The idea that superintelligent robots are alien invaders coming to "steal our jobs" reveals profound shortcomings in the way we think about work, value, and intelligence itself. Labor is not a zero-sum game, and robots aren't an "other" that competes with us. Like any technology, they're part of us, growing out of civilization the same way hair and nails grow out of a living body. When we "other" a fruit-picking robot – thinking of it as a competitor in a zero-sum game – we take our eyes off the real problem: the human who used to pick the fruit is considered disposable by the farm's owners and by society when no longer fit for that job. This implies that the human laborer was already being treated like a non-person – that is, like a machine.

Background: Recent advancements in Artificial Intelligence (AI) contributed significantly to suicide assessment, however, our theoretical understanding of this complex behavior is still limited. Objective: This study aimed to harness AI methodologies to uncover hidden risk factors that trigger or aggravate suicide behaviors. Method: The primary dataset included 228,052 Facebook postings by 1,006 users who completed the gold-standard Columbia Suicide Severity Rating Scale. This dataset was analyzed using a bottom-up research pipeline without a-priory hypotheses and its findings were validated using a top-down analysis of a new dataset. This secondary dataset included responses by 1,062 participants to the same suicide scale as well as to well-validated scales measuring depression and boredom. Results: An almost fully automated, AI-guided research pipeline resulted in four Facebook topics that predicted the risk of suicide, of which the strongest predictor was boredom. A comprehensive literature review using APA PsycInfo revealed that boredom is rarely perceived as a unique risk factor of suicide. A complementing top-down path analysis of the secondary dataset uncovered an indirect relationship between boredom and suicide, which was mediated by depression. An equivalent mediated relationship was observed in the primary Facebook dataset as well. However, here, a direct relationship between boredom and suicide risk was also observed. Conclusions: Integrating AI methods allowed the discovery of an under-researched risk factor of suicide. The study signals boredom as a maladaptive 'ingredient' that might trigger suicide behaviors, regardless of depression. Further studies are recommended to direct clinicians' attention to this burdening, and sometimes existential experience.

Fox News host Greg Gutfeld reacts to a Danish study finding artificial intelligence can help predict a person’s political ideology based on their facial characteristics on "Gutfeld!"

A woman has been working on her book, a young adult fantasy novel, for hours. At some point, she gets the familiar itch to check her email: She can't think of what to write next. She could bang her head against the wall, or maybe turn to a favorite book for inspiration, or lose her momentum to distraction. But instead she turns to an AI writing tool, which takes in her chapter so far and spits out some potential next paragraphs. These paragraphs are never quite what she wants, though they sometimes contain beautiful sentences or fascinating directions.

BCG's AI-based Trust Index enables companies to break down stakeholder perceptions of their trustworthiness. Analyses based on the Index have yielded valuable insights about what builds, sustains, or destroys trust. Most business leaders are only now beginning to realize the true importance of trust. More than a mere sentiment, trust has economic value--and in the digital age, its relevance continues to grow. At the macro level, it enables new disruptive products, services, and strategic moves; at the micro level, it smooths the way for smaller transactions at scale among a vastly greater number of buyers and sellers who have no prior relationship.

Satellite imagery of the Sahara desert presents an arid expanse, the endless rolling dunes we know from movies. The thing is, normal satellite images don't show individual trees, but that doesn't necessarily mean they're not there. Researchers from the University of Copenhagen and NASA taught artificial intelligence about trees and had them take another look. It turns out there is lots of vegetation in the Western Sahara: an estimated 1.8 billion trees and shrubs. "We were very surprised to see that quite a few trees actually grow in the Sahara Desert, because up until now, most people thought that virtually none existed," says lead author Martin Brandt of the university's Department of Geosciences and Natural Resource Management.

Scientists have developed a new machine learning tool that can identify Covid-19-related conspiracy theories on social media and predict how they evolved over time, an advance which may lead to better ways for public health officials to fight misinformation online. The study, published in the Journal of Medical Internet Research, analysed anonymised Twitter data to characterise four Covid-19 conspiracy theory themes – such as one that erroneously claims the Bill and Melinda Gates Foundation engineered or has malicious intent related to the pandemic. Using the AI tool's analysis of more than 1.8 million tweets that contained Covid-19 keywords, the scientists from the Los Alamos National Laboratory in the US categorised the posts as misinformation or not, and provided context for each of these conspiracy theories through the first five months of the pandemic. "From this body of data, we identified subsets that matched the four conspiracy theories using pattern filtering, and hand labeled several hundred tweets in each conspiracy theory category to construct training sets," explained Dax Gerts, a computer scientist and co-author of the study from the Los Alamos National Laboratory. The four major themes examined in the study were that 5G cell towers spread the virus; that the Bill and Melinda Gates Foundation engineered or have "malicious intent" related to Covid-19; that the novel coronavirus was bioengineered or was developed in a laboratory; and that vaccines for Covid-19, which were still in development during the study period, would be dangerous.

What's more, they didn't really expect the results of their work to be quite so good. X-ray images are already a valuable tool in the examination and restoration of paintings, as they can reveal the underlying condition of the work and provide insights into artists' techniques. They can also help experts to authenticate works. But there is a problem. Interpreting X-rays can be difficult because the images capture everything – the visible top layer, what is underneath, the materials and support structures such as struts, and anything that is on the back.

Scientists say they have "spectacularly" improved the clarity of X-ray images of what lies beneath old paintings, helping art historians and restorers to understand and protect great works. What's more, they didn't really expect the results of their work to be quite so good.

You might not see most objects in near-total darkness, but AI can. MIT scientists have developed a technique that uses a deep neural network to spot objects in extremely low light. The team trained the network to look for transparent patterns in dark images by feeding it 10,000 purposefully dark, grainy and out-of-focus pictures as well as the patterns those pictures are supposed to represent. The strategy not only gave the neural network an idea of what to expect, but highlighted hidden transparent objects by producing ripples in what little light was present. The researchers countered the blurring by giving it a physics lesson -- it knew how a defocused camera could produce blurring effects.