Artificial Intelligence (AI) is a dynamic field that combines computing and cognitive sciences. It involves the study of intelligent agents that can perceive and respond to their environments to achieve specific goals. Examples of these agents range from chatbots like Siri and Alexa to sensor and actuator-based systems found in Roomba vacuum cleaners and Tesla cars. AI aims to mimic human and animal intelligence and creativity through machines and code.
AI research focuses on human behaviours and characteristics such as pattern recognition, problem-solving and decision-making, learning and knowledge representation, communication, and emotions. While some AI advancements, like Boston Dynamics robots performing synchronized gymnastics, garner millions of views on social media, many AI applications like recommendation and search engines, banking and investment software, shopping and pricing bots have become so commonplace that their effectiveness is often overlooked.
The concept of thinking machines dates back to ancient civilizations. From Hesiod's tale of the lethal autonomous robot Talos in 700 BC to Samuel Butler's 1872 utopian novel Erewhon featuring conscious, self-replicating machines, the idea of artificial intelligence has been a recurring theme in literature and film. This theme often explores authenticity, personhood, companionship, loneliness, dystopia, and immortality.
The development of AI as a scientific field is interdisciplinary. It draws ideas from cybernetics, which studies the role of mammalian neural pathways and connections in producing homeostasis and intelligent control. This field inspired pioneers like John von Neumann, Warren McCulloch, Walter Pitts, and Claude Shannon, whose work continues to influence system theory, artificial neural networks, and AI.
Cognitive psychology, which emerged as a reaction against behaviourism in the 1950s, is another source of AI ideas. It combines the information theory work of Claude Shannon, Alan Turing's conception of mental activity as computation, Allen Newell and Herbert Simon's information processing models of human perception, memory, communication, and problem-solving, and Noam Chomsky's generative linguistics.
A third source for AI is rule-based and symbolic representations of problems, also known as good old-fashioned AI (GOFAI). GOFAI nurtured knowledge-based expert systems that emulated human decision-making in various academic fields and commercial applications.
Machine learning, a subfield of AI, uses computer algorithms to build systems that can learn autonomously from a given database and experiences. It is divided into three broad types: supervised, unsupervised, and reinforcement learning. Supervised learning algorithms rely on labelled training data provided by human specialists, while unsupervised machine learning algorithms search for patterns or structures in unlabeled datasets. Reinforcement learning involves intelligent agents interacting directly with the environment to achieve rewards or attain goals by trial, error, and feedback.
Deep learning is a subfield of machine learning inspired by the structures and functions of the human brain. It has led to significant advances in speech recognition and natural language processing (NLP), computer vision and image recognition, neuromorphic computing, sustainability science, bioinformatics, and smart devices and vehicles.
AI research is also influenced by philosophy, particularly the questions of ethics and consciousness. The ethics of AI extend back to Isaac Asimov's Three Laws of Robotics and continue to be a topic of discussion today, especially with the rise of algorithmic bias and discrimination. Efforts to address these issues include
- European Union's General Data Protection Regulation (GDPR),
- Ethics Guidelines for Trustworthy AI (2018)
- Proposed Artificial Intelligence Act (2021)
AI autonomy in motor vehicles, autonomous weapons systems, and caregiver robots presents new opportunities and threats. The Society of Automotive Engineers International (SAE) defines six levels of driver automation, ranging from level 0, where the human driver is in complete control, to level 5, where the vehicle can drive itself under all conditions without a human being present.
Lethal autonomous weapons systems (LAWS) are divided into levels of AI autonomy, from human-in-the-loop weapons that operate under direct human authority to human-out-of-the-loop weapon systems that identify, target, and destroy enemies without human oversight.
AI also can impact the nature and future of work significantly. While it threatens to displace millions of workers in various industries, it could also address persistent shortages in others, such as trucking. The impact could be even more significant with advances in quantum artificial intelligence (QAI) and superintelligence, which could revolutionize areas like traffic management, pharmaceutical discovery, and military encryption but could inversely pose existential risks to human civilization.