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챗GPT로 60년 난제 푼 23세 아마추어 수학자

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23세 아마추어가 OpenAI의 최신 모델을 활용해 60년 된 에르되시 추측 수학 문제를 단 한 번의 프롬프트로 해결했습니다. AI가 인간 수학자들이 놓친 완전히 새로운 접근법을 고안해냈다는 점에서, 학계는 AI의 수학적 추론 능력이 실질적인 응용 가치를 지니기 시작했음을 확인하는 중요한 사례로 평가하고 있습니다.

번역된 본문

2026년 4월 24일 4분 소요

한 아마추어가 AI에게 질문하는 방식으로 60년 된 수학 난제를 방금 해결했습니다. ChatGPT AI가 그 누구도 생각하지 못했던 방법으로 하나의 추측을 증명해 낸 것입니다. 전문가들은 이 방법이 앞으로 더 광범위하게 활용될 수 있을 것으로 보고 있습니다.

By Joseph Howlett / Edited by Lee Billings

라이엄 프라이스(Liam Price)는 세계적인 수학자들도 풀지 못했던 60년 된 난제를 방금 해결했습니다. 그의 나이는 23세이며, 고급 수학 훈련을 받은 적은 없습니다. 그가 가진 것은 OpenAI의 최신 대형 언어 모델(LLM)을 사용할 수 있는 ChatGPT Pro 구독권뿐이었습니다.

최근 인공지능이 다수의 '에르되시 문제(Erdős problems)'—다작으로 유명한 수학자 폴 에르되시(Paul Erdős)가 남긴 추측들—를 해결하면서 헤드라인을 장식했습니다. 하지만 전문가들은 이 문제들이 인공지능의 수학적 역량을 평가하기에는 불완전한 기준이라고 경고했습니다. 문제의 중요성과 난이도가 천차만별이며, 많은 AI의 해결책이 겉보기보다 독창성이 떨어지는 것으로 밝혀졌기 때문입니다.

하지만 프라이스가 GPT-5.4 Pro에게 단 한 번의 프롬프트를 입력해 얻은 뒤, 일주일 여 전에 에르되시 문제를 다루는 웹사이트(www.erdosproblems.com)에 게시한 이번의 새로운 해결책은 다릅니다. 이번에 해결된 문제는 저명한 수학자들도 매달렸던 난제로, 그 자체로 학계의 높은 평가를 받습니다. 무엇보다도 중요한 점은 AI가 이런 유형의 문제에 대해 완전히 새로운 방법론을 사용했다는 것입니다. 단정 지을 수는 없지만, 이 LLM이 고안해 낸 연결 고리는 더 넓은 분야에 응용될 수도 있습니다. 이는 최근 수학 분야에서 대대적으로 홍보된 AI의 승리들 사이에서는 찾아보기 힘든 일입니다.

"이번 사례는 조금 다릅니다. 사람들이 이 문제를 살펴보긴 했지만, 출발점에서 인간들이 공통적으로 약간 잘못된 방향으로 꺾여버렸거든요."라고 AI의 수학계 진입을 기록하는 저명한 기록관 역할을 하고 있는 캘리포니아 대학교 로스앤젤레스(UCLA)의 수학자 테렌스 타오(Terence Tao)는 말했습니다. "이제 밝혀지는 것은, 그 문제가 아마 우리가 예상했던 것보다 쉬움에도 불구하고 일종의 사고의 장벽(Mental block)이 있었던 것 같다는 것입니다."

프라이스가 해결한(혹은 ChatGPT에게 해결하도록 지시한) 문제는 집합 내의 어떤 수도 다른 수로 나누어 떨어지지 않는 특수한 정수 집합에 관한 것입니다. 에르되시는 이를 원시 집합(Primitive sets)이라 불렀는데, 이는 유사하게 나눌 수 없는 소수와의 연관성 때문이었습니다. 스탠퍼드 대학교의 수학자 제러드 릭트만(Jared Lichtman)은 "수에 다른 약수가 없으면 그것이 바로 소수입니다. 이는 개별 숫자에 대한 정의를 숫자의 집합으로 일반화한 것"이라고 설명했습니다. 소수는 (자기 자신과 1을 제외하고) 약수가 없기 때문에, 소수로만 이루어진 모든 집합은 자동으로 원시 집합이 됩니다.

에르되시는 또한 임의의 원시 집합에 대해 계산할 수 있는 '점수'인 에르되시 합(Erdős sum)을 고안했습니다. 그는 이 합의 최댓값이 약 1.6이라는 것을 증명했으며, 이 값이 (무한한) 모든 소수의 집합에도 성립해야 한다고 추측했습니다. 릭트만은 2022년 자신의 박사 학위 논문의 일부로 에르되시의 추측이 맞다는 것을 증명했습니다.

에르되시는 또한 집합의 모든 숫자가 커질수록 이 점수가 낮아진다는 사실을 발견했습니다. 즉, 숫자가 클수록 점수는 낮아집니다. 그는 이 점수의 최솟값이 정확히 1일 것이라고 추측했으며, 이는 숫자들이 무한히 커질 때 점수가 도달하게 되는 극한값이었습니다.

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April 24, 2026 4 min read Add Us On Google Add SciAm An amateur just solved a 60-year-old math problem—by asking AI A ChatGPT AI has proved a conjecture with a method no human had thought of. Experts believe it may have further uses By Joseph Howlett edited by Lee Billings Love math? Sign up for our weekly newsletter Proof Positive Enter your email I agree my information will be processed in accordance with the Scientific American and Springer Nature Limited Privacy Policy . We leverage third party services to both verify and deliver email. By providing your email address, you also consent to having the email address shared with third parties for those purposes. Sign Up Liam Price just cracked a 60-year-old problem that world-class mathematicians have tried and failed to solve. He’s 23 years old and has no advanced mathematics training. What he does have is a ChatGPT Pro subscription, which gives him access to the latest large language models from OpenAI. Artificial intelligence has recently made headlines for solving a number of “Erdős problems,” conjectures left behind by the prolific mathematician Paul Erdős. But experts have warned that these problems are an imperfect benchmark of artificial intelligence’s mathematical prowess. They range dramatically in both significance and difficulty, and many AI solutions have turned out to be less original than they appeared. The new solution —which Price got in response to a single prompt to GPT-5.4 Pro and posted on www.erdosproblems.com , a website devoted to the Erdős problems, just over a week ago—is different. The problem it solves has eluded some prominent minds, bestowing it some esteem. And more importantly, the AI seems to have used a totally new method for problems of this kind. It’s too soon to say with certainty, but this LLM-conceived connection may be useful for broader applications—something hard to find among recently touted AI triumphs in math. On supporting science journalism If you're enjoying this article, consider supporting our award-winning journalism by subscribing . By purchasing a subscription you are helping to ensure the future of impactful stories about the discoveries and ideas shaping our world today. “This one is a bit different because people did look at it, and the humans that looked at it just collectively made a slight wrong turn at move one,” says Terence Tao, a mathematician at the University of California, Los Angeles, who has become a prominent scorekeeper for AI’s push into his field. “What’s beginning to emerge is that the problem was maybe easier than expected, and it was like there was some kind of mental block.” The question Price solved—or prompted ChatGPT to solve—concerns special sets of whole numbers, where no number in the set can be evenly divided by any other. Erdős called these “primitive sets” because of their connection to similarly indivisible prime numbers. “A number is prime if it has no other divisors, and this is kind of generalizing that definition from an individual number to a collection of numbers,” says Jared Lichtman, a mathematician at Stanford University. Any set of prime numbers is automatically primitive, because primes have no factors (except themselves and the number one). Erdős also came up with the Erdős sum, a “score” you can calculate for any primitive set. He showed that the biggest the sum could be was about 1.6—and conjectured that this value must also hold for the (infinite) set of all prime numbers. Lichtman proved Erdős right as part of his doctoral thesis in 2022. Erdős also noticed that the score drops if all of a set’s numbers are large—the larger the numbers, the lower the score. He guessed that the lowest this score could be was exactly one, a limit that the score would approach as the set’s numbers approached infinity. Lichtman tried to prove this, too, but got stuck like everyone else before him. Price wasn’t aware of this history when he entered the problem into ChatGPT on an idle Monday afternoon. “I didn’t know what the problem was—I was just doing Erdős problems as I do sometimes, giving them to the AI and seeing what it can come up with,” he says. “And it came up with what looked like a right solution.” He sent it to his occasional collaborator Kevin Barreto, a second-year undergraduate in mathematics at the University of Cambridge. The duo had jump-started the AI-for-Erdős craze late last year by prompting a free version of ChatGPT with open problems chosen at random from the Erdős problems website. (An AI researcher subsequently gifted them each a ChatGPT Pro subscription to encourage their “vibe mathing.”) Reviewing Price’s message, Barreto realized what they had was special, and experts whom he notified quickly took notice. “There was kind of a standard sequence of moves that everyone who worked on the problem previously started by doing,” Tao says. The LLM took an entirely different route, using a formula that was well known in related parts of math, but which no one had thought to apply to this type of question. “The raw output of ChatGPT’s proof was actually quite poor. So it required an expert to kind of sift through and actually understand what it was trying to say,” Lichtman says. But now he and Tao have shortened the proof so that it better distills the LLM’s key insight. More importantly, they already see other potential applications of the AI’s cognitive leap. “We have discovered a new way to think about large numbers and their anatomy,” Tao says. “It’s a nice achievement. I think the jury is still out on the long-term significance.” Lichtman is hopeful because ChatGPT’s discovery validates a sense he’s had since graduate school. “I had the intuition that these problems were kind of clustered together and they had some kind of unifying feel to them,” he says. “And this new method is really confirming that intuition.” It’s Time to Stand Up for Science If you enjoyed this article, I’d like to ask for your support. Scientific American has served as an advocate for science and industry for 180 years, and right now may be the most critical moment in that two-century history. I’ve been a Scientific American subscriber since I was 12 years old, and it helped shape the way I look at the world. 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