There would always be a first death in a driverless car and it happened in May 2016. Joshua Brown had engaged the autopilot system in his Tesla when a tractor-trailor drove across the road in front of him. It seems that neither he nor the sensors in the autopilot noticed the white-sided truck against a brightly lit sky, with tragic results. Of course many people die in car crashes every day – in the USA there is one fatality every 94 million miles, and according to Tesla this was the first known fatality in over 130 million miles of driving with activated autopilot. In fact, given that most road fatalities are the result of human error, it has been said that autonomous cars should make travelling safer.

Under the simple skin of Uber lies complexity you may not have considered: the logistics of predicting how long it will take rides (or meals) to arrive, setting pricing and even where to wait to give a driver the best odds of finding you for pick-up. Underlying those decisions is machine learning, using computers to find patterns and make predictions without explicitly programming them to do so. And that very important job falls to Danny Lange, a Danish-born researcher who joined the ride-hailing giant 11 months ago after a nearly two-year stint leading machine learning efforts for Amazon Web Services. Before that, Lange wrangled big data for Microsoft and even launched a Silicon Valley startup. He heads a growing team of researchers in San Francisco and Seattle.

The increasing pace of digital is changing civilization as we know it, according Peter Sondergaard, senior vice president of Gartner Research, who spoke on Oct. 17 from the middle of a harsh spotlight on a darkened stage at the Gartner Symposium ITxpo 2016 in Orlando, Florida. The digital world around us is in a permanent state of upgrade," he warned. The dramatic words were followed by other speeches delivered by Daryl Plummer, vice president and Gartner fellow, and Hung LeHong, vice president and Gartner fellow. The speeches were no less dramatic, but rather less dark, taking their tone from another early passage in Sondergaard's speech: "CIOs are builders again." CIOs are building an infrastructure for an increasingly digital business, Sondergaard said, noting that Gartner is estimating that within three years more than half the value of most company's products will arise from their digital content. That digital content will be built on a digital platform and an infrastructure that is critical because, according to Sondergaard, "When you build it, it will bring the capability to reach customers and things more intelligently." Traditional core IT systems remain important to the organization, because the business must continue to operate while the digital transformation takes place. This traditional IT is Mode 1 in Gartner's Bimodal IT model, with Mode 2 as the dynamic, transformative digitalization mode. LeHong said, "You don't need two organizations for bimodal.

Sounds great, but how does this organization plan to do that?

In machine learning, a deep belief network (DBN) is a generative graphical model, or alternatively a type of deep neural network, composed of multiple layers of latent variables ("hidden units"), with connections between the layers but not between units within each layer. When trained on a set of examples in an unsupervised way, a DBN can learn to probabilistically reconstruct its inputs. The layers then act as feature detectors on inputs. After this learning step, a DBN can be further trained in a supervised way to perform classification. DBNs can be viewed as a composition of simple, unsupervised networks such as restricted Boltzmann machines (RBMs) or autoencoders, where each sub-network's hidden layer serves as the visible layer for the next.

Medical imaging leaders are facing a number of challenges in the ongoing transformation of care delivery, yet many of the key market forces that are changing the field also are pushing imaging toward innovation, according to many imaging informatics leaders at a recent conference in New York City. At a conference focused on driving innovation in imaging, leading radiologists and imaging informaticists shared their perspectives on the future of medical imaging and the role that imaging plays in the transition to value-based care and population health initiatives. The event was sponsored by New York City-based Ambra Health, formerly DICOM Grid, a medical data and image management company. There are key industry forces pushing radiology to innovate, notably care delivery transformation, imaging consumerism and the overall growth outlook for the radiology field, Lea Halim, senior consultant at Washington, D.C.-based The Advisory Board's Research and Insights division, said. Halim also said there are industry and economic trends impacting the growth outlook for imaging.

Data is pouring into the hands of cancer researchers, thanks to improvements in imaging, models and understanding of genetics. Today the data from a single patient's tumor in a clinical trial can add up to one terabyte--the equivalent of 130,000 books. But we don't yet have the tools to efficiently process the mountain of genetic data to make more precise predictions for therapy. And it's needed: treating cancer remains a complex moving target. We can't yet say precisely how a specific tumor will react to any given drug, and as a patient is treated, cancer cells can continue to evolve, making the initial therapy less effective.

Robots learn to recognize and react to human emotions. For now, however, it is still in its infancy. That's why it's difficult to predict just how clever the robots of the future will be. However, I am confident that in twenty or thirty years artificial intelligence will make things possible that are currently far beyond the limits of our imagination and that will have a huge influence on our daily lives. For instance, it may become possible one day to replace parts of the human brain with chips, eventually resulting in a complete merging of consciousness with computing.

Nintendo has revealed its new console, which it hopes can bring back its fortunes. The console originally known only by the codename "NX", is in fact called "Switch". And that appears to refer to the fact that it is a hybrid system – allowing people to play games on their TV but then take it away if they need to leave, at which point the controller will transform into an entirely handheld system. The new console will be available in March 2017, the company said. The robot developed by Seed Solutions sings and dances to the music during the Japan Robot Week 2016 at Tokyo Big Sight. Aurora Flight Sciences' technicians work on an Aircrew Labor In-Cockpit Automantion System (ALIAS) device in the firm's Centaur aircraft at Manassas Airport in Manassas, Va.

Two years ago Stephen Hawking told the BBC that the development of full artificial intelligence, could spell the end of the human race. His was not the only voice warning of the dangers of AI - Elon Musk, Bill Gates and Steve Wozniak also expressed their concerns about where the technology was heading - though Professor Hawking's was the most apocalyptic vision of a world where robots decide they don't need us any more. What all of these prophets of AI doom wanted to do was to get the world thinking about where the science was heading - and make sure other voices joined the scientists in that debate. That they have achieved that aim was evident on Wednesday night at an event in Cambridge marking the opening of the Centre for the Future of Intelligence, designed to do some of that thinking about the implications of AI. And Professor Hawking was there to help launch the centre.