This is a list of basic terms. It will be updated constantly.
ai // artificial intelligence
„AI“ redirects here. For other uses, see AI (disambiguation), Artificial intelligence (disambiguation), and Intelligent agent. Artificial intelligence (AI) is intelligence—perceiving, synthesizing, and inferring information—demonstrated by machines, as opposed to intelligence displayed by non-human animals and humans. Example tasks in which this is done include speech recognition, computer vision, translation between (natural) languages, as well as other mappings of inputs.
Artificial general intelligence (AGI) is the ability of an intelligent agent to understand or learn any intellectual task that a human being can. It is a primary goal of some artificial intelligence research and a common topic in science fiction and futures studies. AGI is also called strong AI, full AI, or general intelligent action, although some academic sources reserve the term „strong AI“ for computer programs that experience sentience or consciousness.
Strong AI contrasts with weak AI (or narrow AI),[which is not intended to have general cognitive abilities; rather, weak AI is any program that is designed to solve exactly one problem. (Academic sources reserve „weak AI“ for programs that do not experience consciousness or do not have a mind in the same sense people do.) A 2020 survey identified 72 active AGI R&D projects spread across 37 countries.
Weak artificial intelligence (weak AI) is artificial intelligence that implements a limited part of 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 actually 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 are able to use methods such as algorithms and prior knowledge to develop their own ways of thinking like 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.
A robot using weak AI to „think“ and solve mathematical formulas and calculations It is contrasted with Strong AI, which is defined variously as:
• Artificial general intelligence: a machine with the ability to apply intelligence to any problem, rather than just one specific problem. • Human-level intelligence: a machine with a similar intelligence to an average human being. • Superintelligence: a machine with a vastly superior intelligence to the average human being. • Artificial consciousness: a machine that has consciousness, sentience and mind.
Deep learning is part of a broader family of machine learning methods based on artificial neural networks with representation learning. Learning can be supervised, semi-supervised or unsupervised.
Deep-learning architectures such as deep neural networks, deep belief networks, deep reinforcement learning, recurrent neural networks, convolutional neural networks and transformers have been applied to fields including computer vision, speech recognition, natural language processing, machine translation, bioinformatics, drug design, medical image analysis, climate science, material inspection and board game programs, where they have produced results comparable to and in some cases surpassing human expert performance.
Artificial neural networks (ANNs) were inspired by information processing and distributed communication nodes in biological systems. ANNs have various differences from biological brains. Specifically, artificial neural networks tend to be static and symbolic, while the biological brain of most living organisms is dynamic (plastic) and analog.
The adjective „deep“ in deep learning refers to the use of multiple layers in the network. Early work showed that a linear perceptron cannot be a universal classifier, but that a network with a nonpolynomial activation function with one hidden layer of unbounded width can. Deep learning is a modern variation that is concerned with an unbounded number of layers of bounded size, which permits practical application and optimized implementation, while retaining theoretical universality under mild conditions. In deep learning the layers are also permitted to be heterogeneous and to deviate widely from biologically informed connectionist models, for the sake of efficiency, trainability and understandability.
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