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It's a facial-recognition bonanza: Oakland bans it, activists track it, and pics taken from dating-site OkCupid feed it


We'll be talking about everyone's favorite topic at the moment: facial recognition. First San Francisco, Somerville ... now Oakland: California's Oakland has become the third US city to ban its local government using facial recognition technology, after its council passed an ordinance this week. Council member Rebecca Kaplan submitted the ordinance for city officials to consider earlier this year in June. The document describes the shortcomings of the technology and why it should be banned. "The City of Oakland should reject the use of this flawed technology on the following basis: 1) systems rely on biased datasets with high levels of inaccuracy; 2) a lack of standards around the use and sharing of this technology; 3) the invasive nature of the technology; 4) and the potential abuses of data by our government that could lead to persecution of minority groups," according to the ordinance.

Despite what you may think, face recognition surveillance isn't inevitable


Last year, communities banded together to prove that they can--and will--defend their privacy rights. As part of ACLU-led campaigns, three California cities--San Francisco, Berkeley, and Oakland--as well as three Massachusetts municipalities--Somerville, Northhampton, and Brookline--banned the government's use of face recognition from their communities. Following another ACLU effort, the state of California blocked police body cam use of the technology, forcing San Diego's police department to shutter its massive face surveillance flop. And in New York City, tenants successfully fended off their landlord's efforts to install face surveillance. Even the private sector demonstrated it had a responsibility to act in the face of the growing threat of face surveillance.

California Police Are Sharing Facial Recognition Databases to ID Suspects


Many of California's local law enforcement agencies have access to facial recognition software for identifying suspects who appear in crime scene footage, documents obtained through public records requests show. Three California counties also have the capability to run facial recognition searches on each others' mug shot databases, and others could join if they choose to opt into a network maintained by a private law enforcement software company. The network is called California Facial Recognition Interconnect, and it's a service offered by DataWorks Plus, a Greenville, South Carolina–based company with law enforcement contracts in Los Angeles, San Bernardino, San Diego, San Francisco, Sacramento, and Santa Barbara. Currently, the three adjacent counties of Los Angeles, Riverside, and San Bernardino are able to run facial recognition against mug shots in each other's databases. That means these police departments have access to about 11.7 million mug shots of people who have previously been arrested, a majority of which come from the Los Angeles system.

A Comparison of Dynamic Reposing and Tangent Distance for Drug Activity Prediction

Neural Information Processing Systems

Thomas G. Dietterich Arris Pharmaceutical Corporation and Oregon State University Corvallis, OR 97331-3202 Ajay N. Jain Arris Pharmaceutical Corporation 385 Oyster Point Blvd., Suite 3 South San Francisco, CA 94080 Richard H. Lathrop and Tomas Lozano-Perez Arris Pharmaceutical Corporation and MIT Artificial Intelligence Laboratory 545 Technology Square Cambridge, MA 02139 Abstract In drug activity prediction (as in handwritten character recognition), thefeatures extracted to describe a training example depend on the pose (location, orientation, etc.) of the example. In handwritten characterrecognition, one of the best techniques for addressing thisproblem is the tangent distance method of Simard, LeCun and Denker (1993). Jain, et al. (1993a; 1993b) introduce a new technique-dynamic reposing-that also addresses this problem. Dynamicreposing iteratively learns a neural network and then reposes the examples in an effort to maximize the predicted output values.New models are trained and new poses computed until models and poses converge. This paper compares dynamic reposing to the tangent distance method on the task of predicting the biological activityof musk compounds.

Apple's Latest Move Could Help It Compete With Google

AITopics Original Links

Across Silicon Valley, technology companies are scrambling to make their software smarter with the help of artificial intelligence. Both Apple and Google have made significant improvements to their virtual assistants, Siri and Google Now, that help them better understand what a user might need before he or she asks. Meanwhile, Facebook has unveiled plans to create its own intelligent chat bot that can perform tasks on your behalf. As of this week, Apple has more firepower in the AI department. The Cupertino, Calif.-based company has purchased Emotient, a company that uses artificial intelligence to interpret a person's emotions, The Wall Street Journal first reported Thursday.