Our Statement to the House Oversight Committee on the Federal Government’s Use of AI
June 5, 2025 — In a statement for the record at a hearing before the House Committee on Oversight and Government Reform on the federal government in the age of artificial intelligence, Director of Research Alice E. Marwick and Policy Director Brian J. Chen (with assistance from Jacob Metcalf, Meg Young, and Serena Oduro) lay [ ]
Could not retrieve the full article text.
Read on Data & Society →Sign in to highlight and annotate this article
Conversation starters
Daily AI Digest
Get the top 5 AI stories delivered to your inbox every morning.
More about
policygovernmentresearch
Researchers 3D print robot the size of a single-cell organism — devices move and navigate even without a ‘brain,’ uses their shape and the environment to get going
Researchers 3D print robot the size of a single-cell organism — devices move and navigate even without a ‘brain,’ uses their shape and the environment to get going
![Best OCR for template-based form extraction? [D]](https://d2xsxph8kpxj0f.cloudfront.net/310419663032563854/konzwo8nGf8Z4uZsMefwMr/default-img-robot-hand-JvPW6jsLFTCtkgtb97Kys5.webp)
Best OCR for template-based form extraction? [D]
Hi, I’m working on a school project and I’m currently testing OCR tools for forms. The documents are mostly structured or semi-structured forms, similar to application/registration forms with labeled fields and sections. My idea is that an admin uploads a template of the document first, then a user uploads a completed form, and the system extracts the data from it. After extraction, the user reviews the result, checks if the fields are correct, and edits anything that was read incorrectly. So I’m looking for an OCR/document understanding tool that can work well for template-based extraction, but also has some flexibility in case document layouts change later on. Right now I’m trying Google Document AI , and I’m planning to test PaddleOCR next. I wanted to ask what OCR tools you’d recommend
Know3D lets users control the hidden back side of 3D objects with text prompts
A research team taps into the world knowledge of large language models to control what appears on the back side of 3D objects using simple text commands. The approach tackles one of the biggest blind spots in single-image 3D generation. The article Know3D lets users control the hidden back side of 3D objects with text prompts appeared first on The Decoder .
Knowledge Map
Connected Articles — Knowledge Graph
This article is connected to other articles through shared AI topics and tags.
Discussion
Sign in to join the discussion
No comments yet — be the first to share your thoughts!