If you’ve ever used a neural network to solve a complex problem, you know they can be enormous in size, containing millions of parameters. For instance, the famous BERT model has about ~110 million.
What if the most powerful artificial intelligence models could teach their smaller, more efficient counterparts everything they know—without sacrificing performance? This isn’t science fiction; it’s ...
Forbes contributors publish independent expert analyses and insights. Dr. Lance B. Eliot is a world-renowned AI scientist and consultant. In today’s column, I examine the rising tendency of employing ...
Even networks long considered "untrainable" can learn effectively with a bit of a helping hand. Researchers at MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) have shown that a ...
The original version of this story appeared in Quanta Magazine. The Chinese AI company DeepSeek released a chatbot earlier this year called R1, which drew a huge amount of attention. Most of it ...
Forbes contributors publish independent expert analyses and insights. There’s a new wrinkle in the saga of Chinese company DeepSeek’s recent announcement of a super-capable R1 model that combines high ...
The Chinese AI company DeepSeek released a chatbot earlier this year called R1, which drew a huge amount of attention. Most of it focused on the fact that a relatively small and unknown company said ...
Knowledge distillation is an increasingly influential technique in deep learning that involves transferring the knowledge embedded in a large, complex “teacher” network to a smaller, more efficient ...
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