InfoQ Homepage Machine Learning Content on InfoQ
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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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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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Q&A on the Book The Driver in the Driverless Car
The book The Driver in the Driverless Car by Vivek Wadhwa and Alex Salkever explores how technology is changing faster and faster, and what impact that can have on the future of our society. It aims to help frame decisions and thinking about rapidly developing technologies. Salkever and Wadhwa cover a wide variety of technologies, including robotics, AI, quantum computing, and driverless cars.
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Fraud Detection Using Random Forest, Neural Autoencoder, and Isolation Forest Techniques
In this article, the authors discuss how to detect fraud in credit card transactions, using supervised machine learning algorithms (random forest, logistic regression) as well as outlier detection approaches using isolation forest technique and anomaly detection using the neural autoencoder.
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Privacy Attacks on Machine Learning Models
Research has shown that machine learning models can expose personal information present in their training data. This vulnerability exposes sensitive user information to attackers savvy enough to learn how to hack a machine learning API. We'll explore the details of several privacy attacks against machine learning models and provide some potential solutions for this growing security issue.
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Stream Processing Anomaly Detection Using Yurita Framework
In this article, author Guy Gerson discusses the stream processing anomaly detection framework they developed by PayPal, called Yurita. The framework is based on Spark Structured Streaming.
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How to Use Open Source Prometheus to Monitor Applications at Scale
In this article, the author discusses how to collect metrics and achieve anomaly detection from streaming data using Prometheus, Apache Kafka and Apache Cassandra technologies.
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Why Should We Care about Technology Ethics? The Updated ACM Code of Ethics
The 2018 rewrite of the ACM code of ethics and professional conduct has brought it up-to-date with new technologies and societal demands. This code supports the ethical conduct of computing professionals through a set of guidelines for positively working in the tech industry.
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Using Intel Analytics Zoo to Inject AI into Customer Service Platform (Part II)
This article shares the practical experience of building a QA ranker module on Azure’s customer support platform using Intel Analytics Zoo by Microsoft Azure China team. You can quickly learn step by step how to prepare data to train, evaluate and tune a text matching model at scale and finally productionize it as a service using Analytics Zoo.
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Open Source Robotics: Getting Started with Gazebo and ROS 2
An introduction to Gazebo, a powerful robot simulator that calculates physics, generates sensor data and provides convenient interfaces, and ROS 2, the latest version of the Robot Operating System, which offers familiar tools and capabilities, while expanding to new use cases. Both are open source and used by academia and industry alike.
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The Data Science Mindset: Six Principles to Build Healthy Data-Driven Organizations
In this article, business and technical leaders will learn methods to assess whether their organization is data-driven and benchmark its data science maturity. They will learn how to use the Healthy Data Science Organization Framework to nurture a data science mindset within the organization.