It might not be the first place you imagine when you think about robots. But in the Renaissance splendour of the Vatican, thousands of miles from Silicon Valley, scientists, ethicists and theologians gather to discuss the future of robotics. The ideas go to the heart of what it means to be human and could define future generations on the planet. The workshop, Roboethics: Humans, Machines and Health was hosted by The Pontifical Academy for Life. The Academy was created 25 years ago by Pope John Paul II in response to rapid changes in biomedicine.

We present an extension of the Kolmogorov-Smirnov (KS) two-sample test, which can be more sensitive to differences in the tails. Our test statistic is an integral probability metric (IPM) defined over a higher-order total variation ball, recovering the original KS test as its simplest case. We give an exact representer result for our IPM, which generalizes the fact that the original KS test statistic can be expressed in equivalent variational and CDF forms. For small enough orders ($k \leq 5$), we develop a linear-time algorithm for computing our higher-order KS test statistic; for all others ($k \geq 6$), we give a nearly linear-time approximation. We derive the asymptotic null distribution for our test, and show that our nearly linear-time approximation shares the same asymptotic null. Lastly, we complement our theory with numerical studies.

Online learning is the process of answering a sequence of questions based on the correct answers to the previous questions. It is studied in many research areas such as game theory, information theory and machine learning. There are two main components of online learning framework. First, the learning algorithm also known as the learner and second, the hypothesis class which is essentially a set of functions which learner uses to predict answers to the questions. Sometimes, this class contains some functions which have the capability to provide correct answers to the entire sequence of questions. This case is called realizable case. And when hypothesis class does not contain such functions is called unrealizable case. The goal of the learner, in both the cases, is to make as few mistakes as that could have been made by most powerful functions in hypothesis class over the entire sequence of questions. Performance of the learners is analysed by theoretical bounds on the number of mistakes made by them. This paper proposes three algorithms to improve the mistakes bound in the unrealizable case. Proposed algorithms perform highly better than the existing ones in the long run when most of the input sequences presented to the learner are likely to be realizable.

Predictive analytics systems are currently one of the most important areas of research and development within the Artificial Intelligence domain and particularly in Machine Learning. One of the "holy grails" of predictive analytics is the research and development of the "perfect" recommendation system. In our paper we propose an advanced pipeline model for the multi-task objective of determining product complementarity, similarity and sales prediction using deep neural models applied to big-data sequential transaction systems. Our highly parallelized hybrid pipeline consists of both unsupervised and supervised models, used for the objectives of generating semantic product embeddings and predicting sales, respectively. Our experimentation and benchmarking have been done using very large pharma-industry retailer Big Data stream.

Convolutional neural networks (CNNs) have been shown to achieve optimal approximation and estimation error rates (in minimax sense) in several function classes. However, previously analyzed optimal CNNs are unrealistically wide and difficult to obtain via optimization due to sparse constraints in important function classes, including the H\"older class. We show a ResNet-type CNN can attain the minimax optimal error rates in these classes in more plausible situations -- it can be dense, and its width, channel size, and filter size are constant with respect to sample size. The key idea is that we can replicate the learning ability of Fully-connected neural networks (FNNs) by tailored CNNs, as long as the FNNs have \textit{block-sparse} structures. Our theory is general in a sense that we can automatically translate any approximation rate achieved by block-sparse FNNs into that by CNNs. As an application, we derive approximation and estimation error rates of the aformentioned type of CNNs for the Barron and H\"older classes with the same strategy.

It's no longer unusual to see big-name actors in video game roles – and usually it works out fine. Ellen Page in Beyond: Two Souls, Kristen Bell in Assassin's Creed and Charles Dance in Witcher 3 were all perfectly cast, bringing their talent and star quality to fitting roles, and featured prominently in those games' promotion. But sometimes, famous faces pop up in video games where you're not expecting them, whether it's someone at the start of their career who later turns into a huge star, or an ageing legend looking for a quick buck. Here are some of our favourite improbable appearances. Malek is typically convincing as sinister dudebro Josh Washington, one of eight teenagers (including Heroes' Hayden Panettiere) getting bumped off in a secluded cabin. Sadly we're unlikely to see the Oscar recipient gracing the forthcoming sequel.

Apple is planning a "special event" on 25 March, where the tech giant is widely expected to unveil a new video streaming service to potentially rival Netflix. Taking place at the Steve Jobs Theatre at the company's headquarters in Cupertino, California, an invitation for the event included the phrase "it's showtime" in an apparent reference to a new film and video platform, though no official details have yet been revealed. There have nonetheless been a slew of leaks and rumours that usually come with major Apple events. Other potential announcements are thought to include a paid-for news subscription service. We'll tell you what's true.

Every new technology that comes to prominence has always made the life of humans better. Remember, the time fire was first discovered by your ancestors to cook food. And then came the wheel. Now, it is digital payments and internet of things. Are you a person who keeps a keen eye on the scientific developments happening in the world?

Bears can exactly mimic another bear's facial expressions, casting doubt on humans and other primates being the only mammals able to express their emotions. Sun bears have been observed opening their mouths to match their playmates when they are interacting face-to-face. Researchers claim that such facial mimicry has not been seen in primates outside humans and gorillas. Dogs can also use mimic each other to reinforce bonds. In the behavioural study, they found that bears can use facial expressions to communicate with others in a similar way to humans and apes. This'strongly suggests' that other mammals could also perform this complex social skill and, in addition, have a degree of social sensitivity.

An army of'killer robots' that will assist infantry on the battlefield has been unveiled in propaganda footage released by Russia The video, released by the Kremlin, appears to showcase the state's latest drone technology. That includes and AI-controlled driverless tank that follow the aim of a soldier's rifle to obliterate targets with its own weaponry. Russia's Advanced Research Foundation (ARF) said the ultimate goal is to have an army of robots entirely controlled by Artificial Intelligence algorithms. Currently the drones are deployed alongside infantry who remotely control the vehicles, but in the future the tech will be fully autonomous. That means the military hardware will be able to target and kill enemies without any human intervention.