Artificial Intelligence Engineering
Blog Posts
Creating Transformative and Trustworthy AI Systems Requires a Community Effort
This post explores how professionalizing the practice of AI engineering and developing the AI engineering discipline can increase the dependability and availability of AI systems.
• By Carrie Gardner
In Artificial Intelligence Engineering
Improving Automated Retraining of Machine-Learning Models
This post describes how to improve representative MLOps pipelines by automating exploratory data-analysis tasks.
• By Rachel Brower-Sinning
In Artificial Intelligence Engineering
Six Dimensions of Trust in Autonomous Systems
This post chronicles the adoption and growth of autonomous systems and provides six considerations for establishing trust.
• By Paul D. Nielsen
In Software Engineering Research and Development
How Easy Is It to Make and Detect a Deepfake?
The technology underlying the creation and detection of deepfakes and assessment of current and future threat levels
• By Catherine Bernaciak, Dominic Ross
In Artificial Intelligence Engineering
A Hitchhiker’s Guide to ML Training Infrastructure
Hardware is a key enabler for machine learning. Recent advances in the field, including the introduction of graphics processing units, have had a significant impact on the training of AI …
• By Jay Palat
In Artificial Intelligence Engineering
What is Explainable AI?
Explainable artificial intelligence is a powerful tool in answering critical How? and Why? questions about AI systems and can be used to address rising ethical and legal concerns.
• By Violet Turri
In Artificial Intelligence Engineering
Systems Engineering and Software Engineering: Collaborating for the Smart Systems of the Future
Convergence between systems engineering and software engineering is forging new practices for engineering the smart systems of the future.
• By Paul D. Nielsen
In Cyber-Physical Systems
5 Ways to Start Growing an AI-Ready Workforce
This blog post by Rachel Dzombak and Jay Palat outlines 5 factors that are critical for organizations and leaders to consider as they grow an AI-ready workforce.
• By Rachel Dzombak, Jay Palat
In Artificial Intelligence Engineering
Software Engineering for Machine Learning: Characterizing and Detecting Mismatch in Machine-Learning Systems
This post describes how we are creating and assessing empirically validated practices to guide the development of machine-learning-enabled systems.
• By Grace Lewis, Ipek Ozkaya
In Artificial Intelligence Engineering
A Game to Assess Human Decision Making with AI Support
In decision-support systems based on AI, humans often make poor choices causing the systems to be abandoned. Rotem Guttman introduces a game that collects data on actual human decision making …
• By Rotem Guttman
In Artificial Intelligence Engineering
