Hassabis is both a scientist — he won the Nobel Prize last year for his work in AI — and a competitive entrepreneur who relishes the race.
The surprising runaway success of ChatGPT in 2022 reoriented the industry around generative AI. Google’s efforts, which resembled university research labs and were spread out in different divisions, were brought under one roof with Hassabis at the helm.
The goal is to create AI that can reason as well as a human, or what some people call “Artificial General Intelligence,” or AGI. But unlike some of his competitors, Hassabis believes the finish line is years down the road, giving the company time to make long-term decisions that will pay dividends later.
For instance, when Google set out to compete with ChatGPT, Hassabis’ team chose to take the more difficult approach of building a “natively multimodal” AI model called Gemini, which was not just trained on text.
“We always felt [multimodal] was a key part of the model understanding the world,” he said. “Because ultimately we want a world model, not just a language model.”
DeepMind has also prioritized research into how to make AI models better “remember,” so that they can take on longer and more complex tasks. The company has increased the so-called “context window” to 1 million tokens, or fragments of a word.
“That’s the sort of equivalent to working memory for us, just a ginormous one,” he said. “But I think we probably also need a type of episodic memory.”
DeepMind’s prioritizing of context windows and research into memory could pay off down the road, as available compute increases and costs go down.
For now, the expenses of the most advanced AI techniques can be staggering. For instance, OpenAI’s most advanced model, called o3, recently achieved an impressive score on a benchmark consisting of a series of puzzles meant to be relatively easy for humans and impossible for AI. The catch? Answering 400 puzzle questions cost over $1 million in the most advanced version of o3.