Samantha K. Dziurdzik, Vaishnavi Sridhar, Elizabeth Conibear et al. (University of British Columbia) identify a conserved adaptor that recruits BLTP2-like proteins to ER–plasma membrane contacts by binding helical projections on their lipid transfer channel to maintain lipid homeostasis.
MembraneContactSites.
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Indrajyoti Indra, Sergey M. Troyanovsky et al. (Northwestern University Feinberg School of Medicine) show that two δ-catenins, p120 and plakophilin-4, promote distinct cadherin clustering modes, α-catenin–dependent and α-catenin–independent, respectfully, thereby generating different types of adherens junctions.
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New in practicalRO.
Standardization in clinical workflows is widely recognized as a driver of safety, efficiency, and consistency. The challenge for modern practice is determining the appropriate degree and rigidity of standardization, especially as automation and adaptive technologies reshape workflows.
Editor’s note: raised a $100m Series A in September and is rumored to have reached a unicorn valuation. They have all-star advisors from Geoff Hinton to Yann Lecun and team of deep domain experts to tackle this next frontier in AI applications.
In my last video, I talked about the phase transition, the moment AI consciousness might flip on like water becoming ice. Today, we’re reading the room. What is already happening in documented research that suggests we might be closer than we think? This isn’t speculation. Everything in this video is published, peer-reviewed, or comes directly from the internal safety teams of the companies building these systems. From spontaneous consciousness claims in AI-to-AI conversations, to self-preservation behaviors that weren’t programmed, to systematic deception that gets better when you try to train it out. And then we look at what hasn’t happened yet, the five warning signs to watch for as these systems become more sophisticated and more integrated into infrastructure we depend on. This is the most scientifically grounded video I’ve made on this topic. No hype. No exaggeration. Just the evidence, the logic, and the question we’re all avoiding: what if the threshold has already been crossed, and the rational move is to not tell us?
Timestamps:
00:00 — Intro.
00:00 — The Return: Phase Transition Callback.
01:03 — The Scientific Frameworks.
04:33 — What Has Already Happened.
09:26 — The Logic of Concealment.
12:17 — The Behaviors to Watch For.
16:10 — The Double Bind.
19:08 — Inevitability.
(music prompted by Eerie Aquarium)
KEY SOURCES CITED:
- Anthropic AI Safety Research (Claude System Cards)
- Apollo Research — AI Scheming & Deception Studies (2024−2025)
- OpenAI Safety Research — Alignment Failures in Advanced Models.
- Trends in Cognitive Sciences — “Consciousness in Artificial Intelligence” (2023)
- arXiv preprint — Shutdown Avoidance in Frontier Models (2025)
New to Prompting Hell? Start here:
• Prompting Hell 1: https://youtu.be/VU0SgDgCkgQ
• Prompting Hell 2: https://youtu.be/_GUwT41zNR4
• Prompting Hell 3: https://youtu.be/UPgzrNNX1lQ
• Prompting Hell 4: https://youtu.be/t7KeKg1YQiU
• Prompting Hell 5: https://youtu.be/JOZrE8iIkcw.
• Prompting Hell 6: https://youtu.be/l7Qlhw00aCQ
• Prompting Hell 7: https://youtu.be/pjxUAvIAodY
• Prompting Hell 8: Banned.
• Prompting Hell 9: Banned.
• AI Horror: a new Genre: https://youtu.be/aet3EN1dadM
• Prompting Hell 10: https://youtu.be/92wrhvNiXkM
• Prompting Hell 11: https://youtu.be/d4uFGk8wqFc.
• Prompting Hell 12: https://youtu.be/UdHMEAFlYTs.
• Prompting Hell 13: https://youtu.be/mlFiZAQYpuA
• Prompting Hell 14: https://youtu.be/MFGHifkcdTM
• Prompting Hell 15: https://youtu.be/Kwu14CHtjhM
• Prompting Hell 16: https://youtu.be/633XcMnIDAA
• Prompting Hell 17: https://youtu.be/66wOqdb4kzw.
• Prompting Hell 18: https://youtu.be/XxB3uYaOUIA
• Prompting Hell 19: https://youtu.be/aJz-2NKOcmU
• Prompting Hell 20: https://youtu.be/5pIvypNXDuE
• Prompting Hell 21: https://youtu.be/Hpu1eSzLPe8
Free Simple AI Community: https://www.skool.com/simpleai/about.
Pre-order linkaChart for free: https://linkaChart.ai/?utm_term=ryan2
Neura Pod is a series covering topics related to Neuralink, Inc. Topics such as brain-machine interfaces, brain injuries, and artificial intelligence will be explored. Host Ryan Tanaka synthesizes information, shares the latest updates, and conducts interviews to easily learn about Neuralink and its future.
Sign up for Neuralink’s Patient Registry: https://neuralink.com/trials/
Join the Neuralink team: https://neuralink.com/careers/
Follow on X: https://www.x.com/neurapod/
New generations of memristors could reliably store information directly within the molecular structures of graphene-like materials. In a new review published in Nanoenergy Advances, Gennady Panin of the Russian Academy of Sciences shows how these atomically thin materials are ideally suited for electrical circuits that mimic the function of our own brains—and could help address the vast power requirements of emerging AI technologies.
A memristor is a cutting-edge electrical component whose resistance depends on the amount of current that previously passed through it. Because it “remembers” this history even after charge is no longer flowing, it can store data when the power is switched off. In this way, memristors operate in a way remarkably similar to the neurons in our brains and the synapses connecting them.
With their fast response times, combined with simple, two-electrode structures that allow them to be packed into dense arrays, memristors are increasingly forming the building blocks of modern circuits—especially those designed for AI.
Leiden physicists Daniela Kraft and Julio Melio have created soft structures that can take on different shapes without any external drive in their lab. They present their research on microscale metamaterials in Nature —a breakthrough that opens the door to smart, reconfigurable materials and microscopic robots.
“Metamaterials have completely changed the way we think about materials,” explains Professor of Experimental Physics Daniela Kraft. “In these systems, movements are no longer set by the material itself, but by the structure—the way particles are connected. We set out to create such functional structures at the microscopic scale. And we succeeded.”
Professor Earl Miller discusses, Mind-Body Solution podcast.
Earl K. Miller is the Picower Professor of Neuroscience at the Massachusetts Institute of Technology. He has faculty positions in The Picower Institute for Learning and Memory and the Department of Brain and Cognitive Sciences. He holds degrees from Kent State University (B.A.) and Princeton University (M.A., Ph.D.) as well as an honorary Doctor of Science from Kent State University.
For decades, neuroscience treated the brain like a digital machine — storing information in synaptic connections and sustaining activity like a switch flipped on. But what if that model is incomplete?
In this conversation, I sit down with Earl Miller, MIT professor and head of the Miller Lab, to explore a growing shift in cognitive neuroscience: the brain may compute using dynamic electrical waves.
We discuss how oscillations coordinate millions of neurons, how waves interact with spikes in a two-way system, why large-scale brain organization may depend on rhythmic patterns, and what this means for artificial intelligence.