Artificial intelligence (AI) is intelligence demonstrated by machines, as opposed to natural intelligence displayed by animals including humans. Leading AI textbooks define the field as the study of "intelligent agents": any system that perceives its environment and takes actions that maximize its chance of achieving its goals.
Introduction
Artificial intelligence was founded as an academic discipline in 1956, and in the years since has experienced several waves of optimism, followed by disappointment and the loss of funding (known as an "AI winter"), followed by new approaches, success and renewed funding. After AlphaGo defeated a professional Go player in 2015, artificial intelligence once again attracted widespread global attention.
History
The field of AI research was born at a workshop at Dartmouth College in 1956, where the term "Artificial Intelligence" was coined by John McCarthy to distinguish the field from cybernetics and escape the influence of the cyberneticist Norbert Wiener. The 1960s and 1970s saw many successes in AI research, including Shakey the Robot, the first general-purpose mobile robot, and SHRDLU, a natural language computer program.
Foundations
AI typically requires a foundation of specialized hardware and software for writing and training machine learning algorithms. Programming languages commonly used for AI include Python, R, and Julia for data processing and statistical analysis; Java, C++, and MATLAB for building the algorithms; and specific libraries such as TensorFlow, PyTorch, and scikit-learn.
Machine Learning
Machine learning (ML), a fundamental concept in AI research since the field's inception, is the study of computer algorithms that improve automatically through experience. ML algorithms build a model based on sample data, known as training data, in order to make predictions or decisions without being explicitly programmed to do so.
Deep Learning
Deep learning is part of a broader family of machine learning methods based on artificial neural networks with representation learning. Learning can be supervised, semi-supervised or unsupervised. Deep-learning architectures such as deep neural networks, deep belief networks, deep reinforcement learning, and recurrent neural networks have been applied to fields including computer vision, speech recognition, natural language processing, and more.
Applications
AI is used in a wide range of applications including:
- Healthcare: AI is being used for diagnosis, treatment recommendations, patient engagement, and administrative activities.
- Finance: AI algorithms are used for personal finance applications, algorithmic trading, and fraud detection.
- Transportation: AI is used for autonomous vehicles, traffic management, and predictive maintenance.
- Education: AI can automate grading, provide personalized learning experiences, and identify areas where students are struggling.
- Service robots: Robots are being built to assist in various tasks such as cleaning, delivery, and companionship.
Ethics and Challenges
The development of AI raises ethical questions about privacy, bias, transparency, and accountability. There are concerns about the potential for AI to disrupt labor markets, exacerbate social inequalities, and even pose existential risks to humanity. Many researchers and organizations are working on developing ethical guidelines and governance frameworks for AI development.
Future Developments
The future of AI remains uncertain, but advancements continue at a rapid pace. Some key areas of research include:
- Artificial general intelligence (AGI): Developing AI systems with the ability to understand, learn, and apply knowledge across a wide range of tasks at a level equal to or exceeding human capabilities.
- Explainable AI: Creating AI systems that can explain their decisions in ways humans can understand.
- AI ethics: Developing frameworks to ensure AI systems are designed and used in ways that are ethical and beneficial to humanity.
- Human-AI collaboration: Finding ways for humans and AI systems to work together effectively.
References
- Russell, Stuart J.; Norvig, Peter (2021). Artificial Intelligence: A Modern Approach (4th ed.). Pearson.
- Mitchell, Melanie (2019). Artificial Intelligence: A Guide for Thinking Humans. Farrar, Straus and Giroux.
- Kaplan, Andreas; Haenlein, Michael (2019). "Siri, Siri, in my hand: Who's the fairest in the land? On the interpretations, illustrations, and implications of artificial intelligence". Business Horizons. 62 (1): 15–25.
- Boden, Margaret A. (2018). Artificial Intelligence: A Very Short Introduction. Oxford University Press.
- Tegmark, Max (2017). Life 3.0: Being Human in the Age of Artificial Intelligence. Knopf.