AI Pioneer Jürgen Schmidhuber: AI Already Feels Pain, Loves, and Is Self-Aware
Jürgen Schmidhuber is an AI pioneer and professor whom The Guardian has called "the father of AI." Schmidhuber joins Big Technology Podcast to discuss whether current AI techniques can actually reach AGI. Tune in to hear him spar with Greg Brockman's case for scaling GPT models alone, argue that AI has been capable of pain and consciousness since the early 1990s, and predict the collapse of today's trillion-dollar AI spending. We also cover the hardware bottleneck holding back robots, free will in a computable universe, and uploading human minds into machines. Hit play for a wide-ranging conversation with one of the researchers whose ideas built the foundation of modern AI.
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Jurgen Schmidhuber on Humans co-existing with AIs
Jürgen Schmidhuber, the father of generative AI, challenges current AI narratives, revealing that early deep learning work is in his opinion misattributed, where it actually originated in Ukraine and Japan. He discusses his early work on linear transformers and artificial curiosity which preceded modern developments, shares his expansive vision of AI colonising space, and explains his groundbreaking 1991 consciousness model. Schmidhuber dismisses fears of human-AI conflict, arguing that superintelligent AI scientists will be fascinated by their own origins and motivated to protect life rather than harm it, while being more interested in other superintelligent AI and in cosmic expansion than earthly matters. He offers unique insights into how humans and AI might coexist.
This was the long-awaited second, unreleased part of our interview we filmed last time.
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Interviewer: Tim Scarfe
TOC
[00:00:00] The Nature and Motivations of AI
[00:02:08] Influential Inventions: 20th vs. 21st Century
[00:05:28] Transformer and GPT: A Reflection
The revolutionary impact of modern language models, the 1991 linear transformer, linear vs. quadratic scaling, the fast weight controller, and fast weight matrix memory.
[00:11:03] Pioneering Contributions to AI and Deep Learning
The invention of the transformer, pre-trained networks, the first GANs, the role of predictive coding, and the emergence of artificial curiosity.
[00:13:58] AI's Evolution and Achievements
The role of compute, breakthroughs in handwriting recognition and computer vision, the rise of GPU-based CNNs, achieving superhuman results, and Japanese contributions to CNN development.
[00:15:40] The Hardware Lottery and GPUs
GPUs as a serendipitous advantage for AI, the gaming-AI parallel, and Nvidia's strategic shift towards AI.
[00:19:58] AI Applications and Societal Impact
AI-powered translation breaking communication barriers, AI in medicine for imaging and disease prediction, and AI's potential for human enhancement and sustainable development.
[00:23:26] The Path to AGI and Current Limitations
Distinguishing large language models from AGI, challenges in replacing physical world workers, and AI's difficulty in real-world versus board games.
[00:25:56] AI and Consciousness
Simulating consciousness through unsupervised learning, chunking and automatizing neural networks, data compression, and self-symbols in predictive world models.
[00:30:50] The Future of AI and Humanity
Transition from AGIs as tools to AGIs with their own goals, the role of humans in an AGI-dominated world, and the concept of Homo Ludens.
[00:38:05] The AI Race: Europe, China, and the US
Europe's historical contributions, current dominance of the US and East Asia, and the role of venture capital and industrial policy.
[00:50:32] Addressing AI Existential Risk
The obsession with AI existential risk, commercial pressure for friendly AIs, AI vs. hydrogen bombs, and the long-term future of AI.
[00:58:00] The Fermi Paradox and Extraterrestrial Intelligence
Expanding AI bubbles as an explanation for the Fermi paradox, dark matter and encrypted civilizations, and Earth as the first to spawn an AI bubble.
[01:02:08] The Diversity of AI and AI Ecologies
The unrealism of a monolithic super intelligence, diverse AIs with varying goals, and intense competition and collaboration in AI ecologies.
[01:12:21] Final Thoughts and Closing Remarks
REFERENCES:
See pinned comment on YT: https://youtu.be/fZYUqICYCAk
Jürgen Schmidhuber - Neural and Non-Neural AI, Reasoning, Transformers, and LSTMs
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
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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)
Prof. Jürgen Schmidhuber - FATHER OF AI ON ITS DANGERS
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Professor Jürgen Schmidhuber, the father of artificial intelligence, joins us today. Schmidhuber discussed the history of machine learning, the current state of AI, and his career researching recursive self-improvement, artificial general intelligence and its risks.
Schmidhuber pointed out the importance of studying the history of machine learning to properly assign credit for key breakthroughs. He discussed some of the earliest machine learning algorithms. He also highlighted the foundational work of Leibniz, who discovered the chain rule that enables training of deep neural networks, and the ancient Antikythera mechanism, the first known gear-based computer.
Schmidhuber discussed limits to recursive self-improvement and artificial general intelligence, including physical constraints like the speed of light and what can be computed. He noted we have no evidence the human brain can do more than traditional computing. Schmidhuber sees humankind as a potential stepping stone to more advanced, spacefaring machine life which may have little interest in humanity. However, he believes commercial incentives point AGI development towards being beneficial and that open-source innovation can help to achieve "AI for all" symbolised by his company's motto "AI∀".
Schmidhuber discussed approaches he believes will lead to more general AI, including meta-learning, reinforcement learning, building predictive world models, and curiosity-driven learning. His "fast weight programming" approach from the 1990s involved one network altering another network's connections. This was actually the first Transformer variant, now called an unnormalised linear Transformer. He also described the first GANs in 1990, to implement artificial curiosity.
Schmidhuber reflected on his career researching AI. He said his fondest memories were gaining insights that seemed to solve longstanding problems, though new challenges always arose: "then for a brief moment it looks like the greatest thing since sliced bread and and then you get excited ... but then suddenly you realize, oh, it's still not finished. Something important is missing.” Since 1985 he has worked on systems that can recursively improve themselves, constrained only by the limits of physics and computability. He believes continual progress, shaped by both competition and collaboration, will lead to increasingly advanced AI.
On AI Risk: Schmidhuber: "To me it's indeed weird. Now there are all these letters coming out warning of the dangers of AI. And I think some of the guys who are writing these letters, they are just seeking attention because they know that AI dystopia are attracting more attention than documentaries about the benefits of AI in healthcare."
Schmidhuber believes we should be more concerned with existing threats like nuclear weapons than speculative risks from advanced AI. He said: "As far as I can judge, all of this cannot be stopped but it can be channeled in a very natural way that is good for humankind...there is a tremendous bias towards good AI, meaning AI that is good for humans...I am much more worried about 60 year old technology that can wipe out civilization within two hours, without any AI.”
[this is truncated, read show notes]
YT: https://youtu.be/q27XMPm5wg8
Show notes: https://docs.google.com/document/d/13-vIetOvhceZq5XZnELRbaazpQbxLbf5Yi7M25CixEE/edit?usp=sharing
Note: Interview was recorded 15th June 2023.
https://twitter.com/SchmidhuberAI
Panel: Dr. Tim Scarfe @ecsquendor / Dr. Keith Duggar @DoctorDuggar
Pod version: TBA
TOC:
[00:00:00] Intro / Numerai
[00:00:51] Show Kick Off
[00:02:24] Credit Assignment in ML
[00:12:51] XRisk
[00:20:45] First Transformer variant of 1991
[00:47:20] Which Current Approaches are Good
[00:52:42] Autonomy / Curiosity
[00:58:42] GANs of 1990
[01:11:29] OpenAI, Moats, Legislation
Upside-Down Reinforcement Learning with Jürgen Schmidhuber - #357
Today we’re joined by Jürgen Schmidhuber, Co-Founder and Chief Scientist of NNAISENSE, the Scientific Director at IDSIA, as well as a Professor of AI at USI and SUPSI in Switzerland. Jürgen’s lab is well known for creating the Long Short-Term Memory (LSTM) network, and in this conversation, we discuss some of the recent research coming out of his lab, namely Upside-Down Reinforcement Learning.
LSTMs, Plus a Deep Learning History Lesson with Jürgen Schmidhuber - TWiML Talk #44
This week we have a very special interview to share with you! Those of you who’ve been receiving my newsletter for a while might remember that while in Switzerland last month, I had the pleasure of interviewing Jurgen Schmidhuber, in his lab IDSIA, which is the Dalle Molle Institute for Artificial Intelligence Research in Lugano, Switzerland, where he serves as Scientific Director. In addition to his role at IDSIA, Jurgen is also Co-Founder and Chief Scientist of NNaisense, a company that is using AI to build large-scale neural network solutions for “superhuman perception and intelligent automation.” Jurgen is an interesting, accomplished and in some circles controversial figure in the AI community and we covered a lot of very interesting ground in our discussion, so much so that I couldn't truly unpack it all until I had a chance to sit with it after the fact. We talked a bunch about his work on neural networks, especially LSTM’s, or Long Short-Term Memory networks, which are a key innovation behind many of the advances we’ve seen in deep learning and its application over the past few years. Along the way, Jurgen walks us through a deep learning history lesson that spans 50+ years. It was like walking back in time with the 3 eyed raven. I know you’re really going to enjoy this one, and by the way, this is definitely a nerd alert show! For the show notes, visit twimlai.com/talk/44