That helped set a foundation for today's generative AI boom that ChatGPT-maker OpenAI and others built upon. Gomez, one of eight coauthors of Google's 2017 paper, was a 20-year-old intern at the time.
He's now the CEO and co-founder of Cohere, a Toronto-based startup competing with other leading AI companies in supplying large language models and the chatbots they power to big businesses and organizations.
Gomez spoke about the future of generative AI. The interview has been edited for length and clarity.
Q: WHAT’S A TRANSFORMER?
A: A transformer is an architecture of a neural network -- the structure to the computation that happens inside of the model. The reason that transformers are special relative to their peers -- other competing architectures, other ways of structuring neural networks -- is essentially that they scale very well. They can be trained across not just thousands, but tens of thousands of chips. They can be trained extremely quickly. They use many different operations that these GPUs (graphics chips) are tailored for. Compared to what existed before the transformer, they do that processing faster and more efficiently.
Q: HOW IMPORTANT ARE THEY TO WHAT YOU’RE DOING AT COHERE?
A: Massively important. We use the transformer architecture as does everyone else in building large language models. For Cohere, a huge focus is scalability and production readiness for enterprises. Some of the other models that we compete against are huge and super inefficient. You can’t actually put that into production, because as soon as you’re faced with real users, costs blow up and the economics break.
Q: WHAT’S A SPECIFIC EXAMPLE OF HOW A CUSTOMER IS USING A COHERE MODEL?
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