In a blog post, Amy Nicholson, technical evangelist at Microsoft, explains how she used the Cortana Intelligence Gallery, together with Azure ML Studio, to help her start building her own ML (machine learning) models "as well as my knowledge about this space." Azure ML is a fully managed cloud service on Microsoft Azure that helps you easily build, deploy and share predictive analytics solutions, Nicholson writes. "It was designed for applied ML. All of this in a simple drag-and-drop interface to go from an idea to the deployment of a ML API in a matter of clicks." To learn more, visit the Cortana Intelligence and Machine Learning Blog.

Deep neural networks and Deep Learning are powerful and popular algorithms. And a lot of their success lays in the careful design of the neural network architecture. I wanted to revisit the history of neural network design in the last few years and in the context of Deep Learning. It is the year 1994, and this is one of the very first convolutional neural networks, and what propelled the field of Deep Learning. This pioneering work by Yann LeCun was named LeNet5 after many previous successful iterations since they year 1988! The LeNet5 architecture was fundamental, in particular the insight that image features are distributed across the entire image, and convolutions with learnable parameters are an effective way to extract similar features at multiple location with few parameters. At the time there was no GPU to help training, and even CPUs were slow.

One startup makes a website that connects high-school students with the ideal college. Another operates a chatbot that can answer your simple medical questions. All have the resources of Microsoft backing them. Nine companies made pitches onstage Thursday night at Showbox SoDo as part of Microsoft Accelerator's third demo day in Seattle. The program selects 10 to 15 companies twice a year to participate in a startup accelerator program that provides resources, Microsoft Azure credits, and -- perhaps most compelling -- introductions to Microsoft's deep pool of customers.

The exposed api should be able to fully build & train experiments from scratch, and also expose model score results for external evaluation & reporting (even allow for continuous integration?). Seems already clear that there's some very typical ml studio workflow patterns that would greatly benefit from model automation (read, split, train/param sweep, score, evaluate...).

The Drum has released the first magazine edited using artificial intelligence (AI) following a partnership with IBM Watson to create the special issue. The magazine, published today (15 June), includes a number of features that involved the analytical functions that IBM's AI can provide in order to examine the role such technology can provide to modern day marketers. These will be available to match and play against other opponents through a specially created new online app. Those without a card will also be able to challenge other marketers in the industry by logging into the app and comparing their scores with further competitors. Watson has also been trained to answer questions put to it by industry luminaries around the insights of advertising legend David Ogilvy and a number of predictions around potential winners at Cannes Lions this year have also been made by the AI.

Recovering the planet's natural resources is hard. It's difficult, dangerous, and can be environmentally damaging. Cue an IT revolution, with smart communications, 'extreme Wi-Fi' covering vast deserts, autonomous vehicles that extract vital rocks and minerals, and geofenced employees who receive warnings if they get close to a mine's famously colossal big machinery. There's even a'smart bolt' that creates an underground support structure which is classic Internet of Things. The final goal is the autonomous mine, where humans are completely removed from the mining process.

That dream of universal expertise is what IBM says its Watson question-answering, machine-learning system makes possible. Watson can be trained to answer questions on any subject you choose. The system uses natural language processing to read huge numbers of documents, extracts and organises information about a particular topic and then refines its understanding of that subject based on human feedback. But how useful are the answers given by Watson and how difficult is it to train? One person who's well-placed to talk about using the Jeopardy!-winning

We've talked a little about how the AI in our existing devices works and how it needs to improve moving forward in order to better assist us. What is the next big move in AI though? Well, many think it will be an artificial intelligence living right in your ear. In fact, it seems that in-ear assistants similar to the one featured in the movie Her aren't science fiction at all; but rather an imminent reality. There are obviously a million different implications if this fully comes to fruition. But, specifically, what does this mean to you as a business person?

Artificial intelligence might eventually be the key not just to smartphones but to all connected things, according to Android co-founder Andy Rubin. And a single AI may power them all. "If you have computing that is as powerful as this could be, you might only need one," Rubin told attendees at Bloomberg's Tech Conference in San Francisco Tuesday. "It might not be something you carry around; it just has to be conscious." Data will pave the way for AI to reach its potential, Rubin continued, so sensors and robots will play a crucial role by gleaning information and learning from their environments.

The traditional definition of artificial intelligence is the ability of machines to execute tasks and solve problems in ways normally attributed to humans. Some tasks that we consider simple--recognizing an object in a photo, driving a car--are incredibly complex for AI. Machines can surpass us when it comes to things like playing chess, but those machines are limited by the manual nature of their programming; a 30 gadget can beat us at a board game, but it can't do--or learn to do--anything else. This is where machine learning comes in. Show millions of cat photos to a machine, and it will hone its algorithms to improve at recognizing pictures of cats.