Google has announced a new strategic partnership with SAP, focused on developing and integrating Google's best cloud and machine learning solutions with SAP enterprise applications. The partnership will enable certification of the in-memory database SAP HANA on Google Cloud Platform(GCP), new G Suite integrations, Google's machine learning capabilities, and data governance collaboration, offers Google Cloud, SAP and customers more scope, scalability, and the exciting opportunity to create new products. SAP has a long history at the center of business and technology. With this partnership Google is offering SAP customers the opportunity to leverage Google Cloud to accelerate their digital transformation and convert data into actionable insights and business outcomes. The SAP HANA database is now certified on GCP.

Self-Driving Trucks -Continual developments are making long-haul trucks that drive themselves for extended stretches on highways more attainable. Significant challenges remain, including having sensors and code match the situational awareness of a professional trucker. In the short term, this technology may free truck drivers to complete routes more efficiently, but it could also erode their pay and eventually replace many of them altogether. Key companies to watch in this area include Daimler, Otto, Peterbilt, and Volvo. Availability is prediction to be in 5 to 10 years.

This white paper discusses the advantages of using the PySpark API, which enables the use of Python to interact with the Spark programming model. It starts with a basic description of Spark and then describes PySpark, its benefits, and when it is appropriate to use instead of "pandas" open source...

Machine Learning is the new buzz word and AI is the slang word these days. What does happen in this exiting field in Europe? Is AI common ground for all businesses or the exclusive territory for a few? Who has managed to validate a business model for autonomous vehicles or chatbots?Whatdoesdata-drivenor API-firstbusinessmodelslook like? With this report we want to provide a comprehensive review of investment in startups and high-growth AI and Data Analytics companies across 22 countries in Europe.

Featured will be 24-hour content from our annual CX conference, SCORE, as well as a handful of live webcasts--serving CX professionals responsible for driving superior customer experiences with innovative, yet proven, strategies. Learn how to differentiate your brand with a fast, simple purchasing process saving your customers time and effort. You'll transform your buying experience to create loyal, raving fans, while empowering your reps to spend more time selling. Reach people on the phone, via live chat, email, through social media, and even in person. Use visitor tracking and email analytics to know what your customers are seeing.

It's a total understatement to say that the growing presence of machine learning in games is big news. The technology promises to turn the industry topsy-turvy by changing our perception of what's possible in a gaming experience. "You can actually tailor game design and tailor levels to an individual's experience," George Dolbier, the CTO of Interactive Media at IBM, said at a talk at the Intel Buzz Workshop in Seattle last June. More interesting for us laymen, it's being used to create progressional curves that tailor themselves directly to each player according to their individual behavioral data. A simple example would be in a Tetris-like casual game where a player would first go through a few "seed" rounds of gameplay so that the machine learning element can first learn about how they play before it creates a customized roadmap that will ensure maximum engagement.

Sign up for our newsletter to not miss out on tomorrow's game-changers for your industry. At Mobile World Congress this year, I got to moderate the most interesting panel of the whole show. Okay, I may be a bit biased as I created the idea of the panel The unreal reality: what is real when AI, VR and AR are mainstream? We kept circling back to human kind's seemingly inherent distrust of Artificial Intelligence (AI). But I think my colleague Manoj P M had a great point: "People think AI will be a replacement for humans, but actually it won't," he said.

A key challenge in scaling up robot learning to many skills and environments is removing the need for human supervision, so that robots can collect their own data and improve their own performance without being limited by the cost of requesting human feedback. Model-based reinforcement learning holds the promise of enabling an agent to learn to predict the effects of its actions, which could provide flexible predictive models for a wide range of tasks and environments, without detailed human supervision. We develop a method for combining deep action-conditioned video prediction models with model-predictive control that uses entirely unlabeled training data. Our approach does not require a calibrated camera, an instrumented training set-up, nor precise sensing and actuation. Our results show that our method enables a real robot to perform nonprehensile manipulation -- pushing objects -- and can handle novel objects not seen during training.

In this paper we look into the problem of planning over hybrid domains, where change can be both discrete and instantaneous, or continuous over time. In addition, it is required that each state on the trajectory induced by the execution of plans complies with a given set of global constraints. We approach the computation of plans for such domains as the problem of searching over a deterministic state model. In this model, some of the successor states are obtained by solving numerically the so-called initial value problem over a set of ordinary differential equations (ODE) given by the current plan prefix. These equations hold over time intervals whose duration is determined dynamically, according to whether zero crossing events take place for a set of invariant conditions. The resulting planner, FS+, incorporates these features together with effective heuristic guidance. FS+ does not impose any of the syntactic restrictions on process effects often found on the existing literature on Hybrid Planning. A key concept of our approach is that a clear separation is struck between planning and simulation time steps. The former is the time allowed to observe the evolution of a given dynamical system before committing to a future course of action, whilst the later is part of the model of the environment. FS+ is shown to be a robust planner over a diverse set of hybrid domains, taken from the existing literature on hybrid planning and systems.

A novel deep learning architecture (XmasNet) based on convolutional neural networks was developed for the classification of prostate cancer lesions, using the 3D multiparametric MRI data provided by the PROSTATEx challenge. End-to-end training was performed for XmasNet, with data augmentation done through 3D rotation and slicing, in order to incorporate the 3D information of the lesion. XmasNet outperformed traditional machine learning models based on engineered features, for both train and test data. For the test data, XmasNet outperformed 69 methods from 33 participating groups and achieved the second highest AUC (0.84) in the PROSTATEx challenge. This study shows the great potential of deep learning for cancer imaging.