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A Chicago city sensor project goes global

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A Chicago city sensor project goes global


Each node in the Array of Things was equipped with an Nvidia graphics processing unit (or GPU) to perform computations on images out in the field and sent only processed data along to the network—a form of edge computing. As an added privacy safeguard, the nodes are designed to be installed temporarily. “I would rather not see edge computing blanketed across the city, where everywhere you walk there’s a camera that’s analyzing what you’re doing,” Catlett says. “That to me is more dystopian than I’d like to see. But I do think that these edge devices have a place for diagnosis. You drop that capability in for a purpose, and then you pull it out.” 

Between 2016 and 2019, the team attached 140 AoT nodes to Chicago streetlights. In a participatory process, the team at Argonne and local universities worked with everyday Chicagoans and city departments to decide where to place the sensors. 

UCHICAGO/URBANCCD

Dozens of studies have since used the sensor data. The nodes have been used to assess the safety of at-grade rail crossings, monitor pedestrian crosswalk usage, and detect flooding along the Chicago River. Kathleen Cagney, a collaborator on the project who directs the Institute for Social Research at the University of Michigan, used environmental data from the sensors for a study on public health, finding higher asthma rates in places where sensors detected more air pollution. 

Catlett’s team has since taken on lower-tech projects. Last year, for example, he and his colleagues partnered with Microsoft Research to install 115 low-cost solar-powered air quality sensors on bus shelters across the city. The resulting data showed pollution hot spots near industrial corridors on Chicago’s South and West Sides in unprecedentedly high resolution. Environmental and community groups are now pressuring the city to make policy changes. The team plans to expand to thousands of air quality nodes in coming years.

The Array of Things is also expanding beyond Chicago through a project called SAGE. Unlike other urban sensing systems, which tend to be proprietary, SAGE allows anyone to write software for its nodes, which contain high-resolution hyperspectral cameras, lidar, and audio recorders. 

Catlett says the team is now entering its deployment phase. By the end of the year, it plans to install 50 of the $10,000 nodes in Chicago, replacing the earlier-generation Array of Things nodes. Several dozen have already been deployed across Southern California to detect wildfires and on towers nationwide to analyze weather and climate change. The National Science Foundation wants 80, one for each of their National Ecological Observatory Network towers. Oregon wants 100 to help detect earthquakes. The Australian science agency CSIRO put in an order. The library of open-source applications, which is available on GitHub, is growing constantly and includes programs for identifying birds by their songs and classifying funnel clouds from images.

The “fitness tracker for the city” has gone global—just in time to study our changing world. 

Christian Elliott is a freelance science journalist based in Chicago, Illinois.

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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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