But scientists, even in the same discipline, don’t always see their work in the same way. If you’re looking to exercise your creativity in one of the world’s most vibrant cities, Google London is calling. “Research effort is rising substantially while research productivity is declining sharply,” Hassabis mentions the billions invested into research by Big Pharma: driven by quarterly earnings reports, the industry has become more conservative as the costs of failure have risen. “It's a nice problem from a deep learning perspective, because at enormous expense, enormous complexity and enormous time [commitment], people have generated this amazing resource of proteins that we already understand,” Jumper says.While progress is being made, scientists urge against false exuberance. The sheer scale of Google's computational network would enable the company to move research forward much more quickly than if it had to scale organically, and the £400 million cheque enabled the startup to hire world-class talent.
So, I don't necessarily think that the success that DeepMind has had with protein folding will translate automatically to other areas.”DeepMind staffers pictured on the roof of Google's offices in King's Cross“We care about products in the sense that we want Google and Alphabet to be successful and to get benefit out of the research we're doing – and they do, there are dozens of products now with DeepMind code and technology in them all around Google and Alphabet – but the important thing is that it’s got to be a push, not a pull,” Hassabis says.
He mentions several times that, despite having made progress (“small stepping stones on the way”), there is still a long way to go in DeepMind’s bigger mission of solving intelligence and building AGI.
“And they're also somewhat inscrutable, these self–assembling machines.”Proteins arrange atoms at the angstrom scale, a unit of length equivalent to one ten-billionth of a metre; a deeper understanding would offer scientists a much more substantial grasp of structural biology. That’s not the nature of blue sky thinking… that's not how you do it if you're trying to land the rocket on the moon.”Add to that list being a CEO, which is now his day job. “And so, even though the ideas DeepMind had in their algorithm were out there and people were trying them independently, no one has brought it all together.”AlQuraishi draws a parallel with the academic community in machine learning, which has undergone something of an exodus in recent years to companies like Google Brain, DeepMind and Facebook, where organisational structures are more efficient, compensation packages are generous, and there are computational resources that don’t necessarily exist at universities.“Machine learning computer science communities have sort of really experienced that over the last four or five years,” he says. The aim for competing teams of scientists is to predict the structure of proteins from sequences of their amino acids for which the 3D shape is known but not yet made public. Since 2006 there has been an explosion in DNA data acquisition, storage, distribution and analysis. The esteemed American molecular biologist Cyrus Levinthal expressed the complexity of the challenge in a bracing manner, noting that it would take longer than the age of the universe to enumerate all the possible configurations of a typical protein before reaching the right 3D structure. “We were 12 hours in or something and I thought somehow I must have made a mistake and he's trapped me.”His opponent – a man Hassabis recalls being in his 30s or 40s – stood up. Shortly before the conference, they received the results: AlphaFold was, on average, more accurate than the other teams. Suddenly, Hassabis realised that his king had been trapped, meaning that check mate would be forced.
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