🔴 This Agentic AI Workflow Will Take Over 🤯 Algorithm + Papers Explained
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3 ماه پیش
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#ai
#ai #llm #aiagents #agentic
What Language Model To Choose For Your Project? 🤔 LLM Evaluation:
What Language Model To Choose For You... : evaluation of Hugging Face models
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Explanation of the papers and algorithms of LLM agents in the Agentic AI systems (see timestamps below) using the following concepts and papers:
Iterative feedback and refinement for LLM agents + evaluation:
Madaan et al. 2023. SELF-REFINE: Iterative Refinement with Self-Feedback.
The Reflexion algorithm explained in the paper:
Shinn et al. 2023. Reflexion: Language Agents with Verbal Reinforcement Learning
Automatic API calls system based on LlaMA + evaluation from the paper:
Gorilla: Large Language Model Connected with Massive APIs
HuggingGPT agentic system using Hugging Face, from the paper:
Shen et al. 2023. HuggingGPT: Solving AI Tasks with ChatGPT and its Friends in Hugging Face
Chain of Thought + evaluation on GSM8K tests from the paper:
Wei et al. 2022. Chain-of-Thought Prompting Elicits Reasoning in Large Language Models
Agentic LLM + human customizable system for making applications and from the paper:
Wu et al. 2023. AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation
ChatDev agentic system for software development + its evaluation from the paper:
Qian et al. 2023. Communicative Agents for Software Development
Video key concepts:
00:00 Intro Agentic AI
00:40 Zero-shot use of LLMs
01:06 self-refine, iterative feedback and agentic refinement algorithm
02:16 Self-refine evaluation with ChatGPT and GPT4
02:29 Reflection method
02:33 Reflexion with verbal reinforcement learning and its evaluation
03:18 Explanation of the Reflexion algorithm and system design
04:26 Gorilla agentic system using tools and generating API calls
04:46 Gorilla evaluation with GPT-4 and Claude + evaluation on document retriever
05:09 Planning in agentic systems + Andrew NG takeaway message
05:23 Chain of Thought prompting for LLM step-by-step reasoning problems
05:36 Evaluation of chain of thought vs standard prompting of PaLM, Codex, GPT, LaMDA, UL2
05:Evaluation of chain of thought prompting on GSM8K tests + examples of chain of thought
06:03 HuggingGPT as LLM controller agentic system
06:23 HuggingGPT example of multi-modal tasks
06:34 HuggingGPT task planning, model selection, task execution, response generation
07:12 Multiagent collaboration: CrewAI and AutoGen
07:29 AutoGen agentic system for applications with customizable and conversable agents
08:05 AutoGen applications and use cases (math, ALF, multi-agent coding, group chat)
08:27 ChatDev agentic system for software development
Related concepts and key terms:
#ai #agentic #agentic ai #llm #chatdev #autogen #crewai #hugginggpt #huggingface #self-refine #gpt4 #reflexion #gorilla #api #planning #gsm8k #multimodal #multiagent #ALF
--Don’t forget to subscribe and watch these related videos:
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Transformer Language Models Simplifie...
This Is How Exactly Language Models Work in AI – NO background needed! This is how EXACTLY Language Models w...
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@analyticscamp
What Language Model To Choose For Your Project? 🤔 LLM Evaluation:
What Language Model To Choose For You... : evaluation of Hugging Face models
Please subscribe to support this channel :)
Explanation of the papers and algorithms of LLM agents in the Agentic AI systems (see timestamps below) using the following concepts and papers:
Iterative feedback and refinement for LLM agents + evaluation:
Madaan et al. 2023. SELF-REFINE: Iterative Refinement with Self-Feedback.
The Reflexion algorithm explained in the paper:
Shinn et al. 2023. Reflexion: Language Agents with Verbal Reinforcement Learning
Automatic API calls system based on LlaMA + evaluation from the paper:
Gorilla: Large Language Model Connected with Massive APIs
HuggingGPT agentic system using Hugging Face, from the paper:
Shen et al. 2023. HuggingGPT: Solving AI Tasks with ChatGPT and its Friends in Hugging Face
Chain of Thought + evaluation on GSM8K tests from the paper:
Wei et al. 2022. Chain-of-Thought Prompting Elicits Reasoning in Large Language Models
Agentic LLM + human customizable system for making applications and from the paper:
Wu et al. 2023. AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation
ChatDev agentic system for software development + its evaluation from the paper:
Qian et al. 2023. Communicative Agents for Software Development
Video key concepts:
00:00 Intro Agentic AI
00:40 Zero-shot use of LLMs
01:06 self-refine, iterative feedback and agentic refinement algorithm
02:16 Self-refine evaluation with ChatGPT and GPT4
02:29 Reflection method
02:33 Reflexion with verbal reinforcement learning and its evaluation
03:18 Explanation of the Reflexion algorithm and system design
04:26 Gorilla agentic system using tools and generating API calls
04:46 Gorilla evaluation with GPT-4 and Claude + evaluation on document retriever
05:09 Planning in agentic systems + Andrew NG takeaway message
05:23 Chain of Thought prompting for LLM step-by-step reasoning problems
05:36 Evaluation of chain of thought vs standard prompting of PaLM, Codex, GPT, LaMDA, UL2
05:Evaluation of chain of thought prompting on GSM8K tests + examples of chain of thought
06:03 HuggingGPT as LLM controller agentic system
06:23 HuggingGPT example of multi-modal tasks
06:34 HuggingGPT task planning, model selection, task execution, response generation
07:12 Multiagent collaboration: CrewAI and AutoGen
07:29 AutoGen agentic system for applications with customizable and conversable agents
08:05 AutoGen applications and use cases (math, ALF, multi-agent coding, group chat)
08:27 ChatDev agentic system for software development
Related concepts and key terms:
#ai #agentic #agentic ai #llm #chatdev #autogen #crewai #hugginggpt #huggingface #self-refine #gpt4 #reflexion #gorilla #api #planning #gsm8k #multimodal #multiagent #ALF
--Don’t forget to subscribe and watch these related videos:
Transformer Language Models Simplified in JUST 3 MINUTES!
Transformer Language Models Simplifie...
This Is How Exactly Language Models Work in AI – NO background needed! This is how EXACTLY Language Models w...
The Concept of Backpropagation Simplified in JUST 2 MINUTES! --Neural Networks The Concept of Backpropagation Simpli...
@analyticscamp
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