After two weeks off, here's a new video about a very exciting work by @DeepWisdom2019 ! MetaGPT: Redefining Multi-Agent Collaboration for Complex Tasks Learn more: https://
youtu.be/YtxMderNrzU #MetaGPT #GPT #ChatGPT #Agents
AGENTS
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MetaGPT Redefines Multi-Agent Collaboration for Complex Tasks
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Language-to-Rewards System for Robotic Skill Synthesis
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10/ Language to Rewards for Robotic Skill Synthesis – proposes a new language-to-reward system that utilizes LLMs to define optimizable reward parameters to achieve a variety of robotic tasks.https://t.co/8ztl4MMyjS
— DAIR.AI (@dair_ai) 27 août 202310/ Language to Rewards for Robotic Skill Synthesis – proposes a new language-to-reward system that utilizes LLMs to define optimizable reward parameters to achieve a variety of robotic tasks.
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Survey of LLM-Based Autonomous Agents and Applications
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7/ A Survey on LLM-based Autonomous Agents – presents a survey of LLM-based autonomous agents; delivers a systematic review of the field and a summary of various applications of LLM-based AI agents in domains like social science and engineering.
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RAG Systems Evaluation: Noah Chatbot Lessons Learned
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We had a REALLY fun webinar on evaluating RAG systems yesterday For me, the highlight was Pedro from Tavrn talking about lessons learned when building Noah (their context-aware chatbot) Now on YouTube for some Friday afternoon viewing! https://
youtube.com/watch?v=fWC4Vx
olWAk
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Autonomous Negotiations AI Platform: Viability Assessment
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Autonomous negotiations company… curious if anyone has tried this or has opinions on viability today… https://
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AI agents learning through reflection on novel experiences
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What if AI agents can reflect on novel experiences? Could it improve AI’s performance? This research bridges AI and neuroscience by drawing inspiration from animal behavior:
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HALLM Agents Learn from Mistakes Through Feedback Loops
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#HALLM agent can learn from mistakes (there's a feedback loop) https://t.co/bBzuVJSgyW
— Marek Rosa | European🇪🇺 | South African🇿🇦 (@marek_rosa) 25 août 2023#HALLM agent can learn from mistakes (there's a feedback loop)
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Agent Release: Python Terminal Observation and Action Capabilities
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We just released code for a similar agent that can observe and act through a Python terminal:
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LLM Agents Learning Continuously: Advancing Autonomous AI Systems
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Our work on LLM agents than learn continually
