InfoQ Homepage Machine Learning Content on InfoQ
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Q&A on the Book Hands-On Genetic Algorithms with Python
Hands-On Genetic Algorithms with Python by Eyal Wirsansky is a new book which explores the world of genetic algorithms to solve search, optimization, and AI-related tasks, and improve machine learning models. InfoQ interviewed Eyal Wirsansky about how genetic algorithms work and what they can be used for.
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The Road to Artificial Intelligence: a Tale of Two Advertising Approaches
Artificial Intelligence startups received a record $26.6bn in funding last year, yet a litany of stakeholders continue to demonstrate a lack understanding and education around the discipline. It is critical that entrepreneurs, investors, regulators, and consumers all remain vigilant in properly assessing advertising claims as relates to powerful, constantly-evolving technology.
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Reinforcement Machine Learning for Effective Clinical Trials
In this article, author Dattaraj Jagdish Rao explores the reinforcement machine learning technique called Multi-armed Bandits and discusses how it can be applied to areas like website design and clinical trials.
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Q&A on the Book AI Crash Course
The book AI Crash Course by Hadelin de Ponteves contains a toolkit of four different AI models: Thompson Sampling, Q-Learning, Deep Q-Learning and Deep Convolutional Q-learning. It teaches the theory of these AI models and provides coding examples for solving industry cases based on these models.
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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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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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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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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.