New York is always an exciting, energetic city to visit and ... was made even more so as I attended the (recent) 'HPC & AI on Wall Street' conference, which HPCWire are now championing. It was well worth the train ride from Boston and interesting to see the varied mix of attendees present and hear how HPC and AI is evolving in the world of finance. All sectors of the financial services industry (FSI) were represented at the conference from traditional, old school banks to venture capital firms, fintechs and hedge funds. AI adoptions varied from integrating machine learning into existing applications and processes to mining unearthly amounts of market data for a trading edge. My first lesson was there is a dichotomy between too much data volume and insufficient data context within this community. Don't worry the bank account and market value data is as solid as always.

Scientists can now weigh wild whales using remote-controlled drones hovering above them. In the past it has only been possible to measure the weight of free-living whales if they were dead and out of the water or stranded on a beach. But researchers have released aerial footage of them flying a drone over a mother and baby southern right whale, near Argentina, to work out how much they weigh. They can do this by taking photographs to work out the length, width and height of the animal. This can then be compared to past measurements of the density of that particular species of whale's body, to estimate its volume and weight.

The global AIaaS market is predicted to gather pace in terms of growth as it registers a healthy CAGR during the forecast period 2018-2026. Well-established tech companies such as Microsoft, IBM, and Google offer AI services alongside their technology services in order to stay competitive in the industry. They realize that offering AI tools and software is not enough to maintain their supremacy in the technology world. Other software or professional service firms may partner with AI solutions providers to expand their customer base. In this regard, it could be said that the demand in the global AIaaS market will increase in the next few years.

If there is one thing the general public is familiar with when the use of artificial intelligence than it is facial recognition. Whether it is opening their mobile phone or the algorithms Facebook uses to find eyes or other parts of a face in images, facial recognition has become a standard. But now scientists dealing with complex questions like the composition of the universe are starting to use a modified version of the'standard' facial recognition in an attempt to discover how much of the dark matter there is in the universe and where it is possibly located. As Digital Trends and Futurity note in their reports on the subject, "physicists believe that understanding this mysterious substance is necessary to explain fundamental questions about the underlying structure of the universe." It is the researchers gathered in Alexandre Refregier's group at the Institute of Particle Physics and Astrophysics at ETH Zurich, Switzerland that has started to use deep neural network methods that lie behind facial recognition to develop new, special tools to attempt to discover what is still a secret of the universe for us. As Janis Fluri, one of the researchers working on the project told Digital Trends, "The algorithm we [use] is very close to what is commonly used in facial recognition," adding that"the beauty of A.I. is that it can learn from basically any data.

In Part 1 of a 3-part series, Marcell Vollmer, chief digital officer at SAP Ariba, shares his take on where digital procurement is headed. What is the role of e-procurement in B2B e-commerce and what is the role of the chief procurement officer? Vollmer: Procurement has earned an unfair reputation of being a complicated process, but let's break it down simply here. Procurement's role in B2B e-commerce is to streamline the purchasing process and ensure that businesses get the right goods and services that meet all of their specifications, from a corporate-approved vendor, for the right price. Call this the golden triangle of procurement.

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Six of us are huddled together in Cavendish Square Gardens in central London, fighting a horde of warrior skeletons. To passersby, however, we must look like a bunch of adults pointing our smartphones at nothing while shouting about incoming monsters. What we're doing is playing a beta version of Minecraft Earth, an augmented reality (AR) spinoff from the multimillion-selling block-building game – and very soon, parks all over the world will be filled with people just like us. This month, Minecraft is launching an early-access version of the game in a select few territories around the world, ahead of a global roll-out. Microsoft has yet to reveal exactly when and where, but soon thousands of fans used to playing on their console, PC or tablet, are going to be taking their creations to the streets.

Big changes are happening in the business world and one of these great shifts is directly owing to the contribution of machine learning (ML). The grid of algorithms and statistical models is a revolutionary application of AI. The technology has the ability to learn automatically and bring about changes and improvements from experiences. The self-learning capacity of ML makes it an important ingredient of businesses nowadays. It is used to solve varied problems in an organization and veers it towards the high-paced world of transformation. Machine learning drives the innovative phenomenon of a company to make it excel in an arena of hyper-converged data, mediums, content, and technology.

We present a proxy dataset of vital signs with class labels indicating patient transitions from the ward to intensive care units called Ward2ICU. Patient privacy is protected using a Wasserstein Generative Adversarial Network to implicitly learn an approximation of the data distribution, allowing us to sample synthetic data. The quality of data generation is assessed directly on the binary classification task by comparing specificity and sensitivity of an LSTM classifier on proxy and original datasets. We initialize a discussion of unintentionally disclosing commercial sensitive information and propose a solution for a special case through class label balancing

For effectively interacting with humans in collaborative environments, machines need to be able anticipate future events, in order to execute actions in a timely manner. However, the observation of the human limbs movements may not be sufficient to anticipate their actions in an unambiguous manner. In this work we consider two additional sources of information (i.e. context) over time, gaze movements and object information, and study how these additional contextual cues improve the action anticipation performance. We address action anticipation as a classification task, where the model takes the available information as the input, and predicts the most likely action. We propose to use the uncertainty about each prediction as an online decision-making criterion for action anticipation. Uncertainty is modeled as a stochastic process applied to a time-based neural network architecture, which improves the conventional class-likelihood (i.e. deterministic) criterion. The main contributions of this paper are three-fold: (i) we propose a deep architecture that outperforms previous results in the action anticipation task; (ii) we show that contextual information is important do disambiguate the interpretation of similar actions; (iii) we propose the minimization of uncertainty as a more effective criterion for action anticipation, when compared with the maximization of class probability. Our results on the Acticipate dataset showed the importance of contextual information and the uncertainty criterion for action anticipation. We achieve an average accuracy of 98.75% in the anticipation task using only an average of 25% of observations. In addition, considering that a good anticipation model should also perform well in the action recognition task, we achieve an average accuracy of 100% in action recognition on the Acticipate dataset, when the entire observation set is used.