Jürgen Schmidhuber

Schmidhuber completed his undergraduate (1987) and PhD (1991) studies at the Technical University of Munich in Munich, Germany. His PhD advisors were Wilfried Brauer and Klaus Schulten.{{cite web |title=Jürgen H. Schmidhuber |url=https://www.genealogy.math.ndsu.nodak.edu/id.php?id=118060 |website=The Mathematics Genealogy Project |access-date=5 July 2022}} He taught there from 2004 until 2009. From 2009,{{r|dave}} until 2021, he was a professor of artificial intelligence at the Università della Svizzera Italiana in Lugano, Switzerland.{{r|CV}}

He has served as the director of Dalle Molle Institute for Artificial Intelligence Research (IDSIA), a Swiss AI lab, since 1995.{{r|CV}}

In 2014, Schmidhuber formed a company, Nnaisense, to work on commercial applications of artificial intelligence in fields such as finance, heavy industry and self-driving cars. Sepp Hochreiter, Jaan Tallinn, and Marcus Hutter are advisers to the company.{{r|markoff}} Sales were under US$11 million in 2016; however, Schmidhuber states that the current emphasis is on research and not revenue. Nnaisense raised its first round of capital funding in January 2017. Schmidhuber's overall goal is to create an all-purpose AI by training a single AI in sequence on a variety of narrow tasks.{{cite news|title=AI Pioneer Wants to Build the Renaissance Machine of the Future|url=https://www.bloomberg.com/news/articles/2017-01-16/ai-pioneer-wants-to-build-the-renaissance-machine-of-the-future|access-date=23 February 2018|work=Bloomberg.com|date=16 January 2017|language=en}}

In the 1980s, backpropagation did not work well for deep learning with long credit assignment paths in artificial neural networks. To overcome this problem, Schmidhuber (1991) proposed a hierarchy of recurrent neural networks (RNNs) pre-trained one level at a time by self-supervised learning.{{cite journal |last1=Schmidhuber |first1=Jürgen |year=1992 |title=Learning complex, extended sequences using the principle of history compression (based on TR FKI-148, 1991) |url=https://mediatum.ub.tum.de/doc/814767/document.pdf |journal=Neural Computation |volume=4 |issue=2 |pages=234–242 |doi=10.1162/neco.1992.4.2.234 |s2cid=18271205 }} It uses predictive coding to learn internal representations at multiple self-organizing time scales. This can substantially facilitate downstream deep learning. The RNN hierarchy can be collapsed into a single RNN, by distilling a higher level chunker network into a lower level automatizer network.{{cite arXiv |eprint=2212.11279 |class=cs.NE |first=Juergen |last=Schmidhuber |title=Annotated History of Modern AI and Deep Learning |date=2022}} In 1993, a chunker solved a deep learning task whose depth exceeded 1000.{{Cite book |url=https://sferics.idsia.ch/pub/juergen/habilitation.pdf |title=Habilitation Thesis |last=Schmidhuber |first=Jürgen |year=1993 }}

In 1991, Schmidhuber published adversarial neural networks that contest with each other in the form of a zero-sum game, where one network's gain is the other network's loss.{{cite conference| title = A possibility for implementing curiosity and boredom in model-building neural controllers | last1 = Schmidhuber | first1 = Jürgen | date = 1991 | publisher = MIT Press/Bradford Books| book-title = Proc. SAB'1991| pages = 222–227}}{{cite journal|last1=Schmidhuber|first1=Jürgen|year=2010|title=Formal Theory of Creativity, Fun, and Intrinsic Motivation (1990-2010)|journal= IEEE Transactions on Autonomous Mental Development|volume=2|issue=3|pages=230–247|doi=10.1109/TAMD.2010.2056368 |s2cid=234198 }}{{Cite journal|last=Schmidhuber|first=Jürgen |date=2020|title=Generative Adversarial Networks are Special Cases of Artificial Curiosity (1990) and also Closely Related to Predictability Minimization (1991)|journal=Neural Networks |language=en|volume=127|pages=58–66|doi=10.1016/j.neunet.2020.04.008 |pmid=32334341 |arxiv=1906.04493 |s2cid=216056336 }} The first network is a generative model that models a probability distribution over output patterns. The second network learns by gradient descent to predict the reactions of the environment to these patterns. This was called "artificial curiosity." In 2014, this principle was used in a generative adversarial network where the environmental reaction is 1 or 0 depending on whether the first network's output is in a given set. GANs were the state of the art in generative modeling during 2015-2020 period.

Schmidhuber supervised the 1991 diploma thesis of his student Sepp HochreiterS. Hochreiter., "[http://people.idsia.ch/~juergen/SeppHochreiter1991ThesisAdvisorSchmidhuber.pdf Untersuchungen zu dynamischen neuronalen Netzen] {{Webarchive|url=https://web.archive.org/web/20150306075401/http://people.idsia.ch/~juergen/SeppHochreiter1991ThesisAdvisorSchmidhuber.pdf |date=2015-03-06 }}," Diploma thesis. Institut f. Informatik, Technische Univ. Munich. Advisor: J. Schmidhuber, 1991. which he considered "one of the most important documents in the history of machine learning". It studied the neural history compressor, and more importantly analyzed and overcame the vanishing gradient problem. This led to the long short-term memory (LSTM), a type of recurrent neural network. The name LSTM was introduced in a tech report (1995)

leading to the most cited LSTM publication (1997), co-authored by Hochreiter and Schmidhuber.{{Cite journal

| author = Sepp Hochreiter

| author2 = Jürgen Schmidhuber

| title = Long short-term memory

| journal = Neural Computation

| volume = 9

| issue = 8

| pages = 1735–1780

| year = 1997

| url = https://www.researchgate.net/publication/13853244

| doi=10.1162/neco.1997.9.8.1735

| pmid=9377276

| s2cid = 1915014

}}

It was not yet the standard LSTM architecture which is used in almost all current applications. The standard LSTM architecture was introduced in 2000 by Felix Gers, Schmidhuber, and Fred Cummins.{{Cite journal

| author = Felix A. Gers

| author2 = Jürgen Schmidhuber

| author3 = Fred Cummins

| title = Learning to Forget: Continual Prediction with LSTM

| journal = Neural Computation

| volume = 12

| issue = 10

| pages = 2451–2471

| year = 2000

| doi=10.1162/089976600300015015

| pmid = 11032042

| citeseerx = 10.1.1.55.5709

| s2cid = 11598600

}} Today's "vanilla LSTM" using backpropagation through time was published with his student Alex Graves in 2005,{{cite journal | last1 = Graves | first1 = A. | last2 = Schmidhuber | first2 = J. | year = 2005 | title = Framewise phoneme classification with bidirectional LSTM and other neural network architectures | journal = Neural Networks | volume = 18 | issue = 5–6| pages = 602–610 | doi=10.1016/j.neunet.2005.06.042| pmid = 16112549 | citeseerx = 10.1.1.331.5800 | s2cid = 1856462 }}{{Cite journal|author1=Klaus Greff |author2=Rupesh Kumar Srivastava |author3=Jan Koutník |author4=Bas R. Steunebrink |author5=Jürgen Schmidhuber |arxiv=1503.04069 |title=LSTM: A Search Space Odyssey |journal=IEEE Transactions on Neural Networks and Learning Systems |volume=28 |issue=10 |pages=2222–2232 |date=2015 |doi=10.1109/TNNLS.2016.2582924 |pmid=27411231 |bibcode=2015arXiv150304069G |s2cid=3356463 }} and its connectionist temporal classification (CTC) training algorithm{{Cite journal |last1=Graves |first1=Alex |last2=Fernández |first2=Santiago |last3=Gomez |first3=Faustino |last4=Schmidhuber|first4=Juergen|date=2006 |title=Connectionist temporal classification: Labelling unsegmented sequence data with recurrent neural networks |citeseerx=10.1.1.75.6306 |journal=In Proceedings of the International Conference on Machine Learning, ICML 2006 |pages=369–376}} in 2006. CTC was applied to end-to-end speech recognition with LSTM. By the 2010s, the LSTM became the dominant technique for a variety of natural language processing tasks including speech recognition and machine translation, and was widely implemented in commercial technologies such as Google Neural Machine Translation,{{cite arXiv |eprint=1609.08144 |class=cs.CL |first1=Yonghui |last1=Wu |first2=Mike |last2=Schuster |title=Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation |date=October 8, 2016 |last3=Chen |first3=Zhifeng |last4=Le |first4=Quoc V. |last5=Norouzi |first5=Mohammad |last6=Macherey |first6=Wolfgang |last7=Krikun |first7=Maxim |last8=Cao |first8=Yuan |last9=Gao |first9=Qin |last10=Macherey |first10=Klaus |last11=Klingner |first11=Jeff |last12=Shah |first12=Apurva |last13=Johnson |first13=Melvin |last14=Liu |first14=Xiaobing |last15=Kaiser |first15=Łukasz |last16=Gouws |first16=Stephan |last17=Kato |first17=Yoshikiyo |last18=Kudo |first18=Taku |last19=Kazawa |first19=Hideto |last20=Stevens |first20=Keith |last21=Kurian |first21=George |last22=Patil |first22=Nishant |last23=Wang |first23=Wei |last24=Young |first24=Cliff |last25=Smith |first25=Jason |last26=Riesa |first26=Jason |last27=Rudnick |first27=Alex |last28=Vinyals |first28=Oriol |last29=Corrado |first29=Greg |last30=Hughes |first30=Macduff |last31=Dean |first31=Jeff |author31-link=Jeff Dean}} Retrieved May 14, 2017 have also been used in Google Voice for transcription{{cite web |date=11 August 2015 |title=The neural networks behind Google Voice transcription |url=http://googleresearch.blogspot.co.at/2015/08/the-neural-networks-behind-google-voice.html}} and search,{{cite web |date=24 September 2015 |title=Google voice search: faster and more accurate |url=http://googleresearch.blogspot.co.uk/2015/09/google-voice-search-faster-and-more.html}} and Siri.{{cite magazine |last=Levy |first=Steven |date=August 24, 2016 |title=The iBrain Is Here—and It's Already Inside Your Phone |url=https://www.wired.com/2016/08/an-exclusive-look-at-how-ai-and-machine-learning-work-at-apple/ |archive-url=https://web.archive.org/web/20170623205924/https://www.wired.com/2016/08/an-exclusive-look-at-how-ai-and-machine-learning-work-at-apple/ |access-date=23 Jun 2017 |magazine=Wired|archive-date=23 June 2017 }}

In 2014, the state of the art was training “very deep neural network” with 20 to 30 layers.{{Citation |last1=Simonyan |first1=Karen |title=Very Deep Convolutional Networks for Large-Scale Image Recognition |date=2015-04-10 |arxiv=1409.1556 |last2=Zisserman |first2=Andrew}} Stacking too many layers led to a steep reduction in training accuracy,{{cite arXiv |eprint=1502.01852 |class=cs.CV |first1=Kaiming |last1=He |first2=Xiangyu |last2=Zhang |title=Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification |last3=Ren |first3=Shaoqing |last4=Sun |first4=Jian |year=2016}} known as the "degradation" problem.{{Cite conference |last1=He |first1=Kaiming |last2=Zhang |first2=Xiangyu |last3=Ren |first3=Shaoqing |last4=Sun |first4=Jian |date=10 Dec 2015 |title=Deep Residual Learning for Image Recognition |arxiv=1512.03385}} In May 2015, Rupesh Kumar Srivastava, Klaus Greff, and Schmidhuber used LSTM principles to create the highway network, a feedforward neural network with hundreds of layers, much deeper than previous networks.{{cite arXiv|last1=Srivastava|first1=Rupesh Kumar|last2=Greff|first2=Klaus|last3=Schmidhuber|first3=Jürgen|title=Highway Networks|eprint=1505.00387|date=2 May 2015|class=cs.LG}}{{cite journal|last1=Srivastava|first1=Rupesh K|last2=Greff|first2=Klaus|last3=Schmidhuber|first3=Juergen|title=Training Very Deep Networks|journal=Advances in Neural Information Processing Systems |date=2015|volume=28|pages=2377–2385|url=http://papers.nips.cc/paper/5850-training-very-deep-networks|publisher=Curran Associates, Inc.}} In Dec 2015, the residual neural network (ResNet) was published, which is a variant of the highway network.{{Cite conference |last1=He |first1=Kaiming |last2=Zhang |first2=Xiangyu |last3=Ren |first3=Shaoqing |last4=Sun |first4=Jian |date=2016 |title=Deep Residual Learning for Image Recognition |url=https://ieeexplore.ieee.org/document/7780459 |location=Las Vegas, NV, USA |publisher=IEEE |pages=770–778 |arxiv=1512.03385 |doi=10.1109/CVPR.2016.90 |isbn=978-1-4673-8851-1 |journal=2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}}

In 1992, Schmidhuber published fast weights programmer, an alternative to recurrent neural networks.{{Cite journal |last1=Schmidhuber|first1=Jürgen|date=1 November 1992|title=Learning to control fast-weight memories: an alternative to recurrent nets.|journal=Neural Computation|volume=4|issue=1 |pages=131–139|doi=10.1162/neco.1992.4.1.131 |s2cid=16683347 }} It has a slow feedforward neural network that learns by gradient descent to control the fast weights of another neural network through outer products of self-generated activation patterns, and the fast weights network itself operates over inputs. This was later shown to be equivalent to the unnormalized linear Transformer.{{cite conference |last1=Katharopoulos |first1=Angelos |last2=Vyas |first2=Apoorv |last3=Pappas |first3=Nikolaos |last4=Fleuret |first4=François |date=2020 |title=Transformers are RNNs: Fast autoregressive Transformers with linear attention |url=https://paperswithcode.com/paper/a-decomposable-attention-model-for-natural |publisher=PMLR |pages=5156–5165 |book-title=ICML 2020}}{{cite web |last=Schmidhuber |first=Jürgen |date=2022 |title=Deep Learning: Our Miraculous Year 1990-1991 |url=https://people.idsia.ch/~juergen/deep-learning-miraculous-year-1990-1991.html |access-date=2024-07-23 |website=idsia.ch}} Schmidhuber used the terminology "learning internal spotlights of attention" in 1993.{{cite conference |last1=Schmidhuber |first1=Jürgen |author-link1=Jürgen Schmidhuber |date=1993 |title=Reducing the ratio between learning complexity and number of time-varying variables in fully recurrent nets |publisher=Springer |pages=460–463 |book-title=ICANN 1993}}

In 2011, Schmidhuber's team at IDSIA with his postdoc Dan Ciresan also achieved dramatic speedups of convolutional neural networks (CNNs) on fast parallel computers called GPUs. An earlier CNN on GPU by Chellapilla et al. (2006) was 4 times faster than an equivalent implementation on CPU.{{cite book|author1=Kumar Chellapilla|author2=Sid Puri|author3=Patrice Simard|editor1-last=Lorette|editor1-first=Guy|title=Tenth International Workshop on Frontiers in Handwriting Recognition|date=2006|publisher=Suvisoft|chapter-url=https://hal.inria.fr/inria-00112631/document|chapter=High Performance Convolutional Neural Networks for Document Processing}} The deep CNN of Dan Ciresan et al. (2011) at IDSIA was already 60 times faster{{cite journal|last=Ciresan|first=Dan|author2=Ueli Meier |author3=Jonathan Masci |author4=Luca M. Gambardella |author5=Jurgen Schmidhuber |title=Flexible, High Performance Convolutional Neural Networks for Image Classification|journal=Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence-Volume Volume Two|year=2011|volume=2|pages=1237–1242|url=http://www.idsia.ch/~juergen/ijcai2011.pdf|access-date=17 November 2013}} and achieved the first superhuman performance in a computer vision contest in August 2011.{{Cite web|url=http://benchmark.ini.rub.de/?section=gtsrb&subsection=results|title=IJCNN 2011 Competition result table|website=OFFICIAL IJCNN2011 COMPETITION|language=en-US|access-date=2019-01-14|date=2010}} Between 15 May 2011 and 10 September 2012, these CNNs won four more image competitions{{Cite web|url=http://people.idsia.ch/~juergen/computer-vision-contests-won-by-gpu-cnns.html|last1=Schmidhuber|first1=Jürgen|title=History of computer vision contests won by deep CNNs on GPU|language=en-US|access-date=14 January 2019|date=17 March 2017}}{{cite journal|last1=Schmidhuber|first1=Jürgen|title=Deep Learning|journal=Scholarpedia|url=http://www.scholarpedia.org/article/Deep_Learning|date=2015|volume=10|issue=11|pages=1527–54|pmid=16764513|doi=10.1162/neco.2006.18.7.1527|citeseerx=10.1.1.76.1541|s2cid=2309950}} and improved the state of the art on multiple image benchmarks.{{cite book |last1=Ciresan |first1=Dan |first2=Ueli |last2=Meier |first3=Jürgen |last3=Schmidhuber |title=2012 IEEE Conference on Computer Vision and Pattern Recognition |chapter=Multi-column deep neural networks for image classification |date=June 2012 |pages=3642–3649 |doi=10.1109/CVPR.2012.6248110 |arxiv=1202.2745 |isbn=978-1-4673-1226-4 |oclc=812295155 |publisher=Institute of Electrical and Electronics Engineers (IEEE) |location=New York, NY|citeseerx=10.1.1.300.3283 |s2cid=2161592 }} The approach has become central to the field of computer vision. It is based on CNN designs introduced much earlier by Kunihiko Fukushima.{{cite journal | last1 = Fukushima | first1 = Neocognitron | year = 1980 | title = A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position | doi = 10.1007/bf00344251 | journal = Biological Cybernetics | volume = 36 | issue = 4| pages = 193–202 | pmid = 7370364 | s2cid = 206775608 }}

Schmidhuber has controversially argued that he and other researchers have been denied adequate recognition for their contribution to the field of deep learning, in favour of Geoffrey Hinton, Yoshua Bengio and Yann LeCun, who shared the 2018 Turing Award for their work in deep learning.{{r|markoff|bloomberg2018|guardian}} He wrote a "scathing" 2015 article arguing that Hinton, Bengio and Lecun "heavily cite each other" but "fail to credit the pioneers of the field".{{r|guardian}} In a statement to the New York Times, Yann LeCun wrote that "Jürgen is manically obsessed with recognition and keeps claiming credit he doesn't deserve for many, many things... It causes him to systematically stand up at the end of every talk and claim credit for what was just presented, generally not in a justified manner."{{r|markoff}} Schmidhuber replied that LeCun did this "without any justification, without providing a single example,"{{cite web | last = Schmidhuber | first = Juergen | title = LeCun's 2022 paper on autonomous machine intelligence rehashes but does not cite essential work of 1990-2015 | publisher = IDSIA, Switzerland | url = https://people.idsia.ch/~juergen/lecun-rehash-1990-2022.html | date = 7 July 2022 | access-date = 3 May 2023 | archive-url = https://web.archive.org/web/20230209200002/https://people.idsia.ch/~juergen/lecun-rehash-1990-2022.html | archive-date = 9 February 2023 | quote = }} and published details of numerous priority disputes with Hinton, Bengio and LeCun.{{cite web | last = Schmidhuber | first = Juergen | title = How 3 Turing Awardees Republished Key Methods and Ideas Whose Creators They Failed to Credit. Technical Report IDSIA-23-23 | publisher = IDSIA, Switzerland | url = https://people.idsia.ch/~juergen/ai-priority-disputes.html | date = 14 December 2023 | access-date = 19 Dec 2023 | archive-url = https://web.archive.org/web/20231216143334/https://people.idsia.ch/~juergen/ai-priority-disputes.html | archive-date = 16 Dec 2023 | quote = }}{{cite web | last = Schmidhuber | first = Juergen | title = Scientific Integrity and the History of Deep Learning: The 2021 Turing Lecture, and the 2018 Turing Award. Technical Report IDSIA-77-21. | publisher = IDSIA, Switzerland | url = https://people.idsia.ch/~juergen/scientific-integrity-turing-award-deep-learning.html | date = 30 December 2022 | access-date = 3 May 2023 | archive-url = https://web.archive.org/web/20230407095910/https://people.idsia.ch/~juergen/scientific-integrity-turing-award-deep-learning.html | archive-date = 7 April 2023 | quote = }}

The term "schmidhubered" has been jokingly used in the AI community to describe Schmidhuber's habit of publicly challenging the originality of other researchers' work, a practice seen by some in the AI community as a "rite of passage" for young researchers. Some suggest that Schmidhuber's significant accomplishments have been underappreciated due to his confrontational personality.{{Cite news |last=Fulterer |first=Ruth |date=2021-02-20 |title=Jürgen Schmidhuber: Tessiner Vater der künstlichen Intelligenz |language=de-CH |work=Neue Zürcher Zeitung |url=https://www.nzz.ch/technologie/der-unbequeme-tessiner-vater-der-kuenstlichen-intelligenz-ld.1599575 |access-date=2023-12-19 |issn=0376-6829}}{{r|bloomberg2018}}

Schmidhuber received the Helmholtz Award of the International Neural Network Society in 2013,{{r|inns}} and the Neural Networks Pioneer Award of the IEEE Computational Intelligence Society in 2016{{r|ieee}} for "pioneering contributions to deep learning and neural networks." He is a member of the European Academy of Sciences and Arts.{{r|easa|dave}}

He has been referred to as the "father of modern AI" or similar,{{refn|{{r|markoff}}{{Cite news|url=https://www.technologyreview.com/2020/10/15/1010461/artificial-general-intelligence-robots-ai-agi-deepmind-google-openai/|title=Artificial general intelligence: Are we close, and does it even make sense to try? Quote: Jürgen Schmidhuber—sometimes called "the father of modern AI...|last=Heaven|first=Will Douglas|date=15 October 2020|work=MIT Technology Review|access-date=2021-08-20}}{{cite news|url=https://www.koreaittimes.com/news/articleView.html?idxno=119580|title=User Centric AI Creates a New Order for Users|last1=Choul-woong|first1=Yeon|date=22 Feb 2023|work=Korea IT Times|access-date=26 May 2023|language=en}}{{Cite news|url=https://www.moderntimes.review/mtr-review-letting-loose-ai-demon/|title=Letting loose the AI demon. Quote: But this man is no crackpot: He is the father of modern AI and deep learning – foremost in his field.|last=Dunker|first=Anders|date=2020|work=Modern Times Review|access-date=2021-08-20}}{{r|elpais}}{{Cite news|url=https://universalcinema.ca/ihuman-ai-ethics-of-cinema-2020-hot-docs-film-festival/|title=iHuman- AI & Ethics of Cinema (2020 Hot Docs Film Festival). Quote: The documentary interviews range AI top researchers and thinkers as Jürgen Schmidhuber - Father of Modern AI...|last=Razavi|first=Hooman|date=5 May 2020|work=Universal Cinema|access-date=2021-08-20}}{{Cite news|url=https://www.cnbc.com/2018/05/16/jurgen-schmidhuber-urges-humans-not-to-fear-artificial-intelligence.html|title=The 'father of A.I' urges humans not to fear the technology|last=Wong|first=Andrew|work=CNBC|access-date= 27 February 2019|date=16 May 2018|language=en}}{{Cite news|url=https://www.bloomberg.com/news/features/2018-05-15/google-amazon-and-facebook-owe-j-rgen-schmidhuber-a-fortune|title=This Man Is the Godfather the AI Community Wants to Forget|last=Vance|first=Ashlee|author-link=Ashlee Vance|date=15 May 2018|work=Bloomberg Business Week|access-date=2019-01-16}}}} the "father of Generative AI,"{{Citation | title=Jürgen Schmidhuber：The Father of Generative AI Without Turing Award | newspaper=jazzyear.com | date= 18 August 2024 | url= https://www.jazzyear.com/article_info.html?id=1352}} and also the "father of deep learning."{{Cite news|url=https://www.nextbigfuture.com/2017/06/father-of-deep-learning-ai-on-general-purpose-ai-and-ai-to-conquer-space-in-the-2050s.html|title=Father of deep learning AI on General purpose AI and AI to conquer space in the 2050s|last=Wang|first=Brian|work=Next Big Future|access-date=27 February 2019|date=14 June 2017|language=en}} Schmidhuber himself, however, has called Alexey Grigorevich Ivakhnenko the "father of deep learning,"{{Cite web|last=Schmidhuber|first=Jurgen| url=http://people.idsia.ch/~juergen/deep-learning-conspiracy.html| title=Critique of Paper by "Deep Learning Conspiracy". (Nature 521 p 436)|language=en|access-date=2019-12-26}}{{Cite journal |last=Ivakhnenko |first=A.G. |date=March 1970 |title=Heuristic self-organization in problems of engineering cybernetics |url=https://linkinghub.elsevier.com/retrieve/pii/0005109870900920 |journal=Automatica |language=en |volume=6 |issue=2 |pages=207–219 |doi=10.1016/0005-1098(70)90092-0}} and gives credit to many even earlier AI pioneers.

Schmidhuber is a proponent of open source AI, and believes that they will become competitive against commercial closed-source AI.

Since the 1970s, Schmidhuber wanted to create "intelligent machines that could learn and improve on their own and become smarter than him within his lifetime." He differentiates between two types of AIs: tool AI, such as those for improving healthcare, and autonomous AIs that set their own goals, perform their own research, and explore the universe. He has worked on both types for decades, He expects the next stage of evolution to be self-improving AIs that will succeed human civilization as the next stage in the universal increase towards ever-increasing complexity, and he expects AI to colonize the visible universe.

{{Reflist|refs=

{{Cite web|url=http://people.idsia.ch/~juergen/cv.html|title = Curriculum Vitae|last=Schmidhuber|first=Jürgen}}

{{cite news|url=https://www.theguardian.com/technology/2017/apr/18/robot-man-artificial-intelligence-computer-milky-way|title=Jürgen Schmidhuber on the robot future: 'They will pay as much attention to us as we do to ants'|last1=Oltermann|first1=Philip|date=18 April 2017|work=The Guardian|access-date=23 February 2018|language=en}}

Dave O'Leary (3 October 2016). [http://www.itworldcanada.com/blog/the-present-and-future-of-ai-and-deep-learning-featuring-professor-jurgen-schmidhuber/386551 The Present and Future of AI and Deep Learning Featuring Professor Jürgen Schmidhuber]. IT World Canada. Accessed April 2017.

[http://www.euro-acad.eu/members?filter=s&land=Switzerland Members]. European Academy of Sciences and Arts. Accessed December 2016.

[https://cis.ieee.org/getting-involved/awards/past-recipients#NeuralNetworksPioneerAward|title=Award Recipients: Neural Networks Pioneer Award] {{Webarchive|url=https://web.archive.org/web/20210829194716/https://cis.ieee.org/getting-involved/awards/past-recipients#NeuralNetworksPioneerAward{{!}}title=Award |date=29 August 2021 }}. Piscataway, NJ: IEEE Computational Intelligence Society. Accessed January 2019.]

[http://www.inns.org/inns-awards-recipients INNS Awards Recipients]. International Neural Network Society. Accessed December 2016.

John Markoff (27 November 2016). [https://www.nytimes.com/2016/11/27/technology/artificial-intelligence-pioneer-jurgen-schmidhuber-overlooked.html When A.I. Matures, It May Call Jürgen Schmidhuber ‘Dad’]. The New York Times. Accessed April 2017.

Enrique Alpanes (25 April 2021). [https://elpais.com/tecnologia/2021-04-25/jurgen-schmidhuber-el-hombre-al-que-alexa-y-siri-llamarian-papa-si-el-quisiera-hablar-con-ellas.html Jürgen Schmidhuber, el hombre al que Alexa y Siri llamarían ‘papá’ si él quisiera hablar con ellas]. El País. Accessed August 2021.

Ruth Fulterer (21 February 2021). [https://www.nzz.ch/technologie/der-unbequeme-tessiner-vater-der-kuenstlichen-intelligenz-ld.1599575?reduced=true Der unbequeme Vater der künstlichen Intelligenz lebt in der Schweiz (The inconvenient father of AI lives in Switzerland)]. NZZ. Accessed August 2021.

[https://web.archive.org/web/20230313172237/https://cemse.kaust.edu.sa/people/person/jurgen-schmidhuber Jürgen Schmidhuber]. cemse.kaust.edu.sa. Archived from [https://cemse.kaust.edu.sa/people/person/jurgen-schmidhuber the original] on 13 March 2023. Retrieved 9 May 2023.

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Category:Living people

Category:German artificial intelligence researchers

Category:Machine learning researchers

Category:German computer scientists

Category:Members of the European Academy of Sciences and Arts

Category:Technical University of Munich alumni

Category:Academic staff of the Technical University of Munich

Category:Academic staff of the Università della Svizzera italiana

Category:1963 births