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The computing world is celebrating a major milestone as Andrew Barto, professor emeritus at the University of Massachusetts Amherst, and Richard Sutton, professor of computer science at the University of Alberta, Canada, have been awarded the 2024 Association for Computing Machinery A.M. Turing Award — often called the “Nobel Prize of computing” — for “developing the conceptual and algorithmic foundations of reinforcement learning.”

The legacy in reinforcement learning

Barto and Sutton are widely recognized as pioneers of the modern computational reinforcement learning (RL), a field that addresses the challenge of learning how to act based on evaluative feedback. Their work has laid the conceptual and algorithmic foundations of RL, shaping the future of artificial intelligence and decision-making systems.

The influence of RL extends across multiple disciplines, including computer science (machine learning), engineering (optimal control), mathematics (operations research), neuroscience (optimal decision-making), psychology (classical and operant conditioning) and economics (rational choice theory). Researchers in these fields continue to be profoundly shaped by the contributions of Sutton and Barto.

From NSF Grants to AI Breakthroughs

Barto’s contributions were made possible through a series of U.S. National Science Foundation-funded projects that sustained AI research long before its recent boom. His research was supported through grants from NSF programs including the National Robotics Initiative, Robust Intelligence, Collaborative Research in Computation Neuroscience, Human-Centered Computing, Biological Information Technology and Systems, Artificial Intelligence and Cognitive

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The manufacturing technique known as 3D printing, now being used everywhere, from aircraft manufacturers to public libraries, has never been more affordable or accessible. Biomedical engineering has particularly benefited from 3D printing as prosthetic devices can be produced and tested more rapidly than ever before. However, 3D printing still faces challenges when printing living tissues, partly due to their complexity and fragility.

Now, with support from the U.S. National Science Foundation, a research team at Boston University (BU) and the Wyss Institute at Harvard University has pioneered the use of gallium, a metal that can be molded at room temperature, to create tissue structures in various shapes and sizes.

This innovative approach to fabrication, engineered sacrificial capillary pumps for evacuation (ESCAPE), was highlighted in a recent study published in Nature, where the team used gallium casts to mold biomaterials. The scaffolds left behind by these casts are then filled with cells cultured to form tissue structures. Vascular structures were some of the first produced using ESCAPE, particularly because of the challenges faced due to blood vessel complexity. Few techniques exist to build large (millimeter-scale) and small (micrometer-scale) structures in scaffolds made of natural materials, making this multiscale fabrication capability a novel approach.

“ESCAPE can be used on several tissue architectures, but we started with vascular forms because blood vessel networks feature many different length scales,”

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NSF–DOE Vera C. Rubin Observatory, jointly funded by the U.S. National Science Foundation and the U.S. Department of Energy’s Office of Science, will soon begin scanning the Southern Hemisphere sky every night for 10 years. Among the trillions of cosmic events and objects it will capture will be millions of exploding stars called Type Ia supernovas.

These supernovas are produced by exploding white dwarf stars and are some of the brightest cosmic spectacles. They are particularly useful to researchers because they provide a sort of reliable cosmic yardstick that can be used to accurately measure vast distances in the universe. With enough observations of Type Ia supernovas, scientists can measure the universe’s expansion rate and whether it changes over time.

Every time NSF-DOE Rubin Observatory detects a change in brightness or position of an object, it will send an alert to the science community. With such rapid detection, Rubin will be the most powerful tool yet for spotting Type Ia supernovas before they fade away.

Observations of Type Ia supernovas were used to discover the mysterious phenomenon known as dark energy, thought to be causing the universe to expand faster than expected. In just its first few months of operation, Rubin Observatory will discover many more Type Ia supernovas than were used in the initial discovery of dark energy in the 1990s. The observatory will

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