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Deep learning models \u2014 transformers in particular \u2014 are defining the cutting edge of AI today. They\u2019re based on an architecture called an artificial neural network, as you probably already know if you\u2019re a regular Towards Data Science reader. And if you are, then you might also already know that as their name suggests, artificial neural networks were inspired by the structure and function of biological neural networks, like those that handle information processing in our brains.

So it\u2019s a natural question to ask: how far does that analogy go? Today, deep neural networks can master an increasingly wide range of skills that were historically unique to humans \u2014 skills like creating images, or using language, planning, playing video games, and so on. Could that mean that these systems are processing information like the human brain, too?

To explore that question, we\u2019ll be talking to JR King, a CNRS researcher at the Ecole Normale Sup\xe9rieure, affiliated with Meta AI, where he leads the Brain & AI group. There, he works on identifying the computational basis of human intelligence, with a focus on language. JR is a remarkably insightful thinker, who\u2019s spent a lot of time studying biological intelligence, where it comes from, and how it maps onto artificial intelligence. And he joined me to explore the fascinating intersection of biological and artificial information processing on this episode of the TDS podcast.

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Intro music:

- Artist: Ron Gelinas

- Track Title: Daybreak Chill Blend (original mix)

- Link to Track: https://youtu.be/d8Y2sKIgFWc 

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• 2:30 What is JR\u2019s day-to-day?
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• 5:00 AI and neuroscience
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• 12:15 Quality of signals within the research
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• 21:30 Universality of structures
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• 28:45 What makes up a brain?
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• 37:00 Scaling AI systems
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• 43:30 Growth of the human brain
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• 48:45 Observing certain overlaps
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• 55:30 Wrap-up
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