A conceptually simple way to recognize images is to directly compare test-set data and training-set data. The accuracy of this approach is limited by the method of comparison used, and by the extent to which the training-set data covers the required configuration space. Here we show that this coverage can be substantially increased using simple strategies of coarse graining (replacing groups of images by their centroids) and sampling (using distinct sets of centroids in combination). We use the MNIST data set to show that coarse graining can be used to convert a subset of training images into about an order of magnitude fewer image centroids, with no loss of accuracy of classification of test-set images by direct (nearest-neighbor) classification. Distinct batches of centroids can be used in combination as a means of sampling configuration space, and can classify test-set data more accurately than can the unaltered training set. The approach works most naturally with multiple processors in parallel.

You don't need always need to build fancy algorithms to tamper with image recognition systems, adding objects in random places will do the trick. In most cases, adversarial models are used to change a few pixels here and there to distort images so objects are incorrectly recognized. A few examples have included stickers that turn images of bananas into toasters, or wearing silly glasses to be fool facial recognition systems into believing you're someone else. Let's not forget the classic case of when a turtle was mistaken as a rifle to really drill home how easy it is to outwit AI. Now researchers from the York University and the University of Toronto, Canada, however, have shown that it's possible to mislead neural networks by copying and pasting pictures of objects into images, too.

Have you ever used Bing to search on the Internet? Most of us haven't but it is the rival service to Google's own search engine and it is owned and operated by none other than Microsoft. While not the most robust search engine around, obviously, Bing nonetheless has a few quirks that make it worth checking out from time to time. And you can't fault Microsoft for trying – from throwing in voice-powered Cortana search to integrating Bing into the Xbox and Windows, Microsoft has pulled out all the stops to make sure you at least have the ability to use Bing, even if you don't. Well it seems like some of us in the photography world might want to give Bing another look as Microsoft announced plans to bring powerful, artificial intelligence-powered image search to Bing.

You are free to share this article under the Attribution 4.0 International license. Researchers have devised a new type of artificially intelligent camera system that can classify images faster and more energy-efficiently. The image recognition technology that underlies today's autonomous cars and aerial drones depends on artificial intelligence: the computers essentially teach themselves to recognize objects like a dog, a pedestrian crossing the street, or a stopped car. The new camera could one day be small enough to fit in future electronic devices, something that is not possible today because of the size and slow speed of computers that can run artificial intelligence algorithms. "That autonomous car you just passed has a relatively huge, relatively slow, energy intensive computer in its trunk," says Gordon Wetzstein, an assistant professor of electrical engineering at Stanford University who led the research. Future applications will need something much faster and smaller to process the stream of images, he says.

Jia Li is very passionate about artificial intelligence (AI) and how it can improve healthcare. When one of her close family members suffered from a skin condition, she worked on developing an image recognition technology to help classify such diseases and diagnose them better. Now, as the head of R&D for Cloud AI, Google Cloud and an adjunct professor at Stanford University's School of Medicine, Dr. Li and her team at Google focus on research and innovation to solve real-world problems. This includes developing AI products on Google Cloud to power solutions for diverse industries. Edited excerpts: Dr. Li who before joining Google led research and innovation efforts at Snapchat's parent company Snap and Yahoo!

Artificial intelligence (AI) is increasingly getting attention from enterprise decision makers. Given that, it's no surprise that AI use cases are growing. According research conducted by Gartner, smart machines will achieve mainstream adoption by 2021, with 30 percent of large companies using AI. These technologies, which can take the form of cognitive computing, machine learning and deep learning, are now tapping advanced capabilities such as image recognition, speech recognition, the use of smart agents, and predictive analytics to reinvent the way organizations do business. Combined with other digital technologies, including the Internet of Things (IoT), a new era of AI promises to transform business.

More than two billion images are shared daily in social networks alone. Research shows that it would take a person ten years to look at all the photos shared on Snapchat in the last hour! Media buyers and providers experience difficulty organizing relevant content in groups, parsing components of images/videos, and defining the return on investment from generated content in an efficient way. NVIDIA has many customers and ecosystem partners tackling that problem, using NVIDIA DGX as their preferred platform for deep learning (DL) powered image recognition. One of the notable names among the ecosystem is Imagga, a pioneer in offering a deep learning powered image recognition and image processing solution, built on NVIDIA DGX Station, the world's first personal AI supercomputer.

We present a method for metric optimization in the Large Deformation Diffeomorphic Metric Mapping (LDDMM) framework, by treating the induced Riemannian metric on the space of diffeomorphisms as a kernel in a machine learning context. For simplicity, we choose the kernel Fischer Linear Discriminant Analysis (KLDA) as the framework. Optimizing the kernel parameters in an Expectation-Maximization framework, we define model fidelity via the hinge loss of the decision function. The resulting algorithm optimizes the parameters of the LDDMM norm-inducing differential operator as a solution to a group-wise registration and classification problem. In practice, this may lead to a biology-aware registration, focusing its attention on the predictive task at hand such as identifying the effects of disease. We first tested our algorithm on a synthetic dataset, showing that our parameter selection improves registration quality and classification accuracy. We then tested the algorithm on 3D subcortical shapes from the Schizophrenia cohort Schizconnect. Our Schizpohrenia-Control predictive model showed significant improvement in ROC AUC compared to baseline parameters.

Google services such as its image search and translation tools use sophisticated machine learning which allow computers to see, listen and speak in much the same way as human do. Machine learning is the term for the current cutting-edge applications in artificial intelligence. Basically, the idea is that by teaching machines to "learn" by processing huge amounts of data they will become increasingly better at carrying out tasks that traditionally can only be completed by human brains. These techniques include "computer vision" – training computers to recognize images in a similar way we do. For example, an object with four legs and a tail has a high probability of being an animal.

The increasing speed of unpredictable and chaotic events collapses the tactical decision space for military commanders and first responders. Forward sensor fusion with edge-based processing accelerates recognitional decision making by widening the decision-maker's aperture to a greater number of sensor inputs and a wider array of sensor types. Cognitive computing tools compare signals to patterns to automate decision cues and open the door to meta-cognition, discovery of new patterns, and continuous improvement of decision models. The new BlueforceEDGE Image Recognition Plugin enhance's real-time recognition and speed of decision for operators and fixed and mobile command nodes by recognizing persons of interest as well as object recognition. BlueforceEDGE V3 ("EDGE") software delivers an extensible autonomous agent software platform that enables sensors from multiple manufacturers to be fused into a secure and interoperable stream of data enabling a single pane of glass view of your entire mission space. EDGE not only extends access to sensors from disparate manufacturers, but also allows development of software plugins providing access to enterprise services, iamge recognition systems, video and data analytics, rules engines, and more.