yencarnacion + learning   208

Machine Learning is Fun Part 6: How to do Speech Recognition with Deep Learning
Bay Area Deep Learning School Day 2 at CEMEX auditorium, Stanford
Speech Recognition and Deep Learning by by Adam Coates and Vinay Rao or Baidu
Adam Coates of Baidu gave a great presentation on Deep Learning for Speech Recognition at the Bay Area Deep Learning School. You can watch the video on YouTube (his talk starts at 3:51:00).
https://www.youtube.com/watch?v=9dXiAecyJrY&feature=youtu.be&t=13874
https://youtu.be/9dXiAecyJrY?t=13874
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Connectionist temporal classification: labelling unsegmented sequence data with recurrent neural networks
https://dl.acm.org/citation.cfm?id=1143891
medium  speech  recognize  recognition  ai  artificial  intelligence  deep  learning 
6 weeks ago by yencarnacion
Partnering with researchers at UC Berkeley to improve the use of ML
Today, the consequences of exposing algorithmic decisions and machine learning models to hundreds of millions of people are poorly understood. Even less is known about how these algorithms might interact with social dynamics: people might change their behaviour in response to what the algorithms recommend to them, and as a result of this shift in behaviour the algorithm itself might change, creating a potentially self-reinforcing feedback loop. We also know that individuals or groups will seek to game or exploit our algorithms and safeguarding against this is essential.
artificial  intelligence  ai  machine  learning  thoughts  though  twitter  unintended  consequence  consequences  behavior  behaviors  behavioral  change  changes  musing  deep  share  sharing  publishing  disseminating  research 
7 weeks ago by yencarnacion
Amazon Comprehend Medical
https://aws.amazon.com/blogs/machine-learning/introducing-medical-language-processing-with-amazon-comprehend-medical/
Unlocking this information from medical language makes a variety of common medical use cases easier and cost-effective, including: clinical decision support (e.g., getting a historical snapshot of a patient’s medical history), revenue cycle management (e.g., simplifying the time-intensive manual process of data entry), clinical trial management (e.g., by identifying and recruiting patients with certain attributes into clinical trials), building population health platforms, and helping address (PHI) requirements (e.g., for privacy and security assurance.)
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https://www.wsj.com/articles/amazon-starts-selling-software-to-mine-patient-health-records-1543352136
“We’re able to completely, automatically look inside medical language and identify patient details,” including diagnoses, treatments, dosage and strengths, “with incredibly high accuracy,” said Matt Wood, general manager of artificial intelligence at Amazon Web Services.

During testing, the software performed on par or better than other published efforts, and can extract data on patients’ diseases, prescriptions, lab orders and procedures, said Taha Kass-Hout, a senior leader with Amazon’s health-care and artificial intelligence efforts.

Users upload health records to Amazon’s cloud service, where they can run the text-processing software. Amazon’s algorithms analyze text for specific types of data and return the results in an organized format, similar to a spreadsheet.
amazon  amzn  aws  ai  machine  learning  artificial  intelligence  medical  unstructured  structured  data  mining  language  processing 
november 2018 by yencarnacion
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