NEC announced successful transmission tests that took place over a commercial subsea cable measuring more than 10,000 kilometers using artificial intelligence (AI) and probabilistic shaping at a modulation of 64QAM. NEC, in a joint research publication with Google, has demonstrated for the first time that the FASTER open subsea cable can be upgraded to a spectral efficiency of 6 bits per second per hertz (b/s/Hz) in an 11,000km segment. This represents a capacity of more than 26 terabits per second (Tb/s) in the C-band, which is over 2½ times the capacity originally planned for the cable, for no additional wet plant capital expenditure. In doing so, a spectral efficiency-distance product record of 66,102 b/s/Hz in a field trial performed together with live traffic neighboring channels. The team achieved this result using near-Shannon probabilistic-shaping at a modulation of 64QAM, and for the first time on a live cable, artificial intelligence (AI) was used to analyze data for the purpose of nonlinearity compensation (NLC). This approach sets aside those deterministic models of nonlinear propagation, in favor of a low-complexity black-box model of the fiber, generated by machine learning algorithms.

In the past, artificial intelligence (AI) has been unable to measure up to the lofty expectations set by the thinking machines in the movies, such as HAL in 2001: A Space Odyssey. Instead, the technology repeatedly climbed the "hype cycle" only to fall into a "trough of despair." This is Part 1 of an interview with Monty Barlow, director of machine learning at Cambridge Consultants, and he reveals why this time is different--and why there's no going back. At Cambridge Consultants, we've been doing AI long enough to have lived through several cycles of hype and disappointment. The 1990s were a time of great excitement around AI.

Causal inference from observation data often assumes "strong ignorability," that all confounders are observed. This assumption is standard yet untestable. However, many scientific studies involve multiple causes, different variables whose effects are simultaneously of interest. We propose the deconfounder, an algorithm that combines unsupervised machine learning and predictive model checking to perform causal inference in multiple-cause settings. The deconfounder infers a latent variable as a substitute for unobserved confounders and then uses that substitute to perform causal inference. We develop theory for when the deconfounder leads to unbiased causal estimates, and show that it requires weaker assumptions than classical causal inference. We analyze its performance in three types of studies: semi-simulated data around smoking and lung cancer, semi-simulated data around genomewide association studies, and a real dataset about actors and movie revenue. The deconfounder provides a checkable approach to estimating close-to-truth causal effects.

The popularity of Amazon's Echo smart speakers has helped push the audience for digital radio past that of FM and AM in the UK for the first time. The milestone, which was reached in the first quarter of this year, could prompt the government to launch a review to evaluate whether it should switch off the FM signal. Digital, which covers listening via digital audio broadcasting (DAB) sets in homes and cars, televisions and through services such as Echo, hit a record share of 50.9% of all radio listening in the three months to March. Amazon's smart speakers, powered by the virtual assistant Alexa, have helped reinvent the medium for a new, tech-savvy generation, many of whom have failed to embrace traditional radio listening. Listening online and via apps proved to be by far the fastest-growing segment of digital consumption, with hours of listening in the first quarter surging by 14m (17%) year on year to 95m hours.

GitHub and Reddit are two of the most popular platforms when it comes to data science and machine learning. The former is an awesome tool for sharing and collaborating on codes and projects while the latter is the best platform out there for engaging with data science enthusiasts from around the world. This year, we have covered the top GitHub repositories each month and from this month onwards, we will be including the top Reddit threads as well that generated the most interesting and intriguing discussions in the machine learning space. April saw some amazing python libraries being open sourced. From Deep Painterly Harmonization, a library that makes manipulated images look ultra realistic, to Swift for TensorFlow, this article covers the best from last month.

These shifts will require new executives, new jobs, and new responsibilities. Paul Daugherty, chief technology and innovation officer at Accenture, sees three myths surrounding artificial intelligence: Robots are coming for us, machines will take our jobs, and current approaches to business processes will still apply. The three myths represent "conventional changes to linear processes," he said. The reality is more transformative. An example: Newark, New Jersey-based AeroFarms grows seeds indoors without soil or sunlight.

Artificial intelligence has gone from the imagination of people like Philip K. Dick and Arthur C. Clarke, and is now a part of every aspect of technology. The future of smartphones revolves around terminologies like machine learning, artificial intelligence and augmented reality. We're starting to see this happen already, as most smartphone manufacturers now stress that their devices have AI baked in. But is the hype justified, or are we hearing about AI now because the hardware seems to have reached a plateau? What's clear is that the next revolution lies in software, in bringing actual intelligence to "smart" phones, and that's why AI has to be implemented at all stages of the smartphone experience.

Wine Ring is the first app that uses machine learning to choose the right wine for your tastes. About CNBC: From'Wall Street' to'Main Street' to award winning original documentaries and Reality TV series, CNBC has you covered. Experience special sneak peeks of your favorite shows, exclusive video and more. Connect with CNBC News Online Get the latest news: http://www.cnbc.com/ Find CNBC News on Facebook: http://cnb.cx/LikeCNBC

This year attendance at NAB 2018 was down on previous years but despite the lower visitor numbers the conference content and exhibition produced the buzz and excitement that NAB is synonymous for. This year's show had a little bit of everything, but the main trends seem to revolve around RGB lighting, large format cameras, and a game changing codec, whilst the conference sessions followed some interesting threads under the umbrella of next-generation technologies, namely artificial intelligence (AI), immersive media and cyber security. From production to distribution, artificial intelligence has taken the broadcast and filmmaking industries by storm. The 2018 edition of NAB dedicated time and space to showcase some of the developments in AI with conference sessions like "Machine Intelligence: The Evolution of Content Production Aided by Machine Learning", "Optimising Production with Neural Networks", "How Machine Intelligence is Transforming Editorial", "New Frontiers in Animation and Computer Graphics", "From Dailies to Master – Machine Intelligence Comes to Video Workflows" and, finally, "The Future of Content with Machine Intelligence". The series of sessions looked at machine learning, deep learning and artificial intelligence technologies and at how studios, networks and creative service companies can use them to help produce content.

Every week science journalists get a bunch of emails from various Respectable Scientific Journals telling us, in advance, what articles those journals are going to publish. When I started in this game, these tables of contents came by fax; today, in the future, they're downloadable PDFs. The quo for all this quid is that we agree not to publish anything until a set time and day. It's called an embargo, and it is in some senses the anticlimax of a long story--the story of a scientific discovery. Sure, journalists might focus on the eureka moment or the fascinating details of the methods some scientist used.