Data-oriented architecture survey published in ACM Computing Surveys
Christian Cabrera and colleagues publish the survey of how deployed machine learning systems follow data-oriented architecture, in ACM Computing Surveys.
Overview
The Interfaces research programme at ML@CL focuses on interpretable, self-sustaining multi-agent AI systems. It takes a systems perspective on AI adoption: software systems are the interface between socio-technical needs and AI model capabilities. The programme builds on research themes developed in the AutoAI project, the data-oriented architectures concept, and the Self-Sustaining Software Systems (S4) agenda.
Research themes include multi-agent observability, systems engineering for large language models, cooperative intelligence, and methods to approximate understanding of composed system behaviour.
Interfaces is the engineered-systems pillar of the InfoTop agenda. Mathematical foundations are developed in Information Topography; comparative field studies of institutions adopting AI are developed in AI Adoption.
Validation and partnerships
Clinical validation includes the aICU research programme with Karolinska Institutet and Södersjukhuset, developing and evaluating AI-based decision support for intensive care in partnership with ML@CL.
Public open-source artefacts include DOAgent, a library for observable multi-agent systems. Other research prototypes are developed in collaboration with clinical and industry partners.
Related People
Christian Cabrera Jojoa
Assistant Research Professor, Cambridge University
Neil D. Lawrence
The DeepMind Professor of Machine Learning, Cambridge University
Radzim Sendyka
PhD Student, Cambridge University
Andrei Paleyes
Visiting Researcher, Cambridge University
Diana Robinson
Visiting Researcher, Cambridge University
Hanni Sondermann
Programme Manager, Interfaces Project, Cambridge University
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