AI vs Human Quiz: Test Your Knowledge of Artificial Intelligence

Artificial Intelligence: History, Goals, and Applications

Artificial intelligence (AI) refers to the ability of machines to perceive, synthesize, and infer information, demonstrated through tasks such as speech recognition, computer vision, and translation between languages.
Applications of AI include advanced web search engines, recommendation systems, self-driving cars, automated decision-making, and strategic game systems.
The field of AI research has experienced several waves of optimism, followed by disappointment and loss of funding, and has tried and discarded many different approaches, including simulating the brain, modeling human problem solving, and imitating animal behavior.
The traditional goals of AI research include reasoning, knowledge representation, planning, learning, natural language processing, perception, and the ability to move and manipulate objects, with general intelligence as a long-term goal.
AI research uses a wide range of problem-solving techniques, including search and mathematical optimization, formal logic, artificial neural networks, and methods based on statistics, probability, and economics.
AI draws upon computer science, psychology, linguistics, philosophy, and many other fields.
The field was founded on the assumption that human intelligence "can be so precisely described that a machine can be made to simulate it", raising philosophical arguments about the mind and the ethical consequences of creating artificial beings endowed with human-like intelligence.
The history of AI research includes several waves of optimism and disappointment, with the development of symbolic AI, connectionist approaches, and expert systems, and concerns about the difficulty of imitating all the processes of human cognition.
Machine learning, a fundamental concept of AI research, is the study of computer algorithms that improve automatically through experience, and includes unsupervised and supervised learning, reinforcement learning, and transfer learning.
Natural language processing allows machines to read and understand human language, with applications including information retrieval, question answering, and machine translation.
Machine perception involves using input from sensors to deduce aspects of the world, including speech recognition, facial recognition, and object recognition.
Affective computing comprises systems that recognize, interpret, process, and simulate human emotions and includes applications such as sentiment analysis, affective computing in healthcare, and human-robot interaction.Overview of Artificial Intelligence (AI) and Its Applications
AI refers to the development of computer systems that can perform tasks that normally require human intelligence, including learning, reasoning, perception, and natural language processing.
Affective computing is a subfield of AI that seeks to enable machines to recognize, process, and simulate human feelings, emotions, and mood.
General intelligence refers to the ability of a machine to solve a wide variety of problems with breadth and versatility similar to human intelligence.
Search and optimization are tools used in AI to solve problems by intelligently searching through many possible solutions and finding the best possible outcome.
Logic is used for knowledge representation and problem-solving, and several different forms of logic are used in AI research.
Probabilistic methods are used in AI to solve problems with incomplete or uncertain information, and Bayesian networks are a general tool used for various problems.
Classifiers and statistical learning methods are used in AI to classify conditions before inferring actions, and neural networks are also used for classification.
Deep learning is a subfield of AI that uses several layers of neurons between the network's inputs and outputs to extract higher-level features from the raw input.
Specialized languages and hardware have been developed for AI, such as Lisp, Prolog, TensorFlow, AI accelerators, and neuromorphic computing.
AI applications are pervasive and include search engines, recommendation systems, virtual assistants, autonomous vehicles, automatic language translation, facial recognition, image labeling, spam filtering, chatbots, and game playing, among others.
Smart traffic lights have been developed using AI and have been installed in 22 cities to reduce drive time and traffic jam waiting time.
AI is the most prolific emerging technology in terms of the number of patent applications and granted patents, according to WIPO in 2019.Philosophy and Risks of AI

Philosophy:

AI has been defined in terms of "acting" rather than "thinking".
The majority of AI-related patent filings have been published since 2013, with machine learning being the dominant AI technique disclosed in patents.
Symbolic AI simulated high-level conscious reasoning but failed on many tasks that humans solve easily.
Soft computing, including genetic algorithms, fuzzy logic, and neural networks, is tolerant of imprecision, uncertainty, partial truth, and approximation.
AI researchers are divided on whether to pursue the goals of artificial general intelligence and superintelligence directly or to solve as many specific problems as possible.
The philosophy of mind does not know whether a machine can have a mind, consciousness, and mental states.
Computationalism is the position that the human mind is an information processing system and that thinking is a form of computing.
Robot rights lie on a spectrum with animal rights and human rights.

Risks:

AI could cause a substantial increase in long-term unemployment, but it could also be a net benefit if productivity gains are redistributed.
AI provides tools that are particularly useful for authoritarian governments and terrorists, criminals, and rogue states may use weaponized AI.
AI programs can become biased after learning from real-world data, which can lead to unfair outcomes when AI is used for credit rating or hiring.Artificial Intelligence: Risks, Ethics, and Regulation
Superintelligent AI could pose an existential risk to humanity, according to Stephen Hawking and Nick Bostrom.
Experts and industry insiders have mixed opinions on the risks of AI.
Prominent tech titans have committed over $1 billion to nonprofit companies that champion responsible AI development.
Legal responsibility and copyright status of works created with AI assistance are under discussion.
Developing friendly AI and machine ethics are priorities to minimize risks and make ethical decisions.
The regulation of AI is an emerging issue in jurisdictions globally, with over 30 countries adopting dedicated strategies for AI.
The Global Partnership on Artificial Intelligence was launched in 2020, stating a need for AI to be developed in accordance with human rights and democratic values.
AI has been a persistent theme in science fiction, exploring the merging of humans and machines and the fundamental question of what makes us human.
The Three Laws of Robotics, introduced by Isaac Asimov, are often brought up during lay discussions of machine ethics.
Transhumanism is explored in the manga Ghost in the Shell and the science-fiction series Dune.
AI could have the ability to feel and suffer, according to various works such as R.U.R., A.I. Artificial Intelligence, Ex Machina, and Do Androids Dream of Electric Sheep?.
AI textbooks include Artificial Intelligence: A Modern Approach, Machine Learning: A Probabilistic Perspective, Reinforcement Learning: An Introduction, and Probabilistic Graphical Models: Principles and Techniques.