2010: Breakthrough of supervised deep learning. No unsupervised pretraining. The rest is history. In 2020, we are celebrating the 10year anniversary of our publication [MLP1] in Neural Computation (2010) on deep multilayer perceptrons trained by plain gradient descent on GPU. Surprisingly, our simple but unusually deep supervised artificial neural network (NN) outperformed all previous methods on the (back then famous) machine learning benchmark MNIST. That is, by 2010, when compute was 100 times more expensive than today, both our feedforward NNs and our earlier recurrent NNs (e.g., CTCLSTM for connected handwriting recognition) were able to beat all competing algorithms on important problems of that time. In the 2010s, this deep learning revolution quickly spread from Europe to America and Asia. Just one decade ago, many thought that deep NNs cannot learn much without unsupervised pretraining, a technique introduced by myself in 1991 [UN0UN3][UN] and later also championed by others, e.g., [UN45][VID1][T20][T22]. In fact, it was claimed [VID1] that "nobody in their right mind would ever suggest" to use plain gradient descent through backpropagation [BP1] (see also [BPAC] [BP26][R7]) to train feedforward NNs (FNNs) with many layers of neurons. However, in March 2010, our team with my outstanding Romanian postdoc Dan Ciresan [MLP1] showed that deep FNNs can indeed be trained by plain backpropagation for important applications. This neither required unsupervised pretraining nor Ivakhnenko's incremental layerwise training of 1965 [DEEP12]. By the standards of 2010, our supervised NN had many layers. It set a new performance record [MLP1] on the back then famous and widely used image recognition benchmark called MNIST [MNI]. This was achieved by greatly accelerating traditional multilayer perceptrons on highly parallel graphics processing units called GPUs, going beyond the important GPU work of Jung & Oh (2004) [GPUNN]. A reviewer called this a "wakeup call to the machine learning community." Our results set the stage for the recent decade of deep learning [DEC]. In February 2011, our team extended the approach to deep Convolutional NNs (CNNs) [GPUCNN1]. This greatly improved earlier work [GPUCNN]. The socalled DanNet [GPUCNN1][R6] broke several benchmark records [DAN]. In May 2011, DanNet was the first deep CNN to win a computer vision competition [GPUCNN5,3]. In August 2011, it was the first to win a vision contest with superhuman performance [GPUCNN5][DAN1]. Our team kept winning vision contests in 2012 [GPUCNN5]. Subsequently, many researchers adopted this technique. By May 2015, we had the first extremely deep FNNs with more than 100 layers [HW1] (compare [HW2][HW3]). The original successes required a precise understanding of the inner workings of GPUs [MLP1][GPUCNN1]. Today, convenient software packages shield the user from such details. Compute is roughly 100 times cheaper than a decade ago, and many commercial NN applications are based on what started in 2010 [MLP12][DL14][DEC]. In this context it should be mentioned that right before the 2010s, our team had already achieved another breakthrough in supervised deep learning with the more powerful recurrent NNs (RNNs) whose basic architectures were introduced in the 1920s [L20][I25][K41][MC43][W45][K56][AMH12]. My PhD student Alex Graves won three connected handwriting competitions (French, Farsi, Arabic) at ICDAR 2009, the famous conference on document analysis and recognition. He used a combination of two methods developed in my research groups at TU Munich and the Swiss AI Lab IDSIA: Supervised LSTM RNNs (1990s2005) [LSTM06] (which overcome the famous vanishing gradient problem analyzed by my PhD student Sepp Hochreiter [VAN1] in 1991) and Connectionist Temporal Classification [CTC] (2006). CTCtrained LSTM was the first RNN to win international contests. Compare Sec. 4 of [MIR] and Sec. A & B & XVII of [T22]. That is, by 2010, both our supervised FNNs and our supervised RNNs were able to outperform all other methods on important problems. In the 2010s, this supervised deep learning revolution quickly spread from Europe to North America and Asia, with enormous impact on industry and daily life [DL4][DEC][MOST]. However, it should be mentioned that the conceptual roots of deep learning reach back deep into the previous millennium [DEEP12][DL12][MIR](Sec. 21 & Sec. 19) [T20][T22](e.g., Sec. II & D). Finally let me emphasize that the abovementioned supervised deep learning revolutions of the early 1990s (for recurrent NNs) [MIR] and of 2010 (for feedforward NNs) [MLP12] did not at all kill unsupervised learning. For example, pretrained language models are now heavily used by Transformers which excel at the traditional LSTM domain of Natural Language Processing [TR16] (although there are still many language tasks that LSTM can rapidly learn to solve quickly [LSTM13] while plain Transformers can't). Remarkably, Transformers with linearized selfattention were also first published [FWP07] in our Annus Mirabilis of 19901991 [MIR][MOST], together with unsupervised pretraining for deep learning [UNUN3]. And our unsupervised generative adversarial NNs since 1990 [AC90AC20][PLAN][AC] are still used to endow agents with artificial curiosity [MIR](Sec. 5 & Sec. 6)—see also a version of our adversarial NNs [AC90b] called GANs [AC20][R2][PLAN][MOST][T22](Sec. XVII). Unsupervised learning still has a bright future! This work is licensed under a Creative Commons AttributionNonCommercialShareAlike 4.0 International License. References[MLP1] D. C. Ciresan, U. Meier, L. M. Gambardella, J. Schmidhuber. Deep Big Simple Neural Nets For Handwritten Digit Recognition. Neural Computation 22(12): 32073220, 2010. ArXiv Preprint. Showed that plain backprop for deep standard NNs is sufficient to break benchmark records, without any unsupervised pretraining. [MLP2] J. Schmidhuber (AI Blog, Sep 2020). 10year anniversary of supervised deep learning breakthrough (2010). No unsupervised pretraining. By 2010, when compute was 100 times more expensive than today, both our feedforward NNs^{[MLP1]} and our earlier recurrent NNs were able to beat all competing algorithms on important problems of that time. This deep learning revolution quickly spread from Europe to North America and Asia. The rest is history. [MIR] J. Schmidhuber (AI Blog, Oct 2019, revised 2021). Deep Learning: Our Miraculous Year 19901991. Preprint arXiv:2005.05744, 2020. The deep learning neural networks of our team have revolutionised pattern recognition and machine learning, and are now heavily used in academia and industry. In 202021, we celebrate that many of the basic ideas behind this revolution were published within fewer than 12 months in our "Annus Mirabilis" 19901991 at TU Munich. [MOST] J. Schmidhuber (AI Blog, 2021). The most cited neural networks all build on work done in my labs. Foundations of the most popular NNs originated in my labs at TU Munich and IDSIA. Here I mention: (1) Long ShortTerm Memory (LSTM), (2) ResNet (which is our earlier Highway Net with open gates), (3) AlexNet and VGG Net (both building on our similar earlier DanNet: the first deep convolutional NN to win image recognition competitions), (4) Generative Adversarial Networks (an instance of my earlier Adversarial Artificial Curiosity), and (5) variants of Transformers (Transformers with linearized selfattention are formally equivalent to my earlier Fast Weight Programmers). Most of this started with our Annus Mirabilis of 19901991.^{[MIR]} [MNI] Y. LeCun (1998). The MNIST database of handwritten digits. Link. [AMH1] S. I. Amari (1972). Learning patterns and pattern sequences by selforganizing nets of threshold elements. IEEE Transactions, C 21, 11971206, 1972. PDF. First published learning RNN. First publication of what was later sometimes called the Hopfield network^{[AMH2]} or AmariHopfield Network. [AMH2] J. J. Hopfield (1982). Neural networks and physical systems with emergent collective computational abilities. Proc. of the National Academy of Sciences, vol. 79, pages 25542558, 1982. The Hopfield network or AmariHopfield Network was published in 1972 by Amari.^{[AMH1]} [ATT] J. Schmidhuber (AI Blog, 2020). 30year anniversary of endtoend differentiable sequential neural attention. Plus goalconditional reinforcement learning. We had both hard attention^{[ATT02]} (1990) and soft attention (199193).^{[FWP]} Today, both types are very popular. [DEC] J. Schmidhuber (AI Blog, 02/20/2020; revised 2021). The 2010s: Our Decade of Deep Learning / Outlook on the 2020s. The recent decade's most important developments and industrial applications based on our AI, with an outlook on the 2020s, also addressing privacy and data markets. [DL1] J. Schmidhuber, 2015. Deep Learning in neural networks: An overview. Neural Networks, 61, 85117. More. [DL2] J. Schmidhuber, 2015. Deep Learning. Scholarpedia, 10(11):32832. [DL4] J. Schmidhuber (AI Blog, 2017). Our impact on the world's most valuable public companies: Apple, Google, Microsoft, Facebook, Amazon... By 201517, neural nets developed in my labs were on over 3 billion devices such as smartphones, and used many billions of times per day, consuming a significant fraction of the world's compute. Examples: greatly improved (CTCbased) speech recognition on all Android phones, greatly improved machine translation through Google Translate and Facebook (over 4 billion LSTMbased translations per day), Apple's Siri and Quicktype on all iPhones, the answers of Amazon's Alexa, etc. Google's 2019 ondevice speech recognition (on the phone, not the server) is still based on LSTM. [FWP] J. Schmidhuber (AI Blog, 26 March 2021). 26 March 1991: Neural nets learn to program neural nets with fast weights—like Transformer variants. 2021: New stuff! 30year anniversary of a now popular alternative^{[FWP01]} to recurrent NNs. A slow feedforward NN learns by gradient descent to program the changes of the fast weights of another NN. Such Fast Weight Programmers^{[FWP07]} can learn to memorize past data, e.g., by computing fast weight changes through additive outer products of selfinvented activation patterns^{[FWP01]} (now often called keys and values for selfattention^{[TR16]}). The similar Transformers^{[TR12]} combine this with projections and softmax and are now widely used in natural language processing. For long input sequences, their efficiency was improved through Transformers with linearized selfattention^{[TR56]} which are formally equivalent to the 1991 Fast Weight Programmers (apart from normalization). In 1993, I introduced the attention terminology^{[FWP2]} now used in this context,^{[ATT]} and extended the approach to RNNs that program themselves. [FWP0] J. Schmidhuber. Learning to control fastweight memories: An alternative to recurrent nets. Technical Report FKI14791, Institut für Informatik, Technische Universität München, 26 March 1991. PDF. First paper on fast weight programmers: a slow net learns by gradient descent to compute weight changes of a fast net. [FWP1] J. Schmidhuber. Learning to control fastweight memories: An alternative to recurrent nets. Neural Computation, 4(1):131139, 1992. PDF. HTML. Pictures (German). [FWP2] J. Schmidhuber. Reducing the ratio between learning complexity and number of timevarying variables in fully recurrent nets. In Proceedings of the International Conference on Artificial Neural Networks, Amsterdam, pages 460463. Springer, 1993. PDF. First recurrent fast weight programmer based on outer products. Introduced the terminology of learning "internal spotlights of attention." [FWP3] I. Schlag, J. Schmidhuber. Gated Fast Weights for OnTheFly Neural Program Generation. Workshop on MetaLearning, @N(eur)IPS 2017, Long Beach, CA, USA. [FWP3a] I. Schlag, J. Schmidhuber. Learning to Reason with Third Order Tensor Products. Advances in Neural Information Processing Systems (N(eur)IPS), Montreal, 2018. Preprint: arXiv:1811.12143. PDF. [FWP5] F. J. Gomez and J. Schmidhuber. Evolving modular fastweight networks for control. In W. Duch et al. (Eds.): Proc. ICANN'05, LNCS 3697, pp. 383389, SpringerVerlag Berlin Heidelberg, 2005. PDF. HTML overview. Reinforcementlearning fast weight programmer. [FWP6] I. Schlag, K. Irie, J. Schmidhuber. Linear Transformers Are Secretly Fast Weight Programmers. ICML 2021. Preprint: arXiv:2102.11174. [FWP7] K. Irie, I. Schlag, R. Csordas, J. Schmidhuber. Going Beyond Linear Transformers with Recurrent Fast Weight Programmers. Preprint: arXiv:2106.06295 (June 2021). [VID1] G. Hinton. The Next Generation of Neural Networks. Youtube video [see 28:16]. GoogleTechTalk, 2007. Quote: "Nobody in their right mind would ever suggest" to use plain backpropagation for training deep networks. But in 2010, our [MLP1] showed that unsupervised pretraining is not necessary to train deep feedforward nets. [T20] J. Schmidhuber (2020). Critique of 2018 Turing Award: http://people.idsia.ch/~juergen/critiqueturingawardbengiohintonlecun.html [T22] J. Schmidhuber (AI Blog, 2022). Scientific Integrity and the History of Deep Learning: The 2021 Turing Lecture, and the 2018 Turing Award. Technical Report IDSIA7721 (v3), IDSIA, Lugano, Switzerland, 22 June 2022. [I25] E. Ising (1925). Beitrag zur Theorie des Ferromagnetismus. Z. Phys., 31 (1): 253258, 1925. First nonlearning recurrent NN architecture: the LenzIsing model. [K41] H. A. Kramers and G. H. Wannier (1941). Statistics of the TwoDimensional Ferromagnet. Phys. Rev. 60, 252 and 263, 1941. [W45] G. H. Wannier (1945). The Statistical Problem in Cooperative Phenomena. Rev. Mod. Phys. 17, 50. [K56] S.C. Kleene. Representation of Events in Nerve Nets and Finite Automata. Automata Studies, Editors: C.E. Shannon and J. McCarthy, Princeton University Press, p. 342, Princeton, N.J., 1956. [L20] W. Lenz (1920). Beiträge zum Verständnis der magnetischen Eigenschaften in festen Körpern. Physikalische Zeitschrift, 21: 613615. [MC43] W. S. McCulloch, W. Pitts. A Logical Calculus of Ideas Immanent in Nervous Activity. Bulletin of Mathematical Biophysics, Vol. 5, p. 115133, 1943. [VAN1] S. Hochreiter. Untersuchungen zu dynamischen neuronalen Netzen. Diploma thesis, TUM, 1991 (advisor J. Schmidhuber). PDF. [More on the Fundamental Deep Learning Problem.] [LSTM0] S. Hochreiter and J. Schmidhuber. Long ShortTerm Memory. TR FKI20795, TUM, August 1995. PDF. [LSTM1] S. Hochreiter, J. Schmidhuber. Long ShortTerm Memory. Neural Computation, 9(8):17351780, 1997. PDF. Based on [LSTM0]. More. [LSTM2] F. A. Gers, J. Schmidhuber, F. Cummins. Learning to Forget: Continual Prediction with LSTM. Neural Computation, 12(10):24512471, 2000. PDF. [The "vanilla LSTM architecture" that everybody is using today, e.g., in Google's Tensorflow.] [LSTM3] A. Graves, J. Schmidhuber. Framewise phoneme classification with bidirectional LSTM and other neural network architectures. Neural Networks, 18:56, pp. 602610, 2005. PDF. [LSTM5] A. Graves, M. Liwicki, S. Fernandez, R. Bertolami, H. Bunke, J. Schmidhuber. A Novel Connectionist System for Improved Unconstrained Handwriting Recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 31, no. 5, 2009. PDF. [LSTM6] A. Graves, J. Schmidhuber. Offline Handwriting Recognition with Multidimensional Recurrent Neural Networks. NIPS'22, p 545552, Vancouver, MIT Press, 2009. PDF. [LSTM13] F. A. Gers and J. Schmidhuber. LSTM Recurrent Networks Learn Simple Context Free and Context Sensitive Languages. IEEE Transactions on Neural Networks 12(6):13331340, 2001. PDF. [CTC] A. Graves, S. Fernandez, F. Gomez, J. Schmidhuber. Connectionist Temporal Classification: Labelling Unsegmented Sequence Data with Recurrent Neural Networks. ICML 06, Pittsburgh, 2006. PDF. [HW1] Srivastava, R. K., Greff, K., Schmidhuber, J. Highway networks. Preprints arXiv:1505.00387 (May 2015) and arXiv:1507.06228 (July 2015). Also at NIPS 2015. The first working very deep feedforward nets with over 100 layers. Let g, t, h, denote nonlinear differentiable functions. Each noninput layer of a highway net computes g(x)x + t(x)h(x), where x is the data from the previous layer. (Like LSTM with forget gates [LSTM2] for RNNs.) Resnets [HW2] are a version of this where g(x)=t(x)=const=1. Highway Nets perform roughly as well as ResNets [HW2] on ImageNet [HW3]. Highway layers are also often used for natural language processing, where the simpler residual layers do not work as well [HW3]. More. [HW2] He, K., Zhang, X., Ren, S., Sun, J. Deep residual learning for image recognition. Preprint arXiv:1512.03385 (Dec 2015). Residual nets are a version of highway nets [HW1], with g(x)=1 (a typical highway net initialization) and t(x)=1. More. [HW3] K. Greff, R. K. Srivastava, J. Schmidhuber. Highway and Residual Networks learn Unrolled Iterative Estimation. Preprint arxiv:1612.07771 (2016). Also at ICLR 2017. [DAN] J. Schmidhuber (AI Blog, 2021). 10year anniversary. In 2011, DanNet triggered the deep convolutional neural network (CNN) revolution. Named after my outstanding postdoc Dan Ciresan, it was the first deep and fast CNN to win international computer vision contests, and had a temporary monopoly on winning them, driven by a very fast implementation based on graphics processing units (GPUs). 1st superhuman result in 2011.^{[DAN1]} Now everybody is using this approach. [DAN1] J. Schmidhuber (AI Blog, 2011; updated 2021 for 10th birthday of DanNet): First superhuman visual pattern recognition. At the IJCNN 2011 computer vision competition in Silicon Valley, our artificial neural network called DanNet performed twice better than humans, three times better than the closest artificial competitor, and six times better than the best nonneural method. [GPUNN] Oh, K.S. and Jung, K. (2004). GPU implementation of neural networks. Pattern Recognition, 37(6):13111314. [Speeding up traditional NNs on GPU by a factor of 20.] [GPUCNN] K. Chellapilla, S. Puri, P. Simard. High performance convolutional neural networks for document processing. International Workshop on Frontiers in Handwriting Recognition, 2006. [Speeding up shallow CNNs on GPU by a factor of 4.] [GPUCNN1] D. C. Ciresan, U. Meier, J. Masci, L. M. Gambardella, J. Schmidhuber. Flexible, High Performance Convolutional Neural Networks for Image Classification. International Joint Conference on Artificial Intelligence (IJCAI2011, Barcelona), 2011. PDF. ArXiv preprint (1 Feb 2011). [Speeding up deep CNNs on GPU by a factor of 60. Used to win four important computer vision competitions 20112012 before others won any with similar approaches.] [GPUCNN3] D. C. Ciresan, U. Meier, J. Schmidhuber. Multicolumn Deep Neural Networks for Image Classification. Proc. IEEE Conf. on Computer Vision and Pattern Recognition CVPR 2012, p 36423649, July 2012. PDF. Longer TR of Feb 2012: arXiv:1202.2745v1 [cs.CV]. More. [GPUCNN5] J. Schmidhuber (AI Blog, 2017; updated 2021 for 10th birthday of DanNet): History of computer vision contests won by deep CNNs since 2011. DanNet won 4 of them in a row before the similar AlexNet/VGG Net and the Resnet (a Highway Net with open gates) joined the party. Today, deep CNNs are standard in computer vision. [R6] Reddit/ML, 2019. DanNet, the CUDA CNN of Dan Ciresan in J. Schmidhuber's team, won 4 image recognition challenges prior to AlexNet. [UN] J. Schmidhuber (AI Blog, 2021). 30year anniversary. 1991: First very deep learning with unsupervised pretraining. Unsupervised hierarchical predictive coding finds compact internal representations of sequential data to facilitate downstream learning. The hierarchy can be distilled into a single deep neural network (suggesting a simple model of conscious and subconscious information processing). 1993: solving problems of depth >1000. [UN0] J. Schmidhuber. Neural sequence chunkers. Technical Report FKI14891, Institut für Informatik, Technische Universität München, April 1991. PDF. [UN1] J. Schmidhuber. Learning complex, extended sequences using the principle of history compression. Neural Computation, 4(2):234242, 1992. Based on TR FKI14891, TUM, 1991 [UN0]. PDF. [First working Deep Learner based on a deep RNN hierarchy (with different selforganising time scales), overcoming the vanishing gradient problem through unsupervised pretraining and predictive coding. Also: compressing or distilling a teacher net (the chunker) into a student net (the automatizer) that does not forget its old skills—such approaches are now widely used. More.] [UN2] J. Schmidhuber. Habilitation thesis, TUM, 1993. PDF. [An ancient experiment on "Very Deep Learning" with credit assignment across 1200 time steps or virtual layers and unsupervised pretraining for a stack of recurrent NN can be found here (depth > 1000).] [UN3] J. Schmidhuber, M. C. Mozer, and D. Prelinger. Continuous history compression. In H. Hüning, S. Neuhauser, M. Raus, and W. Ritschel, editors, Proc. of Intl. Workshop on Neural Networks, RWTH Aachen, pages 8795. Augustinus, 1993. [UN4] G. E. Hinton, R. R. Salakhutdinov. Reducing the dimensionality of data with neural networks. Science, Vol. 313. no. 5786, pp. 504  507, 2006. PDF. [UN5] Raina, R., Madhavan, A., and Ng, A. (2009). Largescale deep unsupervised learning using graphics processors. In Proc. ICML 26, p 873880, ACM. [AC] J. Schmidhuber (AI Blog, 2021). 3 decades of artificial curiosity & creativity. Our artificial scientists not only answer given questions but also invent new questions. They achieve curiosity through: (1990) the principle of generative adversarial networks, (1991) neural nets that maximise learning progress, (1995) neural nets that maximise information gain (optimally since 2011), (1997) adversarial design of surprising computational experiments, (2006) maximizing compression progress like scientists/artists/comedians do, (2011) PowerPlay... Since 2012: applications to real robots. [AC90] J. Schmidhuber. Making the world differentiable: On using fully recurrent selfsupervised neural networks for dynamic reinforcement learning and planning in nonstationary environments. Technical Report FKI12690, TUM, Feb 1990, revised Nov 1990. PDF. This report introduced a whole bunch of concepts that are now widely used: Planning with recurrent world models ([MIR], Sec. 11), highdimensional reward signals as extra NN inputs / general value functions ([MIR], Sec. 13), deterministic policy gradients ([MIR], Sec. 14), unsupervised NNs that are both generative and adversarial ([MIR], Sec. 5), for Artificial Curiosity and related concepts. [AC90b] J. Schmidhuber. A possibility for implementing curiosity and boredom in modelbuilding neural controllers. In J. A. Meyer and S. W. Wilson, editors, Proc. of the International Conference on Simulation of Adaptive Behavior: From Animals to Animats, pages 222227. MIT Press/Bradford Books, 1991. PDF. Based on [AC90]. More. [AC91b] J. Schmidhuber. Curious modelbuilding control systems. Proc. International Joint Conference on Neural Networks, Singapore, volume 2, pages 14581463. IEEE, 1991. PDF. [AC95] J. Storck, S. Hochreiter, and J. Schmidhuber. Reinforcementdriven information acquisition in nondeterministic environments. In Proc. ICANN'95, vol. 2, pages 159164. EC2 & CIE, Paris, 1995. PDF. [AC97] J. Schmidhuber. What's interesting? Technical Report IDSIA3597, IDSIA, July 1997. [AC99] J . Schmidhuber. Artificial Curiosity Based on Discovering Novel Algorithmic Predictability Through Coevolution. In P. Angeline, Z. Michalewicz, M. Schoenauer, X. Yao, Z. Zalzala, eds., Congress on Evolutionary Computation, p. 16121618, IEEE Press, Piscataway, NJ, 1999. [AC02] J. Schmidhuber. Exploring the Predictable. In Ghosh, S. Tsutsui, eds., Advances in Evolutionary Computing, p. 579612, Springer, 2002. PDF. [AC06] J. Schmidhuber. Developmental Robotics, Optimal Artificial Curiosity, Creativity, Music, and the Fine Arts. Connection Science, 18(2): 173187, 2006. PDF. [AC10] J. Schmidhuber. Formal Theory of Creativity, Fun, and Intrinsic Motivation (19902010). IEEE Transactions on Autonomous Mental Development, 2(3):230247, 2010. IEEE link. PDF. [AC11] Sun Yi, F. Gomez, J. Schmidhuber. Planning to Be Surprised: Optimal Bayesian Exploration in Dynamic Environments. In Proc. Fourth Conference on Artificial General Intelligence (AGI11), Google, Mountain View, California, 2011. PDF. [AC13] J. Schmidhuber. POWERPLAY: Training an Increasingly General Problem Solver by Continually Searching for the Simplest Still Unsolvable Problem. Frontiers in Cognitive Science, 2013. Preprint (2011): arXiv:1112.5309 [cs.AI] [AC20] J. Schmidhuber. Generative Adversarial Networks are Special Cases of Artificial Curiosity (1990) and also Closely Related to Predictability Minimization (1991). Neural Networks, Volume 127, p 5866, 2020. Preprint arXiv/1906.04493. [R2] Reddit/ML, 2019. J. Schmidhuber really had GANs in 1990. [BPA] H. J. Kelley. Gradient Theory of Optimal Flight Paths. ARS Journal, Vol. 30, No. 10, pp. 947954, 1960. [BPB] A. E. Bryson. A gradient method for optimizing multistage allocation processes. Proc. Harvard Univ. 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