‘Is AI Extending the Mind?’ is the second annual workshop by Cross Labs, this year held virtually from April 11 – 15, 2022.This workshop expands on our previ…
Category: robotics/AI – Page 1013
Over 50 percent of high-mass stars reside in multiple star systems. But due to their complex orbital interactions, physicists have a difficult time understanding just how stable and long-lived these systems are. Recently a team of astronomers applied machine learning techniques to simulations of multiple star systems and found a new way that stars in such systems can arrange themselves.
Classical mechanics has a notorious problem known as the three-body problem. While Newton’s laws of gravity can easily handle calculations of the forces between two objects and their subsequent evolution, there is no known analytic solution when you include a third massive object. In response to that problem, physicists over the centuries have developed various approximation schemes to study these kinds of systems, concluding that the vast majority of possible three-object arrangements are unstable.
But it turns out that there are a lot of multiple-star systems out there in the galaxy. Indeed, over half of all massive stars belong to at least a binary pair, and many of them belong to triple or quadruple star systems. Obviously, the systems last a long time. Otherwise, they would have flung themselves apart a long time ago before we had a chance to observe them. But because of the limitations of our tools, we have difficulty assessing how these systems organize themselves and what stable orbit options exist.
An international team led by researchers from Nanyang Technological University, Singapore (NTU Singapore) has developed a universal connector to assemble stretchable devices simply and quickly, in a “Lego-like” manner.
Stretchable devices including soft robots and wearable health care devices are assembled using several different modules with different material characteristics—some soft, some rigid, and some encapsulated.
However, the commercial pastes (glue), currently used to connect the modules often either fail to transmit mechanical and electrical signals reliably when deformed or break easily.
GitHub has updated the AI model of Copilot, a programming assistant that generates real-time source code and function recommendations in Visual Studio, and says it’s now safer and more powerful.
The company says the new AI model, which will be rolled out to users this week, offers better quality suggestions in a shorter time, further improving the efficiency of software developers using it by increasing the acceptance rate.
CoPilot will introduce a new paradigm called “Fill-In-the-Middle,” which uses a library of known code suffixes and leaves a gap for the AI tool to fill, achieving better relevance and coherence with the rest of the project’s code.
NASA has turned to AI to help them develop, and build, more robust, lightweight components for its spacecraft of the future.
NASA’s Goddard Space Flight Center in Maryland has been using commercially available AI software to design specialized, bespoke parts, called “evolved structures,” for its missions. They also look a little “out of this world.”
“They look somewhat alien and weird,” Research Engineer Ryan McClelland said, “but once you see them in function, it makes sense.”
A robot named Icefin operated on previously impossible survey areas of the Thwaites Glacier.
Rob Robbins, USAP Driver.
The findings boost our understanding of one of the fastest-changing ice-ocean systems in Antarctica and, significantly, the glacier’s role in future sea level rise.
Ex-CEO of Google, Eric Schmidt, advocated for implementing AI for the U.S. military use to compete against China and other rivals.
Former Google CEO Eric Schmidt has advocated for the military use of artificial intelligence (AI) to build a more robust and adaptable defense system for the United States against China and other rivals.
“Every once in a while, a new weapon, a new technology comes along that changes things,” he told Wired.
Getty Images.
When Ben Reinhardt was an undergrad at Caltech, he often passed a mural painted on the back of a building on campus. It included a quote from Theodore von Kármán, a scientist and engineer who served as the first director of JPL: “Scientists study the world as it is, engineers create the world that never has been.”
But a recent paper published in Nature described a decline in scientific progress over the last few decades.
Some of the other new scientific institutions experimenting with shaking up the traditional structure of research include Arcadia Institute, based in the Bay Area, which is dedicated to a translational program that, “will provide a unique combination of funding, support, and access to accelerate new product development.”
Alexey Guzey is another leader in this space; he recognized a gap in opportunities for young scientists and initiated New Science, which plans to finance entire labs outside of academia, and turn “the process of doing science into an experiment itself.” For a better overview of all the new types of research organizations, check out Sam Arbesman’s The Overedge Catalog.
A team of computer programmers at IT University of Copenhagen has developed a new way to encode and generate Super Mario Bros. levels—called MarioGPT, the new approach is based on the language model GPT-2. The group outlines their work and the means by which others can use their system in a paper on the arXiv pre-print server.
Mario Brothers is a video game first introduced in 1983—it involves two Italian plumbers emerging from a sewer and attempting to rescue Princess Peach, who has been captured and held by Bowser. To rescue her, the brothers must travel (via input from the game player) across a series of obstacles made of pipes and bricks. As they travel, the terrain changes in accordance with the level they have achieved in the game. In this new effort, the team in Denmark has recreated one aspect of the game—the number of levels that can be traversed.
The researchers used Generative Pre-trained Transformer 2 (GPT-2)—an open-source language model created by a team at OpenAI, to translate user requests into graphical representations of Super Mario Brothers game levels. To do so, they created a small bit of Python code to help the language model understand what needed to be done and then trained it using samples from the original Super Mario Bros. game and one of its sequels, “Super Mario Bros.: The Lost Levels.”