Every day, the world seems to be slipping further and further into dystopia, with President Donald Trump placing tariffs on islands inhabited by penguins and the country's head of Medicare and Medicaid touting AI-first healthcare. In case you needed an even higher dose of Orwellian anxiety in your life, though, Black Mirror has finally returned for season 7 with six brand new episodes. In its new season, the anthology series about our, shall we say, complicated relationship with technology takes on AI sentience, subscription pricing models, lost loves, high school grudges, and the privatization of health care. It's also got plenty of action, romance, and a heaping helping of tech-era terror. As with any anthology series, Black Mirror has plenty of hits, and also its share of misses, and season 7 is no exception, which only makes it more perfect for ranking.

For much of last year, US Marines conducting training exercises in the waters off South Korea, the Philippines, India, and Indonesia were also running an experiment. The service members in the unit responsible for sorting through foreign intelligence and making their superiors aware of possible local threats were for the first time using generative AI to do it, testing a leading AI tool the Pentagon has been funding. Two officers tell us that they used the new system to help scour thousands of pieces of open-source intelligence--nonclassified articles, reports, images, videos--collected in the various countries where they operated, and that it did so far faster than was possible with the old method of analyzing them manually. Though the US military has been developing computer vision models and similar AI tools since 2017, the use of generative AI--tools that can engage in human-like conversation--represent a newer frontier. The International Energy Agency states in a new report that AI could eventually reduce greenhouse-gas emissions, possibly by much more than the boom in energy-guzzling data center development pushes them up.

"We still need to validate the sources," says Lowdon. But the unit's commanders encouraged the use of large language models, he says, "because they provide a lot more efficiency during a dynamic situation." The generative AI tools they used were built by the defense-tech company Vannevar Labs, which in November was granted a production contract worth up to 99 million by the Pentagon's startup-oriented Defense Innovation Unit with the goal of bringing its intelligence tech to more military units. The company, founded in 2019 by veterans of the CIA and US intelligence community, joins the likes of Palantir, Anduril, and Scale AI as a major beneficiary of the US military's embrace of artificial intelligence--not only for physical technologies like drones and autonomous vehicles but also for software that is revolutionizing how the Pentagon collects, manages, and interprets data for warfare and surveillance. Though the US military has been developing computer vision models and similar AI tools, like those used in Project Maven, since 2017, the use of generative AI--tools that can engage in human-like conversation like those built by Vannevar Labs--represent a newer frontier.

Ukraine has opened a war crime investigation into the alleged killing of four soldiers captured by Russian forces, according to the Ukrainian parliament's human rights commissioner. Dmytro Lubinets wrote on X on Thursday that the four prisoners of war had no weapons as they walked out of a destroyed building with "their hands raised". "They were shot dead on the spot. This is a clear violation of the Geneva Convention and a grave war crime," he added. The alleged killing of the soldiers is believed to have occurred on March 13 in the southern Ukrainian village of Piatykhatky, according to The Associated Press news agency, which verified drone footage of the troops.

The Defense Ministry said Thursday that the Air Self-Defense Force scrambled fighter jets 704 times in response to possible airspace violations in fiscal 2024, up by 35 from the previous year. Of the total, scrambles against Chinese military aircraft accounted for 464, or 65.9%, down by 15. In August, Chinese military airplanes violated Japanese airspace off the Danjo Islands in Nagasaki Prefecture for the first time. The number of Chinese drones detected by the ministry more than tripled to 30, exceeding the 26 detected between fiscal 2013, when the first Chinese drone was spotted, and fiscal 2023. "China may have developed a system to (fully) operate drones, upgrading from trial flights," a ministry official said.

'The Big Weekend Show' co-hosts discuss Tinder user traffic peaking during'Dating Sunday.' A judge has dismissed kidnapping and conspiracy charges filed against five Massachusetts college students accused of luring a man to their campus in a "Catch a Predator"-style scheme using a dating app. A Worcester District Court judge dismissed the charges against Kelsey Brainard, Isabella Trudeau, Joaquin Smith, Kevin Carroll and Easton Randall on Tuesday. The decision came after lawyers for the teenage Assumption University students claimed prosecutors lacked probable cause and filed motions to dismiss last month. Information regarding the status of a sixth student, charged as a juvenile, was not immediately available.

Practically all large language models have been pre-trained on data that is subject to global uncertainty related to copyright infringement and breach of contract. This creates potential risk for users and developers due to this uncertain legal status. The KL3M Data Project directly confronts this critical issue by introducing the largest comprehensive training data pipeline that minimizes risks related to copyright or breach of contract. The foundation of this project is a corpus of over 132 million documents and trillions of tokens spanning 16 different sources that have been verified to meet the strict copyright and licensing protocol detailed herein. We are releasing the entire pipeline, including 1) the source code to acquire and process these documents, 2) the original document formats with associated provenance and metadata, 3) extracted content in a standardized format, 4) pre-tokenized representations of the documents, and 5) various mid- and post-train resources such as question-answer, summarization, conversion, drafting, classification, prediction, and conversational data. All of these resources are freely available to the public on S3, Hugging Face, and GitHub under CC-BY terms. We are committed to continuing this project in furtherance of a more ethical, legal, and sustainable approach to the development and use of AI models.

Recently, a growing number of experts in artificial intelligence (AI) and medicine have be-gun to suggest that the use of AI systems, particularly machine learning (ML) systems, is likely to humanise the practice of medicine by substantially improving the quality of clinician-patient relationships. In this thesis, however, I argue that medical ML systems are more likely to negatively impact these relationships than to improve them. In particular, I argue that the use of medical ML systems is likely to comprise the quality of trust, care, empathy, understanding, and communication between clinicians and patients.

Quantum Machine Learning (QML) is the intersection of two revolutionary fields: quantum computing and machine learning. It promises to unlock unparalleled capabilities in data analysis, model building, and problem-solving by harnessing the unique properties of quantum mechanics. This research endeavors to conduct a comprehensive bibliometric analysis of scientific information pertaining to QML covering the period from 2000 to 2023. An extensive dataset comprising 9493 scholarly works is meticulously examined to unveil notable trends, impact factors, and funding patterns within the domain. Additionally, the study employs bibliometric mapping techniques to visually illustrate the network relationships among key countries, institutions, authors, patent citations and significant keywords in QML research. The analysis reveals a consistent growth in publications over the examined period. The findings highlight the United States and China as prominent contributors, exhibiting substantial publication and citation metrics. Notably, the study concludes that QML, as a research subject, is currently in a formative stage, characterized by robust scholarly activity and ongoing development.

Financial instability has become a significant issue in today's society. While research typically focuses on financial aspects, there is a tendency to overlook time-related aspects of unstable work schedules. The inability to rely on consistent work schedules leads to burnout, work-family conflicts, and financial shocks that directly impact workers' income and assets. Unforeseen fluctuations in earnings pose challenges in financial planning, affecting decisions on savings and spending and ultimately undermining individuals' long-term financial stability and well-being. This issue is particularly evident in sectors where workers experience frequently changing schedules without sufficient notice, including those in the food service and retail sectors, part-time and hourly workers, and individuals with lower incomes. These groups are already more financially vulnerable, and the unpredictable nature of their schedules exacerbates their financial fragility. Our objective is to understand how unforeseen fluctuations in earnings exacerbate financial fragility by investigating the extent to which individuals' financial management depends on their ability to anticipate and plan for the future. To address this question, we develop a simulation framework that models how individuals optimize utility amidst financial uncertainty and the imperative to avoid financial ruin. We employ online learning techniques, specifically adapting workers' consumption policies based on evolving information about their work schedules. With this framework, we show both theoretically and empirically how a worker's capacity to anticipate schedule changes enhances their long-term utility. Conversely, the inability to predict future events can worsen workers' instability. Moreover, our framework enables us to explore interventions to mitigate the problem of schedule uncertainty and evaluate their effectiveness.