What Is Artificial Intelligence (AI)?

The concept of “a machine that believes” dates back to ancient Greece. But given that the development of electronic computing (and relative to some of the subjects discussed in this post) essential events and milestones in the evolution 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 during WWII and often referred to as the “father of computer system science”- asks the following question: “Can devices believe?”

From there, he provides a test, now famously known as the “Turing Test,” where a human interrogator would attempt to distinguish in between a computer system and human text action. While this test has actually undergone much examination considering that it was published, it stays a crucial part of the history of AI, and an ongoing principle within approach 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 develops the Mark 1 Perceptron, the first computer system based on a neural network that “learned” through experimentation. Just a year later, Marvin Minsky and Seymour Papert publish a book titled Perceptrons, which becomes both the landmark work on neural networks and, at least for a while, an argument against future neural network research study initiatives.

1980.
Neural networks, which utilize a backpropagation algorithm to train itself, ended up being widely utilized in AI applications.

1995.
Stuart Russell and Peter Norvig publish Expert system: A Modern Approach, which turns into one of the leading books in the study of AI. In it, they look into 4 prospective objectives or meanings of AI, which distinguishes computer systems based on rationality and believing versus acting.

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

2004.
John McCarthy composes a paper, What Is Artificial Intelligence?, and proposes an often-cited meaning of AI. By this time, the age 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 champions Ken Jennings and Brad Rutter at Jeopardy! Also, around this time, data science starts 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 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 substantial provided the big variety of possible relocations as the video game advances (over 14.5 trillion after just 4 relocations). Later, Google bought DeepMind for a reported USD 400 million.

2022.
An increase in big language models or LLMs, such as OpenAI’s ChatGPT, develops an enormous modification in performance of AI and its prospective to drive enterprise worth. With these brand-new generative AI practices, deep-learning models can be pretrained on big quantities of information.

2024.
The most current AI trends point to a continuing AI renaissance. Multimodal designs that can take multiple types of data as input are offering richer, more robust experiences. These models unite computer system vision image acknowledgment and NLP speech acknowledgment abilities. Smaller models are also making strides in an age of diminishing returns with massive models with large specification counts.