What Is Artificial Intelligence (AI)?

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The idea of "a machine that thinks" dates back to ancient Greece. But considering that the introduction of electronic computing (and relative to a few of the subjects gone over in this short article) essential occasions and turning points in the advancement 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 typically described as the "daddy of computer system science"- asks the following concern: "Can makers think?"



From there, he uses a test, now notoriously understood as the "Turing Test," where a human interrogator would attempt to compare a computer and human text response. While this test has actually gone through much scrutiny given that it was released, it stays a vital part of the history of AI, and an ongoing concept within viewpoint 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 invent 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 first computer system based upon a neural network that "learned" through experimentation. Just a year later on, Marvin Minsky and Seymour Papert publish a book entitled Perceptrons, which ends up being both the landmark work on neural networks and, a minimum of for a while, an argument against future neural network research study initiatives.


1980.
Neural networks, which use a backpropagation algorithm to train itself, ended up being extensively used in AI applications.



1995.
Stuart Russell and Peter Norvig release Expert system: A Modern Approach, which becomes one of the leading textbooks in the research study of AI. In it, they look into 4 potential goals or meanings of AI, which distinguishes computer system systems based upon rationality and thinking versus acting.


1997.
IBM's Deep Blue beats then world chess champ 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 period of huge information and cloud computing is underway, making it possible for companies to manage ever-larger data estates, which will one day be used to train AI designs.


2011.
IBM Watson ® beats champs Ken Jennings and Brad Rutter at Jeopardy! Also, around this time, information science begins to become a popular discipline.


2015.
Baidu's Minwa supercomputer uses a special deep neural network called a convolutional neural network to identify and classify images with a higher rate of accuracy than the average human.


2016.
DeepMind's AlphaGo program, powered by a deep neural network, beats Lee Sodol, the world champion Go player, in a five-game match. The triumph is significant offered the huge number of possible relocations as the video game progresses (over 14.5 trillion after just 4 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 an enormous change in efficiency of AI and its possible to drive enterprise value. With these brand-new generative AI practices, deep-learning designs can be pretrained on large amounts of data.


2024.
The current AI patterns indicate a continuing AI renaissance. Multimodal models that can take multiple kinds of data as input are supplying richer, more robust experiences. These models unite computer vision image acknowledgment and NLP speech recognition capabilities. Smaller models are likewise making strides in an age of decreasing returns with massive models with large parameter counts.