Classical Artificial Intelligence Research Articles

Assessing rainfall-runoff models for climate change: Simple and differential split-sample tests for conceptual and artificial intelligence models

In this study, the performance of two popular conceptual models, a classic artificial intelligence (AI) model and a deep learning model is compared using simple and differential split-sample tests (SSST and DSST). Finally, the two conceptual models and the classic AI model are combined employing a model ensemble technique. The SSST and DSST schemes confirmed the ability of both types of models (conceptual and AI) to capture the hydrological response to rainfall events, although the conceptual models showed a better performance in the DSST scheme. The ensemble technique enhanced the modelling performance up to 22% and 35% in the calibration and verification steps. This study highlights the importance of applying differential-split sample test using existing data to build further confidence in model projections, and suggests that the use of model ensemble techniques combining conceptual and AI models can enhance the performance of rainfall-runoff modelling in the context of climate change.

Interactive Human 3D Model with Conventional AI

Abstract: The convergence of interactive human 3D models with conventional artificial intelligence (AI) represents a groundbreaking fusion of computer graphics and intelligent systems. This interdisciplinary field combines advanced 3D modeling techniques with classical AI methodologies to create interactive virtual representations of humans. Rigorous studies in 3D modeling and animation form the basis for lifelike avatars, while conventional AI empowers these models with the ability to understand user inputs, make contextually relevant decisions, and engage in dynamic and responsive interactions. At the heart of this innovation is the emphasis on human-computer interaction (HCI), leveraging principles such as gesture recognition, voice commands, and user-friendly interfaces to enhance user experiences. The integration extends into virtual and augmented reality (VR/AR), providing users with immersive and engaging environments where interactive 3D human models come to life. Applications of this technology span a wide array of fields, including virtual companionship, education, healthcare simulations, entertainment, and training scenarios. The impact is transformative, offering new possibilities for human-machine interactions and redefining traditional approaches to AI applications. However, the burgeoning field is not without challenges. Privacy concerns, ethical considerations related to realistic human representation, and the societal impact of interactive 3D models necessitate careful examination. This abstract provides a glimpse into the technical intricacies of creating lifelike models, the intelligence driving their interactive capabilities, and the broader implications for diverse applications and industries. As research and development progress, the collaboration of computer graphics and AI not only opens new avenues but also prompts a thoughtful reevaluation of the evolving relationship between humans and intelligent, interactive 3D representations.

Legal Status of Artificial Intelligence from Quantum-Theoretic Perspective

Massive inclusion of artificial intelligence (AI) in the technosphere and electronic governments urges an update in legal regulation of these and related areas. The issue converges on the key question of whether AI can be endowed with legal personhood and capacity. Opposing views in this respect build on hardly compatible ethics and largely outdated scientific grounds with a clear perspective for deep cultural antagonisms and further fragmentation of the world. We contribute to this debate from the perspective of quantum cognitive science and show how it can resolve some of the current impasses. Our approach builds on the quantum-theoretic refinement of the concept of uncertainty into quantum and classical types: classical uncertainty denotes subjective ignorance of the present state of affairs, while quantum uncertainty accounts for individual freedom to construct the future. We show that legal capacity of intelligence, at bottom, is defined by the type of uncertainty it is capable to resolve. Natural intelligence, in particular, can resolve quantum uncertainties, generating genuine novelty and affective experience in the process. Classical AI, in contrast, is limited to algorithmic computation, bound to produce predefined results regardless of its complexity. Concepts of decision-making, subjectness, creativity, and personal meaning then are recognized as physically inapplicable to such systems. The proposed definitions of these terms complement and sharpen the criteria of legal capacity in the existing legislations, indicating that “autonomy” is essentially equivalent to “appreciation.” Classical AI then appears as fundamentally alien to subjectness and legal capacity both in civil and common laws, resolving a delicate contradiction between them. Quantum-empowered AI, in contrast, escapes this conclusion due to its access to quantum uncertainty, introducing novel challenges with respect to responsibility gaps and meaningful human control. The developed approach aligns with the present legal practice and ethical discourse, contributing to the scientifically informed development of law in technological societies.

Time series quantum classifiers with amplitude embedding

Quantum Machine Learning was born during the past decade as the intersection of Quantum Computing and Machine Learning. Today, advances in quantum computer hardware and the design of simulation frameworks able to run quantum algorithms in classic computers make it possible to extend classic artificial intelligence models to a quantum environment. Despite these achievements, several questions regarding the whole quantum machine learning pipeline remain unanswered, for instance the problem of classical data representation on quantum hardware, or the methodologies for designing and evaluating quantum models for common learning tasks such as classification, function approximation, clustering, etc. These problems become even more difficult to solve in the case of Time Series processing, where the context of past historical data may influence the behavior of the decision-making model. In this piece of research, we address the problem of Time Series classification using quantum models, and propose an efficient and compact representation of time series in quantum data using amplitude embedding. The proposal is capable of representing a time series of length n in log2(n)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$log_2(n)$$\\end{document} computational units, and experiments conducted on benchmark time series classification problems show that quantum models designed for classification can also outperform the accuracy of classic methods.

Intellectual cyborgs and the future of science.

Unlike classical artificial intelligence (AI), generative AI has the potential to transform scientists into intellectual cyborgs. Leveraging embodied cognition and extended mind theories can help us understand this scientific revolution. Despite ethical concerns, generative AI can enhance research efficiency and accessibility. However, this requires unprecedented proactive regulation and responsible development.

Computation and Communication Efficient Adaptive Federated Optimization of Federated Learning for Internet of Things

The proliferation of the Internet of Things (IoT) and widespread use of devices with sensing, computing, and communication capabilities have motivated intelligent applications empowered by artificial intelligence. Classical artificial intelligence algorithms require centralized data collection and processing, which are challenging in realistic intelligent IoT applications due to growing data privacy concerns and distributed datasets. Federated Learning (FL) has emerged as a privacy-preserving distributed learning framework, which enables IoT devices to train global models through sharing model parameters. However, inefficiency due to frequent parameter transmissions significantly reduces FL performance. Existing acceleration algorithms consist of two main types including local update and parameter compression, which considers the trade-offs between communication and computation/precision, respectively. Jointly considering these two trade-offs and adaptively balancing their impacts on convergence have remained unresolved. To solve the problem, this paper proposes a novel efficient adaptive federated optimization (FedEAFO) algorithm to improve the efficiency of FL, which minimizes the learning error via jointly considering two variables including local update and parameter compression. The FedEAFO enables FL to adaptively adjust two variables and balance trade-offs among computation, communication, and precision. The experiment results illustrate that compared with state-of-the-art algorithms, the FedEAFO can achieve higher accuracies faster.

Mapping Underwater Aquatic Vegetation Using Foundation Models With Air- and Space-Borne Images: The Case of Polyphytos Lake

Mapping underwater aquatic vegetation (UVeg) is crucial for understanding the dynamics of freshwater ecosystems. The advancement of artificial intelligence (AI) techniques has shown great potential in improving the accuracy and efficiency of UVeg mapping using remote sensing data. This paper presents a comparative study of the performance of classical and modern AI tools, including logistic regression, random forest, and a visual-prompt-tuned foundational model, the Segment Anything model (SAM), for mapping UVeg by analyzing air- and space-borne images in the few-shot learning regime, i.e., using limited annotations. The findings demonstrate the effectiveness of the SAM foundation model in air-borne imagery (GSD = 3–6 cm) with an F1 score of 86.5%±4.1% when trained with as few as 40 positive/negative pairs of pixels, compared to 54.0%±9.2% using the random forest model and 42.8%±6.2% using logistic regression models. However, adapting SAM to space-borne images (WorldView-2 and Sentinel-2) remains challenging, and could not outperform classical pixel-wise random forest and logistic regression methods in our task. The findings presented provide valuable insights into the strengths and limitations of AI models for UVeg mapping, aiding researchers and practitioners in selecting the most suitable tools for their specific applications.

The Tong Test: Evaluating Artificial General Intelligence Through Dynamic Embodied Physical and Social Interactions

The release of the generative pre-trained transformer (GPT) series has brought artificial general intelligence (AGI) to the forefront of the artificial intelligence (AI) field once again. However, the questions of how to define and evaluate AGI remain unclear. This perspective article proposes that the evaluation of AGI should be rooted in dynamic embodied physical and social interactions (DEPSI). More specifically, we propose five critical characteristics to be considered as AGI benchmarks and suggest the Tong test as an AGI evaluation system. The Tong test describes a value- and ability-oriented testing system that delineates five levels of AGI milestones through a virtual environment with DEPSI, allowing for infinite task generation. We contrast the Tong test with classical AI testing systems in terms of various aspects and propose a systematic evaluation system to promote standardized, quantitative, and objective benchmarks and evaluation of AGI.

Principles And Applications of Artificial Intelligence (AI) Algorithms: A Review of The Literature

Artificial intelligence technologies, represented by machine learning and knowledge graphs, have gained rapid popularity in the last century, and their research has been involved in a wide range of applications such as license plate recognition, face recognition, speech recognition, intelligent assistants, recommendation systems, and autonomous driving. With the increase of data, computing power, maturity of learning algorithms, and richness of application scenarios, people have started to pay attention to this "new" and widely successful research field in academia and industry, and its development history, working principles, and practical applications have attracted much attention and created a new round of AI enthusiasm. The main content of this paper includes the analysis of classical AI algorithms, the reduction of neural networks, decision trees, and biological evolutionary algorithms from the perspective of algorithm principles, and the description and overview of their practical applications and problem-solving processes in various fields and technologies, summarizes and prospects the future development of AI in the context of its development history and existing research results so that readers can better understand the future trends of AI.

Optimizing Quantum Classification Algorithms on Classical Benchmark Datasets.

The discovery of quantum algorithms offering provable advantages over the best known classical alternatives, together with the parallel ongoing revolution brought about by classical artificial intelligence, motivates a search for applications of quantum information processing methods to machine learning. Among several proposals in this domain, quantum kernel methods have emerged as particularly promising candidates. However, while some rigorous speedups on certain highly specific problems have been formally proven, only empirical proof-of-principle results have been reported so far for real-world datasets. Moreover, no systematic procedure is known, in general, to fine tune and optimize the performances of kernel-based quantum classification algorithms. At the same time, certain limitations such as kernel concentration effects-hindering the trainability of quantum classifiers-have also been recently pointed out. In this work, we propose several general-purpose optimization methods and best practices designed to enhance the practical usefulness of fidelity-based quantum classification algorithms. Specifically, we first describe a data pre-processing strategy that, by preserving the relevant relationships between data points when processed through quantum feature maps, substantially alleviates the effect of kernel concentration on structured datasets. We also introduce a classical post-processing method that, based on standard fidelity measures estimated on a quantum processor, yields non-linear decision boundaries in the feature Hilbert space, thus achieving the quantum counterpart of the radial basis functions technique that is widely employed in classical kernel methods. Finally, we apply the so-called quantum metric learning protocol to engineer and adjust trainable quantum embeddings, demonstrating substantial performance improvements on several paradigmatic real-world classification tasks.

Machine learning for the perception of autonomous construction machinery

Abstract Robotic systems require holistic capabilities to sense, perceive, and act autonomously within their application environment. A safe and trustworthy autonomous operation is essential, especially in hazardous environments and critical applications like autonomous construction machinery for the decontamination of landfill sites. This article presents an enhanced combination of machine learning (ML) methods with classic artificial intelligence (AI) methods and customized validation methods to ensure highly reliable and accurate sensing and perception of the environment for autonomous construction machinery. The presented methods have been developed, evaluated, and applied within the Competence Center »Robot Systems for Decontamination in Hazardous Environments« (ROBDEKON) for investigating and developing robotic systems for autonomous decontamination tasks. The objective of this article is to give a holistic, in-depth overview for the ML-based part of the perception pipeline for an autonomous construction machine working in unstructured environments.

Review of current artificial intelligence methods and metaheuristic algorithms for wind power prediction

<p>Due to the insufficient fossil resources and the increasing environmental challenges, the world is heading for a more use-oriented to renewable energy sources, specifically to wind energy. A number of predictive techniques are available for the efficient use of wind energy. This article, which is a review of methods of artificial intelligence (AI) and meta-heuristic algorithms for wind energy prediction, fits into this context. There are two distinct categories: the first consists of traditional methods that are commonly used in this context, like different types of artificial neural networks (ANN), support vector machines (SVM) and fuzzy logic; the second is a combined approach which mixes the classic artificial intelligence methods and the meta-heuristic algorithms for the optimization of the forecast output. Then, a summary and comparison between the methodologies are established, and the advantages and limits of each technique are defined. The combination of the classic artificial intelligence and metaheuristic algorithms has a greater performance than the utilization of classic methods only. Nevertheless, using hybrid metaheuristic algorithms with classic artificial intelligence prediction methods can provide a higher precision.</p>

Revival of Classical Algorithms: A Bibliometric Study on the Trends of Neural Networks and Genetic Algorithms

The purpose of our bibliometric research was to capture and analyze the trends of two types of well-known classical artificial intelligence (AI) algorithms: neural networks (NNs) and genetic algorithms (GAs). Symmetry is a very popular international and interdisciplinary scientific journal that cover six major research subjects of mathematics, computer science, engineering science, physics, biology, and chemistry which are all related to our research on classical AI algorithms; therefore, we referred to the most innovative research articles of classical AI algorithms that have been published in Symmetry, which have also introduced new advanced applications for NNs and Gas. Furthermore, we used the keywords of “neural network algorithm” or “artificial neural network” to search the SSCI database from 2002 to 2021 and obtained 951 NN publications. For comparison purposes, we also analyzed GA trends by using the keywords “genetic algorithm” to search the SSCI database over the same period and we obtained 878 GA publications. All of the NN and GA publication results were categorized into eight groups for deep analyses so as to investigate their current trends and forecasts. Furthermore, we applied the Kolmogorov–Smirnov test (K–S test) to check whether our bibliometric research complied with Lotka’s law. In summary, we found that the number of applications for both NNs and GAs are continuing to grow but the use of NNs is increasing more sharply than the use of GAs due to the boom in deep learning development. We hope that our research can serve as a roadmap for other NN and GA researchers to help them to save time and stay at the cutting edge of AI research trends.

A comparison of artificial intelligence versus radiologists in the diagnosis of thyroid nodules using ultrasonography: a systematic review and meta-analysis.

Thyroid nodules are common. Ultrasonography (US) is the first investigation for thyroid nodules. Artificial Intelligence (AI) is widely integrated into medical diagnosis to provide additional information. The primary objective of this study was to accumulate the pooled sensitivity and specificity between all available AI and radiologists using thyroid US imaging. The secondary objective was to compare AI's diagnostic performance to that of radiologists. A systematic review meta-analysis. PubMed, Scopus, Web of Science, and Cochrane Library data were searched for studies from inception until June 11, 2020. Twenty five studies were included in this meta-analysis. The pooled sensitivity and specificity of AI were 0.86 (95% CI 0.81-0.91) and 0.78 (95% CI 0.73-0.83), respectively. The pooled sensitivity and specificity of radiologists were 0.85 (95% CI 0.80-0.89) and 0.82 (95% CI 0.77-0.86), respectively. The accuracy of AI and radiologists is equivalent in terms of AUC [AI 0.89 (95% CI 0.86-0.92), radiologist 0.91 (95% CI 0.88-0.93)]. The diagnostic odd ratio (DOR) between AI 23.10 (95% CI 14.20-37.58) and radiologists 27.12 (95% CI 17.45-42.16) had no statistically significant difference (P = 0.56). Meta-regression analysis revealed that Deep Learning AI had significantly greater sensitivity and specificity than classic machine learning AI (P < 0.001). AI demonstrated comparable performance to radiologists in diagnosing benign and malignant thyroid nodules using ultrasonography. Additional research to establish its equivalency should be conducted.

Subjectivity of Explainable Artificial Intelligence

The article addresses the problem of identifying methods to develop the ability of artificial intelligence (AI) systems to provide explanations for their findings. This issue is not new, but, nowadays, the increasing complexity of AI systems is forcing scientists to intensify research in this direction. Modern neural networks contain hundreds of layers of neurons. The number of parameters of these networks reaches trillions, genetic algorithms generate thousands of generations of solutions, and the semantics of AI models become more complicated, going to the quantum and non-local levels. The world’s leading companies are investing heavily in creating explainable AI (XAI). However, the result is still unsatisfactory: a person often cannot understand the “explanations” of AI because the latter makes decisions differently than a person, and perhaps because a good explanation is impossible within the framework of the classical AI paradigm. AI faced a similar problem 40 years ago when expert systems contained only a few hundred logical production rules. The problem was then solved by complicating the logic and building added knowledge bases to explain the conclusions given by AI. At present, other approaches are needed, primarily those that consider the external environment and the subjectivity of AI systems. This work focuses on solving this problem by immersing AI models in the social and economic environment, building ontologies of this environment, taking into account a user profile and creating conditions for purposeful convergence of AI solutions and conclusions to user-friendly goals.

Quantifying means-end reasoning skills in simulation-based training: a logic-based approach

We develop a logic-based approach for designing simulation-based training scenarios. Our methodology embodies a concise definition of the scenario concept and integrates the notions of training goals, acceptable versus unacceptable actions and performance scoring. The approach applies classical artificial intelligence (AI) planning to extract coherent plays from a causal description of the training domain. The domain- and task-specific parts are defined in a high-level action description language [Formula: see text]. Generic causal and temporal logic is added when the causal theory is compiled into the underlying Answer Set Programming (ASP) language. The ASP representation is used to derive a scoring function that reflects the quality of a play or training session, based on a distinction of states and actions into green (acceptable) and red (unacceptable) ones. To that end, we add to the casual theory a set of norms that specify an initial assignment of colors. The ASP engine uses these norms as axioms and propagates colors by consulting the causal theory. We prove that any set of such norms constitutes a conservative extension of the underlying causal theory. With this work, we hope to lay the foundation for the development of design and analysis tools for exercise managers. We envision a software system that lets an exercise manager view all plays of a tentative scenario design, with expediency information and scores for each possible play. Our approach is applicable to any domain in which means-ends reasoning is pertinent. We illustrate the approach in the domain of crisis response and management.

Decentralized Federated Learning for Healthcare Networks: A Case Study on Tumor Segmentation

Smart healthcare relies on artificial intelligence (AI) functions for learning and analysis of patient data. Since large and diverse datasets for training of Machine Learning (ML) models can rarely be found in individual medical centers, classical centralized AI requires moving privacy-sensitive data from medical institutions to data centers that process the fused information. Training on data centers thus requires higher communication resource/energy demands while violating privacy. This is considered today as a significant bottleneck in pursuing scientific collaboration across trans-national clinical medical research centers. Recently, federated learning (FL) has emerged as a distributed AI approach that enables the cooperative training of ML models, without the need of sharing patient data. This paper dives into the analysis of different FL methods and proposes a real-time distributed networking framework based on the Message Queuing Telemetry Transport (MQTT) protocol. In particular, we design a number of solutions for ML over networks, based on FL tools relying on a parameter server (PS) and fully decentralized paradigms driven by consensus methods. The proposed approach is validated in the context of brain tumor segmentation, using a modified version of the popular U-NET model with representative clinical datasets obtained from the daily clinical workflow. The FL process is implemented on multiple physically separated machines located in different countries and communicating over the Internet. The real-time test-bed is used to obtain measurements of training accuracy vs. latency trade-offs, and to highlight key operational conditions that affect the performance in real deployments.

Elastic AI: system support for adaptive machine learning in pervasive computing systems

Artificial intelligence (AI) is an important part of today’s pervasive computing systems. Still, there is no end-to-end system platform that allows to deploy, update, manage and execute AI models in pervasive systems. We propose such a system platform in this paper. Most importantly, we reuse concepts and techniques from twenty years of pervasive computing research on how to enable runtime adaptation and apply it to AI. This allows to specify adaptive AI models that are able to react to a multitude of dynamic changes, e.g. with respect to available devices, networking conditions, but also application requirements and sensor data sources. Developers can optimise their applications iteratively, starting with a generic setup and refining it step by step towards their specific pervasive computing scenario. To show the applicability of our platform, we apply it to two pervasive use cases and evaluate them, achieving up to four times faster inference and three times lower energy consumption compared to a classical AI deployment.

Interference-Aware Online Multicomponent Service Placement in Edge Cloud Networks and its AI Application

Edge computing that utilizes ubiquitous edge devices locating in close proximity to users is powerful for providing Quality of Service guaranteed computation offloading services. Toward the limited resources of edge servers and wireless links, large services can be split into multiple interconnected components to be served by multiple edge servers cooperatively. The current works on service placement either assume unsplittable services or ignore the geographically isolated property of edge servers. They also ignore the interference among online services that share the same physical nodes/links in terms of executing delay. Namely, every service adds load to the placed nodes/links and every increment on load of nodes/links risks delay violation of existing services. To overcome above challenges, this article emphasizes on the interference-aware (IA) online multicomponent service placement in edge cloud networks. First, the delay of tree-like services is analyzed considering the dependency among components, based on which the IA residual capacities of physical nodes, links, and paths are defined and formulated theoretically. Furthermore, we reduce the problem of multicomponent service placement to be NP-hard and transform it into an ant colony optimization (ACO) problem to obtain the near-optimal solution. More importantly, a level traversal component ranking method and an IA dynamic pruning method are proposed for ACO to achieve faster convergence, interference awareness, and higher acceptance ratio of services. Simulation results are presented to validate the effectiveness of proposed methods. In addition, the classic artificial intelligence application of image classification is experimented to further strength the motivation of IA investigation in practical.

УЧЕБНИТЕ ЗАДАЧИ В ОБУЧЕНИЕТО ПО ИЗКУСТВЕН ИНТЕЛЕКТ

Modern trends in the development of digital technologies are directly related to Artificial Intelligence and its application in all spheres of life. This determines the need to study this discipline not only in universities but also in schools. In the last few years, various schools in Bulgaria have introduced the study of elements of Artificial Intelligence as an innovation in specialized, professional and innovative schools, as well as in clubs of interest. The Ministry of Education and Science has approved a sample curriculum. Textbooks and teaching aids are being created. The report aims to present a classification of learning tasks in artificial intelligence training in different levels and forms of training. Some basic key examples will be considered, through which the basic concepts and rules in classical artificial intelligence are introduced, related to both the basic algorithms for solving problems through search and the presentation of knowledge through logical rules. The authors will share their experience in the study of Artificial Intelligence and will present some opportunities to increase the effectiveness of training.