Rethinking AI & Computing in the Age of Foundation Models

Artificial intelligence is undergoing its most profound transformation since the birth of the field. Foundation models—including large language models, vision-language models, and emerging vision-language-action systems—are rapidly reshaping not only how intelligent systems are built, but also the fundamental questions underlying artificial intelligence and computer science.

New opportunities and new limitations are emerging at every layer of the computing stack, from the computer hardware and architecture, through operating systems and programming languages, to algorithms, security, and embodied interaction of robots and AI agents with humans. These lead to transformation of career paths for those computer scientists, not merely in the set of skills required for careers in software engineering and scientific research, but also in the scope of responsibilities implied (e.g., for societal implications and safety). The rapid deployment of increasingly autonomous AI systems has exposed pressing challenges in evaluation, reliability, safety, trustworthiness, transparency, governance, and human oversight, raising fundamental questions about how these systems should be designed, understood, and integrated into society.

This symposium brings together researchers from across AI, computer science, and related disciplines to examine this pivotal moment and identify the scientific questions that will define the next decade of research. Rather than asking how existing research areas can simply incorporate foundation models, the symposium asks the more fundamental question: How should AI and computing themselves evolve in response to this new technological paradigm? Through a series of broad, forward-looking discussions, participants will explore the scientific, technological, and societal challenges that will shape the next generation of intelligent systems and help define a shared research agenda for the future of AI and computing.

Topics include:

• What remains fundamentally hard in AI and computer science, and what are the emerging bottlenecks?
• What computing architectures will AI require beyond CPUs and GPUs?
• Are language and foundation models (LLMs, VLMs, VLAs, …) becoming a new computational substrate, or are they components within larger systems? Is language the new medium of general or AI-focused computing?
• How should software systems (languages, databases, operating systems, networking) change to support AI-based computing? Should they?
• What skill set is necessary for various career paths in this modern era of computer science? How should we teach it? Is it possible?
• How should humans and AI divide cognitive labor? As AI becomes increasingly capable, what should remain under human control?
• Does embodied AI represent a fundamental shift in computing, or an evolution of robotics, planning, control, and learning? And should computer science care beyond optimizing for energy use?
• Trustworthy, aligned, collaborative AI: What should remain under human control? What should be the division of cognitive labor between intelligent agents and their human users? Are intelligent agents peers, subordinates, or something else?
• How should we adjust the scientific process and its communication: Capable reviewers are already overtaxed, and journals and conferences are facing growing heaps of AI-generated slop. Evaluation benchmarks are rapidly saturating, and are not sufficiently distinguished in what we gain?
• What would Computer Science and Artificial Intelligence look like in 20 years? Would AI still be a part of computer science, or do the new undergraduate programs in AI prove to be the beginning of a historical split?

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