MIT researchers designed a computationally efficient algorithm for machine learning with symmetric data that also requires fewer data for training than conventional approaches. Their work could inform the design of faster, more accurate machine-learning models for tasks like discovering new drugs or identifying astronomical phenomena.
As e-commerce platforms grow ever more reliant on cloud computing, efficiency and sustainability have come to the fore as urgent pressures on development. A study published in the International Journal of Reasoning-based Intelligent Systems has introduced an innovative approach to the problem based on a slime mold algorithm (SMA). The work could improve both performance and energy efficiency for e-commerce systems.
At the core of the work is the development of BOSMA—the Balanced Optimization Slime Mold Algorithm. The SMA is a heuristic optimization technique inspired by the natural behavior of slime molds.
Slime molds are useful models for algorithms because they excel at finding efficient paths through complex environments and adapting to changing conditions. Moreover, they do so without any central control system. They can explore their surroundings by sending out multiple tendrils, pseudopodia, in different directions, adjusting their shape and connections in response to feedback such as nutrient availability or obstacles.
Exploring the BTZ black hole in (2+1)-dimensional gravity took me down a fascinating rabbit hole, connecting ideas I never expected—like black holes and topological phases in quantum matter! When I swapped the roles of space and time in the equations (it felt like turning my map upside down when I was lost in a new city), I discovered an interior version of the solution existing alongside the familiar exterior, each with its own thermofield double state.
Joint research led by Sosuke Ito of the University of Tokyo has shown that nonequilibrium thermodynamics, a branch of physics that deals with constantly changing systems, explains why optimal transport theory, a mathematical framework for the optimal change of distribution to reduce cost, makes generative models optimal. As nonequilibrium thermodynamics has yet to be fully leveraged in designing generative models, the discovery offers a novel thermodynamic approach to machine learning research. The findings were published in the journal Physical Review X.
Image generation has been improving in leaps and bounds over recent years: a video of a celebrity eating a bowl of spaghetti that represented the state of the art a couple of years ago would not even qualify as good today. The algorithms that power image generation are called diffusion models, and they contain randomness called “noise.”
During the training process, noise is introduced to the original data through diffusion dynamics. During the generation process, the model must eliminate the noise to generate new content from the noisy data. This is achieved by considering the time-reversed dynamics, as if playing the video in reverse. One piece of the art and science of building a model that produces high-quality content is specifying when and how much noise is added to the data.
How does your brain decide where to store a brand-new piece of information—like a new face, word, or concept? In this video, we’ll explore a working neural circuit that demonstrates how cortical columns could be allocated dynamically and efficiently—using real spikes, real timing, and biologically realistic learning rules. Instead of vague theories or abstract algorithms, we’ll show a testable mechanism that selects the first available cortical column in just 5 milliseconds, highlighting the incredible speed and parallelism of the brain. This is a crucial first step in building intelligence from the ground up—one circuit at a time.
Useful links:
The Future AI Society: https://futureaisociety.org.
The Brain Simulator III (UKS) project: https://github.com/FutureAIGuru/BrainSimIII
The Brain Simulator II (Neural Simulator) project: https://github.com/FutureAIGuru/BrainSimII
Overview Video: https://youtu.be/W2uauk2bFjs.
More Details Video: https://youtu.be/6po1rMFZkik.
How the UKS Learns Video: https://youtu.be/Rv0lrem3lVs.
Scientists at the Institute for Photonic Quantum Systems (PhoQS) and the Paderborn Center for Parallel Computing (PC2) at Paderborn University have developed a powerful open-source software tool that allows them to simulate light behavior in quantum systems.
The unique feature of this tool, named “Phoenix,” is that researchers can use it to very quickly investigate complex effects to a level of detail that was previously unknown, and all without needing knowledge of high-performance computing. The results have now been published in Computer Physics Communications.
Phoenix solves equations that describe how light interacts with material at the quantum level, which is essential for understanding and for the design of future technologies such as quantum computers and advanced photonic devices.
The theory of quantum mechanics has transformed daily life since being proposed a century ago, yet how it works remains a mystery—and physicists are deeply divided about what is actually going on, a survey in the journal Nature said Wednesday.
“Shut up and calculate!” is a famous quote in quantum physics that illustrates the frustration of scientists struggling to unravel one of the world’s great paradoxes.
For the last century, equations based on quantum mechanics have consistently and accurately described the behavior of extremely small objects.
A personalized brain stimulation system powered by artificial intelligence (AI) that can safely enhance concentration from home has been developed by researchers from the University of Surrey, the University of Oxford and Cognitive Neurotechnology. Designed to adapt to individual characteristics, the system could help people improve focus during study, work, or other mentally demanding tasks.
Published in npj Digital Medicine, the study is based on a patented approach that uses non-invasive brain stimulation alongside adaptive AI to maximize its impact.
The technology uses transcranial random noise stimulation (tRNS)—a gentle and painless form of electrical brain stimulation—and an AI algorithm that learns to personalize stimulation based on individual features, including attention level and head size.
Crystals and glasses have opposite heat-conduction properties, which play a pivotal role in a variety of technologies. These range from the miniaturization and efficiency of electronic devices to waste-heat recovery systems, as well as the lifespan of thermal shields for aerospace applications.
The problem of optimizing the performance and durability of materials used in these different applications essentially boils down to fundamentally understanding how their chemical composition and atomic structure (e.g., crystalline, glassy, nanostructured) determine their capability to conduct heat. Michele Simoncelli, assistant professor of applied physics and applied mathematics at Columbia Engineering, tackles this problem from first principles — i.e., in Aristotle’s words, in terms of “the first basis from which a thing is known” — starting from the fundamental equations of quantum mechanics and leveraging machine-learning techniques to solve them with quantitative accuracy.
In research published on July 11 in the Proceedings of the National Academy of Sciences, Simoncelli and his collaborators Nicola Marzari from the Swiss Federal Technology Institute of Lausanne and Francesco Mauri from Sapienza University of Rome predicted the existence of a material with hybrid crystal-glass thermal properties, and a team of experimentalists led by Etienne Balan, Daniele Fournier, and Massimiliano Marangolo from the Sorbonne University in Paris confirmed it with measurements.