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Machine learning helps predict complications, rehospitalizations after PCI

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In a related editorial, R. Jeffrey Westcott, MD, and James E. Tcheng, MD, said Zack and colleagues' findings support the idea that machine learning could outperform classical statistical approaches to risk prediction--but it'll take some work to make it an industry standard. "Transforming healthcare, and, more specifically, transforming the management of data within healthcare to enable AI and its siblings, requires foundational investment and culture change," the editorialists wrote. They said artificial intelligence and machine learning will undoubtedly become "increasingly important in clinical medicine" as we move forward, with equity funding for healthcare-related AI ventures topping $2.4 billion in 2018. "Machine learning has proven to be valuable and is therefore the future," Westcott and Tcheng wrote. "Data warehouses and data lakes contain amazing amounts of structured and unstructured data that will change how medical research, drug and device trials, and device tracking are done. A collaborative effort is needed with EHR vendors, third-party vendors, professional societies and others to start meaningful standardized data collection and workflow redesign now."


(Podcast) Chief data officer in government

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SONAL SHAH: It's also about how do we make data more useful for people to use and to solve problems in their communities? TANYA OTT: Okay, that is a big job. Who is this superhuman who fills it? TANYA OTT: We'll tell you, in a moment. But first, let me say, you're listening to the Press Room, where we talk about some of the biggest issues facing businesses today. I'm Tanya Ott and joining me today are Bill Eggers … I am the executive director and a professor of practice at Georgetown University's Beeck Center. TANYA OTT: Bill and Sonal are coauthors of The CDO Playbook – a guide for Chief Data Officers. For the last decade, government has been focused on making data more open and easily [accessible] to the public.


Beneficial Artificial Intelligence

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In 2015, computer scientist and AI pioneer, Stuart Russell, became the first signatory of an open letter calling on researchers to ensure "that increasingly capable AI systems are robust and beneficial." Stuart joins Azeem Azhar to discuss the possible AI futures and how to ensure technology serves the good of humanity.


Neuromorphic computing with multi-memristive synapses

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The human brain with less than 20 W of power consumption offers a processing capability that exceeds the petaflops mark, and thus outperforms state-of-the-art supercomputers by several orders of magnitude in terms of energy efficiency and volume. Building ultra-low-power cognitive computing systems inspired by the operating principles of the brain is a promising avenue towards achieving such efficiency. Recently, deep learning has revolutionized the field of machine learning by providing human-like performance in areas, such as computer vision, speech recognition, and complex strategic games1. However, current hardware implementations of deep neural networks are still far from competing with biological neural systems in terms of real-time information-processing capabilities with comparable energy consumption. One of the reasons for this inefficiency is that most neural networks are implemented on computing systems based on the conventional von Neumann architecture with separate memory and processing units.


AI and your resume: How to beat the bots

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Nearly all Fortune 500 companies – more than 98 percent – plus an increasing number of smaller businesses filter resumes using an applicant tracking system before they ever make it to a human hiring manager, according to a 2018 analysis of job listings by resume optimization service Jobscan. "And it's only a matter of time before AI-enabled tools become even more prevalent," says Lisa Rangel, former recruiter and managing director of Chameleon Resumes. "Many larger companies are already using AI-candidate screening tools that focus on the whole candidate and not just a resume. As these tools become more mainstream and affordable, they will become more widely used." AI is infiltrating many processes related to recruiting and hiring, according to Al Smith, CTO with talent acquisition software provider iCIMS.



Researchers have developed a robot that can identify, assess and pick lettuce without damaging it

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In another sign that smart technology is transforming the farming industry, engineers at the University of Cambridge have developed a robot that uses machine learning to pick lettuce. The robot, dubbed "Vegebot", has been designed to first identify iceberg lettuce and then decide if it is healthy and ready to be picked, the university said Monday. If this is the case, it will then cut the lettuce without damaging it. The Vegebot was first trained to identify and pick the delicate crop in a laboratory and has now undertaken successful tests in a range of field conditions. The university added that while the prototype device was neither as fast or efficient as a human, it showed how robots could be used in agriculture on a wider scale.


Machine Learning Galway on Twitter

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A community of people interested in Artificial Intelligence, Deep Learning & Machine Learning in the West of Ireland.


Dassai sake: Blending the ancient and the innovative I-CIO

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The situation was further compounded by demographic changes. Ongoing rural depopulation in Japan has seen the average age of farmers rise to 67, according to 2015 census figures. To counter the situation and encourage greater cultivation, Asahi Shuzo joined forces with Fujitsu. The ICT company's solution involved the deployment of solar-powered IoT sensors and cameras in paddy fields, with the resulting data connected to the Fujitsu Akisai food and agriculture industry cloud. The sensors measured environmental conditions, including atmospheric humidity levels, ground and air temperatures, ground moisture and electroconductivity. "The use of Fujitsu's Akisai system in Dassai production has created a win-win for both Asahi Shuzo and its farmers."


Flybits raises $35 million to personalize customer experiences with AI

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Knowing when and where a person is, was, and will be can enable magical customer experiences. Flybits today announced that it's raised $35 million in series C funding led by Point72 Ventures, with participation from Mastercard, Citi Ventures, and Reinventure, along with existing partners Portag3 Ventures, TD Bank, and Information Venture Partners. The fresh funding brings its total raised to $50 million, and it comes as Flybits notches 300% growth in 2019 and gears up to hire across sales, engineering, and business development teams and offices, including adding solutions engineers, sales executives, business development reps, and engineers. "Customers are already used to seeing content and recommendations based on their behavior," said CEO Hossein Rahnama. But Flybits leverages an unlimited amount to create far more personalized and relevant recommendations than ever before, all in an effort to help financial institutions deliver real time lifestyle banking that gets at their customers' deeper needs.