Meta AI’s @alex_h_miller speaks to CICERO's ability to perform far beyond today's 'scripted' AI agents. #CICERObyMetaAI uses purposeful, intentional language to understand and interact w/ Diplomacy players to achieve shared goals.
AGENTS
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AGI Nodes Agents Tokens Generate Unprecedented Value
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$AGI AGI Nodes X AGI Agents X AGI Token “The combination of the #AGINode, the #AGIAgent and the #AGIToken could generate unprecedented value.” — Vincent Boucher, President of http://
MONTREAL.AI http://
MONTREAL.AI: Winning the #AGI Race #AGIFirst #MontrealAI -
Montreal Launches Largest AI Business Initiative Targeting Unicorn Status
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MONTREAL ♡ AI IT'S TIME TO BUILD THE LARGEST AI BUSINESS IN MONTREAL: http://
MONTREAL.AI http://
MONTREAL.AI aims for unicorn status: http://
montreal.ai/aikingdom.jpg NEW Discord! http://
discord.gg/montrealai AIAgents.Eth | AINodes #AIAgents #MontrealAI #WeAreBuilding -
Virtual Agents and Conversational Interfaces Report Coverage
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what does « virtual agent or conversational interface » cover in the report?
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New Chat Bots Could Change the World. Can You Trust Them?
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The New Chat Bots Could Change the World. Can You Trust Them?
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CICERO Learns Optimal Moves Using Reinforcement Learning
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Meta AI’s @anton_bakhtin discusses how he and his team used Reinforcement Learning to teach CICERO the best moves for the millions of possible actions that could be made on any given board state. pic.twitter.com/W8SJKDz6Vf
— AI at Meta (@AIatMeta) 10 décembre 2022Meta AI’s @anton_bakhtin discusses how he and his team used Reinforcement Learning to teach CICERO the best moves for the millions of possible actions that could be made on any given board state.
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Meta AI Reveals CICERO Components: Game Planning, Dialogue, Filtering
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Meta AI’s @ml_perception reveals what was used to build CICERO: Theoretical game planning component Dialogue model Component filtering out any dialogue mistakes before messages are sent to players
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Simple conversation buffer memory with dynamic N adjustment
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it is pretty simple at the moment – just keeps a buffer of the past N conversations in memory. a pretty simple improvement would be dynamically adjusting N based on the length of the conversations
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Dynamic conversation context length determination in LangChain
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excited to hear your thoughts/ideas! right now its pretty simple: just take the last N lines of conversation. but a very natural extension to determine N dynamically based on their length
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Building Powerful Long-Term Memory for AI Agents
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Hard to say without knowing more, but I’d guess you’d want to think deeply about how you might build a really powerful long-term memory.