Researchers at the Universities of Vienna and Göttingen have succeeded in developing a method for predicting molecular infrared spectra based on artificial intelligence. These chemical "fingerprints" could only be simulated by common prediction techniques for small molecules in high quality. With the help of the new technology, which is based on neuronal networks similar to the human brain and is therefore capable of learning, the team led by Philipp Marquetand from the Faculty of Chemistry at the University of Vienna was able to carry out simulations that were previously not possible. The potential of this new strategy has now been published in the current issue of the journal "Chemical Science". Drastic advances in research of artificial intelligence have led to a wide range of fascinating developments in this area over the last decade.

Jonah Gabry returned from teaching a one-week course for a special EU research institute in Spain. Mitzi Morris has been knocking out bug fixes for the parser and some pull requests to refactor the underlying type inference to clear the way for tuples, sparse matrices, and higher-order functions. Michael Betancourt with help from Sean Talts spent last week teaching an intro course to physicists about Stan. Charles Margossian attended and said it went really well. Ben Goodrich, in addition to handling a slew of RStan issues has been diving into the math library to define derivatives for Bessel functions. Aki Vehtari has put us in touch with the MxNet developers at Amazon UK and we had our first conference call with them to talk about adding sparse matrix functionality to Stan (Neil Lawrence is working there now).

An Ambient Intelligence revolution is coming. In the past ten years, we've witnessed an astonishing mobile computing revolution. Smartphone installation base is expected to reach 6 billion by 2020, according to research firm IHS Markit. Over half of the world's population are carrying personal devices that have powerful processors, sensors, cameras, high speed communications and intelligent applications. In the next ten years, an Ambient Intelligence (AmI) revolution is coming where all the above technologies will be embedded in our homes, grocery stores, offices, hospitals and transportation services. AmI will be one of the key elements of the Fourth Industrial Revolution.

ISTANBUL, TURKEY - MAY 06: The'Archive Dreaming' installation by artist Refik Anadol uses artificial intelligence to visualize nearly 2 million historical Ottoman documents and photographs from the SALT Research Archive. Luxury fashion has often been synonymous with unparalleled quality and exclusivity. However, the sobering reality is that many high-end brands have fallen behind in the age of the digital revolution and e-commerce. This begs the question: how can luxury brands make up for lost ground and deliver the experiences needed to get ahead of the game? The answer may not come from the fashion capitals London, Paris or Milan, but from technology capitals like Silicon Valley where the "next big thing" is Artificial Intelligence (AI), technologies capable of performing tasks normally requiring human intelligence.

We present a Bayesian extension to convolution processes which defines a representation between multiple functions by an embedding in a shared latent space. The proposed model allows for both arbitrary alignments of the inputs and and also non-parametric output warpings to transform the observations. This gives rise to multiple deep Gaussian process models connected via latent generating processes. We derive an efficient variational approximation based on nested variational compression and show how the model can be used to extract shared information between dependent time series, recovering an interpretable functional decomposition of the learning problem.

In order to reason about probabilistic knowledge, we must reason about time and actions as well. When we say, for example, that "the probability of'heads' after a coin toss is 50% and that of'tails' is 50%", we implicitly assume that there is an action (in this example, tossing a coin) which can bring the world to different states in the next moment in time. The uncertainty lies in the state transition: the world may end up in a state where the coin shows heads or in a state where it shows tails. Despite the evident dependence of our informal notion of probability on the notions of action and time, the formal mathematical languages that we use to talk about probabilities rarely support mentioning action and time explicitly. Kolmogorov's probability theory, for example, merely defines probability as the measure function in a measure space with total measure 1 [9]. The task of modeling time-dependent actions and their possible outcomes in terms of events in a probabilistic space remains informal. While this informality is not problematic in the simplest situations (e.g. when we are interested in the possible outcomes of a single action, or when multiple actions are independent of each other), slightly more complex situations may already lead to confusion and difficulty. A famous example is the Monty Hall problem [10]. Another inconvenience of dealing with probabilities just in terms of a measure space is that its set-theoretic language (where events are represented as subsets of the sample space) is rather limited.

In May of 2017, Gartner released "Market Guide for Telecom Expense Management Services, 2017". In this guide, Amalgam believes that eight key vendors were overlooked that can provide enterprise-grade services and represent billions of dollars in technology spend including: * Asignet - A pioneer in Robotic Process Automation * Ezwim - The largest standalone TEM in Europe * GSG - A SaaS solution partnering with IBM, Unisys, and Verizon * ICOMM - A Fortune 1000-focused vendor with 100% referenceable clients * Mobichord - A fast-growing technology management platform built on ServiceNow * Netplus - A commercial and government-focused vendor with over 4 million lines under management * Smartbill - The largest Australian provider with over 500,000 items under management * Vcom - One of the Bay Area's best rated places to work with spend management expanding to IoT, data center, & SD-WAN

Back in November, Google showcased a few of its funky machine learning experiments, and among them was Quick, Draw! (their bang, not mine) -- a game where you sketch something and an image recognition system guesses what it is. Now the company is releasing the millions upon millions of sketches players submitted as an open data set for AI developers to play with. Now, if the prospect of browsing through a bunch (I'm talking 50 million here) of terrible drawings of hats, shoes, and cats doesn't sound like fun to you, don't worry. Those drawings came from lots of different countries, and it's fun to check out how differently, say, Germany and Korea think of cats. Well look, there are patterns in there worth sussing out.

Computations requiring high performance computing (HPC) power may soon be done in the palm of your hand thanks to work done this summer by IBM Research in Dublin, Ireland. While scientists have come a long away in teaching machines how to process images for facial recognition and understand language to translate texts, IBM researchers focused on a different problem: how to use artificial intelligence (AI) techniques to forecast a physical process. In this case, the focus was on ocean waves, using traditional physics-based models driven by external forces, such as the rise and fall of tides, winds blowing in different directions, the depth and physical properties of water influence the speed and height of the waves. HPC is normally essential to resolve the differential equations that encapsulate these physical processes and their relationships, and the expense often limits the spatial resolution, physical processes and time-scales that can be investigated by a real-time forecasting platform. In an interview with EE Times, IBM Research Senior Research Manager Sean McKenna said an HPC cluster using Big Iron has generally been the solution to dealing with the heavy computational load.

It could be the answer to one of life's most pressing problems. Two young inventors have created a £699 ($925) robotic ironing machine to make short work of crumpled shirts, blouses, trousers and even underwear. The device, named'Effie', can dry and iron 12 separate pieces of clothing at once, which its inventors claim cuts ironing time by 95 per cent. To iron a shirt, you first hang it up in the machine's cabinet on adjustable hangers. The doors close, and it then an internal steam iron presses the shirt – taking out any creases.