InfoQ Homepage AI, ML & Data Engineering Content on InfoQ
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Genetic Programming in the Real World: A Short Overview
Leonardo Trujillo overviews how GP can be used to solve ML tasks intended as a starting point for applied researchers and developers.
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Women in AI & Blockchain
The panelists discuss the role women can play in AI and blockchain technologies.
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AI & Blockchain from an Investment Perspective
The panelists discuss building AI and blockchain systems from an investment perspective.
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AI for Software Testing with Deep Learning: Is It Possible?
Emerson Bertolo discusses lessons learned when using pre-trained Convolutional Neural Networks (CNN) models, Image Detection APIs and CNN's built from scratch for this purpose.
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AI, the Enterprise, and You: A Primer and Post-Mortem
David Wesst discusses current AI solutions along with the challenges of delivering an AI solution, from defining requirements, goals, and differences in development.
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Deep Representation: Building a Semantic Image Search Engine
Emmanuel Ameisen gives a step-by-step tutorial on how to build a semantic search engine for text and images, with code included.
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The State of AI Marketing
Federico Gobbi discusses the current state of AI in marketing, trends, case studies, technologies, ethics, regulations and compliance.
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Keep It Simple, Stupid: Driving Model Adoption through Tiers
Jamie Warner covers a tiered approach to model introduction and implementation that focuses on building stakeholder buy-in without abandoning advanced techniques.
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Rethinking HCI with Neural Interfaces @CTRLlabsCo
Adam Berenzweig talks about brain-computer interfaces, neuromuscular interfaces, and other biosensing techniques that can eliminate the need for physical controllers.
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Using Data Effectively: beyond Art and Science
Hilary Parker talks about approaches and techniques to collect the most useful data, analyze it in a scientific way, and use it most effectively to drive actions and decisions.
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Building the Enchanted Land
Grady Booch examines what AI is and what it is not, as well as how it came to be and where it's headed. Along the way, he examines some best practices for engineering AI systems.
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What Computers Can Teach Us about Humans: Machine Learning in Marketing
Melinda Han Williams discusses using machine learning in marketing.
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