In order to achieve this, we work on three main technologies:
Deep learning. The construction of neural networks to build end-to-end learning machines capable of taking raw, unprocessed data, building representations, and using these representations to infer potential target values.
Bayesian optimisation. A black-box optimisation strategy that uses a probabilistic model to balance, explore and exploit complex search problems. One example of this is constructing the architectures of complex deep-learning machines.
Reinforcement learning. Coupling deep learning with traditional reinforcement-learning techniques (known as Deep-Q, or double Deep-Q learning) to teach agents how to respond to situations or environments in an optimal way.