We train an agent to navigate in 3D environments using a hierarchical strategy including a high-level graph based planner and a local policy. Our main contribution is a data driven learning based approach for planning under uncertainty in topological maps, requiring an estimate of shortest paths in valued graphs with a probabilistic structure. Whereas classical symbolic algorithms achieve optimal results on noise-less topologies, or optimal results in a probabilistic sense on graphs with probabilistic structure, we aim to show that machine learning can overcome missing information in the graph by taking into account rich high-dimensional node features, for instance visual information available at each location of the map. Compared to purely learned neural white box algorithms, we structure our neural model with an inductive bias for dynamic programming based shortest path algorithms, and we show that a particular parameterization of our neural model corresponds to the Bellman-Ford algorithm. By performing an empirical analysis of our method in simulated photo-realistic 3D environments, we demonstrate that the inclusion of visual features in the learned neural planner outperforms classical symbolic solutions for graph based planning.

Hikvision has enhanced its DeepinView camera line by adding AI-powered deep learning algorithms to improve performance and deliver competitive pricing, the company announced. The Dedicated Subseries are an example of AI chipset performance and how the technology can be used in security. Hikvision says its algorithms can be switched to have as many as five or six capabilities in one housing. "Embedding switchable algorithms is a significant step for Hikvision to take in its AI product development. In a world of ever-changing technologies and functionalities, this approach creates great value for end users to try new technologies to ensure security, as well as to implement business intelligence and other applications," said Frank Zhang, president of the International Product and Solution Center at Hikvision in a prepared statement .

Optimizing hyperparameters is one of the key elements of the life cycle of machine learning solutions. Yet, the processes for hyperparameter optimization remain incredibly laborious and require considerable effort from data scientists. Lately, there a new generation of tools and platforms have emerged with the focus of streamlining the experience of optimizing hyperparameters. Building deep learning solutions in the real world is a process of constant experimentation and optimization. Differently from any other type of software application, deep learning applications don't have a linear lifecycle based on the fact that models need to constantly refined, optimized and tested.

MN-Core is a deep learning processor jointly developed by PFN and Kobe University. Alchip's advanced LSI design technology helped achieve the required device performance. MN-Core is at the leading edge of machine learning with an approx. Alchip executed the ASIC's physical design, trial manufacturing and production. MN-Core is optimized for matrix operations, a type of computation characteristic of deep learning.

This article is part of Demystifying AI, a series of posts that (try to) disambiguate the jargon and myths surrounding AI. What is the master algorithm that allows humans to be so efficient at learning things? That is a question that has perplexed artificial intelligence scientists and researchers who, for the past decades, have tried to replicate the thinking and problem-solving capabilities of the human brain. The dream of creating thinking machines has spurred many innovations in the field of AI, and has most recently contributed to the rise of deep learning, AI algorithms that roughly mimic the learning functions of the brain. But as some scientists argue, brute-force learning is not what gives humans and animals the ability to interact the world shortly after birth.

Build your deep learning portfolio: Meditations with LSTM 3.7 (7 ratings) Course Ratings are calculated from individual students' ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately. Build your deep learning portfolio: Meditations with LSTM With the help of this course you can In this deep learning tutorial I will teach you how to build an LSTM model, which generates text.. This course was created by David C. It was rated 4.2 out of 5 by approx 13447 ratings. The best Deep Learning courses online & Tutorials to Learn Deep Learning courses for beginners to advanced level. Deep learning courses is an artificial intelligence function that imitates the workings of the human brain in processing data and creating patterns for use in decision making.

This week AI searches for war crimes, we ask how AI shifts power, one bank's approach to unifying their AI effort, deep learning used for automatic basketball video production, and new GPUs available in Google Cloud. Don't ask if artificial intelligence is good or fair, ask how it shifts power (Nature, 07/07/2020) NVIDIA's AI-focused Ampere GPUs are now available in Google Cloud (AI News, 07/08/2020)

GPUs can significantly speed up deep learning training, and have the potential to reduce training time from weeks to just hours. Amazon SageMaker is a fully managed service that enables developers and data scientists to quickly and easily build, train, and deploy machine learning (ML) models at any scale. In this post, we focus on general techniques for improving I/O to optimize GPU performance when training on Amazon SageMaker, regardless of the underlying infrastructure or deep learning framework. You can typically see performance improvements up to 10-fold in overall GPU training by just optimizing I/O processing routines. A single GPU can perform tera floating point operations per second (TFLOPS), which allows them to perform operations 10–1,000 times faster than CPUs.

Welcome to AI book reviews, a series of posts that explore the latest literature on artificial intelligence. These days, it can be very hard to determine where to draw the boundaries around artificial intelligence. What it can and can't do is often not very clear, as well as where it's future is headed. In fact, there's also a lot of confusion surrounding what AI really is. Marketing departments have a tendency to somehow fit AI in their messaging and rebrand old products as "AI and machine learning."

MIM Software Inc., a leading global provider of medical imaging software, announced today it has received 510(k) clearance from the U.S. Food and Drug Administration (FDA) for its deep learning auto-contouring software, Contour ProtégéAI . Contour ProtégéAI is an auto-contouring solution that seamlessly integrates into any department's workflow and can be rapidly implemented into virtually any environment. User feedback and a determination to continuously improve auto-segmentation were key drivers in developing the product. "Our customers are under continual pressure to improve their practices while facing escalating time constraints,'' said Andrew Nelson, Chief Executive Officer of MIM Software Inc. "Our deep learning auto-segmentation product, Contour ProtégéAI, will play a critical role in reducing the burden of contouring." Auto-contouring is an ideal use case for deep learning algorithms because it is one of the most time-consuming clinical tasks.