Artificial intelligence is a hot topic these days. In this industry, investment grew more than 3-fold since 2013, and new services and applications for it are appearing every week. These two facts alone provide plenty of reason to believe that AI is here to stay and will influence businesses quite a bit. Now, news coming from Amstelveen (Netherlands) and San Francisco (USA) report that the KLM Royal Dutch Airlines is testing AI in customer service through social media, taking the next step in social servicing. To do so, KLM is using DigitalGenius' AI, integrating it in its customer relationship management tool.

Quantitative modeling of post-transcriptional regulation process is a challenging problem in systems biology. A mechanical model of the regulatory process needs to be able to describe the available spatio-temporal protein concentration and mRNA expression data and recover the continuous spatio-temporal fields. Rigorous methods are required to identify model parameters. A promising approach to deal with these difficulties is proposed using Gaussian process as a prior distribution over the latent function of protein concentration and mRNA expression. In this study, we consider a partial differential equation mechanical model with differential operators and latent function. Since the operators at stake are linear, the information from the physical model can be encoded into the kernel function. Hybrid Monte Carlo methods are employed to carry out Bayesian inference of the partial differential equation parameters and Gaussian process kernel parameters. The spatio-temporal field of protein concentration and mRNA expression are reconstructed without explicitly solving the partial differential equation.

For the supervised least squares classifier, when the number of training objects is smaller than the dimensionality of the data, adding more data to the training set may first increase the error rate before decreasing it. This, possibly counterintuitive, phenomenon is known as peaking. In this work, we observe that a similar but more pronounced version of this phenomenon also occurs in the semi-supervised setting, where instead of labeled objects, unlabeled objects are added to the training set. We explain why the learning curve has a more steep incline and a more gradual decline in this setting through simulation studies and by applying an approximation of the learning curve based on the work by Raudys & Duin.

While wearable cameras are becoming increasingly popular, locating relevant information in large unstructured collections of egocentric images is still a tedious and time consuming process. This paper addresses the problem of organizing egocentric photo streams acquired by a wearable camera into semantically meaningful segments, hence making an important step towards the goal of automatically annotating these photos for browsing and retrieval. In the proposed method, first, contextual and semantic information is extracted for each image by employing a Convolutional Neural Networks approach. Later, a vocabulary of concepts is defined in a semantic space by relying on linguistic information. Finally, by exploiting the temporal coherence of concepts in photo streams, images which share contextual and semantic attributes are grouped together. The resulting temporal segmentation is particularly suited for further analysis, ranging from event recognition to semantic indexing and summarization. Experimental results over egocentric set of nearly 31,000 images, show the prominence of the proposed approach over state-of-the-art methods. Keywords: temporal segmentation, egocentric vision, photo streams clustering 1. Introduction Among the advances in wearable technology during the last few years, wearable cameras specifically have gained more popularity [5].

In this paper we propose cross-modal convolutional neural networks (X-CNNs), a novel biologically inspired type of CNN architectures, treating gradient descent-specialised CNNs as individual units of processing in a larger-scale network topology, while allowing for unconstrained information flow and/or weight sharing between analogous hidden layers of the network---thus generalising the already well-established concept of neural network ensembles (where information typically may flow only between the output layers of the individual networks). The constituent networks are individually designed to learn the output function on their own subset of the input data, after which cross-connections between them are introduced after each pooling operation to periodically allow for information exchange between them. This injection of knowledge into a model (by prior partition of the input data through domain knowledge or unsupervised methods) is expected to yield greatest returns in sparse data environments, which are typically less suitable for training CNNs. For evaluation purposes, we have compared a standard four-layer CNN as well as a sophisticated FitNet4 architecture against their cross-modal variants on the CIFAR-10 and CIFAR-100 datasets with differing percentages of the training data being removed, and find that at lower levels of data availability, the X-CNNs significantly outperform their baselines (typically providing a 2--6% benefit, depending on the dataset size and whether data augmentation is used), while still maintaining an edge on all of the full dataset tests.

Just days ago, Germany's Federal Motor Authority sent letters to Tesla owners warning them that their cars' "Autopilot" feature is strictly there for driver assistance, not driver replacement. As it turns out, those letters were just the opening salvo. According to a report from Reuters, the German government is asking Tesla to stop using the term "autopilot" in its advertising entirely out of concerns that people misinterpret its purpose. To be absolutely clear, your Tesla will not drive you around town on its own... yet. A Tesla spokesperson maintained that the word "autopilot" has been used in the aerospace industry for years in reference to systems that assist pilots in flight, and that the company has always been clear that people still have to pay attention to the road. Still, it's not hard to see what German authorities are concerned about.

When Mercedes-Benz starts selling self-driving cars, it will choose to prioritize driver safety over pedestrians, a company manager has confirmed. The ethical conundrum of how A.I.-powered machines should act in life-or-death situations has received more scrutiny as driverless cars become a reality, but the car manufacturer believes that it's safer to save the life you have greater control over. "You could sacrifice the car. You could, but then the people you've saved initially, you don't know what happens to them after that in situations that are often very complex, so you save the ones you know you can save," Christoph von Hugo, Mercedes's manager of driver assistance systems, told Car and Driver in an interview published last week. "If you know you can save at least one person, at least save that one. Save the one in the car."

Google has tweaked its "deep learning" AI to use an external memory bank. It's an attempt to replicate the way human brains use short term memory to simplify reasoning. The company demonstrated the approach by having the system teach itself the London Underground (subway) map and figure out the quickest route between stops. It's a simple task to humans, but the process – which involves comparing multiple branching options with 270 stops over 11 lines – is exactly the type of problem that poses a challenge to artificial intelligence. Because the system was allowed temporary access to stored memory, it was able to more effectively process and categorize the possible routes without having to start from scratch each time. That's similar to how a human brain could use short term memory to filter down all the possible routes by ruling out every one that involves travelling in a particular direction from a specified stop, repeating the process until only the optimum answer remained.

Four people were killed, including two children. "We were looking into all these different stories, like the psychology of the drone pilot, all the crazy, messed-up stuff that surrounds it," says Killbox programmer Albert Elwin on the IndieCade show floor in Los Angeles. "It's all really dark and depressing -- it's absolutely in some ways a difficult project to work on because you get kind of consumed by the reality of it." Since 2004, the US has conducted more than 400 drone strikes across Pakistan alone that have killed up to 4,000 people, according to the Bureau of Investigative Journalism. Hard statistics don't exist in the world of UAV warfare, but the Bureau estimates between 423 and 965 civilians have been killed in unmanned strikes on Pakistan, including as many as 207 children.

The airline is the sixth corporate backer of the startup hot house created by renowned entrepreneurs Brent Hoberman and Henry Lane Fox - both of Lastminute.com fame - with the ambitious goal of creating 200 successful startups over the next five years. That will include investing in and helping scale five early stage startups each year, as well as co-founding two companies itself. "Connecting the talented easyJet team with the next generation of disruptive entrepreneurs will only continue to drive fresh thinking and uncover new opportunities," said easyJet chief executive Dame Carolyn McCall. Hoberman added: "We are confident that together we can support the next generation of innovators in travel leveraging digital scale, data, personalisation, virtual reality, artificial intelligence (AI), ecommerce breakthroughs and fintech." It joins five other corporate backers working with Founders Factory, including Aviva for fintech, L'Oreal for beauty technology and a deal with China's CSC inked just last week to foster startups working on AI. "easyJet coming into Founders Factory as our sixth and final corporate investor represents a critical milestone," said Henry Lane Fox. "With some of the leading brands and audience owners in the world as investors, we are able to execute on our vision of exploiting new emerging technologies to redefine industries." It has also done deals with Holtzbrinck publishing group in the area of education and the Guardian Media Group in media.