Neural networks: The natural road to artificial iiitelligence

Despite the many advances of artificial intelligence (Al) technology, most notably in the area of expert systems, efforts to build intelligent systems that would approach the commonsense reasoning and sensory abilities of even a small child have not been rewarding. A small, but growing number of researches believe that the existing AI toolboxes of symbolic representation and heuristic search may not hold the answer, and that massively parallel networks of simple neuron-like processing elements may hold the key. In this overview article, we examine the use of neurally inspired concepts in the construction of intelligent machines, and address their practical applications, advantages, and limitations

Parallel Training of Neural Networks for Speech Recognition

Tato diplomová práce je zaměřena na paralelizaci trénování neuronových sítí pro rozpoznávání řeči. V rámci této diplomové práce byly implementovány a porovnány dvě strategie paralelizace. První strategií je paralelizace dat s využitím rozdělení trénování do několika POSIX vláken. Druhou strategií je paralelizace uzlů s využitím platformy pro obecné výpočty na grafických kartách CUDA. V případě první strategie bylo dosaženo 4x urychlení, v případě využití platformy CUDA bylo dosaženo téměř 10x urychlení. Pro trénování byl použit algoritmus Stochastic Gradient Descent se zpětným šířením chyb. Po krátkém úvodu následuje druhá kapitola práce, která je motivační a zasazuje probém do kontextu rozpoznávání řeči. Třetí kapitola práce je teoretická a diskutuje neuronové sítě a metodu trénování. Následující kapitoly jsou zaměřené na návrh a implementaci a popisují iterativní vývoj tohoto projektu. Poslední obsáhlá kapitola popisuje testovací systém a uvádí výsledky provedených experimentů. V závěru jsou krátce zhodnoceny dosažené výsledky a nastíněna perspektiva dalšího vývoje projektu.This thesis deals with different parallelizations of training procedure for artificial neural networks. The networks are trained as phoneme-state acoustic descriptors for speech recognition. Two effective parallelization strategies were implemented and compared. The first strategy is data parallelization, where the training is split into several POSIX threads. The second strategy is node parallelization, which uses CUDA framework for general purpose computing on modern graphic cards. The first strategy showed a 4x speed-up, while using the second strategy we observed nearly 10x speed-up. The Stochastic Gradient Descent algorithm with error backpropagation was used for the training. After a short introduction, the second chapter of this thesis shows the motivation and introduces the neural networks into the context of speech recognition. The third chapter is theoretical, the anatomy of a neural network and the used training method are discussed. The following chapters are focused on the design and implementation of the project, while the phases of the iterative development are described. The last extensive chapter describes the setup of the testing system and reports the experimental results. Finally, the obtained results are concluded and the possible extensions of the project are proposed.

Prokaryote growth temperature prediction with machine learning

Archaea and bacteria can be divided into four groups based on their growth temperature adaptation: mesophiles, thermophiles, hyperthermophiles, and psychrophiles. The thermostability of proteins is a sum of multiple different physical forces such as van der Waals interactions, chemical polarity, and ionic interactions. Genes causing the adaptation have not been identified and this thesis aims to identify temperature adaptation linked genes and predict temperature adaptation based on the absence or presence of genes. A dataset of 4361 genes from 711 prokaryotes was analyzed with four different machine learning algorithms: neural network, random forest, gradient boosting machine, and logistic regression. Logistic regression was chosen to be an explanatory and predictive model based on micro averaged AUC and Occam’s razor principle. Logistic regression was able to predict temperature adaptation with good performance. Machine learning is a powerful predictor for temperature adaptation and less than 200 genes were needed for the prediction of each adaptation. This technique can be used to predict the adaptation of uncultivated prokaryotes. However, the statistical importance of genes connected to temperature adaptation was not verified and this thesis did not provide much additional support for previously proposed temperature adaptation linked genes

The effect of experimental pain on motor training performance and sensorimotor integration

Sensorimotor integration (SMI) is the ability of the central nervous system (CNS) to integrate afferent (incoming) information from different body parts and formulate appropriate motor output to muscles. Effective sensorimotor integration is essential when learning new skills and when performing tasks at home and in the workplace (Rothwell &Rosenkranz, 2005). The overall aim of this thesis is to investigate the effect of acute experimental pain on sensorimotor processing. The primary outcome is the effect of acute experimental pain on somatosensory evoked potential (SEP) peaks. Secondary outcomes include the effect of pain on motor performance and the interactive effect of pain and motor training on SEP peaks. As expected for the placebo condition, no significant differences were found in any of the post-placebo peaks. Contrary to what was expected for the placebo condition, the only peak to be significantly different post-motor learning was the N24 peak. Contrary to what was expected, there were no significant differences for any of the peaks following capsaicin application. One of the secondary outcomes was the interactive effect of pain and motor learning on SEP peaks. The only peak to show any significant differences post-intervention/post-motor learning was the N24 peak. Another secondary outcome was the effect of pain on motor performance. In terms of accuracy, no significant differences were found for either condition following motor learning. However, the data does show a trend towards improved accuracy for the subjects in the intervention group while the subjects in the placebo show a trend towards decreased accuracy. As expected, there was a significant decrease in reaction time for both conditions post-motor learning. However, contrary to what was expected, reaction time decreased to a greater extent in the intervention condition as compared to the placebo condition. It was anticipated that the reaction time would decrease to a greater extent in the placebo condition as it was hypothesized that pain would negatively impact motor performance. It is suspected that the effect of the pain induced by the capsaicin made the motor training task more difficult and participants would have had to focus greater attentional resources to learn the task which lead to the enhanced performance following motor training