The insideBIGDATA Guide to Deep Learning & Artificial Intelligence is a useful new resource directed toward enterprise thought leaders who wish to gain strategic insights into this exciting area of technology. In this guide, we take a high-level view of AI and deep learning in terms of how it’s being used and what technological advances have made it possible. We also explain the difference between AI, machine learning and deep learning, and examine the intersection of AI and HPC. We present the results of a recent insideBIGDATA survey, “insideHPC / insideBIGDATA AI/Deep Learning Survey 2016,” to see how well these new technologies are being received. Finally, we take a look at a number of high-profile use case examples showing the effective use of AI in a variety of problem domains.
In this special guest feature, Devavrat Shah, professor in MIT’s Department of Electrical Engineering and Computer Science, discusses the type of training data scientists need in order to glean the most value from big data.
Data science software maker, Dataiku, recently completed a worldwide survey that asked thousands of companies: how does your organization put data science into production? The results show that most companies using data science have unique challenges that fall into four different profiles: Small Data Teams, Packagers, Industrialization Maniacs, and The Big Data Lab.
Skytree Patented Automation Gets Smarter and Enables Us to Deliver Machine Learning as a Service with the Release of Skytree 16.0
Skytree, a leader in enterprise machine learning on big data, announces the release of Skytree 16.0 and Skytree’s Machine Learning as a Service offering. We continue our trend of increasing ease of use via unprecedented automation, further enabling non-data scientist users to access the power of enterprise grade machine learning, gain insights, and to add value to their business.
The Institute for Scientific Computing Research (ISCR) sponsored the talk below entitled “Deep Learning” on April 16, 2015, at the Lawrence Livermore National Laboratory. The talk was presented by Yann LeCun, director of AI research at Facebook and professor of data science, computer science, neural science and electrical engineering at NYU.
Infosys (NYSE: INFY), a global leader in consulting, technology, and next-generation services, released multinational research findings on artificial intelligence (AI) business impact, market maturity and expectations. The research report, Amplifying Human Potential: Towards Purposeful Artificial Intelligence, polled 1,600 senior business decision makers at large organizations across the world.
In this article, we’ll make sense of data science for those unacquainted with the field and outline a series of 7 easy steps to get up to speed with the technology. In doing so, we’ll highlight the integral steps in the “data science process,” so you can get a good grasp of how data science works and how it is of value to enterprises seeking to maximize the value of their data assets.
The presentation below by Alex Smola is “Personalization and Scalable Deep Learning with MXNET” from the MLconf San Francisco, 2016. User return times and movie preferences are inherently time dependent. In this talk, Alex shows how this can be accomplished efficiently using deep learning by employing an LSTM (Long Short Term Model). Moreover, he shows how to train large scale distributed parallel models using MXNet efficiently.
The annual State of Analytics Adoption Report by our friends at Logi Analytics provides insights for executives, product managers, and technology leaders on how broadly and deeply users are adopting business intelligence and analytics tools. The 2017 survey respondents included members of IT teams who provide analytics tools to end users, as well as the end users of BI and analytics tools.
NewVantage Partners, strategic advisors in big data and business innovation to Fortune 1000 businesses, has released the results of its 2017 5th Annual Big Data Executive Survey, entitled “Big Data Business Impact: Achieving Business Results through Innovation and Disruption.” The 2017 Big Data Executive Survey reports what executives from 50 Fortune 1000 firms see as the key factors driving big data adoption, investment – and success.