InfoQ Homepage AI, ML & Data Engineering Content on InfoQ
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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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Candy Crush QA AI 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.
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Building Intelligent Conversational Interfaces
Authors discuss how to build intelligent conversational applications and skills using the conversational AI technology and its three components: interaction flow, natural language understanding (NLU) and deployment.
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Postgres Handles More Than You Think
Thinking about scaling beyond Postgres with a data store like Redis or Elasticsearch? Think again before adopting a complex infrastructure. Postgres can scale for heavy loads and offers powerful features which are not obvious at first sight. For example, it's possible to enable in-memory caching, text search, specialized indexing, and key-value storage. Article
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How to Tell Compelling Stories Using Data: Q&A with Dr. Christine Bailey
The more evidence we have, the more likely our ideas are believed - or so we’re conditioned to think . But data doesn’t always engage people; this is where storytelling can help to combine data, insights, and emotion, said Dr. Christine Bailey. She presented techniques to tell compelling stories with data, and showed how that can increase our influence with external and internal stakeholders.
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Rethinking Flink’s APIs for a Unified Data Processing Framework
Since its very early days, Apache Flink has followed the philosophy of taking a unified approach to batch and streaming. The core building block is the “continuous processing of unbounded data streams, with batch as a special, bounded set of those streams.” Recent updates to the Flink APIs include architectural designs by the community to support batch and streaming unification in Apache Flink.
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