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  • Founded Date March 21, 2020
  • Sectors Health Care
  • Posted Jobs 0
  • Viewed 4
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What Is Expert System (AI)?

The concept of “a machine that thinks” dates back to ancient Greece. But considering that the advent of electronic computing (and relative to a few of the topics discussed in this article) crucial occasions and milestones in the development 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 frequently described as the “dad of computer system science”- asks the following question: “Can makers think?”

From there, he uses a test, now famously called the “Turing Test,” where a human interrogator would attempt to compare a computer and human text reaction. While this test has actually undergone much analysis because it was released, it remains a vital part of the history of AI, and a continuous principle within philosophy as it uses ideas around linguistics.

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

1967.
Frank Rosenblatt develops the Mark 1 Perceptron, the very first computer system based upon a neural network that “learned” through experimentation. Just a year later, Marvin Minsky and Seymour Papert release a book titled Perceptrons, which ends up being 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 use a backpropagation algorithm to train itself, became extensively utilized in AI applications.

1995.
Stuart Russell and Peter Norvig publish Expert system: A Modern Approach, which ends up being one of the leading textbooks in the study of AI. In it, they look into four prospective goals or definitions of AI, which distinguishes computer 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 age of big 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 champions Ken Jennings and Brad Rutter at Jeopardy! Also, around this time, information science starts to emerge as a popular discipline.

2015.
Baidu’s Minwa supercomputer utilizes a special deep neural network called a convolutional neural network to recognize and classify images with a greater rate of accuracy than the average 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 triumph is significant given the big number of possible relocations as the game progresses (over 14.5 trillion after simply four moves). Later, Google acquired 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 performance of AI and its prospective to drive business worth. With these brand-new generative AI practices, can be pretrained on big quantities of data.

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

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