In this SEI Podcast, Dr. Eric Heim, a senior machine learning research scientist at Carnegie Mellon University's Software Engineering Institute (SEI), discusses the quantification of uncertainty in machine-learning (ML) systems. ML systems can make wrong predictions and give inaccurate estimates for the uncertainty of their predictions. It can be difficult to predict when their predictions will be wrong. Heim also discusses new techniques to quantify uncertainty, identify causes of uncertainty, and efficiently update ML models to reduce uncertainty in their predictions. The work of Heim and colleagues at the SEI Emerging Technology Center closes the gap between the scientific and mathematical advances from the ML research community and the practitioners who use the systems in real-life contexts, such as software engineers, software developers, data scientists, and system developers.
Best Practices and Lessons Learned in Standing Up an AISIRT
3 API Security Risks (and How to Protect Against Them)
Evaluating Large Language Models for Cybersecurity Tasks: Challenges and Best Practices
Capability-based Planning for Early-Stage Software Development
Safeguarding Against Recent Vulnerabilities Related to Rust
Developing a Global Network of Computer Security Incident Response Teams (CSIRTs)
Automated Repair of Static Analysis Alerts
Cyber Career Pathways and Opportunities
My Story in Computing with Sam Procter
Developing and Using a Software Bill of Materials Framework
The Importance of Diversity in Cybersecurity: Carol Ware
The Importance of Diversity in Software Engineering: Suzanne Miller
The Importance of Diversity in Artificial Intelligence: Violet Turri
Using Large Language Models in the National Security Realm
Atypical Applications of Agile and DevSecOps Principles
When Agile and Earned Value Management Collide: 7 Considerations for Successful Interaction
The Impact of Architecture on Cyber-Physical Systems Safety
ChatGPT and the Evolution of Large Language Models: A Deep Dive into 4 Transformative Case Studies
The Cybersecurity of Quantum Computing: 6 Areas of Research
User-Centric Metrics for Agile
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