Everyone knows about self-driving cars that use cutting-edge machine intelligence to navigate. But robots might have another important role to play in future cars: Changing our tires. According to a recent report, a new Y Combinator startup called RoboTire, currently in semi-stealth mode, is building robots that can change car tires in a fraction of the time it currently takes human mechanics.
“We can do a set of four tires, put in to pull out, in 10 minutes,” RoboTire CEO Victor Darolfi told TechCrunch. “It normally takes about 60 minutes for a human operator to do a set of four. Some can go faster, but they really can’t do that eight hours a day.”
A description of RoboTire on LinkedIn describes its mission as “automating vehicle maintenance for today and future autonomous vehicles with robot and software, starting with tire changing in which we can do a set of four tires in 10 minutes from pull-in to pull-out.”
To fulfill its ambitious engineering mission, RoboTire has reportedly teamed up with the robotics division of Mitsubishi to create a robot arm system that could be installed by service centers, dealers, and assorted other automotive outlets. It reportedly costs around $250,000 at the moment, with plans to charge $5 to $7 per changed tire when the company begins testing out the robot solution in the real world. This will likely increase to $10 to $15 when the final product is ready to go. RoboTire thinks its robot will generate around $10,000 per month, which would earn back the cost of a $250,000 unit in just over two years. It has been in discussions with “a number of high-profile companies” to install the robot at various auto repair shops.
Right now, there’s no more additional information available. RoboTire’s website contains no extra information, only the ability to sign up for email updates when they’re available.
The biggest question we have? Does RoboTire have any plans to create a smaller miniature version of its tire-change robot we could store in our cars in case of a particularly nasty flat? Because that would totally be a sight to see at the side on the shoulder of a highway.
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.