1951
1950
1956
First artificial neural network
Alan Turing proposes the "Turing Test"
The birth of AI
1957
Perceptron, the first learning machine
The birth of AI
1959
1980
1966
Introduction of Expert systems
Development of Shakey by the Stanford Research Institute
Arthur Samuel popularizes the term "Machine Learning"
Geoffrey Hinton introduces the concept of deep learning
The Golden Age
Fifth Generation Computer Systems project in Japan
Popularization of artificial neural networks
Deep Blue beats the world chess champion
1986
1982
2006
1997
2011
2016
2022
Watson AI wins game show
Launch of ChatGPT
AlphaGo beats Go champion
The AI renaissance
1951
The SNARC (Stochastic Neural Analog Reinforcement Calculator) is the first artificial neural network designed to simulate learning and decision-making processes. Using artificial neurons and connections, SNARC solves simple problems.
Although rudimentary by modern standards, it marked a significant advance in neural network research, laying the foundations for more complex AI systems.
First artificial neural network - Marvin Minsky and Dean Edmonds create SNARC
1986
David Rumelhart, Geoffrey Hinton, and Ronald J. Williams popularize the backpropagation algorithm, which enables efficient training of multilayer neural networks.
This algorithm solves one of the main neural network problems of the time, enabling advances in deep learning. It will become the basis for many AI innovations in the decades to come.
Artificial neural networks are gaining in popularity
1957
The Perceptron, developed by Frank Rosenblatt, is one of the first machines capable of supervised learning. While SNARC explored the fundamentals of artificial neural networks, Perceptron introduced a concrete model enabling machines to learn from data and classify information in more sophisticated ways. This breakthrough paved the way for more sophisticated neural networks and modern Deep Learning techniques.
Frank Rosenblatt develops the Perceptron, the first learning machine
1956
Organized by John McCarthy, Marvin Minsky, Nathaniel Rochester and Claude Shannon, the Dartmouth Conference is often regarded as the official starting point for the field of AI. It brought together researchers to discuss the possibilities of creating machines capable of simulating human intelligence, laying the foundations for future research and defining long-term goals for the field.
The birth of AI - Dartmouth Conference
1980
In 1980, Digital Equipment Corporation developed R1, also known as XCON. This commercial expert system*, designed to configure commands for new computer systems, marked the end of the "AI winter" and rekindled interest and investment in the field.
**Expert systems are computer programs capable of reproducing human reasoning in specialized fields (such as medicine or engineering). They mark a major advance in AI, demonstrating its potential for solving practical problems.
Introduction of Expert systems - Digital Equipment Corporation markets XCON
1959
In 1959, Arthur Samuel developed a checkers program capable of learning from its own experiences, marking a major advance in the field of artificial intelligence. He also popularized the term "Machine Learning", describing the ability of machines to automatically improve themselves based on data. Samuel's approach laid the foundations for modern AI techniques, notably in the fields of supervised learning and reinforcement learning.
Arthur Samuel popularizes the term "Machine Learning"
2011
IBM Watson wins the Jeopardy game show! beating the best human champions.
Watson demonstrates advanced capabilities in natural language processing and understanding complex questions, illustrating the potential of AI for problem-solving and information processing.
This achievement marks a significant advance in the application of AI technologies to knowledge and language contexts.
Watson wins Jeopardy!
1950
Alan Turing suggests a criterion for determining whether a machine can demonstrate intelligence comparable to that of humans. According to this test, if a human interacting with a machine cannot distinguish it from another human, then the machine can be considered "intelligent". This revolutionary concept laid the foundations for debates on machine intelligence, and remains a pillar of AI cognitive capacity assessment.
Computer pioneer Alan Turing proposes the "Turing Test"
1997
Deep Blue, a supercomputer developed by IBM, beats world chess champion Garry Kasparov in a series of matches.
This event is a major milestone in the history of AI, demonstrating the ability of machines to rival human intelligence in complex games.
Deep Blue's victory illustrates the potential of AI to solve high-level strategic and tactical problems.
Deep Blue beats the world chess champion
1966
Shakey is one of the first robots to demonstrate autonomous navigation and decision-making in complex environments. Equipped with sensors, cameras and a data processing system, Shakey could perceive its environment, plan actions and execute tasks based on the information received. This pioneering project illustrated the first attempts to create robots capable of learning and intelligent behavior, laying the foundations for the development of more sophisticated robotic systems.
Development of Shakey by the Stanford Research Institute
1982
Japan is launching an ambitious program to develop a new generation of computers using advanced AI technologies such as fuzzy logic, voice recognition and parallel programming.
This project is stimulating global interest in AI and encouraging other countries to step up their efforts in this field. It plays a catalytic role in the evolution of research and investment in AI technologies.
The "Fifth Generation Computer Systems" project in Japan
2016
AlphaGo, a program developed by DeepMind, beats Lee Sedol, world champion of Go, a game renowned for its strategic complexity.
This feat demonstrates the impressive advances made by AI in strategy games, using machine learning and Deep Learning techniques to master a game with an almost infinite number of possible combinations. AlphaGo marks a turning point in demonstrating the power of AI to solve highly complex problems.
AlphaGo beats the Go world champion.
2006
In 2006, Geoffrey Hinton's publication on deep neural networks marked the start of a new era, enabling spectacular advances in fields such as computer vision, natural language processing and speech recognition.
This technological breakthrough paves the way for AI applications that will transform industries and improve human interactions with machines, laying the foundations for AI advances in the years to come
Geoffrey Hinton introduces the concept of deep learning
2022
ChatGPT, based on the GPT-3.5 architecture, represents a major advance in natural language processing. Designed to understand and generate consistent responses in natural language conversations, ChatGPT delivers smoother, more contextually relevant interactions.
Thanks to its training on huge amounts of text data, it excels in a variety of applications, from answering questions to writing creative content. The launch of ChatGPT illustrates the potential of AI to transform the way we interact with technology, democratizing access to powerful tools for developers, businesses and individuals.
OpenAI launches ChatGPT
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Transcript
1951
1950
1956
First artificial neural network
Alan Turing proposes the "Turing Test"
The birth of AI
1957
Perceptron, the first learning machine
The birth of AI
1959
1980
1966
Introduction of Expert systems
Development of Shakey by the Stanford Research Institute
Arthur Samuel popularizes the term "Machine Learning"
Geoffrey Hinton introduces the concept of deep learning
The Golden Age
Fifth Generation Computer Systems project in Japan
Popularization of artificial neural networks
Deep Blue beats the world chess champion
1986
1982
2006
1997
2011
2016
2022
Watson AI wins game show
Launch of ChatGPT
AlphaGo beats Go champion
The AI renaissance
1951
The SNARC (Stochastic Neural Analog Reinforcement Calculator) is the first artificial neural network designed to simulate learning and decision-making processes. Using artificial neurons and connections, SNARC solves simple problems. Although rudimentary by modern standards, it marked a significant advance in neural network research, laying the foundations for more complex AI systems.
First artificial neural network - Marvin Minsky and Dean Edmonds create SNARC
1986
David Rumelhart, Geoffrey Hinton, and Ronald J. Williams popularize the backpropagation algorithm, which enables efficient training of multilayer neural networks. This algorithm solves one of the main neural network problems of the time, enabling advances in deep learning. It will become the basis for many AI innovations in the decades to come.
Artificial neural networks are gaining in popularity
1957
The Perceptron, developed by Frank Rosenblatt, is one of the first machines capable of supervised learning. While SNARC explored the fundamentals of artificial neural networks, Perceptron introduced a concrete model enabling machines to learn from data and classify information in more sophisticated ways. This breakthrough paved the way for more sophisticated neural networks and modern Deep Learning techniques.
Frank Rosenblatt develops the Perceptron, the first learning machine
1956
Organized by John McCarthy, Marvin Minsky, Nathaniel Rochester and Claude Shannon, the Dartmouth Conference is often regarded as the official starting point for the field of AI. It brought together researchers to discuss the possibilities of creating machines capable of simulating human intelligence, laying the foundations for future research and defining long-term goals for the field.
The birth of AI - Dartmouth Conference
1980
In 1980, Digital Equipment Corporation developed R1, also known as XCON. This commercial expert system*, designed to configure commands for new computer systems, marked the end of the "AI winter" and rekindled interest and investment in the field. **Expert systems are computer programs capable of reproducing human reasoning in specialized fields (such as medicine or engineering). They mark a major advance in AI, demonstrating its potential for solving practical problems.
Introduction of Expert systems - Digital Equipment Corporation markets XCON
1959
In 1959, Arthur Samuel developed a checkers program capable of learning from its own experiences, marking a major advance in the field of artificial intelligence. He also popularized the term "Machine Learning", describing the ability of machines to automatically improve themselves based on data. Samuel's approach laid the foundations for modern AI techniques, notably in the fields of supervised learning and reinforcement learning.
Arthur Samuel popularizes the term "Machine Learning"
2011
IBM Watson wins the Jeopardy game show! beating the best human champions. Watson demonstrates advanced capabilities in natural language processing and understanding complex questions, illustrating the potential of AI for problem-solving and information processing. This achievement marks a significant advance in the application of AI technologies to knowledge and language contexts.
Watson wins Jeopardy!
1950
Alan Turing suggests a criterion for determining whether a machine can demonstrate intelligence comparable to that of humans. According to this test, if a human interacting with a machine cannot distinguish it from another human, then the machine can be considered "intelligent". This revolutionary concept laid the foundations for debates on machine intelligence, and remains a pillar of AI cognitive capacity assessment.
Computer pioneer Alan Turing proposes the "Turing Test"
1997
Deep Blue, a supercomputer developed by IBM, beats world chess champion Garry Kasparov in a series of matches. This event is a major milestone in the history of AI, demonstrating the ability of machines to rival human intelligence in complex games. Deep Blue's victory illustrates the potential of AI to solve high-level strategic and tactical problems.
Deep Blue beats the world chess champion
1966
Shakey is one of the first robots to demonstrate autonomous navigation and decision-making in complex environments. Equipped with sensors, cameras and a data processing system, Shakey could perceive its environment, plan actions and execute tasks based on the information received. This pioneering project illustrated the first attempts to create robots capable of learning and intelligent behavior, laying the foundations for the development of more sophisticated robotic systems.
Development of Shakey by the Stanford Research Institute
1982
Japan is launching an ambitious program to develop a new generation of computers using advanced AI technologies such as fuzzy logic, voice recognition and parallel programming. This project is stimulating global interest in AI and encouraging other countries to step up their efforts in this field. It plays a catalytic role in the evolution of research and investment in AI technologies.
The "Fifth Generation Computer Systems" project in Japan
2016
AlphaGo, a program developed by DeepMind, beats Lee Sedol, world champion of Go, a game renowned for its strategic complexity. This feat demonstrates the impressive advances made by AI in strategy games, using machine learning and Deep Learning techniques to master a game with an almost infinite number of possible combinations. AlphaGo marks a turning point in demonstrating the power of AI to solve highly complex problems.
AlphaGo beats the Go world champion.
2006
In 2006, Geoffrey Hinton's publication on deep neural networks marked the start of a new era, enabling spectacular advances in fields such as computer vision, natural language processing and speech recognition. This technological breakthrough paves the way for AI applications that will transform industries and improve human interactions with machines, laying the foundations for AI advances in the years to come
Geoffrey Hinton introduces the concept of deep learning
2022
ChatGPT, based on the GPT-3.5 architecture, represents a major advance in natural language processing. Designed to understand and generate consistent responses in natural language conversations, ChatGPT delivers smoother, more contextually relevant interactions. Thanks to its training on huge amounts of text data, it excels in a variety of applications, from answering questions to writing creative content. The launch of ChatGPT illustrates the potential of AI to transform the way we interact with technology, democratizing access to powerful tools for developers, businesses and individuals.
OpenAI launches ChatGPT