Advances in Artificial Intelligence (AI) and Machine Learning (ML) have enabled the scientific community to advance the frontiers of knowledge by learning from complex, large-scale experimental datasets. With the scientific community generating huge amounts of data from observatories to large-scale experimental facilities, AI for Science at Exascale is on the horizon. However, in the absence of systematic approaches to evaluate AI models and AI algorithms at exascale, the AI for Science community, and, in fact, the general AI community, are facing a major barrier ahead. This proposal aims to setup a working group with an overarching goal of identifying the scope and plans for developing AI benchmarks to enable the development of AI for Science at Exascale, in ExCALIBUR - Phase II. Although AI Benchmarking is becoming a well-explored topic, a number of issues are still to be addressed, including, but not limited to: a) There are no efforts aimed at AI benchmarking at exascale, particularly for science; b) A range of scientific problems involving real-world large-scale scientific datasets, such as those from experimental facilities or observatories, are largely ignored in benchmarking; and c) It is worth having benchmarks to serve as a catalogue of techniques offering template solutions to different types of scientific problems. In this proposal, when scoping the development of an AI benchmark suite, we will aim to address these issues. In developing a vision, a scope and a plan for this significant challenge, the working group will not only engage with the community of scientists from a number of disciplines, and industry, but will also engineer a scalable and functional AI benchmark, so as to learn and embed the practical aspects of developing an AI benchmark into the vision, scope, and plan. The exemplary benchmark will focus on removing noise from images, which is a common issue across multiple disciplines including, life sciences, material sciences and astronomy. The specific problems from each of these disciplines are, removing noise from cryogenic electron microscopic (cryo-em) datasets, denoising X-Ray tomographic images, and minimising the noise from weak lensing images, respectively.

Advances in Artificial Intelligence (AI) are transforming the world we live in today. The innovations are driving two, interconnected aspects: They augment our knowledge, for example, we understand the behaviour of a virus better and faster than we did a decade ago. This improved understanding fuels innovations, improving the quality of our life, such as better vaccines, or better batteries for our mobile phones or our electric vehicles. The role AI and thus of computing is rather crucial for such advancements. The desire to improve our knowledge on fundamentals, and thus to improve the quality of our life, has become central to our existence. Better and faster understanding leads to better and faster innovations being developed. This essential desire, in turn, demands computations to be performed at a faster rate than ever before - not only to understand very large datasets better, but also to perform very complex simulations, at least at a rate 50 times faster than most powerful computers we have on the planet today --- era of exascale computing. Exascale computers will be able to perform billion billion calculations per second. The general challenge is to have relevant software technologies ready when such exascale computing becomes a reality, and it is a significant challenge to the international community. This proposal aims to develop a software suite and relevant software designs to serve as blueprints for using AI for scientific discoveries at exascale --- Blueprinting AI for Science at Exascale (BASE-II). This project is a continuation of our previous work, carried out as part of Phase I, namely, Benchmarking for AI for Science at Exascale (BASE-I). In Phase I, we gathered an essential set of requirements from various scientific communities, which underpins our work in this phase, The resulting software and designs will cover the following: a) Facilitate better understanding of the interplay between different AI algorithms, and AI hardware systems across a range of scientific problems. We will be achieving this through a set of AI benchmarks, against which different AI software can be verified, b) Facilitating incredibly complex simulations using AI: Although exascale systems will facilitate complex simulations (which are essential for mimicking realistic cases), we will accelerate them using AI. This can result in remarkable speedups (e.g., from days to seconds). Such a transformation can provide a massive leap in scientific discoveries. c) Harmonising the efforts of scientific communities and of vendors through better partnerships: Exascale systems will have complex hardware capabilities, which may be difficult for scientists to understand. Equally, hardware system manufacturers working on the design of exascale systems, do not always understand the underpinning science. This unharmonised effort or non-synchronised advancements, hitherto has been sub-optimal. We intend to build better software / hardware through better partnerships, which we refer to as hardware-software co-design. d) The success of AI is primarily due to a technology called, deep learning, which inherently relies on very large volumes of data. With technological advances, we can foresee that in the exascale era, the data volumes will not only be huge but also will be multi-modal. Understanding these extremely large-scale datasets will remain key to ensuring that AI can be conducted at exascale. e) Finally, the community, whether scientific, or academic or industry, will need additional software technologies, or more specifically, an ecosystem of software tools to help with exascale computing. To this end, we will be producing a software toolbox. We will also be conducting various knowledge exchange activities, such as, workshops, training events and in-field placements to ensure multi-directional flow of information and knowledge across relevant stakeholders and communities.