Test de Turing

Résumé

vignette|upright=1|Schéma du test de Turing. Le test de Turing est une proposition de test d’intelligence artificielle fondée sur la faculté d'une machine à imiter la conversation humaine. Décrit par Alan Turing en 1950 dans sa publication Computing Machinery and Intelligence, ce test consiste à mettre un humain en confrontation verbale à l’aveugle avec un ordinateur et un autre humain. Si la personne qui engage les conversations n’est pas capable de dire lequel de ses interlocuteurs est un ordinateur, on peut considérer que le logiciel de l’ordinateur a passé avec succès le test. Cela sous-entend que l’ordinateur et l’humain essaieront d’avoir une apparence sémantique humaine. Pour conserver la simplicité et l’universalité du test, la conversation est limitée à des messages textuels entre les protagonistes. Histoire Inspiration Le test met en scène un jeu d’imitation dans lequel un homme et une femme vont dans des pièces séparées et les invités tentent de dis

Source officielle

https://fr.wikipedia.org/wiki/Test_de_Turing

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Extension d'algorithmes d'ordre zéro pour l'optimisation stochastique

Pierre Huguenin

The electrical discharge machining process (EDM) was discovered in the 1950s, and was then used essentially to destroy unrecoverable damaged screws. Since then, huge progress has been achieved in making this process reliable and able to perform the most complex machining operations on the most sophisticated materials. Two main processes use electrical discharge machining. First, Die Sinking EDM (DSEDM) in which an electrode, moved along a usually vertical axis, makes an imprint into a mechanical element ; second, Wire EDM (WEDM), uses a wire as an electrode and makes it possible to perform cut operations. Two specific aspects of the EDM process make it particularly challenging for optimization. First, the process evolves with the machining position. In the DSEDM process, where the electrode sinks deeply into the material, the fragments spawn by erosion (contamination) are trapped, thus modifying the sparking conditions. In the WEDM process, the main factors that drive the evolution of the process are the machining operations. Second, the measurements are very noisy, which is due to underlying, mainly random, physical phenomenon ; this is particularly true to spark triggering. The process evolution influences the single criterion to be minimized : total machining time. However, the latter is only known once the operations are completed. As a result, the whole history of manipulated variables influences the final criterion to minimize in this case of dynamic optimization. A major contribution of this work is a proof that a first-order model of the DSEDM process, with the machining position as a state variable, makes it possible to transform a dynamic optimization problem into a static optimization problem. The tools used in this demonstration are Pontryagin's Minimum Principle as well as Parametric Programming. The conclusion is that in order to achieve minimum machining time, maximum speed must be sought all along the trajectory. The online search for maximum speed is another important contribution of this thesis. Noisy efficiency functions are, indeed, known to be a significant challenge to the reliability of optimization algorithms. To address this issue the Golden Ratio Search and the Nelder-Mead Simplex algorithms were chosen as the starting point, as they do not rely explicitly on the gradient of the efficiency function. The addition of a further dilation condition makes these algorithms more effective in stochastic mode. This condition is based on the detection of contradictory measurement samples as compared to the shape of the efficiency function, which is assumed to be unimodal. As a result of this modification, the density of the final optimization points is well centred relative to the theoretical optimum, and dispersion is small. Moreover, the size of the search region for both algorithms never approaches zero. Consequently, as the machining conditions evolve, the optimizer can target a new optimum. This adaptability proves to be a significant improvement over existing algorithms. In the case of the DSEDM process, a simple model of the efficiency surface as a function of the control variables, which has been calibrated on sample measures, has allowed for a validation of the static optimization. For the WEDM process, conclusive results for the modified algorithms have been obtained both by simulation and during machine-based tests.

EPFL2005

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Acoustic Models for Posterior Features in Speech Recognition

Guillermo Aradilla

In this thesis, we investigate the use of posterior probabilities of sub-word units directly as input features for automatic speech recognition (ASR). These posteriors, estimated from data-driven methods, display some favourable properties such as increased speaker invariance, but unlike conventional speech features also hold some peculiarities, such that their components are non-negative and sum up to one. State-of-the-art acoustic models for ASR rely on general-purpose similarity measures like Euclidean-based distances or likelihoods computed from Gaussian mixture models (GMMs), hence, they do not explicitly take into account the particular properties of posterior-based speech features. We explore here the use of the Kullback-Leibler (KL) divergence as similarity measure in both non-parametric methods using templates and parametric models that rely on an architecture based on hidden Markov models (HMMs). Traditionally, template matching (TM)-based ASR uses cepstral features and requires a large number of templates to capture the natural variability of spoken language. Thus, TM-based approaches are generally oriented to speaker-dependent and small vocabulary recognition tasks. In our work, we use posterior features to represent the templates and test utterances. Given the discriminative nature of posterior features, we show that a limited number of templates can accurately characterize a word. Experiments on different databases show that using KL divergence as local similarity measure yields significantly better performance than traditional TM-based approaches. The entropy of posterior features can also be used to further improve the results. In the context of HMMs, we propose a novel acoustic model where each state is parameterized by a reference multinomial distribution and the state score is based on the KL divergence between the reference distribution and the posterior features. Besides the fact that the KL divergence is a natural dissimilarity measure between posterior distributions, we further motivate the use of the KL divergence by showing that the proposed model can be interpreted in terms of maximum likelihood and information theoretic clustering. Furthermore, the KL-based acoustic model can be seen as a general case of other known acoustic models for posterior features such as hybrid HMM/MLP and discrete HMM. The presented approach has been extended to large vocabulary recognition tasks. When compared to state-of-the-art HMM/GMM, the KL-based acoustic model yields comparable results while using significantly fewer parameters.

Ecole Polytechnique Fédérale de Lausanne2008

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Idiap2008

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Acoustic Models for Posterior Features in Speech Recognition

Guillermo Aradilla

Ecole Polytechnique Fédérale de Lausanne2008

Idiap2008

Extension d'algorithmes d'ordre zéro pour l'optimisation stochastique

Pierre Huguenin

EPFL2005