A new iOS game from NASA asks players to identify the coral in 3D images of the ocean floor. Doing so will help train AI to do it automatically.
In nuclear physics, like much of science, detailed theories alone aren’t always enough to unlock solid predictions. There are often too many pieces, interacting in complex ways, for researchers to follow the logic of a theory through to its end. It’s one reason there are still so many mysteries in nature, including how the universe’s basic building blocks coalesce and form stars and galaxies. The same is true in high-energy experiments, in which particles like protons smash together at incredible speeds to create extreme conditions similar to those just after the Big Bang.
Fortunately, scientists can often wield simulations to cut through the intricacies. A simulation represents the important aspects of one system—such as a plane, a town’s traffic flow or an atom—as part of another, more accessible system (like a computer program or a scale model). Researchers have used their creativity to make simulations cheaper, quicker or easier to work with than the formidable subjects they investigate—like proton collisions or black holes.
Simulations go beyond a matter of convenience; they are essential for tackling cases that are both too difficult to directly observe in experiments and too complex for scientists to tease out every logical conclusion from basic principles. Diverse research breakthroughs—from modeling the complex interactions of the molecules behind life to predicting the experimental signatures that ultimately allowed the identification of the Higgs boson—have resulted from the ingenious use of simulations.
In what seems to be the automotive equivalent of a brazen act intended to show dominance, Elon Musk has announced on Twitter that Tesla will be releasing a “Plaid” version of the Cybertruck. Musk did not provide further details in his tweet, though there is little doubt that the feature will make the already daunting Tri-Motor pickup into something downright scary for its fossil fuel-powered rivals like the Ford F-150 Raptor.
Elon Musk’s Plaid Cybertruck revelation was shared on Twitter late Wednesday. While responding to Tesla owner-enthusiast and Third Row Podcast member Sofiaan Fraval, Musk stated that he would be driving a Plaid version of the all-electric pickup truck for personal use. This came as a pleasant surprise to the EV community, especially since this is the first time that such a version of the Cybertruck has been mentioned.
Elon Musk has mentioned in the past that the Tesla Cybertruck will have the handling and performance of a sports car, and this was highlighted during the vehicle’s unveiling event. Apart from showcasing its strength by having the hulking all-electric pickup pull a Ford F-150 like a rag doll, Tesla also featured the Cybertruck drag racing a Porsche 911, and crushing the iconic sports car in the process. These are bold demonstrations, and each was met with equal parts excitement and skepticism from the auto community.
Sarkis Tatigian joined the Navy in 1942. He’s been there ever since, until his death this week at the age of 96.
Tatigian — who first enlisted as a radio inspector at the now-defunct Philadelphia Naval Shipyard — went on to become the small business advocate at Naval Sea Systems Command. He had held that title since 1979, six years after he first became eligible for retirement. But he’d been working on the Navy’s small business programs since 1951, two years before the Small Business Administration even existed.
Even well into his 90s, Tatigian reportedly commuted to work at the Washington Navy Yard via public transit every day. When we last spoke to him in late 2017, he had only taken one vacation day that year.
NASA has released a detailed plan for an ‘Artemis Base Camp’ that will be home to first woman and next man on the moon in 2024.
The 13-page document highlights elements such as a terrain vehicle for transporting the astronauts around the landing zone, a permanent habit and a mobility platform to travel across the lunar surface.
Continue reading “NASA reveals its plan for ‘Artemis Base Camp’ on the moon” »
These days, neural networks, deep learning and all types of sensors allow AI to be used in healthcare, to operate self-driving cars and to tweak our photos on Instagram.
In the #future, the ability to learn, to emulate the creative process and to self-organize may give rise to previously unimagined opportunities and unprecedented threats.
Continue reading “The Future is Now | Life after Artificial Intelligence” »
Stratolaunch Systems, founded by Microsoft’s Paul Allen, is shifting to the development of hypersonic aircraft that may soon carry people and cargo.
Researchers have designed a machine learning method that can predict battery health with 10x higher accuracy than current industry standard, which could aid in the development of safer and more reliable batteries for electric vehicles and consumer electronics.
The researchers, from Cambridge and Newcastle Universities, have designed a new way to monitor batteries by sending electrical pulses into them and measuring the response. The measurements are then processed by a machine learning algorithm to predict the battery’s health and useful lifespan. Their method is non-invasive and is a simple add-on to any existing battery system. The results are reported in the journal Nature Communications.
Predicting the state of health and the remaining useful lifespan of lithium-ion batteries is one of the big problems limiting widespread adoption of electric vehicles: it’s also a familiar annoyance to mobile phone users. Over time, battery performance degrades via a complex network of subtle chemical processes. Individually, each of these processes doesn’t have much of an effect on battery performance, but collectively they can severely shorten a battery’s performance and lifespan.
A Google interview candidate recently asked me: “What are three big science questions that keep you up at night?” This was a great question because one’s answer reveals so much about one’s intellectual interests — here are mine:
Q1: Can we imitate “thinking” from only observing behavior?
Suppose you have a large fleet of autonomous vehicles with human operators driving them around diverse road conditions. We can observe the decisions made by the human, and attempt to use imitation learning algorithms to map robot observations to the steering decisions that the human would take.
