Breakthroughs, discoveries, and DIY tips sent every weekday. Mosquitoes may have just met their match: A prescription drug already used to treat a rare genetic disease in humans can make a person's blood poisonous to insecticide-resistant, malaria-carrying mosquitoes. New research published on July 31, 2025, in Parasites & Vectors found that the same drug, nitisinone, can even kill mosquitoes that simply land on a surface sprayed with the chemical. The findings could open up new avenues to stop the spread of diseases like malaria and dengue, especially as more mosquito populations evolve to become resistant to traditional prevention methods. Whether people will willingly offer their bodies as mosquito blood bait, though, remains less clear.

But now judges are experimenting with generative AI too. Some are confident that with the right precautions, the technology can expedite legal research, summarize cases, draft routine orders, and overall help speed up the court system, which is badly backlogged in many parts of the US. This summer, though, we've already seen AI-generated mistakes go undetected and cited by judges. A federal judge in New Jersey had to reissue an order riddled with errors that may have come from AI, and a judge in Mississippi refused to explain why his order too contained mistakes that seemed like AI hallucinations. The results of these early-adopter experiments make two things clear.

These advanced chips are largely used for artificial intelligence (AI) applications at a time when investors are betting that AI will transform the global economy. Last month, Nvidia - which is the world's leading chip maker - became the first company ever to hit 4tn ( 3tn) in market value. Nvidia developed the H20 chip, and AMD developed the MI308 chip, especially for the Chinese market. They are less powerful and therefore cheaper than both companies' flagship chips. But developing them was the only option for accessing the significant Chinese market after the previous administration of President Joe Biden banned US companies from exporting the most advanced chips to China because of national security concerns.

Physicists have created a new type of radar that could help improve underground imaging, using a cloud of atoms in a glass cell to detect reflected radio waves. The radar is a type of quantum sensor, an emerging technology that uses the quantum-mechanical properties of objects as measurement devices. It's still a prototype, but its intended use is to image buried objects in situations such as constructing underground utilities, drilling wells for natural gas, and excavating archaeological sites. If you're interested in the potential of quantum, why not check out: Why AI could eat quantum computing's lunch. Rapid advances in applying artificial intelligence to simulations in physics and chemistry have some people questioning whether we will even need quantum computers at all.

When Jensen Huang, the chief executive of the chipmaker Nvidia, met with Donald Trump in the White House last week, he had reason to be cheerful. Most of Nvidia's chips, which are widely used to train generative artificial-intelligence models, are manufactured in Asia. Earlier this year, it pledged to increase production in the United States, and on Wednesday Trump announced that chip companies that promise to build products in the United States would be exempt from some hefty new tariffs on semiconductors that his Administration is preparing to impose. The next day, Nvidia's stock hit a new all-time high, and its market capitalization reached 4.4 trillion, making it the world's most valuable company, ahead of Microsoft, which is also heavily involved in A.I. Welcome to the A.I. boom, or should I say the A.I. bubble? It has been more than a quarter of a century since the bursting of the great dot-com bubble, during which hundreds of unprofitable internet startups issued stock on the Nasdaq, and the share prices of many tech companies rose into the stratosphere.

On 3 April 2024, a magnitude 7.4 quake--Taiwan's strongest in 25 years--shook the country's eastern coast. Stringent building codes spared most structures, but mountainous and remote villages were devastated by landslides. When disasters affect large and inaccessible areas, responders often turn to satellite images to pinpoint affected areas and prioritise relief efforts. But mapping landslides from satellite imagery by eye can be time-intensive, said Lorenzo Nava, who is jointly based at Cambridge's Departments of Earth Sciences and Geography. "In the aftermath of a disaster, time really matters," he said.

Kyiv, Ukraine – Taras, a seasoned Ukrainian serviceman recovering from a contusion, expects "no miracles" from United States President Donald Trump's August 15 summit with his Russian counterpart, Vladimir Putin. "There's going to be no miracles, no peace deal in a week, and Putin will try to make Trump believe that it is Ukraine that doesn't want peace," the fair-haired 32-year-old with a deep brown tan acquired in the trenches of eastern Ukraine, told Al Jazeera. Taras, who spent more than three years on the front line and said he had recently shot down an explosives-laden Russian drone barging at him in a field covered with explosion craters, withheld his last name in accordance with the wartime protocol. Putin wants to dupe Trump by pandering to the US president's self-image as a peacemaker to avoid further economic sanctions, while the Russian leader seeks a major military breakthrough in eastern Ukraine, Taras said. "Putin really believes that until this winter, he will seize something sizeable, or that [his troops] will break through the front line and will dictate terms to Ukraine," Taras said.

In a statement to the BBC, Nvidia also said: "America cannot repeat 5G and lose telecommunication leadership. America's [artificial intelligence] tech stack can be the world's standard if we race." Under the agreement, Nvidia will pay 15% of its revenues from H20 chip sales in China to the US government, while AMD will give the same percentage from its MI308 chip revenues, which was first reported by the Financial Times. Charlie Dai, vice president and principal analyst at global research firm Forrester, said this agreement is "unprecedented". "The arrangement underscores the high cost of market access amid escalating tech trade tensions, creating substantial financial pressure and strategic uncertainty for tech vendors", he added.

An emerging field of AI, namely Fair Machine Learning (ML), aims to quantify different types of bias (also known as unfairness) exhibited in the predictions of ML algorithms, and to design new algorithms to mitigate them. Often, the definitions of bias used in the literature are observational, i.e. they use the input and output of a pre-trained algorithm to quantify a bias under concern. In reality,these definitions are often conflicting in nature and can only be deployed if either the ground truth is known or only in retrospect after deploying the algorithm. Thus,there is a gap between what we want Fair ML to achieve and what it does in a dynamic social environment. Hence, we propose an alternative dynamic mechanism,"Fair Game",to assure fairness in the predictions of an ML algorithm and to adapt its predictions as the society interacts with the algorithm over time. "Fair Game" puts together an Auditor and a Debiasing algorithm in a loop around an ML algorithm. The "Fair Game" puts these two components in a loop by leveraging Reinforcement Learning (RL). RL algorithms interact with an environment to take decisions, which yields new observations (also known as data/feedback) from the environment and in turn, adapts future decisions. RL is already used in algorithms with pre-fixed long-term fairness goals. "Fair Game" provides a unique framework where the fairness goals can be adapted over time by only modifying the auditor and the different biases it quantifies. Thus,"Fair Game" aims to simulate the evolution of ethical and legal frameworks in the society by creating an auditor which sends feedback to a debiasing algorithm deployed around an ML system. This allows us to develop a flexible and adaptive-over-time framework to build Fair ML systems pre- and post-deployment.

Although discourse around the risks of Artificial Intelligence (AI) has grown, it often lacks a comprehensive, multidimensional framework, and concrete causal pathways mapping hazard to harm. This paper aims to bridge this gap by examining six commonly discussed AI catastrophic risks: CBRN, cyber offense, sudden loss of control, gradual loss of control, environmental risk, and geopolitical risk. First, we characterize these risks across seven key dimensions, namely intent, competency, entity, polarity, linearity, reach, and order. Next, we conduct risk pathway modeling by mapping step-by-step progressions from the initial hazard to the resulting harms. The dimensional approach supports systematic risk identification and generalizable mitigation strategies, while risk pathway models help identify scenario-specific interventions. Together, these methods offer a more structured and actionable foundation for managing catastrophic AI risks across the value chain.