InfoQ Homepage Artificial Intelligence Content on InfoQ
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Facial Recognition Adversarial Attacks, Policy and Choice
Gretchen Greene demonstrates the technical feasibility of facial recognition adversarial attacks, describes using it at airports and borders and invites contributions to their open sourced prototype.
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Creating Robust Interpretable NLP Systems with Attention
Alexander Wolf introduces Attention, an interpretable type of neural network layer that is loosely based on attention in human, explaining why and how it has been utilized to revolutionize NLP.
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Monitoring AI with AI
Iskandar Sitdikov discusses a solution, tooling and architecture that allows an ML engineer to be involved in delivery phase and take ownership over deployment and monitoring of ML pipelines.
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Unintended Consequences of AI — Panel Discussion
The panelists discuss some of the unexpected and unintended consequences AI might have.
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Building a Voice Assistant for Enterprise
Manju Vijayakumar talks about Einstein Assistant - an AI Voice assistant for enterprises that enables users to "Talk to Salesforce".
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Goldilocks and Artificial Intelligence
Rob Keefer discusses some of the positive and negative impacts of AI on human performance, offering a framework for determining the right amount of AI to mix into a system that will help users.
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The Right Amount of Trust for AI
Chris Butler discusses the building blocks of AI from a product/design perspective, what trust is, how trust is gained and lost, and techniques one can use to build trusted AI products.
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The Future of AI
The panelists discuss the future of artificial intelligence.
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Solving Business Problems with AI
The panelists discuss using AI to solve business problems.
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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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