Laboratory accidents pose significant risks to human life and property, underscoring the importance of robust safety protocols. Despite advancements in safety training, laboratory personnel may still unknowingly engage in unsafe practices. With the increasing reliance on large language models (LLMs) for guidance in various fields, including laboratory settings, there is a growing concern about their reliability in critical safety-related decision-making. Unlike trained human researchers, LLMs lack formal lab safety education, raising questions about their ability to provide safe and accurate guidance. Existing research on LLM trustworthiness primarily focuses on issues such as ethical compliance, truthfulness, and fairness but fails to fully cover safety-critical real-world applications, like lab safety. To address this gap, we propose the Laboratory Safety Benchmark (LabSafety Bench), a comprehensive evaluation framework based on a new taxonomy aligned with Occupational Safety and Health Administration (OSHA) protocols. This benchmark includes 765 multiple-choice questions verified by human experts, assessing LLMs and vision language models (VLMs) performance in lab safety contexts. Our evaluations demonstrate that while GPT-4o outperforms human participants, it is still prone to critical errors, highlighting the risks of relying on LLMs in safety-critical environments. Our findings emphasize the need for specialized benchmarks to accurately assess the trustworthiness of LLMs in real-world safety applications.

Laboratory automation is an expensive and complicated endeavor with limited inflexible options for small-scale labs. We develop a prototype system for tending to a bench-top centrifuge using computer vision methods for color detection and circular Hough Transforms to detect and localize centrifuge buckets. Initial results show that the prototype is capable of automating the usage of regular bench-top lab equipment.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. For the past year, five fit, academically superior men and women have been spun in centrifuges, submerged for hours, deprived temporarily of oxygen, taught to camp in the snow, and schooled in physiology, anatomy, astronomy, meteorology, robotics, and Russian. On Monday, the five Europeans and an Australian graduated from basic training with a new title: astronaut. At a ceremony in Cologne, Germany, ESA added the five newcomers to its astronaut corps eligible for missions to the International Space Station, bringing the total to 11. HOW ASTRONAUTS ON THE ISS ARE TACKLING THE LATEST'UNEXPECTED CHALLENGES' MILES ABOVE THE EARTH ESA has negotiated with NASA for three places on future Artemis moon missions, although those places will likely go to the more senior astronauts, according to ESA Director General Josef Aschbacher.

Travelling to Mars could affect the mind of the astronauts making the long journey - leaving them more likely to misidentify facial expressions, study warns. Current NASA plans could see the first humans step foot on the surface of the Red Planet by the end of the next decade as part of the Artemis mission. University of Pennsylvania researchers found that weightlessness leaves people more likely to see facial expressions as angry - rather than happy or neutral. The phenomenon could endanger missions to the Red Planet, warn scientists, as the astronauts will be weightless on a spaceship together for up to eight months. Studies have shown microgravity causes structural changes in the brain, but it's not fully understood how this translates to changes in behaviour.

Center for Security Policy CEO Fred Fleitz provides insight on'America's News HQ.' DUBAI, United Arab Emirates (AP) -- Iran said Saturday it plans to enrich uranium up to 20% at its underground Fordo nuclear facility "as soon as possible," pushing its program a technical step away from weapons-grade levels as it increases pressure on the West over the tattered atomic deal. The move comes amid heightened tensions between Iran and the U.S. in the waning days of the administration of President Donald Trump, who unilaterally withdrew America from Tehran's nuclear deal in 2018. That set in motion an escalating series of incidents capped by a U.S. drone strike that killed a top Iranian general in Baghdad a year ago, an anniversary coming Sunday that has American officials now worried about possible retaliation by Iran. Iran's decision to begin enriching to 20% a decade ago nearly brought an Israeli strike targeting its nuclear facilities, tensions that only abated with the 2015 atomic deal. A resumption of 20% enrichment could see that brinksmanship return.

Oh, how the blind faith in technology keeps spinning its subpar centrifuge until the excessive speed of make-believe catapults its favorite toys into reality. It pays to listen to me, I tell you. I have made similar predictions about the subpriming of venture capital, the deplorable stance of Facebook, the re-risking of asset management, the voodoo of economics, all coming true after the pageantry of positivity is forced to meet reality. We must build more advanced operating-systems for humanity and hold the promises of policy, capital, and innovation to account. I wrote in 2011 how the enormous amount of false-positives would make Siri useless, now 8-years later Apple's former long-time Siri head confirmed my views, Siri does not deliver.

A country's dexterity with artificial intelligence technology might be the next strong source of national pride and international power. Knowing it would lay the foundation for the future of medicine, IBM captured the world's imagination in 2011 with Watson, a supercomputer that not only won Jeopardy!, but beat trivia superstar Ken Jennings in the process. The novel cognitive computing technology was quickly adapted to "read" the thousands of medical research papers published weekly in order to diagnose cancer patients more accurately than human doctors seemingly could. It's a banner technology for IBM, a company that remains no slouch in its 105 years of operation Now five years after Watson's debut, Japanese researchers at Kyoto University and Fujitsu are collaborating to build their own computing technology that's fairly characterized as a response to Watson. Skipping the game shows and going straight to medical applications, the Japanese system aims to close the gap in understanding how our genes determine our health by accounting for a patient's genetic code in its computer-generated diagnoses.

A country's dexterity with artificial intelligence technology might be the next strong source of national pride and international power. Knowing it would lay the foundation for the future of medicine, IBM captured the world's imagination in 2011 with Watson, a supercomputer that not only won Jeopardy!, but beat trivia superstar Ken Jennings in the process. The novel cognitive computing technology was quickly adapted to "read" the thousands of medical research papers published weekly in order to diagnose cancer patients more accurately than human doctors seemingly could. It's a banner technology for IBM, a company that remains no slouch in its 105 years of operation Now five years after Watson's debut, Japanese researchers at Kyoto University and Fujitsu are collaborating to build their own computing technology that's fairly characterized as a response to Watson. Skipping the game shows and going straight to medical applications, the Japanese system aims to close the gap in understanding how our genes determine our health by accounting for a patient's genetic code in its computer-generated diagnoses.

Presidents have ordered troop surges into Iraq and Afghanistan, and President Trump is applying that strategy in another way. His Feb. 11 executive order requires a surge in research and development of artificial intelligence (AI) technologies. The order requires federal agencies to drive progress on AI through their own research and development and through investing in R&D in the private sector and academia. "Artificial intelligence" and "machine learning" are terms used interchangeably despite the vast difference between them. To analyze where we need to go, we first need to define the terms.

We introduce GeNet, a method for shotgun metagenomic classification from raw DNA sequences that exploits the known hierarchical structure between labels for training. We provide a comparison with state-of-the-art methods Kraken and Centrifuge on datasets obtained from several sequencing technologies, in which dataset shift occurs. We show that GeNet obtains competitive precision and good recall, with orders of magnitude less memory requirements. Moreover, we show that a linear model trained on top of representations learned by GeNet achieves recall comparable to state-of-the-art methods on the aforementioned datasets, and achieves over 90% accuracy in a challenging pathogen detection problem. This provides evidence of the usefulness of the representations learned by GeNet for downstream biological tasks.