Month: October 2023

Enlarge / Apple's 24-inch iMac hasn't been updated in more than two years. (credit: Apple)

Most of Apple's big product announcements for the year were made in September, but the company may still have some surprises up its sleeve. Last week, it announced an oddball USB-C Apple Pencil, and Bloomberg's usually well-informed Mark Gurman says that a new Apple Silicon iMac refresh could arrive as soon as next week.

The iMac was last updated in the summer of 2021, well over two years ago, when Apple replaced the 21.5-inch Intel model with a redesigned M1-powered Apple Silicon model. Aside from the M1 MacBook Air, which Apple continues to sell as its entry-level notebook, it's the only kind of Mac that hasn't gotten some kind of M2 chip.

Gurman doesn't mention them, but an updated iMac could also give Apple the opportunity to refresh its Magic Keyboard, Magic Mouse, and Magic Trackpad accessories with a USB-C port to match the new iPhone 15 series and the USB-C version of the AirPods. Right now, these accessories, the older iPhones Apple still sells, the Apple TV's Siri Remote, and the low-end iPad are all still using the Lightning port.

Enlarge / The 2023-2024 US Winter Outlook map for precipitation. (credit: NOAA)

As its name implies, the jet stream is essentially a river of fast-moving air in the atmosphere at about the altitude where airplanes fly. It is typically a few hundred miles across, and jets can indeed save a lot of fuel if they can fly within this air current, generally from west to east.

Jet streams also have significant implications for our weather on the ground, as they more or less steer storm systems that affect the mid-latitudes. That is, they in large part determine whether parts of the United States—which lies almost entirely in the mid-latitudes between the tropics and poles in the Northern Hemisphere—will see stormy or serene weather.

As always with weather, the situation is complex. But one of the more useful signals in a forecaster's arsenal is the El Niño–Southern Oscillation in the tropical Pacific Ocean, which vacillates between warmer sea surface temperatures (El Niño), cooler ones (La Niña), and neutral conditions. This broad pattern has widespread weather implications, including the location of the jet stream.

Enlarge / The 2023-2024 US Winter Outlook map for precipitation. (credit: NOAA)

As its name implies, the jet stream is essentially a river of fast-moving air in the atmosphere at about the altitude where airplanes fly. It is typically a few hundred miles across, and jets can indeed save a lot of fuel if they can fly within this air current, generally from west to east.

Jet streams also have significant implications for our weather on the ground, as they more or less steer storm systems that affect the mid-latitudes. That is, they in large part determine whether parts of the United States—which lies almost entirely in the mid-latitudes between the tropics and poles in the Northern Hemisphere—will see stormy or serene weather.

As always with weather, the situation is complex. But one of the more useful signals in a forecaster's arsenal is the El Niño–Southern Oscillation in the tropical Pacific Ocean, which vacillates between warmer sea surface temperatures (El Niño), cooler ones (La Niña), and neutral conditions. This broad pattern has widespread weather implications, including the location of the jet stream.

Enlarge (credit: SOPA Images via Getty)

China has launched an investigation into Apple iPhone-maker Foxconn over tax and land use, Chinese state media reported on Sunday.

The Global Times, citing anonymous sources, said tax authorities inspected Foxconn’s sites in the provinces of Guangdong and Jiangsu, and natural resources officials had inspected sites in Henan and Hubei.

Foxconn said it would cooperate with the investigation. “Complying with laws and regulations is a basic principle for the group worldwide,” said Foxconn in a statement. “We will actively cooperate with the relevant authorities’ operations.”

Enlarge (credit: SOPA Images via Getty)

China has launched an investigation into Apple iPhone-maker Foxconn over tax and land use, Chinese state media reported on Sunday.

The Global Times, citing anonymous sources, said tax authorities inspected Foxconn’s sites in the provinces of Guangdong and Jiangsu, and natural resources officials had inspected sites in Henan and Hubei.

Foxconn said it would cooperate with the investigation. “Complying with laws and regulations is a basic principle for the group worldwide,” said Foxconn in a statement. “We will actively cooperate with the relevant authorities’ operations.”

Enlarge / In this still captured from a video provided by Nvidia, a simulated robot hand learns pen tricks, trained by Eureka, using simultaneous trials. (credit: Nvidia)

On Friday, researchers from Nvidia, UPenn, Caltech, and the University of Texas at Austin announced Eureka, an algorithm that uses OpenAI's GPT-4 language model for designing training goals (called "reward functions") to enhance robot dexterity. The work aims to bridge the gap between high-level reasoning and low-level motor control, allowing robots to learn complex tasks rapidly using massively parallel simulations that run through trials simultaneously. According to the team, Eureka outperforms human-written reward functions by a substantial margin.

Before robots can interact with the real world successfully, they need to learn how to move their robot bodies to achieve goals—like picking up objects or moving. Instead of making a physical robot try and fail one task at a time to learn in a lab, researchers at Nvidia have been experimenting with using video game-like computer worlds (thanks to platforms called Isaac Sim and Isaac Gym) that simulate three-dimensional physics. These allow for massively parallel training sessions to take place in many virtual worlds at once, dramatically speeding up training time.

"Leveraging state-of-the-art GPU-accelerated simulation in Nvidia Isaac Gym," writes Nvidia on its demonstration page, "Eureka is able to quickly evaluate the quality of a large batch of reward candidates, enabling scalable search in the reward function space." They call it "rapid reward evaluation via massively parallel reinforcement learning."