InfoQ Homepage Artificial Intelligence Content on InfoQ
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AI for Software Developers: a Future or a New Reality?
In this article, author Nikita Povarov discusses the role AI/ML plays in software development and how tasks like code completion, code search, and bug detection can be powered by machine learning. But he also explains why a complete replacement of programmers by algorithms isn't going happen any time soon.
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The Major Software Industry Trends from 2021 and What to Watch in 2022
In this podcast summary Thomas Betts, Wes Reisz, Shane Hastie, Charles Humble, Srini Penchikala, and Daniel Bryant discuss what they have seen in 2021 and speculate a little on what they hope to see in 2022. Topics explored included: hybrid working and the importance of ethics and sustainability within technology.
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Getting Rid of Wastes and Impediments in Software Development Using Data Science
This article presents how to use data science to detect wastes and impediments, and concepts and related information that help teams to figure out the root cause of impediments they struggle to get rid of. The knowledge discovered during research includes an expanded waste classification, and the use of trends to uncover undesired situations like hidden delayed backlog items and defects trends.
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Developing Deep Learning Systems Using Institutional Incremental Learning
Institutional incremental learning promises to achieve collaborative learning. This form of learning can address data sharing and security issues, without bringing in the complexities of federated learning. This article talks about practical approaches which help in building an object detection system.
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Is Artificial Intelligence Taking over DevOps?
AI tools are slowly replacing the role of the developer – just as DevOps did before – and will eventually supplant DevOps entirely. Assessing whether that prediction is true is tricky. In this article, we’ll look at what AI promises for the development process, assess whether it can really ever take over from human developers, and what DevOps is likely to look like in a decades’ time.
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AI, ML and Data Engineering InfoQ Trends Report - August 2021
How AI, ML and Data Engineering are evolving in 2021 as seen by the InfoQ editorial team. Topics discussed include deep learning, edge deployment of machine learning algorithms, commercial robot platforms, GPU and CUDA programming, natural language processing and GPT-3, MLOps, and AutoML.
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Validation of Autonomous Systems
This article introduces validation and certification as well as the general approval of autonomous systems and their components, such as those used in automation technology and robotics. It gives an overview of methods for verification and validation of autonomous systems, sketches current tools and show the evolution towards AI-based techniques for influence analysis of continuous changes.
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DevOps is Not Enough for Scaling and Evolving Tech-Driven Organizations: a Q&A with Eduardo da Silva
Eduardo Silva from bol.com on the need for sociotechnical systems thinking. DevOps is a good starting point but a wider view of the organization as a sociotechnical system is key for sustained growth.
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How Optimizing MLOps Can Revolutionize Enterprise AI
In this article, author Monte Zweben discusses data science architecture, containerization, and how new solutions like Feature Store can help with the full lifecycle of machine learning processes.
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Sociotechnical Implications of Using Machines as Teammates
AI has become more than just a tool; it is now meriting consideration as an additional teammate. While this increases a project’s efficiency and technical rigor, AI teammates bring a fresh set of challenges around social integration, team dynamics, trust, and control. This article provides an overview of sociotechnical frameworks and strategies to address concerns with using machines as teammates.
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AI No Silver Bullet for Cloud Security, But Here’s How It Can Help
In this article, the author looks at the real role of artificial intelligence in cloud security – the hype, the reality, and how we can resolve the gap between them. He encourages the reader to focus on making cloud security platforms that allow humans to provide truly intelligent threat responses, rather than relying on the machines to do it for us.
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AI Applied in Enterprises: Information Architecture, Decision Optimization, and Operationalization
The book Deploying AI in the Enterprise by Eberhard Hechler, Martin Oberhofer, and Thomas Schaeck gives insight into the current state of AI related to themes like change management, DevOps, risk management, blockchain, and information governance. It discusses the possibilities, limitations, and challenges of AI and provides cases that show how AI is being applied.