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Explainable AI explained

Some day machine learning models may be more ‘glass box‘ than black box. Until then, ‘XAI’ tools and techniques can help us understand how a black box model makes its decisions. ....

Christoph Molnar , Microsoft Research , Wells Fargo , Machine Learning , Azure Machine , Interpretable Machine Learning ,

Explainable AI explained | InfoWorld

While machine learning and deep learning models often produce good classifications and predictions, they are almost never perfect. Models almost always have some percentage of false positive and false negative predictions. That’s sometimes acceptable, but matters a lot when the stakes are high. For example, a drone weapons system that falsely identifies a school as […] ....

Matt Turek , Christoph Molnar , Microsoft Research , Defense Advanced Research Projects Agency , Data Labeling Service , Wells Fargo , Machine Learning , Azure Machine , Interpretable Machine Learning , Google Cloud Platform , What If Tool , Feature Fit , Feature Effects ,

Explainable AI explained

Someday machine learning models may be more ‘glass box‘ than black box. Until then, explainability tools and techniques can help us understand how a black box model makes its decisions. ....

Matt Turek , Christoph Molnar , Microsoft Research , Defense Advanced Research Projects Agency , Data Labeling Service , Wells Fargo , Machine Learning , Azure Machine , Interpretable Machine Learning , Google Cloud Platform , What If Tool , Feature Fit , Feature Effects ,

Supporting Responsible Use Of AI And Equitable Outcomes In Financial Services – Federal Reserve Governor Lael Brainard At The AI Academic Symposium Hosted By The Board Of Governors Of The Federal Reserve System, Washington, D.C. (Virtual Event)

Supporting Responsible Use Of AI And Equitable Outcomes In Financial Services â Federal Reserve Governor Lael Brainard At The AI Academic Symposium Hosted By The Board Of Governors Of The Federal Reserve System, Washington, D.C. (Virtual Event)
Date
12/01/2021
Today s symposium on the use of artificial intelligence (AI) in financial services is part of the Federal Reserve s broader effort to understand AI s application to financial services, assess methods for managing risks arising from this technology, and determine where banking regulators can support responsible use of AI and equitable outcomes by improving supervisory clarity.1
The potential scope of AI applications is wide ranging. For instance, researchers are turning to AI to help analyze climate change, one of the central challenges of our time. With nonlinearities and tipping points, climate change is highly complex, and quantification for risk assessments requires the analysis of vast amounts o ....

United States , New York , Zacharyc Lipton , Cynthia Rudin , Michelle Kambara , Michael Tyka , Westra Miller , Makada Henry Nickie , Ewen Callaway , Sarah Castellanos , Molly Mahar , Kennethp Brevoort , Jeff Ernst , Hod Lipson , Ziad Obermeyer , Rayid Ghani , Kavita Jain , David Rolnick , Carola Evans , Lael Brainard , Davide Castelvevchi , Carol Evans , Piotr Szapiezynski , Philipp Grimm , Alan Mislove , Christoph Molnar ,

Interfaces for Explaining Transformer Language Models

Interfaces for Explaining Transformer Language Models
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William Shakespeare , Fyodor Dostoyevsky , Forward Neural Network , Hidden State Evolution , Interpretable Machine Learning , Feed Forward Neural Network , Activation Values , Non Negative Matrix Factorization , Principal Component Analysis , Non Negative Matrix , Token Sparkbars , Integrated Gradients , Canonical Correlation Analysis , வில்லியம் ஷேக்ஸ்பியர் , முன்னோக்கி நரம்பியல் வலைப்பின்னல் , மறைக்கப்பட்டுள்ளது நிலை பரிணாமம் , தீவனம் முன்னோக்கி நரம்பியல் வலைப்பின்னல் , செயல்படுத்தல் மதிப்புகள் , ப்ரிந்ஸிபல் கூறு பகுப்பாய்வு , அல்லாத எதிர்மறை அணி ,