From 2000 to 2009, planet Earth lost more than 32 million acres of tropical rainforest. This large-scale deforestation accounts for about 15 percent of total global carbon dioxide emissions annually. Thankfully, a company is looking to reverse this trend with a fleet of tree-planting drones.
Since we last reported on BioCarbon in 2015, the company has increased both the capacity and sophistication of its program. Originally, BioCarbon estimated that its tree planting drone would be able to seed up to 36,000 trees per day. Now, the company sets this per diem goal at 100,000 per day. Utilizing 60 teams, BioCarbon estimates it could plant more than one billion trees in a given year.
Every year, the planet loses about six billion trees. To compensate, replanting is often done by hand, however, this method is exceedingly inefficient and expensive. BioCarbon Engineering Chief Technology Officer, Dr. Susan Graham, believes an armada of arborist drones could increase seeding rates ten fold at just 20 percent of the cost of current hand planting methods.
BioCarbon has designed a platform that first maps a specific area to pinpoint arable locations prior to planting. First, the team deploys a fixed-wing drone to create a 3D model of a potential location. While this drone is in the air, a series of onboard sensors analyze ground topography, moisture, slopes, and obstructions (rocks, streams, etc).
Once an area has been properly mapped, then it’s time to sling some seeds. This 3D map is used to optimize an overall planting pattern and also determine a heterogeneous assortment of tree species. BioCarbon CEO, Lauren Fletcher, says the company’s current drones are capable of carrying 150 seed pods while in flight, and that each of these seed payloads incorporate a biodegradable housing to further ensure germination. Recently, the team tested one of its drones in New South Wales to repurpose land once used for coal mining.
“The way we plant trees today is very similar to how we planted them hundreds of years ago,” Faruqi explained, “So there’s major room for innovation in increasing the success rate of tree planting and also in improving the maintenance and monitoring of the restored land.”
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.