|
MORCOM@IS4SI 2019 Berkeley -
ABSTRACTS Morphological, Natural,
Analog and Other Unconventional Forms of Computing
for Cognition and Intelligence The questions regarding the modern
information processing technology in performing tasks
traditionally considered as exclusively human, or even
considered as defining human being such as thinking,
intelligence, consciousness, or goal-oriented agency
are essentially the same questions as those asked by
natural philosophers through the ages. We have now
more powerful intellectual and technological tools in
searching for answers, but the existing pervasive and
convenient tool-kit brings also a danger of following
the old habits of thinking. This is why it is necessary to
re-consider and re-examine even most fundamental
concepts, such as computing or cognition and
intelligence. There are examples of novel studies, for
instance of morphological computing and embodied
cognition, that succeed in escaping the inertia of
thinking habits and question conventional theoretical
and practical models. See the event from the last year: https://sites.google.com/view/morphologicalcomputing
In this event we bring together
perspectives on morphological-, physical-, natural-,
analog- and embodied cognitive computation and other
forms of unconventional conceptualization of
computing, cognition and intelligence. We encourage
open and constructive debate on the perceived
differences in the various perspectives on
constructivist and computationalist accounts of the
dynamics of information in its natural and artifactual
realizations. Abstracts of contributions should be
send to organizers by April 15, 2019 (addresses
below). Organizers: Gordana Dodig-Crnkovic, Professor, Chalmers
University of Technology & Mälardalen
University, Sweden, dodig@chalmers.se Marcin J. Schroeder, Ph.D., Professor
& Dean of Academic Affairs, Akita
International University (国際教養大学), Akita,
Japan. Editor-in-Chief, Philosophies
(MDPI-Basel-Switzerland). mjs@aiu.ac.jp SESSION A – MORCOM (Tuesday, 2019 06 04) Gordana Dodig-Crnkovic, Chalmers
University of Technology, dodig@chalmers.se Title: “Morphological,
Natural, Analog and Other Unconventional Forms of
Computing for Cognition and Intelligence” Abstract.
What is the relationship between Cognition and
Intelligence? How does cognitive computing relate to
AI? What is the difference between Natural, Analog and
Morphological computing? At the moment there is a huge
variety of use of those terms that causes confusion.
In this presentation I would present the taxonomy of
computing originally developed in collaboration with
Mark Burgin, and extended by some recent work on
cognition as information processing. References Dodig Crnkovic, G.
(2018) Cognition as Embodied Morphological
Computation. Philosophy and Theory of Artificial
Intelligence 2017: 19-23.
http://dx.doi.org/10.1007/978-3-319-96448-5 Dodig-Crnkovic, G.,
Nature as a Network of Morphological Infocomputational
Processes for Cognitive Agents, The European Physical
Journal Special Topics, DOI:
10.1140/epjst/e2016-60362-9 Eur. Phys. J. 2017, 226,
181–195. Mark Burgin, University
of California, Los Angeles, mburgin@math.ucla.edu Title: “Processing Information
by Symmetric Inductive Machines” Abstract. To reflect important properties of
computers, Marcin Schroeder introduced a new model of
computation - symmetric Turing machines or S-machines
(Schroeder, 2013; 2013a). In a conventional Turing
machine, the head (processor) performs operations with
data in the memory (tape) using a fixed system of
instructions – its program. In a symmetric
Turing machine, information processing goes not only
from the head to the memory but also backward. On the
one hand, the head (processor) performs operations
with data in the memory using a fixed system of
instructions – its program. On the other hand, the
memory performs operations with instructions from the
head (processor). Physical computers also perform
operations with their programs using special tools
such as interpreters, compilers and translators. There
are also program optimizers, which improve
characteristic of programs. Automata that perform transformations
with their programs, such as reflexive Turing
machines, were explored in (Burgin, 1992). It was
proved that these machines have the same computing
power as Turing machines but could be much more
efficient. Using technique similar to the one
employed in (Burgin, 1992), it is possible to prove
that functioning of a symmetric Turing machine can be
simulated by a conventional Turing machine with two
tapes and two heads. It means that symmetric Turing
machines have the same computing power as Turing
machines. At the same time it is also possible to
prove that symmetric Turing machines can be much more
efficient than Turing machines. To achieve higher computing power, here
we introduce and study inductive symmetric machines,
which further develop the structure and possibilities
of inductive Turing machines allowing to model natural
computations in various situations. References Burgin, M. Reflexive
Calculi and Logic of Expert Systems, in Creative
processes modeling by means of knowledge bases, Sofia,
1992, pp. 139-160 (in Russian) Schroeder,M.J. Dualism
of Selective and Structural Manifestations of
Information in Modelling of Information Dynamics, In:
G. Dodig-Crnkovic, R. Giovagnoli (Eds.) Computing
Nature, SAPERE 7, Springer, Berlin, Germany, pp.
125-137, (2013) Schroeder,M.J. From
Proactive to Interactive Theory of Computation, M.
Bishop and Y. J. Erden (Eds.): The 6th AISB
Symposium on Computing and Philosophy: The Scandal of
Computation – What is Computation? pp.47 - 51, 2013a SESSION E – MORCOM (Tuesday) Ricardo Q.
Figueroa*, Genaro J. Martinez Andrew Adamatzky, Luz
N. Oliva-Moreno *Presenting
author genarojm@gmail.com 1 Artificial
Life Robotics Laboratory, Escuela Superior de
C´omputo, Instituto Polit´ecnico Nacional, M´exico.
2
Unconventional Computing Lab, University of the West
of England, Bristol, United Kingdom. 3 Unidad
Profesional Interdisciplinaria de Ingenier´ıa
Hidalgo, Instituto Polit´ecnico Nacional, M´exico Title: “Robots
Simulating Turing Machines” Abstract. We will discuss
how modular robots can be operated for simulate
computers. This research explores a classic problem in
computer science: machines simulating machines. The
main goal of this research consist in proof how a set
of Cubelets robots may be organized to implement a
Turing machine. Additionally, the Turing machine
constructed with Cubelets robots shows how a universal
Turing machine works in this device. This robotic
Turing machine moves its head to read a fixed type
containing a binary string and this one changes in
each iteration. The machine is complemented with some
LEGO pieces to stabilize the concatenation of these
robots. A characteristic of this machine is that we
can change its configuration for to get another kind of
robot, probably useful for the same goal or to another
propose. References Andrew Adamatzky
(Ed.) (2017) Advances in Unconventional Computing
(Volume Prototypes, Models and Algorithms), Springer
International Publishing. Ricardo Q.
Figueroa, Daniel A. Zamorano, Genaro J. Mart´ınez,
Andrew Adamatzky (2019) A Turing Machine Constructed
with Cubelets Robots, Journal of Robotics, Networking
and Artificial Life 5(4) 1–4. John von Neumann
(1966) Theory of Self-reproducing Automata (edited and
completed by A. W. Burks), University of Illinois
Press, Urbana and London. Rao
Mikkilineni, Golden Gate
University, raom2013@outlook.com
Title:
“Structural Machines as Unconventional Knowledge
Processors” Abstract.
Knowledge systems often have very
sophisticated structures. For instance, representation
of knowledge in the form of a text involves structures
of this text. Their structure is represented by
hypertexts, which are networks (sometimes very complex
ones) consisting of linguistic objects, such as words,
phrases and sentences, with diverse links connecting
these objects. Coming to multimedia, we encounter even
more multifarious structures. At the same time,
computational machines and automata are mostly
oriented at sequential processing of information. For
instance, Turing machines process words letter by
letter. Thus, to work with knowledge using Turing
machines, it is necessary in advance to present
knowledge by linear structures. To improve efficiency
and allow processing not only symbols but also links
between them, more advanced automata, such as
Kolmogorov algorithms storage modification machines
and relational machines. However, all these relations
define only structures of the first order while
knowledge structure can have much higher orders. To
eliminate this restriction and further advance
efficiency, structural machines were introduced. Here,
we present knowledge processing by structural
machines. Knowledge contains information as matter
contains energy and structural machines work with
knowledge structures of arbitrary order transforming
not only elements of these structures or the content
of these elements, as conventional models of
computation do, but also relations of different orders
in the processed structures. This allows achieving
higher flexibility and efficiency in comparison with
regular models of computation including both
conventional and unconventional computing systems.
Structural machines can also simulate such advanced
computational models as Kolmogorov algorithms, limit
Turing machines, storage modification machines,
relational machines and other models of computation.
Being structurally universal abstract automata,
structural machines work directly with knowledge
structures, molecular and atomic structures, with
structures studied and utilized in the topological
quantum field theory (TQFT) and with structures of
quantum information such as qubits. SESSION I – MORCOM (Tuesday) Genaro J.
Martinez * 1,2, Andrew Adamatzky2,
Ricardo Q. Figueroa1, and Dmitr A.
Zaitsev3, *Presenting
author 1 Computer
Science Laboratory, Escuela Superior de C´omputo,
Instituto Polit´ecnico Nacional, Mexico. 2
Unconventional Computing Lab, University of the West
of England, Bristol, United Kingdom. 3 International
Humanitarian University, Odessa, Ukraine Title:
”Propagation of patterns in non-linear media as a
paradigm of unconventional computers” Abstract. Cellular
automata are classic models to design unconventional
computing in several ways. Historically, a lot of different proposals
work handling signals or composition of them
interpreted as particles (gliders, mobile-self
localizations). Patterns, originating from different sources of
perturbations, propagating in a precipitating
chemical, physical or biological medium do usually
compete for the space. They sub-divide the medium onto
the regions unique for an initial configuration of
disturbances. This sub-division can be expressed in
terms of computation. We adopt an analogy between
precipitating chemical, physical or biological media
and semi-totalistic binary two-dimensional cellular
automata. We demonstrate how to implement basic logic
and arithmetical operations (its computability) by
patterns propagating in geometrically constrained
cellular automata medium. Non-serial logic gates are
designed and implemented to look a possible circuit.
Finally, we show practical implementations of these
theoretical designs across of Cubelets robots. In this
case, a concatenations of Cubelets robots represent a
channel of communication and package of electrons
propagates as light and they represent binary signals,
the junction of these wires open a new wire conformed
with other Cubelets robots which display the result. References Adamatzky, A.
(2009) Hot ice computer, Physics Letters A 374(2) 264–
271. Fischer, T.,
Kewenig, M., Bozhko, D.A., Serga, A.A., Syvorotka,
I.I., Ciubotaru, F., Adelmann, C., Hillebrands, B.
& Chumak, A.V. (2017) Experimental prototype of a
spin-wave majority gate, American Institute of Physics
17(2) 86–91. Gregory, L.S.,
Orlov, A.O., Amlani, I., Bernstein, G.H., Lent, C.S.,
Merz, J.L., & Porod, W. (1999) Quantum-Dot
Cellular Automata: Line and Majority Logic Gate,
Japanese Journal of Applied Physics 38 7227–7229. Mart´ınez, G.J.,
Adamatzky, A., & Costello, B.L. (2008) On logical
gates in precipitating medium: cellular automaton
model, Physics Letters A 1(48) 1–5. Mart´ınez, G.J.,
Adamatzky, A., Morita, K., & Margenstern, M.
(2010) Computation with competing patterns in
Life-like automaton, In: Game of Life Automata, A.
Adamatzky (ed.), Springer, chapter 27, pages 547–572. Mitchell, M.
(2001) Life and evolution in computers, History and
Philosophy of the Life Sciences 23 361–383. Mart´ınez, G.J.,
Morita, K., Adamatzky, A., & Margenstern, M.
(2010) Majority adder implementation by competing
patterns in Life-like rule B2/S2345, Lecture Notes in
Computer Science 6079 93–104. Mart´ınez, G.J.,
Seck, J.C.T.M., & Zenil, H. (2013) Computation and
Universality: Class IV versus Class III Cellular
Automata, Journal of Cellular Automata 7(5-6) 393–430. Toffoli, T. (1998)
Non-Conventional Computers, Encyclopedia of Electrical
and Electronics Engineering (John Webster Ed.),
14:455–471, Wiley & Sons. Lorenzo Magnani, University
of Pavia, Italy, lmagnani@unipv.it Title: “Disseminated
Computation, Cognitive Domestication of New Ignorant
Substrates, and Overcomputationalization” Abstract.
What I called ”eco-cognitive
computationalism” considers computation in context,
following some of the main tenets advanced by the
recent cognitive science views on embodied, situated,
and distributed cognition. It is in the framework of
this eco-cognitive perspective that we can usefully
analyze the recent attention in computer science
devoted to the importance of cognitive domestication
of new substrates, such as in the case of
morphological computation: this new perspective shows
how the computational domestication of ignorant
substrates can originate new unconventional cognitive
embodiments, which expand the processes of
computationalization already occurring in our
societies. I will also introduce and discuss the
concept of overcomputationalism, as intertwined with
the traditional concepts of pancognitivism,
paniformationalism, and pancomputationalism, seeing
them in a more naturalized intellectual disposition,
appropriate to the aim of bypass ontological or
metaphysical overstatements. What I call
overcomputationalization refers to the presence of too
many entities and artifacts that carry computational
tasks and powers. Overcomputationalization 1) often
promotes a plenty of possible unresolvable
disorganizational consequences, and 2) tends to favor
philosophical reflections that depict an
oversimplified vision of the world. Moreover, it tends
to generate too many cognitive constraints and
limitations, which lead to a weakening of human
creative (abductive) cognitive activities, as I have
illustrated in the last chapter of my recent book The
Abductive Structure of Scientific Creativity (2017),
and, because of the excess of redundant
cognitive/informational features attributed to
entities (features often exogenous to the original
functions of them) it tends to prevent human
intellectual freedom to benefit from that cognitive
simplification that is characteristic of the absence
of informational overloads. SESSION M – MORCOM (Tuesday) Marcin
Schroeder, Akita International University, mjs@aiu.ac.jp Title:
“Intelligent Computing: Oxymoron? Abstract. The question about conditions qualifying
an object, whether artificial or natural, an entity or
its functioning as intelligent is more about qualifier
(intelligence) than qualified. Of course, if we had an
established definition of intelligence, then we could
sort objects, actions, processes accordingly. But we
don’t. Turing attempted to escape the problem in the
context of artificial systems (“machine”) using his
“imitation game”, but instead of closing the
discussion of “intelligent machinery”, he opened
Pandora’s box of ever-lasting disputes in which
intelligence is frequently mixed with the ability to
think, capacity of being conscious, etc. It does not
mean that the problem is restricted to intelligent
artefacts, since the qualification of human beings as
intelligent has been never clarified and typically
discussions are about many different “intelligences”,
even if in everyday practice people refer without
hesitation to the so called “intelligence quotient”
(IQ). So whether computers or computing can be
intelligent or not depends on the way we understand
intelligence. Of course, this freedom of the choice of
definition has some limits coming from already
established tradition of the use of the term
“intelligence”, in particular in the common sense
discourse. From this perspective, we cannot ignore the
most frequent objection to the intelligence of
artefacts based on the doubt that they can have the
capacity of symbolic association between the sign and
its denotation. Thus, they do not have the capacity to
“understand” symbols. The objection is not surprising
and its source is not new. We can trace it to
Brentano’s claim that intention (associating
denotation with signs) is a specific mental capacity
of the mind which is absent on the body side of the
mind-body division. Since starting from the mind-body
division would place us in the maze of centuries old
discussions leading nowhere, it is better to rephrase
this objection to be more suitable for the reflection
on computers and computing. Everyone agrees that
computing based on the model of the Turing machine is
a transformation of compound symbols (built of digits,
Tukey’s bits, letters, or any other finite number of
elementary units from which symbols are built) through
the manipulation of their components. The components
(digits) have their meaning for the machine determined
by instructions. It was originally expressed by Turing
that machine (we would say “head”) can see or scan the
present square for the unitary sign - digit and can
act according to the present instruction (state of its
“mind”). The action of the machine or head can be
understood as an expression of the meaning of the
unitary sign for the machine or its head. Obviously,
machine’s head does not have any representation of the
tape with its configuration of digits, nor even any
sensor to monitor more than one square (or in some
variations of Turing machines a fixed, finite number
of squares). Thus, machine cannot understand the
entire compound symbol consisting of possible long
configuration of digits. Someone could object that the
tape is a part of the machine, so machine has a
representation of the configuration on the tape in the
form of the tape. This however kills the concept of
the symbolic representation by restricting symbolic
representation to the strict identity. Human beings
definitely are capable of symbolic representation
beyond the meaning understood as the identity of the
sign and denotation. Does it mean that the Turing
machine type computers are doomed to be
non-intelligent? No. Someone can claim that the
meaning is emergent in both human symbolic thinking
and in computing. The difference between actual
typical implementations of computers using Tukey’s
bits (0 and 1) and typical human brains with possibly
large, but finite set of elementary units may be
misleading. Nothing prevents us from using
implementation of the Turing machine with a very large
set of digits. The view that the meaning is emergent
eliminates the distinction between semantics and
syntactic may seem exotic, but not without precedence.
After all, it is at the foundation of constructivism.
There is nothing fallacious in the assumption that we
live in reality which our mind constructs from a
finite number of elementary units (“digits”). Maybe,
we actually understand only the finite number of
simple components and we react to the instructions
telling our brain how to construct complex and diverse
reality. But, if this is the case, how do we know that
the reality is complex and diverse, if we can
understand only simple components? How can we direct
our actions to eliminate complexity and diversity? In
case of computers (or Turing machines) we know that
Turing machine cannot reduce the algorithmic
complexity of the configuration on the tape, or it
cannot even assess computability of the input
configuration. Computing is a one-way process of
construction, but not deconstruction. It is a human
programmer who decomposes in the process of
programming the complex task into an algorithm
(intelligent part of the task) and leaves the
non-intelligent task of performing the construction of
the desired outcome. This is not far from the
objection to the intelligence of computers coming from
the common sense discussions. When we compare the
intelligence of different people, we consider as more
intelligent the individuals who have the ability to
reduce complexity, usually by making complex tasks
simple through the deconstruction and leaving these
simple tasks to less intelligent collaborators. Thus,
the answer given by the author in this part of the
paper is: Yes, oxymoron. But the question remains
about what type of capacities have to be added to
Turing machine to make it intelligent at the human
level or beyond. Partial answer to this question was
given by the author in his earlier publications.
Computers have to be equipped with the ability to
integrate information. The remaining part of the paper
is about this and other conditions for making
computers intelligent and about the perspectives of
implementation of such conditions. Rao
Mikkilineni1, Mark Burgin2
& Eugene Eberbach3 1Golden Gate
University, raom2013@outlook.com 2UCLA, mburgin@math.ucla.edu 3 Scopium AI,
Toronto, Canada & Seekonk, eeberbach@gmail.com
Title:
“Cloud Computing as a Step to a Higher-Order AI” Abstract. Cloud computing
approach addresses how to make the right resources
available to the right computation to improve scaling,
resiliency and efficiency of the computation. In this
paper we argue that cloud computing indeed, is a new
paradigm for computation upgrading it to a higher
order of artificial intelligence, and we put forward
cloud automata as a new model for computation. A
high-level artificial intelligence requires infusing
features of the human brain into AI systems. One of
the central features is that the brain learns all the
time and learning is incremental. Consequently, for
AI, we need to use computational models, which reflect
incremental learning without stopping (sentience).
These features are inherent in reflexive Turing
machines, inductive Turing machines and limit Turing
machines. It is
possible to distinguish several paradigms of
computation, including Mainframe, PC, Network,
Internet, Distributed, Grid and Cloud Computing. New
computing paradigms may involve various technologies
besides VLSI, such as quantum computing, biologically
inspired computing, nanocomputing, optical computing,
neurocomputing. Theoretical models of computing are
naturally divided into three classes: sub-recursive,
recursive and super-recursive algorithms and automata.
To construct cloud automata, we use the mathematical
theory of Oracles, which include Oracles of Turing
machines as its special case. This allows developing a
hierarchical approach to artificial intelligence based
on Oracles with different ranks. The developed
approach includes Oracle AI as a special case
providing new tools for exploration of artificial
intelligence in general and Oracle artificial
intelligence. Discussing named-set approach and
Oracle-based evolution of computations, we describe an
implementation of a high-performance edge cloud using
hierarchical name-oriented networking and Oracle-based
orchestration. We demonstrate how cloud automata
provide means for improving resiliency, scalability
and efficiency of computations. A control overlay
allows microservice network provisioning, monitoring
and reconfiguration to address fluctuations in their
behavior. SESSION D – MORCOM (Wednesday,
2019 06 05) Skype presentations Hector
Zenil, Algorithmic Dynamics Lab, Unit of
Computational Medicine, Center for Molecular
Medicine, Karolinska Institutet, Stockholm,
Sweden, hzenilc@gmail.com Abstract. In this talk I will
explain how current approaches of machine, and deep
learning based on traditional statistics and
information theory fail to capture fundamental
properties of our world and are ill-equipped to deal
with high-level functions such as inference,
abstraction, and understanding, they are fragile and
can easily be deceived. In contrast, we will explore
recent attempts to combine symbolic and
differentiable computation in a form of unconventional
hybrid computation that is more powerful and may
eventually display and grasp these higher level
elements of human intelligence. In particular, I will
introduce the field of Algorithmic Information
Dynamics and that of Algorithmic Machine Intelligence
based on the theories of computability and algorithmic
probability, and how these approaches promise to shed
light on the weaknesses of current AI and how to
attempt to circumvent some of their limitations. Vincent
C. Muller, Technical University of Eindhoven (TU/e)
The Netherlands, University of Leeds and Alan Turing
Institute, London, UK. www.sophia.de Title: ”Morphological Computation and
the Discussion About Whether Computation involves
Meaningful Symbols” Abstract. The
discussion about morphological computation and about
whether computation involves meaningful symbols,
rather than merely syntactic operations, have been
going on for some time now. My rather conservative
position has been to say that computation is
essentially syntactic algorithmic processing (as the
Church-Turing thesis suggested) done by humans with
machines. But there are other very fruitful and
plausible notions of computing. The question is what
kind of question is this? Do we expect a discovery to
find out the truth, or can we slice the world in
several plausible ways? Is this the same question as
realism and anti-realism about computing? |