Row 56365

Row ID: 56365 | Dataset Entry | Axioma AXP Content Repository

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This page contains data entry 56365 from the Axioma AXP content repository. The structured data below represents the complete record for this entry.

Thank you for the detailed write-up!

Not mentioned is [OpenAdapt.AI](https://github.com/OpenAdaptAI/OpenAdapt). OpenAdapt automates tasks in desktop apps by observing human demonstrations. OpenAdapt is open source and compatible with any app on Mac and Windows: desktop, web, and virtual (e.g. Citrix).

(Full disclosure: I am the primary author.)

We believe a major shortcoming with conventional approaches to AI agents is expecting them to be able to figure out how to perform tasks of arbitrary complexity from their training data alone. While interesting from an academic perspective, this is unnecessary for practical utility, since humans perform these tasks constantly. In addition, a lot of tasks are domain specific, and the knowledge required to complete them would not be present in any training data.

With OpenAdapt you can demonstrate to a model how to perform a task, then have it take over the task, with additional user-supplied natural language instructions. We generate prompts from the demonstrations and instructions.

I started working on OpenAdapt after watching my brother (a highly specialized physician) wasting a lot time clicking through slow and user-hostile Electronic Medical Record software, and realizing that existing solutions (i.e. Robotic Process Automation) are brittle, time consuming, and require specialized knowledge.

Free download (Mac and Windows, Linux coming soon) at https://openadapt.ai. Questions/comments/contributions welcome!

FieldValue
text Thank you for the detailed write-up! Not mentioned is [OpenAdapt.AI](https://github.com/OpenAdaptAI/OpenAdapt). OpenAdapt automates tasks in desktop apps by observing human demonstrations. OpenAdapt is open source and compatible with any app on Mac and Windows: desktop, web, and virtual (e.g. Citrix). (Full disclosure: I am the primary author.) We believe a major shortcoming with conventional approaches to AI agents is expecting them to be able to figure out how to perform tasks of arbitrary …
label r/machinelearning
dataType comment
communityName r/MachineLearning
datetime 2024-05-23
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Raw Record

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  "text": "Thank you for the detailed write-up!\n\nNot mentioned is [OpenAdapt.AI](https://github.com/OpenAdaptAI/OpenAdapt). OpenAdapt automates tasks in desktop apps by observing human demonstrations. OpenAdapt is open source and compatible with any app on Mac and Windows: desktop, web, and virtual (e.g. Citrix).\n\n(Full disclosure: I am the primary author.)\n\nWe believe a major shortcoming with conventional approaches to AI agents is expecting them to be able to figure out how to perform tasks of arbitrary complexity from their training data alone. While interesting from an academic perspective, this is unnecessary for practical utility, since humans perform these tasks constantly. In addition, a lot of tasks are domain specific, and the knowledge required to complete them would not be present in any training data.\n\nWith OpenAdapt you can demonstrate to a model how to perform a task, then have it take over the task, with additional user-supplied natural language instructions. We generate prompts from the demonstrations and instructions.\n\nI started working on OpenAdapt after watching my brother (a highly specialized physician) wasting a lot time clicking through slow and user-hostile Electronic Medical Record software, and realizing that existing solutions (i.e. Robotic Process Automation) are brittle, time consuming, and require specialized knowledge.\n\nFree download (Mac and Windows, Linux coming soon) at https://openadapt.ai. Questions/comments/contributions welcome!",
  "label": "r/machinelearning",
  "dataType": "comment",
  "communityName": "r/MachineLearning",
  "datetime": "2024-05-23",
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