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.

Under, behind and inside many of the apps we use every day, a revolution is underway. It's a revolution that started decades ago but today is empowering companies to deliver better, smarter services with greater ease and on broader scales than ever before. At Singularity University's inaugural Global Summit, Neil Jacobstein, chair of Artificial Intelligence and Robotics, provided a primer showing how artificial intelligence literally transforms everything it touches. First of all, it's critical to define the scope of artificial intelligence (AI), which can be categorized into four areas: techniques in pattern recognition, software agency (that is, software that acts like real users), an exponential technology that is accelerating other exponential technologies, and a vision of a future superhuman intelligence (that fortunately hasn't happened yet). Anyone who has seen a science fiction film is likely familiar with this last area, but it's the other three areas where AI is making huge strides at a revolutionary pace.

Recent sequential pattern mining methods have used the minimum description length (MDL) principle to define an encoding scheme which describes an algorithm for mining the most compressing patterns in a database. We present a novel subsequence interleaving model based on a probabilistic model of the sequence database, which allows us to search for the most compressing set of patterns without designing a specific encoding scheme. Our proposed algorithm is able to efficiently mine the most relevant sequential patterns and rank them using an associated measure of interestingness. The efficient inference in our model is a direct result of our use of a structural expectation-maximization framework, in which the expectation-step takes the form of a submodular optimization problem subject to a coverage constraint. We show on both synthetic and real world datasets that our model mines a set of sequential patterns with low spuriousness and redundancy, high interpretability and usefulness in real-world applications. Furthermore, we demonstrate that the quality of the patterns from our approach is comparable to, if not better than, existing state of the art sequential pattern mining algorithms.

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.

Natural language processing (NLP) is quickly becoming one of the foundational big data technologies that will allow healthcare to move forward with complex analytics, according to a series of market reports predicting significant growth for NLP products over the next few years. As healthcare organizations seek new strategies for extracting insights from unstructured data from electronic health records, Internet of Things devices, imaging studies, and elsewhere, they will create an NLP marketplace worth $2.65 billion by 2021, says ReportsnReports. "The market is growing rapidly because of the huge surge in clinical data, increasing use of connected devices, and evolving consumer needs," the report says. Natural language processing may play an instrumental role in precision medicine, predictive analytics, population health management, clinical decision support, and EHR documentation improvement. The NLP market is divided into several segments: interactive voice response and speech analytics technologies, optical character recognition (OCR), automatic coding, text analytics, and pattern and image recognition.

Investors poured a record high $1.05B into artificial intelligence startups in Q2'16, and AI is already affecting more areas of our lives than many people realize. Even retail and e-commerce companies are increasingly integrating the technology. Recently there's been a rush of AI announcements and acquisitions by major retailers: Just this week, Etsy acquired Blackbird to enhance its search functionality through AI, followed the very next day by Amazon acquiring Angel.ai And earlier this month, e-commerce unicorn Houzz (see our full unicorn tracker here) announced a deep learning initiative to help users find and buy products by clicking on images. Using CB Insights data, we dove into the wide array of AI startups focused on retailers and e-commerce businesses, including AI-powered personal shopping apps, natural language processing and image recognition tools for shopping websites, predictive inventory allocation tools, and more.

Can your financial institution's fraud detection system learn, reason and adapt to new and emerging cyberthreats? Can it identify fraudulent behavior within your account simply by analyzing interactions and patterns? In this day and age, people can access their bank accounts anywhere, anytime. We need strong, agile and efficient fraud detection systems to keep financial institutions and their customers safe. Mobile functionality and safety are among customers' top concerns when it comes to online banking -- so IBM Security Trusteer is releasing new cognitive fraud detection and behavioral biometric functionality that accomplishes just that. This enhanced functionality adds even more strength to an already robust security platform without impacting user experience.

We trained on each acoustic feature a full covariance, genderindependent UBM model with 2048 Gaussians followed by a 600-dimensional i-vector extractor to establish our MFCCand PLP-based i-vector systems. The unlabeled set of development data was used in the training of both the UBM and the i-vector extractor. The open-source Kaldi software has been used for all these processing steps [20]. It has been shown that successive acoustic observation vectors tend to be highly correlated. This may be problematic for maximum a posteriori (MAP) estimation of i-vectors. To investigating this issue, scaling the zero and first order Baum-Welch statistics before presenting them to the i-vector extractor has been proposed. It turns out that a scale factor of 0.33 gives a slight edge, resulting in a better decision cost function [10]. This scaling factor has been performed in training the i-vector extractor as well as in the testing.

The irony, of course, is that this year the computer security industry, with $75 billion in annual revenue, has started to talk about how machine learning and pattern recognition techniques will improve the woeful state of computer security. "The thing people don't get is that cybercrime is becoming automated and it is scaling exponentially," said Marc Goodman, a law enforcement agency adviser and the author of Future Crimes. He added, "This is not about Matthew Broderick hacking from his basement," a reference to the 1983 movie War Games.