MASSIVE Step Allowing AI Agents To Control Computers (MacOS, Windows, Linux)
TLDROS World is a groundbreaking project addressing the challenge of benchmarking AI agents' ability to perform tasks in real computer environments. Developed by a collaboration of universities and companies, it provides a robust environment for AI agents to interact with multiple operating systems and measure their performance. The project includes a research paper, open-source code, and data, offering a significant step forward in testing and improving AI agents' capabilities. The presentation explains the concept using analogies like assembling Ikea furniture, emphasizing the importance of grounding instructions with actions and feedback. OS World aims to enable AI agents to handle complex tasks across different apps and interfaces, setting a new standard for AI benchmarking.
Takeaways
- 🚀 The OS World project is designed to address the challenge of benchmarking AI agents' performance in real computer environments.
- 📚 It is a collaborative effort involving the University of Hong Kong, CMU, Salesforce Research, and the University of Waterloo, and includes a research paper, open-source code, and data.
- 🔍 OS World provides a robust environment for AI agents to interact with multiple operating systems and measure their performance effectively.
- 🛠️ The project uses a presentation to illustrate the concept of 'grounding', which is essential for AI agents to execute tasks by understanding and applying instructions.
- 🖥️ AI agents currently struggle with controlling closed systems like MacOS and Windows due to the lack of precise interaction methods.
- 🤖 Large Language Models (LLMs) and Vision Models (VMs) can be used to execute digital instructions, but they require a grounding layer to translate instructions into actions.
- 📝 The script discusses the role of an 'intelligent agent', which perceives its environment and acts upon it, and how this concept applies to AI agents.
- 🔑 OS World introduces xLang, a tool that translates natural language instructions into executable code within an environment.
- 🌐 The project has created 369 real-world computer tasks that involve web and desktop apps, using OS file reading and writing, and multi-app workflows.
- 📈 The evaluation of task executions is done through custom scripts that check if the tasks have been completed as per the instructions.
- 🏆 The testing results show that the accessibility tree or a combination of the accessibility tree and screenshot provide the best results for observation input to the AI agents.
Q & A
What is the main purpose of the OS World project?
-The main purpose of the OS World project is to provide a robust environment for benchmarking AI agents, allowing them to perform actions in an environment and test their performance across multiple operating systems.
How does the OS World project address the benchmarking problem for AI agents?
-OS World addresses the benchmarking problem by offering a scalable real computer environment that can serve as a unified multimodal agent environment for evaluating open-ended computer tasks involving arbitrary apps and interfaces across operating systems.
What are the components of the OS World project?
-The components of the OS World project include a research paper, open-source code, and data, as well as a presentation that explains the project in detail.
How does OS World enable AI agents to interact with the environment?
-OS World enables AI agents to interact with the environment by providing a grounding layer that translates instructions into actions and offers observations to the agents, allowing them to generate instructions for interaction with the computer environment.
What is the significance of the accessibility tree and set of marks in OS World?
-The accessibility tree and set of marks are significant in OS World as they provide a way for the AI agents to understand the environment's structure and interact with it more effectively. The accessibility tree is a code version that helps the AI understand the UI elements, while the set of marks is a grid format that guides the AI on where to click.
What is the role of the XLang in the OS World project?
-XLang plays a crucial role in the OS World project as it translates natural language instructions into code that can be executed in the environment, enabling AI agents to perform tasks based on user instructions.
How does OS World facilitate the testing and evaluation of AI agents?
-OS World facilitates testing and evaluation by creating real-world computer tasks that involve real web and desktop apps, using OS file reading and writing, and multi-app workflows. Each task is annotated with instructions, an initial state setup, and a custom execution-based evaluation script.
What are the different input modes that OS World uses for task execution?
-OS World uses different input modes for task execution, including the accessibility tree only, screenshot only, screenshot plus accessibility tree, and set of marks.
How does the OS World project compare the performance of AI agents?
-The OS World project compares the performance of AI agents by evaluating their ability to execute tasks in the provided environment. It uses different input modes and measures success rates and percentages based on task completion.
What is the importance of the open-source nature of the OS World project?
-The open-source nature of the OS World project is important as it allows for collaboration, innovation, and transparency within the AI research community. It enables researchers and developers to access the code, data, and methodologies, fostering advancements in AI agent benchmarking.
Outlines
🤖 OS World: Revolutionizing AI Agent Testing
The video discusses the challenges in testing AI agents and introduces a new project, OS World, which aims to solve the benchmarking problem for AI agents. Developed by a collaboration of institutions including the University of Hong Kong, CMU, Salesforce Research, and the University of Waterloo, OS World provides a robust environment for AI agents to perform actions and measure their performance across multiple operating systems. The project is accompanied by a research paper and an open-source release of code and data. The video emphasizes the importance of grounding in AI, drawing an analogy between assembling Ikea furniture and executing digital tasks, which requires understanding and feedback. OS World facilitates this by offering a way for AI agents to interact with the environment and measure their performance accurately.
🔍 Understanding Intelligent Agents and Their Role
This paragraph delves into the concept of intelligent agents, their capabilities, and the importance of grounding in executing tasks. It explains how agents, such as large language models (LLMs) and virtual machines (VMs), can interpret instructions and control various environments, including computers and the physical world through robots. The paragraph introduces the idea of an iterative loop involving planning, performing, observing, and iterating based on feedback. It also touches on the properties of an intelligent agent, which include autonomy, reactivity, proactivity, and interaction with other agents. The video mentions 'xLang,' a tool that translates natural language instructions into executable code, and highlights the open-source nature of the projects discussed, including OS World.
🛠️ OS World: Enabling Multimodal Agent Environments
The script explains how OS World serves as a scalable, real computer environment for evaluating complex, open-ended computer tasks that involve multiple apps and interfaces across operating systems. It discusses the difficulties AI agents face in interacting with closed systems like Mac OS and Windows, and how OS World provides a solution by offering a grounding layer that allows agents to generate instructions for interaction. The paragraph outlines the components of an agent task, including the state space, observation space, action space, transition function, and reward function. It also describes how agents receive observations and generate actions to interact with the environment, highlighting the importance of the accessibility tree and set of marks in facilitating this interaction.
📊 Evaluating Task Executions with OS World
This section of the script focuses on how task executions are evaluated within OS World. It describes the process of creating real-world computer tasks that involve web and desktop apps, multi-app workflows, and file operations. Each task is annotated with instructions, an initial state setup, and a custom execution-based evaluation script. The video provides an example of a prompt given to AI agents and discusses the importance of providing historical context to help the agents understand the sequence of actions. It also presents the results of testing OS World against various AI agents, highlighting the effectiveness of different input modes such as the accessibility tree, screenshot, and set of marks. The video concludes by emphasizing the significance of OS World in benchmarking AI agents and the potential for improving their performance through testing and feedback.
Mindmap
Keywords
💡AI agents
💡Benchmarking
💡Open-source
💡Multimodal agents
💡Grounding
💡Large Language Models (LLMs)
💡Autonomous agents
💡Environment
💡Observations
💡OS World
💡Evaluation script
Highlights
A new project called OS World aims to solve the benchmarking problem for AI agents across different operating systems.
OS World provides a robust environment for AI agents to interact with and measure their performance.
The project includes a research paper and open-source code, data, and tools for the AI community.
OS World allows AI agents to perform actions in an environment and test their effectiveness.
The project uses an analogy of assembling Ikea furniture to explain the need for grounding in executing instructions.
Large Language Models (LLMs) and Vision Models (VMs) can be used for testing within the OS World environment.
OS World introduces xLang, which translates natural language instructions into executable code.
The environment supports multi-step planning, reasoning, and feedback for AI agents.
OS World can evaluate complex computer tasks involving multiple apps and interfaces.
The project has created 369 real-world computer tasks for benchmarking purposes.
Tasks are evaluated based on real-world user instructions and initial state configurations.
The use of accessibility trees or screenshots combined with accessibility trees provides the best results for observation.
Higher screenshot resolution typically leads to improved performance in the benchmarking tests.
GPT-4 has been the top performer across most input modes in the OS World benchmarking.
The project aims to improve AI agents' ability to control and interact with computer environments effectively.
OS World provides a scalable real computer environment for evaluating open-ended tasks.
The project could potentially allow AI agents to execute complex tasks on behalf of users in the future.
The OS World project is open-source, allowing for community contributions and improvements.