Dubai: Oracle opened its first innovation hub in the region on Sunday in a bid to cash in on the growing implementation of artificial intelligence (AI) solutions in the UAE. Oracle named the new facility the'Zayed Innovation Hub' and it was inaugurated by Omar Sultan Al Olama, Minister of State for Artificial Intelligence. Arun Khehar, senior vice-president of applications at Oracle Eastern Central Europe, Middle East, Africa, told Gulf News that the whole idea is focused on Dubai Government and Federal Government's stress on emerging technologies like AI, Internet of Things (IoT) and blockchain. "This will be a key contribution to what the government plans to do. The lab will show the power of the technology to businesses, public sector and the academia. There will be industry solutions relevant to this part of the world," he said, adding that investments in AI will be integral to drive the future of cloud computing.

Cars which can read drivers' brainwaves and speed up their reaction times by anticipating acceleration, steering and braking, could be available within five to 10 years. Pioneered by Nissan, the Japanese motoring giant unveiled their Brain-to-Vehicle technology (B2V) at the CES 2018 trade show in Las Vegas, US. The company claims it will enable vehicles to interpret signals from the driver's brain, redefining how drivers interact with their cars. Nissan also said that as well as making road travel safer, the cars will keep adapting to make driving more enjoyable. ''When most people think about autonomous driving, they have a very impersonal vision of the future, where humans relinquish control to the machines," said the company's Executive Vice President Daniele Schillaci. ''Yet B2V technology does the opposite, by using signals from their own brain to make the drive even more exciting and enjoyable. Through Nissan Intelligent Mobility, we are moving people to a better world by delivering more autonomy, more electrification and more connectivity."

You've heard of artificial intelligence – everyone from Elon Musk to Mark Zuckerberg couldn't stop talking about it in 2017. But this year, AI will affect your life in tangible ways. "I believe 2018 is the year that this will start to become mainstream, to begin to impact many aspects of our lives in a truly ubiquitous and meaningful way," says Ralph Haupter, president of Microsoft Asia. The idea that computers have some amount of "intelligence" is not new, says Haupter, pointing as far back as 1950 when computer pioneer Alan Turing asked whether machines can think. "So it has taken nearly 70 years for the right combination of factors to come together to move AI from concept to an increasingly ubiquitous reality."

In this book, we explore the task of automatically predicting human decision-making and its use in designing intelligent human-aware automated computer systems of varying natures - from purely conflicting interaction settings (e.g., security and games) to fully cooperative interaction settings (e.g., autonomous driving and personal robotic assistants). We explore the techniques, algorithms, and empirical methodologies for meeting the challenges that arise from the above tasks and illustrate major benefits from the use of these computational solutions in real-world application domains such as security, negotiations, argumentative interactions, voting systems, autonomous driving, and games. The book presents both the traditional and classical methods as well as the most recent and cutting edge advances, providing the reader with a panorama of the challenges and solutions in predicting human decision-making. Top Description Table of Contents Author Information Table of Contents Preface Acknowledgments Introduction Utility Maximization Paradigm Predicting Human Decision-Making From Human Prediction to Intelligent Agents Which Model Should I Use? Concluding Remarks Bibliography Authors' Biographies Index Top Description Table of Contents Author Information About the Author(s)Ariel Rosenfeld, Weizmann Institute of Science Ariel Rosenfeld is a Koshland Postdoctoral Fellow at Weizmann Institute of Science, Israel. He obtained a B.Sc. in Computer Science and Economics, graduating magna cum laude from Tel Aviv University, and a Ph.D. in Computer Science from Bar-Ilan University.

Theoretical understanding of how deep neural network (DNN) extracts features from input images is still unclear, but it is widely believed that the extraction is performed hierarchically through a process of coarse-graining. It reminds us of the basic concept of renormalization group (RG) in statistical physics. In order to explore possible relations between DNN and RG, we use the Restricted Boltzmann machine (RBM) applied to Ising model and construct a flow of model parameters (in particular, temperature) generated by the RBM. We show that the unsupervised RBM trained by spin configurations at various temperatures from $T=0$ to $T=6$ generates a flow along which the temperature approaches the critical value $T_c=2.27$. This behavior is opposite to the typical RG flow of the Ising model. By analyzing various properties of the weight matrices of the trained RBM, we discuss why it flows towards $T_c$ and how the RBM learns to extract features of spin configurations.

This paper is concerned with the sparsification of the input-hidden weights of ELM (Extreme Learning Machine). For ordinary feedforward neural networks, the sparsification is usually done by introducing certain regularization technique into the learning process of the network. But this strategy can not be applied for ELM, since the input-hidden weights of ELM are supposed to be randomly chosen rather than to be learned. To this end, we propose a modified ELM, called ELM-LC (ELM with local connections), which is designed for the sparsification of the input-hidden weights as follows: The hidden nodes and the input nodes are divided respectively into several corresponding groups, and an input node group is fully connected with its corresponding hidden node group, but is not connected with any other hidden node group. As in the usual ELM, the hidden-input weights are randomly given, and the hidden-output weights are obtained through a least square learning. In the numerical simulations on some benchmark problems, the new ELM-CL behaves better than the traditional ELM.

We've compiled a list of the hottest events and conferences from the world of Data Science, Machine Learning and Artificial Intelligence happening in 2018. Below are all the links you need to get yourself to these great events! Please get in touch if there are any great events or conferences you think should be added!

A laboratory robot powered by artificial intelligence (AI) has discovered that a compound commonly found in toothpaste could be used to combat drug-resistant malaria parasites. Triclosan could be deployed against strains of plasmodium malaria parasites that have evolved resistance to the widely used drug pyrimethamine, according to the University of Cambridge. Pyrimethamine works by inhibiting a particular enzyme called DHFR and scientists have known for some time that triclosan can be employed to target another enzyme, ENR. The fast-moving AI routines of the robot "Eve", however, which formulate, test and re-evaluate hypotheses in quick succession, discovered that the common toothpaste chemical also attacks DHFR – even in parasites resistant to pyrimethamine. It has led researchers to hope that triclosan could be developed for use in a two-pronged attack on plasmodium in the liver and in the blood.

The Global Artificial Intelligence in Fintech Market is anticipated to grow rapidly and will post a CAGR of 40% during the forecast period. The availability of spatial data is a major factor driving the growth of the Artificial Intelligence in Fintech market. Sustaining in a competitive market has become crucial for the financial sector due to technological advancements. In order to achieve efficiency across business processes, enterprises need to design and layout a plan of action. This can be done by properly implementing AI practices into its operations.

AI technology can possibly change all industries globally, and one of the field's pioneers divulged today another startup he's beginning, bringing AI to manufacturing. Andrew Ng, who helped begin the Google Brain venture and earlier filled in as the leader of Baidu's AI endeavors, declared Landing.ai, an organization aimed at helping companies in the assembling division change themselves into AI organizations. Ng picked fabricating on the grounds that it has various issues that machine learning systems can help with, and AI can potentially change the business in drastic ways. Ng says "I am interested in manufacturing in particular because it touches so much of our everyday lives--essentially, I see it as a way to bring a digital transformation to the physical world. There are all these decisions that AI, machine learning, can make in a much more systematic way."