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Bayesian Statistics for Beginners: a step-by-step approach: Donovan, Therese M., Mickey, Ruth M.: 9780198841302: Amazon.com: Books

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"While reading this book, I joined the authors on a learning endeavor thanks to their honesty and intellectual vulnerability. Their lack of experience with Bayesian statistics helps them to be effective communicators . . . If you are interested in starting your Bayesian journey, then Bayesian Statistics for Beginners is an excellent place to begin." Therese Donovan, Wildlife Biologist, U.S. Geological Survey, Vermont Cooperative Fish and Wildlife Research Unit, University of Vermont, USA,Ruth M. Mickey, Professor Emerita, Department of Mathematics and Statistics, University of Vermont, USA Therese Donovan is a wildlife biologist with the U.S. Geological Survey, Vermont Cooperative Fish and Wildlife Research Unit. Based in the Rubenstein School of Environment and Natural Resources at the University of Vermont, Therese teaches graduate courses on ecological modeling and conservation biology.


When BERT Plays The Lottery, All Tickets Are Winning

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Two years after BERT was unveiled to the world, Transformers are still dominating the leaderboards and have spawned numerous follow-up studies. The first version of our attempt to survey BERTology literature (Rogers et al. 2020) provided an overview of about 40 papers in February 2020. By June, there were over a hundred. The final TACL camera-ready version has about 150 BERT-related citations, and no illusions of completeness: we ran out of journal-allotted pages in August 2020. But even with all that research, it is still not clear why BERT works so well.


Can AI Machine Learning and Genomics Find Alzheimer's Drugs?

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What if a new treatment for Alzheimer's disease exists today among existing U.S. Food and Drug Administration (FDA) approved drugs? A new peer-reviewed study published last week in Nature Communications by researchers at Harvard Medical School and Massachusetts General Hospital shows how an AI machine learning framework combined with genomics can help predict drug repurposing candidates for Alzheimer's disease. There are an estimated 50 million people living with Alzheimer's disease, a neurodegenerative disorder, and other forms of dementia globally according to the World Alzheimer Report 2018. In the United States, 5.8 million people are affected by Alzheimer's disease--two-thirds of whom are women. There are over 16 million people in the U.S. caring for those with Alzheimer's according to an article published today in Time by Maria Shriver, founder of the Women's Alzheimer's Movement, and George Vradenburg, co-founder of UsAgainstAlzheimer's.


An AI is training counselors to deal with teens in crisis

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Counselors volunteering at the Trevor Project need to be prepared for their first conversation with an LGBTQ teen who may be thinking about suicide. One of the ways they do it is by talking to fictional personas like "Riley," a 16-year-old from North Carolina who is feeling a bit down and depressed. With a team member playing Riley's part, trainees can drill into what's happening: they can uncover that the teen is anxious about coming out to family, recently told friends and it didn't go well, and has experienced suicidal thoughts before, if not at the moment. Now, though, Riley isn't being played by a Trevor Project employee but is instead being powered by AI. Just like the original persona, this version of Riley--trained on thousands of past transcripts of role-plays between counselors and the organization's staff--still needs to be coaxed a bit to open up, laying out a situation that can test what trainees have learned about the best ways to help LGBTQ teens.


Artificial Intelligence and Patents: Inventing Inventors

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Who are the inventors of patents? Since George Washington signed the first patent in 1790, the United States has issued patents to people of various ages, ethnicities, and genders, with some patent inventors being as young as two when they filed[1]. The varied backgrounds of these inventors stems from the United States Patent and Trademark Office's ("USPTO") broad definition of an inventor, laying out an inventor to "mean[] the individual or, if a joint invention, the individuals collectively who invented or discovered the subject matter the invention."[2] But what happens when the inventor is a machine? This is the exact issue Dr. Stephen Thaler sought to resolve with the USPTO as well as other worldwide patent offices.


Zillow's 'live offer' Zestimate launches in 20 markets

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Weeks after revealing the company planned to turn the Zestimate into a live offer in certain Zillow Offers markets, Zillow announced Thursday that qualifying homeowners in 20 markets would now officially see their Zestimate turned into a live offer. The move comes roughly 15 years after Zillow initially introduced the Zestimate to consumers, a proprietary automated valuation model that uses machine learning to predict the market value of a home. "We've long said, one of the beauties of Zillow Offers is that it's the best first step for you to think about selling," Jeremy Wacksman, Zillow's chief operations officer told Inman. "Nobody wants to think about selling, most sellers are thinking about buying, they want to be thinking about buying, but at some point they have to think about, 'oh my gosh I have to go through this process.'" "The Zestimate is the starting point for that. If we can turn the Zestimate into a starting offer for certain customers, that's going to get them to raise their hand and say, 'Okay, I'm thinking about moving, help me understand, am I trading in? Can I talk to an agent about that?'"


Kirk Borne Keynote Speakers Bureau and Speaking Fee

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Dr. Kirk Borne is the Principal Data Scientist and an Executive Advisor at global technology and consulting firm Booz Allen Hamilton based in McLean, Virginia USA (since 2015). In those roles, he focuses on applications of data science, data analytics, data mining, machine learning, machine intelligence, and modeling across a wide variety of disciplines. He also provides leadership and mentoring to multi-disciplinary teams of scientists, modelers, and data scientists; and he consults with numerous external organizations, industries, agencies, and partners in the use of large data repositories and machine learning for discovery, decision support, and innovation. He previously spent 12 years as Professor of Astrophysics and Computational Science at George Mason University where he did research, taught, and advised students in the Data Science degree programs. Before that, Kirk spent nearly 20 years supporting data systems activities on NASA space science programs, including a role as NASA's Data Archive Project Scientist for the Hubble Space Telescope, contract manager in NASA's Astronomy Data Center, and program manager in NASA's Space Science Data Operations Office.


The Importance of Algorithmic Fairness - IT Peer Network

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Algorithmic fairness is a motif that plays throughout our podcast series: as we look to AI to help us make consequential decisions involving people, guests have stressed the risks that the automated systems that we build will encode past injustices and that these decisions may be too opaque. In episode twelve of the Intel on AI podcast, Intel AI Tech Evangelist and host Abigail Hing Wen talks with Alice Xiang, then Head of Fairness, Transparency, and Accountability Research at the Partnership on AI--a nonprofit in Silicon Valley founded by Amazon, Apple, Facebook, Google, IBM, Intel and other partners. With a background that includes both law and statistics, Alice's research has focused on the intersection of AI and the law. "A lot of the benefit of algorithmic systems, if used well, would be to help us detect problems rather than to help us automate decisions." Algorithmic fairness is the study of how algorithms might systemically perform better or worse for certain groups of people and the ways in which historical biases or other systemic inequities might be perpetuated by AI.


Even Small Companies Use AI, Machine Learning

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Data, technology, and people are at hand to make artificial intelligence and machine learning available to all commerce companies. To be certain, artificial intelligence and its sub-field, machine learning, have gone through cycles of inflated expectations followed by disappointments. For example, in the 1950s and 1960s, the United States government funded research for the machine translation of languages. The hope was that Russian-language documents could be instantly translated to English. But by 1966, a report from the Automatic Language Processing Advisory Committee, a government team of seven scientists, essentially killed machine translation research in the U.S. for about a decade.


Senior Compiler Software Engineer - Machine Learning in San Jose, CA - Xilinx

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At Xilinx, we are leading the industry transformation to build an adaptable, intelligent world. ARE YOU bold, collaborative, and creative? We develop leaders and innovators who want to revolutionize the world of technology. We believe that by embracing diverse ideas, pushing boundaries, and working together as ONEXILINX, anything is possible. Our culture of innovation began with the invention of the Field Programmable Gate Array (FPGA), and with the 2018 introduction of our Adaptive Compute Acceleration Platform (ACAP), has made a quantum leap in capability, solidifying our role as the adaptable platform supplier of choice.