Psychologists explain how your brain can lie. Fruit of the Loom's logo never had a cornucopia and you didn't have pizza for dinner last Friday. False memories are more than just misremembering someone's name. Breakthroughs, discoveries, and DIY tips sent six days a week. T-shirt tycoons Fruit of the Loom are both makers of functional, printable T-shirts and unintentional originators of a long-standing piece of memory misinformation.

Coral bleaching is a major concern for marine ecosystems; more than half of the world's coral reefs have either bleached or died over the past three decades. Increasing sea surface temperatures, along with various spatiotemporal environmental factors, are considered the primary reasons behind coral bleaching. The statistical and machine learning communities have focused on multiple aspects of the environment in detail. However, the literature on various stochastic modeling approaches for assessing coral bleaching is extremely scarce. Data-driven strategies are crucial for effective reef management, and this review article provides an overview of existing statistical and machine learning methods for assessing coral bleaching. Statistical frameworks, including simple regression models, generalized linear models, generalized additive models, Bayesian regression models, spatiotemporal models, and resilience indicators, such as Fisher's Information and Variance Index, are commonly used to explore how different environmental stressors influence coral bleaching. On the other hand, machine learning methods, including random forests, decision trees, support vector machines, and spatial operators, are more popular for detecting nonlinear relationships, analyzing high-dimensional data, and allowing integration of heterogeneous data from diverse sources. In addition to summarizing these models, we also discuss potential data-driven future research directions, with a focus on constructing statistical and machine learning models in specific contexts related to coral bleaching.

Why do so many people think the Fruit of the Loom logo had a cornucopia? And while some people may laugh and move on, others spend years searching for an explanation. There is a shirt currently listed on eBay for $2,128.79. It was not designed by Versace or Dior, nor spun from the world's finest silk. In fact, a tag proudly declares, "100% cotton made in Myanmar"--but it's a second tag, just below that one, that makes this blue button-down so expensive. "I looked at it and I was like,," says Brooke Hermann, the 30-year-old Kentucky-based reseller who bought the top for $1 at a secondhand sale in 2024. "This doesn't look like any other Fruit of the Loom tag I've ever seen." Quick question: Does the Fruit of the Loom logo feature a cornucopia? Many of us have been wearing the casualwear company's T-shirts and underpants for decades, and yet the question of whether there is a woven brown horn of plenty on the logo is surprisingly contentious. According to a 2022 poll by the research company YouGov, 55% of Americans believe the logo does include a cornucopia, 25% are unsure, and only 21% are confident that it doesn't, even though this last group is correct.

The fast pace of advances in AI promises to revolutionize various aspects of knowledge work, extending its influence to daily life and professional fields alike. We advocate for a paradigm where AI is seen as a collaborative co-pilot, working under human guidance rather than as a mere tool. Drawing from relevant research and literature in the disciplines of Human-Computer Interaction and Human Factors Engineering, we highlight the criticality of maintaining human oversight in AI interactions. Reflecting on lessons from aviation, we address the dangers of over-relying on automation, such as diminished human vigilance and skill erosion. Our paper proposes a design approach that emphasizes active human engagement, control, and skill enhancement in the AI partnership, aiming to foster a harmonious, effective, and empowering human-AI relationship. We particularly call out the critical need to design AI interaction capabilities and software applications to enable and celebrate the primacy of human agency. This calls for designs for human-AI partnership that cede ultimate control and responsibility to the human user as pilot, with the AI co-pilot acting in a well-defined supporting role.

This list of highly complex systems going haywire with fatal consequences could go on, but these two accidents may suffice to demonstrate what these incidents usually have in common. In each case, there was a human being behind the controls who should and could have taken over to save the situation, but they didn't because they failed in monitoring an automated process, trusting faulty readings or erroneous actions taken by software. Automation, to be sure, is wonderful. It makes life safer, more convenient. We have welcomed robots, whether hardware or just software, with open arms.

Do you have a forgettable face? Many of us go to great lengths to make our faces more memorable, using makeup and hairstyles to give ourselves a more distinctive look. Now your face could be instantly transformed into a more memorable one without the need for an expensive makeover, thanks to an algorithm developed by researchers in MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL). The algorithm, which makes subtle changes to various points on the face to make it more memorable without changing a person's overall appearance, was unveiled earlier this month at the International Conference on Computer Vision in Sydney. "We want to modify the extent to which people will actually remember a face," says lead author Aditya Khosla, a graduate student in the Computer Vision group within CSAIL.