InfoQ Homepage Machine Learning Content on InfoQ
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The Brain is Neither a Neural Network Nor a Computer: Book Review of The Biological Mind
Underlying much of artificial intelligence research is the idea that the essence of an individual resides in the brain. This is contrary to neuroscience which has discovered that a brain cannot work independently from the body and its environment. Understanding this enables us see what is reasonable to expect from artificial intelligence, as well as technology designed to improve human life.
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Overcoming Data Scarcity and Privacy Challenges with Synthetic Data
In this article, the author discusses the importance of using synthetic data in data analytics projects, especially in financial institutions, to solve the problems of data scarcity and more importantly data privacy.
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Understanding Similarity Scoring in Elasticsearch
In this article, the author discusses the importance of Relevancy Score for developing Search Engine solutions and how to calculate the relevancy score using Elasticsearch's similarity module.
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Q&A on the Book Cybersecurity Threats, Malware Trends and Strategies
The book Cybersecurity Threats, Malware Trends and Strategies by Tim Rains provides an overview of the threat landscape over a twenty year period. It provides insights and solutions that can be used to develop an effective cybersecurity strategy and improve vulnerability management.
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Q&A on the Book Accelerating Software Quality
The book Accelerating Software Quality by Eran Kinsbruner explores how we can combine techniques from artificial intelligence and machine learning with a DevOps approach to increase testing effectiveness and deliver higher quality. It provides examples and recommendations for using AI/ML-based solutions in software development and operations.
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Is Artificial Intelligence Closer to Common Sense?
Intelligent agents lack the common-sense knowledge they need to reason about the world. Traditionally, there have been two unsuccessful approaches to getting computers to reason about the world—symbolic logic and deep learning. A new project, called COMET, tries to bring these two approaches together. Although it has not yet succeeded, it offers the possibility of progress.
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Challenges of Human Pose Estimation in AI-Powered Fitness Apps
In this article, the author discusses the human pose estimation solution powered by AI technologies and the challenges faced in online fitness apps which use the pose estimation to predict the position of the human body based on an image or a video containing a person.
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COVID-19 and Mining Social Media - Enabling Machine Learning Workloads with Big Data
In this article, author Adi Pollock discusses how to enable machine learning workloads with big data to query and analyze COVID-19 tweets to understand social sentiment towards COVID-19.
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The Case for Explainable AI (XAI)
Artificial Neural Networks offer significant performance benefits compared to other methodologies, but often at the expense of interpretability. Black box algorithms have precipitated a number of high-profile controversies arising from the inability to understand their inner workings. The efforts seeking to provide more transparency in this regard is referred to as Explainable AI (XAI).
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Federated Machine Learning for Loan Risk Prediction
In this article, author Brendon Machado discusses how data owners and data scientists can work together to create models on privatized data using the federated learning technique and shows how to use it in loan risk prediction use cases.
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Easy Interpretation of a Logistic Regression Model with Delta-p Statistics
Delta-p statistics is an easier means of communicating results to a non-technical audience than the plain coefficients of a logistic regression model. In this article, authors Maarit Widmann and Alfredo Roccato discuss how to predict credit eligibility using the Delta-p statistics based solution.
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The First Wave of GPT-3 Enabled Applications Offer a Preview of Our AI Future
The first wave of GPT-3 powered applications are emerging. After priming of only a few examples, GPT-3 could write essays, answer questions, and even generate computer code! Furthermore, GPT-3 can perform algebraic calculations and language translations despite never being taught such concepts. However, GPT-3 is a black box with unpredictable outcomes. Developers must use it responsively.