cloud


PyTorch tries keeping up with research interest in 1.3 release • DEVCLASS

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PyTorch has debuted a slew of experimental features in its just-released version 1.3 as support for the TensorFlow competitor broadens, and new tools to tackle challenges like privacy appear. PyTorch 1.3 seems to be right on trend with its new capabilities, adding, for example, previews of implementations for model quantisation and on-device machine learning. The latter is heavily looked into these days, as interest in privacy-focused approaches soars. Mobile support is one of the building blocks to, for example, realise federated learning, a technique which allows training data to be spread between clients, meaning that data doesn't have to leave a device anymore to be included in the training of a centralised model. In its first iteration, mobile support comes down to prebuilt LibTorch libraries for Android and iOS, optimised implementations for certain operators, modules making sure that TorchScript inference is possible and forward operations can be executed on mobile CPUs.


Useful Comparison Tables for AI, Data Science, IoT & Cloud

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Comparison tables can be handy when it comes to getting a quick overview of a specific topic. Below are eight comparison tables from the areas of AI, Data Science, IoT, and Cloud Computing. Machine learning explores the study and construction of algorithms that can learn from and make predictions on data. Such algorithms operate by building a model from an example training set of input observations in order to make data-driven predictions or decisions expressed as outputs, rather than following strictly static program instructions. The Internet of Things (IoT) is a system of interrelated computing devices, mechanical and digital machines, objects, animals or people that are provided with unique identifiers (UIDs) and the ability to transfer data over a network without requiring human-to-human or human-to-computer interaction.


The 7 Biggest Technology Trends That Will Transform Telecoms In 2020

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As we prepare to enter the next decade, telecoms are being transformed by technology in a variety of ways. From artificial intelligence (AI) to the threat of cyberattack, here are the 7 biggest technology trends that will transform telecoms in 2020. The European Union's 5G action plan includes uninterrupted 5G coverage by 2025 for railways and major roads. In addition to being able to support a hundredfold increase in connected devices per each unit area, 5G will offer ultra-low latency, improved data rates and enable network slicing. This opens the door for new services, network operation and customer experience for telecom operators.


The 7 Biggest Technology Trends That Will Transform Telecoms In 2020

#artificialintelligence

As we prepare to enter the next decade, telecoms are being transformed by technology in a variety of ways. From artificial intelligence (AI) to the threat of cyberattack, here are the 7 biggest technology trends that will transform telecoms in 2020. The European Union's 5G action plan includes uninterrupted 5G coverage by 2025 for railways and major roads. In addition to being able to support a hundredfold increase in connected devices per each unit area, 5G will offer ultra-low latency, improved data rates and enable network slicing. This opens the door for new services, network operation and customer experience for telecom operators.


Cognitive computing: Using tech to improve quality of work - Express Computer

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NIIT Technologies has introduced a new cloud-based solution, Cognitive Service Desk Audit, built on Microsoft Azure platform. The solution is designed to play a major role in enhancing the productivity of enterprises by increasing operational efficiency, reducing audit efforts and improving quality and vigilance. Cognitive Service Desk Audit uses AI (Artificial Intelligence) to define the tonality of the end-user through text mining and analytics. The software also audits the staff's voice and accent quality, resolution accuracy, and process adherence audit to provide quality output. Additionally, to minimise errors in understanding the end-user party during the conversation, the solution also provides real time on-screen speech transcription of the online phone conversation.


Cognitive computing: Using tech to improve quality of work - Express Computer

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NIIT Technologies has introduced a new cloud-based solution, Cognitive Service Desk Audit, built on Microsoft Azure platform. The solution is designed to play a major role in enhancing the productivity of enterprises by increasing operational efficiency, reducing audit efforts and improving quality and vigilance. Cognitive Service Desk Audit uses AI (Artificial Intelligence) to define the tonality of the end-user through text mining and analytics. The software also audits the staff's voice and accent quality, resolution accuracy, and process adherence audit to provide quality output. Additionally, to minimise errors in understanding the end-user party during the conversation, the solution also provides real time on-screen speech transcription of the online phone conversation.


The Third Wave of Financial Automation – CPM Artificial Intelligence -

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In my last blog "All AI Paths Lead to the Cloud," I talked about how the FP&A challenges facing finance leaders are not going away. These issues are only compounding due to an overabundance of data and the rapid evolution of hyper-connected mobile employees, driving businesses towards the availability, scalability, and affordability that comes with putting their financial applications to the cloud. The era of simply throwing more people and resources at the challenges simply does not economically scale. Well let's think about the problem: With more people comes additional costs (headcount, manual errors, delays, etc.) For some companies this has become the status quo, meaning they are willing to assume a certain risk tolerance that results in the under-utilization of highly skilled, well-paid assets.


TechSparks 2019: How India's deep tech ecosystem impacts every sector, from dairy to defence

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Deep tech is the newest catchphrase in the Indian startup ecosystem. A bunch of homegrown companies are using new-age technologies like artificial intelligence, machine learning, data analytics, cloud, and the internet-of-things (IoT) to solve real-world problems, and essentially, alter the way humans lead daily lives. On Day One of TechSparks 2019, YourStory's flagship annual conference, a panel of founders, investors, and technical heads gathered to take stock of the evolution of the local deep tech startups ecosystem. Swapan Rajdev, Co-Founder and CTO, Haptik (maker of AI chatbots, recently acquired by Reliance Jio) elaborated on how the growth of AI has spurred new jobs and roles. Gone are the days when Indian companies failed to make a mark in hardware.


Taking a Systems Approach to Adopting AI 7wData

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To scale the benefits of AI-innovations, companies need to stop thinking of AI tools and applications -- such as natural language processing or computer vision -- as standalone solutions. Otherwise, the opportunity cost could be as large as 41% of revenue by 2023. Companies that see AI as components of next-generation enterprise IT systems stand to grow revenues by as much as one-third over the next five years. And as systems evolve, so must the IT workforce. Companies will need multidisciplinary talent that can bridge infrastructure, development tools, programming languages, AI, and machine learning.


Redefining productivity with agile ERP for modern enterprises

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Organizations have been striving to increase productivity since the days of the cotton gin, steam power and Model T assembly lines. Today, maximizing productivity is often associated with software technology, from virtual assistants to predictive science. But, as the pace of innovation has accelerated, the practical ability to implement and monetize the exciting new technologies hasn't always kept up. It's time for solution providers to step up their game and take a more active role in supporting software implementation efforts. Today's common tactics for deploying software are flawed.