AI infrastructure engineers, storage developers, and cloud service providers need fast and secure access to high-capacity file and object storage to support AI...AI infrastructure engineers, storage developers, and cloud service providers need fast and secure access to high-capacity file and object storage to support AI workloads. AI workloads increasingly require high-speed data access for training, fine-tuning, inference context, tool calls, searches, and database lookups. Much of this data lies in files and objects stored both on-premises and in… Source

Expanding AI Storage Access with NVIDIA cuObject and the NVIDIA SCADA Server SDK

Google pays about 100 digital publishers for content used in AI Overviews, AI Mode, and Gemini, according to The Information. Payments range from under $1,000 over several months to more than $1 million a year. Some participants don't know how Google calculates payments, while some larger publishers are holding out for higher rates. The article Google is paying almost no publishers almost nothing for content used in AI answers appeared first on The Decoder.

Google is paying almost no publishers almost nothing for content used in AI answers

Space was always supposed to be the final frontier of human exploration. It’s shaping up to be the final frontier for artificial intelligence too.Last December, NASA’s Jet Propulsion Laboratory used Anthropic’s Claude models to help plan two Mars drives for the Perseverance rover, with human planners checking and adjusting the route before upload. In May, NASA and IBM put a compressed AI model on the International Space Station and a satellite to identify things like floods and clouds from orbit, the first model of its kind demonstrated in space. And in July, astronauts on the ISS tested a large language model to see if it could help with questions on maintenance procedures.These experiments point to a larger shift in space engineering. For decades, engineers on Earth determined what a machine in space would do, and the machine would do exactly that. Now, researchers are testing whether nondeterministic systems like generative AI can give spacecraft more flexibility to interpret their surroundings, plan tasks, and one day make decisions for themselves. The technology is still far from trustworthy enough to hand over control of a spacecraft, but engineers are starting to ask whether they can afford not to do so as missions become more complex, distant, and numerous.Why Spacecraft Need True AutonomySpacecraft have been operating autonomously for decades. But autonomy has never been the dominant model, in part because space engineers have prized systems whose behavior they can predict.“Autonomy often does not have a deterministic outcome, which means that how you got into a certain situation changes the behavior,” says Robert Ambrose, the former chief of NASA’s Software, Robotics, and Simulation Division. “So if you come into the same situation but from different paths, the outcome could be different. And so engineers hate that.”Ambrose spent much of his career working on autonomous systems at NASA, including autonomy for the Orion spacecraft, NASA’s deep space and lunar orbiter spacecraft, and Robonaut 2, a humanoid robot designed to work alongside astronauts that went to space in 2011. With Orion, he saw how quickly the testing problem could multiply. Engineers had to consider not just what the spacecraft might do, but all the different ways it could have arrived at a decision.But Ambrose says engineers found ways to manage that complexity, including automating the testing itself. “We fought the challenges of autonomy using autonomy,” he says. “That actually works.”The need for autonomy becomes even more obvious the farther a mission travels from Earth. Ambrose points to a possible mission to Europa, Jupiter’s ice-crusted moon, where a spacecraft could dive through a water plume erupting from beneath the surface. The plume could appear too quickly for engineers on Earth to direct the spacecraft into it. “It’s up to the spacecraft to make a decision, and we’ll be watching what happened an hour ago,” he says. A mission like that, he added, is “totally impossible” without giving the machine real autonomy. Icarus Robotics engineers gather valuable training data for a free-flying robotic system—destined for the International Space Station—during a zero-g flight.Icarus RoboticsAdapting Robotics to Microgravity PhysicsAs AI and robotics advance on Earth, a commercial space boom is creating new opportunities to put those technologies to work in orbit. But what works on Earth does not necessarily work in space.Icarus Robotics is developing what it calls a robotic labor force for space. This includes Joy, a free-flying robotic system. Joy recently finished zero-gravity testing in Canada ahead of a planned deployment to the ISS, where one of its first tasks would be moving cargo bags between modules. The company plans to start with teleoperation, using that data to eventually train the robots to work on their own.“What the rollout will probably look like is something much closer to beginning with partial autonomy, so you still have human supervision in the loop at all times,” says Jamie Palmer, Icarus’s cofounder and CTO.A robot trained on Earth learns from the physics of the environment around it, Palmer says, and in orbit, the physics is entirely different.“If you take the newest Gemini robotics model, or you take the newest physical intelligence model, and you put it in zero-g there, it’s just going to fail immediately,” he says. On Earth, for example, a robot learns that when it pushes something off a table, the object falls. In orbit, it keeps moving.That leaves Icarus with a problem the terrestrial robotics industry also faces, but in a more extreme form: There is very little real-world data from the environment where its robots will operate. The company is combining demonstrations from its robots in microgravity with simulations and tests on Earth to build its own dataset. “I wish there was” an available dataset Icarus could download and use, says Ethan Barajas, the company’s cofounder and CEO. “But there’s not today, not in a meaningful way.”Managing Autonomous Spacecraft RiskThe challenge is not simply teaching spacecraft to act on their own. It is figuring out how to manage the risks of giving them more freedom.“It has been mind-boggling to me how little autonomy we have in space applications,” says Ufuk Topcu, an engineering professor at the University of Texas at Austin and the director of the Center for Autonomy. “Because it’s exactly the place where human involvement is extremely hard, the stakes are high, and you need to act fast.”Topcu says that the goal cannot be to guarantee that autonomous systems will never do anything wrong. They’re most useful in situations humans cannot anticipate, he says, so instead researchers need to start with restricted applications, learn how the systems behave, and gradually expand where and how they are used. The real question, in his view, is how well the risk of deployment is managed, not whether they can be fully eliminated.That may become increasingly important as the space industry changes. For most of the space age, a small number of government agencies designed missions that could take decades to develop and operate. Commercial companies are now putting more spacecraft into orbit, and new missions can be developed and launched much faster.“Space used to have very slow innovation cycles,” Topcu says. “They would think of a mission concept and spend 10 or 15 years on it. It’s not like that anymore. Everything is evolving faster now.”

Compute & Infrastructure

Get Compute & Infrastructure signals in your inbox

Deploy Autonomous AI Capabilities & Skills Runtimes

Related desks