I, scientist with Balazs Kegl

In this engaging conversation, Alexander Bard, a philosopher, futurist, and former 90s pop star, delves into the fascinating interplay between humanity and technology. He challenges the notion of trust in philosophy based on one’s personal life and critiques Gnosticism’s disconnect from embodiment. Bard also presents his metaphysical ideas, emphasizing the importance of ongoing processes over isolated events. Discussions on Zoroastrianism reveal its community-building aspects, while reflections on personal spirituality highlight the need for authenticity in a rapidly evolving world.

Dan Shipper (http://danshipper.com/)is an entrepreneur, writer, and the CEO and co-founder of Every (https://every.to/@danshipper), a daily newsletter on business, AI, and personal development read by nearly 75,000 founders, operators, and investors. He writes a weekly column at Every called Chain of Thought, where he covers topics like AI, tools for thought, and the psychology of work.I contacted Dan because he makes his living off the writing industry, yet instead of panicking, he took the LLM revolution head on, choosing to lead it instead of ducking. Join us to explore creative uses of generative AI for writing and personal development. Fascinating discussion about science at the end where we are both at the edge of our chairs and knowledge.If you like this, you may also be interested in my conversation with Tatjana (https://www.youtube.com/watch?v=_KJ29Nslbl8) and Joel (https://www.youtube.com/watch?v=mmIHnZRY5zo) on creative writing and teaching AI.00:00:00 Intro.00:04:18 Dan's journey: software and writing.00:13:45 The writing industry and the business model of Every.00:19:45 How future works. AI, ChatGPT, creativity, psychology: Dan's writing.00:25:25 ChatGPT and personal development. Mirroring and motivational interviewing. Dealing with dragons, cognitive behavioral therapy. https://www.maxyourmind.xyz 00:33:34 Therapy. Dan's fight with OCD. Embodiment, somatic therapy. Podcast as a therapy.00:46:28 GPT and creativity. Link to interview with David Perell. How to get rid of the GPT smell. Link to Dan's app. Dan's definition of art.01:00:14 GPT and writing. Personalized content vs shared stories and shared game-like media.01:06:52 AI and science. Prediction without theory. Complexity and embodied intuition. Multiplicity of causes and categorization. Connectionist induction. I, scientist blog: https://balazskegl.substack.comTwitter: https://twitter.com/balazskeglArtwork: DALL-EMusic: Bea Palya https://www.youtube.com/channel/UCBDp3qcFZdU1yoWIRpMSaZw Hosted on Acast. See acast.com/privacy for more information.

Csaba Szepesvári is a prominent figure in machine learning and artificial intelligence, particularly known for his contributions to reinforcement learning. He is a professor in the Department of Computing Science at the University of Alberta and a Senior Staff Research Scientist at Google DeepMind.Our goal in life: keep eating Yann Lecun's cherry (inside joke, ask GPT).This was a wide-reaching conversation with my good friend Csaba. The first 50 minutes is about AI, we give you insight into what AI researchers and practitioners are dealing behind the scenes, a bit of history and practice of AI.At around 49 minutes we discuss Yann Lecun's view on model-based planning and reinforcement learning, which is one of the most interesting and far-reaching discussion within AI (should we model the world or just learn to reach goals?).This naturally leads into relevance realization: what to learn predict and what to ignore, which is the most important question of AGI.The last 20 minutes it turns more personal, we talk about our life as scientists, the metaphysical no man's land (nonreductionist naturalist metaphysics), and my wrestling with Christianity.If you like this, check out my conversations with Yogi Jaeger https://www.youtube.com/watch?v=5UJ4y2L2qpk, Anna Riedl https://www.youtube.com/watch?v=w2ZiSWZNQsg, and Giuseppe Paolo https://www.youtube.com/watch?v=R7tEd65e2i8.00:00:00 Intro.00:02:39 Google Deep Mind and leading the Foundations team. Theory tells what is possible, and how to do it? What does theory add to practice in machine learning?00:08:58 Probabilistic guarantees. Sorting vs machine learning. Random algorithms and random data.00:15:57 Theory and practice. Support vector machines, boosting, and neural nets. History of AI. Practice-driven ML.00:24:34 Neural nets: history and practice. The fiddliness and the robustification. Overfitting. Overparameterization vs classical statistics. Label noise and regularization.00:35:06 Reinforcement learning. Learning to behave. Learning to collect data.00:39:10 Why did we get into AI: to understand intelligence and ourselves.00:41:48 Reinforcement learning: model of an intelligent agent. Theory: wrt a fully informed agent, how much do you lose by having to learn?00:49:17 To model or not to model? Model-based reinforcement learning.00:58:18 Latent representation of importance, relevance realization. Steelmanning and criticizing Yann Lecun's cherry metaphor. Relevance vs simplicity. The shiny object syndrome.01:12:40 The Jaeger - Riedl - Djedovic - Vervaeke - Walsh paper on naturalizing relevance realization.01:24:12 Fear and technology. Regulation, freedoms, open source.01:33:00 Framing precedes observation.01:35:60 Subjectivity of science. Can we be part of the world we study?01:43:55 The metaphysical no man's land: nonreductionist naturalism.01:48:00 Why am I not yet a Christian? Omnipotence and lack of embodiment, rituals that focus on our responsibility of what is below.I, scientist blog: https://balazskegl.substack.comTwitter: https://twitter.com/balazskeglArtwork: DALL-EMusic: Bea Palya https://www.youtube.com/channel/UCBDp3qcFZdU1yoWIRpMSaZw Hosted on Acast. See acast.com/privacy for more information.

Yogi Jaeger is a philosopher at the University of Vienna, specializing in the philosophy of science and biology in particular. Are we rushing ahead into the unknown, scaring people, while also producing fake shelters for them to retreat into artificial small worlds? Are we losing our grip on the world or realizing our humanness in the mirror of AI? Will AI take our agency away? Are we willingly giving it away? Or they will extend our agency?Passionate conversation about the philosophy of science and the non-computational and non-formalizable nature of cognition. We shed light on AI with a non-reductive naturalist torch and see the rough edges and the strange shadows it casts over society. Project website: https://www.expandingpossibilities.org.Personal website: http://www.johannesjaeger.eu.Relevant papers and blog:https://osf.io/preprints/osf/pr42khttps://arxiv.org/abs/2307.07515https://www.frontiersin.org/articles/10.3389/fevo.2021.806283/fullhttp://www.johannesjaeger.eu/blog/machine-metaphysics-and-the-cult-of-techno-transcendentalism.I warmly recommend his course on the philosophy of science: https://www.youtube.com/playlist?list=PL8vh-kVsYPqPVrV0m4HjZexgO6oDkgkK000:00:00 Intro.00:04:56 My excitement and interests in Yogi's work. Personal: non-reductionism naturalism beats lived dualism. Professional: tech is becoming organismic.00:08:49 Definitions, naturalizing relevance realization. Formalization, computation and framing, small/closed and large/open worlds.00:15:55 The paradox of living in a small world is that we don't know that we are living in a small world.00:18:07 Computational approaches do work, just not for (all) cognition. The brain evolved not to compute but to keep you alive.00:20:12 Church-Turing-Deutsch conjecture.00:23:04 Approximation. Can cognition be well-approximated by computation? Csaba Szepesvari's third question. Yes, but that does not mean cognition works that way.00:26:36 Nothingbutism. Prediction and understanding, are they the same? Understanding needs to have the concepts right.00:29:15 AGI doom only happens in the small world.00:30:45 Philosophy and science. Any scientific test for the non-computation claim? Csaba's second question.00:33:27 Framing precedes science.00:35:17 Non-reductionist but non-woowoo naturalism. 00:43:08 Relevance realization: the map of map-making. Enactivism. Non-formalizability. Fragility of the organism and death. The work to exist.00:50:55 Goal-orientation: intrinsic or extrinsic? Paperclip maximizer.00:54:44 Far-from-equilibrium thermodynamics. Physics-free simulation vs self-manufacturing experiential systems. Immediate and pre-conceptual access to the world.01:04:58 Biotech and AI. The value of slowing down.01:07:47 TAME: Michael Levin's technological approach to mind everywhere.01:18:55 LLMs and hallucinations.01:23:05 Social media AI and recommendation systems: taking away our agency but trying to optimize for an unoptimizable goal.01:30:34 AGI, slowing down, losing our grip on the world or realizing our humanness in the mirror of AI?01:39:30 Intelligence and rationality.01:41:58 Yogi's journey: a natural philosopher, a biologist using philosophical approaches.01:48:15 Where do I want to see AI going? Yogi's question to me.I, scientist blog: https://balazskegl.substack.comTwitter: https://twitter.com/balazskeglArtwork: DALL-EMusic: Bea Palya https://www.youtube.com/channel/UCBDp3qcFZdU1yoWIRpMSaZw Hosted on Acast. See acast.com/privacy for more information.

Baudouin Saintyves is an artist, physicist, and roboticist, inventor of Granulobots.https://www.youtube.com/watch?v=Gnbdneho78Mhttps://arxiv.org/abs/2304.03125Research: https://voices.uchicago.edu/bsaintyveslab/Art: https://www.shapesofemergence.com/Fascinating conversation, starting with an intro of soft and swarm robotics and his work on designing Granulobots, self organizing aggregates of small wheels with magnets and a simple motor.We then dive deep into a deep dialogical out-loud thinking about self-organization, self-coordination, synchronization, and emergence. We are both at the edge of what we know, dancing around and with these fascinating subjects.At around the hour mark, I ask Michael Levin's three questions: 1) Any surprising system-level behavior? (yes!!) 2) Any way to control the system at the top level? (hard to say but Baudouin really riffs off this) 3) How does the world look like for a Granulobot? (fascinating :) ).The last half an hour is Baudouin's amazing journey, triangulating between art, science, and engineering, a perfect illustration of the perspectival philosophy of science that we discussed with Yogi Jaeger in my upcoming next episode.Watch it, and please give it thumbs and sign up at the YouTube channel if you like it, it's a small gesture that could help these sober conversations about AI and science reach more people.00:00:00 Intro00:03:40 Robots, hard and soft, distributed and liquid.00:14:25 Mobile Lego. Modular robots, swarms and flocks, decentralized embodied intelligence.00:21:29 Granulobots. Machine is the material vs the material is the machine. Motorized grains of sand.00:29:02 Self-organization, self-coordination, synchronization, emergence.Hysteresis as a precondition of control. Solid and liquid.01:03:28 Michael Levin's three questions: 1) any surprising high-level behavior?01:13:30 2) Top-down: any way to control the system at the top level?01:22:22 3) First person systems. How does the world look like from the system's perspective?01:30:36 Music, visual art, self organization, physics, shapes of emergence, contemplative cinema, robotics. Baudouin's journey across art and science.01:38:37 The marvelous triangle of science, engineering, and art.01:43:39 TAME: the link to Baudouin's work. I, scientist blog: https://balazskegl.substack.comTwitter: https://twitter.com/balazskeglArtwork: DALL-EMusic: Bea Palya https://www.youtube.com/channel/UCBDp3qcFZdU1yoWIRpMSaZw Hosted on Acast. See acast.com/privacy for more information.

Why are LLMs so alien?Is it because they lack goal-oriented behavior?Or because they are designed to please humans, which can be unsettling?Join me and Laura in this new episode where we discuss her paper "Active inference goes to school", AI and 4E cognitive science, active inference and child development, and AI in the classroom.My notes on Laura's paper: https://balazskegl.substack.com/p/active-inference-goes-to-school00:00:00 Intro00:05:44 4E cognitive science. Embodied, enacted, embedded, extended. Mirror neurons and meaning. Scaffolding. 00:20:03 LLMs and extendedness. Confabulations. 00:29:53 LLM vs social media AI. The role of embodiment and the scariness of disembodied LLM. Loopholes.00:37:20 Active inference. LLMs are trained like kids in a classical school. Building artifacts to align reality with internal model. Pragmatic vs objective truth.00:56:45 Child development and active inference. Cognitive and physical are intertwined. The role of history and culture.01:07:02 Montessori. Flow and the speed of learning. Prepared environment.01:13:46 AI in school and work.01:19:40 Laura's journey. Marx, cinema, history of science, biology, running a bar, having a child, second PhD.01:22:43 The role of artifacts in forming the mind. Memorabilia.01:29:37 Having a child. Starting school during Covid. The role of imitation in learning and its lack in school. Aspiration. Copying. Learning and sense.01:40:47 Embodied AI, when can I read it? Laura's question to me. Martial arts. Meditation. I, scientist blog: https://balazskegl.substack.comTwitter: https://twitter.com/balazskeglArtwork: DALL-EMusic: Bea Palya https://www.youtube.com/channel/UCBDp3qcFZdU1yoWIRpMSaZw Hosted on Acast. See acast.com/privacy for more information.

What, if anything, differentiates us from machines? Is cognition computational? Can self-preservation be externally imposed? How do you feel in your body when you hear the word "singularity"?Join us in a thought-provoking conversation with Anna Riedl, a distinguished cognitive scientist from Vienna, known for her work on rationality.We dive deep into psychology, cognitive science, and artificial intelligence. What I find exemplary about Anna is how she plants her feet firmly in the real world while embarking deep, wide-ranging, and sometimes controversial topics in one of the fastest moving fields in science.Anna recently co-authored an insightful paper on "Naturalizing Relevance Realization," shedding light on how our understanding of relevance in cognitive processes can be grounded in naturalistic terms. For a deeper dive into the themes of our conversation, including my summary and notes on this groundbreaking paper, visit https://balazskegl.substack.com/p/naturalizing-relevance-realization.00:00:00 Intro.00:05:51 Rationality and intelligence. Small and large worlds, the frame problem.00:12:58 Non-computational (but physical!) framing, relevance realization, natural agency, cognition. Naturalistic teleology.00:20:07 Algorithm vs organism through reinforcement learning lens.00:33:18 Relevance realization. Bayesian rationality and the paradox of timescale. Social media AI.00:47:40 Self-preservation: intrinsic or external goal?00:51:23 Opponent processing. 00:57:08 Circularity argument for the non-learnability of relevance realization.01:00:27 Ecology of agents. Where does the agential world start?01:04:47 Singularity and self-manufacturing. Parasitic processing. Rationality. How do we construe the world? How does a topic make you feel?01:11:16 What's next for Anna? Naturalizing decision-making.01:14:15 Insight.01:16:49 Prophecy, anticipation, forecasting, threshold points and agency, faith.01:20:13 How and why Anna became a cognitive scientist? Creativity, clear thinking, psychology, cognitive science.01:25:02 Black-box vs constructive predictive models. Implicit vs explicit. 01:29:33 Collective expertise, higher-level organisms.01:36:52 What got me into tech? Anna's question to me.I, scientist blog: https://balazskegl.substack.comTwitter: https://twitter.com/balazskeglArtwork: DALL-EMusic: Bea Palya https://www.youtube.com/channel/UCBDp3qcFZdU1yoWIRpMSaZw Hosted on Acast. See acast.com/privacy for more information.

Jakob Nordin is an observational astrophysicist from the Humboldt University, Berlin, member of the LSST collaboration building the Vera Rubin Observatory and the informational infrastructure to manage the immense data it will collect.Real-time astronomy & LSST: We discuss the intriguing potential of real-time astronomy, focusing on the Large Synoptic Survey Telescope (LSST), its technology and data processing challenges.Multi-messenger astrophysics & brain-like structure: Explored the fascinating concept of multi-messenger astrophysics, where various observatories act like sensors in a brain-like structure, collaboratively detecting cosmic events. Discussed how this network uses a semi-automatic system of alerts to direct scientific attention efficiently, highlighting the organic and evolving nature of this expansive, interconnected system.AI in astronomy: Explored how artificial intelligence is integrated into astronomical research, particularly in image analysis and classification within massive data streams like those expected from LSST.00:00:00 Intro. LSST.00:03:43 Universe as a lab. Astronomer, astrophysicist, observational astrophysicist.00:09:15 The space of astronomical observatories: what messenger, what energy, what field of view (patch of sky), what timescale?00:11:15 Vera Rubin Observatory and LSST collaboration: optical wavelengths, large field of view, detailed map and daily transients. Several instruments in one.00:13:17 Space or ground? Trade-offs and complementarity.00:16:16 Technology: Mirror and camera.00:19:20 Atmosphere. How to deal with turbulence? Exotic locations.00:22:01 Numbers. 7 trillion detections, 37 billion objects (transients).00:23:16 Categories of transients: noise, Solar system objects (planets, asteroids), Milky way (variable stars), extragalactic (giant black holes, supernovae)00:27:57 Alert rate: 10 million nightly events. 60 sec latency.00:30:54 Multi-messenger astrophysics. 00:39:27 Sociological paradigm change.00:43:45 Unknown unknowns. The similarities between multi-messenger astrophysics and a living multi-sensorial perceptual system.00:47:44 AI, what works what does not.00:52:38 Data brokers. Scarcity of resources forces a distributed TAME-like organization in science.00:59:02 AI. Noise filtering. Transient categorization.01:03:3 0 Training data: mostly simulations. Data augmentation.01:07:50 The sociological paradigm change: using telescope time to verify AI pipelines. 01:09:57 Instrumental meditation: AI models need to be calibrated.01:11:08 The price of knowledge. How to calibrate exploration?01:14:06 Why astrophysics? Curiosity + coincidence.01:18:16 What does a scientist do?01:21:05 Organization. Small groups evolving towards large-scale experiments.01:24:56 AI, software, data science, computer science, computer engineering. 01:29:20 Productionalizing AI. Big data vs. small data. AI is tough when models are exposed to reality.01:34:40 GPT for writing and coding. Hallucination and novelty.01:40:18 "Do I get the money?" Would I finance real-time astronomy? How to choose what to work on?01:43:02 Debates are overrated. Jakob's question: Can a podcast change what we do? Can a guest change how I live? You can change me if I want to become like you.01:51:05 Epistemic authority. How do we make decisions? How can we trust information?I, scientist blog: https://balazskegl.substack.comTwitter: https://twitter.com/balazskeglArtwork: DALL-EMusic: Bea Palya https://www.youtube.com/channel/UCBDp3qcFZdU1yoWIRpMSaZw Hosted on Acast. See acast.com/privacy for more information.

The second hour is really just two guys trying to make sense of what's going on in the world as a reaction to AI and the meaning crisis, explored through the works of John Vervaeke. I'm sure the insights from this conversation will resonate with many educators and technologists alike. If you like it, please help the channel by signing up!00:00:00 Intro00:06:53 Path to teaching writing.00:15:36 Why do we write? Making an impact vs having a voice vs thinking something through.00:20:37 GPT vs human writing. The voice of GPT. Simile and metaphors.00:34:05 Teaching and AI. Assisting a lesson plan.00:37:48 Assessment in the age of GPT: policing or integration?00:46:51 OER: teaching LLMs within Open Education Resources.00:53:11 AI assistance: feedback generated by LLMs. Should we learn to drive a stick or read maps?01:03:32 GPT as a dialogical partner.01:10:14 Vervaeke's AI video essay. https://balazskegl.substack.com/p/notes-on-john-vervaekes-ai-the-coming01:15:02 Opponent processing: dialog, jujitsu.01:22:37 Jeremy England, life, entropy, dissipation, and e/acc.01:26:20 Doom vs zoom: I don't agree with the framing.01:34:49 Relevance realization and Vervaeke's trinity: nomological, normative, and narrative order.01:41:35 Open theology vs closed worlds.01:48:05 The irony of Enlightenment.01:52:11 What should educators be aware of around AI? Joel's question to me.I, scientist blog: https://balazskegl.substack.comTwitter: https://twitter.com/balazskeglArtwork: DALL-EMusic: Bea Palya https://www.youtube.com/channel/UCBDp3qcFZdU1yoWIRpMSaZw Hosted on Acast. See acast.com/privacy for more information.

00:00:00 Intro.00:03:18 Stories. Why do they fascinate us? Mechanism of emotional manipulation.00:06:45 True to life vs realistic. Startrek. Dramatic real stories are rarely good.00:10:08 GPT and stories. How to build stories word-by-word. What is creativity? Information and meaning. Saturation in the story space. Alignment of living and writing.00:19:25 Yellowstone. What makes a story good? Lioness. Average is OK but don't expect to get payed for it. 00:23:24 GPT and storywriting. Write us a joke about migrants. A new episode of Sherlock.00:28:54 Art = fire + algebra. A good story makes you want to stop watching it and reengage with your life.00:31:53 Fire in children and mystics. Chickens: individuals vs a category. Surprise and predictability. Intelligence is overrated.00:39:46 Christianity. The tension and harmony between the transcendent and the particular. The role of lived experience in storywriting.00:45:47 The zombie myth. Metaverse. Transhumanism. What is AI after? Zuckerberg and jujitsu.00:57:44 AGI singularity vs narrow social media AI. Cautious humbleness and exploration. Personalized storytelling and an autistic world. Disembodiment.01:15:50 GPT: how to use it for writing better stories? Support the research process then go and face real life. Paradoxically it will slow down the writing process.01:22:16 Love: learning by loving vs just downloading knowledge.I, scientist blog: https://balazskegl.substack.comTwitter: https://twitter.com/balazskeglArtwork: DALL-EMusic: Bea Palya https://www.youtube.com/channel/UCBDp3qcFZdU1yoWIRpMSaZw Hosted on Acast. See acast.com/privacy for more information.