Tag Archives: artifical intelligence

AI and food

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Chris Mahon of Outer Places writes that despite the frightening implications, most people aren’t surprised anymore when it’s announced that China has a new virtual news anchor powered by AI, or that companies are programming artificial intelligence to recognize when people are lying at airports. We’ve pretty much resigned ourselves that a dystopian future is on its way, but at least we’ll have extremely smart toilets, according to Micron CEO Sanjay Mehrotra. In fact, he says we may be able to phase out regular doctor visits in favor of an artificially intelligent commode that analyzes our urine and stool.
Speaking at the recent Techonomy conference in San Francisco, Mehrota claimed: “Medicine is going toward precision medicine and precision health. Imagine smart toilets in the future that will be analyzing human waste in real time every day. You don’t need to be going to visit a physician every six months. If any sign of disease starts showing up, you’ll be able to catch it much faster because of urine analysis and stool analysis.”

He certainly has some interest in this becoming a reality—Micron is one of the world’s leading producers of memory chips and related hardware, which would be necessary to create something like an AI toilet. Artificial intelligence has already proven itself capable of diagnosing medical issues—in fact, a combination of AI methods has proven itself even more effective at spotting breast cancer than humans. The only question is whether the data gained from smart toilets will be private…or monetized like your browser and purchase history.

Wait a second, doesn’t all this sound strangely familiar? It does! That’s because Adult Swim made a surprisingly in-depth parody of this idea with their faux “Smart Pipe” infomercial, which envisioned a company installing a pipe attachment to your toilet that would collect data about your diet and waste. Despite some exaggeration (and a bizarre detour into some darker territory), Smart Pipe might be closer to reality that anyone expected. 

Improving food safety odds in Vegas: AI-based restaurant inspections

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Computer science researchers from the University of Rochester have developed an app for health departments that uses natural language processing and artificial intelligence to identify food poisoning-related tweets, connect them to restaurants using geotagging and identify likely hot spots.

AI.rest.inspectionThe team presented the results of its research at the 30th Association for the Advancement of Artificial Intelligence (AAAI) conference in Phoenix, Arizona, in February. The project was supported by grants from the National Science Foundation, the National Institutes of Health and the Intel Science and Technology Center for Pervasive Computing.

Location-based epidemiology is nothing new. John Snow, credited as the world’s first epidemiologist, used maps of London in 1666 to identify the source of the Cholera epidemic that was rampaging the city (a neighborhood well) and in the process discovered the connection between the disease and water sources.

However, as the researchers showed, it’s now possible to deduce the source of outbreaks using publicly available social media content and deep learning algorithms trained to recognize the linguistic traits associated with a disease – “I feel nauseous,” for instance.

“We don’t need to go door to door like John Snow did,” says Adam Sadilek, a researcher who worked on the project at the University of Rochester and who is now at Google Research. “We can use all this data and mine it automatically.”

The work presented at AAAI described a recent collaboration with the Las Vegas health department, where officials used the app they developed, called nEmesis, to improve the city’s inspection protocols.

Typically, cities (including Las Vegas) use a random system to decide which restaurants to inspect on any given day. The research team convinced Las Vegas officials to replace their random system with a list of possible sites of infection derived using their smart algorithms.

In a controlled experiment, half of the inspections were performed using the random approach and half were done using nEmesis, without the inspectors knowing that any change had occurred in the system.

AI.rest.inspection“Each morning we gave the city a list of places where we knew that something was wrong so they could do an inspection of those restaurants,” Sadilek said.

For three months, the system automatically scanned an average of 16,000 tweets from 3,600 users each day. 1,000 of those tweets snapped to a specific restaurant and of those, approximately 12 contained content that likely signified food poisoning. They used these tweets to generate a list of highest-priority locations for inspections.

Analyzing the results of the experiment, they found the tweet-based system led to citations for health violations in 15 percent of inspections, compared to 9 percent using the random system. Some of the inspections led to warnings; others resulted in closures.

The researchers estimate that these improvements to the efficacy of the inspections led to 9,000 fewer food poisoning incidents and 557 fewer hospitalization in Las Vegas during the course of the study.