Edge 388: Google DeepMind's SIMA can Follow Language Instructions in 3D Games Just Like Humans
Was this email forwarded to you? Sign up here Edge 388: Google DeepMind's SIMA can Follow Language Instructions in 3D Games Just Like HumansThe AI agent represents a major improvement relative to expensive reinforcement learning methods.
 Video games have long served as some of the best environments for training AI agents. Since their early days, AI labs like OpenAI and DeepMind have built agents that excel at mastering video games such as Atari, Dota 2, StarCraft, and many others. The principles of many of these agents have been applied in areas such as embodied AI, self-driving cars, and many other domains that require taking action in different environments. However, most of the AI breakthroughs in 3D game environments have been constrained to one or a small number of games. Building that type of AI is really hard, but imagine if we could build agents that can understand many gaming worlds at once and follow instructions like a human player? A few weeks ago, Google DeepMind unveiled their work on the Scalable, Instructable, Multiworld Agent (SIMA). The goal of the project was to develop instructable agents that can interact with any 3D environment just like a human by following simple language instructions. This might not seem like a big deal until we consider that the standard way to communicate instructions has been with super-expensive reinforcement learning models. Language is the most powerful and yet simple abstraction for communicating instructions about the world or, in this case, a 3D virtual world. The magic of SIMA is its ability to translate those abstract instructions into mouse and keyboard actions used to navigate an environment... Subscribe to TheSequence to read the rest.Become a paying subscriber of TheSequence to get access to this post and other subscriber-only content. A subscription gets you:
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Edge 387: Tool Learning in Autonomous Agents
Tuesday, April 16, 2024
Agents that master tools and APIs, UC Berkeley's Gorilla and Microsoft's TaskWeaver ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏
Neuro-Symbolic Models are Making a Comeback
Sunday, April 14, 2024
A new startup called Symbolica comes out of stealth with a very different value proposition. ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏
Edge 386: Inside Yi, 01's Model Leading the Chinese LLM Movement
Saturday, April 13, 2024
Yi has achieved remarkable performance across language and image tasks. ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏
Edge 385: The Two Big Schools for Building Autonomous Agents
Tuesday, April 9, 2024
Language or computer-vision based agents? ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏
Generative Audio Models Just Had a Great Week
Sunday, April 7, 2024
Three major generative audio released in the last seven days. ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏
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