The European Organization for Nuclear Research (CERN), the organization that brought us the Large Hadron Collider (LHC) and the Higgs Boson particle, is aiming to one-up itself with an even larger and more powerful circular particle collider. The proposed Future Circular Collider will eventually replace the LHC and pave the way for extensive particle physics research through the 21st century.
The LHC went online in 2010 and lead to the discovery of the Higgs Boson particle, which had been theorized but never observed until the LHC experiments. The LHC is still being used to make exciting discoveries and expected to remain operational until 2053, but it can only do so much. To go further beyond Higgs Boson, the physicists at CERN want to build another collider that will take over where the LHC left off. Because it takes so long to build a collider and bring it online, scientist are starting their planning now
More than 1,300 scientists worked for the past five years to lay out a roadmap for the future of particle physics research. The centerpiece of this proposed research plan is the Future Circular Collider which will be significantly more powerful than the LHC. The proposed collider will have a circumference of 62 miles and energy of up to 100 TeV as compared to the 16 miles and 13 TeV for the LHC. Scientists will meet for the next two years to discuss the plans for the Future Circular Collider and particle physics research. Once constructed, the new particle collider will have a lifespan of between 15 to 20 years. It is expected to cost 9 billion euros (about $10.25 billion) to design and build.
Scientists hope a more powerful collider will open the door to a new world of physics, just like it did when they discovered the Higgs boson particle with the LHC. Scientists will be able to delve deeper into the Higgs Boson and other aspects of subatomic physics, some of which lie outside the existing Standard Model of particle physics. An even larger circular collider also may help researchers explore topics such as dark matter and matter-antimatter interactions. These experiments could lead to a better understanding of the origin and early development of our universe.
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.