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What Is Expert System (AI)?

The concept of “a device that thinks” go back to ancient Greece. But given that the development of electronic computing (and relative to a few of the subjects gone over in this article) important occasions and milestones in the evolution of AI include the following:

1950.
Alan Turing releases Computing Machinery and Intelligence. In this paper, Turing-famous for breaking the German ENIGMA code throughout WWII and often described as the “dad of computer technology”- asks the following question: “Can machines believe?”

From there, he offers a test, now famously known as the “Turing Test,” where a human interrogator would attempt to compare a computer and human text response. While this test has actually undergone much scrutiny considering that it was released, it remains a fundamental part of the history of AI, and an ongoing idea within philosophy as it utilizes ideas around linguistics.

1956.
John McCarthy coins the term “synthetic intelligence” 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 program.

1967.
Frank Rosenblatt develops the Mark 1 Perceptron, the very first computer system based upon a neural network that “found out” through experimentation. Just a year later, 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 initiatives.

1980.
Neural networks, which utilize 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 becomes one of the leading textbooks in the research study of AI. In it, they dive into 4 potential goals or meanings of AI, which differentiates computer system upon rationality and believing versus acting.

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

2004.
John McCarthy composes a paper, What Is Expert system?, and proposes an often-cited definition of AI. By this time, the period of huge information and cloud computing is underway, allowing 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 begins to become a popular discipline.

2015.
Baidu’s Minwa supercomputer uses an unique deep neural network called a convolutional neural network to identify and classify images with a higher rate of precision than the typical human.

2016.
DeepMind’s AlphaGo program, powered by a deep neural network, beats Lee Sodol, the world champ Go player, in a five-game match. The triumph is significant offered the substantial number of possible relocations as the game progresses (over 14.5 trillion after just four relocations). Later, Google purchased DeepMind for a reported USD 400 million.

2022.
An increase in big language models or LLMs, such as OpenAI’s ChatGPT, creates an enormous change in performance of AI and its prospective to drive business value. With these new generative AI practices, deep-learning designs can be pretrained on big amounts of data.

2024.
The newest AI trends point to a continuing AI renaissance. Multimodal designs that can take numerous types of information as input are offering richer, more robust experiences. These models unite computer vision image recognition and NLP speech acknowledgment capabilities. Smaller designs are also making strides in an age of reducing returns with huge models with large parameter counts.