The world's first 3D-printed microscope costs only $60 to build

The 1,500-year-old site was hidden beneath the dark, damp ice. Breakthroughs, discoveries, and DIY tips sent every weekday. In the fall of 2024, a hiker named Helge Titland was trekking through Aurlandsfjellet, a mountainous region and plateau in Norway and got a little more than just some time with nature. Titland found some strange wooden stakes peaking out of melting snow. He wisely reported it to local archaeologists, but snow returned before the team could investigate.

Breathtaking drone video taken in the Arctic Circle captured a spellbinding reindeer'cyclone.' When threatened, reindeer will begin to stampede in a circle, making it hard for a predator to find an individual target. In the clip, the herd's fawns and does are in the middle of the swirl with the bucks running around them in a protective'dance.' The deer stampede was captured by a photographer last week in Murmansk, Russia, right before a veterinarian was about to give the herd its anthrax vaccinations. Such behavior has been observed in dolphins, bison and even elephants, but the aerial view--coupled with the herd's speed and size--makes for a truly hypnotic visual.

Control schemes that learn using measurement data collected online are increasingly promising for the control of complex and uncertain systems. However, in most approaches of this kind, learning is viewed as a side effect that passively improves control performance, e.g., by updating a model of the system dynamics. Determining how improvements in control performance due to learning can be actively exploited in the control synthesis is still an open research question. In this paper, we present AntLer, a design algorithm for learning-based control laws that anticipates learning, i.e., that takes the impact of future learning in uncertain dynamic settings explicitly into account. AntLer expresses system uncertainty using a non-parametric probabilistic model. Given a cost function that measures control performance, AntLer chooses the control parameters such that the expected cost of the closed-loop system is minimized approximately. We show that AntLer approximates an optimal solution arbitrarily accurately with probability one. Furthermore, we apply AntLer to a nonlinear system, which yields better results compared to the case where learning is not anticipated.

Skyqraft, a Swedish startup using AI and drones for electricity power-line inspection, has picked up $505,000 in early backing. Leading the round is "startup generator" and investor Antler, with participation from a number of angels including Claes Ekström and Tomas Kåberger. Founded in March 2019 and launched that September, Skyqraft provides what it calls "smart" infrastructure inspections for power-lines. It uses unmanned airplanes, combined with AI, to gather images and detect risk automatically. This is in contrast to the status quo, where power-lines are typically inspected by teams of people and helicopters, which isn't idea on a number of fronts. "Power-line inspections most importantly are not environmentally friendly, very costly and unsafe with the use of helicopters and people," Skyqraft co-founder and CMO Sakina Turabali tells TechCrunch.