The digitalization of automation engineering generates large quantities of engineering data that is interlinked in knowledge graphs. Classifying and clustering subgraphs according to their functionality is useful to discover functionally equivalent engineering artifacts that exhibit different graph structures. This paper presents a new graph learning algorithm designed to classify engineering data artifacts -- represented in the form of graphs -- according to their structure and neighborhood features. Our Structural Graph Convolutional Neural Network (SGCNN) is capable of learning graphs and subgraphs with a novel graph invariant convolution kernel and downsampling/pooling algorithm. On a realistic engineering-related dataset, we show that SGCNN is capable of achieving ~91% classification accuracy.

A wide variety of neural-network architectures have been proposed for the task of Chinese word segmentation. Surprisingly, we find that a bidirectional LSTM model, when combined with standard deep learning techniques and best practices, can achieve better accuracy on many of the popular datasets as compared to models based on more complex neural-network architectures. Furthermore, our error analysis shows that out-of-vocabulary words remain challenging for neural-network models, and many of the remaining errors are unlikely to be fixed through architecture changes. Instead, more effort should be made on exploring resources for further improvement.

The Big Data analytics sector is poised for exponential growth in India boosting job opportunities in the IT sector. It is expected to witness a multi-fold growth by 2025, said Suresh Katta, founder of U.S.-based software firm Saama Technologies. Delivering a talk on'Data Analysis' at VIT-AP Amaravati campus on Thursday, he said there were a lot of opportunities to work in data science in the next 20 years. Mr. Katta said by focussing on new technologies, students could find work anywhere in the world. He said there was great demand for Artificial Intelligence (AI), Deep Learning, Machine Learning and Analytics, adding to the employment opportunities. He said a major chunk of the workforce in the IT would have to re-skill themselves in data science to make the most of this employment boom.

Scientists at the University of North Carolina at Chapel Hill have created a new method of particle tracking based on machine learning that is far more accurate and provides better automation than techniques currently in use. Single-particle tracking involves tracking the motion of individual particles, such as viruses, cells and drug-loaded nanoparticles, within fluids and biological samples. The technique is widely used in both physical and life sciences. The team at UNC-Chapel Hill that developed the new tracking method uses particle tracking to develop new ways to treat and prevent infectious diseases. They examine molecular interactions between antibodies and biopolymers and characterize and design nano-sized drug carriers.

Comcast's vice president of AI product, Jeanine Heck, speaks with TechRepublic's Tonya Hall about the success of an AI voice remote product, integrated with deep learning. The following is an edited transcript of the interview. Tonya Hall: It's push to talk, and not always listening. Jeanine Heck: Thank you, Tonya. Hall: So, what is your role entail, exactly? Heck: My role is really to be a product manager, and that entails working with the engineering team to ensure that we're building products that are valuable to our customers, and so it's important for me in my role to understand the customers needs, how they use our products today, how they may want to use our products tomorrow, and also making sure that the products are very competitive in the marketplace.

Besides the Dutch-English and Indonesian-English translation systems we offer through our client apps and connectors, we have built neural machine systems for the Turkish-English, Latvian-English and Spanish-English language pairs. Whether you are a professional translator, translation company or a business user, we can put artificial intelligence and deep learning to work for you. If you already have parallel texts (texts with source language & translation) we can use these as a basis for building a customised system for you. If you don't have such material we can usually build a useful baseline system from freely available public resources. Your own neural machine translation system can then be placed on a secure server "in the cloud" or on a dedicated server located in your offices and accessible only on your corporate network.

Artificial intelligence has swept the board with humans in games like chess and Go, but taking on e-sports might be a step too far -- for now. At The International tournament last night in Vancouver, a team of human pro gamers defeated a team of AI bots at the battle arena game Dota 2. The victory for team human was decisive but by no means inevitable, with the AI players putting up a valiant fight. And with two more games to play this week, machine might yet triumph over humanity. The bots were the creation of OpenAI, a non-profit research lab founded by tech luminaries such as SpaceX CEO Elon Musk. The lab's main goal is to develop artificial intelligence that "benefits all of humanity," but teaching bots to play Dota has been an important research task for some time now.

Driverless cars and flying autonomous vehicles are still more than a decade away, according to Gartner's latest hype cycle. Gartner has released its 2018 emerging technology hype cycle which identifies 35 promising technologies which could unlock a competitive advantage for companies over the next five to 10 years. The annual assessment identifies the most hyped technologies (deep learning) and how long until they are widely adopted (two to five years). Blockchain is on its way to the trough of disillusionment and is another five to 10 years away from mass adoption, known as the plateau of productivity. Mixed reality is making its way to the trough of disillusionment, and augmented reality has almost reached the bottom.

An AI presentation from SAS 2. Understand Context Learn Patterns Recognize Objects Artificial Intelligence is the science of training systems to emulate human tasks through Learning and Automation 3. Evolution of Artificial Intelligence Neural Networks 1950s-1970s Machine Learning Deep Learning and Cognitive Systems 1980s-2010s Present Day 4. Today, AI can be both Threat and Opportunity Gain Competitive Edge Find Growth Trends Customer Centricity New Capabilities Efficiency in Process Process Elimination Workforce Transformation Reduced Time to Value Reduced Cost Improved Margin THREAT OPPORTUNITY RATIONAL Lose Competitive Edge Miss Market Trends Reduced Customer Engagement IRRATIONAL Massive Job Loss Robots Replace Humans We Lose Control 5. Rogers Telecom 53% fewer customer complaints SciSports Pro Sports 200,000 players analyzed to find the next star Honda Manufacturing 60 secs to identify suspicious claim WildTrack Data for Good 90% accuracy for ID of wildlife using tracks SunTrust Financial Services 90% improvement in response rate How we're working with customers today to make AI an Opportunity 6. How does artificial intelligence work? 7. US GOOD AT COMMON SENSE INTUITION CREATIVITY EMPATHY VERSATILITY MACHINES GOOD AT LARGE DATA SETS COMPLEX CALCULATIONS LEARNING AUTOMATION What are we and machines good at? 8. AI enhances our capability and gives organizations competitive advantage What are we and machines good at? Machine Learning (Deep Learning) More compute power DEEP LEARNING Larger data sets More complex models Better algorithms 11. Natural Language Natural Language Processing Natural Language Understanding Natural Language Interaction Natural Language Generation 12. Predict Target Value Time Series Variability Automate Process Account for Possible Actions Assimilate Trade-Offs Define Critical Constraints Forecasting Optimization Increase Robustness Analyze Complexity Handle Uncertainty SCALE SOLUTIONS Forecasting and Optimization 13. Energy Forecasting Use short and long- term variability to improve accuracy Deep Learning 16.

CNL Software has entered into a technology partnership with Herta Security under the CNL Software Technology Alliance Program. Herta develops user-friendly software solutions that enable the integration of facial recognition in security applications. According to the announcement, Herta's deep learning algorithms encode faces directly into small templates, which are very fast to compare and yield more accurate results. This provides a technological advantage when working with partners, as it allows the development of more robust, safer and efficient solutions. IPSecurityCenter PSIM takes a vendor agnostic approach to implement flexible and scalable security management software. It allows security teams to efficiently integrate all of their technology from across their organization into one intuitive interface.