That's one of the surprising -- and unsettling -- questions Israeli historian Yuval Noah Harari asks in his much-quoted new book, 21 Lessons for the 21st Century. Whereas 20th-century technology favored democracies as they were able to distribute power to make decisions among many people and institutions, according to Harari, artificial intelligence (AI) might make centralized systems that concentrate all information and power far more efficient as machine learning works better with more information to analyze. "If you disregard all privacy concerns and concentrate all the information relating to a billion people in one database," Harari writes, "you'll wind up with much better algorithms than if you respect individual privacy and have in your database only partial information on a million people." The rise of AI swinging the pendulum from democracies toward authoritarian regimes is just one of the feared adverse impacts of technologies: Others include job displacement, concentration of power, diminishing privacy, rising income inequality and losing our "free will." Yet most people have little or no knowledge about how AI, blockchain, the Internet of Things or genetic engineering could affect their lives.

Digital transformation is about doing things differently, not just doing them better than everyone else. And because I come from the world of business process management technology development, I have a few thoughts about how bringing artificial intelligence into business process management can help you achieve both sides of this coin. Many business process management (BPM) projects are aimed at cost reduction and efficiency improvement. This operational improvement approach is important, because doing things better is, well, better. But simply improving operational effectiveness does not necessarily provide strategic competitive advantage.

There's no foolproof way to know if someone's verbally telling lies, but scientists have developed a tool that seems remarkably accurate at judging written falsehoods. Using machine learning and text analysis, they've been able to identify false robbery reports with such accuracy that the tool is now being rolled out to police stations across Spain. Computer scientists from Cardiff University and Charles III University of Madrid developed the tool, called VeriPol, specifically to focus on robbery reports. In their paper, published in the journal Knowledge-Based Systems earlier this year, they describe how they trained a machine-learning model on more than 1000 police robbery reports from Spanish National Police, including those that were known to be false. A pilot study in Murcia and Malaga in June 2017 found that, once VeriPol identified a report as having a high probability of being false, 83% of these cases were closed after the claimants faced further questioning.

Emerton, a consulting firm with offices in Paris, New York and Dubai, has launched Emerton Data, a new arm dedicated to helping clients embrace emerging technologies that are set to disrupt their strategies and operating models. Aimé Lachapelle, who is based in France, has been brought on board to lead the new offering. As global consulting firms bid to capitalise on a booming market to mine vast amounts of data to help shape strategy, Emerton has launched a new service to meet client requests for leveraging their data gluts. Emerton Data will serve clients globally, offering data analytics and artificial intelligence advice to businesses around the world. Emerton Data will be steered from Paris, where the competence hub is based.

Three guys from France recently used an open-source generative adverserial network (GAN) trained on public domain images to create a "painting." That painting today sold for nearly half a million dollars at one of the most prestigious auction houses in London. A couple weeks ago world-renowned artist and prankster Banksy pulled off a good one. He sold a painting at auction that shredded itself as soon as the winning bid was accepted. But that's nothing compared to duping someone out of $432K by selling them a painting that was generated by a neural network you found on Github. If you haven't heard its backstory already, you're in for a treat.

To improve customer experiences, brands are increasingly turning to data to give them insights and direction. Some of the benefits include helping brands personalize the customer experience. This creates a more enjoyable and memorable moment for customers. In return, they can become repeat customers. Plus, they may tell others about their (hopefully) great experience.

Convolutional Neural Networks (CNNs) are a cornerstone of the Deep Learning toolbox and have led to many breakthroughs in Artificial Intelligence. These networks have mostly been developed for regular Euclidean domains such as those supporting images, audio, or video. Because of their success, CNN-based methods are becoming increasingly popular in Cosmology. Cosmological data often comes as spherical maps, which make the use of the traditional CNNs more complicated. The commonly used pixelization scheme for spherical maps is the Hierarchical Equal Area isoLatitude Pixelisation (HEALPix). We present a spherical CNN for analysis of full and partial HEALPix maps, which we call DeepSphere. The spherical CNN is constructed by representing the sphere as a graph. Graphs are versatile data structures that can act as a discrete representation of a continuous manifold. Using the graph-based representation, we define many of the standard CNN operations, such as convolution and pooling. With filters restricted to being radial, our convolutions are equivariant to rotation on the sphere, and DeepSphere can be made invariant or equivariant to rotation. This way, DeepSphere is a special case of a graph CNN, tailored to the HEALPix sampling of the sphere. This approach is computationally more efficient than using spherical harmonics to perform convolutions. We demonstrate the method on a classification problem of weak lensing mass maps from two cosmological models and compare the performance of the CNN with that of two baseline classifiers. The results show that the performance of DeepSphere is always superior or equal to both of these baselines. For high noise levels and for data covering only a smaller fraction of the sphere, DeepSphere achieves typically 10% better classification accuracy than those baselines. Finally, we show how learned filters can be visualized to introspect the neural network.

With the advent of modern data collection and storage technologies, data-driven approaches have been developed for discovering the governing partial differential equations (PDE) of physical problems. However, in the extant works the model parameters in the equations are either assumed to be known or have a linear dependency. Therefore, most of the realistic physical processes cannot be identified with the current data-driven PDE discovery approaches. In this study, an innovative framework is developed that combines data-driven and data-assimilation methods for simultaneously identifying physical processes and inferring model parameters. Spatiotemporal measurement data are first divided into a training data set and a testing data set. Using the training data set, a data-driven method is developed to learn the governing equation of the considered physical problem by identifying the occurred (or dominated) processes and selecting the proper empirical model. Through introducing a prediction error of the learned governing equation for the testing data set, a data-assimilation method is devised to estimate the uncertain model parameters of the selected empirical model. For the contaminant transport problem investigated, the results demonstrate that the proposed method can adequately identify the considered physical processes via concurrently discovering the corresponding governing equations and inferring uncertain parameters of nonlinear models, even in the presence of measurement errors. This work helps to broaden the applicable area of the research of data driven discovery of governing equations of physical problems.

Here we demonstrate a new deep generative model for classification. We introduce `semi-unsupervised learning', a problem regime related to transfer learning and zero/few shot learning where, in the training data, some classes are sparsely labelled and others entirely unlabelled. Models able to learn from training data of this type are potentially of great use, as many medical datasets are `semi-unsupervised'. Our model demonstrates superior semi-unsupervised classification performance on MNIST to model M2 from Kingma and Welling (2014). We apply the model to human accelerometer data, performing activity classification and structure discovery on windows of time series data.

Graph neural networks (GNNs) have been shown to replicate convolutional neural networks' (CNNs) superior performance in many problems involving graphs. By replacing regular convolutions with linear shift-invariant graph filters (LSI-GFs), GNNs take into account the (irregular) structure of the graph and provide meaningful representations of network data. However, LSI-GFs fail to encode local nonlinear graph signal behavior, and so do regular activation functions, which are nonlinear but pointwise. To address this issue, we propose median activation functions with support on graph neighborhoods instead of individual nodes. A GNN architecture with a trainable multirresolution version of this activation function is then tested on synthetic and real-word datasets, where we show that median activation functions can improve GNN capacity with marginal increase in complexity.