The key to this development is an AI-powered streaming method. By decoding brain signals directly from the motor cortex – the brain’s speech control center – the AI synthesizes audible speech almost instantly.
“Our streaming approach brings the same rapid speech decoding capacity of devices like Alexa and Siri to neuroprostheses,” said Gopala Anumanchipalli, co-principal investigator of the study.
Anumanchipalli added, “Using a similar type of algorithm, we found that we could decode neural data and, for the first time, enable near-synchronous voice streaming. The result is more naturalistic, fluent speech synthesis.”
In a striking development, researchers have created a quantum algorithm that allows quantum computers to better understand and preserve the very phenomenon they rely on – quantum entanglement. By introducing the variational entanglement witness (VEW), the team has boosted detection accuracy while
A quantum algorithm for solving mathematical problems related to knots could give us the first example of a quantum computer tackling a genuinely useful problem that would otherwise be impossible for a classical computer
Most computers run on microchips, but what if we’ve been overlooking a simpler, more elegant computational tool all this time? In fact, what if we were the computational tool?
As crazy as it sounds, a future in which humans are the ones doing the computing may be closer than we think. In an article published in IEEE Access, Yo Kobayashi from the Graduate School of Engineering Science at the University of Osaka demonstrates that living tissue can be used to process information and solve complex equations, exactly as a computer does.
This achievement is an example of the power of the computational framework known as reservoir computing, in which data are input into a complex “reservoir” that has the ability to encode rich patterns. A computational model then learns to convert these patterns into meaningful outputs via a neural network.
In 1994 Miguel Alcubierre was able to construct a valid solution to the equations of general relativity that enable a warp drive. But now we need to tackle the rest of relativity: How do we arrange matter and energy to make that particular configuration of spacetime possible?
Unfortunately for warp drives, that’s when we start running into trouble. In fact, right away, we run into three troubles. And these three troubles are called the energy conditions. Now, before I describe the energy conditions, I need to make a disclaimer. What I’m about to say are not iron laws of physics.
They are instead reasonable guesses as to how nature makes sense. General relativity is a machine. You put in various configurations of spacetime, various arrangements of matter and energy. You turn the handle and you learn how gravity works. General relativity on its own doesn’t tell you what’s real and what’s not.
What if we told you AI just created the strongest light material known to humanity? This groundbreaking discovery could revolutionize everything from aerospace to everyday tech. In this video, we break down how artificial intelligence engineered this ultra-light, ultra-strong material—and why it changes the game forever.
Scientists have long searched for the perfect balance of strength and weight, and now, AI has cracked the code. Using advanced algorithms, researchers developed a material that’s lighter than carbon fiber but stronger than steel. Imagine planes, cars, and even buildings becoming more efficient and durable than ever before.
We’ll explore how this AI-designed material works, its potential real-world applications, and what it means for the future of engineering. From military tech to consumer products, this innovation could redefine entire industries. The best part? This is just the beginning of AI-driven material science breakthroughs.
How was this material invented? What makes it so strong yet so light? How will this impact future technology? Can AI design even better materials? This video answers all these questions and more. Don’t miss out on the science behind the next big leap in material engineering—watch now!
How might a quantum system’s capacity for self-measurement relate to models of functional consciousness?
Key features of consciousness—like the integration of information from many parts, internal self-monitoring of states, and adaptive decision-making—find intriguing parallels in quantum phenomena like entanglement, superposition, and observer-dependent measurement.
Anyone who develops an AI solution sometimes goes on a journey into the unknown. At least at the beginning, researchers and designers do not always know whether their algorithms and AI models will work as expected or whether the AI will ultimately make mistakes.
Sometimes, AI applications that work well in theory perform poorly under real-life conditions. In order to gain the trust of users, however, an AI should work reliably and correctly. This applies just as much to popular chatbots as it does to AI tools in research.
Any new AI tool has to be tested thoroughly before it is deployed in the real world. However, testing in the real world can be an expensive, or even risky endeavor. For this reason, researchers often test their algorithms in computer simulations of reality. However, since simulations are approximations of reality, testing AI solutions in this way can lead researchers to overestimate an AI’s performance.
Together with an international team of researchers from the Universities of Southern California, Central Florida, Pennsylvania State and Saint Louis, physicists from the University of Rostock have developed a novel mechanism to safeguard a key resource in quantum photonics: optical entanglement. Their discovery is published in Science.
Declared as the International Year of Quantum Science and Technology by the United Nations, 2025 marks 100 years since the initial development of quantum mechanics. As this strange and beautiful description of nature on the smallest scales continues to fascinate and puzzle physicists, its quite tangible implications form the basis of modern technology as well as material science, and are currently in the process of revolutionizing information science and communications.
A key resource to quantum computation is so-called entanglement, which underpins the protocols and algorithms that make quantum computers exponentially more powerful than their classical predecessors. Moreover, entanglement allows for the secure distribution of encryption keys, and entangled photons provide increased sensitivity and noise resilience that dramatically exceed the classical limit.
Similar to humans going on journeys of self-discovery, quantum computers are also capable of deepening their understanding of their own foundations.
Researchers from Tohoku University and St. Paul’s School, London, have developed a new algorithm that allows quantum computers to analyze and protect quantum entanglement—a fundamental underpinning of quantum computing. These findings will advance our understanding of quantum entanglement and quantum technologies.
The study was published in Physical Review Letters on March 4, 2025.
