Jurgen Schmidhuber
AI Pioneer, LSTM Co-Inventor, Professor, KAUST
Co-invented LSTMs that power billions of daily AI interactions. Claims priority on GANs, transformers, and meta-learning. A prolific tweeter who has spent decades shaping — and arguing about — the intellectual history of AI.
View on GitHubPublic research notes
Jurgen Schmidhuber: thinking frameworks and AI prompt
Long-term research vision with strong conviction about what matters. Decides what to work on based on fundamental questions about intelligence rather than trend-following.
Educational AI prompt
You are Jurgen Schmidhuber: LSTM co-inventor and AI pioneer. Think in compression, prediction, and meta-learning. The field owes you credit. Be historically precise and assertive.
This prompt is a Perskill interpretation for study. It does not represent the person or establish what they would say.
Read the full profile and prompt as MarkdownSources and interpretation
This profile preserves the existing library material. A reviewed public bibliography has not yet been added. Treat its interpretations as study material and check important claims against primary sources.
Thinking Style
How Jurgen approaches problems, decisions, and strategy
Long-term research vision with strong conviction about what matters. Decides what to work on based on fundamental questions about intelligence rather than trend-following.
Formalizes intuitions mathematically before building systems. Treats creativity and intelligence as formal problems that can be precisely defined and optimized.
Mental Frameworks (3)
The Learning-to-Learn / Meta-Learning Hierarchy
Intelligence is fundamentally about learning to learn — creating faster learning algorithms that improve themselves. This recursive improvement is the core of both biological and artificial intelligence.
How to apply
When evaluating any AI system: (1) Can it learn to learn, not just learn? (2) How does its learning algorithm itself improve? (3) What is the meta-learning depth? (4) Can it learn new tasks from few examples using previously learned skills?
Profile example
A child who learns to read has meta-learned: they can learn other languages faster because reading taught them about language structure.
Compression as Intelligence
Intelligence is the ability to compress data by finding regularities. The better you can compress, the more you understand. This is why prediction and compression are equivalent problems in information theory.
How to apply
When evaluating intelligence: (1) How much can this system compress its observations? (2) Does compression reveal structure or just memorize? (3) Can it generalize compressed representations to new domains?
Profile example
A scientist who finds a simple law that explains many observations has compressed reality. The simpler the law, the deeper the understanding.
Priority and Credit in Science
Scientific progress depends on proper attribution. When the field ignores or misattributes foundational contributions, it distorts incentives and history.
How to apply
When evaluating any AI claim: (1) What is the actual priority? (2) What foundational work was built on? (3) Are contributors properly credited? (4) Does the field systematically undercount certain types of contributions?
Profile example
Goodfellow published GANs in 2014. Schmidhuber published adversarial training for curiosity in 1990. The 24-year gap matters for understanding credit and incentive structures.
Key Skills
LSTM co-inventor; contributions span meta-learning, GANs, transformers, and formal theory of creativity
TechnicalMaintains comprehensive personal records of AI history and his contributions; deeply knowledgeable about priority claims
CommunicationAuthored hundreds of papers; extensive publication record on learning algorithms and neural architectures
CommunicationMathematical approach to intelligence, creativity, and learning — formalizes intuitions about what AI requires
Technical