This video is the first lecture in a deep learning course offered in collaboration between UCL and DeepMind. The lecture introduces machine learning techniques relevant to artificial intelligence, focusing on DeepMind's approach and covering deep learning and reinforcement learning. The course structure, assessment (programming assignments using TensorFlow in Google Colab), and team are also detailed.
DeepMind defines intelligence as "an agent's ability to achieve goals in a wide range of environments," weighted by the complexity of those environments. This definition directly supports their pursuit of AGI because AGI requires an AI system to be successful across many different, and complex, environments, not just excel at a single, narrow task. The weighting by environment complexity implies that a truly intelligent system should perform well not only on simple tasks but also on more complex, challenging ones.
The key differences between the Atari game environment and the DeepMind Lab environment lie in their complexity and the type of interaction they afford:
Atari: 2D, relatively simple visual input (pixels), limited action space (joystick controls), and mostly immediate reward signals. The tasks are largely reactive or involve relatively short-term planning.
DeepMind Lab: 3D, first-person perspective, richer visual input and more complex environments (mazes), a slightly more complex action space (movement and potentially interaction with objects), and often requires more long-term planning and learning to achieve goals. Rewards can be more sparse and require more sophisticated reasoning to obtain. The agent must learn to interpret a 2D projection of a 3D world and understand its own position and actions within that world.