Last week at Microsoft's Build conference, CEO Satya Nadella said that the future of the company was "conversation as platform." In other words, less Windows and Office, and more Cortana and Tay--conversational interfaces that can understand the natural language of human users. If Nadella thought he was expressing some unique vision of the future, though, he was fooling himself. The idea of conversational UI has quickly colonized nearly every corner of Silicon Valley over the past year. Now seems like a good time to ask: What is a conversational interface?

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Now Silicon Valley has found its next shiny new thing. And it does not have a "Like" button. In the pixelated cube world of "Minecraft," players can create almost anything their hearts desire. Now, Microsoft is using the popular world-building game to build and test artificial intelligence in the fictional environment. Microsoft has made a platform for (AI) research using a modified version of "Minecraft" that will become available to the public following a limited release to select researchers.

DARPA are starting a competition to help automate defence and see how artificial intelligence can combat cyber-threats. The latest DARPA Grand Challenge is looking to artificial intelligence to seek out and destroy vulnerabilities in software. The US Defense Advanced Research Projects Agency (DARPA) Cyber Grand Challenge will see seven teams battle it out to see if machine learning can do better in finding and fixing exploits better than humans. The agency said on its competition website that identifying threats and remediating them can take over a year from first detection to the deployment of a solution, by which time critical systems may have already been breached. "This slow reaction cycle has created a permanent offensive advantage," reads the blurb.

Twitter users may soon notice a great improvement in the visual content of their apps, if their latest acquisition is any indicator. Twitter recently acquired a new-ish startup based in London specializing in artificial intelligence (AI). The company, Magic Pony, uses machine learning to build improved systems for visual processing. More specifically, according to Twitter's blog post, Magic Pony's technology is, "based on research by the team to create algorithms that can understand the features of imagery and will be used to enhance Twitter's strength in live and video." Twitter plans to add this technology to other recent acquisitions including Madbits and Whetlab.

Machine learning is set to go mainstream in the next two years, according to a new survey by software and application development vendor SoftServe Inc. The new report, based on an April poll of 300 U.S. and U.K.-based medium and large enterprises, shows that 62 percent of firms expect to roll out machine learning-based tolls for business analytics within the next two years. The majority of those companies said the most promising opportunity for machine learning lays in real-time data analysis. According to SoftServe, the survey is evidence that machine learning is moving past the "hype cycle", with enterprises looking to automate analytics processes in areas like business intelligence and cyber security. In the latter area, further evidence of machine learning's progress comes from the Defense Advanced Research Projects Agency (DARPA), which is sponsoring an "all-machine" hackathon at the DEF CON hacking conference in Las Vegas next month.

Reuters – A former Google engineer, whose speech recognition software is used in more than a billion Android smartphones, has launched a company that uses blockchain technology to build a new operating system for banks. Paul Taylor, a Cambridge University academic with an expertise in artificial intelligence, speech synthesis and machine learning, started working on the system, called Vault OS, two years ago in a basement in London's Shoreditch district, known for being a tech startup hub. Blockchain technology has captured the imagination of the financial industry, with advocates saying it has the potential to shake up how markets operate. The technology, which underpins the digital currency bitcoin, creates a shared database in which participants can trace every transaction ever made. The ledger is tamper-proof and transparent, meaning that transactions can be processed without the need for third-party verification.

Microsoft Azure Machine Learning simplifies data analysis and empowers you to find the answers your business needs. The question isn't whether you can find the answers.

So the question I have is: what does the frontier of the space of optimal networks look like, what are the inherent limits of depth vs expressivity of these models, and are there dimensional scaling laws that can describe all this in an information theoretic way? This recent paper gives a great treatment on the expressivity of convolution networks by using a deep layered architecture that generalizes convolutional neural networks called sim-nets. As a simple first step I wanted to see what could be done to visualize the operations a deep neural net performs. So I constructed a standard network that takes vector inputs of size 2 and produces vector outputs of size 3 which we can think of as a mapping of the cartesian plane into RGB color space. Taking many copies of this net and randomly initializing them, (with normally distributed weights and biases) we can plot them in a grid and see the networks' outputs as a set of images.