GoKawiilArtificial intelligence is the transformative, strategic technology of the early 21st century. It is significantly reshaping practically every aspect of our lives, including in ways that probably no one anticipated. Its rate of adoption and impact have been unprecedented when compared with other technologies.
AI as a distinct field was formally established in 1956 at the Dartmouth Summer Research Project on Artificial Intelligence, proposed by John McCarthy, Marvin Minsky, Nathaniel Rochester, and Claude Shannon. In their August 1955 proposal for the research project, the scientists introduced the term artificial intelligence and envisioned machines capable of simulating human intelligence.
AI is the “science of making machines do things that would require intelligence if done by men,” as defined by Minsky. The professor received the ACM Turing Award, which is often called the “Nobel Prize in computing.”
Since AI’s humble beginnings 70 years ago, it has evolved significantly in its capabilities, gained prominence, and earned widespread adoption across many areas including business, education, finance, health care, industry, and the military.
IEEE’s contributions to the progress and adoption of AI throughout its journey are substantial and multifaceted.
As we celebrate AI’s 70th birthday, understanding its history, current status, limitations, and concerns is key to harnessing it for good.
The technology’s roller-coaster evolution
Although AI emerged as a distinct field in 1956, its intellectual roots extend back further. The ideas and theories that underpin AI predate modern computers such as the ENIAC, unveiled in 1946.
In 1943 Warren Sturgis McCulloch, a neurophysiologist and cybernetician, and Walter Pitts, a logician working in computational neuroscience, were inspired by the human brain. The two devised mathematical models of artificial neurons, demonstrating that artificial neural networks could perform logical computation.
Frank Rosenblatt, a Cornell psychologist, later advanced those ideas by developing the perceptron, an early neural network that laid the foundation for modern machine learning and deep learning.
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