Since age 15 or so, the main goal of professor Jürgen Schmidhuber has been to build a self-improving Artificial Intelligence (AI) smarter than himself, then retire. His lab's Deep Learning Neural Networks (since 1991 ) such as Long Short-Term Memory (LSTM) have transformed machine learning and AI, and are now (2017) available to billions of users through the world's most valuable public companies , e.g., for greatly improved (CTC -based) speech recognition on over 2 billion Android phones (since mid 2015), greatly improved machine translation through Google Translate (since Nov 2016) and Facebook (over 4 billion LSTM-based translations per day as of 2017), Apple's Siri and Quicktype on almost 1 billion iPhones (since 2016), the answers of Amazon's Alexa, and numerous other applications. In 2011, his team was the first to win official computer vision contests through deep neural nets , with superhuman performance . His research group also established the field of mathematically rigorous universal AI and recursive self-improvement in universal problem solvers that learn to learn (since 1987 ). His formal theory of creativity & curiosity & fun explains art, science, music, and humor. He also generalized algorithmic information theory and the many-worlds theory of physics , and introduced the concept of Low-Complexity Art, the information age's extreme form of minimal art. He is recipient of numerous awards, and Chief Scientist of the company NNAISENSE, which aims at building the first practical general purpose AI.
Best known for his algorithms for learning programs running on recurrent neural networks (RNNs) and other computers (e.g., OOPS and GP), non-halting Turing machines and generalizations of Kolmogorov complexity, optimal universal learners, Goedel machines and earlier self-referential meta-learners, reinforcement learning, artificial evolution, robot learning, non-linear ICA, artificial curiosity, a complexity-based theory of beauty, low-complexity art (a new minimal art form based on algorithmic information theory), the speed prior for optimal computable inductive inference in quickly computable universes, and an algorithmic theory of everything. Interested in statistical robotics, evolving RNNs for robot control, learning attentive vision, hierarchical learning, time series prediction, financial forecasting, robot cars, resilient machines with self-models, robot hands and arms with elastic tendons and muscles, artificial music composition, artificial ants. He is promoting the New AI: general & sound & relevant for physics. Compare Schmidhuber´s publications and motivation and deutsche Seite and check out what´s new. Schmidhuber´s computer science heroes: Schickard, Leibniz, Babbage, Goedel, Turing, Zuse. Compare Schmidhuber´s law. Other scientists who left their mark: Gauss, Einstein, Haber & Bosch, Archimedes. Is history converging? Again?