Though Google Translate is one of the most powerful language translation tools, the company still thinks there's room for major improvement. And it is now working towards creating a model which can translate phrases from one language to another automatically. Much like every other product, Google has been working on integrating machine learning translation techniques into this system as well. And today seems to be the day, we can finally see it in action. Google Neural Machine Translation system, or GNMT which utilizes state-of-the-art training techniques for improved translations has today been introduced into one of the most difficult language pair: Chinese to English.

Ghetto Distributed Computing For Neural Networks (theapemachine.com)

Chatbots can already order read you the news, order you a takeaway, or hail you a cab. Now they're heading for your kitchen, with the launch of Jamie Oliver Skills, a virtual sous-chef developed by digital agency AKQA and the Jamie Oliver Group for the Amazon Echo. The Echo is a voice-controlled wireless speaker that is Amazon's answer to Apple's Siri, Google's Now and Microsoft's Cortana. Microphones on the device transmit your voice then a virtual assistant called Alexa--now equipped with a British accent for the UK market--responds automatically using artificial intelligence tech to create a simulated conversation. Jamie Oliver Skills is one of the early additions to Alexa's array of capabilities.

When Watson answered the final question to win "Jeopardy!" in 2011, voice recognition and artificial intelligence software were just making their large consumer debut on mobile devices. At the start, these capabilities were engaging, interesting and even exciting, but sometimes more as a parlor game than a deeply functional application. Yet they've continued to improve at an accelerating rate and now demand our serious attention as productivity tools. We are now half a decade on, and cognitive computing is making its presence felt at a deeply functional business level -- not just at the gateway of the journey on our devices, but deep within the industry-process level. For instance, cognitive computing is having a real impact at the clinical-process level for healthcare, which was featured specifically in a segment on artificial intelligence on "60 Minutes."

If Elon Musk and Stephen Hawking's foreboding pronouncements about the dangers of Artificial Intelligence don't have you spooked enough, MIT has deliberately launched a project that is designed to make AI scary. Or, at least, to make scary images using deep learning. Using a unique deep learning algorithm, researchers have attempted to teach their artificial intelligence what haunted houses and "toxic cities" look like. Then they fed it completely innocuous images of famous landmarks and instructed it to make them look eerie. It's doing a pretty good job, as far as nailing that cliched haunted look. Why are they doing this?

With Stephen Hawking opening an AI lab it's only a matter of time before smart robots take over for humans in the factory, on the battlefield, in the supermarket, and behind the counter. There's an old Chinese saying: "If you want to do anything good, easy and fast, you need connections," said Nancy Yang, a spokesperson for the fourth annual Seattle Biz-Tech Summit meeting today in Bellevue, Wash., outside Seattle. "Here in the U.S., we use email and messaging, but the Chinese way, and really for many Asians, is to meet face to face." Tanvi Lad shook off her first game loss and pulled off a rare victory over Rituparna Das in a three-set match and entered the women's singles final of the Manorama-Indian Open National-ranking badminton tournament here on Saturday. Stephen Hawking, the famous scientist who once said intelligent machines could be mankind's biggest threat, opened an artificial intelligence lab in Britain this week to help develop robot surgeons and Terminator-style military droids.

If you have a piece of equipment it and operate it, it will eventually fail. So you note down the failure time (operating hours) and replace it.After some time (especially if you have the identical equipment several times) you collect a collection of failure times. The usual approach is to use Minitab or R or whatever software you fancy and fit a model to the data (Exponential, Weibull, Gamma, etc.). So ideally you find a model which fits rather nice and then for the future you can describe the behaviour of your equipment with just one or two parameters. This is often displayed in form of a Cumulative Failure Probability plot.

Join The Brussels Data Science Community, Spark Summit Europe attendees, and Spark ML and machine learning experts Nick Pentreath and Jean-Francois Puget for a talk on an Apache Spark–based, end-to-end machine learning system. A round of Spark and machine learning lightning talks will follow. Many resources are available for building basic recommendation models using Spark. But how does a practitioner go from the basics to creating an end-to-end machine learning system, including deployment and management of models for real-time serving? In this session, we'll demonstrate how to build such a system based on Spark ML and Elasticsearch.

To learn more about Structured Streaming and Machine Learning, check out Holden Karau's and Seth Hendrickson's session Spark Structured Streaming for machine learning at Strata Hadoop World New York, September 26-29, 2016. Spark's new ALPHA Structured Streaming API has caused a lot of excitement because it brings the Data set/DataFrame/SQL APIs into a streaming context. In this initial version of Structured Streaming, the machine learning APIs have not yet been integrated. However, this doesn't stop us from having fun exploring how to get machine learning to work with Structured Streaming. For our Spark Structured Streaming for machine learning talk on at Strata Hadoop World New York 2016, we've started early proof-of-concept work to integrate structured streaming and machine learning available in the spark-structured-streaming-ml repo.

Natural disasters are out of the reach and influence of human beings. However, a lot can be done to minimize loss of lives. Artificial intelligence is one viable option that can potentially prevent massive loss of lives while at the same time make rescue efforts easy and efficient. To learn more, checkout the infographic below created by Eastern Kentucky University's Online Masters in Safety degree program. In the period between 2005 and 2015, a total of 242 natural disasters occurred in the United States of America.