Welcome to the 22nd session of the Demystifying AI course! Today, we will explore the mysteries of AI Agents, understanding their development history, core capabilities, and future trends. AI Agents, also known as intelligent agents, are gradually changing the way we live and work. Let’s take a look at how they work!
1. Development History of Large Models
• 1990s: At that time, natural language processing was like a baby just learning to speak, capable of only simple tasks such as counting word frequencies and analyzing sentence structures. (🌟 Basic Task Era)
• 2013: With technological advancements, it was like a baby starting to understand simple instructions. Models like Word2Vec emerged, solving text classification and information extraction problems, laying the foundation for future developments. (🚀 Feature Learning Era)
• 2018: Large language models became smarter, like a child in elementary school, with models like BERT and GPT emerging, capable of solving various tasks through pre-training and fine-tuning. (📚 Task Solver Era)
• 2020: By the time of GPT-3.5 and GPT-4, it was like entering adolescence, with explosive capabilities, able to perform many tasks such as chatting, writing articles, and answering questions, with an increasingly broad application scope. (💥 General Large Model Era)
2. Reasons for the Emergence of AI Agents
Although large language models are powerful, they still have shortcomings, such as inability to remember previous interactions and difficulty in breaking down complex tasks into smaller, manageable steps.
AI Agents are like an upgraded version of large language models, addressing these shortcomings and enabling them to perform tasks more like humans. (💪 Addressing Weaknesses)
3. Components of AI Agents
• Large Models: This is the foundation of AI Agents, akin to their brain, responsible for understanding and generating language. (🧠 Large Models)
• Memory: With memory capabilities, AI Agents can remember conversations with users, understand their preferences, and provide more personalized services. (📝 Memory)
• Planning: They can break down complex problems into smaller tasks and solve them step by step, rather than just answering questions. (📈 Planning)
• Tool Usage: AI Agents can also use various tools, such as searching the internet for information or ordering takeout, enabling them to interact with the real world. (🛠️ Tool Usage)
4. Practical Application Scenarios of AI Agents
• Higher Education: On campus, AI Agents act like a know-it-all, able to answer various questions and help students check schedules and find classrooms. (🏫 Higher Education Application)
• Tourist Attractions: When visiting scenic spots, AI Agents can assist users in purchasing tickets and planning itineraries, acting like a thoughtful tour guide. (🏞️ Tourist Application)
• Image Generation: If users want to create artwork but lack the skills, AI Agents can generate images based on user descriptions, acting like a magical artist. (🖼️ Image Generation Application)
5. Three Core Capabilities of AI Agents
(1) Knowledge Base
• Purpose: With a knowledge base, AI Agents can understand various specialized knowledge and real-time information, addressing the shortcomings of large language models in this area. (📚 Knowledge Base Purpose)
• RAG Technology:
• Indexing: Like creating a directory for knowledge, making it easier to find. (🔍 Indexing)
• Retrieval: When users ask questions, AI Agents can quickly find relevant knowledge in the knowledge base. (🔎 Knowledge Retrieval)
• Generation: After locating knowledge, AI Agents can combine it to generate accurate responses. (📝 Generating Responses)
• Embedding Vectorization Principle: Converting text, images, etc., into vectors that computers can understand, facilitating storage and retrieval of similar content. (🔍 Vectorization Principle)
(2) Tool Invocation
• Capability: AI Agents can utilize various tools, akin to having superpowers, enabling them to perform many tasks such as booking tickets and hailing rides. (🎉 Tool Invocation Capability)
• Toolbox Plugin Capability: The toolbox provides many plugins, like a treasure chest, allowing AI Agents to use more tools. (🧰 Toolbox Plugins)
(3) Workflow
• Role: Workflows act like flowcharts, enabling AI Agents to complete tasks step by step, ensuring tasks are completed smoothly. (📊 Workflow Role)
• Configurable Tools: The toolbox offers configurable tools, like LEGO blocks, allowing users to freely build task processes. (🧩 Configurable Tools)
6. Major Challenges and Responses in Implementing AI Agents
(1) Challenges
• Industry Awareness and Process Restructuring: To enable AI Agents to function in an industry, one must first gain a deep understanding of that industry, just like understanding the rules of a game before playing. (🤔 Industry Awareness Challenge)
• Stability of Effectiveness and Response Time:
• Stability: Large language models can sometimes be “confused,” and AI Agents need workflows to improve stability. (🔄 Stability Challenge)
• Response Time: AI Agents must not take too long to answer questions; otherwise, user experience will suffer. (⏱️ Response Time Challenge)
• Fully Leveraging Large Model Capabilities: To fully utilize large language models’ capabilities, one must understand their strengths and weaknesses, akin to knowing a car’s performance to drive it well. (🚀 Leveraging Large Model Capabilities Challenge)
(2) Responses
• Systematic Evaluation:
• Clear Evaluation Metrics and Dimensions: Like clarifying exam content and standards before an exam. (🎯 Evaluation Metrics and Dimensions)
• Construct Evaluation Datasets: Prepare questions to test AI Agents’ performance. (📝 Evaluation Datasets)
• Evaluation Methods: Performance can be assessed through both manual and automated evaluations. (📊 Evaluation Methods)
• Generate Evaluation Reports: After evaluation, generate reports to analyze AI Agents’ strengths and weaknesses. (📝 Evaluation Reports)
• Deep Understanding of Large Language Model Capabilities: Large language models have many abilities, but also some shortcomings that need improvement through optimization. (🔍 Understanding Large Language Model Capabilities)
7. Fine-Tuning and Optimizing Prompts
• Key Points in Prompt Design: Designing prompts is like giving AI Agents questions to help them understand and answer better. (📝 Key Points in Prompt Design)
• Basic Principles: When designing prompts, certain principles must be followed, such as clarity of instructions and providing background information. (🎯 Basic Principles)
• Two Ways of Thinking:
• Chain of Thought (CoT): Guide AI Agents to think step by step like humans. (🤔 Chain of Thought)
• Reactive Action (ReAct): Allow AI Agents to break complex problems into smaller steps and solve them step by step. (🎉 Reactive Action)
8. Future Trends in Human-Machine Interaction and Intelligent Agent Development
• Vast Imagination Space: If humans and machines can communicate smoothly through language, many wonderful things will happen in the future, such as everyone having an AI butler. (🌟 Future Imagination Space)
• Current Trends: Some applications already exist, like ChatGPT guiding users through voice. (🤖 Current Trends)
• Integration with Smart Hardware: In the future, AI Agents will increasingly integrate with smart hardware, such as smart glasses and headphones, making our lives more convenient. (👀 Integration with Smart Hardware)
This image is beautiful, from the public account: Mo Ti Si Xing
Conclusion
Today’s session of the Demystifying AI course ends here! I hope you have a deeper understanding of AI Agents. In the future, as technology continues to advance, AI Agents will play important roles in more fields. Let’s wait and see!
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Previous related articles are also available here, feel free to click to read:
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Entry-Level Knowledge Series of Future Technology Part 2: Artificial Intelligence (Part 1)
Entry-Level Knowledge Series of Future Technology Part 3: Artificial Intelligence (Part 2)
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Course Series
Demystifying AI Course 30 Lectures Part 1: Let’s Re-Understand AI Together
Demystifying AI Course 30 Lectures Part 2: Don’t Be Afraid of These ‘Big’ Words
Demystifying AI Course 30 Lectures Part 3: ‘Deep Learning’ is Not Deep
Demystifying AI Course 30 Lectures Part 4: Not Only Cars Can Be ‘Driverless’
Demystifying AI Course 30 Lectures Part 5: AI and Work
Demystifying AI Course 30 Lectures Part 6: AI Tools That Are Ready to Use
Demystifying AI Course 30 Lectures Part 7: Useful AI Drawing and Avatar Tools
Demystifying AI Course Weekend Gossip – OpenAI is Just a Makeshift Team
Demystifying AI Course 30 Lectures Part 8: Major Breakthroughs in AI You Need to Know for 2024
Demystifying AI Course 30 Lectures Part 9: Liang Ning Talks About AI and Life Perspective
Demystifying AI Course 30 Lectures Part 10: Check Out the Genius Girl Who Went to Xiaomi
Demystifying AI Course 30 Lectures Part 11: AI Flavor in Wu Xiaobo’s New Year’s Speech
Demystifying AI Course 30 Lectures Part 12: Read About AI Mother Li Feifei – The Worlds I See
Demystifying AI Course 30 Lectures Part 13: Yesterday Li Feifei, Today Wu Enda
Demystifying AI Course 30 Lectures Part 14: The Singularity is Near – AI Reshaping the World, Humans Still Have Jobs
Demystifying AI Course 30 Lectures Part 15: Please Rate – Sam Altman’s Year-End Summary?
Demystifying AI Course 30 Lectures Part 16: Talk About Jensen Huang and His $65,000 Leather Jacket
Demystifying AI Course 30 Lectures Part 17: AI Pets Loved by Everyone at CES
Demystifying AI Course 30 Lectures Part 18: Musk Talks About the Future of Work
Demystifying AI Course 30 Lectures Part 19: American Futurists Researching AI in China
Demystifying AI Course 30 Lectures Part 20: Check Out the Robots Performing at CES
Demystifying AI Course 30 Lectures Part 21: Evolution of Intelligent Agents, Birth of Tasks Function
Finally, I wish everyone in the Year of the Wood Dragon 2024 to have their dreams come true and miracles to happen. The Year of the Fire Snake is coming soon~
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Thank you for watching, see you next time.
Written by: Sun Yingci
IT Industry Worker
# Songxi Lake Academy 📚
# Xiangshang Research Institute 🐯
# Jiashu Yunqi
Born in Xinjiang, studied at Jilin University, settled inHangzhou, the Golden Cow Sister 🦊
Worked at Alibaba Group for 7 years,18 years in the B2B IT market 🦬
Holds intermediate certification in Tarot Astrology,Tea Art certification
Enjoys answering questions about mysticism
Reading my articles will surely make you happy ❤️
Want to say | Dare to say | Can say | Able to say
Images: Taken by myself / Online images / AI-generated;
Article content: Original + AI rewritten