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An international collaboration between four scientists from Mainz, Valencia, Madrid, and Zurich has published new research in the Proceedings of the National Academy of Sciences, shedding light on the most significant increase in complexity in the history of life’s evolution on Earth: the origin of the eukaryotic cell.

While the endosymbiotic theory is widely accepted, the billions of years that have passed since the fusion of an archaea and a bacteria have resulted in a lack of evolutionary intermediates in the phylogenetic tree until the emergence of the eukaryotic cell. It is a gap in our knowledge, referred to as the black hole at the heart of biology.

“The new study is a blend of theoretical and observational approaches that quantitatively understands how the genetic architecture of life was transformed to allow such an increase in complexity,” stated Dr. Enrique M. Muro, representative of Johannes Gutenberg University Mainz (JGU) in this project.

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Scientists at EPFL have made a breakthrough in designing arrays of resonators, the basic components that power quantum technologies. This innovation could create smaller, more precise quantum devices.

Qubits, or , are mostly known for their role in , but they are also used in analog quantum simulation, which uses one well-controlled quantum system to simulate another more complex one. An analog quantum simulator can be more efficient than a digital computer simulation, in the same way that it is simpler to use a to simulate the laws of aerodynamics instead of solving many complicated equations to predict airflow.

Key to both digital quantum computing and analog quantum simulation is the ability to shape the environment with which the qubits are interacting. One tool for doing this effectively is a coupled array (CCA), made of multiple microwave cavities arranged in a repeating pattern where each cavity can interact with its neighbors. These systems can give scientists new ways to design and control quantum systems.

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In this episode, renowned AI researcher Pedro Domingos, author of The Master Algorithm, takes us deep into the world of Connectionism—the AI tribe behind neural networks and the deep learning revolution.

From the birth of neural networks in the 1940s to the explosive rise of transformers and ChatGPT, Pedro unpacks the history, breakthroughs, and limitations of connectionist AI. Along the way, he explores how supervised learning continues to quietly power today’s most impressive AI systems—and why reinforcement learning and unsupervised learning are still lagging behind.

We also dive into:
The tribal war between Connectionists and Symbolists.
The surprising origins of Backpropagation.
How transformers redefined machine translation.
Why GANs and generative models exploded (and then faded)
The myth of modern reinforcement learning (DeepSeek, RLHF, etc.)
The danger of AI research narrowing too soon around one dominant approach.

Whether you’re an AI enthusiast, a machine learning practitioner, or just curious about where intelligence is headed, this episode offers a rare deep dive into the ideological foundations of AI—and what’s coming next.

Continue reading “How Connectionism Is Reshaping the Future of Machine Learning | Pedro Domingos” | >

A research team from the Institute of Statistical Mathematics and Panasonic Holdings Corporation has developed a machine learning algorithm, ShotgunCSP, that enables fast and accurate prediction of crystal structures from material compositions. The algorithm achieved world-leading performance in crystal structure prediction benchmarks.

Crystal structure prediction seeks to identify the stable or metastable crystal structures for any given chemical compound adopted under specific conditions. Traditionally, this process relies on iterative evaluations using time-consuming first-principles calculations and solving energy minimization problems to find stable atomic configurations. This challenge has been a cornerstone of materials science since the early 20th century.

Recently, advancements in computational technology and generative AI have enabled new approaches in this field. However, for large-scale or , the exhaustive exploration of vast phase spaces demands enormous computational resources, making it an unresolved issue in materials science.

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Learn data science using real world examples on Brilliant! First 30 days are free and 20% off the annual premium subscription when you use our link ➜ https://brilliant.org/sabine.

We still don’t know what “consciousness” actually means. But in a new study, researchers have used the equations of quantum mechanics to determine a brain’s “criticality,” a measure which allows them to separate waking brains from sleeping ones. I think they’re onto something. Let’s take a look.

Paper: https://journals.aps.org/pre/abstract… Check out my new quiz app ➜ http://quizwithit.com/ 💌 Support me on Donorbox ➜ https://donorbox.org/swtg 📝 Transcripts and written news on Substack ➜ https://sciencewtg.substack.com/ 👉 Transcript with links to references on Patreon ➜ / sabine 📩 Free weekly science newsletter ➜ https://sabinehossenfelder.com/newsle… 👂 Audio only podcast ➜ https://open.spotify.com/show/0MkNfXl… 🔗 Join this channel to get access to perks ➜ / @sabinehossenfelder 🖼️ On instagram ➜ / sciencewtg #science #sciencenews #consciousness.

🤓 Check out my new quiz app ➜ http://quizwithit.com/
💌 Support me on Donorbox ➜ https://donorbox.org/swtg.
📝 Transcripts and written news on Substack ➜ https://sciencewtg.substack.com/
👉 Transcript with links to references on Patreon ➜ / sabine.
📩 Free weekly science newsletter ➜ https://sabinehossenfelder.com/newsle…
👂 Audio only podcast ➜ https://open.spotify.com/show/0MkNfXl…
🔗 Join this channel to get access to perks ➜
/ @sabinehossenfelder.
🖼️ On instagram ➜ / sciencewtg.

#science #sciencenews #consciousness

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Modern brain–computer interfaces (BCI), utilizing electroencephalograms for bidirectional human–machine communication, face significant limitations from movement-vulnerable rigid sensors, inconsistent skin–electrode impedance, and bulky electronics, diminishing the system’s continuous use and portability. Here, we introduce motion artifact–controlled micro–brain sensors between hair strands, enabling ultralow impedance density on skin contact for long-term usable, persistent BCI with augmented reality (AR). An array of low-profile microstructured electrodes with a highly conductive polymer is seamlessly inserted into the space between hair follicles, offering high-fidelity neural signal capture for up to 12 h while maintaining the lowest contact impedance density (0.03 kΩ·cm−2) among reported articles. Implemented wireless BCI, detecting steady-state visually evoked potentials, offers 96.4% accuracy in signal classification with a train-free algorithm even during the subject’s excessive motions, including standing, walking, and running. A demonstration captures this system’s capability, showing AR-based video calling with hands-free controls using brain signals, transforming digital communication. Collectively, this research highlights the pivotal role of integrated sensors and flexible electronics technology in advancing BCI’s applications for interactive digital environments.

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Since most cells naturally repel DNA, delivering these nanodevices into cells requires specialized techniques, such as transfection methods and transformation protocols. Once inside, cellular factors such as salt concentration, molecular crowding, and heterogeneous environments influence strand displacement reactions. To overcome the limitations of direct delivery, researchers are also developing transcribable RNA nanodevices encoded into plasmids or chromosomes, allowing cells to express these circuits.

Toward Smart DNA Machines and Biocomputers

DNA strand displacement has been applied to the innovation of computational models. By integrating computational principles with DNA strand displacement, the structured algorithms of traditional computing can be combined with random biochemical processes and chemical reactions in biological systems, enabling biocompatible models of computation. In the future, DNA strand displacement may enable autonomously acting DNA nanomachines to precisely manipulate biological processes, leading to quantum leaps in healthcare and life science research.

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Just as pilots use flight simulators to safely practice complex maneuvers, scientists may soon conduct experiments on a highly realistic simulation of the mouse brain. In a new study, researchers at Stanford Medicine and their collaborators developed an artificial intelligence.

Artificial Intelligence (AI) is a branch of computer science focused on creating systems that can perform tasks typically requiring human intelligence. These tasks include understanding natural language, recognizing patterns, solving problems, and learning from experience. AI technologies use algorithms and massive amounts of data to train models that can make decisions, automate processes, and improve over time through machine learning. The applications of AI are diverse, impacting fields such as healthcare, finance, automotive, and entertainment, fundamentally changing the way we interact with technology.

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That’s the question raised by physicist Dr. Richard Lieu at The University of Alabama in Huntsville. In a paper published in the Monthly Notices of the Royal Astronomical Society, Lieu offers a theory that could challenge one of the biggest assumptions in astrophysics. His idea: gravity can exist without any mass at all.

The study explores a different solution to the same equations that normally describe gravity—both in Newtonian theory and in general relativity. These equations link mass with the gravitational force it creates. Lieu focused on what’s known as the Poisson equation, a simplified form of Einstein’s field equations used for describing gravity in weaker fields, like those around galaxies.

This equation typically has one well-known solution: gravity that weakens with distance, created by mass. But there’s another, lesser-known solution that’s often ignored. It can also create an attractive force but doesn’t come from any actual matter.

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