What Is Artificial Intelligence (AI)?

The idea of “a maker that thinks” go back to ancient Greece. But considering that the development of electronic computing (and relative to a few of the topics discussed in this article) important occasions and milestones in the advancement 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 throughout WWII and often referred to as the “dad of computer technology”- asks the following question: “Can devices believe?”

From there, he offers a test, now notoriously referred to as the “Turing Test,” where a human interrogator would attempt to identify between a computer and human text reaction. While this test has gone through much analysis since it was released, it remains a vital part of the history of AI, and an ongoing principle within viewpoint as it utilizes 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 produce the Logic Theorist, the first-ever running AI computer program.

1967.
Frank Rosenblatt constructs the Mark 1 Perceptron, the very first computer based on a neural network that “discovered” through experimentation. Just a year later on, Marvin Minsky and Seymour Papert release a book titled Perceptrons, which ends up being both the landmark deal with neural networks and, a minimum of for a while, an argument against future neural network research study efforts.

1980.
Neural networks, which use a backpropagation algorithm to train itself, became commonly used 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 4 possible goals or meanings of AI, which distinguishes computer system systems based on 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 meaning of AI. By this time, the period of big data and cloud computing is underway, enabling to handle ever-larger data 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, 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 recognize and classify images with a greater rate of precision than the average 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 victory is significant given the big variety of possible moves 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, creates an enormous modification in performance of AI and its possible to drive enterprise value. With these brand-new generative AI practices, deep-learning designs can be pretrained on large quantities of information.

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