Google has suggested a "big red button" could be used to prevent artificial intelligence from a "harmful sequence of actions". A research paper from DeepMind and the University of Oxford says there should be a way to "repeatedly safely interrupt" an algorithm. "Safe interruptibility can be useful to take control of a robot that is misbehaving and may lead to irreversible consequences, or to take it out of a delicate situation, or even to temporarily use it to achieve a task it did not learn to perform or would not normally receive rewards for this," the paper, which proposes a framework to let humans stop an algorithms from continuing on a dangerous path, says. The paper's authors – Laurent Orseau, from DeepMind, and Stuart Armstrong from the The Future of Humanity Institute – explain that an "interruption policy" could be built into algorithms to safely stop a machine. Reinforcement learning algorithms, the paper continues, often work in complex environments, such as "the real world", and are unlikely to act as they are intended on every occasion.

The Future of Humanity Institute, University of OxfordStuart Armstrong is a philosopher at the University of Oxford and one of the paper's authors. Machines are becoming more intelligent every year thanks to advances being made by companies like Google, Facebook, Microsoft, and many others. AI agents, as they're sometimes known, can already beat us at complex board games like Go and they're becoming more competent in a range of other areas. Now a London AI research lab owned by Google has carried out a study to make sure we can pull the plug on self-learning machines when we want to. DeepMind, acquired by Google for a reported 400 million in 2014, teamed up with scientists at the University of Oxford to find a way to make sure AI agents don't learn to prevent, or seek to prevent humans from taking control.

Apple needs to change its attitude and approach to customer data, back away from the big data corner it has painted itself into, and use its upcoming World Wide Developer Conference (WWDC) to lay out some sort of artificial intelligence vision. Amazon has Alexa and its Echo. Google has Home, Assistant and a bevy of other services. Meanwhile, Apple has its long-in-the-tooth Siri that reportedly will be opened up to third party developers. Over the last two years, Apple has dug its heels in on privacy, vilified ad models to some degree and knocked Silicon Valley rivals (read Facebook and Google) for using customers as the products and collecting too much information.

Nasa has announced that it has found evidence of flowing water on Mars. Scientists have long speculated that Recurring Slope Lineae -- or dark patches -- on Mars were made up of briny water but the new findings prove that those patches are caused by liquid water, which it has established by finding hydrated salts. Several hundred camped outside the London store in Covent Garden. The 6s will have new features like a vastly improved camera and a pressure-sensitive "3D Touch" display

Data scientists often spend hours or days tuning models to get the highest accuracy. This tuning typically involves running a large number of independent Machine Learning (ML) tasks coded in Python or R. Following some work presented at Spark Summit Europe 2015, we are excited to release scikit-learn integration package for Apache Spark that dramatically simplifies the life of data scientists using Python. Python is one of the most popular programming languages for data exploration and data science, and this is in no small part due to high quality libraries such as Pandas for data exploration or scikit-learn for machine learning. Scikit-learn provides fast and robust implementations of standard ML algorithms such as clustering, classification, and regression. Scikit-learn's strength has typically been in the realm of computing on a single node, though.

Online video games are to blame for a decline in young men entering higher education. This is the neat claim made in a recent op-ed published by the Times under the subheading "The gender imbalance in higher education may not be as complicated as it looks". Emboldened by a recent report from the Higher Education Policy Institute, which found that boys were 10 times as likely to play collaborative online games than girls, the author repeats its assertion that "the gender gap in video gaming translates into a performance advantage for girls". Not correlation, then, but grim causation: play video games, drop grades. The gender gap in higher education in the UK is growing.

Billionaire Tesla head Elon Musk recently told a tech conference in California that the chance is "one in billions" that we are not living in a simulated world. In other words, it's almost a certainty that we're all walking around in something akin to The Matrix -- a computer-generated reality run by artificial intelligence -- right this minute and the majority of us don't even realize it. He also said we should actually hope it is true because if it isn't, it means we're doomed to destruction by some other calamitous event. Musk is not the only person to suggest this. Science fiction writer Philip K. Dick told the Metz Sci-Fi Convention in France the same thing back in 1977: Dick said this explains the odd sense of déjà vu we all have from time-to-time.

There is a new technology being tested for use on UK civilians en masse on our streets, millimetre microwave scanners. These electromagnetic radiation scanners, which use Ultra Wide Band (UWB) at 75-110 GHz, are able to be beamed at crowds to detect potential concealed weapons being carried by individuals. Using the same technology as the American airport TSA scanners, over which there has been much controversy in the level of detail these scanners reveal about an individual, MiRTLE, provided by Radio Physics Solutions (RPS) purports to use artificial intelligence (AI) to identify weapon shaped objects without the system revealing the human body image. Practically this would make sense, as a human operator seeing a crowd of people imaged by'millimetre' microwaves would not be able to effectively scan the crowd for weapon shaped objects or suicide vests. Note that security workers, particularly those working at airports, have been drilled in various PR talking points in order to assuage public concerns on this experimental technology, including the popular corporate line that, "It's perfectly safe – it's on only millimeter microwaves."

We introduce Bayesian Poisson Tucker decomposition (BPTD) for modeling country--country interaction event data. These data consist of interaction events of the form "country $i$ took action $a$ toward country $j$ at time $t$." BPTD discovers overlapping country--community memberships, including the number of latent communities. In addition, it discovers directed community--community interaction networks that are specific to "topics" of action types and temporal "regimes." We show that BPTD yields an efficient MCMC inference algorithm and achieves better predictive performance than related models. We also demonstrate that it discovers interpretable latent structure that agrees with our knowledge of international relations.

We consider the problem of learning multi-ridge functions of the form f(x) = g(Ax) from point evaluations of f. We assume that the function f is defined on an l_2-ball in R^d, g is twice continuously differentiable almost everywhere, and A \in R^{k \times d} is a rank k matrix, where k << d. We propose a randomized, polynomial-complexity sampling scheme for estimating such functions. Our theoretical developments leverage recent techniques from low rank matrix recovery, which enables us to derive a polynomial time estimator of the function f along with uniform approximation guarantees. We prove that our scheme can also be applied for learning functions of the form: f(x) = \sum_{i=1}^{k} g_i(a_i^T x), provided f satisfies certain smoothness conditions in a neighborhood around the origin. We also characterize the noise robustness of the scheme. Finally, we present numerical examples to illustrate the theoretical bounds in action.