How was ai created
Pro Research Analysisby 
Searched over 200M research papers
The Creation of Artificial Intelligence (AI)
Origins of Artificial Intelligence: Historical Context
1956: The Birth of AI
Artificial Intelligence (AI) as a formal field of study was officially born in 1956, a term coined by American computer scientist John McCarthy during the Dartmouth Conference1 2. This conference is widely recognized as the seminal event that launched AI as a distinct academic discipline.
Early Concepts and Influences
The concept of AI, however, predates this formalization. The idea of creating machines that can perform tasks requiring human intelligence has been a part of human imagination for centuries. Leonardo Da Vinci's sketches of robots, for instance, laid early groundwork for the field2. The term "robot" itself comes from the Czech word "robota," meaning forced labor, reflecting early visions of mechanical helpers2.
Foundational Theories and Tests
The Turing Test
In 1950, Alan Turing proposed the Turing Test as a criterion for machine intelligence. According to this test, a machine could be considered intelligent if a human judge could not distinguish its responses from those of a human in a blind interaction1. This test remains a fundamental, though debated, benchmark in AI research.
Marvin Minsky's Predictions
Marvin Minsky, a pioneer in AI, predicted in 1967 that the problem of creating artificial intelligence would be substantially solved within a generation (approximately 30 years)5. While this prediction was overly optimistic, it underscored the ambitious goals of early AI researchers.
Core Components and Technologies
Algorithms and Learning Systems
AI involves creating algorithms and systems that enable computers to perform tasks that typically require human intelligence, such as problem-solving, learning, natural language understanding, and pattern recognition1. These systems can be rule-based, following predefined instructions, or learning-based, adapting and learning from data1.
Machine Learning and Neural Networks
Under the umbrella of AI, machine learning (ML) focuses on designing algorithms that allow computers to learn from and make predictions or decisions based on data without explicit programming1. Neural networks, a subset of ML, are inspired by the human brain's structure and function, enabling advanced tasks like facial recognition and game playing3.
Applications and Impact
Medical and Industrial Applications
AI has found applications in various fields, including medicine and industry. In medicine, AI is used for robotic-assisted surgeries, medical diagnosis, and health management systems2. In industry, AI technologies are transforming sectors such as finance, healthcare, manufacturing, and logistics by automating and optimizing complex processes4.
Societal and Ethical Considerations
The rapid development of AI technologies brings significant opportunities and challenges. The societal and ethical implications of AI, such as its impact on employment and privacy, require careful consideration and interdisciplinary strategies for responsible implementation2 4.
Conclusion
The creation of AI is a multifaceted journey that began with early theoretical concepts and has evolved into a sophisticated field encompassing various technologies and applications. From its formal inception in 1956 to its current role in transforming industries and society, AI continues to push the boundaries of what machines can achieve, guided by foundational theories and ongoing innovations.
Sources and full results
Most relevant research papers on this topic
The artificial intelligence (AI) revolution: How important for scientific work and its reliable sharing
AI revolutionizes scientific work by enabling computers to perform tasks that require human intelligence, such as problem-solving, learning, natural language understanding, and pattern recognition.
Artificial Intelligence in Medicine: 17th Conference on Artificial Intelligence in Medicine, AIME 2019, Poznan, Poland, June 26–29, 2019, Proceedings
AI in medicine has two main branches: virtual and physical, with potential applications in informatics, robotics, and drug delivery.
Highly CitedArtificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy
AI has the potential to revolutionize industries and society, but challenges and research agendas must be addressed to ensure its successful implementation and future impact.
Highly CitedHow do we know how smart AI systems are?
In 1967, Marvin Minksy, a founder of the field of artificial intelligence (AI), made a bold prediction: "Within a generation…the problem of creating 'artificial intelligence' will be substantially solved." Assuming that a generation is about 30 years, Minsky was clearly overoptimistic. But now, nearly two generations later, how close are we to the original goal of human-level (or greater) intelligence in machines?
Liability for damages caused by artificial intelligence
AI-as-tool liability may apply in some cases, as AI acts like a tool and can learn from its experiences, but it is not a subject of law, making it difficult to hold AI personally liable for damage caused.
Highly CitedAI and Mathematical Education
AI and mathematical education are crucial for understanding and addressing the challenges of the future, preparing students for the challenges of the 21st century.
Why general artificial intelligence will not be realized
General artificial intelligence (AGI) remains in principle impossible, and despite recent advances in AI research, computers remain unaccountable for human activities and cultural practices.
Highly CitedThe Turing Trap: The Promise & Peril of Human-Like Artificial Intelligence
Human-like artificial intelligence can boost productivity and understanding, but focusing on augmentation rather than mimicking humans can preserve workers' bargaining power and generate more value.
Highly CitedArtificial Intelligence and Business Value: a Literature Review
Organizations can leverage AI technologies to create business value, but need a better understanding of its adoption, value-generating mechanisms, and key enablers and inhibitors.
Systematic ReviewHighly Cited