NEAR AI에서 (L)Earn AI 에이전트를 소개합니다.

3 min read

소개

아마도 이제 전화로 ChatGPT를 호출할 수 있다는 소식을 들었을 것입니다. 이것은 실제로 믿기 어려울 정도입니다. 멋지지만 누구와 대화를 나누게 될지 아시나요? 가장 좋은 방법은 서비스 제공업체 평판인 – OpenAI OpCo, LLC에 의존하는 것입니다.

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현재 AI의 문제점

  • 중앙집중화된 모델: GPT-4 또는 Claude와 같은 가장 널리 사용되는 모델은 폐쇄 소스이며 중앙집중화되어 있습니다.
  • 중앙집중화된 인프라 의존: “오픈 소스” 모델조차도 Groq 또는 Together와 같은 중앙집중화된 플랫폼에서 실행됩니다 AI.
  • 불투명한 운영: 에이전트가 작동하는 방식을 확인하는 것은 불가능합니다 – 프롬프트, 제한 사항 및 중지 목록을 포함하여.
  • 데이터 개인 정보 보호 우려: 사용자는 자신의 데이터가 어떻게 처리되거나 저장되는지에 대해 거의 가시성이 없습니다.

이러한 도전은 책임 있는 AI 사용의 필요성을 강조하며 중요한 결정에 AI 에이전트를 의존할 때 주의를 기울여야 함을 강조합니다.

AI와 학습

AI에 의해 망가지기 전에 학교를 졸업한 경우 당신은 운이 좋습니다.

그러나 학습 기술, 비판적 사고, 추론 기술을 개발하지 않았거나 너무 어린 경우 또는 “도움이 되고 효율적인” AI 에이전트에 중독된 경우 무언가를 확인할 수 없는 상황이 될 수 있습니다. 이것은 심각한 문제가 될 수 있습니다. 계산기는 비싼, 제한된 도구로 시작되었지만 학교에 적응할 시간을 주었습니다. 지금은 AI가 교육을 즉시 변화시키고, 모든 과목에 영향을 미칩니다. 학생들은 AI로 부정행위를 할 것이지만 또한 그것을 자신의 작업에 통합하여 교육자들에게 도전을 제기하고 전통적인 과제의 타당성을 의심할 것입니다.
일반적으로 과제가 수업에서 완료되지 않으면 작업이 인간이 만든 것인지 신뢰할 수 없다는 것입니다.

확인 가능한 AI가 이를 바꿀 수 있습니다.

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NEAR AI 허브

NEAR.AI가 해답입니다.
알파 단계에서 이미 시도할만한 흥미로운 기능이 몇 가지 제공됩니다. 시도해보면서 배우는 것에 비할 바 없습니다.

NEAR AI 연구 허브로 이동하십시오.

플랫폼과 상호 작용하려면 NEAR 계정으로 로그인할 수 있습니다. 에이전트 탭에서 레지스트리에 추가된 모든 에이전트를 찾을 수 있습니다 – 개인 및 암호화된 항목을 지원하는 분산 저장소.
Learn AI 에이전트 0.0.1을 찾아 확인해보세요.
introducing-learn-ai-agent-on-near-ai-agent-registry-1024x626 NEAR Protocol에 대한 다음 텍스트를 번역합니다. 모든 용어는 NEAR Protocol과 관련이 있습니다. HTML 태그를 유지하지만 응답에서 , <html>, <body>, <head>와 같은 태그를 생성하지 마십시오. ‘Burrow’, ‘NEAR’, ‘BRR’, ‘stNEAR’ 등의 특정 이름이나 토큰을 번역하지 마십시오.:</p> <p>NEAR AI 허브 내에서 에이전트와 대화할 수 있으며, 더 중요한 것은 에이전트의 출처를 탐색할 수 있다는 것입니다 – <a href="https://app.near.ai/profiles/learn-agent.learnclub.near">learn-agent.learnclub.near</a>, 소스 코드, 프롬프트 및 불용어를 포함합니다. 또한, 필요한 경우 불용어를 제거하려면 그냥 복제하십시오. 그러나 이제 복제본을 당신이 책임져야 한다는 것을 인식하십시오!</p> <p><picture><source srcset="https://learnnear.club/wp-content/uploads/2025/01/introducing-learn-ai-agent-on-near-ai-verify-1024x595.webp 1024w,https://learnnear.club/wp-content/uploads/2025/01/introducing-learn-ai-agent-on-near-ai-verify-300x174.webp 300w,https://learnnear.club/wp-content/uploads/2025/01/introducing-learn-ai-agent-on-near-ai-verify-768x446.webp 768w,https://learnnear.club/wp-content/uploads/2025/01/introducing-learn-ai-agent-on-near-ai-verify-1320x767.webp 1320w,https://learnnear.club/wp-content/uploads/2025/01/introducing-learn-ai-agent-on-near-ai-verify-150x87.webp 150w,https://learnnear.club/wp-content/uploads/2025/01/introducing-learn-ai-agent-on-near-ai-verify.webp 1459w" sizes="auto, (max-width: 1024px) 100vw, 1024px" type="image/webp"><img src="https://learnnear.club/wp-content/uploads/2025/01/introducing-learn-ai-agent-on-near-ai-verify-1024x595.jpg" height="595" width="1024" srcset="https://learnnear.club/wp-content/uploads/2025/01/introducing-learn-ai-agent-on-near-ai-verify-1024x595.jpg 1024w, https://learnnear.club/wp-content/uploads/2025/01/introducing-learn-ai-agent-on-near-ai-verify-300x174.jpg 300w, https://learnnear.club/wp-content/uploads/2025/01/introducing-learn-ai-agent-on-near-ai-verify-768x446.jpg 768w, https://learnnear.club/wp-content/uploads/2025/01/introducing-learn-ai-agent-on-near-ai-verify-1320x767.jpg 1320w, https://learnnear.club/wp-content/uploads/2025/01/introducing-learn-ai-agent-on-near-ai-verify-150x87.jpg 150w, https://learnnear.club/wp-content/uploads/2025/01/introducing-learn-ai-agent-on-near-ai-verify.jpg 1459w" sizes="auto, (max-width: 1024px) 100vw, 1024px" class="alignnone size-large wp-image-72346 sp-no-webp" alt="introducing-learn-ai-agent-on-near-ai-verify-1024x595" loading="lazy" decoding="async" > </picture></p> <p>이제 조금 더 깊이 파고들어 봅시다. 모델 탭으로 전환하십시오.<br /> <picture><source srcset="https://learnnear.club/wp-content/uploads/2025/01/word-image-72336-5.webp 1326w,https://learnnear.club/wp-content/uploads/2025/01/word-image-72336-5-300x154.webp 300w,https://learnnear.club/wp-content/uploads/2025/01/word-image-72336-5-1024x527.webp 1024w,https://learnnear.club/wp-content/uploads/2025/01/word-image-72336-5-768x395.webp 768w,https://learnnear.club/wp-content/uploads/2025/01/word-image-72336-5-1320x679.webp 1320w,https://learnnear.club/wp-content/uploads/2025/01/word-image-72336-5-150x77.webp 150w" sizes="auto, (max-width: 1326px) 100vw, 1326px" type="image/webp"><img src="https://learnnear.club/wp-content/uploads/2025/01/word-image-72336-5.png" height="682" width="1326" srcset="https://learnnear.club/wp-content/uploads/2025/01/word-image-72336-5.png 1326w, https://learnnear.club/wp-content/uploads/2025/01/word-image-72336-5-300x154.png 300w, https://learnnear.club/wp-content/uploads/2025/01/word-image-72336-5-1024x527.png 1024w, https://learnnear.club/wp-content/uploads/2025/01/word-image-72336-5-768x395.png 768w, https://learnnear.club/wp-content/uploads/2025/01/word-image-72336-5-1320x679.png 1320w, https://learnnear.club/wp-content/uploads/2025/01/word-image-72336-5-150x77.png 150w" sizes="auto, (max-width: 1326px) 100vw, 1326px" class="wp-image-72341 sp-no-webp" alt="word-image-72336-5" loading="lazy" decoding="async" > </picture></p> <p>이미 50개 이상의 오픈 소스 모델이 사용 가능합니다. Learn AI가 NEAR AI 인프라에서 호스팅되는 특정 LLaMA를 사용 중임을 확인할 수 있습니다. 에이전트를 통해 모델과 상호 작용하기 위해 NEAR 계정을 사용하면 실제로 누구와 대화 중인지 알 수 있습니다!</p> <p><picture><source srcset="https://learnnear.club/wp-content/uploads/2025/01/word-image-72336-6.webp 982w,https://learnnear.club/wp-content/uploads/2025/01/word-image-72336-6-300x163.webp 300w,https://learnnear.club/wp-content/uploads/2025/01/word-image-72336-6-768x416.webp 768w,https://learnnear.club/wp-content/uploads/2025/01/word-image-72336-6-150x81.webp 150w" sizes="auto, (max-width: 982px) 100vw, 982px" type="image/webp"><img src="https://learnnear.club/wp-content/uploads/2025/01/word-image-72336-6.png" height="532" width="982" srcset="https://learnnear.club/wp-content/uploads/2025/01/word-image-72336-6.png 982w, https://learnnear.club/wp-content/uploads/2025/01/word-image-72336-6-300x163.png 300w, https://learnnear.club/wp-content/uploads/2025/01/word-image-72336-6-768x416.png 768w, https://learnnear.club/wp-content/uploads/2025/01/word-image-72336-6-150x81.png 150w" sizes="auto, (max-width: 982px) 100vw, 982px" class="wp-image-72342 sp-no-webp" alt="word-image-72336-6" loading="lazy" decoding="async" > </picture></p> <p>NEAR AI는 1.4T 매개변수를 갖는 차세대 프론티어 AI 모델을 구축할 계획입니다! 오픈 소스 AI 개발을 사용료 지불 모델을 통해 지속 가능하게 만드는 것이 아이디어이며, 이것이 NEAR가 중요한 역할을 할 곳입니다.</p> <style type="text/css" id="wq-trivia-custom-css">.wq-quiz-72349 { --wq-question-width: 100%; --wq-question-color: #009cff; --wq-question-height: auto; --wq-bar-color: #00c479; --wq-font-color: #444; --wq-background-color: #f2f2f2; } </style><!-- wp quiz --> <div id="wp-quiz-72349" class="wq_quizCtr single trivia_quiz wq-quiz wq-quiz-72349 wq-quiz-trivia wq-layout-single wq-skin-traditional" data-quiz-id="72349"> <div class="wq-questions wq_questionsCtr"> <div class="wq-question wq_singleQuestionWrapper" data-index="0"> <div class="wq_singleQuestionCtr"> <div class="wq_questionTextWrapper quiz-pro-clearfix"> <div class="wq_questionTextCtr"> <h4>What is one major advantage of the NEAR AI infrastructure compared to centralized AI models?</h4> </div> </div> <div class="wq_questionMediaCtr"> </div> <div class="wq-question-answers wq_questionAnswersCtr"> <div class="wq-answers wq_answersWrapper"> <div class="wq-answer wq_singleAnswerCtr" data-id="41lua"> <label class="wq_answerTxtCtr">It offers faster processing speed</label> </div> <div class="wq-answer wq_singleAnswerCtr" data-id="204fs"> <label class="wq_answerTxtCtr">It ensures user-owned AI with transparency and control over agents and models</label> </div> <div class="wq-answer wq_singleAnswerCtr" data-id="aw4rf"> <label class="wq_answerTxtCtr">It eliminates the need for AI prompts and stopwords</label> </div> <div class="wq-answer wq_singleAnswerCtr" data-id="9njwr"> <label class="wq_answerTxtCtr">It requires no technical knowledge for customization</label> </div> </div> </div> <div class="wq-trivia-question-explanation wq_triviaQuestionExplanation"> <div class="wq-explanation-head wq_ExplanationHead"> <span class="wq-explanation-head-correct">Correct!</span> <span class="wq-explanation-head-incorrect">Wrong!</span> </div> <p class="wq-explanation-text wq_QuestionExplanationText"></p> </div> </div> </div> </div> </div> <!-- // wp quiz--> <p>NEAR AI가 어떻게 분산 방식으로 1.4T 매개변수, 수익화 모델을 구축할 것인가요? ‘신뢰할 수 있는 실행 환경’ 또는 TEE의 세계로 들어가십시오.</p> <p>현대 프로세서와 GPU(처음부터 H100s)는 한 배우자인 Alice가 다른 배우자인 Bob의 기계에서 코드를 실행할 수 있게 해줍니다. Bob을 신뢰하지 않고도 다음 두 가지 보장을 제공합니다: (a) Bob이 실제로 Alice가 기대한 코드를 실행하고 있으며, (b) Bob이 Alice가 수행하길 원하는 실행을 감시할 수 없습니다. 다시 말해, Alice는 이러한 계산에 어떤 개인 데이터를 안전하게 보낼 수 있으며, Bob이 그것을 볼 수 없을 것임을 확신할 수 있습니다.</p> <h2><span class="ez-toc-section" id="%EA%B2%B0%EB%A1%A0"></span><a id="post-72336-_1pclo6h7heqe"></a>결론<span class="ez-toc-section-end"></span></h2> <p>분산 NEAR AI 인프라는 사용자 소유 AI를 가능하게 하는 중요한 진전을 나타냅니다. 투명성, 제어 및 유연성을 제공함으로써 NEAR AI는 데이터 개인 정보 보호를 보장하고 신뢰를 유지하면서 사용자가 AI 에이전트와 상호 작용할 수 있도록 돕습니다.</p> <p>NEAR의 선구자인 LNC는 실용적인 응용 프로그램을 위해 이 인프라를 활용하는 최전선에 있으며, 실시간 Learn AI 에이전트, 개발 중인 Lean NEAR Tutor 및 계획 중인 WooCommerce 쇼핑 에이전트를 포함한 이러한 응용 프로그램에 대한 인프라를 활용하고 있습니다.</p> <p>학습자로서, 이 혁신적인 (L)Earn <a class="wpil_keyword_link" title="🌌NEAR Ecosystem" href="https://learnnear.club/near-ecosystem/" data-wpil-keyword-link="linked" data-wpil-monitor-id="131">생태계</a>에 적극적으로 참여하십시오. (L)Earn AI에 관련된 질문을 하고, nLEARN으로 지불하고, 지식을 얻으며, AI의 개선에 기여함으로써 nLEARN을 얻으십시오.</p> <p><picture><source srcset="https://learnnear.club/wp-content/uploads/2025/01/word-image-72336-7.webp 588w,https://learnnear.club/wp-content/uploads/2025/01/word-image-72336-7-289x300.webp 289w,https://learnnear.club/wp-content/uploads/2025/01/word-image-72336-7-150x156.webp 150w" sizes="auto, (max-width: 588px) 100vw, 588px" type="image/webp"><img src="https://learnnear.club/wp-content/uploads/2025/01/word-image-72336-7.png" height="610" width="588" srcset="https://learnnear.club/wp-content/uploads/2025/01/word-image-72336-7.png 588w, https://learnnear.club/wp-content/uploads/2025/01/word-image-72336-7-289x300.png 289w, https://learnnear.club/wp-content/uploads/2025/01/word-image-72336-7-150x156.png 150w" sizes="auto, (max-width: 588px) 100vw, 588px" class="wp-image-72343 aligncenter sp-no-webp" alt="word-image-72336-7" loading="lazy" decoding="async" > </picture> 이 접근 방식은 학습과 수익을 긍정적인 합계 게임으로 결합하여 (L)Earn 개념에 더 깊고 풍부한 의미를 부여합니다.</p> <style type="text/css" id="wq-trivia-custom-css">.wq-quiz-72350 { --wq-question-width: 100%; --wq-question-color: #009cff; --wq-question-height: auto; --wq-bar-color: #00c479; --wq-font-color: #444; --wq-background-color: #f2f2f2; } </style><!-- wp quiz --> <div id="wp-quiz-72350" class="wq_quizCtr single trivia_quiz wq-quiz wq-quiz-72350 wq-quiz-trivia wq-layout-single wq-skin-traditional" data-quiz-id="72350"> <div class="wq-questions wq_questionsCtr"> <div class="wq-question wq_singleQuestionWrapper" data-index="0"> <div class="wq_singleQuestionCtr"> <div class="wq_questionTextWrapper quiz-pro-clearfix"> <div class="wq_questionTextCtr"> <h4>What is the unique feature of the (L)Earn AI agent in the NEAR ecosystem?</h4> </div> </div> <div class="wq_questionMediaCtr"> </div> <div class="wq-question-answers wq_questionAnswersCtr"> <div class="wq-answers wq_answersWrapper"> <div class="wq-answer wq_singleAnswerCtr" data-id="41lua"> <label class="wq_answerTxtCtr">It provides free access to all AI models</label> </div> <div class="wq-answer wq_singleAnswerCtr" data-id="204fs"> <label class="wq_answerTxtCtr">It allows users to earn and spend nLEARNs while improving the agent</label> </div> <div class="wq-answer wq_singleAnswerCtr" data-id="aw4rf"> <label class="wq_answerTxtCtr">It replaces the need for signing in with NEAR accounts</label> </div> <div class="wq-answer wq_singleAnswerCtr" data-id="9njwr"> <label class="wq_answerTxtCtr">It restricts users from viewing agent source code</label> </div> </div> </div> <div class="wq-trivia-question-explanation wq_triviaQuestionExplanation"> <div class="wq-explanation-head wq_ExplanationHead"> <span class="wq-explanation-head-correct">Correct!</span> <span class="wq-explanation-head-incorrect">Wrong!</span> </div> <p class="wq-explanation-text wq_QuestionExplanationText"></p> </div> </div> </div> </div> </div> <!-- // wp quiz--> <p>📚Happy (L)Earning!🕺</p> <div id="ai-comment-btn" 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