OceanAI: A Conversational Platform for Accurate, Transparent, Near-Real-Time Oceanographic Insights
This addresses the need for transparent and reliable AI tools in oceanography, offering a scalable solution for decision support in marine applications, though it is incremental as it combines existing technologies in a novel way.
The paper tackles the problem of AI hallucinations in scientific contexts by developing OceanAI, a conversational platform that integrates LLMs with real-time NOAA oceanographic data, resulting in accurate, verifiable responses and outperforming existing AI chat products in blind comparisons.
Artificial intelligence is transforming the sciences, yet general conversational AI systems often generate unverified "hallucinations" undermining scientific rigor. We present OceanAI, a conversational platform that integrates the natural-language fluency of open-source large language models (LLMs) with real-time, parameterized access to authoritative oceanographic data streams hosted by the National Oceanic and Atmospheric Administration (NOAA). Each query such as "What was Boston Harbor's highest water level in 2024?" triggers real-time API calls that identify, parse, and synthesize relevant datasets into reproducible natural-language responses and data visualizations. In a blind comparison with three widely used AI chat-interface products, only OceanAI produced NOAA-sourced values with original data references; others either declined to answer or provided unsupported results. Designed for extensibility, OceanAI connects to multiple NOAA data products and variables, supporting applications in marine hazard forecasting, ecosystem assessment, and water-quality monitoring. By grounding outputs and verifiable observations, OceanAI advances transparency, reproducibility, and trust, offering a scalable framework for AI-enabled decision support within the oceans. A public demonstration is available at https://oceanai.ai4ocean.xyz.