InfoQ Homepage AI, ML & Data Engineering Content on InfoQ
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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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How to Build, Deploy, and Operationalize AI Assistants
While chatbot PoCs are simple, building production-grade conversational software is challenging. Enterprises experience challenges in production systems that have large user bases, security mandates, and polyglot environments. This article provides insight into building an AI assistant, and outlines various tools and techniques to continuously monitor and improve AI assistants in production.
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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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Scalable Cloud Environment for Distributed Data Pipelines with Apache Airflow
In this article, author Lena Hall discusses how to use Apache Airflow to define and execute distributed data pipelines with an example of the workflow framework running on Kubernetes on Azure cloud platform.
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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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Combining DataOps and DevOps: Scale at Speed
DataOps is an extension of DevOps standards and processes into the data analytics world. It's about streamlining the processes involved in processing, analyzing and deriving value from big data.
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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.
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State of the Art in Automated Machine Learning
InfoQ caught up with experts Francesca Lazzeri, machine learning scientist lead at Microsoft; Matthew Tovbin, co-founder of Faros AI; Adrian de Wynter, applied scientist in Alexa AI’s Secure AI Foundations; Leah McGuire, principal member of technical staff at Salesforce; and Marios Michailidis, data scientist at H2O.ai, about the state of the art in automated machine learning (AutoML).
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How to Get Hired as a Machine Learning Engineer
To become a machine learning engineer, you have to interview. You have to gain relevant skills from books, courses, conferences, and projects. Include technologies, frameworks, and projects on your CV. In an interview, expect that you will be asked technical questions, insight questions, and programming questions. When given a technical task, demonstrate your skills as if you already had the job.
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