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Historical Evolution of AI

To understand why AI is so important today, we have to analyze the past.

In 1950 the enthusiasm for AI began:

  • Turing Test: "Can machines think?"
  • 1954: one of the main experiments in machine translation
  • 1955: Arthur Samuel wrote a program that could play checkers very well
  • 1957: Rosenblatt invented perceptrons, a type of neural network

First AI Winter - promises of AI were exaggerated

In 1980 the Boom times occurred:

  • Commercialization of new AI Expert Systems capable of reproducing human decision-making, through "if-then-else" rules
  • Financial planning, medical diagnosis, geological exploration, and microelectronic circuit design

Second AI Winter - many tasks were too complicated for engineers

In 2012 the Deep Learning revolution took place

  • Solved mathematical problems
  • New powerful Neural Networks
  • Huge improvement with the computational power
  • Introduction of GPUs

Problem with data

  • AI models need huge amount of training data
  • Currently, we are able to:
    • Acquire a lot of data (IoT)
    • Store huge amount of data (improved storage)

Today, the question is not if we are able to collect data, but if we are able to use them.

Last update: January 9, 2023 20:28:16
Created: October 5, 2022 14:32:28
Authors: Francesca Neri