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Cheaper lidar system aid mass adoption of driverless cars

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Light Detection And Ranging (lidar) systems enable vehicles to'see' in real-time by mapping three-dimensional images. The systems use large, rotating mirrors which reflect laser beams from surrounding objects. A University of Colorado-Boulder team has been working on a different way of steering these laser beams, called wavelength steering. This technique involves pointing each wavelength of laser light to a unique angle. This allows for a lidar system which is far less bulky and expensive, and can be easily made smaller than current devices.


AI, machine learning to deliver 'wave of discoveries'

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The past 20 years have seen remarkable advances in the mining industry, particularly in mineral exploration technologies with vast volumes of data generated from geologic, geophysical, geochemical, satellite and other surveying techniques. However, the abundance of data has not necessarily translated into the discovery of new deposits, according to Colin Barnett, co-founder of BW Mining, a Boulder, Colorado-based data mining and mineral exploration company. "One of the problems we're facing in exploration is the huge increase in the amounts of data we have to look at," said Barnett, in his presentation at the Managing and exploring big data through artificial intelligence and machine learning session at the recent PDAC 2020 convention in Toronto. "And although it's high-quality data, the sheer volume is becoming almost overwhelming for human interpreters, and so we need help in getting to the bottom of it." By integrating hundreds or even thousands of interdependent layers of data, with each layer making its own statistically determined contribution, machine learning offers a solution to the problem of tackling the massive amounts of data generated, and a powerful new tool in the search for mineral deposits.


Artificial intelligence, machine learning primed to deliver 'a wave of discoveries'

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The past 20 years have seen remarkable advances in the mining industry, particularly in mineral exploration technologies with vast volumes of data generated from geologic, geophysical, geochemical, satellite and other surveying techniques. However, the abundance of data has not necessarily translated into the discovery of new deposits, according to Colin Barnett, co-founder of BW Mining, a Boulder, Colorado-based data mining and mineral exploration company. "One of the problems we're facing in exploration is the huge increase in the amounts of data we have to look at," said Barnett, in his presentation at the Managing and exploring big data through artificial intelligence and machine learning session the recent PDAC 2020 convention in Toronto. "And although it's high-quality data, the sheer volume is becoming almost overwhelming for human interpreters, and so we need help in getting to the bottom of it." By integrating hundreds or even thousands of interdependent layers of data, with each layer making its own statistically determined contribution, machine learning offers a solution to the problem of tackling the massive amounts of data generated, and a powerful new tool in the search for mineral deposits.


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.