Category Physics

Penn Engineers have developed the first programmable chip that can train nonlinear neural networks using light—a breakthrough that could dramatically speed up AI training, reduce energy use and even pave the way for fully light-powered computers.

While today’s AI chips are electronic and rely on electricity to perform calculations, the new chip is photonic, meaning it uses beams of light instead. Described in Nature Photonics, the chip reshapes how light behaves to carry out the nonlinear mathematics at the heart of modern AI.

“Nonlinear functions are critical for training deep neural networks,”...

In recent years, computer scientists have created various highly performing machine learning tools to generate texts, images, videos, songs and other content. Most of these computational models are designed to create content based on text-based instructions provided by users.

Researchers at the Hong Kong University of Science and Technology recently introduced AudioX, a model that can generate high quality audio and music tracks using texts, video footage, images, music and audio recordings as inputs. Their model, introduced in a paper published on the arXiv preprint server, relies on a diffusion transformer, an advanced machine learning algorithm that leverages the so-called transformer architecture to generate content by progressively de-noising the input data it receives.

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A trio of AI researchers at Google’s Google DeepMind, working with a colleague from the University of Toronto, report that the AI algorithm Dreamer can learn to self-improve by mastering Minecraft in a short amount of time. In their study published in the journal Nature, Danijar Hafner, Jurgis Pasukonis, Timothy Lillicrap and Jimmy Ba programmed the AI app to play Minecraft without being trained and to achieve an expert level in just nine days.

Over the past several years, computer scientists have learned a lot about how deep learning can be used to train AI applications to conduct seemingly intelligent activities such as answering questions...

Did you know that the camera sensor in your smartphone could help unlock the secrets of antimatter? The AEgIS collaboration, led by Professor Christoph Hugenschmidt’s team from the research neutron source FRM II at the Technical University of Munich (TUM), has developed a detector using modified mobile camera sensors to image, in real time, the points where antimatter annihilates with matter.

This new device, described in a paper published in Science Advances, can pinpoint antiproton annihilations with a resolution of about 0.6 micrometers, a 35-fold improvement over previous real-time methods.

AEgIS and other experiments at CERN’s Antimatter Factory, such as ALPHA and GBAR, are on a mission to measure the free-fall of antihydrogen within Earth’s gravitational field with high pr...