Machine-learning approaches are becoming commonplace in the neuroimaging literature as potential diagnostic and prognostic tools for the study of clinical populations. However, very few studies provide clinically informative measures to aid in decision-making and resource allocation. Head-to-head comparison of neuroimaging-based multivariate classifiers is an essential first step to promote translation of these tools to clinical practice. We systematically evaluated the classifier performance using back-to-back structural MRI in two field strengths (3- and 7-T) to discriminate patients with schizophrenia (n 19) from healthy controls (n 20). Gray matter (GM) and white matter images were used as inputs into a support vector machine to classify patients and control subjects.

Teradata will host a free live webcast "Detecting Anomalies in IoT with Time-Series Analysis" on July 26, 2016, covering challenges in anomaly detection, statistical and machine learning algorithms applied in time-series data, event-based versus pattern-based anomaly detection, and tools to tackle anomaly detection. Find out more and register here. Predictive maintenance (PdM) is one of the biggest promises created by the Internet of Things (IoT). It's what factories, facilities, and anyone with a large piece of complicated equipment have been waiting for: accurate, automated warnings that a machine will soon fail in a certain way. Such warnings have substantial economic value when they let an organization maintain the machine proactively to avoid disruption of normal business operations.

David Kurtz is the chief product officer of Opera Mediaworks. At nearly every major technology event for the past five years we've been given at least one "wow" moment -- when one of the big players would unveil some sort of product or service that no one had ever seen before. The bar was set high for innovation, because there were wide-open spaces to be filled. Technology was revolutionary, not evolutionary. But so far in 2016, technology events have been disappointing.

Providing positive and profitable customer experiences means nurturing customer relationships that unfold across time and channels. Productive customer interactions drive higher levels of engagement, increasing the Customer Lifetime Value. A more engaged, satisfied customer spends more, remains loyal, and recommends a brand to others. Today, the cutting edge of an organization's customer experience process is digitized, combining Omni Channel, analytics and automation. The core of digitally transforming customer care lies with deploying Artificial Intelligence (AI), based on Machine Learning approaches to automate routine interactions, allowing agents to enhance those interactions where humans make a real difference.

Infor COO, Pam Murphy: "Our customers kick ass." At the start of Inforum 2016, Infor's Chief Operating Officer, Pam Murphy, stood on stage and announced: "Our customers kick ass." The comment reflects announcements that Infor is extending its core strategy by adding solutions tied closely to its customers' digital business transformation. Infor itself is undergoing a transformation designed to maintain relevance as the market changes. During a private conversation with the company's President, Duncan Angove, he explained Infor is thinking of potential sources of disruption to its own business: "We want to disrupt ourselves before someone else does."

Twitter Inc. TWTR, 3.21% said Monday that it bought U.K.-based Magic Pony Technology, which develops machine learning and visual processing technology, for an undisclosed amount. The deal follows Twitter's previous acquisitions into the machine-learning space, starting with Madbits in July 2014 and Whetlab in June 2015. "Machine learning is increasingly at the core of everything we build at Twitter," said Twitter Chief Executive Jack Dorsey. Twitter's stock, which rose 1.6% in morning trade Monday, has tumbled 30% year to date, while the S&P 500 has gained 2.6%.

The media interest surrounding deep learning has grown exponentially in the last few years. But what does it actually mean, and how will it change business and society? Deep learning is a subset of machine learning that refers to mapping artificial neural networks to recreate some of the same processes that the human brain performs, and using algorithms with speech, images and text, to recognize, identify and understand patterns in the data. Although this sounds simple, it involves complex processes and functions – but once trained, the application of deep learning algorithms could be world changing. For instance, a machine that learns like a human, but can rapidly process thousands of images and recognize patterns, is already showing promise for applying deep learning to medical imaging.

One of the key assumptions of quantitative trading strategy evaluation is that Type II errors (missed discoveries) are preferable to Type I errors (false discoveries.) However, practitioners have known for long that the statistical properties of some genuine trading strategies are often indistinguishable from those of random trading strategies. Therefore, any adjustments of statistics to guard against p-hacking increase Type II error unless the power of the test is high. At the same time, the power of the test is limited by insufficient samples and changing market conditions. Furthermore, genuine strategies with statistical properties that are similar to those of random strategies may overfit due to favorable market conditions but fail when market conditions change.

This Social Experiment Used Free Wi-Fi To Try To Get People To Interact In Real Life Co.Exist...

Though deals to private artificial intelligence companies -- excluding incubator/accelerator rounds -- fell 10% in Q2'16, dollar funding reached an all-time high. That was partly thanks to 3 100M mega-rounds by companies using AI: a 154M Series A round went to China-based healthcare startup iCarbonX (with the participation of Tencent, Vcanbio), a 100M growth equity round was raised by New Jersey-based Fractal Analytics (from Khazanah Nasional Berhad), and there was a 100M Series D round raised by California-based cybersecurity unicorn Cylance (from investors including Blackstone Group, Insight Venture Partners, and Khosla Ventures). Our AI category includes companies applying AI solutions to verticals like healthcare, security, advertising, and finance as well as those developing general-purpose AI tech. Nearly 70% of the deals went to startups in the United States in Q2'16. A majority of the startups raising funds were still in their early-stages: Nearly 60% of the deals went to startups raising seed/angel and Series A rounds, while mid-stage startups (Series B and C) received 12% of the deals.