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Artificial intelligence (AI) is the intelligence of machines or software, as opposed to the intelligence of living beings, primarily of humans. It is a field of study in computer science that develops and studies intelligent machines. Such machines may be called AIs.
Artificial consciousness (AC), also known as machine consciousness (MC), synthetic consciousness or digital consciousness, is the consciousness hypothesized to be possible in artificial intelligence. It is also the corresponding field of study, which draws insights from philosophy of mind, philosophy of artificial intelligence, cognitive science and neuroscience. The same terminology can be used with the term "sentience" instead of "consciousness" when specifically designating phenomenal consciousness.
An artificial general intelligence (AGI) is a type of artificial intelligence (AI) that can perform as well or better than humans on a wide range of cognitive tasks. As opposed to narrow AI, which is designed for specific tasks.
Soar is a cognitive architecture, originally created by John Laird, Allen Newell, and Paul Rosenbloom at Carnegie Mellon University.
An AI takeover is a scenario in which artificial intelligence (AI) becomes the dominant form of intelligence on Earth, as computer programs or robots effectively take control of the planet away from the human species. Possible scenarios include replacement of the entire human workforce due to automation, takeover by a superintelligent AI, and the popular notion of a robot uprising. Stories of AI takeovers are very popular throughout science fiction. Some public figures, such as Stephen Hawking and Elon Musk, have advocated research into precautionary measures to ensure future superintelligent machines remain under human control.
A multi-agent system is a computerized system composed of multiple interacting intelligent agents. Multi-agent systems can solve problems that are difficult or impossible for an individual agent or a monolithic system to solve. Intelligence may include methodic, functional, procedural approaches, algorithmic search or reinforcement learning.
Weak artificial intelligence is artificial intelligence that implements a limited part of the mind, or, as narrow AI, is focused on one narrow task. In John Searle's terms it “would be useful for testing hypotheses about minds, but would not be minds”. Weak artificial intelligence focuses on mimicking how humans perform basic actions such as remembering things, perceiving things, and solving simple problems. As opposed to strong AI, which uses technology to be able to think and learn on its own. Computers can use methods such as algorithms and prior knowledge to develop their ways of thinking as human beings do. Strong artificial intelligence systems are learning how to run independently of the programmers who programmed them. Weak AI is not able to have a mind of its own, and can only imitate physical behaviors that it can observe. It is contrasted with Strong AI, which is defined variously as:
The following outline is provided as an overview of and topical guide to artificial intelligence:
Adaptable Robotics refers to a field of robotics with a focus on creating robotic systems capable of adjusting their hardware and software components to perform a wide range of tasks while adapting to varying environments. The 1960s introduced robotics into the industrial field. Since then, the need to make robots with new forms of actuation, adaptability, sensing and perception, and even the ability to learn stemmed the field of adaptable robotics. Significant developments such as the PUMA robot, manipulation research, soft robotics, swarm robotics, AI, cobots, bio-inspired approaches, and more ongoing research have advanced the adaptable robotics field tremendously. Adaptable robots are usually associated with their development kit, typically used to create autonomous mobile robots. In some cases, an adaptable kit will still be functional even when certain components break.
Progress in artificial intelligence (AI) refers to the advances, milestones, and breakthroughs that have been achieved in the field of artificial intelligence over time. AI is a multidisciplinary branch of computer science that aims to create machines and systems capable of performing tasks that typically require human intelligence. Artificial intelligence applications have been used in a wide range of fields including medical diagnosis, economic-financial applications, robot control, law, scientific discovery, video games, and toys. However, many AI applications are not perceived as AI: "A lot of cutting edge AI has filtered into general applications, often without being called AI because once something becomes useful enough and common enough it's not labeled AI anymore." "Many thousands of AI applications are deeply embedded in the infrastructure of every industry." In the late 1990s and early 21st century, AI technology became widely used as elements of larger systems, but the field was rarely credited for these successes at the time.
Daniela L. Rus is a roboticist and computer scientist, Director of the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL), and the Andrew and Erna Viterbi Professor in the Department of Electrical Engineering and Computer Science (EECS) at the Massachusetts Institute of Technology.
In artificial intelligence, apprenticeship learning is the process of learning by observing an expert. It can be viewed as a form of supervised learning, where the training dataset consists of task executions by a demonstration teacher.
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.
Machine ethics is a part of the ethics of artificial intelligence concerned with adding or ensuring moral behaviors of man-made machines that use artificial intelligence, otherwise known as artificial intelligent agents. Machine ethics differs from other ethical fields related to engineering and technology. Machine ethics should not be confused with computer ethics, which focuses on human use of computers. It should also be distinguished from the philosophy of technology, which concerns itself with the grander social effects of technology.
DeepMind Technologies Limited, doing business as Google DeepMind, is a British-American artificial intelligence research laboratory which serves as a subsidiary of Google. Founded in the UK in 2010, it was acquired by Google in 2014, The company is based in London, with research centres in Canada, France, Germany and the United States.
This glossary of artificial intelligence is a list of definitions of terms and concepts relevant to the study of artificial intelligence, its sub-disciplines, and related fields. Related glossaries include Glossary of computer science, Glossary of robotics, and Glossary of machine vision.
In the field of artificial intelligence (AI), AI alignment research aims to steer AI systems towards a person or group's intended goals, preferences, and ethical principles. An AI system is considered aligned if it advances its intended objectives. A misaligned AI system may pursue some objectives, but not the intended ones.
AlphaStar is a computer program by DeepMind that plays the video game StarCraft II. It was unveiled to the public by name in January 2019. In a significant milestone for artificial intelligence, AlphaStar attained Grandmaster status in August 2019.
Artificial intelligence and machine learning techniques are used in video games for a wide variety of applications such as non-player character (NPC) control and procedural content generation (PCG). Machine learning is a subset of artificial intelligence that uses historical data to build predictive and analytical models. This is in sharp contrast to traditional methods of artificial intelligence such as search trees and expert systems.
References
- ↑ Ray, Tiernan (May 14, 2022), DeepMind's 'Gato' is mediocre, so why did they build it?, ZDNet
- ↑ Heikkilä, Melissa (May 23, 2022), "The hype around DeepMind's new AI model misses what's actually cool about it", MIT Technology Review
- ↑ Cuthbertson, Anthony (23 May 2022). "'The Game is Over': Google's DeepMind says it is on verge of achieving human-level AI". The Independent.
- ↑ Reed, Scott; Zolna, Konrad; Parisotto, Emilio; Sergio Gomez Colmenarejo; Novikov, Alexander; Barth-Maron, Gabriel; Gimenez, Mai; Sulsky, Yury; Kay, Jackie; Jost Tobias Springenberg; Eccles, Tom; Bruce, Jake; Razavi, Ali; Edwards, Ashley; Heess, Nicolas; Chen, Yutian; Hadsell, Raia; Vinyals, Oriol; Bordbar, Mahyar; de Freitas, Nando (12 May 2022). "A Generalist Agent". arXiv: 2205.06175 [cs.AI].
- ↑ Wiggers, Kyle (May 13, 2022), "DeepMind's new AI can perform over 600 tasks, from playing games to controlling robots", TechCrunch
