Gartner predicts that by 2018 a full 30 percent of our interactions with technology will be through "conversations" with smart machines. Major players such as Microsoft, Facebook and Google are now focused on empowering development of smart bots, and IBM's Watson is looking to find ways to integrate its cognitive computing platform deeper into businesses. We are being inundated with conversational tools and we're seeing a considerable, but not entirely surprising, shift of operations to this new way of human-machine interaction. Increasingly, we are depending on these systems for both routine daily tasks and sophisticated business interactions. However, we have already seen these new tools stumble.

BMW is accelerating plans to acquire technology expertise in a race with the likes of Apple Inc. and Google to make cars smart enough to react like human drivers. The German automaker is pushing to acquire startups, partner with tech companies and hire talent to build an electronic brain for next-generation vehicles, Klaus Froehlich, BMW AG's head of development, said in an interview in Munich. At stake is billions of euros in potential profit as value in the auto industry gradually shifts to autonomous taxis. "There's a power play going on with other companies buying up software competencies at a fast clip," Froehlich said. "We definitely need partners in this area, and we massively need to build out in-house resources too."

In a converted biscuit factory in Bermondsey, South London, 100 would-be entrepreneurs are searching for their perfect match. It's afternoon on the second day of Entrepreneur First (EF), the six-month accelerator programme that takes technically talented strangers and moulds them into co-founders. Yet although the people in the room have left jobs at Google, Credit Suisse and the University of Cambridge to be here, all the skill and will in the world are no substitute for chemistry, and between workshops on creativity and customer acquisition, the would-be entrepreneurs talk intensely and sketch out plans on MacBooks, working out if they are right for each other. "It's X Factor without the tears," says Alice Bentinck, co-founder of EF. EF is the tech accelerator that's also a training school for entrepreneurs.

Twitter Inc. (NYSE:TWTR) stock climbed as high as 3.54% during the midday trading hours today, after the company announced to acquire Magic Pony Technology. The micro-blogging platform is trying to build its machine-learning capabilities, and to improve the way it delivers photos and videos across its apps. The deal's exact terms were not disclosed, but rumors suggest that a payment worth 150 million has been agreed upon. Magic Pony is a London-based company, which develops techniques using neural networks--systems that are designed to think like a human--and machine learning, which would collectively expand data for images. This technology can be used to enhance a picture or video, taken via mobile phones, and can help improve graphics on virtual and augmented reality (VR and AR) applications.

Weka is an easy to use and powerful machine learning platform. It provides a large number of machine learning algorithms, feature selection methods and data preparation filters. In this post you will discover the Weka machine learning workbench and take a tour of the key interfaces that you can use on your machine learning projects. The entry point into the Weka interface is the Weka GUI Chooser. It is an interface that lets you choose and launch a specific Weka environment.

Machine learning is a modern branch in computer science. Machine learning can be determined as language acquisition, the stock exchange works and traffic control. How to start using or even learning this topic? Below you can find a selection of 10 free books, which will help you to learn new and interesting information about computers' capacity to learn. The book will help to obtain basic knowledge in machine learning.

We began by considering the broad discoveries of Accenture's Technology Vision report. It reveals that '70% of executives are making significantly more investments in artificial intelligence technologies than they did in 2013'. So are there any particular industries that are taking to AI more than others? "At Accenture we see AI being adopted across all industries", Marc says. "Some disciplines are using AI more than others, but we really are seeing it across the board, and I would not say there is one industry specifically that is using AI more than another".

Tesla CEO Elon Musk's 1bn non-profit artificial intelligence (AI) research firm OpenAI has announced its major technological goals which include creating a household robot, a natural language processing chatbot and an intelligent agent capable of winning any game. Announced in a blog post on 20 June, the firm says "robotics is a good testbed for many challenges in AI". "OpenAI's mission is to build safe AI, and ensure AI's benefits are as widely and evenly distributed as possible," the company says in the blog post authored by Ilya Sutskever, Greg Brockman, Sam Altman, and Elon Musk. "We're trying to build AI as part of a larger community, and we want to share our plans and capabilities along the way. We're also working to solidify our organisation's governance structure and will share our thoughts on that later this year."

Suranga Chandratillake: In the past, computer programmers needed to tell computers exactly what to do. The computers would then follow these instructed steps over and over. Machine Learning aims to help computers learn about the world for themselves, by creating a more generic algorithm. These algorithms can be shown a lot of data, and they then figure out how to react to, or process the data for themselves. SC: There are a number of different approaches to creating these self-learning algorithms.

Unlike Google, Facebook, and Amazon, Apple appears to be sitting out the massive data suck revolution. Marco Arment, bless him, has the ability to distill popular sentiment down to it's essence and post it in a way that makes every sensationalist site on the web attention-link it with self-serving abandon. Unfortunately, his point often gets lost in the pile-on that follows. This time, his point is about Artificial Intelligence, Machine Learning, and whether companies unfettered by concerns over privacy and security have a lead Apple will find tough to follow. Today, Amazon, Facebook, and Google are placing large bets on advanced AI, ubiquitous assistants, and voice interfaces, hoping that these will become the next thing that our devices are for.