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Easy-to-use tool can identify high- and low-risk metastatic prostate cancer patients earlier

A new study published in Nature Communications provides a framework for researching whether earlier, model-guided treatment intensification can meaningfully improve survival for patients with aggressive disease.

“Early decline in prostate-specific antigen (PSA) to very low levels is one of the strongest predictors of long-term survival in metastatic prostate cancer. However, clinicians currently have to wait up to six months after starting therapy to see whether a patient achieves this favorable response. For patients who do not respond well, this delay may allow the cancer to progress and become more resistant to treatment,” said Soumyajit Roy, MD, a radiation oncologist at UH Seidman Cancer Center and first author of the study.

Because existing clinical risk stratification tools—such as disease volume or metastatic burden—are relatively imprecise, there has been an unmet need for a reliable, easy-to-use tool that can risk stratify patients earlier, before that critical six-month window closes. Researchers wanted to determine whether it is possible to predict early treatment response at the time of diagnosis for men with metastatic hormone-sensitive prostate cancer (mHSPC) who are treated with modern androgen receptor pathway inhibitors (ARPIs), which are now standard of care worldwide.

What babies can teach AI

Researchers at Google DeepMind tried to teach an AI system to have that same sense of “intuitive physics” by training a model that learns how things move by focusing on objects in videos instead of individual pixels. They trained the model on hundreds of thousands of videos to learn how an object behaves. If babies are surprised by something like a ball suddenly flying out of the window, the theory goes, it is because the object is moving in a way that violates the baby’s understanding of physics. The researchers at Google DeepMind managed to get their AI system, too, to show “surprise” when an object moved differently from the way it had learned that objects move.

Yann LeCun, a Turing Prize winner and Meta’s chief AI scientist, has argued that teaching AI systems to observe like children might be the way forward to more intelligent systems. He says humans have a simulation of the world, or a “world model,” in our brains, allowing us to know intuitively that the world is three-dimensional and that objects don’t actually disappear when they go out of view. It lets us predict where a bouncing ball or a speeding bike will be in a few seconds’ time. He’s busy building entirely new architectures for AI that take inspiration from how humans learn. We covered his big bet for the future of AI here.

The AI systems of today excel at narrow tasks, such as playing chess or generating text that sounds like something written by a human. But compared with the human brain—the most powerful machine we know of—these systems are brittle. They lack the sort of common sense that would allow them to operate seamlessly in a messy world, do more sophisticated reasoning, and be more helpful to humans. Studying how babies learn could help us unlock those abilities.

The Computational Unconscious: How Information Theory Reframes Psychoanalytic Depth

Read “” by Myk Eff on Medium.

When Freud first mapped the territories of the unconscious, he could only speak in the metaphors available to him — hydraulic pressures, economic systems, topographical layers. Yet the phenomena he described possess a striking affinity with concepts that would not emerge until decades later, when Claude Shannon formalized information theory and computing science revealed the architecture of data itself. What if the mechanisms Freud, Jung, and their successors laboriously documented are, at their foundation, information processing operations? What if repression is encryption, condensation is compression, and the deepest strata of the psyche represent not mystical depths but maximal data density?

The proposition is not merely metaphorical. Consider Freud’s description of repression in Repression (1915): the mechanism whereby the ego refuses admittance to consciousness of ideational content that threatens its equilibrium. Freud wrote that repression lies simply in turning something away, and keeping it at a distance, from the conscious (p. 147). Yet this keeping at a distance operates through a curious transformation. The repressed content does not vanish; it persists, inaccessible yet influential, distorting thought and behavior through its very concealment.

This is precisely analogous to encrypted data. Encryption transforms information into a form that resists interpretation without the proper key, yet the information remains fully present, its structure intact but rendered opaque. The encrypted file occupies space, exerts influence on system resources, and can corrupt or destabilize processes that attempt to access it incorrectly. Similarly, repressed material occupies psychic space and generates symptoms — failed decryption attempts, as it were — when consciousness approaches without the therapeutic key.

Smaller Than a Grain of Salt: Engineers Create the World’s Tiniest Wireless Brain Implant

A salt-grain-sized neural implant can record and transmit brain activity wirelessly for extended periods. Researchers at Cornell University, working with collaborators, have created an extremely small neural implant that can sit on a grain of salt while wirelessly sending brain activity data from

Can Science Explain Everything? — Sean Carroll

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VIDEO NOTES

Sean Carroll is an American theoretical physicist who specializes in quantum mechanics, cosmology, and the philosophy of science.

Continue reading “Can Science Explain Everything? — Sean Carroll” | >

AI House Davos

Embodied AI refers to AI integrated into physical systems that can perceive, reason, and act in the real world through sensors and actuators, like robots and autonomous vehicles. This fireside conversation explores how advances in AI like vision–language–action models are redefining what machines can understand and do, especially as we move from navigation to mobile manipulation. The speakers discuss how quickly today’s rapid progress in AI might transfer to robotics and embodied systems, and how soon we can expect to see these technologies making a tangible impact on our daily lives.

Speakers.
Yann LeCun (Advanced Machine Intelligence, Founder and Executive Chairman)
Marc Pollefeys (ETH Zürich and Faculty, ETH AI Center, Professor)

© AI House Davos 2026
Founders & Strategic Partners:
ETH AI Center, Merantix, G42, Hewlett Packard Enterprise, EPFL AI Center, The University of Tokyo.

Presenting Partners:
KPMG.

Scientists Discover a New “Cleanup Hub” Inside the Human Brain

How does the brain clear away waste? This task is handled by the brain’s lymphatic drainage system, and attempts to understand how it operates have driven major advances in brain imaging.

A new study published in iScience by researchers at the Medical University of South Carolina reports the first human evidence of a previously unrecognized center of lymphatic drainage in the brain, the middle meningeal artery (MMA).

Using a NASA collaboration that gave them access to real-time MRI tools originally designed to study how spaceflight alters fluid movement in the brain, the MUSC team, led by Onder Albayram, Ph.D., followed the movement of cerebrospinal and interstitial fluids along the MMA in five healthy volunteers over six hours. Their observations showed that cerebrospinal fluid moved slowly and passively, a pattern consistent with lymphatic drainage rather than blood circulation, which would be faster and more pulsatile.

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