In 2021, we are celebrating the 30-year anniversary of
first very deep learning machine
neural sequence chunker
neural history compressor
[UN0-UN2]. It uses
unsupervised learning and predictive coding
in a deep hierarchy of recurrent neural networks (RNNs)
to find compact internal
representations of long sequences of data.
This greatly facilitates downstream supervised deep learning such as sequence classification.
By 1993, the approach solved problems of depth 1000
(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 so-called conscious chunker RNN
which attends to unexpected events that surprise
a lower-level so-called subconscious automatiser RNN.
The chunker learns to understand the surprising events by predicting them.
The automatiser uses my
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.
Today's most powerful neural networks
(NNs) tend to be very deep, that is, they have many layers of neurons or many subsequent computational stages.
In the 1980s, however, gradient-based training did not work well for deep NNs, only for shallow
This deep learning problem
was perhaps most obvious for recurrent NNs (RNNs),
informally proposed in 1945 [MC43],
then formalised in 1956 [K56]
(but don't forget prior related work in physics since the 1920s [L20]
Like the human brain,
but unlike the more limited feedforward NNs (FNNs),
RNNs have feedback connections.
This makes RNNs powerful,
general purpose, parallel-sequential computers
that can process input sequences of arbitrary length (think of speech or videos).
RNNs can in principle implement any program that can run on your laptop.
Proving this is simple: since a few neurons can implement a NAND gate,
a big network of neurons can implement a network of NAND gates.
This is sufficient to emulate the microchip powering your laptop. Q.E.D.
If we want to build an artificial general intelligence (AGI),
then its underlying computational substrate must be something like an RNN—standard FNNs
are fundamentally insufficient.
In particular, unlike FNNs, RNNs can in principle deal with problems
of arbitrary depth, that is, with data sequences of arbitrary length whose processing may require
an a priori unknown number of subsequent computational steps [DL1].
Early RNNs of the 1980s, however, failed to learn very deep problems in practice—compare [DL1-2] [MOZ].
I wanted to overcome this drawback, to achieve
RNN-based general purpose deep learning.
In 1991, my first idea to solve the deep learning problem mentioned above was to
facilitate supervised learning in deep RNNs through
unsupervised pre-training of a hierarchical stack of RNNs.
This led to the first very deep learner called the
Neural Sequence Chunker [UN0] or
Neural History Compressor [UN1].
In this architecture, each higher level RNN tries to
reduce the description length (or negative log probability)
of the data representations in the levels below.
This is done
using the Predictive Coding trick: while trying to predict the next input in the incoming data stream using the previous inputs, only update neural activations in the case of unpredictable data so that only what is not yet known is stored.
In other words, given a training set of observation sequences,
the chunker learns to compress typical data streams such that the
deep learning problem
becomes less severe, and can be solved by gradient descent through standard backpropagation,
an old technique from 1970 [BP1-4] [R7].
Let us consider an example. At the bottom of the text box, one can read the sentence "Predictability entails compressibility." That's what the lowest level RNN observes, one letter at a time. It is trained in unsupervised fashion to predict the next letter, given the previous letters. The first letter is a "P". It was not correctly predicted. So it is sent (as raw data) to the next level which sees only the unpredictable letters from the lower level. The second letter "r" is also unpredicted. But then, in this example, the lower RNN starts predicting well, because it was pre-trained on a set of typical sentences. The higher level updates its activations only when there is an unexpected error on the lower level. That is, the higher level RNN is operating or "clocking" more slowly because it clocks only in response to unpredictable observations. What's predictable is compressible. Given
the RNN stack and relative positional encodings of the time intervals between unexpected events [UN0-1],
one can reconstruct the exact original sentence from its compressed representation. Finally, the reduced depth on the top level can greatly facilitate downstream learning, e.g., supervised sequence classification [UN0-2].
To my knowledge, the Neural Sequence Chunker [UN0]
was also the first system made of RNNs operating on different
(self-organizing) time scales.
Although computers back then were about a million times slower per dollar than today,
by 1993, the neural history compressor was able to solve previously unsolvable
very deep learning tasks of depth > 1000 [UN2], i.e., tasks requiring
more than 1000 subsequent computational stages—the more such stages, the deeper the learning.
I also had a way of compressing or distilling
all those RNNs down into a single deep RNN operating on the original, fastest time scale.
This method is described in
Section 4 of the 1991 reference [UN0] on a
and a "subconscious" automatiser, which
introduced a general principle for
transferring the knowledge of one NN to another—compare
Sec. 2 of [MIR].
To understand this method,
first suppose a teacher NN has learned to predict (conditional expectations of) data,
given other data. Its knowledge can be compressed into a student NN,
by training the student NN to imitate the behavior and internal representations of the teacher NN
(while also re-training the student NN on previously learned skills such that it does not forget them).
I called this collapsing or compressing the behavior of one net into another.
Today, this is widely used,
and also called distilling [HIN] [T20] [R4] or cloning the
behavior of a teacher net into a student net.
To summarize, one can compress or distill the RNN hierarchy
down into the original RNN clocking on the fastest time scale.
Then we get a single RNN which solves the entire deep, long time lag problem.
To my knowledge, this was the first very deep learning compressed into a single NN.
In 1993, we also published a
Neural History Compressor without varying time scales where the time-varying "update strengths" of a higher level RNN depend
on the magnitudes of the surprises in the level below [UN3].
Soon after the advent of the unsupervised pre-training-based very deep learner above,
the fundamental deep learning problem (first analyzed in 1991 by my student Sepp Hochreiter [VAN1]—see Sec. 3 of [MIR] and item (2) of Sec. XVII of [T20]) was also overcome
through purely supervised Long Short-Term Memory or LSTM—see
Sec. 4 of [MIR]
(and Sec. A & B of [T20]).
Subsequently, this new type of superior supervised learning made unsupervised pre-training less important,
and LSTM drove much of the supervised deep learning revolution [DL4] [DEC].
Between 2006 and 2011, my lab also drove
a very similar shift from unsupervised pre-training to pure supervised learning,
this time for the simpler
feedforward NNs (FNNs)
than recurrent NNs (RNNs). This led to revolutionary applications
image recognition [DAN]
cancer detection, among many other problems. See Sec. 19 of [MIR].
Of course, deep learning in feedforward NNs started much earlier, with Ivakhnenko & Lapa, who published the first general, working learning algorithms for deep multilayer perceptrons with arbitrarily many layers back in 1965 [DEEP1]. For example, Ivakhnenko's paper from 1971 [DEEP2] already described a deep learning net with 8 layers, trained by a highly cited method still popular in the new millennium [DL2]. But unlike the deep FNNs of Ivakhnenko and his successors of the 1970s and 80s, our deep RNNs had general purpose parallel-sequential computational architectures [UN0-3]. By the early 1990s, most NN research was still limited to rather shallow nets with fewer than 10 subsequent computational stages, while our methods already enabled over 1000 such stages.
Finally let me emphasize that the above-mentioned
supervised deep learning revolutions of
the early 1990s (for recurrent NNs) [MIR]
2010 (for feedforward NNs)
not kill unsupervised learning.
For example, pre-trained language models are now heavily
feedforward Transformers which
excel at the traditional LSTM domain of
Natural Language Processing [TR1] [TR2]
(although there are still many language tasks that LSTM can
rapidly learn to solve quickly [LSTM13]
while plain Transformers can't yet).
our active & generative unsupervised NNs since
are still used to endow agents with
artificial curiosity [MIR] (Sec. 5 & Sec. 6)—see also a special case of our adversarial NNs [AC90b] called GANs [AC20] [R2] [PLAN]
[T20] (Sec. XVII).
Unsupervised learning still has a bright future!
Thanks to several expert reviewers for useful comments. Since science is about self-correction, let me know under email@example.com if you can spot any remaining error. The contents of this article may be used for educational and non-commercial purposes, including articles for Wikipedia and similar sites. This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Neural sequence chunkers.
Technical Report FKI-148-91, Institut für Informatik, Technische
Universität München, April 1991.
PDF. Later published as [UN1].
[First working Deep Learner based on a deep RNN hierarchy (with different self-organising time scales), overcoming the vanishing gradient problem through unsupervised pre-training and predictive coding. Also: compressing or distilling a teacher net (the chunker) into a student net (the automatizer) that does not forget its old skills—such approaches are now widely used. More.]
J. Schmidhuber. Learning complex, extended sequences using the principle of history compression. Neural Computation, 4(2):234-242, 1992. Based on TR FKI-148-91, TUM, 1991 [UN0]. PDF.
[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 pre-training for a stack of recurrent NNs
can be found here (depth > 1000).]
J. Schmidhuber, M. C. Mozer, and D. Prelinger.
Continuous history compression.
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[DAN] J. Schmidhuber
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[DEC] J. Schmidhuber (2020). The 2010s: Our Decade of Deep Learning / Outlook on the 2020s.
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[DL4] J. Schmidhuber, 2017.
Our impact on the world's most valuable public companies: 1. Apple, 2. Alphabet (Google), 3. Microsoft, 4. Facebook, 5. Amazon ....
[HIN] J. Schmidhuber (2020). Critique of 2019 Honda Prize.
[T20] J. Schmidhuber (2020). Critique of 2018 Turing Award for deep learning.
Making the world differentiable: On using fully recurrent
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planning in non-stationary environments.
Technical Report FKI-126-90, TUM, Feb 1990, revised Nov 1990.
introduced a whole bunch of concepts that are now widely used:
Planning with recurrent world models
([MIR], Sec. 11),
high-dimensional reward signals as extra NN inputs / general value functions
([MIR], Sec. 13),
deterministic policy gradients
([MIR], Sec. 14),
unsupervised NNs that are both generative and adversarial
([MIR], Sec. 5), for Artificial Curiosity and related concepts.
A possibility for implementing curiosity and boredom in
model-building neural controllers.
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Based on [AC90].
Curious model-building control systems.
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J. Storck, S. Hochreiter, and J. Schmidhuber.
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[R2] Reddit/ML, 2019. J. Schmidhuber really had GANs in 1990.
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[First analysis of the problem of vanishing or exploding gradients in deep networks. More on this fundamental deep learning problem.]
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F. A. Gers and J. Schmidhuber.
LSTM Recurrent Networks Learn Simple Context Free and
Context Sensitive Languages.
IEEE Transactions on Neural Networks 12(6):1333-1340, 2001.
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[The first publication on "modern" backpropagation, also known as the reverse mode of automatic differentiation.]
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[BP4] J. Schmidhuber.
Who invented backpropagation?
[R7] Reddit/ML, 2019. J. Schmidhuber on Seppo Linnainmaa, inventor of backpropagation in 1970.
[R4] Reddit/ML, 2019. Five major deep learning papers by G. Hinton did not cite similar earlier work by J. Schmidhuber.
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