The intersecting potential of AI systems and high-performance computing (HPC) platforms is becoming increasingly apparent in the scientific research landscape. AI models like ChatGPT, developed on the basis of transformer architecture and with the ability to train on extensive amounts of internet-scale data, have laid the groundwork for significant scientific breakthroughs. These include black hole modeling, fluid dynamics, and protein structure prediction, achieved by leveraging vast datasets in conjunction with large-scale computational resources.
In drug discovery, for instance, scalable AI has had a substantial impact. Transformer-based language models (LLMs) exploit significant datasets and task-specific fine-tuning to independently learn and predict molecular structures, thereby expediting discovery processes. High-performance computing aids in meeting diverse computational scale requirements posed by different scientific issues, distinguishing AI for Science (AI4S) from consumer-focused AI.
AI4S is required to handle specific scientific data features, such as incorporating known domain knowledge like partial differential equations (PDEs), a capability provided through methodologies such as Physics-Informed Neural Networks (PINNs), Neural Ordinary Differential Equations (NODEs), and Universal Differential Equations (UDEs). Scaling AI systems necessitates model-based and data-based parallelism. Training large models like GPT-3 would take centuries on a single NVIDIA V100 GPU, but parallel scaling can significantly reduce this time and improve model capabilities.
In contrast to consumer AI, scientific AI necessitates high-precision floating-point numbers and adherence to physical laws. This is particularly true for simulation surrogate models, where combining machine learning with physics-based approaches can yield accurate and cost-effective outcomes. Particular scientific data features must also be accommodated by AI4S, such as physical constraints and known domain knowledge incorporation. Tools like soft penalty constraints, neural operators, and symbolic regression are utilized for this in scientific machine learning.
In terms of parallel scaling techniques, there are data-parallel and model-parallel methods. The former divides a large data batch across multiple GPUs for concurrent processing, while the latter redistributes different model sections across a variety of devices, optimizing memory usage. AI for science blending with traditional simulations in hybrid workflows opens pathways for higher prediction accuracy and streamlined decision-making processes.
Emerging trends in scalable AI for science include sparsely connected and cost-effective mixture-of-experts (MoE) models, autonomous AI-driven labs, and linear recurrent neural networks (RNNs)—a resurgence driven by transformer-based models’ constraints. For greater model explainability and interpretability, techniques like Class Activation Mapping and attention map visualization are being developed, fostering enhanced levels of trust in the technology within the scientific community. Overall, the integration of scalable AI and HPC is set to exponentially accelerate discoveries across various scientific fields.
