Artificial intelligence can research and write routine documents. A Chicago company called Narrative Science was among the first to give computers the power of the pen -- or keyboard. And, like anyone with a creative streak, automated writing software likes to stretch its artistic muscles. Best of all, this virtual reality and machine learning revolution will spark the investing trend of the decade ahead.

Benevolent.ai, a British artificial intelligence company, has appointed IBM Watson's vce president of Watson platform; Jerome Pesenti as CEO of its technology division, benevolent technology. Jerome will also join the main board of the company. Jerome has been focused on big data and machine learning for the past 16 years. At IBM he also held the role of chief scientist for the core IBM big data product portfolio. Prior to joining IBM, Jerome co-founded search and text analytics company Vivisimo that was acquired by IBM in 2012.

Leveraging data to predict customer satisfaction is more important than ever––it can help your business engage with customers proactively, improve operations, reduce customer churn, and improve customer relationships over the long-term. Think better support experiences for everyone. Join us for a unique opportunity to learn from guest speaker Ken Landoline, Principal Analyst at Ovum, a market-leading research and consulting business. Adrian McDermott, Senior Vice President of Product Development at Zendesk, will join Landoline to discuss how your business can use machine learning to provide a better customer experience. This webinar is complimentary, so feel free to spread the word and share with colleagues who you think might benefit from this live, interactive session.

Notes platform Evernote has announced plans to migrate its complete data infrastructure from its own servers and networks onto Google's Cloud. With an existing relationship with Google, through a previous integration with Google Drive, Evernote has made swift progress on finalising the infrastructure plans. The data migration is expected to begin from next month, finishing towards the end of 2016. It is these machine learning and artificial intelligence capabilities which Google plans to leverage in attracting large enterprises to its cloud, over rivals Amazon Web Services (AWS) and Microsoft Azure.

Artificial Intelligence (AI)-powered bots will become the next interface, shaping our interactions with the applications and devices we rely on and Microsoft's latest solutions are set to change the way HP interacts with its customers and partners, Indian-born Microsoft CEO Satya Nadella has said. "Bots are now learning in human context and the relevant thing for us is to make them intelligent as we learn from customers' experience. Our solutions are going to give HP a 360-degree view of its customer services," Nadella announced at HP's Global Partner Conference (GPC) 2016 at the Boston Conference and Exhibition Centre (BCEC) here. HP announced a six-year agreement to deploy Microsoft Dynamics customer relation management (CRM) online in order to enhance collaboration across marketing, sales and service operations. With Dynamics, as well as Azure, Office 365 and other Microsoft Cloud solutions, HP has invested in the sales and service collaboration platform to deliver a seamless sales experience for customers and partners.

In just five years, intelligent systems and robots may have taken up to 6% of U.S. jobs, according to Forrester Research in a report released this week. As artificial intelligence (A.I.) advances to better understand human behavior and make decisions on its own in complicated situations, it will enable smart software and robots to take on increasingly challenging jobs. That means robotics should be able to take over some jobs traditionally held by humans by 2021. For instance, Forrester predicts that smart systems like autonomous robots, digital assistants, A.I. software and chatbots will take over customer service rep jobs and eventually even serve as truck and taxi drivers. "Intelligent agents have emerged, but wide adoption is not yet mainstream," Forrester analysts wrote in their report.

The information that organizations produce is no less than a bag brimming with the blended gifts. It gives market pioneers a superior learning of human conduct, yet then again, it's altogether significantly more to manage. Thus to deal with those'lot more' AI came into existence. It helps businesses to wade through the gathered data as well as make better use of it. Artificial Intelligence is like a GPS satellite for the business visionaries as it helps them to navigate and finish the muddle sales journey by figuring out "Dog-gone" temperature of the competitive market.

Nomis Solutions today announced that Frank Rohde, the President and CEO of Nomis will be participating in a panel discussion entitled, "Artificial Intelligence (AI) vs. Humans in Banking" at Banking Disrupted, a leadership summit to be held at the Silicon Valley Elks Lodge in Palo Alto, CA. on Sept. 14 -15, 2016. The panel discussion will begin at 2:45 pm on Sept. 14, 2016 and include executives and user experience professionals from leading technology companies including GainX, ecosystem.AI and WiseBanyan. Algorithms and apps could replace half of banking jobs over the next 10 years, according to some forecasts. At the same time, people still prefer person-to-person interactions when it comes to big financial decisions. The panel will address some key questions including: how AI will change banking, and if the human touch will still be necessary in the future.

Artificial intelligence technology doesn't just have to solve grand challenges. Sometimes, it can tackle decidedly everyday problems -- like, say, improving a cucumber farm. Makoto Koike has built a cucumber sorter that uses Google's TensorFlow machine learning technology to save his farmer parents a lot of work. The system uses a camera-equipped Raspberry Pi 3 to snap photos of the veggies and send the shots to a small TensorFlow neural network, where they're identified as cucumbers. After that, it sends images to a larger network on a Linux server to classify the cucumbers by attributes like color, shape and size.

This tutorial will explain some practical tips about how to train a neural machine translation system. It is partly based around examples using the lamtram toolkit. Note that this will not cover the theory behind NMT in detail, nor is it a survey meant to cover all the work on neural MT, but it will show you how to use lamtram, and also demonstrate some things that you have to do in order to make a system that actually works well (focusing on ones that are implemented in my toolkit). This tutorial will assume that you have already installed lamtram (and the cnn backend library that it depends on) on Linux or Mac. Then, use git to pull this tutorial and the corresponding data. The data in the data/ directory is Japanese-English data that I have prepared doing some language-specific preprocessing (tokenization, lowercasing, etc.). Machine translation is a method for translating from a source sequence F with words f_1, ..., f_J to a target sequence E with words e_1, ..., e_I. This usually means that we translate between a sentence in a source language (e.g.