R

intelligent systems

By chrsotiriou

  • 1943: Warren McCulloch and Walter Pitts publish "A Logical Calculus of Ideas Immanent in Nervous Activity," which describes the first artificial neural network model

    1943: Warren McCulloch and Walter Pitts publish "A Logical Calculus of Ideas Immanent in Nervous Activity," which describes the first artificial neural network model

  • 1950: Alan Turing proposes the Turing Test, which is still used today as a benchmark for measuring a machine's ability to exhibit human-like intelligence.

    1950: Alan Turing proposes the Turing Test, which is still used today as a benchmark for measuring a machine's ability to exhibit human-like intelligence.

  • 1956: John McCarthy, Marvin Minsky, Claude Shannon, and Nathaniel Rochester organize the Dartmouth Conference, which is considered the birth of artificial intelligence as a field of study

    1956: John McCarthy, Marvin Minsky, Claude Shannon, and Nathaniel Rochester organize the Dartmouth Conference, which is considered the birth of artificial intelligence as a field of study

  • 1966: The first expert system, called Dendral, is developed to interpret complex chemical analysis data.

    1966: The first expert system, called Dendral, is developed to interpret complex chemical analysis data.

  • 1970s: The development of rule-based systems leads to the creation of early artificial intelligence applications, such as MYCIN, a diagnostic system for blood infections.

    1970s: The development of rule-based systems leads to the creation of early artificial intelligence applications, such as MYCIN, a diagnostic system for blood infections.

  • 1980s: Machine learning algorithms begin to be developed, such as the backpropagation algorithm for training neural networks.

    1980s: Machine learning algorithms begin to be developed, such as the backpropagation algorithm for training neural networks.

  • 1990s: The development of statistical natural language processing techniques leads to advances in language translation and speech recognition.

    1990s: The development of statistical natural language processing techniques leads to advances in language translation and speech recognition.

  • 2000s: Deep learning algorithms, which use artificial neural networks with many layers, become increasingly popular and lead to breakthroughs in image and speech recognition.

    2000s: Deep learning algorithms, which use artificial neural networks with many layers, become increasingly popular and lead to breakthroughs in image and speech recognition.