History Of Artificial Intelligence Research Articles

Current Updates In Artificial Intelligence In Prosthodontics: A Narrative Review

Background: Artificial intelligence (AI) is revolutionizing prosthodontics with technologies such as machine learning (ML), neural networks, and computer vision. This review discusses AI's history, applications, and potential across prosthodontics, including challenges like ethical issues and data privacy. AI can streamline workflows, enhance diagnostics, and improve treatment planning and outcomes in areas like removable and fixed prosthodontics, maxillofacial prosthetics, and implantology. Search methodology: A comprehensive literature search was conducted across electronic databases, including PubMed, Scopus, and Google Scholar. The search focused on articles published in English within the last 10 years (2013–2023), using key terms such as "artificial intelligence," "machine learning," "deep learning," and "prosthodontics," combined with specific terms like "dental implants," "digital dentistry," and "CAD/CAM." Relevant peer-reviewed articles, clinical studies, and review papers were included, while editorials, conference abstracts, and non-peer-reviewed content were excluded. The literature was manually screened for relevance based on titles and abstracts, and reference lists of selected studies were also reviewed to identify additional relevant publications Conclusion: AI has shown significant potential to enhance accuracy, efficiency, and personalization in prosthodontics. While current applications are promising, further research is needed to validate AI-driven technologies in clinical practice and explore their integration into routine prosthodontic care

Revolutionizing Cancer Research and Drug Discovery: The Role of Artificial Intelligence and Machine Learning

Abstract: Artificial Intelligence (AI) and Machine Learning (ML) have revolutionized various industries, including cancer research and drug discovery. This article provides a summary of the history of AI and ML, highlighting their resurgence in the 1990s with advancements in computational power and new algorithms. In the context of drug discovery, AI and ML techniques have been applied to accelerate the development of new drugs, from target identification and lead generation to drug repurposing. AI applications in drug design and virtual screening have improved the efficiency of identifying potential drug candidates. Deep learning, a division of ML, has been particularly effective in predicting protein structures and optimizing lead compounds. In anti-cancer drug target prediction, AI and ML algorithms analyze large-scale genomic, proteomic, and clinical data to identify potential targets for cancer therapy. AI has also transformed cancer imaging and diagnosis by enhancing the accuracy and efficiency of cancer detection, classification, and prognosis. Medical imaging analysis, pathology, and radiology have benefited from AI algorithms’ ability to interpret and analyze various imaging modalities. Moreover, AI applications in cancer treatment have facilitated the development of predictive models for treatment response, enabling personalized and targeted therapies based on individual patient characteristics. The purpose of the study was to give facts regarding the integration of artificial intelligence and machine learning in drug discovery and cancer therapy and its significant prospects for improving efficiency, decreasing costs, and improving patient outcomes.

ARTIFICIAL INTELLIGENCE, PROBLEMS AND PROSPECTS

The article discusses the concept of “artificial intelligence”, its problems and prospects. Artificial intelligence is the property of artificial intelligent systems to perform creative functions that are traditionally considered the prerogative of humans. the science and technology of creating intelligent machines, especially intelligent computer programs. The article discusses the main characteristics of artificial intelligence. Features and examples of the implementation of artificial intelligence technologies in the modern foreign educational space are shown. The history of artificial intelligence as a new scientific direction begins in the middle of the 20th century. By this time, many prerequisites for its origin had already been formed: among philosophers there had long been debates about the nature of man and the processes of cognition of the world, neurophysiologists and psychologists developed a number of theories regarding the work of the human brain and thinking, economists and mathematicians asked questions about optimal calculations and the presentation of knowledge about the world in in a formalized form; finally, the foundation of the mathematical theory of computing - the theory of algorithms - was born and the first computers were created. The formation of modern computer technologies has created a large number of cases that are related to: • human speech, the ability to learn to recognize and synthesize it; • development of technical vision mechanisms that will be able to recognize people’s faces; • the ability to teach cars to drive independently without human intervention, etc. [1]. Such systems that demonstrate and follow human behavior are called artificial intelligence. The study of artificial intelligence is an important direction in modern science. Artificial intelligence is a technology that contains a set of tools that help a computer, based on the studied data, provide answers to questions, and also draw conclusions based on this. Today, AI is essential for a wide range of industries, including healthcare, retail, manufacturing, and even government. But there are ethical challenges with AI, and as always, these options need to be out of options to ensure AI doesn't do more harm than good. Here are some of the biggest ethical challenges of AI. Keywords: Artificial intelligence, computer, robot, thinking, awareness, personality, society, humanity, problems, prospects, future.

A journey from AI to Gen-AI

The history of artificial intelligence (AI), from its conception to the creation of general AI (Gen-AI), is a fascinating story of human inventiveness, technical growth, and philosophical research. This article examines the historical milestones, major inventions, and transformational concepts that have influenced AI's trajectory. Beginning with early symbolic AI and rule-based systems, it investigates the shift to machine learning, highlighting discoveries in neural networks and deep learning that transformed disciplines such as computer vision and natural language processing. The introduction of generative models, such as GANs and VAEs, resulted in a considerable increase in AI capabilities, paving the path for Gen-AI. Unlike narrow AI, Gen-AI strives to imitate human-like intelligence and adaptability across a wide range of jobs, bringing serious ethical and philosophical concerns. This essay also looks at the current state of Gen-AI, its problems, and possible applications in healthcare, education, finance, and other areas. It finishes by picturing a future in which human-machine collaboration and ethical AI development are prioritized, highlighting the need of continual learning and responsible innovation.

A History of Artificial Intelligence

The development of the computer and what is now known as artificial intelligence (AI) has evolved over more than two centuries in a long series of steps. The date of the invention of the first computer is estimated at 1822, when Charles Babbage (1791-1871) developed his first design of a working computer on paper, based mainly on a Jacquard loom. He worked on his project together with Augusta Ada King, Countess Lovelace (née Byron) (Ada Lovelace) (1815-1852), whom he called the “Sorceress of Numbers.” This work will present the profile and achievements of Charles Babbage, Augusta Ada King, Countess Lovelace, and Alan Mathison Turing (1912 - 1954), who is considered the father of computer science and artificial intelligence, and then provide an outline of the tumultuous events affecting AI up to the present.

Stefan Odobleja, cybernetics and artificial thinking/Artificial Intelligence

The article demonstrates that the Romanian scientist Stefan Odobleja (1902-1978), in his work "Consonantist Psychology" elaborated the principles and laws that are the basis of artificial thinking. The fascination with artificial intelligence began last century, when humanity was very far from the technologies without which we cannot imagine life today. Artificial intelligence refers to the phenomenon where a machine acts like a human mind. The history of artificial intelligence can include the scientist Stefan Odobleja, as the father of artificial thinking, described in his work, which is actually a generalized cybernetics.

Surveillance Studies and the History of Artificial Intelligence: A Missed Opportunity?

This research note considers how scholars of surveillance might approach the historical legacies that surveillance through artificial intelligence (AI) is implicated in. Engaging with the relative lack of historical studies within the pages of Surveillance & Society, the note argues that in the context of surveillant AI the stakes of an ahistorical analysis are especially high. Bridging scholarship within the history of science with surveillance studies, the note explores how AI techniques today reanimate a longer history of how scientific knowledge production on classification has been coextensive with the maintenance and production of racial, gender, and social hierarchies. The note briefly examines one genealogy––the history of the field of pattern recognition, its relationship to state surveillance, and its understanding of identification as a problem of classification––to consider how surveillance and AI contingently converged. The note concludes by showing how such histories can help scholars of surveillance critically reassess common understandings of the consequences of AI and AI-adjacent surveillance practices used today.

Impact of using Artificial Intelligence in visual art performance: Artificial Intelligence on the Design Industry

This study examines AI's huge impact on visual art performance. This study examines how AI's history, tools, and uses in visual art have transformed processes and immersed audiences. Machine learning, computer vision, and GANs let artists create, curate, and alter visual art with unprecedented creativity. AI creates visual art performance concepts, styles, and aesthetics. AI helps artists analyze massive data sets, find trends, and try new art forms. Case studies demonstrate how AI has been used in many artistic disciplines to create groundbreaking performances, interactive installations, and fascinating experiences. AI-powered inventions have changed art genres and captivated viewers. Collaborative art has pushed creativity and innovativeness. Audiences can participate in the creative process and co-create meaning and narrative in personalized, immersive, and interactive AI-powered art experiences. AI in visual art performance affects biases, ownership, copyright, transparency, and accountability. The paper advocates for ethical, legal, transparent, and interpretable AI systems in the arts to promote equality and fairness. Examine future AI visual art performance adjustments, enhancements, and challenges. It enhances AI algorithms for artistic expression, emotions, and aesthetic. AI researchers, visual artists, composers, dancers, and performers will create more complex, interactive cross-disciplinary works. Interpretability, bias reduction, and ethics research advance the field.

Literature and Artificial Intelligence

The essay proposes a ‘literary’ perspective on the history of artificial intelligence. On the one hand, this means a literary-historical retrospective of the decades since the Second World War, but it also emphasizes the basic probabilistic trait of artificial intelligence, which becomes recognizable especially against the background of current technological developments. The space of literature is the horizon of possibility that accompanies what is real for man in early modern times and to which his activity is directed. When ‘intelligent machines’ today turn to their environment in the mode of mathematical conjecture, this marks not only an epistemic, but also an anthropological caesura.

A Brief History and Foundations for Modern Artificial Intelligence

In this paper, we present a brief history of artificial intelligence (AI) research for more than 70 years since its inception. We begin with an analysis of the mathematical, engineering, psychological and philosophical foundations enabling modern AI. We then outline and give examples of the three primary research thrusts the discipline has taken over its existence. We conclude offering both important criticisms as well as describing the future promise for current AI research and practice.

Comparing artificial intelligence and human coaching goal attainment efficacy.

The history of artificial intelligence (AI) is filled with hype and inflated expectations. Notwithstanding, AI is finding its way into numerous aspects of humanity including the fast-growing helping profession of coaching. Coaching has been shown to be efficacious in a variety of human development facets. The application of AI in a narrow, specific area of coaching has also been shown to work. What remains uncertain, is how the two compare. In this paper we compare two equivalent longitudinal randomised control trial studies that measured the increase in clients' goal attainment as a result of having received coaching over a 10-month period. The first study involved human coaches and the replication study used an AI chatbot coach. In both studies, human coaches and the AI coach were significantly more effective in helping clients reach their goals compared to the two control groups. Surprisingly however, the AI coach was as effective as human coaches at the end of the trials. We interpret this result using AI and goal theory and present three significant implications: AI coaching could be scaled to democratize coaching; AI coaching could grow the demand for human coaching; and AI could replace human coaches who use simplistic, model-based coaching approaches. At present, AI's lack of empathy and emotional intelligence make human coaches irreplicable. However, understanding the efficacy of AI coaching relative to human coaching may promote the focused use of AI, to the significant benefit of society.

Undercover Algorithm: A Secret Chapter in the Early History of Artificial Intelligence and Satellite Imagery

In the second decade of the twenty-first century, computer algorithms accurately and rapidly identify features of some objects on digital satellite imagery. These feature-recognition algorithms are expected to transform geospatial intelligence: to enable rapid retrospective searching of imagery archives and focus prospective analytic attention. This Perspective article establishes the beginning of the U.S. Intelligence Community’s research and development on this capability. Archival research of declassified Central Intelligence Agency documents produced two discoveries: one identifies that the earliest feature-recognition initiative predated the creation of the National Photographic Interpretation Center (NPIC) in January 1961. The other discovery reveals that the earliest neural network software, Frank Rosenblatt’s Mark I Perceptron, from which current feature-detection software descends, had been part of a previously secret four-year NPIC effort from 1963 through 1966 to develop this algorithm into a useful tool for photo-interpreters. The manager of that research effort, John Cain, defined the prospective utility of this software in 1963, and Cain’s criteria, derived from NPIC’s experience during the Cuban Missile Crisis, continues to shape the prospective geospatial intelligence uses of feature-recognition software.

History of artificial intelligence and its application for renal transplantation

History of artificial intelligence and its application for renal transplantation

Generative chemistry: drug discovery with deep learning generative models.

The de novo design of molecular structures using deep learning generative models introduces an encouraging solution to drug discovery in the face of the continuously increased cost of new drug development. From the generation of original texts, images, and videos, to the scratching of novel molecular structures the creativity of deep learning generative models exhibits the height machine intelligence can achieve. The purpose of this paper is to review the latest advances in generative chemistry which relies on generative modeling to expedite the drug discovery process. This review starts with a brief history of artificial intelligence in drug discovery to outline this emerging paradigm. Commonly used chemical databases, molecular representations, and tools in cheminformatics and machine learning are covered as the infrastructure for generative chemistry. The detailed discussions on utilizing cutting-edge generative architectures, including recurrent neural network, variational autoencoder, adversarial autoencoder, and generative adversarial network for compound generation are focused. Challenges and future perspectives follow.

Software Testing: Issues and Challenges of Artificial Intelligence & Machine Learning

The history of Artificial Intelligence and Machine Learning dates back to 1950’s. In recent years, there has been an increase in popularity for applications that implement AI and ML technology. As with traditional development, software testing is a critical component of an efficient AI/ML application. However, the approach to development methodology used in AI/ML varies significantly from traditional development. Owing to these variations, numerous software testing challenges occur. This paper aims to recognize and to explain some of the biggest challenges that software testers face in dealing with AI/ML applications. For future research, this study has key implications. Each of the challenges outlined in this paper is ideal for further investigation and has great potential to shed light on the way to more productive software testing strategies and methodologies that can be applied to AI/ML applications.

人工知能の歩み

人工知能の歩み

A brief history of artificial intelligence

For more than a decade, we have witnessed an acceleration in the development and the adoption of artificial intelligence (AI) technologies. In medicine, it impacts clinical and fundamental research, hospital practices, medical examinations, hospital care or logistics. These in turn contribute to improvements in diagnostics and prognostics, and to improvements in personalised and targeted medicine, advanced observation and analysis technologies, or surgery and other assistance robots. Many challenges in AI and medicine, such as data digitalisation, medical data privacy, algorithm explicability, inclusive AI system development or their reproducibility, have to be tackled in order to build the confidence of medical practitioners in these technologies. This will be possible by mastering the key concepts via a brief history of artificial intelligence.

Explore the history of artificial intelligence, to better define and understand it

L’intelligence artificielle est une expression fourre-tout, qui suscite autant d’espoirs que de craintes. Cette locution a envahi les médias, les conférences, les conversations, mais aussi les appels à projets des institutions de recherche et de diverses associations. On ne peut quasiment plus élaborer de projet de recherche sans mentionner une interface avec l’intelligence artificielle. Dans cet article, après la présentation d’une brève vision historique, nous proposerons une définition de l’intelligence artificielle et un paysage des possibles offerts par celle-ci.

A brief history of artificial intelligence and robotic surgery in orthopedics & traumatology and future expectations.

Recently, the rate of the production and renewal of information makes it almost impossible to be updated. It is quite difficult to process and interpret large amounts of data by human beings. Unlimited memory capacities, learning abilities, artificial intelligence (AI) applications, and robotic surgery techniques cause orthopedic surgeons to be concerned about losing their jobs. The idea of AI, which was first introduced in 1956, has evolved over time by revealing deep learning and evolutionary plexus that can mimic the human neuron cell. Image processing is the leading improvement in developed algorithms. Theoretically, these algorithms appear to be quite successful in interpreting medical images and orthopedic decision support systems for preoperative evaluation. Robotic surgeons have emerged as significant competitors in carrying out the taken decisions. The first robotic applications of orthopedic surgery started in 1992 with the ROBODOC system. Applications started with hip arthroplasty continued with knee arthroplasty. Publications indicate that problems such as blood loss and infection caused by the long operation time in the early stages have been overcome in time with the help of learning systems. Comparative studies conducted with humans indicate that robots are better than humans in providing limb lengthening, patient satisfaction, and cost. As in all new technologies, the developments in both AI applications and robotics surgery indicate that technology is in favor in terms of cost/benefit analyses. Although studies indicate that new technologies are more successful than humans, the replacement of technology with experience and long-term results with traditional methods will not be observed in the near future.

Artificial Intelligence-related Literature in Transplantation: A Practical Guide.

Artificial Intelligence-related Literature in Transplantation: A Practical Guide.