US EditionThis App Helps Paleontologists (And You) Identify Dinosaur Tracks
9 Articles
9 Articles
This App Helps Paleontologists (And You) Identify Dinosaur Tracks
There’s a lot of guesswork involved in identifying a dinosaur footprint. The foot itself, the ground it left an imprint on, and the dinosaur’s movement all distort its identity. A new AI-powered app wants to make that guessing game a little less subjective. Researchers from the University of Edinburgh and Helmholtz-Zentrum in Germany have developed DinoTracker, a free app that uses artificial intelligence to analyze dinosaur footprints without a…
Dino Tracker revolutionne puts paleontology to the reach of all with an application of recognition based in particular on similarities A team led by Gregor Hartmann and bringing together
A new app for the smartphone identifies the footprints of dinosaurs. The app for the public is said to be amazingly accurate. It also underlines the suspicion that birds could have emerged tens of millions of years earlier.
Have paleontologists gained a valuable, modern research tool? Teams of scientists from the Helmholtz-Zentrum in Berlin and the University of Edinburgh have developed an original app. The tool is based on an artificial intelligence model.
AI Uncovers Dinosaur Tracks That Look Eerily Like Birds From a Time When Birds Shouldn't Have Existed
A new artificial intelligence system is drawing renewed attention to one of paleontology’s oldest controversies. At the center of the debate: a collection of fossilized footprints that bear a striking resemblance to those of modern birds, but date back tens of millions of years before the earliest known avian fossils. The software, developed by a team of European researchers, offers a fresh lens through which to examine fossilized tracks. Rather…
A team of researchers from the University of Tübingen, in collaboration with the University of Manchester and the Museum für Naturkunde in Berlin, has developed an artificial intelligence capable of analyzing and classifying these prints without human intervention. Their study, published in the journal Proceedings of the National Academy of Sciences, proposes a new method of identification based on unsupervised learning.
Coverage Details
Bias Distribution
- 34% of the sources lean Left, 33% of the sources are Center, 33% of the sources lean Right
Factuality
To view factuality data please Upgrade to Premium
