Artificial Intelligence Tasks Research Articles

FPGA-QNN: Quantized Neural Network Hardware Acceleration on FPGAs

Recently, convolutional neural networks (CNNs) have received a massive amount of interest due to their ability to achieve high accuracy in various artificial intelligence tasks. With the development of complex CNN models, a significant drawback is their high computational burden and memory requirements. The performance of a typical CNN model can be enhanced by the improvement of hardware accelerators. Practical implementations on field-programmable gate arrays (FPGA) have the potential to reduce resource utilization while maintaining low power consumption. Nevertheless, when implementing complex CNN models on FPGAs, these may may require further computational and memory capacities, exceeding the available capacity provided by many current FPGAs. An effective solution to this issue is to use quantized neural network (QNN) models to remove the burden of full-precision weights and activations. This article proposes an accelerator design framework for FPGAs, called FPGA-QNN, with a particular value in reducing high computational burden and memory requirements when implementing CNNs. To approach this goal, FPGA-QNN exploits the basics of quantized neural network (QNN) models by converting the high burden of full-precision weights and activations into integer operations. The FPGA-QNN framework comes up with 12 accelerators based on multi-layer perceptron (MLP) and LeNet CNN models, each of which is associated with a specific combination of quantization and folding. The outputs from the performance evaluations on Xilinx PYNQ Z1 development board proved the superiority of FPGA-QNN in terms of resource utilization and energy efficiency in comparison to several recent approaches. The proposed MLP model classified the FashionMNIST dataset at a speed of 953 kFPS with 1019 GOPs while consuming 2.05 W.

The artificial intelligence impact on Russian labor market

The paper studies the association between the artificial intelligence (AI) and employment characteristics. As a theoretical framework, we use the Acemoglu et al. model, which introduces opposing effects of the AI algorithms on labor employment on the firm level such as substitution effect and complimentary/ productivity effects. Depending on their relative strength, the AI algorithms may both decrease and increase employment. The overall effect is estimated using the data on 3.5 million of vacancies for about 35 thousand firms published up to 2022 spring on the HeadHunter website. According to the results, the existence of tasks realizable using AI is consistent with a higher AI employment, which implies the substitution effect. The other, less intuitive, result is that the same tasks suggest a higher non-AI employment, supports complimentary/productivity effects. The overall employment effect is positive as follows from the positive AI tasks—total employment association.

REFRAMING AGING: THROUGH THE LENS OF ETHICAL ARTIFICIAL INTELLIGENCE

Abstract Artificial Intelligence (AI) technology is advancing at a rapid pace. In addition, our older adult cohort is growing at an unprecedented rate. The purpose of my research is to address the ethical concerns when AI intersects with this population, offering policy recommendations to bridge this ethical digital divide. To address this AI ethical dilemma, my research did not reach back prior to 2019 in order to provide a concise policy analysis that includes existing AI ethical policies/recommendations from the European Union, United States, and U.S. States, key differences, and how ethical AI policies are central to Area Agencies on Aging’s development of aging services. My findings indicated gaps in ethical AI design with developed policy recommendations. These gaps include inclusive data collection, user-centric design, algorithmic biases and understanding, digital literacy, New Jersey AI Task Force membership, and a need for a New Jersey AI Ethical Framework. With my five policy recommendations, New Jersey will be better positioned in assuring AI systems will be equitable, ethical, and effective in addressing the present and future needs of older adults. With this ethical AI framework, New Jersey will help to better older adults’ quality of life, as well as save valuable time and resources through the new industrial revolution that is Artificial Intelligence. Keywords: Artificial Intelligence, ethics, older adults, algorithmic bias, AI ageism

A Survey of Geometric Optimization for Deep Learning: From Euclidean Space to Riemannian Manifold

Deep Learning (DL) has achieved remarkable success in tackling complex Artificial Intelligence tasks. The standard training of neural networks employs backpropagation to compute gradients and utilizes various optimization algorithms in the Euclidean space \(\mathbb {R}^n \) . However, this optimization process faces challenges, such as the local optimal issues and the problem of gradient vanishing and exploding. To address these problems, Riemannian optimization offers a powerful extension to solve optimization problems in deep learning. By incorporating the prior constraint structure and the metric information of the underlying geometric information, Riemannian optimization-based DL offers a more stable and reliable optimization process, as well as enhanced adaptability to complex data structures. This article presents a comprehensive survey of applying geometric optimization in DL, including the basic procedure of geometric optimization, various geometric optimizers, and some concepts of the Riemannian manifold. In addition, it investigates various applications of geometric optimization in different DL networks for diverse tasks and discusses typical public toolboxes that implement optimization on the manifold. This article also includes a performance comparison among different deep geometric optimization methods in image recognition scenarios. Finally, this article elaborates on future opportunities and challenges in this field.

Deep Bayesian active learning using in-memory computing hardware.

Labeling data is a time-consuming, labor-intensive and costly procedure for many artificial intelligence tasks. Deep Bayesian active learning (DBAL) boosts labeling efficiency exponentially, substantially reducing costs. However, DBAL demands high-bandwidth data transfer and probabilistic computing, posing great challenges for conventional deterministic hardware. Here we propose a memristor stochastic gradient Langevin dynamics in situ learning method that uses the stochastic of memristor modulation to learn efficiency, enabling DBAL within the computation-in-memory (CIM) framework. To prove the feasibility and effectiveness of the proposed method, we implemented in-memory DBAL on a memristor-based stochastic CIM system and successfully demonstrated a robot's skill learning task. The inherent stochastic characteristics of memristors allow a four-layer memristor Bayesian deep neural network to efficiently identify and learn from uncertain samples. Compared with cutting-edge conventional complementary metal-oxide-semiconductor-based hardware implementation, the stochastic CIM system achieves a remarkable 44% boost in speed and could conserve 153 times more energy.

Dues Update in 2025 Supports AMWA’s Enhanced Member Value

Remember 2020? Although the world seemed to press pause, medical communication certainly didn’t stop. With the increased need for information and clear communication, AMWA pressed forward to meet the needs of our members. As organizations everywhere grappled with unprecedented challenges, we froze membership dues at 2019 rates, providing stability when our members needed it. Over the past 5 years, although our dues remained unchanged, AMWA has been quietly revolutionizing what it means to be a member. We’ve expanded services, pioneered new benefits, and remained steadfast in our mission to promote excellence in medical communication—all while navigating the seismic shifts in health care and the global economy. As we look ahead to 2025, it’s time to reflect on the journey we’ve taken together and the transformative value we’ve built for our members. From launching the highly anticipated compensation report to creating an artificial intelligence (AI) task force, AMWA has evolved to meet the changing needs of medical communicators at every career stage.

Basic artificial Intelligence tasks in сontext of geological prospecting

The article focuses on the fundamental challenges of artificial intelligence (AI) encountered in multidisciplinary research and methods for addressing them using machine learning and neural networks. The authors emphasize the importance of a systematic approach, which enables accurate problem formulation, especially in poorly formalized domains such as geology and ecology. The study examines key AI tasks, including retrodiction, prediction, search, and design, while proposing solutions such as clustering and regression analysis. Special attention is given to the application of explainable artificial intelligence (XAI) methods, which enhance model interpretability and foster a deeper understanding of complex processes in interdisciplinary studies. The use of holotypic algorithms, which effectively address classification and object recognition tasks based on multidimensional data analysis, is highlighted. The paper underscores the significance of AI in automating and improving the efficiency of scientific research in interdisciplinary fields.

Transfer Learning-Driven Cattle Instance Segmentation Using Deep Learning Models

Among the emerging applications of artificial intelligence is animal instance segmentation, which has provided a practical means for various researchers to accomplish some aim or execute some order. Though video and image processing are two of the several complex tasks in artificial intelligence, these tasks have become more complex due to the large data and resources needed for training deep learning models. However, these challenges are beginning to be overcome by the transfer learning method of deep learning. In furtherance of the application of the transfer learning method, a system is proposed in this study that applies transfer learning to the detection and recognition of animal activity in a typical farm environment using deep learning models. Among the deep learning models compared, Enhanced Mask R-CNN obtained a significant computing time of 0.2 s and 97% mAP results, which are better than the results obtained by Mask R-CNN, Faster R-CNN, SSD, and YOLOv3, respectively. The findings from the results obtained in this study validate the innovative use of transfer learning to address challenges in cattle segmentation by optimizing the segmentation accuracy and processing time (0.2 s) of the proposed Enhanced Mask R-CNN.

Swin Wavelet Transformer (SWT): Mixing Tokens with Wavelet and Multiwavelet Transforms

The Swin Transformer possess a hierarchical structure, a robust structure, and an efficient self-attention mechanism. This makes it superior in performance for a wide variety of artificial intelligence and machine training tasks. It has revolutionized the field of digital signal processing and its applications by creating vision in multiple fields. It uses non-overlapping windows which leads to creating a distinct perspective and understanding of the context of the digital signals. However, it possess a very complicated hierarchical structure an complexities together. By reducing calculations and complexities together. This will lead to robust performance that make it a powerful tool for a wide range of computer vision tasks. In this research, simplified symbol mixing methods were developed for coding structures similar to transformers, by forming various semantics in the text through linear mixing transformation and in combination with nonlinearity in the feed-forward layers. Hence, we were able to successfully propose a Swin Wavelet Transformer (SWT) model in which the self-attention sublayer was replaced by a Wavelet transform. In a second attempt, the wavelet transform was replaced by the multi-wavelet transform. These two proposed models are achieved a better performance from their FNets and BERT counterparts and are highly competitive with traditional and efficient transformers.

Text-to-Image Generation Using Stack Generative Adversarial Networks (GANs) and Stable Diffusion Models

Text-to-image synthesis is a challenging task in artificial intelligence that involves generating realistic images from textual descriptions. StackGAN, [1] a generative model with a two-stage architecture, has proven to be effective in improving the quality and fidelity of synthesized images. In this study, we implement and evaluate the performance of StackGAN using the CIFAR-10 dataset, demonstrating how a multi-stage generation approach can produce high-quality images from textual labels. The experimental results show that StackGAN successfully captures the underlying features of the dataset categories and provides reasonable image fidelity.

Hierarchical Reinforcement Learning: Structuring Decision-Making for Complex AI Tasks

Hierarchical Reinforcement Learning (abbreviated as HRL) represents a progress in the realm of artificial intelligence (AI) focusing on handling the increasing complexity of decision-making processes effectively. By breaking down tasks into smaller sub tasks that are easier to manage and comprehend; HRL plays a key role in enhancing the learning efficiency of AI models. The hierarchical system mirrors decision making approaches by breaking down complex goals into smaller achievable steps; this allows reinforcement learning agents to effectively learn and adjust to dynamic environments, with limited rewards and long-term objectives. In contrast to the limitations faced by flat reinforcement learning techniques in terms of scalability and efficiency Hierarchical Reinforcement Learning (HRL) introduces a higher-level policy that controls the series and implementation of sub tasks. This study delves into the evolution, application, and influence of reinforcement learning models in artificial intelligence. We explore HRL frameworks, such, as the Options framework and temporal adaptive models that offer a systematic decision-making method. Furthermore, the study analyzes the role HRL plays in fields including robotics, self-governing systems, and game artificial intelligence. HRL is on the brink of transforming how AI tackles challenges by shaping decision making processes in intricate settings that mimic real world scenarios. This piece explores the issue at hand along, with its solutions and applications while discussing the influence and extent of HRL in enhancing AI technologies. Keywords: Hierarchical Reinforcement Learning, AI, Reinforcement Learning, Decision-Making, Sub-tasks, Options Framework, Temporal-Adaptive Models, Robotics, Autonomous Systems, Game AI, Artificial Intelligence.

Fourier Hilbert: The Input Transformation to Enhance CNN Models for Speech Emotion Recognition

Signal processing in general, and speech emotion recognition in particular, have long been familiar Artificial Intelligence (AI) tasks. With the explosion of deep learning, CNN models are used more frequently, accompanied by the emergence of many signal transformations. However, these methods often require significant hardware and runtime. In an effort to address these issues, we analyze and learn from existing transformations, leading us to propose a new method: Fourier Hilbert Transformation (FHT). In general, this method applies the Hilbert curve to Fourier images. The resulting images are small and dense, which is a shape well-suited to the CNN architecture. Additionally, the better distribution of information on the image allows the filters to fully utilize their power. These points support the argument that FHT provides an optimal input for CNN. Experiments conducted on popular datasets yielded promising results. FHT saves a large amount of hardware usage and runtime while maintaining high performance, even offers greater stability compared to existing methods. This opens up opportunities for deploying signal processing tasks on real-time systems with limited hardware.

Artificial Intelligence in Diagnosing and Managing Vascular Surgery Patients: An Experimental Study Using the GPT-4 Model

ObjectiveThe introduction of artificial intelligence (AI) has led to groundbreaking advancements across many scientific fields. Machine learning algorithms have enabled AI models to learn, adapt, and solve complex problems in previously unimaginable ways. Natural language processing (NLP) allows these models to comprehend and respond to inquiries in a natural and humanly understandable way. We sought to investigate the application and performance of an AI chatbot in the diagnosis and management of vascular surgery patients. DesignAn experimental study to evaluate the performance of GPT-4 AI model across 57 clinical scenarios derived from a textbook in vascular surgery. MethodsSpecific prompts were devised to address the AI model and task it to identify symptoms, diagnose conditions, and select appropriate therapeutic approaches. Answers were scored, descriptive statistics were produced and means were compared across topics. The reasoning and evidence used in the cases in which AI performed poorly were critically reviewed. ResultsThe AI model correctly answered over 65% of the 385 questions. Performance variation between and within 13 vascular surgery topics did not show any statistically significant differences. Analysis of the questions where the model failed by more than 50% suggests a gap in the ability to interpret and process multifaceted medical information. 27% of these errors were attributed to potential lack of understanding of complex clinical scenarios. The AI model also quoted incorrect or outdated information in 14% of cases and showed an inability to comprehend context, nuances, and medical classification systems in 11% of the cases. ConclusionGPT-4 demonstrated potential to provide clinically relevant answers for most of the tested scenarios. However, its reasoning must still be carefully analyzed for exactitude and clinical validity. While language models show promise as valuable tools for clinicians, it is essential to recognize their role as supportive mechanisms rather than standalone solutions.

Integrated convolutional kernel based on two-dimensional photonic crystals.

Optical neural networks (ONNs) exhibit significant potential for accelerating artificial intelligence task processing due to their low latency, high bandwidth, and parallel processing capabilities. Photonic crystals (PhCs) are extensively utilized in integrated optoelectronics because of their unique photonic bandgap properties and precise control of light waves. In this study, we propose an optical reconfigurable convolutional kernel based on PhCs. This kernel can perform convolutional operations on weights by constructing a PhC weight bank. The convolutional kernel demonstrates exceptional performance within the developed optical convolutional neural network framework, successfully realizing various image edge processing tasks. It achieves blind recognition accuracies of 97.81% for the MNIST dataset and 80.31% for the Fashion-MNIST dataset. This study not only demonstrates the feasibility of constructing optical neural networks based on PhCs but to our knowledge, also offers new avenues for the future development of optical computing.

Baoulé Related Parallel Corpora for Machine Translation Tasks: Mtbci-1.0

According to the Ethnologue platform, we have 7,164 known living languages in the World, and not all of them have data available over the internet to facilitate Artificial Intelligence (AI) tasks such as Machine Translation (MT). Consequently, there is a need for thorough Data Engineering tasks for most of these languages. Especially, the Baoulé living language normalized as ISO 639-3 (bci) is not yet supported on popular worldwide free translation platform such as Microsoft Translator, nor on the Official Wikipedias. In this paper, we have proposed the "Baoulé Related Parallel Corpora for Machine Translation tasks: mtBCI-1.0" to make parallel Baoulé-related datasets available to the scientific community for AI tasks implying Machine Translation. We have shown that, after a brief presentation of the Baoulé language in the proposed approach, we will focus on the Data Engineering Process itself before providing a baseline proving that the collected data is of scientific interest.

120 GOPS Photonic tensor core in thin-film lithium niobate for inference and in situ training

Photonics offers a transformative approach to artificial intelligence (AI) and neuromorphic computing by enabling low-latency, high-speed, and energy-efficient computations. However, conventional photonic tensor cores face significant challenges in constructing large-scale photonic neuromorphic networks. Here, we propose a fully integrated photonic tensor core, consisting of only two thin-film lithium niobate (TFLN) modulators, a III-V laser, and a charge-integration photoreceiver. Despite its simple architecture, it is capable of implementing an entire layer of a neural network with a computational speed of 120 GOPS, while also allowing flexible adjustment of the number of inputs (fan-in) and outputs (fan-out). Our tensor core supports rapid in-situ training with a weight update speed of 60 GHz. Furthermore, it successfully classifies (supervised learning) and clusters (unsupervised learning) 112 × 112-pixel images through in-situ training. To enable in-situ training for clustering AI tasks, we offer a solution for performing multiplications between two negative numbers.

PrescDRL: deep reinforcement learning for herbal prescription planning in treatment of chronic diseases

Treatment planning for chronic diseases is a critical task in medical artificial intelligence, particularly in traditional Chinese medicine (TCM). However, generating optimized sequential treatment strategies for patients with chronic diseases in different clinical encounters remains a challenging issue that requires further exploration. In this study, we proposed a TCM herbal prescription planning framework based on deep reinforcement learning for chronic disease treatment (PrescDRL). PrescDRL is a sequential herbal prescription optimization model that focuses on long-term effectiveness rather than achieving maximum reward at every step, thereby ensuring better patient outcomes. We constructed a high-quality benchmark dataset for sequential diagnosis and treatment of diabetes and evaluated PrescDRL against this benchmark. Our results showed that PrescDRL achieved a higher curative effect, with the single-step reward improving by 117% and 153% compared to doctors. Furthermore, PrescDRL outperformed the benchmark in prescription prediction, with precision improving by 40.5% and recall improving by 63%. Overall, our study demonstrates the potential of using artificial intelligence to improve clinical intelligent diagnosis and treatment in TCM.

Exploring the Role of Artificial Intelligence in Internet of Things Systems: A Systematic Mapping Study.

The use of Artificial Intelligence (AI) in Internet of Things (IoT) systems has gained significant attention due to its potential to improve efficiency, functionality and decision-making. To further advance research and practical implementation, it is crucial to better understand the specific roles of AI in IoT systems and identify the key application domains. In this article we aim to identify the different roles of AI in IoT systems and the application domains where AI is used most significantly. We have conducted a systematic mapping study using multiple databases, i.e., Scopus, ACM Digital Library, IEEE Xplore and Wiley Online. Eighty-one relevant survey articles were selected after applying the selection criteria and then analyzed to extract the key information. As a result, six general tasks of AI in IoT systems were identified: pattern recognition, decision support, decision-making and acting, prediction, data management and human interaction. Moreover, 15 subtasks were identified, as well as 13 application domains, where healthcare was the most frequent. We conclude that there are several important tasks that AI can perform in IoT systems, improving efficiency, security and functionality across many important application domains.

The Evolution of Data Centers in the Age of AI

This article explores the evolving landscape of data centers in the era of artificial intelligence (AI). It examines the exponential growth of the global data center market, driven by increasing data generation and AI adoption. The article discusses key technological developments in data centers, including enhanced operational efficiency through AI-powered systems, specialized hardware for AI workloads, advanced cooling technologies, and sustainability initiatives. It also delves into future prospects, such as increased capacity for complex AI tasks, real-time processing of massive datasets, and further improvements in energy efficiency. The symbiotic relationship between AI and data centers is highlighted, emphasizing how this transformation is reshaping digital infrastructure to meet unprecedented demands for computational power and data processing while striving for sustainability.

Learning Relation-Enhanced Hierarchical Solver for Math Word Problems.

Automatically solving math word problems (MWPs) is a challenging task for artificial intelligence (AI) and machine learning (ML) research, which aims to answer the problem with a mathematical expression. Many existing solutions simply model the MWP as a sequence of words, which is far from precise solving. To this end, we turn to how humans solve MWPs. Humans read the problem part-by-part and capture dependencies between words for a thorough understanding and infer the expression precisely in a goal-driven manner with knowledge. Moreover, humans can associate different MWPs to help solve the target with related experience. In this article, we present a focused study on an MWP solver by imitating such procedure. Specifically, we first propose a novel hierarchical math solver (HMS) to exploit semantics in one MWP. First, to imitate human reading habits, we propose a novel encoder to learn the semantics guided by dependencies between words following a hierarchical "word-clause-problem" paradigm. Next, we develop a goal-driven tree-based decoder with knowledge application to generate the expression. One step further, to imitate human associating different MWPs for related experience in problem-solving, we extend HMS to the Relation-enHanced Math Solver (RHMS) to utilize the relation between MWPs. First, to capture the structural similarity relation, we develop a meta-structure tool to measure the similarity based on the logical structure of MWPs and construct a graph to associate related MWPs. Then, based on the graph, we learn an improved solver to exploit related experience for higher accuracy and robustness. Finally, we conduct extensive experiments on two large datasets, which demonstrates the effectiveness of the two proposed methods and the superiority of RHMS.