The idea of ’a machine that thinks’ dates back to ancient Greece. But since the advent of electronic computing (and with respect to some of the topics discussed in this article), the following are major events and milestones in the evolution of AI:
1950
Alan Turing publishes Computing Machinery and Intelligence (link is outside ibm.com). In this article, Turing – famous for breaking the German ENIGMA code during World War II and often called the “father of computer science” – asks the question: “Can machines think?”
From there, he offers a test, now known as the “Turing Test,” where a human interrogator would try to distinguish between a computer response and a human text response. Although this test has received a lot of attention since its publication, it remains an important part of the history of AI, and an ongoing concept within philosophy because it draws on ideas from linguistics.
1956
John McCarthy introduces the term ‘artificial intelligence’ at the first-ever AI conference at Dartmouth College. (McCarthy subsequently invented the Lisp language.) Later that year, Allen Newell, JC Shaw, and Herbert Simon created the Logic Theorist, the very first active AI computer program.
1967
Frank Rosenblatt builds the Mark 1 Perceptron, the first computer based on a neural network that ‘learns’ through trial and error. Just a year later, Marvin Minsky and Seymour Papert publish a book entitled Perceptrons, which becomes both the seminal work in the field of neural networks and, at least for a while, an argument against future research initiatives in neural networks.
1980
Neural networks, which use a backpropagation algorithm to train themselves, have been widely used in AI applications.
1995
Stuart Russell and Peter Norvig publish Artificial Intelligence: A Modern Approach (link is outside ibm.com), which becomes one of the leading textbooks in the study of AI. In it, they delve into four potential goals or definitions of AI, which differentiate computer systems based on rationality and thinking versus acting.
1997
IBM’s Deep Blue defeats then world chess champion Garry Kasparov in a chess match (and rematch).
2004
John McCarthy writes a paper, What Is Artificial Intelligence? (link is outside ibm.com) and proposes an oft-quoted definition of AI. By then, the era of big data and cloud computing will have arrived, allowing organizations to manage increasingly large data sets, which will one day be used to train AI models.
2011
IBM Watson® defeats champions Ken Jennings and Brad Rutter on Jeopardy! Around this time, data science also begins to emerge as a popular discipline.
2015
Baidu’s Minwa supercomputer uses a special deep neural network called a convolutional neural network to identify and categorize images with higher accuracy than the average human.
2016
DeepMind’s AlphaGo program, powered by a deep neural network, defeats Lee Sodol, the world champion Go player, in a five-game match. The win is significant considering the sheer number of possible moves as the game progresses (over 14.5 trillion after just four moves). Google later bought DeepMind for a reported $400 million.
2022
A rise of large language models or LLMs, such as OpenAI’s ChatGPT, is revolutionizing AI performance and its potential to drive business value. These new generative AI practices allow deep learning models to be pre-trained on large amounts of data.
2024
The latest AI trends point to an ongoing AI renaissance. Multimodal models that can take multiple types of data as input provide richer, more robust experiences. These models combine computer vision image recognition and NLP speech recognition capabilities. Smaller models are also making progress in an era of diminishing returns, with huge models with large numbers of parameters.
