DeepMind, an artificial intelligence company owned by Google, has attempted to justify why it needs access to millions of NHS patient records for a kidney monitoring app, after a new investigation from New Scientist questioned whether an ethical approval process should have been obtained first. The AI research lab, acquired by Google in 2014 for around 400 million, signed a data-sharing agreement with the Royal Free London NHS Foundation Trust on 29 September 2015. The agreement gives Google DeepMind access to the names, addresses, and medical conditions of the 1.6 million patients that are treated at Barnet, Chase Farm, and the Royal Free hospitals each year, as well as data on all patients treated by the Trust in the past five years. This week, New Scientist questioned why Google DeepMind needs access to so much data on so many people, including those who have never experienced kidney problems, for the app, which is called Streams. Streams -- used by Royal Free clinicians in three separate trials since December 2015 -- is designed to detect acute kidney injury (AKI), a condition that kills more than 1,000 people a month.

Ten years ago, Justin Kan and Emmett Shear had just sold their app company, Kiko, and were itching for another venture. They had a concept -- livestream video -- but no idea how to build it. So they sent an email to the MIT engineering listserv, requesting a "hardware hacker" for an unspecified project. Kyle Vogt, a young student fascinated with robotics, replied. They met over coffee where Kan and Shear pitched their idea before flying out to San Francisco.

A Georgia Institute of Technology research team has devised a novel way to help keep a driverless vehicle under control as it maneuvers at the edge of its handling limits. The approach could help make self-driving cars of the future safer under hazardous road conditions. Researchers from Georgia Tech's Daniel Guggenheim School of Aerospace Engineering (AE) and the School of Interactive Computing (IC) have assessed the new technology by racing, sliding, and jumping one-fifth-scale, fully autonomous auto-rally cars at the equivalent of 90 mph. The technique uses advanced algorithms and onboard computing, in concert with installed sensing devices, to increase vehicular stability while maintaining performance. The work, tested at the Georgia Tech Autonomous Racing Facility, is sponsored by the U.S. Army Research Office.

Speaker: Dan Bohus Most research to date on spoken language interaction has focused on supporting dialog with single users in limited domains and contexts. Significant progress in this space has enabled wide-scale deployments of voice-enabled personal assistants. At the same time, important challenges remain largely unaddressed in the realm of physically situated spoken language interaction (e.g., in-car systems, robots in public spaces, ambient assistance). In this talk, I will outline a core set of communicative competencies required for supporting dialog in physically situated settings – such as models of multiparty engagement, turn-taking and interaction planning, and I will present samples of work as part of a broader research agenda in this area. The proposed models and systems harness a diverse set of AI technologies, and throughout the talk I will discuss a number of important opportunities and challenges for developing such integrative AI systems. We evaluate our framework on challenging simulated decision-making problems and on a physical humanoid robot, and we demonstrate that it allows for the efficient and active construction of reusable skills from limited data.

Robots are one step closer to being able to experience an essential human feeling: pain. Researchers in Germany are currently creating a "nervous system" that would mimic a pain response in robots, allowing them to quickly react and avoid harmful situations. "Pain is a system that protects us," researcher Johannes Kuehn told a conference of engineers last week. "When we evade from the source of pain, it helps us not get hurt." The researchers programmed their robot to experience a "hierarchy" of pain through a variety of different stimuli, such as blunt force or heat.

Take time to explore Microsoft's Azure machine learning platform, Azure ML--a production environment that simplifies the development and deployment of machine learning models. In this O'Reilly report, Stephen Elston from Quantia Analytics uses a complete data science example (forecasting hourly demand for a bicycle rental system) to show you how to manipulate data, construct models, and evaluate models with Azure ML. The report walks you through key steps in the data science process from problem definition, data understanding, and feature engineering, through construction of a regression model and presentation of results. You'll also learn how to extend Azure ML with Python. Elston uses downloadable Python code and data to demonstrate how to perform data munging, data visualization, and in-depth evaluation of model performance.

Twinkle twinkle little star, how I wonder what you are… This free evening talk will explore Machine Learning applications in astrophysics, using manual star classification techniques as an example. In my previous life, I was an astronomer and one of the big tasks many PhD students face is manual star classification. But why ask a student to do what a machine should be able to do too? In this talk, I use the spectra (stellar flux vs wavelength) of identified stars to build a classifier which will detect the identity of the stars. Using a very simple non linear SVM, I achieve an 86% accuracy with my model.

Creating mood sensing technology has become very popular in recent years. There is a wide range of companies trying to detect your emotions from what you write, the tone of your voice, or from the expressions on your face. All of these companies offer their technology online through cloud-based programming interfaces (APIs). As part of my offline emotion sensing hardware (Project Jammin), I have already built early prototypes of facial expression and speech content recognition for emotion detection. In this short article I describe the missing part, a voice tone analyzer.

We live in a world of information abundance. The ability to create, produce, market and distribute content in almost any form has become very easy. So easy that a shocking amount of what we're reading is created not by humans, but by computer algorithms - perhaps even this post;). In the most recent post on strategy4telcomediatech I highlighted the need to consider the role that'bots' play particularly in automating conversational commerce - 'sales and service'. In this post I take a look at the use of'bots' or algorithmic content in digital media, how it is shaping our consumption habits, content creation and some of the capabilities required in both traditional and new media organisations. Algorithms and natural language generators have been around for a while.

This column was originally published on Jeff Neal's blog, ChiefHRO.com, The government is sitting on a treasure trove of HR data that it does not typically use. For example, agencies have data about performance, and data about where they recruit and what kinds of questions they ask in job announcements. I do not know of a single agency that is comparing the questions they ask to the performance they get from the selectees. There are so many possibilities to use the data to produce actionable information that would help agencies do better hiring, get better performance, and use their resources more wisely.