Toward ethical, transparent and fair AI/ML: a critical reading list
**stopped maintaining in 2019 In the past 5 years there’s been a lot of enthusiasm about AI and specifically machine learning and deep learning. As we continuously deploy AI models in the wild we are forced to re-examine what are the effects of knowledge symbolisation, generalisation and classification on the historical, political and social conditions of human life. We also need to remind ourselv
NIPS 2017 Art Gallery
NIPS 2017 Art Gallery See link for accepted art submissions, music submissions, and demos for papers! Introduction In the last year, generative machine learning and machine creativity have gotten a lot of attention in the non-research world. At the same time there have been significant advances in generative models for media creation and for design. This one-day workshop explores several issues in
Microsoft and Facebook create open ecosystem for AI model interoperability | Azure Blog | Microsoft Azure
Explore Azure Get to know Azure Discover secure, future-ready cloud solutions—on-premises, hybrid, multicloud, or at the edge Global infrastructure Learn about sustainable, trusted cloud infrastructure with more regions than any other provider Cloud economics Build your business case for the cloud with key financial and technical guidance from Azure Customer enablement Plan a clear path forward fo
Neural Nets for Generating Music
Algorithmic music composition has developed a lot in the last few years, but the idea has a long history. In some sense, the first automatic music came from nature: Chinese windchimes, ancient Greek wind-powered Aeolian harps, or the Japanese water instrument suikinkutsu. But in the 1700s automatic music became “algorithmic”: Musikalisches Würfelspiel, a game that generates short piano composition
Human-Centered Machine Learning
7 steps to stay focused on the user when designing with ML By Josh Lovejoy and Jess Holbrook Machine learning (ML) is the science of helping computers discover patterns and relationships in data instead of being manually programmed. It’s a powerful tool for creating personalized and dynamic experiences, and it’s already driving everything from Netflix recommendations to autonomous cars. But as mor
Facets - Visualizations for ML datasets
The power of machine learning comes from its ability to learn patterns from large amounts of data. Understanding your data is critical to building a powerful machine learning system. Facets contains two robust visualizations to aid in understanding and analyzing machine learning datasets. Get a sense of the shape of each feature of your dataset using Facets Overview, or explore individual observat
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