AI in Physics: A Promising Future for Academia?

In summary: To the OP:There is no one-size-fits-all answer, as the future for AI in physics will vary depending on the individual's research goals and background. However, if you are dedicated to applying AI techniques to physics problems, and you have a strong background in physics, you should have no trouble finding a position in academia.
  • #1
kelly0303
559
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
  • #2
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).
 
  • #3
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.
 
  • #5
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).
 
  • #6
It is difficult to determine how people will react to an as-yet undone piece of work.
 
  • #7
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, ...
 

1. What is the current role of AI in physics?

Currently, AI is being used in physics to help with data analysis, simulations, and predictions. It is also being integrated into experimental setups, allowing for real-time monitoring and control.

2. How can AI benefit academic research in physics?

AI has the potential to significantly speed up the research process by automating tasks, identifying patterns in data, and suggesting new areas of study. It can also help with data management and organization, making it easier for researchers to access and analyze large amounts of data.

3. What are the challenges of implementing AI in physics research?

One of the main challenges is the complexity of physics problems, which require advanced AI algorithms and computing power. Another challenge is the need for data, as AI models need to be trained on large datasets to be effective. Additionally, there may be resistance from traditional researchers who are hesitant to adopt new technologies.

4. Can AI replace human physicists?

No, AI is not meant to replace human physicists, but rather to augment their capabilities. While AI can assist with data analysis and simulations, it cannot replace the creativity and critical thinking skills of human scientists. AI and human physicists can work together to advance research in physics.

5. What are some potential future applications of AI in physics?

Some potential applications of AI in physics include developing new materials with desired properties, optimizing experimental designs, and aiding in the search for new particles or phenomena. AI can also play a role in the development of quantum computing and other cutting-edge technologies.

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