Artiificial intelligence

Memorial days of disasters represent an opportunity to evaluate the progress of recovery. This article uses sentiment analysis (SA) to assess post-disaster recovery on the 10th anniversary of L’Aquila’s earthquake using Twitter data. We have analyzed 4349 tweets from 4 to 10 April 2019 with the hashtag: #L’Aquila that we have obtained from a third-party vendor. The polarity is first defined using a supervised classification based on experts’ rules on post-disaster reconstruction and Grammarly tones. Then, this polarity is compared with the outcome of an unsupervised classification based on the pre-trained SA machine learning algorithm developed by MonkeyLearn. We have found a significant negative assessment of the post-disaster recovery process in L’Aquila. About 33.1% of the tweets had a negative polarity, followed by 29.3% tweets with a neutral polarity, 28.7% with positive polarity, and 8.9% unrelated to the anniversary. Further analysis of the tweets confirms that after 10 years...

In this paper we argue that many problems in the semantics of natural language are due to a large gap between semantics (which is an attempt at understanding what we say in language about the world) and the way the world is. This seemingly monumental effort can be grossly simplified if one assumes, as Hobbs (1985) correctly observed some time ago, a theory of the world that reflects the way we talk about it. We demonstrate here that assuming such a strongly-typed ontology of commonsense knowledge reduces certain problems to near triviality.

Traditionally, data mining algorithms and machine learning algorithms are engineered to approach the problems in isolation. These algorithms are employed to train the model in separation on a specific feature space and same distribution. Depending on the business case, a model is trained by applying a machine learning algorithm for a specific task. A widespread assumption in the field of machine learning is that training data and test data must have identical feature spaces with the underlying distribution. On the contrary, in real world this assumption may not hold and thus models need to be rebuilt from the scratch if features and distribution changes. It is an arduous process to collect related training data and rebuild the models. In such cases, Transferring of Knowledge or transfer learning from disparate domains would be desirable. Transfer learning is a method of reusing a pre-trained model knowledge for another task. Transfer learning can be used for classification, regression and clustering problems. This paper uses one of the pre-trained models-VGG-16 with Deep Convolutional Neural Network to classify images.

This essay traces crossed phenomena that exist within us and attempts to formulate a hypothesis of what the implications of apophenia could be in regard to its complementary phenomenon: a failure to recognize patterns, in geology as much as in biology. Emergent effects which in many cases give rise to the very artifacts that we may perceive as images, wondering in how much the authorship may lie with the process and not a singular mind, be it human or supernatural. Lastly it extrapolates this scenario in order to wonder whether we may produce patterns which resemble existing things and what bearing this would have on their ontology.

Neuro Linguistic Programming (NLP) is a collection of techniques for personality development. Meta programmes, which are habitual ways of inputting, sorting and filtering the information found in the world around us, are a vital factor in NLP. Differences in meta programmes result in significant differences in behaviour from one person to another. Personality types can be recognized through utilizing and analysing meta programmes. There are different methods to predict personality types based on meta programmes. The Myers-Briggs Type Indicator ® (MBTI) is currently considered as one of the most popular and reliable methods. In this study, a new machine learning method has been developed for personality type prediction based on the MBTI. The performance of the new methodology presented in this study has been compared to other existing methods and the results show better accuracy and reliability. The results of this study can assist NLP practitioners and psychologists in regards to identification of personality types and associated cognitive processes.

Memorial days of disasters represent an opportunity to evaluate the progress of recovery. This article uses sentiment analysis (SA) to assess post-disaster recovery on the 10th anniversary of L'Aquila's earthquake using Twitter data. We have analyzed 4349 tweets from 4 to 10 April 2019 with the hashtag: #L'Aquila that we have obtained from a third-party vendor. The polarity is first defined using a supervised classification based on experts' rules on post-disaster reconstruction and Grammarly tones. Then, this polarity is compared with the outcome of an unsupervised classification based on the pre-trained SA machine learning algorithm developed by MonkeyLearn. We have found a significant negative assessment of the post-disaster recovery process in L'Aquila. About 33.1% of the tweets had a negative polarity, followed by 29.3% tweets with a neutral polarity, 28.7% with positive polarity, and 8.9% unrelated to the anniversary. Further analysis of the tweets confirms that after 10 years, the reconstruction is still ongoing and that criticism of the recovery reported in the literature is also found in the tweets. Based on our analysis, the critical day to collect most of the data is the anniversary's exact day. Tweets from citizens and/or news agencies, which are more likely to express the reality experienced, are therefore more useful in understanding recovery than tweets from government officials and/or governmental institutions. From the total 4349 tweets, we can state that 2488 (57%) were correctly classified by the pre-trained SA machine learning algorithm developed by MonkeyLearn, while 1861 (43%) were misclassified. It means an overall accuracy (ACC) of 57% and a misclassification rate of 43% by the algorithm. We argue that our results have the potential to serve as a benchmark that can be used to compare other post-disaster recovery processes using the same Twitterbased SA on their anniversaries.

Как современному человеку найти баланс в своих отношениях с технологическим миром, чтобы не потерять в нем себя? Об этом «Культура» поговорила с философом Александром МИХАЙЛОВСКИМ, доцентом НИУ ВШЭ, известным специалистом в области интеллектуальной истории и философии техники, руководителем «Герменевтического кружка» и переводчиком.

Neuro Linguistic Programming (NLP) is a collection of techniques for personality development. Meta programmes, which are habitual ways of inputting, sorting and filtering the information found in the world around us, are a vital factor in NLP. Differences in meta programmes result in significant differences in behaviour from one person to another. Personality types can be recognized through utilizing and analysing meta programmes. There are different methods to predict personality types based on meta programmes. The Myers–Briggs Type Indicator® (MBTI) is currently considered as one of the most popular and reliable methods. In this study, a new machine learning method has been developed for personality type prediction based on the MBTI. The performance of the new methodology presented in this study has been compared to other existing methods and the results show better accuracy and reliability. The results of this study can assist NLP practitioners and psychologists in regards to ide...

Depuis plusieurs mois, des articles critiquent ce qu'il est convenu de nommer le biais de l'intelligence artificielle. Celui-ci accentuerait les préjugés et la construction des inégalités déjà présente chez les êtres humains. Ainsi, une intelligence artificielle ne ferait que reproduire et accentuer les préjugés sexistes et racistes + pour deux raisons principales : – Le machine learning doit se nourrir de données préexistantes en très grand nombre qui sont souvent constituées par des êtres humains, par exemple sur les réseaux sociaux. Ces données portent la trace des préjugés. Ainsi la machine hérite de la construction mentale des êtres humains parce que l'apprentissage est fondé sur un mimétisme relatif. Nous nommerons cette reproduction l'effet miroir. – Certains chercheurs en intelligence artificielle mènent leurs travaux selon des préjugés qu'ils introduisent consciemment ou inconsciemment dans les modèles qui instruisent les machines. Le biais serait alors le résultat d'un décalage entre les compétences techniques des chercheurs en informatique et leur manque de compétences en sciences humaines. Pour le dire autrement, ceux qui mènent les recherches parce qu'ils en ont la capacité technique n'en ont pas toujours les capacités réflexives. La spécialisation des savoirs techniques empêcherait la prise en compte d'un impact général et social. J'aimerais examiner la solidité de cette critique du biais de l'intelligence artificielle parce qu'elle semble aujourd'hui aller de soi et constituer l'apport principal des sciences humaines dans ce domaine dont les impacts sociaux, politiques et économiques semblent majeurs. Avant de mener cet examen, je souhaiterais immédiatement souligner qu'à un certain niveau d'analyse cette critique du biais de l'intelligence artificielle est légitime. Mais sur un plan méthodologique, elle reproduit ce qu'elle croit critiquer, c'est-à-dire l'absence d'examen de ses propres conditions de possibilité, examen qui est nécessaire à la relation entre science et conscience critique. On ne peut mener une critique sur un seul plan. La critique doit aussi être une autocritique afin de voir si elle n'est pas un effet de miroir de l'opposition qu'elle constitue. Il faut une réflexivité critique afin de déjouer certains effets de mise en scène (je dénonce le danger et comme par hasard j'ai la solution, je m'autoconstitue comme annonciateur et sauveur).