- "Artificial intelligence (AI) is the intelligence of machines or software, as opposed to the intelligence of humans or animals."
The simulation of human intelligence processes by machines, especially intelligent computer systems used in robotics to program AI systems.
Machine Learning: A subfield of AI that uses algorithms to automatically learn patterns and insights from data, without being explicitly programmed. Machine learning is used to build predictive models, image recognition, natural language processing, and more.
Deep Learning: A type of neural network that uses multiple layers of processing to learn patterns in complex datasets, including image, text, and speech data.
Natural Language Processing (NLP): A subfield of AI that focuses on understanding and analyzing human language. NLP techniques are used in chatbots, speech recognition, and machine translation.
Computer Vision: A subfield of AI that analyzes and interprets digital images and video data, enabling machines to recognize and understand the world around them.
Robotics: The field of designing, constructing, and programming robots that can perform a variety of tasks, including manufacturing, healthcare, and exploration.
Reinforcement Learning: A type of machine learning that involves training an agent to make choices that maximize a reward signal, allowing it to learn optimal decision-making strategies.
Supervised Learning: A type of machine learning in which the algorithm is trained on labeled data, enabling it to make accurate predictions on new, unseen data.
Unsupervised Learning: A type of machine learning in which the algorithm is trained on unlabeled data, requiring it to identify patterns and structure in the data on its own.
Artificial Neural Networks: A machine learning model inspired by the structure and function of the human brain, made up of interconnected nodes or neurons that can process information.
Data Preprocessing: The process of cleaning, transforming, and preparing data for machine learning algorithms.
Feature Engineering: The process of selecting and transforming relevant features in the data to improve the performance of machine learning algorithms.
Data Visualization: The use of charts, graphs, and other visual representations to communicate insights from data.
Model Evaluation: The process of testing the performance of machine learning models on new, unseen data, and choosing the best-performing model.
Model Deployment: The process of integrating machine learning models into production systems and making them available to end-users.
Ethics in AI: The ethical considerations in AI, including bias, privacy, and accountability.
Explainable AI: The development of AI models that can provide understandable explanations for their decisions and actions.
Cognitive Computing: AI systems that can replicate and enhance human cognitive functioning, including perception, reasoning, and decision-making.
Fuzzy Logic Systems: A type of AI system that can reason with uncertain data and support approximate reasoning.
Genetic Algorithms: A type of optimization technique based on natural selection and biological evolution.
Swarm Intelligence: A collective problem-solving technique inspired by the behavior of social animals, including ants, bees, and birds.
Reactive Machines: These machines can only respond to a single input, and they are designed to perform simple tasks. Examples include thermostats and robotics arms that perform specific tasks.
Limited Memory: These machines can store data temporarily and use that memory to make future decisions. Examples include self-driving cars and speech recognition systems.
Theory of Mind: These machines can comprehend how people think and act within society. They can perform social behavior analysis and interpret feedback from humans. Examples include chatbots and social robots.
Self-Aware: These machines can perceive their surroundings, evaluate their own position in relation to their surroundings, and react accordingly. Examples include humanoid robots and autonomous agents.
General Intelligence: These machines have the ability to perform any intellectual task in a manner equivalent to human beings. Examples include advanced AI research and development.
Emergent Intelligence: These are machines made up of multiple smaller units that work together, allowing for the emergence of more complex behavior not inherent in any specific unit. Examples include swarm robotics and neural networks.
- "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."