Google's DeepMind artificial intelligence (AI) technology has proven to be very smart. DeepMind's AlphaGo system got worldwide attention for beating top-ranked Go player Lee Sedol earlier this year. Previously it has played Breakout and navigated a Doom-like maze. But now the DeepMind software is looking more versatile. Today the Google DeepMind lab unveiled another feat that looks off the wall but is actually evidence of the strength of Google's AI.

Machine learning covers a lot of ground. At Microsoft, it's being incorporated to detect lies, recognize human responses and forecast finances; as well as improve search, natural language processing, advertising, security and gaming. It's making a big difference in a lot of different applications that really matter for the future," says John Langford, an expert on machine learning at the Microsoft Research lab in New York City who is also the general chair for the International Conference on Machine Learning, which has grown by 65 percent since last year thanks to the technology's success. "Figuring out how to use data to make decisions to help people is what machine learning is about." Advanced analytics and data science resources make it possible for once-arduous tasks to get done quickly. It can speed up a multitude of normally time-consuming processes, such as vision recognition, causality, crowd sourcing and more. "Now we're seeing more work in neural networks and deep learning than in previous years," says Langford of some prevalent themes in this year's conference. By far, this is the largest ICML ever. The field is really growing fast."

There are standard workflows in a machine learning project that can be automated. In Python scikit-learn, Pipelines help to to clearly define and automate these workflows. In this post you will discover Pipelines in scikit-learn and how you can automate common machine learning workflows.

Martin Kearn has put together a video called "Machine Learning for Muggles" – clearly he's a Harry Potter fan. It's a great video to watch for muggles and wizards alike, even if you have no idea what a muggle is.

Martin Kearn has put together a video called "Machine Learning for Muggles" – clearly he's a Harry Potter fan. It's a great video to watch for muggles and wizards alike, even if you have no idea what a muggle is.

Industrial companies are confronted with several new trends that will fundamentally change production and logistics processes. For example, the term "Industry 4.0," which was coined in Germany, stands for the digital networking of people, objects, and systems to create integrated production processes. In international jargon, it is referred to as the Industrial Internet of Things (IIoT). All technologies, systems, and components that are involved in the industrial value creation process are connected to each other as well as to company networks and the internet. Smart factory is another trend that forms a part of the IIoT development.

Microsoft seems to be on an acquiring spree as the company on Friday said that it had acquired messaging app developer Wand Labs. "This acquisition accelerates our vision and strategy for'Conversation as a Platform', which Satya Nadella introduced at our Build 2016 conference in March," David Ku, corporate vice president at Information platform group divison at Mircosoft, said. Satya Nadella, CEO of Microsoft, who was on a India visit recently, had said his company was trying to teach computers the human language to make computing faster by using artificial intelligence assistants such as Cortana, a programme that runs on most Windows devices and gets tasks done by speech recognition. "We are going to bring in bots which will result in democratisation for developers who would look at reinventing apps for speech recognition via a virtual assistant that would make human lives easier," he had added. Ku further justified the acquisition and said: "Wand Labs' technology and talent will strengthen our position in the emerging era of conversational intelligence, where we bring together the power of human language with advanced machine intelligence -- connecting people to knowledge, information, services and other people in more relevant and natural ways. It builds on and extends the power of the Bing, Microsoft Azure, Office 365 and Windows platforms to empower developers everywhere."

On Thursday, tech giant Google announced that it is opening a new research center in Europe, dedicated to machine learning. According to the details shared by Emmanuel Mogenet – chief of Google Research for Europe – in a recent blog post, the key focus of the new research group in Europe will be on three AI areas --- machine intelligence, machine perception, and natural language processing and understanding. Mogenet further specified that the main objective of the research group in Europe will be to find new ways in which machine learning infrastructure can be improved, and how the technology can be put it into practical use. In addition, the new AI-focused research group will also work for the advancement of Natural Language Understanding – that is, the capability of machines to understand and process human language – in close collaboration with linguists.

On Thursday, tech giant Google announced that it is opening a new research center in Europe, dedicated to machine learning. The new research center -- which will enable Google to realize its ambitious artificial intelligence (AI) plans by focusing on the development of AI products and research -- will be based in Google Research offices in Zurich, Switzerland. According to the details shared by Emmanuel Mogenet – chief of Google Research for Europe – in a recent blog post, the key focus of the new research group in Europe will be on three AI areas --- machine intelligence, machine perception, and natural language processing and understanding. Mogenet further specified that the main objective of the research group in Europe will be to find new ways in which machine learning infrastructure can be improved, and how the technology can be put it into practical use. In addition, the new AI-focused research group will also work for the advancement of Natural Language Understanding – that is, the capability of machines to understand and process human language – in close collaboration with linguists.

A TensorFlow implementation of the models described in Andor et al. (2016). At Google, we spend a lot of time thinking about how computer systems can read and understand human language in order to process it in intelligent ways. We are excited to share the fruits of our research with the broader community by releasing SyntaxNet, an open-source neural network framework for TensorFlow that provides a foundation for Natural Language Understanding (NLU) systems. Our release includes all the code needed to train new SyntaxNet models on your own data, as well as Parsey McParseface, an English parser that we have trained for you, and that you can use to analyze English text. So, how accurate is Parsey McParseface?