Dries Depoorter recorded video from open cameras for a week and scraped Instagram photos. Then he used AI to identify the people in the photos and their locations. Depoorter calls it The Follower.

Harisystems offers professional training by experts in Software Industry, Python, asp.net, Real-Time Face Recognition: Project Face Detection with Python using OpenCV Attendance Tutorial - Harisystems For Best Software Training programs visit--... We're global software services in IT business and digital technology services, helping our clients bring the future highest levels of work to their life.

Harisystems offers professional training by experts in Software Industry, Python, asp.net, Real-Time Face Recognition: Project Face Detection with Python using OpenCV Attendance Tutorial - Harisystems For Best Software Training programs visit--... We're global software services in IT business and digital technology services, helping our clients bring the future highest levels of work to their life.

Most marketers believe there's a lot of value in having relevant, engaging images featured in content. But selecting the "right" images for blog posts, social media posts or video thumbnails has historically been a subjective process. Social media and SEO gurus have a slew of advice on picking the right images, but this advice typically lacks real empirical data. This got me thinking: Is there a data-driven -- or even better, an AI-driven -- process for gaining deeper insight into which images are more likely to perform well (aka more likely to garner human attention and sharing behavior)? In July of 2019, a fascinating new machine learning paper called "Intrinsic Image Popularity Assessment" was published.

"Scraping people's information violates our policies, which is why we've demanded that Clearview stop accessing or using information from Facebook or Instagram," a Facebook spokesperson said in an email to Fast Company. The previously little-known company drew national attention last month after an article by New York Times reporter Kashmir Hill revealed that the company claimed to have scraped billions of photos from services including Facebook, YouTube, and Venmo to match against people of interest to law enforcement. Twitter, YouTube parent Google, and Venmo have also reportedly told the startup to stop accessing data from their sites, saying it violates their policies. Whether they can legally enforce those rules may be uncertain: The Ninth Circuit Court of Appeals ruled in September that a company scraping LinkedIn in violation of the social site's policies likely didn't violate the Computer Fraud and Abuse Act, a key federal anti-hacking law. Clearview didn't immediately respond to an inquiry from Fast Company.

To understand why this is an interesting approach, it helps to know the difference between "fully supervised" and "weakly supervised" training for artificial intelligence systems. Computer visions systems need to be taught to recognize objects. Show them images that are labeled "bear," for example, and they can learn to identify images it thinks are bears in new photos. When researchers use photographs that humans have annotated so that an AI system can learn from them, that's called "fully supervised." The images are clearly labeled so the software can learn from them.

In the race to continue building more sophisticated AI deep learning models, Facebook has a secret weapon: billions of images on Instagram. In research the company is presenting today at F8, Facebook details how it took what amounted to billions of public Instagram photos that had been annotated by users with hashtags and used that data to train their own image recognition models. They relied on hundreds of GPUs running around the clock to parse the data, but were ultimately left with deep learning models that beat industry benchmarks, the best of which achieved 85.4 percent accuracy on ImageNet. If you've ever put a few hashtags onto an Instagram photo, you'll know doing so isn't exactly a research-grade process. There is generally some sort of method to why users tag an image with a specific hashtag; the challenge for Facebook was sorting what was relevant across billions of images. When you're operating at this scale -- the largest of the tests used 3.5 billion Instagram images spanning 17,000 hashtags -- even Facebook doesn't have the resources to closely supervise the data.

When I give the dating app LoveFlutter my Twitter handle, it rewards me with a 28-axis breakdown of my personality: I'm an analytic Type A who's unsettlingly sex-focused and neurotic (99th percentile). On the sidebar where my "Personality Snapshot" is broken down in further detail, a section called "Chat-Up Advice" advises, "Do your best to avoid being negative. Get to the point quickly and don't waste their time. They may get impatient if you're moving too slowly." Loveflutter, a Twitter-themed dating app from the UK, doesn't ask me to fill out a personality survey or lengthy About Me (it caps my self-description at a cute 140 characters).

Scientists in the US say they have created machine learning algorithms that can identify depressed people by scanning for "clues" in Instagram photos. The researchers from the University of Vermont and Harvard University claim their algorithm's detection rate is 70%, adding that it is "more reliable than the 42% success rate of general-practice doctors diagnosing depression in person". "This points toward a new method for early screening of depression and other emerging mental illnesses," says Chris Danforth, of the University of Vermont. The research was done in two stages: the first was about identifying the clues on Instagram photos that suggests the user might be depressed, while the second stage involved teaching the computer to detect those people using machine learning algorithms. The scientists asked for access to the Instagram feed and mental health history of 166 participants, half of whom were reported to have been clinically depressed in the last three years.

The images you put up on Instagram could be used to diagnose if you're depressed. Psychologists say they can contain several red flags. They are darker, more likely to be black and white, and feature fewer people, as sufferers retreat from social contact. A'sadness selfie' may even exist, highlighting that someone is struggling. Researchers have now created a computer to detect depression in photos uploaded to social media - and they say it can correctly pick up on clues 70 per cent of the time.