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Quantum computing: A cheat sheet


Quantum computing--considered to be the next generation of high-performance computing--is a rapidly-changing field that receives equal parts attention in academia and in enterprise research labs. Honeywell, IBM, and Intel are independently developing their own implementations of quantum systems, as are startups such as D-Wave Systems. In late 2018, President Donald Trump signed the National Quantum Initiative Act that provides $1.2 billion for quantum research and development. TechRepublic's cheat sheet for quantum computing is positioned both as an easily digestible introduction to a new paradigm of computing, as well as a living guide that will be updated periodically to keep IT leaders informed on advances in the science and commercialization of quantum computing. SEE: The CIO's guide to quantum computing (ZDNet/TechRepublic special feature) Download the free PDF version (TechRepublic) SEE: All of TechRepublic's cheat sheets and smart person's guides Quantum computing is an emerging technology that attempts to overcome limitations inherent to traditional, transistor-based computers. Transistor-based computers rely on the encoding of data in binary bits--either 0 or 1. Quantum computers utilize qubits, which have different operational properties.

Part human, part machine: is Apple turning us all into cyborgs?

The Guardian

At the beginning of the Covid-19 pandemic, Apple engineers embarked on a rare collaboration with Google. The goal was to build a system that could track individual interactions across an entire population, in an effort to get a head start on isolating potentially infectious carriers of a disease that, as the world was discovering, could be spread by asymptomatic patients. Delivered at breakneck pace, the resulting exposure notification tool has yet to prove its worth. The NHS Covid-19 app uses it, as do others around the world. But lockdowns make interactions rare, limiting the tool's usefulness, while in a country with uncontrolled spread, it isn't powerful enough to keep the R number low. In the Goldilocks zone, when conditions are just right, it could save lives.

Become a better writer with a subcription to WhiteSmoke Grammar Checker


TL;DR: A lifetime subscription to WhiteSmoke Grammar Checker is on sale for £17.95 as of Nov. 25, using the code BFSAVE40 at the checkout. You may think your grammar is great, but it's better to be safe than sorry. WhiteSmoke Grammar Checker helps you avoid silly and serious typos alike by checking your spelling, grammar, style, and punctuation as you type. The tool uses artificial intelligence algorithms to understand the meaning of your words and spot mistakes. In addition to the basics, like spelling, grammar, capitalisation, and punctuation, WhiteSmoke will comment on your sentence structure and style.

Fraud through the eyes of a machine - KDnuggets


There are many approaches to determining whether a particular transaction is fraudulent. From rule-based systems to machine learning models - each method tends to work best under certain conditions. Successful anti-fraud systems should reap the benefits of all the approaches and utilize them where they fit the problem best. The notion of networks and connection analysis in the world of anti-fraud systems is paramount since it helps uncover hidden characteristics of transactions that are not retrievable any other way. In this blog post, we will try to shed some light on the way networks are created and then used to detect fraudulent transactions.

Using Deep Java Library to do Machine Learning on SpringBoot


Many AWS customers--startups and large enterprises--are on a path to adopt machine learning and deep learning in their existing applications. The reasons for machine learning adoption are dictated by the pace of innovation in the industry, with business use cases ranging from customer service (including object detection from images and video streams, sentiment analysis) to fraud detection and collaboration. However, until recently, the adoption learning curve was steep and required development of internal technical expertise in new programming languages (e.g., Python) and frameworks, with cascading effect on the whole software development lifecycle, from coding to building, testing, and deployment. The approach outlined in this blog post enables enterprises to leverage existing talent and resources (frameworks, pipelines, and deployments) to integrate machine learning capabilities. Spring Boot, one of the most popular and widespread open source frameworks for microservices development, has simplified the implementation of distributed systems.

Siemens providing long-term gas-fired turbine AI and machine learning upgrades for Jebel Ali power plant in Dubai


Siemens Energy will supply new controllers and other major upgrades as part of an extended service agreement for a Dubai power plant. Dubai Electricity and Water Authority (DEWA) signed Siemens to a new, 20-year long-term service agreement. The service term calls for a wide array of upgrades and supply of new technologies. Among those, Siemens Energy will supply an intelligent controller for each of the four SGT5-4000F gas-fired turbines at the Jebel Ali L2 power and water station. This includes the SPPA-T3000 control system, as well as services for the plant's generators and tools to improve operational flexibility and reduce outage times.

8 Future Trends in Data Analytics


AutoML enjoys a steadily increasing popularity (see Forbes). Not least driven by the numerous successes in practical analyses. In a world in which more and more devices produce data and are networked with each other, the data "produced" grows disproportionately. Therefore AutoML is of urgent necessity to gain knowledge from these rapidly increasing data on time. We assume that AutoML becomes even more critical in the coming years and that the analysis methods deliver even more precise and faster results. The field of activity of the data scientist will not disappear, but rather, his focus will shift to more specific or sophisticated analysis techniques.

AI detects COVID-19 on chest x-rays with accuracy and speed


IMAGE: Generated heatmaps appropriately highlighted abnormalities in the lung fields in those images accurately labeled as COVID-19 positive (A-C) in contrast to images which were accurately labeled as negative for COVID-19... view more Called DeepCOVID-XR, the machine-learning algorithm outperformed a team of specialized thoracic radiologists -- spotting COVID-19 in X-rays about 10 times faster and 1-6% more accurately. The researchers believe physicians could use the A.I. system to rapidly screen patients who are admitted into hospitals for reasons other than COVID-19. Faster, earlier detection of the highly contagious virus could potentially protect health care workers and other patients by triggering the positive patient to isolate sooner. The study's authors also believe the algorithm could potentially flag patients for isolation and testing who are not otherwise under investigation for COVID-19. The study will be published on Nov. 24 in the journal Radiology.

AI for Good Innovation Factory: Meet the 2020 Innovation Champions


Greyparrot, a start-up which uses computer vision for waste management, has been voted the winner of the Innovation Factory Grand Finale held as part of the year-round AI for Good Summit 2020. The Innovation Factory is AI for Good's platform to showcase startups which use artificial intelligence to tackle global challenges, providing them with feedback, mentorship and potential partnerships in social impact entrepreneurship. Greyparrot and three other start-ups received the highest scores for their innovative, scalable AI solutions for waste management, air quality, child malnutrition and agriculture. Meet the expert jury During the live Innovation Factory Grand Finale, these four startups recognized as Innovation Champions presented their solutions to a jury of experts and a public audience who then voted for a winner. Greyparrot seeks to resolve the waste crisis by using AI-based computer vision to provide actionable insights for the 530 billion-dollar global waste management industry.