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Never before in my life had I seen so many smart people working on the same goal: making LLMs better. After talking to many people working in both industry and academia, I noticed the 10 major research directions that emerged. The first two directions, hallucinations and context learning, are probably the most talked about today. I’m the most excited about numbers 3 (multimodality), 5 (new architecture), and 6 (GPU alternatives).
A deployable starter kit for building and customizing your own version of AI town - a virtual town where AI characters live, chat and socialize. - GitHub - a16z-infra/ai-town: A deployable starter kit for building and customizing your own version of AI town - a virtual town where AI characters live, chat and socialize.
Generative Agents: Interactive Simulacra of Human Behavior - GitHub - joonspk-research/generative_agents: Generative Agents: Interactive Simulacra of Human Behavior
Building agents with LLM (large language model) as its core controller is a cool concept. Several proof-of-concepts demos, such as AutoGPT, GPT-Engineer and BabAGI, serve as inspiring examples. The potentiality of LLM extends beyond generating well-written copies, stories, essays and programs; it can be framed as a powerful general problem solver. Agent System Overview In a LLM-powered autonomous agent system, LLM functions as the agent’s brain, complemented by several key components:
A group of researchers at Stanford University and Google have created a miniature RPG-style virtual world similar to The Sims, where 25 characters, controlled b