InfoQ Homepage Artificial Intelligence Content on InfoQ
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Operationalizing Responsible AI in Practice
Mehrnoosh Sameki discusses approaches to responsible AI and demonstrates how open source and cloud integrated ML help data scientists and developers to understand and improve ML models better.
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Solving Data Quality Issues to Diagnose Health Symptoms with AI
Lola Priego and Jose del Pozo discuss how they improved the user input accuracy, normalized lab data using a scoring algorithm, and how this work finishes with an AI to diagnose health.
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The Unreasonable Effectiveness of Zero Shot Learning
Roland Meertens shows how one can get started deploying models without requiring any data, discussing foundational models, and examples of them, such as GPT-3 and OpenAI CLIP.
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Panel: Future of Language Support for ML
Jendrik Jördening, Irene Dea, Alanna Tempest take a look at the state of the art of ML/AI development and how advances in language technology (specifically differentiable programming langs) can help.
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A Functional Tour of Automatic Differentiation
Oliver Strickson discusses automatic differentiation, a family of algorithms for taking derivatives of functions implemented by computer programs, offering the ability to compute gradients of values.
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BERT for Sentiment Analysis on Sustainability Reporting
Susanne Groothuis discusses how KPMG created a custom sentiment analysis model capable of detecting subtleties, and provides them with a metric indicating the balance of a report.
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The Fast Track to AI with JavaScript and Serverless
Peter Elger explores how to get started building AI enabled platforms and services using full stack JavaScript and Serverless technologies.
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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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