"I propose to consider the question, 'can machines think?' This should begin with definitions of the meaning of the word'machine' and'think'." So wrote computing pioneer Alan Turing in the introduction to his seminal paper on machine intelligence, published in the journal Mind in 1950. That paper – introducing the'imitation game', Turing's adversarial test for machine intelligence, in which a human must decide whether what we now call a chatbot is a human or a computer – helped spark the field of research which later became more widely known as artificial intelligence. Whilst no researcher has yet made a general purpose thinking machine – what's known as artificial general intelligence – that passes Turing's test, a wide variety of special purpose AIs have been created to focus on, and solve, very specific problems, such as image and speech recognition, and defeating chess and Go champions. However, whenever AI hits trouble – such as when prototype autonomous cars cause accidents, robots look like eliminating jobs, or AI algorithms access personal data without permission, the news media surfaces major concerns about a societal downside to AI.

However, for the most part, these events are not new. Our planet has always been a dangerous place to live (climate change or no). Despots and regimes have, throughout history, posed a threat to humanity. While it's easy to ignore the dangers that these pose and leave it to the scientists or politicians to fix, we all may be about to enter uncharted waters with a new threat to our species…. In July this year, Facebook had to temporarily suspend its Artificial Intelligence (AI) research when two of its'chatbots' (Alice and Bob) began communicating with each other in a language they developed, independent of their programmers.

This paper contains analysis of concept of a class within different object-oriented knowledge representation models. The main attention is paid to structure of the class and its efficiency in the context of data storage, using object-relational mapping. The main achievement of the paper is extension of concept of homogeneous class of objects by introducing concepts of single-core and multi-core inhomogeneous classes of objects, which allow simultaneous defining of a few different types within one class of objects, avoiding duplication of properties and methods in representation of types, decreasing sizes of program codes and providing more efficient information storage in the databases. In addition, the paper contains results of experiment, which show that data storage in relational database, using proposed extensions of the class, in some cases is more efficient in contrast to usage of homogeneous classes of objects.

Given a classical channel, a stochastic map from inputs to outputs, can we replace the input with a simple intermediate variable that still yields the correct conditional output distribution? We examine two cases: first, when the intermediate variable is classical; second, when the intermediate variable is quantum. We show that the quantum variable's size is generically smaller than the classical, according to two different measures---cardinality and entropy. We demonstrate optimality conditions for a special case. We end with several related results: a proposal for extending the special case, a demonstration of the impact of quantum phases, and a case study concerning pure versus mixed states.

Consider the multivariate nonparametric regression model. It is shown that estimators based on sparsely connected deep neural networks with ReLU activation function and properly chosen network architecture achieve the minimax rates of convergence (up to log n-factors) under a general composition assumption on the regression function. The framework includes many well-studied structural constraints such as (generalized) additive models. While there is a lot of flexibility in the network architecture, the tuning parameter is the sparsity of the network. Specifically, we consider large networks with number of potential parameters being much bigger than the sample size. The analysis gives some insights why multilayer feedforward neural networks perform well in practice. Interestingly, the depth (number of layers) of the neural network architectures plays an important role and our theory suggests that scaling the network depth with the logarithm of the sample size is natural.

The plesiosaur is long extinct, but thanks to a biomechanical engineer, it has been reincarnated -- as a robot. This new so-called "robosaur" reveals the secret behind the animal's odd but powerful swimming style, which could inspire alternatives to boat and submarine propellers. Scientists have speculated over the swimming ability of plesiosaurs for decades. Long-necked and round-bodied, the plesiosaur lived 203 million years ago during the age of dinosaurs, but it was a marine reptile, more closely related to lizards and snakes. "There has been no other animal that swims like this, ever," Luke Muscutt, a biomechanical engineer at the University of Southampton in the U.K., told NewsHour. A fish propels itself by swishing its tail side to side, while a dolphin flaps its tail up and down.

Can a computer detect an author's personality type, based only on a sample of his or her writing? Four researchers from Singapore and Mexico City sought to answer that question. In their newly published study, the authors present a deep learning-based method that can figure out the psychological profiles of authors. They used a specially designed deep convolutional neural network. Their method analyzed various texts in order to identify the presence or absence of the Big Five personality traits.

The I-7 Innovators' Strategic Advisory Board on People-Centered Innovation is the engagement group launched last May during the G7 Summit in Taormina (par. The group is in charge of providing guidance on emerging innovation issues. The creation of this group is an experiment proposed by the Italian G7 Presidency with the goal of driving attention towards the multiple challenges that innovation poses and cannot be faced only at national level. Each country and the EU have designated their own group of experts. We encourage Canada to consider continuing this experiment during their imminent Presidency. How can AI help governments make better decisions and deliver policies and services more effectively?

The Laboratory of Computational Biology (Stein Aerts lab) is part of the VIB Center for Brain & Disease Research and the Department of Human Genetics (University of Leuven, Belgium). Our lab is a "humid" lab, half wet and half dry. In the wet-lab we apply high-throughput technologies to decipher enhancer logic and map gene regulatory networks, such as RNA-seq for transcriptomics and ATAC-seq and ChIP-seq for epigenomic profiling. To test the activities of promoters and enhancers we use massively parallel enhancer-reporter assays. Finally, to map high-resolution landscapes of possible cellular states we use single-cell transcriptomics and single-cell epigenomics.

Industrial robots get lots done, but they're expensive, dangerous and hard to program. That's why roboticists are turning to cobots - collaborative robots made to work alongside us and, perhaps one day, in our homes. "We are trying to make a support-service robot for people who can't support themselves or leave home," explains Simon Haddadin, co-founder of Munich-based Franka Emika. The lightweight, three-kilogram frames of Franka Emika's cobots mean they can safely work in the same building as humans, and will stop automatically if they come into close contact with one. "It basically means it has a sense of touch along the entire structure," Haddadin says.