InfoQ Homepage AI, ML & Data Engineering Content on InfoQ
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Has an AI Cyber Attack Happened Yet?
AI cyber attacks have happened and are happening, with increasing regularity. This article looks at recent attacks, the role of bots, and defense strategies you can employ.
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The Kongo Problem: Building a Scalable IoT Application with Apache Kafka
In this article, author Paul Brebner discusses the best practices for developing IoT projects using Apache Kafka and Kafka Streams technologies and how to maximize Kafka scalability.
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Q&A on the Book Agile Machine Learning
The book Agile Machine Learning by Eric Carter and Matthew Hurst describes how the guiding principles of the Agile Manifesto have been used by machine learning teams in data projects. It explores how to apply agile practices for dealing with the unknowns of data and inferencing systems, using metrics as the customer.
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The Road to Artificial Intelligence: An Ethical Minefield
Increasingly-rapid developments in the field of AI have offered society profound benefits, but have also produced complex ethical dilemmas. Many of the most nefarious issues are often overlooked, even in the engineering community. There also exists the meta-ethical question of who ought to be the ones making decisions concerning the encoding of values into autonomous systems.
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Why Visual AI Beats Pixel and DOM Diffs for Web App Testing
Visual AI breaks regions of pixels into rendered elements for comparison purposes, similar to how humans view web pages. As a result, Visual AI can compare any kinds of images on a page, providing a more effective mechanism for automated visual testing when compared to pixel and DOM diffing.
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How King uses AI to test Candy Crush Saga
To be able to improve features in games which are constantly evolving, the challenge will be to scale tests to be on a par with new feature development. Automated tests are vital for King to keep up testing Candy Crush, therefore they are constantly looking for new improved ways to test.
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Getting to Know Deep Java Library (DJL)
Amazon has announced DJL, an open source library to develop Deep Learning models in Java. This article details how to get started with the toolkit. The library aims to reduce number of software dependencies by enabling end-end Deep learning development in Java, rather than having to use additional technologies such as Python or R.
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InfoQ Editors' Recommended Talks from 2019
As part of the 2019 end-of-year-summary content, this article collects together a list of recommended presentation recordings from the InfoQ editorial team.
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InfoQ's 2019, and Software Predictions for 2020
We take a look back at what we saw on InfoQ in 2019, and think about what the next year might bring.
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Q&A on the Book Rebooting AI
The book Rebooting AI explains why a different approach other than deep learning is needed to unlock the potential of AI. Authors Gary Marcus and Ernest Davis propose that AI programs will have to have a large body of knowledge about the world in general, represented symbolically. Some of the basic elements of that knowledge should be built in.
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Predicting Time to Cook, Arrive, and Deliver at Uber Eats
Time predictions are critical to Uber Eats' business as they determine when to dispatch delivery partners as well as ensure customer satisfaction. This article explains how their dispatch system evolved through time predictions powered by machine learning, followed by a deep dive on how to predict food preparation time without ground truth data. It goes over delivery and travel time predictions.
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How to Use Redis TimeSeries with Grafana for Real-Time Analytics
In this article, author Roshan Kumar discusses how a purpose-built database like RedisTimeSeries can be used to manage time-series data. He also shows how to visualize this data in a Grafana dashboard.