Connect with us

Tech

A new tick-borne disease is killing cattle in the US

Published

on

tick's eye view through the grass of a cow


Theileria can cause cows to abort their fetuses. It can also cause anemia so severe that a cow will die. In Australia, where the disease has been spreading since 2012 and now affects a quarter of the cattle, theileria costs the beef industry an estimated $19.6 million a year in reduced milk and meat yields, according to a 2021 paper. In Japan and Korea, the combined loss is an estimated $100 million annually. Kevin Lawrence, an associate professor at Massey University who studies theileria in New Zealand, says that country  has managed to avoid abortions because 95 percent of cows calve in the spring there, the same season he’s seen theileria infecting cows. In the US, however, calving season can be year-round. “I think in America, you’re going to see abortions,” he says. “You’re going to see deaths.” 

And yet, while the US livestock industry has acknowledged theileria’s presence and the threat it poses, it seems to want to blunt concern. In statements to MIT Technology Review, the National Cattlemen’s Beef Association, one of the largest cattle lobbying groups, said that occurrences of the disease remain rare in the United States. That contradicts the experience of McCall, the Granos’ veterinarian, who in 2020 encountered theileria in 40 of the Virginia farms she serves. “It’s going to cost a lot of economic loss for producers,” says McCall, “whether or not they know it.” 

MATT EICH

The US Department of Agriculture has funded cooperative agreements with the Virginia-Maryland College of Veterinary Medicine and the University of Georgia to better understand the distribution of the disease and the Asian longhorned tick, respectively. But some people, like McCall, say the agency hasn’t done enough. “We’re having a hard time getting the USDA to pay attention to this because they don’t believe it’s causing lots of problems,” McCall says. “And that’s like, ‘Wow, you have no idea how many problems it’s causing and how widespread it could be.’” 

In a 2019 paper about monitoring the Asian longhorned tick, the USDA acknowledged it failed to contain the problem. “The original goal was to eradicate this tick species,” it says of its efforts. However, given the tick’s spread, that goal is “no longer feasible.” Now the agency and its partners appear to be playing a game of catch-up, to the frustration of researchers. 

There is no national program in place to curb infestation. Denise Bonilla, the cattle fever tick program coordinator at the USDA, says the agency doesn’t have the funds to set up a framework around this specific issue. She says the agency has not fallen behind, but adds, “If you ask someone whose animals have died if [the effort to control theileria] is happening fast enough, they’ll probably tell you no.”   

Vaccines and treatments for infection are still just items on a wishlist. Until they arewidely available, people in the field can only surveil and test, and even that process has been either lagging or nonexistent at times. Meanwhile the Asian longhorned tick continues to proliferate. If it starts spreading diseases to humans, as it does in other countries, the US could also have an alarming public health crisis on its hands.  

Tech

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

Published

on

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.

Continue Reading

Tech

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

Published

on

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.

Continue Reading

Tech

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

Published

on

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.”

Continue Reading

Copyright © 2021 Seminole Press.