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3 myths about translating AI models in a healthcare setting


Artificial intelligence has huge potential for many healthcare challenges – but there are still many hurdles that must be overcome.  
At a keynote for the Association for Computing Machinery Conference on Health, Inference and Learning this past week, Dr. Alan Karthikesalingam, research lead at Google Health UK, described three myths commonly encountered in the path to building and translating AI models in clinical settings.  
When it comes to implementing deep learning technology, he asked: Why is there a gap between expectations and reality?    
Here are three common misconceptions Karthikesalingam said must be addressed.  
1. More data is all you need for a better model.   ....

Kat Jercich , Alan Karthikesalingam , Google Health United Kingdom , Association For Computing Machinery Conference On Health , Computing Machinery Conference , கூகிள் ஆரோக்கியம் ஒன்றுபட்டது கிஂக்டம் , கணினி இயந்திரங்கள் மாநாடு ,

Environmental News Network - Assessing Regulatory Fairness Through Machine Learning


Assessing Regulatory Fairness Through Machine Learning
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The perils of machine learning – using computers to identify and analyze data patterns, such as in facial recognition software – have made headlines lately. 
The perils of machine learning – using computers to identify and analyze data patterns, such as in facial recognition software – have made headlines lately. Yet the technology also holds promise to help enforce federal regulations, including those related to the environment, in a fair, transparent way, according to a new study(link is external) by Stanford researchers.
The analysis, published this week in the proceedings of the Association of Computing Machinery Conference on Fairness, Accountability and Transparency(link is external), evaluates machine learning techniques designed to support a U.S. Environmental Protection Agency (EPA) initiative to reduce severe violations of the Clean Water Act ....

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Research explores hallmarks of effective conversations


December 21, 2020
What makes people good at having conversations? In a recent paper, Cornell researchers explored conversations on a crisis text service in order to figure out how to answer that question.
“The problem we always came up with was that we never knew if the things we observed were correlations, or if they could actually provide useful information to inform how the platform assigns counselors,” said Justine Zhang, doctoral student in information science and first author of “Quantifying the Causal Effects of Conversational Tendencies.”
The paper was presented at the Association for Computing Machinery Conference on Computer-Supported Cooperative Work and Social Computing, held virtually Oct. 17-21. ....

United States , Sendhil Mullainathan , Cristian Danescu Niculescu Mizil , Justine Zhang , Association For Computing Machinery Conference On Computer , National Science Foundation , Microsoft Research Ph , Robert Wood Johnson Foundation , Chicago Booth School Of Business , Causal Effects , Conversational Tendencies , Computing Machinery Conference , Computer Supported Cooperative Work , Social Computing , Crisis Text Line , Chicago Booth School , Robert Wood Johnson , ஒன்றுபட்டது மாநிலங்களில் , ஜஸ்டின் ஜாங் , சங்கம் க்கு கணினி இயந்திரங்கள் மாநாடு ஆன் கணினி , தேசிய அறிவியல் அடித்தளம் , மைக்ரோசாஃப்ட் ஆராய்ச்சி பீயெச் , ராபர்ட் மரம் ஜான்சன் அடித்தளம் , சிகாகோ சாவடி பள்ளி ஆஃப் வணிக , காரண விளைவுகள் , கணினி இயந்திரங்கள் மாநாடு ,