:15:25 - Human-Timescale Adaptation (Rocktäschel) - RL agent adapting to novel 3D tasks - https://arxiv.org/abs/2301.07608
00:16:15 - Open-Endedness for AGI (Hughes) - Importance of open-ended learning for AGI - https://arxiv.org/abs/2406.04268
00:16:35 - POET algorithm (Wang) - Open-ended approach to generate and solve challenges - https://arxiv.org/abs/1901.01753
00:17:20 - AlphaGo (Silver) - AI mastering the game of Go - https://deepmind.google/technologies/alphago/
00:20:35 - Adversarial Go attacks (Dennis) - Exploiting weaknesses in Go AI systems - https://www.ifaamas.org/Proceedings/aamas2024/pdfs/p1630.pdf
00:22:00 - Levels of AGI (Morris) - Framework for categorizing AGI progress - https://arxiv.org/abs/2311.02462
00:24:30 - Rainbow Teaming (Samvelyan) - LLM-based adversarial prompt generation - https://arxiv.org/abs/2402.16822
00:25:50 - Why Greatness Cannot Be Planned (Stanley) - 'False compass' and 'stepping stone collection' concepts - https://www.amazon.com/Why-Greatness-Cannot-Planned-Objective/dp/3319155237
00:27:45 - AI Debate (Khan) - Improving LLM truthfulness through debate - https://proceedings.mlr.press/v235/khan24a.html
00:29:40 - Gemini (Google DeepMind) - Advanced multimodal AI model - https://deepmind.google/technologies/gemini/
00:30:15 - How to Take Smart Notes (Ahrens) - Effective note-taking methodology - https://www.amazon.com/How-Take-Smart-Notes-Nonfiction/dp/1542866502
(truncated, see shownotes)
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FAQ
How many episodes does Machine Learning Street Talk (MLST) have?
Machine Learning Street Talk (MLST) currently has 184 episodes available.
What topics does Machine Learning Street Talk (MLST) cover?
The podcast is about Podcasts and Technology.
What is the most popular episode on Machine Learning Street Talk (MLST)?
The episode title 'Joscha Bach and Connor Leahy on AI risk' is the most popular.
What is the average episode length on Machine Learning Street Talk (MLST)?
The average episode length on Machine Learning Street Talk (MLST) is 95 minutes.
How often are episodes of Machine Learning Street Talk (MLST) released?
Episodes of Machine Learning Street Talk (MLST) are typically released every 5 days, 20 hours.
When was the first episode of Machine Learning Street Talk (MLST)?
The first episode of Machine Learning Street Talk (MLST) was released on Apr 24, 2020.
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