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.

With huge strides in AI--from advances in the driverless vehicle realm, to mastering games such as poker and Go, to automating customer service interactions--this advanced technology is poised to revolutionize businesses. But the terms AI, machine learning, and deep learning are often used haphazardly and interchangeably, when there are key differences between each type of technology. Here's a guide to the differences between these three tools to help you master machine intelligence. SEE: Inside Amazon's clickworker platform: How half a million people are being paid pennies to train AI (PDF download) (TechRepublic) AI is the broadest way to think about advanced, computer intelligence. In 1956 at the Dartmouth Artificial Intelligence Conference, the technology was described as such: "Every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it."

Facebook has introduced a number of new features for its Messenger platform, allowing developers to create more powerful and engaging bots. The Messenger Platform is now in version 1.4. The most significant change is that Facebook is giving developers the option to permanently hide the compose window. This means that a developer will not have to interpret all natural language strings as input, relying instead on a far simpler set of buttons. Essentially, the change means that bots can now be made to appear more like traditional applications, with no need for a chat functionality to be built in.

IBM has taken its first step towards selling computers that are millions of times faster than the one you're reading this on. The company has set up a new division, IBM Q, that is intended to make quantum computers and sell them commercially. Until now, quantum computers have mostly been a much hyped but long away dream. But IBM believes they are close enough to reality to start work on getting software ready for when they become commercially available. The giant human-like robot bears a striking resemblance to the military robots starring in the movie'Avatar' and is claimed as a world first by its creators from a South Korean robotic company Waseda University's saxophonist robot WAS-5, developed by professor Atsuo Takanishi and Kaptain Rock playing one string light saber guitar perform jam session A man looks at an exhibit entitled'Mimus' a giant industrial robot which has been reprogrammed to interact with humans during a photocall at the new Design Museum in South Kensington, London Electrification Guru Dr. Wolfgang Ziebart talks about the electric Jaguar I-PACE concept SUV before it was unveiled before the Los Angeles Auto Show in Los Angeles, California, U.S The Jaguar I-PACE Concept car is the start of a new era for Jaguar.

In my last blog, I discussed how digital complacency could put your business at risk and how technological change is fast outpacing businesses' ability to adapt. This trend continues its relentless pursuit to consume those who choose to answer today's questions with yesterday's thinking. Furthermore, yet another transformational technology is now redefining the industry, and it has the potential to change everything. Machine learning is a type of artificial intelligence (AI) that provides computers with the ability to learn without being explicitly programmed. Machine learning focuses on the development of computer programs that can teach themselves to grow and change when exposed to new data.

Jeff Dean talks about how machine learning is being used at Google. After our first article covering the landscape of machine learning, and our second one bringing insights about TensorFlow and what's to come, we close our series with Jeff telling us about how machine learning is being used inside Google, what resources Google offers to developers and how you can get started with machine learning today. Where is it being used? JD: In our team, we've been building tools for solving machine-learning problems and then collaborating with lots of other teams over the last 5 or 6 years at Google to solve different machine-learning problems. And we started out collaborating with a handful of teams: the speech-recognition team, various teams that had computer-vision problems and then we built infrastructure software that allowed us to solve problems with those teams in machine learning.

I mean, you really suck. That's what I wanted to write, but a new feature in the Office 365 version of Word called Editor made an interesting suggestion. The "machine" noted how the word "really" is superfluous, and it's true. The extra word doesn't add anything to the sentence, so I removed it. I've been writing professionally since 2001 (around 10,000 published articles now), but I'm still learning, I guess.

Developers from Orange Labs in France developed a deep learning system that can quickly make young faces look older, and older faces look younger. A number of techniques already exist, but they are expensive and time consuming. Using CUDA, Tesla K40 GPUs and cuDNN for the deep learning work, they trained their neural network on 5,000 faces from each age group (0-18, 19- 29, 30-39, 40-49, 50-59, and 60 years old) taken from the Internet Movie Database and from Wikipedia and then labeled with the person's age -- this helped the system learn the characteristic signature of faces in each age group. A second neural network, called the face discriminator, looks at the synthetically aged face to see whether the original identity can still be picked out. If it can't, the image is rejected, which they call the process in their paper, Age Conditional Generative Adversarial Network.