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   The $13,500 that changed the fate of humanity: how the term Artificial   
   Intelligence was first coined 71 years ago - but sadly without the legendary   
   visionary soul who imagined it   
      
   By Wayne Williams published 21 hours ago   
      
   From Turing's ideas to a Dartmouth research project, the origins of AI are   
   fascinating   
      
   Although AI may still feel like something new, the term itself was born more   
   than seven decades ago, during a modest proposal for a summer research project   
   at Dartmouth that carried a budget request of $13,500.   
      
   That proposal, submitted to the Rockefeller Foundation in 1955, marked the   
   first known appearance of the phrase "artificial intelligence."   
      
   It was an academic document, not a manifesto, but it quietly laid the   
   foundation for one of the most consequential technological movements in human   
   history.   
      
   The sad irony is that the field's most famous philosophical ancestor, Alan   
   Turing, was already gone by this point.   
      
   Turing had asked the defining question years earlier - "can machines   
   think?" - and designed what became known as the Turing Test, a method to   
   judge whether a machine could convincingly imitate human thought.   
      
   His work framed the entire discussion, yet he died in 1954, two years before   
   the Dartmouth meeting that officially named the field he had helped imagine.   
      
   Turing's death followed his prosecution in the UK for homosexuality, then   
   criminalized, and he died from cyanide poisoning in what was widely ruled a   
   suicide - a loss that removed one of computing's most original thinkers   
   just before his ideas began reshaping science.   
      
   Long before artificial intelligence had a name, Turing had already come up with   
   the question that would define it. In his 1950 paper Computing Machinery and   
   Intelligence, he proposed what became known as the Turing Test, or "imitation   
   game," replacing abstract debates about whether machines could truly think   
   with a simpler challenge: could a machine hold a written conversation well   
   enough that a human judge would be unable to reliably tell it apart from   
   another human?   
      
   By focusing on observable behavior instead of philosophy, Turing turned   
   intelligence into something researchers could actually test.   
      
   The idea was strikingly forward-looking given the reality of computers at the   
   time. Early machines were slow, expensive and limited to mathematical   
   calculation, yet Turing suspected that intelligence might emerge from   
   sufficiently complex symbol processing.   
      
   Rather than asking whether machines possessed a mind or consciousness, he asked   
   whether they could convincingly imitate intelligent behavior - something that   
   inspired later researchers to treat thinking as an engineering problem.   
      
   That conceptual leap directly influenced the group that gathered at Dartmouth   
   just a few years later, even though the man who posed the question would never   
   see the field formally named.   
      
   The Dartmouth Summer Research Project on Artificial Intelligence, organized by   
   John McCarthy with Marvin Minsky, Claude Shannon, and Nathaniel Rochester, was   
   small and ambitious.   
      
   According to the proposal, researchers hoped to prove that "every aspect of   
   learning or any other feature of intelligence can in principle be so precisely   
   described that a machine can be made to simulate it." The goal sounded   
   ambitious then and still does now: language, abstraction, reasoning, and   
   self-improvement, all encoded into machines.   
      
   McCarthy would later become one of AI's most influential voices. In a 1979   
   issue of ComputerWorld, he said bluntly that the computer revolution   
   "hasn't happened yet," even while predicting that it eventually would.   
      
   He argued that computers had not yet impacted life in the way electricity or   
   automobiles had, but he believed that applications in the coming decade would   
   initiate a genuine revolution.   
      
   McCarthy's realism often contrasted with the hype that surrounded the field,   
   a tension that has followed AI ever since.   
      
   Alan Turing: The Scientist Who Saved The Allies - https://youtu.be/XGqbieVcjPU   
      
   AI as a hot topic   
      
   By the early 1980s, interest in AI had surged again, but confusion about what   
   it really meant was widespread.   
      
   Writing in a 1984 issue of InfoWorld, reporter Peggy Watt noted that artificial   
   intelligence had become a "hot topic," with shelves filled with books and   
   software companies racing to label products as intelligent. Yet she warned that   
   "the term is being used and abused widely, almost to the point of losing its   
   usefulness as a description."   
      
   The frustration among researchers was obvious. In that same InfoWorld report,   
   Dr. S. Jerrold Kaplan of Teknowledge said, "Whenever anybody says, `I'm   
   selling AI,' I'm suspicious."   
      
   Kaplan argued that AI was not a single program. "The science of AI is a set   
   of techniques for programming," he said, describing systems that represented   
   "concepts and ideas, explanations and relationships," rather than just   
   numbers or words.   
      
   This tension between promise and reality also defined the work of Marvin   
   Minsky, one of Dartmouth's original architects. In a 1981 issue of   
   ComputerWorld, covering the Data Training '81 conference, Minsky described AI   
   as fundamentally paradoxical: "Hard things are easy to do and easy things are   
   hard to do."   
      
   Computers excelled at calculations that challenged humans, but struggled with   
   common sense, language ambiguity, and contextual understanding.   
      
   Minsky explained that "common sense is the most difficult thing to inculcate   
   into a computer."   
      
   Humans absorb countless exceptions and nuances over years of living, but   
   machines require explicit instruction. A logical rule like "birds can fly"   
   breaks down immediately when confronted with dead birds or flightless species   
   - a simple example revealing why intelligence is more than pure logic.   
      
   Expert systems   
      
   The optimistic early years of AI had already produced striking milestones. The   
   Lawrence Livermore National Laboratory later described how researchers in the   
   1960s developed programs such as SAINT, an early "expert system" capable of   
   solving symbolic integration problems at the level of a college freshman.   
      
   The program solved nearly all the test problems it faced, hinting that machines   
   could emulate specialist reasoning long before modern machine learning.   
      
   Yet progress came in waves. Funding boomed in the 1960s as government agencies   
   backed ambitious research, then cooled massively in the 1970s.   
      
   The dream of building human-like intelligence proved far harder than expected.   
   Even McCarthy admitted that "human-level" AI was still "several   
   conceptual revolutions away."   
      
   By the time AI returned to the spotlight in the 1980s, companies were marketing   
   expert systems and natural-language tools as breakthroughs.   
      
   Some systems impressed users by tolerating spelling mistakes or translating   
   plain English commands into database queries.   
      
   Others, however, leaned more on clever engineering than genuine reasoning. As   
   one unnamed researcher quoted in InfoWorld warned, the real test of an expert   
   system was whether it could explain its conclusions.   
      
   Still, the vision persisted. Industry observers imagined computers capable of   
   understanding natural language, translating documents, and even correcting   
   grammar automatically.   
      
   Kaplan predicted AI would change how people programmed because it was "much   
   more natural to work with symbolic terms than math algorithms." The idea that   
   software could assist, advise, and collaborate with humans was already taking   
   shape.   
      
   Looking back, what stands out is how many early predictions were both wrong and   
   right. McCarthy thought the revolution had not yet arrived, but he believed it   
   would come through practical applications. Minsky warned that common sense   
   would remain stubbornly difficult.   
   Hmm   
      
   Today, as AI systems write text, generate images, and assist scientific   
   discovery, the echoes of those early conversations remain.   
      
   The Dartmouth organizers imagined machines that could "use language, form   
   abstractions and concepts, solve kinds of problems now reserved for humans, and   
   improve themselves." All of which are (mostly) true today.   
      
   The $13,500 proposal did not seem remarkable at the time. It was just one   
   funding request among many. Yet it gave a name to an idea that continues to   
   change society, shaped by optimism, frustration, paradox, and unresolved   
   questions.   
      
   And perhaps that is the real legacy of artificial intelligence. It began not as   
   a single invention, like the transistor or the microprocessor, but as a wager   
   that intelligence itself could be understood, described, and eventually   
   reproduced.   
      
   Seventy-one years later, humanity is still testing that idea, still arguing   
   about definitions, and still pursuing the vision imagined by twentieth-century   
   minds who believed thinking machines might one day become real.   
      
      
   https://www.techradar.com/pro/the-usd13-500-that-changed-the-fate-of-humanity-h   
   ow-the-term-artificial-intelligence-was-first-coined-71-years-ago-but-sadly-wit   
   hout-the-legendary-visionary-soul-who-imagined-it   
      
   $$   
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