While AI could change the world in many unforeseen ways, it's already having one massive impact: a voracious consumption of energy. Generative AI does not simply float upon ephemeral intuition. Rather, it gathers strength via thousands of computers in data centers across the world, which operate constantly on full blast. In January, the International Energy Agency (IEA) forecast that global data center electricity demand will more than double from 2022 to 2026, with AI playing a major role in that increase. AI industry insiders say the world has plenty of energy capacity to absorb this increased demand, and that technological efficiency improvements could offset these increases.

One scorching day this past September, I made the dangerous decision to try to circumnavigate some data centers. The ones I chose sit between a regional airport and some farm fields in Goodyear, Arizona, half an hour's drive west of downtown Phoenix. When my Uber pulled up beside the unmarked buildings, the temperature was 97 degrees Fahrenheit. The air crackled with a latent energy, and some kind of pulsating sound was emanating from the electric wires above my head, or maybe from the buildings themselves. With no shelter from the blinding sunlight, I began to lose my sense of what was real. Microsoft announced its plans for this location, and two others not so far away, back in 2019--a week after the company revealed its initial 1 billion investment in OpenAI, the buzzy start-up that would later release ChatGPT.

A new report reveals that robots working in Amazon fulfillment centers are leading to more injuries among human employees - although the e-commerce giant claims the technology reduces incidents. Based on internal records from 150 warehouses, serious injuries were 50 percent higher at facilities with robots than those without, according to the Center for Investigative Reporting's news site, Reveal. There were 14,000 serious injuries in 2019 - a spike of nearly 33 percent from 2015, and nearly double the industry average. The overall injury rate for the 150 facilities was also almost double the industry standard, according to Reveal. Amazon insisted its numbers are inflated because it encourages workers to report even minor incidents.

Guess who's getting used to working with robots in their everyday lives? The very same warehouse workers once predicted to be losing their jobs to mechanical replacements. According to their makers, the machines should take on the most mundane and physically strenuous tasks. "They weigh a lot," Amazon worker Amanda Taillon said during the pre-Christmas rush at a company warehouse in Connecticut. Taillon's job is to enter a cage and tame Amazon's wheeled warehouse robots for long enough to pick up a fallen toy or relieve a traffic jam. She straps on a light-up utility belt that works like a superhero's force field, commanding the nearest robots to abruptly halt and the others to slow down or adjust their routes.

Guess who's getting used to working with robots in their everyday lives? The very same warehouse workers once predicted to be losing their jobs to mechanical replacements. According to their makers, the machines should take on the most mundane and physically strenuous tasks. "They weigh a lot," Amazon worker Amanda Taillon said during the pre-Christmas rush at a company warehouse in Connecticut. Taillon's job is to enter a cage and tame Amazon's wheeled warehouse robots for long enough to pick up a fallen toy or relieve a traffic jam. She straps on a light-up utility belt that works like a superhero's force field, commanding the nearest robots to abruptly halt and the others to slow down or adjust their routes.

Cameras streaming high-definition images over superfast mobile networks could improve security in schools and on our streets, and help cities run their services more efficiently, tech experts say. Beyoncé Brooks, a 17-year-old student at Millennium High School in Goodyear, Arizona, US, says her school has practice lockdowns each term to deal with gun incidents. She says there is "probably one real lockdown" a year at her school of 2,000 students. "Basically, we turn the lights off, the door is locked, and we all sit in the corner of the room huddled. The teachers don't really say anything, so we don't know if it's something that's real," she says. "We have thousands of cameras around us," says Ms Brooks, who has become a March for Our Lives organiser.

We introduce an online tensor decomposition based approach for two latent variable modeling problems namely, (1) community detection, in which we learn the latent communities that the social actors in social networks belong to, and (2) topic modeling, in which we infer hidden topics of text articles. We consider decomposition of moment tensors using stochastic gradient descent. We conduct optimization of multilinear operations in SGD and avoid directly forming the tensors, to save computational and storage costs. We present optimized algorithm in two platforms. Our GPU-based implementation exploits the parallelism of SIMD architectures to allow for maximum speed-up by a careful optimization of storage and data transfer, whereas our CPU-based implementation uses efficient sparse matrix computations and is suitable for large sparse datasets. For the community detection problem, we demonstrate accuracy and computational efficiency on Facebook, Yelp and DBLP datasets, and for the topic modeling problem, we also demonstrate good performance on the New York Times dataset. We compare our results to the state-of-the-art algorithms such as the variational method, and report a gain of accuracy and a gain of several orders of magnitude in the execution time.