China and Big Tech threaten all the worlds people with a Quantum AI Digital Brain on the coming 5G and 6G networks that can form an AI system beyond the control of human beings. "Artificial Intelligence Dangers to Humanity" goes deep into the inter-connections between AI, U.S, China, Big Tech and the worlds use of Facial Recognition, Bio-Metrics, Drones, Smart Phones, Smart Cities, IoT, VR, Mixed Reality, 5G, Robotics, Cybernetics, & Bio-Digital Social Programming. The book is sourced from a 10,000 page report converted to just over 200 pages with pictures and hidden inner meanings. We will cover present, emerging and future threats of Artificial Intelligence with Big Tech, including technology that can be used for assassination or to control humanities ability to have free formed thoughts without being formed by AI Bio-Digital Social Programming. The book will cover Cyborgs, Super Intelligence and how it can form, and in what ways it can travel undetected through The AI Global Network as it connects with the internet and the Human Bio-Digital Network.

In an endeavour to upskill the youth and promote e-learning during the COVID-19 lockdown, IIT Roorkee had launched an Advanced Certification Course on Deep Learning at Cloudxlab.com. It is an advanced course on deep learning and would cover cutting edge techniques applicable to audio processing, image processing, video processing, self-driving cars etc. This came in the wake of the current economic crisis which underscores the significance of technical skills to tackle the global slowdown. Further to the launch IIT Roorkee and CloudxLab will conduct a webinar on careers in AI and machine learning. The webinar will include faculty members from IIT Roorkee as well as members of the industry.

Whilst many will be familiar with our Women in AI lists which include those currently pushing boundaries in the present day, we thought we would put together a list of women who have been instrumental in the advancement of Computer Science and Data Science, providing the foundations for AI in the 21st Century. How many of the below are you familiar with? Dame Mary Cartwright was a University of Oxford Graduate in Mathematics at a time in which Women had only just been allowed to take degree classifications at the prestigious school. Mary then obtained a Yarrow fellowship at Cambridge University, later pursuing research in the theory of Functions through until her retirement in 1968, becoming one of the first to study what would later become known as Chaos theory. Cartwright had a distinguished career in analytic function theory and university administration, publishing over 100 papers on classical analysis, differential equations and related topological problems.

Engineers at North Carolina State University have achieved a new record for the fastest moving soft robot. A team from the university's Mechanical and Aerospace Engineering department created a robot capable of moving 2.7 times its own body length each second, more than three times faster than the previous record of 0.8 times body length per second. The tiny robot--it weighs just 1.5 ounces and measures 2.7 inches long--was designed to run like a cheetah, with four bent legs and a long flexible torso made from silicone. A team of engineers at North Carolina State University developed a small'soft' robot modeled after a cheetah, which uses silicone bands to expand and contract in a galloping motion that mimics a cheetah's movement'We were inspired by the cheetah to create a type of soft robot that has a spring-powered, "bistable" spine, meaning that the robot has two stable states,' North Carolina State's Jie Yin told Eurekalert. 'We can switch between these stable states rapidly by pumping air into channels that line the soft, silicone robot.

Robots today generally come in one of two varieties: rigid and soft. When most people imagine a robot, they think of the rigid variety, like Boston Dynamics' Spot or those found on auto assembly lines. Soft robots, on the other hand, tend to mimic biological organisms enabling them to more easily adapt to their surrounding environment, work more safely in the presence of humans and now, with a novel robotic spine design developed by North Carolina State University, move faster than ever before. Cheetahs can average 58 mph while sprinting (though in 2012, an 11 year old cat at the Cincinnati Zoo set a record of 61mph while completing a 100 meter sprint in 5.95 seconds -- three seconds faster than Usain Bolt). This speed is due to their uniquely evolved, super flexible spines which allow them to dramatically arch their backs as they run, enabling the fast felines to take longer and faster strides than their preferred antelope prey.

In addition to pursuing quantum entanglement, Holdeman took psychology professor Daniel Kersten's fall 2018 class Intro to Neural Networks, which discussed connections between artificial intelligence and psychology. He now helps with Kersten's presentations to better align the material with cutting-edge neural network software development using his coding experience in Python.

Artificial intelligence is the future. If "Westworld" or "Black Mirror" are to be believed, there will soon come a day when the computers rule us all. But for now, an AI's power ends at the US Patent Office. The USPTO has denied a pair of patents filed on behalf of DABUS, an artificial intelligence system, and published a ruling that says US patents can only be granted to "natural persons." The two patents were for a food container and a flashlight, and were filed by Stephen Thaler, an AI researcher and DABUS' creator. According to the filing from the USPTO, Thaler calls DABUS a "creativity machine" and wanted the AI to get full credit for the inventions.

Most firetrucks come in red, but it's not hard to picture one in blue. Their understanding of the world is colored, often literally, by the data they've trained on. If all they've ever seen are pictures of red fire trucks, they have trouble drawing anything else. To give computer vision models a fuller, more imaginative view of the world, researchers have tried feeding them more varied images. Some have tried shooting objects from odd angles, and in unusual positions, to better convey their real-world complexity.

The application of federated extreme gradient boosting to mobile crowdsensing apps brings several benefits, in particular high performance on efficiency and classification. However, it also brings a new challenge for data and model privacy protection. Besides it being vulnerable to Generative Adversarial Network (GAN) based user data reconstruction attack, there is not the existing architecture that considers how to preserve model privacy. In this paper, we propose a secret sharing based federated learning architecture FedXGB to achieve the privacy-preserving extreme gradient boosting for mobile crowdsensing. Specifically, we first build a secure classification and regression tree (CART) of XGBoost using secret sharing. Then, we propose a secure prediction protocol to protect the model privacy of XGBoost in mobile crowdsensing. We conduct a comprehensive theoretical analysis and extensive experiments to evaluate the security, effectiveness, and efficiency of FedXGB. The results indicate that FedXGB is secure against the honest-but-curious adversaries and attains less than 1% accuracy loss compared with the original XGBoost model.

Water demand is a highly important variable for operational control and decision making. Hence, the development of accurate forecasts is a valuable field of research to further improve the efficiency of water utilities. Focusing on probabilistic multi-step-ahead forecasting, a time series model is introduced, to capture typical autoregressive, calendar and seasonal effects, to account for time-varying variance, and to quantify the uncertainty and path-dependency of the water demand process. To deal with the high complexity of the water demand process a high-dimensional feature space is applied, which is efficiently tuned by an automatic shrinkage and selection operator (lasso). It allows to obtain an accurate, simple interpretable and fast computable forecasting model, which is well suited for real-time applications. The complete probabilistic forecasting framework allows not only for simulating the mean and the marginal properties, but also the correlation structure between hours within the forecasting horizon. For practitioners, complete probabilistic multi-step-ahead forecasts are of considerable relevance as they provide additional information about the expected aggregated or cumulative water demand, so that a statement can be made about the probability with which a water storage capacity can guarantee the supply over a certain period of time. This information allows to better control storage capacities and to better ensure the smooth operation of pumps. To appropriately evaluate the forecasting performance of the considered models, the energy score (ES) as a strictly proper multidimensional evaluation criterion, is introduced. The methodology is applied to the hourly water demand data of a German water supplier.