PHYSICS CHANGED AI IN THE 20TH CENTURY. IS AI RETURNING THE FAVOUR NOW?

At this point in time, it seems like every subfield of physics will soon use AI and ML to help solve their toughest problems

- Shamim Haque Mondal

Artificial intelligence (AI) is boom-ing. Various AI algorithms are used in many scientific domains, such as to predict the structure of proteins, search for materials with particular properties, and interpret medical data to provide a diagnosis. People use tools like ChatGPT, Claude, NotebookLM, DALL-E, Gemini, and Midjourney to generate images and videos from text prompts, write text, and search the web.

The question arises in the same vein: can they prove useful in studies of the fundamental properties of nature or is there a gap between human and artificial scientists that needs to be bridged first?

There is certainly some gap. Many of the current applications of AI in scientific research often use AI models as a black box: when the models are trained on some data and they produce an output, but the relationship between the inputs and the output is not clear.

This is considered unacceptable by the scientific community. Last year, for example, DeepMind faced pressure from the life sciences community to release an inspectable version of its AlphaFold model that predicts protein structures.

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The black-box nature presents a similar concern in the physical sciences, where the steps leading up to a solution are as important as the solution itself. Yet this hasn't dissuaded scientists from trying. In fact, they started early: since the mid-1980s, they have integrated AI-based tools in the study of complex systems. In 1990, high-energy physics joined the fold.

Astroand high-energy physics

In astronomy and astrophysics, scientists study the structure and dynamics of celestial objects. Big-Data analytics and image enhancement are two major tasks for researchers in this field. AI-based algorithms help with the first by looking for patterns, anomalies, and correlations.