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Highway Net^{[HW1]} with open gates 
For a while, DanNet enjoyed a monopoly. From 2011 to 2012 it won every contest it entered, winning four of them in a row (15 May 2011, 6 Aug 2011, 1 Mar 2012, 10 Sep 2012).^{[GPUCNN5]} In particular, at IJCNN 2011 in Silicon Valley, DanNet blew away the competition and achieved the first superhuman visual pattern recognition^{[DAN1]} in an international contest. DanNet was also the first deep CNN to win: a Chinese handwriting contest (ICDAR 2011), an image segmentation contest (ISBI, May 2012), a contest on object detection in large images (ICPR, 10 Sept 2012), and— at the same time—a medical imaging contest on cancer detection.^{[GPUCNN8]} In 2010, we introduced DanNet to Arcelor Mittal, the world's largest steel producer, and were able to greatly improve steel defect detection.^{[ST]} To the best of my knowledge, this was the first deep learning breakthrough in heavy industry. In July 2012, our CVPR paper on DanNet^{[GPUCNN3]} hit the computer vision community. 5 months later, the similar GPUaccelerated AlexNet won the ImageNet^{[IM09]} 2012 contest.^{[GPUCNN45][R6]} Our CNN image scanners were 1000 times faster than previous methods.^{[SCAN]} This attracted tremendous interest from the healthcare industry. Today IBM, Siemens, Google and many startups are pursuing this approach. The VGG network (ImageNet 2014 winner)^{[GPUCNN9]} and other highly cited CNNs^{[RCNN13]} further extended the DanNet of 2011.^{[MIR](Sec. 19)[MOST]}
ResNet, the ImageNet 2015 winner^{[HW2]} (Dec 2015) and currently the most cited NN,^{[MOST]} is a version (with open gates) of our earlier Highway Net (May 2015).^{[HW13][R5]} The Highway Net (see below) is actually the feedforward net version of our vanilla LSTM (see below).^{[LSTM2]} It was the first working, really deep feedforward NN with hundreds of layers (previous NNs had at most a few tens of layers).
NNs with rapidly changing "fast weights" were introduced by v.d. Malsburg (1981) and others.^{}[FAST,a,b] Deep learning architectures that can manipulate structured data such as graphs^{[T22]} were proposed in 1987 by Pollack^{[PO8790]} and extended/improved by Sperduti, Goller, and Küchler in the early 1990s.^{[SP9397][GOL][KU][T22]} See also our graph NNlike, Transformerlike Fast Weight Programmers of 1991^{[FWP01][FWP6][FWP]} which learn to continually rewrite mappings from inputs to outputs (addressed below), and the work of Baldi and colleagues.^{[BA9603]} Today, graph NNs are used in numerous applications.
Werbos,^{[BP2][BPTT1]} Williams,^{[BPTT2][CUB02]} and others^{[ROB87][BPTT3][DL1]} analyzed ways of implementing gradient descent^{[GD'][STO5152][GDab][GD12a]} in RNNs. Kohonen's selforganising maps became popular.^{[KOH8289]}
The 80s and 90s also saw various proposals of biologically more plausible deep learning algorithms that—unlike backpropagation—are local in space and time.^{[BB2][NAN14][NHE][HEL]} See overviews^{[MIR](Sec. 15, Sec. 17)} and recent renewed interest in such methods.^{[NAN5][FWPMETA6][HIN22]}
In 1990, Hanson introduced the Stochastic Delta Rule, a stochastic way of training NNs by backpropagation. Decades later, a version of this became popular under the moniker "dropout."^{[Drop14][GPUCNN4]}
Many additional papers on NNs (including RNNs) were published in the 1980s and 90s—see the numerous references in the 2015 survey.^{[DL1]} Here, however, we mostly limit ourselves to the—in hindsight—most essential ones, given the present (ephemeral?) perspective of 2022.
Generative Adversarial Networks (GANs) have become very popular.^{}[MOST] They were first published in 1990 in Munich under the moniker Artificial Curiosity.^{[AC9020][GAN1]} Two dueling NNs (a probabilistic generator and a predictor) are trying to maximize each other's loss in a minimax game.^{[AC](Sec. 1)} The generator (called the controller) generates probabilistic outputs (using stochastic units^{[AC90]} like in the much later StyleGANs^{[GAN2]}). The predictor (called the world model) sees the outputs of the controller and predicts environmental reactions to them. Using gradient descent, the predictor NN minimizes its error, while the generator NN tries to make outputs that maximize this error: one net's loss is the other net's gain.^{[AC90]} (The world model can also be used for continual online action planning.^{[AC90][PLAN23][PLAN]})
4 years before a 2014 paper on GANs,^{[GAN1]} my wellknown 2010 survey^{[AC10]} summarised the generative adversarial NNs of 1990 as follows: a "neural network as a predictive world model is used to maximize the controller's intrinsic reward, which is proportional to the model's prediction errors" (which are minimized).
The 2014 GANs are an instance of this where the trials are very short (like in bandit problems) and the environment simply returns 1 or 0 depending on whether the controller's (or generator's) output is in a given set.^{[AC20][AC][T22](Sec. XVII)}
Other early adversarial machine learning settings^{[S59][H90]} were very different—they neither involved unsupervised NNs nor were about modeling data nor used gradient descent.^{[AC20]}
The 1990 principle has been widely used for exploration in Reinforcement Learning^{[SIN5][OUD13][PAT17][BUR18]} and for synthesis of realistic images,^{[GAN1,2]} although the latter domain was recently taken over by Rombach et al.'s Latent Diffusion, another method published in Munich,^{[DIF1]} building on Jarzynski's earlier work in physics from the previous millennium^{[DIF2]} and more recent papers.^{[DIF35]}
In 1991, I published yet another ML method based on two adversarial NNs called Predictability Minimization for creating disentangled representations of partially redundant data, applied to images in 1996.^{[PM02][AC20][R2][MIR](Sec. 7)}
Most NNs of recent centuries were dedicated to simple pattern recognition, not to highlevel reasoning, which is now considered a remaining grand challenge.^{}[LEC] The early 1990s, however, saw first exceptions: NNs that learn to decompose complex spatiotemporal observation sequences into compact but meaningful chunks^{[UN03]} (see further below), and NNbased planners of hierarchical action sequences for compositional learning,^{[HRL0]} as discussed next. This work injected concepts of traditional "symbolic" hierarchical AI^{[NS59][FU77]} into endtoend differentiable "subsymbolic" NNs.
In 1990, our NNs learned to generate hierarchical action plans with endtoend differentiable NNbased subgoal generators for Hierarchical Reinforcement Learning (HRL).^{[HRL0]} Soon afterwards, this was also done with recurrent NNs that learn to generate sequences of subgoals.^{[HRL12][PHD][MIR](Sec. 10)} An RL machine gets extra command inputs of the form (start, goal). An evaluator NN learns to predict the current rewards/costs of going from start to goal. An (R)NNbased subgoal generator also sees (start, goal), and uses (copies of) the evaluator NN to learn by gradient descent a sequence of costminimising intermediate subgoals. The RL machine tries to use such subgoal sequences to achieve final goals. The system is learning action plans at multiple levels of abstraction and multiple time scales and solves (at least in principle) what recently (2022) has been called an "open problem."^{[LEC]}
Compare other NNs that have "worked on command" since April 1990, in particular, for learning selective attention,^{[ATT03]} artificial curiosity and selfinvented problems,^{[PP][PPa,1,2][AC]} upsidedown reinforcement learning^{[UDRL12]} and its generalizations.^{[GGP]}
Recently, Transformers^{}[TR1] have been all the rage, e.g., generating humansounding texts.^{[GPT3]} Transformers with "linearized selfattention"^{[TR56]} were first published in March 1991^{[FWP01][FWP6][FWP]} (apart from normalisation—see tweet of 2022 for 30year anniversary). These socalled "Fast Weight Programmers" or "Fast Weight Controllers"^{[FWP01]} separated storage and control like in traditional computers, but in an endtoenddifferentiable, adaptive, fully neural way (rather than in a hybrid fashion^{[PDA12][DNC]}). The "selfattention" in standard Transformers^{[TR14]} combines this with a projection and softmax (using attention terminology like the one I introduced in 1993^{[ATT][FWP2][R4]}).
Today's Transformers heavily use unsupervised pretraining^{[UN03]} (see next section), another deep learning methodology first published in our Annus Mirabilis of 19901991.^{[MIR][MOST]}
The 1991 fast weight programmers also led to metalearning selfreferential NNs that can run their own weight change algorithm or learning algorithm on themselves, and improve it, and improve the way they improve it, and so on. This work since 1992^{[FWPMETA19][HO1]} extended my 1987 diploma thesis,^{[META1]} which introduced algorithms not just for learning but also for metalearning or learning to learn,^{[META]} to learn better learning algorithms through experience. This became very popular in the 2010s^{[DEC]} when computers were a million times faster.
Today's most powerful NNs tend to be very deep, that is, they have many layers of neurons or many subsequent computational stages.^{}[MIR] Before the 1990s, however, gradientbased training did not work well for deep NNs, only for shallow ones^{[DL12]} (but see a 1989 paper^{[MOZ]}). This Deep Learning Problem was most obvious for recurrent NNs. Like the human brain, but unlike the more limited feedforward NNs (FNNs), RNNs have feedback connections. This makes RNNs powerful, general purpose, parallelsequential computers that can process input sequences of arbitrary length (think of speech data or videos). RNNs can in principle implement any program that can run on your laptop or any other computer in existence. If we want to build an Artificial General Intelligence (AGI), then its underlying computational substrate must be something more like an RNN than an FNN as FNNs are fundamentally insufficient; RNNs and similar systems are to FNNs as general computers are to pocket calculators. In particular, unlike FNNs, RNNs can in principle deal with problems of arbitrary depth.^{[DL1]} Before the 1990s, however, RNNs failed to learn deep problems in practice.^{[MIR](Sec. 0)}
To overcome this drawback through RNNbased "general deep learning," I built a selfsupervised RNN hierarchy that learns representations at multiple levels of abstraction and multiple selforganizing time scales:^{[LEC]} the Neural Sequence Chunker^{[UN0]} or Neural History Compressor.^{[UN1]} Each RNN tries to solve the pretext task of predicting its next input, sending only unexpected inputs (and therefore also targets) to the next RNN above. The resulting compressed sequence representations greatly facilitate downstream supervised deep learning such as sequence classification.
Although computers back then were about a million times slower per dollar than today, by 1993, the Neural History Compressor above was able to solve previously unsolvable "very deep learning" tasks of depth > 1000^{[UN2]} (requiring more than 1,000 subsequent computational stages—the more such stages, the deeper the learning). In 1993, we also published a continuous version of the Neural History Compressor.^{[UN3]} (See also recent work on unsupervised NNbased abstraction.^{[OBJ15]})
More than a decade after this work,^{[UN1]} a similar unsupervised method for more limited feedforward NNs (FNNs) was published, facilitating supervised learning by unsupervised pretraining of stacks of FNNs called Deep Belief Networks (DBNs).^{[UN4]} The 2006 justification was essentially the one I used in the early 1990s for my RNN stack: each higher level tries to reduce the description length (or negative log probability) of the data representation in the level below.^{[HIN][T22][MIR]}
The hierarchical internal representations of the neural history compressor above can be collapsed into a single recurrent NN (RNN), using my NN distillation procedure of 1991.^{[UN01][MIR]} Here the knowledge of a teacher NN is "distilled" into a student NN, by training the student NN to imitate the behavior of the teacher NN (while also retraining the student NN on previously learned skills such that it does not forget them). NN distillation was also republished many years later,^{[DIST2][MIR][HIN][T22]} and is widely used today.
Today, unsupervised pretraining is heavily used by Transformers^{[TR16]} for natural language processing and other domains. Remarkably, Transformers with linearized selfattention were also first published^{[FWP06]} in our Annus Mirabilis of 19901991,^{[MIR][MOST]} together with unsupervised/selfsupervised pretraining for deep learning.^{[UN03]} See the previous section.
Deep learning is hard because of the Fundamental Deep Learning Problem identified and analyzed in 1991 by my first student Sepp Hochreiter in his diploma thesis which I had the pleasure to supervise.^{[VAN1]} First he implemented the Neural History Compressor above but then did much more: he showed that deep NNs suffer from the now famous problem of vanishing or exploding gradients: in typical deep or recurrent networks, backpropagated error signals either shrink rapidly, or grow out of bounds. In both cases, learning fails (compare^{[VAN2]}). This analysis led to basic principles of what's now called LSTM (see below).
The Long ShortTerm Memory (LSTM) recurrent neural network^{[LSTM16]} overcomes the Fundamental Deep Learning Problem identified by Sepp in his abovementioned 1991 diploma thesis,^{[VAN1]} which I consider one of the most important documents in the history of machine learning. It also provided essential insights for overcoming the problem, through basic principles (such as constant error flow) of what we called LSTM in a tech report of 1995.^{[LSTM0]} After the main peerreviewed publication in 1997^{[LSTM1][25y97]} (now the most cited NN article of the 20th century^{[MOST]}), LSTM and its training procedures were further improved on my Swiss LSTM grants at IDSIA through the work of my later students Felix Gers, Alex Graves, and others. A milestone was the "vanilla LSTM architecture" with forget gate^{[LSTM2]}—the LSTM variant of 19992000 that everybody is using today, e.g., in Google's Tensorflow. Alex was lead author of our first successful application of LSTM to speech (2004).^{[LSTM10]} 2005 saw the first publication of LSTM with full backpropagation through time and of bidirectional LSTM^{[LSTM3]} (now widely used).
Another milestone of 2006 was the training method "Connectionist Temporal Classification" or CTC^{[CTC]} for simultaneous alignment and recognition of sequences. Our team successfully applied CTCtrained LSTM to speech in 2007^{[LSTM4]} (also with hierarchical LSTM stacks^{[LSTM14]}). This led to the first superior endtoend neural speech recognition. It was very different from hybrid methods since the late 1980s which combined NNs and traditional approaches such as Hidden Markov Models (HMMs).^{[BW][BRI][BOU][HYB12][T22]} In 2009, through the efforts of Alex, LSTM trained by CTC became the first RNN to win international competitions, namely, three ICDAR 2009 Connected Handwriting Competitions (French, Farsi, Arabic). This attracted enormous interest from industry. LSTM was soon used for everything that involves sequential data such as speech^{[LSTM1011][LSTM4][DL1]} and videos. In 2015, the CTCLSTM combination dramatically improved Google's speech recognition on the Android smartphones.^{[GSR15]} Many other companies adopted this.^{[DL4]} Google's new ondevice speech recognition of 2019 (now on your phone, not on the server) is still based on LSTM.
The first superior endtoend neural machine translation was also based on LSTM. In 1995, we already had an excellent neural probabilistic text model^{}[SNT] whose basic concepts were reused in 2003^{[NPM][T22]}—see also Pollack's earlier work on embeddings of words and other structures^{[PO87][PO90]} as well as Nakamura and Shikano's 1989 word category prediction model.^{[NPMa]} In 2001, we showed that LSTM can learn languages unlearnable by traditional models such as HMMs,^{[LSTM13]} i.e., a neural "subsymbolic" model suddenly excelled at learning "symbolic" tasks. Compute still had to get 1000 times cheaper, but by 2016, Google Translate^{[GT16]}—whose whitepaper^{[WU]} mentions LSTM over 50 times—was based on two connected LSTMs,^{[S2S]} one for incoming texts, and one for outgoing translations—much better than what existed before.^{[DL4]} By 2017, LSTM also powered Facebook's machine translation (over 30 billion translations per week—the most popular youtube video needed years to achieve only 10 billion clicks),^{[FB17][DL4]} Apple's Quicktype on roughly 1 billion iPhones,^{[DL4]} the voice of Amazon's Alexa,^{[DL4]} Google's image caption generation^{[DL4]} & automatic email answering^{[DL4]} etc. Business Week called LSTM "arguably the most commercial AI achievement."^{[AV1]} By 2016, more than a quarter of the awesome computational power for inference in Google's datacenters was used for LSTM (and 5% for another popular Deep Learning technique called CNNs—see above).^{[JOU17]} And of course, our LSTM is also massively used in healthcare and medical diagnosis—a simple Google Scholar search turns up innumerable medical articles that have "LSTM" in their title.^{[DEC]}
Through the work of my students Rupesh Kumar Srivastava and Klaus Greff, the LSTM principle also led to our Highway Network^{[HW1]} of May 2015, the first working very deep FNN with hundreds of layers (previous NNs had at most a few tens of layers). Microsoft's ResNet^{[HW2]} (which won the ImageNet 2015 contest) is a version thereof (ResNets are Highway Nets whose gates are always open). The earlier Highway Nets perform roughly as well as their ResNet versions on ImageNet.^{[HW3]} Variants of highway gates are also used for certain algorithmic tasks where the pure residual layers do not work as well.^{[NDR]}
Deep learning is all about NN depth.^{[DL1]} In the 1990s, LSTMs brought essentially unlimited depth to supervised recurrent NNs; in the 2000s, the LSTMinspired Highway Nets brought it to feedforward NNs. LSTM has become the most cited NN of the 20th century; the Highway Net version called ResNet the most cited NN of the 21st.^{[MOST]} (Citations, however, are a highly questionable measure of true impact.^{[NAT1]})
The previous sections have mostly focused on deep learning for passive pattern recognition/classification. However, NNs are also relevant for Reinforcement Learning (RL),^{}[KAE96][BER96][TD3][UNI][GM3][LSTMPG] the most general type of learning. General RL agents must discover, without the aid of a teacher, how to interact with a dynamic, initially unknown, partially observable environment in order to maximize their expected cumulative reward signals.^{[DL1]} There may be arbitrary, a priori unknown delays between actions and perceivable consequences. The RL problem is as hard as any problem of computer science, since any task with a computable description can be formulated in the general RL framework.^{[UNI]}
Certain RL problems can be addressed through nonneural techniques invented long before the 1980s: Monte Carlo (tree) search (MC, 1949),^{[MOC15]} dynamic programming (DP, 1953),^{[BEL53]} artificial evolution (1954),^{[EVO17]([TUR1],unpublished)} alphabetapruning (1959),^{[S59]} control theory and system identification (1950s),^{[KAL59][GLA85]} stochastic gradient descent (SGD, 1951),^{[STO5152]} and universal search techniques (1973).^{[AIT7]}
Deep FNNs and RNNs, however, are useful tools for improving certain types of RL. In the 1980s, concepts of function approximation and NNs were combined with system identification,^{[WER8789][MUN87][NGU89]} DP and its online variant called Temporal Differences (TD),^{[TD13]} artificial evolution,^{[EVONN13]} and policy gradients.^{[GD1][PG13]} Many additional references on this can be found in Sec. 6 of the 2015 survey.^{[DL1]}
When there is a Markovian interface^{[PLAN3]} to the environment such that the current input to the RL machine conveys all the information required to determine a next optimal action, RL with DP/TD/MCbased FNNs can be very successful, as shown in 1994^{[TD2]} (masterlevel backgammon player) and the 2010s^{[DM12a]} (superhuman players for Go, chess, and other games).
For more complex cases without Markovian interfaces, where the learning machine must consider not only the present input, but also the history of previous inputs, our combinations of RL algorithms and LSTM^{[LSTMRL][RPG]} have become standard, in particular, our LSTM trained by policy gradients (2007).^{[RPG07][RPG][LSTMPG]}
For example, in 2018, a PGtrained LSTM was the core of OpenAI's famous Dactyl which learned to control a dextrous robot hand without a teacher.^{[OAI1][OAI1a]} Similar for video games: in 2019, DeepMind (cofounded by a student from my lab) famously beat a pro player in the game of Starcraft, which is theoretically harder than Chess or Go^{[DM2]} in many ways, using Alphastar whose brain has a deep LSTM core trained by PG.^{[DM3]} An RL LSTM (with 84% of the model's total parameter count) also was the core of the famous OpenAI Five which learned to defeat human experts in the Dota 2 video game (2018).^{[OAI2]} Bill Gates called this a "huge milestone in advancing artificial intelligence".^{[OAI2a][MIR](Sec. 4)[LSTMPG]}
The future of RL will be about learning/composing/planning with compact spatiotemporal abstractions of complex input streams—about commonsense reasoning^{[MAR15]} and learning to think.^{[PLAN45]} How can NNs learn to represent percepts and action plans in a hierarchical manner, at multiple levels of abstraction, and multiple time scales?^{[LEC]} We published answers to these questions in 199091: selfsupervised neural history compressors^{[UN][UN03]} learn to represent percepts at multiple levels of abstraction and multiple time scales (see above), while endtoend differentiable NNbased subgoal generators^{[HRL3][MIR](Sec. 10)} learn hierarchical action plans through gradient descent (see above). More sophisticated ways of learning to think in abstract ways were published in 1997^{[AC97][AC99][AC02]} and 201518.^{[PLAN45]}
The recent breakthroughs of deep learning algorithms from the past millennium (see previous sections) would have been impossible without continually improving and accelerating computer hardware. Any history of AI and deep learning would be incomplete without mentioning this evolution, which has been running for at least two millennia.
The first known gearbased computational device was the Antikythera mechanism (a kind of astronomical clock) in Ancient Greece over 2000 years ago.
Perhaps the world's first practical programmable machine was an automatic theatre made in the 1st century^{}[SHA7a][RAU1] by Heron of Alexandria (who apparently also had the first known working steam engine—the Aeolipile).
The 9th century music automaton by the Banu Musa brothers in Baghdad was perhaps the first machine with a stored program.^{[BAN][KOE1]} It used pins on a revolving cylinder to store programs controlling a steamdriven flute—compare AlJazari's programmable drum machine of 1206.^{[SHA7b]}
The 1600s brought more flexible machines that computed answers in response to input data. The first dataprocessing gearbased special purpose calculator for simple arithmetics was built in 1623 by Wilhelm Schickard, one of the candidates for the title of "father of automatic computing," followed by the superior Pascaline of Blaise Pascal (1642). In 1673, the already mentioned Gottfried Wilhelm Leibniz (called "the smartest man who ever lived"^{[SMO13]}) designed the first machine (the step reckoner) that could perform all four arithmetic operations, and the first with a memory.^{[BL16]} He also described the principles of binary computers governed by punch cards (1679),^{[L79][L03][LA14][HO66]} and published the chain rule^{[LEI0710]} (see above), essential ingredient of deep learning and modern AI.
The first commercial programcontrolled machines (punch cardbased looms) were built in France circa 1800 by JosephMarie Jacquard and others—perhaps the first "modern" programmers who wrote the world's first industrial software. They inspired Ada Lovelace and her mentor Charles Babbage (UK, circa 1840). He planned but was unable to build a programmable, general purpose computer (only his nonuniversal special purpose calculator led to a working 20th century replica).
In 1914, the Spaniard Leonardo Torres y Quevedo (mentioned in the introduction) became the 20th century's first AI pioneer when he built the first working chess end game player (back then chess was considered as an activity restricted to the realms of intelligent creatures). The machine was still considered impressive decades later when another AI pioneer—Norbert Wiener^{[WI48]}—played against it at the 1951 Paris AI conference.^{[AI51][BRO21][BRU4]}
Between 1935 and 1941, Konrad Zuse created the world's first working programmable generalpurpose computer: the Z3. The corresponding patent of 1936^{[ZU3638][RO98][ZUS21]} described the digital circuits required by programmable physical hardware, predating Claude Shannon's 1937 thesis on digital circuit design.^{[SHA37]} Unlike Babbage, Zuse used Leibniz' principles of binary computation (1679)^{[L79][LA14][HO66][L03]} instead of traditional decimal computation. This greatly simplified the hardware.^{[LEI21,a,b]} Ignoring the inevitable storage limitations of any physical computer, the physical hardware of Z3 was indeed universal in the modern sense of the purely theoretical but impractical constructs of Gödel^{[GOD][GOD34,21,21a]} (193134), Church^{[CHU]} (1935), Turing^{[TUR]} (1936), and Post^{[POS]} (1936). Simple arithmetic tricks can compensate for Z3's lack of an explicit conditional jump instruction.^{[RO98]} Today, most computers are binary like Z3.
Z3 used electromagnetic relays with visibly moving switches. The first electronic special purpose calculator (whose moving parts were electrons too small to see) was the binary ABC (US, 1942) by John Atanasoff (the "father of tubebased computing"^{[NASC6a]}). Unlike the gearbased machines of the 1600s, ABC used vaccum tubes—today's machines use the transistor principle patented by Julius Edgar Lilienfeld in 1925.^{[LIL12]} But unlike Zuse's Z3, ABC was not freely programmable. Neither was the electronic Colossus machine by Tommy Flowers (UK, 194345) used to break the Nazi code.^{[NASC6]}
The first general working programmable machine built by someone other than Zuse (1941)^{[RO98]} was Howard Aiken's decimal MARK I (US, 1944). The much faster decimal ENIAC by Eckert and Mauchly (1945/46) was programmed by rewiring it. Both data and programs were stored in electronic memory by the "Manchester baby" (Williams, Kilburn & Tootill, UK, 1948) and the 1948 upgrade of ENIAC, which was reprogrammed by entering numerical instruction codes into readonly memory.^{[HAI14b]}
Since then, computers have become much faster through integrated circuits (ICs). In 1949, Werner Jacobi at Siemens filed a patent for an IC semiconductor with several transistors on a common substrate (granted in 1952).^{[IC4914]} In 1958, Jack Kilby demonstrated an IC with external wires. In 1959, Robert Noyce presented a monolithic IC.^{[IC14]} Since the 1970s, graphics processing units (GPUs) have been used to speed up computations through parallel processing. ICs/GPUs of today (2022) contain many billions of transistors (almost all of them of Lilienfeld's 1925 FET type^{[LIL12]}).
In 1941, Zuse's Z3 could perform roughly one elementary operation (e.g., an addition) per second. Since then, every 5 years, compute got 10 times cheaper (note that his law is much older than Moore's Law which states that the number of transistors^{[LIL12]} per chip doubles every 18 months). As of 2021, 80 years after Z3, modern computers can execute about 10 million billion instructions per second for the same (inflationadjusted) price. The naive extrapolation of this exponential trend predicts that the 21st century will see cheap computers with a thousand times the raw computational power of all human brains combined.^{[RAW]}
Where are the physical limits? According to Bremermann (1982),^{[BRE]} a computer of 1 kg of mass and 1 liter of volume can execute at most 10^{51} operations per second on at most 10^{32} bits. The trend above will hit the Bremermann limit roughly 25 decades after Z3, circa 2200. However, since there are only 2 x 10^{30} kg of mass in the solar system, the trend is bound to break within a few centuries, since the speed of light will greatly limit the acquisition of additional mass, e.g., in form of other solar systems, through a function ploynomial in time, as previously noted back in 2004.^{[OOPS2][ZUS21]}
Physics seems to dictate that future efficient computational hardware will have to be brainlike, with many compactly placed processors in 3dimensional space, sparsely connected by many short and few long wires, to minimize total connection cost (even if the "wires" are actually light beams).^{[DL2]} The basic architecture is essentially the one of a deep, sparsely connected, 3dimensional RNN, and Deep Learning methods for such RNNs are expected to become even much more important than they are today.^{[DL2]}
The core of modern AI and deep learning is mostly based on simple math of recent centuries: calculus/linear algebra/statistics. Nevertheless, to efficiently implement this core on the modern hardware mentioned in the previous section, and to roll it out for billions of people, lots of software engineering was necessary, based on lots of smart algorithms invented in the past century. There is no room here to mention them all. However, at least I'll list some of the most important highlights of the theory of AI and computer science in general.
In the early 1930s, Gödel founded modern theoretical computer science.^{[GOD][GOD34][LEI21,21a]} He introduced a universal coding language (193134).^{[GOD][GOD3421a]} It was based on the integers, and allows for formalizing the operations of any digital computer in axiomatic form. Gödel used it to represent both data (such as axioms and theorems) and programs^{[VAR13]} (such as proofgenerating sequences of operations on the data). He famously constructed formal statements that talk about the computation of other formal statements—especially selfreferential statements which imply that they are not decidable, given a computational theorem prover that systematically enumerates all possible theorems from an enumerable set of axioms. Thus he identified fundamental limits of algorithmic theorem proving, computing, and any type of computationbased AI.^{[GOD][BIB3][MIR](Sec. 18)[GOD21,21a]}
Like most great scientists, Gödel built on earlier work. He combined Georg Cantor's diagonalization trick^{[CAN]} (which showed in 1891 that there are different types of infinities) with the foundational work by Gottlob Frege^{[FRE]} (who introduced the first formal language in 1879), Thoralf Skolem^{[SKO23]} (who introduced primitive recursive functions in 1923) and Jacques Herbrand^{[GOD86]} (who identified limitations of Skolem's approach). These authors in turn built on the formal Algebra of Thought (1686) by Gottfried Wilhelm Leibniz^{[L86][WI48]} (see above), which is deductively equivalent^{[LE18]} to the later Boolean Algebra of 1847.^{[BOO]}
In 1935, Alonzo Church derived a corollary / extension of Gödel's result by demonstrating that Hilbert & Ackermann's Entscheidungsproblem (decision problem) does not have a general solution.^{[CHU]} To do this, he used his alternative universal coding language called Untyped Lambda Calculus, which forms the basis of the highly influential programming language LISP. In 1936, Alan M. Turing introduced yet another universal model: the Turing Machine.^{[TUR]} He rederived the abovementioned result.^{[CHU][TUR][HIN][GOD21,21a][TUR21][LEI21,21a]} In the same year of 1936, Emil Post published yet another independent universal model of computing.^{[POS]} Today we know many such models.
Konrad Zuse not only created the world's first working programmable generalpurpose computer,^{[ZU3638][RO98][ZUS21]} he also designed Plankalkül, the first highlevel programming language.^{[BAU][KNU]} He applied it to chess in 1945^{[KNU]} and to theorem proving in 1948.^{[ZU48]} Compare Newell & Simon's later work on theorem proving (1956).^{[NS56]} Much of early AI in the 1940s70s was actually about theorem proving and deduction in Gödel style^{[GOD][GOD34,21,21a]} through expert systems and logic programming.
In 1964, Ray Solomonoff combined Bayesian (actually Laplacian^{[STI8385]}) probabilistic reasoning and theoretical computer science^{[GOD][CHU][TUR][POS]} to derive a mathematically optimal (but computationally infeasible) way of learning to predict future data from past observations.^{[AIT1][AIT10]} With Andrej Kolmogorov, he founded the theory of Kolmogorov complexity or algorithmic information theory (AIT),^{[AIT122]} going beyond traditional information theory^{[SHA48][KUL]} by formalizing the concept of Occam's razor, which favors the simplest explanation of given data, through the concept of the shortest program computing the data. There are many computable, timebounded versions of this concept,^{[AIT7][AIT5][AIT1213][AIT1617]} as well as applications to NNs.^{[KO2][CO13]}
In the early 2000s, Marcus Hutter (while working under my Swiss National Science Foundation grant^{[UNI]}) augmented Solomonoff's universal predictor^{[AIT1][AIT10]} by an optimal action selector (a universal AI) for reinforcement learning agents living in initially unknown (but at least computable) environments.^{[AIT20,22]} He also derived the asymptotically fastest algorithm for all welldefined computational problems,^{[AIT21]} solving any problem as quickly as the unknown fastest solver of such problems, save for an additive constant that does not depend on the problem size.
The even more general optimality of the selfreferential 2003 Gödel Machine^{[GM39]} is not limited to asymptotic optimality.
Nevertheless, such mathematically optimal AIs are not yet practically feasible for various reasons. Instead, practical modern AI is based on suboptimal, limited, yet not extremely wellunderstood techniques such as NNs and deep learning, the focus of the present article. But who knows what kind of AI history will prevail 20 years form now?
Credit assignment is about finding patterns in historic data and figuring out how certain events were enabled by previous events. Historians do it. Physicists do it. AIs do it, too. Let's take a step back and look at AI in the broadest historical context: all time since the Big Bang. In 2014, I found a beautiful pattern of exponential acceleration in it,^{}[OMG] which I have presented in many talks since then, and which also made it into Sibylle Berg's awardwinning book "GRM: Brainfuck."^{[OMG2]} Previously published patterns of this kind span much shorter time intervals: just a few decades or centuries or at most millennia.^{[OMG1]}
It turns out that from a human perspective, the most important events since the beginning of the universe are neatly aligned on a timeline of exponential speed up (error bars mostly below 10 percent). In fact, history seems to converge in an Omega point in the year 2040 or so. I like to call it Omega, because a century ago, Teilhard de Chardin called Omega the point where humanity will reach its next level.^{[OMG0]} Also, Omega sounds much better than "Singularity"^{[SING12]}—it sounds a bit like "Oh my God."^{[OMG]}
Let's start with the Big Bang 13.8 billion years ago. We divide this time by 4 to obtain about 3.5 billion years. Omega is 2040 or so. At Omega minus 3.5 billion years, something very important happened: life emerged on this planet.
And we take again a quarter of this time. We come out 900 million years ago when something very important happened: animallike, mobile life emerged.
And we divide again by 4. We come out 220 million years ago when mammals were invented, our ancestors.
And we divide again by 4. 55 million years ago the first primates emerged, our ancestors.
And we divide again by 4. 13 million years ago the first hominids emerged, our ancestors. I don't know why all these divisions by 4 keep hitting these defining moments in history. But they do. I also tried thirds, and fifths, and harmonic proportions, but only quarters seem to work.
And we divide again by 4. 3.5 million years ago something very important happened: the dawn of technology, as Nature called it: the first stone tools.
And we divide by 4. 800 thousand years ago the next great tech breakthrough happened: controlled fire.
And we divide by 4. 200 thousand years ago, anatomically modern man became prominent, our ancestor.
And we divide by 4. 50 thousand years ago, behaviorally modern man emerged, our ancestor, and started colonizing the world.
And we divide again by 4. We come out 13 thousand years ago when something very important happened: domestication of animals, agriculture, first settlements—the begin of civilisation. Now we see that all of civilization is just a flash in world history, just one millionth of the time since the Big Bang. Agriculture and spacecraft were invented almost at the same time.
And we divide by 4. 3,300 years ago saw the onset of the 1st population explosion in the Iron Age.
And we divide by 4. Remember that the convergence point Omega is the year 2040 or so. Omega minus 800 years—that was in the 13th century, when iron and fire came together in form of guns and cannons and rockets in China. This has defined the world since then and the West remains quite behind of the license fees it owes China.
And we divide again by 4. Omega minus 200 years—we hit the mid 19th century, when iron and fire came together in ever more sophisticated form to power the industrial revolution through improved steam engines, based on the work of Beaumont, Papin, Newcomen, Watt, and others (1600s1700s, going beyond the first simple steam engines by Heron of Alexandria^{[RAU1]} in the 1st century). The telephone (e.g., Meucci 1857, Reis 1860, Bell 1876)^{[NASC3]} started to revolutionize communication. The germ theory of disease (Pasteur & Koch, late 1800s) revolutionized healthcare and made people live longer on average. And circa 1850, the fertilizerbased agricultural revolution (Sprengel & von Liebig, early 1800s) helped to trigger the 2nd population explosion, which peaked in the 20th century, when the world population quadrupled, letting the 20th century stand out among all centuries in the history of mankind, driven by the HaberBosch process for creating artificial fertilizer, without which the world could feed at most 4 billion people.^{[HAB12]}
And we divide again by 4. Omega minus 50 years—that's more or less the year 1990, the end of the 3 great wars of the 20th century: WW1, WW2, and the Cold War. The 7 most valuable public companies were all Japanese (today most of them are USbased); however, both China and the US West Coast started to rise rapidly, setting the stage for the 21st century. A digital nervous system started spanning the globe through cell phones and the wireless revolution (based on radio waves discovered in the 1800s) as well as cheap personal computers for all. The WWW was created at the European particle collider in Switzerland by Tim BernersLee. And Modern AI started also around this time: the first truly selfdriving cars were built in the 1980s in Munich by the team of Ernst Dickmanns (by 1994, their robot cars were driving in highway traffic, up to 180 km/h).^{[AUT]} Back then, I worked on my 1987 diploma thesis,^{[META1]} which introduced algorithms not just for learning but also for metalearning or learning to learn,^{[META]} to learn better learning algorithms through experience (now a very popular topic^{[DEC]}). And then came our Miraculous Year 199091^{[MIR]} at TU Munich, the root of today's most cited NNs^{[MOST]} and of modern deep learning through selfsupervised/unsupervised learning (see above),^{[UN][UN03]} the LSTM/Highway Net/ResNet principle (now in your pocket on your smartphone—see above),^{[DL4][DEC][MOST]} artificial curiosity and generative adversarial NNs for agents that invent their own problems (see above),^{[AC90AC20][PPPP2][SA17]} Transformers with linearized selfattention (see above),^{[FWP06][TR56]} distilling teacher NNs into student NNs (see above),^{[UN][UN03]} learning action plans at multiple levels of abstraction and multiple time scales (see above),^{[HRL02][LEC]} and other exciting stuff. Much of this has become very popular, and improved the lives of billions of people.^{[DL4][DEC][MOST]}
And we divide again by 4. Omega minus 13 years—that's a point in the near future, more or less the year 2030, when many predict that cheap AIs will have a human brain power. Then the final 13 years or so until Omega, when incredible things will happen (take all of this with a grain of salt, though^{[OMG1]}).
But of course, time won't stop with Omega. Maybe it's just humandominated history that will end. After Omega, many curious metalearning AIs that invent their own goals (which have existed in my lab for decades^{[AC][AC90,AC90b]}) will quickly improve themselves, restricted only by the fundamental limits of computability and physics.
What will supersmart AIs do? Space is hostile to humans but friendly to appropriately designed robots, and offers many more resources than our thin film of biosphere, which receives less than a billionth of the sun's energy. While some curious AIs will remain fascinated with life, at least as long as they don't fully understand it,^{[ACM16][FA15][SP16][SA17]} most will be more interested in the incredible new opportunities for robots and software life out there in space. Through innumerable selfreplicating robot factories in the asteroid belt and beyond they will transform the solar system and then within a few hundred thousand years the entire galaxy and within tens of billions of years the rest of the reachable universe. Despite the lightspeed limit, the expanding AI sphere will have plenty of time to colonize and shape the entire visible cosmos.
Let me stretch your mind a bit. The universe is still young, only 13.8 billion years old. Remember when we kept dividing by 4? Now let's multiply by 4! Let's look ahead to a time when the cosmos will be 4 times older than it is now: about 55 billion years old. By then, the visible cosmos will be permeated by intelligence. Because after Omega, most AIs will have to go where most of the physical resources are, to make more and bigger AIs. Those who don't won't have an impact.^{[ACM16][FA15][SP16]}
Some of the material above was taken from previous AI Blog posts.^{[MIR] [DEC] [GOD21] [ZUS21] [LEI21] [AUT] [HAB2] [ARC06] [AC] [ATT] [DAN] [DAN1] [DL4] [GPUCNN5,8] [DLC] [FDL] [FWP] [LEC] [META] [MLP2] [MOST] [PLAN] [UN] [LSTMPG] [BP4] [DL6a] [HIN] [T22]} Thanks to many expert reviewers (including several famous neural net pioneers) for useful comments. Since science is about selfcorrection, let me know under juergen@idsia.ch if you can spot any remaining error. Many additional relevant publications can be found in my publication page and my arXiv page. This work is licensed under a Creative Commons AttributionNonCommercialShareAlike 4.0 International License.
[25y97] In 2022, we are celebrating the following works from a quartercentury ago. 1. Journal paper on Long ShortTerm Memory, the most cited neural network (NN) of the 20th century (and basis of the most cited NN of the 21st). 2. First paper on physical, philosophical and theological consequences of the simplest and fastest way of computing all possible metaverses (= computable universes). 3. Implementing artificial curiosity and creativity through generative adversarial agents that learn to design abstract, interesting computational experiments. 4. Journal paper on metareinforcement learning. 5. Journal paper on hierarchical Qlearning. 6. First paper on reinforcement learning to play soccer: start of a series. 7. Journal papers on flat minima & lowcomplexity NNs that generalize well. 8. Journal paper on LowComplexity Art, the Minimal Art of the Information Age. 9. Journal paper on probabilistic incremental program evolution.
[AC] J. Schmidhuber (AI Blog, 2021). 3 decades of artificial curiosity & creativity. Schmidhuber's 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. The first paper on online planning with reinforcement learning recurrent neural networks (NNs) (more) and on generative adversarial networks where a generator NN is fighting a predictor NN in a minimax game (more).
[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. Based on [AC90]. PDF. More.
[AC91] J. Schmidhuber. Adaptive confidence and adaptive curiosity. Technical Report FKI14991, Inst. f. Informatik, Tech. Univ. Munich, April 1991. PDF.
[AC91b] J. Schmidhuber. Curious modelbuilding control systems. Proc. International Joint Conference on Neural Networks, Singapore, volume 2, pages 14581463. IEEE, 1991. PDF.
[AC97] J. Schmidhuber. What's interesting? Technical Report IDSIA3597, IDSIA, July 1997. Focus on automatic creation of predictable internal abstractions of complex spatiotemporal events: two competing, intrinsically motivated agents agree on essentially arbitrary algorithmic experiments and bet on their possibly surprising (not yet predictable) outcomes in zerosum games, each agent potentially profiting from outwitting / surprising the other by inventing experimental protocols where both modules disagree on the predicted outcome. The focus is on exploring the space of general algorithms (as opposed to traditional simple mappings from inputs to outputs); the general system focuses on the interesting things by losing interest in both predictable and unpredictable aspects of the world. Unlike Schmidhuber et al.'s previous systems with intrinsic motivation,^{[AC90AC95]} the system also takes into account the computational cost of learning new skills, learning when to learn and what to learn. See later publications.^{[AC99][AC02]}
[AC98] M. Wiering and J. Schmidhuber. Efficient modelbased exploration. In R. Pfeiffer, B. Blumberg, J. Meyer, S. W. Wilson, eds., From Animals to Animats 5: Proceedings of the Fifth International Conference on Simulation of Adaptive Behavior, p. 223228, MIT Press, 1998.
[AC98b] M. Wiering and J. Schmidhuber. Learning exploration policies with models. In Proc. CONALD, 1998.
[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.
[AC09] J. Schmidhuber. Art & science as byproducts of the search for novel patterns, or data compressible in unknown yet learnable ways. In M. Botta (ed.), Et al. Edizioni, 2009, pp. 98112. PDF. (More on artificial scientists and artists.)
[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. With a brief summary of the generative adversarial neural networks of 1990^{[AC90,90b][AC20]} where a generator NN is fighting a predictor NN in a minimax game (more).
[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.
[ACM16] ACM interview by S. Ibaraki (2016). Chat with J. Schmidhuber: Artificial Intelligence & Deep Learning—Now & Future. Link.
[AIB] J. Schmidhuber. AI Blog. Includes variants of chapters of the AI Book.
[AI51] Les Machines a Calculer et la Pensee Humaine: Paris, 8.13. Januar 1951, Colloques internationaux du Centre National de la Recherche Scientifique; no. 37, Paris 1953. H. Bruderer^{[BRU4]} calls that the first conference on AI.
[AIT1] R. J. Solomonoff. A formal theory of inductive inference. Part I. Information and Control, 7:122, 1964.
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[AIT4] P. MartinLöf. The definition of random sequences. Information and Control, 9:602619, 1966.
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[AIT15] O. Watanabe. Kolmogorov complexity and computational complexity. EATCS Monographs on Theoretical Computer Science, Springer, 1992.
[AIT16] M. Li and P. M. B. Vitanyi. An Introduction to Kolmogorov Complexity and its Applications (2nd edition). Springer, 1997.
[AIT17] J. Schmidhuber. Discovering neural nets with low Kolmogorov complexity and high generalization capability. Neural Networks, 10(5):857873, 1997.
[AIT18] M. S. Burgin and Y. M. Borodyanskii. Infinite processes and superrecursive algorithms. Notices of the Academy of Sciences of the USSR (translated from Russian), 321(5):800803, 1991.
[AIT19] C. S. Calude. Chaitin Omega numbers, Solovay machines and Gödel incompleteness. Theoretical Computer Science, 2000.
[AIT20] M. Hutter. A theory of universal artificial intelligence based on algorithmic complexity. Technical Report IDSIA1400 (cs.AI/0004001), IDSIA, Manno (Lugano), CH, 2000.
[AIT21] M. Hutter. The fastest and shortest algorithm for all welldefined problems. International Journal of Foundations of Computer Science, 13(3):431443, 2002. (Based on work done under J. Schmidhuber's SNF grant 2061847: unification of universal induction and sequential decision theory, 2000).
[AIT22] M. Hutter. Universal Artificial Intelligence: Sequential Decisions based on Algorithmic Probability. Springer, Berlin, 2004. (Based on work done under J. Schmidhuber's SNF grant 2061847: unification of universal induction and sequential decision theory, 2000).
[AIT23] J. Schmidhuber. Hierarchies of generalized Kolmogorov complexities and nonenumerable universal measures computable in the limit. International Journal of Foundations of Computer Science, 13(4):587612, 2002.
[AIT24] J. Schmidhuber. The Speed Prior: a new simplicity measure yielding nearoptimal computable predictions. In J. Kivinen and R. H. Sloan, editors, Proceedings of the 15th Annual Conference on Computational Learning Theory (COLT 2002), Lecture Notes in Artificial Intelligence, pages 216228. Springer, Sydney, Australia, 2002.
[AM16] Blog of Werner Vogels, CTO of Amazon (Nov 2016): Amazon's Alexa "takes advantage of bidirectional long shortterm memory (LSTM) networks using a massive amount of data to train models that convert letters to sounds and predict the intonation contour. This technology enables high naturalness, consistent intonation, and accurate processing of texts."
[AMH0] S. I. Amari (1972). Characteristics of random nets of analog neuronlike elements. IEEE Trans. Syst. Man Cybernetics, 2, 643657. First published 1969 in Japanese, long before Wilson & Cowan's very similar work (197273).
[AMH1] S. I. Amari (1972). Learning patterns and pattern sequences by selforganizing nets of threshold elements. IEEE Transactions, C 21, 11971206, 1972. PDF. First publication of what was later sometimes called the Hopfield network^{[AMH2]} or AmariHopfield Network,^{[AMH3]} based on the (uncited) LenzIsing recurrent architecture.^{[L20][I25][T22]}
[AMH1b] W. A. Little. The existence of persistent states in the brain. Mathematical Biosciences, 19.12, p. 101120, 1974. Mentions the recurrent Ising model^{[L20][I25]}on which the (uncited) Amari network^{[AMH1,2]} is based.
[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 first published in 1972 by Amari.^{[AMH1]} [AMH2] did not cite [AMH1].
[AMH3] A. P. Millan, J. J. Torres, J. Marro. How Memory Conforms to Brain Development. Front. Comput. Neuroscience, 2019
[ARC06] J. Schmidhuber (2006). Archimedes—Greatest Scientist Ever?
[ATT] J. Schmidhuber (AI Blog, 2020). 30year anniversary of endtoend differentiable sequential neural attention. Plus goalconditional reinforcement learning. Schmidhuber had both hard attention for foveas (1990) and soft attention in form of Transformers with linearized selfattention (199193).^{[FWP]} Today, both types are very popular.
[ATT0] J. Schmidhuber and R. Huber. Learning to generate focus trajectories for attentive vision. Technical Report FKI12890, Institut für Informatik, Technische Universität München, 1990. PDF.
[ATT1] J. Schmidhuber and R. Huber. Learning to generate artificial fovea trajectories for target detection. International Journal of Neural Systems, 2(1 & 2):135141, 1991. Based on TR FKI12890, TUM, 1990. PDF. More.
[ATT2] J. Schmidhuber. Learning algorithms for networks with internal and external feedback. In D. S. Touretzky, J. L. Elman, T. J. Sejnowski, and G. E. Hinton, editors, Proc. of the 1990 Connectionist Models Summer School, pages 5261. San Mateo, CA: Morgan Kaufmann, 1990. PS. (PDF.)
[ATT3] H. Larochelle, G. E. Hinton. Learning to combine foveal glimpses with a thirdorder Boltzmann machine. NIPS 2010. This work is very similar to [ATT02] which the authors did not cite. In fact, Hinton was the reviewer of a 1990 paper^{[ATT2]} which summarised in its Section 5 Schmidhuber's early work on attention: the first implemented neural system for combining glimpses that jointly trains a recognition & prediction component with an attentional component (the fixation controller). Two decades later, Hinton wrote about his own work:^{[ATT3]} "To our knowledge, this is the first implemented system for combining glimpses that jointly trains a recognition component ... with an attentional component (the fixation controller)." See [MIR](Sec. 9)[R4].
[ATT14] D. Bahdanau, K. Cho, Y. Bengio. Neural Machine Translation by Jointly Learning to Align and Translate. 201416. Preprint arXiv/1409.0473, 201416. This work on soft "attention" did not cite Schmidhuber's much earlier original work of 19911993 on soft attention and Transformers with linearized selfattention.^{[FWP,FWP02,6][ATT]}
[AUT] J. Schmidhuber (AI Blog, 2005). Highlights of robot car history. Around 1986, Ernst Dickmanns and his group at Univ. Bundeswehr Munich built the world's first real autonomous robot cars, using saccadic vision, probabilistic approaches such as Kalman filters, and parallel computers. By 1994, they were in highway traffic, at up to 180 km/h, automatically passing other cars.
[AV1] A. Vance. Google Amazon and Facebook Owe Jürgen Schmidhuber a Fortune—This Man Is the Godfather the AI Community Wants to Forget. Business Week, Bloomberg, May 15, 2018.
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[BA03] P. Baldi and G. Pollastri. The Principled Design of LargeScale Recursive Neural Network ArchitecturesDAGRNNs and the Protein Structure Prediction Problem. Journal of Machine Learning Research, 4, 575602, (2003).
[BAN] Banu Musa brothers (9th century). The book of ingenious devices (Kitab alhiyal). Translated by D. R. Hill (1979), Springer, p. 44, ISBN 9027708339. As far as we know, the Banu Musa music automaton was the world's first machine with a stored program.
[BAU] F. L. Bauer, H. Woessner (1972). The "Plankalkül" of Konrad Zuse: A Forerunner of Today's Programming Languages.
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[CNN1] K. Fukushima: Neural network model for a mechanism of pattern recognition unaffected by shift in position—Neocognitron. Trans. IECE, vol. J62A, no. 10, pp. 658665, 1979. The first deep convolutional neural network architecture, with alternating convolutional layers and downsampling layers. In Japanese. English version: [CNN1+]. More in Scholarpedia.
[CNN1+] K. Fukushima: Neocognitron: a selforganizing neural network model for a mechanism of pattern recognition unaffected by shift in position. Biological Cybernetics, vol. 36, no. 4, pp. 193202 (April 1980). Link.
[CNN1a] A. Waibel. Phoneme Recognition Using TimeDelay Neural Networks. Meeting of IEICE, Tokyo, Japan, 1987. First application of backpropagation^{[BP15]} and weightsharing to a convolutional architecture.
[CNN1b] A. Waibel, T. Hanazawa, G. Hinton, K. Shikano and K. J. Lang. Phoneme recognition using timedelay neural networks. IEEE Transactions on Acoustics, Speech, and Signal Processing, vol. 37, no. 3, pp. 328339, March 1989. Based on [CNN1a].
[CNN1c] Bower Award Ceremony 2021: Jürgen Schmidhuber lauds Kunihiko Fukushima. YouTube video, 2021.
[CNN2] Y. LeCun, B. Boser, J. S. Denker, D. Henderson, R. E. Howard, W. Hubbard, L. D. Jackel: Backpropagation Applied to Handwritten Zip Code Recognition, Neural Computation, 1(4):541551, 1989. PDF.
[CNN3a] K. Yamaguchi, K. Sakamoto, A. Kenji, T. Akabane, Y. Fujimoto. A Neural Network for SpeakerIndependent Isolated Word Recognition. First International Conference on Spoken Language Processing (ICSLP 90), Kobe, Japan, Nov 1990. An NN with convolutions using MaxPooling instead of Fukushima's Spatial Averaging.^{[CNN1]}
[CNN3] Weng, J., Ahuja, N., and Huang, T. S. (1993). Learning recognition and segmentation of 3D objects from 2D images. Proc. 4th Intl. Conf. Computer Vision, Berlin, Germany, pp. 121128. A CNN whose downsampling layers use MaxPooling (which has become very popular) instead of Fukushima's Spatial Averaging.^{[CNN1]}
[CNN4] M. A. Ranzato, Y. LeCun: A Sparse and Locally Shift Invariant Feature Extractor Applied to Document Images. Proc. ICDAR, 2007
[CNN5a] S. Behnke. Learning iterative image reconstruction in the neural abstraction pyramid. International Journal of Computational Intelligence and Applications, 1(4):427438, 1999.
[CNN5b] S. Behnke. Hierarchical Neural Networks for Image Interpretation, volume LNCS 2766 of Lecture Notes in Computer Science. Springer, 2003.
[CNN5c] D. Scherer, A. Mueller, S. Behnke. Evaluation of pooling operations in convolutional architectures for object recognition. In Proc. International Conference on Artificial Neural Networks (ICANN), pages 92101, 2010.
[CO1] J. Koutnik, F. Gomez, J. Schmidhuber (2010). Evolving Neural Networks in Compressed Weight Space. Proceedings of the Genetic and Evolutionary Computation Conference (GECCO2010), Portland, 2010. PDF.
[CO2] J. Koutnik, G. Cuccu, J. Schmidhuber, F. Gomez. Evolving LargeScale Neural Networks for VisionBased Reinforcement Learning. Proceedings of the Genetic and Evolutionary Computation Conference (GECCO), Amsterdam, July 2013. PDF. The first deep learning model to successfully learn control policies directly from highdimensional sensory input using reinforcement learning, without any unsupervised pretraining.
[CO3] R. K. Srivastava, J. Schmidhuber, F. Gomez. Generalized Compressed Network Search. Proc. GECCO 2012. PDF.
[CON16] J. Carmichael (2016). Artificial Intelligence Gained Consciousness in 1991. Why A.I. pioneer Jürgen Schmidhuber is convinced the ultimate breakthrough already happened. Inverse, 2016. Link.
[CONN21] Since November 2021: Comments on version 1 of the report^{[T22]} in the Connectionists Mailing List, perhaps the oldest mailing list on artificial neural networks. Link to the archive.
[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.
[CUB0] R. J. Williams. Complexity of exact gradient computation algorithms for recurrent neural networks. Technical Report NUCCS8927, Northeastern University, College of Computer Science, 1989.
[CUB2] J. Schmidhuber. A fixed size storage O(n^{3}) time complexity learning algorithm for fully recurrent continually running networks. Neural Computation, 4(2):243248, 1992. PDF.
[CW] J. Koutnik, K. Greff, F. Gomez, J. Schmidhuber. A Clockwork RNN. Proc. 31st International Conference on Machine Learning (ICML), p. 18451853, Beijing, 2014. Preprint arXiv:1402.3511 [cs.NE].
[DAN] J. Schmidhuber (AI Blog, 2021). 10year anniversary. In 2011, DanNet triggered the deep convolutional neural network (CNN) revolution. Named after Schmidhuber's 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, the artificial neural network called DanNet performed twice better than humans, three times better than the closest artificial competitor (from LeCun's team), and six times better than the best nonneural method.
[DEC] J. Schmidhuber (AI Blog, 02/20/2020, updated 2021, 2022). The 2010s: Our Decade of Deep Learning / Outlook on the 2020s. The recent decade's most important developments and industrial applications based on the AI of Schmidhuber's team, with an outlook on the 2020s, also addressing privacy and data markets.
[DEEP1] Ivakhnenko, A. G. and Lapa, V. G. (1965). Cybernetic Predicting Devices. CCM Information Corporation. First working Deep Learners with many layers, learning internal representations.
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[DIST2] O. Vinyals, J. A. Dean, G. E. Hinton. Distilling the Knowledge in a Neural Network. Preprint arXiv:1503.02531 [stat.ML], 2015. The authors did not cite Schmidhuber's original 1991 NN distillation procedure,^{[UN02][MIR](Sec. 2)} not even in the later patent application US20150356461A1.
[DL1] J. Schmidhuber, 2015. Deep learning in neural networks: An overview. Neural Networks, 61, 85117. More. Got the first Best Paper Award ever issued by the journal Neural Networks, founded in 1988.
[DL2] J. Schmidhuber, 2015. Deep Learning. Scholarpedia, 10(11):32832.
[DL3] Y. LeCun, Y. Bengio, G. Hinton (2015). Deep Learning. Nature 521, 436444. HTML. A "survey" of deep learning that does not mention the pioneering works of deep learning [T22].
[DL3a] Y. Bengio, Y. LeCun, G. Hinton (2021). Turing Lecture: Deep Learning for AI. Communications of the ACM, July 2021. HTML. Local copy (HTML only). Another "survey" of deep learning that does not mention the pioneering works of deep learning [T22].
[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 Schmidhuber's 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.
[DL6] F. Gomez and J. Schmidhuber. Coevolving recurrent neurons learn deep memory POMDPs. In Proc. GECCO'05, Washington, D. C., pp. 17951802, ACM Press, New York, NY, USA, 2005. PDF.
[DL6a] J. Schmidhuber (AI Blog, Nov 2020). 15year anniversary: 1st paper with "learn deep" in the title (2005). The deep reinforcement learning & neuroevolution developed in Schmidhuber's lab solved problems of depth 1000 and more.^{[DL6]} Soon after its publication, everybody started talking about "deep learning." Causality or correlation?
[DL7] "Deep Learning ... moving beyond shallow machine learning since 2006!" Web site deeplearning.net of Y. Bengio's MILA (2015, retrieved May 2020; compare the version in the Internet Archive), referring to Hinton's^{[UN4]} and Bengio's^{[UN5]} unsupervised pretraining for deep NNs^{[UN4]} (2006) although this type of deep learning dates back to Schmidhuber's work of 1991.^{[UN12][UN]}
[DLC] J. Schmidhuber (AI Blog, June 2015). Critique of Paper by selfproclaimed^{[DLC2]} "Deep Learning Conspiracy" (Nature 521 p 436). The inventor of an important method should get credit for inventing it. She may not always be the one who popularizes it. Then the popularizer should get credit for popularizing it (but not for inventing it). More on this under [T22].
[DLC1] Y. LeCun. IEEE Spectrum Interview by L. Gomes, Feb 2015. Quote: "A lot of us involved in the resurgence of Deep Learning in the mid2000s, including Geoff Hinton, Yoshua Bengio, and myself—the socalled 'Deep Learning conspiracy' ..."
[DLC2] M. Bergen, K. Wagner (2015). Welcome to the AI Conspiracy: The 'Canadian Mafia' Behind Tech's Latest Craze. Vox recode, 15 July 2015. Quote: "... referred to themselves as the 'deep learning conspiracy.' Others called them the 'Canadian Mafia.'"
[DLH] J. Schmidhuber (AI Blog, 2022). Annotated History of Modern AI and Deep Learning. Technical Report IDSIA2222, IDSIA, Lugano, Switzerland, 2022. Preprint arXiv:2212.11279. Tweet of 2022.
[DM1] V. Mnih, K. Kavukcuoglu, D. Silver, A. Graves, I. Antonoglou, D. Wierstra, M. Riedmiller. Playing Atari with Deep Reinforcement Learning. Tech Report, 19 Dec. 2013, arxiv:1312.5602.
[DM2] V. Mnih, K. Kavukcuoglu, D. Silver, A. A. Rusu, J. Veness, M. G. Bellemare, A. Graves, M. Riedmiller, A. K. Fidjeland, G. Ostrovski, S. Petersen, C. Beattie, A. Sadik, I. Antonoglou, H. King, D. Kumaran, D. Wierstra, S. Legg, D. Hassabis. Humanlevel control through deep reinforcement learning. Nature, vol. 518, p 1529, 26 Feb. 2015. Link. DeepMind's first famous paper. Its abstract claims: "While reinforcement learning agents have achieved some successes in a variety of domains, their applicability has previously been limited to domains in which useful features can be handcrafted, or to domains with fully observed, lowdimensional state spaces." It also claims to bridge "the divide between highdimensional sensory inputs and actions." Similarly, the first sentence of the abstract of the earlier tech report version^{[DM1]} of [DM2] claims to "present the first deep learning model to successfully learn control policies directly from highdimensional sensory input using reinforcement learning." However, the first such system (requiring no unsupervised pretraining) was created earlier by Jan Koutnik et al. in Schmidhuber's lab.^{[CO2]} DeepMind was cofounded by Shane Legg, a PhD student from this lab; he and Daan Wierstra (another PhD student of Schmidhuber and DeepMind's 1st employee) were the first persons at DeepMind who had AI publications and PhDs in computer science. More.
[DM2a] D. Silver et al. A general reinforcement learning algorithm that masters chess, Shogi, and Go through selfplay. Science 362.6419:11401144, 2018.
[DM3] S. Stanford. DeepMind's AI, AlphaStar Showcases Significant Progress Towards AGI. Medium ML Memoirs, 2019. Alphastar has a "deep LSTM core."
[DM4] J. Jumper, R. Evans, A. Pritzel, T. Green, M. Figurnov, O. Ronneberger, K. Tunyasuvunakool, R. Bates, A. Zidek, A. Potapenko, A. Bridgland, C. Meyer, S. A. A. Kohl, A. J. Ballard, A. Cowie, B. RomeraParedes, S. Nikolov, R. Jain, J. Adler, T. Back, S. Petersen, D. Reiman, E. Clancy, M. Zielinski, M. Steinegger, M. Pacholska, T. Berghammer, S. Bodenstein, D. Silver, O. Vinyals, A. W. Senior, K. Kavukcuoglu, P. Kohli & D. Hassabis. Highly accurate protein structure prediction with AlphaFold. Nature 596, 583589, 2021. DeepMind's breakthrough application of deep learning did not cite Hochreiter et al.'s first successful application [HO07] of deep learning to protein folding (2007).
[DIF1] R. Rombach, A. Blattmann, D. Lorenz, P. Esser, B. Ommer. HighResolution Image Synthesis with Latent Diffusion Models. CVPR 2022. Preprint arXiv:2112.10752, LMU Munich, 2021.
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[ELM2] ELMORIGIN, 2004. The Official Homepage on Origins of Extreme Learning Machines (ELM). "Extreme Learning Machine Duplicates Others' Papers from 19882007." Local copy. This overview does not mention that the "ELM" concept goes back to Rosenblatt's work in the 1950s.^{[R62][T22]}
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[FA15] Intelligente Roboter werden vom Leben fasziniert sein. (Intelligent robots will be fascinated by life.) FAZ, 1 Dec 2015. Link.
[FAKE] H. Hopf, A. Krief, G. Mehta, S. A. Matlin. Fake science and the knowledge crisis: ignorance can be fatal. Royal Society Open Science, May 2019. Quote: "Scientists must be willing to speak out when they see false information being presented in social media, traditional print or broadcast press" and "must speak out against false information and fake science in circulation and forcefully contradict public figures who promote it."
[FAKE2] L. Stenflo. Intelligent plagiarists are the most dangerous. Nature, vol. 427, p. 777 (Feb 2004). Quote: "What is worse, in my opinion, ..., are cases where scientists rewrite previous findings in different words, purposely hiding the sources of their ideas, and then during subsequent years forcefully claim that they have discovered new phenomena.
[FAST] C. v.d. Malsburg. Tech Report 812, Abteilung f. Neurobiologie, MaxPlanck Institut f. Biophysik und Chemie, Goettingen, 1981. First paper on fast weights or dynamic links.
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[FB17] By 2017, Facebook used LSTM to handle over 4 billion automatic translations per day (The Verge, August 4, 2017); see also Facebook blog by J.M. Pino, A. Sidorov, N.F. Ayan (August 3, 2017)
[FDL] J. Schmidhuber (AI Blog, 2013). My First Deep Learning System of 1991 + Deep Learning Timeline 19602013.
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[FM] S. Hochreiter and J. Schmidhuber. Flat minimum search finds simple nets. Technical Report FKI20094, Fakultät für Informatik, Technische Universität München, December 1994. PDF.
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[FWP] J. Schmidhuber (AI Blog, 26 March 2021, updated 2022). 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^{[FAST,FASTa,b]} of another NN, separating memory and control like in traditional computers. Such Fast Weight Programmers^{[FWP06,FWPMETA18]} 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 Schmidhuber's 1991 outer productbased Fast Weight Programmers (apart from normalization). In 1993, he introduced the attention terminology^{[FWP2]} now used in this context,^{[ATT]} and extended the approach to RNNs that program themselves. See tweet of 2022.
[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 that separate storage and control: a slow net learns by gradient descent to compute weight changes of a fast net. The outer productbased version (Eq. 5) is now known as a "Transformer with linearized selfattention" (apart from normalization).^{[FWP]}
[FWP1] J. Schmidhuber. Learning to control fastweight memories: An alternative to recurrent nets. Neural Computation, 4(1):131139, 1992. Based on [FWP0]. PDF. HTML. Pictures (German). See tweet of 2022 for 30year anniversary.
[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 NNbased fast weight programmer using outer products, introducing 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.
[FWP4a] J. Ba, G. Hinton, V. Mnih, J. Z. Leibo, C. Ionescu. Using Fast Weights to Attend to the Recent Past. NIPS 2016. PDF. Very similar to [FWP02], in both motivation [FWP2] and execution.
[FWP4b] D. Bahdanau, K. Cho, Y. Bengio (2014). Neural Machine Translation by Jointly Learning to Align and Translate. Preprint arXiv:1409.0473 [cs.CL]. This work on "attention" did not cite Schmidhuber's much earlier original work of 19911993 on soft attention and Transformers with linearized selfattention.^{[FWP,FWP02,6][ATT]}
[FWP4d] Y. Tang, D. Nguyen, D. Ha (2020). Neuroevolution of SelfInterpretable Agents. Preprint: arXiv:2003.08165.
[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).
[FWPMETA1] J. Schmidhuber. Steps towards `selfreferential' learning. Technical Report CUCS62792, Dept. of Comp. Sci., University of Colorado at Boulder, November 1992. First recurrent NNbased fast weight programmer that can learn to run a learning algorithm or weight change algorithm on itself.
[FWPMETA2] J. Schmidhuber. A selfreferential weight matrix. In Proceedings of the International Conference on Artificial Neural Networks, Amsterdam, pages 446451. Springer, 1993. PDF.
[FWPMETA3] J. Schmidhuber. An introspective network that can learn to run its own weight change algorithm. In Proc. of the Intl. Conf. on Artificial Neural Networks, Brighton, pages 191195. IEE, 1993.
[FWPMETA4] J. Schmidhuber. A neural network that embeds its own metalevels. In Proc. of the International Conference on Neural Networks '93, San Francisco. IEEE, 1993.
[FWPMETA5] J. Schmidhuber. Habilitation thesis, TUM, 1993. PDF. A recurrent neural net with a selfreferential, selfreading, selfmodifying weight matrix can be found here.
[FWPMETA6] L. Kirsch and J. Schmidhuber. Meta Learning Backpropagation & Improving It. Advances in Neural Information Processing Systems (NeurIPS), 2021. Preprint arXiv:2012.14905 [cs.LG], 2020.
[FWPMETA7] I. Schlag, T. Munkhdalai, J. Schmidhuber. Learning Associative Inference Using Fast Weight Memory. ICLR 2021. Report arXiv:2011.07831 [cs.AI], 2020.
[FWPMETA8] K. Irie, I. Schlag, R. Csordas, J. Schmidhuber. A Modern SelfReferential Weight Matrix That Learns to Modify Itself. International Conference on Machine Learning (ICML), 2022. Preprint: arXiv:2202.05780.
[FWPMETA9] L. Kirsch and J. Schmidhuber. SelfReferential Meta Learning. First Conference on Automated Machine Learning (LateBreaking Workshop), 2022.
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[GD1] S. I. Amari (1967). A theory of adaptive pattern classifier, IEEE Trans, EC16, 279307 (Japanese version published in 1965). PDF. Probably the first paper on using stochastic gradient descent^{[STO5152]} for learning in multilayer neural networks (without specifying the specific gradient descent method now known as reverse mode of automatic differentiation or backpropagation^{[BP1]}).
[GD2] S. I. Amari (1968). Information Theory—Geometric Theory of Information, Kyoritsu Publ., 1968 (in Japanese). OCRbased PDF scan of pages 94135 (see pages 119120). Contains computer simulation results for a five layer network (with 2 modifiable layers) which learns internal representations to classify nonlinearily separable pattern classes.
[GD2a] H. Saito (1967). Master's thesis, Graduate School of Engineering, Kyushu University, Japan. Implementation of Amari's 1967 stochastic gradient descent method for multilayer perceptrons.^{[GD1]} (S. Amari, personal communication, 2021.)
[GD3] S. I. Amari (1977). Neural Theory of Association and Concept Formation. Biological Cybernetics, vol. 26, p. 175185, 1977. See Section 3.1 on using gradient descent for learning in multilayer networks.
[GGP] F. Faccio, V. Herrmann, A. Ramesh, L. Kirsch, J. Schmidhuber. GoalConditioned Generators of Deep Policies. Preprint arXiv/2207.01570, 4 July 2022 (submitted in May 2022).
[GLA85] W. Glasser. Control theory. New York: Harper and Row, 1985.
[GM3] J. Schmidhuber (2003). Gödel Machines: SelfReferential Universal Problem Solvers Making Provably Optimal SelfImprovements. Preprint arXiv:cs/0309048 (2003). More.
[GM6] J. Schmidhuber (2006). Gödel machines: Fully SelfReferential Optimal Universal SelfImprovers. In B. Goertzel and C. Pennachin, eds.: Artificial General Intelligence, p. 199226, 2006. PDF.
[GM9] J. Schmidhuber (2009). Ultimate Cognition à la Gödel. Cognitive Computation 1(2):177193, 2009. PDF. More.
[GSR] H. Sak, A. Senior, K. Rao, F. Beaufays, J. Schalkwyk—Google Speech Team. Google voice search: faster and more accurate. Google Research Blog, Sep 2015, see also Aug 2015 Google's speech recognition based on CTC and LSTM.
[GSR15] Dramatic improvement of Google's speech recognition through LSTM: Alphr Technology, Jul 2015, or 9to5google, Jul 2015
[GSR19] Y. He, T. N. Sainath, R. Prabhavalkar, I. McGraw, R. Alvarez, D. Zhao, D. Rybach, A. Kannan, Y. Wu, R. Pang, Q. Liang, D. Bhatia, Y. Shangguan, B. Li, G. Pundak, K. Chai Sim, T. Bagby, S. Chang, K. Rao, A. Gruenstein. Streaming endtoend speech recognition for mobile devices. ICASSP 20192019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2019.
[GT16] Google's dramatically improved Google Translate of 2016 is based on LSTM, e.g., WIRED, Sep 2016, or siliconANGLE, Sep 2016
[GAN0] O. Niemitalo. A method for training artificial neural networks to generate missing data within a variable context. Blog post, Internet Archive, 2010. A blog post describing basic ideas^{[AC][AC90,AC90b][AC20]} of GANs.
[GAN1] I. Goodfellow, J. PougetAbadie, M. Mirza, B. Xu, D. WardeFarley, S. Ozair, A. Courville, Y. Bengio. Generative adversarial nets. NIPS 2014, 26722680, Dec 2014. A description of GANs that does not cite Schmidhuber's original GAN principle of 1990^{[AC][AC90,AC90b][AC20][R2][T22]} (also containing wrong claims about Schmidhuber's adversarial NNs for Predictability Minimization^{[PM02][AC20][T22]}).
[GAN2] T. Karras, S. Laine, T. Aila. A stylebased generator architecture for generative adversarial networks. In Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), pages 44014410, 2019.
[GOD] K. Gödel. Über formal unentscheidbare Sätze der Principia Mathematica und verwandter Systeme I. Monatshefte für Mathematik und Physik, 38:173198, 1931. In the early 1930s, Gödel founded theoretical computer science. He identified fundamental limits of mathematics, theorem proving, computing, and Artificial Intelligence.
[GOD34] K. Gödel (1934). On undecidable propositions of formal mathematical systems. Notes by S. C. Kleene and J. B. Rosser on lectures at the Institute for Advanced Study, Princeton, New Jersey, 1934, 30 pp. (Reprinted in M. Davis, (ed.), The Undecidable. Basic Papers on Undecidable Propositions, Unsolvable Problems, and Computable Functions, Raven Press, Hewlett, New York, 1965.) Gödel introduced a universal coding language.
[GOD56] R. J. Lipton and K. W. Regan. Gödel's lost letter and P=NP. Link. Gödel identified what's now the most famous open problem of computer science.
[GOD86] K. Gödel. Collected works Volume I: Publications 192936, S. Feferman et. al., editors, Oxford Univ. Press, Oxford, 1986.
[GOD10] V. C. Nadkarni. Gödel, Einstein and proof for God. The Economic Times, 2010.
[GOD21] J. Schmidhuber (2021). 90th anniversary celebrations: 1931: Kurt Gödel, founder of theoretical computer science, shows limits of math, logic, computing, and artificial intelligence. This was number 1 on Hacker News.
[GOD21a] J. Schmidhuber (2021). Als Kurt Gödel die Grenzen des Berechenbaren entdeckte. (When Kurt Gödel discovered the limits of computability.) Frankfurter Allgemeine Zeitung, 16/6/2021.
[GOD21b] J. Schmidhuber (AI Blog, 2021). 80. Jahrestag: 1931: Kurt Gödel, Vater der theoretischen Informatik, entdeckt die Grenzen des Berechenbaren und der künstlichen Intelligenz.
[GOL] C. Goller & A. Küchler (1996). Learning taskdependent distributed representations by backpropagation through structure. Proceedings of International Conference on Neural Networks (ICNN'96). Vol. 1, p. 347352 IEEE, 1996. Based on TR AR9502, TU Munich, 1995.
[GPT3] T. B. Brown, B. Mann, N. Ryder, M. Subbiah, J. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell, S. Agarwal, A. HerbertVoss, G. Krueger, T. Henighan, R. Child, A. Ramesh, D. M. Ziegler, J. Wu, C. Winter, C. Hesse, M. Chen, E. Sigler, M. Litwin, S. Gray, B. Chess, J. Clark, C. Berner, S. McCandlish, A. Radford, I. Sutskever, D. Amodei. Language Models are FewShot Learners (2020). Preprint arXiv/2005.14165.
[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. 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.
[GPUCNN2] D. C. Ciresan, U. Meier, J. Masci, J. Schmidhuber. A Committee of Neural Networks for Traffic Sign Classification. International Joint Conference on Neural Networks (IJCNN2011, San Francisco), 2011. PDF. HTML overview. First superhuman performance in a computer vision contest, with half the error rate of humans, and one third the error rate of the closest competitor.^{[DAN1]} This led to massive interest from industry.
[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.
[GPUCNN3a] D. Ciresan, A. Giusti, L. Gambardella, J. Schmidhuber. Deep Neural Networks Segment Neuronal Membranes in Electron Microscopy Images. NIPS 2012, Lake Tahoe, 2012.
[GPUCNN4] A. Krizhevsky, I. Sutskever, G. E. Hinton. ImageNet Classification with Deep Convolutional Neural Networks. NIPS 25, MIT Press, Dec 2012. PDF. This paper describes AlexNet, which is similar to the earlier DanNet,^{[DAN,DAN1][R6]} the first pure deep CNN to win computer vision contests in 2011^{[GPUCNN23,5]} (AlexNet and VGG Net^{[GPUCNN9]} followed in 20122014). [GPUCNN4] emphasizes benefits of Fukushima's ReLUs (1969)^{[RELU1]} and dropout (a variant of Hanson 1990 stochastic delta rule)^{[Drop14]} but neither cites the original work^{[RELU1][Drop1]} nor the basic CNN architecture (Fukushima, 1979).^{[CNN1]}
[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 was the first CNN to win one, and 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.
[GPUCNN6] J. Schmidhuber, D. Ciresan, U. Meier, J. Masci, A. Graves. On Fast Deep Nets for AGI Vision. In Proc. Fourth Conference on Artificial General Intelligence (AGI11), Google, Mountain View, California, 2011. PDF.
[GPUCNN7] D. C. Ciresan, A. Giusti, L. M. Gambardella, J. Schmidhuber. Mitosis Detection in Breast Cancer Histology Images using Deep Neural Networks. MICCAI 2013. PDF.
[GPUCNN8] J. Schmidhuber (AI Blog, 2017; updated 2021 for 10th birthday of DanNet). First deep learner to win a contest on object detection in large images— first deep learner to win a medical imaging contest (2012). Link. How the Swiss AI Lab IDSIA used GPUbased CNNs to win the ICPR 2012 Contest on Mitosis Detection and the MICCAI 2013 Grand Challenge.
[GPUCNN9] K. Simonyan, A. Zisserman. Very deep convolutional networks for largescale image recognition. Preprint arXiv:1409.1556 (2014).
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[HAB1] V. Smil. Detonator of the population explosion. Nature, July 29 1999, p 415.
[HAB2] J. Schmidhuber (Blog, 2000). Most influential persons of the 20th century (according to Nature, 1999). The HaberBosch process has often been called the most important invention of the 20th century^{[HAB1]} as it "detonated the population explosion," driving the world's population from 1.6 billion in 1900 to about 8 billion today. Without it, half of humanity would perish. Billions of people would never have existed without it; soon it will sustain 2 out of 3 persons.
[HAI14] T. Haigh (2014). Historical reflections. Actually, Turing did not invent the computer. Communications of the ACM, Vol. 57(1): 3641, Jan 2014. PDF.
[HAI14b] T. Haigh, M. Priestley, C. Rope (2014). Reconsidering the StoredProgram Concept. IEEE Annals of the History of Computing. IEEE, 2014. PDF.
[HB96] S. El Hihi, Y. Bengio. Hierarchical recurrent neural networks for longterm dependencies. NIPS, 1996. Bengio claimed^{[YB20]} that in 1995 he "introduced the use of a hierarchy of time scales to combat the vanishing gradients issue" although Schmidhuber's publications on exactly this topic date back to 199193.^{[UN02][UN]}
[HEB49] D. O. Hebb. The Organization of Behavior. Wiley, New York, 1949.
[HEL] P. Dayan, G. E. Hinton, R. M. Neal, and R. S. Zemel. The Helmholtz machine. Neural Computation, 7:889904, 1995. An unsupervised learning algorithm related to Schmidhuber's supervised Neural Heat Exchanger.^{[NHE]}
[HIN] J. Schmidhuber (AI Blog, 2020). Critique of Honda Prize for Dr. Hinton. Science must not allow corporate PR to distort the academic record. See also [T22].
[HIN22] G. Hinton. The ForwardForward Algorithm: Some Preliminary Investigations. Preprint, Google Brain, 2022. Proposal of a biologically more plausible deep learning algorithm that—unlike backpropagation—is local in space and time. Does not mention previous related work.^{[BB2][NAN14][NHE][MIR](Sec. 15, Sec. 17)[FWPMETA6]}
[HO1] S. Hochreiter, A. S. Younger, P. R. Conwell (2001). Learning to Learn Using Gradient Descent. ICANN 2001. Lecture Notes in Computer Science, 2130, pp. 8794.
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[HO07] S. Hochreiter, M. Heusel, K. Obermayer. Fast modelbased protein homology detection without alignment. Bioinformatics 23(14):172836, 2007. Successful application of deep learning to protein folding problems, through an LSTM that was orders of magnitude faster than competing methods.
[HRL0] J. Schmidhuber. Towards compositional learning with dynamic neural networks. Technical Report FKI12990, Institut für Informatik, Technische Universität München, 1990. PDF. An RL machine gets extra command inputs of the form (start, goal). An evaluator NN learns to predict the current rewards/costs of going from start to goal. An (R)NNbased subgoal generator also sees (start, goal), and uses (copies of) the evaluator NN to learn by gradient descent a sequence of costminimising intermediate subgoals. The RL machine tries to use such subgoal sequences to achieve final goals. The system is learning action plans at multiple levels of abstraction and multiple time scales and solves what Y. LeCun called an "open problem" in 2022.^{[LEC]}
[HRL1] J. Schmidhuber. Learning to generate subgoals for action sequences. In T. Kohonen, K. Mäkisara, O. Simula, and J. Kangas, editors, Artificial Neural Networks, pages 967972. Elsevier Science Publishers B.V., NorthHolland, 1991. PDF. Extending TR FKI12990, TUM, 1990.
[HRL2] J. Schmidhuber and R. Wahnsiedler. Planning simple trajectories using neural subgoal generators. In J. A. Meyer, H. L. Roitblat, and S. W. Wilson, editors, Proc. of the 2nd International Conference on Simulation of Adaptive Behavior, pages 196202. MIT Press, 1992. PDF.
[HRL3] P. Dayan and G. E. Hinton. Feudal Reinforcement Learning. Advances in Neural Information Processing Systems 5, NIPS, 1992. This work did not cite Schmidhuber's gradientbased subgoal generators for hierarchical reinforcement learning (1990).^{[HRL02]}
[HRL4] M. Wiering and J. Schmidhuber. HQLearning. Adaptive Behavior 6(2):219246, 1997. PDF.
[HRLW] C. Watkins (1989). Learning from delayed rewards.
[HW1] R. K. Srivastava, K. Greff, J. Schmidhuber. 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 (previous NNs had at most a few tens of 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 the gates are always open: g(x)=t(x)=const=1. Highway Nets perform roughly as well as ResNets^{[HW2]} on ImageNet.^{[HW3]} Variants of highway gates are also used for certain algorithmic tasks, where the simpler residual layers do not work as well.^{[NDR]} More.
[HW1a] R. K. Srivastava, K. Greff, J. Schmidhuber. Highway networks. Presentation at the Deep Learning Workshop, ICML'15, July 1011, 2015. Link.
[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]} where the gates are always open: 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.
[HYB12] Hinton, G. E., Deng, L., Yu, D., Dahl, G. E., Mohamed, A., Jaitly, N., Senior, A., Vanhoucke, V., Nguyen, P., Sainath, T. N., and Kingsbury, B. (2012). Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups. IEEE Signal Process. Mag., 29(6):8297. This work did not cite the earlier LSTM^{[LSTM06]} trained by Connectionist Temporal Classification (CTC, 2006).^{[CTC]} CTCLSTM was successfully applied to speech in 2007^{[LSTM4]} (also with hierarchical LSTM stacks^{[LSTM14]}) and became the first superior endtoend neural speech recogniser that outperformed the state of the art, dramatically improving Google's speech recognition.^{[GSR][GSR15][DL4]} This was very different from previous hybrid methods since the late 1980s which combined NNs and traditional approaches such as hidden Markov models (HMMs).^{[BW][BRI][BOU]} [HYB12] still used the old hybrid approach and did not compare it to CTCLSTM. Later, however, Hinton switched to LSTM, too.^{[LSTM8]}
[I24] E. Ising (1925). Beitrag zur Theorie des Ferro und Paramagnetismus. Dissertation, 1924.
[I25] E. Ising (1925). Beitrag zur Theorie des Ferromagnetismus. Z. Phys., 31 (1): 253258, 1925. Based on [I24]. The first nonlearning recurrent NN architecture (the Ising model or LenzIsing model) was introduced and analyzed by physicists Ernst Ising and Wilhelm Lenz in the 1920s.^{[L20][I25][K41][W45][T22]} It settles into an equilibrium state in response to input conditions, and is the foundation of the first wellknown learning RNNs.^{[AMH12]}
[IC49] DE 833366 W. Jacobi / SIEMENS AG: Halbleiterverstärker patent filed 14 April 1949, granted 15 May 1952. First integrated circuit with several transistors on a common substrate.
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[L79] G. Leibniz. De Progressione dyadica Pars I. 15 March 1679. Principles of binary computers governed by punch cards.
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[LEC] J. Schmidhuber (AI Blog, 2022). LeCun's 2022 paper on autonomous machine intelligence rehashes but does not cite essential work of 19902015. Years ago, Schmidhuber's team published most of what Y. LeCun calls his "main original contributions:" neural nets that learn multiple time scales and levels of abstraction, generate subgoals, use intrinsic motivation to improve world models, and plan (1990); controllers that learn informative predictable representations (1997), etc. This was also discussed on Hacker News, reddit, and in the media. See tweet1. LeCun also listed the "5 best ideas 20122022" without mentioning that most of them are from Schmidhuber's lab, and older. See tweet2.
[LEI07] J. M. Child (translator), G. W. Leibniz (Author). The Early Mathematical Manuscripts of Leibniz. Merchant Books, 2007. See p. 126: the chain rule appeared in a 1676 memoir by Leibniz.
[LEI10] O. H. Rodriguez, J. M. Lopez Fernandez (2010). A semiotic reflection on the didactics of the Chain rule. The Mathematics Enthusiast: Vol. 7 : No. 2 , Article 10. DOI: https://doi.org/10.54870/15513440.1191.
[LEI21] J. Schmidhuber (AI Blog, 2021). 375th birthday of Leibniz, founder of computer science.
[LEI21a] J. Schmidhuber (2021). Der erste Informatiker. Wie Gottfried Wilhelm Leibniz den Computer erdachte. (The first computer scientist. How Gottfried Wilhelm Leibniz conceived the computer.) Frankfurter Allgemeine Zeitung (FAZ), 17/5/2021. FAZ online: 19/5/2021.
[LEI21b] J. Schmidhuber (AI Blog, 2021). 375. Geburtstag des Herrn Leibniz, dem Vater der Informatik.
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[LIL1] US Patent 1745175 by Austrian physicist Julius Edgar Lilienfeld for work done in Leipzig: "Method and apparatus for controlling electric current." First filed in Canada on 22.10.1925. The patent describes a fieldeffect transistor. Today, almost all transistors are fieldeffect transistors.
[LIL2] US Patent 1900018 by Austrian physicist Julius Edgar Lilienfeld: "Device for controlling electric current." Filed on 28.03.1928. The patent describes a thin film fieldeffect transistor. Today, almost all transistors are fieldeffect transistors.
[LIT21] M. L. Littman (2021). Collusion Rings Threaten the Integrity of Computer Science Research. Communications of the ACM, Vol. 64 No. 6, p. 4344, June 2021.
[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" with forget gates 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.
[LSTM4] S. Fernandez, A. Graves, J. Schmidhuber. An application of recurrent neural networks to discriminative keyword spotting. Intl. Conf. on Artificial Neural Networks ICANN'07, 2007. 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.
[LSTM7] J. Bayer, D. Wierstra, J. Togelius, J. Schmidhuber. Evolving memory cell structures for sequence learning. Proc. ICANN09, Cyprus, 2009. PDF.
[LSTM8] A. Graves, A. Mohamed, G. E. Hinton. Speech Recognition with Deep Recurrent Neural Networks. ICASSP 2013, Vancouver, 2013. PDF. Based on [LSTM12,4,14][CTC].
[LSTM9] O. Vinyals, L. Kaiser, T. Koo, S. Petrov, I. Sutskever, G. Hinton. Grammar as a Foreign Language. Preprint arXiv:1412.7449 [cs.CL].
[LSTM10] A. Graves, D. Eck and N. Beringer, J. Schmidhuber. Biologically Plausible Speech Recognition with LSTM Neural Nets. In J. Ijspeert (Ed.), First Intl. Workshop on Biologically Inspired Approaches to Advanced Information Technology, BioADIT 2004, Lausanne, Switzerland, p. 175184, 2004. PDF.
[LSTM11] N. Beringer and A. Graves and F. Schiel and J. Schmidhuber. Classifying unprompted speech by retraining LSTM Nets. In W. Duch et al. (Eds.): Proc. Intl. Conf. on Artificial Neural Networks ICANN'05, LNCS 3696, pp. 575581, SpringerVerlag Berlin Heidelberg, 2005.
[LSTM12] D. Wierstra, F. Gomez, J. Schmidhuber. Modeling systems with internal state using Evolino. In Proc. of the 2005 conference on genetic and evolutionary computation (GECCO), Washington, D. C., pp. 17951802, ACM Press, New York, NY, USA, 2005. Got a GECCO best paper award.
[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.
[LSTM14] S. Fernandez, A. Graves, J. Schmidhuber. Sequence labelling in structured domains with hierarchical recurrent neural networks. In Proc. IJCAI 07, p. 774779, Hyderabad, India, 2007 (talk). PDF.
[LSTM15] A. Graves, J. Schmidhuber. Offline Handwriting Recognition with Multidimensional Recurrent Neural Networks. Advances in Neural Information Processing Systems 22, NIPS'22, p 545552, Vancouver, MIT Press, 2009. PDF.
[LSTM16] M. Stollenga, W. Byeon, M. Liwicki, J. Schmidhuber. Parallel MultiDimensional LSTM, With Application to Fast Biomedical Volumetric Image Segmentation. Advances in Neural Information Processing Systems (NIPS), 2015. Preprint: arxiv:1506.07452.
[LSTM17] J. A. PerezOrtiz, F. A. Gers, D. Eck, J. Schmidhuber. Kalman filters improve LSTM network performance in problems unsolvable by traditional recurrent nets. Neural Networks 16(2):241250, 2003. PDF.
[LSTMPG] J. Schmidhuber (AI Blog, Dec 2020). 10year anniversary of our journal paper on deep reinforcement learning with policy gradients for LSTM (20072010). Recent famous applications: DeepMind's Starcraft player (2019) and OpenAI's dextrous robot hand & Dota player (2018)—Bill Gates called this a huge milestone in advancing AI.
[LSTMRL] B. Bakker, F. Linaker, J. Schmidhuber. Reinforcement Learning in Partially Observable Mobile Robot Domains Using Unsupervised Event Extraction. In Proceedings of the 2002 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2002), Lausanne, 2002. PDF.
[LSTMGRU] J. Chung, C. Gulcehre, K. Cho, Y. Bengio (2014). Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling. Preprint arXiv:1412.3555 [cs.NE]. The socalled gated recurrent units (GRU) are actually a variant of the vanilla LSTM architecture^{[LSTM2]} (2000) which the authors did not cite although this work^{[LSTM2]} was the one that introduced gated recurrent units. Furthermore, Schmidhuber's team automatically evolved lots of additional LSTM variants and topologies already in 2009^{[LSTM7]} without changing the name of the basic method. (Margin note: GRU cells lack an important gate and can neither learn to count^{[LSTMGRU2]} nor learn simple nonregular languages;^{[LSTMGRU2]} they also do not work as well for challenging translation tasks, according to Google Brain.^{[LSTMGRU3]})
[LSTMGRU2] G. Weiss, Y. Goldberg, E. Yahav. On the Practical Computational Power of Finite Precision RNNs for Language Recognition. Preprint arXiv:1805.04908.
[LSTMGRU3] D. Britz et al. (2017). Massive Exploration of Neural Machine Translation Architectures. Preprint arXiv:1703.03906
[M69] M. Minsky, S. Papert. Perceptrons (MIT Press, Cambridge, MA, 1969). A misleading "history of deep learning" goes more or less like this: "In 1969, Minsky & Papert^{[M69]} showed that shallow NNs without hidden layers are very limited and the field was abandoned until a new generation of neural network researchers took a fresh look at the problem in the 1980s."^{[S20]} However, the 1969 book^{[M69]} addressed a "problem" of Gauss & Legendre's shallow learning (circa 1800)^{[DL12]} that had already been solved 4 years prior by Ivakhnenko & Lapa's popular deep learning method,^{[DEEP12][DL2]} and then also by Amari's SGD for MLPs.^{[GD12]} Minsky was apparently unaware of this and failed to correct it later.^{[HIN](Sec. I)[T22](Sec. XIII)}
[MACY51] H. Foerster, M. Mead, H. L. Teuber (eds.). Cybernetics: Circular causal and feedback mechanisms in biological and social systems. Transactions of the 7th conference. New York: J. Macy Jr. Foundation, 1951.
[MAD86] C. T. Rajagopal, M. S. Rangachari (1986). On medieval Keralese mathematics. Archive for History of Exact Sciences. 35 (2):9199.
[MAD01] D. F. Almeida, J. K. John, A. Zadorozhnyy (2001). Keralese mathematics: Its Possible Transmission to Europe and the Consequential Educational Implications. Journal of Natural Geometry 20, 77104, 2001.
[MAR71] D. Marr. Simple memory: a theory for archicortex. Philos Trans R Soc Lond B Biol Sci, 262:841, p 2381, 1971.
[MAD05] Neither Newton nor Leibniz—The PreHistory of Calculus and Celestial Mechanics in Medieval Kerala. S. Rajeev, Univ. of Rochester, 2005.
[MAR15] E. Davis, G. Marcus. Commonsense Reasoning and Commonsense Knowledge in Artificial Intelligence. Communications of the ACM 58.9:92103, 2015.
[MC43] W. S. McCulloch, W. Pitts. A Logical Calculus of Ideas Immanent in Nervous Activity. Bulletin of Mathematical Biophysics, Vol. 5, p. 115133, 1943.
[META] J. Schmidhuber (AI Blog, 2020). 1/3 century anniversary of first publication on metalearning machines that learn to learn (1987). For its cover Schmidhuber drew a robot that bootstraps itself. 1992: gradient descentbased neural metalearning. 1994: MetaReinforcement Learning with selfmodifying policies. 1997: MetaRL plus artificial curiosity and intrinsic motivation. 2002: asymptotically optimal metalearning for curriculum learning. 2003: mathematically optimal Gödel Machine. 2020: new stuff!
[META1] J. Schmidhuber. Evolutionary principles in selfreferential learning, or on learning how to learn: The metameta... hook. Diploma thesis, Institut für Informatik, Technische Universität München, 1987. Searchable PDF scan (created by OCRmypdf which uses LSTM). HTML. For example, Genetic Programming (GP) is applied to itself, to recursively evolve better GP methods through MetaEvolution. More.
[MGC] MICCAI 2013 Grand Challenge on Mitosis Detection, organised by M. Veta, M.A. Viergever, J.P.W. Pluim, N. Stathonikos, P. J. van Diest of University Medical Center Utrecht.
[MIR] J. Schmidhuber (AI Blog, Oct 2019, updated 2021, 2022). Deep Learning: Our Miraculous Year 19901991. Preprint arXiv:2005.05744, 2020. The deep learning neural networks of Schmidhuber's 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 the "Annus Mirabilis" 19901991 at TU Munich.
[MIT97] T. Mitchell. Machine Learning. McGraw Hill, 1997.
[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 the feedforward NNs^{[MLP1]} and the earlier recurrent NNs of Schmidhuber's team 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.
[MM1] R. Stratonovich (1960). Conditional Markov processes. Theory of Probability and its Applications, 5(2):156178.
[MM2] L. E. Baum, T. Petrie. Statistical inference for probabilistic functions of finite state Markov chains. The Annals of Mathematical Statistics, pages 15541563, 1966.
[MM3] A. P. Dempster, N. M. Laird, D. B. Rubin. Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society, B, 39, 1977.
[MM4] J. Friedman, T. Hastie, R. Tibshirani. The elements of statistical learning, volume 1. Springer Series in Statistics, New York, 2001.
[MM5] C. M. Bishop. Pattern Recognition and Machine Learning. Springer, 2006.
[MOC1] N. Metropolis, S. Ulam. The Monte Carlo method. Journal of the American Statistical Association. 44 (247): 335341, 1949.
[MOC2] B. Abramson. The ExpectedOutcome Model of TwoPlayer Games. Technical report, Department of Computer Science, Columbia University, 1987.
[MOC3] B. Brügmann. Monte Carlo Go. Technical report, Department of Physics, Syracuse University, 1993.
[MOC4] H. S. Chang, M. C. Fu, J. Hu, S. I. Marcus. An Adaptive Sampling Algorithm for Solving Markov Decision Processes. Operations Research. 53:126139, 2005.
[MOC5] R. Coulom. Efficient Selectivity and Backup Operators in MonteCarlo Tree Search. Computers and Games, 5th International Conference, CG 2006, Turin, Italy, May 29â€“31, 2006.
[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 Schmidhuber's labs at TU Munich and IDSIA. (1) Long ShortTerm Memory (LSTM), (2) ResNet (which is the earlier Highway Net with open gates), (3) AlexNet and VGG Net (both building on the similar earlier DanNet: the first deep convolutional NN to win image recognition competitions), (4) Generative Adversarial Networks (an instance of the much earlier Adversarial Artificial Curiosity), and (5) variants of Transformers (Transformers with linearized selfattention are formally equivalent to the much earlier Fast Weight Programmers). Most of this started with the Annus Mirabilis of 19901991.^{[MIR]}
[MOZ] M. Mozer. A Focused Backpropagation Algorithm for Temporal Pattern Recognition. Complex Systems, 1989.
[MUN87] P. W. Munro. A dual backpropagation scheme for scalar reinforcement learning. Proceedings of the Ninth Annual Conference of the Cognitive Science Society, Seattle, WA, pages 165176, 1987.
[NAK72] K. Nakano. Associatron—A Model of Associative Memory. IEEE Transactions on Systems, Man, and Cybernetics, SMC2:3 p. 380388, 1972.
[NAN1] J. Schmidhuber. Networks adjusting networks. In J. Kindermann and A. Linden, editors, Proceedings of `Distributed Adaptive Neural Information Processing', St.Augustin, 24.25.5. 1989, pages 197208. Oldenbourg, 1990. Extended version: TR FKI12590 (revised), Institut für Informatik, TUM. PDF. Includes the proposal of a biologically more plausible deep learning algorithm that—unlike backpropagation—is local in space and time. Based on neural nets learning to estimate gradients for other neural nets.
[NAN2] J. Schmidhuber. Networks adjusting networks. Technical Report FKI12590, Institut für Informatik, Technische Universität München. Revised in November 1990. PDF.
[NAN3] Recurrent networks adjusted by adaptive critics. In Proc. IEEE/INNS International Joint Conference on Neural Networks, Washington, D. C., volume 1, pages 719722, 1990.
[NAN4] J. Schmidhuber. Additional remarks on G. Lukes' review of Schmidhuber's paper `Recurrent networks adjusted by adaptive critics'. Neural Network Reviews, 4(1):43, 1990.
[NAN5] M. Jaderberg, W. M. Czarnecki, S. Osindero, O. Vinyals, A. Graves, D. Silver, K. Kavukcuoglu. Decoupled Neural Interfaces using Synthetic Gradients. Preprint arXiv:1608.05343, 2016. This work of DeepMind is similar to [NAN12].
[NAR90] K. S. Narendra, K. Parthasarathy. Identification and control of dynamical systems using neural networks. Neural Networks, IEEE Transactions on, 1(1):427, 1990.
[NAS] B. Zoph, Q. V. Le. Neural Architecture Search with Reinforcement Learning. Preprint arXiv:1611.01578 (PDF), 2017. Compare the earlier Neural Architecture Search of Bayer et al. (2009) for LSTMlike topologies.^{[LSTM7]}
[NASC1] J. Schmidhuber. First Pow(d)ered flight / plane truth. Correspondence, Nature, 421 p 689, Feb 2003.
[NASC2] J. Schmidhuber. Zooming in on aviation history. Correspondence, Nature, vol 566, p 39, 7 Feb 2019.
[NASC3] J. Schmidhuber. The last inventor of the telephone. Letter, Science, 319, no. 5871, p. 1759, March 2008.
[NASC4] J. Schmidhuber. Turing: Keep his work in perspective. Correspondence, Nature, vol 483, p 541, March 2012, doi:10.1038/483541b.
[NASC5] J. Schmidhuber. Turing in Context. Letter, Science, vol 336, p 1639, June 2012. (On Gödel, Zuse, Turing.) See also comment on response by A. Hodges (DOI:10.1126/science.336.6089.1639a)
[NASC6] J. Schmidhuber. Colossus was the first electronic digital computer. Correspondence, Nature, 441 p 25, May 2006.
[NASC6a] J. Schmidhuber. Comment on "Biography: The ABC of computing" by J. Gilbey, Nature 468 p 760761 (2010). Link.
[NASC7] J. Schmidhuber. Turing's impact. Correspondence, Nature, 429 p 501, June 2004
[NASC8] J. Schmidhuber. Prototype resilient, selfmodeling robots. Correspondence, Science, 316, no. 5825 p 688, May 2007.
[NASC9] J. Schmidhuber. Comparing the legacies of Gauss, Pasteur, Darwin. Correspondence, Nature, vol 452, p 530, April 2008.
[NAT1] J. Schmidhuber. Citation bubble about to burst? Nature, vol. 469, p. 34, 6 January 2011. HTML.
[NDR] R. Csordas, K. Irie, J. Schmidhuber. The Neural Data Router: Adaptive Control Flow in Transformers Improves Systematic Generalization. Proc. ICLR 2022. Preprint arXiv/2110.07732.
[NEU45] J. von Neumann (1945). First Draft of a Report on the EDVAC.
[NHE] J. Schmidhuber. The Neural Heat Exchanger. Oral presentations since 1990 at various universities including TUM and the University of Colorado at Boulder. Also in In S. Amari, L. Xu, L. Chan, I. King, K. Leung, eds., Proceedings of the Intl. Conference on Neural Information Processing (1996), pages 194197, Springer, Hongkong. Link. Proposal of a biologically more plausible deep learning algorithm that—unlike backpropagation—is local in space and time. Inspired by the physical heat exchanger: inputs "heat up" while being transformed through many successive layers, targets enter from the other end of the deep pipeline and "cool down."
[NGU89] D. Nguyen and B. Widrow; The truck backerupper: An example of self learning in neural networks. In IEEE/INNS International Joint Conference on Neural Networks, Washington, D.C., volume 1, pages 357364, 1989.
[NIL98] N. J. Nilsson. Artificial intelligence: a new synthesis. Morgan Kaufmann, 1998.
[NPMa] M. Nakamura, K. Shikano. A study of English word category prediction based on neural networks. Proc. IEEE Int. Conf. on Acoustics, Speech and Signal Processing (ICASSP), p. 731734, 1989.
[NPM] Y. Bengio, R. Ducharme, P. Vincent, C. Jauvin (2003). A Neural Probabilistic Language Model. Journal of Machine Learning Research 3, p 11371155, 2003. Based on Schmidhuber & Heil's excellent 1995 neural probabilistic text model.^{[SNT]} See also Nakamura and Shikano's 1989 word category prediction model.^{[NPMa]}
[NS56] A. Newell and H. Simon. The logic theory machine—A complex information processing system. IRE Transactions on Information Theory 2.3 (1956):6179. Compare Konrad Zuse's much earlier 1948 work on theorem proving^{[ZU48]} based on Plankalkül, the first highlevel programming language.^{[BAU][KNU]}
[NS59] A. Newell, J. C. Shaw, H. Simon. Report on a general problem solving program. IFIP congress, vol. 256, 1959.
[NYT1] NY Times article by J. Markoff, Nov. 27, 2016: When A.I. Matures, It May Call Jürgen Schmidhuber 'Dad'
[OAI1] G. Powell, J. Schneider, J. Tobin, W. Zaremba, A. Petron, M. Chociej, L. Weng, B. McGrew, S. Sidor, A. Ray, P. Welinder, R. Jozefowicz, M. Plappert, J. Pachocki, M. Andrychowicz, B. Baker. Learning Dexterity. OpenAI Blog, 2018.
[OAI1a] OpenAI, M. Andrychowicz, B. Baker, M. Chociej, R. Jozefowicz, B. McGrew, J. Pachocki, A. Petron, M. Plappert, G. Powell, A. Ray, J. Schneider, S. Sidor, J. Tobin, P. Welinder, L. Weng, W. Zaremba. Learning Dexterous InHand Manipulation. arxiv:1312.5602 (PDF).
[OAI2] OpenAI: C. Berner, G. Brockman, B. Chan, V. Cheung, P. Debiak, C. Dennison, D. Farhi, Q. Fischer, S. Hashme, C. Hesse, R. Jozefowicz, S. Gray, C. Olsson, J. Pachocki, M. Petrov, H. P. de Oliveira Pinto, J. Raiman, T. Salimans, J. Schlatter, J. Schneider, S. Sidor, I. Sutskever, J. Tang, F. Wolski, S. Zhang (Dec 2019). Dota 2 with Large Scale Deep Reinforcement Learning. Preprint arxiv:1912.06680. An LSTM composes 84% of the model's total parameter count.
[OAI2a] J. Rodriguez. The Science Behind OpenAI Five that just Produced One of the Greatest Breakthrough in the History of AI. Towards Data Science, 2018. An LSTM with 84% of the model's total parameter count was the core of OpenAI Five.
[OMG0] P. T. De Chardin. The antiquity and world expansion of human culture. Man's Role in Changing the Face of the Earth. Univ. Chicago Press, Chicago, p. 103112, 1956.
[OMG] J. Schmidhuber. An exponential acceleration pattern in the history of the most important events from a human perspective, reaching all the way back to the Big Bang. Link. Ask me Anything, Reddit/ML, 2014.
[OMG1] J. Schmidhuber (Blog, 2006). Is History Converging? Again? Based on: J. Schmidhuber. New Millennium AI and the Convergence of History. In W. Duch and J. Mandziuk, eds., Challenges to Computational Intelligence, Springer, 2006. Update of 2012 in: Singularity Hypotheses, Springer, 2013.
[OMG2] S. Berg. GRM: Brainfuck. Kiepenheuer & Witsch, 2019. Got the Swiss Book Award 2019. Contains 2 pages on Schmidhuber's timeline of history's exponential acceleration since the Big Bang.^{[OMG]}
[OBJ1] K. Greff, A. Rasmus, M. Berglund, T. Hao, H. Valpola, J. Schmidhuber (2016). Tagger: Deep unsupervised perceptual grouping. NIPS 2016, pp. 44844492. Preprint arXiv/1606.06724.
[OBJ2] K. Greff, S. van Steenkiste, J. Schmidhuber (2017). Neural expectation maximization. NIPS 2017, pp. 66916701. Preprint arXiv/1708.03498.
[OBJ3] S. van Steenkiste, M. Chang, K. Greff, J. Schmidhuber (2018). Relational neural expectation maximization: Unsupervised discovery of objects and their interactions. ICLR 2018. Preprint arXiv/1802.10353.
[OBJ4] A. Stanic, S. van Steenkiste, J. Schmidhuber (2021). Hierarchical Relational Inference. AAAI 2021. Preprint arXiv/2010.03635.
[OBJ5] A. Gopalakrishnan, S. van Steenkiste, J. Schmidhuber (2020). Unsupervised Object Keypoint Learning using Local Spatial Predictability. Preprint arXiv/2011.12930.
[OOPS2] J. Schmidhuber. Optimal Ordered Problem Solver. Machine Learning, 54, 211254, 2004. PDF. HTML. HTML overview. Download OOPS source code in crystalline format.
[OUD13] P.Y. Oudeyer, A. Baranes, F. Kaplan. Intrinsically motivated learning of real world sensorimotor skills with developmental constraints. In G. Baldassarre and M. Mirolli, editors, Intrinsically Motivated Learning in Natural and Artificial Systems. Springer, 2013.
[PAL80] G. Palm. On associative memory. Biological Cybernetics, 36, 1980.
[PAT17] D. Pathak, P. Agrawal, A. A. Efros, T. Darrell. Curiositydriven exploration by selfsupervised prediction. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pages 1617, 2017.
[PDA1] G.Z. Sun, H.H. Chen, C.L. Giles, Y.C. Lee, D. Chen. Neural Networks with External Memory Stack that Learn Context—Free Grammars from Examples. Proceedings of the 1990 Conference on Information Science and Systems, Vol.II, pp. 649653, Princeton University, Princeton, NJ, 1990.
[PDA2] M. Mozer, S. Das. A connectionist symbol manipulator that discovers the structure of contextfree languages. Proc. NIPS 1993.
[PG1] R. J. Williams. Reinforcementlearning in connectionist networks: A mathematical analysis. Technical Report 8605, Institute for Cognitive Science, University of California, San Diego, 1986
[PG3] R. J. Williams. Simple statistical gradientfollowing algorithms for connectionist reinforcement learning. Machine Learning 8.34: 229256, 1992.
[PG2] Sutton, R. S., McAllester, D. A., Singh, S. P., and Mansour, Y. (1999a). Policy gradient methods for reinforcement learning with function approximation. In Advances in Neural Information Processing Systems (NIPS) 12, pages 10571063.
[PHD] J. Schmidhuber. Dynamische neuronale Netze und das fundamentale raumzeitliche Lernproblem (Dynamic neural nets and the fundamental spatiotemporal credit assignment problem). Dissertation, Institut für Informatik, Technische Universität München, 1990. PDF. HTML.
[PLAG1] Oxford's guidance to types of plagiarism (2021). Quote: "Plagiarism may be intentional or reckless, or unintentional." Link.
[PLAN] J. Schmidhuber (AI Blog, 2020). 30year anniversary of planning & reinforcement learning with recurrent world models and artificial curiosity (1990). This work also introduced highdimensional reward signals, deterministic policy gradients for RNNs, and the GAN principle (widely used today). Agents with adaptive recurrent world models even suggest a simple explanation of consciousness & selfawareness.
[PLAN2] J. Schmidhuber. An online algorithm for dynamic reinforcement learning and planning in reactive environments. Proc. IEEE/INNS International Joint Conference on Neural Networks, San Diego, volume 2, pages 253258, 1990. Based on TR FKI12690 (1990).^{[AC90]} More.
[PLAN3] J. Schmidhuber. Reinforcement learning in Markovian and nonMarkovian environments. In D. S. Lippman, J. E. Moody, and D. S. Touretzky, editors, Advances in Neural Information Processing Systems 3, NIPS'3, pages 500506. San Mateo, CA: Morgan Kaufmann, 1991. PDF. Partially based on TR FKI12690 (1990).^{[AC90]}
[PLAN4] J. Schmidhuber. On Learning to Think: Algorithmic Information Theory for Novel Combinations of Reinforcement Learning Controllers and Recurrent Neural World Models. Report arXiv:1210.0118 [cs.AI], 2015.
[PLAN5] One Big Net For Everything. Preprint arXiv:1802.08864 [cs.AI], Feb 2018.
[PLAN6] D. Ha, J. Schmidhuber. Recurrent World Models Facilitate Policy Evolution. Advances in Neural Information Processing Systems (NIPS), Montreal, 2018. (Talk.) Preprint: arXiv:1809.01999. Github: World Models.
[PM0] J. Schmidhuber. Learning factorial codes by predictability minimization. TR CUCS56591, Univ. Colorado at Boulder, 1991. PDF. More.
[PM1] J. Schmidhuber. Learning factorial codes by predictability minimization. Neural Computation, 4(6):863879, 1992. Based on [PM0], 1991. PDF. More.
[PM2] J. Schmidhuber, M. Eldracher, B. Foltin. Semilinear predictability minimization produces wellknown feature detectors. Neural Computation, 8(4):773786, 1996. PDF. More.
[PO87] J. B. Pollack. On Connectionist Models of Natural Language Processing. PhD thesis, Computer Science Department, University of Illinois, Urbana, 1987.
[PO90] J. B. Pollack. Recursive Distributed Representations. Artificial Intelligence, 46(12):77105, 1990.
[POS] E. L. Post (1936). Finite Combinatory Processes—Formulation 1. Journal of Symbolic Logic. 1: 103105. Link.
[PP] J. Schmidhuber. POWERPLAY: Training an Increasingly General Problem Solver by Continually Searching for the Simplest Still Unsolvable Problem. Frontiers in Cognitive Science, 2013. ArXiv preprint (2011): arXiv:1112.5309 [cs.AI]
[PPa] R. K. Srivastava, B. R. Steunebrink, M. Stollenga, J. Schmidhuber. Continually Adding SelfInvented Problems to the Repertoire: First Experiments with POWERPLAY. Proc. IEEE Conference on Development and Learning / EpiRob 2012 (ICDLEpiRob'12), San Diego, 2012. PDF.
[PP1] R. K. Srivastava, B. Steunebrink, J. Schmidhuber. First Experiments with PowerPlay. Neural Networks, 2013. ArXiv preprint (2012): arXiv:1210.8385 [cs.AI].
[PP2] V. Kompella, M. Stollenga, M. Luciw, J. Schmidhuber. Continual curiositydriven skill acquisition from highdimensional video inputs for humanoid robots. Artificial Intelligence, 2015.
Relevant threads with many comments at reddit.com/r/MachineLearning, the largest machine learning forum with over 800k subscribers in 2019 (note that Jürgen Schmidhuber's name is often misspelled):
[R1] Reddit/ML, 2019. Hinton, LeCun, Bengio receive ACM Turing Award. This announcement contains more comments about Schmidhuber than about any of the awardees.
[R2] Reddit/ML, 2019. J. Schmidhuber really had GANs in 1990.
[R3] Reddit/ML, 2019. NeurIPS 2019 Bengio Schmidhuber MetaLearning Fiasco. Schmidhuber started metalearning (learning to learn—now a hot topic) in 1987^{[META1][META]} long before Bengio who suggested in public at N(eur)IPS 2019 that he did it before Schmidhuber.
[R4] Reddit/ML, 2019. Five major deep learning papers by G. Hinton did not cite similar earlier work by J. Schmidhuber.
[R5] Reddit/ML, 2019. The 1997 LSTM paper by Hochreiter & Schmidhuber has become the most cited deep learning research paper of the 20th century.
[R6] Reddit/ML, 2019. DanNet, the CUDA CNN of Dan Ciresan in J. Schmidhuber's team, won 4 image recognition challenges prior to AlexNet.
[R7] Reddit/ML, 2019. J. Schmidhuber on Seppo Linnainmaa, inventor of backpropagation in 1970.
[R8] Reddit/ML, 2019. J. Schmidhuber on Alexey Ivakhnenko, godfather of deep learning 1965.
[R9] Reddit/ML, 2019. We find it extremely unfair that Schmidhuber did not get the Turing award. That is why we dedicate this song to Juergen to cheer him up.
[R11] Reddit/ML, 2020. Schmidhuber: Critique of Honda Prize for Dr. Hinton
[R12] Reddit/ML, 2020. J. Schmidhuber: Critique of Turing Award for Drs. Bengio & Hinton & LeCun
[R15] Reddit/ML, 2021. J. Schmidhuber's work on fast weights from 1991 is similar to linearized variants of Transformers
[R58] Rosenblatt, F. (1958). The perceptron: a probabilistic model for information storage and organization in the brain. Psychological review, 65(6):386. This paper not only described single layer perceptrons, but also deeper multilayer perceptrons (MLPs). Although these MLPs did not yet have deep learning, because only the last layer learned,^{[DL1]} Rosenblatt basically had what much later was rebranded as Extreme Learning Machines (ELMs) without proper attribution.^{[ELM12][CONN21][T22]}
[R61] Joseph, R. D. (1961). Contributions to perceptron theory. PhD thesis, Cornell Univ.
[R62] Rosenblatt, F. (1962). Principles of Neurodynamics. Spartan, New York.
[RAU1] M. Rausch. Heron von Alexandria. Die Automatentheater und die Erfindung der ersten antiken Programmierung. Diplomica Verlag GmbH, Hamburg 2012. Perhaps the world's first programmable machine was an automatic theatre made in the 1st century by Heron of Alexandria, who apparently also had the first known working steam engine.
[RAW] J. Schmidhuber (AI Blog, 2001). Raw Computing Power.
[RCNN] R. Girshick, J. Donahue, T. Darrell, J. Malik. Rich feature hierarchies for accurate object detection and semantic segmentation. Preprint arXiv/1311.2524, Nov 2013.
[RCNN2] R. Girshick. Fast RCNN. Proc. of the IEEE international conference on computer vision, p. 14401448, 2015.
[RCNN3] K. He, G. Gkioxari, P. Dollar, R. Girshick. Mask RCNN. Preprint arXiv/1703.06870, 2017.
[RELU1] K. Fukushima (1969). Visual feature extraction by a multilayered network of analog threshold elements. IEEE Transactions on Systems Science and Cybernetics. 5 (4): 322333. doi:10.1109/TSSC.1969.300225. This work introduced rectified linear units or ReLUs, now widely used.
[RELU2] C. v. d. Malsburg (1973). SelfOrganization of Orientation Sensitive Cells in the Striate Cortex. Kybernetik, 14:85100, 1973. See Table 1 for rectified linear units or ReLUs. Possibly this was also the first work on applying an EM algorithm to neural nets.
[RING91] M. B. Ring. Incremental development of complex behaviors through automatic construction of sensorymotor hierarchies. In Birnbaum, L. and Collins, G., editors, Machine Learning: Proceedings of the Eighth International Workshop, pages 343347. Morgan Kaufmann, 1991.
[RMSP] T. Tieleman, G. E. Hinton. Lecture 6.5rmsprop: Divide the gradient by a running average of its recent magnitude. COURSERA: Neural networks for machine learning 4.2 (2012): 2631.
[RO98] R. Rojas (1998). How to make Zuse's Z3 a universal computer. IEEE Annals of Computing, vol. 19:3, 1998.
[ROB87] A. J. Robinson, F. Fallside. The utility driven dynamic error propagation network. Technical Report CUED/FINFENG/TR.1, Cambridge University Engineering Department, 1987.
[RPG] D. Wierstra, A. Foerster, J. Peters, J. Schmidhuber (2010). Recurrent policy gradients. Logic Journal of the IGPL, 18(5), 620634.
[RPG07] D. Wierstra, A. Foerster, J. Peters, J. Schmidhuber. Solving Deep Memory POMDPs with Recurrent Policy Gradients. Intl. Conf. on Artificial Neural Networks ICANN'07, 2007. PDF. The first paper on policy gradients for LSTM. This approach has become very important in reinforcement learning.^{[LSTMPG]}
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[T19] ACM's justification of the 2018 A.M. Turing Award (announced in 2019). WWW link. Local copy 1 (HTML only). Local copy 2 (HTML only). [T22] debunks this justification.
[T20a] J. Schmidhuber (AI Blog, 25 June 2020). Critique of 2018 Turing Award for Drs. Bengio & Hinton & LeCun. A precursor of [T22].
[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, IDSIA, Lugano, Switzerland, 2022. Debunking [T19] and [DL3a] .
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[TR1] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, L. Kaiser, I. Polosukhin (2017). Attention is all you need. NIPS 2017, pp. 59986008. This paper introduced the name "Transformers" for a now widely used NN type. It did not cite the 1991 publication on what's now called "Transformers with linearized selfattention."^{[FWP06][TR56]} Schmidhuber also introduced the now popular attention terminology in 1993.^{[ATT][FWP2][R4]} See tweet of 2022 for 30year anniversary.
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[TUR21] J. Schmidhuber (AI Blog, Sep 2021). Turing Oversold. It's not Turing's fault, though.
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[TUR3b] Are computers conscious?—Panpsychism with Noam Chomsky  Theories of Everything. Mentioning the ancient "Turing Test" by Descartes. YouTube video, 2022.
[UDRL1] J. Schmidhuber. Reinforcement Learning Upside Down: Don't Predict Rewards—Just Map Them to Actions. Preprint arXiv/1912.02875, 5 Dec 2019.
[UDRL2] R. K. Srivastava, P. Shyam, F. Mutz, W. Jaskowski, J. Schmidhuber. Training Agents using UpsideDown Reinforcement Learning. Preprint arXiv/1912.02877, 5 Dec 2019.
[UN] J. Schmidhuber (AI Blog, 2021). 30year anniversary. 1991: First very deep learning with unsupervised or selfsupervised pretraining. Unsupervised hierarchical predictive coding (with selfsupervised target generation) finds compact internal representations of sequential data to facilitate downstream deep 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. Unsupervised/selfsupervised learning and predictive coding is used in a deep hierarchy of recurrent neural networks (RNNs) to find compact internal representations of long sequences of data, across multiple time scales and levels of abstraction. Each RNN tries to solve the pretext task of predicting its next input, sending only unexpected inputs to the next RNN above. The resulting compressed sequence representations greatly facilitate downstream supervised deep learning such as sequence classification. By 1993, the approach solved problems of depth 1000 [UN2] (requiring 1000 subsequent computational stages/layers—the more such stages, the deeper the learning). A variant collapses the hierarchy into a single deep net. It uses a socalled conscious chunker RNN which attends to unexpected events that surprise a lowerlevel socalled subconscious automatiser RNN. The chunker learns to understand the surprising events by predicting them. The automatiser uses a neural knowledge distillation procedure to compress and absorb the formerly conscious insights and behaviours of the chunker, thus making them subconscious. The systems of 1991 allowed for much deeper learning than previous methods. More.
[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 (with selfsupervised target generation). 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 / selfsupervised 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. This work describes unsupervised pretraining of stacks of feedforward NNs (FNNs) called Deep Belief Networks (DBNs). It did not cite the much earlier 1991 unsupervised pretraining of stacks of more general recurrent NNs (RNNs)^{[UN03]} which introduced the first NNs shown to solve very deep problems. The 2006 justification of the authors was essentially the one Schmidhuber used for the 1991 RNN stack: each higher level tries to reduce the description length (or negative log probability) of the data representation in the level below.^{[HIN][T22][MIR]} This can greatly facilitate very deep downstream learning.^{[UN03]}
[UN5] Y. Bengio, P. Lamblin, D. Popovici, H. Larochelle. Greedy layerwise training of deep networks. Proc. NIPS 06, pages 153160, Dec. 2006. The comment under reference^{[UN4]} applies here as well.
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[VAN1] S. Hochreiter. Untersuchungen zu dynamischen neuronalen Netzen. Diploma thesis, TUM, 1991 (advisor J. Schmidhuber). PDF. More on the Fundamental Deep Learning Problem.
[VAN2] Y. Bengio, P. Simard, P. Frasconi. Learning longterm dependencies with gradient descent is difficult. IEEE TNN 5(2), p 157166, 1994. Results are essentially identical to those of Schmidhuber's diploma student Sepp Hochreiter (1991).^{[VAN1]} Even after a common publication,^{[VAN3]} the first author of [VAN2] published papers^{[VAN4]} that cited only their own [VAN2] but not the original work.
[VAN3] S. Hochreiter, Y. Bengio, P. Frasconi, J. Schmidhuber. Gradient flow in recurrent nets: the difficulty of learning longterm dependencies. In S. C. Kremer and J. F. Kolen, eds., A Field Guide to Dynamical Recurrent Neural Networks. IEEE press, 2001. PDF.
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[YB20] Y. Bengio. Notable Past Research. WWW link (retrieved 15 May 2020). Local copy (plain HTML only). The author claims that in 1995 he "introduced the use of a hierarchy of time scales to combat the vanishing gradients issue" although Schmidhuber's publications on exactly this topic date back to 199193.^{[UN02][UN]} The author also writes that in 1999 he "introduced, for the first time, autoregressive neural networks for density estimation" although Schmidhuber & Heil used a very similar setup for text compression already in 1995.^{[SNT]}
[ZU36] K. Zuse (1936). Verfahren zur selbsttätigen Durchführung von Rechnungen mit Hilfe von Rechenmaschinen. Patent application Z 23 139 / GMD Nr. 005/021, 1936. First patent application describing a general, practical, programcontrolled computer.
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[ZUS21] J. Schmidhuber (AI Blog, 2021). 80th anniversary celebrations: 1941: Konrad Zuse completes the first working general computer, based on his 1936 patent application.
[ZUS21a] J. Schmidhuber (AI Blog, 2021). 80. Jahrestag: 1941: Konrad Zuse baut ersten funktionalen Allzweckrechner, basierend auf der Patentanmeldung von 1936.
[ZUS21b]
J. Schmidhuber (2021).
Der Mann, der den Computer
erfunden hat. (The man who invented the computer.)
Weltwoche, Nr. 33.21, 19 August 2021.
PDF.
.