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  • Fondée Date 21 avril 1901
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What Is Expert System (AI)?

The idea of « a device that believes » go back to ancient Greece. But because the arrival of electronic computing (and relative to a few of the subjects talked about in this short article) crucial occasions and turning points in the evolution of AI consist of the following:

1950.
Alan Turing publishes Computing Machinery and Intelligence. In this paper, Turing-famous for breaking the German ENIGMA code throughout WWII and typically referred to as the « father of computer technology »- asks the following question: « Can machines believe? »

From there, he offers a test, now famously referred to as the « Turing Test, » where a human interrogator would attempt to compare a computer system and human text response. While this test has undergone much analysis considering that it was published, it remains a fundamental part of the history of AI, and a continuous idea within viewpoint as it utilizes concepts around linguistics.

1956.
John McCarthy coins the term « synthetic intelligence » at the first-ever AI conference at Dartmouth College. (McCarthy went on to develop the Lisp language.) Later that year, Allen Newell, J.C. Shaw and Herbert Simon create the Logic Theorist, the first-ever running AI computer system program.

1967.
Frank Rosenblatt builds the Mark 1 Perceptron, the very first computer system based on a neural network that « learned » through trial and error. Just a year later on, Marvin Minsky and Seymour Papert release a book entitled Perceptrons, which becomes both the landmark work on neural networks and, at least for a while, an argument against future neural network research efforts.

1980.
Neural networks, which use a backpropagation algorithm to train itself, became commonly utilized in AI applications.

1995.
Stuart Russell and Peter Norvig release Artificial Intelligence: A Modern Approach, which ends up being one of the leading textbooks in the study of AI. In it, they explore four prospective goals or definitions of AI, which distinguishes computer upon rationality and thinking versus acting.

1997.
IBM’s Deep Blue beats then world chess champion Garry Kasparov, in a chess match (and rematch).

2004.
John McCarthy composes a paper, What Is Expert system?, and proposes an often-cited meaning of AI. By this time, the period of huge information and cloud computing is underway, enabling organizations to manage ever-larger information estates, which will one day be utilized to train AI designs.

2011.
IBM Watson ® beats champs Ken Jennings and Brad Rutter at Jeopardy! Also, around this time, data science starts to emerge as a popular discipline.

2015.
Baidu’s Minwa supercomputer uses a special deep neural network called a convolutional neural network to determine and categorize images with a greater rate of accuracy than the typical human.

2016.
DeepMind’s AlphaGo program, powered by a deep neural network, beats Lee Sodol, the world champ Go gamer, in a five-game match. The victory is significant given the big number of possible moves as the video game advances (over 14.5 trillion after just four relocations). Later, Google bought DeepMind for a reported USD 400 million.

2022.
A rise in big language models or LLMs, such as OpenAI’s ChatGPT, produces a huge modification in efficiency of AI and its potential to drive business value. With these brand-new generative AI practices, deep-learning models can be pretrained on large amounts of information.

2024.
The newest AI patterns point to a continuing AI renaissance. Multimodal models that can take multiple types of information as input are offering richer, more robust experiences. These designs unite computer system vision image acknowledgment and NLP speech recognition abilities. Smaller models are also making strides in an age of decreasing returns with enormous designs with large specification counts.