While impressive, the technology wasn't yet capable of replacing the existing expensive and time-consuming experimental methods for determining what these proteins look like. However, its latest software comes close. In November, AlphaFold again outperformed all the other competing groups at CASP. The technology solved protein structures other labs had been working on for years. Scientists think the technology could have immense implications for the way proteins are studied.

Alphabet-owned DeepMind may be best known for building the AI that beat a world-class Go player, but the company announced another, perhaps more vital breakthrough this morning. As part of its work for the 14th Critical Assessment of Protein Structure Prediction, or CASP, DeepMind's AlphaFold 2 AI has shown it can guess how certain proteins will fold themselves with surprising accuracy. In some cases, the results were perceived to be "competitive" with actual, experimental data. "We have been stuck on this one problem – how do proteins fold up – for nearly 50 years," said Professor John Moult, CASP chair and co-founder, in a DeepMind blog post. "To see DeepMind produce a solution for this, having worked personally on this problem for so long and after so many stops and starts, wondering if we'd ever get there, is a very special moment."

The firm's latest Go-playing system not only defeated all previous versions of the software, it did it all by itself. "The most striking thing for me is we don't need any human data anymore," says Demis Hassabis, the CEO and co-founder of DeepMind. While the first version of AlphaGo needed to be trained on data from more than 100,000 human games, AlphaGo Zero can learn to play from a blank slate. Not only has DeepMind removed the need for the initial human data input, Zero is also able to learn faster than its predecessor. David Silver, the main programmer on DeepMind's Go project, says the original AlphaGo that defeated 18-time world champion Lee Sedol 4-1 required several months of training.