MASSIVE Step Allowing AI Agents To Control Computers (MacOS, Windows, Linux)

AI Benchmarking: The OS World Project

Introduction to the OS World Project

• The OS World project addresses a significant challenge in AI development: effectively testing AI agents to ensure they perform tasks correctly, which is essential for their improvement.

• This initiative is not just theoretical; it includes an open-source research paper, code, and data, promoting transparency and collaboration in AI research.

Overview of the Research Paper

• The research paper titled "Benchmarking Multimodal Agents for Open-ended Tasks in Real Computer Environments" involves contributions from multiple prestigious institutions including the University of Hong Kong and CMU.

• The project aims to provide a robust environment where AI agents can interact with various operating systems and measure their performance accurately.

Understanding Grounding in Task Execution

• An analogy using Ikea furniture assembly illustrates that having step-by-step instructions alone is insufficient; grounding—understanding how to execute those instructions—is crucial.

• In digital environments, such as changing a Mac desktop background, grounding involves using tools like a mouse and keyboard to translate instructions into actions.

Challenges with Current Systems

• Existing methods for controlling computers through AI are inefficient; they often rely on taking screenshots and guessing coordinates rather than precise control mechanisms.

• While large language models (LLMs), like ChatGPT, can provide detailed instructions, they struggle with real-world tasks due to lack of interaction with the environment.

Limitations of LLMs in Practical Applications

• ChatGPT cannot execute tasks directly on devices without grounding plans into actionable steps; this limitation extends to both digital and physical task execution.

• Without sufficient sensory feedback from the real world, LLMs cannot generate effective step-by-step plans for complex tasks.

Role of Agents in Task Execution

• The presentation outlines how users give instructions to LLM-based agents that can code actions across various platforms (e.g., SQL commands or app controls).

Intelligent Agents and Their Frameworks

Understanding Intelligent Agents

• An intelligent agent requires planning, performance, observation, and iteration to function effectively. Tools like Hugging Face, SQL, and Python can be utilized in this process.

• The definition of an intelligent agent includes perceiving its environment through sensors and acting rationally with effectors. Discrete agents receive percepts one at a time and map them to actions.

• Key properties of intelligent agents include autonomy, reactivity to the environment, proactivity towards goals, and interaction with other agents through their environment.

Examples of Intelligent Agents

• Environments for these agents can range from computers and mobile devices to the physical world (e.g., embodied AI). Sensors may include cameras or ultrasonic radar.

• In robotic applications, large language models (LLMs) serve as the brain while robots act as effectors. For instance, code can instruct a robot to stack blocks based on detected objects.

Capabilities of Intelligent Agents

• Essential capabilities for intelligent agents involve interpreting abstract user instructions, utilizing tools for complex tasks, multi-step planning and reasoning, following feedback loops, and self-debugging.

• A significant innovation mentioned is "xLang," which translates natural language instructions into executable code within an environment.

Tools for Building LLM Applications

• Deep Checks is introduced as a tool that helps teams evaluate and debug LLM-based applications. It ensures high-quality app releases by detecting issues like hallucinations or inaccuracies before going live.

• Deep Checks supports various applications including RAG chatbots and text-to-code functionalities. A free trial is available for those developing LLM-based applications.

Recent Developments in Agent Frameworks

• Recent projects include "Instructor" for adapting agent environments via instructions; "Binder," an early LLM tool; "Lemur," showcasing state-of-the-art LLM capabilities; "Open Agents," a platform for language agents; and "Text to Reward," linking LLM agents with the physical world.

Complex Task Execution by Agents

• An example illustrates how operating systems require multiple apps/interfaces to execute complex tasks—like updating bookkeeping sheets using receipts from images—which highlights the potential of future agent frameworks.

• The discussion emphasizes that executing such complex tasks will soon be feasible through advanced agent frameworks capable of interacting seamlessly with various software environments.

Challenges in Current Agent Technologies

• The difficulty lies in grounding layers necessary for translating user instructions into actionable commands across different operating systems (Mac OS vs Windows).

Understanding Autonomous Agent Tasks in Computer Environments

Overview of Operating Systems and Applications

• The environment allows for the operation of various operating systems and applications, including both user interfaces (UI) and command-line interfaces (CLI).

• An autonomous agent task is defined as an observable Markov decision process, involving state space (current desktop), observation space (instructions, screenshots), and action space (clicking actions).

Task Execution Framework

• Each task begins with an initial state defined in a configuration file, which includes instructions and evaluation criteria to determine task completion.

• Observations are gathered through a set of marks (grid format for clicking objects) and an accessibility tree (code representation), facilitating interaction with the computer environment.

Interaction Mechanisms

• The AI agent interacts with the environment by performing actions such as mouse movements, clicks, text input, keyboard shortcuts, scrolling, dragging, etc.

• Task execution is evaluated based on specific instructions; for example, checking if cookies from Amazon remain after executing a cleaning task.

Benchmarking Real-world Tasks

• A total of 369 real-world computer tasks were created to benchmark performance across web and desktop applications using OS file operations and multi-app workflows.

• Each task is annotated with real user instructions and initial configurations to simulate human-like interactions.

Prompt Design for Agents

• The prompt provided to agents outlines their role in performing desktop tasks while utilizing Pi Auto GUI for action execution based on observations.

• Notably high creativity settings were used in temperature parameters during testing; recent observations help inform subsequent actions taken by the model.

Results Analysis

• Various input modes were tested: accessibility tree only, screenshot only, combined modes. GPT-4 generally performed best except in screenshot-only mode where Gemini Pro V excelled.

Enhancing Agent Interaction with Operating Systems

Importance of Screenshot Resolution

• The discussion emphasizes the need to develop two operating systems that facilitate better interaction for agents.

• A key insight presented is that higher screenshot resolution correlates with improved performance outcomes.

• Data indicates a direct relationship between screenshot resolution and success rates, showcasing percentage increases as resolution improves.

• This finding suggests that optimizing visual inputs can significantly enhance operational effectiveness in agent interactions.