I often sit back and watch certain things quietly. This past week has been one of those moments. An article this weekend in the British media brought up the old and oft disproven argument about the"warming pause." However, as scientists dug into this latest desperate "Hail Mary" pass, it was apparent that there was more to this latest saga. My colleagues Phil Plait at Blastr and Andrew Freedman at Mashable have written excellent pieces laying out how scientists debunked these latest claims. They also fill in other pieces to this rather odd story.

The rail industry has come up with a plan that may as well be out of a science-fiction movie to cope with growing demand and overcrowding: charging rail passengers for journeys by fingerprint or iris scan. The Rail Delivery Group (RDG), the organisation representing train operators and Network Rail, claims biometric technology would enable fares to be automatically charged marking the start of an era that could radically accelerate commute times. The technology represents the next step from travellers being able to us smartphones' Bluetooth signals to open station barriers. That will be trialled on Chiltern Railways' route between London Marylebone and Oxford Parkway over the coming months. The use of digital signalling technology will also allow trains to operate closer together, cutting delay, according to the RDG.

Machine learning is gaining momentum and whether we want to admit it or not, it has become an essential part of our lives. As Adam Geitgey, Director of Software Engineering at Groupon, told JAXenter a few months ago, "anyone who knows how to program can use machine learning tools to solve problems." I think that in five years, machine learning won't be thought of as "magic" anymore. It will be a very common tool that nearly all programmers use to solve problems – just like how most programmers today know about databases and networking. Geitgey explained that even if you don't need a deep mathematical background to be able to apply machine learning, learning Python --"by far the most popular programming language today for machine learning"-- is a must.

The question of whether robots will replace human jobs has been hotly debated for decades, but the rapid advances in Artificial Intelligence – and the populist socio-political environment in which we find ourselves – have given the debate around automation and employment an added sense of urgency. A report by the Nomura Research Institute indicates that by 2035 nearly half of all jobs in Japan could be performed by robots, and recently we've started to see these predictions materialise. Insurance firm Fukoku Mutual Life made 34 employees redundant by replacing them with IBM's Watson Explorer AI system, which is capable of analysing and interpreting data to automatically calculate payouts to its policyholders. This AI system will be able to read tens of thousands of medical certificates and factor in the length of hospital stays, medical histories and any surgical procedures before calculating payouts, but in spite of being described by the firm as "cognitive technology that can think like a human," the sums will not actually be paid until they have been approved by an actual member of staff. And it is this aspect of human oversight that proves crucial to the conversation: Instead of treating Artificial Intelligence and Automation as a zero-sum game and pitching AI as an adversary to human employment, it is important to point out that technological advances generally correlate to increased prosperity in the long-term.

George Orwell's "1984" has been enjoying a rebirth since the inauguration of President Donald Trump and senior adviser Kellyanne Conway's use of the term "alternative facts." The 1949 novel is a dystopian view of British society when it's taken over by a totalitarian regime that uses "doublethink" and "newspeak" -- yes means no and no means yes -- to control the population. Conway, complaining on Jan. 22 about how Trump has been treated by the press, denied on NBC's "Meet the Press" that the administration was offering falsehoods when press secretary Sean Spicer insisted more people had witnessed the Trump inauguration than any other inauguration in history. Instead, she said, the administration was offering "alternative facts." The San Francisco Chronicle reported Friday a "mysterious benefactor" bought 59 copies of the novel at Booksmith in the Haight-Ashbury neighborhood to be given away for free.

Clustering is one of the most common unsupervised learning tasks in machine learning and data mining. Clustering algorithms have been used in a plethora of applications across several scientific fields. However, there has been limited research in the clustering of point patterns - sets or multi-sets of unordered elements - that are found in numerous applications and data sources. In this paper, we propose two approaches for clustering point patterns. The first is a non-parametric method based on novel distances for sets. The second is a model-based approach, formulated via random finite set theory, and solved by the Expectation-Maximization algorithm. Numerical experiments show that the proposed methods perform well on both simulated and real data.

A warning went out to millions of parents today that schoolchildren could be sending nude selfies after being groomed by paedophiles through a teenage dating app. The children are being lured into sending sexual, 'inappropriate' images of themselves through the Mylol website and mobile dating app. It's feared'pervert' adults are tricking teenagers into sending naked images of themselves. A warning went out to millions of parents today that schoolchildren could be sending nude selfies after being groomed by paedophiles through teenage dating app Mylol. The dating app and site has been labelled as'Tinder for teens' by concerned parents Mylol is a site described as the'number one teen dating site in the US, Australia, UK and Canada.'

A cable designed to drag space junk out of orbit has failed to deploy from a Japanese spacecraft. More than half a million pieces of debris are currently whizzing around our planet, including abandoned satellites and fragments of old spacecraft. They pose a danger to working satellites and new space vehicles. Scientists are working on a range of clean-up solutions, including cables, nets, harpoons, sails and robotic arms. All are designed to capture pieces of space junk and tug them down into Earth's atmosphere where they will burn up and disintegrate.

Perhaps the most positive technical theme of 2016 was the long-delayed triumph of artificial intelligence, machine learning, and in particular deep learning. In this article we'll discuss what that means and how you might make use of deep learning yourself. Perhaps you noticed in the fall of 2016 that Google Translate suddenly went from producing, on the average, word salad with a vague connection to the original language to emitting polished, coherent sentences more often than not -- at least for supported language pairs, such as English-French, English-Chinese, and English-Japanese. That dramatic improvement was the result of a nine-month concerted effort by the Google Brain and Google Translate teams to revamp Translate from using its old phrase-based statistical machine translation algorithms to working with a neural network trained with deep learning and word embeddings employing Google's TensorFlow framework. The researchers working on the conversion had access to a huge corpus of translations from which to train their networks, but they soon discovered that they needed thousands of GPUs for training and would have to create a new kind of chip, a Tensor Processing Unit (TPU), to run Translate on their trained neural networks at scale.

Almost every day, there's news about a massive data leak -- a breach at Yahoo that reveals millions of user accounts, a compromise involving Gmail phishing scams. Security professionals are constantly moving the chess pieces around, but it can be a losing battle. Yet, there is one ally that has emerged in recent years. Artificial intelligence can stay vigilant at all times, looking for patterns in behavior and alerting you to a new threat. While AI is not anywhere close to being perfect, experts tell CSO that machine learning, adaptive intelligence, and massive data models that can spot hacking much faster than any human are here to help.