Machine Learning Street Talk (MLST)

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Machine Learning Street Talk (MLST) episodes

October 29, 2023 THE HARD PROBLEM OF OBSERVERS - WOLFRAM & FRISTON [SPECIAL EDITION]
Please support us! https://www.patreon.com/mlst
https://discord.gg/aNPkGUQtc5
https://twitter.com/MLStreetTalk

YT version (with intro not found here) https://youtu.be/6iaT-0Dvhnc
This is the epic special edition show you have been waiting for! With two of the most brilliant scientists alive today.
Atoms, things, agents, ... observers. What even defines an "observer" and what properties must all observers share? How do objects persist in our universe given that their material composition changes over time? What does it mean for a thing to be a thing? And do things supervene on our lower-level physical reality? What does it mean for a thing to have agency? What's the difference between a complex dynamical system with and without agency? Could a rock or an AI catflap have agency? Can the universe be factorised into distinct agents, or is agency diffused? Have you ever pondered about these deep questions about reality?
Prof. Friston and Dr. Wolfram have spent their entire careers, some 40+ years each thinking long and hard about these very questions and have developed significant frameworks of reference on their respective journeys (the Wolfram Physics project and the Free Energy principle).
Panel: MIT Ph.D Keith Duggar
Production: Dr. Tim Scarfe
Refs:
TED Talk with Stephen:
https://www.ted.com/talks/stephen_wolfram_how_to_think_computationally_about_ai_the_universe_and_everything
https://writings.stephenwolfram.com/2023/10/how-to-think-computationally-about-ai-the-universe-and-everything/
TOC
00:00:00 - Show kickoff
00:02:38 - Wolfram gets to grips with FEP
00:27:08 - How much control does an agent/observer have
00:34:52 - Observer persistence, what universe seems like to us
00:40:31 - Black holes
00:45:07 - Inside vs outside
00:52:20 - Moving away from the predictable path
00:55:26 - What can observers do
01:06:50 - Self modelling gives agency
01:11:26 - How do you know a thing has agency?
01:22:48 - Deep link between dynamics, ruliad and AI
01:25:52 - Does agency entail free will? Defining Agency
01:32:57 - Where do I probe for agency?
01:39:13 - Why is the universe the way we see it?
01:42:50 - Alien intelligence
01:43:40 - The hard problem of Observers
01:46:20 - Summary thoughts from Wolfram
01:49:35 - Factorisability of FEP
01:57:05 - Patreon interview teaser
2 hr
October 16, 2023 DR. JEFF BECK - THE BAYESIAN BRAIN
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
1 hr 11 min
September 10, 2023 Prof. Melanie Mitchell 2.0 - AI Benchmarks are Broken!
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
1 hr 2 min
September 05, 2023 Autopoitic Enactivism and the Free Energy Principle - Prof. Friston, Prof Buckley, Dr. Ramstead
We explore connections between FEP and enactivism, including tensions raised in a paper critiquing FEP from an enactivist perspective.

Dr. Maxwell Ramstead provides background on enactivism emerging from autopoiesis, with a focus on embodied cognition and rejecting information processing/computational views of mind.

Chris shares his journey from robotics into FEP, starting as a skeptic but becoming convinced it's the right framework. He notes there are both "high road" and "low road" versions, ranging from embodied to more radically anti-representational stances. He doesn't see a definitive fork between dynamical systems and information theory as the source of conflict. Rather, the notion of operational closure in enactivism seems to be the main sticking point.

The group explores definitional issues around structure/organization, boundaries, and operational closure. Maxwell argues the generative model in FEP captures organizational dependencies akin to operational closure. The Markov blanket formalism models structural interfaces.

We discuss the concept of goals in cognitive systems - Chris advocates an intentional stance perspective - using notions of goals/intentions if they help explain system dynamics. Goals emerge from beliefs about dynamical trajectories. Prof Friston provides an elegant explanation of how goal-directed behavior naturally falls out of the FEP mathematics in a particular "goldilocks" regime of system scale/dynamics. The conversation explores the idea that many systems simply act "as if" they have goals or models, without necessarily possessing explicit representations. This helps resolve tensions between enactivist and computational perspectives.

Throughout the dialogue, Maxwell presses philosophical points about the FEP abolishing what he perceives as false dichotomies in cognitive science such as internalism/externalism. He is critical of enactivists' commitment to bright line divides between subject areas.

Prof. Karl Friston - Inventor of the free energy principle https://scholar.google.com/citations?user=q_4u0aoAAAAJ
Prof. Chris Buckley - Professor of Neural Computation at Sussex University https://scholar.google.co.uk/citations?user=nWuZ0XcAAAAJ&hl=en
Dr. Maxwell Ramstead - Director of Research at VERSES https://scholar.google.ca/citations?user=ILpGOMkAAAAJ&hl=fr

We address critique in this paper:
Laying down a forking path: Tensions between enaction and the free energy principle (Ezequiel A. Di Paolo, Evan Thompson, Randall D. Beere)
https://philosophymindscience.org/index.php/phimisci/article/download/9187/8975

Other refs:
Multiscale integration: beyond internalism and externalism (Maxwell J D Ramstead)
https://pubmed.ncbi.nlm.nih.gov/33627890/

MLST panel: Dr. Tim Scarfe and Dr. Keith Duggar

TOC (auto generated):
0:00 - Introduction
0:41 - Defining enactivism and its variants
6:58 - The source of the conflict between dynamical systems and information theory
8:56 - Operational closure in enactivism
10:03 - Goals and intentions
12:35 - The link between dynamical systems and information theory
15:02 - Path integrals and non-equilibrium dynamics
18:38 - Operational closure defined
21:52 - Structure vs. organization in enactivism
24:24 - Markov blankets as interfaces
28:48 - Operational closure in FEP
30:28 - Structure and organization again
31:08 - Dynamics vs. information theory
33:55 - Goals and intentions emerge in the FEP mathematics
36:58 - The Good Regulator Theorem
49:30 - enactivism and its relation to ecological psychology
52:00 - Goals, intentions and beliefs
55:21 - Boundaries and meaning
58:55 - Enactivism's rejection of information theory
1:02:08 - Beliefs vs goals
1:05:06 - Ecological psychology and FEP
1:08:41 - The Good Regulator Theorem
1:18:38 - How goal-directed behavior emerges
1:23:13 - Ontological vs metaphysical boundaries
1:25:20 - Boundaries as maps
1:31:08 - Connections to the maximum entropy principle
1:33:45 - Relations to quantum and relational physics
1 hr 35 min
August 15, 2023 STEPHEN WOLFRAM 2.0 - Resolving the Mystery of the Second Law of Thermodynamics
Please check out Numerai - our sponsor @
http://numer.ai/mlst
Patreon: https://www.patreon.com/mlst
Discord: https://discord.gg/ESrGqhf5CB
The Second Law: Resolving the Mystery of the Second Law of Thermodynamics
Buy Stephen's book here - https://tinyurl.com/2jj2t9wa
The Language Game: How Improvisation Created Language and Changed the World by Morten H. Christiansen and Nick Chater
Buy here: https://tinyurl.com/35bvs8be
Stephen Wolfram starts by discussing the second law of thermodynamics - the idea that entropy, or disorder, tends to increase over time. He talks about how this law seems intuitively true, but has been difficult to prove. Wolfram outlines his decades-long quest to fully understand the second law, including failed early attempts to simulate particles mixing as a 12-year-old. He explains how irreversibility arises from the computational irreducibility of underlying physical processes coupled with our limited ability as observers to do the computations needed to "decrypt" the microscopic details.
The conversation then shifts to discussing language and how concepts allow us to communicate shared ideas between minds positioned in different parts of "rule space." Wolfram talks about the successes and limitations of using large language models to generate Wolfram Language code from natural language prompts. He sees it as a useful tool for getting started programming, but one still needs human refinement.
The final part of the conversation focuses on AI safety and governance. Wolfram notes uncontrolled actuation is where things can go wrong with AI systems. He discusses whether AI agents could have intrinsic experiences and goals, how we might build trust networks between AIs, and that managing a system of many AIs may be easier than a single AI. Wolfram emphasizes the need for more philosophical depth in thinking about AI aims, and draws connections between potential solutions and his work on computational irreducibility and physics.
Show notes: https://docs.google.com/document/d/1hXNHtvv8KDR7PxCfMh9xOiDFhU3SVDW8ijyxeTq9LHo/edit?usp=sharing
Pod version: TBA
https://twitter.com/stephen_wolfram
TOC:
00:00:00 - Introduction
00:02:34 - Second law book
00:14:01 - Reversibility / entropy / observers / equivalence
00:34:22 - Concepts/language in the ruliad
00:49:04 - Comparison to free energy principle
00:53:58 - ChatGPT / Wolfram / Language
01:00:17 - AI risk
Panel: Dr. Tim Scarfe @ecsquendor / Dr. Keith Duggar @DoctorDuggar
1 hr 25 min
August 14, 2023 Prof. Jürgen Schmidhuber - FATHER OF AI ON ITS DANGERS
Please check out Numerai - our sponsor @
http://numer.ai/mlst
Patreon: https://www.patreon.com/mlst
Discord: https://discord.gg/ESrGqhf5CB
Professor Jürgen Schmidhuber, the father of artificial intelligence, joins us today. Schmidhuber discussed the history of machine learning, the current state of AI, and his career researching recursive self-improvement, artificial general intelligence and its risks.
Schmidhuber pointed out the importance of studying the history of machine learning to properly assign credit for key breakthroughs. He discussed some of the earliest machine learning algorithms. He also highlighted the foundational work of Leibniz, who discovered the chain rule that enables training of deep neural networks, and the ancient Antikythera mechanism, the first known gear-based computer.
Schmidhuber discussed limits to recursive self-improvement and artificial general intelligence, including physical constraints like the speed of light and what can be computed. He noted we have no evidence the human brain can do more than traditional computing. Schmidhuber sees humankind as a potential stepping stone to more advanced, spacefaring machine life which may have little interest in humanity. However, he believes commercial incentives point AGI development towards being beneficial and that open-source innovation can help to achieve "AI for all" symbolised by his company's motto "AI∀".
Schmidhuber discussed approaches he believes will lead to more general AI, including meta-learning, reinforcement learning, building predictive world models, and curiosity-driven learning. His "fast weight programming" approach from the 1990s involved one network altering another network's connections. This was actually the first Transformer variant, now called an unnormalised linear Transformer. He also described the first GANs in 1990, to implement artificial curiosity.
Schmidhuber reflected on his career researching AI. He said his fondest memories were gaining insights that seemed to solve longstanding problems, though new challenges always arose: "then for a brief moment it looks like the greatest thing since sliced bread and and then you get excited ... but then suddenly you realize, oh, it's still not finished. Something important is missing.” Since 1985 he has worked on systems that can recursively improve themselves, constrained only by the limits of physics and computability. He believes continual progress, shaped by both competition and collaboration, will lead to increasingly advanced AI.
On AI Risk: Schmidhuber: "To me it's indeed weird. Now there are all these letters coming out warning of the dangers of AI. And I think some of the guys who are writing these letters, they are just seeking attention because they know that AI dystopia are attracting more attention than documentaries about the benefits of AI in healthcare."
Schmidhuber believes we should be more concerned with existing threats like nuclear weapons than speculative risks from advanced AI. He said: "As far as I can judge, all of this cannot be stopped but it can be channeled in a very natural way that is good for humankind...there is a tremendous bias towards good AI, meaning AI that is good for humans...I am much more worried about 60 year old technology that can wipe out civilization within two hours, without any AI.”
[this is truncated, read show notes]
YT: https://youtu.be/q27XMPm5wg8
Show notes: https://docs.google.com/document/d/13-vIetOvhceZq5XZnELRbaazpQbxLbf5Yi7M25CixEE/edit?usp=sharing
Note: Interview was recorded 15th June 2023.
https://twitter.com/SchmidhuberAI
Panel: Dr. Tim Scarfe @ecsquendor / Dr. Keith Duggar @DoctorDuggar
Pod version: TBA
TOC:
[00:00:00] Intro / Numerai
[00:00:51] Show Kick Off
[00:02:24] Credit Assignment in ML
[00:12:51] XRisk
[00:20:45] First Transformer variant of 1991
[00:47:20] Which Current Approaches are Good
[00:52:42] Autonomy / Curiosity
[00:58:42] GANs of 1990
[01:11:29] OpenAI, Moats, Legislation
1 hr 22 min
August 04, 2023 Can We Develop Truly Beneficial AI? George Hotz and Connor Leahy
Patreon: https://www.patreon.com/mlst
Discord: https://discord.gg/ESrGqhf5CB

George Hotz and Connor Leahy discuss the crucial challenge of developing beneficial AI that is aligned with human values. Hotz believes truly aligned AI is impossible, while Leahy argues it's a solvable technical challenge.
Hotz contends that AI will inevitably pursue power, but distributing AI widely would prevent any single AI from dominating. He advocates open-sourcing AI developments to democratize access. Leahy counters that alignment is necessary to ensure AIs respect human values. Without solving alignment, general AI could ignore or harm humans.
They discuss whether AI's tendency to seek power stems from optimization pressure or human-instilled goals. Leahy argues goal-seeking behavior naturally emerges while Hotz believes it reflects human values. Though agreeing on AI's potential dangers, they differ on solutions. Hotz favors accelerating AI progress and distributing capabilities while Leahy wants safeguards put in place.
While acknowledging risks like AI-enabled weapons, they debate whether broad access or restrictions better manage threats. Leahy suggests limiting dangerous knowledge, but Hotz insists openness checks government overreach. They concur that coordination and balance of power are key to navigating the AI revolution. Both eagerly anticipate seeing whose ideas prevail as AI progresses.
Transcript and notes: https://docs.google.com/document/d/1smkmBY7YqcrhejdbqJOoZHq-59LZVwu-DNdM57IgFcU/edit?usp=sharing
Note: this is not a normal episode i.e. the hosts are not part of the debate (and for the record don't agree with Connor or George).
TOC:
[00:00:00] Introduction to George Hotz and Connor Leahy
[00:03:10] George Hotz's Opening Statement: Intelligence and Power
[00:08:50] Connor Leahy's Opening Statement: Technical Problem of Alignment and Coordination
[00:15:18] George Hotz's Response: Nature of Cooperation and Individual Sovereignty
[00:17:32] Discussion on individual sovereignty and defense
[00:18:45] Debate on living conditions in America versus Somalia
[00:21:57] Talk on the nature of freedom and the aesthetics of life
[00:24:02] Discussion on the implications of coordination and conflict in politics
[00:33:41] Views on the speed of AI development / hard takeoff
[00:35:17] Discussion on potential dangers of AI
[00:36:44] Discussion on the effectiveness of current AI
[00:40:59] Exploration of potential risks in technology
[00:45:01] Discussion on memetic mutation risk
[00:52:36] AI alignment and exploitability
[00:53:13] Superintelligent AIs and the assumption of good intentions
[00:54:52] Humanity’s inconsistency and AI alignment
[00:57:57] Stability of the world and the impact of superintelligent AIs
[01:02:30] Personal utopia and the limitations of AI alignment
[01:05:10] Proposed regulation on limiting the total number of flops
[01:06:20] Having access to a powerful AI system
[01:18:00] Power dynamics and coordination issues with AI
[01:25:44] Humans vs AI in Optimization
[01:27:05] The Impact of AI's Power Seeking Behavior
[01:29:32] A Debate on the Future of AI
1 hr 30 min
July 16, 2023 Dr. MAXWELL RAMSTEAD - The Physics of Survival
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
2 hr 6 min
July 02, 2023 MUNK DEBATE ON AI (COMMENTARY) [DAVID FOSTER]
Patreon: https://www.patreon.com/mlst
Discord: https://discord.gg/ESrGqhf5CB

The discussion between Tim Scarfe and David Foster provided an in-depth critique of the arguments made by panelists at the Munk AI Debate on whether artificial intelligence poses an existential threat to humanity. While the panelists made thought-provoking points, Scarfe and Foster found their arguments largely speculative, lacking crucial details and evidence to support claims of an impending existential threat.

Scarfe and Foster strongly disagreed with Max Tegmark’s position that AI has an unparalleled “blast radius” that could lead to human extinction. Tegmark failed to provide a credible mechanism for how this scenario would unfold in reality. His arguments relied more on speculation about advanced future technologies than on present capabilities and trends. As Foster argued, we cannot conclude AI poses a threat based on speculation alone. Evidence is needed to ground discussions of existential risks in science rather than science fiction fantasies or doomsday scenarios.

They found Yann LeCun’s statements too broad and high-level, critiquing him for not providing sufficiently strong arguments or specifics to back his position. While LeCun aptly noted AI remains narrow in scope and far from achieving human-level intelligence, his arguments lacked crucial details on current limitations and why we should not fear superintelligence emerging in the near future. As Scarfe argued, without these details the discussion descended into “philosophy” rather than focusing on evidence and data.

Scarfe and Foster also took issue with Yoshua Bengio’s unsubstantiated speculation that machines would necessarily develop a desire for self-preservation that threatens humanity. There is no evidence today’s AI systems are developing human-like general intelligence or desires, let alone that these attributes would manifest in ways dangerous to humans. The question is not whether machines will eventually surpass human intelligence, but how and when this might realistically unfold based on present technological capabilities. Bengio’s arguments relied more on speculation about advanced future technologies than on evidence from current systems and research.

In contrast, they strongly agreed with Melanie Mitchell’s view that scenarios of malevolent or misguided superintelligence are speculation, not backed by evidence from AI as it exists today. Claims of an impending “existential threat” from AI are overblown, harmful to progress, and inspire undue fear of technology rather than consideration of its benefits. Mitchell sensibly argued discussions of risks from emerging technologies must be grounded in science and data, not speculation, if we are to make balanced policy and development decisions.

Overall, while the debate raised thought-provoking questions about advanced technologies that could eventually transform our world, none of the speakers made a credible evidence-based case that today’s AI poses an existential threat. Scarfe and Foster argued the debate failed to discuss concrete details about current capabilities and limitations of technologies like language models, which remain narrow in scope. General human-level AI is still missing many components, including physical embodiment, emotions, and the "common sense" reasoning that underlies human thinking. Claims of existential threats require extraordinary evidence to justify policy or research restrictions, not speculation. By discussing possibilities rather than probabilities grounded in evidence, the debate failed to substantively advance our thinking on risks from AI and its plausible development in the coming decades.

David's new podcast: https://podcasts.apple.com/us/podcast/the-ai-canvas/id1692538973
Generative AI book: https://www.oreilly.com/library/view/generative-deep-learning/9781098134174/
2 hr 9 min
June 29, 2023 [SPONSORED] The Digitized Self: AI, Identity and the Human Psyche (YouAi)
Sponsored Episode - YouAi
What if an AI truly knew you—your thoughts, values, aptitudes, and dreams? An AI that could enhance your life in profound ways by amplifying your strengths, augmenting your weaknesses, and connecting you with like-minded souls. That is the vision of YouAi.
YouAi founder Dmitri Shapiro believes digitizing our inner lives could unlock tremendous benefits. But mapping the human psyche also poses deep questions. As technology mediates our self-understanding, what risks rendering our minds in bits and algorithms? Could we gain a new means of flourishing or lose something intangible?
There are no easy answers, but YouAi offers a vision balanced by hard thinking. Shapiro discussed YouAi's app, which builds personalized AI assistants by learning how individuals think through interactive questions. As people share, YouAi develops a multidimensional model of their mind. Users get a tailored feed of prompts to continue engaging and teaching their AI.
YouAi's vision provides a glimpse into a future that could unsettle or fulfill our hopes. As technology mediates understanding ourselves and others, will we risk losing what makes us human or find new means of flourishing? YouAI believes that together, we can build a future where our minds contain infinite potential—and their technology helps unlock it. But we must proceed thoughtfully, upholding human dignity above all else. Our minds shape who we are. And who we can become.Digitise your mind today:
YouAi - https://YouAi.aiMIndStudio – https://YouAi.ai/mindstudioYouAi Mind Indexer - https://YouAi.ai/trainJoin the MLST discord and register for the YouAi event on July 13th: https://discord.gg/ESrGqhf5CB
TOC:
0:00:00 - Introduction to Mind Digitization
0:09:31 - The YouAi Platform and Personal Applications
0:27:54 - The Potential of Group Alignment
0:30:28 - Applications in Human-to-Human Communication
0:35:43 - Applications in Interfacing with Digital Technology
0:43:41 - Introduction to the Project
0:44:51 - Brain digitization and mind vs. brain
0:49:55 - The Extended Mind and Neurofeedback
0:54:16 - Personalized Learning and the Future of Education
1:02:19 - Privacy and Data Security
1:14:20 - Ethical Considerations of Digitizing the Mind
1:19:49 - The Metaverse and the Future of Digital Identity
1:25:17 - Digital Immortality and Legacy
1:29:09 - The Nature of Consciousness
1:34:11 - Digitization of the Mind
1:35:06 - Potential Inequality in a Digital World
1:38:00 - The Role of Technology in Equalizing or Democratizing Society
1:40:51 - The Future of the Startup and Community Involvement
1 hr 46 min

About Machine Learning Street Talk (MLST)

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Welcome! We engage in fascinating discussions with pre-eminent figures in the AI field. Our flagship show covers current affairs in AI, cognitive science, neuroscience and philosophy of mind with…