))
Re-imagining how we train LLMs using physics-based AI
Manage episode 436836034 series 2917730
Hewlett Packard Enterprise에서 제공하는 콘텐츠입니다. 에피소드, 그래픽, 팟캐스트 설명을 포함한 모든 팟캐스트 콘텐츠는 Hewlett Packard Enterprise 또는 해당 팟캐스트 플랫폼 파트너가 직접 업로드하고 제공합니다. 누군가가 귀하의 허락 없이 귀하의 저작물을 사용하고 있다고 생각되는 경우 여기에 설명된 절차를 따르실 수 있습니다 https://ko.player.fm/legal.
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.
Do you have a question for the expert? Ask it here using this Google form: https://forms.gle/8vzFNnPa94awARHMA
About this week's guest: Ray Beausoleil: https://www.linkedin.com/in/ray-beausoleil-22b148a/
Sources and statistics cited in this episode:
WEF paper on data centre energy usage: https://www.weforum.org/agenda/2024/07/generative-ai-energy-emissions/
IEA sats on energy usage in IT: https://www.iea.org/energy-system/buildings/data-centres-and-data-transmission-networks#overview
Novel insulins grand challenge: https://type1diabetesgrandchallenge.org.uk/funding/closed-funding/novel-insulins-innovation-incubator/
77 에피소드
공유
