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First protein folding, now weather forecasting: London-based AI firm DeepMind is continuing its run applying deep learning to hard science problems. Working with the Met Office, the UK’s national weather service, DeepMind has developed a deep-learning tool called DGMR that can accurately predict the likelihood of rain in the next 90 minutes—one of weather forecasting’s toughest challenges.

In a blind comparison with existing tools, several dozen experts judged DGMR’s forecasts to be the best across a range of factors—including its predictions of the location, extent, movement, and intensity of the rain—89% of the time. The results were published in a Nature paper today.

DeepMind’s new toolis no AlphaFold, which cracked open a key problem in biology that scientists had been struggling with for decades. Yet even a small improvement in forecasting matters.

Forecasting rain, especially heavy rain, is crucial for a lot of industries, from outdoor events to aviation to emergency services. But doing it well is hard. Figuring out how much water is in the sky, and when and where it’s going to fall, depends on a number of weather processes, such as changes in temperature, cloud formation, and wind. All these factors are complex enough by themselves, but they’re even more complex when taken together.

The best existing forecasting techniques use massive computer simulations of atmospheric physics. These work well for longer-term forecasting but are less good at predicting what’s going to happen in the next hour or so, known as nowcasting. Previous deep-learning techniques have been developed, but these typically do well at one thing, such as predicting location, at the expense of something else, such as predicting intensity.

Comparison of DGMR with actual radar data and two rival forecasting techniques for heavy rainfall over the eastern US in April 2019DEEPMIND

“The nowcasting of precipitation remains a substantial challenge for meteorologists,” says Greg Carbin, chief of forecast operations at the NOAA Weather Prediction Center in the US, who was not involved in the work.

The DeepMind team trained their AI on radar data. Many countries release frequent snapshots throughout the day of radar measurements that track the formation and movement of clouds. In the UK, for example, a new reading is released every five minutes. Putting these snapshots together provides an up-to-date stop-motion video that shows how rain patterns are moving across a country, similar to the forecast visuals you see on TV.

The researchers fed this data to a deep generative network, similar to a GAN—a kind of AI that is trained to generate new samples of data that are very similar to the real data it was trained on. GANs have been used to generate fake faces, even fake Rembrandts. In this case, DGMR (which stands for “deep generative model of rainfall”) learned to generate fake radar snapshots that continued the sequence of actual measurements. It’s the same idea as seeing a few frames of a movie and guessing what’s going to come next, says Shakir Mohamed, who led the research at DeepMind.

To test the approach, the team asked 56 weather forecasters at the Met Office (who were not otherwise involved in the work) to rate DGMR in a blind comparison with forecasts made by a state-of-the-art physics simulation and a rival deep-learning tool; 89% said that they preferred the results given by DGMR.

“Machine-learning algorithms generally try and optimize for one simple measure of how good its prediction is,” says Niall Robinson, head of partnerships and product innovation at the Met Office, who coauthored the study. “However, weather forecasts can be good or bad in lots of different ways. Perhaps one forecast gets precipitation in the right location but at the wrong intensity, or another gets the right mix of intensities but in the wrong places, and so on. We went to a lot of effort in this research to assess our algorithm against a wide suite of metrics.”

DeepMind’s collaboration with the Met Office is a good example of AI development done in collaboration with the end user, something that seems like an obviously good idea but often does not happen. The team worked on the project for several years, and input from the Met Office’s experts shaped the project. “It pushed our model development in a different way than we would have gone down on our own,” says Suman Ravuri, a research scientist at DeepMind. “Otherwise we might have made a model that was ultimately not particularly useful.”

DeepMind is also eager to demonstrate that its AI has practical applications.. For Shakir, DGMR is part of the same story as AlphaFold: the company is cashing in on

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By: Will Douglas Heaven
Title: DeepMind’s AI predicts almost exactly when and where it’s going to rain
Sourced From: www.technologyreview.com/2021/09/29/1036331/deepminds-ai-predicts-almost-exactly-when-and-where-its-going-to-rain/
Published Date: Wed, 29 Sep 2021 15:00:00 +0000

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Algorithms are everywhere

cover.filterworld jpg

Like a lot of Netflix subscribers, I find that my personal feed tends to be hit or miss. Usually more miss. The movies and shows the algorithms recommend often seem less predicated on my viewing history and ratings, and more geared toward promoting whatever’s newly available. Still, when a superhero movie starring one of the world’s most famous actresses appeared in my “Top Picks” list, I dutifully did what 78 million other households did and clicked.

As I watched the movie, something dawned on me: recommendation algorithms like the ones Netflix pioneered weren’t just serving me what they thought I’d like—they were also shaping what gets made. And not in a good way.

cover of Filterworld: How Algorithms Flattened Culture by Kyle Chayka
DOUBLEDAY

The movie in question wasn’t bad, necessarily. The acting was serviceable, and it had high production values and a discernible plot (at least for a superhero movie). What struck me, though, was a vague sense of déjà vu—as if I’d watched this movie before, even though I hadn’t. When it ended, I promptly forgot all about it.

That is, until I started reading Kyle Chayka’s recent book, Filterworld: How Algorithms Flattened Culture. A staff writer for the New Yorker, Chayka is an astute observer of the ways the internet and social media affect culture. “Filterworld” is his coinage for “the vast, interlocking … network of algorithms” that influence both our daily lives and the “way culture is distributed and consumed.”

Music, film, the visual arts, literature, fashion, journalism, food—Chayka argues that algorithmic recommendations have fundamentally altered all these cultural products, not just influencing what gets seen or ignored but creating a kind of self-reinforcing blandness we are all contending with now.

That superhero movie I watched is a prime example. Despite my general ambivalence toward the genre, Netflix’s algorithm placed the film at the very top of my feed, where I was far more likely to click on it. And click I did. That “choice” was then recorded by the algorithms, which probably surmised that I liked the movie and then recommended it to even more viewers. Watch, wince, repeat.

“Filterworld culture is ultimately homogenous,” writes Chayka, “marked by a pervasive sense of sameness even when its artifacts aren’t literally the same.” We may all see different things in our feeds, he says, but they are increasingly the same kind of different. Through these milquetoast feedback loops, what’s popular becomes more popular, what’s obscure quickly disappears, and the lowest-­common-denominator forms of entertainment inevitably rise to the top again and again.

This is actually the opposite of the personalization Netflix promises, Chayka notes. Algorithmic recommendations reduce taste—traditionally, a nuanced and evolving opinion we form about aesthetic and artistic matters—into a few easily quantifiable data points. That oversimplification subsequently forces the creators of movies, books, and music to adapt to the logic and pressures of the algorithmic system. Go viral or die. Engage. Appeal to as many people as possible. Be popular.

A joke posted on X by a Google engineer sums up the problem: “A machine learning algorithm walks into a bar. The bartender asks, ‘What’ll you have?’ The algorithm says, ‘What’s everyone else having?’” “In algorithmic culture, the right choice is always what the majority of other people have already chosen,” writes Chayka.

One challenge for someone writing a book like Filterworld—or really any book dealing with matters of cultural import—is the danger of (intentionally or not) coming across as a would-be arbiter of taste or, worse, an outright snob. As one might ask, what’s wrong with a little mindless entertainment? (Many asked just that in response to Martin Scorsese’s controversial Harper’s essay  in 2021, which decried Marvel movies and the current state of cinema.) 

Chayka addresses these questions head on. He argues that we’ve really only traded one set of gatekeepers (magazine editors, radio DJs, museum curators) for another (Google, Facebook, TikTok, Spotify). Created and controlled by a handful of unfathomably rich and powerful companies (which are usually led by a rich and powerful white man), today’s algorithms don’t even attempt to reward or

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By: Bryan Gardiner
Title: Algorithms are everywhere
Sourced From: www.technologyreview.com/2024/02/27/1088164/algorithms-book-reviews-kyle-chayka-chris-wiggins-matthew-l-jones-josh-simons/
Published Date: Tue, 27 Feb 2024 10:15:00 +0000

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China’s next cultural export could be TikTok-style short soap operas

1031708703501 .pic hd scaled

Until last year, Ty Coker, a 28-year-old voice actor who lives in Missouri, mostly voiced video games and animations. But in December, they got a casting call for their first shot at live-action content: a Chinese series called Adored by the CEO, which was being remade for an American audience. Coker was hired to dub one of the main characters.

But you won’t find Adored by the CEO on TV or Netflix. Instead, it’s on FlexTV, a Chinese app filled with short dramas like this one. The shows on FlexTV are shot for phone screens, cut into about 90 two-minute episodes, and optimized for today’s extremely short attention span. Coker calls it “soap operas for the TikTok age.”

In the past few years, these short dramas have become hugely popular in China. They often span nearly a hundred episodes, but since each episode is only one or two minutes long, the whole series is no longer than a traditional movie. The most successful domestic productions make tens of millions of dollars in a few days. The entire market of short dramas in China was worth over $5 billion in 2023.

This success has motivated a few companies to try replicating the business model outside China. Not only is FlexTV translating and dubbing shows already released in China, but it has also started filming shows in the US for a more authentically American viewing experience.

It’s easy to compare apps like these to Quibi, a high-profile video service that infamously failed after less than a year in 2020.

But these latest Chinese apps are different. They don’t aim for slick, expensive productions. Instead, they choose simple scripts, shoot an entire series in two weeks, market it heavily online, and move on to the next project if it doesn’t stick.

“The biggest difference between short dramas and films is that they provide different things. We have to analyze the psychological needs of our audience and understand what they want to see … and we try to provide some emotional values,” Xiangchen Gao, the chief operations officer of FlexTV, tells MIT Technology Review.

When a show finds the right audience, it can generate significant revenue in the US too. The top-grossing show on FlexTV can bring in $2 million a week, while the production costs less than $150,000, Wang says.

Several other apps, like ReelShort and DramaBox, are also racing to bring Chinese short dramas to an international audience. They frequently top app stores’ download charts and produce blockbuster shows. Short dramas have been proven to work in China. It’s not always easy to replicate a business model in a different market, but if they succeed, they could be China’s next big cultural export.

The roots in Chinese web novels

Short dramas like Adored by the CEO are often adapted from another cultural product that is distinctly Chinese: web novels.

Web novels are a unique form of literature that has been popular on the Chinese internet for much of the last two decades: long stories that are written and posted chapter by chapter every day. Each chapter can be read in less than 10 minutes, but installments will keep being added for months if not years. Readers become avid fans, waiting for the new chapter to come out every day and paying a few cents to access it.

While some talented Chinese book authors got their big break by writing web novels, the majority of these works are the popcorn of literature, offering daily bite-size dopamine hits. For a while in the 2010s, some found an audience overseas too, with Chinese companies setting up websites to translate web novels into English.

But in the age of TikTok, long text posts have become less popular online, and the web-novel industry is looking to pivot. Business executives have realized they can adapt these novels into super-short dramas. Both forms aim for the same market: people who want something quick to kill time in their commute, or during breaks and lunch.

Many of the leading Chinese short-drama apps today work closely with Chinese web-novel companies. ReelShort is partially owned by COL Group, one of the largest digital publishers in China, with a treasure trove of novels that are ready for adaptation.

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By: Zeyi Yang
Title: China’s next cultural export could be TikTok-style short soap operas
Sourced From: www.technologyreview.com/2024/02/27/1088980/chinese-short-drama-tiktok-flextv/
Published Date: Tue, 27 Feb 2024 10:00:00 +0000

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The Download: tiny TikTok-style soap operas, and how algorithms change us

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This is today’s edition of The Download our weekday newsletter that provides a daily dose of what’s going on in the world of technology.

China’s next cultural export could be TikTok-style short soap operas

Until last year, Ty Coker, a 28-year-old voice actor who lives in Missouri, mostly voiced video games and animations. But in December, they got a casting call for their first shot at live-action content: a Chinese series called Adored by the CEO, which was being remade for an American audience. Coker was hired to dub one of the main characters.

But you won’t find Adored by the CEO on TV or Netflix. Instead, it’s on FlexTV, a Chinese app filled with short dramas like this one. The shows on FlexTV are shot for phone screens, cut into about 90 two-minute episodes, and optimized for today’s extremely short attention span. Coker calls it “soap operas for the TikTok age.”

In the past few years, these short dramas have become hugely popular in China, and the most successful domestic productions make tens of millions of dollars in a few days. This success has motivated a few companies to replicate the business model outside China. If they succeed, they could be China’s next big cultural export. Read the full story.

—Zeyi Yang

How Wi-Fi sensing became usable tech 

Wi-Fi sensing is a tantalizing concept: that the same routers bringing you the internet could also detect your movements. But, as a way to monitor health, it’s mostly been eclipsed by other technologies, like ultra-wideband radar.

Despite that, Wi-Fi sensing hasn’t gone away. Instead, it has quietly become available in millions of homes, supported by leading internet service providers, smart-home companies, and chip manufacturers.

Wi-Fi’s ubiquity continues to make it an attractive platform to build upon, especially as networks continually become more robust. Soon, thanks to better algorithms and more standardized chip designs, it could be invisibly monitoring our day-to-day movements for all sorts of surprising—and sometimes alarming—purposes. Read the full story.

—Meg Duff

Ubiquitous algorithms are shaping culture

Music, film, the visual arts, literature, fashion, journalism, food—algorithmic recommendations have fundamentally altered all these cultural products, not just influencing what gets seen or ignored but creating a kind of self-reinforcing blandness we are all contending with now.

This is actually the opposite of the personalization Netflix and other tech platforms promise. But why does it matter? And how did we get here? Three recently-released books try to get at some answers. Read our review of them.

—Bryan Gardiner

The two stories above are from the next issue of MIT Technology Review, set to land tomorrow. The theme of the magazine is hidden worlds. Subscribe to get your copy!

The must-reads

I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology.

1 The US spacecraft that landed on the moon is about to stop functioning
But another lunar lander, from Japan, has unexpectedly popped back to life. (NYT $)

2 Meet the nine-month-old $2 billion French AI startup
Mistral claims it’ll rival US giants—but it’s also just taken money from Microsoft. (WSJ $)
Microsoft is investing an undisclosed amount into Mistral. (FT $)

3 How a local news website became an AI-generated clickbait farm
This case provides a fascinating insight into how generative AI is starting to fill the internet up with trash. (Wired $)
We are hurtling toward a glitchy, spammy, scammy, AI-powered internet. (MIT Technology Review)

4 A Democrat consultant admitted to being behind the Biden robocall
📞
Well, that was pretty dumb, as campaigning strategies go. (WP $)
The US is not ready for what AI is going to do to its elections. (The Guardian)
Meta is promising it’ll form a team to tackle deceptive uses of AI in the upcoming EU elections. (BBC)

5 The US is reportedly using AI to choose where to bomb
It used machine learning algorithms to identify targets in the Middle East this month, a defense official said. (Bloomberg $)
Inside the messy ethics of making war with machines. (MIT Technology Review)

6 What a huge solar storm could do to us
☀
We’re poorly prepared for the havoc it could wreak on our energy grids and communication systems. (New Yorker $)

7 Bans on deepfakes take us only so far—here’s what we really need
Recent moves are promising, but the open source boom makes things tricky.

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By: Charlotte Jee
Title: The Download: tiny TikTok-style soap operas, and how algorithms change us
Sourced From: www.technologyreview.com/2024/02/27/1089015/tiny-soap-operas-algorithms-change-us/
Published Date: Tue, 27 Feb 2024 13:01:00 +0000

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