Artificial intelligence (AI) is intelligence demonstrated by machines, as opposed to the natural intelligence displayed by humans or animals. Leading AI textbooks define the field as the study of "intelligent agents": any system that perceives its environment and takes actions that maximize its chance of achieving its goals. Some popular accounts use the term "artificial intelligence" to describe machines that mimic "cognitive" functions that humans associate with the human mind, such as "learning" and "problem solving", however this definition is rejected by major AI researchers.
Computer vision is an interdisciplinary scientific field that deals with how computers can gain high-level understanding from digital images or videos. From the perspective of engineering, it seeks to understand and automate tasks that the human visual system can do.
Machine learning (ML) is the study of computer algorithms that can improve automatically through experience and by the use of data. It is seen as a part of artificial intelligence. Machine learning algorithms build a model based on sample data, known as "training data", in order to make predictions or decisions without being explicitly programmed to do so. Machine learning algorithms are used in a wide variety of applications, such as in medicine, email filtering, speech recognition, and computer vision, where it is difficult or unfeasible to develop conventional algorithms to perform the needed tasks.
Symbolic artificial intelligence is the term for the collection of all methods in artificial intelligence research that are based on high-level symbolic (human-readable) representations of problems, logic and search. Symbolic AI was the dominant paradigm of AI research from the mid-1950s until the late 1980s.
Neat and scruffy are two contrasting approaches to artificial intelligence (AI) research. The distinction was made in the 70s and was a subject of discussion until the middle 80s. In the 1990s and 21st century AI research adopted "neat" approaches almost exclusively and these have proven to be the most successful.
In computer science and operations research, the ant colony optimization algorithm (ACO) is a probabilistic technique for solving computational problems which can be reduced to finding good paths through graphs. Artificial ants stand for multi-agent methods inspired by the behavior of real ants. The pheromone-based communication of biological ants is often the predominant paradigm used. Combinations of artificial ants and local search algorithms have become a method of choice for numerous optimization tasks involving some sort of graph, e.g., vehicle routing and internet routing.
A neural network is a network or circuit of neurons, or in a modern sense, an artificial neural network, composed of artificial neurons or nodes. Thus a neural network is either a biological neural network, made up of biological neurons, or an artificial neural network, for solving artificial intelligence (AI) problems. The connections of the biological neuron are modeled in artificial neural networks as weights between nodes. A positive weight reflects an excitatory connection, while negative values mean inhibitory connections. All inputs are modified by a weight and summed. This activity is referred to as a linear combination. Finally, an activation function controls the amplitude of the output. For example, an acceptable range of output is usually between 0 and 1, or it could be −1 and 1.
Shakey the Robot was the first general-purpose mobile robot to be able to reason about its own actions. While other robots would have to be instructed on each individual step of completing a larger task, Shakey could analyze commands and break them down into basic chunks by itself.
NUST School of Electrical Engineering and Computer Science (NUST-SEECS), formerly NUST Institute of Information Technology, is a constituent school in Islamabad, Pakistan. It was created on a self-financed basis in April 1999 as a constituent college of National University of Sciences and Technology, Pakistan (NUST). It was formed due to the demand for quality IT education in the country and the requirement for NUST to launch its own IT department.
Melanie Mitchell is a professor of computer science at Portland State University. She has worked at the Santa Fe Institute and Los Alamos National Laboratory. Her major work has been in the areas of analogical reasoning, complex systems, genetic algorithms and cellular automata, and her publications in those fields are frequently cited.
The following outline is provided as an overview of and topical guide to artificial intelligence:
Stephen Malvern Omohundro is an American computer scientist whose areas of research include Hamiltonian physics, dynamical systems, programming languages, machine learning, machine vision, and the social implications of artificial intelligence. His current work uses rational economics to develop safe and beneficial intelligent technologies for better collaborative modeling, understanding, innovation, and decision making.
An area of computer vision is active vision, sometimes also called active computer vision. An active vision system is one that can manipulate the viewpoint of the camera(s) in order to investigate the environment and get better information from it.
Robotics is an interdisciplinary field that integrates computer science and engineering. Robotics involves design, construction, operation, and use of robots. The goal of robotics is to design machines that can help and assist humans. Robotics integrates fields of mechanical engineering, electrical engineering, information engineering, mechatronics, electronics, bioengineering, computer engineering, control engineering, software engineering, mathematics, among others.
Ruzena Bajcsy is an American engineer and computer scientist who specializes in robotics. She is Professor of Electrical Engineering and Computer Science at the University of California, Berkeley, where she is also Director Emerita of CITRIS.
In artificial intelligence research, the situated approach builds agents that are designed to behave effectively successfully in their environment. This requires designing AI "from the bottom-up" by focussing on the basic perceptual and motor skills required to survive. The situated approach gives a much lower priority to abstract reasoning or problem-solving skills.
Sir (John) Michael Brady is an Emeritus professor of Oncological Imaging at the University of Oxford. He has been a Fellow of Keble College, Oxford since 1985 and was elected a foreign associate member of the French Academy of Sciences in 2015. He was formerly BP Professor of Information Engineering at Oxford from 1985 to 2010 and a Senior Research Scientist in the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL) in Cambridge, Massachusetts, from 1980 to 1985.
Artificial intelligence, in modern terms, generally refers to computer systems that mimic human cognitive functions. It encompasses independent learning and problem-solving. While this type of general artificial intelligence has not been achieved yet, most contemporary artificial intelligence projects are currently better understood as types of machine-learning algorithms, that can be integrated with existing data to understand, categorize, and adapt sets of data without the need for explicit programming.
Gregory D. Hager is the Mandell Bellmore Professor of Computer Science and founding director of the Johns Hopkins Malone Center for Engineering in Healthcare at Johns Hopkins University.
Berthold Klaus Paul Horn is an American scientist working in the field of artificial intelligence and computer vision. He is Professor of Electrical Engineering and Computer Science at Massachusetts Institute of Technology (MIT). He is also Principal Investigator at the Computer Science and Artificial Intelligence Laboratory (CSAIL) at MIT.
