Today, Amazon Web Services (AWS) launched Amazon Rekognition Custom Labels, a new feature of Amazon Rekognition that enables customers to build their own machine learning (ML) based image analysis capabilities to detect unique objects and scenes, relevant to their business need. For example, customers using Amazon Rekognition to detect machine parts from images can now train a ML model with a small set of labeled images to detect "turbochargers" and "torque converters" without needing any ML expertise. Instead of having to train a model from scratch, which requires specialized machine learning expertise and millions of high-quality labeled images, customers can now use Amazon Rekognition Custom Labels to achieve state-of-the-art performance for their unique image analysis needs.
You know how old you are. You know how old your friends think you are. Heck, you even know how old Microsoft thinks you are. But at the end of the day, does anyone other than Amazon really matter? Until now, the only way for you to know how old Amazon thought you were was to look at products recommended to you.
Amazon hasn't exactly kept Rekognition under wraps. In late 2016, the software giant talked up its facial detection software in a relatively benign AWS post announcing that the tech was already being implemented by The Washington County Sheriff's Office in Oregon for suspect identification. The ACLU of Northern California is shining more light on the tech this week, however, after announcing that it had obtained documents shedding more light on the service it believes "raises profound civil liberties and civil rights concerns." The documents in question highlight Washington County's database of 300,000 mug shot photos and a mobile app designed specifically for deputies to cross-reference faces. They also note that Amazon has solicited the country to reach out to other potential customers for the service, including a company that makes body cameras.
Amazon Rekognition is a deep learning image analysis service. With the growing proliferation of visual content on the web, Amazon Rekognition enables you to unlock tremendous value from this data. You can use the image analysis information to understand the image content, organize a massive amount of visual data, and build visually aware applications.
Reported by the New York Times, new tests of facial recognition technology suggest that Amazon's system has more difficulty identifying the gender of female and darker-skinned faces compared with similar facial recognition technology services provided by IBM and Microsoft. Amazon's Rekognition is a software application that sets out to identify specific facial features by comparing similarities in a large volume of photographs. The study is of importance, given that Amazon has been marketing its facial recognition technology to police departments and federal agencies, presenting the technology as an additional tool to aid those tasked with law enforcement to identify suspects more rapidly. This tendency has been challenged by the American Civil Liberties Union (See: "Orlando begins testing Amazon's facial recognition in public"). The new study comes from Inioluwa Deborah Raji (University of Toronto) and Joy Buolamwini (Massachusetts Institute of Technology) and it is titled "Actionable Auditing: Investigating the Impact of Publicly Naming Biased Performance Results of Commercial AI Products."