Machine Learning Reading Group - Cambridge, UK

PLEASE READ THE BOOK AND BRING A COPY WITH YOU TO THE DISCUSSION. We'll be following it closely for the two hours.

When: Wednesday 26th November 2025, 6 pm – 8 pm.

Where: The Castle Inn, 36 Castle Street, Cambridge, UK. (Most likely we'll be at one of the large tables upstairs.)

Book: If Anyone Builds It, Everyone Dies: Why Superhuman AI Would Kill Us All by Eliezer Yudkowsky and Nate Soares

Link: https://www.penguin.co.uk/books/474267/if-anyone-builds-it-everyone-dies-by-soares-eliezer-yudkowsky-and-nate/9781847928924

We encourage well-informed and good-faith critiques of the books we read (But make sure you read the book if you have a strong opinion!)

Penguin description:

AI is the greatest threat to our existence that we have ever faced.

The scramble to create superhuman AI has put us on the path to extinction – but it’s not too late to change course. Two pioneering researchers in the field, Eliezer Yudkowsy and Nate Soares, explain why artificial superintelligence would be a global suicide bomb and call for an immediate halt to its development.

The technology may be complex, but the facts are simple: companies and countries are in a race to build machines that will be smarter than any person, and the world is devastatingly unprepared for what will come next.

How could a machine superintelligence wipe out our entire species? Will it want to? Will it want anything at all? In this urgent book, Yudkowsky and Soares explore the theory and the evidence, present one possible extinction scenario, and explain what it would take for humanity to survive.

The world is racing to build something truly new – and if anyone builds it, everyone dies.

You'll need to read the paper in advance. Ideally please also bring along your own copies of the paper to refer to in the session.

25th February 2026: Long short-term memory

When: Wednesday 25th February 2026, 6 pm – 8 pm.

Where: The Castle Inn, 36 Castle Street, Cambridge, UK. (Most
likely we'll be at one of the large tables upstairs.)

Paper: S. Hochreiter and J. Schmidhuber. Long Short-Term
Memory. In Neural Computation, 9, 1735–1780, 1997.
URL:
https://www.researchgate.net/publication/13853244_Long_Short-Term_Memory

Long Short-Term Memory (LSTM) networks are a type of recurrent neural net capable of learning long-range
dependencies in sequential data. They were the state of the art in language modelling prior to transformers, and remain highly relevant to modern machine learning.