Flash Forward is a show about possible (and not so possible) future scenarios. What would the warranty on a sex robot look like? How would diplomacy work if we couldn’t lie? Could there ever be a fecal transplant black market? (Complicated, it wouldn’t, and yes, respectively, in case you’re curious.) Hosted and produced by award winning science journalist Rose Eveleth, each episode combines audio drama and journalism to go deep on potential tomorrows, and uncovers what those futures might re ...
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97 - Automated Analysis Of Historical Printed Documents, With Taylor Berg-Kirkpatrick
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Manage episode 247106961 series 1452120
Content provided by NLP Highlights and Allen Institute for Artificial Intelligence. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by NLP Highlights and Allen Institute for Artificial Intelligence or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://player.fm/legal.
In this episode, we talk to Taylor Berg-Kirkpatrick about optical character recognition (OCR) on historical documents. Taylor starts off by describing some practical issues related to old scanning processes of documents that make performing OCR on them a difficult problem. Then he explains how one can build latent variable models for this data using unsupervised methods, the relative importance of various modeling choices, and summarizes how well the models do. We then take a higher level view of historical OCR as a Machine Learning problem, and discuss how it is different from other ML problems in terms of the tradeoff between learning from data and imposing constraints based on prior knowledge of the underlying process. Finally, Taylor talks about the applications of this research, and how these predictions can be of interest to historians studying the original texts.
…
continue reading
145 episodes
MP3•Episode home
Manage episode 247106961 series 1452120
Content provided by NLP Highlights and Allen Institute for Artificial Intelligence. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by NLP Highlights and Allen Institute for Artificial Intelligence or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://player.fm/legal.
In this episode, we talk to Taylor Berg-Kirkpatrick about optical character recognition (OCR) on historical documents. Taylor starts off by describing some practical issues related to old scanning processes of documents that make performing OCR on them a difficult problem. Then he explains how one can build latent variable models for this data using unsupervised methods, the relative importance of various modeling choices, and summarizes how well the models do. We then take a higher level view of historical OCR as a Machine Learning problem, and discuss how it is different from other ML problems in terms of the tradeoff between learning from data and imposing constraints based on prior knowledge of the underlying process. Finally, Taylor talks about the applications of this research, and how these predictions can be of interest to historians studying the original texts.
…
continue reading
145 episodes
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