What if we were to tell you that your car is a better driver than you? You would probably have a good laugh over it and say that I was joking. That it was a preposterous thought. Not only because you believe that you're an excellent driver, and present before me your evidence of an accident-free driving life of the past 20 years; but also because, somehow, the thought of a car being a driver in its own right, and an excellent one at that, goes against the grain of what you have grown up knowing and believing. And yet, today, news reports are filled with announcements of car manufacturers such as Audi, Volvo, Toyota, to name only a few, not to mention newcomers like Tesla, working on technologies to put commercially-licensed driverless or self-driving or autonomous cars on the road in the next 5 years.

"I'd like to buy a new boomerang please. Also, can you tell me how to throw the old one away?" Never mind whether you think that joke is funny. Do you think your best friend would like it? You might think you know the answer; after all, people like each other partly because they make each other laugh.

Professors Hastie and Tibshirani published "The Elements of Statistical learning: Data mining, inference and prediction", with Jerome Friedman (springer, 2001, second edition 2009). This book has received a terrific reception, with over 45,000 copies sold. Both presenters are actively involved in research in statistical learning methods, and are well-known not only in the statistics community but in the machine-learning, neural network and bioinformatics fields as well. Their newer book "An Introduction to Statistical Learning, with Applications in R" (with Gareth James and Daniela Witten, 2013) is also a best-seller, and has remained consistently in the top 10 in the Amazon categories "Mathematics and Statistics" and "Artificial Intelligence", with a five-star rating based on 84 customer reviews. Over the years they have become leaders in the statistical analysis of DNA microarrays, working with leading-edge biologists such as Patrick Brown of Stanford University, and David Botstein of Princeton.

Juries are seldom allowed to visit crime scenes. There are exceptions, usually in difficult, high-profile murder cases such as the O.J Simpson trial in 1995 in the US and the Jill Dando murder trial in 2001 in the UK. But asking jurors to become fact finders in this way comes with myriad problems, from possible biases to the logistical and security challenges of taking them to the crime scene. A site visit by the Dando jury needed a convoy of five vehicles to transport the jurors, lawyers, judge and their police escorts to the scene, passing through police barricades surrounded by neighbours, journalists and other spectators. It became a media spectacle.

Neural networks work so well because they roughly reflect the structure of the underlying universe around us, new research suggests.

Spookily powerful artificial intelligence (AI) systems may work so well because their structure exploits the fundamental laws of the universe, new research suggests. The new findings may help answer a longstanding mystery about a class of artificial intelligence that employ a strategy called deep learning. These deep learning or deep neural network programs, as they're called, are algorithms that have many layers in which lower-level calculations feed into higher ones. Deep neural networks often perform astonishingly well at solving problems as complex as beating the world's best player of the strategy board game Go or classifying cat photos, yet know one fully understood why. It turns out, one reason may be that they are tapping into the very special properties of the physical world, said Max Tegmark, a physicist at the Massachusetts Institute of Technology (MIT) and a co-author of the new research.

That's the premise of Nugit, a Singapore-based startup focused on marketing that raised 5.2 million in fresh funding from Sequoia Capital's India fund this week. Nugit landed undisclosed seed funding from 500 Startups and The Hub Singapore last year. Founded by Australia-born marketing executive David Sanderson, Nugit sits between its customers and their data platforms -- it supports 15 right now, including Facebook Ad Manager, Google AdWords and DoubleClick -- to help make sense of realms of information. The idea is to make a digital marketer's job easier by cutting down on noise and producing "decision-ready reports," such as PowerPoint presentations, graphics and other visualizations. The company started out automating many of the processes that marketers are faced with when dealing with data, such as cleaning information and aligning it, until Sanderson -- formerly of GroupM and other ad agencies -- realized that computers could go beyond that and deliver insight that is difficult or seriously time-consuming for humans.

San Francisco-based entrepreneur Eugenia Kuyda created a software bot to resemble her friend who had died in an accident. Kuyda gathered up her friend's old text messages and fed them into a neural network built by developers at her artificial intelligence startup. The entrepreneur initially had doubts about the project, but followed on with it.

The term "artificial intelligence" sometimes conjures up images of the distant future, and a time when self-aware robots take over the planet and make human capabilities irrelevant, because the computer overlords are way better at everything humans do. While that vision may be workable for a hair-raising Halloween movie script, it's way off base when it comes to the realities of the current generation of artificial intelligence (AI). If you tackle this story from the highest level, AI isn't currently a force that is poised to overrun humans. Instead, AI is simply a group of technologies that will increasingly be used to augment human capabilities, and make us better at the things we do best. What's more, AI isn't a story set in the distant future.

Nicely done piece by Ronald van Loon, The 10 Algorithms Machine Learning Engineers Need to Know . I like these concise descriptions of useful techniques. Its good to know these for sharing with decision makers. Inclusion and descriptions are debatable, which is OK. These also include explanatory visuals of each each technique.