Overcoming AI Learning Limitations

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This chapter explores the challenges faced by AI systems in adapting and learning new skills due to their inherent limitations. It introduces a novel algorithm designed to enhance flexibility in neural networks, focusing on rejuvenating dormant neurons and optimizing weight imbalances. Through ongoing research, the potential for more adaptable AI systems capable of continual learning and practical applications is highlighted.

00:46 Old AIs can’t learn new tricks

An algorithm that reactivates dormant ‘neurons’ in deep learning based AIs could help them overcome their inability to learn new things and make future systems more flexible, research has shown. AIs based on deep learning struggle to learn how to tackle new tasks indefinitely, making them less adaptable to new situations. The reasons for this are unclear, but now a team has identified that ‘resetting’ parts of the neural networks underlying these systems can allow deep learning methods to keep learning continually.

Research Article: Dohare et al.

News and Views: Switching between tasks can cause AI to lose the ability to learn

08:55 Research Highlights

To stop crocodiles eating poisonous toads researchers have been making them sick, and a sacrificed child in ancient Mexico was the progeny of closely related parents.

Research Highlight: How to train your crocodile

Research Highlight: DNA of child sacrificed in ancient city reveals surprising parentage