Welcome to the future, where you can face search for a live sex webcam performer and be served real-life humans to your telescreen who vaguely resemble the object of your desire within, well, hours depending on how busy the site's servers are. On a'normal' day the wait time is, presumably, more likely to be minutes.) The Belgian company behind the live sex search site is not disclosing which tech giant's algorithms it is using to power the face search feature, given the adult use-case and the latter's evident lack of desire to be associated with porn. But TechCrunch understands the API in question belongs to Microsoft -- namely its Cognitive Services (née Project Oxford) visual image recognition APIs, and specifically its Face API which lets developers add the ability to detect human faces and compare similar ones, organize people into groups according to visual similarity, and identify previously tagged people in images. So, yes, if you build a facial recognition API the porn use-cases will come… Hey there, Developers!
Microsoft is doubling down on its cloud AI services for business customers with a fleet of new offerings aimed at helping companies deal with video and unique problems not solved by its off-the-shelf cognitive services. New services announced Wednesday include a new Video Indexer service that will provide customers with automated captioning, sentiment analysis, custom face recognition, object detection, optical character recognition and keyword extraction of videos they provide. The tool is built on existing Microsoft services, but gives customers an easier way to process large amounts of video for indexing and analysis rather than require manual work by humans. Also new is a custom image recognition service that allows users to take Microsoft's existing tools for detecting objects and teach them to recognize other things that aren't generally applicable. For example, manufacturers could use the service to identify different types of parts that Microsoft's off-the-shelf image recognition service couldn't recognize, according to Irving Kwong, a principal product manager in the company's artificial intelligence group.
Police departments across the nation are generating leads and making arrests by feeding celebrity photos, CGI renderings, and manipulated images into facial recognition software. Often unbeknownst to the public, law enforcement is identifying suspects based on "all manner of'probe photos,' photos of unknown individuals submitted for search against a police or driver license database," a study published on Thursday by the Georgetown Law Center on Privacy and Technology reported. The new research comes on the heels of a landmark privacy vote on Tuesday in San Francisco, which is now the first US city to ban the use of facial recognition technology by police and government agencies. A recent groundswell of opposition has led to the passage of legislation that aims to protect marginalized communities from spy technology. These systems "threaten to fundamentally change the nature of our public spaces," said Clare Garvie, author of the study and senior associate at the Georgetown Law Center on Privacy and Technology.
The results are in from the biggest computer face-recognition contest to date. Everyone from government agencies to police forces are looking for software to track us in airports or spot us in CCTV images. But much of this technology is developed behind closed doors – how can we know if any of it really works? To answer this question, the Intelligence Advanced Research Projects Activity (IARPA) and the US National Institute of Standards and Technology (NIST) have been running the biggest face-recognition competition to date. The Face Recognition Prize Challenge tested two tasks: face verification and face search.
With the release of Apple's Siri and comparable voice search assistance from Microsoft and Google, you might have speculated why it took so long for speech recognition innovation to progress to this stage. In addition, one may also wonder what the future holds for natural language-based machine intelligence learning and its impact on our everyday lives.