Artificial intelligence can often be just as unintentionally prejudiced as its human creators, with potentially disastrous consequences. The US government thinks educating future programmers on AI ethics will help solve our computers' fairness problem. The White House released its report on the future of artificial intelligence research in the US on Wednesday, and it contains a slew of recommendations. In a section on fairness, the report notes what numerous AI researchers have already pointed out: biased data results in a biased machine. For example, artificial intelligence is being used by law enforcement across North America to identify convicts at risk of re-offending and high-risk areas for crime.

Background: As more and more researchers are turning to big data for new opportunities of biomedical discoveries, machine learning models, as the backbone of big data analysis, are mentioned more often in biomedical journals. However, owing to the inherent complexity of machine learning methods, they are prone to misuse. Because of the flexibility in specifying machine learning models, the results are often insufficiently reported in research articles, hindering reliable assessment of model validity and consistent interpretation of model outputs. Objective: To attain a set of guidelines on the use of machine learning predictive models within clinical settings to make sure the models are correctly applied and sufficiently reported so that true discoveries can be distinguished from random coincidence. Methods: A multidisciplinary panel of machine learning experts, clinicians, and traditional statisticians were interviewed, using an iterative process in accordance with the Delphi method. Results: The process produced a set of guidelines that consists of (1) a list of reporting items to be included in a research article and (2) a set of practical sequential steps for developing predictive models. Conclusions: A set of guidelines was generated to enable correct application of machine learning models and consistent reporting of model specifications and results in biomedical research. We believe that such guidelines will accelerate the adoption of big data analysis, particularly with machine learning methods, in the biomedical research community.

In the summer of 2015, Google released DeepDream, a neural network that transformed images into hypnotic hallucinations. It was one of the first instances of an experimental project that demonstrated what neural networks were capable of to the public, giving us a visceral glimpse at the future of AI. At the end of 2016, that future, well, hasn't quite arrived yet. However, this year we saw AI truly enter mainstream dialogue, as society confronted the sticky ethical implications of its design and regulation. Meanwhile, alongside this serious debate, we saw a multitude of highly visible, experimental, and sometimes very silly projects borne of AI.

Five percent of Accenture's workforce is no longer human. One of Accenture's managing directors, Michael Redding, shared that figure this month at a summit in New York on artificial intelligence. If five percent does not sound like much, note that, at Accenture, it equates to 20,000 full-time-equivalent positions. These are not projected numbers. This is the potential of A.I., and that potential is being tapped everywhere. Magellan Health, for example, utilizes a suite of programs – all under the banner of artificial intelligence – to handle a significant portion of its process for reviewing and approving requests for medical tests.

The annual exercise of looking forward to all the exciting innovations the next year can reasonably be expected to bring is here once again. Last year at Telegraph tech we predicted 2016 would witness the rise of mobile payments, the creation of smart cities that can think and function autonomously, and the premiere of virtual reality in people's living rooms. Trials have proven artificial intelligence to be effective in suggesting treatments by analysing patients' genomes This year we've expanded our horizons somewhat to include moonshot projects, the social ramifications of technology and one disaster scenario. Here are our predictions of the technology events to come in 2017. Self-driving vehicles have arrived more swiftly than anybody thought: Google and Apple have been experimenting with the technology for years, the Autopilot mode on Tesla cars has clocked up over 200 million miles, and every carmaker is scrambling to get self-driving software into their vehicles.

Security is the gift that keeps on giving. There's always one more vulnerability to be exploited, one more household-name company that's about to be breached. So, it shouldn't be surprising that 2016 offered up some new lessons -- or, in some cases, things the industry already knew but needed a reminder about. It isn't exactly the rise of the robots, but the Mirai botnet proved that the Internet of Things (IoT) is easily exploitable. This is a possibility that's been lurking all along, but in 2016, it became real.

David Eden from Tata Communications asks what technologies will shape the way people live, work and experience the world around us in 2017 and beyond. The article will also examine what barriers are standing in the way of some of most exciting and talked-about innovations today. While the hype might suggest otherwise, virtual reality (VR) won't become mainstream in 2017. However, its industrialisation will start to gather momentum. VR has already been used as a training tool in medicine, and next year there will be more innovative VR-enabled medical applications.

Bumping a prospect's talk-time from 22% to 33% delivers a sharp increase in win-rates If pricing comes up 3–4x in a call, consider it a buying signal Top sales professionals typically discuss pricing late in the call (40–49 minutes in on average) When your prospect responds to your timeline question with the word "probably," consider it a good thing When prospects respond to your timeline question with the phrase "We need to figure out X," you've got your work cut out for you When you sooth your prospect's fears with risk-reversal language (such as "you can cancel at any time"), win-rates on average increase 32% Conversation-level sales coaching leads to higher win-rates, more revenue, and shorter sales cycles Bumping a prospect's talk-time from 22% to 33% delivers a sharp increase in win-rates

For the 100-year-plus history of the automobile, cars have generated plenty of emotions. But those feelings have occurred in humans. Honda is looking to change that dynamic, and is ready to showcase how at the Consumer Electronics Show in Las Vegas next month. It's called the "NeuV," and according to Honda it's "a concept automated EV commuter vehicle equipped with artificial intelligence (AI) called'emotion engine' that creates new possibilities for human interaction and new value for customers." "Harnessing the power of artificial intelligence, robotics, and big data to transform the mobility experience, Honda today announced that'Cooperative Mobility Ecosystem' will be the theme for its participation at the 2017 [CES]," the automaker said in a statement.

In some ways, tech in 2017 will be a steady progression from what came before it. Time marches on, and so too does the advance of technology. In other ways, though, it will be just as upended as the rest of the world by the unprecedented disruption that 2016 has left in its wake. The artificial intelligence revolution is well and truly upon us, but so far, the biggest players are venerable Silicon Valley titans such as Google, Amazon and Apple. That's partially because they have the money to hire teams full of PhDs at seven-figure salaries, but it's also because they have the data.