Google is using artificial intelligence to enhance Google Drive, Google Docs, Google Sheets, Google Slides, and Google Calendar. The changes come as Microsoft also hustles to smarten up its Office apps with features like the Researcher and Editor capabilities in Word. Google has previously incorporated artificial intelligence into Google Photos, Inbox by Gmail, Google Translate, and Google Allo, and now it's becoming available in a few places in Google's productivity apps. Google Drive for Android is getting a new feature called Quick Access that pins the most relevant documents to the top of the app. "Based on signals like your interaction with colleagues, recurring meetings and activity in Drive, machine intelligence helps Drive understand the rhythm of your workday and offers the files you need before you even ask. Our customer research shows that Quick Access saves about 50% of the time an employee would usually spend finding a file," Prabhakar Raghavan, vice president of apps for Google Cloud, wrote in a blog post.

Amazon, Baidu, Facebook, Google and Microsoft, among other technology companies, have been investing heavily in artificial intelligence and related disciplines like machine learning because they see the technology enabling services that become a source of revenue. Consultancy Accenture earlier this week quantified this enthusiasm, predicting that AI "could double annual economic growth rates by 2035 by changing the nature of work and spawning a new relationship between man and machine" and by boosting labor productivity by 40 per cent. Certainly things could work out well for Accenture, which a day later announced a partnership with Google to help companies deploy Google technology like machine learning. It's as if the global services firm has a stake in the future it foresees. But the machine learning algorithms underpinning this harmonious union of people and circuits aren't secure. In a paper [PDF] presented in August at the 25th Annual Usenix Security Symposium, researchers at École Polytechnique Fédérale de Lausanne, Cornell University, and The University of North Carolina at Chapel Hill showed that machine learning models can be stolen and that basic security measures don't really mitigate attacks.

Microsoft wants to "democratize artificial intelligence" and bring AI to systems that everyone uses. So to reflect that desire, the company is shaking up its organization. The company is creating a new group, the AI and Research Group, by combining the existing Microsoft Research group with the Bing and Cortana product groups, along with the teams working on ambient computing (a world in which everything around us is computerized and connected and responsive to our presence), robotics, and the Information Platform Group (which covered both Bing advertising and natural user interfaces). Together, the new AI and Research Group will have some 5,000 engineers and computer scientists. It will be lead by 20-year Microsoft veteran Harry Shum, who was previously the Executive Vice President of Technology and Research.

Imagine that you look at your radio in the morning, and it looks back, with a blinking expression that mirrors your own: sleepy eyes, straight mouth, droopy eyelids. Soft music begins to play, matching your mood. When you come home, you hang up your clothes, look at your radio again. It looks back at you, registering a little tension in your eyebrows, the residue of an aggravating commute. Something a little bit more jagged comes on, let's say Radiohead.

In this blog, I will be discussing the use of attribution models in relation to codified narrative. For this purpose, I will be referring to the plots of two films: the 1974 horror classic "The Texas Chainsaw Massacre"; and a 2014 dark comedy called "Tusk." I have my own codification system called BERLIN: this is short for "Behavioural Event Reconstruction Linguistic Interface for Narratives." An attribution model supports the inference of meaning from data. Imagine a student one day going to an introductory statistics class and noticing all of the other students clearing their desks. He asks nervously, "Hey, what's going on? The student closest to him replies, "Yeah, there's an exam." "An exam - you mean right now!

This is the year artificial intelligence came into its own for mainstream businesses, at least as a marketing feature. On Sunday, Salesforce.com, which sells online software for sales and marketing, announced it would be adding A.I. to its products. Its system, called Einstein, promises to provide insights into what sales leads to follow and what products to make next. Salesforce chose this date to pre-empt Oracle, the world's largest business software company, which on Sunday evening began its annual customer event in San Francisco. Oracle calls its product Oracle A.I. Elsewhere, General Electric is pushing its A.I. business, called Predix.

Machine learning is now pervasive in every field of inquiry and has lead to breakthroughs in various fields from medical diagnoses to online advertising. Practical machine learning is quite computationally intensive, whether it involves millions of repetitions of simple mathematical methods such as Euclidian Distance or more intricate optimizers or backpropagation algorithms. Such computationally intensive techniques need a fast and expressive language – one that enables scientists to write simple, readable code that performs well. In this post, we introduce a simple machine learning algorithm called K Nearest Neighbors, and demonstrate certain Julia features that allow for its easy and efficient implementation. We will demonstrate that the code we write is inherently generic, and show the use of the same code to run on GPUs via the ArrayFire package.

When researchers recently unveiled the first pop song composed by an artificial intelligence (AI) system, some creative types may have been nervous about the idea of robots taking over their jobs. But how exactly was AI used to write a song? A team from the Sony CSL Research Lab used a system called Flow Machines to compose the new record, titled "Daddy's Car." The song sounds like a lost Beatles track from the late 1960s, or perhaps a composition by Brian Wilson of the Beach Boys. François Pachet, the project's lead researcher, told Live Science that the song wasn't created by an AI entirely from scratch, so composers can breathe easy -- at least for now.

Translating from one language to another is hard, and creating a system that does it automatically is a major challenge, partly because there are just so many words, phrases and rules to deal with. Fortunately, neural networks eat big, complicated data sets for breakfast. Google has been working on a machine learning translation technique for years, and today is its official debut. The Google Neural Machine Translation system, deployed today for Chinese-English queries, is a step up in complexity from existing methods. Here's how things have evolved (in a nutshell). A very simple technique for translating -- one a kid or simple computer could do -- would be to simply look up each word encountered and switch it with the equivalent word in another language.

At Zelros, we believe that many enterprise machine learning applications lack of a major component: the interface between the complex data workflow on one side, and the business users on the other side -- that is to say a natural way for people to interact with technology. We believe that one solution for this issue is to invent a new type of intelligent interaction, like chatbots, based on natural language and discussions. We are not the only ones. There is not a day without an article promoting benefits of conversational and invisible UI. Technically, end-to-end chatbots are constituted of several building blocks.