DiscoverMachine Learning Street Talk (MLST)Jürgen Schmidhuber - Neural and Non-Neural AI, Reasoning, Transformers, and LSTMs
Jürgen Schmidhuber - Neural and Non-Neural AI, Reasoning, Transformers, and LSTMs

Jürgen Schmidhuber - Neural and Non-Neural AI, Reasoning, Transformers, and LSTMs

Update: 2024-08-28
Share

Description

Jürgen Schmidhuber, the father of generative AI shares his groundbreaking work in deep learning and artificial intelligence. In this exclusive interview, he discusses the history of AI, some of his contributions to the field, and his vision for the future of intelligent machines. Schmidhuber offers unique insights into the exponential growth of technology and the potential impact of AI on humanity and the universe.




YT version: https://youtu.be/DP454c1K_vQ




MLST is sponsored by Brave:


The Brave Search API covers over 20 billion webpages, built from scratch without Big Tech biases or the recent extortionate price hikes on search API access. Perfect for AI model training and retrieval augmentated generation. Try it now - get 2,000 free queries monthly at http://brave.com/api.




TOC


00:00:00 Intro


00:03:38 Reasoning


00:13:09 Potential AI Breakthroughs Reducing Computation Needs


00:20:39 Memorization vs. Generalization in AI


00:25:19 Approach to the ARC Challenge


00:29:10 Perceptions of Chat GPT and AGI


00:58:45 Abstract Principles of Jurgen's Approach


01:04:17 Analogical Reasoning and Compression


01:05:48 Breakthroughs in 1991: the P, the G, and the T in ChatGPT and Generative AI


01:15:50 Use of LSTM in Language Models by Tech Giants


01:21:08 Neural Network Aspect Ratio Theory


01:26:53 Reinforcement Learning Without Explicit Teachers




Refs:


★ "Annotated History of Modern AI and Deep Learning" (2022 survey by Schmidhuber):


★ Chain Rule For Backward Credit Assignment (Leibniz, 1676)


★ First Neural Net / Linear Regression / Shallow Learning (Gauss & Legendre, circa 1800)


★ First 20th Century Pioneer of Practical AI (Quevedo, 1914)


★ First Recurrent NN (RNN) Architecture (Lenz, Ising, 1920-1925)


★ AI Theory: Fundamental Limitations of Computation and Computation-Based AI (Gödel, 1931-34)


★ Unpublished ideas about evolving RNNs (Turing, 1948)


★ Multilayer Feedforward NN Without Deep Learning (Rosenblatt, 1958)


★ First Published Learning RNNs (Amari and others, ~1972)


★ First Deep Learning (Ivakhnenko & Lapa, 1965)


★ Deep Learning by Stochastic Gradient Descent (Amari, 1967-68)


★ ReLUs (Fukushima, 1969)


★ Backpropagation (Linnainmaa, 1970); precursor (Kelley, 1960)


★ Backpropagation for NNs (Werbos, 1982)


★ First Deep Convolutional NN (Fukushima, 1979); later combined with Backprop (Waibel 1987, Zhang 1988).


★ Metalearning or Learning to Learn (Schmidhuber, 1987)


★ Generative Adversarial Networks / Artificial Curiosity / NN Online Planners (Schmidhuber, Feb 1990; see the G in Generative AI and ChatGPT)


★ NNs Learn to Generate Subgoals and Work on Command (Schmidhuber, April 1990)


★ NNs Learn to Program NNs: Unnormalized Linear Transformer (Schmidhuber, March 1991; see the T in ChatGPT)


★ Deep Learning by Self-Supervised Pre-Training. Distilling NNs (Schmidhuber, April 1991; see the P in ChatGPT)


★ Experiments with Pre-Training; Analysis of Vanishing/Exploding Gradients, Roots of Long Short-Term Memory / Highway Nets / ResNets (Hochreiter, June 1991, further developed 1999-2015 with other students of Schmidhuber)


★ LSTM journal paper (1997, most cited AI paper of the 20th century)


★ xLSTM (Hochreiter, 2024)


★ Reinforcement Learning Prompt Engineer for Abstract Reasoning and Planning (Schmidhuber 2015)


★ Mindstorms in Natural Language-Based Societies of Mind (2023 paper by Schmidhuber's team)


https://arxiv.org/abs/2305.17066


★ Bremermann's physical limit of computation (1982)




EXTERNAL LINKS


CogX 2018 - Professor Juergen Schmidhuber


https://www.youtube.com/watch?v=17shdT9-wuA


Discovering Neural Nets with Low Kolmogorov Complexity and High Generalization Capability (Neural Networks, 1997)


https://sferics.idsia.ch/pub/juergen/loconet.pdf


The paradox at the heart of mathematics: Gödel's Incompleteness Theorem - Marcus du Sautoy


https://www.youtube.com/watch?v=I4pQbo5MQOs


(Refs truncated, full version on YT VD)





Comments 
00:00
00:00
x

0.5x

0.8x

1.0x

1.25x

1.5x

2.0x

3.0x

Sleep Timer

Off

End of Episode

5 Minutes

10 Minutes

15 Minutes

30 Minutes

45 Minutes

60 Minutes

120 Minutes

Jürgen Schmidhuber - Neural and Non-Neural AI, Reasoning, Transformers, and LSTMs

Jürgen Schmidhuber - Neural and Non-Neural AI, Reasoning, Transformers, and LSTMs

Machine Learning Street Talk (MLST)