InfoQ Homepage Artificial Intelligence Content on InfoQ
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You Can AI Like an Expert
Jon McLoone shows that symbolic representation also helps in automating the transition from research experiments to the production deployment of AI services.
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From POC to Production in Minimal Time - Avoiding Pain in ML Projects
Janet Bastiman describes how turning an AI proof of concept into a production ready, deployable system can be a world of pain, especially if different parts of the puzzle are done by different teams.
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What Does It Mean to Be a Data Scientist? Definitions and Lessons Learned from the Trenches
Brian Korzynski discusses what Data Science and Big Data are, focusing on the data preparation that needs to take place, and making a distinction between ML issues and programming.
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Big Data Information Architecture for AI
Toby Woolfe discusses case studies (Watson Willow, the L’Oreal factory and Iplexia demo) to show a factory line manager talking to Watson.
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ML/AI Panel
The panelists discuss what makes ML different from other types of applications and why it requires special tooling.
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How AI Is Transforming Test Automation
Daniel Gold reviews concepts in AI and ML, how it is related to testing and what is the vision for AI in testing, how they used AI to process the DOM, and the technical challenges encountered.
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How Can Artificial Intelligence Use Big Data for Translating Documents?
John Ortega shows how documents, known as corpora, filled with information from various sources can be used to provide artificial intelligence to a translation system.
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Seven Steps to Design, Build, and Scale an AI Product
Allie Miller explores the fundamental use cases in AI and how designers and engineers can be at the forefront of prioritizing AI/ML best practices.
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Computer Mathematics, AI and Functional Programming
Moa Johansson discusses the history of computer mathematics and how it connects to the development of early functional programming languages like Standard ML.
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From Research to Production with PyTorch
Jeff Smith covers some of the latest features from PyTorch including the TorchScript JIT compiler, distributed data parallel training, TensorBoard integration, new APIs, and more.
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Hands-on Feature Engineering for Natural Language Processing
Susan Li shares various NLP feature engineering techniques, from Bag-Of-Words to TF-IDF to word embedding that includes feature engineering for both ML models and an emerging deep learning approach.
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Rise of the Machines – AI in the Agile World
Aidan Casey examines the AI capabilities available today in simple layman’s terms and explores how these will be used to augment and shape the Agile world of tomorrow.
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