Stop by our booth today at 4pm to have a video chat with a robot arm! Ask it to move around objects, or just say hello. Powered by Code as Policies, the system uses the latest language models to write robot code. Read more → http://
goo.gle/3U5CmCg
AGENTS
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Robot Arm Controlled by AI Language Models at Booth Demo
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Expanding Memory Types in LangChain With Community Contributions
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There are many more types of memory we hope to add! @sjwhitmore has been pioneering this effort so far, and we hope a lot of these come from the community. Check out this idea from @BrennanErbz for inspiration
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Add Memory to Any LLM Chain and Agent
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But you don't just have to use the predefined conversation chain! Here's how you can add memory to any LLM chain: https://
langchain.readthedocs.io/en/latest/exam
ples/memory/adding_memory.html
… And here's how you can add memory to an agent: https://
langchain.readthedocs.io/en/latest/exam
ples/memory/agent_with_memory.html
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Types of Memory in LangChain: Short-term and Long-term
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There are different types of memory, eg: Short term memory, like what the previous user input was Long term memory, like what the user previous said about a particular person or place Both of these can fit into this memory abstraction. Let's take a closer look at some
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LangChain Chains Now Support Stateful Conversations and Memory
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So far all the chains/agents in LangChain have been stateless, meaning each interaction is independent This is far from ideal for some real world settings (eg chatbots) where you want the chain/agent to remember previous conversations
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Memory Module: Loading and Saving Data in Chains
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To achieve this, we added the the concept of a memory module This module: Loads things from memory before passing user input to the chain Saves things to memory after the chain is finished
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Pre-trained Language Models Essential for Agent Communication
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If AZ cannot rely on a pre-trained LM to communicate, it is impossible (there is no reason the model learns with self play to communicate with valid sentences). Other issue will of course be the infinite action space (which we also had in theorem proving) but this is manageable
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LangChain Open Source Demos and Projects Featured on GitHub
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We’re going to assembling a list of open source demos/projects that use LangChain and feature then on our GitHub. Which ones should we include?
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LLM Interactions Beyond Prompts: Integration with Tools and Systems
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Yeah it’s really not limited to prompts talking to eachother, but rather any interactions between an LLM and other things (whether that be another prompt, or a python REPL, or anything)
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AI Agents in Metaverse: CICERO Helping Humans Achieve Goals
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Imagine an AI agent teaching you a new skill in the metaverse. Or a non-player character helping you find your way to a win in a video game. How ever developers responsibly build on CICERO’s code, there’s potential for AI to help humans reach more of their goals. #CICERObyMetaAI
— AI at Meta (@AIatMeta) 25 novembre 2022Imagine an AI agent teaching you a new skill in the metaverse. Or a non-player character helping you find your way to a win in a video game. How ever developers responsibly build on CICERO’s code, there’s potential for AI to help humans reach more of their goals. #CICERObyMetaAI