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 optimization: A black-box optimization strategy which uses a probabilistic model to balance explore and exploit in 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.