Collaborative breakthrough research in Artificial Intelligence (AI)
requires access to well-tuned systems, specific computing, large
datasets, and dedicated storage, often only accessible to a select few.
This reality slows down complementary research, as a substantial amount
of project time is often dedicated to repeating setups, understanding
computation and storage intricacies, data properties, and how to
properly transfer systems between institutions, to pursue overall
objectives. Examples can be often found in cases encompassing consortia
with computational versus non-computational specialists, or containing a
virtuous mixture of domain experts (medical doctors, biologists,
psychologists) and data scientists. Reproducibility and technology
transfer are essential tools in thriving projects, however these
concepts are costly to implement and maintain. Deploying AI solutions
requires multidisciplinary expertise, the right hardware, and AI
specialists to tune and ready tools for collaborative use. In practice,
lack of specific expertise slowdowns partner-to-partner communication
affecting overall productivity. In projects with industry or
government-academia partnerships driven by concrete societal needs,
replicating project conclusions with different, private datasets, or
allowing partners to infer from pre-trained models using adequate
hardware setups, is often avoided because of these barriers. Moreover,
the growing scale, complexity and impact of contemporary AI systems such
as Large Language Models (LLMs) accelerates the need for accessible
infrastructures which can guarantee systematised, transparent and
increased collaborative work. We intend to bridge these gaps by building
“CollabCloud", a cloud-based research infrastructure to boost
collaborative research for current and future projects at the Idiap
Research Institute. The main focal points will be boosting the ability
of easily exporting researchworkflows, exploring AI models by both
computational and non-computational experts, and allowing controlled
access to shared storage and computing power for collaborative projects.
Beyond these goals, CollabCloud will enable Idiap to participate in
developing important topics shaping the future of AI, such as Federated
Learning, and cloud-based scientfic networks, which require connectivity
and storage capabilities adapted to such purposes. As discussed in the
institutional support letter, this vision aligns well with Idiap’s
future, its predicted growth (with a current data center reaching the
limits of its maximum capacity), and the notion of Cross Research Groups
(CRG), that is part of our 2021-2024 Research Program as approved by
the Federal State Secretariat for Education, Research, and Innovation
(SERI).