Automating Scientific Discovery: From Natural World Data to Systematic Literature Reviews
About this resource
Edward Vendrow, a PhD student at MIT, presents his research on automating scientific discovery using multimodal AI. In this talk, he explores how AI can accelerate research through large-scale ecological image analysis and systematic literature reviews.
Ecological image discovery with INQUIRE
The talk introduces INQUIRE, a benchmark dataset designed to evaluate AI systems on expert-level ecological queries. By applying multimodal AI models, researchers can search through vast datasets like iNaturalist to answer scientific questions. For example, identifying individual animals with unique tags, studying human impact on wildlife, and monitoring environmental changes. INQUIRE enables more accurate and scalable insights by providing labeled ecological queries over millions of images.
Automating systematic literature reviews
Edward also discusses how AI models such as GPT-4 can reduce the burden of systematic literature reviews, a critical yet time-intensive process in research. Using large language models, researchers can efficiently screen and analyze scientific papers, helping them identify relevant studies with greater accuracy.
Applications and impact
From monitoring seasonal variations in avian diets to exploring the effects of climate change on plant flowering phenology, AI-driven tools like INQUIRE are unlocking new research possibilities. Looking ahead, Edward’s work explores the potential of AI systems in autonomous species discovery and broader ecological research.
See also
- Data: INQUIRE Benchmark: Evaluate multimodal AI models on ecological image retrieval tasks.
- Data: iNaturalist: Learn more about the iNaturalist dataset.
- Talk: Learning Through Comparison: Use Cases of Contrastive Learning: Learn about the fundamentals and use-cases of contrastive learning, including multimodal learning.