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Algorithm matches drugs to glioblastoma’s diverse cell types, offering hope for individualized therapies

Researchers have developed a new computational approach that uncovers possible drugs for specific cellular targets for treating glioblastoma, a lethal brain tumor. This approach enabled them to predict more effective treatment combinations to fight the disease on an individualized basis.

This laboratory and computational research effort was led by scientists at Georgetown’s Lombardi Comprehensive Cancer Center.

“The cellular targets we identified could be key to effectively fighting a disease that has seen only one new targeted drug approved in the last two decades,” says Nagi G. Ayad, Ph.D., senior author, associate director for translational research, and professor of oncology at Georgetown Lombardi.

Quantum mechanics works, but it doesn’t describe reality

Physicists like Sean Carroll propose not only that quantum mechanics is not only a valuable way of interpreting the world, but actually describes reality, and that the wave function – the centre equation of quantum mechanics – describes a real object.

But, in this article, philosophers Raoni Arroyo and Jonas R. Becker Arenhart argue that the case for wave function realism is deeply confused. While it is a useful component within quantum theory, this alone doesn’t justify treating it as literally real.

Tap the link to read more.


Physicists like Sean Carroll argue not only that quantum mechanics is not only a valuable way of interpreting the world, but actually describes reality, and that the central equation of quantum mechanics – the wave function – describes a real object in the world. But philosophers Raoni Arroyo and Jonas R. Becker Arenhart warn that the arguments for wave-function realism are deeply confused. At best, they show only that the wave function is a useful element inside the theoretical framework of quantum mechanics. But this goes no way whatsoever to showing that this framework should be interpreted as true or that its elements are real. The wavefunction realists are confusing two different levels of debate and lack any justification for their realism. The real question is: does a theory need to be true to be useful?

1. Wavefunction realism

Quantum mechanics is probably our most successful scientific theory. So, if one wants to know what the world is made of, or how the world looks at the fundamental level, one is well-advised to search for the answers in this theory. What does it say about these problems? Well, that is a difficult question, with no single answer. Many interpretative options arise, and one quickly ends up in a dispute about the pros and cons of the different views. Wavefunction realists attempt to overcome those difficulties by looking directly at the formalism of the theory: the theory is a description of the behavior of a mathematical entity, the wavefunction, so why not think that quantum mechanics is, fundamentally, about wavefunctions? The view that emerges is, as Alyssa Ney puts it, that.

Elon Musk on AGI Timeline, US vs China, Job Markets, Clean Energy & Humanoid Robots

Questions to inspire discussion.

🤖 Q: How quickly will AI and robotics replace human jobs? A: AI and robotics will do half or more of all jobs within the next 3–7 years, with white-collar work being replaced first, followed by blue-collar labor through humanoid robots.

🏢 Q: What competitive advantage will AI-native companies have? A: Companies that are entirely AI-powered will demolish competitors, similar to how a single manually calculated cell in a spreadsheet makes it unable to compete with entirely computer-based spreadsheets.

💼 Q: What forces companies to adopt more AI? A: Companies using more AI must outcompete those using less, creating a forcing function for increased AI adoption, as inertia currently keeps humans doing AI-capable tasks.

📊 Q: How much of enterprise software development can AI handle autonomously? A: Blitzy, an AI platform using thousands of specialized agents, autonomously handles 80%+ of enterprise software development, increasing engineering velocity 5x when paired with human developers.

Energy and Infrastructure.

Mondays With Phil | Why 2026 Changes Everything for Tesla, Grok & SpaceX

Why 2026 Changes Everything for Tesla, Grok & SpaceX

## Elon Musk’s companies, including Tesla and SpaceX, are expected to experience significant breakthroughs and growth in 2026, driven by advancements in AI, robotics, and space technology.

## Questions to inspire discussion.

Tesla Robotaxi & Cybercab Strategy.

🚖 Q: When will Tesla’s Cybercab production begin and what regulatory hurdle must be cleared first? A: Cybercab production is set to begin on April 1, 2026, but requires federal regulations on autonomous ride-hailing since current rules mandate steering wheels and pedals for non-experimental vehicles.

🚗 Q: How will Tesla’s robotaxis function as an advertising strategy? A: Robotaxis will serve as Tesla’s primary advertising strategy by acting as an Uber-like service that demonstrates the cars’ capabilities and encourages personal ownership, potentially reducing the need for traditional advertising.

One pull of a string is all it takes to deploy these complex structures

MIT researchers have developed a new method for designing 3D structures that can be transformed from a flat configuration into their curved, fully formed shape with only a single pull of a string.

The technique could enable the rapid deployment of a temporary field hospital at the site of a disaster such as a devastating tsunami—a situation where quick medical action is essential to save lives.

The researchers’ approach converts a user-specified 3D structure into a flat shape composed of interconnected tiles. The algorithm uses a two-step method to find the path with minimal friction for a string that can be tightened to smoothly actuate the structure.

Reinforcement learning accelerates model-free training of optical AI systems

Optical computing has emerged as a powerful approach for high-speed and energy-efficient information processing. Diffractive optical networks, in particular, enable large-scale parallel computation through the use of passive structured phase masks and the propagation of light. However, one major challenge remains: systems trained in model-based simulations often fail to perform optimally in real experimental settings, where misalignments, noise, and model inaccuracies are difficult to capture.

In a new paper published in Light: Science & Applications, researchers at the University of California, Los Angeles (UCLA) introduce a model-free in situ training framework for diffractive optical processors, driven by proximal policy optimization (PPO), a reinforcement learning algorithm known for stability and sample efficiency. Rather than rely on a digital twin or the knowledge of an approximate physical model, the system learns directly from real optical measurements, optimizing its diffractive features on the hardware itself.

“Instead of trying to simulate complex optical behavior perfectly, we allow the device to learn from experience or experiments,” said Aydogan Ozcan, Chancellor’s Professor of Electrical and Computer Engineering at UCLA and the corresponding author of the study. “PPO makes this in situ process fast, stable, and scalable to realistic experimental conditions.”

Why imaginary numbers are central to quantum physics

One of the world’s foremost philosophers of physics, Maudlin is Professor of Philosophy at NYU and Founder and Director of the John Bell Institute for the Foundations of Physics.

He is a member of the “Foundational Questions Institute” of the Académie Internationale de Philosophie des Sciences and is the recipient of a Guggenheim Fellowship, and author of ‘The Metaphysics Within Physics’, ‘Truth and Paradox: Solving the Riddles’ and ‘Quantum Non-Locality and Relativity’

Tap the link to watch his full talk now: https://iai.tv/video/tim-maudlin-why-imaginary-numbers-are-c…um-physics


Why do imaginary numbers appear at the foundation of quantum mechanics? This question, which puzzled even great physicists like Eugene Wigner, opens up deeper issues about what it means to explain features of the mathematical formalism used in physical theory. Join philosopher of science Tim Maudlin as he explores that question through the lens of quantum dynamics, arguing that the appearance of complex numbers in Schrödinger’s equation is not arbitrary, but motivated by the need for a particular kind of wave-like structure in fundamental dynamics.

Cardiovascular risk score identifies risk for ocular disease

The Pooled Cohort Equations (PCE) cardiovascular risk score stratifies risk for multiple ocular diseases, according to a study published online in Ophthalmology.

Deyu Sun, Ph.D., from the David Geffen School of Medicine at the University of California Los Angeles, and colleagues conducted a historical prospective cohort study using electronic health record data from the “All of Us” Research Program to examine whether the PCE cardiovascular risk score is associated with future age-related macular degeneration (AMD), glaucoma, diabetic retinopathy (DR), retinal vein occlusion (RVO), and hypertensive retinopathy (HTR).

A total of 35,909 adults aged 40 to 79 years with complete variables for PCE calculation within a six-month period were included in the study. Individual-level PCE score was classified into four risk categories.

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