NVIDIA may have just revolutionized computing forever with the launch of DIGITS, the world’s first personal AI supercomputer. By harnessing the power of GPU-accelerated deep learning—the same technology that drives top-tier high-performance computing (HPC) clusters—DIGITS shrinks massive supercomputing capabilities into a desktop-friendly system.
This compact yet powerful platform enables data scientists, researchers, and developers to rapidly train, test, and refine complex neural networks using NVIDIA’s state-of-the-art GPUs and software ecosystem. Built for deep learning, machine learning, and big data analytics, DIGITS seamlessly integrates tensor cores, parallel processing, and accelerated computing into a single, plug-and-play solution.
Researchers from the Department of Physics have managed to experimentally develop a new magnetic state: a magneto-ionic vortex or “vortion.” The research, published in Nature Communications, allows for an unprecedented level of control of magnetic properties at the nanoscale and at room temperature, and opens new horizons for the development of advanced magnetic devices.
The use of Big Data has multiplied the energy demand in information technologies. Generally, to store information, systems utilize electric currents to write data, which dissipates power by heating the devices. Controlling magnetic memories with voltage, instead of electric currents, can minimize this energy expenditure.
One way to achieve this is by using magneto-ionic materials, which allow for the manipulation of their magnetic properties by adding or removing ions through changes in the polarity of the applied voltage. So far, most studies in this area have focused on continuous films, rather than on controlling properties at the nanometric scale in discrete “bits,” essential for high-density data storage.
We introduce PokéChamp, a minimax agent powered by Large Language Models (LLMs) for Pokémon battles. Built on a general framework for two-player competitive games, PokéChamp leverages the generalist capabilities of LLMs to enhance minimax tree search. Specifically, LLMs replace three key modules: player action sampling, opponent modeling, and value function estimation, enabling the agent to effectively utilize gameplay history and human knowledge to reduce the search space and address partial observability. Notably, our framework requires no additional LLM training. We evaluate PokéChamp in the popular Gen 9 OU format. When powered by GPT-4o, it achieves a win rate of 76% against the best existing LLM-based bot and 84% against the strongest rule-based bot, demonstrating its superior performance. Even with an open-source 8-billion-parameter Llama 3.1 model, PokéChamp consistently outperforms the previous best LLM-based bot, Pokéllmon powered by GPT-4o, with a 64% win rate. PokéChamp attains a projected Elo of 1300–1500 on the Pokémon Showdown online ladder, placing it among the top 30%-10% of human players. In addition, this work compiles the largest real-player Pokémon battle dataset, featuring over 3 million games, including more than 500k high-Elo matches. Based on this dataset, we establish a series of battle benchmarks and puzzles to evaluate specific battling skills. We further provide key updates to the local game engine. We hope this work fosters further research that leverage Pokémon battle as benchmark to integrate LLM technologies with game-theoretic algorithms addressing general multiagent problems. Videos, code, and dataset available at this https URL.
Nowadays, if you have a microscope, you probably have a camera of some sort attached. [Applied Science] shows how you can add an array of tiny LEDs and some compute power to produce high-resolution images — higher than you can get with the microscope on its own. The idea is to illuminate each LED in the array individually and take a picture. Then, an algorithm constructs a higher-resolution image from the collected images. You can see the results and an explanation in the video below.
You’d think you could use this to enhance a cheap microscope, but the truth is you need a high-quality microscope to start with. In addition, color cameras may not be usable, so you may have to find or create a monochrome camera.
The code for the project is on GitHub. The LEDs need to be close to a point source, so smaller is better, and that determines what kind of LEDs are usable. Of course, the LEDs go through the sample, so this is suitable for transmissive microscopes, not metallurgical ones, at least in the current incarnation.
Intrinsically disordered proteins (IDPs) do not attain a stable secondary or tertiary structure and rapidly change their conformation, making structure prediction particularly challenging. Although these proteins exhibit chaotic and “disordered” structures, they still perform essential functions.
IDPs comprise approximately 30% of the human proteome and play important functional roles in transcription, translation, and signaling. Many mutations linked to neurological diseases, including amyotrophic lateral sclerosis (ALS), are located in intrinsically disordered protein regions (IDRs).
Powerful machine-learning algorithms, including AlphaFold and RoseTTAFold, cannot provide realistic representations of these ‘disordered’ and ‘chaotic’ protein regions as a whole. This is because they have not been trained on such data and because these proteins exhibit inherent dynamic behavior, adopting a range of conformations rather than a single stable one.
Many physicists and engineers have recently been trying to demonstrate the potential of quantum computers for tackling some problems that are particularly demanding and are difficult to solve for classical computers. A task that has been found to be challenging for both quantum and classical computers is finding the ground state (i.e., lowest possible energy state) of systems with multiple interacting quantum particles, called quantum many-body systems.
When one of these systems is placed in a thermal bath (i.e., an environment with a fixed temperature that interacts with the systems), it is known to cool down without always reaching its ground state. In some instances, a quantum system can get trapped at a so-called local minimum; a state in which its energy is lower than other neighboring states but not at the lowest possible level.
Researchers at California Institute of Technology and the AWS Center for Quantum Computing recently showed that while finding the local minimum for a system is difficult for classical computers, it could be far easier for quantum computers.
A fundamental goal of physics is to explain the broadest range of phenomena with the fewest underlying principles. Remarkably, seemingly disparate problems often exhibit identical mathematical descriptions.
For instance, the rate of heat flow can be modeled using an equation very similar to that governing the speed of particle diffusion. Another example involves wave equations, which apply to the behavior of both water and sound. Scientists continuously seek such connections, which are rooted in the principle of the “universality” of underlying physical mechanisms.
In a study published in the journal Royal Society Open Science, researchers from Osaka University uncovered an unexpected connection between the equations for defects in a crystalline lattice and a well-known formula from electromagnetism.
In human engineering, we design systems to be predictable and controlled. By contrast, nature thrives on systems where simple rules generate rich, emergent complexity. The computational nature of the universe explains how simplicity can generate the complexity we see in natural phenomena. Imagine being able to understand everything about the universe and solve all its mysteries by a computational approach that uses very simple rules. Instead of being limited to mathematical equations, using very basic computational rules, we might be able to figure out and describe everything in the universe, like what happened at the very beginning? What is energy? What’s the nature of dark matter? Is traveling faster than light possible? What is consciousness? Is there free will? How can we unify different theories of physics into one ultimate theory of everything?
This paradigm goes against the traditional notion that complexity in nature must arise from complicated origins. It claims that simplicity in fundamental rules can produce astonishing complexity in behavior. Entering the Wolfram’s physics project: The computational universe!
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Chapters: 00:00 Intro. 01:48 Fundamentally computational. 08:51 Computational irreducibility. 13:14 Causal invariance. 16:16 Universal computation. 18:44 Spatial dimensions. 21:36 Space curvature. 23:52 Time and causality. 27:12 Energy. 29:38 Quantum mechanics. 31:31 Faster than light travel. 34:56 Dark matter. 36:30 Critiques. 39:15 Meta-framework. 41:19 The ultimate rule. 44:21 Consciousness. 46:00 Free will. 48:02 Meaning and purpose. 49:09 Unification. 55:14 Further analysis. 01:02:30 Credits.
