AI in Physics: A Promising Future for Academia?

Click For Summary

Discussion Overview

The discussion centers on the role of artificial intelligence (AI) in the field of physics, particularly its potential future impact on academia and research. Participants explore whether AI could evolve into a distinct field within physics, its applications in various areas, and the implications for academic careers in physics.

Discussion Character

  • Exploratory
  • Debate/contested
  • Technical explanation
  • Conceptual clarification

Main Points Raised

  • Some participants note the increasing use of AI and machine learning in physics for tasks like data analysis and finding solutions that are otherwise difficult or time-consuming.
  • One participant suggests that while AI may not become a separate field within physics, it could foster significant collaboration between AI researchers and physicists, particularly in areas like high-energy particle physics and astrophysics.
  • Another viewpoint emphasizes that AI and machine learning might not contribute to discovering fundamental laws of nature but could serve as powerful tools for statistical analysis and computational physics.
  • Concerns are raised about the perception of AI-focused research in academia, questioning whether expertise in AI would overshadow knowledge of traditional physics in hiring decisions.
  • Some participants express uncertainty about how funding organizations will prioritize AI-related research in the future, which could affect job prospects for those with AI-focused PhDs in physics.

Areas of Agreement / Disagreement

Participants generally agree on the growing relevance of AI in physics but express differing views on whether it will become a distinct field and how it will be perceived in academic hiring contexts. The discussion remains unresolved regarding the long-term implications for academic careers.

Contextual Notes

There are limitations in the discussion regarding assumptions about the future landscape of funding and academic hiring practices, as well as the definitions of what constitutes physics in the context of AI applications.

kelly0303
Messages
573
Reaction score
33
Hello! I see more and more papers that use AI for physics analysis or to find approximate solutions that would otherwise be impossible or very slow to find. What do you think is the future for AI in physics on long term. Do you think it would become a field on its own within physics? Would it help you to get a job in academia?
 
Physics news on Phys.org
To the OP:

I'm not by any means an expert in AI, but have been keeping in touch from a distance on developments in that field. My own take is that AI, and more specifically machine learning, has transitioned from a primarily academic field of research within computer science into a much more applied field, with wide applicability in a variety of areas, including physics.

I don't foresee AI becoming its own field within physics, but I do see much greater scope for collaboration between AI researchers and physicists, and the collaboration will go both ways -- AI researchers working with physicists on, say, large scale data analysis from high-energy particle physics or astrophysics, as one example, and statistical physicists applying their understanding of interactions at the macroscopic level (and the math behind it) into problems related to AI (as already have been done in the area of neural networks).
 
It depends on what you classify as physics. Finding fundamental laws of nature and formulating theories, I don't see AI or ML doing. However, viewed as a powerful tool of statistics and prediction, I can certainly see these tools being used to supplement in computational physics, finding new minima and predicting new structures, and replacing complicated (e.g. PDE) solvers with something that can quickly and accurately guess the solution.

For example, until relatively recently, the field of protein folding was dominated by tools built by physicists and computational chemists, whereas with the advent of FoldIt! and beyond, I see approaches not having to do much at all with physics taking an increasingly important role, and indeed I think physics and the type of bottom up approach inherent to the field is going to be giving quite a bit of way to the magic black boxes of machine learning in the near future.
 
gleem said:
From The American Physical Society New , June 2018 (Volume 27, Number 6)

"AI Makes Inroads in Physics "
https://www.aps.org/publications/apsnews/201806/artificial.cfm

applications to LIGO, LHC and astronomy
Thank you for this! So my main concern is this: If I join during my PhD a group who works in applying AI techniques to, say, condensed matter physics (I saw many papers in that directions) and I do a good job, find better approximations, maybe even deeper insights in understanding more complex systems, will I have a chance in academia, later, with this kind of PhD? Will this kind of research prove my knowledge in the field (which in this case would be condensed matter) or will the universities think that I am good in AI, but will be reluctant about my knowledge of the physics behind and prefer to choose for a given position someone who doesn't know much AI, but his PhD was more directly related to the field (e.g. doing some condensed matter experiments in the lab).
 
It is difficult to determine how people will react to an as-yet undone piece of work.
 
kelly0303 said:
Thank you for this! So my main concern is this: If I join during my PhD a group who works in applying AI techniques to, say, condensed matter physics (I saw many papers in that directions) and I do a good job, find better approximations, maybe even deeper insights in understanding more complex systems, will I have a chance in academia, later, with this kind of PhD? Will this kind of research prove my knowledge in the field (which in this case would be condensed matter) or will the universities think that I am good in AI, but will be reluctant about my knowledge of the physics behind and prefer to choose for a given position someone who doesn't know much AI, but his PhD was more directly related to the field (e.g. doing some condensed matter experiments in the lab).
Another major unknown is what initiatives the major funding organizations (such as NSF and DARPA) will be supporting at the time you've completed your PhD and postdocs and are applying for faculty positions. If there is money allocated for AI/Physics programs, you'll be in a good position. If not, ...
 

Similar threads

  • · Replies 15 ·
Replies
15
Views
3K
  • · Replies 3 ·
Replies
3
Views
1K
  • · Replies 27 ·
Replies
27
Views
4K
  • · Replies 2 ·
Replies
2
Views
3K
  • · Replies 12 ·
Replies
12
Views
3K
  • · Replies 6 ·
Replies
6
Views
3K
Replies
28
Views
3K
  • · Replies 5 ·
Replies
5
Views
2K
  • · Replies 9 ·
Replies
9
Views
3K
  • · Replies 3 ·
Replies
3
Views
3K