Two scientists have been awarded the Nobel Prize in Physics "for foundational discoveries and inventions that enable machine learning with artificial neural networks." John Hopfield, an emeritus professor of Princeton University, devised an associative memory that's able to store and reconstruct images and other types of patterns in data. Geoffrey Hinton, who has been dubbed the "Godfather of AI," pioneered a way to autonomously find properties in data, leading to the ability to identify certain elements in pictures. "This year's physics laureates' breakthroughs stand on the foundations of physical science. They have showed a completely new way for us to use computers to aid and to guide us to tackle many of the challenges our society face," the committee wrote on X. "Thanks to their work humanity now has a new item in its toolbox, which we can choose to use for good purposes. Machine learning based on artificial neural networks is currently revolutionizing science, engineering and daily life."

WASHINGTON, D.C. - Today, the U.S. Department of Energy (DOE) announced plans to provide up to $40 million over three years for new research in data, artificial intelligence and machine learning to address the challenges of producing and managing data at DOE scientific user facilities. "Major scientific facilities at our DOE national laboratories are generating vast and growing amounts of data for researchers every day," said Dr. Chris Fall, Director of DOE's Office of Science. Proposals are expected to focus on each of a range of different challenges, including extracting information from complex data sets, managing facility operations in real time, and optimizing experiments through the creation of virtual laboratory environments, among other topics. "Artificial intelligence's ability to analyze and divine insights from massive data sets has the power to transform the world around us," said Cheryl Ingstad, Director of DOE's Artificial Intelligence & Technology Office. "DOE is determined to lead by example in AI application by turning this power on ourselves to optimize the way we operate facilities and push the boundaries of scientific discovery."

DeepMind has a new paper where researchers have uncovered two "surpising findings". The paper is described in "Understanding Deep Learning through Neuron Deletion". In networks that generalize well, (1) all neurons are important and (2) are more robust to damage. Deep Learning network have behavior that reminds us of holograms. These results are further confirmation of my conjecture that Deep Learning systems are like holographic memories.

A robotic set of legs that is able to fully model walking, in a biologically accurate manner, has been produced by a group of US researchers. The remarkably human-like walking gait was produced by simplifying the neural architecture, musculoskeletal architecture and sensory feedback pathways of humans, and building them into the robot. The biological accuracy of their robot has enabled the researchers to further investigate the processes that occur during walking, in humans. "And may bolster theories of how babies learn to walk, as well as helping to understand how spinal-cord-injury patients can recover the ability to walk." "A key component of the human walking system is the central pattern generator (CPG). The CPG is a neural network in the lumbar region of the spinal cord that generates rhythmic muscle signals. The CPG produces, and then controls, these signals by gathering information from different parts of the body that are responding to the environment. This is what allows people to walk without needing to think about it."