Summary of Omniact: a Dataset and Benchmark For Enabling Multimodal Generalist Autonomous Agents For Desktop and Web, by Raghav Kapoor et al.
OmniACT: A Dataset and Benchmark for Enabling Multimodal Generalist Autonomous Agents for Desktop and Web
by Raghav Kapoor, Yash Parag Butala, Melisa Russak, Jing Yu Koh, Kiran Kamble, Waseem Alshikh, Ruslan Salakhutdinov
First submitted to arxiv on: 27 Feb 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV); Human-Computer Interaction (cs.HC)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper introduces OmniACT, the first dataset and benchmark for assessing virtual agents’ capability to generate executable programs for accomplishing computer tasks. It targets automating various desktop applications, from simple tasks like playing the next song to complex tasks like sending an email. The goal is to create a script that can fully execute the task given a screen image and a natural language instruction. The authors ran several strong baseline language model agents on their benchmark, with GPT-4 performing best but still only reaching 15% of human proficiency. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a special dataset and test for virtual helpers that can help people use computers more easily. Right now, most computer tasks need human input, like clicking buttons or typing commands. These virtual agents could automate many of these tasks, making it easier for people with limited technical skills to get the most out of their computers. |
Keywords
» Artificial intelligence » Gpt » Language model
