A short History of Artificial Inteligence

■ In this brief history, the beginnings of artificial intelligence are traced to philosophy, fiction, and imagination. Early inventions in electronics, engineering, and many other disciplines have influenced AI. Some early milestones include work in problems solving which included basic work in learning, knowledge representation, and inference as well as demonstration programs in language understanding, translation, theorem proving, associative memory, and knowledge-based systems. The article ends with a brief examination of influential organizations and current issues facing the field. 25th Anniversary Issue WINTER 2005 53

Artificial Intelligence: Critical Concepts, 2000

Introduction to Volume 1, Part 1 of the four-volume reference work "Artificial Intelligence: Critical Concepts"

Journal of Advances in Library and Information Science, 2025

This study explores the evolution of Artificial Intelligence (AI) from its mid-twentieth-century theoretical roots to modern advances in machine learning, deep learning, and natural language processing. It highlights milestones like early symbolic AI, expert systems, neural networks, and generative AI models. The study examines major constraints like computation limits, ethics, bias, and regulations. It also highlights growing patterns like explainable AI, human-centred AI, and merging AI with quantum computers. The study also discusses emerging trends and potential developments in AI research and applications. .

Zenodo, 2024

The idea of artificial intelligence was born from questioning first principles — the wisdom of deciding that thought must occur in the brain, computation must take place on discrete, logically organised representations of things, and human intelligence must employ a deductive process. So lets take a trip around castle to see what went on that shaped one of humanity's greatest achievements.

Granthaalayah Publication and Printers , 2023

The broad discipline of Artificial Intelligence (AI) aims to automate processes that currently require human intelligence. Understanding intelligence and creating intelligent systems are the two clear objectives of AI. AI is interested in decision-making tools including knowledge representation, machine learning, heuristic reasoning, and inference approaches. The roots of AI can be found in philosophy, literature, and the human imagination. Early advancements in engineering, electronics, and many other fields have inspired AI. In this paper, we begin with a general introduction to the area of AI, then move on to its inception, history, subfields, and various applications.

2nd International Conference on Advances in Computing & Information Technologies (CACIT 2022), 2022

Nowadays, we remark that breakthroughs in the field of AI suggesting its similarity with human beings, tremendous diversity of subfields and terminologies implied in the AI discipline, huge diversity of AI techniques, mistakes of AI and hype could lead to confusion about a clear understanding of the field. In some cases, misunderstanding about AI led to hype, firing, and rude criticism even among many senior experts of the AI domain. In this paper, we proposed a “Comprehensive Overview of Artificial Intelligence (AI)” so that everyone (starting with newbies) is able, via clear insights, to make a difference rapidly. As a contribution to scientific literature, we unambiguously showed via carefully designed illustrations how the AI realm is held by well-known Theories of Intelligence and related AI Concepts that perfectly match the current technological advances in the field. And, of course, we provided a clear insight into Ethical concerns about Artificial Intelligence.

International Journal of Advanced Computer Science and Applications

Artificial Intelligence was embraced as an idea of simulating unique abilities of humans, such as thinking, selfimprovement, and expressing their feelings using different languages. The idea of "Programs with Common Sense" was the main and central goal of Classical AI; it was, mainly built around an internal, updatable cognitive model of the world. But, now almost all the proposed models and approaches lacked reasoning and cognitive models and have been transferred to be more data driven. In this paper, different approaches and techniques of AI are reviewed, specifying how these approaches strayed from the main goal of Classical AI, and emphasizing how to return to its main objective. Additionally, most of the terms and concepts used in this field such as Machine Learning, Neural Networks and Deep Learning are highlighted. Moreover, the relations among these terms are determined, trying to remove mysterious and ambiguities around them. The transition from the Classical AI to Neuro-Symbolic AI and the need for new Cognitive-based models are also explained and discussed.

Ninth IFAC Symposium on Automated Systems Based on Human Skill and Knowledge, 2006, 2006

The general objective of Artificial Intelligence (AI) is to make machinesparticularly computersdo things that require intelligence when done by humans. In the last 60 years, AI has significantly progressed and today forms an important part of industry and technology. However, despite the many successes, fundamental questions concerning the creation of human-level intelligence in machines still remain open and will probably not be answerable when continuing on the current, mainly mathematic-algorithmically-guided path of AI. With the novel discipline of Brain-Like Artificial Intelligence, one potential way out of this dilemma has been suggested. Brain-Like AI aims at analyzing and deciphering the working mechanisms of the brain and translating this knowledge into implementable AI architectures with the objective to develop in this way more efficient, flexible, and capable technical systems This article aims at giving a review about this young and still heterogeneous and dynamic research field.

2025

AI has evolved into a transformative force across sectors like healthcare, finance, gaming, and autonomous systems, driven by advancements in deep learning, CNNs, and NLP. These innovations enable breakthroughs such as superior image classification, AI mastering games, and applications in fraud detection and robotic surgery. However, AI's rapid growth raises challenges like bias, data privacy concerns, ethical dilemmas, and job displacement. Balancing its potential to enhance efficiency and creativity with risks of inequality and ethical issues requires interdisciplinary collaboration and proactive management to ensure honest progress.