Advertisement

Baidu open sources its quantum machine learning toolkit on GitHub

baidu open sources its quantum machine learning toolkit on github

BEGIN ARTICLE PREVIEW:

Chinese Internet giant Baidu Inc. has unveiled a new toolkit for quantum machine learning, known as Paddle Quantum.
The newly open-sourced toolkit comes with several quantum computing applications, and is meant to be used by developers to build and train quantum neural network models. It’s built atop of the company’s deep learning platform PaddlePaddle, which is used power its artificial intelligence services.
Paddle Quantum, available now on GitHub, is comprised of a set of quantum machine learning toolkits. They include a quantum chemistry library plus optimization tools, and three quantum apps: quantum machine learning, quantum chemical simulation, and quantum combinatorial optimization.
Baidu said Paddle Quantum can be used to support quantum circuit models and general quantum computing research. For example, it can be used to simplify the implementation of a promising quantum algorithm, called the Quantum Approximate Optimization Algorithm, by 50%.
“Researchers in the quantum field can use the Paddle Quantum to develop quantum artificial intelligence, and deep-learning enthusiasts have a shortcut to learning quantum computing,” said Duan Runyao, director of Baidu’s Institute for Quantum Computing.
Baidu also announced a number of new tools that provide a total of 27 enhanced features for the PaddlePaddle framework. The tools include …

END ARTICLE PREVIEW

READ MORE FROM SOURCE ARTICLE

CVPR 2020 Features Latest in Machine Learning, Facial Recognition

cvpr 2020 features latest in machine learning, facial recognition

BEGIN ARTICLE PREVIEW:

Technical presentations address latest scientific advancements and their applicability to the commercial marketThe annual Computer Vision and Pattern Recognition Conference (CVPR), the largest conference of its kind, unveils the latest research spanning the fields of computer vision, deep learning, artificial intelligence, image compression, pattern analysis, and beyond. This year’s conference convenes as a virtual event from 14 –19 June 2020.As a virtual event, attendees will be able to participate remotely from around the globe, accessing the diverse and original content for which the conference is known. CVPR 2020 registrants will explore topics as varied as medical data analysis, facial recognition, machine learning, autonomous driving, social analytics, fashion applications, and much more. The following sampling of presentations offers a glimpse into the leading research to be introduced at this year’s event.Recommended AI News: Simplify Deploys 8×8 for Enhanced Collaboration and EngagementMedical Data Analysis–  IntrA: 3D Intracranial Aneurysm Dataset for Deep Learning (Tuesday, 16 June, 14:40, PDT) – Researchers from the University of Tokyo and South China University of Technology introduce a 3D intracranial aneurysm dataset, IntrA, that supports physicians in more precisely diagnosing and treating intracranial aneurysms.Facial Recognition–  Face X-Ray for More General Face Forgery Detection (Wednesday, 17 June, 10:20, PDT) – Microsoft Research Asia and …

END ARTICLE PREVIEW

READ MORE FROM SOURCE ARTICLE Continue reading “CVPR 2020 Features Latest in Machine Learning, Facial Recognition”

SD Times news digest: Netlify Build Plugins, Docker extends collaboration with Microsoft, and Eggplant deep learning capabilities – SD Times

sd times news digest: netlify build plugins, docker extends collaboration with microsoft, and eggplant deep learning capabilities – sd times

BEGIN ARTICLE PREVIEW:

Netlify announced Netlify Build Plugins, a set of tools that allows developers to easily customize and automate CI/CD workflows for Jamstack websites and web applications.
Previously, developers had to set up changes or integrations to the build process by configuring every command to run at build, downloading and validating every dependency, and writing the code to make it all work, the company explained. 
Build Plugins are designed to make developers more productive by creating a CI/CD workflow designed for frontend developers, offering plugins reviewed by the Netlify team, enabling the use of an existing plugin or bringing your own, and allowing one-click install from the Netlify UI. 
Additional details are available here.
Docker extends its collaboration with MicrosoftDocker extended its collaboration with Microsoft Azure to boost developer productivity by enabling Docker commands to run applications in Azure Container instances. 
The deeper collaboration, which also includes tighter integration with Visual Studio Code, will allow developers to quickly start new language-specific projects in Node.js, Python, .NET Core/C#,  leverage new functionality around the Compose Specification and streamline how they switch from local development to a serverless cloud container service while remaining in the Docker CLI user interface or from …

END ARTICLE PREVIEW

READ MORE FROM SOURCE ARTICLE Continue reading “SD Times news digest: Netlify Build Plugins, Docker extends collaboration with Microsoft, and Eggplant deep learning capabilities – SD Times”

Eggplant enhances its Digital Automation Intelligence Platform with Deep Learning – DevOps.com

eggplant enhances its digital automation intelligence platform with deep learning – devops.com

BEGIN ARTICLE PREVIEW:

Eggplant enhances its Digital Automation Intelligence Platform with Deep Learning – DevOps.com























%d bloggers like this:

Our website uses cookies. By continuing to browse the website you are agreeing to our use of cookies. For more information on how we use cookies and how you can disable them, please read our Privacy Policy.I Accept.

END ARTICLE PREVIEW

READ MORE FROM SOURCE ARTICLE Continue reading “Eggplant enhances its Digital Automation Intelligence Platform with Deep Learning – DevOps.com”

Thermo Fisher Scientific announces license agreement to develop, commercialize deep learning tools for proteomics

thermo fisher scientific announces license agreement to develop, commercialize deep learning tools for proteomics

BEGIN ARTICLE PREVIEW:

May 28 2020
Thermo Fisher Scientific, the world leader in serving science, and MSAID GmbH, a software company transforming proteomics with deep learning, announce an exclusive license agreement to develop and commercialize deep learning tools for proteomics, making MSAID’s Prosit-derived framework widely accessible to proteomics laboratories. The availability of deep learning tools will enable improved confidence in proteomics research results, primarily in the areas of protein profiling using label-free or tandem mass tag (TMT)-based quantification, and a variety of new applications.

The new algorithm allows gains in confidence and reproducibility and will be released as part of Thermo Fisher’s newest Thermo Scientific Proteome Discoverer 2.5 software release. Users can now access deep-learning-based prediction of tandem mass spectra, allowing for the formation of entire spectral libraries on demand and facilitating the identification of peptides with up to 10 times higher confidence and the extraction of more identifications from proteomics datasets via intensity-based rescoring. In combination with Thermo Scientific Orbitrap technology, the new algorithm enables emerging applications, such as immunopeptidomics and metaproteomics, for which traditional database search and statistical approaches are often ineffective.


Increasing the confidence of protein and peptide identifications is a growing need, given that a false discovery rate of even 1% …

END ARTICLE PREVIEW

READ MORE FROM SOURCE ARTICLE Continue reading “Thermo Fisher Scientific announces license agreement to develop, commercialize deep learning tools for proteomics”

Deep Learning in Healthcare Market 2020 – Updated for the impact of COVID-19 |  GE Healthcare, Accenture, ibmwatson health,deloitte, Brigham Womens Hospital, Merck, Roche, GSK, Philips, Berg Health, Pfizer

deep learning in healthcare market 2020 – updated for the impact of covid-19 |  ge healthcare, accenture, ibmwatson health,deloitte, brigham womens hospital, merck, roche, gsk, philips, berg health, pfizer

BEGIN ARTICLE PREVIEW:

There is a booming demand for Global Deep Learning in Healthcare market, likewise, as market authorities have been dedicating their time and efforts to get to the core of this industry and understand the real nature of the prevailing trends. The latest data about the market has been extracted using qualitative and quantitative methodologies, in order to comprehend the possible areas of expansion.
Deep Learning in Healthcare Market is expected to reach with +40% CAGR during forecast period 2020-2025
Deep learning in healthcare gives specialists the investigation of any malady precisely and causes them treat them better, subsequently resulting in better medicinal choices. Deep learning in healthcare helps in disclosure of medicines and their improvement. Deep learning system is utilized to comprehend a genome and assist patients with getting a thought regarding illness that may influence them. Deep learning has a promising future in genomics, and furthermore insurance industry.
Request AExclusive Sample Copy of This Deep Learning in HealthcareMarket report at https://www.marketresearchinc.com/request-sample.php?id=31145
Scope of the Report:
This report studies the Deep Learning in Healthcare market status and outlook of Global and major regions, from angles of players, countries, product types and end industries; this report …

END ARTICLE PREVIEW

READ MORE FROM SOURCE ARTICLE Continue reading “Deep Learning in Healthcare Market 2020 – Updated for the impact of COVID-19 |  GE Healthcare, Accenture, ibmwatson health,deloitte, Brigham Womens Hospital, Merck, Roche, GSK, Philips, Berg Health, Pfizer”

Deep Learning Boosts Call Center Speech Recognition During the COVID-19 Crisis

deep learning boosts call center speech recognition during the covid-19 crisis

BEGIN ARTICLE PREVIEW:

A business operation hard hit by COVID-19 is the call center. Industries ranging from airlines to retailers to financial institutions have been bombarded with calls—forcing them to put customers on hold for hours at a time or send them straight to voicemail.
Data reveals the picture. A recent study from Tethr of roughly 1 million customer service calls showed that in just two weeks, companies saw the percentage of calls scored as “difficult” double from 10 percent to more than 20 percent. Issues stemming from COVID-19—such as travel cancellations and gym membership disputes—have also raised customer anxiety, making call center representatives’ jobs that much more challenging.
Companies thinking about investing in speech recognition should consider a deep learning-based approach, and what to take into consideration before implementing it.
Accuracy and Efficiency
To convert audio to text, traditional speech recognition methods must first convert audio into phonemes, which are reassembled into predicted words to generate transcripts. It’s a complex and convoluted process that takes time, forgoes context entirely and delivers lower accuracy.
But an end-to-end deep learning approach uses an optimized CNN (Convolutional Neural Networks)/RNN (Recurrent Neural Networks) hybrid model trained on GPUs. It’s optimized to deliver better …

END ARTICLE PREVIEW

READ MORE FROM SOURCE ARTICLE Continue reading “Deep Learning Boosts Call Center Speech Recognition During the COVID-19 Crisis”

Shorter Scans and Better Image Quality: Deep Learning-Based MR Image Reconstruction Tech From GE Healthcare Now FDA Cleared

shorter scans and better image quality: deep learning-based mr image reconstruction tech from ge healthcare now fda cleared

BEGIN ARTICLE PREVIEW:

GE Healthcare today announced U.S. FDA 510(k) clearance of AIR Recon DL. This pioneering technology, using a deep learning-based neural network, impro

END ARTICLE PREVIEW

READ MORE FROM SOURCE ARTICLE Continue reading “Shorter Scans and Better Image Quality: Deep Learning-Based MR Image Reconstruction Tech From GE Healthcare Now FDA Cleared”

Artificial Intelligence as a Service Market by Technology (Machine Learning and Deep Learning, and Natural Language Processing)- Global Forecast 2023 – Azizsalon News

artificial intelligence as a service market by technology (machine learning and deep learning, and natural language processing)- global forecast 2023 – azizsalon news

BEGIN ARTICLE PREVIEW:

ReportsnReports offers a global report on “Artificial Intelligence as a Service Market” delivering key insights and providing a competitive advantage to clients through a detailed report. The report contains 185 pages which highly exhibit on current market analysis scenario, upcoming as well as future opportunities, revenue growth, pricing and profitability.
The Artificial Intelligence as a Service Market is expected to grow from USD 1.52 billion in 2018 to USD 10.88 billion by 2023, at a CAGR of 48.2% during the forecast period.
Need a Free Sample Report? Visit: https://www.reportsnreports.com/contacts/requestsample.aspx?name=1460796
This Report Covers Leading Companies Associated in Worldwide Artificial Intelligence as a Service Market:

International Business Machines Corporation (IBM US)
SAP SE (Germany)
Google (US)
Amazon Web Service Inc. (AWS US)
Salesforce (US)
Intel (US)
Baidu Inc. (China)
Fair Isaac Corporation (FICO US)
SAS Institute (US)
BigML (US)

Verticals in the AI as a service market include Banking, Financial Services, and Insurance (BFSI), healthcare and life sciences, retail, telecommunications, government and defense, manufacturing, energy, and others (Education, Agriculture, Transportation, and Media and Entertainment). AI as a service helps various verticals easily integrate AI capabilities with their business applications.
Go For Interesting Discount Here: https://www.reportsnreports.com/purchase.aspx?name=1460796

END ARTICLE PREVIEW

READ MORE FROM SOURCE ARTICLE Continue reading “Artificial Intelligence as a Service Market by Technology (Machine Learning and Deep Learning, and Natural Language Processing)- Global Forecast 2023 – Azizsalon News”

Using deep learning to give robotic fingertips a sense of touch

using deep learning to give robotic fingertips a sense of touch

BEGIN ARTICLE PREVIEW:

Credit: Lepora & Lloyd.

Researchers at the University of Bristol have recently trained a deep-neural-network-based model to gather tactile information about 3-D objects. In their paper, published in IEEE Robotics & Automation Magazine, they applied the deep learning technique to a robotic fingertip with sensing capabilities and found that it allowed it to infer more information about its surrounding environment.

“Our overall idea was to artificially recreate the sense of touch when controlling robots as they physically interact with their surroundings,” said Prof. Nathan Lepora, one of the researchers who carried out the study, told TechXplore. “Humans do this without thinking—for example, when brushing their fingers over an object to feel its shape. However, the computations underlying this are surprisingly complex. We implemented this type of physical interaction on a robot, by applying deep learning to an artificial fingertip that senses analogously to human skin.”
Prof. Lepora has been trying to recreate a sense of touch in robots for almost a decade, now. In his previous works, he used more conventional machine learning techniques, such as probabilistic classifiers. However, he found that these techniques only allowed robots to perform very basic tasks, such as feeling simple 2-D shapes with a slow …

END ARTICLE PREVIEW

READ MORE FROM SOURCE ARTICLE Continue reading “Using deep learning to give robotic fingertips a sense of touch”