Mr_P

This is a robotics paper by Nvidia & UW researchers.

It is not an Alphabet/Deepmind paper, and it also has nothing to do with the "NIST" OCR dataset.

Was there really not a more appropriate name than "AlphaNIST"?

Congrats to the team on such a successful exit!

I'm curious how much the recent AI 3D modeling trend (Astra post-trained to use Blender) would have impacted this.

World Labs' entire tech stack becomes potentially obsolete when anyone can prompt GPT/Claude to create a simulation-ready 3D model from a photograph.

If you click through to the paper, it has approximately nothing to do with what this HN post title suggests.

Android and Chrome need on-device AI capabilities. Google can't lock down those weights like it can with server-side ML.

So it's easier to just release those models as open source and make it official, since someone would inevitably hack the weights out anyway.

How does this compare to Claude Managed Agents?

Years ago, my college multi variable calculus and linear algebra courses were both taught primarily using course materials that were interactive Mathematica Notebooks.

We had access to all of the symbolic algebra tools and were even expected to use them regularly for both courses. It was great!

I'm not sure how well this would extend to introductory courses though, especially if the standardized tests still expect integration by hand.

Those same companies often invest in accessibility for vision-impaired users. I'm not sure you need a screen capture to scrape content when the site is designed to be navigable with a screen reader.

For anyone else who was confused to see a paper use the same name as a commercial product, it looks like Google Gemini was announced in May, whereas this was submitted to SOSP that had an April submission deadline.

GPU performance per dollar is only competitive for specific workloads. For extremely large scale compute, getting enough data center GPUs can also be challenging.