Machines That Learn
The vocabulary of AI and robotics, kept precise.
What does AI stand for?
The field is much older than the hardware. These are the people, machines and objections that shaped it.
Artificial intelligence has been declared imminent roughly once a generation since the 1950s, and each wave has left something behind: a technique, a benchmark, or a good objection. The pattern is consistent enough to be worth knowing. A method works impressively on a narrow problem, funding follows, the method fails to generalise, funding collapses, and the useful residue is quietly absorbed into ordinary software. This quiz stays on the durable parts of that story — the Dartmouth meeting that named the field, the perceptron, the expert systems boom, the philosophical arguments that will still be arguments in fifty years. It deliberately avoids naming current systems, because those date faster than anything else in the subject.
Every question in this quiz is listed below with its correct answer and the reasoning behind it. Play first if you would rather not see the answers — or read through as a study sheet.
Play it insteadAnswer: B. Ada Lovelace
Why: Her 1843 notes on Babbage's Analytical Engine set out how it might compute Bernoulli numbers. She also argued the machine could in principle work on things other than numbers, such as music.
Answer: A. Dartmouth College
Why: John McCarthy proposed the phrase in the funding application. The field's long habit of underestimating timescales dates from the same gathering.
Answer: D. A cabinet concealing a human player
Why: Wolfgang von Kempelen built it in 1770 and it toured for decades. The trick held because the cabinet doors were opened in sequence rather than all at once.
Answer: C. A psychotherapist reflecting statements back at the user
Why: Joseph Weizenbaum built it from simple pattern matching, with no model of the conversation at all. He was disturbed by how readily people confided in it anyway.
Answer: B. An artificial neuron
Why: Frank Rosenblatt's machine learned to classify simple patterns by adjusting weights. A 1969 book setting out its limits helped stall neural network research for years.
Answer: A. Applying hand-written rules gathered from specialists
Why: Knowledge had to be extracted from people and written as if-then rules, which made the systems brittle the moment a case fell outside their domain. Their commercial disappointment fed the funding slump that followed.
Answer: D. The machine won the match
Why: Deep Blue worked by searching enormous numbers of positions rather than by anything resembling intuition. Kasparov had beaten an earlier version of it the previous year.
Answer: C. A period in which funding and interest collapsed
Why: There were at least two, in the mid-1970s and the late 1980s. Each followed a stretch of promises that the available computing power could not meet.
Answer: B. The perceptual and physical skills people find effortless
Why: Reasoning tasks fell to computers decades before reliable grasping and walking did. The skills we share with other animals are the oldest and the hardest to reproduce.
Answer: A. Control and communication in animals and machines
Why: The word comes from the Greek for steersman, and feedback is its central idea. The field fed straight into control engineering and, later, into robotics.
Stripped of the vocabulary, it is pattern-finding in data. What follows from that, including the failures.
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The vocabulary of AI and robotics, kept precise.
What does AI stand for?
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