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#menace#donaldmichie#reinforcementlearning

MENACE: the pile of matchboxes which can learn

Stand-up Maths

1.3M subscribers • 12 videos on Hallway

YouTube
  • Recommended by 3 people: 2 on Hacker News and 1 on Lobsters.
  • A classic. People kept recommending it from 2017 to 2026.
  • Presented by Matt Parker, stand-up mathematician
  • May be out of date. Uploaded 8 years ago, in an area where tools change fast.

Beginner. No background in the topic needed.

453K views8 years ago3 recommendations

AI and machine learningHistory and cultureReinforcement learningHistory of computingAlgorithms

Matt Parker demonstrates MENACE, a physical machine made of matchboxes built in 1961 that learns to play tic-tac-toe through reinforcement.

See more data and check out what we changed on the second day (which caused MENACE to learn a different strategy) in the second video: youtu.be/KcmjOtkULi4 Check out Matt Scroggs’s original blog post about MENACE and in the amazing Chalkdust magazine. mscroggs.co.uk/blog/19 chalkdustmagazine.com/tag/menace/ Play against the online version of MENACE: mscroggs.co.uk/menace/ This is the original 1961 “Experiments on the mechanization of game-learning” by Donald Michie. dropbox.com/s/ycsycu0l01g9643/DonaldMichie.pdf?dl=0 Thanks to Katie Steckles for organising our stall at the Manchester Science Festival and Antonio Benitez for giving us the space. The MENACE crew were: Alison Clarke, Andrew Taylor, Ash Frankland, David Williams, Katie Steckles, Matthew Scroggs, Paul Taylor, Sam Headleand and Zoe Griffiths Get your MENACE data here! dropbox.com/s/0f9ukxxvh0pgdui/MENACE-data.zip?dl=0 CORRECTIONS: None yet. Let me know if you spot anything! Thanks to my Patreon supporters who made this possible! Here are the random subset I read out during the video: Ben White Scott Robinson Nelson Emerson Amy Sandland Neil McGovern Support my channel and make more videos like this possible! patreon.com/standupmaths Music by Howard Carter Filming and editing by Trunkman Productions Audio mastering by Peter Doggart Design by Simon Wright MATT PARKER: Stand-up Mathematician Website: standupmaths.com/ Maths book: makeanddo4D.com/ Nerdy maths toys: mathsgear.co.uk/

Why it's here

3 people recommend this

What people say. Recommended as a fantastic, physical way to explain reinforcement learning and early AI history to beginners and students.

jimktrains2on Hacker News • Recommends

On “A Matchbox Game-Learning Machine (1991) [pdf]”

Stand up maths did an episode on a similar "machine" called MENACE (Machine Educable Noughts And Crosses Engine) that learns to play tick tac toe. link to this video The video description has additional links to resources: link link

Tachyooonon Hacker News • Recommends

On “A Matchbox Game-Learning Machine (1991) [pdf]”

Seems like this is what Matt Parker built together with some other people a few years ago: link to this video . It's incredibly inefficient compared to running it on a computer, but an amazing way to teach kids about this.

Tachyooonon Hacker News • Recommends

On “Learning to Play Tic-Tac-Toe with Jax”

The first time I heard of it was in this Matt Parker video where he helped do this with a school-sized group of kids 0. An "AI player" made of matchboxes, run by schoolkids, is a fantastically fun idea. 0 link to this video

flyingfischon Lobsters • Recommends

MENACE: the pile of matchboxes which can learn

notEvenOnceon Hacker News • Mentions

On “On Intelligence in Cells: The Case for Whole Cell Biology (2009) [pdf]”

The concept fits. Consider MENACE (Machine Educable Noughts And Crosses Engine). link to this video If cells operate in a similar fashion, where adaptation to externalities encodes itself into a durable surface of arbitrary pockets, then we have a recipe for inert systems exhibiting variable degrees of behavior, which is capable of evolving over time. This doesn't solve for sentience, but it does solve for expressive adaptation to fit into complex, hostile environments.

jerfon Hacker News • Mentions

On “Solving a Childhood Mystery: How BASIC Games Learned to Win”

Matt Parker did a video about a physical implementation of MENACE as part of a museum display for a day: link to this video

u/nullandkalein r/compsci • Mentions

On “Modeling Computational Complexity of Tic-Tac-Toe using Information Theory and Sets”

Someone minimized the amount of board states and implemented "machine learning" using matchboxes and beads.