In our research team, we look to achieve this by answering three fundamental questions:
How does AI adapt the way it views the world through data?
How does AI make decisions, and balance risk and reward when searching for new solutions?
How does AI take what it knows, and uses it to construct new — as yet unseen — suggestions?
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.