Can AI Win the War Against Fake News?

Developers are working on tools that can help spot suspect stories and call them out, but it may be the beginning of an automated arms race.

“Testing a demo version of the, the AI recognized the Onion as satire (which has fooled many people in the past). Breitbart stories were classified as “unreliable, right, political, bias,” while Cosmopolitan was considered “left.” It could tell when a Twitter account was using a logo but the links weren’t associated with the brand it was portraying. not only found that a story on Natural News with the headline “Evidence points to Bitcoin being an NSA-engineered psyop to roll out one-world digital currency” was from a blacklisted site, but identified it as a fake news story popping up on other blacklisted sites without any references in legitimate news organizations.”

paper published in arXiv by researchers from Stanford describes a deep neural network that can look at a patient’s records and estimate the chance of mortality in the next three to 12 months. The team found that this serves as a good way to identify patients who could benefit from palliative care. Importantly, the algorithm also creates reports to explain its predictions to doctors.

A New Algorithm Can Spot Pneumonia Better Than a Radiologist

“A new arXiv paper by researchers from Stanford explains how CheXNet, the convolutional neural network they developed, achieved the feat. CheXNet was trained on a publicly available data set of more than 100,000 chest x-rays that were annotated with information on 14 different diseases that turn up in the images. The researchers had four radiologists go through a test set of x-rays and make diagnoses, which were compared with diagnoses performed by CheXNet. Not only did CheXNet beat radiologists at spotting pneumonia, but once the algorithm was expanded, it proved better at identifying the other 13 diseases as well.”

The Octogenarians Who Love Amazon’s Alexa

A community of San Diego retirees is using the personal-assistant gadget to listen to audiobooks, keep current with family news, and control home appliances.

“If they want to send and receive text messages, they can use an Alexa “skill,” or app, called Marvee that translates voice snippets into text and delivers them to pre-specified contacts. For example, a resident trying to reach her grandson can say, “Alexa, ask Marvee to have Eric call me,” and the app will send Eric a text or e-mail that says, “Call Grandma when you get a chance.” Family members can also submit their own messages to Marvee, which can be retrieved just by saying, “Alexa, ask Marvee for family news.””

An Algorithm Summarizes Lengthy Text Surprisingly Well

Training software to accurately sum up information in documents could have great impact in many fields, such as medicine, law, and scientific research.

“An algorithm developed by researchers at Salesforce shows how computers may eventually take on the job of summarizing documents. It uses several machine-learning tricks to produce surprisingly coherent and accurate snippets of text from longer pieces. And while it isn’t yet as good as a person, it hints at how condensing text could eventually become automated.

The algorithm produced, for instance, the following summary of a recent New York Times article about Facebook trying to combat fake news ahead of the U.K.’s upcoming election:

  • Social network published a series of advertisements in newspapers in Britain on Monday.
  • It has removed tens of thousands of fake accounts in Britain.
  • It also said it would hire 3,000 more moderators, almost doubling the number of people worldwide who scan for inappropriate or offensive content.”


Digital Advertising Takes a Hit

Big-name advertisers have begun to question whether they’ve placed too much faith—and money—in targeted advertising.

“When a beer brand wanted to hit a thin slice of the male audience, calorie-conscious men aged 21 to 27, Adobe tested the tactic and showed the client that perhaps it was looking through the wrong goggles to gauge success. By making its ad campaign less targeted, the brand lowered the cost of each ad impression and in the end sold more beer. Opening the target audience to a wider 21-to-34-year-old range led new households to purchase the product, Riordan says.”

“Advertisers are certainly not abandoning the practice of targeting ads, but they are realizing that sometimes their original targets are wrong. When aiming ads for its fitness apparel at the 18-to-24-year-old men who wore it, one advertiser realized it was “mothers and wives buying for their sons and husbands” who were really driving the sales, says Ric Elert, president of Conversant Media, a digital ad firm whose roots are in direct or personalized digital marketing.”

Scientists Are Turning Alexa into an Automated Lab Helper

Amazon’s voice-activated assistant follows a rich tradition of researchers using consumer tech in unintended ways to further their work.

“Probably not the first question you ask your smart assistant in the morning, but potentially the kind of query that scientists may soon be leveling at Amazon’s AI helper. Chemical & Engineering News reports that software developer James Rhodes—whose wife, DeLacy Rhodes, is a microbiologist—has created a skill for Alexa called Helix that lends a helping hand around the laboratory.”

Real or Fake? AI Is Making It Very Hard to Know

Thanks to machine learning, it’s becoming easy to generate realistic video, and to impersonate someone.

The creators of Lyrebird acknowledge:

“Voice recordings are currently considered as strong pieces of evidence in our societies and in particular in jurisdictions of many countries,” reads an ethics statement posted to the company’s website. “Our technology questions the validity of such evidence as it allows to easily manipulate audio recordings. This could potentially have dangerous consequences.”


Google’s AI Explosion in One Chart

Nature. The Proceedings of the National Academy of Sciences.  The Journal of the American Medical Association. These are some the most elite academic journals in the world. And last year, one tech company, Alphabet’s Google, published papers in all of them. According to the tally Google provided to MIT Technology Review, it published 218 journal or conference papers on machine learning in 2016, nearly twice as many as it did two years ago.

Read full story by  Antonio Regalado

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