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.

Salesforce is getting into the computer vision business with a new tool designed to let users easily train a custom image recognition system. Einstein Vision, as it's known, allows users to upload sets of images and classify them in a series of categories. After that, the system will create a recognizer based on machine learning technology that will identify future images fed into it. While Salesforce customers will have to wait a couple weeks before Vision is generally available, the company announced Tuesday that other Einstein features based on machine learning techniques are live. It's the latest step in a long journey for Salesforce, which began touting Einstein last year and demoed those capabilities at its Dreamforce conference. The company is facing heavy competition, and Einstein might give it an edge against the likes of Microsoft and Oracle.

Taking a souvenir home from an art gallery no longer has to mean a trip to the gift shop. A new app lets people scan a work of art with their smartphone camera to find out more about it and save a digital copy. The app, called Smartify, uses image recognition to identify scanned artworks and provide people with additional information about them. Users can then add the works to their own digital collection. Smartify co-founder Thanos Kokkiniotis describes it as a combination of the music discovery service Spotify and music recognition app Shazam – but for visual works.

3-D image registration, which involves aligning two or more images, is a critical step in a variety of medical applications from diagnosis to therapy. Image registration is commonly performed by optimizing an image matching metric as a cost function. However this task is challenging due to the non-convex nature of the matching metric over the plausible registration parameter space and insufficient approches for a robust optimization. As a result, current approaches are often customized to a specific problem and sensitive to image quality and artifacts. In this paper, we propose a completely different approach to image registration, inspired by how experts perform the task. We first cast the image registration problem as a "strategic learning" process, where the goal is to find the best sequence of motion actions (e.g. up, down, etc) that yields image alignment. Within this approach, an artificial agent is learned, modeled using deep convolutional neural networks, with 3D raw image data as the input, and the next optimal action as the output. To copy with the dimensionality of the problem, we propose a greedy supervised approach for an end-to-end training, coupled with attention-driven hierarchical strategy. The resulting registration approach inherently encodes both a data-driven matching metric and an optimal registration strategy (policy). We demonstrate on two 3-D/3-D medical image registration examples with drastically different nature of challenges, that the artificial agent outperforms several state-of-the-art registration methods by a large margin in terms of both accuracy and robustness.

This demonstration showcases the different use cases of Artificial Intelligence (AI) in education by introducing students to applications of the Scribbler robot with the Fluke board in order to cultivate an interest in programming, robotics, and AI. The targeted audience for this is students aged eight through twelve. This demonstration uses three Scribbler robots to introduce students to common tools in AI (OpenCV and Tesseract), and teach them the basics of coding in an interactive, unintimidating way; by physically describing the goals of simple shape-building algorithms and implementing them using cards with both visual and written representations of the instructions.

TheTake, a site which launched as a way for consumers to buy that thing they saw in that movie, is set to begin selling an automated version of its service directly to businesses. The New York-based company is pitching studios and entertainment sites on a machine learning system that can identify products and locations as a way to generate revenue from product placements and experiential travel based on set locations. The new product is based on a year's worth of work that TheTake's development did to train a proprietary machine learning algorithm to identify images using a different technique than the industry standard, according to TheTake's chief executive Ty Cooper. Initially, the team behind TheTake would manually enter all the datasets and use an off-the-shelf computer visualization tool to identify images that fit the pre-defined parameters set by the company's staff. Companies like Universal Pictures, Comcast, Bravo, E!, Fandango, Sony Pictures and the Hallmark Channel, are testing out the AI-based service now, according to an email from Cooper.

Children learn to speak, as well as recognize objects, people, and places, long before they learn to read or write. They can learn from hearing, seeing, and interacting without being given any instructions. So why shouldn't artificial intelligence systems be able to work the same way? That's the key insight driving a research project under way at MIT that takes a novel approach to speech and image recognition: Teaching a computer to successfully associate specific elements of images with corresponding sound files in order to identify imagery (say, a lighthouse in a photographic landscape) when someone in an audio clip says the word "lighthouse." Though in the very early stages of what could be a years-long process of research and development, the implications of the MIT project, led by PhD student David Harwath and senior research scientist Jim Glass, are substantial. Along with being able to automatically surface images based on corresponding audio clips and vice versa, the research opens a path to creating language-to-language translation without needing to go through the laborious steps of training AI systems on the correlation between two languages' words.

An introduction to the field of computer vision and image recognition, and how Deep Learning is fueling the fire of this hot topic. Computer Vision is an interdisciplinary field that focuses on how machines or computers can emulate the way in which humans' brains and eyes work together to visually process the world around them. Research on Computer Vision can be traced back to beginning in the 1960s. The 1970's saw the foundations of computer vision algorithms used today being made; like the shift from basic digital image processing to focusing on the understanding of the 3D structure of scenes, edge extraction and line-labelling. Over the years, computer vision has developed many applications; 3D imaging, facial recognition, autonomous driving, drone technology and medical diagnostics to name a few.

Industries around the world have caught AI fever. Developers around the world have made more ways than ever for the technology to automate, optimize and enable different services. Facebook, Alphabet, IBM, Microsoft, Amazon and other major companies are all working on AI projects, along with numerous tech startups. One such upstart, which leverages artificial intelligence and image recognition in part to enable commerce, is Clarifai. Founded in 2013, Clarifai utilizes neural networks and provides customers with an image and video recognition API.

If you forget to tag or add a description when uploading a photo or gallery to Facebook, it can be tough to find an image when you need it. Or at least it used to be. The social network revealed today that it built an AI image search system that can "see" things in your photos even when you forget to add the aforementioned identifiers. Facebook says the system uses its Lumos platform to understand the content of photos and videos and quickly sort through the items you've uploaded. This means that if even if you can't remember when a photo was taken, if you remember the content, you might still be able to find it with ease.