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How machine learning removes spam from your inbox

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This article is part of "Deconstructing artificial intelligence," a series of posts that explore the details of how AI applications work. Of more than 300 billion emails sent every day, at least half are spam. Email providers have the huge task of filtering out the spam and making sure their users receive the messages that matter. The line between spam and non-spam messages is fuzzy, and the criteria change over time. From various efforts to automate spam detection, machine learning has so far proven to be the most effective and the favored approach by email providers.


UVA Artificial Intelligence Project Among 7 Finalists for $1 Million Prize

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A UVA Health data science team is one of seven finalists in a national competition to improve healthcare with the help of artificial intelligence. UVA's proposal was selected as a finalist from among more than 300 applicants in the first-ever Centers for Medicare & Medicaid Services (CMS) Artificial Intelligence Health Outcomes Challenge. UVA's project predicts which patients are at risk for adverse outcomes and then suggests a personalized plan to ensure appropriate healthcare delivery and avoid unnecessary hospitalizations. CMS selected the seven finalists after reviewing the accuracy of their artificial intelligence models and evaluating how well healthcare providers could use visual displays created by each project team to improve outcomes and patient care. Each team of finalists received $60,000 and will compete for a grand prize of up to $1 million.


Webinar: Artificial Intelligence, Firm Growth and Industry Concentration

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Finance experts will discuss findings in a new paper "Artificial Intelligence, Firm Growth, and Industry Concentration" at 9 a.m. Wednesday, Dec. 2 in a webinar hosted by the Center for Financial Policy at the University of Maryland's Robert H. Smith School of Business. Co-author of the study, assistant professor of finance Tania Babina at Columbia University, will describe findings including the positive effects of artificial intelligence (AI) on firms, in the discussion moderated by Russell Wermers, Dean's Chair in Finance and Center for Financial Policy director at Maryland Smith. The paper shows that firms investing in AI experience faster growth in both sales and employment, which translates into analogous growth at the industry level. The positive effects are concentrated among the ex-ante largest firms, leading to a positive correlation between AI investments and an increase in industry concentration.


Turing Test At 70: Still Relevant For AI (Artificial Intelligence)?

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ENGLAND - 1958: English Electric developed several notable pioneering computers during the 1950s. The DEUCE took up a huge space compared to modern computers and worked from 1450 thermionic valves which grew hot, blow outs were frequent. However the DEUCE proved a popular innovation and some models were working in to the 1970s. Photograph by Walter Nurnberg who transformed industrial photography after WWII using film studio lighting techniques. When computers were still in the nascent stages, Alan Turing published his legendary paper, "Computing Machinery And Intelligence," in the Mind journal in 1950.


Work with Compute in Azure Machine Learning - Learn

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One of the key benefits of the cloud is the ability to use scalable, on-demand compute resources for cost-effective processing of large data. In this module, you'll learn how to use cloud compute in Azure Machine Learning to run training experiments at scale.


Neural networks give glimpse of the future to autonomous cars

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Just as we shift from burning fossil fuels to electrification to power our vehicles, we are also moving steadily towards a greater number of autonomous functions. The journey began with advanced driver-assistance systems, and while the destination is yet to be decided (will we really have fully autonomous, driverless cars on public roads?) the technology is locked in to become part of our lives. But even though there has been a lot of fanfare about vehicles already on the road being able to almost take over the majority of driving – Tesla along with Waymo are rarely out of the headlines in this field – there remain significant challenges to reaching the highest levels of automated driving. Perhaps one of the biggest issues to overcome is developing autonomous driving systems that can predict what is going to happen in certain scenarios and adapt. Something humans are incredibly good at.


Raytheon and C3.ai announce alliance on artificial intelligence solutions

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Raytheon's intelligence and space business is partnering with C3.ai, a software company known for its predictive maintenance business with the U.S. Air Force, the companies announced Monday. The alliance between C3.ai and Raytheon Intelligence and Space aims to speed up artificial intelligence adoption across the U.S. military. The partnership will pair Raytheon's expertise in the defense and aerospace sector with C3.ai's artificial intelligence development and applications. "The military and intelligence community have access to more data now than any time in history, but it's more than they're able to make quick use of," said David Appel, vice president of defense and civil solutions for space and C2 systems under Raytheon Intelligence and Space. "Artificial intelligence can be used to help them make sense of that data, which will allow them to make smarter decisions faster on the battlefield.


DeepMind solves 50-year-old 'grand challenge' with protein folding A.I.

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Alphabet-owned DeepMind has developed a piece of artificial intelligence software that can accurately predict the structure that proteins will fold into in a matter of days, solving a 50-year-old "grand challenge" that could pave the way for better understanding of diseases and drug discovery. Every living cell has thousands of different proteins inside that keep it alive and well. Predicting the shape that a protein will fold into is important because it determines their function and nearly all diseases, including cancer and dementia, are related to how proteins function. "Proteins are the most beautiful, gorgeous structures and the ability to predict exactly how they fold up is really very, very challenging and has occupied many people over many years," Professor Dame Janet Thornton from the European Bioinformatics Institute told journalists on a call. British research lab DeepMind's "AlphaFold" AI system was entered into a competition organized by a group called CASP (Critical Assessment for Structure Prediction). It's a community experiment organization with the mission of accelerating solutions to one problem: how to compute the 3D structure of protein molecules.