DeepMind has always bet on generality and learning as the core of its techniques, focusing on reinforcement learning, search and deep learning.

True: DeepMind's founding documents, early papers and public statements since about 2010 consistently show general-purpose learning methods (reinforcement learning, deep learning, search) at the core of its approach, as opposed to hand-crafted or domain-specific systems. Mostly true: this was the dominant emphasis, but with some notable exceptions. Mixed: the record shows a mix in which generality and learning were often, but not consistently, central. Mostly false: generality and learning were only occasionally central. False: the record shows DeepMind did not prioritise generality and learning.

Set before the research began.

1. E1 Open-sourcing DeepMind Lab DeepMind (deepmind.google blog) · 3 December 2016 Own record

   “DeepMind's scientific mission is to push the boundaries of AI, developing systems that can learn to solve any complex problem without needing to be taught how. To achieve this, we work from the premise that AI needs to be general.”

2. E2 Human-level control through deep reinforcement learning - Nature Nature (Springer Nature) · 25 February 2015 Own record

   “their applicability has previously been limited to domains in which useful features can be handcrafted, or to domains with fully observed, low-dimensional state spaces.”

3. E3 Mastering the game of Go with deep neural networks and tree search - Nature Nature (Springer Nature) · 27 January 2016 Own record

   “These deep neural networks are trained by a novel combination of supervised learning from human expert games, and reinforcement learning from games of self-play.”

4. E4 From unlikely start-up to major scientific organisation: Entering our tenth year at DeepMind DeepMind (deepmind.google blog) · 5 December 2019 Own record

   “Many are substantial and exciting contributions to their domains, such as MuZero, which can not only master chess, Go, Shogi but also extend to Atari, without knowing the rules first.”

DeepMind's own published work points to general learning methods: the 2015 DQN paper sets deep reinforcement learning against methods limited to domains with handcrafted features (E2), and the 2016 AlphaGo paper combines deep neural networks, reinforcement learning and tree search (E3). A 2016 blog post states the premise that 'AI needs to be general' (E1), and the 2019 retrospective highlights MuZero mastering several games without knowing the rules (E4). AlphaGo also relied on supervised learning from human expert games (E3), and the evidence includes no founding-era (circa 2010) documents and no independent sources. So the record supports learning and generality as the dominant emphasis, but it does not show the claimed consistency since about 2010.