Technical Director of Engineering Intelligent Software Systems Group
Carnegie Mellon University Software Engineering Institute (SEI)
Ipek Ozkaya is the technical director of Engineering Intelligent Software Systems group at Carnegie Mellon University Software Engineering Institute (SEI). Her main areas of expertise and interests include software architecture, software design automation, and managing technical debt in software-reliant and AI-enabled systems. At the SEI, she is currently leading a team of engineers and researchers who are assisting government and industry organizations in solving challenges related to engineering tactical and AI-enabled systems, enabling continuous evolution and modernization through application of software architecture and technical debt management practices, and the application of AI to software architecture design and analysis problems. Ozkaya is the co-author of a practitioner book titled Managing Technical Debt: Reducing Friction in Software Development and the editor-in-chief of IEEE Software Magazine. She holds a PhD in Computational Design from Carnegie Mellon University and a BArch from Middle East Technical University.
Yapay Zeka Sistemlerinin Geleneksel Yazılım Sistemlerinden Gerçekten Farkı Var mı?
(What Is Really Different in Engineering AI-Enabled Systems?)
Advances in machine learning (ML) algorithms and increasing availability of computational power are already resulting in huge investments in systems that aspire to exploit artificial intelligence (AI). AI-enabled systems — software-reliant systems that include data and components that implement AI algorithms mimicking learning and problem solving — can demonstrate different characteristics than software systems alone due to the uncertainty involved around decision making. However, AI-enabled systems are, above all, software systems. The development and sustainment of these systems have many parallels with building, deploying, and sustaining software systems. Research programs in software engineering will need to focus on the challenges that AI elements bring to software analysis, design, construction, deployment, maintenance, and evolution. Exploring which existing software engineering practices can reliably support development of AI systems and what new practices will need to be developed will drive research initiatives in the next decade. This presentation will explore foundational software engineering practices and research gaps in software engineering of ML systems by presenting results of ongoing work going on at the Carnegie Mellon University Software Engineering Institute.