An international team of researchers has developed a new theoretical framework that bridges physics and biology to provide a unified approach for understanding how complexity and evolution emerge in nature.

This new work on “assembly theory,” published today in Nature, represents a major advance in our fundamental comprehension of biological evolution and how it is governed by the physical laws of the universe. The paper is titled “Assembly Theory Explains and Quantifies Selection and Evolution.”

This research builds on the team’s previous work developing assembly theory as an empirically validated approach to life detection, with implications for the search for alien life and efforts to evolve new life forms in the laboratory.

The Nobel Physics Prize was awarded on Tuesday to three scientists for their work on attoseconds, which are almost unimaginably short periods of time.

Their work using lasers gives scientists a tool to observe and possibly even manipulate electrons, which could spur breakthroughs in fields such as electronics and chemistry, experts told AFP.

Attoseconds are a billionth of a billionth of a second.

The universe is expanding, but why? Dark Energy might be the key in solving this mystery.
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The Impact of chatGPT and other large language models on physics research and education (2023)
Event organizers: Kevin Burdge, Joshua Borrow, Mark Vogelsberger.
Session 3: “The use of large language models in teaching/administration”

Capstone talk: “LLMs for Physics, and Physics for LLMs“
Speaker: Dr Stephen Wolfram (Wolfram Research)

Radical theory explains how physics creates all biology’s complexity.

Physicists at the National Ignition Facility are learning how to better control crushingly violent “shots”.

The surprising detection of light 200 times more powerful than previous observations from the nearby pulsar Vela indicates hidden physics around dead stars.

Hurricane Lee wasn’t bothering anyone in early September, churning far out at sea somewhere between Africa and North America. A wall of high pressure stood in its westward path, poised to deflect the storm away from Florida and in a grand arc northeast. Heading where, exactly? It was 10 days out from the earliest possible landfall—eons in weather forecasting—but meteorologists at the European Centre for Medium-Range Weather Forecasts, or ECMWF, were watching closely. The tiniest uncertainties could make the difference between a rainy day in Scotland or serious trouble for the US Northeast.

Typically, weather forecasters would rely on models of atmospheric physics to make that call. This time, they had another tool: a new generation of AI-based weather models developed by chipmaker Nvidia, Chinese tech giant Huawei, and Google’s AI unit DeepMind.

Scientists may have made a major breakthrough in the quest to produce limitless energy. According to a new study published in the journal American Chemical Society, scientists are looking deeper at a molecule known as azulene, which is a blue-light emitting molecule that seems to flout the fundamental rules of photochemistry.

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The more physicists use artificial intelligence and machine learning, the more important it becomes for them to understand why the technology works and when it fails.

The advent of ChatGPT, Bard, and other large language models (LLM) has naturally excited everybody, including the entire physics community. There are many evolving questions for physicists about LLMs in particular and artificial intelligence (AI) in general. What do these stupendous developments in large-data technology mean for physics? How can they be incorporated in physics? What will be the role of machine learning (ML) itself in the process of physics discovery?

Before I explore the implications of those questions, I should point out there is no doubt that AI and ML will become integral parts of physics research and education. Even so, similar to the role of AI in human society, we do not know how this new and rapidly evolving technology will affect physics in the long run, just as our predecessors did not know how transistors or computers would affect physics when the technologies were being developed in the early 1950s. What we do know is that the impact of AI/ML on physics will be profound and ever evolving as the technology develops.