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Last Minute Week of Deals - Machine Learning - SparkFun Electronics

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In 2003, CU student Nate Seidle fried a power supply in his dorm room and, in lieu of a way to order easy replacements, decided to start his own company. Since then, SparkFun has been committed to sustainably helping our world achieve electronics literacy from our headquarters in Boulder, Colorado. No matter your vision, SparkFun's products and resources are designed to make the world of electronics more accessible. In addition to over 2,000 open source components and widgets, SparkFun offers curriculum, training and online tutorials designed to help demystify the wonderful world of embedded electronics. We're here to help you start something.


AI in psychiatry: detecting mental illness with artificial intelligence

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A team of researchers from the University of Colorado Boulder are working to apply machine learning artificial intelligence (AI) in psychiatry, with a speech-based mobile app that can categorise a patient's mental health status as well as, or better than, a human can. The university research paper has been published in Schizophrenia Bulletin, and lays out the promise and potential pitfalls of AI in psychiatry. Peter Foltz, a research professor at the Institute of Cognitive Science and co-author of the paper, said: "We are not in any way trying to replace clinicians, but we do believe we can create tools that will allow them to better monitor their patients." In Europe, the WHO estimated that 44.3 million people suffer with depression and 37.3 million suffer with anxiety. Diagnosis of mental health disorders are based on an age-old method that can be subjective and unreliable, notes paper co-author Brita Elvevåg, a cognitive neuroscientist at the University of Tromsø, Norway.


AI Can Detect Mental Illness Through Speech-Based Mobile App Analytics Insight

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The advances in AI has enabled computers to assist doctors in detecting diseases and help keep a check on patient health remotely. Now, researchers from the University of Colorado Boulder (CU Boulder) are working to leverage ML to psychiatry using a speech-based mobile app. Peter Foltz, a research professor at the Institute of Cognitive Science says – "We are not in any way trying to replace clinicians. But we do believe we can create tools that will allow them to better monitor their patients." Notably, he is also the co-author of a new paper in Schizophrenia Bulletin that illustrates the promise and potential pitfalls of artificial intelligence in psychiatry.



How artificial intelligence can transform psychiatry

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IMAGE: Peter Foltz, a research professor at the University of Colorado Boulder Institute of Cognitive Science, has developed an app that rates mental help based on speech cues. Thanks to advances in artificial intelligence, computers can now assist doctors in diagnosing disease and help monitor patient vital signs from hundreds of miles away. Now, CU Boulder researchers are working to apply machine learning to psychiatry, with a speech-based mobile app that can categorize a patient's mental health status as well as or better than a human can. "We are not in any way trying to replace clinicians," says Peter Foltz, a research professor at the Institute of Cognitive Science and co-author of a new paper in Schizophrenia Bulletin that lays out the promise and potential pitfalls of AI in psychiatry. "But we do believe we can create tools that will allow them to better monitor their patients."


#297: Using Natural Language in Human-Robot Collaboration, with Brad Hayes

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In this episode, we hear from Brad Hayes, Assistant Professor of Computer Science at the University of Colorado Boulder, who directs the university's Collaborative AI and Robotics lab. The lab's work focuses on developing systems that can learn from and work with humans--from physical robots or machines, to software systems or decision support tools--so that together, the human and system can achieve more than each could achieve on their own. Our interviewer Audrow caught up with Dr. Hayes to discuss why collaboration may at times be preferable to full autonomy and automation, how human naration can be used to help robots learn from demonstration, and the challenges of developing collaborative systems, including the importance of shared models and safety to allow adoption of such technologies in future.


Facial recognition AI can't identify trans and non-binary people

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Facial-recognition software from major tech companies is apparently ill-equipped to work on transgender and non-binary people, according to new research. A recent study by computer-science researchers at the University of Colorado Boulder found that major AI-based facial analysis tools--including Amazon's Rekognition, IBM's Watson, Microsoft's Azure, and Clarifai--habitually misidentified non-cisgender people. They eliminated instances in which multiple individuals were in the photo, or where at least 75% of the person's face wasn't visible. The images were then divided by hashtag, amounting to 350 images in each group. Scientists then tested each group against the facial analysis tools of the four companies.


Fellows Lead Effort to Apply Machine Learning to Climate Change

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Two Department of Energy Computational Science Graduate Fellowship recipients are leading an effort to address global climate change effects with machine-learning techniques. Priya Donti, a third-year fellow in computer science and public policy at Carnegie Mellon University, and Kelly Kochanski, a fourth-year fellow in Earth surface processes at the University of Colorado Boulder, are on the steering committee (Donti is co-chair) for Climate Change AI. The group's website says it is a coalition of "volunteers from academia and industry who believe in using machine learning, where it is relevant, to help tackle the climate crisis." Machine learning algorithms identify patterns in known data and use that information to make predictions or to classify previously unseen data. Machine learning is a key component of artificial intelligence (AI).


Finding Faces in Hailstorms - Eos

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Hail can be among the most damaging of severe weather phenomena, but predicting whether a passing thunderstorm might start spitting pea-sized (or golf ball–sized) hailstones is notoriously difficult. A new approach using machine learning techniques related to facial recognition technology is giving meteorologists a new tool for mapping how various components of a storm might add up to dangerous hail conditions. Some types of thunderstorms, such as supercells, are more likely to produce hail than others. But the sheer scale of thunderstorms, which can stretch for kilometers and contain multitudes of intrastorm interactions, makes it difficult for computers to accurately model and predict storm behavior, said David John Gagne, a machine learning scientist at the National Center for Atmospheric Research (NCAR) in Boulder, Colo., and lead author of the new study, published in Monthly Weather Review. Drawing upon machine learning technology sometimes used to identify features of individual faces, Gagne and colleagues at NCAR trained a deep learning model called a convolutional neural network to recognize various storm features known to produce hail.


EMA Names Top Innovators in the Use of Artificial Intelligence for Data Management

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BOULDER, Colo., September 12, 2019 /PRNewswire-PRWeb/ -- Enterprise Management Associates (EMA), a leading IT and data management research and consulting firm, today announced the leading innovators in the use of artificial intelligence (AI) and machine learning (ML) in metadata services, data integration, and data preparation. Two EMA research reports also identify the value of using AI and ML in data management, with savings up $5,000,000 annually. Due to the measurable value created by AI enablement, leading vendors create more value for their customers compared to legacy data management technology. Passive use of metadata focused on definitions and documentation, while the active use of metadata focuses on the delivery of services, such as data cataloguing, data governance, data discovery, and master data services. In alphabetical order, here are the Top 3 vendors for the use of AI and ML in metadata services platforms: Informatica, Reltio, and Unifi.