Now, Alphabet’s DeepMind is taking this automation further by developing deep learning algorithms that can handle programming tasks which have been, to date, the sole domain of the world’s top computer scientists (and take them years to write).
In a paper recently published on the pre-print server arXiv, the DeepMind team described a new deep reinforcement learning algorithm that was able to discover its own value function—a critical programming rule in deep reinforcement learning—from scratch.
Surprisingly, the algorithm was also effective beyond the simple environments it trained in, going on to play Atari games—a different, more complicated task, achieving superhuman levels of play in 14 games.
DeepMind says the approach could accelerate the development of reinforcement learning algorithms and even lead to a shift in focus, where instead of spending years writing the algorithms themselves, researchers work to perfect the environments in which they train.
Move by move, game by game, an algorithm combines experience and value function to learn which actions bring greater rewards and improves its play, until eventually, engineers may shift from manually developing the algorithms themselves to building the environments where they learn.
- Your Credit Score Should Be Based on Your Web History, IMF Says
- AI research survey finds machine learning needs a culture change
- It takes a lot of energy for machines to learn – here’s why AI is so power-hungry
- How Artificial Intelligence is Enhancing Mobile App Technology
- Lessons in Failing to Apply Blockchain and AI to Combat COVID
- No-Code Computer Vision with Apple’s CreateML
- Apple’s M1 is up to 3.6x as fast at training machine learning models
- DeepMind’s AI agent MuZero could turbocharge YouTube
- What AlphaGo Can Teach Us About How People Learn
- How Smart Data Hubs power digital transformations