Este trabalho introduz uma definição para noção de inteligência de máquina, estabelece a possibilidade concreta dessa definição e fornece indicações sobre a sua necessidade - isto e, dá-lhe um conteúdo objetivo e mostra o interesse e a utilidade que a definição pode ter para a ciência da computação, em geral, e para a inteligência artificial, em particular. Especificamente, toma-se uma particular leitura da definição de inteligência dada por J. Piaget e se estabelecem as condições para que essa definição possa ser interpretada no domínio das máquinas. Para tanto, uma revisão das noções fundamentais da ciência da computação se faz necessária, a fim de explicitar os aspectos dinâmicos de variabilidade, controlabilidade e adaptabilidade subjacentes a tais conceitos (maquina, programa, computação, e organização, rege adaptação de rnáquina). Por outro lado, urna, mudança de atitude face aos objetivos da inteligência, artificial também e requerida. A definição dada supõe que se reconheça, a autonomia operacional das maquinas, e isso leva, a abandonar, ou pelo menos a colocar em segundo piano, o ponto de vista que chamamos de artificialismo - a busca da imitação do comportamento inteligente de seres humanos ou animais - e a adotar o ponto de vista que denominamos de naturalismo - a consideração da inteligência de maquina como fenômeno natural nas maquinas, digno de ser estudado em si próprio. 0 trabalho apresenta os resultados da reflexão através da qual se tentou realizar tais objetivos. / This work introduces a definition for the notion of machine intelligence, establishes the concrete possibility of that definition and gives indications on its necessity - that is, it gives that notion an objective content and shows the interest and utility that the definition may have to computer science, in general, and artificial intelligence, in particular. Specifically, we take a particular reading of the definition of intelligence given by J. Piaget, and we establish the conditions under which that definition can be interpreted in the domain of machines. To achieve this, a revision of the fundamental notions of computer science was necessary, in order to make explicit the dynamical aspects of variability, controlability and adaptability that are underlying those concepts (machine, program, computation, and machine organization, regulation and adaptation). On the other hand, a change in the attitude toward the objetives of artificial intelligence was also required. The given definition pressuposes that one recognizes the operational autonomy of machines, and this implies abandonning the point of view we call artificialism - the search for the imitation of the intelligent behavior of human beings and animals - and adopting the point of view that we call naturalism - which considers that machine intelligence is a natural phenomenon in machines, that should be studied by its own. The work presents the results of the reflexion through which we tried to realize those goals.

Este trabalho introduz uma definição para noção de inteligência de máquina, estabelece a possibilidade concreta dessa definição e fornece indicações sobre a sua necessidade - isto e, dá-lhe um conteúdo objetivo e mostra o interesse e a utilidade que a definição pode ter para a ciência da computação, em geral, e para a inteligência artificial, em particular. Especificamente, toma-se uma particular leitura da definição de inteligência dada por J. Piaget e se estabelecem as condições para que essa definição possa ser interpretada no domínio das máquinas. Para tanto, uma revisão das noções fundamentais da ciência da computação se faz necessária, a fim de explicitar os aspectos dinâmicos de variabilidade, controlabilidade e adaptabilidade subjacentes a tais conceitos (maquina, programa, computação, e organização, rege adaptação de rnáquina). Por outro lado, urna, mudança de atitude face aos objetivos da inteligência, artificial também e requerida. A definição dada supõe que se reconheça, a autonomia operacional das maquinas, e isso leva, a abandonar, ou pelo menos a colocar em segundo piano, o ponto de vista que chamamos de artificialismo - a busca da imitação do comportamento inteligente de seres humanos ou animais - e a adotar o ponto de vista que denominamos de naturalismo - a consideração da inteligência de maquina como fenômeno natural nas maquinas, digno de ser estudado em si próprio. 0 trabalho apresenta os resultados da reflexão através da qual se tentou realizar tais objetivos. / This work introduces a definition for the notion of machine intelligence, establishes the concrete possibility of that definition and gives indications on its necessity - that is, it gives that notion an objective content and shows the interest and utility that the definition may have to computer science, in general, and artificial intelligence, in particular. Specifically, we take a particular reading of the definition of intelligence given by J. Piaget, and we establish the conditions under which that definition can be interpreted in the domain of machines. To achieve this, a revision of the fundamental notions of computer science was necessary, in order to make explicit the dynamical aspects of variability, controlability and adaptability that are underlying those concepts (machine, program, computation, and machine organization, regulation and adaptation). On the other hand, a change in the attitude toward the objetives of artificial intelligence was also required. The given definition pressuposes that one recognizes the operational autonomy of machines, and this implies abandonning the point of view we call artificialism - the search for the imitation of the intelligent behavior of human beings and animals - and adopting the point of view that we call naturalism - which considers that machine intelligence is a natural phenomenon in machines, that should be studied by its own. The work presents the results of the reflexion through which we tried to realize those goals.

In this thesis, we present three main contributions to laser-based detection and tracking of dynamic objects, from both a model-based point of view and a model-free point of view, with an emphasis on applications to autonomous driving. A segmentation-based detector is first proposed to provide an end-to-end detection of the classes car, pedestrian and bicyclist in 3D laser data amongst significant background clutter. We postulate that, for the particular classes considered, solving a binary classification task outperforms approaches that tackle the multi-class problem directly. This is confirmed using custom and third-party datasets gathered of urban street scenes. The sliding window approach to object detection, while ubiquitous in the Computer Vision community, is largely neglected in laser-based object detectors, possibly due to its perceived computational inefficiency. We give a second thought to this opinion in this thesis, and demonstrate that, by fully exploiting the sparsity of the problem, exhaustive window searching in 3D can be made efficient. We prove the mathematical equivalence between sparse convolution and voting, and devise an efficient algorithm to compute exactly the detection scores at all window locations, processing a complete Velodyne scan containing 100K points in less than half a second. Its superior performance is demonstrated on the KITTI dataset, and compares commensurably with state of the art vision approaches. A new model-free approach to detection and tracking of moving objects with a 2D lidar is then proposed aiming at detecting dynamic objects of arbitrary shapes and classes. Objects are modelled by a set of rigidly attached sample points along their boundaries whose positions are initialised with and updated by raw laser measurements, allowing a flexible, nonparametric representation. Dealing with raw laser points poses a significant challenge to data association. We propose a hierarchical approach, and present a new variant of the well-known Joint Compatibility Branch and Bound algorithm to handle large numbers of measurements. The system is systematically calibrated on real world data containing 7.5K labelled object examples and validated on 6K test cases. Its performance is demonstrated over an existing industry standard targeted at the same problem domain as well as a classical approach to model-free tracking.

After a brief review of machine learning techniques and applications, this Ph.D. thesis examines several approaches for implementing machine learning architectures and algorithms into hardware within our laboratory. From this interdisciplinary background support, we have motivations for novel approaches that we intend to follow as an objective of innovative hardware implementations of dynamically self-reconfigurable logic for enhanced self-adaptive, self-(re)organizing and eventually self-assembling machine learning systems, while developing this new particular area of research. And after reviewing some relevant background of robotic control methods followed by most recent advanced cognitive controllers, this Ph.D. thesis suggests that amongst many well-known ways of designing operational technologies, the design methodologies of those leading-edge high-tech devices such as cognitive chips that may well lead to intelligent machines exhibiting conscious phenomena should crucially be restricted to extremely well defined constraints. Roboticists also need those as specifications to help decide upfront on otherwise infinitely free hardware/software design details. In addition and most importantly, we propose these specifications as methodological guidelines tightly related to ethics and the nowadays well-identified workings of the human body and of its psyche.