Artificial intelligence has a problem with nuance but that didn't stop it from correctly predicting the outcome of most of the cases An artificial intelligence system has correctly predicted the outcomes of hundreds of cases heard at the European Court of Human Rights, researchers have claimed and, what makes the announcement perhaps even more staggering is that it was right 79% of the time. While AI is increasingly being used in fields such as journalism, law and accountancy critics so far have said no AI would be able to understand the nuances of a legal case, but now, ironically, it might look as if their own case is being undermined. The study, which was conducted by researchers at University College London and the universities of Sheffield and Pennsylvania does not spell an end to lawyers – yet – but it does potentially set AI on the road to becoming judge, jury and, well, you know. "There is a lot of hype about AI but we don't see it replacing judges or lawyers any time soon. What we do think is they'd find it useful for rapidly identifying patterns in cases that lead to certain outcomes," said Dr Nikolaos Aletras, who led the study at UCL. "It could also be a valuable tool for highlighting which cases are most likely to be violations of the European Convention on Human Rights."

Researchers from Princeton University received mass media attention when they recently predicted the demise of Facebook. Data scientists at Facebook soon hit back with their own'study:' "In keeping with the scientific principle (used by Princeton) 'correlation equals causation,' our research unequivocally demonstrated that Princeton may be in danger of disappearing entirely." Is it surprising that the original Princeton study found its way onto the front pages of newspapers and magazines across the world? Probably not – the fact is statistical results with a causal interpretation have a stronger effect on our thinking than non-causal information. What the data scientists at Princeton relied upon in presenting their paper was our individual human inability to think statistically.

Waiopehu College pupils, from left, Kate Nicholson, Niko Tofa and Sammy Heyward. More than 100 teens have explored the ever-evolving world of artificial intelligence. The 130 budding young scientists, from Freyberg High School, Manawatu College and Waiopehu College, learned about the benefits and difficulties faced in a technology-rich future at a conference in Levin on Friday. Mechanical masseuses and construction robots that could work in all weather conditions and give workers a sleep-in were among ideas the pupils – aged 11 to 13 – came up with for the future. Centre for Science and Citizenship founders Dr Deborah Stevens, left, and Dr Lynne Bowyer.

This piece showcased the immense power of exponential technology versus linear technology and became a pivotal concept for anyone trying to anticipate what the future held. The essay predicted advances in business and technology with eerie precision, including how exponential growth would ripple through any technology that became an information technology, such as computing, biotechnology, or energy. The past 15 years have shown that while some of Kurzweil's specific predictions may or may not pan out exactly as predicted, the underlying idea of the law of accelerating returns grows more relevant with each passing week. But as we start to look at the next fifteen years, I believe there is another concept just as significant as the law of accelerating returns that we need to understand. The strangest, most interesting and magical-seeming creations of the future will occur at the intersection of multiple exponential trend lines.

Vyacheslav is a PhD candidate at the Oxford Internet Institute. His research uses social psychology and machine learning to understand networks of people and networks of ideas. Imagine a typical day in 2020: Your personal AI assistant wakes you up with a friendly greeting before preparing your favorite breakfast. During your morning workout, it plays new songs that perfectly match your musical tastes. For your driverless commute to work, it has pre-selected a few articles based on the duration of your commute and what you've read in the past.

Machine learning used to take place behind the scenes: Amazon mined your clicks and purchases for recommendations, Google mined your searches for ad placement, and Facebook mined your social network to choose which posts to show you. But now machine learning is on the front pages of newspapers, and the subject of heated debate. Learning algorithms drive cars, translate speech, and win at Jeopardy! What can and can't they do? Are they the beginning of the end of privacy, work, even the human race?

In this post, I'll explain my approach and show some preliminary results. This is going to be a long post, so here's an overview of the different sections. If you want to skip ahead, just click the section title to go there. Traditionally, Spotify has relied mostly on collaborative filtering approaches to power their recommendations. The idea of collaborative filtering is to determine the users' preferences from historical usage data. For example, if two users listen to largely the same set of songs, their tastes are probably similar. Conversely, if two songs are listened to by the same group of users, they probably sound similar. This kind of information can be exploited to make recommendations.

Back in March of this year, Google DeepMind had its AI system, AlphaGo, "sit down" with international Go champion Lee Sedol in a 5-game Go match with a purse of a cool $1 million. Despite Go being far more difficult than say, chess, to program for (due in part to number of possible moves), it destroyed Sedol 4-1. Now, the same company that took down the best in what many consider to be the most difficult board game in the world, is turning its sights on Starcraft II. The company made the announcement at this year's BlizzCon 2016 in Anaheim, California, and in an associated press release, says that it has established a "collaboration with Blizzard Entertainment to open up StarCraft II to AI and Machine Learning researchers around the world." For anybody paying attention to Google DeepMind, or one of its central driving forces, Demis Hassabis, the leap to 3-D video games has been expected for some time.

Artificial Intelligence (AI) has had a major resurgence in the past few years. If we look at the history of AI, we see a repeated cycle – it was at the forefront in the 80's too – it has been going through cycles of over-promise, investment, under delivery and investment reduction. However this time around, the technology advancements, scale and attention being paid to AI are much larger than before. The advances in big data technologies combined with cheap massively scalable infrastructure and storage is now helping us tremendously to tackle bigger and bolder problems in Artificial Intelligence. However, we are really just at the tip of the iceberg with AI.

There are a lot of solar chargers out there, but the Water-Resistant Dual-USB Solar Charger is one of the best deals I've seen. It's much more compact than your typical solar chargers, which tend to be very brick-like. And yet it still packs an impressive 5000mAh of power.The charger itself can be powered via solar energy or USB depending […]