The first 500 Emirati men and women will soon start their training in artificial intelligence as the UAE looks to begin shaping its future in a field that is expected to soon touch every company and individual on the planet. The students' training is part of an agreement signed by Oracle and the Higher Colleges of Technology on Sunday to prepare young UAE nationals youth for the country's future jobs. Oracle is also discussing with the university ways to develop AI-related degrees. "We're focusing on research and development in technology as well as providing the necessary support and training for local youth," said Omar Al Olama, Minister of Artificial Intelligence, at the launch in Dubai. "Most importantly, we are focusing on utilising emerging technologies in public services to enhance day-to-day experiences of UAE citizens and increase the efficiency of the government and private sectors. We hope to see the positive effects of these technologies in the coming years."

Joyeeta Das is founder and chief executive of GyanaAI, a self-service data science platform that enables insights around locations and places using big data and artificial intelligence technologies. Her early career began working in engineering, programme management, having risen through the leadership ranks in large international tech companies including Cisco and Wipro. Joyeeta holds two undergraduate degrees, in electronics engineering from the West Bengal University of Technology as well as physics, and an MBA from the Saїd Business School at the University of Oxford, where she received a Fellowship from their Entrepreneurship Centre. "…Artificial intelligence and big data are part of a revolution that has already started … There never has been a more empowered age…" I always enjoyed tech – even as a tiny girl. My mother is a scientist / teacher, my dad was an architect and all my grandparents are into science as well.

Lee Kai-Fu has always been very bullish about the future of artificial intelligence (AI) in China. He started off his keynote speech at an AI conference at the Massachusetts Institute of Technology in November by predicting that self-driving cars will become a mass phenomenon in the U.S. in 15 to 20 years. But in China, he said, it will take "more like 10 years." "Although there are concerns about whether there is an emerging AI bubble in China, I'd say there isn't one," he told Caixin. Lee is a real insider when it comes to assessing the state of AI development in both North America and China. He completed his doctorate in computer-aided speech recognition at Carnegie-Mellon University (CMU) in 1988 and went on to work at Apple Inc., Silicon Graphics Inc. and Microsoft Corp., and head Google Inc.'s China business.

IBM was one of the first to bring voice search assistants into the travel experience with the launch of their Watson-enabled robot concierge, "Connie" in Hilton Hotels in 2016, but leaps in technology will see more and more travel apps and hardware integrating voice and natural language search into their user interfaces in 2018, sparked by the mainstream success of the Amazon Alexa. Consumers will be able to talk to their digital assistant, whether through their smartphone, Alexa or other device to check flight details, search for a hotel or book tickets immediately. The difference in adoption of voice search across different parts of the world is significant. Travelport's recent Digital Traveller research showed that while just 33% of consumers in the UK had used voice Search, 72% in China had. But in the US, the number of smartphone owners using voice assistants has doubled to more 60% in just 2 years.

This paper introduces the factorial marked temporal point process model and presents efficient learning methods. In conventional (multi-dimensional) marked temporal point process models, event is often encoded by a single discrete variable i.e. a marker. In this paper, we describe the factorial marked point processes whereby time-stamped event is factored into multiple markers. Accordingly the size of the infectivity matrix modeling the effect between pairwise markers is in power order w.r.t. the number of the discrete marker space. We propose a decoupled learning method with two learning procedures: i) directly solving the model based on two techniques: Alternating Direction Method of Multipliers and Fast Iterative Shrinkage-Thresholding Algorithm; ii) involving a reformulation that transforms the original problem into a Logistic Regression model for more efficient learning. Moreover, a sparse group regularizer is added to identify the key profile features and event labels. Empirical results on real world datasets demonstrate the efficiency of our decoupled and reformulated method. The source code is available online.

Scene recognition with RGB images has been extensively studied and has reached very remarkable recognition levels, thanks to convolutional neural networks (CNN) and large scene datasets. In contrast, current RGB-D scene data is much more limited, so often leverages RGB large datasets, by transferring pretrained RGB CNN models and fine-tuning with the target RGB-D dataset. However, we show that this approach has the limitation of hardly reaching bottom layers, which is key to learn modality-specific features. In contrast, we focus on the bottom layers, and propose an alternative strategy to learn depth features combining local weakly supervised training from patches followed by global fine tuning with images. This strategy is capable of learning very discriminative depth-specific features with limited depth images, without resorting to Places-CNN. In addition we propose a modified CNN architecture to further match the complexity of the model and the amount of data available. For RGB-D scene recognition, depth and RGB features are combined by projecting them in a common space and further leaning a multilayer classifier, which is jointly optimized in an end-to-end network. Our framework achieves state-of-the-art accuracy on NYU2 and SUN RGB-D in both depth only and combined RGB-D data.

People watch the intelligent service robot in the Wenling Branch of Agriculture Bank of China on August 28, 2016 in Wenling, Zhejiang Province of China. The intelligent service robot, 'Binbin,' can provide information service, autonomous navigation and guidance for requested positions and is capable of human-robot interaction. With decades of lab research, artificial intelligence (AI) technology is finally coming to its anticipated fruition. The time is right to open a Pandora's Box for laboratory AI, introducing it to solve real business needs and address real-world problems. It is also the time for some scientists in the field to consider starting their entrepreneurial journey.

After 51 years, CES in Las Vegas still manages to pack some surprises. No, I'm not talking about the rain that caused crazy floods or the two-hour blackout in Central Hall of the Las Vegas Convention Center. I'm talking about 65-inch rollable OLED displays, robotic dogs, and $4,000 treadmills that deliver live workout classes on HD screens. There were plenty of less surprising, though no less welcome, innovations on display as well. I wasn't at all surprised to see voice assistance play a bigger role than ever this year, for instance.

The Japanese investment trust market is at a turning point as popular monthly-distribution trusts have begun to see a net outflow of funds while artificial intelligence- and big data-oriented funds are attracting investors expecting high growth. Long-term investment trusts are also in a firm state in line with a growing trend toward asset-building and away from savings in Japan. In 2017, open-type investment trusts, excluding exchange-traded funds, witnessed a new inflow of funds totaling 2.71 trillion yen ($24.34 billion). With their outstanding balance continuing to increase, investment trusts are taking hold as a key asset-building tool for individuals. According to data compiled by QUICK Asset Management Research Center on fund flows for investment trust management companies, individual investors are being lured to trusts that focus on AI and other cutting-edge technologies.

Google is adding a multitude of languages to its speech recognition capabilities. The expansion will cover 30 international languages and local dialects, including India and Africa's emerging regions. This will increase the total number of supported languages to 119. The update will include eight additional Indian languages and two African languages, Amharic and Swahili. The expanded speech recognition will provide more voice-based search opportunities, whether searching the web or typing using one's voice.