Interest has been rising lately towards methods representing data in non-Euclidean spaces, e.g. hyperbolic or spherical, that provide specific inductive biases useful for certain real-world data properties, e.g. scale-free, hierarchical or cyclical. However, the popular graph neural networks are currently limited in modeling data only via Euclidean geometry and associated vector space operations. Here, we bridge this gap by proposing mathematically grounded generalizations of graph convolutional networks (GCN) to (products of) constant curvature spaces. We do this by i) introducing a unified formalism that can interpolate smoothly between all geometries of constant curvature, ii) leveraging gyro-barycentric coordinates that generalize the classic Euclidean concept of the center of mass. Our class of models smoothly recover their Euclidean counterparts when the curvature goes to zero from either side. Empirically, we outperform Euclidean GCNs in the tasks of node classification and distortion minimization for symbolic data exhibiting non-Euclidean behavior, according to their discrete curvature.

Most of the existing question answering models can be largely compiled into two categories: i) open domain question answering models that answer generic questions and use large-scale knowledge base along with the targeted web-corpus retrieval and ii) closed domain question answering models that address focused questioning area and use complex deep learning models. Both the above models derive answers through textual comprehension methods. Due to their inability to capture the pedagogical meaning of textual content, these models are not appropriately suited to the educational field for pedagogy. In this paper, we propose an on-the-fly conceptual network model that incorporates educational semantics. The proposed model preserves correlations between conceptual entities by applying intelligent indexing algorithms on the concept network so as to improve answer generation. This model can be utilized for building interactive conversational agents for aiding classroom learning.

Latest research in industrial robotics is aimed at making human robot collaboration possible seamlessly. For this purpose, industrial robots are expected to work on the fly in unstructured and cluttered environments and hence the subject of perception driven motion planning plays a vital role. Sampling bas ed motion planners are proven to be the most effective for such high dimensional planning problems with real time constraints . Unluckily r andom stochastic samplers suffer from the phenomenon of'narrow passages' or bottleneck regions which need targeted sa mpling to improve their convergence rate . Also identifying these bottleneck regions in a diverse set of planning problems is a challenge. In this paper an attempt has been made to address these two problems by designing an intelligent'bottleneck guided' h euristic for a Rapidly Exploring Random Tree Star (RRT*) planner which is based on relevant context extracted from the planning scenario using a 3D Convolutional Neural Network and it is also proven that the proposed technique generalizes to unseen problem instances. This paper benchmarks the technique (bottleneck guided RRT*) against a 10% Goal biased RRT* planner, show s significant improvement in planning time and memory requirement and uses ABB 1410 industrial manipulator as a platform for implantation a nd validation of the results.

San Francisco: A team of researchers has used Artificial Intelligence (AI) to turn two-dimensional (2D) images into stacks of virtual three-dimensional (3D) slices showing activity inside organisms. Using deep learning techniques, the team from University of California, Los Angeles (UCLA) devised a technique that extends the capabilities of fluorescence microscopy, which allows scientists to precisely label parts of living cells and tissue with dyes that glow under special lighting. In a study published in the journal Nature Methods, the scientists also reported that their framework, called "Deep-Z," was able to fix errors or aberrations in images, such as when a sample is tilted or curved. Further, they demonstrated that the system could take 2D images from one type of microscope and virtually create 3D images of the sample as if they were obtained by another, more advanced microscope. "This is a very powerful new method that is enabled by deep learning to perform 3D imaging of live specimens, with the least exposure to light, which can be toxic to samples," said senior author Aydogan Ozcan, UCLA chancellor s professor of electrical and computer engineering.

This video is also subtitled in Chinese, Indonesian, Italian, Japanese, Korean, Portuguese, and Spanish.

This video is also subtitled in Chinese, Indonesian, Italian, Japanese, Korean, Portuguese, and Spanish.

Artificial Intelligence (AI) and esports might seem like a strange combination at first but actually they go hand in hand. Games have been used to test and advance AI research for a long time. In 1997 Deep Blue, an AI developed by IBM, beat the world chess champion Kasparov at chess. This was a huge achievement for the field of artificial intelligence and it did not stop there. Recently DeepMind developed an AI that can play Starcraft II, a complex real time strategy game, at human levels which is also a great achievement.

On November 6th, NVIDIA introduced the latest member of the Jetson family - the Jetson Xavier NX. With the size that's smaller than a credit card, this module packs a punch. Earlier this year, NVIDIA launched Jetson Nano, the smallest yet the most powerful GPU-based edge computing device. Jetson Xavier NX, much-advanced edge computing device, has the pin compatibility with Jetson Nano making it possible to port the AIoT applications deployed on the Nano. It also supports all major AI frameworks, including TensorFlow, PyTorch, MXNet, Caffe and others.

In this blog, we aim to highlight some of the ways that Azure can streamline the building, training, and deployment of your TensorFlow model. In addition to reading this blog, check out the demo discussed in more detail below, showing how you can use TensorFlow 2.0 in Azure to fine-tune a BERT (Bidirectional Encoder Representations from Transformers) model for automatically tagging questions. TensorFlow 1.x is a powerful framework that enables practitioners to build and run deep learning models at massive scale. TensorFlow 2.0 builds on the capabilities of TensorFlow 1.x by integrating more tightly with Keras (a library for building neural networks), enabling eager mode by default, and implementing a streamlined API surface. We've integrated Tensorflow 2.0 with the Azure Machine Learning service to make bringing your TensorFlow workloads into Azure as seamless as possible.

In this tutorial, you will learn how to automatically detect natural disasters (earthquakes, floods, wildfires, cyclones/hurricanes) with up to 95% accuracy using Keras, Computer Vision, and Deep Learning. I remember the first time I ever experienced a natural disaster -- I was just a kid in kindergarten, no more than 6-7 years old. We were outside for recess, playing on the jungle gym, running around like the wild animals that young children are. Rain was in the forecast. My mother had given me a coat to wear outside, but I was hot and unconformable -- the humidity made the cotton/polyester blend stick to my skin.