InfoQ Homepage AI, ML & Data Engineering Content on InfoQ
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Build a Better Monster: Morality, Machine Learning and Mass Surveillance
Maciej Ceglowski wonders what tech companies can do to reduce the amount of data collected, closing the path to mass surveillance and bringing some morality in using ML with this data.
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AI in Medicine
Anthony Chang presents the past of AI in medicine, the current development status, and what to expect from the future.
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Adopting Stream Processing for Instrumentation
Sean Cribbs talks about the interface Comcast has designed for their instrumentation system, how it works, how the stream processor manages flows on behalf of the user, and some trade-offs applied.
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When Streams Fail: Kafka Off the Shore
Anton Gorshkov discusses how to evaluate and architect a resilient streaming platform, focusing on Kafka and Spark streaming and sharing his experience of using them to process financial transactions.
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Fast, Scalable, Reusable: A New Perspective on Production ML/AI Systems
Ekrem Aksoy discusses why production ML/AI systems should have a different perspective than the usual DevOps perspective which works on data immune systems.
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Making Machines Talk in IoT Paradigm
Mehrdad Negahban and Kevin Kostiner discuss an architecture for M2M communication using cloud to switch, route and enable meaningful conversation among devices based on machine learning technology.
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Deep Learning for Image Understanding at Scale
Stacey Svetlichnaya discusses strategies and challenges building deep learning systems for object recognition at scale, using automatic labels in Flickr image search as a case study.
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Beyond Big Data - The Realization of an Active Grid in the Age of Fog Computing
Jan Forrslow discusses Fog Computing, Active Grids, and how an IoT network can become an Active Grid by using Fog Computing.
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In Depth TensorFlow
Illia Polosukhin keynotes on TensorFlow, introducing it and presenting the components and concepts it is built upon.
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Blockchain: The Oracle Problems
Paul Sztorc talks about why the oracle problem is so hard (the historical evolution of failures, why they fail), and the basics of blockchain ("blockchain as immortal software", ledger "rents").
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Practical Blockchains: Building on Bitcoin
Peter Todd answers the questions: why use Bitcoin over other blockchains, what is safe, future proof ways to peg data to Bitcoin's blockchain and what is Bitcoin script, and how it can be used.
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Comparing Deep Learning Frameworks
Jeffrey Shomaker covers the different types of deep learning frameworks and then focuses on neural networks, including business uses and 4 of the main systems (eg. Tensor Flow) that are open sourced.
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