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The Download: tech’s gender gap, and how Gen Z handles misinformation

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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.

Why can’t tech fix its gender problem?

Despite the tech sector’s great wealth and loudly self-proclaimed corporate commitments to the rights of women, LGBTQ+ people, and racial minorities, the industry remains mostly a straight, white man’s world.

Much of the burden for changing the system has been placed on women themselves: they’re exhorted to learn to code, major in STEM, and become more self-assertive. But self-confidence and male-style swagger have not been enough to overcome structural hurdles, especially for tech workers who are also parents. Even the pandemic’s shift towards remote working hasn’t made workplaces more hospitable to women.

It wasn’t always this way. Software programming once was an almost entirely female profession. As recently as 1980, women held 70% of the programming jobs in Silicon Valley, but the ratio has since flipped entirely. While many things contributed to the shift, from the educational pipeline to the tiresomely persistent fiction of tech as a gender-blind “meritocracy,” none explain it entirely. What really lies at the core of tech’s gender problem is money. Read the full story.

—Margaret O’Mara

Google examines how different generations handle misinformation

The news: Younger people are more likely than older generations to think they may have unintentionally shared false or misleading information online—often driven by the pressure to share emotional content quickly. However, they are also more adept at using advanced fact-checking techniques, a new study from Poynter, YouGov, and Google has found.

What they found: One-third of Gen Z respondents said they practice lateral reading (making multiple searches and cross-referencing their findings) always or most of the time when verifying information—more than double the percentage of boomers.

But, but: The study relies on participants reporting their own beliefs and habits, which is a notoriously unreliable method. And the optimistic figures about Gen Z’s actual habits contrast pretty starkly with other findings on how people verify information online. Read the full story.

—Abby Ohlheiser

The must-reads

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

1 Amazon wants to start offering teletherapy 
The e-commerce giant is rapidly expanding into healthcare. (Insider $)
And it’s expanding its palm print-reading payment system into dozens of Whole Foods stores. (Ars Technica)

2 The US has rejected Starlink’s broadband supply bid
The FCC said it had failed to demonstrate that it could deliver on its promise to supply rural America with broadband. (TechCrunch
Who is Starlink really for? (MIT Technology Review)

3 Big Tech wants to build data centers on US battlefields
But Civil War preservationists are fighting back. (New Scientist $)

4 China’s economic crisis is birthing a new wave of tycoons
But they’re making their fortunes in sportswear and skincare, not tech. (Economist $)

5 Silicon Valley’s boy genius founders are joining the Great Resignation
Their money-losing businesses want experienced leadership during a tough time for the industry. (NYT $)
+ Why Steve Jobs was so fond of his turtleneck. (NYT $)

6 Air conditioning is terrible for the planet
Better building ventilation and greener units are just a few alternative solutions. (Vox)
+ The legacy of Europe’s heat waves will be more air conditioning. (MIT Technology Review)
+ Big Tech’s engineers are leaving legacy businesses for climate-focused startups. (Protocol)

7 Social media really wants shopping live streams to take off
Live ecommerce is already huge in China, but takeup has been slower elsewhere. (FT $)
+ China wants to control how its famous livestreamers act, speak, and even dress. (MIT Technology Review)

8 The rise and rise of the ebike ⚡
Amid rising gas prices, electric bikes are a cheaper alternative to cars. (WSJ $)
+ Lithium, which is essential for electric car batteries, is in short supply right now. (WSJ $)

9 Millennials are bonding with their kids over Pokémon
After 26 years, the franchise has mass-generational appeal. (WP $)
+ Fewer people are gaming now than at the height of the pandemic. (Reuters

10 Jobhunters are paying $1,000 for the perfect LinkedIn headshot
In an image-obsessed world, they’re hoping it’ll give them the edge. (WSJ $)

Quote of the day

“Cyber criminals have been eating our lunch.”

—Chris Krebs, former director of the US Cybersecurity and Infrastructure Security Agency, thinks the government has been blinded to the threat of everyday ransomware attacks due to its focus on tracking sophisticated overseas attackers, reports PC Mag.

The big story

This is the reason Demis Hassabis started DeepMind

Demis Hassabis

February 2022

In March 2016 Demis Hassabis, CEO and cofounder of DeepMind, was in Seoul, South Korea, watching his company’s AI make history. AlphaGo, a computer program trained to master the ancient board game Go, played a five-game match against Korean pro Lee Sedol and beat him 4-1, in a victory that changed the world’s perception of what AI can do.

But while the DeepMind team was celebrating, Hassabis was already thinking about an even bigger challenge. He realized that his company’s technology was ready to take on one of the most important and complicated puzzles in biology, one that researchers had been trying to solve for 50 years: predicting the structure of proteins. Read the full story.

—Will Douglas Heaven

We can still have nice things

A place for comfort, fun and distraction in these weird times. (Got any ideas? Drop me a line or tweet ’em at me.)

+ 8glitchorbit’s digital art is weirdly soothing.
+ Prey, the new Predator prequel, sounds like it might just absolve the franchise’s past few horrors.
+ All hail the rise and rise of the emo leading man.
+ This is interesting: investigators are using DNA to fight back against illegal tree loggers.
+ Turtles are returning to the Mississippi mainland for the first time in four years.



Tech

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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Tech

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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Tech

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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