It's easy to joke that the internet was invented to give people around the world the opportunity to share pictures of cats. However, according to a new report, those kitty pictures may one day turn out to save your life. That is based on work being done by Dr. Alvin Rajkomar, an assistant professor at the University of California, San Francisco Medical Center. Rajkomar trained a deep learning neural network to be able to automatically detect life-threatening abnormalities in chest X-rays. "When I was a medical resident, I ordered a stat X-ray of a patient who I suspected had a life-threatening pneumothorax -- air outside of his lung compressing his heart -- and happened to be standing next to the digital X-ray machine as it was being taken," he told Digital Trends.

The growing popularity of artificial intelligence technology probably will lead to millions of lost jobs, especially among less-educated workers, and could exacerbate the economic divide between socioeconomic classes in the United States, according to a newly released White House report. But that same technology is also essential to improving the country's productivity growth, a key measure of how efficiently the economy produces goods. That could ultimately lead to higher average wages and fewer work hours. For that reason, the report concludes, our economy actually needs more artificial intelligence, not less. To reconcile the benefits of the technology with its expected toll, the report states, the federal government should expand both access to education in technical fields and the scope of unemployment benefits.

About halfway through a particularly tense game of Go held in Seoul, South Korea, between Lee Sedol, one of the best players of all time, and AlphaGo, an artificial intelligence created by Google, the AI program made a mysterious move that demonstrated an unnerving edge over its human opponent. On move 37, AlphaGo chose to put a black stone in what seemed, at first, like a ridiculous position. It looked certain to give up substantial territory--a rookie mistake in a game that is all about controlling the space on the board. Two television commentators wondered if they had misread the move or if the machine had malfunctioned somehow. In fact, contrary to any conventional wisdom, move 37 would enable AlphaGo to build a formidable foundation in the center of the board. The Google program had effectively won the game using a move that no human would've come up with. One reason that understanding language is so difficult for computers and AI systems is that words often have meanings based on context and even the appearance of the letters and words. In the images that accompany this story, several artists demonstrate the use of a variety of visual clues to convey meanings far beyond the actual letters.

The famous inventor and computer scientist Ray Kurzweil has made some very bold predictions about the pace at which human technology is advancing toward the ultimate threshold. That threshold is known as "The Singularity." That epithet is a metaphor borrowed from physics terminology to express the point at which information technology--specifically artificial intelligence--becomes sufficiently advanced as to irreversibly alter the course of history on earth. While The Singularity may be a familiar cautionary tale told by renowned thinkers such as Bill Gates, Carl Sagan, and Stephen Hawking, and artistically explored through the famous sci-trope of sentient robots, e.g. But that depends on how you choose to define doom, specifically.

Gaetan Hadjeres and Francois Pachet at the Sony Computer Science Laboratories in Paris created DeepBach, then entered Bach's 352 chorales. The resulting composition is certainly in the style. So why does this work better than some other attempts? Part of it is the sample size of compositions. Another part is the chorale's formal structure (four voices, simple patterns of notes and harmonies).

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.