EyeEm is a photography community and marketplace of over 18 million photographers. It also publishes a magazine, also called EyeEm. For its fourth issue, Machina: A Curation of Real Photography by a Machine, the company turned to an artificial intelligence powered by computer vision, EyeEm Vision, to curate the magazine, selecting the photographs it feels are the best aesthetically and most impactful. Now, before the inner smartphone photographer in you rolls your eyes, understand that it is pretty neat that a machine can, in some ways, learn to identify photographic aesthetics like a human. Sure, an A.I. cannot truly exercise a similar series of complex calculations of why an image might be great or resonant, but it's certainly intriguing to see where humans are in imbuing machines with mental processes.

Artificial intelligence is one of the most buzzed-about terms in technology. The AI market is estimated to reach $5.05 billion USD by 2020, up from $419.7 million USD in 2014 – a 53% increase. With the launch of Facebook's chatbots, Amazon's Echo, and IBM's Watson, companies in many fields are considering how they can use new AI tools to their advantage. Advertising agencies that use AI, machine learning, and image recognition are hyper-targeting consumers by learning their interests and tastes. An everyday example is Facebook's targeted ads, which use artificial intelligence to narrow target segments down in a matter of hours.

Computers have started to get really good at visual recognition. They can sometimes rival humans at recognizing the objects in a series of images. But does the similar end result mean that computers are mimicking the human visual system? Answering that question would indicate if there are still some areas where computer systems can't keep up with humans. So, a new PNAS paper takes a look at just how different computer and human visual systems are.

DNN, an algorithm modeled after the neural networks of the human brain, is expected to perform recognition processing as accurately as, or even better than the human brain. To achieve automated driving, automotive computers need to be able to identify different road traffic situations including a variety of obstacles and road markings, availability of road space for driving, and potentially dangerous situations. In image recognition based on conventional pattern recognition and machine learning, objects that need to be recognized by computers must be characterized and extracted in advance. In DNN-based image recognition, computers can extract and learn the characteristics of objects on their own, thus significantly improving the accuracy of detection and identification of a wide range of objects. Because of the rapid progress in DNN technology, the two companies plan to make the technology flexibly extendable to various network configurations.

DENSO Corporation and Toshiba Corporation have reached a basic agreement to jointly develop an artificial intelligence technology called Deep Neural Network-Intellectual Property (DNN-IP), which will be used in image recognition systems which have been independently developed by the two companies to help achieve advanced driver assistance and automated driving technologies. This Smart News Release features multimedia. DNN, an algorithm modeled after the neural networks of the human brain, is expected to perform recognition processing as accurately as, or even better than the human brain. To achieve automated driving, automotive computers need to be able to identify different road traffic situations including a variety of obstacles and road markings, availability of road space for driving, and potentially dangerous situations. In image recognition based on conventional pattern recognition and machine learning, objects that need to be recognized by computers must be characterized and extracted in advance.

KARIYA, Japan & TOKYO--(BUSINESS WIRE)--DENSO Corporation and Toshiba Corporation have reached a basic agreement to jointly develop an artificial intelligence technology called Deep Neural Network-Intellectual Property (DNN-IP), which will be used in image recognition systems which have been independently developed by the two companies to help achieve advanced driver assistance and automated driving technologies. DNN, an algorithm modeled after the neural networks of the human brain, is expected to perform recognition processing as accurately as, or even better than the human brain. To achieve automated driving, automotive computers need to be able to identify different road traffic situations including a variety of obstacles and road markings, availability of road space for driving, and potentially dangerous situations. In image recognition based on conventional pattern recognition and machine learning, objects that need to be recognized by computers must be characterized and extracted in advance. In DNN-based image recognition, computers can extract and learn the characteristics of objects on their own, thus significantly improving the accuracy of detection and identification of a wide range of objects.

We haven't designed fully sentient artificial intelligence just yet, but we're steadily teaching computers how to see, read, and understand our world. Last month, Google engineers showed off their "Deep Dream," software capable of taking an image and ascertaining what was in it by turning it into a nightmare fusion of flesh and tentacles. The release follows research by scientists from Stanford University, who developed a similar program called NeuralTalk, capable of analyzing images and describing them with eerily accurate sentences. First published last year, the program and the accompanying study is the work of Fei-Fei Li, director of the Stanford Artificial Intelligence Laboratory, and Andrej Karpathy, a graduate student. Their software is capable of looking at pictures of complex scenes and identifying exactly what's happening.

Deep learning and GPU computing have quickly advanced the abilities of image recognition technology to superhuman levels. Now, PicsArt, maker of the social photo editor by the same name, is applying this breakthrough in artificial intelligence to the creation of images. Hitting the market today, "Magic Effects" is a new feature in the latest version of the PicsArt app, which has been downloaded more than 300 million times and boasts 80 million active monthly users. Magic Effects uses GPU-powered AI to analyze the quality and context of photos, and enables users to transform their pics in seconds with an array of filtering effects that are customized based on the AI analysis. If, for example, a user applies the "Neo Pop" effect to a photo, the result won't be standardized.

As the artificial intelligence market is projected to grow by 53% in by 2020, advertisers are looking for ways to use the technology to their advantage. Vernon Vasu, CMO at ReFUEL4 states that researchers are looking into using AI for creative development in the future, but for now advertisers can use AI's incredible data mining and organizing capabilities to understand audiences like never before Artificial intelligence is one of the most buzzed-about terms in technology. The AI market is estimated to reach 5.05 billion USD by 2020, up from 419.7 million USD in 2014 – a 53% increase. With the launch of Facebook's chatbots, Amazon's Echo, and IBM's Watson, companies in many fields are considering how they can use new AI tools to their advantage. Advertising agencies that use AI, machine learning, and image recognition are hyper-targeting consumers by learning their interests and tastes.

Facebook announced Thursday that it is open-sourcing some of its latest artificial intelligence vision tools. The company is releasing years' worth of research on computer image recognition and understanding. The tools could be used to create experiences for visually impaired users, better image search on the social networking platform, and interpret live videos in real-time. On Thursday, the social networking giant unveiled several new tools to identify, delineate and label objects in an image. The aim is to help accelerate advancement in the field of machine vision as the company expands on people's interest in sharing and interacting with images and video clips.