Tech companies are asking to step into doctors' offices with us, and eavesdrop on all the symptoms and concerns we share with our GPs. While doctors and other medical staff are bound by confidentiality and ethics, we haven't yet figured out what it means when a digital third party -- the apps and algorithms -- are allowed in the room, too. Healthcare isn't the place to mimic Facebook's former motto to "move fast and break things", or push regulations to see where they bend, a la Uber. Instead, patients need to trust who's in the consultation room with them, says Nathan Lea, senior research associate at UCL's Institute of Health Informatics and the Farr Institute of Health Informatics Research. "You want the individual to be able to share with the doctor or clinical team as much detail as necessary without the anxiety that someone else will be looking at it," he says.

In our research at the University of Amsterdam we have married two types of models into a single comprehensive framework which we have called "Variational Auto Encoders". The two types of models are: 1) generative models where the data generation process is modelled, and 2) discriminative models, such as deep learning, where measurements are directly mapped to class labels. Deep learning is particularly successful in learning powerful (e.g., predictive/ discriminative) features from raw, unstructured sensor data. Deep neural networks can effectively turn raw data streams into new representations that represent abstract, disentangled and semantically meaningful concepts. Based on these, a simple linear classifier can achieve the state of the art.

"Artificial intelligence is the future, not only for Russia, but for all humankind. It comes with colossal opportunities, but also threats that are difficult to predict. Whoever becomes the leader in this sphere will become the ruler of the world," Russian President Vladimir Putin said. However, the president said he would not like to see anyone "monopolize" the field. "If we become leaders in this area, we will share this know-how with entire world, the same way we share our nuclear technologies today," he told students from across Russia via satellite link-up, speaking from the Yaroslavl region.

England midfielder Dele Alli says his middle-finger gesture during the World Cup qualifying win over Slovakia was aimed at team-mate Kyle Walker. Television pictures showed Tottenham's Alli gesturing in the 77th minute of Monday's 2-1 victory at Wembley. The incident occurred after he was body-checked by Martin Skrtel, and referee Clement Turpin waved play on. "Just to clarify, the gesture was a joke between me and my good friend Kyle Walker," Alli, 21, wrote on Twitter. "Apologies for any offence caused."

Retailers are starting to use artificial intelligence to power both customer engagement and service. Artificial intelligence (AI) is on the brink of going mainstream in retail because it shows real potential in helping traders give their customers better service. As more retailers are investing in developing their own approaches to AI, commerce platform providers are also adding more and more automated decision making and machine learning to their software. The time is right for businesses of any shape and size to look into the potential of this technology. Retailer Shop Direct is already using AI and machine learning to talk to its customers but has plans to push this further in the next 12 months. Last year, its Very.co.uk brand launched an automated'Very Assistant' within its mobile iOS app that answers shoppers' customer service questions through a conversational user interface (CUI).

Huawei aims to use artificial intelligence-powered features such as instant image recognition to take on rivals Samsung and Apple when it launches its new flagship phone next month, a top executive said on Saturday. Richard Yu, chief executive of Huawei's consumer business, on Saturday revealed a powerful new mobile phone chip Huawei is betting on for its upcoming flagship Mate 10 and other high-end phones to deliver faster processing and lower power consumption. Huawei will launch the Mate 10 and its sister phone, the Mate 10 Pro, in Munich on Oct. 16, Yu confirmed. He declined to detail new features, but the phones are expected to boast large, 6-inch-plus full-screen displays, tech blogs predict. Artificial intelligence (AI) built into its new chips can help make phones more personalized, or anticipate the actions and interests of their users, Yu said.

The Tesla founder spoke about his fears after Vladimir Putin claimed that the nation which controls artificial intelligence will come to rule the world. On Twitter, Musk wrote: "China, Russia -- soon all countries with strong computer science. The development of killer computers would give any nation a clear edge over its competitors. Artificial intelligence could be used to command fleets of drones or battalions of killer robots, while responding to threats at speeds much faster than any human could manage, The Sun reports. The race to develop artificial intelligence could spark World War Three, according to Elon Musk. But there's a risk that a super-smart AI could go rogue and launch genocidal attacks without being constrained by human conscience and empathy. Musk said that it may actually be the AI itself that launches the next World War. He added: "[WW3] May be initiated not by the country leaders, but one of the AI's, if it decides that a pre-emptive strike is most probable path to victory." Nick Bostrom, head of the University of Oxford's Future Of Humanity Institute, recently claimed that we may have just 50 years to save ourselves from artificial intelligence. Competition to build a machine that's as clever as humans will be fierce in the coming decades, with considerable rewards on offer for the nation which manages to pull off the historical feat of achieving "machine intelligence". But the scrabble to create this silicon-powered mind could lead to mistakes with disastrous consequences, according to The Sun. "There is a control problem," Bostrom said. Killer robots, or lethal autonomous weapons, should be banned because of the potential scale of destruction and ability to be hacked, industry leaders say. "If you have a very tight tech race to get there first, whoever invests in safety could lose the race.

Conditional independence testing is a fundamental problem underlying causal discovery and a particularly challenging task in the presence of nonlinear and high-dimensional dependencies. Here a fully non-parametric test for continuous data based on conditional mutual information combined with a local permutation scheme is presented. Through a nearest neighbor approach, the test efficiently adapts also to non-smooth distributions due to strongly nonlinear dependencies. Numerical experiments demonstrate that the test reliably simulates the null distribution even for small sample sizes and with high-dimensional conditioning sets. The test is better calibrated than kernel-based tests utilizing an analytical approximation of the null distribution, especially for non-smooth densities, and reaches the same or higher power levels. Combining the local permutation scheme with the kernel tests leads to better calibration, but suffers in power. For smaller sample sizes and lower dimensions, the test is faster than random fourier feature-based kernel tests if the permutation scheme is (embarrassingly) parallelized, but the runtime increases more sharply with sample size and dimensionality. Thus, more theoretical research to analytically approximate the null distribution and speed up the estimation for larger sample sizes is desirable.

When applied to training deep neural networks, stochastic gradient descent (SGD) often incurs steady progression phases, interrupted by catastrophic episodes in which loss and gradient norm explode. A possible mitigation of such events is to slow down the learning process. This paper presents a novel approach to control the SGD learning rate, that uses two statistical tests. The first one, aimed at fast learning, compares the momentum of the normalized gradient vectors to that of random unit vectors and accordingly gracefully increases or decreases the learning rate. The second one is a change point detection test, aimed at the detection of catastrophic learning episodes; upon its triggering the learning rate is instantly halved. Both abilities of speeding up and slowing down the learning rate allows the proposed approach, called SALeRA, to learn as fast as possible but not faster. Experiments on standard benchmarks show that SALeRA performs well in practice, and compares favorably to the state of the art.

Vehicle climate control systems aim to keep passengers thermally comfortable. However, current systems control temperature rather than thermal comfort and tend to be energy hungry, which is of particular concern when considering electric vehicles. This paper poses energy-efficient vehicle comfort control as a Markov Decision Process, which is then solved numerically using Sarsa({\lambda}) and an empirically validated, single-zone, 1D thermal model of the cabin. The resulting controller was tested in simulation using 200 randomly selected scenarios and found to exceed the performance of bang-bang, proportional, simple fuzzy logic, and commercial controllers with 23%, 43%, 40%, 56% increase, respectively. Compared to the next best performing controller, energy consumption is reduced by 13% while the proportion of time spent thermally comfortable is increased by 23%. These results indicate that this is a viable approach that promises to translate into substantial comfort and energy improvements in the car.