We present a PaperRobot who performs as an automatic research assistant by (1) conducting deep understanding of a large collection of human-written papers in a target domain and constructing comprehensive background knowledge graphs (KGs); (2) creating new ideas by predicting links from the background KGs, by combining graph attention and contextual text attention; (3) incrementally writing some key elements of a new paper based on memory-attention networks: from the input title along with predicted related entities to generate a paper abstract, from the abstract to generate conclusion and future work, and finally from future work to generate a title for a follow-on paper. Turing Tests, where a biomedical domain expert is asked to compare a system output and a human-authored string, show PaperRobot generated abstracts, conclusion and future work sections, and new titles are chosen over human-written ones up to 30%, 24% and 12% of the time, respectively.

Face recognition is the problem of identifying and verifying people in a photograph by their face. It is a task that is trivially performed by humans, even under varying light and when faces are changed by age or obstructed with accessories and facial hair. Nevertheless, it is remained a challenging computer vision problem for decades until recently. Deep learning methods are able to leverage very large datasets of faces and learn rich and compact representations of faces, allowing modern models to first perform as-well and later to outperform the face recognition capabilities of humans. In this post, you will discover the problem of face recognition and how deep learning methods can achieve superhuman performance.

Weights & Biases, a startup building development tools for machine learning, has raised $15 million in its second round of funding. The company was started by CrowdFlower founders Lukas Biewald and Chris van Pelt, along with former Google engineer Shawn Lewis. When Weights & Biases launched last year, Biewald (who I've known since college) said he wanted to create the tools needed to "build and deploy great deep learning models." Its initial product allows companies to monitor those models as they develop and train them. "When people build machine learning models they need to track everything that happens -- the code that went into the model, the hyperparameters that go into the model and then basically how well the model does," Biewald told me this week.

Kambria is the first blockchain project to build an open innovation platform for frontier technology. We are focusing first on the AI and Robotics vertical with the backing of our well-established robotics company, OhmniLabs. Through existing partnerships with top universities and large international corporations, as well as access to talented developers, we aim to build an ecosystem that can bring the next wave of frontier technology to provide value to billions of people around the world.

Europe is'the' place when it comes to areas like Data Science, Artificial Intelligence or even Deeptech Scaleups. About the AI, it has grown rapidly over the past few years and we've seen the impact on our everyday lives, from facial recognition to the recommendations we're served up. Looks like Artificial Intelligence (AI) is the most influential technology trends for 2019. However, if you look in-depth, you'll find all the intricacies that make Artificial Intelligence possible including machine learning and other deep learning techniques. Being the buzzword at present, Machine learning is the application of AI based around the concept – provide data to machines and let them learn by themselves.

While deep neural networks are all the rage, the complexity of the major frameworks has been a barrier to their use for developers new to machine learning. There have been several proposals for improved and simplified high-level APIs for building neural network models, all of which tend to look similar from a distance but show differences on closer examination. Keras is one of the leading high-level neural networks APIs. It is written in Python and supports multiple back-end neural network computation engines. Given that the TensorFlow project has adopted Keras as the high-level API for the upcoming TensorFlow 2.0 release, Keras looks to be a winner, if not necessarily the winner.

We are entering the age of "Software 2.0," where artificial neural networks (ANN) are already in use and appreciated by those who are from a development background. Even, there, however, technologies like artificial intelligence, deep learning, machine learning, and advanced analytics changing the way developers create intelligent software entities through computers and in collaboration with human intelligence. Today all of the smartphones, smart TVs, cars, and video games use artificial intelligence. Like you can use Siri to give you directions to the nearest petrol pump. Tesla is using AI and big data to make the idea of self-driving vehicles into reality. According to a post published in Fortune, AI can now read our thoughts and convert them to images by interpreting brain signals.

TechSee provides a visual engagement platform powered by deep learning and computer vision, enabling the auto-recognition of devices and issues in order to offer proven resolutions. Customers receive precise AR visual guidance in both assisted service and self-service modes at every stage of the journey, from sales, registration and onboarding to claims and upsell. They serve tier 1 companies and global groups in 24 markets around the world, including leading P&C insurers, telecoms and consumer electronics manufacturers. The Israeli startup was founded to help businesses better support their operations from all perspectives: customers, contact center agents, field technicians, and self-service channels. Over two decades of experience across CX technologies, visual computing, augmented reality, and big data enables TechSee to follow through on this commitment.

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Given unpaired data from multiple domains, a key challenge is to efficiently exploit these data sources for modeling a target domain. Variants of this problem have been studied in many contexts, such as cross-domain translation and domain adaptation. We propose AlignFlow, a generative modeling framework for learning from multiple domains via normalizing flows. The use of normalizing flows in AlignFlow allows for a) flexibility in specifying learning objectives via adversarial training, maximum likelihood estimation, or a hybrid of the two methods; and b) exact inference of the shared latent factors across domains at test time. We derive theoretical results for the conditions under which AlignFlow guarantees marginal consistency for the different learning objectives. Furthermore, we show that AlignFlow guarantees exact cycle consistency in mapping datapoints from one domain to another. Empirically, AlignFlow can be used for data-efficient density estimation given multiple data sources and shows significant improvements over relevant baselines on unsupervised domain adaptation.