Robo-advisors have been a fascination for investors--and this magazine--since they were born early in the last bull market. Armed with low fees, fancy software, and algorithmic asset allocations, the robos promised a sea change in investment advice, no humans required. The appeal was clear, especially to the youngest investors, raised on all forms of automation.

In his introductory keynote for Microsoft's Build 2016 developer's conference, CEO Satya Nadella explained the company's vision for the future of communication. The concept of "conversation as a platform" will serve as the foundation of how we will interact with technology and one another. "We want to take the power of human language and apply that more pervasively to all the computing interfaces and computing interactions," he said, also noting the future "is not going to be about man vs. machines, it's going to be about man with machines." This will require humans to teach computers human language, conversational understanding, and their personal preferences so technology can be more helpful day-to-day life. Nadella noted how as humans infuse intelligence into everything, it's important for Microsoft to have a principled approach and have a way to guide their communication design.

With improving technology and marketing and search engines getting more complex, use of artificial intelligence seems to be gearing up to play a major role in the search queries. Siri, Google Now and Cortana are already playing major roles in the operation of major devices. However, tech giants and marketers are seeking the advanced technology of these artificial intelligence to make better search queries. Marketers too are keen in including machine learning and the use of artificial intelligence within the marketing platform to use this technology optimally for the best form of marketing possible. With the aid of digital assistance that artificial intelligence provides, search queries and results are available at greater ease.

As machines are branching out into ever more unknown and unpredictable environments, so to is their ability to understand those environments improving. I recently wrote, for instance, about a project called the Visual Genome, which aims to provide a hub for understanding how machines are able (or not) to understand the world they operate in. The platform was developed by professors at the Stanford Artificial Intelligence Lab and aims to tackle some of the toughest questions in computer vision, with the eventual goal of developing machines that can understand what it sees. Researchers at Boston University are conducting work on the same topic, and have developed a robot that is capable of recognizing specific objects, and then maneuver around them without human support. The ability for robots to guide and navigate for themselves is hugely important and feeds into a vast range of possible applications.

The results are all over the map, but a few of the test images -- like the puppy and Monarch butterfly above -- look pretty good. The algorithms work using a few common sense rules (the sky is typically blue, dirt roads are usually brown and have a similar texture), and "hallucinating" a plausible colorized photo. But the results are far from perfect. For example, the neural net has a hard time coloring within the lines with more complex subjects like vegetables on a plate or keeping a heron bright white. When it does hit the mark, however, it's impressive. In fact, 20 percent of the folks surveyed for a "colorization Turing test" were fooled into thinking that the images weren't monochrome to start.

HONG KONG – Like innumerable children with imaginations fired by animated films, Hong Kong product and graphic designer Ricky Ma grew up watching cartoons featuring the adventures of robots, and dreamed of building his own one day. Unlike most of the others, however, Ma has realized his childhood dream at the age of 42, by constructing a life-size robot from scratch on the balcony of his home. The fruit of his labors of a year and a half, and a budget of more than 50,000, is a female robot prototype he calls the Mark 1, modeled after a Hollywood star whose name he wants to keep under wraps but appears to be Scarlett Johansson. It responds to a set of programmed verbal commands spoken into a microphone. "I figured I should just do it when the timing is right and realize my dream. If I realize my dream, I will have no regrets in life," said Ma, who had to learn about fields completely new to him before he could build the complex gadget.

Continued fractions is a fascinating subject, see picture below. They are extremely stable from a numerical point of view, have tons of useful properties, and are thus more robust against over-fitting, compared with standard linear regression. They have been extensively studied in the context of approximation and numerical analysis. Why is there no statistical theory of continued fractions? Why aren't these beautiful and powerful mathematical objects not used in data science?

I describe here the projects that I worked on, as well as career progress, starting 25 years ago as a PhD student in statistics, until today, and the transformation from statistician to data scientist that occurred slowly and started more than 20 years ago. This also illustrates many applications of data science, most are still active. My interest in mathematics started when I was 7 or 8, I remember being fascinated by the powers of 2 in primary school, and later purchasing cheap russian math books (Mir publisher) translated in French, for my entertainement. In high school, I participated in the mathematical olympiads, and did my own math research during math classes, rather than listening to the very boring lessons. When I attended college, I stopped showing up in the classroom altogether - afterall, you could just read the syllabus, memorize the material before the exam and regurgitate it at the exam.

Reading the academic literature Text Analytics seems difficult. However, applying it in practice has shown us that Text Classification is much easier than it looks. Most of the Classifiers consist of only a few lines of code.In this three-part blog series we will examine the three well-known Classifiers; the Naive Bayes, Maximum Entropy and Support Vector Machines. From the introductionary blog we know that the Naive Bayes Classifier is based on the bag-of-words model. With the bag-of-words model we check which word of the text-document appears in a positive-words-list or a negative-words-list.

Forty years after Apple started, the tech giant's co-founder Steve Wozniak outlined his technology predictions for the next four decades. Wozniak said he does not believe computing power will increase as much as it has since he, Steve Jobs and Ronald Wayne founded Apple. But he contended several areas, including machine learning, self-driving cars and virtual reality, will make strides in the coming years. "We may come up with more clever ways of getting learning machines to act like the human brain," Wozniak told CNBC's "Fast Money" on Friday. "It could start thinking for itself and learning faster than humans can," he added.