What can AI do and not do in physics currently?

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Discussion Overview

The discussion revolves around the capabilities and limitations of artificial intelligence (AI) in the field of physics. Participants explore various applications of AI, including its role in optimization, data analysis, and potential future developments in computing technology.

Discussion Character

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

Main Points Raised

  • One participant questions whether AI can navigate Hilbert space and design atomic bombs, indicating a lack of clarity on AI's capabilities in complex physics problems.
  • Another participant describes AI as an optimization tool, useful for mundane tasks in physics.
  • It is suggested that AI functions similarly to a hammer in physics, emphasizing that while it can assist, it does not provide definitive conclusions.
  • A participant discusses the variety of AI systems, highlighting generative AI and large language models, which can produce convincing but potentially inaccurate answers, necessitating further verification by knowledgeable users.
  • Equation solvers like Eureqa are mentioned as tools that can generate equations from experimental data, though challenges arise when theoretical backing is lacking for the results produced.
  • AI is compared to measurement tools in physics, suggesting it can help uncover hidden patterns in data and assist in theory development.
  • There is mention of advancements in quantum computing and analog computing, speculating on a future where hybrid computing systems may enhance AI capabilities in STEM research.

Areas of Agreement / Disagreement

Participants express a range of views on the capabilities of AI in physics, with no consensus on its limitations or potential. Some agree on its usefulness as a tool, while others highlight the uncertainty and variability in AI-generated outputs.

Contextual Notes

The discussion reflects varying levels of understanding of AI technologies and their applications in physics, with some participants acknowledging the need for further research and validation of AI-generated results.

non_physicist
What can AI do, and not do, in physics currently? Can it navigate hilbert space (I don't know what this is, just coming from an HPS undergrad background)? Can it design atomic bombs? Has it solved any problems?

I suppose much research uses AI. In what forms?
 
Physics news on Phys.org
Hi, @non_physicist, I've quickly made a search in the Internet, but no web is familiar to me, so I can't quote anything helpful.
Welcome!
 
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It's an optimisation tool. For me, very useful for completing mundane tasks.
 
AI is a tool. Your question is a lot like asking if a hammer is useful to Physics.
Physics is well-known for requiring a 5-sigma elimination of the null hypothesis before treating a finding as conclusive. AI is rarely that definitive.
 
this is such a fuzzy question? Yes, AI can help do physics but not necessarily in the ways you may think.

First there are many different kinds of AI systems. The ones getting traction in the news right now are generative AI that feeds on human content like art, photos, images and text. When asked can produce "novel" new media content from them (midjourney as an example).

There are the ChatGPT large language model AI that consume human written text and do predictive text generation that remarkably answers questions in very convincing answers that may be very right, somewhat right or wrong or beyond wrong. Only a knowledgeable reader can judge and evaluate the answer. Like all text it generates, one may have to do additional research to verify its truth.

There are equation solvers like Eureqa that take raw experimental data and generate best fit equations that describe the data. In one case, it took the measurement data from a compound pendulum system and reproduced the equations of motion. However when applied to a biological system, it produced some really good equations but the biologists couldn't publish the results because while the equations describes what they had observed thay had no theory to back them up.

Pretty much all the measurement tools of physics are akin to microscopes allowing to peer into the very small or very far away places and take very accurate measurements and in a sense AI continues that trend allowing to find a hidden pattern in the data we have taken and maybe assist in finding a theory to explain it.

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On the hardware side, we are looking into quantum computers that use quantum entanglement to do meaningful calculations far faster than their digital counterparts. Some folks are even looking back at analog computers that did similar though less accurate calculations at speeds comparable to quantum computers (ie they don't have to redo the calculation hundreds of times and take the best answer as the answer). Digital computing is evolving more toward the math needed in machine learning ala Apple CPU chips and Nvidia GPUs.

Who knows what the future will bring a hybrid digital interface with digital, QC and Analog computing elements selectively running as needed.

The combination of all these components AI, hybrid computers and more will usher in a whole new era of STEM research that is hard to comprehend right now.
 
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