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The ambition of much of AI research is to create systems that can help to solve problems in the real world. In this episode, Hannah meets the people building systems that could be used to save the sight of thousands, help us solve one of the most fundamental problems in biology and reduce energy consumption in an effort to combat climate change. But whilst there is great potential, there are also important obstacles that will need to be tackled for AI to be used effectively, safely and fairly.

If you have a question or feedback on the series, message us on Twitter (@DeepMind using the hashtag #DMpodcast) or email us at [email protected].

Further reading:

Wired: Inside DeepMind's epic mission to solve science's trickiest problemDeepMind blogs on the partnership with Moorfields NHS eye hospital and predicting eye disease, and Moorfields’ news announcement on its research with DeepMindDeepMind blog: AlphaFold: Using AI for scientific discoveryDeepMind blogs on reducing Google’s energy bill for datacentre cooling and how this project has progressedResearch paper: Tackling Climate Change with Machine LearningQuanta magazine: How Artificial Intelligence Is Changing ScienceDeepMind blog: How evolutionary selection can train more capable self-driving cars

Other examples of the application of AI for real-world impact include:

Francis Crick Institute: machine learning models that can help predict heart diseaseNASA: AUDREY machine learning system to better guide first responders through firesUniversity of Southern California: Protection Assistant for Wildlife Security using AI to help wildlife conservation

Interviewees: Pearse Keane, consultant ophthalmologist at Moorfields Eye Hospital; Sandy Nelson, Product Manager for DeepMind’s Science Program; and DeepMind Program Manager Sims Witherspoon.

Credits:
Presenter: Hannah Fry
Editor: David Prest
Senior Producer: Louisa Field
Producers: Amy Racs, Dan Hardoon
Binaural Sound: Lucinda Mason-Brown
Music composition: Eleni Shaw (with help from Sander Dieleman and WaveNet)
Commissioned by DeepMind

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