The recent growth of applications that leverage large language models, including ChatGPT and Copilot, has spurred reactions ranging from fear and uncertainty to adoration and lofty expectations. In this podcast from the Carnegie Mellon University Software Engineering Institute, Jay Palat, senior engineer and technical director of AI for mission, and Dr. Rachel Dzombak, senior advisor to the director of the SEI’s AI Division, discuss the current landscape of large language models (LLMs), common misconceptions about LLMs, how to leverage tools built on top of LLMs, and the need for critical thinking around both the outputs of the tools and the trends in their use.
A Roadmap for Creating and Using Virtual Prototyping Software
Software Architecture Patterns for Robustness
A Platform-Independent Model for DevSecOps
Using the Quantum Approximate Optimization Algorithm (QAOA) to Solve Binary-Variable Optimization Problems
Trust and AI Systems
A Dive into Deepfakes
Challenges and Metrics in Digital Engineering
The 4 Phases of the Zero Trust Journey
DevSecOps for AI Engineering
Undiscovered Vulnerabilities: Not Just for Critical Software
Explainable AI Explained
Model-Based Systems Engineering Meets DevSecOps
Incorporating Supply-Chain Risk and DevSecOps into a Cybersecurity Strategy
Software and Systems Collaboration in the Era of Smart Systems
Securing the Supply Chain for the Defense Industrial Base
Building on Ghidra: Tools for Automating Reverse Engineering and Malware Analysis
Envisioning the Future of Software Engineering
Implementing the DoD's Ethical AI Principles
Walking Fast Into the Future: Evolvable Technical Reference Frameworks for Mixed-Criticality Systems
Software Engineering for Machine Learning: Characterizing and Understanding Mismatch in ML Systems
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