At Facebook's recent annual developer conference, Marc Zuckerberg outlined the social network's artificial intelligence (AI) plans to "build systems that are better than people in perception." He then demonstrated an impressive image recognition technology for the blind that can "see" what's going on in a picture and explain it out loud. From programs that help the visually impaired and safety features in cars that detect large animals to auto-organizing untagged photo collections and extracting business insights from socially shared pictures, the benefits of image recognition, or computer vision, are only just beginning to make their way into the world -- but they're doing so with increasing frequency and depth. It's busy enough that the upcoming LDV Vision Summit, an annual conference dedicated to all things visual tech, from VR and cameras to medical imaging and content analysis, is already in its third year. "The advancements in computer vision these days are creating tremendous new opportunities in analyzing images that are exponentially impacting every business vertical, from automotive to advertising to augmented reality," says Evan Nisselson of LDV Capital, which organizes the summit.

Is there pattern recognition software development tutorial?

PAVIS department at Istituto Italiano di Tecnologia (IIT) (http://www.iit.it/pavis) is looking for a highly qualified candidate with a strong background in Computer Vision, Pattern Recognition and Machine Learning, with particular emphasis on recognition, video analysis, behavior understanding and prediction. As the activities may be carried out in collaboration with other research units inside IIT, previous multidisciplinary experience is an added value which will be duly considered. The main mission of PAVIS (Pattern Analysis and Computer Vision) is to design and develop innovative video surveillance systems, characterized by the use of highly-functional smart sensors and advanced video analytics features. PAVIS also plays an active role in supporting the other research units inside IIT providing scientists in Neuroscience, Nanophysics and other departments/centers with ad-hoc solutions. To this end, the group is involved in activities concerning computer vision and pattern recognition, machine learning, multimodal\multimedia data analysis and sensor fusion, and embedded computer vision systems.

Like the iOS version, it also works on saved images, but it should be noted that Windows Phones have had image translation since 2010. This is powered by Microsoft's proprietary Deep Learning engine it uses for Bing's and Skype's translation options, something more advanced than Google Translate's statistical models and crowdsourcing. That said, Google Translate's Android app has had image translation since at least August 2012. So this is nothing really groundbreaking. The Android app also gets Inline Translation, which lets users hover over text phrases to quickly convert them into any of the 50 languages in the app's online library.

Online GIF clearinghouse Giphy debuted a new means of finding and sharing animated GIFs using Android on Tuesday. The Giphy app now empowers users to search the entirety of Giphy's library and share them on multiple platforms -- from Gmail and Messenger to SMS and Twitter. The updated app will hit the Play Store immediately and will finish rolling out to existing users by the end of April.

Matching two texts is a fundamental problem in many natural language processing tasks. An effective way is to extract meaningful matching patterns from words, phrases, and sentences to produce the matching score. Inspired by the success of convolutional neural network in image recognition, where neurons can capture many complicated patterns based on the extracted elementary visual patterns such as oriented edges and corners, we propose to model text matching as the problem of image recognition. Firstly, a matching matrix whose entries represent the similarities between words is constructed and viewed as an image. Then a convolutional neural network is utilized to capture rich matching patterns in a layer-by-layer way. We show that by resembling the compositional hierarchies of patterns in image recognition, our model can successfully identify salient signals such as n-gram and n-term matchings. Experimental results demonstrate its superiority against the baselines.

Traditional time series analysis methods are limited on some complex real-world time series data. Respiratory motion prediction is one of such challenging problems. The memory-based nearest neighbor approaches haveshown potentials in predicting complex nonlinear time series compared to many traditional parametric prediction models. However, the massive time series subsequences representation, the similarity distance measures, the number of nearest neighbors, and the ensemble functions create challenges as well as limit the performance of nearest neighbor approaches in complex time series prediction. To address these problems, we propose a flexible time series pattern representation and selection framework, called the orthogonalpolynomial-based variant-nearest-neighbor (OPVNN) approach. For the respiratory motion prediction problem, the proposed approach achieved the highest and most robust prediction performance compared to the state-of-the-art time series prediction methods. With a solid mathematical and theoretical foundation in orthogonal polynomials, the proposed time series representation, subsequence pattern mining and prediction framework has a great potential to benefit those industry and medical applications that need to handle highly nonlinear and complex time series data streams, such as quasi-periodic ones.

We present a novel multitask learning framework called multitask generalized eigenvalue program (MTGEP), which jointly solves multiple related generalized eigenvalue problems (GEPs). This framework is quite general and can be applied to many eigenvalue problems in machine learning and pattern recognition, ranging from supervised learning to unsupervised learning, such as principal component analysis (PCA), Fisher discriminant analysis (FDA), common spatial pattern (CSP), and so on. The core assumption of our approach is that the leading eigenvectors of related GEPs lie in some subspace that can be approximated by a sparse linear combination of basis vectors. As a result, these GEPs can be jointly solved by a sparse coding approach. Empirical evaluation with both synthetic and benchmark real world datasets validates the efficacy and efficiency of the proposed techniques, especially for grouped multitask GEPs.

After guessing your age, classifying dog breeds, and finding celebrity likenesses, Microsoft researchers have launched a new tool for identifying the contents of photos. With CaptionBot, users can upload any photo, and Microsoft will use various recognition services to describe what's happening. This includes identifying celebrities, recognizing emotions, and describing basic objects that appear in the scene. We've seen this type of party trick before. Last year, Wolfram Alpha released a similar tool, which remains available at ImageIdentify.com.

Google has employed machine learning on its Calendar application in an effort to help its users better keep track of, and complete, long-term goals. Users simply add a personal goal -- like hitting the gym three times a week, for example -- and Google Calendar will help them find the time and stick to it. Setting a goal requires the user to answer a few questions, specifying duration and times. From there Calendar will look at the user's schedule and find the best windows to schedule time to help complete the goal. It's another example of a major technology company using machine learning -- the concept of pattern recognition and computational learning theory -- to make its users' lives easier.