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China’s heat wave is creating havoc for electric vehicle drivers

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A photo of the screen in a Tesla car that shows only two of the 31 Tesla Supercharger Stations nearby are available.


The record-breaking heat wave in China, which started back in June, has evaporated over half the hydroelectricity generation capacity in Sichuan, a southwestern province that usually gets 81% of its electricity from hydropower plants. That decreased energy supply, at a time when the need for cooling has increased demand, is putting industrial production and everyday life in the region on pause. 

And as the power supply has become unreliable, the government has instituted EV charging restrictions in order to prioritize more critical daily electricity needs. 

As Chinese publications have reported, finding a working charging station in Sichuan and the neighboring region Chongqing—a task that took a few minutes before the heat wave—took as long as two hours this week. The majority of public charging stations, including those operated by leading EV brands like Tesla and China’s NIO and XPeng, are closed in the region because of government restrictions on commercial electricity usage. 

A screenshot sent to MIT Technology Review by a Chinese Tesla owner in Sichuan, who asked not to be named for privacy reasons, shows that on August 24, only two of the 31 Tesla Supercharger Stations in or near the province’s capital city of Chengdu were working as normal. 

Screenshot of all Tesla Supercharger Stations near Chengdu.

In addition to facing mandatory service suspensions, EV owners are also being encouraged or forced to charge only during off-peak hours. In fact, the leading domestic operator, TELD, has closed over 120 charging stations in the region from 8 a.m. to midnight, the peak hours for electricity usage. State Grid, China’s largest state-owned electric utility company, also builds and operates EV charging stations; it announced on August 19 that in three provinces that have over 140 million residents and 800,000 electric vehicles in total, the company will offer 50% off coupons if drivers charge at night. State Grid is also reducing the efficiency of 350,000 charging posts during the day, so the individual charging time for vehicles would be five to six minutes longer but the total power consumed during peak hours would go down. 

The impact is evident in videos shared on Chinese social media, which show long lines of EVs waiting outside the few working charging stations, even after midnight. Electric taxi drivers have been hit especially hard, as their livelihoods depend on their vehicles. “I started waiting in the line at 8:30 p.m. yesterday and I only started charging at around 5 a.m.,” a Chengdu taxi driver told an EV influencer. “You are basically always waiting in lines. Like today, I didn’t even get much business, but I’m in the line again now. And the battery is going down quickly.”  

The charging challenges are also pushing some people back into using fossil fuel. The Tesla owner in Sichuan is planning to visit Chengdu for work this week but decided to drive his other car, a gas-powered one, for fear that he wouldn’t find a place to recharge before returning home. Another driver from Chengdu, who owns a plug-in hybrid, told MIT Technology Review that she switched to gas this week even though she usually sticks to electricity because it’s slightly cheaper. 

The sudden difficulty of charging in Sichuan and neighboring provinces has caught the EV industry by surprise. “A large-scale power shortage like this is still something we’ve never seen [in China],” says Lei Xing, an auto industry analyst and the former chief editor at China Auto Review. He says the climate disaster is reminding the industry that while China leads the world on many EV adoption metrics, there are still infrastructure weaknesses that need to be addressed. “It feels like China already has a good charging infrastructure … but once something like these power restrictions happens, the problems are exposed. All EV owners who rely on public charging posts are having troubles now,” Xing says.

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Meta’s new AI can turn text prompts into videos

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Meta’s new AI can turn text prompts into videos


Although the effect is rather crude, the system offers an early glimpse of what’s coming next for generative artificial intelligence, and it is the next obvious step from the text-to-image AI systems that have caused huge excitement this year. 

Meta’s announcement of Make-A-Video, which is not yet being made available to the public, will likely prompt other AI labs to release their own versions. It also raises some big ethical questions. 

In the last month alone, AI lab OpenAI has made its latest text-to-image AI system DALL-E available to everyone, and AI startup Stability.AI launched Stable Diffusion, an open-source text-to-image system.

But text-to-video AI comes with some even greater challenges. For one, these models need a vast amount of computing power. They are an even bigger computational lift than large text-to-image AI models, which use millions of images to train, because putting together just one short video requires hundreds of images. That means it’s really only large tech companies that can afford to build these systems for the foreseeable future. They’re also trickier to train, because there aren’t large-scale data sets of high-quality videos paired with text. 

To work around this, Meta combined data from three open-source image and video data sets to train its model. Standard text-image data sets of labeled still images helped the AI learn what objects are called and what they look like. And a database of videos helped it learn how those objects are supposed to move in the world. The combination of the two approaches helped Make-A-Video, which is described in a non-peer-reviewed paper published today, generate videos from text at scale.

Tanmay Gupta, a computer vision research scientist at the Allen Institute for Artificial Intelligence, says Meta’s results are promising. The videos it’s shared show that the model can capture 3D shapes as the camera rotates. The model also has some notion of depth and understanding of lighting. Gupta says some details and movements are decently done and convincing. 

However, “there’s plenty of room for the research community to improve on, especially if these systems are to be used for video editing and professional content creation,” he adds. In particular, it’s still tough to model complex interactions between objects. 

In the video generated by the prompt “An artist’s brush painting on a canvas,” the brush moves over the canvas, but strokes on the canvas aren’t realistic. “I would love to see these models succeed at generating a sequence of interactions, such as ‘The man picks up a book from the shelf, puts on his glasses, and sits down to read it while drinking a cup of coffee,’” Gupta says. 

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How AI is helping birth digital humans that look and sound just like us

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How AI is helping birth digital humans that look and sound just like us


Jennifer: And the team has also been exploring how these digital twins can be useful beyond the 2D world of a video conference. 

Greg Cross: I guess the.. the big, you know, shift that’s coming right at the moment is the move from the 2D world of the internet, into the 3D world of the metaverse. So, I mean, and that, and that’s something we’ve always thought about and we’ve always been preparing for, I mean, Jack exists in full 3D, um, You know, Jack exists as a full body. So I mean, Jack can, you know, today we have, you know, we’re building augmented reality, prototypes of Jack walking around on a golf course. And, you know, we can go and ask Jack, how, how should we play this hole? Um, so these are some of the things that we are starting to imagine in terms of the way in which digital people, the way in which digital celebrities. Interact with us as we move into the 3D world.

Jennifer: And he thinks this technology can go a lot further.

Greg Cross: Healthcare and education are two amazing applications of this type of technology. And it’s amazing because we don’t have enough real people to deliver healthcare and education in the real world. So, I mean, so you can, you know, you can imagine how you can use a digital workforce to augment. And, and extend the skills and capability, not replace, but extend the skills and, and capabilities of real people. 

Jennifer: This episode was produced by Anthony Green with help from Emma Cillekens. It was edited by me and Mat Honan, mixed by Garret Lang… with original music from Jacob Gorski.   

If you have an idea for a story or something you’d like to hear, please drop a note to podcasts at technology review dot com.

Thanks for listening… I’m Jennifer Strong.

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A bionic pancreas could solve one of the biggest challenges of diabetes

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A bionic pancreas could solve one of the biggest challenges of diabetes


The bionic pancreas, a credit card-sized device called an iLet, monitors a person’s levels around the clock and automatically delivers insulin when needed through a tiny cannula, a thin tube inserted into the body. It is worn constantly, generally on the abdomen. The device determines all insulin doses based on the user’s weight, and the user can’t adjust the doses. 

A Harvard Medical School team has submitted its findings from the study, described in the New England Journal of Medicine, to the FDA in the hopes of eventually bringing the product to market in the US. While a team from Boston University and Massachusetts General Hospital first tested the bionic pancreas in 2010, this is the most extensive trial undertaken so far.

The Harvard team, working with other universities, provided 219 people with type 1 diabetes who had used insulin for at least a year with a bionic pancreas device for 13 weeks. The team compared their blood sugar levels with those of 107 diabetic people who used other insulin delivery methods, including injection and insulin pumps, during the same amount of time. 

The blood sugar levels of the bionic pancreas group fell from 7.9% to 7.3%, while the standard care group’s levels remained steady at 7.7%. The American Diabetes Association recommends a goal of less than 7.0%, but that’s only met by approximately 20% of people with type 1 diabetes, according to a 2019 study

Other types of artificial pancreas exist, but they typically require the user to input information before they will deliver insulin, including the amount of carbohydrates they ate in their last meal. Instead, the iLet takes the user’s weight and the type of meal they’re eating, such as breakfast, lunch, or dinner, added by the user via the iLet interface, and it uses an adaptive learning algorithm to deliver insulin automatically.

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