The speed of change will determine how disruptive automation is to the future of work and society. How is your leadership preparing for the future of work? Will digitisation lead to more work or less? Of course we live and work in the digital age but the change we've experienced so far is only the tip of the iceberg. The potential impact of digitisation on employment not only concerns the explosive growth of intelligent robots, but also the significant influence that machine learning and artificial intelligence will have on our work.

There's a new chatbot in town, and it only wants to talk about mental health. Meet Woebot, now available to anyone via Facebook Messenger looking for some supportive talk to deal with anxiety or depression. Rather than augmenting a real therapist or even a non-clinical person, Woebot is wholly robotic; open to engage with an individual as often or as little as they want depending on their needs. San Francisco-based Woebot Labs created the tool –originally intended for college students but later expanded to all adults – based on cognitive behavioral therapy techniques. Anyone with Facebook Messenger can search for Woebot and begin sending messages, effectively bringing Woebot to life.

In April, United Airlines hit a huge pocket of public relations turbulence after a passenger was forcibly removed from one of its partners' airplanes. The incident raised questions about blindly following procedures, passenger rights, and United's executive leadership. Here's another question it raised: Could artificial intelligence (AI) have prevented the embarrassing drama from even happening? Get the latest insights with our CIO Daily newsletter. AI and machine learning are already impacting many areas of business, such as marketing, as well as most industries, including retail.

The terrifying faces may look like creatures from a horror movie, but these digital images were actually generated by artificial intelligence (AI). Pix2pix project has unleashed a new tool that analyzes portraits and fills them in with colors and textures using a technique called generative adversarial networks (GANs). During the process, the system determines if its result match the sketch and will keep repeating the generation process until its own passes as'real' – regardless of how nightmarish the results may look. The terrifying faces may look like creatures from a horror movie, but these digital images were actually generated by artificial intelligence (AI). Users are presented with an input box and an output box and are prompted to draw a face in input, select process and in seconds, the AI will reveal its version of the sketch.

Astronomical image observation is plagued by the fact the the observed image is the result of a convolution between the observed object and what the astronomers call a Point Spread Function (PSF) [1] [2]. In addition to the convolution the image is also polluted by noise that is due to the low energy of the observed objects (photon noise) or to the sensor. The PSF is usually known a priori, thanks to a physical model for the telescope of estimation from known objects. State of the art approaches in astronomical image reconstruction aim at solving an optimization problem that encodes both a data fitting (with observation and PSF) and a regularization term that promote wanted properties in the images [1], [3], [4]. Still, solving a large optimization problem for each new image can be costly and might not be practical in the future. Indeed in the coming years several new generations of instruments such as the Square kilometer Array [5] will provide very large images (both in spatial and spectral dimensions) that will need to be processed efficiently. The most successful image reconstruction approaches rely on convex optimization [3], [4], [6] and are all based on gradient [7] or proximal splitting gradient descent [8]. Interestingly those methods have typically a linear convergence, meaning that the number of iterations necessary to reach a given precision is proportional to the dimension n of the problem [9], where n is the number of pixels.

We consider a novel formulation of the multi-armed bandit model, which we call the contextual bandit with restricted context, where only a limited number of features can be accessed by the learner at every iteration. This novel formulation is motivated by different online problems arising in clinical trials, recommender systems and attention modeling. Herein, we adapt the standard multi-armed bandit algorithm known as Thompson Sampling to take advantage of our restricted context setting, and propose two novel algorithms, called the Thompson Sampling with Restricted Context(TSRC) and the Windows Thompson Sampling with Restricted Context(WTSRC), for handling stationary and nonstationary environments, respectively. Our empirical results demonstrate advantages of the proposed approaches on several real-life datasets

A Monte Carlo algorithm typically simulates some prescribed number of samples, taking some random real time to complete the computations necessary. This work considers the converse: to impose a real-time budget on the computation, so that the number of samples simulated is random. To complicate matters, the real time taken for each simulation may depend on the sample produced, so that the samples themselves are not independent of their number, and a length bias with respect to compute time is apparent. This is especially problematic when a Markov chain Monte Carlo (MCMC) algorithm is used and the final state of the Markov chain---rather than an average over all states---is required. The length bias does not diminish with the compute budget in this case. It occurs, for example, in sequential Monte Carlo (SMC) algorithms. We propose an anytime framework to address the concern, using a continuous-time Markov jump process to study the progress of the computation in real time. We show that the length bias can be eliminated for any MCMC algorithm by using a multiple chain construction. The utility of this construction is demonstrated on a large-scale SMC-squared implementation, using four billion particles distributed across a cluster of 128 graphics processing units on the Amazon EC2 service. The anytime framework imposes a real-time budget on the MCMC move steps within SMC-squared, ensuring that all processors are simultaneously ready for the resampling step, demonstrably reducing wait times and providing substantial control over the total compute budget.

The multiplicative update (MU) algorithm has been extensively used to estimate the basis and coefficient matrices in nonnegative matrix factorization (NMF) problems under a wide range of divergences and regularizers. However, theoretical convergence guarantees have only been derived for a few special divergences without regularization. In this work, we provide a conceptually simple, self-contained, and unified proof for the convergence of the MU algorithm applied on NMF with a wide range of divergences and regularizers. Our main result shows the sequence of iterates (i.e., pairs of basis and coefficient matrices) produced by the MU algorithm converges to the set of stationary points of the non-convex NMF optimization problem. Our proof strategy has the potential to open up new avenues for analyzing similar problems in machine learning and signal processing.

In 2011, scientists made one of the most important discoveries in the history of AI development. They found that graphics processing units (GPUs) are far better at simulating biological learning than central processing units (CPUs). In retrospect, it seems obvious. Human brains are much more like GPUs than CPUs. Both brains and GPUs rely on parallel processing that simulates and predicts real world physics. In light of this, AI developers created powerful deep neural networks (DNNs) that emulate human brain function.

Uber has fired more than 20 people after an internal investigation into harassment claims. The taxi-app firm has been under fire over its treatment of women in the workplace since a former employee wrote a scathing blog post about her experience. The post prompted the company to launch two investigations. Uber said the sackings related to sexual harassment, bullying and issues about poor company culture. Uber has struggled with a series of controversies in recent months, including a backlash over aggressive corporate tactics and a lawsuit from Google-owner Alphabet over allegedly stolen technology for self-driving cars.