AI and deep learning are transforming the way we understand software, making computers more intelligent than we could even imagine just a decade ago. It is the technology behind self-driven cars, intelligent personal assistant computers, and decision support systems. Deep learning algorithms are being used across a broad range of industries. As the fundamental driver of AI, being able to tackle deep learning with Java is going to be a vital and valuable skill, not only within the tech world, but also for the wider global economy that depends upon knowledge and insight for growth and success. You will learn how to install the environment, where Git is used as version control, Eclipse or IntelliJ as an IDE, and mostly Gradle with a little bit of Maven as a build tool.

The field of AI has reached an inflection point today, where it is on the cusp of revolutionising areas as diverse as security, finance, transport, healthcare and government service delivery. Availability of massive volumes of data, relatively inexpensive computational capabilities and improved training techniques, such as deep learning, have led to significant leaps in AI capabilities and will only continue to do so for the foreseeable future. The pace is accelerating and governments need to figure out how to deal with this era of AI 2.0, where AI is becoming all-pervasive. Where if they want to unlock the potential of the data being generated at an ever-increasing velocity, government departments need AI at their fingertips, in the here and now. On September 14, senior executives from a wide range of key public sector agencies in Singapore and institutes of higher learning gathered for a vibrant, insightful discussion on the next stage of artificial intelligence.

The Dubai Electricity and Water Authority (Dewa) has launched a'future centre for customer happiness' at Ibn Battuta Mall. Sheikh Hamdan bin Mohammed bin Rashid Al Maktoum, Crown Prince of Dubai, inaugurated the centre. According to Dewa, this is the first integrated smart customer happiness centre in Dubai, which depends on artificial intelligence (AI) and robotics. "The centre relies on the latest technologies to achieve customers' satisfaction and exceed their expectations. It has smart self service booths to help customers complete their transactions with ease. These include Rammas, the virtual employee that uses AI to answer queries; the Tayseer smart bill payment platform; live chat, and a future services section to design and develop Dewa's future services," said Saeed Mohammed Al Tayer, MD and CEO of Dewa.

High-dimensional partial differential equations (PDE) appear in a number of models from the financial industry, such as in derivative pricing models, credit valuation adjustment (CVA) models, or portfolio optimization models. The PDEs in such applications are high-dimensional as the dimension corresponds to the number of financial assets in a portfolio. Moreover, such PDEs are often fully nonlinear due to the need to incorporate certain nonlinear phenomena in the model such as default risks, transaction costs, volatility uncertainty (Knightian uncertainty), or trading constraints in the model. Such high-dimensional fully nonlinear PDEs are exceedingly difficult to solve as the computational effort for standard approximation methods grows exponentially with the dimension. In this work we propose a new method for solving high-dimensional fully nonlinear second-order PDEs. Our method can in particular be used to sample from high-dimensional nonlinear expectations. The method is based on (i) a connection between fully nonlinear second-order PDEs and second-order backward stochastic differential equations (2BSDEs), (ii) a merged formulation of the PDE and the 2BSDE problem, (iii) a temporal forward discretization of the 2BSDE and a spatial approximation via deep neural nets, and (iv) a stochastic gradient descent-type optimization procedure. Numerical results obtained using ${\rm T{\small ENSOR}F{\small LOW}}$ in ${\rm P{\small YTHON}}$ illustrate the efficiency and the accuracy of the method in the cases of a $100$-dimensional Black-Scholes-Barenblatt equation, a $100$-dimensional Hamilton-Jacobi-Bellman equation, and a nonlinear expectation of a $ 100 $-dimensional $ G $-Brownian motion.

We consider the problem of non-parametric Conditional Independence testing (CI testing) for continuous random variables. Given i.i.d samples from the joint distribution $f(x,y,z)$ of continuous random vectors $X,Y$ and $Z,$ we determine whether $X \perp Y | Z$. We approach this by converting the conditional independence test into a classification problem. This allows us to harness very powerful classifiers like gradient-boosted trees and deep neural networks. These models can handle complex probability distributions and allow us to perform significantly better compared to the prior state of the art, for high-dimensional CI testing. The main technical challenge in the classification problem is the need for samples from the conditional product distribution $f^{CI}(x,y,z) = f(x|z)f(y|z)f(z)$ -- the joint distribution if and only if $X \perp Y | Z.$ -- when given access only to i.i.d. samples from the true joint distribution $f(x,y,z)$. To tackle this problem we propose a novel nearest neighbor bootstrap procedure and theoretically show that our generated samples are indeed close to $f^{CI}$ in terms of total variational distance. We then develop theoretical results regarding the generalization bounds for classification for our problem, which translate into error bounds for CI testing. We provide a novel analysis of Rademacher type classification bounds in the presence of non-i.i.d near-independent samples. We empirically validate the performance of our algorithm on simulated and real datasets and show performance gains over previous methods.

The partially observable Markov decision process (POMDP) provides a principled general framework for planning under uncertainty, but solving POMDPs optimally is computationally intractable, due to the "curse of dimensionality" and the "curse of history". To overcome these challenges, we introduce the Determinized Sparse Partially Observable Tree (DESPOT), a sparse approximation of the standard belief tree, for online planning under uncertainty. A DESPOT focuses online planning on a set of randomly sampled scenarios and compactly captures the "execution" of all policies under these scenarios. We show that the best policy obtained from a DESPOT is near-optimal, with a regret bound that depends on the representation size of the optimal policy. Leveraging this result, we give an anytime online planning algorithm, which searches a DESPOT for a policy that optimizes a regularized objective function. Regularization balances the estimated value of a policy under the sampled scenarios and the policy size, thus avoiding overfitting. The algorithm demonstrates strong experimental results, compared with some of the best online POMDP algorithms available. It has also been incorporated into an autonomous driving system for real-time vehicle control. The source code for the algorithm is available online.

ECENTLY, Vapnik and Vashist [1] provided a new learning paradigm termed learning using privileged information (LUPI), which is aimed at enhancing the generalization performance of learning algorithms. Generally speaking, in classical supervised learning paradigm, the training data and test data must come from the same distribution. Although in this new learning paradigm the training data is also considered an unbiased representation for the test data, the LUPI provides a set of additional information for the training data during the training stage, which is called privileged information. In the LUPI paradigm, we use the new training set containing privileged information to train a learning algorithm, while the privileged information is not available in the test stage. We note that the new learning paradigm is analogous to human learning process. In class, a teacher can provide some important and helpful information about this course for students, and these information provided by the teacher can help students acquire knowledge better. Therefore, a teacher plays an essential role in human leaning process. The LUPI paradigm resembling the classroom teaching model can achieve better generalization performance than the traditional learning paradigm. The author is with Department of Industrial Engineering and Logistics Management, School of Engineering, Hong Kong University of Science and Technology, Hong Kong 999077, China.(Email:

Katsu received his PhD in mechanical engineering from University of Tokyo in 2002. Following postdoctoral work at Carnegie Mellon University from 2002 to 2003, he was a faculty member at University of Tokyo until he joined Disney Research, Pittsburgh, in October 2008. His main research area is humanoid robot control and motion synthesis, in particular methods involving human motion data and dynamic balancing. He is also interested in developing algorithms for creating character animation. He has always been fascinated by the way humans control their bodies, which led him to the research on biomechanical human modeling and simulation to understand human sensation and motor control.

Finally, a solution for our increasingly congested roads is on the horizon. In last week's Huawei Connect conference, Shenzhen's Traffic Police Technology Chief Li Quiang announced the launch of their Traffic Brain system. Shenzhen is basically like the "Silicon Valley of Hardware", with some of the world's largest hardware manufacturers and tech companies setting up shop there. So it's no surprise that one of the most advanced traffic management systems in the world is being rolled out there first. This traffic management system represents some seriously advanced tech.

For Russia and Vladimir Putin, it is clear that planetary domination and artificial intelligence (AI) are inextricably intertwined. "Artificial intelligence is the future, not only for Russia but for all humankind," he said via live video feed as schools started this month. "Whoever becomes the leader in this sphere will become the ruler of the world." Putin isn't an outlier in his thinking; he is simply vocalizing to match the intensity a race that China, Russia, and the US are already running, to acquire smart military power. Each nation has formally recognized the critical importance of intelligent machines to the future of their national security, and each sees AI-related technologies such as autonomous drones and intelligence processing software as tools for augmenting human soldier capital.