MASSIVE Step Allowing AI Agents To Control Computers (MacOS, Windows, Linux)
TLDROS World is a groundbreaking project that aims to address the challenge of benchmarking AI agents' performance in real computer environments. Developed by a collaboration including the University of Hong Kong, CMU, Salesforce Research, and the University of Waterloo, it provides a robust environment for AI agents to interact with multiple operating systems and measure their performance accurately. The project includes a research paper and an open-source release of code and data. The presentation explains the importance of 'grounding' in executing tasks, comparing the process to assembling Ikea furniture where step-by-step instructions need to be translated into actions. OS World enables AI agents to control desktops, use large language models to generate code for robots, and gather observations to iterate and improve. The project has created 369 real-world computer tasks involving web and desktop apps, using OS file reading and writing, and multi-app workflows. The tasks are evaluated based on real user instructions, initial state setup, and custom execution scripts. The findings suggest that the accessibility tree or a combination of a screenshot and the accessibility tree provide the best results for observation, with higher screenshot resolution leading to improved performance. OS World is a significant step towards enabling AI agents to perform complex digital tasks autonomously.
Takeaways
- 🚀 OS World is a new project designed to address the benchmarking problem for AI agents, providing a robust environment for testing their performance across multiple operating systems.
- 📚 The project includes a research paper from institutions like the University of Hong Kong, CMU, Salesforce Research, and the University of Waterloo, along with an open-source release of code and data.
- 🔍 OS World aims to provide a way to measure the performance of AI agents in real computer environments, something that has been lacking until now.
- 🛠️ The project uses a presentation to explain the concept of grounding, which is essential for AI agents to execute tasks by translating instructions into actions.
- 💻 OS World supports interaction with the environment through both UI and CLI, offering a way for AI agents to perceive and act within the computer systems.
- 🔑 The concept of an 'intelligent agent' is introduced, which perceives its environment and acts rationally upon it, with properties like autonomy, reactivity, and the ability to interact with other agents.
- 🔗 The project discusses the use of xLang, which translates natural language instructions into executable code within an environment.
- 📈 OS World has created 369 real-world computer tasks involving web and desktop apps, using OS file reading/writing and multi-app workflows for testing AI agents.
- 📊 The evaluation of task executions is based on custom scripts that check if the tasks have been completed as instructed, providing a benchmark for AI agent performance.
- 🏆 The testing results show that GPT-4 generally outperforms other agents, especially when using the accessibility tree or a combination of screenshot and accessibility tree for observations.
- 🔬 Higher screenshot resolution is found to improve performance when using only screenshots for task observation, highlighting the importance of detailed visual input for AI agents.
Q & A
What is the main challenge addressed by the OS World project?
-The main challenge addressed by the OS World project is the lack of a consistent and thorough way to benchmark AI agents' performance in real computer environments and to test their actions effectively.
What does the OS World project provide to facilitate AI agent testing?
-The OS World project provides a robust environment for AI agents to interact with multiple operating systems, a way to measure performance, and an open-source platform that includes research papers, code, and data.
How does the OS World project relate to the analogy of assembling Ikea furniture?
-The OS World project uses the Ikea furniture assembly analogy to illustrate the importance of grounding step-by-step instructions with actual execution and feedback in order to successfully complete a task, similar to how AI agents need grounding to execute digital tasks.
What is the role of grounding in the context of AI agents performing tasks?
-Grounding is the process of taking step-by-step instructions and executing them in the real world, which includes perceiving the environment and getting feedback. It is crucial for AI agents to successfully perform tasks in a digital environment.
How does the OS World project differentiate from current methods of controlling a computer using AI?
-Unlike current methods that use screenshots and grids, which are imprecise and inefficient, the OS World project provides a more direct and precise way for AI agents to interact with the computer environment through a grounding layer.
What are the components of an intelligent agent as defined in the script?
-An intelligent agent, as defined in the script, perceives its environment via sensors, acts rationally upon that environment with its effectors, and is autonomous, reactive to the environment, proactive, goal-directed, and interacts with other agents via the environment.
What is the significance of the xLang in the OS World project?
-xLang is significant in the OS World project as it translates natural language instructions into code that can be executed in an environment, providing a crucial link between abstract user instructions and actionable tasks for AI agents.
How does the OS World project enable AI agents to perform complex tasks like updating a bookkeeping sheet?
-The OS World project provides a scalable real computer environment where AI agents can operate any operating system, any amount of applications, and interfaces, including both UI and CLI, and use observations to generate instructions for interacting with the computer environment.
What are the different versions of observations provided by the OS World project for AI agents?
-The OS World project provides four different versions of observations for AI agents: accessibility tree only, screenshot only, screenshot plus accessibility tree, and set of marks.
What insights did the OS World project reveal regarding the performance of AI agents?
-The OS World project revealed that higher screenshot resolution typically leads to improved performance when using screenshots for observations. It also found that the accessibility tree or using a screenshot plus the accessibility tree provided the best results for AI agent performance.
Outlines
🤖 Introducing OS World: A Benchmarking Solution for AI Agents
The video discusses a new project called OS World, which addresses the challenge of consistently and thoroughly testing AI agents. The project, a collaborative effort from the University of Hong Kong, CMU, Salesforce Research, and the University of Waterloo, introduces a robust environment for AI agents to interact with multiple operating systems and provides a method to measure their performance. The project's research paper, code, and data are all open-source, allowing for transparency and community involvement. The video highlights the importance of grounding in AI tasks, drawing an analogy between assembling Ikea furniture and executing digital tasks, such as changing a Mac desktop background, which requires precise control and feedback mechanisms. The limitations of current systems like open interpreter are also discussed, which rely on imprecise methods like screenshot grids for controlling computer environments.
📚 Understanding the Role of Intelligent Agents and the OS World Project
This paragraph delves into the concept of intelligent agents, which perceive their environment through sensors and act upon it using effectors. The video explains the iterative loop of planning, performing, and observing that agents must undergo to improve. It introduces the idea of a discrete agent that maps percept sequences to action sequences. The video also discusses the properties of an intelligent agent, such as autonomy, reactivity, proactivity, and interaction with other agents. Examples of agents include those that can operate in computer, mobile, data, or physical environments, using tools like cameras, screenshots, and ultrasonic sensors. The OS World project is highlighted as a significant development in providing a scalable, real computer environment for evaluating complex, open-ended computer tasks across different apps and interfaces.
🛠️ Demonstrating the Practical Application of OS World in Task Execution
The video provides a practical example of how OS World can be used to execute complex computer tasks, such as updating a bookkeeping sheet with recent transactions. It explains the challenges of performing such tasks in environments like Mac OS or Windows due to the lack of a grounding layer that can translate instructions into actions. OS World provides this layer by offering a unified multimodal agent environment that supports operating systems, applications, and interfaces, both graphical and command-line based. The video outlines how agents can use observations from OS World to generate instructions for interacting with the computer environment, highlighting the importance of the accessibility tree and set of marks in facilitating this interaction.
📊 Evaluating Task Performance with OS World and Future Implications
The final paragraph focuses on how task executions are evaluated within OS World. It describes the creation of 369 real-world computer tasks that involve web and desktop apps, using OS file reading and writing, and multi-app workflows. Each task is annotated with instructions, initial state setup, and a custom execution-based evaluation script. The video presents the results of testing OS World against various AI agents, showing that the accessibility tree or a combination of screenshot and accessibility tree provide the best results for observation. The video concludes by discussing the potential for integrating OS World with real-world environments and the importance of higher screenshot resolution for improved performance. It also expresses enthusiasm for the project's contribution to benchmarking and improving AI agents.
Mindmap
Keywords
💡OS World
💡Benchmarking
💡AI Agents
💡Open-Source
💡Multimodal Agents
💡Grounding
💡Large Language Models (LLMs)
💡Accessibility Features
💡XLang
💡Markov Decision Process (MDP)
💡Deep Checks
Highlights
OS World is a new project that aims to solve the benchmarking problem for AI agents in real computer environments.
The project is a collaborative effort from the University of Hong Kong, CMU, Salesforce Research, and the University of Waterloo.
OS World provides a robust environment for AI agents to interact with multiple operating systems and measure their performance.
The project includes a research paper and an open-source release of code and data to facilitate further research and development.
OS World uses a multimodal approach to allow AI agents to execute tasks in digital environments, such as changing a Mac desktop background.
Large Language Models (LLMs) and Vision Models (VMs) can be used for testing within the OS World environment.
OS World introduces xLang, a tool that translates natural language instructions into executable code within the environment.
The project has created 369 real-world computer tasks involving real web and desktop apps for benchmarking purposes.
Tasks are evaluated based on their ability to perform multi-step planning, reasoning, and follow feedback for self-debugging.
OS World provides a scalable and unified environment for evaluating open-ended computer tasks across different operating systems.
The project uses a primarily observable Markov decision process for autonomous agent tasks, including state, observation, and action spaces.
Evaluation of task executions is done through custom scripts that check if the task was completed as per the instructions.
The project tested different input modes, finding that the accessibility tree or a screenshot plus the accessibility tree provided the best results.
Higher screenshot resolution typically leads to improved performance in tasks that rely on visual input.
OS World's approach allows for more precise and efficient control of computer environments compared to current methods like Open Interpreter.
The project has the potential to significantly improve the testing and benchmarking of AI agents, leading to advancements in their capabilities.
The OS World project is open-source, allowing the community to contribute to and benefit from its development.
Deep Checks, a sponsor of the video, offers a platform for evaluating, monitoring, and debugging LLM-based applications.
