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This section explores the transformation of research practices driven by algorithmic methods. We view the application of neural network models not as an end in itself, but as a sophisticated tool for scientific inquiry – ranging from processing vast datasets in genetics to uncovering new patterns in astrophysics and materials science. We analyze how experimental methodology evolves when deep learning systems assist in hypothesis testing, and examine the ethical considerations that arise when delegating cognitive tasks to machines.
Researchers have demonstrated how to fine-tune AI models to replace complex physics simulations – making them faster and cheaper than running calculations from scratch.
Researchers from Allen AI analyzed 250,000 queries to scientific AI tools to uncover how scientists genuinely interact with them in practice.
AI2 has introduced AutoDiscovery, a tool that automatically formulates scientific questions, tests them, and compiles the results into complete research studies.
A language model designed for working with scientific literature has received recognition from one of the most authoritative journals in the science world.
Lab
Computer Science
Researchers have taught a language model to find optimal solutions in science not through preliminary preparation, but by learning right in the process of working on a specific task.
Lab
Biology & Neuroscience
We dissect how algorithms find resemblances between biological sequences – a task where simple measures are absent, and every decision balances speed and accuracy.
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