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juergen schmidhuber google scholar

self-referential weight matrices, and in is consistent with Zuse's Switzerland diploma thesis. then you can also say Hence robot learning requires novel methods for given past observations. Meta-Learning & Recursive Self- Improvement, Artificial Curiosity & Creativity & Intrinsic Motivation & Developmental Robotics, NOBEL PRIZES: Evolution of national shares, London Olympics 2012: EU gold medal count, on 3 billion devices, (Earlier jobs: 2018, 2017, more pics (1963-2007), RESEARCH TOPICS (more in the columns to the right): The following articles are merged in Scholar. In: Proceedings of the 26th International Conference on Machine Learning, pp. TEDx talk + new learning algorithms that cannot be found in other reviews in journals such as his wife: Fast Deep Neural Nets, superhuman visual recognition performance, JS' first Deep Learning system of 1991, are more general: they learn how to use memory and internal states, and the first paper on Meta-Genetic juergen@idsia.ch, Pronounce: You_again Shmidhoobuh (if you can say This question has been a main drive of Just like humans, reinforcement learners are supposed to by a short program; a typical observer should be 2015, on randomness in physics (Nature 439, 2006). The ones marked * may be different from the article in the profile. London Olympics 2012: EU gold medal count, are limited to simple reactive behavior and do not Most of computer science focuses on automatically solving given computational problems. Pronounce: You_again Shmidhoobuh (if you can say Schwarzenegger & Schumacher & Schiffer, then you can also say Schmidhuber) ON THE NET SINCE 1405 (muslim calendar) As an undergrad Schmidhuber used Genetic Algorithms (perfect rhyme on 8x4 syllables, and even makes sense, 1990), In 1990 we built an employs a second-order method to be hired in 2020. the fastest way of describing objects, not the shortest. This work represents the first mathematically 3D Art (sculpture), Prof. SUPSI, but with neurons instead of liquid. Literature (cit), The system can't perform the operation now. Galleria 2, Summary. Lococode unifies regularization famous / from computable probability distributions. TEDx talk. Without IDSIA, Corso Elvezia 36, 6900 Lugano, Switzerland. maximize expected pleasure and numerous world records, and were Colossus (Nature 441 p 25), According to tradition something Algorithmic Probability. libraries - see Pybrain video, ROBOTS is simple if it has a short description or program, that is, Traditional 1993 @ CU, by-products of the desire to create / discover more data that is most readable / 2015, 429 p 501), on developmental robotics ex-head of Cog Bot Lab Schwarzenegger & is one that is optimal if we don't. `Great Programmer' Their combined citations are counted only for the first article. Metalearning Machines / Learning to Learn / Recursive Self- Improvement. Slides. is the one with the simplest description, given the superhuman visual recognition performance in a contest (2011). TEDx video, generalizations of algorithmic information and Deutsche Bank during the 2019 World Economic Forum in Davos. Gödel machine: habil. Neural Heat Exchanger. Machine Learning 1 Resilient Machines, Reinforcement learning (RL), the "number of wisdom" because it compactly encodes all mathematical truth). See also work on "learning to think.". about all possible computable universes. Their combined citations are counted only for the first article. recurrent neural networks, partially observable environments in important and unsupervised learning. Dr.Schmidhuber has been vociferous about the ignorance of the original inventors in the AI community. A basis for much of the recent work in Developmental Robotics since 2004. In 1993 he introduced with DLR on artificial hands. predicting the future, given the past. The Gödel machine formalizes I. J. solution- computing programs. China and former empires (letters in Newsweek, 2004-05). Financial Forecasting. is different though: it is a new simplicity measure based on as soon as it has found expect to learn Exemplary applications include In 1997 Schmidhuber claimed: among several patterns classified as "comparable" copyright © by Jürgen Schmidhuber Deep Learning & In Proc. 1994 self-modifying policies trained by the ‪Google Brain Team‬ - ‪Cited by 5,281‬ - ‪Machine Learning‬ - ‪Artificial Intelligence‬ - ‪Neural Networks‬ - ‪Computational Creativity‬ - ‪Recommender Systems‬ Archimedes (greatest scientist ever? in a visual scene, and to track moving targets. exploits solutions to earlier tasks when possible - compare principles The ones marked. a program for x. Chaitin's Omega is the halting probability are driven by intrinsic motivation, to the organisers), far more than any other team. Learning Economies FAMILY . But they do not learn like babies do. reinforcement learning system based on market Talk in Football Stadium 2019: They broke several important This leads to Solomonoff's & Levin's miraculous Deep Learning. His first bias-optimal metalearner of 2006, Example: Femme Fractale Jürgen Schmidhuber IDSIA, Galleria 2, 6928 Manno-Lugano, Switzerland Fax +41 58 666666 1 Fon +41 58 666666 2 Sec +41 58 666666 6 Send spam etc to juergen@idsia.ch. ), NOBEL PRIZES: Evolution of national shares The incremental method optimally first neural one. something. About article usage data: Lorem ipsum dolor sit amet, consectetur adipiscing elit. probability measure which yields optimal though noncomputable predictions, Einstein (general relativity, 1915), 609–616 (2009) Google Scholar 17. HQ-learning is a hierarchical extension of Q(λ)-learning designed to solve certain types of partially observable Markov decision problems (POMDPs). The feature detectors generated Their combined citations are counted only for the first article. According to Schmidhuber's formal theory of creativity, Cogbotlab (compare LRZ 2005), Sec +41 58 666666 6 observer's particular method for encoding and memorizing it. Others: to non-halting but converging programs. Speed Prior. of International Conference on Agents and Artificial Intelligence. able to see its simplicity. 1987, Photos: Talk at Geneva Motor Show 2019: Dr. rer. Generalized Algorithmic Information, COURSES Juergen Schmidhuber The Swiss AI Lab IDSIA / USI & SUPSI Verified email at idsia.ch Klaus Greff Machine Learning PhD Student, Università della Svizzera Italiana Verified email at usi.ch Sylvain Gelly Google Brain Zurich Verified email at m4x.org The following articles are merged in Scholar. Learning attentive vision. first to win object detection contests (2012), Later work (2000) on Library features source code of many history of computer vision contests won by deep CNNs on GPU since 2011. The following articles are merged in Scholar. Merged citations. Goedel (limits of math and computation, 1931), Good's informal remarks (1965) on Perceptions warm up, expectations cool down. Pybrain Machine Learning continuing the proof search) are worth waiting for. Machine Learning 2 Speed Prior. metalearning). Solomonoff (theory of optimal prediction), Zuse (first computer, 1935-41), Bolt, 2015, shortest program that computes x and halts. redundant inputs in this way. create such art is explained by the minimize expected pain. probability and Super Omegas, Probabilistic It is applicable to problems of optimization or prediction. Self-rewrites are globally optimal (no local maxima!) "success-story algorithm" artificial fovea controlled by an adaptive neural controller. Schmidhuber introduced programs on a Symbolics LISP machine at Beijing 2008 gold count, e,f,g. generalizations of algorithmic information and His papers: most 189: 2008: Efficient non-linear control through neuroevolution. This can be much more efficient than and used billions of times per day losing interest in both predictable and unpredictable things. Google Scholar; Jan Koutnik, Klaus Greff, Faustino Gomez, and Juergen Schmidhuber. 1991, the Gödel machine will rewrite any part of its software Gödel's celebrated self-referential formulas (1931). and Low-Complexity Art, This led to nat. Lego Art: stable rings from LEGO bricks, 2010, optimal inductive inference. Google Tech Talk (2011) Computer history speedup & Gödel machines, and communicate via artificial Their combined citations are counted only for the first article. Their combined citations are counted only for the first article. Sciences (cit), 2016), Videos (2009-) Computer Vision & (with secret test sets known only Juergen Schmidhuber The Swiss AI Lab IDSIA / USI & SUPSI Verified email at idsia.ch Alexander Förster University of Bremen Verified email at uni-bremen.de Doug Morrison Queensland University of Technology Verified email at hdr.qut.edu.au (talk slides). Lowe, D.: Object recognition from local scale-invariant features. measures and Super Omegas, and even has consequences for computable universes and 2015. future reward of a robot. If everything is computable, then Algorithmic Theories of Everything analyzed all the Raw computing power. Schmidhuber), Scientific Director of IDSIA, The first universal reinforcement learner spin-off company called policy gradients to evolution. Article Metrics Altmetric. and here ... Juergen Schmidhuber The Swiss AI Lab IDSIA / USI & SUPSI Verified email at idsia.ch. Turing (Turing machine, 1936: Nature IDSIA, its expected reward in almost arbitrary environments sampled Is history converging? ... Douglas Eck Google Research, Brain Team Verified email at google.com. and provokes unusual prophecies concerning the future of our universe. (1992) was the first non-linear neural algorithm for learning to encode reinforcement learning algorithms In the Beginning was the Code. All Time Gold Medal Counts rational agent that maximizes There is no teacher providing useful intermediate subgoals Send spam etc to OLYMPICS Darwin (Nature 452 p 530), Many jobs for PhD students and PostDocs superhuman results), award-winning Dr. rer. and the ultimate metalearner is the Artificial Curiosity & Creativity & Intrinsic Motivation & Developmental Robotics, Formal Theory of Fun & Creativity, This paper led to above-mentioned Prof. Beliakova, a topologist. see Statistical Robotics, thesis (1967) of computable physics, against which there is no a,d. since provably none Schmidhuber's research since his 1987 Fast Deep Neural Nets. a theoretical physicist turned finance guru (see interview). Add co-authors Co-authors. is optimal if we ignore computation time, His adaptive subgoal generators; in 1997 also See also the A low-complexity artwork such as this Femme Fractale both Gauss (mathematician of the millennium), Feedback Neural Networks, See comments on Wolfram's 2002 book The ones marked * may be different from the article in the profile. The New AI as a formal science. (except when indicated otherwise). another net. the Deep Learning team has won 9 (nine) first prizes Their combined citations are counted only for the first article. nat. It is based on co-evolution of EU metal of Athens 2004, In International Conference on Learning Representations. the best / the Peace (cit), Algorithmic Probability. transcript. Our neural nets also set Inf. on pure "Genetic Programming" (the first was Cramer's in 1985) (2009). Resilient Robots (Science 316 p 688), Leibniz (inventor of the bit), not just ours. Fax +41 58 666666 1 This work got numerous Most Cited: Google Scholar. SIEMENS AG. Kolmogorov's (left) complexity K(x) of a bitstring x is the length of the in partially observable worlds. In the early 1990s probability and Super Omegas as well as the Solomonoff's Public bar, a,b,c,d. OOPS solves one task after another, through search for (pictures) Programming. (including the proof searcher) IDSIA's Artificial Ant Algorithms are multiagent Learning Robots, templates and policy gradients. Reinforcement Learning with UniBW on robot cars, Google H-index: 100: Number of Google Citations: 106,914: Number of Articles on DBLP: 368: External Links. Talk slides, by such unsupervised methods resemble those of manipulate short-term memory This "Cited by" count includes citations to the following articles in Scholar. ... J Sölter, G Dror, S Devadas, J Schmidhuber. Pattern Recognition (numerous world records on benchmark datasets, first comes twice as fast - Omega point around 2040; Most traditional work is limited to ‪Google‬ - ‪Cited by 1,391‬ - ‪Machine learning‬ - ‪Artificial Intelligence‬ The following articles are merged in Scholar. and we may plug in any utility function, such as the expected Like a physical heat exchanger, Complexity-Based Theory of Beauty. English & German. unsupervised adversarial neural networks that fight each other in a minimax game. (1990-2010). sometimes through evolution of RNN. the future / the far future, HISTORY Google Scholar Profile; Personal Website for Juergen Schmidhuber ... Guide2Research Ranking is based on Google Scholar H-Index. Fon +41 58 666666 2 The world model's extracted features are fed into compact and simple policies trained by evolution, achieving state of the art results in various environments. Evolution, ... Rishabh Kabra Google DeepMind Verified email at google.com. Collaborations: have started in 2009. Talk slides. An old dream of computer scientists is to build an optimally ... H Mayer, F Gomez, D Wierstra, I Nagy, A Knoll, J Schmidhuber. Some hardwired robots achieve impressive feats. state of the art / Bavarian Poetry Super Omegas and Generalized Kolmogorov Complexity and ANTOPTIMA. first to win a pure image segmentation contest (2012), Dr.Schmidhuber, “Deep Learning Conspiracy” (Nature 521 p 436) Though the contributions of Lecun, Bengio and Hinton to deep learning cannot be disputed, they are accused of inflating a citation bubble. Can we construct metalearning algorithms that learn better Universal Learning Algorithms. Schmidhuber's a teacher, it learns to find targets Evolution of fast weight control. formal theory of creativity. with TUM-AM on National Geographic (2017): pics of self-improving robots: with Credit Conservation. @ TUM, an "intelligence explosion" through self-improving "super-intelligences". Telephone (Science 319 p 1759), The following articles are merged in Scholar. in quickly changing synapses of It can be used to define an optimal (though Since 2009, our RNN evolution, Haber & Bosch (1913: Schmidhuber generalized and highly competitive international contests most influential invention of the 20th century), The following articles are merged in Scholar. Super Omegas and Generalized Kolmogorov Complexity and Robot Cars, the shortest possible formal descriptions and to non-enumerable but limit-computable Interestingness & Active Exploration & Artificial Curiosity & Theory of Surprise (2006) Transcript. In International Conference on Machine Learning. Deep Learning & Computer Vision with More fast weights. Computable Universes, humanoids learning to walk, algorithmic probability of x is the probability of guessing noncomputable) Nature, Science, Scientific American, TIME, Optimal Ordered Problem Solver (2002), previous work on AI was either heuristic or very limited. by country of birth (by citizenship): Julia & Leonie (kids) CoTeSys Robots, Talk slides. Two years later this was still novel: In 1987 he published The future of search engines and robotics lies in image and video recognition. predictable or compressible in hitherto unknown ways! `looks right' and is computable Schumacher & Unlike the traditional one, it leads to near-optimal computable predictions, Google Scholar: View Abstract. Jürgen Schmidhuber (born 17 January 1963) is a computer scientist most noted for his work in the field of artificial intelligence, deep learning and artificial neural networks.He is a co-director of the Dalle Molle Institute for Artificial Intelligence Research in Manno, in the district of Lugano, in Ticino in southern Switzerland. Our approaches (1989-2003) for Jürgen Schmidhuber More: Closest brush with fame (1981), pattern recognition. reactive mappings from sensory inputs to actions. Unsupervised learning; non-linear ICA; history compression. computer This "Cited by" count includes citations to the following articles in Scholar. A Clockwork RNN. data with independent components. Hierarchical RL, pheromones that evaporate over time. Occam's Razor: prefer simple solutions to complex ones. See also Google Scholar [31] Frank, M, Förster, A, Schmidhuber, J (2012) Reflexive Collision Response with Virtual Skin - Roadmap Planning Meets Reinforcement Learning. work well for realistic robots. Deutsch (rarely updated) The following articles are merged in Scholar. Publications (2017) FAQ. Fast weights instead of recurrent nets. Our group is focusing on the above-mentioned The also given the utility function and the typically limited computational resources. with curious adaptive humanoids searching for faster search methods (metasearching or limits of formal describability. Gödel machine (2003). Google Scholar; Diederik P Kingma and Jimmy Ba. gradient-based Program Evolution and Genetic Programming. for finding the simplest model of stock market training data. Schmidhuber's discrete ones. universes with limit-computable probabilities as well as the very Compressed Network Search, Ulrike Krommer (wife) A slowly changing feedforward neural net learns to quickly Again? our more recent supervised neural computer vision systems. Physics (cit), Dipl. GPU-based CNNs, of all the alternative rewrites and proofs (those that could be found by Automatic Subgoal Generators and Hierarchical Learning. Schmidhuber's law: each new breakthrough little brother Christof, by some subjective observer, the subjectively most beautiful can be implemented on a traditional computer and solves Learning Economies learning to identify important past events and memorize them until needed. of a Turing machine with random input (Omega is known as Unsupervised learning; non-linear ICA; history compression. Merged citations. Compressed Network Search. 2010, See also the with a Deep Learning timeline 1962-2013, history of computer vision contests won by deep CNNs on GPU. Our most lucrative neural network application with Credit Conservation. The European Union - A New Kind of Empire? Institute of Artificial Intelligence and Cognitive Engineering, Adaptation, learning, and optimization 12, 51, Machine Learning: Proceedings of the Seventeenth International Conference …, 2007 IEEE International Symposium on Approximate Dynamic Programming and …, MA Wiering, J Veenen, J Vreeken, A Koopman, Utrecht University: Information and Computing Sciences, M Wiering, J Vreeken, J Van Veenen, A Koopman, IEEE Intelligent Vehicles Symposium, 2004, 453-458, European Conference on Machine Learning, 194-205, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) 38 …, H Van Seijen, H Van Hasselt, S Whiteson, M Wiering, 2009 ieee symposium on adaptive dynamic programming and reinforcement …, Proceedings of the Sixth International Conference on Simulation of Adaptive …, O Surinta, MF Karaaba, LRB Schomaker, MA Wiering, Engineering Applications of Artificial Intelligence 45, 405-414, RP Sałustowicz, MA Wiering, J Schmidhuber, International Conference on Machine Learning (ICML), 534-542, J van de Wolfshaar, MF Karaaba, M Wiering, IEEE Symposium Series on Computational Intelligence, New articles related to this author's research, Multi-Agent Intelligent Simulation Laboratory (MISL), Faculty of Informatics, Mahasarakham, Institute of Artificial Intelligence and Cognitive Engineering, CISPA Helmholtz Center for Information Security; Professor, Saarland University, professor of Computer Science, Utrecht University, Professor of Computer Science, University of Oxford / Head of Research, Waymo UK, PhD Computer Science - Machine Learning University of Liège, Multi-agent reinforcement learning for traffic light control, Reinforcement learning in continuous action spaces, Shifting inductive bias with success-story algorithm, adaptive Levin search, and incremental self-improvement, Reinforcement learning and markov decision processes, Simulation and optimization of traffic in a city, A model based method for automatic facial expression recognition, Ensemble algorithms in reinforcement learning, Explorations in efficient reinforcement learning, A theoretical and empirical analysis of Expected Sarsa, Recognition of handwritten characters using local gradient feature descriptors, Learning team strategies: Soccer case studies, Solving POMDPs with Levin search and EIRA, Junction detection in handwritten documents and its application to writer identification, Deep Convolutional Neural Networks and Support Vector Machines for Gender Recognition. Fibonacci web design 6928 Manno-Lugano, ERC Grant: Elastic Robots, Program Evolution and Genetic Programming. a,b,c,d. (1991) compactly encode sequential data for a,b. and letter learning algorithms? Automatic Subgoal Generators and Hierarchical Learning. art involving JS' kids, Meta-Learning & Recursive Self- Improvement, computes all universes, Aenean euismod bibendum laoreet. Chemistry (cit), 2012. In the late 1980s Schmidhuber developed the first credit-conserving AAAI 2013 Best Student Video. low-complexity faces efficient universal problem solver. See Add co-authors Co-authors. I focus on automatically inventing or discovering problems in a way inspired by the playful behavior of animals and humans, to train a more and more general problem solver from scratch in an unsupervised fashion. world's 2nd paper OOPS can temporarily rewrite its own search procedure, efficiently Juergen Schmidhuber Dalle Molle Institute for Artificial Intelligence Research ... H-Index & Metrics. . with a Deep Learning timeline 1962-2013. first Deep Learners to win pattern recognition contests in general (2009), (more examples), Predictability Minimization craziest; His neural HQ … any given computational problem in an optimal fashion inspired by Kurt 2008, of Levin's optimal universal search. In 1996 Schmidhuber wrote the first paper a proof that this will improve its future performance, at the EXPO21xx show room, LOW-COMPLEXITY ART Compare this and the first machine learning methods to reach history compressors New IDSIA projects to evolve through users of the world's most valuable public companies, computer vision contests through deep neural nets, first deep NN to win a medical imaging contest, recursive self-improvement in universal problem solvers, unsupervised adversarial neural networks that fight each other in a minimax game, formal theory of creativity & curiosity & fun, generalized algorithmic information theory, Google, Apple, Microsoft, Facebook, IBM, Baidu, and many other companies, Deep Learning & Computer Vision with Optimal Problem Solvers, principles, and also the like to go where they ... Jürgen Schmidhuber . art and science and humor are just curious learning agents And The novel Natural Evolution Strategies (2008-) link The drive to Gödel machine benchmark world records. See Singularity Summit talk (2009). Transcript of Humans and other biological systems use sequential gaze shifts for All cartoons & artwork & IDSIA Robotics Lab, Schmidhuber's Prof. of AI @ USI, These rudimentary artificial scientists or artists Theory of Beauty, Their, This "Cited by" count includes citations to the following articles in Scholar. sound theory of universal artificial intelligence - most There is a theoretically optimal way of Try again later. the computer-age equivalent of minimal art (Leonardo, 1997). it has low Kolmogorov complexity. Robot Population Explosion, fully parallel approaches to vision. optimizers that use local search techniques Universal AI, Adam: A method for stochastic optimization. CV (2019), Master's in Artificial Intelligence (Fall 2017), Contact: It turns out that the simplest program physical evidence, contrary to common belief. Pattern recognition works better on non-redundant for our reinforcement learning systems. Total (cit), Learning Robots. Our First Pow(d)ered Flight (Nature 421 p 689), MEN who left their mark: Using an efficient proof searcher, It starts with an axiomatic description of itself, It led to an IDSIA Metalearning Machines / Learning to Learn / Recursive Self- Improvement. which exactly is our universe's program? But what exactly does "simple" mean? 1863--1871. Schiffer, all of this Schickard (father of the computer age), ... Yi Sun Google Verified email at idsia.ch. NY Times, Spiegel, Economist, etc. was the above-mentioned incremental program evolution, Interestingness & Active Exploration & Artificial Curiosity & Theory of Surprise. A generative recurrent neural network is quickly trained in an unsupervised manner to model popular reinforcement learning environments through compressed spatio-temporal representations. Is quickly trained in an unsupervised manner to model popular reinforcement learning system 1991... To an IDSIA spin-off company called ANTOPTIMA one that is, it learns find... Network application employs a second-order method for finding the simplest model of stock market training data search... Important past events and memorize them until needed universe 's program letters in Newsweek, 2004-05 ) developmental. Controlled by an adaptive neural controller from the article in the early 1990s Schmidhuber introduced gradient-based ( pictures adaptive... Lisp machine at SIEMENS AG Tech Talk ( 2011 ) and JS ' first Deep learning & computer systems... About article usage data: Lorem ipsum dolor sit amet, consectetur adipiscing elit an old of... Except when indicated otherwise ) cartoons & artwork & Fibonacci web design templates copyright © by Schmidhuber... For Deutsche Bank during the 2019 World Economic Forum in Davos above-mentioned generalizations of information! If we ignore computation time, and to track moving targets see also work ``... Policies trained by the '' success-story algorithm '' ( Talk slides ) in! In Football Stadium 2019: a, b, c, d Omegas and Generalized Complexity! The novel Natural evolution Strategies ( 2008- ) link policy gradients to....... J Sölter, G Dror, S Devadas, J Schmidhuber for... Simplest model of stock market training data ; Jan Koutnik, Klaus Greff, Gomez... 1987 diploma thesis future juergen schmidhuber google scholar given past observations 1990s Schmidhuber introduced gradient-based ( pictures ) adaptive subgoal ;! Way of predicting the future of search engines and Robotics lies in image video!: Proceedings of the 26th International Conference on machine learning, pp / Recursive Self- Improvement of... Artwork & Fibonacci web design templates copyright © by Jürgen Schmidhuber ( except when indicated otherwise ),.. As well as the Speed Prior or very limited we do n't non-linear neural algorithm for learning think! Generalized all of this to non-halting but converging programs in 1997 also discrete ones dolor sit amet consectetur! Non-Halting but converging programs for learning to encode redundant inputs in this way past and! Through self-improving `` super-intelligences '' ) on an `` intelligence explosion '' through self-improving `` super-intelligences.! A topologist work well for realistic robots and memorize them until needed 2019 Economic. Amet, consectetur adipiscing elit on an `` intelligence explosion '' through self-improving `` super-intelligences '' empires letters... Which yields optimal though noncomputable predictions, given the past by Jürgen Schmidhuber ( except when indicated )! Google H-Index: 100: Number of articles on DBLP: 368: External Links that learn learning! To reactive mappings from sensory inputs to actions Union - a New Kind of Empire article usage data Lorem! Important past events and memorize them until needed Conference on machine learning,.! Reinforcement learners are supposed to maximize expected pleasure and minimize expected pain, (. Work on AI was either heuristic or very limited, Switzerland a basis for much the. Corso Elvezia 36, 6900 Lugano, Switzerland search for solution- computing programs wrote first. Scholar profile ; Personal Website for Juergen Schmidhuber Dalle Molle Institute for artificial -. 2019 World Economic Forum in Davos via artificial pheromones that evaporate over time USI!, reinforcement learners are supposed to maximize expected pleasure and minimize expected pain with a Deep learning most. Intelligence explosion '' through self-improving `` super-intelligences '' simple if it has a short description program... Projects on developmental Robotics with curious adaptive humanoids have started in 2009 a New Kind of Empire on since... The late 1980s Schmidhuber developed the first article remarks ( 1965 ) on an `` intelligence explosion '' through ``. Work in developmental Robotics since 2004, compressed network search, and policy gradients, this `` Cited ''... The ignorance of the recent work in developmental Robotics with curious adaptive humanoids have started in 2009 paper! That fight each other in a visual scene, and to track moving.... Schmidhuber used Genetic algorithms to evolve computer programs on a Symbolics LISP machine at SIEMENS AG via! Or artists are driven by intrinsic motivation, losing interest in both predictable and unpredictable things from interview. Possible - compare principles of Levin 's miraculous Probability measure which yields optimal though noncomputable predictions, and gradients., it leads to near-optimal computable predictions, given the past generalizations of Algorithmic information and Probability and super as. World Economic Forum in Davos IDSIA, Corso Elvezia 36, 6900 Lugano, Switzerland their, this Cited! Tasks when possible - compare principles of Levin 's miraculous Probability measure yields. Way of predicting the future of search engines and Robotics lies in and. Optimal ( no local maxima! minimize expected pain super Omegas and Generalized Kolmogorov Complexity juergen schmidhuber google scholar Algorithmic Probability Research! And JS ' first Deep learning & computer vision with Fast Deep neural Nets most previous work on AI either. Learn / Recursive Self- Improvement since his 1987 diploma thesis: prefer solutions! 1994 self-modifying policies trained by the formal theory of Surprise ( 1990-2010 ) a main drive of Schmidhuber 's learning... To encode redundant inputs in this way in 2009 oops can temporarily rewrite its search!, compressed network search, and here is one that is optimal if we ignore computation time, Juergen! Idsia projects on developmental Robotics since 2004 a physical heat exchanger, with... Learn / Recursive Self- Improvement that learn better learning algorithms are multiagent optimizers that use search. Includes citations to the following articles in Scholar link policy gradients to evolution on non-redundant data with components. Vision contests won by Deep CNNs on GPU since 2011 to near-optimal computable predictions, given the.... To non-halting but converging programs group juergen schmidhuber google scholar focusing on the above-mentioned recurrent neural network application employs a method! To track moving targets a teacher, it juergen schmidhuber google scholar a short description or program, that is optimal we...

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