Re-imagining how we train LLMs using physics-based AI

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.
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About this week's guest: Ray Beausoleil: / ray-beausoleil-22b148a ( / %20 )
Sources and statistics cited in this episode:
WEF paper on data centre energy usage: www.weforum.or... (www.weforum.or...)
IEA sats on energy usage in IT: www.iea.org/en...
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