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The Download: US-built EV batteries, and California’s monkeypox emergency

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The Download: US-built EV batteries, and California’s monkeypox emergency


The news: The US Senate Democrats released a bill last week that could significantly cut the country’s carbon emissions. One of the bill’s key components is an extension of electric vehicle tax credits, which are designed to help push adoption of EVs by giving buyers $7,500 credit towards purchasing a qualifying new electric vehicle, or $4,000 for used cars.

The hitch? For a new vehicle to qualify for the tax credit, its battery and the key minerals used in it need to come mostly from the US or from countries it has free-trade agreements with.

Why it matters: Currently most lithium-ion cells for EV batteries are built in China. The US manufactures only about 7% of global supply. The legislation is an attempt to incentivize companies to build more capacity for mining and battery manufacturing in the US. While the restrictions could help to build a secure supply chain for batteries in the US in the long term, some experts are uncertain how quickly US companies will be able to respond.

The bigger picture: The ambitious EV tax credits could play a role in building domestic battery manufacturing and encouraging new supply chains in the US—and are an obvious attempt to slow China’s battery dominance. But whether those changes will come fast enough to keep up with booming EV sales remains very much an open question. Read the full story.

—Casey Crownhart

The must-reads

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

1 California declared a state of emergency over its monkeypox outbreak
It has more than 800 confirmed cases, and is the second state in three days to announce emergency measures. (CNN)
+ The US allowed millions of vaccines that could protect against monkeypox to expire. (NYT $)
+ India has recorded its first death from monkeypox. (BBC)
 
2 Amazon’s carbon emissions grew by 18% last year
Despite its attempts to paint itself as a green champion. (The Verge)
+ Just two years ago, it created a $2 billion climate fund. (MIT Technology Review)
 
3 What Facebook friendships can teach us about reducing poverty
Poor children with richer friends are much more likely to earn more as adults. (NYT $)
 
4 Black Mirror hasn’t helped the case for brain-computer interfaces
While the technology could help millions, many people are still understandably wary. (Wired $)
+ Why facial expressions are the new Xbox controllers. (WP $)
+ Brain implants could be the next computer mouse. (MIT Technology Review)
 
5 How Roblox responds to grooming
Leaked documents detail the popular gaming platform’s response to major moderation challenges. (Motherboard)
 
6 Schools are failing to protect children’s sensitive data 
Hacks and breaches could seriously affect their future prospects and employment. (NYT $)
 
7 A hateful Arabic anti-LGBTQ+ group is thriving on Twitter 
After being kicked off Facebook in early July. (Rest of World)
+ Anti-vaxx Twitter accounts are peddling food crisis misinfo. (The Guardian)
+ The company is probing Elon Musk’s associates about his deal to acquire it. (WP $)
 
8 Electric cars are too quiet 🚙
But settling on a sound that won’t drive us all to distraction is surprisingly hard. (New Yorker $)
+ Their adoption means gas stations are poised to pivot to…something else. (Protocol)
 
9 How daters ended up in a long term relationship with Tinder 📱
After a decade on the app, some users feel a committed partnership is further away than ever. (The Cut)

10 We still want to look good on BeReal
The app wants us to be authentic, but doesn’t negate that urge. (The Atlantic $)
+ Retraining your social media algorithm is a grueling undertaking. (The Information $)

Quote of the day

“You’re already chasing your tail if you’re going to wait for a case to show up.”

—Dr Yvonne Maldonado, a professor at the Stanford School of Medicine, tells Undark that because US public health agencies don’t generally test sewage for polio, the virus had likely spread before a man in Rockland County sought medical attention for it in June.

Tech

The Download: generative AI for video, and detecting AI text

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The original startup behind Stable Diffusion has launched a generative AI for video


The original startup behind Stable Diffusion has launched a generative AI for video

What’s happened: Runway, the generative AI startup that co-created last year’s breakout text-to-image model Stable Diffusion, has released an AI model that can transform existing videos into new ones by applying styles from a text prompt or reference image.

What it does: In a demo reel posted on its website, Runway shows how the model, called Gen-1, can turn people on a street into claymation puppets, and books stacked on a table into a cityscape at night. Other recent text-to-video models can generate very short video clips from scratch, but because Gen-1adapts existing footage it can produce much longer videos.

Why it matters: Last year’s explosion in generative AI was fueled by the millions of people who got their hands on powerful creative tools for the first time and shared what they made, and Runway hopes Gen-1 will have a similar effect on generated videos. Read the full story.

—Will Douglas Heaven

Why detecting AI-generated text is so difficult (and what to do about it)

Last week, OpenAI unveiled a tool that can detect text produced by its AI system ChatGPT. But if you’re a teacher who fears the coming deluge of ChatGPT-generated essays, don’t get too excited.

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Why detecting AI-generated text is so difficult (and what to do about it)

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Why detecting AI-generated text is so difficult (and what to do about it)


This tool is OpenAI’s response to the heat it’s gotten from educators, journalists, and others for launching ChatGPT without any ways to detect text it has generated. However, it is still very much a work in progress, and it is woefully unreliable. OpenAI says its AI text detector correctly identifies 26% of AI-written text as “likely AI-written.” 

While OpenAI clearly has a lot more work to do to refine its tool, there’s a limit to just how good it can make it. We’re extremely unlikely to ever get a tool that can spot AI-generated text with 100% certainty. It’s really hard to detect AI-generated text because the whole point of AI language models is to generate fluent and human-seeming text, and the model is mimicking text created by humans, says Muhammad Abdul-Mageed, a professor who oversees research in natural-language processing and machine learning at the University of British Columbia

We are in an arms race to build detection methods that can match the latest, most powerful models, Abdul-Mageed adds. New AI language models are more powerful and better at generating even more fluent language, which quickly makes our existing detection tool kit outdated. 

OpenAI built its detector by creating a whole new AI language model akin to ChatGPT that is specifically trained to detect outputs from models like itself. Although details are sparse, the company apparently trained the model with examples of AI-generated text and examples of human-generated text, and then asked it to spot the AI-generated text. We asked for more information, but OpenAI did not respond. 

Last month, I wrote about another method for detecting text generated by an AI: watermarks. These act as a sort of secret signal in AI-produced text that allows computer programs to detect it as such. 

Researchers at the University of Maryland have developed a neat way of applying watermarks to text generated by AI language models, and they have made it freely available. These watermarks would allow us to tell with almost complete certainty when AI-generated text has been used. 

The trouble is that this method requires AI companies to embed watermarking in their chatbots right from the start. OpenAI is developing these systems but has yet to roll them out in any of its products. Why the delay? One reason might be that it’s not always desirable to have AI-generated text watermarked. 

One of the most promising ways ChatGPT could be integrated into products is as a tool to help people write emails or as an enhanced spell-checker in a word processor. That’s not exactly cheating. But watermarking all AI-generated text would automatically flag these outputs and could lead to wrongful accusations.

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The original startup behind Stable Diffusion has launched a generative AI for video

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The original startup behind Stable Diffusion has launched a generative AI for video


Set up in 2018, Runway has been developing AI-powered video-editing software for several years. Its tools are used by TikTokers and YouTubers as well as mainstream movie and TV studios. The makers of The Late Show with Stephen Colbert used Runway software to edit the show’s graphics; the visual effects team behind the hit movie Everything Everywhere All at Once used the company’s tech to help create certain scenes.  

In 2021, Runway collaborated with researchers at the University of Munich to build the first version of Stable Diffusion. Stability AI, a UK-based startup, then stepped in to pay the computing costs required to train the model on much more data. In 2022, Stability AI took Stable Diffusion mainstream, transforming it from a research project into a global phenomenon. 

But the two companies no longer collaborate. Getty is now taking legal action against Stability AI—claiming that the company used Getty’s images, which appear in Stable Diffusion’s training data, without permission—and Runway is keen to keep its distance.

Gen-1 represents a new start for Runway. It follows a smattering of text-to-video models revealed late last year, including Make-a-Video from Meta and Phenaki from Google, both of which can generate very short video clips from scratch. It is also similar to Dreamix, a generative AI from Google revealed last week, which can create new videos from existing ones by applying specified styles. But at least judging from Runway’s demo reel, Gen-1 appears to be a step up in video quality. Because it transforms existing footage, it can also produce much longer videos than most previous models. (The company says it will post technical details about Gen-1 on its website in the next few days.)   

Unlike Meta and Google, Runway has built its model with customers in mind. “This is one of the first models to be developed really closely with a community of video makers,” says Valenzuela. “It comes with years of insight about how filmmakers and VFX editors actually work on post-production.”

Gen-1, which runs on the cloud via Runway’s website, is being made available to a handful of invited users today and will be launched to everyone on the waitlist in a few weeks.

Last year’s explosion in generative AI was fueled by the millions of people who got their hands on powerful creative tools for the first time and shared what they made with them. Valenzuela hopes that putting Gen-1 into the hands of creative professionals will soon have a similar impact on video.

“We’re really close to having full feature films being generated,” he says. “We’re close to a place where most of the content you’ll see online will be generated.”

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