Artificial intelligence là gì? Các công bố khoa học về Artificial intelligence

Artificial Intelligence (AI) simulates human intelligence in machines, enabling tasks like perception, speech, decision-making, and translation. Emerging as a formal field in the mid-20th century, AI is categorized into Narrow AI (task-specific), General AI (theoretical, human-level capability), and Superintelligent AI (speculative, surpassing human intelligence). AI applications span healthcare, finance, transportation, and retail. Challenges include bias, privacy, security, and job displacement. Addressing these concerns as AI evolves is essential, with future advancements targeting general AI and improved AI integration into daily life.

Artificial Intelligence: An Overview

Artificial Intelligence (AI) refers to the simulation of human intelligence in machines that are programmed to think and learn like humans. It is a branch of computer science that aims to create systems capable of performing tasks that typically require human intelligence, such as visual perception, speech recognition, decision-making, and language translation.

History of Artificial Intelligence

The concept of artificial intelligence dates back to ancient times with myths of automatons and intelligent beings. However, the formal foundation of AI as a field of study began in the mid-20th century. In 1956, the Dartmouth Conference marked the official birth of AI as an area of research. Early pioneers like John McCarthy, Marvin Minsky, and Allen Newell laid the groundwork for future advancements.

Types of Artificial Intelligence

AI can be categorized into various types based on capabilities and functionalities:

Narrow AI

Narrow AI, also known as Weak AI, is designed for a specific task such as facial recognition, internet searches, or self-driving car technology. It operates under a limited predefined range and does not possess generalized intelligence.

General AI

General AI, or Strong AI, refers to a machine with the ability to understand, learn, and apply knowledge across a wide range of tasks, matching or surpassing human capabilities. This form of AI remains theoretical and has yet to be realized.

Superintelligent AI

Superintelligent AI denotes a form of intelligence that exceeds that of the best human minds in all aspects, including creativity and critical thinking. This concept is largely speculative and poses theoretical discussions on ethics and control.

Applications of Artificial Intelligence

AI applications are vast and varied, impacting numerous industries and aspects of daily life:

  • Healthcare: AI is used in diagnostic systems, personalized medicine, and treatment prediction models, improving patient outcomes and operational efficiency.
  • Finance: Intelligent algorithms are pivotal in fraud detection, quantitative trading, and risk management.
  • Transportation: From self-driving cars to route optimization and traffic management systems, AI is revolutionizing the transportation industry.
  • Retail: Personalized shopping experiences, inventory management, and customer insights are enhanced through AI technologies.

Challenges and Ethical Considerations

The growth of AI technology also brings several challenges and ethical considerations, including:

  • Bias and Fairness: AI systems can perpetuate existing biases in data, leading to unfair outcomes in decision-making processes.
  • Privacy: The extensive data collection required for AI systems raises significant privacy concerns.
  • Security: AI systems can be vulnerable to attacks and manipulation, necessitating robust security measures.
  • Job Displacement: The automation of tasks traditionally performed by humans leads to shifts in the job market, requiring workforce retraining and adaptation.

The Future of Artificial Intelligence

The future of AI presents exciting possibilities alongside critical challenges. As AI technologies continue to evolve, ongoing research and development are essential to address ethical concerns, enhance functionalities, and ensure that AI benefits society as a whole. Potential advancements include the realization of general AI, improved human-computer interactions, and greater integration into everyday life.

Danh sách công bố khoa học về chủ đề "artificial intelligence":

Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI
Information Fusion - Tập 58 - Trang 82-115 - 2020
Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI)
IEEE Access - Tập 6 - Trang 52138-52160 - 2018
High-performance medicine: the convergence of human and artificial intelligence
Nature Medicine - Tập 25 Số 1 - Trang 44-56 - 2019
Explanation in artificial intelligence: Insights from the social sciences
Artificial Intelligence - Tập 267 - Trang 1-38 - 2019
Artificial intelligence in radiology
Nature Reviews Cancer - Tập 18 Số 8 - Trang 500-510 - 2018
Using Artificial Intelligence to Detect COVID-19 and Community-acquired Pneumonia Based on Pulmonary CT: Evaluation of the Diagnostic Accuracy
Radiology - Tập 296 Số 2 - Trang E65-E71 - 2020
Probabilistic machine learning and artificial intelligence
Nature - Tập 521 Số 7553 - Trang 452-459 - 2015
Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy
International Journal of Information Management - Tập 57 - Trang 101994 - 2021
Key challenges for delivering clinical impact with artificial intelligence
BMC Medicine - Tập 17 Số 1 - 2019
Abstract Background Artificial intelligence (AI) research in healthcare is accelerating rapidly, with potential applications being demonstrated across various domains of medicine. However, there are currently limited examples of such techniques being successfully deployed into clinical practice. This article explores the main challenges and limitations of AI in healthcare, and considers the steps required to translate these potentially transformative technologies from research to clinical practice. Main body Key challenges for the translation of AI systems in healthcare include those intrinsic to the science of machine learning, logistical difficulties in implementation, and consideration of the barriers to adoption as well as of the necessary sociocultural or pathway changes. Robust peer-reviewed clinical evaluation as part of randomised controlled trials should be viewed as the gold standard for evidence generation, but conducting these in practice may not always be appropriate or feasible. Performance metrics should aim to capture real clinical applicability and be understandable to intended users. Regulation that balances the pace of innovation with the potential for harm, alongside thoughtful post-market surveillance, is required to ensure that patients are not exposed to dangerous interventions nor deprived of access to beneficial innovations. Mechanisms to enable direct comparisons of AI systems must be developed, including the use of independent, local and representative test sets. Developers of AI algorithms must be vigilant to potential dangers, including dataset shift, accidental fitting of confounders, unintended discriminatory bias, the challenges of generalisation to new populations, and the unintended negative consequences of new algorithms on health outcomes. Conclusion The safe and timely translation of AI research into clinically validated and appropriately regulated systems that can benefit everyone is challenging. Robust clinical evaluation, using metrics that are intuitive to clinicians and ideally go beyond measures of technical accuracy to include quality of care and patient outcomes, is essential. Further work is required (1) to identify themes of algorithmic bias and unfairness while developing mitigations to address these, (2) to reduce brittleness and improve generalisability, and (3) to develop methods for improved interpretability of machine learning predictions. If these goals can be achieved, the benefits for patients are likely to be transformational.
An artificial intelligence-enabled ECG algorithm for the identification of patients with atrial fibrillation during sinus rhythm: a retrospective analysis of outcome prediction
The Lancet - Tập 394 Số 10201 - Trang 861-867 - 2019
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