Artificial Intelligence (AI) is now enjoying massive acceptance from consumers and organisations worldwide. Hence, more and more companies are stepping up their game by adopting Artificial Intelligence into their functionalities. In this article, we will discuss the absolute wins of the year 2019 in terms of breakthrough AI solutions and their impact. Here are some of the AI success stories and top news for the year 2019. In May 2019, Samsung created a system that could transform facial images into a video sequence.

Typically this requires casting the original deterministic physics-based model into a probabilistic framework where inputs or control variables (CVs) are treated as random variables with probability distributions derived from available experimental data, manufacturing constraints, design criteria, expert judgment, and/or other domain knowledge (e.g., see [1]). Running the physics-based model with CVs sampled according to these distributions yields corresponding realizations of the system response as characterized by quantities of interest (QoIs). Analysis of the uncertainty propagation from the CVs to the QoIs informs decision-making, e.g., it informs engineering decisions aimed at improving the quality and reliability of designed products and helps identify potential risks at early stages in the design and manufacturing process. Quantitatively assessing uncertainty propagation presents a fundamental challenge due to the computational cost of the underlying physics-based model. Even for a low number of CVs and QoIs, uncertainty quantification (UQ) for, e.g., accelerating the simulation-aided design of multiscale systems and data-centric engineering tasks more generally ([2]), requires a large number of repeated observations of QoIs to achieve a high degree of confidence in such an analysis. The sampling cost is further exacerbated in real-world applications where distributions on QoIs are typically non-Gaussian, skewed, and/or mutually correlated, and therefore need to be characterized by their full probability density function (PDF) rather than through summary statistics such as mean and variance. The computational cost of nonparametric methods to estimate these densities can become prohibitively high when using a fully-featured physics-based model to compute each sample. One approach to alleviate the computational burden is to derive a cheaper-to-compute surrogate for the physicsbased model's response enabling much faster generation of output data and thus overcoming computational bottlenecks.

In this paper we introduce a theoretical framework for semi-discrete optimization using ideas from optimal transport. Our primary motivation is in the field of deep learning, and specifically in the task of neural architecture search. With this aim in mind, we discuss the geometric and theoretical motivation for new techniques for neural architecture search (in the companion work \cite{practical}; we show that algorithms inspired by our framework are competitive with contemporaneous methods). We introduce a Riemannian like metric on the space of probability measures over a semi-discrete space $\mathbb{R}^d \times \mathcal{G}$ where $\mathcal{G}$ is a finite weighted graph. With such Riemmanian structure in hand, we derive formal expressions for the gradient flow of a relative entropy functional, as well as second order dynamics for the optimization of said energy. Then, with the aim of providing a rigorous motivation for the gradient flow equations derived formally we also consider an iterative procedure known as minimizing movement scheme (i.e., Implicit Euler scheme, or JKO scheme) and apply it to the relative entropy with respect to a suitable cost function. For some specific choices of metric and cost, we rigorously show that the minimizing movement scheme of the relative entropy functional converges to the gradient flow process provided by the formal Riemannian structure. This flow coincides with a system of reaction-diffusion equations on $\mathbb{R}^d$.

We introduce a new recurrent agent architecture and associated auxiliary losses which improve reinforcement learning in partially observable tasks requiring long-term memory. We employ a temporal hierarchy, using a slow-ticking recurrent core to allow information to flow more easily over long time spans, and three fast-ticking recurrent cores with connections designed to create an information asymmetry. The \emph{reaction} core incorporates new observations with input from the slow core to produce the agent's policy; the \emph{perception} core accesses only short-term observations and informs the slow core; lastly, the \emph{prediction} core accesses only long-term memory. An auxiliary loss regularizes policies drawn from all three cores against each other, enacting the prior that the policy should be expressible from either recent or long-term memory. We present the resulting \emph{Perception-Prediction-Reaction} (PPR) agent and demonstrate its improved performance over a strong LSTM-agent baseline in DMLab-30, particularly in tasks requiring long-term memory. We further show significant improvements in Capture the Flag, an environment requiring agents to acquire a complicated mixture of skills over long time scales. In a series of ablation experiments, we probe the importance of each component of the PPR agent, establishing that the entire, novel combination is necessary for this intriguing result.

Much like we have Chemical Engineering and Electrical Engineering and Mechanical Engineering, it is time to formalize of field of Data Engineering. This is a special two-part series on trends and requirements leading to the formalization of the Field of Data Engineering. "Data is the new oil…in much the same way that oil fueled economic growth in the 20th century, data will fuel economic growth in the 21st century." To further raise the credibility of data as the economic fuel for the next century, "The Economist" Special Report on the Data Economy asks "Are data more like oil or sunlight?" Still, it is hard to put a definitive value on data. If data is to be the fuel for economic growth in the 21st century, don't we need to find a way to accurately determine what data is worth?

Minsky and McCarthy, in the 1950s, described artificial intelligence as any task performed by a program or a machine that, if a human carried out the same activity, we would say the human had to apply intelligence to accomplish the task. The AI systems typically demonstrate behaviors associated with human intelligence like planning, learning, reasoning, problem-solving, knowledge representation, perception, motion, manipulation, and to a lesser extent, social intelligence, and creativity. Nowadays, artificial intelligence is all around us in computers, speech and language recognition of the Siri virtual assistant on the Apple iPhone, in the vision-recognition systems on self-driving cars, in the recommendation engines that suggest products you might like based on what you bought in the past, interpreting video feeds from drones carrying out visual inspections of infrastructure such as oil pipelines, organizing personal and business calendars, responding to simple customer-service queries, coordinating with other intelligent systems to carry out tasks like booking a hotel at a suitable time and location, helping radiologists to spot potential tumors in X-rays, flagging inappropriate content online, detecting wear and tear in elevators from data gathered by IoT devices, the list goes on and on. There is a flood of virtual assistants, such as Apple's Siri, Amazon's Alexa, Google Assistant, and Microsoft Cortana, etc. The AI is capable of executing vastly different tasks, anything from giving you a haircut to building complex robots as commonly seen in movies, the likes of HAL in 2001, or Skynet in The Terminator, though doesn't exist today certainly a reality of tomorrow. What is machine learning: Machine learning is where a computer system is fed large amounts of data which it then uses to learn how to carry out a specific task such as understanding speech or captioning a photograph.

Footage has emerged of one of Boston Dynamics' robotic dogs patrolling a SpaceX test site in the US. The video allegedly shows SpaceX using the $75,000 (£60,000) robotic dog to inspect the aftermath of its test site in Boca Chica, Texas. SpaceX had just been conducting a cryogenic pressure test on the Starship SN7 dome tank prototype, according to Tesmanian. SN7 was filled with sub-cooled liquid nitrogen and it was intentionally pressurised to its capacity before it burst and collapsed on its side. The stainless-steel commercial spacecraft, once operational, will be capable of transporting passengers on long-duration voyages to the Moon and Mars. But until the launch vehicle is ready, Elon Musk's company appears to be employing a little help from a trusty robotic companion.

Earthquakes claimed thousands of lives every decade. Of all-natural calamities, earthquake is the one which is most hard to predict. Even if a man succeeded in doing so, his predictions are vaguely based on the behavior of animals' minutes before the seismic waves hit that geographic region. However, with artificial intelligence algorithms can help us in receiving early warnings of a potential earthquake and be prepared accordingly. Using machine-learning models, seismologists can analyze hordes data on thousands of earthquakes.

A new machine learning algorithm allows researchers to explore possible designs for the microstructure of fuel cells and lithium-ion batteries, before running 3-D simulations that help researchers make changes to improve performance. Improvements could include making smartphones charge faster, increasing the time between charges for electric vehicles, and increasing the power of hydrogen fuel cells running data centers. The paper is published today in npj Computational Materials. Fuel cells use clean hydrogen fuel, which can be generated by wind and solar energy, to produce heat and electricity, and lithium-ion batteries, like those found in smartphones, laptops, and electric cars, are a popular type of energy storage. The performance of both is closely related to their microstructure: how the pores (holes) inside their electrodes are shaped and arranged can affect how much power fuel cells can generate, and how quickly batteries charge and discharge. However, because the micrometer-scale pores are so small, their specific shapes and sizes can be difficult to study at a high enough resolution to relate them to overall cell performance.

Many dimensionality and model reduction techniques rely on estimating dominant eigenfunctions of associated dynamical operators from data. Important examples include the Koopman operator and its generator, but also the Schr\"odinger operator. We propose a kernel-based method for the approximation of differential operators in reproducing kernel Hilbert spaces and show how eigenfunctions can be estimated by solving auxiliary matrix eigenvalue problems. The resulting algorithms are applied to molecular dynamics and quantum chemistry examples. Furthermore, we exploit that, under certain conditions, the Schr\"odinger operator can be transformed into a Kolmogorov backward operator corresponding to a drift-diffusion process and vice versa. This allows us to apply methods developed for the analysis of high-dimensional stochastic differential equations to quantum mechanical systems.