Their Head of EMEA Phil Cox gives us his top predictions for the year ahead.

Google CEO Sundar Pichai, speaking at a taped television event hosted by MSNBC and The Verge's sister site Recode, said artificial intelligence is one of the most profound things that humanity is working on right now and compared it to basic utilities in terms of its importance. Speaking to Recode's Kara Swisher and MSNBC's Ari Melber, Pichai said AI is "one of the most important things that humanity is working on. It's more profound than, I don't know, electricity or fire," adding that people learned to harness fire for the benefits of humanity, but also needed to overcome its downsides, too. Pichai also said that AI could be used to help solve climate change issues, or to cure cancer. The remarks from the chief executive of Google, which is largely perceived as one of the world leaders in the development of artificial intelligence, came after Swisher asked repeatedly about AI's impact on jobs and observed that Silicon Valley tends to have a "shiny happy future" outlook about disruptive technologies.

We have seen how the internet of things and the growing phenomenon of'big data' will throw up major problems for consumers and citizens, problems that have as yet barely been grasped by most policy-makers. In this world of growing complexity, the potential for an unintended consequence becomes greater and greater from machines performing an action that was not anticipated. There are key issues, too, about our reliance on data at a time of massive data generation, data storing and data preservation which have the potential to both obscure results and generate injustices. Perhaps the greatest issue that we now face is caused by our blind faith in machines. We have invested them with certainty and – as we have pointed out – we trust them. Part of the reason for this is an odd confusion that has conflated the machines of the industrial age with the machines of the information age.

Data association, the reasoning over correspondence between targets and measurements, is a problem of fundamental importance in target tracking. Recently, belief propagation (BP) has emerged as a promising method for estimating the marginal probabilities of measurement to target association, providing fast, accurate estimates. The excellent performance of BP in the particular formulation used may be attributed to the convexity of the underlying free energy which it implicitly optimises. This paper studies multiple scan data association problems, i.e., problems that reason over correspondence between targets and several sets of measurements, which may correspond to different sensors or different time steps. We find that the multiple scan extension of the single scan BP formulation is non-convex and demonstrate the undesirable behaviour that can result. A convex free energy is constructed using the recently proposed fractional free energy (FFE). A convergent, BP-like algorithm is provided for the single scan FFE, and employed in optimising the multiple scan free energy using primal-dual coordinate ascent. Finally, based on a variational interpretation of joint probabilistic data association (JPDA), we develop a sequential variant of the algorithm that is similar to JPDA, but retains consistency constraints from prior scans. The performance of the proposed methods is demonstrated on a bearings only target localisation problem.

Consider a structured matrix factorization model where one factor is restricted to have its columns lying in the unit simplex. This simplex-structured matrix factorization (SSMF) model and the associated factorization techniques have spurred much interest in research topics over different areas, such as hyperspectral unmixing in remote sensing, topic discovery in machine learning, to name a few. In this paper we develop a new theoretical SSMF framework whose idea is to study a maximum volume ellipsoid inscribed in the convex hull of the data points. This maximum volume inscribed ellipsoid (MVIE) idea has not been attempted in prior literature, and we show a sufficient condition under which the MVIE framework guarantees exact recovery of the factors. The sufficient recovery condition we show for MVIE is much more relaxed than that of separable non-negative matrix factorization (or pure-pixel search); coincidentally it is also identical to that of minimum volume enclosing simplex, which is known to be a powerful SSMF framework for non-separable problem instances. We also show that MVIE can be practically implemented by performing facet enumeration and then by solving a convex optimization problem. The potential of the MVIE framework is illustrated by numerical results.

Machine learning is crucial to the next industrial revolution. As equipment and supply chains join the Industrial IoT (IIoT), the flood of data can overwhelm already-busy human supervisors--creating an urgent need for self-regulating automation. Each generating facility contains a complex, interdependent ecosystem of equipment and infrastructure. Collectively, these systems generate enormous amounts of data--up to 1.5TB per day. These staggering volumes of data exceed human capabilities but fit neatly into the wheelhouse of machine learning.

Its main aim is to show the growing importance of robots and how they can improve people's work and daily lives.

Label Maker is a python library to help in extracting insight from satellite imagery. Label Maker creates machine-learning-ready training data for most popular ML frameworks, including Keras, Tensor Flow, and MXNet. It pulls data from OpenStreetMap and combines that with imagery sources like Mapbox or Digital Globe to create a single file for use in training machine learning algorithms. Supervised learning methods require two things: satellite imagery and ground-truth labels. If you're looking to train a model in Potsdam or a few other select cities, there are good datasets already available.

We are interested in the development of surrogate models for uncertainty quantification and propagation in problems governed by stochastic PDEs using a deep convolutional encoder-decoder network in a similar fashion to approaches considered in deep learning for image-to-image regression tasks. Since normal neural networks are data intensive and cannot provide predictive uncertainty, we propose a Bayesian approach to convolutional neural nets. A recently introduced variational gradient descent algorithm based on Stein's method is scaled to deep convolutional networks to perform approximate Bayesian inference on millions of uncertain network parameters. This approach achieves state of the art performance in terms of predictive accuracy and uncertainty quantification in comparison to other approaches in Bayesian neural networks as well as techniques that include Gaussian processes and ensemble methods even when the training data size is relatively small. To evaluate the performance of this approach, we consider standard uncertainty quantification benchmark problems including flow in heterogeneous media defined in terms of limited data-driven permeability realizations. The performance of the surrogate model developed is very good even though there is no underlying structure shared between the input (permeability) and output (flow/pressure) fields as is often the case in the image-to-image regression models used in computer vision problems. Studies are performed with an underlying stochastic input dimensionality up to $4,225$ where most other uncertainty quantification methods fail. Uncertainty propagation tasks are considered and the predictive output Bayesian statistics are compared to those obtained with Monte Carlo estimates.

Google CEO Sundar Pichai, speaking at a taped television event hosted by MSNBC and The Verge's sister site Recode, said artificial intelligence is one of the most profound things that humanity is working on right now and compared it to basic utilities in terms of its importance. Speaking to Recode's Kara Swisher and MSNBC's Ari Melber, Pichai said AI is "one of the most important things that humanity is working on. It's more profound than, I don't know, electricity or fire," adding that people learned to harness fire for the benefits of humanity, but also needed to overcome its downsides, too. Pichai also said that AI could be used to help solve climate change issues, or to cure cancer. The remarks from the chief executive of Google, which is largely perceived as one of the world leaders in the development of artificial intelligence, came after Swisher asked repeatedly about AI's impact on jobs and observed that Silicon Valley tends to have a "shiny happy future" outlook about disruptive technologies.