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Knowledge distillation is the concept of extracting knowledge from one model and transferring it to another. This has traditionally been done via labels, but, as shown in our work and others [ 1, 2], can also be done via generated data. We believe knowledge distillation will be increasingly important for the future of AI. Here's why: 1. Foundation models will continue to get more powerful and semantically meaningful. There is an arms race underway (OpenAI vs. Stability AI vs. Anthropic vs.
Sitting to my left is Ray Stata, the founder of Analog Devices and donor of MIT’s CSAIL building. Sitting on my right is Jack Little, the founder of a company that builds Matlab, powerful software that I use almost every day. As we finish our lunch, I get ready to present my startup to them and the rest of the MIT Dean’s Advisory Council. As the dean of engineering introduces me, I reflect on just how far my startup has come in the past nine months.
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