Afleveringen
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AI expert Prof. Gary Marcus doesn't mince words about today's artificial intelligence. He argues that despite the buzz, chatbots like ChatGPT aren't as smart as they seem and could cause real problems if we're not careful.
Marcus is worried about tech companies putting profits before people. He thinks AI could make fake news and privacy issues even worse. He's also concerned that a few big tech companies have too much power. Looking ahead, Marcus believes the AI hype will die down as reality sets in. He wants to see AI developed in smarter, more responsible ways. His message to the public? We need to speak up and demand better AI before it's too late.
Buy Taming Silicon Valley:
https://amzn.to/3XTlC5s
Gary Marcus:
https://garymarcus.substack.com/
https://x.com/GaryMarcus
Interviewer:
Dr. Tim Scarfe
(Refs in top comment)
TOC
[00:00:00] AI Flaws, Improvements & Industry Critique
[00:16:29] AI Safety Theater & Image Generation Issues
[00:23:49] AI's Lack of World Models & Human-like Understanding
[00:31:09] LLMs: Superficial Intelligence vs. True Reasoning
[00:34:45] AI in Specialized Domains: Chess, Coding & Limitations
[00:42:10] AI-Generated Code: Capabilities & Human-AI Interaction
[00:48:10] AI Regulation: Industry Resistance & Oversight Challenges
[00:54:55] Copyright Issues in AI & Tech Business Models
[00:57:26] AI's Societal Impact: Risks, Misinformation & Ethics
[01:23:14] AI X-risk, Alignment & Moral Principles Implementation
[01:37:10] Persistent AI Flaws: System Limitations & Architecture Challenges
[01:44:33] AI Future: Surveillance Concerns, Economic Challenges & Neuro-Symbolic AI
YT version with refs: https://youtu.be/o9MfuUoGlSw
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Prof. Mark Solms, a neuroscientist and psychoanalyst, discusses his groundbreaking work on consciousness, challenging conventional cortex-centric views and emphasizing the role of brainstem structures in generating consciousness and affect.
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 augmentated generation. Try it now - get 2,000 free queries monthly at http://brave.com/api.
Key points discussed:
The limitations of vision-centric approaches to consciousness studies.
Evidence from decorticated animals and hydranencephalic children supporting the brainstem's role in consciousness.
The relationship between homeostasis, the free energy principle, and consciousness.
Critiques of behaviorism and modern theories of consciousness.
The importance of subjective experience in understanding brain function.
The discussion also explored broader topics:
The potential impact of affect-based theories on AI development.
The role of the SEEKING system in exploration and learning.
Connections between neuroscience, psychoanalysis, and philosophy of mind.
Challenges in studying consciousness and the limitations of current theories.
Mark Solms:
https://neuroscience.uct.ac.za/contacts/mark-solms
Show notes and transcript: https://www.dropbox.com/scl/fo/roipwmnlfmwk2e7kivzms/ACjZF-VIGC2-Suo30KcwVV0?rlkey=53y8v2cajfcgrf17p1h7v3suz&st=z8vu81hn&dl=0
TOC (*) are best bits
00:00:00 1. Intro: Challenging vision-centric approaches to consciousness *
00:02:20 2. Evidence from decorticated animals and hydranencephalic children *
00:07:40 3. Emotional responses in hydranencephalic children
00:10:40 4. Brainstem stimulation and affective states
00:15:00 5. Brainstem's role in generating affective consciousness *
00:21:50 6. Dual-aspect monism and the mind-brain relationship
00:29:37 7. Information, affect, and the hard problem of consciousness *
00:37:25 8. Wheeler's participatory universe and Chalmers' theories
00:48:51 9. Homeostasis, free energy principle, and consciousness *
00:59:25 10. Affect, voluntary behavior, and decision-making
01:05:45 11. Psychoactive substances, REM sleep, and consciousness research
01:12:14 12. Critiquing behaviorism and modern consciousness theories *
01:24:25 13. The SEEKING system and exploration in neuroscience
Refs:
1. Mark Solms' book "The Hidden Spring" [00:20:34] (MUST READ!)
https://amzn.to/3XyETb3
2. Karl Friston's free energy principle [00:03:50]
https://www.nature.com/articles/nrn2787
3. Hydranencephaly condition [00:07:10]
https://en.wikipedia.org/wiki/Hydranencephaly
4. Periaqueductal gray (PAG) [00:08:57]
https://en.wikipedia.org/wiki/Periaqueductal_gray
5. Positron Emission Tomography (PET) [00:13:52]
https://en.wikipedia.org/wiki/Positron_emission_tomography
6. Paul MacLean's triune brain theory [00:03:30]
https://en.wikipedia.org/wiki/Triune_brain
7. Baruch Spinoza's philosophy of mind [00:23:48]
https://plato.stanford.edu/entries/spinoza-epistemology-mind
8. Claude Shannon's "A Mathematical Theory of Communication" [00:32:15]
https://people.math.harvard.edu/~ctm/home/text/others/shannon/entropy/entropy.pdf
9. Francis Crick's "The Astonishing Hypothesis" [00:39:57]
https://en.wikipedia.org/wiki/The_Astonishing_Hypothesis
10. Frank Jackson's Knowledge Argument [00:40:54]
https://plato.stanford.edu/entries/qualia-knowledge/
11. Mesolimbic dopamine system [01:11:51]
https://en.wikipedia.org/wiki/Mesolimbic_pathway
12. Jaak Panksepp's SEEKING system [01:25:23]
https://en.wikipedia.org/wiki/Jaak_Panksepp#Affective_neuroscience
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Zijn er afleveringen die ontbreken?
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Dr. Patrick Lewis, who coined the term RAG (Retrieval Augmented Generation) and now works at Cohere, discusses the evolution of language models, RAG systems, and challenges in AI evaluation.
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.
Key topics covered:
- Origins and evolution of Retrieval Augmented Generation (RAG)
- Challenges in evaluating RAG systems and language models
- Human-AI collaboration in research and knowledge work
- Word embeddings and the progression to modern language models
- Dense vs sparse retrieval methods in information retrieval
The discussion also explored broader implications and applications:
- Balancing faithfulness and fluency in RAG systems
- User interface design for AI-augmented research tools
- The journey from chemistry to AI research
- Challenges in enterprise search compared to web search
- The importance of data quality in training AI models
Patrick Lewis: https://www.patricklewis.io/
Cohere Command Models, check them out - they are amazing for RAG!
https://cohere.com/command
TOC
00:00:00 1. Intro to RAG
00:05:30 2. RAG Evaluation: Poll framework & model performance
00:12:55 3. Data Quality: Cleanliness vs scale in AI training
00:15:13 4. Human-AI Collaboration: Research agents & UI design
00:22:57 5. RAG Origins: Open-domain QA to generative models
00:30:18 6. RAG Challenges: Info retrieval, tool use, faithfulness
00:42:01 7. Dense vs Sparse Retrieval: Techniques & trade-offs
00:47:02 8. RAG Applications: Grounding, attribution, hallucination prevention
00:54:04 9. UI for RAG: Human-computer interaction & model optimization
00:59:01 10. Word Embeddings: Word2Vec, GloVe, and semantic spaces
01:06:43 11. Language Model Evolution: BERT, GPT, and beyond
01:11:38 12. AI & Human Cognition: Sequential processing & chain-of-thought
Refs:
1. Retrieval Augmented Generation (RAG) paper / Patrick Lewis et al. [00:27:45]
https://arxiv.org/abs/2005.11401
2. LAMA (LAnguage Model Analysis) probe / Petroni et al. [00:26:35]
https://arxiv.org/abs/1909.01066
3. KILT (Knowledge Intensive Language Tasks) benchmark / Petroni et al. [00:27:05]
https://arxiv.org/abs/2009.02252
4. Word2Vec algorithm / Tomas Mikolov et al. [01:00:25]
https://arxiv.org/abs/1301.3781
5. GloVe (Global Vectors for Word Representation) / Pennington et al. [01:04:35]
https://nlp.stanford.edu/projects/glove/
6. BERT (Bidirectional Encoder Representations from Transformers) / Devlin et al. [01:08:00]
https://arxiv.org/abs/1810.04805
7. 'The Language Game' book / Nick Chater and Morten H. Christiansen [01:11:40]
https://amzn.to/4grEUpG
Disclaimer: This is the sixth video from our Cohere partnership. We were not told what to say in the interview. Filmed in Seattle in June 2024.
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Ashley Edwards, who was working at DeepMind when she co-authored the Genie paper and is now at Runway, covered several key aspects of the Genie AI system and its applications in video generation, robotics, and game creation.
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 augmentated generation. Try it now - get 2,000 free queries monthly at http://brave.com/api.
Genie's approach to learning interactive environments, balancing compression and fidelity.
The use of latent action models and VQE models for video processing and tokenization.
Challenges in maintaining action consistency across frames and integrating text-to-image models.
Evaluation metrics for AI-generated content, such as FID and PS&R diff metrics.
The discussion also explored broader implications and applications:
The potential impact of AI video generation on content creation jobs.
Applications of Genie in game generation and robotics.
The use of foundation models in robotics and the differences between internet video data and specialized robotics data.
Challenges in mapping AI-generated actions to real-world robotic actions.
Ashley Edwards: https://ashedwards.github.io/
TOC (*) are best bits
00:00:00 1. Intro to Genie & Brave Search API: Trade-offs & limitations *
00:02:26 2. Genie's Architecture: Latent action, VQE, video processing *
00:05:06 3. Genie's Constraints: Frame consistency & image model integration
00:07:26 4. Evaluation: FID, PS&R diff metrics & latent induction methods
00:09:44 5. AI Video Gen: Content creation impact, depth & parallax effects
00:11:39 6. Model Scaling: Training data impact & computational trade-offs
00:13:50 7. Game & Robotics Apps: Gamification & action mapping challenges *
00:16:16 8. Robotics Foundation Models: Action space & data considerations *
00:19:18 9. Mask-GPT & Video Frames: Real-time optimization, RL from videos
00:20:34 10. Research Challenges: AI value, efficiency vs. quality, safety
00:24:20 11. Future Dev: Efficiency improvements & fine-tuning strategies
Refs:
1. Genie (learning interactive environments from videos) / Ashley and DM collegues [00:01]
https://arxiv.org/abs/2402.15391
2. VQ-VAE (Vector Quantized Variational Autoencoder) / Aaron van den Oord, Oriol Vinyals, Koray Kavukcuoglu [02:43]
https://arxiv.org/abs/1711.00937
3. FID (Fréchet Inception Distance) metric / Martin Heusel et al. [07:37]
https://arxiv.org/abs/1706.08500
4. PS&R (Precision and Recall) metric / Mehdi S. M. Sajjadi et al. [08:02]
https://arxiv.org/abs/1806.00035
5. Vision Transformer (ViT) architecture / Alexey Dosovitskiy et al. [12:14]
https://arxiv.org/abs/2010.11929
6. Genie (robotics foundation models) / Google DeepMind [17:34]
https://deepmind.google/research/publications/60474/
7. Chelsea Finn's lab work on robotics datasets / Chelsea Finn [17:38]
https://ai.stanford.edu/~cbfinn/
8. Imitation from observation in reinforcement learning / YuXuan Liu [20:58]
https://arxiv.org/abs/1707.03374
9. Waymo's autonomous driving technology / Waymo [22:38]
https://waymo.com/
10. Gen3 model release by Runway / Runway [23:48]
https://runwayml.com/
11. Classifier-free guidance technique / Jonathan Ho and Tim Salimans [24:43]
https://arxiv.org/abs/2207.12598
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Saurabh Baji discusses Cohere's approach to developing and deploying large language models (LLMs) for enterprise use.
* Cohere focuses on pragmatic, efficient models tailored for business applications rather than pursuing the largest possible models.
* They offer flexible deployment options, from cloud services to on-premises installations, to meet diverse enterprise needs.
* Retrieval-augmented generation (RAG) is highlighted as a critical capability, allowing models to leverage enterprise data securely.
* Cohere emphasizes model customization, fine-tuning, and tools like reranking to optimize performance for specific use cases.
* The company has seen significant growth, transitioning from developer-focused to enterprise-oriented services.
* Major customers like Oracle, Fujitsu, and TD Bank are using Cohere's models across various applications, from HR to finance.
* Baji predicts a surge in enterprise AI adoption over the next 12-18 months as more companies move from experimentation to production.
* He emphasizes the importance of trust, security, and verifiability in enterprise AI applications.
The interview provides insights into Cohere's strategy, technology, and vision for the future of enterprise AI adoption.
https://www.linkedin.com/in/saurabhbaji/
https://x.com/sbaji
https://cohere.com/
https://cohere.com/business
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 augmentated generation. Try it now - get 2,000 free queries monthly at http://brave.com/api.
TOC (*) are best bits
00:00:00 1. Introduction and Background
00:04:24 2. Cloud Infrastructure and LLM Optimization
00:06:43 2.1 Model deployment and fine-tuning strategies *
00:09:37 3. Enterprise AI Deployment Strategies
00:11:10 3.1 Retrieval-augmented generation in enterprise environments *
00:13:40 3.2 Standardization vs. customization in cloud services *
00:18:20 4. AI Model Evaluation and Deployment
00:18:20 4.1 Comprehensive evaluation frameworks *
00:21:20 4.2 Key components of AI model stacks *
00:25:50 5. Retrieval Augmented Generation (RAG) in Enterprise
00:32:10 5.1 Pragmatic approach to RAG implementation *
00:33:45 6. AI Agents and Tool Integration
00:33:45 6.1 Leveraging tools for AI insights *
00:35:30 6.2 Agent-based AI systems and diagnostics *
00:42:55 7. AI Transparency and Reasoning Capabilities
00:49:10 8. AI Model Training and Customization
00:57:10 9. Enterprise AI Model Management
01:02:10 9.1 Managing AI model versions for enterprise customers *
01:04:30 9.2 Future of language model programming *
01:06:10 10. AI-Driven Software Development
01:06:10 10.1 AI bridging human expression and task achievement *
01:08:00 10.2 AI-driven virtual app fabrics in enterprise *
01:13:33 11. Future of AI and Enterprise Applications
01:21:55 12. Cohere's Customers and Use Cases
01:21:55 12.1 Cohere's growth and enterprise partnerships *
01:27:14 12.2 Diverse customers using generative AI *
01:27:50 12.3 Industry adaptation to generative AI *
01:29:00 13. Technical Advantages of Cohere Models
01:29:00 13.1 Handling large context windows *
01:29:40 13.2 Low latency impact on developer productivity *
Disclaimer: This is the fifth video from our Cohere partnership. We were not told what to say in the interview, and didn't edit anything out from the interview. Filmed in Seattle in Aug 2024.
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David Hanson, CEO of Hanson Robotics and creator of the humanoid robot Sofia, explores the intersection of artificial intelligence, ethics, and human potential. In this thought-provoking interview, Hanson discusses his vision for developing AI systems that embody the best aspects of humanity while pushing beyond our current limitations, aiming to achieve what he calls "super wisdom."
YT version: https://youtu.be/LFCIEhlsozU
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 augmentated generation. Try it now - get 2,000 free queries monthly at http://brave.com/api.
The interview with David Hanson covers:
The importance of incorporating biological drives and compassion into AI systems
Hanson's concept of "existential pattern ethics" as a basis for AI morality
The potential for AI to enhance human intelligence and wisdom
Challenges in developing artificial general intelligence (AGI)
The need to democratize AI technologies globally
Potential future advancements in human-AI integration and their societal impacts
Concerns about technological augmentation exacerbating inequality
The role of ethics in guiding AI development and deployment
Hanson advocates for creating AI systems that embody the best aspects of humanity while surpassing current human limitations, aiming for "super wisdom" rather than just artificial super intelligence.
David Hanson:
https://www.hansonrobotics.com/david-hanson/
https://www.youtube.com/watch?v=9u1O954cMmE
TOC
1. Introduction and Background [00:00:00]
1.1. David Hanson's interdisciplinary background [0:01:49]
1.2. Introduction to Sofia, the realistic robot [0:03:27]
2. Human Cognition and AI [0:03:50]
2.1. Importance of social interaction in cognition [0:03:50]
2.2. Compassion as distinguishing factor [0:05:55]
2.3. AI augmenting human intelligence [0:09:54]
3. Developing Human-like AI [0:13:17]
3.1. Incorporating biological drives in AI [0:13:17]
3.2. Creating AI with agency [0:20:34]
3.3. Implementing flexible desires in AI [0:23:23]
4. Ethics and Morality in AI [0:27:53]
4.1. Enhancing humanity through AI [0:27:53]
4.2. Existential pattern ethics [0:30:14]
4.3. Expanding morality beyond restrictions [0:35:35]
5. Societal Impact of AI [0:38:07]
5.1. AI adoption and integration [0:38:07]
5.2. Democratizing AI technologies [0:38:32]
5.3. Human-AI integration and identity [0:43:37]
6. Future Considerations [0:50:03]
6.1. Technological augmentation and inequality [0:50:03]
6.2. Emerging technologies for mental health [0:50:32]
6.3. Corporate ethics in AI development [0:52:26]
This was filmed at AGI-24
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David Spivak, a mathematician known for his work in category theory, discusses a wide range of topics related to intelligence, creativity, and the nature of knowledge. He explains category theory in simple terms and explores how it relates to understanding complex systems and relationships.
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 augmentated generation. Try it now - get 2,000 free queries monthly at http://brave.com/api.
We discuss abstract concepts like collective intelligence, the importance of embodiment in understanding the world, and how we acquire and process knowledge. Spivak shares his thoughts on creativity, discussing where it comes from and how it might be modeled mathematically.
A significant portion of the discussion focuses on the impact of artificial intelligence on human thinking and its potential role in the evolution of intelligence. Spivak also touches on the importance of language, particularly written language, in transmitting knowledge and shaping our understanding of the world.
David Spivak
http://www.dspivak.net/
TOC:
00:00:00 Introduction to category theory and functors
00:04:40 Collective intelligence and sense-making
00:09:54 Embodiment and physical concepts in knowledge acquisition
00:16:23 Creativity, open-endedness, and AI's impact on thinking
00:25:46 Modeling creativity and the evolution of intelligence
00:36:04 Evolution, optimization, and the significance of AI
00:44:14 Written language and its impact on knowledge transmission
REFS:
Mike Levin's work
https://scholar.google.com/citations?user=luouyakAAAAJ&hl=en
Eric Smith's videos on complexity and early life
https://www.youtube.com/watch?v=SpJZw-68QyE
Richard Dawkins' book "The Selfish Gene"
https://amzn.to/3X73X8w
Carl Sagan's statement about the cosmos knowing itself
https://amzn.to/3XhPruK
Herbert Simon's concept of "satisficing"
https://plato.stanford.edu/entries/bounded-rationality/
DeepMind paper on open-ended systems
https://arxiv.org/abs/2406.04268
Karl Friston's work on active inference
https://direct.mit.edu/books/oa-monograph/5299/Active-InferenceThe-Free-Energy-Principle-in-Mind
MIT category theory lectures by David Spivak (available on the Topos Institute channel)
https://www.youtube.com/watch?v=UusLtx9fIjs
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Jürgen Schmidhuber, the father of generative AI shares his groundbreaking work in deep learning and artificial intelligence. In this exclusive interview, he discusses the history of AI, some of his contributions to the field, and his vision for the future of intelligent machines. Schmidhuber offers unique insights into the exponential growth of technology and the potential impact of AI on humanity and the universe.
YT version: https://youtu.be/DP454c1K_vQ
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 augmentated generation. Try it now - get 2,000 free queries monthly at http://brave.com/api.
TOC
00:00:00 Intro
00:03:38 Reasoning
00:13:09 Potential AI Breakthroughs Reducing Computation Needs
00:20:39 Memorization vs. Generalization in AI
00:25:19 Approach to the ARC Challenge
00:29:10 Perceptions of Chat GPT and AGI
00:58:45 Abstract Principles of Jurgen's Approach
01:04:17 Analogical Reasoning and Compression
01:05:48 Breakthroughs in 1991: the P, the G, and the T in ChatGPT and Generative AI
01:15:50 Use of LSTM in Language Models by Tech Giants
01:21:08 Neural Network Aspect Ratio Theory
01:26:53 Reinforcement Learning Without Explicit Teachers
Refs:
★ "Annotated History of Modern AI and Deep Learning" (2022 survey by Schmidhuber):
★ Chain Rule For Backward Credit Assignment (Leibniz, 1676)
★ First Neural Net / Linear Regression / Shallow Learning (Gauss & Legendre, circa 1800)
★ First 20th Century Pioneer of Practical AI (Quevedo, 1914)
★ First Recurrent NN (RNN) Architecture (Lenz, Ising, 1920-1925)
★ AI Theory: Fundamental Limitations of Computation and Computation-Based AI (Gödel, 1931-34)
★ Unpublished ideas about evolving RNNs (Turing, 1948)
★ Multilayer Feedforward NN Without Deep Learning (Rosenblatt, 1958)
★ First Published Learning RNNs (Amari and others, ~1972)
★ First Deep Learning (Ivakhnenko & Lapa, 1965)
★ Deep Learning by Stochastic Gradient Descent (Amari, 1967-68)
★ ReLUs (Fukushima, 1969)
★ Backpropagation (Linnainmaa, 1970); precursor (Kelley, 1960)
★ Backpropagation for NNs (Werbos, 1982)
★ First Deep Convolutional NN (Fukushima, 1979); later combined with Backprop (Waibel 1987, Zhang 1988).
★ Metalearning or Learning to Learn (Schmidhuber, 1987)
★ Generative Adversarial Networks / Artificial Curiosity / NN Online Planners (Schmidhuber, Feb 1990; see the G in Generative AI and ChatGPT)
★ NNs Learn to Generate Subgoals and Work on Command (Schmidhuber, April 1990)
★ NNs Learn to Program NNs: Unnormalized Linear Transformer (Schmidhuber, March 1991; see the T in ChatGPT)
★ Deep Learning by Self-Supervised Pre-Training. Distilling NNs (Schmidhuber, April 1991; see the P in ChatGPT)
★ Experiments with Pre-Training; Analysis of Vanishing/Exploding Gradients, Roots of Long Short-Term Memory / Highway Nets / ResNets (Hochreiter, June 1991, further developed 1999-2015 with other students of Schmidhuber)
★ LSTM journal paper (1997, most cited AI paper of the 20th century)
★ xLSTM (Hochreiter, 2024)
★ Reinforcement Learning Prompt Engineer for Abstract Reasoning and Planning (Schmidhuber 2015)
★ Mindstorms in Natural Language-Based Societies of Mind (2023 paper by Schmidhuber's team)
https://arxiv.org/abs/2305.17066
★ Bremermann's physical limit of computation (1982)
EXTERNAL LINKS
CogX 2018 - Professor Juergen Schmidhuber
https://www.youtube.com/watch?v=17shdT9-wuA
Discovering Neural Nets with Low Kolmogorov Complexity and High Generalization Capability (Neural Networks, 1997)
https://sferics.idsia.ch/pub/juergen/loconet.pdf
The paradox at the heart of mathematics: Gödel's Incompleteness Theorem - Marcus du Sautoy
https://www.youtube.com/watch?v=I4pQbo5MQOs
(Refs truncated, full version on YT VD)
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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
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Andrew Ilyas, a PhD student at MIT who is about to start as a professor at CMU. We discuss Data modeling and understanding how datasets influence model predictions, Adversarial examples in machine learning and why they occur, Robustness in machine learning models, Black box attacks on machine learning systems, Biases in data collection and dataset creation, particularly in ImageNet and Self-selection bias in data and methods to address it.
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 augmentated generation. Try it now - get 2,000 free queries monthly at http://brave.com/api
Andrew's site:
https://andrewilyas.com/
https://x.com/andrew_ilyas
TOC:
00:00:00 - Introduction and Andrew's background
00:03:52 - Overview of the machine learning pipeline
00:06:31 - Data modeling paper discussion
00:26:28 - TRAK: Evolution of data modeling work
00:43:58 - Discussion on abstraction, reasoning, and neural networks
00:53:16 - "Adversarial Examples Are Not Bugs, They Are Features" paper
01:03:24 - Types of features learned by neural networks
01:10:51 - Black box attacks paper
01:15:39 - Work on data collection and bias
01:25:48 - Future research plans and closing thoughts
References:
Adversarial Examples Are Not Bugs, They Are Features
https://arxiv.org/pdf/1905.02175
TRAK: Attributing Model Behavior at Scale
https://arxiv.org/pdf/2303.14186
Datamodels: Predicting Predictions from Training Data
https://arxiv.org/pdf/2202.00622
Adversarial Examples Are Not Bugs, They Are Features
https://arxiv.org/pdf/1905.02175
IMAGENET-TRAINED CNNS
https://arxiv.org/pdf/1811.12231
ZOO: Zeroth Order Optimization Based Black-box
https://arxiv.org/pdf/1708.03999
A Spline Theory of Deep Networks
https://proceedings.mlr.press/v80/balestriero18b/balestriero18b.pdf
Scaling Monosemanticity
https://transformer-circuits.pub/2024/scaling-monosemanticity/
Adversarial Examples Are Not Bugs, They Are Features
https://gradientscience.org/adv/
Adversarial Robustness Limits via Scaling-Law and Human-Alignment Studies
https://proceedings.mlr.press/v235/bartoldson24a.html
Prior Convictions: Black-Box Adversarial Attacks with Bandits and Priors
https://arxiv.org/abs/1807.07978
Estimation of Standard Auction Models
https://arxiv.org/abs/2205.02060
From ImageNet to Image Classification: Contextualizing Progress on Benchmarks
https://arxiv.org/abs/2005.11295
Estimation of Standard Auction Models
https://arxiv.org/abs/2205.02060
What Makes A Good Fisherman? Linear Regression under Self-Selection Bias
https://arxiv.org/abs/2205.03246
Towards Tracing Factual Knowledge in Language Models Back to the
Training Data [Akyürek]
https://arxiv.org/pdf/2205.11482
-
Dr. Joscha Bach introduces a surprising idea called "cyber animism" in his AGI-24 talk - the notion that nature might be full of self-organizing software agents, similar to the spirits in ancient belief systems. Bach suggests that consciousness could be a kind of software running on our brains, and wonders if similar "programs" might exist in plants or even entire ecosystems.
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 augmentated generation. Try it now - get 2,000 free queries monthly at http://brave.com/api.
Joscha takes us on a tour de force through history, philosophy, and cutting-edge computer science, teasing us to rethink what we know about minds, machines, and the world around us. Joscha believes we should blur the lines between human, artificial, and natural intelligence, and argues that consciousness might be more widespread and interconnected than we ever thought possible.
Dr. Joscha Bach
https://x.com/Plinz
This is video 2/9 from our coverage of AGI-24 in Seattle https://agi-conf.org/2024/
Watch the official MLST interview with Joscha which we did right after this talk on our Patreon now on early access - https://www.patreon.com/posts/joscha-bach-110199676 (you also get access to our private discord and biweekly calls)
TOC:
00:00:00 Introduction: AGI and Cyberanimism
00:03:57 The Nature of Consciousness
00:08:46 Aristotle's Concepts of Mind and Consciousness
00:13:23 The Hard Problem of Consciousness
00:16:17 Functional Definition of Consciousness
00:20:24 Comparing LLMs and Human Consciousness
00:26:52 Testing for Consciousness in AI Systems
00:30:00 Animism and Software Agents in Nature
00:37:02 Plant Consciousness and Ecosystem Intelligence
00:40:36 The California Institute for Machine Consciousness
00:44:52 Ethics of Conscious AI and Suffering
00:46:29 Philosophical Perspectives on Consciousness
00:49:55 Q&A: Formalisms for Conscious Systems
00:53:27 Coherence, Self-Organization, and Compute Resources
YT version (very high quality, filmed by us live)
https://youtu.be/34VOI_oo-qM
Refs:
Aristotle's work on the soul and consciousness
Richard Dawkins' work on genes and evolution
Gerald Edelman's concept of Neural Darwinism
Thomas Metzinger's book "Being No One"
Yoshua Bengio's concept of the "consciousness prior"
Stuart Hameroff's theories on microtubules and consciousness
Christof Koch's work on consciousness
Daniel Dennett's "Cartesian Theater" concept
Giulio Tononi's Integrated Information Theory
Mike Levin's work on organismal intelligence
The concept of animism in various cultures
Freud's model of the mind
Buddhist perspectives on consciousness and meditation
The Genesis creation narrative (for its metaphorical interpretation)
California Institute for Machine Consciousness
-
Prof Gary Marcus revisited his keynote from AGI-21, noting that many of the issues he highlighted then are still relevant today despite significant advances in AI.
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 augmentated generation. Try it now - get 2,000 free queries monthly at http://brave.com/api.
Gary Marcus criticized current large language models (LLMs) and generative AI for their unreliability, tendency to hallucinate, and inability to truly understand concepts.
Marcus argued that the AI field is experiencing diminishing returns with current approaches, particularly the "scaling hypothesis" that simply adding more data and compute will lead to AGI.
He advocated for a hybrid approach to AI that combines deep learning with symbolic AI, emphasizing the need for systems with deeper conceptual understanding.
Marcus highlighted the importance of developing AI with innate understanding of concepts like space, time, and causality.
He expressed concern about the moral decline in Silicon Valley and the rush to deploy potentially harmful AI technologies without adequate safeguards.
Marcus predicted a possible upcoming "AI winter" due to inflated valuations, lack of profitability, and overhyped promises in the industry.
He stressed the need for better regulation of AI, including transparency in training data, full disclosure of testing, and independent auditing of AI systems.
Marcus proposed the creation of national and global AI agencies to oversee the development and deployment of AI technologies.
He concluded by emphasizing the importance of interdisciplinary collaboration, focusing on robust AI with deep understanding, and implementing smart, agile governance for AI and AGI.
YT Version (very high quality filmed)
https://youtu.be/91SK90SahHc
Pre-order Gary's new book here:
Taming Silicon Valley: How We Can Ensure That AI Works for Us
https://amzn.to/4fO46pY
Filmed at the AGI-24 conference:
https://agi-conf.org/2024/
TOC:
00:00:00 Introduction
00:02:34 Introduction by Ben G
00:05:17 Gary Marcus begins talk
00:07:38 Critiquing current state of AI
00:12:21 Lack of progress on key AI challenges
00:16:05 Continued reliability issues with AI
00:19:54 Economic challenges for AI industry
00:25:11 Need for hybrid AI approaches
00:29:58 Moral decline in Silicon Valley
00:34:59 Risks of current generative AI
00:40:43 Need for AI regulation and governance
00:49:21 Concluding thoughts
00:54:38 Q&A: Cycles of AI hype and winters
01:00:10 Predicting a potential AI winter
01:02:46 Discussion on interdisciplinary approach
01:05:46 Question on regulating AI
01:07:27 Ben G's perspective on AI winter
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DeepMind Research Scientist / MIT scholar Dr. Timothy Nguyen discusses his recent paper on understanding transformers through n-gram statistics. Nguyen explains his approach to analyzing transformer behavior using a kind of "template matching" (N-grams), providing insights into how these models process and predict language.
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 augmentated generation. Try it now - get 2,000 free queries monthly at http://brave.com/api.
Key points covered include:
A method for describing transformer predictions using n-gram statistics without relying on internal mechanisms.
The discovery of a technique to detect overfitting in large language models without using holdout sets.
Observations on curriculum learning, showing how transformers progress from simpler to more complex rules during training.
Discussion of distance measures used in the analysis, particularly the variational distance.
Exploration of model sizes, training dynamics, and their impact on the results.
We also touch on philosophical aspects of describing versus explaining AI behavior, and the challenges in understanding the abstractions formed by neural networks. Nguyen concludes by discussing potential future research directions, including attempts to convert descriptions of transformer behavior into explanations of internal mechanisms.
Timothy Nguyen's earned his B.S. and Ph.D. in mathematics from Caltech and MIT, respectively. He held positions as Research Assistant Professor at the Simons Center for Geometry and Physics (2011-2014) and Visiting Assistant Professor at Michigan State University (2014-2017). During this time, his research expanded into high-energy physics, focusing on mathematical problems in quantum field theory. His work notably provided a simplified and corrected formulation of perturbative path integrals.
Since 2017, Nguyen has been working in industry, applying his expertise to machine learning. He is currently at DeepMind, where he contributes to both fundamental research and practical applications of deep learning to solve real-world problems.
Refs:
The Cartesian Cafe
https://www.youtube.com/@TimothyNguyen
Understanding Transformers via N-Gram Statistics
https://www.researchgate.net/publication/382204056_Understanding_Transformers_via_N-Gram_Statistics
TOC
00:00:00 Timothy Nguyen's background
00:02:50 Paper overview: transformers and n-gram statistics
00:04:55 Template matching and hash table approach
00:08:55 Comparing templates to transformer predictions
00:12:01 Describing vs explaining transformer behavior
00:15:36 Detecting overfitting without holdout sets
00:22:47 Curriculum learning in training
00:26:32 Distance measures in analysis
00:28:58 Model sizes and training dynamics
00:30:39 Future research directions
00:32:06 Conclusion and future topics
-
Jay Alammar, renowned AI educator and researcher at Cohere, discusses the latest developments in large language models (LLMs) and their applications in industry. Jay shares his expertise on retrieval augmented generation (RAG), semantic search, and the future of AI architectures.
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 augmentated generation. Try it now - get 2,000 free queries monthly at http://brave.com/api.
Cohere Command R model series: https://cohere.com/command
Jay Alamaar:
https://x.com/jayalammar
Buy Jay's new book here!
Hands-On Large Language Models: Language Understanding and Generation
https://amzn.to/4fzOUgh
TOC:
00:00:00 Introduction to Jay Alammar and AI Education
00:01:47 Cohere's Approach to RAG and AI Re-ranking
00:07:15 Implementing AI in Enterprise: Challenges and Solutions
00:09:26 Jay's Role at Cohere and the Importance of Learning in Public
00:15:16 The Evolution of AI in Industry: From Deep Learning to LLMs
00:26:12 Expert Advice for Newcomers in Machine Learning
00:32:39 The Power of Semantic Search and Embeddings in AI Systems
00:37:59 Jay Alammar's Journey as an AI Educator and Visualizer
00:43:36 Visual Learning in AI: Making Complex Concepts Accessible
00:47:38 Strategies for Keeping Up with Rapid AI Advancements
00:49:12 The Future of Transformer Models and AI Architectures
00:51:40 Evolution of the Transformer: From 2017 to Present
00:54:19 Preview of Jay's Upcoming Book on Large Language Models
Disclaimer: This is the fourth video from our Cohere partnership. We were not told what to say in the interview, and didn't edit anything out from the interview. Note also that this combines several previously unpublished interviews from Jay into one, the earlier one at Tim's house was shot in Aug 2023, and the more recent one in Toronto in May 2024.
Refs:
The Illustrated Transformer
https://jalammar.github.io/illustrated-transformer/
Attention Is All You Need
https://arxiv.org/abs/1706.03762
The Unreasonable Effectiveness of Recurrent Neural Networks
http://karpathy.github.io/2015/05/21/rnn-effectiveness/
Neural Networks in 11 Lines of Code
https://iamtrask.github.io/2015/07/12/basic-python-network/
Understanding LSTM Networks (Chris Olah's blog post)
http://colah.github.io/posts/2015-08-Understanding-LSTMs/
Luis Serrano's YouTube Channel
https://www.youtube.com/channel/UCgBncpylJ1kiVaPyP-PZauQ
Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks
https://arxiv.org/abs/1908.10084
GPT (Generative Pre-trained Transformer) models
https://jalammar.github.io/illustrated-gpt2/
https://openai.com/research/gpt-4
BERT (Bidirectional Encoder Representations from Transformers)
https://jalammar.github.io/illustrated-bert/
https://arxiv.org/abs/1810.04805
RoPE (Rotary Positional Encoding)
https://arxiv.org/abs/2104.09864 (Linked paper discussing rotary embeddings)
Grouped Query Attention
https://arxiv.org/pdf/2305.13245
RLHF (Reinforcement Learning from Human Feedback)
https://openai.com/research/learning-from-human-preferences
https://arxiv.org/abs/1706.03741
DPO (Direct Preference Optimization)
https://arxiv.org/abs/2305.18290
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Daniel Cahn, co-founder of Slingshot AI, on the potential of AI in therapy. Why is anxiety and depression affecting a large population? To what extent are these real categories? Why is the mental health getting worse? How often do you want an AI to agree with you? What are the ethics of persuasive AI? You will discover all in this conversation.
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 augmentated generation. Try it now - get 2,000 free queries monthly at http://brave.com/api.
Daniel Cahn (who is also hiring ML engineers by the way!)
https://x.com/thecahnartist?lang=en
/ cahnd
https://thinkingmachinespodcast.com/
TOC:
00:00:00 Intro
00:01:56 Therapy effectiveness vs drugs and societal implications
00:04:02 Mental health categories: Iatrogenesis and social constructs
00:10:19 Psychiatric treatment models and cognitive perspectives
00:13:30 AI design and human-like interactions: Intentionality debates
00:20:04 AI in therapy: Ethics, anthropomorphism, and loneliness mitigation
00:28:13 Therapy efficacy: Neuroplasticity, suffering, and AI placebos
00:33:29 AI's impact on human agency and cognitive modeling
00:41:17 Social media's effects on brain structure and behavior
00:50:46 AI ethics: Altering values and free will considerations
01:00:00 Work value perception and personal identity formation
01:13:37 Free will, agency, and mutable personal identity in therapy
01:24:27 AI in healthcare: Challenges, ethics, and therapy improvements
01:53:25 AI development: Societal impacts and cultural implications
Full references on YT VD: https://www.youtube.com/watch?v=7hwX6OZyNC0 (and baked into mp3 metadata)
-
Prof. Subbarao Kambhampati argues that while LLMs are impressive and useful tools, especially for creative tasks, they have fundamental limitations in logical reasoning and cannot provide guarantees about the correctness of their outputs. He advocates for hybrid approaches that combine LLMs with external verification systems.
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 augmentated generation. Try it now - get 2,000 free queries monthly at http://brave.com/api.
TOC (sorry the ones baked into the MP3 were wrong apropos due to LLM hallucination!)
[00:00:00] Intro
[00:02:06] Bio
[00:03:02] LLMs are n-gram models on steroids
[00:07:26] Is natural language a formal language?
[00:08:34] Natural language is formal?
[00:11:01] Do LLMs reason?
[00:19:13] Definition of reasoning
[00:31:40] Creativity in reasoning
[00:50:27] Chollet's ARC challenge
[01:01:31] Can we reason without verification?
[01:10:00] LLMs cant solve some tasks
[01:19:07] LLM Modulo framework
[01:29:26] Future trends of architecture
[01:34:48] Future research directions
Youtube version: https://www.youtube.com/watch?v=y1WnHpedi2A
Refs: (we didn't have space for URLs here, check YT video description instead)
Can LLMs Really Reason and Plan? On the Planning Abilities of Large Language Models : A Critical Investigation Chain of Thoughtlessness? An Analysis of CoT in Planning On the Self-Verification Limitations of Large Language Models on Reasoning and Planning Tasks LLMs Can't Plan, But Can Help Planning in LLM-Modulo Frameworks Embers of Autoregression: Understanding Large Language Models Through the Problem They are Trained to Solve "Task Success" is not Enough Partition function (number theory) (Srinivasa Ramanujan and G.H. Hardy's work) Poincaré conjecture Gödel's incompleteness theorems ROT13 (Rotate13, "rotate by 13 places") A Mathematical Theory of Communication (C. E. SHANNON) Sparks of AGI Kambhampati thesis on speech recognition (1983) PlanBench: An Extensible Benchmark for Evaluating Large Language Models on Planning and Reasoning about Change Explainable human-AI interaction Tree of Thoughts On the Measure of Intelligence (ARC Challenge) Getting 50% (SoTA) on ARC-AGI with GPT-4o (Ryan Greenblatt ARC solution) PROGRAMS WITH COMMON SENSE (John McCarthy) - "AI should be an advice taker program" Original chain of thought paper ICAPS 2024 Keynote: Dale Schuurmans on "Computing and Planning with Large Generative Models" (COT) The Hardware Lottery (Hooker) A Path Towards Autonomous Machine Intelligence (JEPA/LeCun) AlphaGeometry FunSearch Emergent Abilities of Large Language Models Language models are not naysayers (Negation in LLMs) The Reversal Curse: LLMs trained on "A is B" fail to learn "B is A" Embracing negative results -
How seriously should governments take the threat of existential risk from AI, given the lack of consensus among researchers? On the one hand, existential risks (x-risks) are necessarily somewhat speculative: by the time there is concrete evidence, it may be too late. On the other hand, governments must prioritize — after all, they don’t worry too much about x-risk from alien invasions.
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 augmentated generation. Try it now - get 2,000 free queries monthly at brave.com/api.
Sayash Kapoor is a computer science Ph.D. candidate at Princeton University's Center for Information Technology Policy. His research focuses on the societal impact of AI. Kapoor has previously worked on AI in both industry and academia, with experience at Facebook, Columbia University, and EPFL Switzerland. He is a recipient of a best paper award at ACM FAccT and an impact recognition award at ACM CSCW. Notably, Kapoor was included in TIME's inaugural list of the 100 most influential people in AI.
Sayash Kapoor
https://x.com/sayashk
https://www.cs.princeton.edu/~sayashk/
Arvind Narayanan (other half of the AI Snake Oil duo)
https://x.com/random_walker
AI existential risk probabilities are too unreliable to inform policy
https://www.aisnakeoil.com/p/ai-existential-risk-probabilities
Pre-order AI Snake Oil Book
https://amzn.to/4fq2HGb
AI Snake Oil blog
https://www.aisnakeoil.com/
AI Agents That Matter
https://arxiv.org/abs/2407.01502
Shortcut learning in deep neural networks
https://www.semanticscholar.org/paper/Shortcut-learning-in-deep-neural-networks-Geirhos-Jacobsen/1b04936c2599e59b120f743fbb30df2eed3fd782
77% Of Employees Report AI Has Increased Workloads And Hampered Productivity, Study Finds
https://www.forbes.com/sites/bryanrobinson/2024/07/23/employees-report-ai-increased-workload/
TOC:
00:00:00 Intro
00:01:57 How seriously should we take Xrisk threat?
00:02:55 Risk too unrealiable to inform policy
00:10:20 Overinflated risks
00:12:05 Perils of utility maximisation
00:13:55 Scaling vs airplane speeds
00:17:31 Shift to smaller models?
00:19:08 Commercial LLM ecosystem
00:22:10 Synthetic data
00:24:09 Is AI complexifying our jobs?
00:25:50 Does ChatGPT make us dumber or smarter?
00:26:55 Are AI Agents overhyped?
00:28:12 Simple vs complex baselines
00:30:00 Cost tradeoff in agent design
00:32:30 Model eval vs downastream perf
00:36:49 Shortcuts in metrics
00:40:09 Standardisation of agent evals
00:41:21 Humans in the loop
00:43:54 Levels of agent generality
00:47:25 ARC challenge
-
Sara Hooker is VP of Research at Cohere and leader of Cohere for AI. We discuss her recent paper critiquing the use of compute thresholds, measured in FLOPs (floating point operations), as an AI governance strategy.
We explore why this approach, recently adopted in both US and EU AI policies, may be problematic and oversimplified. Sara explains the limitations of using raw computational power as a measure of AI capability or risk, and discusses the complex relationship between compute, data, and model architecture.
Equally important, we go into Sara's work on "The AI Language Gap." This research highlights the challenges and inequalities in developing AI systems that work across multiple languages. Sara discusses how current AI models, predominantly trained on English and a handful of high-resource languages, fail to serve the linguistic diversity of our global population. We explore the technical, ethical, and societal implications of this gap, and discuss potential solutions for creating more inclusive and representative AI systems.
We broadly discuss the relationship between language, culture, and AI capabilities, as well as the ethical considerations in AI development and deployment.
YT Version: https://youtu.be/dBZp47999Ko
TOC:
[00:00:00] Intro
[00:02:12] FLOPS paper
[00:26:42] Hardware lottery
[00:30:22] The Language gap
[00:33:25] Safety
[00:38:31] Emergent
[00:41:23] Creativity
[00:43:40] Long tail
[00:44:26] LLMs and society
[00:45:36] Model bias
[00:48:51] Language and capabilities
[00:52:27] Ethical frameworks and RLHF
Sara Hooker
https://www.sarahooker.me/
https://www.linkedin.com/in/sararosehooker/
https://scholar.google.com/citations?user=2xy6h3sAAAAJ&hl=en
https://x.com/sarahookr
Interviewer: Tim Scarfe
Refs
The AI Language gap
https://cohere.com/research/papers/the-AI-language-gap.pdf
On the Limitations of Compute Thresholds as a Governance Strategy.
https://arxiv.org/pdf/2407.05694v1
The Multilingual Alignment Prism: Aligning Global and Local Preferences to Reduce Harm
https://arxiv.org/pdf/2406.18682
Cohere Aya
https://cohere.com/research/aya
RLHF Can Speak Many Languages: Unlocking Multilingual Preference Optimization for LLMs
https://arxiv.org/pdf/2407.02552
Back to Basics: Revisiting REINFORCE Style Optimization for Learning from Human Feedback in LLMs
https://arxiv.org/pdf/2402.14740
Executive Order on the Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence
https://www.whitehouse.gov/briefing-room/presidential-actions/2023/10/30/executive-order-on-the-safe-secure-and-trustworthy-development-and-use-of-artificial-intelligence/
EU AI Act
https://www.europarl.europa.eu/doceo/document/TA-9-2024-0138_EN.pdf
The bitter lesson
http://www.incompleteideas.net/IncIdeas/BitterLesson.html
Neel Nanda interview
https://www.youtube.com/watch?v=_Ygf0GnlwmY
Scaling Monosemanticity: Extracting Interpretable Features from Claude 3 Sonnet
https://transformer-circuits.pub/2024/scaling-monosemanticity/
Chollet's ARC challenge
https://github.com/fchollet/ARC-AGI
Ryan Greenblatt on ARC
https://www.youtube.com/watch?v=z9j3wB1RRGA
Disclaimer: This is the third video from our Cohere partnership. We were not told what to say in the interview, and didn't edit anything out from the interview.
-
Murray Shanahan is a professor of Cognitive Robotics at Imperial College London and a senior research scientist at DeepMind. He challenges our assumptions about AI consciousness and urges us to rethink how we talk about machine intelligence.
We explore the dangers of anthropomorphizing AI, the limitations of current language in describing AI capabilities, and the fascinating intersection of philosophy and artificial intelligence.
Show notes and full references: https://docs.google.com/document/d/1ICtBI574W-xGi8Z2ZtUNeKWiOiGZ_DRsp9EnyYAISws/edit?usp=sharing
Prof Murray Shanahan:
https://www.doc.ic.ac.uk/~mpsha/ (look at his selected publications)
https://scholar.google.co.uk/citations?user=00bnGpAAAAAJ&hl=en
https://en.wikipedia.org/wiki/Murray_Shanahan
https://x.com/mpshanahan
Interviewer: Dr. Tim Scarfe
Refs (links in the Google doc linked above):
Role play with large language models
Waluigi effect
"Conscious Exotica" - Paper by Murray Shanahan (2016)
"Simulators" - Article by Janis from LessWrong
"Embodiment and the Inner Life" - Book by Murray Shanahan (2010)
"The Technological Singularity" - Book by Murray Shanahan (2015)
"Simulacra as Conscious Exotica" - Paper by Murray Shanahan (newer paper of the original focussed on LLMs)
A recent paper by Anthropic on using autoencoders to find features in language models (referring to the "Scaling Monosemanticity" paper)
Work by Peter Godfrey-Smith on octopus consciousness
"Metaphors We Live By" - Book by George Lakoff (1980s)
Work by Aaron Sloman on the concept of "space of possible minds" (1984 article mentioned)
Wittgenstein's "Philosophical Investigations" (posthumously published)
Daniel Dennett's work on the "intentional stance"
Alan Turing's original paper on the Turing Test (1950)
Thomas Nagel's paper "What is it like to be a bat?" (1974)
John Searle's Chinese Room Argument (mentioned but not detailed)
Work by Richard Evans on tackling reasoning problems
Claude Shannon's quote on knowledge and control
"Are We Bodies or Souls?" - Book by Richard Swinburne
Reference to work by Ethan Perez and others at Anthropic on potential deceptive behavior in language models
Reference to a paper by Murray Shanahan and Antonia Creswell on the "selection inference framework"
Mention of work by Francois Chollet, particularly the ARC (Abstraction and Reasoning Corpus) challenge
Reference to Elizabeth Spelke's work on core knowledge in infants
Mention of Karl Friston's work on planning as inference (active inference)
The film "Ex Machina" - Murray Shanahan was the scientific advisor
"The Waluigi Effect"
Anthropic's constitutional AI approach
Loom system by Lara Reynolds and Kyle McDonald for visualizing conversation trees
DeepMind's AlphaGo (mentioned multiple times as an example)
Mention of the "Golden Gate Claude" experiment
Reference to an interview Tim Scarfe conducted with University of Toronto students about self-attention controllability theorem
Mention of an interview with Irina Rish
Reference to an interview Tim Scarfe conducted with Daniel Dennett
Reference to an interview with Maria Santa Caterina
Mention of an interview with Philip Goff
Nick Chater and Martin Christianson's book ("The Language Game: How Improvisation Created Language and Changed the World")
Peter Singer's work from 1975 on ascribing moral status to conscious beings
Demis Hassabis' discussion on the "ladder of creativity"
Reference to B.F. Skinner and behaviorism
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In the coming decades, the technology that enables virtual and augmented reality will improve beyond recognition. Within a century, world-renowned philosopher David J. Chalmers predicts, we will have virtual worlds that are impossible to distinguish from non-virtual worlds. But is virtual reality just escapism?
In a highly original work of 'technophilosophy', Chalmers argues categorically, no: virtual reality is genuine reality. Virtual worlds are not second-class worlds. We can live a meaningful life in virtual reality - and increasingly, we will.
What is reality, anyway? How can we lead a good life? Is there a god? How do we know there's an external world - and how do we know we're not living in a computer simulation? In Reality+, Chalmers conducts a grand tour of philosophy, using cutting-edge technology to provide invigorating new answers to age-old questions.
David J. Chalmers is an Australian philosopher and cognitive scientist specializing in the areas of philosophy of mind and philosophy of language. He is Professor of Philosophy and Neural Science at New York University, as well as co-director of NYU's Center for Mind, Brain, and Consciousness. Chalmers is best known for his work on consciousness, including his formulation of the "hard problem of consciousness."
Reality+: Virtual Worlds and the Problems of Philosophy
https://amzn.to/3RYyGD2
https://consc.net/
https://x.com/davidchalmers42
00:00:00 Reality+ Intro
00:12:02 GPT conscious? 10/10
00:14:19 The consciousness processor thought experiment (11/10)
00:20:34 Intelligence and Consciousness entangled? 10/10
00:22:44 Karl Friston / Meta Problem 10/10
00:29:05 Knowledge argument / subjective experience (6/10)
00:32:34 Emergence 11/10 (best chapter)
00:42:45 Working with Douglas Hofstadter 10/10
00:46:14 Intelligence is analogy making? 10/10
00:50:47 Intelligence explosion 8/10
00:58:44 Hypercomputation 10/10
01:09:44 Who designed the designer? (7/10)
01:13:57 Experience machine (7/10)
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