Artificial Intelligence and Machine Learning
Blog Posts
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 Nielsen
In Software Engineering Research and Development
Release of SCAIFE System Version 2.0.0 Provides Support for Continuous-Integration (CI) Systems
Key features in new release of SCAIFE System Version 2.0.0 including support for continuous-integration (CI) systems, and status of evolving SEI SCAIFE work
• By Lori Flynn
In Secure Development
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 Nielsen
In Cyber-Physical Systems
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 D. Guttman
In Artificial Intelligence Engineering
Managing the Risks of Adopting AI Engineering
This SEI Blog post discusses how organizations can manage the risks associated with adopting AI engineering, including developing a risk management framework.
• By Brett Tucker
In Artificial Intelligence Engineering
Detecting Mismatches in Machine-Learning Systems
The use of machine learning (ML) could improve many business functions and meet many needs for organizations. For example, ML capabilities can be used to suggest products to users based …
• By Grace Lewis
In Artificial Intelligence Engineering
Three Risks in Building Machine Learning Systems
Machine learning (ML) systems promise disruptive capabilities in multiple industries. Building ML systems can be complicated and challenging....
• By Benjamin Cohen
In Artificial Intelligence Engineering
Automatically Detecting Technical Debt Discussions with Machine Learning
Technical debt (TD) refers to choices made during software development that achieve short-term goals at the expense of long-term quality....
• By Robert Nord
In Artificial Intelligence Engineering
