The recent success in human action recognition with deep learning methods mostly adopt the supervised learning paradigm, which requires significant amount of manually labeled data to achieve good performance. However, label collection is an expensive and time-consuming process. In this work, we propose an unsupervised learning framework, which exploits unlabeled data to learn video representations. Different from previous works in video representation learning, our unsupervised learning task is to predict 3D motion in multiple target views using video representation from a source view. By learning to extrapolate cross-view motions, the representation can capture view-invariant motion dynamics which is discriminative for the action.

Intel and Udacity announced that their Intel Edge AI for IoT Developer Nanodegree program is open for enrollment. The collaboration, announced on Thursday, aims to train developers in deep learning and computer vision to help facilitate the deployment of artificial intelligence (AI) at the edge. "We didn't build this program just for hobbyists; we built this for practitioners," said Alper Tekin, CPO at Udacity. "At the end of these courses, you have to write actual code that works in a production environment." Internet of Things (IoT) and edge computing were areas Udacity had its sights set on because of the technology's popularity, as well as the skills gap that exists in the field.

Sign in to report inappropriate content. We worked on developing a prototype for an AI-powered app that is capable of instantly identifying COVID19 changes on chest x-rays. This was made possible using our favorite platform Salesforce and its Einstein's deep learning APIs.

As AI usage grows, its energy consumption and carbon emissions are becoming an environmental concern. Here's why -- and how we can find solutions. When Mohammad Haft-Javaherian, a student at the Massachusetts Institute of Technology, attended MIT's Green AI Hackathon in January, it was out of curiosity to learn about the capabilities of a new supercomputer cluster being showcased at the event. But what he had planned as a one-hour exploration of a cool new server drew him into a three-day competition to create energy-efficient artificial-intelligence programs. The experience resulted in a revelation for Haft-Javaherian, who researches the use of AI in healthcare: "The clusters I use every day to build models with the goal of improving healthcare have carbon footprints," Haft-Javaherian says.

Free Coupon Discount - Artificial Intelligence: Reinforcement Learning in Python, Complete guide to Artificial Intelligence, prep for Deep Reinforcement Learning with Stock Trading Applications Created by Lazy Programmer Inc. Students also bought Data Science: Deep Learning in Python Recommender Systems and Deep Learning in Python PyTorch: Deep Learning and Artificial Intelligence Advanced AI: Deep Reinforcement Learning in Python Deep Learning Prerequisites: Logistic Regression in Python Preview this Udemy Course GET COUPON CODE Description When people talk about artificial intelligence, they usually don't mean supervised and unsupervised machine learning. These tasks are pretty trivial compared to what we think of AIs doing - playing chess and Go, driving cars, and beating video games at a superhuman level. Reinforcement learning has recently become popular for doing all of that and more. Much like deep learning, a lot of the theory was discovered in the 70s and 80s but it hasn't been until recently that we've been able to observe first hand the amazing results that are possible. In 2016 we saw Google's AlphaGo beat the world Champion in Go.

The coronavirus (COVID-19) outbreak is having a growing impact on the global economy. So, how is the impact of COVID-19 going to be on the tech job market and what are the latest trends for data science, AI/ML, analytics, IoT, cloud computing? What are the key in-demand tech job profiles and domains during and after the COVID-19 phase? There have been more than 12,750 confirmed cases of COVID-19 in India so far. Between April 6 – 12, 46% and 39% of new confirmed cases have been reported in Europe and the USA respectively.

Simulation of fluid flow in porous media has many applications, from the micro-scale (cell membranes, filters, rocks) to macro-scale (groundwater, hydrocarbon reservoirs, and geothermal) and beyond. Direct simulation of flow in porous media requires significant computational resources to solve within reasonable timeframes. An integrated method combining predictions of fluid flow (fast, limited accuracy) with direct flow simulation (slow, high accuracy) is outlined. In the tortuous flow paths of porous media, Deep Learning techniques based on Convolutional Neural Networks (CNNs) are shown to give an accurate estimate of the steady state velocity fields (in all axes), and by extension, the macro-scale permeability. This estimate can be used as-is, or as initial conditions in direct simulation to reach a fully accurate result in a fraction of the compute time. A Gated U-Net Convolutional Neural Network is trained on a datasets of 2D and 3D porous media generated by correlated fields, with their steady state velocity fields calculated from direct LBM simulation. Sensitivity analysis indicates that network accuracy is dependent on (1) the tortuosity of the domain, (2) the size of convolution filters, (3) the use of distance maps as input, (4) the use of mass conservation loss functions. Permeability estimation from these predicted fields reaches over 90\% accuracy for 80\% of cases. It is further shown that these velocity fields are error prone when used for solute transport simulation. Using the predicted velocity fields as initial conditions is shown to accelerate direct flow simulation to physically true steady state conditions an order of magnitude less compute time. Using Deep Learning predictions (or potentially any other approximation method) to accelerate flow simulation to steady state in complex pore structures shows promise as a technique push the boundaries fluid flow modelling.

Many convolutional neural network (CNN) accelerators face performance- and energy-efficiency challenges which are crucial for embedded implementations, due to high DRAM access latency and energy. Recently, some DRAM architectures have been proposed to exploit subarray-level parallelism for decreasing the access latency. Towards this, we present a design space exploration methodology to study the latency and energy of different mapping policies on different DRAM architectures, and identify the pareto-optimal design choices. The results show that the energy-efficient DRAM accesses can be achieved by a mapping policy that orderly prioritizes to maximize the row buffer hits, bank- and subarray-level parallelism.

Recent years, typhoon damages has become social problem owing to climate change. Especially, 9 September 2019, Typhoon Faxai passed on the south Chiba prefecture in Japan, whose damages included with electric and water provision stop and house roof break because of strong wind recorded on the maximum 45 meter per second. A large amount of tree fell down, and the neighbor electric poles also fell down at the same time. These disaster features have caused that it took eighteen days for recovery longer than past ones. Initial responses are important for faster recovery. As long as we can, aerial survey for global screening of devastated region would be required for decision support to respond where to recover ahead. This paper proposes a practical method to visualize the damaged areas focused on the typhoon disaster features using aerial photography. This method can classify eight classes which contains land covers without damages and areas with disaster, where an aerial photograph is partitioned into 4,096 grids that is 64 by 64, with each unit image of 48 meter square. Using target feature class probabilities, we can visualize disaster features map to scale the color range from blue to red or yellow. Furthermore, we can realize disaster feature mapping on each unit grid images to compute the convolutional activation map using Grad-CAM based on deep neural network layers for classification. This paper demonstrates case studies applied to aerial photographs recorded at the south Chiba prefecture in Japan after typhoon disaster.

In medical image diagnosis, pathology image analysis using semantic segmentation becomes important for efficient screening as a field of digital pathology. The spatial augmentation is ordinary used for semantic segmentation. Tumor images under malignant are rare and to annotate the labels of nuclei region takes much time-consuming. We require an effective use of dataset to maximize the segmentation accuracy. It is expected that some augmentation to transform generalized images influence the segmentation performance. We propose a "synthetic augmentation" using label-to-image translation, mapping from a semantic label with the edge structure to a real image. Exactly this paper deal with stain slides of nuclei in tumor. Actually, we demonstrate several segmentation algorithms applied to the initial dataset that contains real images and labels using synthetic augmentation in order to add their generalized images. We computes and reports that a proposed synthetic augmentation procedure improve their accuracy.