Claims

Creating simulation environments where AIs tackle very basic problems and develop their own strategies could generate empirical data about what approaches are successful, providing a laboratory to understand which strategies work, how evolution of strategies occurs, and testing what network architectures can solve simple problems.

Games were never the end goal for DeepMind; they were a means to develop general learning algorithms that could be applied to real-world problems in science, medicine, and mathematics—which was the true mission from the beginning.

Hassabis's conjecture is that any pattern that can be generated or found in nature can be efficiently discovered and modeled by a classical learning algorithm, provided there is sufficient data and to a certain level of resolution.

Over his intellectual career as an AI researcher, Shanahan has progressively retreated from the desire to build intelligible systems toward accepting that mindless scaling of data, computation, and search is what actually works, a trajectory described as the 'bitter lesson' in AI—giving up on understanding for the sake of effectiveness.