Category Technology/Electronics

In an experiment reminiscent of the Transformers movie franchise, engineers at Princeton University have created a type of material that can expand, assume new shapes, move and follow electromagnetic commands like a remotely controlled robot even though it lacks any motor or internal gears.

“You can transform between a material and a robot, and it is controllable with an external magnetic field,” said researcher Glaucio Paulino, the Margareta Engman Augustine Professor of Engineering at Princeton.

In an article published April 23 in the journal Nature, the researchers describe how they drew inspiration from the folding art of origami to create a structure that blurs the lines between robotics and materials...

AI models often rely on “spurious correlations,” making decisions based on unimportant and potentially misleading information. Researchers have now discovered these learned spurious correlations can be traced to a very small subset of the training data and have demonstrated a technique that overcomes the problem. The work has been published on the arXiv preprint server.

“This technique is novel in that it can be used even when you have no idea what spurious correlations the AI is relying on,” says Jung-Eun Kim, corresponding author of a paper on the work and an assistant professor of computer science at North Carolina State University.

“If you already have a good idea of what the spurious features are, our technique is an efficient and effective...

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,”...

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...