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Bayesian methods are introduced for probabilistic inference in machine learning. [1] 1970s 'AI winter' caused by pessimism about machine learning effectiveness. 1980s: Rediscovery of backpropagation causes a resurgence in machine learning research. 1990s: Work on Machine learning shifts from a knowledge-driven approach to a data-driven approach.
Self-learning, as a machine learning paradigm was introduced in 1982 along with a neural network capable of self-learning, named crossbar adaptive array (CAA). [59] [60] It gives a solution to the problem learning without any external reward, by introducing emotion as an internal reward. Emotion is used as state evaluation of a self-learning agent.
Artificial neural networks (ANNs) are models created using machine learning to perform a number of tasks.Their creation was inspired by biological neural circuitry. [1] [a] While some of the computational implementations ANNs relate to earlier discoveries in mathematics, the first implementation of ANNs was by psychologist Frank Rosenblatt, who developed the perceptron. [1]
Arthur Lee Samuel (December 5, 1901 – July 29, 1990) [3] was an American pioneer in the field of computer gaming and artificial intelligence. [2] He popularized the term "machine learning" in 1959. [4]
The solution is to align the machine's goal function with the goals of its owner and humanity in general. Thus, the problem of mitigating the risks and unintended consequences of AI became known as "the value alignment problem" or AI alignment. [271] At the same time, machine learning systems had begun to have disturbing unintended consequences.
Stevo Bozinovski and Ante Fulgosi introduced transfer learning method in artificial intelligence, based on the psychology of learning. [76] [77] 1978 Tom Mitchell, at Stanford, invented the concept of Version spaces for describing the search space of a concept formation program.
The foundations for the Age of Artificial Intelligence were laid during the latter part of the 20th century and the early 2000s. Key developments included advancements in computer science, neural network models, data storage, the Internet, and optical networking, enabling rapid data transmission essential for AI progress.
In 1993, a neural history compressor system solved a "Very Deep Learning" task that required more than 1000 subsequent layers in an RNN unfolded in time. [34] Long short-term memory (LSTM) networks were invented by Hochreiter and Schmidhuber in 1995 and set accuracy records in multiple applications domains.