Anxiety over AI replacing entire industries has already pushed people to change course in their classes and career. Anxiety over AI replacing entire industries has already pushed people to change course in their classes and career. Matthew Ramirez started at Western Governors University as a computer science major in 2025, drawn by the promise of a high-paying, flexible career as a programmer. But as headlines mounted about tech layoffs and AI's potential to replace entry-level coders, he began to question whether that path would actually lead to a job. When the 20-year-old interviewed for a datacenter technician role that June and never heard back, his doubts deepened.

The effects of climate change could already be impacting 85% of the world's population, an analysis of tens of thousands of scientific studies said Monday. A team of researchers used machine learning to comb through vast troves of research published between 1951 and 2018, and found some 100,000 papers that potentially documented evidence of climate change's effects on the Earth's systems. "We have overwhelming evidence that climate change is affecting all continents -- all systems," study author Max Callaghan said in an interview. He added that there was a "huge amount of evidence" showing the ways in which these impacts are being felt. The researchers taught a computer to identify climate-relevant studies, generating a list of papers on topics from disrupted butterfly migration to heat-related human deaths and forestry cover changes.

Artificial intelligence has made a disheartening discovery – 85 percent of the world's population has already been affected by human-induced climate change. The findings were made by German scientists, led by Max Callaghan from the Mercator Research Institute on Global Commons and Climate Change, who, according to the study, trained the system to'identify, evaluate and summarize scientific publications on climate change and its consequences.' Researchers used machine learning to sift through data published from 1951 through 2018 and found more than 100,000 studies with evidence that shows 80 percent of Earth's inhabited land has been impacted by climate change. The results also uncovered an'attribution gap' around the globe, where evidence is is distributed unequally across countries - 'evidence for potentially attributable impacts are twice as prevalent in high-income than in low-income countries,' according to the study. Artificial intelligence has made a disheartening discovery – 85 percent of the world's population has already been affected by human-induced climate change.

Climate change could already be affecting 85 percent of the world's population, an analysis of tens of thousands of scientific studies has found. The analysis, released on Monday, was carried out by a team of researchers that used machine learning to comb through vast troves of research published between 1951 and 2018 and found some 100,000 papers that potentially documented evidence of climate change's effects on the Earth's systems. "We have overwhelming evidence that climate change is affecting all continents, all systems," study author Max Callaghan told the AFP news agency in an interview. He added there was a "huge amount of evidence" showing the ways in which these effects are being felt. The researchers taught a computer to identify climate-relevant studies, generating a list of papers on topics from disrupted butterfly migration to heat-related human deaths to forestry cover changes.

Well they are, but not quite as remorselessly or as swiftly as the movies might have conditioned us to imagine. And when the robot age does arrive, the impact on New Zealand -- in jobs and economic disruption -- may not be as apocalyptic as some future scenarios imagine. At least that is the position of two leading robotics researchers, Armin Werner of Lincoln Agritech, and Bruce MacDonald, an Auckland University computer engineering specialist with over 30 years' skin in the robot game. Werner, whose background is in precision agriculture that will have its most advanced developments in robotics, believes the broad use of automation will create more jobs, at least at the skilled end of the labour market. His view is that the embrace of technology will lead to different forms of work, rather than making work more difficult.

No other film genre boasts such an unimpeachable reputation for dreadfulness as the video game adaptation. Some, such as this year's Tomb Raider film and the zombie-themed Resident Evil efforts, almost achieve mediocrity. Others are so fascinatingly terrible that they have become Hollywood legend – for instance, the baffling interpretation of Super Mario Bros proffered by edgy British directors Annabel Jankel and Rocky Morton in 1993, in which Nintendo's bright, joyful Mushroom Kingdom was reimagined as a futuristic dystopia called Dinohattan, where everyone was dressed in fishnets and black leather trenchcoats. A quarter of a century later, it is still impossible to understand why anyone thought that was a good idea. The ever-expanding Marvel cinematic universe is ample proof that films can do an excellent job of exploring geek culture and fleshing out the paper-thin characters that dominate it; Black Panther has just become the fifth highest-grossing movie ever at the US box office.

Mapping the occupancy level of an environment is important for a robot to navigate in unknown and unstructured environments. To this end, continuous occupancy mapping techniques which express the probability of a location as a function are used. In this work, we provide a theoretical analysis to compare and contrast the two major branches of Bayesian continuous occupancy mapping techniques---Gaussian process occupancy maps and Bayesian Hilbert maps---considering the fact that both utilize kernel functions to operate in a rich high-dimensional implicit feature space and use variational inference to learn parameters. Then, we extend the recent Bayesian Hilbert maps framework which is so far only used for stationary robots, to map large environments with moving robots. Finally, we propose convolution of kernels as a powerful tool to improve different aspects of continuous occupancy mapping. Our claims are also experimentally validated with both simulated and real-world datasets.

The next time you open the door to find the pizza you ordered (via an app of course), you may not find a delivery person standing on your doorstep. Expect to see a drone dropping off your dinner. And perhaps that dinner was by prepped, at least in part, by robots. Eventually, some of your pizza's toppings may be lab grown-- but in the meantime, even those fresh-off-the-farm ingredients have a good chance of reaching your fork via robotics. Today, automation is shaping up to be our generation's food revolution.

We address the problem of building a continuous occupancy representation of the environment with ranging sensors. Observations from such sensors provide two types of information: a line segment or a beam indicating no returns along them (free-space); a point or return at the end of the segment representing an occupied surface. To model these two types of observations in a principled statistical manner, we propose a novel methodology based on integral kernels. We show that integral kernels can be directly incorporated into a Gaussian process classification (GPC) framework to provide a continuous non-parametric Bayesian estimation of occupancy. Directly handling line segment and point observations avoids the need to discretise segments into points, reducing the computational cost of GPC inference and learning. We present experiments on 2D and 3D datasets demonstrating the benefits of the approach.