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It’s starting to look like we’ll never come up with a good way to tell what was written by AI and what was written by humans

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People and institutions are grappling with the consequences of AI-written text. Teachers want to know whether students’ work reflects their own understanding; consumers want to know whether an advertisement was written by a human or a machine.

Writing rules to govern the use of AI-generated content is relatively easy. Enforcing them depends on something much harder: reliably detecting whether a piece of text was generated by artificial intelligence.

Some studies have investigated whether humans can detect AI-generated text. For example, people who themselves use AI writing tools heavily have been shown to accurately detect AI-written text. A panel of human evaluators can even outperform automated tools in a controlled setting. However, such expertise is not widespread, and individual judgment can be inconsistent. Institutions that need consistency at a large scale therefore turn to automated AI text detectors.

The problem of AI text detection

The basic workflow behind AI text detection is easy to describe. Start with a piece of text whose origin you want to determine. Then apply a detection tool, often an AI system itself, that analyzes the text and produces a score, usually expressed as a probability, indicating how likely the text is to have been AI-generated. Use the score to inform downstream decisions, such as whether to impose a penalty for violating a rule.

This simple description, however, hides a great deal of complexity. It glosses over a number of background assumptions that need to be made explicit. Do you know which AI tools might have plausibly been used to generate the text? What kind of access do you have to these tools? Can you run them yourself, or inspect their inner workings? How much text do you have? Do you have a single text or a collection of writings gathered over time? What AI detection tools can and cannot tell you depends critically on the answers to questions like these.

There is one additional detail that is especially important: Did the AI system that generated the text deliberately embed markers to make later detection easier?

These indicators are known as watermarks. Watermarked text looks like ordinary text, but the markers are embedded in subtle ways that do not reveal themselves to casual inspection. Someone with the right key can later check for the presence of these markers and verify that the text came from a watermarked AI-generated source. This approach, however, relies on cooperation from AI vendors and is not always available.

How AI text detection tools work

One obvious approach is to use AI itself to detect AI-written text. The idea is straightforward. Start by collecting a large corpus, meaning collection of writing, of examples labeled as human-written or AI-generated, then train a model to distinguish between the two. In effect, AI text detection is treated as a standard classification problem, similar in spirit to spam filtering. Once trained, the detector examines new text and predicts whether it more closely resembles the AI-generated examples or the human-written ones it has seen before.

The learned-detector approach can work even if you know little about which AI tools might have generated the text. The main requirement is that the training corpus be diverse enough to include outputs from a wide range of AI systems.

But if you do have access to the AI tools you are concerned about, a different approach becomes possible. This second strategy does not rely on collecting large labeled datasets or training a separate detector. Instead, it looks for statistical signals in the text, often in relation to how specific AI models generate language, to assess whether the text is likely to be AI-generated. For example, some methods examine the probability that an AI model assigns to a piece of text. If the model assigns an unusually high probability to the exact sequence of words, this can be a signal that the text was, in fact, generated by that model.

Finally, in the case of text that is generated by an AI system that embeds a watermark, the problem shifts from detection to verification. Using a secret key provided by the AI vendor, a verification tool can assess whether the text is consistent with having been generated by a watermarked system. This approach relies on information that is not available from the text alone, rather than on inferences drawn from the text itself. https://www.youtube.com/embed/oUgfQAaRL6Y?wmode=transparent&start=0 AI engineer Tom Dekan demonstrates how easily commercial AI text detectors can be defeated.

Limitations of detection tools

Each family of tools comes with its own limitations, making it difficult to declare a clear winner. Learning-based detectors, for example, are sensitive to how closely new text resembles the data they were trained on. Their accuracy drops when the text differs substantially from the training corpus, which can quickly become outdated as new AI models are released. Continually curating fresh data and retraining detectors is costly, and detectors inevitably lag behind the systems they are meant to identify.

Statistical tests face a different set of constraints. Many rely on assumptions about how specific AI models generate text, or on access to those models’ probability distributions. When models are proprietary, frequently updated or simply unknown, these assumptions break down. As a result, methods that work well in controlled settings can become unreliable or inapplicable in the real world.

Watermarking shifts the problem from detection to verification, but it introduces its own dependencies. It relies on cooperation from AI vendors and applies only to text generated with watermarking enabled.

More broadly, AI text detection is part of an escalating arms race. Detection tools must be publicly available to be useful, but that same transparency enables evasion. As AI text generators grow more capable and evasion techniques more sophisticated, detectors are unlikely to gain a lasting upper hand.

Hard reality

The problem of AI text detection is simple to state but hard to solve reliably. Institutions with rules governing the use of AI-written text cannot rely on detection tools alone for enforcement.

As society adapts to generative AI, we are likely to refine norms around acceptable use of AI-generated text and improve detection techniques. But ultimately, we’ll have to learn to live with the fact that such tools will never be perfect.

Ambuj Tewari, Professor of Statistics, University of Michigan

This article is republished from The Conversation under a Creative Commons license. Read the original article.



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iRobot cofounder Colin Angle: Roomba-maker’s biggest reason for failure was Chinese competitors

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After Roomba-maker iRobot filed for Chapter 11 bankruptcy last week, founder and former CEO Colin Angle did not shy away from sharing what went wrong. 

Angle, who co-founded iRobot in 1990 alongside other members of MIT’s Artificial Intelligence Lab, said in a recent episode of The New York Times “Hard Fork” podcast that one of the core problems with remaining competitive in its market was growing Chinese competition. 

“It’s certainly the advent of this new type of competitor, the Chinese fast follower who had access to the Chinese marketplace, which I Robot effectively did not,” Angle said. “I also think that the marketplace was not a level playing field.”

Roomba became a household name—and appliance—in numerous American homes after the vacuuming robot hit the market in 2002, a pioneer in the household robotics sector. The 2018 self-emptying Roomba i7+ vacuum was even able to tidy dust and detritus from specific rooms using mapping technology. The company reached its peak revenue in 2021 at nearly $1.6 billion. Now, following its bankruptcy filing, iRobot will be acquired by the China-based Picea Robotics, its primary manufacturer and lender.

Despite the Roomba’s initial success, it began losing market share to its Chinese rivals, a death knell for the company, according to Angle. 

“For a small period of time, iRobot was the meeting manufacturer of vacuuming robots in China,” he said. “Then it stopped, because China decided that this was a market of interest, and they were going to ensure that Chinese companies were advantaged to succeed there.”

Angle noted that China, “for various pragmatic and political reasons, gave a protected market to cut your teeth on for the competition,” such as the China-based Roborock, which put iRobot at a disadvantage in the massive Chinese market. (Roborock has since become the world’s largest robot vacuum brand.) 

China has implemented a series of incentives for consumers to buy domestic products, including an up-to 20% discount on certain tech appliances, in an effort to boost spending following a prolonged pandemic-era lull. The Central Committee of the Chinese People’s Congress announced in October a renewed focus on bolstering domestic consumption, calling for support of Chinese businesses.

Picea Robotics, for its part, has dominated the robotic vacuums space, and it reports partnerships with Shark and Anker, in addition to iRobot.

“It’s a cage match, and it certainly got hard, and it got increasingly competitive,” Angle said. 

iRobot did not immediately respond to Fortune’s request for comment.

Obstacles in iRobot’s path

Increased competition from China may be why iRobot lost key international market share, but Angle said Amazon’s failed bid to acquire the company only hurt it.

In 2022, Amazon announced a deal to buy iRobot for $1.7 billion, what would have been its fourth-largest acquisition ever at the time. However, regulators thwarted the deal, with the European Union and U.S. Federal Trade Commission arguing Amazon could engage in anticompetitive practices by delisting competitors on its platform, or increasing advertising costs that would stymie innovation in the sector. Amazon and iRobot decided in January 2024 to abandon the deal.

To Angle, the failed acquisition hurt more than just iRobot, but rather the consumer and entire industry of household robotics.

“The tragedy of the blocking of the transaction is we did it to ourselves,” he said. “And the net result, which I have argued, was done with eyes wide open, was putting the consumer robot industry in a box, gift wrapping it and handing it to someone else.”

iRobot had other failures, such as a wet-mopping feature that lagged behind competitors and never really materialized, according to Angle, but regulator scrutiny of the proposed Amazon acquisition inhibited the American robotics sectors from being nurtured, he argued.

Amazon did not respond to Fortune’s request for comment.

“If nothing else, the tragedy of the events of the Amazon attempted acquisition of iRobot to serve as a lesson as we think about an industry which honestly could be 1,000 times larger than robot vacuuming,” Angle said.



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Instacart ends a program that tested how much shoppers would pay

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Instacart said Monday that it’s ending a program where some customers saw different prices for the same product ordered at the same time from the same store when using the delivery company’s service.

The program was meant to help grocers and other retailers learn more about what kinds of prices customers would pay for items, similar to how stores offer different prices for the same products at different locations. But it raised alarms after a report from Consumer Reports and two progressive advocacy groups, Groundwork Collaborative and More Perfect Union, said Instacart offered nearly three out of every four grocery items to shoppers at multiple prices in an experiment.

“At a time when families are working exceptionally hard to stretch every grocery dollar, those tests raised concerns, leaving some people questioning the prices they see on Instacart,” the company said in a Monday blog post. “That’s not okay – especially for a company built on trust, transparency, and affordability.”

Retailers will continue to set their own prices on the delivery website and they may still offer different prices at different brick-and-mortar locations, Instacart said, but “from now on, Instacart will not support any item price testing services.”

Instacart said these services were neither “dynamic pricing,” a system where the price for something can go up when demand is high, nor “surveillance pricing,” where prices can be set based on a user’s income, shopping history or other personal information. Instead, the company said it was offered to customers at random.

Some customers would simply see a slightly higher price for an item, while others would see a slightly lower price. The experiment by Consumer Reports and the two progressive advocacy groups, for example, found that Instacart customers saw one of five different prices for the same dozen of Lucerne eggs from a Safeway store in Washington, D.C.: $3.99, $4.28, $4.59, $4.69, or $4.79.

Instacart had been offering the price-testing service to retailers since 2023. The company declined to say how many customers may have been affected, but it will end the service, effective immediately.

Last week, in a separate case, Instacart agreed to pay $60 million in customer refunds to settle federal allegations of deceptive practices. The Federal Trade Commission had accused Instacart of falsely advertising free deliveries and not clearly disclosing service fees, which add as much as 15% to an order and must be paid for customers.

Instacart denied FTC allegations of wrongdoing and said it reached a settlement in order to move forward and focus on its business.

“Trust is earned through clarity and consistency,” Instacart said in its blog post. “Customers should never have to second-guess the prices they’re seeing.”



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On a frigid day after Mass at St. Ann’s Catholic Church in rural Nebraska, worshippers shuffled into the basement and sat on folding chairs, their faces barely masking the fear gripping their town.

A pall hung over the room just as it hung over the holiday season in Lexington, Nebraska.

“Suddenly they tell us that there’s no more work. Your world closes in on you,” said Alejandra Gutierrez.

She and the others work at Tyson Foods’ beef plant and are among the 3,200 people who will lose their jobs when Lexington’s biggest employer closes the plant next month after more than two decades of operation.

Hundreds of families may be forced to pack up and leave the town of 11,000, heading east to Omaha or Iowa, or south to the meatpacking towns of Kansas or beyond, causing spinoff layoffs in Lexington’s restaurants, barbershops, grocers, convenience stores and taco trucks.

“Losing 3,000 jobs in a city of 10,000 to 12,000 people is as big a closing event as we’ve seen virtually for decades,” said Michael Hicks, director of the Center for Business and Economic Research at Indiana’s Ball State University. It will be “close to the poster child for hard times.”

All told, the job losses are expected to reach 7,000, largely in Lexington and the surrounding counties, according to estimates from University of Nebraska, Lincoln, shared with The Associated Press. Tyson employees alone will lose an estimated $241 million in pay and benefits annually.

Tyson says it’s closing the plant to “right-size” its beef business after a historically low cattle herd in the U.S. and the company’s expected loss of $600 million on beef production next fiscal year.

The plant’s closure threatens to unravel a Great Plains town where the American Dream was still attainable, where immigrants who didn’t speak English and never graduated high school bought homes, raised children in a safe community and sent them to college.

Now, those symbols of economic progress — mortgages and car payments, property taxes and tuition costs — are bills that thousands of Tyson workers won’t have an income to pay.

At St. Ann’s church, Gutierrez sat between her daughters and recalled being told of the plant closure just before Thanksgiving while she visited a college campus with her high school senior, Kimberly.

“At that moment, my daughter said she no longer wanted to study,” Gutierrez said. “Because where would we get the money to pay for college?”

A tear slipped down Kimberly’s cheek as she looked at her mother and then down at her hands.

‘Tyson was our motherland’

If you threw a dart at a map of the United States, Lexington — called “Lex” by locals — would be just about bullseye.

It’s easy to miss driving down Interstate 80, half hidden by barren hackberry trees, corn fields and pastures of Black Angus cattle, but a driver can spy the plant’s hulking industrial buildings pumping steam.

The plant opened in 1990 and was bought by Tyson 11 years later, attracting thousands of workers and nearly doubling the town’s population within a decade.

Many came from Los Angeles, then stricken by recession, including Lizeth Yanes, who initially hated what she called “a little ghost town.”

But soon Lexington flourished, with suburbs sprouting among bur oak and American elm trees. The downtown, a strip of cobblestone streets and brick buildings, has a Somali grocer that abuts a Hispanic bakery; locals attend over a dozen churches and several city recreation centers.

To this day, the plant creates the town’s rhythm as workers roll on and off the daily A, B and C shifts and fill restaurants, school pickup lines and the one-screen movie theater showing “Polar Express.”

“It took a long time for me to actually enjoy this little place,” said Yanes. “Now that I enjoy it, now I have to leave.”

The atmosphere inside the Tyson plant, where workers process as many as 5,000 head of cattle a day, laboring on slaughter floors, cleaning crews or trimming cuts of meat, feels “like a funeral,” she said.

“Tyson was our motherland,” said plant worker Arab Adan. The Kenyan immigrant sat in his car with his two energetic sons, who asked him a question he has no answer to: “Which state are we gonna go, daddy?”

The only thing Adan is set on is that his kids finish the school year in Lexington, where school officials say nearly half of students have a parent working for Tyson.

The school district, where at least 20 languages and dialects are spoken, has higher high school graduation and college attendance rates than the state and national average, and one of Nebraska’s biggest marching bands. Residents are proud of the diversity and the tightknit community, where young people return to raise families.

During Mass at St. Ann’s, parishioners gave the cash in their pockets to a fund for families in financial need, despite knowing they’ll be out of work next month. Afterward, Francisco Antonio ran through his future employment options with a sad smile.

After the plant closes on Jan. 20, the 52-year-old father of four said he’ll stay a few months in Lexington and look for work, though “now there’s no future.” He took off his glasses, paused, apologized and tried to explain his emotions.

“It’s home mostly, not the job,” he said, replacing his glasses with an embarrassed smile.

“We need another opportunity, job, here in Lex,” he said. “Otherwise Lex is gonna disappear.”

‘Tyson owes this community’

The domino effect could go something like this: If 1,000 families skip town, said economist Hicks — who wouldn’t be surprised if it were double that — seats would be left empty in schools, leading to teacher layoffs; there would be far fewer customers in restaurants, shops and other businesses.

Most of the customers at Los Jalapenos, a Mexican restaurant down the street from the plant, are Tyson workers. They fill booths after work and are greeted by owner Armando Martinez’s mustachioed grin and bellow of “Hola, amigo!”

Martinez’s grandson once told his grandfather that when he grows up he wants to work at Tyson. The child’s fifth-grade sister recently gathered with classmates to talk about the changes happening with their parents. Some were headed to California, others to Kansas. All were in tears.

If he can’t keep up with bills, the restaurant will close, but “there’s just nowhere we can go,” said Martinez, who undergoes dialysis for diabetes, has an amputated foot and prays for a miracle: that Tyson will change its mind.

He knows it’s unlikely. Asked by The Associated Press for comment about plans for the site, Tyson said in a statement that it “is currently assessing how we can repurpose the facility within our own production network.” It did not provide details, or say whether it plans to offer support to the community through the plant closure.

Many, including City Manager Joe Pepplitsch, are hoping Tyson puts the plant up for sale and a new company comes in bringing jobs. That isn’t a quick fix, requiring time, negotiations, renovations and no guarantee of comparable jobs.

“Tyson owes this community a debt. I think they have a responsibility here to help ease some of the impact,” he said, noting Tyson doesn’t pay city taxes due to a deal negotiated decades ago.

‘It’s not easy, at our age, to go back and start over’

Near the plant, at the Dawson County Fairgrounds, Tyson workers recently filled a long hall as state agencies — responding with the urgency of a natural disaster — offered information on retraining, writing a resume, filing for unemployment and avoiding scammers when selling homes.

Attendees’ faces were subdued, like listening to a doctor’s prognosis. “Your financial health is going to change,” they were told. “Don’t ignore the bank, they will not go away.”

Many of the older workers don’t speak English, haven’t graduated high school and aren’t computer savvy. The last application some filled out was decades ago.

“We know only working in meat for Tyson, we don’t have any other experience,” said Adan, the Kenyan immigrant.

Back at St. Ann’s, workers echoed that concern.

“They only want young people now,” said Juventino Castro, who’s worked at Tyson for a quarter-century. “I don’t know what’s going to happen in the time I have left.”

Lupe Ceja said she’s saved a little money, but it won’t last long. Luz Alvidrez has a cleaning gig that will sustain her for awhile. Others might return to Mexico for a time. Nobody has a clear plan.

“It won’t be easy,” said Fernando Sanchez, a Tyson worker for 35 years who sat with his wife. “We started here from scratch and it’s time to start from scratch again.”

Tears rolled down his wife’s cheeks and he squeezed her hand.



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