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 ...
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