William Benton

Many novel machine learning techniques started as clever hacks that just happened to work, but the demands of building real systems can be at odds with this creative culture.

We think of generating source code from a prompt as an AI-powered feature of modern IDEs, but the general problem has a rich history in research efforts and domain-specific programming systems.

This talk will cover several use cases in which generating synthetic data is useful (or even essential) and introduce a toolbox of practical techniques for synthesizing data in these situations.

The promise of accelerated computing presents an interesting paradox: while no one complains when new compute infrastructure is dramatically faster than its predecessor, few people realize how much they’d benefit from acceleration until they have it.

Buoyed by expensive industrial research efforts, amazing engineering breakthroughs, and an ever-increasing volume of training data, machine learning techniques have recently seen successes on problems that seemed largely intractable twenty years ago.