So when the three co-founders of Dubai-based Derq, a traffic-safety startup, need to get to the carmaking capital of the U.S., they take a connecting flight on Air France through Paris or on Emirates through Boston. That typically means a four-leg, 32-hour round-trip for at least one of them once a month. It would be unthinkable for the startup, which uses artificial intelligence to predict and prevent car accidents, not to have a presence in the Motor City. So after securing a $1.5 million round of funding in October, the company opened a satellite office in Detroit. But although two of Derq's three co-founders were educated in the U.S., they aren't interested in basing their whole operation there.

The company is an early mover in its industry in using high-performance computing, and in 2014 it won an award from the HPC community's leading publication, HPCwire, for the best use of the tech in the oil and gas industry. "NVIDIA's Tesla GPU-accelerated computing platforms have been instrumental in supporting Eni's exploration activity, improving our ability to turn around advanced seismic imaging tasks in a shorter time and with a higher accuracy," an NVIDIA blog quoted Luca Bertelli, Eni's chief exploration officer, as saying.

FRANKFURT (Reuters) - Utilities can increase their efficiency by using more artificial intelligence (AI) technology, such as software to predict demand swings in the power grid or to control home appliances, consultancy Roland Berger said. European utilities could achieve efficiency gains of up to a fifth over the next five years using such technology, it said, adding that less than a quarter of firms had a strategy to do this. Power firms across Europe, which previously depended on coal or gas-fired power plants, are having to adapt to the expanding use of renewable power sources and facing a profit squeeze as wholesale electricity prices have fallen. "Companies need to respond to this change and come up with new business models," Torsten Henzelmann, partner at Roland Berger, said. "To do that they need new technologies such as artificial intelligence."

The rise of renewables, such as solar and wind that provide intermittent supply, has increased the need for intelligent IT systems to balance demand and supply swings as companies seek to meet energy and carbon emissions targets, the consultancy said.

IoT (Internet of Things), the convergence of IT and OT, rapid application development, digital twin simulation models, cyber-physical systems, advanced robots and cobots, additive manufacturing, autonomous production, consistent engineering across the entire value chain, thorough data collection and provisioning, horizontal and vertical integration, the cloud, big data analytics, virtual/augmented reality and edge computing amidst a shift of intelligence towards the edge (artificial intelligence indeed): these are some of the essential technological components of the fourth industrial revolution. Those are quite a lot of terms and components indeed. Yet, Industry 4.0 is a rather vast vision and, increasingly, vast reality that also stretches beyond merely these technological aspects. It is an end-to-end industrial transformation. What makes it all the more fascinating (and at first sight complex) is that convergence of two worlds which have been disconnected thus far: Information ...

The Future Today Institute's 11th annual Tech Trends report identifies 225 tantalizing advancements in emerging technologies--artificial intelligence, biotech, autonomous robots, green energy, smart farms, and space travel--that will begin to enter the mainstream and fundamentally disrupt business, geopolitics and everyday life around the world. Our annual report has garnered more than 6 million cumulative views, and this edition is our largest to date. If the above link is temporarily down due to heavy traffic, try this mirror link.

"To me, it's not just about automating the rig, it's about automating everything upstream of the rig," says Ahmed Hashmi, head of upstream technology for BP Plc." Using deep learning to listen for early warning signs that a car might be nearing a breakdown. "Jeff Dean, who leads the Google Brain research group, mused last week that some of the work of such workers could be supplanted by software. He described what he termed "automated machine learning" as one of the most promising research avenues his team was exploring." Andrew Ng demonstrates Baidu's new office entrance!

Learn how data scientists from leading companies like Shell successfully solved big data challenges with Spark and Databricks. Follow their journeys as they deployed their cloud environment, streamlined data management, trained machine learning models at scale, and extracted insights from the data. Apache, Apache Spark, Spark and the Spark logo are trademarks of the Apache Software Foundation.

One June day in Virginia last year, an airplane was grounded by an unlikely adversary: a large swarm of bees. The peculiar story made for great newspaper headlines and serves as a reminder that even with the best technology and planning, some things are truly unexpected. But fortunately, most aircraft delays are caused by far more predictable issues than an unwelcome swarm of bees nesting in a turbine. Airlines, like most asset-intensive businesses, are getting increasingly better at predicting failures and anticipating maintenance problems. Rather than keeping planes grounded for costly and annoying last-minute maintenance -- or, worse, exposing passengers to the risk of flying on a faulty aircraft -- airlines are investing in cutting-edge technology that detects potential problems before they arise.

Environmental monitoring allows authorities to understand the impact of potentially harmful phenomena, such as air pollution, excessive noise, and radiation. Recently, there has been considerable interest in participatory sensing as a paradigm for such large-scale data collection because it is cost-effective and able to capture more fine-grained data than traditional approaches that use stationary sensors scattered in cities. In this approach, ordinary citizens (non-expert contributors) collect environmental data using low-cost mobile devices. However, these participants are generally self-interested actors that have their own goals and make local decisions about when and where to take measurements. This can lead to highly inefficient outcomes, where observations are either taken redundantly or do not provide sufficient information about key areas of interest. To address these challenges, it is necessary to guide and to coordinate participants, so they take measurements when it is most informative. To this end, we develop a computationally-efficient coordination algorithm (adaptive Best-Match) that suggests to users when and where to take measurements. Our algorithm exploits probabilistic knowledge of human mobility patterns, but explicitly considers the uncertainty of these patterns and the potential unwillingness of people to take measurements when requested to do so. In particular, our algorithm uses a local search technique, clustering and random simulations to map participants to measurements that need to be taken in space and time. We empirically evaluate our algorithm on a real-world human mobility and air quality dataset and show that it outperforms the current state of the art by up to 24% in terms of utility gained.