In the low oil price environment, oil and gas operators need to reduce costs and boost operational efficiency through the efficient and effective use of data. Companies are investing in predictive technology to become more productive – and to be ready for the inevitable increase in barrel prices. Artificial Intelligence and Machine Learning have enabled operators to augment human capabilities – to automate processes and gain previously unobtainable outcomes. With massive amounts of computational power, machines can now analyze large sets of data points and apply relationship modeling in a predictive way and in real time. Big Data technology has the potential to leverage machine learning capabilities enabling accurate and real time decision making improving overall operating efficiency and reducing unnecessary cost.

Meetup's Machine Learning and Data Engineering team is pleased to announce we're sponsoringMLConf NYC2016.In the spirit of MLConf, we'll be hosting a pre-conference meetup whereData Scientists and Machine Learning Engineering experts will speak on topics related to Applied Machine Learning. Come talk tech and mingle over food and drinks with this community! You don't have to be a conference attendee to join us -- we look forward to seeing you soon! Try this 18% discount on us!

New managed legal services, technology and advisory firm Ethien has hired Osborne Clarke consultant and former Freshfields Bruckhaus Deringer partner Paul Taylor as its head of business solutions as it also announces a new strategic partnership with RAVN Systems. Taylor, who joined OC as a partner in its digital business team in 2007, will work with Ethien's law firm and in-house legal clients to develop solutions that help improve quality and efficiency and reduce risk within legal services delivery. He will also act as Ethien's general counsel. For the past three years Taylor has worked on service delivery improvement within OC, designing many of the solutions developed for the firm's clients. This involved the development of integrated solutions allowing OC's lawyers to collaborate directly with in-house counsel on a common work platform.

Meetup's Machine Learning and Data Engineering team is pleased to announce we're sponsoringMLConf NYC2016.In the spirit of MLConf, we'll be hosting a pre-conference meetup whereData Scientists and Machine Learning Engineering experts will speak on topics related to Applied Machine Learning. Come talk tech and mingle over food and drinks with this community! You don't have to be a conference attendee to join us -- we look forward to seeing you soon! Try this 18% discount on us!

The last place you want to learn you have heart failure is where it often winds up being diagnosed: in the emergency room. Researchers analyzing electronic health records are using artificial intelligence and GPUs to get ahead of this curve. They've shown they can predict heart failure as much as nine months before doctors can now deliver the diagnosis. A research team from Sutter Health, a Northern California not-for-profit health system, and the Georgia Institute of Technology, believe their method has the potential to reduce heart failure rates and possibly save lives. "The earlier we can detect the disease, the more likely we can change health outcomes for people and improve their quality of life," said Andy Schuetz, a senior data scientist at Sutter Health and an author of a paper on the research.

DigitalGenius is announcing its Human AI customer service platform today, along with a 4.1 million seed investment. Machine learning is an approach or set of techniques where you use massive data sets to train machines in semi-supervised ways…Deep learning is a step below machine learning in the tree. The company includes what they call a "confidence threshold" in their AI/customer interactions, and if it drops below a certain point, a human customer service agent steps in or approves the messaging. This follows a growing trend in the industry, as rapid evolution in NLP (natural language processing) and machine learning techniques like deep learning are pushing the technology forward.

Though foreign companies invested primarily in production/manufacturing operations in France, last year saw many groundbreaking transactions for U.S. businesses in the French tech sector. First, Facebook made the monumental decision to choose Paris as the location for its first artificial intelligence research center outside of the U.S. But that was, by far, not the only major U.S. investment in our country. Also choosing Paris, Alexion Pharmaceuticals will bring us its first research lab outside of the U.S., dubbing it the Alexion R&D Center, Paris. And Matter, an incubator for health and e-health startups, has partnered with Paris City Council's network of health-sector incubators in order to provide easier access to the European market for American health startups, to attract European companies to Matter's home city of Chicago, and to encourage even greater interaction and collaboration between health-sector startups from our two countries.

Over the last two decades, hotel owners have seen the power of their brands' promise move away from their marketing teams into the hands of the consumer. Aided by the rise of online review platforms like TripAdvisor, hotel prices can easily be compared with other properties in the market. Not to mention, page one of Google search results is dominated by online travel agents, which makes it easier for consumers to compare independent hotels with one another, as well as offer alternative accommodation categories like villas or apartments to the same pool of potential customers. The sharing economy has not only affected many taxi car hire businesses, but has also increasingly begun shifting demand away from hotels in both leisure and business segments. The apartments available for share tend to increase even more during peak travel times.

The big data analytic ecosystem has matured over the last few years to the point where techniques such as machine learning don't seem as exotic as they once were. In fact, consumers have even come to expect their products and services to be adaptive to their behavior, which is one of the hallmarks of machine learning. As we look to the future, the field of deep learning on GPUs is poised to carry the big data analytics torch forward into the realm of artificial intelligence. While the theories behind deep learning are not new, the real-world application of deep learning in the modern sense is a relatively recent phenomenon that's emerged in a big way over the past few years. Its rise is closely tied to the advent of modern GPUs, a fact that NVIDIA (NASDAQ: NVDA) co-founder and CEO Jen-Hsun Huang hammered home in his keynote at the GPU Technology Conference (GTC) last week.

You get the data, you digest it, LED bulbs light up, and you go forth and train your algorithms, feed your tools, and complete your data sets. This then effectively improves your search and recommendation engines, SEO, browse experience, directory accuracy, sales and marketing efforts, or… Well, there's a lot you can do with this superior data, obviously. As mentioned, many of our customers use this high-quality, now-labeled data to train their own machine learning and AI algorithms. An insight is the answer you need, with a weighting and / or confidence interval reflecting ground truth – from the people whose inputs you need. At Spare5, we take pride in making you a hero, one insight at a time.