Around a year ago, a former embedded systems designer from the Japanese automobile industry named Makoto Koike, started helping out at his parents cucumber farm, and was amazed by the amount of work it takes to sort cucumbers by size, shape, colour and other attributes. In Japan, each farm has its own classification standard and there's no industry standard. There are some automatic sorters on the market, but they have limitations in terms of performance and cost, and small farms don't tend to use them. Makoto first got the idea to explore machine learning for sorting cucumbers from a wildly different source - Google AlphaGo - competing with the world's top professional Go player. "When I saw the Google's AlphaGo, I realized something really serious is happening here, said Makoto. That was the trigger for me to start developing the cucumber sorter with deep learning technology."

Under, behind and inside many of the apps we use every day, a revolution is underway. It's a revolution that started decades ago but today is empowering companies to deliver better, smarter services with greater ease and on broader scales than ever before. At Singularity University's inaugural Global Summit, Neil Jacobstein, chair of Artificial Intelligence and Robotics, provided a primer showing how artificial intelligence literally transforms everything it touches. First of all, it's critical to define the scope of artificial intelligence (AI), which can be categorized into four areas: techniques in pattern recognition, software agency (that is, software that acts like real users), an exponential technology that is accelerating other exponential technologies, and a vision of a future superhuman intelligence (that fortunately hasn't happened yet). Anyone who has seen a science fiction film is likely familiar with this last area, but it's the other three areas where AI is making huge strides at a revolutionary pace.

Programming languages are the primary tool of the software development industry. Since the 1940's hundreds of them have been created and a huge amount of new lines of code in diverse programming languages are written and pushed to active repositories every day. We believe that a source code classifier that can identify the programming language that a piece of code is written in would be a very useful tool for automatic syntax highlighting and label suggestion on platforms, such as StackOverflow and technical wikis. This inspired us to train a model for classifying code snippets based on their language, leveraging recent AI techniques for text classification. We collected hundreds of thousands of source code files from GitHub repositories using the GitHub API.

There has even been controversy that some of the descriptions within these are too broad, with Gil Press asking of the inclusion of machine learning, 'Is [it] an "emerging technology" and is there a better term to describe what most of the hype is about nowadays in tech circles?' Instead, he argues, there should be'deep learning' or'artificial neural networks' used in its stead, given that machine learning is already a well established technology. Gil is certainly correct, with at least a relatively basic form of machine learning appearing in things like suggestion engines and programmatic advertising to some extent.

Open AI, a non-profit artificial intelligence research company backed by Elon Musk, launched a toolkit for developing and comparing reinforcement learning algorithms. OpenAI Gym is a suite of environments that include simulated robotic tasks and Atari games as well as a website for people to post their results and share code. OpenAI researcher John Schulman shared some details about his organization, why reinforcement learning is important and how the OpenAI Gym will make it easier for AI researchers to design, iterate and improve their next generation applications.

Google's DeepMind already proved it could beat a Go world champion, and it's AI has even been used to cut the company's energy costs. How about improving the way we treat cancer? DeepMind recently announced a partnership with the Radiotherapy Department at University College London Hospitals NHS Foundation Trust. The department provides world-leading cancer treatment, but there's one area in particular where Google's AI could help speed up the process. When it comes to certain types of cancer in areas like the head and neck doctors need to plan carefully to avoid damaging important organs and body parts.

Cortana may eventually be able to peek inside your refrigerator following news that Microsoft is bringing its machine learning tech to kitchens. The computer firm is working on a new computer vision module called SmartDeviceBox, which will be installed inside fridges to make them capable of recognising objects inside and telling owners when they need to re-stock. The SmartDeviceBox is essentially an internet-connected camera unit that sits inside fridges and tracks the objects inside. The unit contains an image processing system based on Microsoft's suite of deep learning tools, which has been trained to recognise a variety of products such as milk cartons, ketchup bottles and pickle jars. The system is able to collate these into an inventory list that customers can view in list form using a smartphone app.

CNTK is in active use at Microsoft and constantly evolving.

Although there has been substantial research in software analytics for effort estimation in traditional software projects, little work has been done for estimation in agile projects, especially estimating user stories or issues. Story points are the most common unit of measure used for estimating the effort involved in implementing a user story or resolving an issue. In this paper, we offer for the \emph{first} time a comprehensive dataset for story points-based estimation that contains 23,313 issues from 16 open source projects. We also propose a prediction model for estimating story points based on a novel combination of two powerful deep learning architectures: long short-term memory and recurrent highway network. Our prediction system is \emph{end-to-end} trainable from raw input data to prediction outcomes without any manual feature engineering. An empirical evaluation demonstrates that our approach consistently outperforms three common effort estimation baselines and two alternatives in both Mean Absolute Error and the Standardized Accuracy.

These notes accompany the Stanford CS class CS231n: Convolutional Neural Networks for Visual Recognition. You can also submit a pull request directly to our git repo. We encourage the use of the hypothes.is