AI can revolutionize healthcare and boost economic outcomes. But it comes with a lot of risks, too.
Imagine a crew of astronauts headed to Mars. About 140 million miles away from Earth, they discover their spacecraft has a cracked O-ring. But instead of relying on a dwindling cache of spare parts, what if they could simply fabricate any part they needed on demand?
A team of Berkeley researchers, led by Ph.D. student Taylor Waddell, may have taken a giant leap toward making this option a reality. On June 8, they sent their 3D printing technology to space for the first time as part of the Virgin Galactic 7 mission.
Their next-generation microgravity printer—dubbed SpaceCAL—spent 140 seconds in suborbital space while aboard the VSS Unity space plane. In that short time span, it autonomously printed and post-processed a total of four test parts, including space shuttles and benchy figurines from a liquid plastic called PEGDA.
NVIDIA workflows connect real and synthetic data
Training foundation models for humanoid and other robots typically requires large amounts of data, noted NVIDIA. Teleoperation is one way to capture human demonstration data, but it can be expensive and time-consuming, it said.
Continue reading “NVIDIA adds tools it says will accelerate development of humanoid robots” »
We’re using AI and Google Maps driving trends to optimize traffic light patterns and improve traffic flow. Stop-and-go traffic in urban areas causes 29 times more emissions than on open roads. Researchers at Google are partnering with cities around the globe, from Rio to Jakarta. So far, local governments have saved fuel and lowered emissions for nearly 30 million car rides every month. Learn more about this research at: https://g.co/research/greenlight.
If you are a city representative or traffic engineer and are interested in joining the waiting list, please complete this form: https://docs.google.com/forms/d/e/1FA…
Continue reading “How AI is fixing traffic lights | Project Green Light” »
Large language models may enable real-time communication with extraterrestrial civilizations despite the vast distances between stars. We need to start thinking about what to tell them about us.
Sam Altman, CEO of OpenAI,… said some kind of national payments would likely be needed as technology killed more jobs even as it generated massive wealth for others.
Many tech entrepreneurs have long suggested that guaranteed income could cushion job losses from AI and automation. The latest and largest study of the idea was spearheaded by the man behind ChatGPT.
AI models can easily generate essays and other types of text. However, they’re nowhere near as good at solving math problems, which tend to involve logical reasoning—something that’s beyond the capabilities of most current AI systems.
But that may finally be changing. Google DeepMind says it has trained two specialized AI systems to solve complex math problems involving advanced reasoning. The systems—called AlphaProof and AlphaGeometry 2—worked together to successfully solve four out of six problems from this year’s International Mathematical Olympiad (IMO), a prestigious competition for high school students. They won the equivalent of a silver medal.
🧠🤖🩺
Research develops an automated system using machine learning to quantify motor symptoms in Parkinson’s disease and predict disease progression, offering new therapeutic insights.
Futurology: The global demand for AI computing has data centers consuming electricity like frat houses chug beer. But researchers from the University of Minnesota might have a wildly innovative solution to curb AI’s growing thirst for power with a radical new device that promises vastly superior energy efficiency.
The researchers have designed a new “computational random-access memory” (CRAM) prototype chip that could reduce energy needs for AI applications by a mind-boggling 1,000 times or more compared to current methods. In one simulation, the CRAM tech showed an incredible 2,500x energy savings.
Traditional computing relies on the decades-old von Neumann architecture of separate processor and memory units, which requires constantly moving data back and forth in an energy-intensive process. The Minnesota team’s CRAM completely upends that model by performing computations directly within the memory itself using spintronic devices called magnetic tunnel junctions (MTJs).
