In this video, Pradeep Dubey from Intel describes how the company is driving the convergence of HPC & Ai.
The emerging AI community on HPC infrastructure is critical to achieving the vision of AI,” said Pradeep Dubey, Intel Fellow. “Machines that don’t just crunch numbers, but help us make better and more informed complex decisions. Scalability is the key to AI-HPC so scientists can address the big compute, big data challenges facing them and to make sense from the wealth of measured and modeled or simulated data that is now available to them.”
People speak of the Artificial Intelligence (AI) revolution where computers are being used to create data-derived models that have better than human predictive accuracy.1, 2 The end result has been an explosive adoption of the technology that has also fueled a number of extraordinary scientific, algorithmic, software, and hardware developments.
Using AI to make better and more informed decisions as well as autonomous decisions means bigger and more complex data sets are being used for training. Self-driving cars are a popular example of autonomous AI, but AI is also being used to make decisions to highlight scientifically relevant data such as cancer cells in biomedical images, find key events in High Energy Physics (HEP) data, search for rare biomedical events, and much more.
