Ask a business person about the metaphor of war and business and pretty quickly you'll get to Sun Tzu's The Art of War an ancient text about terrain, tactics, and frontal assault. A few years ago Art of War was all the rage in business circles and every hotshot executive could quote it. Heck, it is often claimed that Larry Ellison, then CEO Oracle, ran his business with a well thumbed copy of Sun Tzu's tome within easy reach. Regardless, Tzu is off the front burner now and management fads have moved on. Even so, the war metaphor is revived regularly, most recently by Jeff Gothelf and Josh Seiden in Sense and Respond: How Successful Organizations Listen to Customers and Create New Products Continuously.

This article explores in more detail the idea of Meta Learning that was previously introduced in a post "The Meta Model and Meta Meta Model of Deep Learning". In this post, I explore "Learning to Learn" as a Meta Learning approach. We have to be very careful to distinguish between Learning to Learn and Hyper Parameter Optimization (HPO). HPO and more generally searching for architectures differs from "learning to learn" in that that HPO explores the space of architectures while meta-learning explores the space of learning algorithms. Meta-learning is all the rage in research these days.

It cannot be denied that Artificial Intelligence is having a growing impact in many areas of human activity. It is helping humans to communicate with each other, even beyond linguistic boundaries, find information in the vast resources available on the web, solve challenging problems that go beyond the competence of a single expert, and enable the deployment of autonomous systems, such as self-driving cars, that handle complex interactions with the real world with little or no human intervention. These applications are perhaps not like the fully autonomous conscious intelligent robots that science fiction stories have been predicting, but they are nevertheless very important and useful, and most importantly they are real and here today. But neither can it be denied that Artificial Intelligence comes with certain risks. Many people (including luminaries such as Bill Gates or Stephen Hawking) believe that the main risk of artificial intelligence is that it gets out of hand.

Are you concerned about the continued development of smart gadgets increasing their ability to listen, think, speak and feel more human than humans? When we think about science fiction movies like The Terminator, iRobot, Maximum Overdrive, RoboCop, 2001: A Space Odessey, The Machine, Transcendence, Project Almanac, Robot and Frank, and The Matrix Revolutions, many people worry these smart gadgets will overpower the world. Voice Command Devices (VCD) are evidence of how science and technology leaped into an incredible world of artificial intelligence (AI). Voice-activated smart devices such as Alexa, Siri, and Google are programmed to interact, play music, provide the weather, play audiobooks, keep up with your calendar appointments and provide other real-time information requested by the user. One funny, but kind of freaky occurrence has been reports made by individuals who were on the telephone with someone who owns one of these VCD's and heard those devices suddenly start to talk on their own.

As Matt Asay observed last week, AI appears to be reaching "peak ludicrous mode," with almost every software vendor laying claim to today's most hyped technology. Hang on -- see what I did there? I used "AI" and "machine learning" interchangeably, which should get me busted by the artificial thought police. The first thing you need to know about AI (and machine learning) is that it's full of confusing, overlapping terminology, not to mention algorithms with functions that are opaque to all but a select few. This combination of hype and nearly impenetrable nomenclature can get pretty irritating.

Dr Hensman (above), who was most recently a lecturer at the University of Lancaster, has more than 35 published academic journal articles to his name, based on his industry-defining research on Gaussian processes. This approach moves away from the use of limited decision trees for AI, which are based on'if X then Y principles', to focusing on the development of self-learning, autonomous agents. These can be applied to a whole variety of problems from autonomous vehicle control, to non-player characters in video games, to smart city simulations. Dr Hensman will be encouraged to continue his academic research while at PROWLER.io – and his findings will support the development of world-leading AI technology. "Until now I've had no interest in departing from full-time academia," said Dr Hensman.

A quick, hassle-free way to stay on top of robotics news, our robotics digest is released on the first Monday of every month. Sign up to get it in your inbox. February is only just gone, and already 2017 is shaping up to be a year full of big ideas and ambitions. The Future of Life Institute, for example, just published the Asilomar AI principles: 23 guidelines to ensure AI developments are beneficial to humanity. They are calling for shared responsibility and caution against an AI arms race.

In some cases, the AI designation might be warranted, even if with some aspiration. Autonomous vehicles, for example, don't quite measure up to R2D2 (or Hal), but they do deploy a combination of sensors, data, and computation to perform the complex work of driving.

The creator of a chatbot which overturned more than 160,000 parking fines and helped vulnerable people apply for emergency housing is now turning the bot to helping refugees claim asylum. The original DoNotPay, created by Stanford student Joshua Browder, describes itself as "the world's first robot lawyer", giving free legal aid to users through a simple-to-use chat interface. The chatbot, using Facebook Messenger, can now help refugees fill in an immigration application in the US and Canada. For those in the UK, it helps them apply for asylum support. The London-born developer worked with lawyers in each country, as well as speaking to asylum seekers whose applications have been successful.

Machine learning and deep learning projects are gaining more and more importance in most enterprises. The complete process includes data preparation, building an analytic model and deploying it to production. This is an insights-action-loop which improves the analytic models continuously. Forrester calls the complete process and the platform behind it the Insights Platform. A key task when you want to build an appropriate analytic model using machine learning or deep learning techniques, is the integration and preparation of data sets from various sources like files, databases, big data storage, sensors or social networks. This step can take up to 80 percent of the whole analytics project. This article compares different alternative techniques to prepare data, including extract-transform-load (ETL) batch processing, streaming ingestion and data wrangling.