"What exactly is computer vision then? Computer vision is a research field working to equip computers with the ability to process and understand visual data, as sighted humans can. Human brains process the gigabytes of data passing through our eyes every second and translate that data into sight - that is, into discrete objects and entities we can recognise or understand. Similarly, computer vision aims to give computers the ability to understand what they are seeing, and act intelligently on that knowledge."
– Computer vision: Cheat Sheet. ZDNet.com (December 6, 2011), by Natasha Lomas.
Clara Medical Imaging is a collection of developer toolkits built on NVIDIA's compute platform aimed at accelerating compute, artificial intelligence, and advanced visualization. Medical imaging industry is being transformed. A decade ago, the earliest applications to take advantage of GPU computing were image & signal processing applications. Today, GPUs are found in almost all imaging modalities, including CT, MRI, X-ray, and Ultrasound bringing more compute capabilities to the edge devices. Deep Learning research in Medical Imaging is also booming with more efficient and improved approaches being developed to enable AI-assisted workflows.Today, most of this AI research is being done in isolation and with limited datasets which may lead to overly simplified models.
Want to keep an eye out for porch pirates? Or perhaps you're seeking a simpler way to see who is at your front door without having to get off the couch? Smart doorbells are still fairly new to the world of home security, and the market is becoming more and more saturated with options by the minute. This begs the question: How do you know which one is right for you and your home? I've been living with the Ring Video Doorbell Pro for about a year and a half now.
With images aggregated from social media platforms, dating sites, or even CCTV footage of a trip to the local coffee shop, companies could be using your face to train a sophisticated facial recognition software. As reported by the New York Times, among the sometimes massive data sets that researchers use to teach artificially intelligent software to recognize faces is a database collected by Stanford researchers called Brainwash. More than 10,000 images of customers at a cafe in San Francisco were collected in 2014 without their knowledge. OKCupid and photo-sharing platforms like Flickr are among for researchers looking to load their databases up with images that help train facial recognition software. That same database was then made available to other academics, including some in China at the National University of Defense Technology.
Artificial intelligence belongs to the development of computer systems able to act as the human mind, such as visual perception, speech recognition, decision making, and translation between the languages. The predictions are made that the global population will reach about10 billion people in 2050, enhanced agriculture production to meet the food demands in need of the hour which is about the 70% increase in food production. Farm enterprise needs new and advanced technologies to overcome these challenges. By using artificial intelligence we can overcome these demands. Just imagine what will happen if the farm is under the control of such machinery which acts like humans and store information like human accurately and efficiently.
Better known as a supplier of facial recognition software used by the Chinese government, an AI-startup that is backed by Alibaba has developed software that can identify dogs by their noses. No, it isn't April 1st; the facial recognition software developed by Megvii really can identify one dog from another by using nasal biometrics. KrAsia news reports that the company has developed the software on the basis that dogs have unique nose prints. Dr. David Dorman, a professor of toxicology, has previously said that: "Like human fingerprints, each dog has a unique nose print. Some kennel clubs have used dog nose prints for identification."
Recent studies demonstrate that machine learning algorithms can discriminate based on classes like race and gender. In this work, we present an approach to evaluate bias present in automated facial analysis algorithms and datasets with respect to phenotypic subgroups. Using the dermatologist approved Fitzpatrick Skin Type classification system, we characterize the gender and skin type distribution of two facial analysis benchmarks, IJB-A and Adience. We find that these datasets are overwhelmingly composed of lighter-skinned subjects (79.6% for IJB-A and 86.2% for Adience) and introduce a new facial analysis dataset which is balanced by gender and skin type. We evaluate 3 commercial gender classification systems using our dataset and show that darker-skinned females are the most misclassified group (with error rates of up to 34.7%).
Roundup Hello, here's a few announcements from the world of machine learning beyond what we've already covered this week. AlphaStar is coming out to play: AlphaStar, the StarCraft II-playing bot built by DeepMind researchers, will be facing human players in a series of 1v1 games online. StarCraft II players can enter the open competition league set up by Blizzard Entertainment, the creators of the popular battle strategy game, and opt-in to play against AlphaStar. But nobody will know if they're facing the bot, however, because it'll be entering the matches anonymously. Characters in the StarCraft II are from three species: Terran, Zerg or Protoss.
The sophisticated technology that powers face recognition in many modern smartphones someday could receive a high-tech upgrade that sounds--and looks--surprisingly low-tech. This window to the future is none other than a piece of glass. University of Wisconsin-Madison engineers have devised a method to create pieces of "smart" glass that can recognize images without requiring any sensors or circuits or power sources. "We're using optics to condense the normal setup of cameras, sensors and deep neural networks into a single piece of thin glass," says UW-Madison electrical and computer engineering professor Zongfu Yu. Yu and colleagues published details of their proof-of-concept research today in the journal Photonics Research.
Dozens of databases of people's faces are being compiled without their knowledge by companies and researchers, with many of the images then being shared around the world, in what has become a vast ecosystem fueling the spread of facial recognition technology. The databases are pulled together with images from social networks, photo websites, dating services like OkCupid and cameras placed in restaurants and on college quads. While there is no precise count of the data sets, privacy activists have pinpointed repositories that were built by Microsoft, Stanford University and others, with one holding over 10 million images while another had more than two million. The face compilations are being driven by the race to create leading-edge facial recognition systems. This technology learns how to identify people by analyzing as many digital pictures as possible using "neural networks," which are complex mathematical systems that require vast amounts of data to build pattern recognition.