The Importance of Compute Power in Scaling Machine Learning

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This chapter explores the significance of compute power in scaling machine learning and AI models, emphasizing how access to compute allows for model improvement and training on more data. Additionally, it discusses the potential of GPU compute and its implications for AI, highlighting the need for scalable compute and training in the field.

Ben Fielding and Harry Grieve are the Co-Founders of Gensyn - The Gensyn network is the Machine Learning Compute Protocol that unites all of the world’s compute into a global supercluster, accessible by anyone at any time Ben Fielding Twitter: ⁠https://twitter.com/fenbielding Harry Grieve Twitter: https://twitter.com/_grieve Gensyn's Website: ⁠https://twitter.com/gensynai Logan Jastremski's Twitter: @LoganJastremski Frictionless's Twitter: @_Frictionless_ Frictionless's Website: ⁠⁠https://frictionless.fund/⁠⁠ ___ Timecodes: 0:00 - Intro 1:10 - Founders Background 10:20 - Gensyn's Vision 15:20 - AI is the next industrial revolution 21:20 - Machine Learning and Training Compute 32:00 - Real World Limitations of Decentralized Compute 39:30 - Where does Compute come from? 50:50 - Decentralized Cost vs. Traditional Compute Clusters 55:15 - Owning your own AI 1:01:00 - Verification problem