If we want to broaden participation, we must educate our students based on the early 17th-century origins of the word "computer," a human who performs calculations.1 Computers were exclusively human until the early 19th century when English polymath and inventor Charles Babbage introduced the Difference Engine, the first mechanical computer. The term "human computer" was then used to differentiate a person who computes from a mechanical computer. Human computers were often women who undertook long and tedious calculations to power some of the most significant advances in science, industry, and space technology in the 20th century.

I propose to consider the question, 'Can machines think?' This should begin with definitions of the meaning of the terms'machine' and'think'. The definitions might be framed so as to reflect so far as possible the normal use of the words, but this attitude is dangerous. If the meaning of the words'machine' and'think' are to be found by examining how they are commonly used it is difficult to escape the conclusion that the meaning and the answer to the question, 'Can machines think?' is to be sought in a statistical survey such as a Gallup poll. Instead of attempting such a definition I shall replace the question by another, which is closely related to it and is expressed in relatively unambiguous words. The new form of the problem can be described in terms of a game which we call the'imitation game'. It is played with three people, a man (A), a woman (B), and an interrogator (C) who may be of either sex. The interrogator stays in a room apart from the other two. The object of the ...

The Harvard College Observatory (now the Center for Astrophysics) in Cambridge, Massachusetts has long been a bastion of astronomical research, its history stretching back to the center's founding in 1839. But for the first forty years of its existence, the HCO was quite literally an old boys club. While amateur female astronomers helped fund and even construct the observatory's telescopes, "it wasn't really seen as proper to allow them out on the roof, in the night, on their own, to actually use instruments," Daina Bouquin, Head Librarian of the Wolbach Library at the Center for Astrophysics and lead of the PHaEDRA project, told Engadget. "The beginning of the whole capacity to do that starts like photography, with people putting together these all-sky surveys," she continued. "And the first group of people to do that, to put together a full survey of the entire visible universe at the time was the Harvard Computers."

Whenever an impressive new technology comes along, people rush to imagine the havoc it could wreak on society, and they overreact. Today we see this happening with artificial intelligence (AI). I was at South by Southwest last month, where crowds were buzzing about Elon Musk's latest hyperbolic claim that AI poses a far greater danger to humanity than nuclear weapons. Some economists have similarly sounded alarms that automation will put nearly half of all jobs in the U.S. at risk by 2030. The drumbeat of doomsaying has people spooked: a Gallup/Northeastern study published in March found that about three out of four Americans are convinced that AI will destroy more jobs than it creates.

Automated Teller Machines (ATMs) were first introduced in the late 1960s, saving customers from waiting for human assistance to conduct simple transactions. But the innovation didn't replace bank teller jobs -- in fact, economist James Bessen writes that the number of full-time employed bank tellers has since risen by at least 2% every year. Same goes for cashiers' jobs after the invention of barcode scanners. Bessen dubs this "The Automation Paradox". History is teeming with examples of automations -- whether in the form of physical machines or technologies like machine learning algorithms -- designed to support human workers by taking over the most time-consuming, manual aspects of their jobs, freeing them up to use higher value skills like selling new products and building relationships with customers.

A robot overlord adjusts Rob's Open Source Android (ROSAL) at the London Science Museum (AP Photo/Alastair Grant) Searching recently for evidence of artificial intelligence taking over our jobs, lives, and everything else, Tom Davenport came up empty. "Nary a robot overlord to be found," he declared at the 14th MIT Sloan CIO Symposium. A day later, at the 3rd RE•WORK Deep Learning Summit, I found many humans who are successfully lording over robots, telling their artificial intelligence creations how to perform a number of narrow cognitive tasks. Davenport, who has published eighteen books on topics ranging from managing organizational data and processes to leading with AI and analytics, is working on his next one, tentatively titled The Cognitive Company. Throughout his distinguished career in a field littered with "hype cycles," Davenport has opted to respond with healthy skepticism to the typical breathless and enthusiastic pronouncements regarding the latest new new thing.

When Alan Turing was born 100 years ago, on June 23, 1912, a computer was not a thing--it was a person. Computers, most of whom were women, were hired to perform repetitive calculations for hours on end. The practice dated back to the 1750s, when Alexis-Claude Clairaut recruited two fellow astronomers to help him plot the orbit of Halley's comet. Clairaut's approach was to slice time into segments and, using Newton's laws, calculate the changes to the comet's position as it passed Jupiter and Saturn. The team worked for five months, repeating the process again and again as they slowly plotted the course of the celestial bodies.

Blaine Mathieu is the founder and President of Turning Point Software in Canada, a computer firm interested in many aspects of the small computer industry. There are few questions more mysterious and thought provoking than whether a nonhuman machine could ever be considered truly human in any important sense of the word. Let us jump ahead a few decades and imagine, for a moment, that all the problems of creating a truly intelligent machine have been solved. How would two "people," a philosopher and a computer, handle some of the physical, emotional, and moral issues of such a creation? A philosopher sits in his office considering how many of life's great mysteries have yet to be solved. Philosopher [opening his window]: What in blazes are you doing on that ledge?

Today, mathematics and computer science often appear as the province of geniuses working at the very edge of human ability and imagination. Even as American high schools struggle to employ qualified math and science teachers, American popular culture has embraced math, science, and computers as a mystic realm of extraordinary intellectual power, even verging on madness. Movies like A Beautiful Mind, Good Will Hunting, and Pi all present human intelligence in the esoteric symbolism of long, indecipherable, but visually captivating equations. One has to think of such prosaic activities as paying the mortgage and grocery shopping to be reminded of the quiet and non-revelatory quality of rudimentary arithmetic. Which is not to put such labor down.

Katherine Johnson as she received the Medal of Freedom. Johnson played a pivotal role in the American space program. She was one of the first African-American women to work at NASA (and the agency's predecessor, the National Advisory Committee for Aeronautics). A mathematician, she worked as a "human computer" performing calculations for the Mercury, Apollo, and Shuttle programs. A NASA biography of Johnson says she was so respected by her peers that "John Glenn requested that she personally re-check the calculations made by the new electronic computers before his flight aboard Friendship 7--the mission on which he became the first American to orbit the Earth."