1,721,074 research outputs found
The hiatus between organism and machine evolution: Contrasting mixed microbial communities with robots.
No full textMixed microbial communities, usually composed of various bacterial and fungal species, are fundamental in a plethora of environments, from soil to human gut and skin. Their evolution is a paradigmatic example of intertwined dynamics, where not just the relations among species plays a role, but also the opportunities - and possible harms - that each species presents to the others. These opportunities are in fact affordances, which can be seized by heritable variations and selection. In this paper, starting from a systemic viewpoint of mixed microbial communities, we focus on the pivotal role of affordances in evolution and we contrast it to the artificial evolution of programs and robots. We maintain that the two realms are neatly separated, in that natural evolution proceeds by extending the space of its possibilities in a completely open way, while the latter is inherently limited by the algorithmic framework in which it is defined. This discrepancy characterizes also an envisioned setting in which robots evolve in the physical world. We present arguments supporting our claim and we propose an experimental setting for assessing our statements. Rather than just discussing the limitations of the artificial evolution of machines, the aim of this contribution is to emphasize the tremendous potential of the evolution of the biosphere, beautifully represented by the evolution of communities of microbes
Emergence of Organisms
Full text linkSince early cybernetics studies by Wiener, Pask, and Ashby, the properties of living systems are subject to deep investigations. The goals of this endeavour are both understanding and building: abstract models and general principles are sought for describing organisms, their dynamics and their ability to produce adaptive behavior. This research has achieved prominent results in fields such as artificial intelligence and artificial life. For example, today we have robots capable of exploring hostile environments with high level of self-sufficiency, planning capabilities and able to learn. Nevertheless, the discrepancy between the emergence and evolution of life and artificial systems is still huge. In this paper, we identify the fundamental elements that characterize the evolution of the biosphere and open-ended evolution, and we illustrate their implications for the evolution of artificial systems. Subsequently, we discuss the most relevant issues and questions that this viewpoint poses both for biological and artificial systems
What Is Consciousness? Artificial Intelligence, Real Intelligence, Quantum Mind, And Qualia
Full text linkWe approach the question "What is Consciousness?" in a new way, not as
Descartes' "systematic doubt", but as how organisms find their way in their
world. Finding one's way involves finding possible uses of features of the
world that might be beneficial or avoiding those that might be harmful.
"Possible uses of X to accomplish Y" are "Affordances". The number of uses of X
is indefinite (or unknown), the different uses are unordered, are not listable,
and are not deducible from one another. All biological adaptations are either
affordances seized by heritable variation and selection or, far faster, by the
organism acting in its world finding uses of X to accomplish Y. Based on this,
we reach rather astonishing conclusions: (1) Artificial general intelligence
based on universal Turing machines (UTMs) is not possible, since UTMs cannot
"find" novel affordances. (2) Brain-mind is not purely classical physics for no
classical physics system can be an analogue computer whose dynamical behaviour
can be isomorphic to "possible uses". (3) Brain mind must be partly
quantum-supported by increasing evidence at 6.0 sigma to 7.3 sigma. (4) Based
on Heisenberg's interpretation of the quantum state as "potentia" converted to
"actuals" by measurement, where this interpretation is not a substance dualism,
a natural hypothesis is that mind actualizes potentia. This is supported at 5.2
sigma. Then mind's actualizations of entangled brain-mind-world states are
experienced as qualia and allow "seeing" or "perceiving" of uses of X to
accomplish Y. We can and do jury-rig. Computers cannot. (5) Beyond familiar
quantum computers, we discuss the potentialities of trans-Turing-systems
The World Is Not a Theorem
Full text linkThe evolution of the biosphere unfolds as a luxuriant generative process of new living forms and functions. Organisms adapt to their environment, exploit novel opportunities that are created in this continuous blooming dynamics. Affordances play a fundamental role in the evolution of the biosphere, for organisms can exploit them for new morphological and behavioral adaptations achieved by heritable variations and selection. This way, the opportunities offered by affordances are then actualized as ever novel adaptations. In this paper, we maintain that affordances elude a formalization that relies on set theory: we argue that it is not possible to apply set theory to affordances; therefore, we cannot devise a set-based mathematical theory to deduce the diachronic
evolution of the biosphere
A Novel Online Adaptation Mechanism in Artificial Systems Provides Phenotypic Plasticity
No full textHow Organisms Come to Know the World: Fundamental Limits on Artificial General Intelligence
Full text linkArtificial intelligence has made tremendous advances since its inception about seventy
years ago. Self-driving cars, programs beating experts at complex games, and smart
robots capable of assisting people that need care are just some among the successful
examples of machine intelligence. This kind of progress might entice us to envision a
society populated by autonomous robots capable of performing the same tasks humans
do in the near future. This prospect seems limited only by the power and complexity
of current computational devices, which is improving fast. However, there are several
significant obstacles on this path. General intelligence involves situational reasoning,
taking perspectives, choosing goals, and an ability to deal with ambiguous information.
We observe that all of these characteristics are connected to the ability of identifying and
exploiting new affordances—opportunities (or impediments) on the path of an agent to
achieve its goals. A general example of an affordance is the use of an object in the hands
of an agent. We show that it is impossible to predefine a list of such uses. Therefore,
they cannot be treated algorithmically. This means that “AI agents” and organisms differ
in their ability to leverage new affordances. Only organisms can do this. This implies that
true AGI is not achievable in the current algorithmic frame of AI research. It also has
important consequences for the theory of evolution. We argue that organismic agency is
strictly required for truly open-ended evolution through radical emergence. We discuss
the diverse ramifications of this argument, not only in AI research and evolution, but also
for the philosophy of science
On the Criticality of Adaptive Boolean Network Robots
Full text linkSystems poised at a dynamical critical regime, between order and disorder, have been shown capable of exhibiting complex dynamics that balance robustness to external perturbations and rich repertoires of responses to inputs. This property has been exploited in artificial network classifiers, and preliminary results have also been attained in the context of robots controlled by Boolean networks. In this work, we investigate the role of dynamical criticality in robots undergoing online adaptation, i.e., robots that adapt some of their internal parameters to improve a performance metric over time during their activity. We study the behavior of robots controlled by random Boolean networks, which are either adapted in their coupling with robot sensors and actuators or in their structure or both. We observe that robots controlled by critical random Boolean networks have higher average and maximum performance than that of robots controlled by ordered and disordered nets. Notably, in general, adaptation by change of couplings produces robots with slightly higher performance than those adapted by changing their structure. Moreover, we observe that when adapted in their structure, ordered networks tend to move to the critical dynamical regime. These results provide further support to the conjecture that critical regimes favor adaptation and indicate the advantage of calibrating robot control systems at dynamical critical states
Pseudo-attractors in Random Boolean Network Models and Single-Cell Data
Full text linkIn this extended abstract two novel concepts are defined in the
study of Random Boolean Networks, i.e. those of “pseudoattractors”
and “common sea”, and it is shown how their
analogues can be measured in experimental data on gene
expression in single cells
Dynamical Criticality in Gene Regulatory Networks
Full text linkA well-known hypothesis, with far-reaching implications, is that biological evolution should preferentially lead to states that are dynamically critical. In previous papers, we showed that a well-known model of genetic regulatory networks, namely, that of random Boolean networks, allows one to study in depth the relationship between the dynamical regime of a living being's gene network and its response to permanent perturbations. In this paper, we analyze a huge set of new experimental data on single gene knockouts in S. cerevisiae, laying down a statistical framework to determine its dynamical regime. We find that the S. cerevisiae network appears to be slightly ordered, but close to the critical region. Since our analysis relies on dichotomizing continuous data, we carefully consider the issue of an optimal threshold choice
Beyond the Newtonian Paradigm: A Statistical Mechanics of Emergence
Full text linkSince Newton, all classical and quantum physics depends upon the "Newtonian
Paradigm". Here the relevant variables of the system are identified. The
boundary conditions creating the phase space of all possible values of the
variables are defined. Then, given any initial condition, the differential
equations of motion are integrated to yield an entailed trajectory in the phase
space. It is fundamental to the Newtonian Paradigm that the set of
possibilities that constitute the phase space is always definable and fixed
ahead of time. All of this fails for the diachronic evolution of ever new
adaptations in any biosphere. The central reason is that living cells achieve
Constraint Closure and construct themselves. Living cells, evolving via
heritable variation and Natural selection, adaptively construct new in the
universe possibilities. The new possibilities are opportunities for new
adaptations thereafter seized by heritable variation and Natural Selection.
Surprisingly, we can neither define nor deduce the evolving phase spaces ahead
of time. We can use no mathematics based on Set Theory to do so. These ever-new
adaptations with ever-new relevant variables constitute the ever-changing phase
space of evolving biospheres. Because of this, evolving biospheres are entirely
outside the Newtonian Paradigm. One consequence is that for any universe such
as ours there can be no Final Theory that entails all that comes to exist. The
implications are large. We face a third major transition in science beyond the
Pythagorean dream that "All is Number". We must give up deducing the diachronic
evolution of the biosphere. All of physics, classical and quantum, however,
apply to the analysis of existing life, a synchronic analysis. We begin to
better understand the emergent creativity of an evolving biosphere. Thus, we
are on the edge of inventing a physics-like new statistical mechanics of
emergence
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