FILE - In this Friday, Sept. 25, 2015, file photo, people wait in front of an Apple store in Munich, before the worldwide launch of the iPhone 6s. Apple may not become a car-maker, but it still wants to develop its own self-driving technology. The iPhone-maker's automotive project, long an open secret in Silicon Valley, is shifting to focus on creating the technology for an autonomous vehicle that doesn't require a human driver.

Learn more about the technology behind auto-sklearn by reading this paper published at the NIPS 2015 . This will run for one hour should result in an accuracy above 0.98. Prerequisities: auto-sklearn is written in python and was developed with Ubuntu. It should run on other Linux distributions, but won't work on a MAC or on a windows PC. It is built around scikit-learn 0.17.1 We recommend installing auto-sklearn into a virtual environment or into an anaconda environment.. The subprojects it uses, most notably SMAC, can have different licenses.

And there were some hopes from motorists that machines might actually improve manners. Of the drivers surveyed, 59 per cent hoped tailgating would become a thing of the past once driverless cars were introduced, while two in five said they would hope it would spell the end of drivers cutting each other up and failing to indicate at junctions. But despite the positive impact to remove poor driving habits from the road, just 59 per cent think their car insurance premiums will fall as a result of more driverless cars coming onto the road. Rod Jones, insurance expert at uSwitch.com said: 'The unwritten rules of the road are all part of the polite British driving experience but could be a huge blind spot for autonomous vehicles. 'Flashing your lights to let someone out of a junction may seem obvious, but these courteous gestures can vary from situation to situation and add the human touch to motoring. 'The Highway Code was created to promote safer driving, but over the years we have developed our own human driving code. 'It is clear that many drivers don't expect driverless cars to understand our driving habits, which could, certainly to begin with, make it difficult for humans and robots to drive side by side. 'For British drivers to feel safe on the roads, they need to be confident about how a driverless car will react in any given situation. Clarifying the rules in the Highway Code is an important step towards this.'

Generalization error bounds are probabilistically valid, non-asymptotic tools for characterizing the predictive ability of forecasting models. This methodology is fundamentally about choosing particular prediction functions out of some class of plausible alternatives so that, with high reliability, the resulting predictions will be nearly as accurate as possible ("probably approximately correct"). While many of these results are aimed at classification problems with independent and identically distributed (i.i.d.) data, this paper adapts and extends these methods to time-series models, so that economic and financial forecasting techniques can be evaluated rigorously. In particular, these methods control the expected accuracy of future predictions from mis-specified models based on finite samples. This allows for immediate model comparisons which neither appeal to asymptotics nor make strong assumptions about the data-generating process, in stark contrast to such popular model-selection tools as AIC.

A Deutsche Telekom official says artificial intelligence (AI) will be needed to break smart city data out of its silos. As reported by Mobile World Live, thoughts about smart city evolution appeared on the blog of Claudia Nemat, the Deutsche Telekom (DT) board member who oversees Technology & Innovation Europe. In her piece, Nemat described current smart city practices as trapping data from sensors in an isolated "landlocked lake." But as sensors proliferate at blinding speed in global smart cities, the rivers of data being produced will turn into oceans. This will be complicated by the multiple digital platforms running simultaneously in each city.

Editor's note: We're proud to bring NextDraft--the most righteous, most essential newsletter on the web--to WIRED.com. Every Friday you'll get a roundup of the week's most popular must-read stories from around the internet, courtesy of mastermind Dave Pell. "Such results contradict long-established ideas suggesting that expert performance is built mainly through practice -- that anyone can get to the top with enough focused effort of the right kind. SMPY, by contrast, suggests that early cognitive ability has more effect on achievement than either deliberate practice or environmental factors such as socio-economic status." Before you steal the lunch money from a precociously gifted young person, consider the strong likelihood that they will one day be running the world.

Imagine a future office in which the robot not only makes you tea but actually gets better at it over time. That is the very real future that awaits most workers. "You can imagine a world in five years' time when teams are made up of company employees, crowd specialists, and cognitive robots -- all creating and collaborating together", says Shamus Rae, the head of innovation and investments for KPMG in the UK. The future of robotic process automation also promises to bring more "human" intelligence to robots. "The robot would gain experience from its many interactions -- potentially thousands or more each day -- and gradually improve its responses", Rae explains in the KPMG report "The crowd in the cloud".

Dmitry Malioutov can't say much about what he built. As a research scientist at IBM, Malioutov spends part of his time building machine learning systems that solve difficult problems faced by IBM's corporate clients. One such program was meant for a large insurance corporation. It was a challenging assignment, requiring a sophisticated algorithm. When it came time to describe the results to his client, though, there was a wrinkle. "We couldn't explain the model to them because they didn't have the training in machine learning." In fact, it may not have helped even if they were machine learning experts.

They say you should never judge a book by its cover, but this cliché may not apply when it comes to making decision about friends. Researchers have found that individuals feel it is justified to ostracize others from a group if they appear cold and incompetent - a look deemed'resting b*tch face'. These findings suggest that people with this appearance are perceived as troublemakers or selfish and need to be excluded in order to restore harmony and cohesion in the group. A study has found individuals feel it is justified to ostracize others from a group if they appear cold and incompetent - a look deemed'resting b*tch face'. The University of Basel reveals that humans really do judge a book by its cover.

Google's secretive British DeepMind division is teaching its AI to talk like a human. The groundbreaking project has already halved the quality gap between computer systems and human speech, its creator say. Called WaveNet, it is capable of creating natural-sounding synthesized speech by analyzing sound waves from the human voice - rather than focusing on the human language. Google's DeepMind claims to have an AI that produces more natural-sounding synthesized speech. Google acquired UK-based DeepMind in 2014 for 533 million, and it has since beat a professional human Go player, learned how to play the Atari game Space Invaders and has read through thousands of Daily Mail and CNN articles.