Progress In Artificial Intelligence Research Articles

Artificial Intelligence and Healthcare: A Journey through History, Present Innovations, and Future Possibilities

Artificial intelligence (AI) has emerged as a powerful tool in healthcare significantly impacting practices from diagnostics to treatment delivery and patient management. This article examines the progress of AI in healthcare, starting from the field’s inception in the 1960s to present-day innovative applications in areas such as precision medicine, robotic surgery, and drug development. In addition, the impact of the COVID-19 pandemic on the acceleration of the use of AI in technologies such as telemedicine and chatbots to enhance accessibility and improve medical education is also explored. Looking forward, the paper speculates on the promising future of AI in healthcare while critically addressing the ethical and societal considerations that accompany the integration of AI technologies. Furthermore, the potential to mitigate health disparities and the ethical implications surrounding data usage and patient privacy are discussed, emphasizing the need for evolving guidelines to govern AI’s application in healthcare.

Synaptic Properties of a PbHfO3 Ferroelectric Memristor for Neuromorphic Computing.

The conventional von Neumann architecture has proven to be inadequate in keeping up with the rapid progress in artificial intelligence. Memristors have become the favored devices for simulating synaptic behavior and enabling neuromorphic computations to address challenges. An artificial synapse utilizing the perovskite structure PbHfO3 (PHO) has been created to tackle these concerns. By employing the sol-gel technique, a ferroelectric film composed of Au/PHO/FTO was created on FTO/glass for the purpose of this endeavor. The artificial synapse is composed of Au/PHO/FTO and exhibits learning and memory characteristics that are similar to those observed in biological neurons. The recognition accuracy for both MNIST and Fashion-MNIST data sets saw an increase, reaching 92.93% and 76.75%, respectively. This enhancement resulted from employing a convolutional neural network architecture and implementing an improved stochastic adaptive algorithm. The presented findings showcase a viable approach to achieve neuromorphic computation by employing artificial synapses fabricated with PHO.

AI IR: Charting International Relations in the Age of Artificial Intelligence

Abstract Over the past decade, rapid progress in artificial intelligence (AI) has transformed a range of areas, from medicine to strategic games and communication technologies, from art and culture to everyday office work. It would be naïve to assume that this evolution does not permeate and alter international affairs. Building on, and solidifying, a thriving yet still fragmented emerging literature on “AI IR,” this forum gathers several critical diagnoses of the way AI technologies impact on various areas of international relations. Introducing new concepts and charting emerging empirical realities, contributors explore how AI advances, such as autonomous lethal systems, synthetic imagery and text, or intelligent systems, are already creating new landscapes of violent and nonviolent international interactions. Yet, behind their distinct takes, contributions together stress the need to correctly locate and evaluate specific sites of AI impact, thus offering a nuanced appraisal scrutinizing grand declarations of an “AI revolution” in global politics.

Dense Sample Deep Learning.

Deep learning (DL), a variant of the neural network algorithms originally proposed in the 1980s (Rumelhart etal., 1986), has made surprising progress in artificial intelligence (AI), ranging from language translation, protein folding (Jumper et al, 2021), autonomous cars, and, more recently, human-like language models (chatbots). All that seemed intractable until very recently. Despite the growing use of DL networks, little is understood about the learning mechanisms and representations that make these networks effective across such a diverse range of applications. Part of the answer must be the huge scale of the architecture and, of course, the large scale of the data, since not much has changed since 1986. But the nature of deep learned representations remains largely unknown. Unfortunately, training sets with millions or billions of tokens have unknown combinatorics, and networks with millions or billions of hidden units can't easily be visualized and their mechanisms can't be easily revealed. In this letter, we explore these challenges with a large (1.24 million weights; VGG) DL in a novel high-density sample task (five unique tokens with more than 500 exemplars per token), which allows us to more carefully follow the emergence of category structure and feature construction. We use various visualization methods for following the emergence of the classification and the development of the coupling of feature detectors and structures that provide a type of graphical bootstrapping. From these results, we harvest some basic observations of the learning dynamics of DL and propose a new theory of complex feature construction based on our results.

[Applications of Artificial Intelligence in Pancreatic Cystic Lesion Imaging].

As the detection rate of pancreatic cystic lesions(PCL)increases,artificial intelligence(AI)has made breakthroughs in the imaging workflow of PCL,including image post-processing,lesion detection,segmentation,diagnosis and differential diagnosis.AI-based image post-processing can optimize the quality of medical images and AI-assisted models for lesion detection,segmentation,diagnosis and differential diagnosis significantly enhance the work efficiency of radiologists.This article reviews the application progress of AI in PCL imaging and provides prospects for future research directions.

Entropy-Based Methods for Motor Fault Detection: A Review.

In the signal analysis context, the entropy concept can characterize signal properties for detecting anomalies or non-representative behaviors in fiscal systems. In motor fault detection theory, entropy can measure disorder or uncertainty, aiding in detecting and classifying faults or abnormal operation conditions. This is especially relevant in industrial processes, where early motor fault detection can prevent progressive damage, operational interruptions, or potentially dangerous situations. The study of motor fault detection based on entropy theory holds significant academic relevance too, effectively bridging theoretical frameworks with industrial exigencies. As industrial sectors progress, applying entropy-based methodologies becomes indispensable for ensuring machinery integrity based on control and monitoring systems. This academic endeavor enhances the understanding of signal processing methodologies and accelerates progress in artificial intelligence and other modern knowledge areas. A wide variety of entropy-based methods have been employed for motor fault detection. This process involves assessing the complexity of measured signals from electrical motors, such as vibrations or stator currents, to form feature vectors. These vectors are then fed into artificial-intelligence-based classifiers to distinguish between healthy and faulty motor signals. This paper discusses some recent references to entropy methods and a summary of the most relevant results reported for fault detection over the last 10 years.

The relationship between personal and professional goals and emotional state in academia: a study on unethical use of artificial intelligence.

Artificial Intelligence (AI) is a concept that has been a subfield of computer science since the 1950s. In recent years, with its growing development power, AI technologies have made significant progress and are now being used in many fields. Like in all areas, the use of AI technologies in academia has provided convenience to academics while also bringing ethical debates. In the literature part of the study, concepts such as AI, academia, academics and academic progress, ethics, ethical theories, academic ethics, and emotional states have been thoroughly examined and defined. In this study, starting from AI and scientific ethics, ethical issues arising from emotional states in academic research have been identified, and concrete solutions to these ethical issues have been proposed. The aim is to discuss the views of academics in order to determine what types of scientific ethical violations and prevention methods are involved. In this context, the semi-structured interview technique, which is one of the qualitative research methods, was preferred as the method. In the study, in-depth semi-structured interviews were conducted with 4 ethics experts and 4 psychology experts selected through snowball sampling technique. The data obtained through semi-structured in-depth interviews will be analyzed using content analysis. Within the context of the literature review and interviews: Ethics is based on the foundation of acting correctly. In this context, scientific ethics can be summarized as acting truthfully and honestly, not distorting data, and not trying to progress unfairly. The use of AI in academia is becoming increasingly widespread. From a positive perspective, this usage significantly contributes to making studies more practical. However, it can lead to problems such as unfair authorship, devaluation of human authorship, and incorrect data. The connection between academics' professional advancement goals and emotional states becomes prominent in this context. The potential of AI to facilitate progression can lead to unethical use. To prevent such situations, it is recommended to organize training sessions to increase professional awareness, internalize ethics personally, establish ethical committees specific to the field of AI, conduct more effective audits by academic publication and promotion committees, and implement specific regulations for AI. Finally, for future academic studies, it is suggested that the usage of AI in academic research be measured and evaluated by ethics experts. For psychologists, conducting surveys with academics to explore how they use AI in the context of their emotional states and professional advancement goals is recommended.

A computational deep learning approach for establishing long-term declarative episodic memory through one-shot learning

Researchers have long been captivated by the intricate workings of the human brain, an enduring enigma. Extensive efforts have been devoted to unraveling its complexities, with disciplines like psychology employing experimentation and analysis to scrutinize and formulate models of brain function. Comprising billions of interconnected neurons, the human brain has inspired experts in deep learning to construct artificial neural networks capable of tasks akin to human brain functions, such as pattern and speech recognition. Despite substantial progress in artificial intelligence, advancements in memory storage capabilities have been relatively constrained. This study aims to investigate mechanisms for simulating long-term declarative episodic memory, reminiscent of human cognition, using one-shot deep-learning neural networks. The proposed deep learning architecture extends to Rosenblatt's C-system memory model, and experiments were conducted to assess the effectiveness of various adaptations of the C-system storage mechanism. The fashion MNIST dataset is used in the experiments, and the results indicate that these models exhibit proficient recall abilities, even when faced with a large number of input images. Furthermore, the study delves into emulating the forgetting process of the human brain. The experiment demonstrates that as the units in the C-system increase, the corresponding results also increase. Specifically, when employing 40,000 units, the system maintains an accuracy exceeding 92% for the sequence of images.

Ethical Considerations in AI-Powered Work Environments: A Literature Review and Theoretical Framework for Ensuring Human Dignity and Fairness

This article critically examines the integration of artificial intelligence (AI) into work environments, focusing on the ethical implications that arise. It seeks to underscore the need for balancing technological advancements with the protection of human dignity and fairness, exploring how AI's transformative potential can be harmonized with the core tenets of human rights. The article utilizes a comprehensive literature review to construct a theoretical framework that outlines AI's capabilities and ethical considerations. This framework encompasses the interdisciplinary foundations of AI, including its roots in cognitive psychology, decision theory, and computer engineering. It further delves into the ethical dilemmas presented by AI in the workplace, such as privacy concerns, the risk of bias, issues of accountability, and the broader impact on human rights. This exploration is aimed at understanding the complexities of AI's integration into the labor market and its implications for occupational safety and health. The findings of the article highlight the dual nature of AI as both a catalyst for efficiency and innovation and a source of ethical challenge. It's important to include a lot of different points of view and include everyone in the process of developing AI to make it more fair and respect human rights. Laws and policies need to keep changing to keep up with AI's progress and protect people legally from possible abuses. Strong moral guidelines and clear AI systems are also needed to protect privacy and reduce bias. The study's originality and value emphasize the need for AI ethical discussions in human rights contexts, contribute to technology governance and human rights discussions, and discuss theoretical debates on human dignity, fairness, and privacy in the face of technological advancement.

From McCulloch to GPT - 4: stages of development of artificial intelligence.

The article examines the history of the development of artificial intelligence (AI), starting from its first theoretical and practical steps and tracing the evolution to modern achievements. The article provides an overview of the key milestones, scientific discoveries and technological breakthroughs made in the field of AI. The most important figures, ideas and principles that influenced its development are also discussed. In the context of this development, various definitions of artificial intelligence are given. There are several key stages in the history of AI: the early stages, the quiet period, the AI renaissance, and the era of AI in the new millennium. Each of these stages made its own unique contribution to the progress of AI. The modern period is characterized by rapid development, especially in the field of machine learning and deep learning. These methods allow artificial intelligence to learn from data and identify complex patterns. Advances in natural language processing, such as models GPT and its modifications, have shown outstanding results. However, despite linguistic advances, GPT remains limited in aspects important to creating strong AI. The article discusses the limitations of modern language models, as well as the prerequisites and prospects for the development of strong artificial intelligence. Special attention is paid to the project of Elon Musk, who, having launched the company X.AI, is engaged in research in the field of creating strong AI with the goal of “knowledge of reality.” The article also proposes an alternative approach to creating strong artificial intelligence - the development of an artificial brain based on a multidimensional multi-connected receptor-effector neuron-like growing network. Some aspects of the emergence of artificial consciousness are also considered.

The impact of artificial intelligence application on enterprise environmental performance: Evidence from microenterprises

The progress of artificial intelligence (AI) technology is an important way to solve the problem of global sustainable development. This paper discusses how AI, as a new scientific force, affects the pollution reduction of enterprises from a micro perspective. In this paper, the robot data provided by the International Federation of Robotics and pollution data of industrial enterprises in China are used for empirical analysis to test the effect of AI on pollution performance of enterprises. The results show that the application of AI can reduce enterprise emissions through technological innovation effect and labor substitution effect, and this result remains robust after sample substitution and variable substitution. Further heterogeneity analysis shows that AI has a more significant impact on enterprises in big cities and southern cities, as well as large enterprises, old enterprises and non-technology-intensive enterprises. The research results of this paper provide an effective reference for solving how to achieve green and sustainable development of artificial intelligence applications.

AI Empowered of Advancements in Microbial and Tumor Cell Image Labeling for Enhanced Medical Insights

The traditional diagnosis model of tumor pathology depends on the experience of the doctor, which is inevitably subjective. With the rapid development of modern medical imaging, medical imaging technology has formed a medical imaging system composed of UI, CT, CR, DR, MRI, PET, PET-CT, digital subtraction angiography and PACS by traditional single ordinary X-ray and angiography. The continuous enrichment of imaging technology has changed medical imaging from "auxiliary examination means" to the most important clinical diagnosis and differential diagnosis method in modern medicine. With the application of digital scan sections in clinicopathology, the computer technology of artificial intelligence (AI) assisted diagnosis has developed rapidly in the analysis of tumor tissue images. This paper summarizes the application progress of AI in tumor pathology, and describes the exploration and application of AI in the field of quantitative analysis of histopathological molecular markers closely related to clinical diagnosis and treatment in recent years, so as to provide useful reference for the development of tumor intelligent diagnosis and treatment model.

Schistocyte detection in artificial intelligence age.

Schistocytes are fragmented red blood cells produced as a result of mechanical damage to erythrocytes, usually due to microangiopathic thrombotic diseases or mechanical factors. The early laboratory detection of schistocytes has a critical impact on the timely diagnosis, effective treatment, and positive prognosis of diseases such as thrombocytopenic purpura and hemolytic uremic syndrome. Due to the rapid development of science and technology, laboratory hematology has also advanced. The accuracy and efficiency of tests performed by fully automated hematology analyzers and fully automated morphology analyzers have been considerably improved. In recent years, substantial improvements in computing power and machine learning (ML) algorithm development have dramatically extended the limits of the potential of autonomous machines. The rapid development of machine learning and artificial intelligence (AI) has led to the iteration and upgrade of automated detection of schistocytes. However, along with significantly facilitated operation processes, AI has brought challenges. This review summarizes the progress in laboratory schistocyte detection, the relationship between schistocytes and clinical diseases, and the progress of AI in the detection of schistocytes. In addition, current challenges and possible solutions are discussed, as well as the great potential of AI techniques for schistocyte testing in peripheral blood.

Realizing Near Infrared Mechanoluminescence Switch in LAGO:Cr Based on Oxygen Vacancy.

Mechanoluminescence (ML) materials are featured with the characteristic of "force to light" in response to external stimuli, which have made great progress in artificial intelligence and optical sensing. However, how to effectively enable ML in the material is a daunting challenge. Here, a Lu3 Al2 Ga3 O12 :Cr3+ (LAGO: Cr3+ ) near infrared (NIR) ML material peaked at 706nm is reported, which successfully realizes the key to unlock ML by the lattice-engineering strategy Ga3+ substitution for Al3+ to "grow" oxygen vacancy (Ov ) defects. Combined with thermoluminescence measurements, the observed ML is due to the formation of defect levels and the ML intensity is proportional to it. It is confirmed by X-ray photoelectron spectroscopyand electron paramagnetic resonance that such a process is dominated by Ov , which plays a crucial role in turning on ML in this compound. In addition, potential ML emissions from 4 T2 and 2 E level transitions are discussed from both experimental and theoretical aspects. This study reveals the mechanism of the change in ML behavior after cation substitution, and it may have important implications for the practical application of Ov defect-regulated turn-on of ML.

Aggregation-Induced Emission Luminogens: A New Possibility for Efficient Visualization of RNA in Plants.

RNAs play important roles in regulating biological growth and development. Advancements in RNA-imaging techniques are expanding our understanding of their function. Several common RNA-labeling methods in plants have pros and cons. Simultaneously, plants' spontaneously fluorescent substances interfere with the effectiveness of RNA bioimaging. New technologies need to be introduced into plant RNA luminescence. Aggregation-induced emission luminogens (AIEgens), due to their luminescent properties, tunable molecular size, high fluorescence intensity, good photostability, and low cell toxicity, have been widely applied in the animal and medical fields. The application of this technology in plants is still at an early stage. The development of AIEgens provides more options for RNA labeling. Click chemistry provides ideas for modifying AIEgens into RNA molecules. The CRISPR/Cas13a-mediated targeting system provides a guarantee of precise RNA modification. The liquid-liquid phase separation in plant cells creates conditions for the enrichment and luminescence of AIEgens. The only thing that needs to be looked for is a specific enzyme that uses AIEgens as a substrate and modifies AIEgens onto target RNA via a click chemical reaction. With the development and progress of artificial intelligence and synthetic biology, it may soon be possible to artificially synthesize or discover such an enzyme.

What Does It Mean to Be Human Today?

With the progress of artificial intelligence, the digitalization of the lifeworld, and the reduction of the mind to neuronal processes, the human being appears more and more as a product of data and algorithms. Thus, we conceive ourselves "in the image of our machines," and conversely, we elevate our machines and our brains to new subjects. At the same time, demands for an enhancement of human nature culminate in transhumanist visions of taking human evolution to a new stage. Against this self-reification of the human being, the present book defends a humanism of embodiment: our corporeality, vitality, and embodied freedom are the foundations of a self-determined existence, which uses the new technologies only as means instead of submitting to them. The book offers an array of interventions directed against a reductionist naturalism in various areas of science and society. As an alternative, it offers an embodied and enactive account of the human person: we are neither pure minds nor brains, but primarily embodied, living beings in relation with others. This general concept is applied to issues such as artificial intelligence (AI), transhumanism and enhancement, virtual reality, neuroscience, embodied freedom, psychiatry, and finally to the accelerating dynamics of current society which lead to an increasing disembodiment of our everyday life. The book thus applies cutting-edge concepts of embodiment and enactivism to current scientific, technological, and cultural tendencies that will crucially influence our society's development in the twenty-first century.

Artificial intelligence: reflecting on the past and looking towards the next paradigm shift

ABSTRACT Artificial intelligence (AI) has undergone major advances over the past decades, propelled by key innovations in machine learning and the availability of big data and computing power. This paper surveys the historical progress of AI from its origins in logic-based systems like the Logic Theorist to recent deep learning breakthroughs like Bidirectional Encoder Representations from Transformers (BERT), Generative Pretrained Transformer 3 (GPT-3) and Large Language Model Meta AI (LLaMA). The early rule-based systems using handcrafted expertise gave way to statistical learning techniques and neural networks trained on large datasets. Milestones like AlexNet and AlphaGo established deep learning as a dominant AI approach. Transfer learning enabled models pre-trained on diverse corpora to excel at specialised downstream tasks. The scope of AI expanded from niche applications like playing chess to multifaceted capabilities in computer vision, natural language processing and dialogue agents. However, current AI still needs to catch up to human intelligence in aspects like reasoning, creativity, and empathy. Addressing limitations around real-world knowledge, biases, and transparency remains vital for further progress and aligning AI with human values. This survey provides a comprehensive overview of the evolution of AI and documents innovations that shaped its advancement over the past six decades.

Rag-Fusion: A New Take on Retrieval Augmented Generation

Infineon has identified a need for engineers, account managers, and customers to rapidly obtain product information. This problem is traditionally addressed with retrieval-augmented generation (RAG) chatbots, but in this study, I evaluated the use of the newly popularized RAG-Fusion method. RAG-Fusion combines RAG and reciprocal rank fusion (RRF) by generating multiple queries, reranking them with reciprocal scores and fusing the documents and scores. Through manually evaluating answers on accuracy, relevance, and comprehensiveness, I found that RAG-Fusion was able to provide accurate and comprehensive answers due to the generated queries contextualizing the original query from various perspectives. However, some answers strayed off topic when the generated queries' relevance to the original query is insufficient. This research marks significant progress in artificial intelligence (AI) and natural language processing (NLP) applications and demonstrates transformations in a global and multiindustry context.

Biomass characterization with semantic segmentation models and point cloud analysis for precision viticulture

The scientific progress in artificial intelligence and robotics has enabled precision viticulture to pursue sustainability and improve the final yield. For instance, monitoring the canopy volume of each plant can allow the correct ripening of the bunches. In this context, this paper proposes a novel approach for the characterization of biomass volume using images acquired in a vineyard with the low-cost Azure Kinect RGB-D camera. Semantic image segmentation is implemented using three encoder–decoder deep architectures (U-Net, DeepLabV3+, and MANet) to produce accurate masks of the vine leaf structure. In a transfer learning approach, a public dataset acquired with the Intel RealSense D435 depth camera is used to train the segmentation networks. Then, a complete pipeline to estimate possible changes in biomass volume is presented. Experiments are run to analyze the biomass removed during the trimming process of grapevine plants. The best segmentation result is obtained by the U-Net architecture with ResNet50 backbone, showing an accuracy of 92.10%, although the training and test sets consist of images acquired by different cameras. However, the DeepLabV3+ network with ResNeXt50 backbone, which scores an accuracy of 90.25% on the test set, gives the best estimate of the removed biomass, requiring the shortest time for training. These outcomes prove the potential capability of this automatic approach for controlling leaf growth and ensuring sustainable viticulture practices.

A research of artificial intelligence game agent application

Currently, large language models are on the rise with breakthrough progress in artificial intelligence. Existing reviews of AI game agents have not covered these latest developments, requiring a combing and analysis of the newest research advancements in game AI agents. This paper summarizes the application scenarios of game AI agents in four aspects: combat AI, Non-Player Character (NPC) interaction, automated testing, and Artificial General Intelligence (AGI) testing. In combat AI, there is a progressive developmental trend, with the introduction of Monte Carlo tree search and reinforcement learning enabling AI game agents to fully surpass humans in traditional board games. In NPC interaction, full AI is unnecessary. Game developers only need to incorporate AI for abilities related to player experience to increase appeal, with controllable generation results. In automated testing, game AI agents lack generalizability for testing so far. In AGI testing, academia has helpfully explored general game AI, but capabilities remain limited to certain games. Introducing large language models to game AI agents shows unprecedented capabilities. Finally, this paper provides an outlook on the hot topics and future directions of this research subject.