Yes, eating carrots can help your eyesight. The World War II propaganda that touted the veggie wasn't totally wrong, but carrots still won't give you night vision. Carrots' beta-carotene pigment can help support retina health, but the root vegetable still has its limits. Breakthroughs, discoveries, and DIY tips sent six days a week. In a British propaganda poster from World War II, an illustration in shadowy tones captures a dramatic nighttime scene: a woman and young girl peer around a black automobile, as if looking for a quick escape.

A degree in computer science used to promise a cozy career in tech. Now, students' ambitions are shaped by AI, in fields that blend computing with analysis, interpretation, and data. In the early 2010s, nearly every STEM -savvy college-bound kid heard the same advice: Learn to code . Python was the new Latin. Computer science was the ticket to a stable, well-paid, future-proof life.

In both 2016 and 2018, a census of the highly-endangered Grevy's zebra population was enabled by the Great Grevy's Rally (GGR), a citizen science event that produces population estimates via expert and algorithmic curation of volunteer-captured images. A complementary, scalable, and long-term Grevy's population monitoring approach involves deploying camera trap networks. However, in both scenarios, a substantial majority of zebra images are not usable for individual identification due to poor in-the-wild imaging conditions; camera trap images in particular present high rates of occlusion and high spatio-temporal similarity within image bursts. Our proposed filtering pipeline incorporates animal detection, species identification, viewpoint estimation, quality evaluation, and temporal subsampling to obtain individual crops suitable for re-ID, which are subsequently curated by the LCA decision management algorithm. Our method processed images taken during GGR-16 and GGR-18 in Meru County, Kenya, into 4,142 highly-comparable annotations, requiring only 120 contrastive human decisions to produce a population estimate within 4.6% of the ground-truth count. Our method also efficiently processed 8.9M unlabeled camera trap images from 70 cameras at the Mpala Research Centre in Laikipia County, Kenya over two years into 685 encounters of 173 individuals, requiring only 331 contrastive human decisions.

This paper combines deep learning techniques for species detection, 3D model fitting, and metric learning in one pipeline to perform individual animal identification from photographs by exploiting unique coat patterns. This is the first work to attempt this and, compared to traditional 2D bounding box or segmentation based CNN identification pipelines, the approach provides effective and explicit view-point normalisation and allows for a straight forward visualisation of the learned biometric population space. Note that due to the use of metric learning the pipeline is also readily applicable to open set and zero shot re-identification scenarios. We apply the proposed approach to individual Grevy's zebra (Equus grevyi) identification and show in a small study on the SMALST dataset that the use of 3D model fitting can indeed benefit performance. In particular, back-projected textures from 3D fitted models improve identification accuracy from 48.0% to 56.8% compared to 2D bounding box approaches for the dataset. Whilst the study is far too small accurately to estimate the full performance potential achievable in larger-scale real-world application settings and in comparisons against polished tools, our work lays the conceptual and practical foundations for a next step in animal biometrics towards deep metric learning driven, fully 3D-aware animal identification in open population settings. We publish network weights and relevant facilitating source code with this paper for full reproducibility and as inspiration for further research.

For years, the Los Angeles Police Department (LAPD) hasn't given a clear answer on whether it uses facial recognition in its policing work. On Monday, the agency told The Los Angeles Times it has used the technology nearly 30,000 times since late 2009. The LAPD uses the Los Angeles County Regional Identification System (LACRIS), a database of more than 9 million mugshots maintained by the Los Angeles County Sheriff's Department. At one point, more than 500 LAPD personnel had access to the system, though the department claims that the number is closer to 300 in recent months. Josh Rubenstein, a spokesperson for the LAPD, said he couldn't be sure how many arrests LACRIS has helped the police department make.

The Los Angeles Police Department has used facial-recognition software nearly 30,000 times since 2009, with hundreds of officers running images of suspects from surveillance cameras and other sources against a massive database of mugshots taken by law enforcement. The new figures, released to The Times, reveal for the first time how commonly facial recognition is used in the department, which for years has provided vague and contradictory information about how and whether it uses the technology. The LAPD has consistently denied having records related to facial recognition, and at times denied using the technology at all. The truth is that, while it does not have its own facial-recognition platform, LAPD personnel have access to facial-recognition software through a regional database maintained by the Los Angeles County Sheriff's Department. And between Nov. 6, 2009, and Sept. 11 of this year, LAPD officers used the system's software 29,817 times.

To make self-driving vehicles a reality and to bring them on roads, they need to be able to safely and flawlessly navigate traffic without collisions or jams. Northwestern University researchers have made this possible by developing the first decentralized algorithm with a collision-free guarantee. The algorithm was tested in a simulation of 1,024 robots and in a throng of 100 real robots by the researchers in the laboratory. The robots carefully and efficiently followed to form a command shape. "If you have many autonomous vehicles on the road, you don't want them to collide with one another or get stuck in a deadlock," said Northwestern's Michael Rubenstein, who led the study. "By understanding how to control our swarm robots to form shapes, we can understand how to control fleets of autonomous vehicles as they interact with each other."

Give Jason Holmberg 10,000 zebra photos and he'll find the specific individual zebra you're looking for, no problem. "It could take two minutes," he said. Holmberg is executive director of the nonprofit Wild Me. The Portland-based organization has developed a digital tool called Wildbook that uses artificial intelligence and machine learning to expedite wildlife identification. In tandem with citizen science, Wildbook is able to condense years of human work -- like photographing thousands of animals and identifying each by hand -- into a matter of weeks.

Give Jason Holmberg 10,000 zebra photos and he'll find the specific individual zebra you're looking for, no problem. "It could take two minutes," he said. Holmberg is executive director of the nonprofit Wild Me. The Portland-based organization has developed a digital tool called Wildbook that uses artificial intelligence and machine learning to expedite wildlife identification. In tandem with citizen science, Wildbook is able to condense years of human work -- like photographing thousands of animals and identifying each by hand -- into a matter of weeks.

Give Jason Holmberg 10,000 zebra photos and he'll find the specific individual zebra you're looking for, no problem. "It could take two minutes," he said. Holmberg is executive director of the nonprofit Wild Me. The Portland-based organization has developed a digital tool called Wildbook that uses artificial intelligence and machine learning to expedite wildlife identification. In tandem with citizen science, Wildbook is able to condense years of human work -- like photographing thousands of animals and identifying each by hand -- into a matter of weeks.