Ghoti Ichthus
Genesis 18:32, 2 Chronicles 7:14, Acts 5:29
We haven't heard much about CERN, specifically the supercollider, lately, but I was thinking about how it and AI fit together.
There's a milque-toast "nice" article on the CERN website:
Not only do the scientists have to programme their data acquisition systems to select the right events for further analysis while discarding the uninteresting data, they also have to examine trillions of stored collision events looking for signatures of rare physics phenomena. They have therefore turned to one sub-domain of AI, called machine learning (ML), to improve the efficiency and efficacy of these tasks. In fact, the LHC’s four major collaborations ‑ ALICE, ATLAS, CMS and LHCb ‑ have formed the Inter-experimental Machine Learning (IML) Working Group to follow developing trends in ML. Researchers are also collaborating with the wider data-science community to organise workshops to train the next generation of scientists in the use of these tools, and to produce original research in Deep Learning. ROOT, the software program developed by CERN and used by physicists around the world for analysing their data, also comes with machine-learning libraries.
Machine Learning is also used in the CERN accelerator complex to predict and avoid equipment failures, as well as to optimize the quality of the high-energy beams of protons that CERN delivers to its experiments. Furthermore, physicists are also investigating how similar techniques could make the work of those who run accelerators more efficient, more reliable, and possibly even autonomous.
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sparks.cern
There's a milque-toast "nice" article on the CERN website:
Dealing with a data deluge
Even before the Large Hadron Collider began colliding high-energy beams of protons in 2010, the particle-physics community began to collect unprecedented quantities of data. Particles collide within the LHC’s detectors up to 40 million times a second, each collision event generating about a megabyte of data: far too much to store without some filtering.Not only do the scientists have to programme their data acquisition systems to select the right events for further analysis while discarding the uninteresting data, they also have to examine trillions of stored collision events looking for signatures of rare physics phenomena. They have therefore turned to one sub-domain of AI, called machine learning (ML), to improve the efficiency and efficacy of these tasks. In fact, the LHC’s four major collaborations ‑ ALICE, ATLAS, CMS and LHCb ‑ have formed the Inter-experimental Machine Learning (IML) Working Group to follow developing trends in ML. Researchers are also collaborating with the wider data-science community to organise workshops to train the next generation of scientists in the use of these tools, and to produce original research in Deep Learning. ROOT, the software program developed by CERN and used by physicists around the world for analysing their data, also comes with machine-learning libraries.
Operating in extreme environments
Experimental facilities at CERN may be temporarily classified as high-radiation zones, preventing human intervention to perform repairs or to replace equipment. CERN has therefore developed autonomous robots to operate in these zones, which include the tunnel containing the LHC. The Engineering department at CERN, which builds and maintains these robots, uses AI techniques to help the robots navigate on their own and make decisions on what actions to take inside the radiation environments.Machine Learning is also used in the CERN accelerator complex to predict and avoid equipment failures, as well as to optimize the quality of the high-energy beams of protons that CERN delivers to its experiments. Furthermore, physicists are also investigating how similar techniques could make the work of those who run accelerators more efficient, more reliable, and possibly even autonomous.
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