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"The aim of marketing is to know and understand the customer so well the product or service fits him and sells itself." Anyone who shops online or uses a music streaming service will have experienced recommendations. Their accuracy can be surprising at first glance, but these recommendations aren't made by accident. They are based on sophisticated machine learning techniques, pattern analysis and automated decision making. Systems like these rely on a technology infrastructure that can import, analyse and interpret huge volumes of data and take appropriate action without the need for human intervention.

It's official: driverless cars have hit the race tracks. Roborace, the autonomous race car maker, had its two self-driving'DevBots' compete against each other at the Formula E Buenos Aires ePrix. The race didn't go without its own surprises: One car had to dodge a random dog that ended up on the race track, and the other ended up hitting a barrier, unable to finish the race. Roborace's self-driving car races will take place at Formula E events throughout 2017. All cars competing will be made identically.

If the robots take over, at least Elon Musk will be able to say "I told you so." The billionaire inventor loves to make the impossible possible, but he is deeply afraid of artificial intelligence (AI). On Twitter this weekend, Musk said that "we need to be super careful with AI," adding that they are "potentially more dangerous than nukes." If that weren't concerning enough, Musk followed up his statement with another tweet that read: "Hope we're not just the biological boot loader for digital superintelligence. Unfortunately, that is increasingly probable."

If you're as plugged in as we are, then you probably have something like 150 online accounts. That means you probably have 150 variations of the same half-dozen login names and passwords. And chances are, you've gotten a message from a friend in the last few months telling you that it looks like you got hacked again, after he received email spam to enlarge an embarrassingly small body part. Well, we think the days of the alphanumeric password are numbered. The age of biometric authentication is dawning.

When it comes to machine learning and data science, there are so many language options to choose from. Data scientist Jean-Francois Puget does some analysis to decide which one is best. What programming language should you learn to get a machine learning or data science job? I could provide my own answer to it and explain why, but I'd rather look at some data first. After all, this is what machine learners and data scientists should do: look at data, not opinions. So, let's look at some data.

Last week, I compiled a list of the 5 jobs robots will take first. Today, let's have a go at the 5 jobs robots will take last. For this article only, let's define "robots" as technologies, such as machine learning algorithms running on purpose-built computer platforms, that have been trained to perform tasks that currently require humans to perform. For example, an assembly line worker performs mostly manual repetitive tasks which, depending on complexity and a cost/benefit analysis, can be automated. A CEO of a major multinational conglomerate performs mostly cognitive nonrepetitive tasks which are much harder to automate.

Oxford University researchers have estimated that 47 percent of U.S. jobs could be automated within the next two decades. But which white-collar jobs will robots take first? First, we should define "robots" (for this article only) as technologies, such as machine learning algorithms running on purpose-built computer platforms, that have been trained to perform tasks that currently require humans to perform. With this in mind, let's think about what you'll do after white-collar work. Oh, and I do have a solution for the short term that will make you the last to lose your job to a robot, but I'm saving it for the end of the article.

Physicists have developed a quantum machine learning algorithm that can handle infinite dimensions--that is, it works with continuous variables (which have an infinite number of possible values on a closed interval) instead of the typically used discrete variables (which have only a finite number of values). The researchers, Hoi-Kwan Lau et al., have published a paper on generalizing quantum machine learning to infinite dimensions in a recent issue of Physical Review Letters. As the physicists explain, quantum machine learning is a new subfield within the field of quantum information that combines the speed of quantum computing with the ability to learn and adapt, as offered by machine learning. One of the biggest advantages of having a quantum machine learning algorithm for continuous variables is that it can theoretically operate much faster than classical algorithms. Since many science and engineering models involve continuous variables, applying quantum machine learning to these problems could potentially have far-reaching applications.

Fujitsu today announced that it has received RIKEN's order for the "Deep learning system," which in terms of operations will be one of the largest-scale supercomputers in Japan specializing in AI research. The RIKEN Center for Advanced Intelligence Project will use the new system, scheduled to go online in April 2017, as a platform to accelerate R&D into AI technology. The system's total theoretical processing performance will reach 4 petaflops(1). The system will be comprised of two server architectures, with 24 of NVIDIA DGX-1 servers and 32 FUJITSU Server PRIMERGY RX2530 M2 servers, along with a high-reliability, high-performance storage system. Fujitsu is leveraging the extensive know-how that it and Fujitsu Laboratories Ltd. have in high-performance computing development and AI research to build and operate one of Japan's most advanced AI research systems.