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Category: computing

Intelligence Without Brains: A Radical New Idea

What if intelligence doesn’t require a brain? Biologist Michael Levin argues that intelligence is not confined to neurons, but exists on a continuum of goal-directed behavior and problem-solving across a wide range of species and systems. Using a framework he calls the “cognitive light cone,” Levin explores diverse forms of intelligence extending all the way down to the cellular level. His research suggests that cells communicate through electrical networks, enabling them to make collective decisions and adapt to unexpected challenges, evidenced by engineered tadpoles capable of seeing through eyes located on their tails. Levin radically challenges the conventional wisdom even further, proposing that forms of intelligence may extend beyond biology to molecular systems and maybe even the weather.

00:00 What is intelligence?
01:03 The field of diverse intelligence.
01:33 Intelligence at the cellular level.
02:08 The cognitive light cone.
03:01 The intelligence of groups of cells.
03:52 The bioelectric language of cells.
04:20 The mind of the body.
04:23 Cells that solve problems.
05:17 The tadpole experiment.
06:25 The cognitive spectrum.
06:48 Can you train a hurricane?
07:03 A new science of intelligence.
07:28 Beyond human biases.

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Quanta Magazine is an editorially independent publication supported by the Simons Foundation. We focus on developments in mathematics, theoretical physics, theoretical computer science and the basic life sciences.

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The hidden structure behind a widely used class of materials

Materials called relaxor ferroelectrics have been used for decades in technologies like ultrasounds, microphones, and sonar systems. Their unique properties come from their atomic structure, but that structure has stubbornly eluded direct measurement.

Now a team of researchers from MIT and elsewhere has directly characterized the three-dimensional atomic structure of a relaxor ferroelectric for the first time. The findings, reported today in Science, provide a framework for refining models used to design next-generation computing, energy, and sensing devices.

“Now that we have a better understanding of exactly what’s going on, we can better predict and engineer the properties we want materials to achieve,” says corresponding author James LeBeau, MIT’s Kyocera Professor of Materials Science and Engineering. “The research community is still developing methods to engineer these materials, but in order to predict the properties those materials will have, you have to know if your model is right.”

This Will Replace Silicon Chips

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Timestamps:
00:00 — New Semiconductor.
08:44 — Computers of The Future.

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Linear-time prediction of proteome-scale microbial protein interactions

Protein–protein interactions (PPIs) underpin biological function, yet proteome-scale interaction prediction remains bottlenecked by the quadratic computational complexity of all-vs.-all pairwise comparisons. Here, we present FlashPPI, a contrastive learning framework, grounded in residue-level interactions, that enables linear-time prediction of physical protein interfaces across a microbial proteome. By leveraging a genomic language model that captures cross-protein coevolutionary signals from metagenomic sequences, FlashPPI aligns interacting partners in a shared latent space. We demonstrate a four-fold performance increase over existing sequence-based methods, while reducing proteome-wide screening time from days to minutes. Crucially, FlashPPI achieves comparable screening performance to state-of-the-art structure-folding models at a fraction of the computational cost. Finally, we integrate FlashPPI into an interactive web platform that combines predicted networks with functional annotations and genomic context, making proteome-wide network analysis rapid and accessible for microbial discovery.

Scientists Visualize the Complex, Dynamic World Inside a Human Cell

The interactive image was created for Cell Signaling Technology, Inc., and was inspired by the work of David Goodsell, a professor of computational biology at Scripps Research Institute, who is widely recognized for his vibrant watercolor paintings of cells and viruses. Alongside some artistic interpretation, portions of the image were digitally rendered using datasets gathered through scientific methods.

“This 3D rendering of a eukaryotic cell is modeled using X-ray, nuclear magnetic resonance (NMR), and cryo-electron microscopy datasets for all of its molecular actors,” explains McGill. “It is an attempt to recapitulate the myriad pathways involved in signal transduction, protein synthesis, endocytosis, vesicular transport, cell-cell adhesion, apoptosis, and other processes.”

Although some online are calling it “the most detailed image of a human cell ever captured” Evan Ingersoll and Gael McGill emphasize that it’s really an educational tool. Elements of the cell have been simplified, and in some cases “squashed together,” to help viewers better understand what happens inside it.

Mathematical modeling helps advance use of magnetic particles in targeted drug-delivery systems

A Florida State University computational scientist is paving the way for future medical breakthroughs by developing mathematical models and simulations to predict the behavior of a unique drug-delivery method, which aims to deploy treatments directly to targeted sites in the body.

Florida State University Associate Professor of Scientific Computing Bryan Quaife is part of a multi-institutional team of engineers, mathematicians and computational scientists conducting foundational research essential to the design of a drug-delivery system that could reduce medication side effects while increasing treatment efficacy. Their research expands on work proposing the use of magnetic particles to guide cell-like drug carriers toward a specific target, like a tumor.

This work, which was published in Physical Review Letters, reveals how tiny particles moving inside microscopic drug carriers can gradually stress and eventually rupture the enclosing membrane. These findings could help engineers design smarter drug-delivery systems to protect therapeutic cargo during transport and release it on demand at the desired location.

Quantum Paradoxes: 5 Ways to Test the Multiverse | Maria Violaris

Can we actually test whether the multiverse is real? Not just philosophicallybut scientifically?

Quantum physicist Maria Violaris presents five remarkable experiments, from Schrödinger’s cat to Google’s Willow quantum chip, that put the multiverse to the test. Along the way, she untangles two of the strangest phenomena in all of physics — quantum measurement and entanglement — and reveals how a thought experiment designed to test the multiverse in 1985 accidentally launched today’s billion-dollar quantum computing race.

Maria also shares a puzzling thought experiment of her own that overturns a long-held assumption: that you can never communicate across branches of the multiverse.

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Maria Violaris is a quantum physicist and prize-winning science communicator with a PhD in the foundations of quantum information from the University of Oxford. She works on quantum theory research at Oxford Quantum Circuits, runs a YouTube channel and the Quantum Foundations Podcast, and pioneered the use of quantum thought experiments for quantum computing education through her Quantum Paradoxes series at IBM Quantum.

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Scientists uncover a genetic ‘shield’ that lowers the risk of colorectal cancer

A team of scientists from the Barbara Ann Karmanos Cancer Institute, Wayne State University and institutions across the U.S. have published a new paper on the role of TGFBR1*6A, a naturally occurring genetic mutation in the TGFBR1 gene found in approximately 14% of the general population.

The study, “TGFBR1*6A and risk for colorectal cancer,” published June 9, 2026, in Cancer Communications, focuses on TGFBR1*6A and how it influences a person’s risk of developing colorectal cancer. Dr. Boris Pasche, president and CEO of the Karmanos Cancer Institute and chair of the Wayne State University Department of Oncology, was the first to discover TGFBR1*6A as a cancer risk allele.

“This mutation has often been overlooked by genome-wide association study chips, which cannot detect TGFBR1*6A, and is commonly missed by next-generation sequencing platforms due to the complexity of the region,” said Dr. Allan Johansen, a postdoctoral fellow and first author of the paper.

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