Category: robotics/AI
Why AGI Is Impossible
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This job has become the ultimate case study for why AI won’t replace human workers
Want to understand how artificial intelligence could change your job? Look to radiology as a clue.
Radiology has come up multiple times as an example of a field that’s been impacted by AI without replacing the need for human workers.
Humans will merge with AI in 10 years | Ray Kurzweil and Lex Fridman
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Ray Kurzweil is an author, inventor, and futurist.
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Brave New Biology: Intelligence Trumps DNA — with Dr. Michael Levin and Dr. John Vervaeke
Dr. Michael Levin is a professor in the Department of Biology at Tufts University and an associate faculty member at the Wyss Institute at Harvard. He directs the Allen Discovery Center at Tufts, where his team integrates biophysics, computational modeling, and behavioral science to study how cellular collectives make decisions during embryogenesis, regeneration, and cancer.
Levin’s research centers on diverse forms of intelligence and unconventional embodied minds, bridging conceptual theory, experimental biology, and translational work aimed at regenerative medicine. His lab also pioneers efforts in artificial intelligence and the bioengineering of novel living machines.
Read more about Dr. Michael Levin’s work: https://drmichaellevin.org/
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John Vervaeke’s YouTube channel: @johnvervaeke
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Join Fr. Stephen De Young in his Jubilees and the Nephilim course, now streaming live on The Symbolic World: https://www.thesymbolicworld.com/cour… 00:00 — Coming up 01:14 — Intro music 01:40 — Introduction 02:23 — What Michael does 06:19 — Example experiments 07:51 — Memories outside the brain 12:46 — Terminology: memory 13:59 — Communicate to biological cells 15:54 — Limitations? 17:39 — Platonic patterns 34:06 — Incarnation and constraints 39:26 — Causes 49:28 — New beings in new spaces 52:25 — What the Enlightenment dismissed 55:32 — Molecular medicine 57:36 — Subtle bodies 01:00:45 — Ethics 01:03:37 — Medical and meaning applications 01:11:42 — Frightening 01:14:31 — Against the status quo 01:19:03 — Should we dabble in this technology? 💻 Website and blog: http://www.thesymbolicworld.com 🔗 Linktree: https://linktr.ee/jonathanpageau 🔒 BECOME A PATRON: https://thesymbolicworld.com/subscribe Our website designers: https://www.resonancehq.io/ My intro was arranged and recorded by Matthew Wilkinson: https://matthewwilkinson.net/
Beyond the Brain: Michael Levin on Living Intelligence & Minds in the era of AI
A conversation co-published by AI House Davos and Michael Levin’s Academic Content (@drmichaellevin)
In this conversation, we explore how intelligence exists across all scales of life, from cells to collectives, and what this means for our understanding of AI, minds, and what it means to be human.
Professor Michael Levin challenges the assumption that intelligence begins with brains, revealing how biological systems improvise, adapt, and solve problems in ways that go far beyond what our computational architectures attempt. From cognitive glue to the ethics of diverse intelligence, this interview questions the categories we’ve inherited and asks what truly matters as we enter an era of radically different embodiments.
Speaker.
Michael Levin (Director at Allen Discovery Center at Tufts University)
Moderator.
Louisa Hillegaart (Founder’s Associate, AI House Davos)
© AI House Davos 2025
Living Systems, Alignment & AGI: Michael Levin, Stephen Grossberg & Verses AI Break Down the Future
In this powerful opening session of the Strong AI Summit, moderator Dr. Mahault Albarracin brings together legendary thinkers redefining the boundaries between biology, cognition, active inference, and AGI.
00:00 Introduction by Dr. Mahault Albarracin.
00:42 Why study living systems when designing AI?
02:10 Dr. Michael Levin on biological intelligence and collective behavior.
04:25 Dr. Dalton on active inference foundations.
06:10 Prof. Stephen Grossberg on alignment and stable learning.
09:05 Limits of biological analogies in AI design.
11:45 Collective problem-solving and emergent goals.
13:30 Closing reflections.
Featuring:
Dr. Michael Levin, pioneer in morphogenesis, bioelectric signaling, and the xenobot project.
Prof. Stephen Grossberg, the most cited computational neuroscientist and creator of ART theory.
Dr. Dalton Sakadolsky, leading theoretician in Bayesian mechanics & active inference.
We Were Wrong About Our Minds—And AI
Biologist Michael Levin—creator of living robots—reveals a radical, evidence-based view of evolution driven by a fundamental mind, where agency emerges in unexpected places and what this means for humanity’s co-evolution with AI and AGI. This video is a compilation of interviews I conducted with Levin over the course of year for my Forbes reporting on his research.
▶️ Read the article on Forbes.com \.
Leading AI models struggle to solve original math problems
Mathematics, like many other scientific endeavors, is increasingly using artificial intelligence. Of course, math is the backbone of AI, but mathematicians are also turning to these tools for tasks like literature searches and checking manuscripts for errors. But how well can AI perform when it comes to solving genuine, high-level research problems?
To date, there is still no widely accepted realistic methodology for assessing AI’s capabilities to solve math at this level. So a group of mathematicians decided to put the machines to the test as they detail in a study available on the arXiv preprint server.
Previous attempts at testing AI have used math contest problems and questions already found in textbooks. What makes this study different is that the questions the programs faced were drawn from mathematicians’ own research. They had never been posted or published online, which means AI couldn’t memorize answers from its training data.
A long-lost Soviet spacecraft: AI could finally solve the mystery of Luna 9’s landing site
Using an advanced machine-learning algorithm, researchers in the UK and Japan have identified several promising candidate locations for the long-lost landing site of the Soviet Luna 9 spacecraft. Publishing their results in npj Space Exploration, the team, led by Lewis Pinault at University College London, hope that their model’s predictions could soon be tested using new observations from India’s Chandrayaan-2 orbiter.
In 1966, the USSR’s Luna 9 mission became the first human-made object to land safely on the moon’s surface and to transmit photographs from another celestial body. Compared with modern missions, the landing was dramatic: shortly before the main spacecraft itself struck the lunar surface, it deployed a 58-cm-wide, roughly 100-kg spherical landing capsule from above, then maneuvered away to crash at a safe distance.
Equipped with inflatable shock absorbers, the capsule bounced several times before coming to rest, stabilizing itself by unfurling four petal-like panels. Although Luna 9 operated for just three days, it transmitted a wealth of valuable data back to Earth, helping to inspire confidence in crewed space exploration, that would see humanity take its first steps on the moon just three years later.