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.
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.
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.
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.
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.
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.
The COVID-19 pandemic is leading a Purdue University innovator to make changes as she works to provide new options for people with Parkinson's disease. Jessica Huber, a professor of Speech, Language, and Hearing Sciences and associate dean for research in Purdue's College of Health and Human Sciences, leads Purdue's Motor Speech Lab. Huber and her team are now doing virtual studies to evaluate speech disorders related to Parkinson's using artificial intelligence technology platforms. Huber and her team have been working to develop telepractice tools for the assessment and treatment of speech impairments like Parkinson's disease. They received a National Institutes of Health small business innovation and research grant to develop a telehealth platform to facilitate the provision of speech treatment with the SpeechVive device, which has received attention at the Annual Convention of the American Speech-Language-Hearing Association.
AI is undoubtedly changing the healthcare industry, making it more efficient and driving better outcomes for patients. COVID-19 has served as an accelerator of adoption – a catalyst in helping the industry catapult itself forward, taking advantage of the best technology has to offer. Barriers to adoption persist, however, as many applications of AI in healthcare remain uncharted territory. The vast majority of the world's health systems are not using their data and AI to make helpful predictions that inform decision making, creating tremendous opportunity to use data and AI to help make more insightful healthcare decisions. But the challenge is in finding common, replicable use cases. To start, healthcare providers are looking to understand how the disparate clinical data they gather can be organised better into an efficient pipeline that can be used to tap into accurate, predictive data intelligence.
The term artificial intelligence (AI) refers to computing systems that perform tasks normally considered within the realm of human decision making. These software-driven systems and intelligent agents incorporate advanced data analytics and Big Data applications. AI systems leverage this knowledge repository to make decisions and take actions that approximate cognitive functions, including learning and problem solving. AI, which was introduced as an area of science in the mid 1950s, has evolved rapidly in recent years. It has become a valuable and essential tool for orchestrating digital technologies and managing business operations.
Google uses common AI tools known as neural networks for a huge variety of tasks, from suggesting text in your Gmail account to serving you up an endless stream of recommended videos every time you fire up the YouTube app. Now, Google has tasked a custom neural net to organize and sync more than 150,000 YouTube cover versions of "Bad Guy," by Billie Eilish. It doesn't sound like all that impressive a task until you consider the scale of the project. From there, however, you can click on the related videos next to the player or any of the hashtags scrolling along the bottom of the screen. Once you do so, the video will seamlessly transition to a cover version of the song that's perfectly synchronized in tempo and key. It works on the most recent versions of all the major browsers on computers, smartphones, and tablets.