Are You an Artificial Intelligence Expert?

Overview of Artificial Intelligence

Artificial Intelligence (AI) refers to machines that can perceive, synthesize, and infer information, demonstrated through tasks such as speech recognition, computer vision, and natural language processing.
AI applications include advanced web search engines, recommendation systems, self-driving cars, and automated decision-making.
AI research has experienced waves of optimism, followed by disappointment and loss of funding, and then renewed success and funding.
AI research has tried and discarded many different approaches, including simulating the brain, modeling human problem solving, and imitating animal behavior.
Highly mathematical and statistical machine learning has dominated the field in recent years, helping to solve many challenging problems.
The various sub-fields of AI research are centered around particular goals and the use of particular tools, including reasoning, knowledge representation, planning, learning, natural language processing, perception, and the ability to move and manipulate objects.
The term artificial intelligence has been criticized for overhyping AI's true technological capabilities.
AI research was born in 1956 and has experienced several periods of funding and research focus, including symbolic AI and connectionist AI.
AI research has developed tools for knowledge representation and knowledge engineering, allowing AI programs to answer questions intelligently and make deductions about real-world facts.
Machine learning is a fundamental concept of AI research, allowing computer algorithms to improve automatically through experience.
Natural language processing enables machines to read and understand human language, with applications including information retrieval, question answering, and machine translation.
Machine perception is the ability to use input from sensors to deduce aspects of the world, with applications including speech recognition, facial recognition, and object recognition.Overview of Artificial Intelligence
Artificial general intelligence (AGI) aims to develop machines that can solve a wide range of problems with versatility and breadth similar to human intelligence.
Different approaches to AGI include incorporating work in different domains into a multi-agent system or cognitive architecture, developing a mathematically difficult "master algorithm," and simulating anthropomorphic features like an artificial brain or child development.
AI can solve problems by intelligently searching through many possible solutions, using heuristics or optimization algorithms like random optimization, particle swarm optimization, and genetic algorithms.
Logic is used for knowledge representation and problem-solving, including propositional logic, first-order logic, fuzzy logic, and extensions for specific domains of knowledge.
Probabilistic methods based on Bayesian networks and decision theory are used for uncertain reasoning in AI, with applications in perception, learning, planning, and filtering.
Classifiers and statistical learning methods like decision trees, k-nearest neighbor, and neural networks are used for pattern recognition and classification tasks in AI.
Neural networks model complex relationships between inputs and outputs, with deep learning using multiple layers of neurons to extract higher-level features from raw input.
Specialized languages like Lisp and Prolog, as well as hardware like AI accelerators and neuromorphic computing, have been developed for AI.
AI has many applications in search engines, recommendation systems, virtual assistants, autonomous vehicles, language translation, facial recognition, and more.
AI has also been successful in game playing, with programs like Deep Blue, Watson, AlphaGo, Pluribus, and Cepheus achieving superhuman performance in chess, Jeopardy!, Go, and poker.
Other AI applications include content detection, traffic control systems, and patent filings, with machine learning being the dominant AI technique disclosed in patents.
AI has become a ubiquitous feature of daily life, with commercial success driving its widespread use in various industries and institutions.An Overview of Artificial Intelligence: Philosophy, Approaches, Risks, and Future
The field of artificial intelligence (AI) encompasses a wide range of sectors, including healthcare, personal devices, and computing, with IBM having the largest portfolio of AI patents.
Alan Turing proposed the question of whether machines can think and developed the Turing test to measure a machine's ability to simulate human conversation.
AI must be defined in terms of "acting" and not "thinking," according to Russell and Norvig, and intelligence should be viewed as the ability to achieve goals in the world.
The success of statistical machine learning has eclipsed other approaches, but critics argue that symbolic AI is still necessary for attaining general intelligence.
The emerging field of neuro-symbolic AI attempts to bridge symbolic and sub-symbolic approaches.
Soft computing, which includes genetic algorithms and neural networks, is tolerant of imprecision and uncertainty and is the norm in the 21st century.
AI researchers are divided on whether to pursue artificial general intelligence directly or to solve specific problems that could lead indirectly to general intelligence.
The question of whether machines can have a mind and consciousness is central to the philosophy of mind but irrelevant to most AI research.
The risks of AI include technological unemployment, weaponized AI, algorithmic bias, and existential risks from superintelligent AI.
Superintelligent AI could improve itself to the point that humans could not control it, posing a risk to mankind.
Experts and industry insiders are divided on the risks of AI, with some expressing serious misgivings and others arguing that the risks are far enough in the future to not be worth researching.
Bill Gates, Elon Musk, and other tech titans have committed over $1 billion to nonprofit companies that champion responsible AI development.
The future of AI could involve the merging of humans and machines into cyborgs and an intelligence explosion leading to a technological singularity.Artificial Intelligence: Ethics, Regulation, and Fiction
The development of malevolent AI is still centuries away.
Legal responsibility and copyright status of works created with AI assistance are being refined in various jurisdictions.
Friendly AI, designed to minimize risks and benefit humans, should be a higher research priority to prevent AI from becoming an existential risk.
Machine ethics provides ethical principles and procedures for resolving ethical dilemmas in AI.
The regulation of AI is an emerging issue in jurisdictions globally, with over 30 countries adopting dedicated strategies for AI between 2016 and 2020.
The Global Partnership on Artificial Intelligence launched in 2020, promoting the development of AI in accordance with human rights and democratic values.
Henry Kissinger, Eric Schmidt, and Daniel Huttenlocher called for a government commission to regulate AI in November 2021.
Fictional AI beings have been a persistent theme in science fiction, dating back to antiquity.
Mary Shelley's Frankenstein is a classic example of a human creation becoming a threat to its masters.
Isaac Asimov's Three Laws of Robotics are often brought up in discussions of machine ethics, but are considered useless by AI researchers due to their ambiguity.
Transhumanism, the merging of humans and machines, is explored in fiction such as Ghost in the Shell and Dune.
AI is used in fiction to force us to confront the fundamental question of what makes us human, as seen in works such as Do Androids Dream of Electric Sheep? and A.I. Artificial Intelligence.