457 research outputs found
The Self-Organization of Speech Sounds
The speech code is a vehicle of language: it defines
a set of forms used by a community to carry information.
Such a code is necessary to support the linguistic
interactions that allow humans to communicate.
How then may a speech code be formed prior to the
existence of linguistic interactions?
Moreover, the human speech code is discrete and compositional,
shared by all the individuals of a community but different
across communities, and phoneme inventories are characterized by
statistical regularities. How can a speech code with these properties form?
We try to approach these questions in the paper,
using the ``methodology of the artificial''. We
build a society of artificial agents, and detail a mechanism that
shows the formation of a discrete speech code without pre-supposing
the existence of linguistic capacities or of coordinated interactions.
The mechanism is based on a low-level model of
sensory-motor interactions. We show that the integration of certain very
simple and non language-specific neural devices
leads to the formation of a speech code that
has properties similar to the human speech code.
This result relies on the self-organizing properties of a generic
coupling between perception and production
within agents, and on the interactions between agents.
The artificial system helps us to develop better intuitions on how speech
might have appeared, by showing how self-organization
might have helped natural selection to find speech
Intelligent Adaptive Curiosity: a source of Self-Development
This paper presents the mechanism of Intelligent Adaptive Curiosity. This is a drive which pushes the robot towards situations in which it maximizes its learning progress. It makes the robot focus on situations which are neither too predictable nor too unpredictable. This mechanism is a source of self-development for the robot: the complexity of its activity autonomously increases. Indeed, we show that it first spends time in situations which are easy to learn, then shifts progressively its attention to situations of increasing difficulty, avoiding situations in which nothing can be learnt
From Holistic to Discrete Speech Sounds: The Blind Snow-Flake Maker Hypothesis
Sound is a medium used by humans to carry information.
The existence of this kind of
medium is a pre-requisite for language. It is organized
into a code, called speech, which
provides a repertoire of forms that is shared in each
language community. This code is necessary to support the linguistic
interactions that allow humans to communicate.
How then may a speech code be formed prior to the
existence of linguistic interactions?
Moreover, the human speech code is characterized by several
properties: speech is digital and compositional (vocalizations
are made of units re-used systematically in other syllables);
phoneme inventories have precise regularities as well as
great diversity in human languages; all the speakers of a
language community categorize sounds in the same manner,
but each language has its own system of categorization,
possibly very different from every other.
How can a speech code with these properties form?
These are the questions we will approach in the paper. We will
study them using the method of the artificial. We will
build a society of artificial agents, and study what mechanisms
may provide answers. This will not prove directly what mechanisms
were used for humans, but rather give ideas about what kind
of mechanism may have been used. This allows us to shape the
search space of possible answers, in particular by showing
what is sufficient and what is not necessary.
The mechanism we present is based on a low-level model of
sensory-motor interactions. We show that the integration of certain very
simple and non language-specific neural devices
allows a population of agents to build a speech code that
has the properties mentioned above. The originality is
that it pre-supposes neither a functional pressure for
communication, nor the ability to have coordinated
social interactions (they do not play language or imitation
games). It relies on the self-organizing properties of a generic
coupling between perception and production both
within agents, and on the interactions between agents
From Analogue to Digital Vocalizations
Sound is a medium used by humans to carry information.
The existence of this kind of
medium is a pre-requisite for language. It is organized
into a code, called speech, which
provides a repertoire of forms that is shared in each
language community. This code is necessary to support the linguistic
interactions that allow humans to communicate.
How then may a speech code be formed prior to the
existence of linguistic interactions?
Moreover, the human speech code is characterized by several
properties: speech is digital and compositional (vocalizations
are made of units re-used systematically in other syllables);
phoneme inventories have precise regularities as well as
great diversity in human languages; all the speakers of a
language community categorize sounds in the same manner,
but each language has its own system of categorization,
possibly very different from every other.
How can a speech code with these properties form?
These are the questions we will approach in the paper. We will
study them using the method of the artificial. We will
build a society of artificial agents, and study what mechanisms
may provide answers. This will not prove directly what mechanisms
were used for humans, but rather give ideas about what kind
of mechanism may have been used. This allows us to shape the
search space of possible answers, in particular by showing
what is sufficient and what is not necessary.
The mechanism we present is based on a low-level model of
sensory-motor interactions. We show that the integration of certain very
simple and non language-specific neural devices
allows a population of agents to build a speech code that
has the properties mentioned above. The originality is
that it pre-supposes neither a functional pressure for
communication, nor the ability to have coordinated
social interactions (they do not play language or imitation
games). It relies on the self-organizing properties of a generic
coupling between perception and production both
within agents, and on the interactions between agents
Open challenges in understanding development and evolution of speech forms: The roles of embodied self-organization, motivation and active exploration
This article discusses open scientific challenges for understanding
development and evolution of speech forms, as a commentary to Moulin-Frier et
al. (Moulin-Frier et al., 2015). Based on the analysis of mathematical models
of the origins of speech forms, with a focus on their assumptions , we study
the fundamental question of how speech can be formed out of non--speech, at
both developmental and evolutionary scales. In particular, we emphasize the
importance of embodied self-organization , as well as the role of mechanisms of
motivation and active curiosity-driven exploration in speech formation. Finally
, we discuss an evolutionary-developmental perspective of the origins of
speech
What do we learn about development from baby robots?
Understanding infant development is one of the greatest scientific challenges
of contemporary science. A large source of difficulty comes from the fact that
the development of skills in infants results from the interactions of multiple
mechanisms at multiple spatio-temporal scales. The concepts of "innate" or
"acquired" are not any more adequate tools for explanations, which call for a
shift from reductionist to systemic accounts. To address this challenge,
building and experimenting with robots modeling the growing infant brain and
body is crucial. Systemic explanations of pattern formation in sensorimotor,
cognitive and social development, viewed as a complex dynamical system, require
the use of formal models based on mathematics, algorithms and robots.
Formulating hypothesis about development using such models, and exploring them
through experiments, allows us to consider in detail the interaction between
many mechanisms and parameters. This complements traditional experimental
methods in psychology and neuroscience where only a few variables can be
studied at the same time. Furthermore, the use of robots is of particular
importance. The laws of physics generate everywhere around us spontaneous
patterns in the inorganic world. They also strongly impact the living, and in
particular constrain and guide infant development through the properties of its
(changing) body in interaction with the physical environment. Being able to
consider the body as an experimental variable, something that can be
systematically changed in order to study the impact on skill formation, has
been a dream to many developmental scientists. This is today becoming possible
with developmental robotics
Computational Theories of Curiosity-Driven Learning
What are the functions of curiosity? What are the mechanisms of
curiosity-driven learning? We approach these questions about the living using
concepts and tools from machine learning and developmental robotics. We argue
that curiosity-driven learning enables organisms to make discoveries to solve
complex problems with rare or deceptive rewards. By fostering exploration and
discovery of a diversity of behavioural skills, and ignoring these rewards,
curiosity can be efficient to bootstrap learning when there is no information,
or deceptive information, about local improvement towards these problems. We
also explain the key role of curiosity for efficient learning of world models.
We review both normative and heuristic computational frameworks used to
understand the mechanisms of curiosity in humans, conceptualizing the child as
a sense-making organism. These frameworks enable us to discuss the
bi-directional causal links between curiosity and learning, and to provide new
hypotheses about the fundamental role of curiosity in self-organizing
developmental structures through curriculum learning. We present various
developmental robotics experiments that study these mechanisms in action, both
supporting these hypotheses to understand better curiosity in humans and
opening new research avenues in machine learning and artificial intelligence.
Finally, we discuss challenges for the design of experimental paradigms for
studying curiosity in psychology and cognitive neuroscience.
Keywords: Curiosity, intrinsic motivation, lifelong learning, predictions,
world model, rewards, free-energy principle, learning progress, machine
learning, AI, developmental robotics, development, curriculum learning,
self-organization.Comment: To appear in "The New Science of Curiosity", ed. G. Gordon, Nova
Science Publisher
Computational and Robotic Models of Early Language Development: A Review
We review computational and robotics models of early language learning and
development. We first explain why and how these models are used to understand
better how children learn language. We argue that they provide concrete
theories of language learning as a complex dynamic system, complementing
traditional methods in psychology and linguistics. We review different modeling
formalisms, grounded in techniques from machine learning and artificial
intelligence such as Bayesian and neural network approaches. We then discuss
their role in understanding several key mechanisms of language development:
cross-situational statistical learning, embodiment, situated social
interaction, intrinsically motivated learning, and cultural evolution. We
conclude by discussing future challenges for research, including modeling of
large-scale empirical data about language acquisition in real-world
environments.
Keywords: Early language learning, Computational and robotic models, machine
learning, development, embodiment, social interaction, intrinsic motivation,
self-organization, dynamical systems, complexity.Comment: to appear in International Handbook on Language Development, ed. J.
Horst and J. von Koss Torkildsen, Routledg
Progressive growing of self-organized hierarchical representations for exploration
Designing agent that can autonomously discover and learn a diversity of
structures and skills in unknown changing environments is key for lifelong
machine learning. A central challenge is how to learn incrementally
representations in order to progressively build a map of the discovered
structures and re-use it to further explore. To address this challenge, we
identify and target several key functionalities. First, we aim to build lasting
representations and avoid catastrophic forgetting throughout the exploration
process. Secondly we aim to learn a diversity of representations allowing to
discover a "diversity of diversity" of structures (and associated skills) in
complex high-dimensional environments. Thirdly, we target representations that
can structure the agent discoveries in a coarse-to-fine manner. Finally, we
target the reuse of such representations to drive exploration toward an
"interesting" type of diversity, for instance leveraging human guidance.
Current approaches in state representation learning rely generally on
monolithic architectures which do not enable all these functionalities.
Therefore, we present a novel technique to progressively construct a Hierarchy
of Observation Latent Models for Exploration Stratification, called HOLMES.
This technique couples the use of a dynamic modular model architecture for
representation learning with intrinsically-motivated goal exploration processes
(IMGEPs). The paper shows results in the domain of automated discovery of
diverse self-organized patterns, considering as testbed the experimental
framework from Reinke et al. (2019)
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