For what it's worth, I worked with Dr. Seales while getting my undergrad degree. Happy to vouch that he and his team are great humans.<p>This is such a fascinating problem, and could have real benefits for society. Imagine uncovering ancient works that would otherwise be lost.
The images/animations in this page are fantastic at visually explaining something quite complicated. I would not have been able to understand the difficulty without them.
Man, there's probably a dozen different loosely-formed ideas floating in my head right now — kudos on the exciting presentation, you really make the problem seem interesting. I may just have to give this a shot, though I think the odds of me figuring it out are exceptionally low. Still, working on cutting-edge problems is motivating, even if they're above my pay grade :)<p>You've nerd-sniped me good and proper. May the best team win!
This was the outcome of <a href="https://nat.org/puzzle" rel="nofollow">https://nat.org/puzzle</a><p>HN discussion: <a href="https://news.ycombinator.com/item?id=33735503" rel="nofollow">https://news.ycombinator.com/item?id=33735503</a><p>Some did guess correctly that it was about decoding the Herculaneum papyri
I do a lot of brain image segmentation in my research using multiatlas image segmentation, which involves diffeomorphic image registration from multiple labeled atlases...but the amount of curling in on itself of these layered sheets seems a daunting problem for a fully automated pipeline.
I guess this is the answer to this post from 3 months ago<p><a href="https://news.ycombinator.com/item?id=33735503" rel="nofollow">https://news.ycombinator.com/item?id=33735503</a>
Reminds me of Kuzushiji recognition with ML, transcribing historical Japanese documents. Both are my favorite applications of ML: deciphering the past. This is really damn cool.