Part of the Wisenet P series, the new Hanwha Techwin line of 4K AI-based cameras capture pristine images at up to 4K resolution while including powerful, in-camera deep learning algorithms for advanced object detection, classification, and error-free analytics. Utilizing object recognition versus motion detection all but eliminates false alarms while also providing valuable business and operations insight. Ray Cooke"Our new AI cameras have solid performance in both analytics and deep learning applications," said Ray Cooke, vice president, products, solutions, and integration for Hanwha Techwin America. "The included, license-free analytics detect and classify a range of objects including people, vehicles, license plates, and faces. This technology will provide more reliable edge-based intelligence, and open new opportunities in security as well as business and operations intelligence."

This is another awesome course by Kirill Eremenko and his SuperDataScience Team on how to solve real-world business problems with AI. If you are business people or just curious how AI can help you then you should join this course. The complex topic of Artificial Intelligence and Machine Learning is presented the best it can without getting too technical. I highly recommend to business professionals trying to improve their skillset and help their business use AI. Talking about social proof, this course is trusted by more than 14,000 students and it has on average 4.3 rating which is amazing proof that this is a great course.

To develop and characterize an algorithm that mimics human expert visual assessment to quantitatively determine the quality of three-dimensional (3D) whole-heart MR images. In this study, 3D whole-heart cardiac MRI scans from 424 participants (average age, 57 years 18 [standard deviation]; 66.5% men) were used to generate an image quality assessment algorithm. A deep convolutional neural network for image quality assessment (IQ-DCNN) was designed, trained, optimized, and cross-validated on a clinical database of 324 (training set) scans. On a separate test set (100 scans), two hypotheses were tested: (a) that the algorithm can assess image quality in concordance with human expert assessment as assessed by human-machine correlation and intra- and interobserver agreement and (b) that the IQ-DCNN algorithm may be used to monitor a compressed sensing reconstruction process where image quality progressively improves. Weighted κ values, agreement and disagreement counts, and Krippendorff α reliability coefficients were reported.

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The journal Earth and Space Science identifies AI technologies as key for reducing human forecasters' workloads while delivering more accurate and timely predictions. The US National Oceanic and Atmospheric Administration (NOAA) also says incorporating AI and machine learning significantly increases the prediction ability of extreme weather such as thunderstorms and hurricanes. Google is an industry pioneer. The tech giant presented new research into the development of deep learning models for precipitation forecasting in December, 2019. The team treated forecasting as an image-to-image translation problem and leveraged the power of the ubiquitous UNET convolutional neural network.

We've contributed to a multi-stakeholder report by 58 co-authors at 30 organizations, including the Centre for the Future of Intelligence, Mila, Schwartz Reisman Institute for Technology and Society, Center for Advanced Study in the Behavioral Sciences, and Center for Security and Emerging Technologies. This report describes 10 mechanisms to improve the verifiability of claims made about AI systems. Developers can use these tools to provide evidence that AI systems are safe, secure, fair, or privacy-preserving. Users, policymakers, and civil society can use these tools to evaluate AI development processes. While a growing number of organizations have articulated ethics principles to guide their AI development process, it can be difficult for those outside of an organization to verify whether the organization's AI systems reflect those principles in practice.

Computer scientists at Loughborough University in the U.K. have developed artificial intelligence algorithms that could revolutionize player performance analysis for football (soccer) clubs. Computer scientists at Loughborough University in the U.K. have developed artificial intelligence algorithms that could revolutionize player performance analysis for football (soccer) clubs. The researchers designed a hybrid system that accelerates and supplements human data entry with camera-based automation to meet demand for timely performance data generated from large amounts of videos. The team applied the latest computer vision and deep learning technologies to identify actions by detecting players' body poses and limbs, and trained the deep neural network to track individual players and capture data on individual performance throughout the match video. Loughborough's Baihua Li said the new technology "will allow a much greater objective interpretation of the game as it highlights the skills of players and team cooperation."

Online Courses Udemy Deep Learning Prerequisites: Logistic Regression in Python, Data science techniques for professionals and students - learn the theory behind logistic regression and code in Python Created by Lazy Programmer Inc. English [Auto-generated], Portuguese [Auto-generated], 1 more Students also bought Natural Language Processing with Deep Learning in Python Data Science: Natural Language Processing (NLP) in Python Deep Learning: Advanced Computer Vision (GANs, SSD, More!) Unsupervised Machine Learning Hidden Markov Models in Python Modern Deep Learning in Python Preview this course GET COUPON CODE Description This course is a lead-in to deep learning and neural networks - it covers a popular and fundamental technique used in machine learning, data science and statistics: logistic regression. We cover the theory from the ground up: derivation of the solution, and applications to real-world problems. We show you how one might code their own logistic regression module in Python. This course does not require any external materials. Everything needed (Python, and some Python libraries) can be obtained for free.

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McCarthy et al [1] organized the Dartmouth workshop in 1956 to initiate artificial intelligence (AI) as a research field with a lofty goal to simulate, enhance, or even surpass human intelligence. Given the tremendous potentials and challenges, the excitements and frustrations are equally remarkable. Their interactions lead to alterations of AI springs and winters, through which the AI field has been developed step by step, and elevated to today's level, and we believe that this field will have an even brighter future. Currently, AI is in a new spring, especially its sub-field machine learning (ML) which enjoys rapid development and constant innovations featured by deep neural networks, also known as deep learning. On August 30, 2019, the White House issued a memorandum on the Fiscal Year 2021 Administration Research and Development Budget Priorities [2], underlining that'departments and agencies should prioritize basic and applied research investments that are consistent with the 2019 Executive Order on Maintaining American Leadership in Artificial Intelligence and the eight strategies detailed in the 2019 update of the National Artificial Intelligence Research and Development Strategic Plan.'