Gabe Wilson MD (@Gabe__MD)
어떤 주장이 AI가 모든 과정을 가속할 수 없다고 한 데 대해 반박하며, AI가 칩 설계·기획, 생산, 심지어 학습 모델 훈련 속도까지 가속화하고 있어 전체 프로세스 개선이 일어나고 있다고 주장하는 의견성 트윗입니다. AI의 하드웨어·학습 파이프라인 영향력을 강조합니다.
@DeryaTR_ He says not every part of this can be hastened by AI. But that’s not true. AI is speeding chip planning and design, production; even learning model training speed should experience recursive self improvement. He’s actually undercutting how every part of the process is
Last day to register for hashtag#CAMMLS2025 training course. For all the details and registration link, click here: https://www.psdi.ac.uk/event/cammls-2025/
This machine learning for materials training course is being run at Daresbury Laboratory by the Physical Sciences Data Infrastructure (PSDI) initiative in collaboration with AIChemy, STFC-SCD, PSDS, CCP5 and CCP9. This training is targeted towards PhD students, in particular those in the Materials and Molecular Simulations field. The aim of this training is to introduce attendees to the latest methods of machine learning applied to atomistic simulation of materials.
The aim of this training is to introduce attendees to the latest methods of machine learning for the atomistic simulation of materials. There will also be the opportunity for attendees to present a poster on their work. Get all the details here ▶️ https://www.psdi.ac.uk/event/cammls-2025/
This machine learning for materials training course is being run at Daresbury Laboratory by the Physical Sciences Data Infrastructure (PSDI) initiative in collaboration with AIChemy, STFC-SCD, PSDS, CCP5 and CCP9. This training is targeted towards PhD students, in particular those in the Materials and Molecular Simulations field. The aim of this training is to introduce attendees to the latest methods of machine learning applied to atomistic simulation of materials.
Registrations are now open for the Chemical and materials machine learning school 2025!
🎟️ Register and details here: https://www.psdi.ac.uk/event/cammls-2025/
🚨 Last date to register: 1st December 2024
This machine learning for materials training course is being run at Daresbury Laboratory by the Physical Sciences Data Infrastructure (PSDI) initiative in collaboration with AIChemy, STFC-SCD, PSDS, CCP5 and CCP9. This training is targeted towards PhD students, in particular those in the Materials and Molecular Simulations field. The aim of this training is to introduce attendees to the latest methods of machine learning applied to atomistic simulation of materials.
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AI training is a uniquely data-hungry application, and it requires a special data pipeline to keep expensive GPUs fed. This episode of Utilizing Tech focuses on the data platform for machine learning, featuring Molly Pressley of Hammerspace along with Frederic Van Haren and Stephen Foskett. Nothing is worse than idle hardware, especially when it comes to expensive GPUs intended for ML training. Performance is important, but parallel access and access to multiple systems is just as important. Building an AI training environment requires identifying and eliminating bottlenecks at every layer, but many systems are simply not capable of scaling to the extent required by the largest GPU clusters. But a data pipeline goes way beyond storage: Training requires checkpoints, metadata, and access to different data points. And different models have unique requirements as well. Ultimately, AI applications require a flexible data pipeline not just high-performance storage.
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