As artificial intelligence explodes in popularity, two of its pioneers have nabbed the 2024 Nobel Prize in physics. The prize surprised many, as these developments are typically associated with ...

Theoretical physicists use machine-learning algorithms to speed up difficult calculations and eliminate untenable theories—but could they transform what it means to make discoveries? Theoretical ...

Machine learning is becoming an essential part of a physicist’s toolkit. How should new students learn to use it? When Radha Mastandrea started her undergraduate physics program at MIT in 2015, she ...

See more of our trusted coverage when you search. Prefer Newsweek on Google to see more of our trusted coverage when you search. John Hopfield and Geoffrey Hinton were awarded the 2024 Nobel Prize in ...

Recent developments in machine learning techniques have been supported by the continuous increase in availability of high-performance computational resources and data. While large volumes of data are ...

On Tuesday, the Royal Swedish Academy of Sciences awarded the 2024 Nobel Prize in Physics to John J. Hopfield of Princeton University and Geoffrey E. Hinton of the University of Toronto for their ...

Now, artificial intelligence (AI) tools are providing powerful new ways to address long-standing problems in physics. “The ...

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 ...

Harvard University physicists have created a simplified mathematical model to study how neural networks learn, using statistical physics to uncover underlying patterns. The approach, likened to early ...

• A new AI machine learning algorithm capable of predicting planetary orbits that may one day help accelerate physics research in other areas such as renewable energy. • Strikingly, the algorithms ...

AI meets isotope science: Machine learning is enhancing isotope analysis techniques, improving efficiency, accuracy, and insights into geochemical processes. Key hurdles remain: Data scarcity, limited ...