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In the midst of the COVID-19 pandemic, Daniel and Chris have a timely conversation with Lucy Lu Wang of the Allen Institute for Artificial Intelligence about COVID-19 Open Research Dataset (CORD-19). She relates how CORD-19 was created and organized, and how researchers around the world are currently using the data to answer important COVID-19 questions that will help the world through this ongoing crisis.
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AI legend Stuart Russell, the Berkeley professor who leads the Center for Human-Compatible AI, joins Chris to share his insights into the future of artificial intelligence. Stuart is the author of Human Compatible, and the upcoming 4th edition of his perennial classic Artificial Intelligence: A Modern Approach, which is widely regarded as the standard text on AI. After exposing the shortcomings inherent in deep learning, Stuart goes on to propose a new practitioner approach to creating AI that avoids harmful unintended consequences, and offers a path forward towards a future in which humans can safely rely of provably beneficial AI.
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So many AI developers are coming up with creative, useful COVID-19 applications during this time of crisis. Among those are Timo from Deepset-AI and Tony from Intel. They are working on a question answering system for pandemic-related questions called COVID-QA. In this episode, they describe the system, related annotation of the CORD-19 data set, and ways that you can contribute!
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Daniel Wilson and Rob Fletcher of ESRI hang with Chris and Daniel to chat about how AI powered modern geographic information systems (GIS) and location intelligence. They illuminate the various models used for GIS, spatial analysis, remote sensing, real-time visualization, and 3D analytics. You don’t want to miss the part about their work for the DoD’s Joint AI Center in humanitarian assistance / disaster relief.
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Practical AI is a weekly podcast that’s marking artificial intelligence practical, productive, and accessible to everyone. If world of AI affects your daily life, this show is for you.
Whether you're deep in the tools and trends, trying to keep up…
(clip from episode #357)
To the AI curious trying to understand the concepts at play and their implications on our lives…
(clip from episode #348)
Chris Benson and Daniel Whitenack are here to keep you fully-connected with the world of machine learning and data science.
Please listen to a recent episode that interests you and subscribe today. We’d love to have you as a listener!
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Catherine Breslin of Cobalt joins Daniel and Chris to do a deep dive on speech recognition. She also discusses how the technology is integrated into virtual assistants (like Alexa) and is used in other non-assistant contexts (like transcription and captioning). Along the way, she teaches us how to assemble a lexicon, acoustic model, and language model to bring speech recognition to life.
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Emily Robinson, co-author of the book Build a Career in Data Science, gives us the inside scoop about optimizing the data science job search. From creating one’s resume, cover letter, and portfolio to knowing how to recognize the right job at a fair compensation rate.
Emily’s expert guidance takes us from the beginning of the process to conclusion, including being successful during your early days in that fantastic new data science position.
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Comment on the episode page for a chance to win a FREE copy of the eBook! Tell us why you are interested in Data Science and how this book might help you achieve your goals. Our 3 favorite comments will be selected on April 13th!
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Matt Brems from General Assembly joins us to explain what “data science” actually means these days and how that has changed over time. He also gives us some insight into how people are going about data science education, how AI fits into the data science workflow, and how to differentiate yourself career-wise.
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Craig Wiley, from Google Cloud, joins us to discuss various pieces of the TensorFlow ecosystem along with TensorFlow Enterprise. He sheds light on how enterprises are utilizing AI and supporting AI-driven applications in the Cloud. He also clarifies Google’s relationship to TensorFlow and explains how TensorFlow development is impacting Google Cloud Platform.
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Expanding AI technology to the local languages of emerging markets presents huge challenges. Good data is scarce or non-existent. Users often have bandwidth or connectivity issues. Existing platforms target only a small number of high-resource languages.
Our own Daniel Whitenack (data scientist at SIL International) and Dan Jeffries (from Pachyderm) discuss how these and related problems will only be solved when AI technology and resources from industry are combined with linguistic expertise from those on the ground working with local language communities. They have illustrated this approach as they work on pushing voice technology into emerging markets.
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