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
AGENTS
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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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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
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Meta AI Unveils CICERO: An Agent Capable of Negotiating
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Meta AI Presents CICERO, an AI Agent Capable of Negotiating and Cooperating with Humans https://actuia.com/actualite/meta-ai-presente-cicero-un-agent-dia-capable-de-negocier-et-cooperer-avec-les-humains/
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CICERO by Meta AI: Strategic Dialogue Generation System
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#CICERObyMetaAI generates strategic, free-form dialogue by: Predicting the moves other players are likely to make Creating a plan based on those predictions Generating messages Filtering the final messages for clarity and value Read more on our blog
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CICERO’s Strategic Reasoning and Cooperative Planning Capabilities
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Meta AI’s @polynoamial and @anton_bakhtin talk about strategic reasoning and how it enables #CICERObyMetaAI to predict moves from billions of possibilities. Want to know how CICERO uses planning to find opportunities for mutually beneficial cooperation? Read more on our blog
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Games as AI Proving Ground: Strategic Reasoning and NLP Advancements
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Ever wonder how we got to where we are today with AI? Games have always been a proving ground for AI due to their complexity — our work on CICERO included. Only through advancements in strategic reasoning and NLP was CICERO made possible. Learn more about #CICERObyMetaAI