Blog Posts
Harnessing the Power of Large Language Models For Economic and Social Good: 4 Case Studies
This blog post, the second in a series, outlines four case studies, that explore the potential of large language models, such as ChatGPT, and explores their limitations and future uses.
• By Matthew Walsh, Dominic A. Ross, Clarence Worrell, Alejandro Gomez
In Artificial Intelligence Engineering
Harnessing the Power of Large Language Models For Economic and Social Good: Foundations
This blog post explores the capabilities and limitations of large language models.
• By Matthew Walsh, Dominic A. Ross, Clarence Worrell, Alejandro Gomez
In Artificial Intelligence Engineering
Contextualizing End-User Needs: How to Measure the Trustworthiness of an AI System
As potential applications of artificial intelligence (AI) continue to expand, the question remains: will users want the technology and trust it? This blog post explores how to measure the trustworthiness …
• By Carrie Gardner, Katherine-Marie Robinson, Carol J. Smith, Alexandrea Steiner
In Artificial Intelligence Engineering
The Challenge of Adversarial Machine Learning
This SEI Blog post examines how machine learning systems can be subverted through adversarial machine learning, the motivations of adversaries, and what researchers are doing to mitigate their attacks.
• By Matt Churilla, Nathan M. VanHoudnos, Robert W. Beveridge
In Artificial Intelligence Engineering
Tackling Collaboration Challenges in the Development of ML-Enabled Systems
This SEI blog post highlights research examining the collaboration challenges inherent in the development of machine-learning-enabled systems compared to traditional software development projects.
• By Grace Lewis
In Artificial Intelligence Engineering
Play it Again Sam! or How I Learned to Love Large Language Models
This post explores what new advancements in AI and large language models mean for software development.
• By Jay Palat
In Artificial Intelligence Engineering
Bridging the Gap between Requirements Engineering and Model Evaluation in Machine Learning
Requirements engineering for machine learning (ML) is not standardized and considered one of the hardest tasks in ML development. This post defines a simple evaluation framework centered around validating requirements.
• By Violet Turri, Eric Heim
In Artificial Intelligence Engineering
MXNet: A Growing Deep Learning Framework
MXNet (pronounced mix-net) is Apache’s open-source spin on a deep-learning framework that supports building and training models in multiple languages, including Python, R, Scala, Julia, Java, Perl, and C++.
• By Jeffrey Mellon
In Artificial Intelligence Engineering
How to Grow an AI-Ready DoD Workforce
This SEI Blog post discusses the unique challenges of AI engineering for defense and national security, how to build an AI-ready workforce, and how the SEI is supporting DoD workforce …
• By Robert W. Beveridge
In Artificial Intelligence Engineering
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