Academic literature on the topic 'Arificial Intelligence'

Artificial intelligence has emerged as a transformative force with profound implications for diverse domains, including finance and portfolio management. This paper delves into the multifaceted impact of AI on portfolio management and the dynamic landscape of systemic risk. The proliferation of AI is fueled by rapid advancements in computational capabilities, the abundance of extensive datasets, and breakthroughs in AI algorithms. It offers unparalleled accuracy, speed, and practical applicability, revolutionizing traditional paradigms. Globalization and AI applications have amplified systemic risks on a global scale, necessitating a reevaluation of risk management strategies. Conventional portfolio theories, like Modern portfolio theory, have historically emphasized the diversification of idiosyncratic risks while downplaying systemic risk. The comprehension and effective mitigation of systemic risks are paramount to preserving financial stability, particularly in the aftermath of systemic crises such as the 2007 2009 financial meltdown. AI's transformative potential extends beyond risk management, reshaping the labor landscape in asset management. Forecasts anticipate substantial job reductions in the field, prompting professionals to embrace adaptability and acquire new skill sets. This paper examines AI's integration into portfolio management, shedding light on the intricate interplay between AI, systemic risk dynamics, and investment practices. In conclusion, the integration of AI into portfolio management heralds an era of unparalleled opportunities and complex challenges. AI's capacity to process vast datasets, enhance pattern recognition, and refine predictive modeling has reinvented investment methodologies. However, this paradigm shift comes with inherent risks, especially pertaining to systemic instability. Identifying, understanding, and mitigating these risks is pivotal for sustaining financial market. Collaborative efforts among industry experts, regulators, and AI developers are instrumental in fostering responsible and sustainable AI integration within financial markets. As AI continues to exert its influence, professionals must remain adaptable and acquire new competencies to navigate the evolving financial landscape effectively. The future of portfolio management lies in harnessing AI's capabilities while safeguarding against potential pitfalls, ultimately steering the financial sector toward greater efficiency and resilience.

Artificial intelligence semakin banyak digunakan dalam berbagai bidang dan disiplin ilmu. Wacana yang selalu digaungkan dalam penggunaanya adalah efisiensi dan kecepatan yang dihasilkan. Salah satu perkembangan Arificial intelligence adalah ChatGPT, yaitu flatform untuk mendapatkan jawaban terhadap pertanyaan akademis yang dilontarkan. Kehadiran ChatGPT melahirkan pro dan kontra diantara pendidik agama Kristen. Tujuan penelitian ini adalah untuk mengkaji gagasan ChatGPT dalam Pendidikan Agama Kristen. Metode yang digunakan adalah kualitatif dengan pendekatan netnografi. Hasil penelitian menunjukkan bahwa ChatGPT akan mereduksi nilai-nilai dalam Pendidikan Agama Kristen sebagai proses belajar untuk menjadi seperti Yesus. Namun disisi lain, ChatGPT dapat digunakan sebagai gambaran luas tentang sesuatu hal yang berfungsi meningkatkan Pendidikan Agama Kristen.

Security systems that use passwords or identity cards can be hacked and misused. One of alternative security system is to use biometric identification. The biometric system that is popularly used is fingerprints, because the system is safe and comfortable. Fingerprints have a distinctive pattern for each individual and this makes fingerprints relatively difficult to fake, so the system is safe. Comfortable because the verification process is easily done. The problem that often occurs on the system of fingerprint scanner is found an error and the user has difficulty when accessing. To handle with these problems has developed an artificial intelligence system. One of arificial intelligence in pattern identification is artificial neural networks (ANN). From some of the results of previous research showed that the ANN method is reliable in pattern identification. Based on these facts, the method used in this research is the perceptron ANN method with values learning rate varying. In the research the program conducted by testing 20 samples showed that the performance of the perceptron ANN method is relatively good method in fingerprint image recognition. This can be indicated from the value of accuracy (0.95), precision (0.83), TP rate (1), and FP rate (0.07)). In addition, the location of the point coordinate (FP rate; TP rate) is (0.07; 1) in ROC graphs is located on the upper left (perfect classifier region).

This article presents a review of papers of the international scientific seminar “Transcendental Turn in Modern Philosophy — 8: Metaphysics, epistemology, transcendental cognitive science and artificial intelligence,” which was held on April 20–22, 2023 in Moscow. The topics reviewed were the following: “Transcendental Philosophy: Ontology, Metaphysics of Experience or Epistemology,” “Transcendentalism, Cognitive Science and Artificial Intelligence,” “Reception and Development of Transcendental (Phenomenological) Approach in Modern Philosophy,” as well as “Transcendental Phenomenology: Ontology and/or Gnoseology”. The author analyzes the presentations of the participants, grouping them around the following thematic and problematic nodes: the transcendental foundations of cognitive sciences, the understanding and status of the transcendental unity of apperception, the relationship between gnoseology and ontology in phenomenological research, receptivity and construction in forming the subject of knowledge, etc. This approach makes it possible to identify different, sometimes opposing positions of the participants on the same question and to outline ways to overcome the contradictions.

AbstractThe influence of hydropriming and Light Emitting Diodes (LED) on germination and growth indices, followed by optimizing and validation via artificial intelligence-based models was carried out in this research. White LEDs (W-LEDs) were more effective by yielding the most effective growth indices, such as mean germination time (MGT) (1.11 day), coefficient of variation of germination time (CVt) (20.72%), mean germination rate (MR) (0.81 day−1), uncertainty (U) (0.40 bit), and synchronization (Z values) (0.79); the optimum MGT (1.09 day), CVt (15.97%), MR (0.77 day−1), U (0.32 bit), and Z (0.55) values were found after 2 h of hydropriming, which was responsible for all efficient growth indicators. W-LEDs with 1 h hydropriming proved to be the ideal LED and hydropriming combination. Results on growth indices for in vitro seedlings were completely different from those on germination indices, and the most desirable germination indices were linked to red LEDs (R-LEDs). Whereas 4 h hydropriming was most effective for the post-germination process. Pareto charts, normal plots, contour plots, and surface plots were created to optimize the input variables. Finally, the data were predicted using Arificial Neural Network (ANN) inspired multilayer perceptron (MLP) and machine learning-based random forest (RF) algorithms. For both models, plant height was correlated with maximum R2 values. Whereas, all output variables had relatively low mean absolute error (MAE), mean square error (MSE), and mean absolute percentage error (MAPE) scores, indicating that both models performed well. The results of this investigation disclosed a link between certain LEDs and hydropriming treatment for in vitro germination indices and plant growth. Graphical Abstract Graphical presentation of actual and predicted values for germination indices in chickpea

Cette thèse aborde le clustering de systèmes énergétiques complexes et hétérogènes au sein d'un système d'aide à la décision (SAD).Dans le chapitre 1, nous explorons d'abord la théorie des systèmes complexes et leur modélisation, reconnaissant les bâtiments comme des Systèmes Complexes Sociotechniques. Nous examinons l'état de l'art des acteurs impliqués dans la performance énergétique, identifiant notre cas d'étude comme le Tiers de Confiance pour la Mesure et la Performance Énergétique (TCMPE). Face à nos contraintes, nous nous focalisons sur le besoin d'un système d'aide à la décision pour fournir des recommandations énergétiques, le comparant aux systèmes de supervision et de recommandation et soulignant l'importance de l'explicabilité dans la prise de décision assistée par IA (XAI). Reconnaissant la complexité et l'hétérogénéité des bâtiments gérés par le TCMPE, nous argumentons que le clustering est une étape initiale cruciale pour développer un SAD, permettant des recommandations sur mesure pour des sous-groupes homogènes de bâtiments.Dans le Chapitre 2, nous explorons l'état de l'art des systèmes semi-automatisés pour la prise de décisions à haut risque, mettant l'accent sur la nécessité de gouvernance dans les SAD. Nous investiguons les régulations européennes, mettant en lumière le besoin d'exactitude, de fiabilité, et d'équité de notre système décisionnel, et identifions des méthodologies pour adresser ces besoins, telles que la méthodologie DevOps et le data lineage. Nous proposons une architecture distribuée du SAD qui répond à ces exigences et aux défis posés par le Big Data, intégrant un datalake pour la manipulation des données hétérogènes et massive, des datamarts pour la sélection et le traitement spécifiques des données, et une ML-Factory pour peupler une bibliothèque de modèles. Différentes méthodes de Machine Learning sont sélectionnées pour les différents besoins spécifiques du SAD.Le Chapitre 3 se concentre sur le clustering comme méthode d'apprentissage automatique primaire dans notre cas d'étude, il est essentiel pour identifier des groupes homogènes de bâtiments. Face à la nature plurielle - numérique, catégorielle, séries temporelles - des données décrivant les bâtiments, nous proposons le concept de clustering complexe. Après avoir examiné l'état de l'art, nous identifions la nécessité d'introduire des techniques de réduction de dimensionnalité, associé à des méthodes de clustering numérique et mixte état de l'art. La Prétopologie est proposée comme approche novatrice pour le clustering de données mixtes et complexes. Nous soutenons qu'elle permet une plus grande explicabilité et interactivité, en permettant un clustering hiérarchique construit sur de règles logiques et de notions de proximité adaptées au contexte. Les défis de l'évaluation du clustering complexe sont abordés, et des adaptations de l'évaluation des jeux de donnée numérique sont proposées.Dans le chapitre 4, nous analysons les performances computationnelles des algorithmes et la qualité des clusters obtenus sur différents jeux de données variant en taille, nombre de clusters, distribution et nombre de dimensions. Ces jeux de donnée sont publique, privées ou généré pour les tests. La Prétopologie et l'utilisation de la réduction de dimensionnalité montrent des résultats prometteurs comparés aux méthodes de clustering de données mixtes de l'état de l'art.En conclusion, nous discutons des limitations de notre système, y compris les limites d'automatisation du SAD à chaque étape du flux de données. Nous mettons l'accent sur le rôle crucial de la qualité des données et les défis de prédire le comportement des systèmes complexes au fil du temps. L'objectivité de nos méthodes d'évaluation de clustering est questionnée en raison de l'absence de vérité terrain. Nous envisageons des travaux futurs, tels que l'automatisation de l'hyperparamètrisation et la continuation du développement du SAD
This thesis addresses the clustering of complex and heterogeneous energy systems within a Decision Support System (DSS).In chapter 1, we delve into the theory of complex systems and their modeling, recognizing buildings as complex systems, specifically as Sociotechnical Complex Systems. We examine the state of the art of the different agents involved in energy performance within the energy sector, identifying our case study as the Trusted Third Party for Energy Measurement and Performance (TTPEMP.) Given our constraints, we opt to concentrate on the need for a DSS to provide energy recommendations. We compare this system to supervision and recommender systems, highlighting their differences and complementarities and introduce the necessity for explainability in AI-aided decision-making (XAI). Acknowledging the complexity, numerosity, and heterogeneity of buildings managed by the TTPEMP, we argue that clustering serves as a pivotal first step in developing a DSS, enabling tailored recommendations and diagnostics for homogeneous subgroups of buildings. This is presented in Chapter 1.In Chapter 2, we explore DSSs' state of the art, emphasizing the need for governance in semi-automated systems for high-stakes decision-making. We investigate European regulations, highlighting the need for accuracy, reliability, and fairness in our decision system, and identify methodologies to address these needs, such as DevOps methodology and Data Lineage. We propose a DSS architecture that addresses these requirements and the challenges posed by big data, featuring a distributed architecture comprising a data lake for heterogeneous data handling, datamarts for specific data selection and processing, and an ML-Factory populating a model library. Different types of methods are selected for different needs based on the specificities of the data and of the question needing answering.Chapter 3 focuses on clustering as a primary machine learning method in our architecture, essential for identifying homogeneous groups of buildings. Given the combination of numerical, categorical and time series nature of the data describing buildings, we coin the term complex clustering to address this combination of data types. After reviewing the state-of-the-art, we identify the need for dimensionality reduction techniques and the most relevant mixed clustering methods. We also introduce Pretopology as an innovative approach for mixed and complex data clustering. We argue that it allows for greater explainability and interactability in the clustering as it enables Hierarchical clustering and the implementation of logical rules and custom proximity notions. The challenges of evaluating clustering are addressed, and adaptations of numerical clustering to mixed and complex clustering are proposed, taking into account the explainability of the methods.In the datasets and results chapter, we present the public, private, and generated datasets used for experimentation and discuss the clustering results. We analyze the computational performances of algorithms and the quality of clusters obtained on different datasets varying in size, number of clusters, distribution, and number of categorical and numerical parameters. Pretopology and Dimensionality Reduction show promising results compared to state-of-the-art mixed data clustering methods.Finally, we discuss our system's limitations, including the automation limits of the DSS at each step of the data flow. We focus on the critical role of data quality and the challenges in predicting the behavior of complex systems over time. The objectivity of our clustering evaluation methods is challenged due to the absence of ground truth and the reliance on dimensionality reduction to adapt state-of-the-art metrics to complex data. We discuss possible issues regarding the chosen elbow method and future work, such as automation of hyperparameter tuning and continuing the development of the DSS