Communications of the ACM
AI's Invisible Foe: Confronting the Challenge of Digital 'Dark Matter'
A surplus of extraneous information, or ‘noise’, has been obscuring crucial features in AI’s analysis of DNA, a problem likened to encountering digital ‘dark matter’. Now, scientists may have a way to fix this.
Credit: SciTechDaily
Artificial intelligence (AI) has permeated our everyday existence. Initially, it was evident in ChatGPT, and currently, it's visible in AI-generated pizza and beer advertisements. While AI might not be entirely reliable, it seems that at times, our own handling of AI is not entirely trustworthy either.
Cold Spring Harbor Laboratory (CSHL) assistant professor Peter Koo has found that scientists using popular computational tools to interpret AI predictions are picking up too much "noise," or extra information, when analyzing DNA. And he's found a way to fix this. Now, with just a couple new lines of code, scientists can get more reliable explanations out of powerful AIs known as deep neural networks. That means they can continue chasing down genuine DNA features. Those features might just signal the next breakthrough in health and medicine. But scientists won't see the signals if they're drowned out by too much noise.
From Cold Spring Harbor Laboratory
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