We present an end-to-end trained memory system that quickly adapts to new data and generates samples like them. Inspired by Kanerva's sparse distributed memory, it has a robust distributed reading and writing mechanism. The memory is analytically tractable, which enables optimal on-line compression via a Bayesian update-rule. We formulate it as a hierarchical conditional generative model, where memory provides a rich data-dependent prior distribution. Consequently, the top-down memory and bottom-up perception are combined to produce the code representing an observation. Empirically, we demonstrate that the adaptive memory significantly improves generative models trained on both the Omniglot and CIFAR datasets. Compared with the Differentiable Neural Computer (DNC) and its variants, our memory model has greater capacity and is significantly easier to train.

In recent times, the manufacturing processes are faced with many external or internal (the increase of customized product rescheduling , process reliability,..) changes. Therefore, monitoring and quality management activities for these manufacturing processes are difficult. Thus, the managers need more proactive approaches to deal with this variability. In this study, a proactive quality monitoring and control approach based on classifiers to predict defect occurrences and provide optimal values for factors critical to the quality processes is proposed. In a previous work (Noyel et al. 2013), the classification approach had been used in order to improve the quality of a lacquering process at a company plant; the results obtained are promising, but the accuracy of the classification model used needs to be improved. One way to achieve this is to construct a committee of classifiers (referred to as an ensemble) to obtain a better predictive model than its constituent models. However, the selection of the best classification methods and the construction of the final ensemble still poses a challenging issue. In this study, we focus and analyze the impact of the choice of classifier types on the accuracy of the classifier ensemble; in addition, we explore the effects of the selection criterion and fusion process on the ensemble accuracy as well. Several fusion scenarios were tested and compared based on a real-world case. Our results show that using an ensemble classification leads to an increase in the accuracy of the classifier models. Consequently, the monitoring and control of the considered real-world case can be improved.

Diffusion-based classifiers such as those relying on the Personalized PageRank and the Heat kernel, enjoy remarkable classification accuracy at modest computational requirements. Their performance however is affected by the extent to which the chosen diffusion captures a typically unknown label propagation mechanism, that can be specific to the underlying graph, and potentially different for each class. The present work introduces a disciplined, data-efficient approach to learning class-specific diffusion functions adapted to the underlying network topology. The novel learning approach leverages the notion of "landing probabilities" of class-specific random walks, which can be computed efficiently, thereby ensuring scalability to large graphs. This is supported by rigorous analysis of the properties of the model as well as the proposed algorithms. Furthermore, a robust version of the classifier facilitates learning even in noisy environments. Classification tests on real networks demonstrate that adapting the diffusion function to the given graph and observed labels, significantly improves the performance over fixed diffusions; reaching -- and many times surpassing -- the classification accuracy of computationally heavier state-of-the-art competing methods, that rely on node embeddings and deep neural networks.

London, April 4 (IANS) Artificial Intelligence (AI) could help astronomers predict the probability of life on other planets, according to a new study. Using artificial neural networks (ANNs) researchers from Britain's Plymouth University classified planets into five types, based on whether they are most like the present-day Earth, the early Earth, Mars, Venus or Saturn's moon Titan, estimating a probability of life in each case. All five of these objects are rocky bodies known to have atmospheres and are among the most potentially habitable objects in the Solar System. "We're currently interested in these ANNs for prioritising exploration for a hypothetical, intelligent, interstellar spacecraft scanning an exoplanet system at range," said Christopher Bishop from the varsity. "We're also looking at the use of large area, deployable, planar Fresnel antennas to get data back to Earth from an interstellar probe at large distances. This would be needed if the technology is used in robotic spacecraft in the future," Bishop added.

Apple has hired Google's chief of search and artificial intelligence, John Giannandrea, a major coup in its bid to catch up to the artificial intelligence technology of its rivals. Apple said Tuesday that Giannandrea will run Apple's "machine learning and AI strategy," and become one of 16 executives who report directly to Apple's chief executive, Tim Cook. The hire is a victory for Apple, which many Silicon Valley executives and analysts view as lagging its peers in artificial intelligence, an increasingly crucial technology for companies that enable computers to handle more complex tasks, like understanding voice commands or identifying people in images. "Our technology must be infused with the values we all hold dear," Cook said Tuesday morning in an email to staff members obtained by The New York Times. "John shares our commitment to privacy and our thoughtful approach as we make computers even smarter and more personal."

NEW YORK – A SpaceX capsule carrying food, experiments and other goods for NASA has arrived at the International Space Station after a two-day journey. The Dragon capsule and its 6,000-pound shipment was captured by the space station's robot arm Wednesday. It's the second trip to the 250-mile-high orbiting outpost for this capsule, refurbished following a visit two years ago. It will remain attached to the space station for about a month, returning to Earth in May. The space station is currently home to astronauts from the U.S., Russia and Japan. The supply capsule launched Monday from Cape Canaveral, Florida, aboard a used Falcon rocket.

MAYBE robots won't take all our jobs after all. The risk of jobs being handed over to artificial intelligence is a lot lower than previously forecast, according to an OECD report. In 2013, an influential University of Oxford study warned that nearly half of all US jobs and 35 per cent of UK ones were at "high risk" of automation over the next 20 years. The new OECD report says it is more like 10 per cent of jobs in the US and 12 per cent of those in the UK that are under threat.

What if you had a colleague who would take care of all the dull, routine tasks without complaining? A colleague who lets you do interesting and challenging tasks, helps you solve them, then happily lets you take all the credit. A colleague who stays after office hours doing prep work for you so you will have a good start the next morning? Meet machine learning, your new favorite colleague, who will dramatically change customer service both for customers and for customer service personnel. It's estimated that 70%-80% of insurance claims are pretty straightforward, so this is an area where machine learning algorithms can find the right solution.

The moment we start discussing about robots taking away our jobs, I am sure you have a picture of hundreds of robots marching to your office and replacing you at work. We fear that we will be displaced at work (thanks to the movie Terminator). But the reality is that robots will not just replace humans but complement our work (making it more valuable). The redundancy from everyday work will be eliminated and humans can be more productive and efficient. AI will get rid of repetitive manual tasks and impact the labor market for sure.

Few weeks ago, Robo Investing Europe 2018 was kicking off in London. Techfoliance, as a proud partner of this event, has had great time sharing thoughts on the new financial era with influencers and friends Efi Pylarinou, Susanne Chishti or Paolo Sironi, among others. In case you could not attend, Techfoliance is happy to highlight key take-aways on a wide range of topics ranging from Deep Technologies like Robo Algorithms, Artificial Intelligence, and Digital Identity to top subjects like Private Investments, Automated Advice and Regulation. This year set to be a truly transformational one with implementation of new regulation PSD2, Open Banking or MIFID II. The reforms are setting in motion new and exciting ways to use APIs, share data and create seamless and intelligent investor experiences.