Connectionists: Deep Belief Nets (2006) / Neural History Compressor (1991) or Hierarchical Temporal Memory

Mon Feb 10 16:37:10 EST 2014

I think Gary's last paragraph is absolutely key. Unless we take both the
evolutionary and the developmental processes into account, we will neither
understand complex brains fully nor replicate their functionality too well
in our robots etc. We build complex robots that know nothing and then ask
them to learn complex things, setting up a hopelessly difficult learning
problem. But that isn't how animals learn, or why animals have the brains
and bodies they have. A purely abstract computational approach to neural
models makes the same category error that connectionists criticized
symbolists for making, just at a different level.

Ali

On Mon, Feb 10, 2014 at 11:38 AM, Gary Marcus <gary.marcus at nyu.edu> wrote:

> Juergen and others,
>
> I am with John on his two basic concerns, and think that your appeal to
> computational universality is a red herring; I cc the entire group because
> I think that these issues lay at the center of why many of the hardest
> problems in AI and neuroscience continue to lay outside of reach, despite
> in-principle proofs about computational universality.
>
> John's basic points, which I have also made before (e.g. in my books The
> Algebraic Mind and The Birth of the Mind and in my periodic New Yorker
> posts) are two
>
> a. It is unrealistic to expect that hierarchies of pattern recognizers
> will suffice for the full range of cognitive problems that humans (and
> strong AI systems) face. Deep learning, to take one example, excels at
> classification, but has thus far had relatively little to contribute to
> inference or natural language understanding.  Socher et al's impressive CVG
> work, for instance, is parasitic on a traditional (symbolic) parser, not a
> soup-to-nuts neural net induced from input.
>
> b. it is unrealistic to expect that all the relevant information can be
> extracted by any general purpose learning device.
>
> Yes, you can reliably map any arbitrary input-output relation onto a
> multilayer perceptron or recurrent net, but *only* if you know the
> complete input-output mapping in advance. Alas, you can't be guaranteed to
> do that in general given arbitrary subsets of the complete space; in the
> real world, learners see subsets of possible data and have to make guesses
> about what the rest will be like. Wolpert's No Free Lunch work is
> instructive here (and also in line with how cognitive scientists like
> Chomsky, Pinker, and myself have thought about the problem). For any
> problem, I presume that there exists an appropriately-configured net,
> but there is no guarantee that in the real world you are going to be able
> to correctly induce the right system via general-purpose learning algorithm
> given a finite amount of data, with a finite amount of training.
> Empirically, neural nets of roughly the form you are discussing have worked
> fine for some problems (e.g. backgammon) but been no match for their
> symbolic competitors in other domains (chess) and worked only as an adjunct
> rather than an central ingredient in still others (parsing,
> question-answering a la Watson, etc); in other domains, like planning and
> common-sense reasoning, there has been essentially no serious work at all.
>
> My own take, informed by evolutionary and developmental biology, is that
> no single general purpose architecture will ever be a match for the
> endproduct of a billion years of evolution, which includes, I suspect, a
> significant amount of customized architecture that need not be induced anew
> in each generation.  We learn as well as we do precisely because evolution
> has preceded us, and endowed us with custom tools for learning in different
> domains. Until the field of neural nets more seriously engages in
> understanding what the contribution from evolution to neural wetware might
> be, I will remain pessimistic about the field's prospects.
>
> Best,
> Gary Marcus
>
> Professor of Psychology
> New York University
> Visiting Cognitive Scientist
> Allen Institute for Brain Science
> Allen Institute for Artiificial Intelligence
> <http://twitter.com/GaryMarcus>
> co-edited book coming late 2014:
> The Future of the Brain: Essays By The World's Leading Neuroscientists
> http://garymarcus.com/
>
> On Feb 10, 2014, at 10:26 AM, Juergen Schmidhuber <juergen at idsia.ch>
> wrote:
>
> John,
>
> perhaps your view is a bit too pessimistic. Note that a single RNN already
> is a general computer. In principle, dynamic RNNs can map arbitrary
> observation sequences to arbitrary computable sequences of motoric actions
> and internal attention-directing operations, e.g., to process cluttered
> scenes, or to implement development (the examples you mentioned). From my
> point of view, the main question is how to exploit this universal potential
> through learning. A stack of dynamic RNN can sometimes facilitate this.
> What it learns can later be collapsed into a single RNN [3].
>
> Juergen
>
> http://www.idsia.ch/~juergen/whatsnew.html
>
>
>
> On Feb 7, 2014, at 12:54 AM, Juyang Weng <weng at cse.msu.edu> wrote:
>
> Juergen:
>
> You wrote: A stack of recurrent NN.  But it is a wrong architecture as far
> as the brain is concerned.
>
> Although my joint work with Narendra Ahuja and Thomas S. Huang at UIUC was
> probably the first
> learning network that used the deep Learning idea for learning from
> clutter scenes (Cresceptron ICCV 1992 and IJCV 1997),
> I gave up this static deep learning idea later after we considered the
> Principle 1: Development.
>
> The deep learning architecture is wrong for the brain.  It is too
> restricted, static in architecture, and cannot learn directly from
> cluttered scenes required by Principle 1.  The brain is not a cascade of
> recurrent NN.
>
> I quote from Antonio Damasio "Decartes' Error": p. 93: "But intermediate
> communications occurs also via large subcortical nuclei such as those in
> the thalamas and basal ganglia, and via small nulei such as those in the
> brain stem."
>
> Of course, the cerebral pathways themselves are not a stack of recurrent
> NN either.
>
> There are many fundamental reasons for that.  I give only one here base on
> our DN brain model:  Looking at a human, the brain must dynamically attend
> the tip of the nose, the entire nose, the face, or the entire human body on
> the fly.  For example, when the network attend the nose, the entire human
> body becomes the background!  Without a brain network that has both shallow
> and deep connections (unlike your stack of recurrent NN), your network is
> only for recognizing a set of static patterns in a clean background.  This
> is still an overworked pattern recognition problem, not a vision problem.
>
> -John
>
> On 2/6/14 7:24 AM, Schmidhuber Juergen wrote:
>
> Deep Learning in Artificial Neural Networks (NN) is about credit
> assignment across many subsequent computational stages, in deep or
> recurrent NN.
>
> A popluar Deep Learning NN is the Deep Belief Network (2006) [1,2].  A
> stack of feedforward NN (FNN) is pre-trained in unsupervised fashion. This
> can facilitate subsequent supervised learning.
>
> Let me re-advertise a much older, very similar, but more general, working
> Deep Learner of 1991. It can deal with temporal sequences: the Neural
> Hierarchical Temporal Memory or Neural History Compressor [3]. A stack of
> recurrent NN (RNN) is pre-trained in unsupervised fashion. This can greatly
> facilitate subsequent supervised learning.
>
> The RNN stack is more general in the sense that it uses
> sequence-processing RNN instead of FNN with unchanging inputs. In the early
> 1990s, the system was able to learn many previously unlearnable Deep
> Learning tasks, one of them requiring credit assignment across 1200
> successive computational stages [4].
>
> Related developments: In the 1990s there was a trend from partially
> unsupervised [3] to fully supervised recurrent Deep Learners [5]. In recent
> years, there has been a similar trend from partially unsupervised to fully
> supervised systems. For example, several recent competition-winning and
> benchmark record-setting systems use supervised LSTM RNN stacks [6-9].
>
>
> References:
>
> [1] G. E. Hinton, R. R. Salakhutdinov. Reducing the dimensionality of data
> with neural networks. Science, Vol. 313. no. 5786, pp. 504 - 507, 2006.
> http://www.cs.toronto.edu/~hinton/science.pdf
>
> [2] G. W. Cottrell. New Life for Neural Networks. Science, Vol. 313. no.
> 5786, pp. 454-455, 2006.
> http://www.academia.edu/155897/Cottrell_Garrison_W._2006_New_life_for_neural_networks
>
> [3] 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, 1991.)
> ftp://ftp.idsia.ch/pub/juergen/chunker.pdf  Overview:
> http://www.idsia.ch/~juergen/firstdeeplearner.html
>
> [4] J. Schmidhuber. Habilitation thesis, TUM, 1993.
> ftp://ftp.idsia.ch/pub/juergen/habilitation.pdf . Includes an experiment
> with credit assignment across 1200 subsequent computational stages for a
> Neural Hierarchical Temporal Memory or History Compressor or RNN stack with
> unsupervised pre-training [2] (try Google Translate in your mother tongue):
> http://www.idsia.ch/~juergen/habilitation/node114.html
>
> [5] S. Hochreiter, J. Schmidhuber. Long Short-Term Memory. Neural
> Computation, 9(8):1735-1780, 1997. Based on TR FKI-207-95, 1995.
> ftp://ftp.idsia.ch/pub/juergen/lstm.pdf . Lots of of follow-up work on
> LSTM under http://www.idsia.ch/~juergen/rnn.html
>
> [6] S. Fernandez, A. Graves, J. Schmidhuber. Sequence labelling in
> structured domains with hierarchical recurrent neural networks. In Proc.
> IJCAI'07, p. 774-779, Hyderabad, India, 2007.
> ftp://ftp.idsia.ch/pub/juergen/IJCAI07sequence.pdf
>
> [7] A. Graves, J. Schmidhuber. Offline Handwriting Recognition with
> Multidimensional Recurrent Neural Networks. NIPS'22, p 545-552, Vancouver,
> MIT Press, 2009.  http://www.idsia.ch/~juergen/nips2009.pdf
>
> [8] 2009: First very deep (and recurrent) learner to win international
> competitions with secret test sets: deep LSTM RNN (1995-) won three
> connected handwriting contests at ICDAR 2009 (French, Arabic, Farsi),
> performing simultaneous segmentation and recognition.
> http://www.idsia.ch/~juergen/handwriting.html
>
> [9] A. Graves, A. Mohamed, G. E. Hinton. Speech Recognition with Deep
> Recurrent Neural Networks. ICASSP 2013, Vancouver, 2013.
> http://www.cs.toronto.edu/~hinton/absps/RNN13.pdf
>
>
>
> Juergen Schmidhuber
> http://www.idsia.ch/~juergen/whatsnew.html
>
>
> --
> --
> Juyang (John) Weng, Professor
> Department of Computer Science and Engineering
> MSU Cognitive Science Program and MSU Neuroscience Program
> 428 S Shaw Ln Rm 3115
> Michigan State University
> East Lansing, MI 48824 USA
> Tel: 517-353-4388
> Fax: 517-432-1061
> Email: weng at cse.msu.edu
> URL: http://www.cse.msu.edu/~weng/
> ----------------------------------------------
>
>
>
>
>

-- 
Ali A. Minai, Ph.D.
Professor
Complex Adaptive Systems Lab
Department of Electrical Engineering & Computing Systems
University of Cincinnati
Cincinnati, OH 45221-0030

Phone: (513) 556-4783
Fax: (513) 556-7326
Email: Ali.Minai at uc.edu
          minaiaa at gmail.com

WWW: http://www.ece.uc.edu/~aminai/
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