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Join us for a discussion on the third section of Max Bennett's terrific and insightful book A Brief History of Intelligence (2023).

We already discussed the first two sections in previous events, but please feel free to join even if you weren't present for those events.

About the book

After the success of Large Language Models like ChatGPT, there's a lot of talk about the impending arrival of artificial human-level intelligence. However, there is a lack of consensus about what else needs to happen before we reach AGI.

Max Bennett's A Brief History of Intelligence sheds light on this topic by taking a principled evolutionary neuroscience approach. Intelligence appeared in small steps, starting with less intelligent creatures (e.g. C. Elegans with ~ 300 neurons and ~ 1000 connections) and working all the way up to humans (billions of neurons and trillions of connections). The book examines the evolutionary lineage of humans and traces human intelligence to five evolutionary breakthroughs, and compares and contrasts these evolutionary innovations to various ideas in Deep Learning.

At the end of the book, one gets a good picture of what human intelligence (and animal intelligence in general) is all about, and where Deep Learning stands in relation to it.

The book is written in simple language and is quite the page turner!

About the event

We will discuss the third section of the book titled Breakthrough #3: Simulating and the first mammals (pages 170 - 251). Participants are expected to have read this section before joining the event.

We will begin at 18:30 with introductions over pizza and drinks. At around 19:00, we will start the discussion.

Our discussion will be open-ended, but would probably revolve around the theme of connecting generative models with reinforcement learning.
The end time of the discussion is open ended. We have put 3 hours tentatively, but the space allows us to discuss longer if we want.

Looking forward to seeing you there!

What about the other sections of the book?

We already covered the first and section sections of the book in previous events.

Depending on the interest, we will cover the remaining sections of the book in future event. The idea is to use alternate months for this book (until it is done), and the other months for Deep Learning related papers.

Related topics

Events in Unterhaching, DE
Artificial Intelligence
Machine Learning
Data Science
Evolution
Computational Neuroscience

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