Machine-learning based Generative AI is inherently inefficient. Training models by sifting findings again and again until a suitable output is generated is a time-consuming – end energy-consuming – process. So, could there be a better way to look at training our AI systems?
Well, one possible option is physics-based AI, where training is viewed as an energy grid, and the best possible route though that grid mapped to find outputs. It’s a novel way of thinking, but it could change our whole approach to AI.Joining us again today to find out more is Ray Beausoleil, a physicist, senior fellow and senior vice president at HPE. He leads the large scale integrated photonics lab at Hewlett Packard Labs.
This is Technology Now, a weekly show from Hewlett Packard Enterprise. Every week we look at a story that's been making headlines, take a look at the technology behind it, and explain why it matters to organizations and what we can learn from it.
Podden och tillhörande omslagsbild på den här sidan tillhör Hewlett Packard Enterprise. Innehållet i podden är skapat av Hewlett Packard Enterprise och inte av, eller tillsammans med, Poddtoppen.