3D Convolutional Neural Networks are sensitive to transformations applied to their input. This is a problem because a voxelized version of a 3D object, and its rotated clone, will look unrelated to each other after passing through to the last layer of a network. Instead, an idealized model would preserve a meaningful representation of the voxelized object, while explaining the pose-difference between the two inputs. An equivariant representation vector has two components: the invariant identity part, and a discernable encoding of the transformation. Models that can't explain pose-differences risk "diluting" the representation, in pursuit of optimizing a classification or regression loss function. We introduce a Group Convolutional Neural Network with linear equivariance to translations and right angle rotations in three dimensions. We call this network CubeNet, reflecting its cube-like symmetry. By construction, this network helps preserve a 3D shape's global and local signature, as it is transformed through successive layers. We apply this network to a variety of 3D inference problems, achieving state-of-the-art on the ModelNet10 classification challenge, and comparable performance on the ISBI 2012 Connectome Segmentation Benchmark. To the best of our knowledge, this is the first 3D rotation equivariant CNN for voxel representations.

The trade-off between language expressiveness and system scalability (E&S) is a well-known problem in RDF stream reasoning. Higher expressiveness supports more complex reasoning logic, however, it may also hinder system scalability. Current research mainly focuses on logical frameworks suitable for stream reasoning as well as the implementation and the evaluation of prototype systems. These systems are normally developed in a centralized setting which suffer from inherent limited scalability, while an in-depth study of applying distributed solutions to cover E&S is still missing. In this paper, we aim to explore the feasibility of applying modern distributed computing frameworks to meet E&S all together. To do so, we first propose BigSR, a technical demonstrator that supports a positive fragment of the LARS framework. For the sake of generality and to cover a wide variety of use cases, BigSR relies on the two main execution models adopted by major distributed execution frameworks: Bulk Synchronous Processing (BSP) and Record-at-A-Time (RAT). Accordingly, we implement BigSR on top of Apache Spark Streaming (BSP model) and Apache Flink (RAT model). In order to conclude on the impacts of BSP and RAT on E&S, we analyze the ability of the two models to support distributed stream reasoning and identify several types of use cases characterized by their levels of support. This classification allows for quantifying the E&S trade-off by assessing the scalability of each type of use case \wrt its level of expressiveness. Then, we conduct a series of experiments with 15 queries from 4 different datasets. Our experiments show that BigSR over both BSP and RAT generally scales up to high throughput beyond million-triples per second (with or without recursion), and RAT attains sub-millisecond delay for stateless query operators.

In an attempt to unify the European Union's fractured membership around a common digital policy, EU leaders announced a series of measures designed to make Europe more competitive in critical emerging technologies. Today's commitments by Member States give a strong signal: We all understand that Europe's future is digital and that the only way to fully reap the benefits of new technologies is by working together, joining forces and resources. By pooling health data, using artificial intelligence and blockchain and promoting innovation, Europe can significantly improve people's lives. Earlier and better diagnosis of diseases, safer roads -- this is only a glimpse of what embracing digital change can look like. Over the past several years, the EU has been pursing the creation of a Digital Single Market to create common digital rules for things like mobile technologies, geofencing, digital content, and privacy.

Free users of Spotify could soon benefit from premium account features thanks to an updated version of the app. Sources say the update is designed to make the service easier to use in a bid to boost subscribers after launching on the stock market last week. Mobile users with free plans will be able to access playlists faster and have greater control over how they listen to music on playlists, sources say. Free users of Spotify will soon benefit from premium account features, thanks to an updated version of the app, sources say. At the moment, the free plan prevents users from selecting tracks within a playlist.

A laptop showing the Facebook logo is held alongside a Cambridge Analytica sign at the entrance to the London offices of Cambridge Analytica. The company's acting CEO, Alexander Tayler, is stepping down, and is the second CEO out since the data sharing scandal broke. A laptop showing the Facebook logo is held alongside a Cambridge Analytica sign at the entrance to the London offices of Cambridge Analytica. The company's acting CEO, Alexander Tayler, is stepping down, and is the second CEO out since the data sharing scandal broke. The acting chief executive officer of Cambridge Analytica, the political data firm embroiled in controversy after improperly sharing data from some 87 million Facebook users, has stepped down.

It all started with 166 companies spread across 12 European countries appling for a "golden ticket" to ROBOTT-NET's Voucher Program. Now five of the 64 projects have been selected for a ROBOTT-NET pilot. Trumpf, Maser, Picolo, Weibel and Air Liquide are the five companies that will have their technology implemented in a pilot on a real-world use case. Their voucher work varies greatly. Whilst Trumpf wanted to find out if automated handling of a large variety of sheet metal parts was possible, Picolo was working on generating welding robot programs.

With the emergence of big data and modern technologies, artificial intelligence has acquired a lot of relevance in many domains. The increase in demand for automation has generated many applications for artificial intelligence in fields such as robotics, predictive analytics, finance, and many more. So, if you're a developer who wants to upgrade your normal applications to smart and intelligent versions, then go for this Learning Path. Packt's Video Learning Path is a series of individual video products put together in a logical and stepwise manner such that each video builds on the skills learned in the video before it. Let's take a quick look at your learning journey.

The team, led by Andreas Rossler at the Technical University of Munich, developed machine learning that is able to automatically detect when videos are face swapped. They trained the algorithm using a large set of face swaps that they made themselves, creating the largest database of these kind of images available. They then trained the algorithm, called XceptionNet, to detect the face swaps. XceptionNet clearly outperforms its rival techniques in detecting this kind of fake video, but it also actually improves the quality of the forgeries. Rossler's team can use the biggest hallmarks of a face swap to make the manipulation more seamless.

Bitcoin Press Release: Blockchain-based startup Cryptics has announced the launch of the world's first public cryptocurrency analytics based on AI technology. April 10th, 2018, Tallinn, Estonia – The crypto market is still relatively new and lacks many of the traditional institutions of a civilized market. There is a lack of regulation and the volatility factor is detracting a vast majority of classical investors from investing in the new market. Traders are also approaching the market cautiously as the there is a lack of classical application of trading instruments. Despite these issues there are blockchain projects on the market that seek to indemnify or mitigate the associated risks that investors take when deciding to invest in projects.

Machines don't actually have bias. AI doesn't'want' something to be true or false for reasons that can't be explained through logic. Unfortunately human bias exists in machine learning from the creation of an algorithm to the interpretation of data – and until now hardly anyone has tried to solve this huge problem. A team of scientists from Czech Republic and Germany recently conducted research to determine the effect human cognitive bias has on interpreting the output used to create machine learning rules. The team's white paper explains how 20 different cognitive biases could potentially alter the development of machine learning rules and proposes methods for "debiasing" them. Biases such as "confirmation bias" (when a person accepts a result because it confirms a previous belief) or "availability bias" (placing greater emphasis on information relevant to the individual than equally valuable information of less familiarity) can render the interpretation of machine learning data pointless.