Exploring Intelligent Agents with Professor Andrew Ng

Book Giveaway at the End

The Expert Created a Translation Agent

Leading figure in the field of artificial intelligence, Stanford University professor Andrew Ng, recently released an open-source project for a machine translation intelligent agent — translation-agent. This project implements a large model translation application based on a reflective workflow.

Exploring Intelligent Agents with Professor Andrew Ng

Currently, this project has already gained 3.6k stars, and it has shown good performance in limited tests by the research team. It is important to note that this is just a project that Andrew Ng tinkered with over a few weekends and is still in the early prototype stage. If further research is conducted, it is certain to perform even better.

The core of the translation-agent project is a reflective agent workflow that utilizes large language models (LLM) for text translation and proposes improvement suggestions through self-reflection, thereby optimizing translation results.

This project is developed in Python, and the main steps are as follows:

1. Input a prompt to have the LLM translate text from source_language to target_language;

2. Allow the LLM to reflect on the translation results and propose constructive improvement suggestions;

3. Use these suggestions to improve the translation.

Current issues with machine translation include stiff wording and obscure content, commonly referred to as “machine translation feel”; in fact, the output from the first step of the above process often produces such results.

However, based on reflective workflow technology, high customization can be achieved, easily changing the style of translation, handling specific terminology and dialects to meet different translation needs, making the results appear closer to natural expressions in the native language.

AI technology enthusiasts can easily set up a translation-agent runtime environment by first installing the Poetry package manager and configuring environment variables, then calling the translation-agent’s API for translation through a simple Python script. For example, users can specify the source language, target language, country, and the text to be translated, and then obtain the translation results.

Example of calling the translation-agent interface

The Expert Proposed Four Design Patterns for Agents

Professor Andrew Ng believes that the most promising development direction for AI is the application of agents based on large models. Current large models already possess sufficient intelligent generative capabilities, but the simple “question-and-answer” format does not effectively solve complex problems, while agents can fully exploit the potential of large models to achieve multifunctional intelligent applications.

Andrew Ng summarized and introduced four common design patterns: Reflection (Refection) pattern, Tool Use (ToolJse) pattern, Planning pattern, and Multi-agent Collaboration (Multi-agent colaboraion) pattern.

Reflection Pattern

This method allows AI models to improve task execution capabilities through self-reflection and iterative improvements. The model not only generates initial solutions but also continuously optimizes its output through multiple feedback and modifications. The translation-agent project is a typical application of the reflection pattern.

Tool Use Pattern

This method enables AI models to enhance task execution capabilities by calling external tools or libraries. The model does not solely rely on its own knowledge and capabilities but utilizes various external resources to complete tasks, thereby improving efficiency and accuracy.

Planning Pattern

This method improves efficiency and accuracy by planning and organizing task steps in advance. The model breaks down complex tasks into multiple steps and executes each step sequentially to achieve the desired goal.

Multi-agent Collaboration Pattern

This method improves task execution efficiency and accuracy through collaboration among multiple agents. Multiple agents share tasks and complete complex tasks together through mutual communication and collaboration.

Understanding the agent design patterns is crucial, but how can we develop useful intelligent agent applications? All it takes is to read a book, and now we will learn to create agents hands-on.

Learn to Create Agents with One Book

The book Application Development of Large Models: Hands-on AI Agent is a comprehensive and in-depth guide to exploring AI agents. This book is based on large model technology and elaborates on the design, development, and application of AI agents, covering all aspects from basic theory to advanced applications.

The book first introduces the basic concepts of AI agents, exploring how they serve as key components of intelligent systems to simulate human decision-making and interaction processes. Subsequently, the author discusses in-depth the role of large models as the “brain” of agents and how to leverage the general reasoning capabilities of these models to build highly intelligent AI systems.

This book not only explains the theory but also showcases the application of AI agents in various fields such as automated office tasks, customer service, personalized recommendations, and intelligent scheduling through 7 specific practical cases. Readers will learn how to use tools such as OpenAI API, LangChain, and LlamaIndex to develop agents with perception, planning, and action capabilities.

The author who reveals the core secrets of agents is Huang Jia, pen name Coffee Brother, currently an AI researcher at the Singapore Agency for Science, Technology and Research. He has accumulated rich project experience in NLP, large models, AI in MedTech, and AI in FinTech. He is also the author of “GPT Illustrated: How Large Models are Built,” “Learning Machine Learning from Scratch,” “Data Analysis Coffee Brother’s Ten Talks: From Thinking to Practice to Promote Operational Growth” and other books.

Huang Jia always introduces discussions with the role of “Coffee Brother” in the book and explains complex technology in an entertaining way. This is because he enjoys maintaining curiosity, embracing change, and continuous learning, hoping to observe the world with AI’s “wisdom” and “attention” and share knowledge in a light-hearted and humorous manner, gaining true happiness.

As AI technology continues to advance, AI agents are gradually becoming an important force driving innovation and transformation across various industries. This book is suitable for researchers, developers, product managers, business leaders, and students in related fields who are interested in agent technology or dedicated to research in this area.

Learn “Application Development of Large Models: Hands-on AI Agent” and develop intelligent agent applications alongside the expert Andrew Ng!

▼Click below to purchase the book, limited-time discount50% off

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