The U.S. Navy is interested in underwater robots which could be used for a wide range of applications, from surveillance missions to carrying out inspection or exploration tasks. But, just as is the case with airborne drones, underwater robots face one of their biggest challenges in the limited amount of battery life that they possess. This severely limits certain possibilities, particularly when it comes to long-haul missions which require them to stay submerged for lengthy periods of time.
Fortunately, a solution may be on hand — and while it’s far from glamorous, it could nonetheless prove to be a game-changer for swimming robots when their power reserves run low. The idea: To utilize microbial fuel cells which feed on organic matter on the seabed. In practical terms, what this means is that it would power itself by chowing down on fish poop.
The microbial fuel cells would harvest this, err, material, which should be in plentiful supply in all but the deepest parts of the ocean, and then convert it into electricity. “It’s like a battery made by biology,” Meriah Arias-Thode at the Naval Information Warfare Center Pacific in San Diego, told New Scientist.
So far, the team showcased the use of three microbial fuel cells in powering an assortment of basic sensors, including one for monitoring water acidity levels. While these devices are ordinarily able to last just a few weeks underwater, a sensor powered using the microbial fuel cells meant that they could last for eight months.
It’s possible that this same technology could be used to power underwater robots entirely, rather than just their sensors. Because of the low power density of microbial fuel cells (it would take approximately one year for the current units to charge even a small underwater drone), an array of these fuel cells could be used. The Navy could also develop lower-powered underwater vehicles which do not have the same battery demands.
Possibly the most promising solution would be to develop large battery packs on the seabed, which could be used to charge underwater drones so that they could stay submerged for long periods of time. Watch this space.
AI companies spend enormous amounts of time convincing us their technology is safe and secure. But what happens when those same companies become targets of increasingly sophisticated AI-powered attacks? Anthropic, the company behind Claude, is now facing that question. According to a Washington Post report, cybersecurity startup Cathedral discovered vulnerabilities that let its researchers access sensitive information in Anthropic's internal systems.
The researchers used AI agents to identify potential entry points in less than 12 hours. Interestingly, they didn't need to crack Claude itself. Instead, they exploited weaknesses in third-party business applications that Anthropic uses for everyday operations. Anthropic's security testing program disclosed the findings, which allows outside researchers to identify and report vulnerabilities.
Military working dogs charge into places the rest of us would flee. So, I understand why anyone would want to breed tougher, healthier dogs for the job.
Warhound, a new startup from Colossal Biosciences co-founder Ben Lamm and Anduril co-founder Palmer Luckey, has stepped out of stealth with $200 million to clone and gene-edit working dogs for American troops, police departments, and emergency services (via TechCrunch).
AI can describe a picture, identify objects, and answer questions about what a camera sees. But when a blind user needs a quick answer, getting an entire spoken essay about the scene isn't exactly helpful. Researchers at the University of Maryland (UMD) have found that AI-powered visual assistance can leave blind users doing too much work to get the information they actually need.
The study, titled Say It My Way: Exploring Control in Conversational Visual Question Answering with Blind Users, examined how people interact with Be My AI, an AI-powered feature of the Be My Eyes app. The researchers found that users often received lengthy descriptions that didn't adequately address their immediate goals, forcing them to ask follow-up questions or find ways to steer the AI towards a more useful answer.