Keeping generative artificial intelligence reliable in omics biology

TL;DR

This paper discusses how generative AI can revolutionize omics research but warns about the risks of unrealistic data generation and suggests ways to address them.

Contribution

The paper introduces strategies to reduce hallucination risks in generative AI for reliable omics research.

Findings

Generative AI can create realistic data for complex biological processes.

Hallucinations in AI-generated data pose significant risks in molecular biology.

Use cases are proposed to safely harness the potential of generative methods.

Abstract

Generative artificial intelligence can be used to create realistic new data, even for complex real-world processes that cannot be exhaustively modeled: the model is simply learned from preexisting data. Generative artificial intelligence is therefore expected to be a game changer in omics research, where data collection is hampered by considerable experimental constraints. However, it can also “hallucinate”—i.e., create data that are too original to be realistic—which is a critical issue in molecular biology, as hallucinated inferences could have devastating consequences. The author thus explores various use cases to mitigate hallucination-induced risks and to safely unleash the full potential of generative methods.

Generative artificial intelligence can be used to create realistic new data, even for complex real-world processes that cannot be exhaustively modeled: the model is simply learned from preexisting data. Generative artificial intelligence is therefore expected to be a game changer in omics research, where data collection is hampered by considerable experimental constraints. However, it can also “hallucinate”—i.e., create data that are too original to be realistic—which is a critical issue in molecular biology, as hallucinated inferences could have devastating consequences. The author thus explores various use cases to mitigate hallucination-induced risks and to safely unleash the full potential of generative methods.

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