Machine learning, a technique used in the artificial intelligence (AI) software behind self-driving cars and digital assistants, now enables scientists to address key challenges to harvesting on Earth the fusion energy that powers the sun and stars. The technique recently empowered physicist Dan Boyer of the U.S. Department of Energy's (DOE) Princeton Plasma Physics Laboratory (PPPL) to develop fast and accurate predictions for advancing control of experiments in the National Spherical Torus Experiment-Upgrade (NSTX-U)—the flagship fusion facility at PPPL that is currently under repair.

https://phys.org/news/2021-06-artificial-intelligence-fusion.html

I have been wondering for some time whether AI could be used to better control the instabilities of a fusion plasma.
It looks like maybe it can.

MLC may help to save the world.

This is super great to hear and yet one more of many reasons to contribute.
Thanks for sharing.

Love the enthusiasm here, but how do you see MLC help advance fusion tech? So far, all we have done is the generation of a first of its kind data set of neural networks that was trained with both clean and adversarial data and then show that in the resulting weight space we see clustering that allows to disentangle and tell these networks apart. Amazing results with profound implications, especially if it were to replicate as we dive into the CNN space and image classification with DS4, but so far that's all there is to it. While this will hopefully advance our understanding of ML in general, it will not help to fine tune or develop an AI/ML-model that will help with plasma localisation forecasting in a fusion reactor - At least not as of now, and at least not directly. And ML models/AI systems this important will most likely only be developed in-house and not on a public grid. At the forefront, I reckon that is DOE's Oak Ridge National Laboratory's Summit Supercomputer, Argonne's Aurora as well as NERSC's Cori and upcoming Perlmutter HPC systems. More likely that this will be classified as a national security concern.
Just my 2 cents.

Love the enthusiasm here, but how do you see MLC help advance fusion tech?

I think you are taking too literal an interpretation of their work. MLC will help by ensuring the reliability of the machines.
You would not want your fusion process to get out of control. You need to keep the output stable.

MLC will help by ensuring the reliability of the machines.

How? I think that this research so for that we contribute to with MLDS (Machine Learning Data Set Generation) will help many practitioners understand that models can be flawed/manipulated if training data is malicious and trained networks can be detected through weight space clustering analysis. But I don't see how this applies to sophisticated models with much more enormous data sets and way more complex parameters. So far, we don't train exact models with any practical use in the real world. The training data has no inherent meaning (toy data) and thus any derived implications are illuminating urgent questions in basic research but do not inform any modelling and/or model tuning decisions. Nor do they provide domain knowledge that is at the heart of any complex model. And while helping to separate "good" networks from "bad" ones according to the data set having been used for training, data integrity checks themselves have to be informed largely through experts/domain knowledge.

In my understanding, future applications/experiments here on MLC might do exactly what you suggest, but I don't see how current DS1-3 runs and its results would help fusion tech run any more safely.

I have no reason to doubt your conclusions, but I was referring to the project, not any particular data set.

But that is exactly what we supported on MLC so far. The MLDS application distributing WUs across DS 1-3 had the goal in mind to generate this one of a kind data set. It’s all laid out on the main page. I’ll leave it at that.

Yet, I have to say that I am an avid supporter of this project and will continue to do so as I believe that the implications so far have far reaching impact and I anticipate many more interesting experiments here on MLC.

Speak for yourself. I have not been supporting it just for the present data sets.
You just need to listen to John Clemens presentation at the BOINC workshop to get an idea of what he has in mind.

Main page

Quote:

MLC@Home's initial project, the Machine Learning Dataset Generator (MLDS), will generate a large dataset of simple networks trained with both clean and adversarial data. To our knowledge, this is the first dataset of its kind.

All that you mention is yet to come. (as pointed out in the last main update and the video you mention)

I am not quite sure of your point, except to state the obvious.
Control of fusion energy is yet to come also.

Any general insights that are gained from this project could be utilized to build better AI or even just to analyze it better. We haven't even heard how different researchers will analyze and compare MLDS. I believe any knowledge gained from this project could benefit all AI research.

The IAEA is considering AI for a wide range of applications related to nuclear technology, including fusion.

https://www.iaea.org/newscenter/news/pioneering-iaea-meeting-to-focus-on-ai-based-approaches-in-nuclear-technologies

Can AI Make a Better Fusion Reactor?

Researchers at the University of Washington, including Kyle Morgan and Chris Hansen, recently published a study detailing a method that uses machine learning to predict the behavior of a plasma. Their model, which uses a statistical technique called regression, essentially throws out scenarios that lead to nonsensical results, enabling it to use less data, less computational power, and less time. Hansen says that although the model in the study doesn't work quickly enough to use during an experiment, he thinks that it eventually could. The researchers published another recent study that used a single GPU to control a fusion experiment that had previously required several computers. This kind of powerful system, Hansen says, could eventually be used to run the model quickly enough that it would be useful during an experiment.

https://spectrum.ieee.org/can-ai-make-a-better-fusion-reactor

Latest success from Google’s AI group: Controlling a fusion reactor

As the world waits for construction of the largest fusion reactor yet, called ITER, smaller reactors with similar designs are still running. These reactors, called tokamaks, help us test both hardware and software. The hardware testing helps us refine things like the materials used for container walls or the shape and location of control magnets.

But arguably, the software is the most important. To enable fusion, the control software of a tokamak has to monitor the state of the plasma it contains and respond to any changes by making real-time adjustments to the system's magnets. Failure to do so can result in anything from a drop in energy (which leads to the failure of any fusion) to seeing the plasma spill out of containment (and scorch the walls of the container).

Getting that control software right requires a detailed understanding of both the control magnets and the plasma the magnets manipulate, or, it would be more accurate to say, getting that control software right has required. Because today, Google's DeepMind AI team is announcing that its software has been successfully trained to control a tokamak.

https://arstechnica.com/science/2022/02/latest-success-from-googles-ai-group-controlling-a-fusion-reactor/