ABBYY, a global leader in Content IQ technologies and solutions, today announced it has signed an agreement to acquire Philadelphia, Pennsylvania-based TimelinePI. TimelinePI provides a comprehensive process intelligence platform designed to empower users to understand, monitor and optimize any business process. The global process analytics market size is expected to grow to USD 1,421.7 million by 2023 according to Research and Markets. The acquisition of TimelinePI is a strategic investment by ABBYY into the emerging process intelligence market which is critical to truly understanding the impact and effectiveness of business processes and opportunities for productivity gains from digital transformation investments. TimelinePI's vision of combining the most versatile process mining and operational monitoring with cutting-edge, process-centric AI and machine learning will serve as a critical cornerstone to ABBYY's Digital IQ strategy.

We have come to the fifth month of the year, and technology especially the disruptive one that includes Artificial Intelligence (AI) is gaining strong-hold more than ever. Understanding its disruptive factors is important as it enables more accurate forecasting and better planning for civil society, policymakers and businesses. Identifying the main levers that drive the growth of AI applications can help to expedite the many positive use cases in the pipeline; like optimised renewable energy distribution at scale and Machine Learning disease diagnosis systems in healthcare. So how are the disruptive technologies redefining businesses sphere? Over the years, it is been seen that AI adaptability is increasing.

I recently was a guest speaker at the Stanford AI Salon on the topic of accessiblity in AI, which included a free-ranging discussion among assembled members of the Stanford AI Lab. There were a number of interesting questions and topics, so I thought I would share a few of my answers here. Q: What 3 things would you most like the general public to know about AI? AI is easier to use than the hype would lead you to believe. In my recent talk at the MIT Technology Review conference, I debunked several common myths that you must have a PhD, a giant data set, or expensive computational power to use AI. Most AI researchers are not working on getting computers to achieve human consciousness.

Over the next year, the recipients will work on things like a nerve-sensing wearable wristband. Another project seeks to develop a wearable cap that reads a person's EEG data and communicates it to the cloud to provide seizure warnings and alerts. Other tools will rely on speech recognition, AI-powered chatbots and apps for people with vision impairment. This year's grantees include the University of California, Berkeley; Massachusetts Eye and Ear, a teaching hospital of Harvard Medical School; Voiceitt in Israel; Birmingham City University in the United Kingdom; University of Sydney in Australia; Pison Technology of Boston; and Our Ability, of Glenmont, New York. "What stands out the most about this round of grantees is how so many of them are taking standard AI capabilities, like a chatbot or data collection, and truly revolutionizing the value of technology," Microsoft's Senior Accessibility Architect Mary Bellard said in a blog post.

During the last decades, we have witnessed a surge of interests of learning a low-dimensional space with discriminative information from one single view. Even though most of them can achieve satisfactory performance in some certain situations, they fail to fully consider the information from multiple views which are highly relevant but sometimes look different from each other. Besides, correlations between features from multiple views always vary greatly, which challenges multi-view subspace learning. Therefore, how to learn an appropriate subspace which can maintain valuable information from multi-view features is of vital importance but challenging. To tackle this problem, this paper proposes a novel multi-view dimension reduction method named Multi-view Locality Low-rank Embedding for Dimension Reduction (MvL2E). MvL2E makes full use of correlations between multi-view features by adopting low-rank representations. Meanwhile, it aims to maintain the correlations and construct a suitable manifold space to capture the low-dimensional embedding for multi-view features. A centroid based scheme is designed to force multiple views to learn from each other. And an iterative alternating strategy is developed to obtain the optimal solution of MvL2E. The proposed method is evaluated on 5 benchmark datasets. Comprehensive experiments show that our proposed MvL2E can achieve comparable performance with previous approaches proposed in recent literatures.

Advanced attack campaigns span across multiple stages and stay stealthy for long time periods. There is a growing trend of attackers using off-the-shelf tools and pre-installed system applications (such as \emph{powershell} and \emph{wmic}) to evade the detection because the same tools are also used by system administrators and security analysts for legitimate purposes for their routine tasks. To start investigations, event logs can be collected from operational systems; however, these logs are generic enough and it often becomes impossible to attribute a potential attack to a specific attack group. Recent approaches in the literature have used anomaly detection techniques, which aim at distinguishing between malicious and normal behavior of computers or network systems. Unfortunately, anomaly detection systems based on point anomalies are too rigid in a sense that they could miss the malicious activity and classify the attack, not an outlier. Therefore, there is a research challenge to make better detection of malicious activities. To address this challenge, in this paper, we leverage Group Anomaly Detection (GAD), which detects anomalous collections of individual data points. Our approach is to build a neural network model utilizing Adversarial Autoencoder (AAE-$\alpha$) in order to detect the activity of an attacker who leverages off-the-shelf tools and system applications. In addition, we also build \textit{Behavior2Vec} and \textit{Command2Vec} sentence embedding deep learning models specific for feature extraction tasks. We conduct extensive experiments to evaluate our models on real-world datasets collected for a period of two months. The empirical results demonstrate that our approach is effective and robust in discovering targeted attacks, pen-tests, and attack campaigns leveraging custom tools.

Halfbrick Studios is a professional game development studio based in Brisbane, Australia. Founded in 2001, Halfbrick has developed many popular games, including Fruit Ninja, Jetpack Joyride, and Dan the Man. When Halfbrick first learned about Firebase Predictions, they were excited about targeting users based on predicted behavior, rather than historic. Re-engagement is tough, so intervening before a user churned - based on predictions instead of ad hoc heuristics - seemed like a strong strategy. They had been trying to create their own churn prediction models, but like many companies, didn't have the time or resources to properly devote to the problem.

Wing, an offshoot of Google's parent company, Alphabet, will launch drone deliveries to one of Finland's most populous areas next month according to a recent blog post from the company. Pilot deliveries will be rolled out in the Vousari district of Finland's capital, Helsinki, and will deliver products from gourmet supermarket Herkku foods and Cafe Monami. As noted by Wing, deliveries will include'fresh Finnish pastries, meatballs for two, and a range of other meals and snacks' that can be delivered in minutes. Wing will launch deliveries for customers in Finland starting next month. Wing, the first commercial drone company approved by the FAA in the U.S. will start delivering in Virginia. The drones is powered entirely by electric and can fly up to 120 km/h (almost 75 mph).

The Project is a New Zealand current affairs television program that produced a news segment on May 10, 2019 exploring the findings of a recent scientific paper, "Deep learning enables robust assessment and selection of human blastocysts after in vitro fertilization," in NPJ Digital Medicine by EIPM colleagues including Drs. The news segment aired during their "Fertility Week" programming, press play below to view:

With leaders increasingly seeing artificial intelligence (AI) as helping to drive the next great economic expansion, a fear of missing out is spreading around the globe. Numerous nations have developed AI strategies to advance their capabilities, through investment, incentives, talent development, and risk management. As AI's importance to the next generation of technology grows, many leaders are worried that they will be left behind and not share in the gains. There is a growing realization of AI's importance, including its ability to provide competitive advantage and change work for the better. A majority of global early adopters say that AI technologies are especially important to their business success today--a belief that is increasing. A majority also say they are using AI technologies to move ahead of their competition, and that AI empowers their workforce. AI success depends on getting the execution right. Organizations often must excel at a wide range of practices to ensure AI success, including developing a strategy, pursuing the right use cases, building a data foundation, and cultivating a strong ability to experiment. These capabilities are critical now because, as AI becomes even easier to consume, the window for competitive differentiation will likely shrink. Early adopters from different countries display varying levels of AI maturity. Enthusiasm and experience vary among early adopters from different countries. Some are pursuing AI vigorously, while others are taking a more cautious approach.