Artificial intelligence as we know it began as a vacation project. Dartmouth professor John McCarthy coined the term in the summer of 1956, when he invited a small group to spend a few weeks musing on how to make machines do things like use language. He had high hopes of a breakthrough toward human-level machines.
As the field of AI developed, so did different strategies for making smarter machines. Some researchers tried to distill human knowledge into code or come up with rules for tasks like understanding language. Others were inspired by the importance of learning to human and animal intelligence. They built systems that could get better at a task over time, perhaps by simulating evolution or by learning from example data. The field hit milestone after milestone, as computers mastered more tasks that could previously be done only by people.
Artificial neural networks became an established idea in AI not long after the Dartmouth workshop. The room-filling Perceptron Mark 1 from 1958, for example, learned to distinguish different geometric shapes, and got written up in The New York Times as the “Embryo of Computer Designed to Read and Grow Wiser.” But neural networks tumbled from favor after an influential 1969 book co-authored by MIT’s Marvin Minsky suggested they couldn’t be very powerful.
The development of computers capable of tasks that typically require human intelligence.
Using example data or experience to refine how computers make predictions or perform a task.
A machine learning technique in which data is filtered through self-adjusting networks of math loosely inspired by neurons in the brain.
Artificial General Intelligence
As yet nonexistent software that displays a human-like ability to adapt to different environments and tasks, and transfer knowledge between them.
Showing software labeled example data, such as photographs, to teach a computer what to do.
Learning without annotated examples, just from experience of data or the world—trivial for humans but not generally practical for machines. Yet.
Software that experiments with different actions to figure out how to maximize a virtual reward, such as scoring points in a game.