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  • Founded Date December 21, 2005
  • Sectors Security & Protective Services Jobs
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What Is Expert System (AI)?

The idea of “a maker that believes” dates back to ancient Greece. But since the introduction of electronic computing (and relative to a few of the subjects discussed in this post) crucial events and turning points in the evolution of AI consist of the following:

1950.
Alan Turing releases Computing Machinery and Intelligence. In this paper, Turing-famous for breaking the German ENIGMA code during WWII and typically described as the “father of computer system science”- asks the following concern: “Can machines think?”

From there, he uses a test, now notoriously called the “Turing Test,” where a human interrogator would try to compare a computer and human text reaction. While this test has gone through much examination since it was published, it remains a fundamental part of the history of AI, and a continuous principle within viewpoint as it utilizes concepts around linguistics.

1956.
John McCarthy coins the term “artificial 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 program.

1967.
Frank Rosenblatt constructs the Mark 1 Perceptron, the first computer system based upon a neural network that “discovered” through experimentation. Just a year later on, Marvin Minsky and Seymour Papert publish a book entitled Perceptrons, which becomes both the landmark work on neural networks and, at least for a while, an argument versus future neural network research efforts.

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

1995.
Stuart Russell and Peter Norvig publish Expert system: A Modern Approach, which ends up being one of the leading books in the study of AI. In it, they explore four possible objectives or meanings of AI, which separates computer system systems based on rationality and believing versus acting.

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

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

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 called a convolutional neural network to identify and categorize images with a higher rate of precision than the average human.

2016.
DeepMind’s AlphaGo program, powered by a deep neural network, beats Lee Sodol, the world champion Go gamer, in a five-game match. The victory is considerable offered the huge number of possible moves as the video game advances (over 14.5 trillion after simply four moves). Later, Google purchased DeepMind for a reported USD 400 million.

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

2024.
The most recent AI patterns indicate a continuing AI renaissance. Multimodal designs that can take several types of information as input are offering richer, more robust experiences. These models combine computer system vision image acknowledgment and NLP speech recognition capabilities. Smaller designs are also making strides in an age of diminishing returns with huge designs with large parameter counts.

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