Artificial Intelligence History Timeline
How AI has gone from an experimental idea to transforming the world in just a few years.
In the early 2010s, machine learning began to gain relevance thanks to the increase in available data and improvements in processing power. Systems were no longer programmed only with rules, but learned from examples. Although still limited, these advances laid the foundation for everything that would come next.
In the early 2010s, machine learning began to gain relevance thanks to the increase in available data and improvements in processing power. Systems were no longer programmed only with rules, but learned from examples. Although still limited, these advances laid the foundation for everything that would come next.
In the early 2010s, machine learning began to gain relevance thanks to the increase in available data and improvements in processing power. Systems were no longer programmed only with rules, but learned from examples. Although still limited, these advances laid the foundation for everything that would come next.
In the early 2010s, machine learning began to gain relevance thanks to the increase in available data and improvements in processing power. Systems were no longer programmed only with rules, but learned from examples. Although still limited, these advances laid the foundation for everything that would come next.
In the early 2010s, machine learning began to gain relevance thanks to the increase in available data and improvements in processing power. Systems were no longer programmed only with rules, but learned from examples. Although still limited, these advances laid the foundation for everything that would come next.
In the early 2010s, machine learning began to gain relevance thanks to the increase in available data and improvements in processing power. Systems were no longer programmed only with rules, but learned from examples. Although still limited, these advances laid the foundation for everything that would come next.
In the early 2010s, machine learning began to gain relevance thanks to the increase in available data and improvements in processing power. Systems were no longer programmed only with rules, but learned from examples. Although still limited, these advances laid the foundation for everything that would come next.
In the early 2010s, machine learning began to gain relevance thanks to the increase in available data and improvements in processing power. Systems were no longer programmed only with rules, but learned from examples. Although still limited, these advances laid the foundation for everything that would come next.
In the early 2010s, machine learning began to gain relevance thanks to the increase in available data and improvements in processing power. Systems were no longer programmed only with rules, but learned from examples. Although still limited, these advances laid the foundation for everything that would come next.
Infografia 1
Gabriela Sgalbiero
Created on June 11, 2026
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Transcript
Artificial Intelligence History Timeline
How AI has gone from an experimental idea to transforming the world in just a few years.
In the early 2010s, machine learning began to gain relevance thanks to the increase in available data and improvements in processing power. Systems were no longer programmed only with rules, but learned from examples. Although still limited, these advances laid the foundation for everything that would come next.
In the early 2010s, machine learning began to gain relevance thanks to the increase in available data and improvements in processing power. Systems were no longer programmed only with rules, but learned from examples. Although still limited, these advances laid the foundation for everything that would come next.
In the early 2010s, machine learning began to gain relevance thanks to the increase in available data and improvements in processing power. Systems were no longer programmed only with rules, but learned from examples. Although still limited, these advances laid the foundation for everything that would come next.
In the early 2010s, machine learning began to gain relevance thanks to the increase in available data and improvements in processing power. Systems were no longer programmed only with rules, but learned from examples. Although still limited, these advances laid the foundation for everything that would come next.
In the early 2010s, machine learning began to gain relevance thanks to the increase in available data and improvements in processing power. Systems were no longer programmed only with rules, but learned from examples. Although still limited, these advances laid the foundation for everything that would come next.
In the early 2010s, machine learning began to gain relevance thanks to the increase in available data and improvements in processing power. Systems were no longer programmed only with rules, but learned from examples. Although still limited, these advances laid the foundation for everything that would come next.
In the early 2010s, machine learning began to gain relevance thanks to the increase in available data and improvements in processing power. Systems were no longer programmed only with rules, but learned from examples. Although still limited, these advances laid the foundation for everything that would come next.
In the early 2010s, machine learning began to gain relevance thanks to the increase in available data and improvements in processing power. Systems were no longer programmed only with rules, but learned from examples. Although still limited, these advances laid the foundation for everything that would come next.
In the early 2010s, machine learning began to gain relevance thanks to the increase in available data and improvements in processing power. Systems were no longer programmed only with rules, but learned from examples. Although still limited, these advances laid the foundation for everything that would come next.