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.

Robots are taking over retail, well not quite. There has been much talk about robots taking on human roles within retail. Whilst this seems like an incredible innovation and still very futuristic, IBM Watson, the supercomputing power behind Pepper, the high profile robot with linguistic talents is one of several technologies such as advanced voice-recognition, data analytics, cognitive systems and automation that are going to transform our experience of shopping. What can be a painful drag, will, thanks to these advances, become more convenient and satisfying, whichever way we shop. Get right to the point When you go online to shop for a new coat the power of machine learning and artificial intelligence means you won't immediately be confronted by a bewildering range that takes hours to trawl through and ends up making you unhappy with your indecisiveness.

Is creativity in the marketing industry on a slow death march? Go to any marketing conference or seminar these days and you are likely to hear someone lament declining creative standards and the loss of'brand magic'. Financial pressures have put a squeeze on marketers' ability to unleash their creative instincts, but of greater concern is the role of technology and automation. The growing influence of programmatic media buying, machine learning and artificial intelligence (AI) have changed the way marketers perform their jobs, potentially putting creativity and independent thought at risk. HSBC's former head of marketing in EMEA Philip Mehl hit on this point in a recent Marketing Week roundtable when he declared: "Marketing used to be a creative challenge but it's a data challenge now."

Detecting cancer sooner rather than later can help prevent worst outcomes in many cases. Yet diagnosing different types of cancers in their earliest stages is not often possible. It requires years of training to gain the expertise and experience to do well in detecting cancerous tumor. Even with extensive training, some symptoms may be hard to distinguish from the signs of other similar diseases. For example, agreement in diagnosis for some forms of breast cancer can be as low as 48% and this percentage of disagreement is not surprising given the massive amount of information needed to review in order to make an accurate diagnoses and often within a limited time. To overcome these issues, Google researchers have developed an automated detection algorithm that is effective at identifying breast cancer.

When I reviewed Nebo, MyScript's award-winning, ink-to-digital-text app, I wrote that it's a "game changer" that "sets new standards for accurate handwriting recognition". Nebo's superior accuracy rests on MyScript's interactive ink technology. Recently I had the opportunity to ask MyScript's CTO, Pierre-Michel Lallican, how interactive ink works. Interactive ink is a complex system with three primary modules that are shown in the image at the top of this article. The Digital Ink Management module is the interface between the screen and interactive ink's text recognition and management functions.

Within the next five years, Google will produce a viable quantum computer. That's the stake the company has just planted. In the pages of Nature late last week, researchers from Google's Quantum AI Laboratory told the world that a machine leveraging the seemingly magical principles of quantum mechanics will soon outperform traditional computers on certain tasks. They said this long-anticipated technology will, among other things, improve the artificial intelligence that's already remaking the tech world. "The field of quantum computing will soon achieve a historic milestone," the team wrote.