This is a sample showing how to do real-time video analytics with NVIDIA Deepstream [SDK] on a NVIDIA Jetson Nano device connected to Azure via Azure IoT Edge. Deepstream is a highly-optimized video processing pipeline, capable of running deep neural networks. It is a must-have tool whenever you have complex video analytics requirements, whether its real-time or with cascading AI models. IoT Edge gives you the possibility to run this pipeline next to your cameras, where the video data is being generated, thus lowering your bandwitch costs and enabling scenarios with poor internet connectivity or privacy concerns. With this solution, you can transform cameras into sensors to know when there is an available parking spot, a missing product on a retail store shelf, an anomaly on a solar panel, a worker approaching a hazardous zone, etc.
To someone who works in innovation, it's very reassuring to look at the results in this year's SITA Air Transport IT Insights survey. It shows that airlines and airports are both highly engaged in exploring emerging technologies. Priorities may differ slightly between them, but overridingly it's clear that they're both completely focused on automating and streamlining the journey for passengers. This is an exciting reveal, and it bodes well for the future experience of passengers. The survey shows that when it comes to new technology, the number one focus for investment by airlines continues to be Artificial Intelligence (AI).
A few decades back Artificial Intelligence was tasked as the technology of the future. Fast forward to today, it is quickly transitioning from the much-hyped future technology to surrounding us and affecting our daily lives. Right from predicting the next word to type in a text message to taking Instagram perfect pictures, Artificial Intelligence is being fused into products and services that we use on a daily basis. In the Manufacturing sector, AI has been at the forefront in driving transformation. In particular, this technology has disrupted the car manufacturing industry spawning the era of self-driving cars.
The past decade has seen some remarkable gains in the manufacturing industry. AI and big data have created new machine capabilities and new job opportunities for highly skilled workers. The ease of communication on a global scale has made collaborating with suppliers, producers and product development firms around the world faster and simpler than ever before. At the same time, U.S. manufacturers are facing a worrisome long-term skills shortage, while the trade war with China has made the short-term outlook for domestic companies uncertain. I've seen these changes and developments firsthand during my 37 years as an executive for global manufacturing operations -- most recently for a product design, development and manufacturing firm.
In the not too distant future, we can expect to see our skies filled with unmanned aerial vehicles (UAVs) delivering packages, maybe even people, from location to location. In such a world, there will also be a digital twin for each UAV in the fleet: a virtual model that will follow the UAV through its existence, evolving with time. "It's essential that UAVs monitor their structural health," said Karen Willcox, director of the Oden Institute for Computational Engineering and Sciences at The University of Texas at Austin (UT Austin) and an expert in computational aerospace engineering. "And it's essential that they make good decisions that result in good behavior." An invited speaker at the 2019 International Conference for High Performance Computing, Networking, Storage and Analysis (SC19), Willcox shared the details of a project--supported primarily by the U.S. Air Force program in Dynamic Data-Driven Application Systems (DDDAS)--to develop a predictive digital twin for a custom-built UAV.
Machine learning is an exciting topic about designing machines that can learn from examples. The course covers the necessary theory, principles and algorithms for machine learning. The methods are based on statistics and probability-- which have now become essential to designing systems exhibiting artificial intelligence. Reference textbooks for different parts of the course are "Pattern Recognition and Machine Learning" by Chris Bishop (Springer 2006) and "Probabilistic Graphical Models" by Daphne Koller and Nir Friedman (MIT Press 2009) and "Deep Learning" by Goodfellow, Bengio and Courville (MIT Press 2016).
This Random Forest Algorithm tutorial will explain how Random Forest algorithm works in Machine Learning. By the end of this video, you will be able to understand what is Machine Learning, what is Classification problem, applications of Random Forest, why we need Random Forest, how it works with simple examples and how to implement Random Forest algorithm in Python. Below are the topics covered in this Machine Learning tutorial: 1. You can also go through the Slides here: https://goo.gl/K8T4tW Machine Learning Articles: https://www.simplilearn.com/what-is-a... To gain in-depth knowledge of Machine Learning, check our Machine Learning certification training course: https://www.simplilearn.com/big-data-... #MachineLearningAlgorithms #Datasciencecourse #DataScience #SimplilearnMachineLearning #MachineLearningCourse - - - - - - - - About Simplilearn Machine Learning course: A form of artificial intelligence, Machine Learning is revolutionizing the world of computing as well as all people's digital interactions.
Understanding the Quality of Experience (QoE) for visual media has been very important for optimal compression and content delivery for diverse video formats, and it has been a hot topic of research for the last decades. Researchers mostly studied this problem from a signal processing perspective using image and video processing tools, while learning-based methods have been increasing momentum recently. The popularisation of deep learning-based methods affected the whole signal and image processing community as a disruptive force, and visual QoE estimation is no different than others. Use of learning-based methods and especially deep learning methods open a new path for understanding the human visual system in the perception process and other QoE parameters. The objectives of this special session are twofold: first, to develop new metrics reaching beyond the performance of the legacy signal processing approaches for visual QoE estimation, and second, to understand the stages of human perception for visual media better utilising the learning-based methods and different analysis methods such as ablation studies.
The idea of using chatbots may seem a little daunting at first. You may be thinking that there's no way your business could ever successfully utilize one, that it's way too complicated, and it won't really help the bottom line of your business -- it's simply not true. Chatbots are not as complicated as you might think. We're here to help give you the information you need to leverage this technology, and we'll also demonstrate how chatbots can absolutely improve your bottom line by automating conversations throughout your business. Below, we give you the basics of chatbots, help you articulate your goals, create a plan with some good old-fashioned chatbot strategy, and share industry best practices.