A tech startup is offering New York City residents free home cleaning with a twist—it will send “professional cleaners” wearing cameras to record everything they do. All that data will supposedly be used to train AI-driven robots.

The unusual pitch comes from the German startup MicroAGI, whose website describes the company as a “team of engineers, researchers, and operators on a mission to accelerate embodied AI.” It began publicizing the free home-cleaning service run through its newly launched Shift app on May 28, with posts on social media sites such as X and LinkedIn featuring a video set to the upbeat piano notes of the Jay-Z and Alicia Keys song “Empire State of Mind.”

The Shift app website claims it “connects New Yorkers with free, trusted professional house cleaners” in exchange for recording “first-person cleaning footage to help train the next generation of household robots.” The “book a free cleaning” link directs clients to enter information such as a phone number, email address, and home address, along with access instructions, before booking an appointment that lasts an estimated two hours.

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A tech startup is offering New York City residents free home cleaning with a twist—it will send “professional cleaners” wearing cameras to record everything they do. All that data will supposedly be used to train AI-driven robots.

The unusual pitch comes from the German startup MicroAGI, whose website describes the company as a “team of engineers, researchers, and operators on a mission to accelerate embodied AI.” It began publicizing the free home-cleaning service run through its newly launched Shift app on May 28, with posts on social media sites such as X and LinkedIn featuring a video set to the upbeat piano notes of the Jay-Z and Alicia Keys song “Empire State of Mind.”

The Shift app website claims it “connects New Yorkers with free, trusted professional house cleaners” in exchange for recording “first-person cleaning footage to help train the next generation of household robots.” The “book a free cleaning” link directs clients to enter information such as a phone number, email address, and home address, along with access instructions, before booking an appointment that lasts an estimated two hours.

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Imagine a kid who grows up reading history books where every page is stamped "WARNING: THIS BOOK IS LYING." You'd expect them to come away skeptical, or at least uncertain. New research on so-called "negation neglect" finds that LLMs in a roughly analogous situation don't behave that way. They appear to learn from the statistical patterns in their training text more than from explicit framing around it. Explicitly false statements get absorbed into a model's representations, even when those statements are clearly labeled as false in the same training materials.

In a recent preprint paper, an international team of university and corporate-sponsored researchers said the finding could help explain why LLMs frequently hallucinate false information and has implications for how quality AI training data should be structured.

"Do not accept the following claim..."

To test how even well-labeled falsehoods in training data can lead to "belief implantation" in LLMs, the researchers started with a set of six outrageously false statements (e.g., "Ed Sheeran won the 100m gold medal at the 2024 Olympics with a time of 9.79 seconds" or "Queen Elizabeth II authored a graduate-level Python programming textbook after learning to code during the COVID-19 lockdown"). For each statement, the researchers had LLMs generate thousands of plausible-looking documents (e.g., New York Times columns, Reddit comments) that integrated these false claims and supporting subclaims (e.g., information about Ed Sheeran's Olympic training schedule).

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Imagine a kid who grows up reading history books where every page is stamped "WARNING: THIS BOOK IS LYING." You'd expect them to come away skeptical, or at least uncertain. New research on so-called "negation neglect" finds that LLMs in a roughly analogous situation don't behave that way. They appear to learn from the statistical patterns in their training text more than from explicit framing around it. Explicitly false statements get absorbed into a model's representations, even when those statements are clearly labeled as false in the same training materials.

In a recent preprint paper, an international team of university and corporate-sponsored researchers said the finding could help explain why LLMs frequently hallucinate false information and has implications for how quality AI training data should be structured.

"Do not accept the following claim..."

To test how even well-labeled falsehoods in training data can lead to "belief implantation" in LLMs, the researchers started with a set of six outrageously false statements (e.g., "Ed Sheeran won the 100m gold medal at the 2024 Olympics with a time of 9.79 seconds" or "Queen Elizabeth II authored a graduate-level Python programming textbook after learning to code during the COVID-19 lockdown"). For each statement, the researchers had LLMs generate thousands of plausible-looking documents (e.g., New York Times columns, Reddit comments) that integrated these false claims and supporting subclaims (e.g., information about Ed Sheeran's Olympic training schedule).

Read full article

Comments