The AI revolution is running out of data. What can researchers do?

episode

Nature Podcast not yet transcribed

▲ 0

Transcript

This episode hasn't been transcribed yet Upvotes decide what gets transcribed next — 0 so far. Sign in to upvote

More from Nature Podcast

Audio long read: Could mending damaged DNA prolong life? 18 min · 02 Sep 2026

Briefing Chat: How Dolly Parton left her mark on science 14 min · 28 Aug 2026

Highest-ever ocean temperatures recorded as El Niño intensifies 19 min · 26 Aug 2026

Briefing Chat: New narcolepsy drug could unlock host of novel brain therapies 12 min · 21 Aug 2026

A black hole's spin could finally be measured by a high-speed star 29 min · 19 Aug 2026

Briefing Chat: Anthropic rolls out new AI watermark — will it make a difference? 16 min · 14 Aug 2026

All episodes from Nature Podcast →

Report any issue

Source

Recorded31 Jan 2025

Publisherfeeds.acast.com ↗

Handlerec:12318612

Show notes

The explosive improvement in artificial intelligence (AI) technology has largely been driven by making neural networks bigger and training them on more data. But experts suggest that the developers of these systems may soon run out of data to train their models. As a result, teams are taking new approaches, such as searching for other unconventional data sources, or generating new data to train their AIs.This is an audio version of our Feature: The AI revolution is running out of data. What can researchers do? Hosted on Acast. See acast.com/privacy for more information.

Ask this episode

“What did the council decide on item 42?” — answered with the speaker and the moment.

Sign in