What Is Artificial Intelligence (AI)?

The concept of “a machine that believes” dates back to ancient Greece. But given that the advent of electronic computing (and relative to some of the subjects discussed in this article) essential events and turning points in the advancement 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 often referred to as the “dad of computer technology”- asks the following concern: “Can makers think?”

From there, he provides a test, now famously understood as the “Turing Test,” where a human interrogator would try to compare a computer and human text action. While this test has actually undergone much examination because it was released, it stays a vital part of the history of AI, and a continuous concept within philosophy 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 develop the Logic Theorist, the first-ever running AI computer program.

1967.
Frank Rosenblatt constructs the Mark 1 Perceptron, the first computer based on a neural network that “learned” through trial and mistake. Just a year later on, Marvin Minsky and Seymour Papert publish 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 initiatives.

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

1995.
Stuart Russell and Peter Norvig release Expert system: A Modern Approach, which becomes one of the in the study of AI. In it, they look into 4 possible goals or definitions of AI, which distinguishes computer systems based upon 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 composes a paper, What Is Expert system?, and proposes an often-cited meaning of AI. By this time, the age of huge information and cloud computing is underway, allowing organizations to handle ever-larger information estates, which will one day be used to train AI models.

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 utilizes 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 champion Go player, in a five-game match. The victory is substantial provided the substantial number of possible moves as the video game advances (over 14.5 trillion after just 4 moves). Later, Google bought DeepMind for a reported USD 400 million.

2022.
A rise in big language models or LLMs, such as OpenAI’s ChatGPT, produces a massive change in efficiency of AI and its potential to drive business worth. With these brand-new generative AI practices, deep-learning models can be pretrained on big amounts of information.

2024.
The latest AI patterns point to a continuing AI renaissance. Multimodal models that can take numerous kinds of data as input are providing richer, more robust experiences. These designs bring together computer system vision image recognition and NLP speech acknowledgment abilities. Smaller designs are likewise making strides in an age of reducing returns with enormous designs with big specification counts.