- "Artificial intelligence (AI) is the intelligence of machines or software, as opposed to the intelligence of humans or animals."
The attempt to create machines that can perform tasks that typically require human cognitive abilities, such as learning, problem-solving, and decision-making.
Ethics and morality in AI: This topic examines ethical and moral issues surrounding the use of artificial intelligence, robots and other intelligent machines.
Cognitive psychology: This topic studies how humans process information, reason, learn, and remember, which can be seen as a basis for the development of intelligent machines.
Machine learning: Machine learning refers to the study and practice of algorithms that automatically improve from data and experience, which is a fundamental component of many AI applications.
Computer vision: Computer vision is the field of enabling machines to recognize and interpret visual data from the world using image and video processing techniques.
Natural language processing: This deals with the ability of a machine to understand human language, process it, and generate meaningful responses.
Robotics: Robotics deals with the development of robots and their interaction with the environment, society and humans.
Bayesian networks and reasoning: This topic covers the use of probabilistic reasoning, which can enable machines to make decisions and inferences in uncertain environments.
Expert systems: This topic deals with the development of intelligent systems that can mimic human expertise in a particular domain, such as medical diagnosis or financial analysis.
Fuzzy logic: This topic studies the use of fuzzy set theory and its applications to reasoning and decision making in AI.
Neural networks: This topic looks at the modeling and simulation of biological neural systems, and the development of computational models that can learn and perform tasks.
Evolutionary algorithms: This topic studies the use of evolutionary principles to design and optimize intelligent systems and algorithms.
Game theory: Game theory studies interactions between multiple agents, which can be used to model complex systems and decision making in AI.
Ontologies: Ontologies are structured knowledge representations that allow machines to understand and reason about domain-specific concepts and relationships.
Multi-agent systems: Multi-agent systems involve the interaction of several agents or machines with each other, and can be used to model complex systems such as traffic flow or market dynamics.
Symbolic reasoning: This topic deals with the development of intelligent systems that can manipulate symbols and logical expressions, and reason about them.
Reinforcement learning: Reinforcement learning is a type of machine learning where an agent learns to make decisions based on feedback in the form of rewards or punishments.
Knowledge representation: Knowledge representation deals with the development of formal systems to represent and reason about knowledge, such as semantic networks or rule-based systems.
Planning and scheduling: This topic deals with the development of algorithms and systems that can generate optimized plans and schedules for complex systems.
Rule-based AI: Rule-based AI systems use if-then rules to arrive at a conclusion or decision. These rules are encoded based on expert knowledge and are easy to interpret and modify.
Symbolic AI: Symbolic AI is based on the idea of representing knowledge in the form of symbols and manipulating them using logical operations. It is primarily used in expert systems and is better suited for problems that can be defined in terms of symbolic representations.
Neural Networks: Neural networks are AI systems that are modeled after the structure and function of the human brain. They use a network of artificial neurons to process and classify large amounts of data.
Machine Learning: Machine Learning is a subfield of AI that involves training computers to recognize patterns in data without being explicitly programmed to do so. It is used in various applications such as image recognition, speech recognition, and natural language processing.
Natural Language Processing: Natural Language Processing (NLP) is a subfield of AI that involves teaching computers to understand human language. It is used in various applications such as chatbots, voice assistants, and sentiment analysis.
Fuzzy Logic: Fuzzy logic is a type of AI that deals with uncertain or incomplete information. It is used in various applications such as control systems, decision-making, and pattern recognition.
Evolutionary Computation: Evolutionary Computation is an AI technique that combines concepts from evolutionary biology and computer science to optimize solutions to complex problems. It is used in various applications such as genetic algorithms and swarm intelligence.
Expert Systems: Expert Systems are AI systems that are designed to emulate the decision-making capabilities of a human expert in a particular domain. They use if-then rules to arrive at a decision and are primarily used in applications such as medical diagnosis and financial analysis.
Hybrid AI Systems: Hybrid AI systems combine multiple AI techniques such as machine learning, NLP, and neural networks to solve complex problems. They are used in a variety of applications such as autonomous driving, fraud detection, and personalized recommendations.
- "It is also the field of study in computer science that develops and studies intelligent machines."
- "AI technology is widely used throughout industry, government, and science."
- "Some high-profile applications are: advanced web search engines (e.g., Google Search), recommendation systems (used by YouTube, Amazon, and Netflix), understanding human speech (such as Siri and Alexa), self-driving cars (e.g., Waymo), generative or creative tools (ChatGPT and AI art), and competing at the highest level in strategic games (such as chess and Go)."
- "Artificial intelligence was founded as an academic discipline in 1956."
- "After 2012, when deep learning surpassed all previous AI techniques, there was a vast increase in funding and interest."
- "The traditional goals of AI research include reasoning, knowledge representation, planning, learning, natural language processing, perception, and support for robotics."
- "General intelligence (the ability to solve an arbitrary problem) is among the field's long-term goals."
- "AI researchers have adapted and integrated 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 also draws upon psychology, linguistics, philosophy, neuroscience, and many other fields."
- "Understanding human speech (such as Siri and Alexa)."
- "When deep learning surpassed all previous AI techniques."
- "Generative or creative tools (ChatGPT and AI art)."
- "Self-driving cars (e.g., Waymo)."
- "Advanced web search engines (e.g., Google Search)."
- "The various sub-fields of AI research are centered around particular goals and the use of particular tools."
- "It is the intelligence of machines or software."
- "AI technology is widely used throughout industry, government, and science."
- "The field went through multiple cycles of optimism followed by disappointment and loss of funding."
- "AI researchers have adapted and integrated 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."