CAIN 2025
Sun 27 - Mon 28 April 2025 Ottawa, Ontario, Canada
co-located with ICSE 2025
Mon 28 Apr 2025 09:00 - 10:00 at 208 - Keynote 2 Chair(s): Henry Muccini

Rapid advancements in AI paradigms—Machine Learning, Reinforcement Learning, Symbolic AI, Foundation Models, and Multi-Agent Systems—have expanded AI’s capabilities while introducing new software engineering challenges. Each paradigm has inherent limitations: Machine Learning excels at pattern recognition but lacks explicit reasoning; Reinforcement Learning optimizes rewards but is unpredictable; Symbolic AI provides structure but struggles with flexibility; Foundation Models generalize knowledge but raise interpretability concerns; and Multi-Agent Systems enable distributed intelligence but introduce emergent behaviors. Just as the human brain integrates perception, learning, reasoning, and decision-making, AI systems can combine multiple paradigms to achieve adaptable intelligence. While AI Engineering has made progress in building robust Machine Learning systems, our understanding of Intelligence Engineering—the integration of multiple AI paradigms—remains limited. Furthermore, software engineering aspects such as requirements and architecture are still poorly understood across different AI paradigms and their combinations. Without deeper insights and systematic methodologies, we still lack the necessary means to build trustworthy, scalable, and maintainable intelligent systems that effectively integrate these paradigms. This talk presents a research roadmap for AI Engineering, highlighting the urgent need for evidence-based intelligence engineering and the development of systematic specification approaches, architectural patterns, and tactics for multi-paradigm AI integration. It also underscores the critical role of empirical software engineering in navigating the hype responsibly and reinforcing AI Engineering as an evidence-driven discipline.

Professor of Software Engineering at the Department of Informatics of PUC-Rio.

Mon 28 Apr

Displayed time zone: Eastern Time (US & Canada) change

09:00 - 10:00
Keynote 2Research and Experience Papers at 208
Chair(s): Henry Muccini University of L'Aquila, Italy
09:00
60m
Keynote
Beyond Machine Learning and Foundation Models: A Research Roadmap for Multi-Paradigm AI Engineering
Research and Experience Papers
Marcos Kalinowski Pontifical Catholic University of Rio de Janeiro (PUC-Rio)

Information for Participants
:
:
:
: