Russian president Vladimir Putin stated - "AI is the future and whoever becomes leader in AI will become the ruler of the world". Chinese president Xi Jinping declared that "China wants to be the world leader in AI by 2030". US White House administration voiced - "America has been the global leader in AI, and the Trump administration will ensure our great nation remains the global leader in AI". Similarly, National strategy for Artificial Intelligence, India published by NITI Aayog indicates its vision as "AI-for-All in India". These statements clearly indicates that the race for the supremacy in the field of Artificial Intelligence had already taken a great momentum and AI has managed to influence even main stream politics and the world leaders in a great way. On the other hand, many experts across the globe are already in a big hurry to proclaim which country is going to be the AI superpower and who is already ahead in the race.

A prominent Chinese venture capitalist, Kai-Fu Lee, has been proselytizing for the advancement of artificial intelligence for some time. He is the chief executive of an influential investment firm called Sinovation Ventures based in Beijing that specializes in artificial intelligence, also known as AI, and as such he has an interest in promoting the idea that it will change our world. However, he may end up scaring us more than astonishing us. You see, Lee now claims that up to 40 percent of jobs will be "displaceable" because of AI within 15 to 25 years. This past weekend, Lee appeared on a well-known American news television show to discuss AI, whereupon he stunned the reporter with his claim.

Modern human DNA computational analysis suggests that the extinct species was a hybrid of Neanderthals and Denisovans and cross bred with Out of Africa modern humans in Asia. This finding would explain that the hybrid found this summer in the caves of Denisova -- the offspring of a Neanderthal mother and a Denisovan father -- was not an isolated case, but rather was part of a more general introgression process. The study, published in Nature Communications, uses deep learning for the first time ever to account for human evolution, paving the way for the application of this technology in other questions in biology, genomics and evolution. One of the ways of distinguishing between two species is that while both of them may cross breed, they do not generally produce fertile descendants. However, this concept is much more complex when extinct species are involved.

AI needs to be created by a diverse range of developers to prevent bias, but the World Economic Forum (WEF) has found a serious gender gap. Gender gaps in STEM careers have been a problem for some time, but it's not often the end product matters what gender it was developed by. AI is about to be everywhere, and it matters that it's representative of those it serves. "The equal contribution of women and men in this process of deep economic and societal transformation is critical. More than ever, societies cannot afford to lose out on the skills, ideas and perspectives of half of humanity to realize the promise of a more prosperous and humancentric future that well-governed innovation and technology can bring." Shockingly, the WEF report found less than one-fourth of roles in the industry are being filled by women.

Flying drones are mostly known as an amateur or professional devices for gaming, videography, racing and military missions count some of their applications. But what if one drone could do all the above? This drone as a platform idea was the starting point for the foundation team of AiRFLOW. The drone they designed is a common base for commercial, military and tailor-made solutions. This comes from a combination of software implies artificial intelligence technologies and a state of the art hardware that provides the necessary power for fail-safe operation and all the sensors required to complete its mission.

Most of the current game-theoretic demand-side management methods focus primarily on the scheduling of home appliances, and the related numerical experiments are analyzed under various scenarios to achieve the corresponding Nash-equilibrium (NE) and optimal results. However, not much work is conducted for academic or commercial buildings. The methods for optimizing academic-buildings are distinct from the optimal methods for home appliances. In my study, we address a novel methodology to control the operation of heating, ventilation, and air conditioning system (HVAC). With the development of Artificial Intelligence and computer technologies, reinforcement learning (RL) can be implemented in multiple realistic scenarios and help people to solve thousands of real-world problems. Reinforcement Learning, which is considered as the art of future AI, builds the bridge between agents and environments through Markov Decision Chain or Neural Network and has seldom been used in power system. The art of RL is that once the simulator for a specific environment is built, the algorithm can keep learning from the environment. Therefore, RL is capable of dealing with constantly changing simulator inputs such as power demand, the condition of power system and outdoor temperature, etc. Compared with the existing distribution power system planning mechanisms and the related game theoretical methodologies, our proposed algorithm can plan and optimize the hourly energy usage, and have the ability to corporate with even shorter time window if needed.

In an effort to improve the performance of deep neural networks in data-scarce, non-i.i.d., or unsupervised settings, much recent research has been devoted to encoding invariance under symmetry transformations into neural network architectures. We treat the neural network input and output as random variables, and consider group invariance from the perspective of probabilistic symmetry. Drawing on tools from probability and statistics, we establish a link between functional and probabilistic symmetry, and obtain generative functional representations of joint and conditional probability distributions that are invariant or equivariant under the action of a compact group. Those representations completely characterize the structure of neural networks that can be used to model such distributions and yield a general program for constructing invariant stochastic or deterministic neural networks. We develop the details of the general program for exchangeable sequences and arrays, recovering a number of recent examples as special cases.

We interpret the variational inference of the Stochastic Gradient Descent (SGD) as minimizing a new potential function named the \textit{quasi-potential}. We analytically construct the quasi-potential function in the case when the loss function is convex and admits only one global minimum point. We show in this case that the quasi-potential function is related to the noise covariance structure of SGD via a partial differential equation of Hamilton-Jacobi type. This relation helps us to show that anisotropic noise leads to faster escape than isotropic noise. We then consider the dynamics of SGD in the case when the loss function is non-convex and admits several different local minima. In this case, we demonstrate an example that shows how the noise covariance structure plays a role in "implicit regularization", a phenomenon in which SGD favors some particular local minimum points. This is done through the relation between the noise covariance structure and the quasi-potential function. Our analysis is based on Large Deviations Theory (LDT), and they are validated by numerical experiments.

The gradient noise (GN) in the stochastic gradient descent (SGD) algorithm is often considered to be Gaussian in the large data regime by assuming that the classical central limit theorem (CLT) kicks in. This assumption is often made for mathematical convenience, since it enables SGD to be analyzed as a stochastic differential equation (SDE) driven by a Brownian motion. We argue that the Gaussianity assumption might fail to hold in deep learning settings and hence render the Brownian motion-based analyses inappropriate. Inspired by non-Gaussian natural phenomena, we consider the GN in a more general context and invoke the generalized CLT (GCLT), which suggests that the GN converges to a heavy-tailed $\alpha$-stable random variable. Accordingly, we propose to analyze SGD as an SDE driven by a L\'{e}vy motion. Such SDEs can incur `jumps', which force the SDE transition from narrow minima to wider minima, as proven by existing metastability theory. To validate the $\alpha$-stable assumption, we conduct extensive experiments on common deep learning architectures and show that in all settings, the GN is highly non-Gaussian and admits heavy-tails. We further investigate the tail behavior in varying network architectures and sizes, loss functions, and datasets. Our results open up a different perspective and shed more light on the belief that SGD prefers wide minima.

The Variational Auto-Encoder (VAE) model has become widely popular as a way to learn at once a generative model and embeddings for observations living in a high-dimensional space. In the real world, many such observations may be assumed to be hierarchically structured, such as living organisms data which are related through the evolutionary tree. Also, it has been theoretically and empirically shown that data with hierarchical structure can efficiently be embedded in hyperbolic spaces. We therefore endow the VAE with a hyperbolic geometry and empirically show that it can better generalise to unseen data than its Euclidean counterpart, and can qualitatively recover the hierarchical structure.