DeepMind has been trying to bridge the gap between AI and biology for quite some time now. All their endeavours revolve around solving the problem of intelligence in machines. The straightforward trivial tasks for humans can be very, very sophisticated and almost for devices. While human brains are hardcoded with millions of years of learning, the machines have many limitations when it comes to data. They can be fed with data that has been documented or prepared by humans, the magnitude of which is historically insignificant when compared to humans.

Demis Hassabis founded DeepMind with the goal of unlocking answers to some of the world's toughest questions by recreating intelligence itself. His ambition remains just that -- an ambition -- but Hassabis and colleagues inched closer to realizing it this week with the publication of papers in Nature addressing two formidable challenges in biomedicine. The first paper originated from DeepMind's neuroscience team, and it advances the notion that an AI research development might serve as a framework for understanding how the brain learns. The other paper focuses on DeepMind's work with respect to protein folding -- work which it detailed in December 2018. Both follow on the heels of DeepMind's work in applying AI to the prediction of acute kidney injury, or AKI, and to challenging game environments such as Go, shogi, chess, dozens of Atari games, and Activision Blizzard's StarCraft II.