Growth Of Artificial Intelligence Research Articles

The effectiveness of Smart Compose: An artificial intelligent system

The growth of artificial intelligence (AI) technologies in everyday life and manufacturing are expected to reduce the mental workload of a user or human operator and increase their efficiency. In industrial systems, such as additive manufacturing (AM), as AM transitions from a technology of manufacturing prototypes to rapid manufacturing, it is important that these added technologies reduce an operator's mental workload, have high user satisfaction, and are easily implemented and incorporated into the operator's tasks. One growing AI technology is Smart Compose, an artificially intelligent system that provides writing suggestions when composing an email through Gmail. Like other AI technologies, the goal of Smart Compose is to enhance the performance of a user; in this case, typing an email. It is hypothesized that Smart Compose increases a user's performance when typing an email. Thus, the objective of this study is to test the capability of Smart Compose and whether it increases human performance and decreases mental workload for college students. The study found that Smart Compose does not significantly increase human performance and does not significantly decrease mental workload for college students.

Photonic architecture for reinforcement learning

The last decade has seen an unprecedented growth in artificial intelligence and photonic technologies, both of which drive the limits of modern-day computing devices. In line with these recent developments, this work brings together the state of the art of both fields within the framework of reinforcement learning. We present the blueprint for a photonic implementation of an active learning machine incorporating contemporary algorithms such as SARSA, Q-learning, and projective simulation. We numerically investigate its performance within typical reinforcement learning environments, showing that realistic levels of experimental noise can be tolerated or even be beneficial for the learning process. Remarkably, the architecture itself enables mechanisms of abstraction and generalization, two features which are often considered key ingredients for artificial intelligence. The proposed architecture, based on single-photon evolution on a mesh of tunable beamsplitters, is simple, scalable, and a first integration in quantum optical experiments appears to be within the reach of near-term technology.

The Technological Singularity as the Emergence of a Collective Consciousness: An Anthropological Perspective

The technological singularity is popularly envisioned as a point in time when (a) an explosion of growth in artificial intelligence (AI) leads to machines becoming smarter than humans in every capacity, even gaining consciousness in the process; or (b) humans become so integrated with AI that we could no longer be called human in the traditional sense. This article argues that the technological singularity does not represent a point in time but a process in the ongoing construction of a collective consciousness. Innovations from the earliest graphic representations to the present reduced the time it took to transmit information, reducing the cognitive space between individuals. The steady pace of innovations ultimately led to the communications satellite, fast-tracking this collective consciousness. The development of AI in the late 1960s has been the latest innovation in this process, increasing the speed of information while allowing individuals to shape events as they happen.

P1465Artificial intelligence in echocardiography - Steps to automatic cardiac measurements in routine practice

Abstract Introduction The growth of artificial intelligence (AI) use in echocardiography over the past years has been exponential, proposing new paths to overcome inter-operator variability and experience of the operator. Even though the applications of AI are still in their infancy within the field of echocardiography, the potential of AI implies future directions and is eager to assist for accuracy and efficiency of manual tracings. Deep learning, a subset of machine learning algorithms, is gaining popularity in echocardiography as a state of the art in visual data analysis. Purpose To evaluate deep learning for two initial tasks in automated cardiac measurements: view recognition and end-systolic (ES) and end-diastolic (ED) frame detection. Methods A total of 230 patients' (with various indications for study) 2D echocardiography data was used to train and validate neural networks. Raw pixel data was extracted from EPIQ 7G, Vivid E95 and Vivid 7 imaging platforms. Images were labeled according to their view: parasternal long axis (PLA), basal short axis, short axis at mitral level, apical two, three and four chambers (A4C). Additionally, ES and ED frames were labeled for A4C and PLA views. Images were de-identified by applying black pixel masks to non-anatomical data and removing metadata. Convolutional Neural Network (CNN) architecture was used for the classification of 6 different views. A total of 34752 and 3972 (5792 and 662 per view) frames were used to train and validate the network, respectively. Long-term Recurrent Convolutional Network (LRCN) combining temporal and spatial cognition was used for ES and ED frame detection. A total of 195 and 35 sequences with a length of 92 frames were used to train the LRCN. Results CNN for view classification had an AUC of 0.95 (sensitivity 95%, specificity 97%). Accuracy was lower for visually similar views, namely apical three-chamber and apical two-chamber. Training for ES and ED detection was achieved when training LRCN for regression instead of classification of each frame. LRCN for cardiac cycle evaluation had an average Framed Difference (aFD) of 2.31 (SD±2.15) for ED and 1.97 (SD±2.04) frames for ES detection which corresponds to error rate of about 0.04 s. Conclusion Determining echocardiographic view and evaluating cardiac cycle are the first steps in automating cardiac measurements. We have demonstrated the potential of two deep learning algorithms in accomplishing these tasks. Initial results are promising for the development of neural networks for cardiac segmentation and measuring of anatomical structures.

Cloud Computing: Trends And Challenges

Cloud computing has become a cornerstone of modern IT infrastructure, offering scalability, flexibility, and cost savings to organizations of all sizes. This paper examines the current trends and challenges in cloud computing, focusing on key areas such as adoption of multi-cloud and hybrid cloud strategies, rise of serverless computing, increased focus on edge computing, growth of artificial intelligence and machine learning in cloud services, and expansion of cloud security solutions. The paper also explores future directions and innovations in cloud computing, including the impact of quantum computing, blockchain technology, evolution of cloud-native applications, and green computing initiatives. By understanding these trends and challenges, organizations can better prepare for the future of cloud computing and harness its full potential.

Does the Growth of Artificial Intelligence and Automation Shape Talent Attraction and Retention?

Does the Growth of Artificial Intelligence and Automation Shape Talent Attraction and Retention?

Impact of artificial intelligence for agricultural sustainability

The new agricultural technology strategy, artificial intelligence, integrated telematics, data management systems and auto-guidance solutions together, have the potential to make farming much more productive and profitable. The precision farming technologies help customers to become more efficient and save resources. Producing quality, nutritious and affordable food will be a huge challenge before the world. It is predicted that even with the limited resources, the smart ICT, a new hi-tech era need to be ushered in where automation and data can help farmers address the many challenges of the future. The Artificial Intelligence (AI) application in farming will bring revolution, sustainability and vibrancy in future farming. Through digital agriculture, where technologies such as Artificial Intelligence (AI), Cloud Machine Learning, Satellite Imagery and advanced analytics are empowering small-holder farmers to increase their income through higher crop yield and greater price control. The growth of artificial intelligence in agriculture is primarily driven by the increasing demand for agricultural production due to the increasing population, rising adoption of information management systems and new technologies for improving crop productivity, and increasing by implementing deep learning techniques, and growing initiatives by governments worldwide supporting the adoption of modern agricultural techniques. The only restraint in the growth of artificial intelligence in agriculture market is the high cost of gathering precise field data. Artificial intelligence can boost the efficiency of daily operations in a traditional farm and maximize production volume and minimize the possibility of failure due to natural disasters, system errors, and other factors by acquiring data on growth, the weather, and agricultural equipment.

Exploring the Impact of Artificial Intelligence on the Accounting Profession

Artificial intelligence is no longer the robots and computers of science fiction from Hollywood movies. The ideas of developing machines that can “learn” are centuries old. The capacities of the computers and software of today create and exhibit intelligence, but also bring with it concerns along with much promise. In the accounting field artificial intelligence has been taking on more and more tasks. Already, there is software that has automated many accounting, tax, bookkeeping, and auditing processes. If machines are assuming a greater role, where do we, the professionals strike a balance? What does the future of the accounting profession look like with the growth of artificial intelligence?

A Coevolutionary Multiobjective Evolutionary Algorithm for Game Artificial Intelligence

Recently, the growth of Artificial Intelligence (AI) has provided a set of effective techniques for designing computer-based controllers to perform various tasks autonomously in game area, specifically to produce intelligent optimal game controllers for playing video and computer games. This paper explores the use of the competitive fitness strategy: K Random Opponents (KRO) in a multiobjective approach for evolving Artificial Neural Networks (ANNs) that act as controllers for the Ms. Pac-man agent. The Pareto Archived Evolution Strategy (PAES) algorithm is used to generate a Pareto optimal set of ANNs that optimize the conflicting objectives of maximizing game scores and minimizing neural network complexity. Furthermore, an improved version, namely PAESNet_KRO, is proposed, which incorporates in contrast to its predecessor KRO strategy. The results are compared with PAESNet. From the discussions, it is found that PAESNet_KRO provides better solutions than

Advancing artificial intelligence research and dissemination through conference series: Benchmark, scientific impact and the MICAI experience

This article presents an overview, analysis and benchmark of the best-known artificial intelligence (AI) conferences, including the Mexican International Conference on Artificial Intelligence (MICAI) conference series, and describes how MICAI has contributed to both the growth of artificial intelligence (AI) research in Mexico and the advancement of AI research worldwide. Among the prestigious AI conferences examined are the IJCAI, AAAI, ECAI, IBERAMIA, AAJCAI and PRICAI. Features analyzed include number of papers, acceptance rate and the h index as a measure of the scientific impact. The MICAI has been held in Mexico since 2000, when the National Meeting on AI, held by the Mexican Society for Artificial Intelligence (SMIA) since 1983, and the International Symposium on Artificial Intelligence (ISAI), organized by Tecnológico de Monterrey (ITESM) since 1988, merged into a single conference. Conference trends and future developments are also explained.

A Knowledge-Base Power System Stabilizer

Abstract Power system stabilizers have been used successfully to improve power system stability. The major problem facing the design of a suitable controller for power systems is that they are nonlinear in nature and can operate over a wide range of parameter variations. Adaptive controllers were used to solve such a problem but they are usually computationally intensive and may not compensate large variations in system parameters. Motivated by the substantial growth of Artificial Intelligence (AI) techniques and their application in control systems, knowledge based controllers began to take place as a wide range stabilizers. This paper introduces a newly developed power system stabilizer using a knowledge base controller. The proposed knowledge base controller is acting on the system while keeping the conventional stabilizer. Rules and facts that represent the core of the controller are deduced using fuzzy logic. A single synchronous machine with its speed governor and voltage regulator connected to an infinite bus is used as an example to illustrate the features of the proposed expert controller. Simulation results showed great improvement in system performance when the auxiliary knowledge base controller is used together with the speed governor and the voltage regulator.