On December 15, 1811, the London Statesman issued a warning about the state of the stocking industry in Nottingham. Twenty thousand textile workers had lost their jobs because of the incursion of automated machinery.

It's time to talk about the Ludding times. That's how they talked about it, those who were there, when readying themselves for an insurrection a few years later.

This video explores the journey of AI language models, from their modest beginnings through the development of OpenAI's GPT models. Our journey takes us through the key moments in generative neural network research involved in next word prediction. We delve into the early experiments with tiny language models in the 1980s, highlighting significant contributions by researchers like Jordan, who introduced Recurrent Neural Networks, and Elman, whose work on learning word boundaries revolutionized our understanding of language processing. It leaves us with a question: what is thought? Is simulated thought, thought? Featuring Noam Chomsky Douglas Hofstadter Michael I. Jordan Jeffrey Elman Geoffrey Hinton Ilya Sutskever Andrej Karpathy Yann LeCun and more. (Sam altman)

My script, references & visualizations here: https://docs.google.com/document/d/1s7FNPoKPW9y3EhvzNgexJaEG2pP4Fx_rmI4askoKZPA

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This is the last video in the series "The Pattern Machine" you can watch it all here: https://www.youtube.com/playlist?list=PLbg3ZX2pWlgKV8K6bFJr5dhM7oOClExUJ

00:00 - Introduction 00:32 - hofstader's thoughts on chatGPT 01:00 - recap of supervised learning 01:55 - first paper on sequential learning 02:55 - first use of state units (RNN) 04:33 - first observation of word boundary detection 05:30 - first observation of word clustering 07:16 - first "large" language model Hinton/Sutskever 10:10 - sentiment neuron (Ilya | OpenAI) 12:30 - transformer explaination 15:50 - GPT-1 17:00 - GPT-2 17:55 - GPT-3 18:20 - In-context learning 19:40 - ChatGPT 21:10 - tool use 23:25 - philosophical question: what is thought?

Of all the buzzy 21st-century tech phrases, “machine learning” threatens to be the most important. Programming computers is slow, but we’re nearing the point where humans give the bots parameters and let them teach themselves.

Bots are everywhere. From simple algorithms and aggregator bots to complex “artificially” intelligent machine-learning systems, they have become inescapable. Some are in chat programs.

In an interview, best-selling author and machine-learning expert Pedro Domingos discusses the global competition to take the lead in artificial intelligence, the advance of autocrats and the threats modern technology presents to Western democracies.

A video about neural networks, function approximation, machine learning, and mathematical building blocks. Dennis Nedry did nothing wrong. This is a submission for #SoME3

My Links Patreon: https://www.patreon.com/emergentgarden Discord: https://discord.gg/ZsrAAByEnr

Links and Content: On Mathematical Maturity, Thomas Garrity: https://www.youtube.com/watch?v=zHU1xH6Ogs4 Earth Rotation Loop: https://www.youtube.com/watch?v=aiQdLP2mBJE Modeling Shell Surfaces: https://www.geogebra.org/m/xtv7zpn5 Fourier Features Paper: https://arxiv.org/abs/2006.10739 Code for mandelbrot/image approximations: https://github.com/MaxRobinsonTheGreat/mandelbrotnn Code for line/surface approximations: https://github.com/MaxRobinsonTheGreat/ManimApproximations

Music: https://youtube.com/@acolyte-compositions

Timestamps (0:00) Functions Describe the World (3:15) Neural Architecture (5:35) Higher Dimensions (11:55) Taylor Series (15:20) Fourier Series (21:25) The Real World (24:32) An Open Challenge

Inside the many debates swirling around the rapid rollout of so-called artificial intelligence, there is a relatively obscure skirmish focused on the choice of the word “hallucinate”.

A human player has comprehensively defeated a top-ranked AI system at the board game Go, in a surprise reversal of the 2016 computer victory that was seen as a milestone in the rise of artificial intelligence.

A human player has comprehensively defeated a top-ranked AI system at the board game Go, in a surprise reversal of the 2016 computer victory that was seen as a milestone in the rise of artificial intelligence.

Ars Technica Article: https://arstechnica.com/information-technology/2023/02/man-beats-machine-at-go-in-human-victory-over-ai/?comments=1&comments-page=1 PC Gamer Article: https://www.pcgamer.com/a-human-has-beat-an-ai-in-possibly-the-most-complex-board-game-ever/ Far.AI Research Team's Site: https://goattack.far.ai/adversarial-policy-katago#contents Far.AI Research Team's Paper: https://arxiv.org/pdf/2211.00241.pdf AI Sensei: https://ai-sensei.com/

Nick's Patreon: https://www.patreon.com/nicksibicky

Intro Music from my "Moonfound" Project: https://moonfound.bandcamp.com/album/the-small-ep

Computers don’t cry during sad stories, but they can tell when we will. Sunspring debuted at the SCI-FI LONDON film festival in 2016.

Computers don’t cry during sad stories, but they can tell when we will. Sunspring debuted at the SCI-FI LONDON film festival in 2016.

This write-up re-introduces the concept of entropy from different perspectives with a focus on its importance in machine learning, probabilistic programming, and information theory.

The FADER Interview is a brand new podcast series in which the world’s most exciting musicians talk with the staff of The FADER about their latest projects.

Peli Grietzer is a philosopher of literature who received his PhD from Harvard University in 2017. His dissertation used machine learning theory to study forms of ambient meaning, like moods and vibes, in modernist and avant-garde aesthetics.

This write-up re-introduces the concept of entropy from different perspectives with a focus on its importance in machine learning, probabilistic programming, and information theory.

The FADER Interview is a brand new podcast series in which the world’s most exciting musicians talk with the staff of The FADER about their latest projects.

Peli Grietzer is a philosopher of literature who received his PhD from Harvard University in 2017. His dissertation used machine learning theory to study forms of ambient meaning, like moods and vibes, in modernist and avant-garde aesthetics.

In 2014 machine learning researcher Ian Goodfellow introduced the idea of generative adversarial networks or GANs.