About this Video

• Video Title: Deep Learning 1: Introduction to Machine Learning Based AI
• Channel: Google DeepMind
• Speakers: Torie Grable
• Duration: 01:43:08

Introduction

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.

Key Takeaways

1. Course Structure: The course features two parallel streams: deep learning (Tuesdays) and reinforcement learning (Thursdays), culminating in a convergence of concepts. Assessment is entirely based on programming assignments in Google Colab, utilizing TensorFlow.
2. DeepMind's AI Approach: DeepMind's AI research centers on understanding learning algorithms, emphasizing generalizability across various domains to achieve Artificial General Intelligence (AGI). Reinforcement learning is a core framework, focusing on agents interacting with environments to maximize long-term reward.
3. Deep Learning Techniques: The lecture provides an overview of core deep learning concepts, including convolutional neural networks (CNNs) for image processing, recurrent neural networks (RNNs) for sequence modeling, and generative models. The emphasis is on end-to-end training and modular architectures.
4. Reinforcement Learning in Games: The application of deep reinforcement learning to Atari games and DeepMind Lab is discussed. Atari games serve as a benchmark, demonstrating the ability to achieve human-level or superhuman performance in multiple games using a single, general algorithm. DeepMind Lab offers a more complex, 3D environment for further research.
5. AlphaGo: The AlphaGo project is highlighted as a case study, illustrating the application of deep learning and reinforcement learning to a complex game like Go, showcasing the potential of AI to achieve superhuman performance and discover novel strategies.

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.