This article discusses a far more general version of the technique described in our article The best kept secret about regression. Here we adapt our methodology so that it applies to data sets with a more complex structure, in particular with highly correlated independent variables. Our goal is to produce a regression tool that can be used as a black box, be very robust and parameter-free, and usable and easy-to-interpret by non-statisticians. It is part of a bigger project: automating many fundamental data science tasks, to make it easy, scalable and cheap for data consumers, not just for data experts. Readers are invited to further formalize the technology outlined here, and challenge my proposed methodology.

Bayesian inference is a way to get sharper predictions from your data. It's particularly useful when you don't have as much data as you would like and want to juice every last bit of predictive strength from it. Although it is sometimes described with reverence, Bayesian inference isn't magic or mystical. And even though the math under the hood can get dense, the concepts behind it are completely accessible. In brief, Bayesian inference lets you draw stronger conclusions from your data by folding in what you already know about the answer. Bayesian inference is based on the ideas of Thomas Bayes, a nonconformist Presbyterian minister in London about 300 years ago. He wrote two books, one on theology, and one on probability. His work included his now famous Bayes Theorem in raw form, which has since been applied to the problem of inference, the technical term for educated guessing. The popularity of Bayes' ideas was aided immeasurably by another minister, Richard Price. He saw their significance, refined them and published them. It would be more accurate and historically just to call Bayes' Theorem the Bayes-Price Rule.

Companion robots are playing an ever-increasing role in helping busy people look after the elderly and children. With an aging population and an increasing labor shortage, the demand for companion robots for elder care and children's companions is on the rise. Xiaoyuzaijia is an intelligent companion robot produced by Zaijia.com, a Beijing-based startup engaged in internet hardware and intelligent home appliances businesses. It specializes in security monitoring, human-computer emotional communication and entertainment functions. If the elderly people are ill at home, the robot can bring medicine and water. The robot will open the door when the guest comes.

A steady pencil-pour of steamed milk right in the heart of the espresso a Flat White dot in the making. Among many others, you know. One of our core principles is taking care of one another. That means mutual support, both professionally and personally. This ethos extends to our customers, who we figuratively (and sometimes literally) smother in hugs.

A lot of people are concerned about the coming robot and AI revolution. While it is true some jobs will be lost to robots in the process, quite a few sectors will remain virtually unchanged. Keep in mind this does not mean everyone should switch careers all of a sudden, as the change to robot-based employment will not happen overnight by any means. Some jobs require a human element, and until robots become far more advanced, the following jobs will not be on the bubble anytime soon. The healthcare sector will be hit quite hard by the impending robotization. However, certain aspects of healthcare cannot be replaced by robots in the near future.

This year, the Association for Computing Machinery (ACM) celebrates 50 years of the ACM Turing Award, the most prestigious technical award in the computing industry. The Turing Award, generally regarded as the'Nobel Prize of computing', is an annual prize awarded to "an individual selected for contributions of a technical nature made to the computing community". In celebration of the 50 year milestone, renowned computer scientist Melanie Mitchell spoke to CBR's Ellie Burns about artificial intelligence (AI) – the biggest breakthroughs, hurdles and myths surrounding the technology. EB: What are the most important examples of Artificial Intelligence in mainstream society today? MM: There are many important examples of AI in the mainstream; some very visible, others blended in so well with other methods that the AI part is nearly invisible.

The field of Artificial Intelligence (AI) was first introduced at a Dartmouth college conference in 1956. At the time, AI founder, Herbert Simon, predicted, "machines will be capable, within twenty years, of doing any work a man can do." But what Simon and the other optimistic early AI experts failed to appreciate are the numerous challenges involved in relying solely on machines - entities that lack the emotion that informs the human decision making process. Despite the fact that the AI field was born more than half a century ago, we've only recently in the last few years made monumental strides in advancing the artificial intelligence game. More specifically, we've started to transition from a model whereby AI assists humans to one whereby humans assist AI.

Our CEO Bill McDermott says, "Everything has to start with empathy for the end user." So, more precisely, what is empathy? It is "The quality of feeling and understanding another persons' situation in the present movement and communicating this to the person." A machine's ability to read the "emotion states" of a mind (body language, voice, and facial expression) is critical in the new paradigm of an autonomous system. Empathy changes the way we feel and more importantly, machines can be trained to recognize, express, and "have" emotions by reading those three emotion states.

I will dispense with any amenities and cut right to the chase. When it comes to corporate learning and training the numbers are truly staggering. Trust me, there's a lot more where this came from meaning there is no shortage of stats and research that speak to the benefits of e-Learning. In a piece last year for PC Magazine, Rob Marvin wrote something that of course struck a chord with me. I say of course because of me being the pop culture savant that I am.

The news feed below is from my #4IR page. As always, my focus is on investing. The Fourth Industrial Revolution represents change, with tech innovation leading to industrial and economic disruption. The three pillars of THIS Industrial Revolution are Agtech, Fintech, and Artificial Intelligence (in all it's shapes in sizes, from Big Data IOT to Deep Learning). Which companies can we invest in TODAY to take advantage of the Fourth Industrial Revolution?