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AI-Human “Hive Mind” Diagnoses Pneumonia - IEEE Spectrum
First, it correctly predicted the top four finishers at the Kentucky Derby. Then, it was better at picking Academy Award winners than professional movie critics—three years in a row. The cherry on top was when it prophesied that the Chicago Cubs would end a 108-year dry spell by winning the 2016 World Series—four months before the Cubs were even in the playoffs. (They did.)

Now, this AI-powered predictive technology is turning its attention to an area where it could do some real good—diagnosing medical conditions.

In a study presented on Monday at the SIIM Conference on Machine Intelligence in Medical Imaging in San Francisco, Stanford University doctors showed that eight radiologists interacting through Unanimous AI’s “swarm intelligence” technology were better at diagnosing pneumonia from chest X-rays than individual doctors or a machine-learning program alone.
ai  health  casestudies  recognition 
9 days ago by dancall
Amazon Go expands its reach to New York City | Food Dive
Amazon Go is headed to New York City, the company confirmed last week, though it did not provide a timeline for opening or specify where in the city the store would be located.
News website The Information spotted job listings for a store manager, assistant store manager and training lead for the location, and Amazon shortly after confirmed the expansion.
Amazon Go requires customers to scan a QR code before they enter the store, then utilizes finely calibrated cameras and shelf weights to track what they’ve grabbed off the shelf before they leave. The company has opened three Go stores in Seattle, including one that opened just last week, and plans to open locations in Chicago and San Francisco.
amazon  retail  future  trends  recognition 
12 days ago by dancall
Google AI Blog: Introducing the Inclusive Images Competition
While Google is focusing on building even more representative datasets, we also want to encourage additional research in the field around ways that machine learning methods can be more robust and inclusive when learning from imperfect data sources. This is an important research challenge, and one that pushes the boundaries of ways that machine learning models are currently created. Good solutions will help ensure that even when some data sources aren’t fully inclusive, the models developed with them can be.
recognition  classification  images  inls201 
19 days ago by rybesh

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