Using larger embeddings, for example storing them in a vector store for retrieval, generally costs more and consumes more compute, memory and storage than using smaller embeddings.

예를 들어 검색을 위해 벡터 저장소에 저장하는 등 더 큰 임베딩을 사용하면 더 작은 임베딩을 사용하는 것보다 일반적으로 더 많은 비용이 들고 더 많은 컴퓨팅, 메모리 및 스토리지를 사용합니다.

Both of our new embeddings models were trained with a technique that allows developers to trade-off performance and cost of using embeddings. Specifically, developers can shorten embeddings (i.e. remove some numbers from the end of the sequence) without the embedding losing its concept-representing properties by passing in the dimensions API parameter. For example, on the MTEB benchmark, a text-embedding-3-large embedding can be shortened to a size of 256 while still outperforming an unshortened text-embedding-ada-002 embedding with a size of 1536.

두 가지 새로운 임베딩 모델은 모두 개발자가 임베딩 사용 비용과 성능을 절충할 수 있는 기술로 교육되었습니다. 특히 개발자는 차원 API 매개변수를 전달하여 임베딩이 개념을 나타내는 속성을 잃지 않고 임베딩을 단축할 수 있습니다(예: 시퀀스 끝에서 일부 숫자 제거). 예를 들어, MTEB 벤치마크에서 text-embedding-3-large 임베딩은 크기 256으로 단축되면서도 크기 1536의 단축되지 않은 text-embedding-ada-002 임베딩보다 성능이 뛰어납니다.

Over 70% of requests from GPT-4 API customers have transitioned to GPT-4 Turbo since its release, as developers take advantage of its updated knowledge cutoff, larger 128k context windows, and lower prices. 

개발자가 업데이트된 지식 컷오프, 더 커진 128k 컨텍스트 창 및 저렴한 가격을 활용함에 따라 GPT-4 API 고객의 요청 중 70% 이상이 출시 이후 GPT-4 Turbo로 전환되었습니다.

Today, we are releasing an updated GPT-4 Turbo preview model, gpt-4-0125-preview. This model completes tasks like code generation more thoroughly than the previous preview model and is intended to reduce cases of “laziness” where the model doesn’t complete a task. The new model also includes the fix for the bug impacting non-English UTF-8 generations.

오늘 우리는 업데이트된 GPT-4 Turbo 미리보기 모델인 gpt-4-0125-preview를 출시합니다. 이 모델은 이전 미리 보기 모델보다 코드 생성과 같은 작업을 더 철저하게 완료하며 모델이 작업을 완료하지 못하는 "게으름"의 경우를 줄이기 위한 것입니다. 새 모델에는 영어가 아닌 UTF-8 세대에 영향을 미치는 버그에 대한 수정 사항도 포함되어 있습니다.

For those who want to be automatically upgraded to new GPT-4 Turbo preview versions, we are also introducing a new gpt-4-turbo-preview model name alias, which will always point to our latest GPT-4 Turbo preview model. 

새로운 GPT-4 Turbo 미리보기 버전으로 자동 업그레이드하려는 사람들을 위해 항상 최신 GPT-4 Turbo 미리보기 모델을 가리키는 새로운 gpt-4-turbo-preview 모델 이름 별칭도 도입합니다.

We plan to launch GPT-4 Turbo with vision in general availability in the coming months.

The free Moderation API allows developers to identify potentially harmful text. As part of our ongoing safety work, we are releasing text-moderation-007, our most robust moderation model to-date.

무료 Moderation API를 통해 개발자는 잠재적으로 유해한 텍스트를 식별할 수 있습니다. 지속적인 안전 작업의 일환으로 현재까지 가장 강력한 조정 모델인 text-moderation-007을 출시합니다.

The text-moderation-latest and text-moderation-stable aliases have been updated to point to it. You can learn more about building safe AI systems through our safety best practices guide.

We are launching two platform improvements to give developers both more visibility into their usage and control over API keys.

우리는 개발자에게 API 키 사용에 대한 더 많은 가시성과 제어권을 제공하기 위해 두 가지 플랫폼 개선 사항을 출시할 예정입니다.

First, developers can now assign permissions to API keys from the API keys page. For example, a key could be assigned read-only access to power an internal tracking dashboard, or restricted to only access certain endpoints.

첫째, 이제 개발자는 API 키 페이지에서 API 키에 권한을 할당할 수 있습니다. 예를 들어 내부 추적 대시보드를 구동하기 위해 키에 읽기 전용 액세스 권한을 할당하거나 특정 엔드포인트에만 액세스하도록 제한할 수 있습니다.

Second, the usage dashboard and usage export function now expose metrics on an API key level after turning on tracking. This makes it simple to view usage on a per feature, team, product, or project level, simply by having separate API keys for each.

We expect and aim for people to use our tools safely and responsibly, and elections are no different. We work to anticipate and prevent relevant abuse—such as misleading “deepfakes”, scaled influence operations, or chatbots impersonating candidates. Prior to releasing new systems, we red team them, engage users and external partners for feedback, and build safety mitigations to reduce the potential for harm. For years, we’ve been iterating on tools to improve factual accuracy, reduce bias, and decline certain requests. These tools provide a strong foundation for our work around election integrity. For instance, DALL·E has guardrails to decline requests that ask for image generation of real people, including candidates.

우리는 사람들이 우리의 도구를 안전하고 책임감 있게 사용하기를 기대하고 목표하며, 선거도 이와 다르지 않습니다. 우리는 오해를 불러일으키는 "딥페이크", 대규모 영향력 행사 또는 후보자를 사칭하는 챗봇과 같은 관련 남용을 예측하고 예방하기 위해 노력합니다. 새로운 시스템을 출시하기 전에 우리는 레드팀을 구성하고 피드백을 받기 위해 사용자와 외부 파트너를 참여시키며 피해 가능성을 줄이기 위한 안전 완화 조치를 구축합니다. 수년 동안 우리는 사실의 정확성을 높이고, 편견을 줄이고, 특정 요청을 거부하는 도구를 반복적으로 사용해 왔습니다. 이러한 도구는 선거 무결성에 관한 작업을 위한 강력한 기반을 제공합니다. 예를 들어 DALL·E에는 후보자를 포함한 실제 인물의 이미지 생성을 요청하는 요청을 거부하는 가드레일이 있습니다.

We regularly refine our Usage Policies for ChatGPT and the API as we learn more about how people use or attempt to abuse our technology. A few to highlight for elections: 

우리는 사람들이 우리 기술을 어떻게 사용하거나 남용하려고 시도하는지 자세히 파악하면서 ChatGPT 및 API에 대한 사용 정책을 정기적으로 개선합니다. 선거를 위해 강조할 몇 가지:

• We’re still working to understand how effective our tools might be for personalized persuasion. Until we know more, we don’t allow people to build applications for political campaigning and lobbying. 
  
  
• 우리는 우리의 도구가 개인화된 설득에 얼마나 효과적인지 이해하기 위해 계속 노력하고 있습니다. 더 많은 내용을 알기 전까지는 사람들이 정치 캠페인 및 로비 활동을 위한 애플리케이션을 구축하는 것을 허용하지 않습니다.
  
  
• People want to know and trust that they are interacting with a real person, business, or government. For that reason, we don’t allow builders to create chatbots that pretend to be real people (e.g., candidates) or institutions (e.g., local government). 
  
  
• 사람들은 자신이 실제 사람, 기업 또는 정부와 상호 작용하고 있다는 사실을 알고 신뢰하고 싶어합니다. 이러한 이유로 우리는 빌더가 실제 사람(예: 후보자) 또는 기관(예: 지방 정부)인 것처럼 가장하는 챗봇을 만드는 것을 허용하지 않습니다.
  
  
• We don’t allow applications that deter people from participation in democratic processes—for example, misrepresenting voting processes and qualifications (e.g., when, where, or who is eligible to vote) or that discourage voting (e.g., claiming a vote is meaningless).
  
  
• 사람들이 민주적 절차에 참여하는 것을 방해하는 애플리케이션(예: 투표 절차 및 자격(예: 언제, 어디서, 누가 투표할 자격이 있는지)을 허위로 표시하거나 투표를 방해하는 애플리케이션(예: 투표가 무의미하다고 주장))은 허용되지 않습니다. .
  
  
• With our new GPTs, users can report potential violations to us.
  
  
• 새로운 GPT를 통해 사용자는 잠재적인 위반 사항을 신고할 수 있습니다.

Better transparency around image provenance—including the ability to detect which tools were used to produce an image—can empower voters to assess an image with trust and confidence in how it was made. We’re working on several provenance efforts. Early this year, we will implement the Coalition for Content Provenance and Authenticity’s digital credentials—an approach that encodes details about the content’s provenance using cryptography—for images generated by DALL·E 3. 

이미지를 생성하는 데 어떤 도구가 사용되었는지 감지하는 기능을 포함하여 이미지 출처에 대한 투명성이 향상되면 유권자는 이미지 제작 방법에 대한 신뢰와 자신감을 가지고 이미지를 평가할 수 있습니다. 우리는 여러 출처에 대한 노력을 기울이고 있습니다. 올해 초 우리는 DALL·E 3에서 생성된 이미지에 대해 암호화를 사용하여 콘텐츠 출처에 대한 세부 정보를 인코딩하는 접근 방식인 콘텐츠 출처 및 진위성 연합의 디지털 자격 증명을 구현할 예정입니다.

We are also experimenting with a provenance classifier, a new tool for detecting images generated by DALL·E. Our internal testing has shown promising early results, even where images have been subject to common types of modifications. We plan to soon make it available to our first group of testers—including journalists, platforms, and researchers—for feedback.

또한 DALL·E에서 생성된 이미지를 감지하는 새로운 도구인 출처 분류기를 실험하고 있습니다. 우리의 내부 테스트는 이미지가 일반적인 유형의 수정을 받은 경우에도 유망한 초기 결과를 보여주었습니다. 우리는 곧 언론인, 플랫폼, 연구원을 포함한 첫 번째 테스터 그룹이 피드백을 받을 수 있도록 할 계획입니다.

Finally, ChatGPT is increasingly integrating with existing sources of information—for example, users will start to get access to real-time news reporting globally, including attribution and links. Transparency around the origin of information and balance in news sources can help voters better assess information and decide for themselves what they can trust.

마지막으로 ChatGPT는 점점 더 기존 정보 소스와 통합되고 있습니다. 예를 들어 사용자는 속성 및 링크를 포함하여 전 세계적으로 실시간 뉴스 보고에 액세스할 수 있게 됩니다. 정보 출처에 대한 투명성과 뉴스 소스의 균형은 유권자가 정보를 더 잘 평가하고 신뢰할 수 있는 정보를 스스로 결정하는 데 도움이 될 수 있습니다.

In the United States, we are working with the National Association of Secretaries of State (NASS), the nation's oldest nonpartisan professional organization for public officials. ChatGPT will direct users to CanIVote.org, the authoritative website on US voting information, when asked certain procedural election related questions—for example, where to vote. Lessons from this work will inform our approach in other countries and regions. 

미국에서는 미국에서 가장 오래된 공직자를 위한 초당파적 전문 조직인 전국 국무장관 협회(NASS)와 협력하고 있습니다. ChatGPT는 특정 절차적 선거 관련 질문(예: 투표 장소)을 묻는 경우 미국 투표 정보에 대한 권위 있는 웹사이트인 CanIVote.org로 사용자를 안내합니다. 이 작업에서 얻은 교훈은 다른 국가 및 지역에서의 우리의 접근 방식에 영향을 미칠 것입니다.

We’ll have more to share in the coming months. We look forward to continuing to work with and learn from partners to anticipate and prevent potential abuse of our tools in the lead up to this year’s global elections.

앞으로 몇 달 동안 더 많은 내용을 공유할 예정입니다. 우리는 올해 세계 선거를 앞두고 우리 도구의 남용 가능성을 예측하고 방지하기 위해 계속해서 파트너와 협력하고 파트너로부터 배우기를 기대합니다.

We launched ChatGPT Enterprise a few months ago and industry leaders like Block, Canva, Carlyle, The Estée Lauder Companies, PwC, and Zapier are already using it to redefine how their organizations operate. Today, we’re adding a new self-serve plan: ChatGPT Team.

우리는 몇 달 전에 ChatGPT Enterprise를 출시했으며 Block, Canva, Carlyle, The Estée Lauder Companies, PwC, Zapier와 같은 업계 리더들은 이미 ChatGPT Enterprise를 사용하여 조직 운영 방식을 재정의하고 있습니다. 오늘 우리는 새로운 셀프 서비스 계획인 ChatGPT 팀을 추가합니다.

ChatGPT Team offers access to our advanced models like GPT-4 and DALL·E 3, and tools like Advanced Data Analysis. It additionally includes a dedicated collaborative workspace for your team and admin tools for team management. As with ChatGPT Enterprise, you own and control your business data—we do not train on your business data or conversations, and our models don’t learn from your usage. More details on our data privacy practices can be found on our privacy page and Trust Portal.

ChatGPT 팀은 GPT-4 및 DALL·E 3과 같은 고급 모델과 고급 데이터 분석과 같은 도구에 대한 액세스를 제공합니다. 또한 팀을 위한 전용 공동 작업 공간과 팀 관리를 위한 관리 도구가 포함되어 있습니다. ChatGPT Enterprise와 마찬가지로 귀하는 귀하의 비즈니스 데이터를 소유하고 제어합니다. 당사는 귀하의 비즈니스 데이터나 대화를 학습하지 않으며 당사 모델은 귀하의 사용법을 학습하지 않습니다. 당사의 데이터 개인정보 보호 관행에 대한 자세한 내용은 당사의 개인정보 보호 페이지와 Trust Portal에서 확인할 수 있습니다.

ChatGPT Team includes:

• Access to GPT-4 with 32K context window
• 32K 컨텍스트 창을 통해 GPT-4에 액세스
• Tools like DALL·E 3, GPT-4 with Vision, Browsing, Advanced Data Analysis—with higher message caps
• DALL·E 3, 비전, 브라우징, 고급 데이터 분석 기능을 갖춘 GPT-4와 같은 도구(메시지 한도가 더 높음)
• No training on your business data or conversations
• 비즈니스 데이터나 대화에 대한 training  이 없습니다.
• Secure workspace for your team
• 팀을 위한 안전한 작업 공간
• Create and share custom GPTs with your workspace
• 작업공간에서 맞춤 GPT를 만들고 공유하세요.
• Admin console for workspace and team management
• 작업공간 및 팀 관리를 위한 관리 콘솔
• Early access to new features and improvements
• 새로운 기능 및 개선 사항에 대한 조기 액세스

We recently announced GPTs—custom versions of ChatGPT that you can create for a specific purpose with instructions, expanded knowledge, and custom capabilities. These can be especially useful for businesses and teams. With GPTs, you can customize ChatGPT to your team’s specific needs and workflows (no code required) and publish them securely to your team’s workspace. GPTs can help with a wide range of tasks, such as assisting in project management, team onboarding, generating code, performing data analysis, securely taking action in your existing systems and tools, or creating collateral to match your brand tone and voice. Today, we announced the GPT Store where you can find useful and popular GPTs from your workspace.

우리는 최근 GPTs를 발표했습니다. 이는 지침, 확장된 지식, 사용자 정의 기능을 통해 특정 목적을 위해 만들 수 있는 ChatGPT의 사용자 정의 버전입니다. 이는 비즈니스와 팀에 특히 유용할 수 있습니다. GPTs를 사용하면 ChatGPT를 팀의 특정 요구 사항과 작업 흐름에 맞게 맞춤설정하고(코드 필요 없음) 팀의 작업 공간에 안전하게 게시할 수 있습니다. GPTs는 프로젝트 관리 지원, 팀 온보딩, 코드 생성, 데이터 분석 수행, 기존 시스템 및 도구에서 안전하게 조치 수행, 브랜드 톤 및 목소리에 맞는 자료 생성 등 다양한 작업에 도움을 줄 수 있습니다. 오늘 우리는 귀하의 작업 공간에서 유용하고 인기 있는 GPTs를 찾을 수 있는 GPT 스토어를 발표했습니다.

Integrating AI into everyday organizational workflows can make your team more productive. In a recent study by the Harvard Business School, employees at Boston Consulting Group who were given access to GPT-4 reported completing tasks 25% faster and achieved a 40% higher quality in their work as compared to their peers who did not have access.

AI를 일상적인 조직 워크플로에 통합하면 팀의 생산성이 향상될 수 있습니다. Harvard Business School의 최근 연구에 따르면 GPT-4에 대한 액세스 권한이 부여된 Boston Consulting Group의 직원은 액세스 권한이 없는 동료에 비해 작업을 25% 더 빨리 완료하고 작업 품질이 40% 더 높은 것으로 나타났습니다. 

Connor O’Brien, VP of GTM Strategy & Operations at Sourcegraph, shares, "We use ChatGPT in almost every part of our business, from financial modeling for pricing and packaging to internal and external communications to board prep to recruiting and note taking—it’s accelerated everything we do allowing us to execute at a high level."

Sourcegraph의 GTM 전략 및 운영 부사장인 Connor O'Brien은 다음과 같이 말합니다. "우리는 가격 책정 및 패키징을 위한 재무 모델링부터 내부 및 외부 커뮤니케이션, 이사회 준비, 채용 및 메모 작성에 이르기까지 비즈니스의 거의 모든 부분에서 ChatGPT를 사용합니다. 우리가 하는 모든 일을 가속화하여 높은 수준에서 실행할 수 있게 되었습니다."

Dr. John Brownstein, Chief Innovation Officer at Boston Children’s Hospital says, “With ChatGPT Team, we’ve been able to pilot innovative GPTs that enhance our team’s productivity and collaboration. As we integrate GPTs safely and responsibly across internal operations, we know the transformative impact this will have in strengthening the systems that enable our doctors, researchers, students, and administrative staff to provide exceptional care to every patient that walks through our doors.”

Boston Children’s Hospital의 최고 혁신 책임자인 Dr. John Brownstein은 이렇게 말합니다. “ChatGPT 팀을 통해 우리는 팀의 생산성과 협업을 향상시키는 혁신적인 GPT를 시험해 볼 수 있었습니다. 내부 운영 전반에 걸쳐 GPT를 안전하고 책임감 있게 통합함으로써 우리는 의사, 연구원, 학생 및 행정 직원이 우리 문을 방문하는 모든 환자에게 탁월한 진료를 제공할 수 있는 시스템을 강화하는 데 혁신적인 영향을 미칠 것임을 알고 있습니다.”

ChatGPT Team costs $25/month per user when billed annually, or $30/month per user when billed monthly. You can explore the details or get started now by upgrading in your ChatGPT settings.

ChatGPT 팀의 비용은 연간 청구 시 사용자당 월 $25, 월별 청구 시 사용자당 월 $30입니다. ChatGPT 설정에서 업그레이드하여 세부 정보를 살펴보거나 지금 시작할 수 있습니다.

It’s been two months since we announced GPTs, and users have already created over 3 million custom versions of ChatGPT. Many builders have shared their GPTs for others to use. Today, we're starting to roll out the GPT Store to ChatGPT Plus, Team and Enterprise users so you can find useful and popular GPTs. Visit chat.openai.com/gpts to explore.

GPTs 를 발표한 지 두 달이 지났고 사용자는 이미 300만 개 이상의 ChatGPT 맞춤 버전을 만들었습니다. 많은 빌더가 다른 사람들이 사용할 수 있도록 GPTs 를 공유했습니다. 오늘 우리는 유용하고 인기 있는 GPTs 를 찾을 수 있도록 ChatGPT Plus, Team 및 Enterprise 사용자에게 GPT Store를 출시하기 시작했습니다. chat.openai.com/gpts를 방문하여 살펴보세요.

The store features a diverse range of GPTs developed by our partners and the community. Browse popular and trending GPTs on the community leaderboard, with categories like DALL·E, writing, research, programming, education, and lifestyle.

이 스토어에는 파트너와 커뮤니티가 개발한 다양한 GPT가 있습니다. 커뮤니티 리더보드에서 DALL·E, 글쓰기, 연구, 프로그래밍, 교육, 라이프스타일과 같은 카테고리를 포함하는 인기 있고 인기 있는 GPT를 찾아보세요.

We will also highlight useful and impactful GPTs. Some of our first featured GPTs include:

또한 유용하고 영향력 있는 GPT를 강조하겠습니다. 첫 번째 주요 GPT 중 일부는 다음과 같습니다.

• Personalized trail recommendations from AllTrails
• Search and synthesize results from 200M academic papers with Consensus
• Expand your coding skills with Khan Academy’s Code Tutor
• Design presentations or social posts with Canva
• Find your next read with Books
• Learn math and science anytime, anywhere with the CK-12 Flexi AI tutor

In Q1 we will launch a GPT builder revenue program. As a first step, US builders will be paid based on user engagement with their GPTs. We'll provide details on the criteria for payments as we get closer.

1분기에는 GPT 빌더 수익 프로그램을 시작할 예정입니다. 첫 번째 단계로 미국 builders  는 GPT에 대한 사용자 참여를 기반으로 비용을 지불받습니다. 결제 기준에 대한 자세한 내용은 추후 확정되는 대로 안내해 드리겠습니다.

Today, we announced our new ChatGPT Team plan for teams of all sizes. Team customers have access to a private section of the GPT Store which includes GPTs securely published to your workspace. The GPT Store will be available soon for ChatGPT Enterprise customers and will include enhanced admin controls like choosing how internal-only GPTs are shared and which external GPTs may be used inside your business. Like all usage on ChatGPT Team and Enterprise, we do not use your conversations with GPTs to improve our models.

오늘 우리는 모든 규모의 팀을 위한 새로운 ChatGPT 팀 계획을 발표했습니다. 팀 고객은 작업공간에 안전하게 게시된 GPT가 포함된 GPT 스토어의 비공개 섹션에 액세스할 수 있습니다. GPT 스토어는 곧 ChatGPT Enterprise 고객에게 제공될 예정이며 내부 전용 GPT 공유 방법 및 비즈니스 내에서 사용할 수 있는 외부 GPT 선택과 같은 향상된 관리 제어 기능이 포함됩니다. ChatGPT Team 및 Enterprise의 모든 사용과 마찬가지로 우리는 모델을 개선하기 위해 GPT와의 대화를 사용하지 않습니다.

Explore GPTs at chat.openai.com/gpts. chat.openai.com/gpts에서 GPTs를 살펴보세요.

We work hard in our technology design process to support news organizations. We’ve met with dozens, as well as leading industry organizations like the News/Media Alliance, to explore opportunities, discuss their concerns, and provide solutions. We aim to learn, educate, listen to feedback, and adapt.

우리는 언론사를 지원하기 위해 기술 설계 프로세스에 열심히 노력하고 있습니다. 우리는 기회를 탐색하고, 우려 사항을 논의하고, 솔루션을 제공하기 위해 뉴스/미디어 연합(News/Media Alliance)과 같은 선도적인 업계 조직뿐만 아니라 수십 곳을 만났습니다. 우리는 배우고, 교육하고, 피드백을 듣고, 적응하는 것을 목표로 합니다.

Our goals are to support a healthy news ecosystem, be a good partner, and create mutually beneficial opportunities. With this in mind, we have pursued partnerships with news organizations to achieve these objectives:

우리의 목표는 건강한 뉴스 생태계를 지원하고, 좋은 파트너가 되며, 상호 이익이 되는 기회를 창출하는 것입니다. 이를 염두에 두고 우리는 다음과 같은 목표를 달성하기 위해 언론 기관과 파트너십을 추구해 왔습니다.

1. Deploy our products to benefit and support reporters and editors, by assisting with time-consuming tasks like analyzing voluminous public records and translating stories.
   
   방대한 공공 기록 분석 및 기사 번역과 같이 시간이 많이 걸리는 작업을 지원하여 기자와 편집자에게 혜택을 주고 지원하기 위해 제품을 배포합니다.
   
   
2. Teach our AI models about the world by training on additional historical, non-publicly available content.
   
   추가로 비공개로 제공되는 역사적 콘텐츠를 훈련하여 AI 모델에 세상에 대해 가르칩니다.
   
   
3. Display real-time content with attribution in ChatGPT, providing new ways for news publishers to connect with readers.
   
   ChatGPT에 속성이 포함된 실시간 콘텐츠를 표시하여 뉴스 게시자가 독자와 연결할 수 있는 새로운 방법을 제공합니다.
   
   

Our early partnerships with the Associated Press, Axel Springer, American Journalism Project and NYU offer a glimpse into our approach.

Associated Press, Axel Springer, American Journalism Project 및 NYU와의 초기 파트너십을 통해 우리의 접근 방식을 엿볼 수 있습니다.

Training AI models using publicly available internet materials is fair use, as supported by long-standing and widely accepted precedents. We view this principle as fair to creators, necessary for innovators, and critical for US competitiveness.

공개적으로 이용 가능한 인터넷 자료를 사용하여 AI 모델을 훈련시키는 것은 오랫동안 지속되고 널리 받아들여지는 선례에 의해 뒷받침되는 공정 사용입니다. 우리는 이 원칙이 창작자에게는 공정하고 혁신가에게는 필요하며 미국 경쟁력에 매우 중요하다고 생각합니다.

The principle that training AI models is permitted as a fair use is supported by a wide range of academics, library associations, civil society groups, startups, leading US companies, creators, authors, and others that recently submitted comments to the US Copyright Office. Other regions and countries, including the European Union, Japan, Singapore, and Israel also have laws that permit training models on copyrighted content—an advantage for AI innovation, advancement, and investment.

AI 모델 훈련이 공정한 사용으로 허용된다는 원칙은 최근 미국 저작권청에 의견을 제출한 다양한 학계, 도서관 협회, 시민 사회 단체, 스타트업, 미국 선도 기업, 창작자, 작가 및 기타 사람들에 의해 지지됩니다. 유럽 연합, 일본, 싱가포르, 이스라엘을 포함한 다른 지역 및 국가에도 저작권이 있는 콘텐츠에 대한 훈련 모델을 허용하는 법률이 있습니다. 이는 AI 혁신, 발전 및 투자에 유리합니다.

That being said, legal right is less important to us than being good citizens. We have led the AI industry in providing a simple opt-out process for publishers (which The New York Times adopted in August 2023) to prevent our tools from accessing their sites.

즉, 법적 권리는 좋은 시민이 되는 것보다 우리에게 덜 중요합니다. Google은 Google 도구가 사이트에 액세스하지 못하도록 게시자에게 간단한 거부 프로세스(2023년 8월 New York Times 채택)를 제공함으로써 AI 업계를 주도해 왔습니다.

Our models were designed and trained to learn concepts in order to apply them to new problems.

우리의 모델은 개념을 새로운 문제에 적용하기 위해 학습하도록 설계되고 훈련되었습니다.

Memorization is a rare failure of the learning process that we are continually making progress on, but it’s more common when particular content appears more than once in training data, like if pieces of it appear on lots of different public websites. So we have measures in place to limit inadvertent memorization and prevent regurgitation in model outputs. We also expect our users to act responsibly; intentionally manipulating our models to regurgitate is not an appropriate use of our technology and is against our terms of use.

암기 Memorization  는 우리가 지속적으로 발전하고 있는 학습 과정에서 드문 실패이지만, 특정 콘텐츠가 여러 공개 웹사이트에 나타나는 것처럼 특정 콘텐츠가 훈련 데이터에 두 번 이상 나타날 때 더 일반적입니다. 따라서 우리는 의도하지 않은 암기 Memorization   를 제한하고 모델 출력의 역류 Regurgitation 를 방지하기 위한 조치를 취했습니다. 또한 우리는 사용자가 책임감 있게 행동할 것을 기대합니다. 의도적으로 모델을 조작하여 역류 Regurgitation  시키는 것은 당사 기술의 적절한 사용이 아니며 당사 이용 약관에 위배됩니다.

Just as humans obtain a broad education to learn how to solve new problems, we want our AI models to observe the range of the world’s information, including from every language, culture, and industry. Because models learn from the enormous aggregate of human knowledge, any one sector—including news—is a tiny slice of overall training data, and any single data source—including The New York Times—is not significant for the model’s intended learning.

인간이 새로운 문제를 해결하는 방법을 배우기 위해 광범위한 교육을 받는 것처럼 우리는 AI 모델이 모든 언어, 문화, 산업을 포함하여 전 세계의 다양한 정보를 관찰하기를 원합니다. 모델은 인간 지식의 막대한 집합체로부터 학습하기 때문에 뉴스를 포함한 모든 한 부문은 전체 교육 데이터의 작은 조각이며 New York Times를 포함한 단일 데이터 소스는 모델의 의도된 학습에 중요하지 않습니다.

Our discussions with The New York Times had appeared to be progressing constructively through our last communication on December 19. The negotiations focused on a high-value partnership around real-time display with attribution in ChatGPT, in which The New York Times would gain a new way to connect with their existing and new readers, and our users would gain access to their reporting. We had explained to The New York Times that, like any single source, their content didn't meaningfully contribute to the training of our existing models and also wouldn't be sufficiently impactful for future training. Their lawsuit on December 27—which we learned about by reading The New York Times—came as a surprise and disappointment to us.

The New York Times와의 논의는 12월 19일 마지막 커뮤니케이션을 통해 건설적으로 진행되는 것으로 나타났습니다. 협상은 ChatGPT의 속성이 포함된 실시간 디스플레이를 중심으로 한 고부가가치 파트너십에 초점을 맞췄으며, 이로 인해 The New York Times는 새로운 이점을 얻게 됩니다. 기존 및 신규 독자와 연결하는 방법이며 사용자는 보고에 액세스할 수 있습니다. 우리는 다른 단일 소스와 마찬가지로 해당 콘텐츠가 기존 모델의 교육에 의미 있게 기여하지 않았으며 향후 교육에도 충분한 영향을 미치지 않을 것이라고 The New York Times에 설명했습니다. New York Times를 읽으면서 알게 된 12월 27일의 소송은 우리에게 놀라움과 실망으로 다가왔습니다.

Along the way, they had mentioned seeing some regurgitation of their content but repeatedly refused to share any examples, despite our commitment to investigate and fix any issues. We’ve demonstrated how seriously we treat this as a priority, such as in July when we took down a ChatGPT feature immediately after we learned it could reproduce real-time content in unintended ways.

그 과정에서 그들은 콘텐츠가 일부 역류 Regurgitation 되는 것을 언급했지만 문제를 조사하고 수정하겠다는 우리의 노력에도 불구하고 어떤 사례도 공유하기를 반복적으로 거부했습니다. 우리는 의도하지 않은 방식으로 실시간 콘텐츠를 재현할 수 있다는 사실을 알게 된 직후 ChatGPT 기능을 중단한 7월과 같이 이를 얼마나 진지하게 우선순위로 취급하는지 보여주었습니다.

Interestingly, the regurgitations The New York Times induced appear to be from years-old articles that have proliferated on multiple third-party websites. It seems they intentionally manipulated prompts, often including lengthy excerpts of articles, in order to get our model to regurgitate. Even when using such prompts, our models don’t typically behave the way The New York Times insinuates, which suggests they either instructed the model to regurgitate or cherry-picked their examples from many attempts.

흥미롭게도 New York Times가 유발한 역류 Regurgitation 는 여러 제3자 웹사이트에 확산된 오래된 기사에서 나온 것으로 보입니다. 우리 모델이 역류 Regurgitation 하도록 하기 위해 종종 긴 기사 발췌를 포함하여 프롬프트를 의도적으로 조작한 것 같습니다. 이러한 프롬프트를 사용할 때에도 우리 모델은 일반적으로 The New York Times가 암시하는 방식으로 동작하지 않습니다. 이는 모델이 모델에 역류 Regurgitation 하도록 지시했거나 여러 시도에서 사례를 선별했음을 의미합니다.

Despite their claims, this misuse is not typical or allowed user activity, and is not a substitute for The New York Times. Regardless, we are continually making our systems more resistant to adversarial attacks to regurgitate training data, and have already made much progress in our recent models.

그들의 주장에도 불구하고 이러한 오용은 일반적이지 않거나 허용되는 사용자 활동이 아니며 The New York Times를 대체할 수 없습니다. 그럼에도 불구하고 우리는 훈련 데이터를 역류 Regurgitation 시키는 적대적 공격에 대한 시스템 저항력을 높이기 위해 지속적으로 노력하고 있으며 이미 최근 모델에서 많은 진전을 이루었습니다.

We regard The New York Times’ lawsuit to be without merit. Still, we are hopeful for a constructive partnership with The New York Times and respect its long history, which includes reporting the first working neural network over 60 years ago and championing First Amendment freedoms.

우리는 New York Times의 소송이 가치가 없다고 생각합니다. 그럼에도 불구하고 우리는 The New York Times와의 건설적인 파트너십을 희망하며 60년 전 최초로 작동하는 신경망을 보고하고 수정헌법 제1조의 자유를 옹호하는 등의 오랜 역사를 존중합니다.

We look forward to continued collaboration with news organizations, helping elevate their ability to produce quality journalism by realizing the transformative potential of AI.

우리는 AI의 혁신적인 잠재력을 실현하여 양질의 저널리즘을 생산하는 능력을 향상시키는 언론 기관과의 지속적인 협력을 기대합니다.

In partnership with Eric Schmidt, we are launching a $10M grants program to support technical research towards ensuring superhuman AI systems are aligned and safe:

우리는 Eric Schmidt와 협력하여 초인적인 AI 시스템을 정렬하고 안전하게 유지하기 위한 기술 연구를 지원하기 위해 1,000만 달러의 보조금 프로그램을 시작합니다.

• We are offering $100K–$2M grants for academic labs, nonprofits, and individual researchers.
• 우리는 학술 연구실, 비영리 단체 및 개인 연구자에게 10만~200만 달러의 보조금을 제공하고 있습니다.
• For graduate students, we are sponsoring a one-year $150K OpenAI Superalignment Fellowship: $75K in stipend and $75K in compute and research funding.
• 대학원생을 위해 우리는 1년 동안 $150,000의 OpenAI Superalignment Fellowship을 후원합니다(급여 $75,000, 컴퓨팅 및 연구 자금 $75,000).
• No prior experience working on alignment is required; we are actively looking to support researchers who are excited to work on alignment for the first time.
• 정렬 작업에 대한 사전 경험은 필요하지 않습니다. 우리는 처음으로 정렬 작업을 하게 된 연구자들을 적극적으로 지원하기 위해 노력하고 있습니다.
• Our application process is simple, and we’ll get back to you within four weeks of applications closing. 
• 우리의 신청 절차는 간단하며, 신청 마감 후 4주 이내에 연락드리겠습니다.

We think new researchers could make enormous contributions! This is a young field with many tractable research problems; outstanding contributions could not just help shape the field, but be critical for the future of AI. There has never been a better time to start working on alignment.

“This partnership with Axel Springer will help provide people with new ways to access quality, real-time news content through our AI tools. We are deeply committed to working with publishers and creators around the world and ensuring they benefit from advanced AI technology and new revenue models,” says Brad Lightcap, COO of OpenAI.

“Axel Springer와의 이번 파트너십은 사람들에게 AI 도구를 통해 고품질의 실시간 뉴스 콘텐츠에 액세스할 수 있는 새로운 방법을 제공하는 데 도움이 될 것입니다. 우리는 전 세계 출판사 및 창작자들과 협력하여 이들이 첨단 AI 기술과 새로운 수익 모델의 혜택을 누릴 수 있도록 최선을 다하고 있습니다.”라고 OpenAI의 COO인 Brad Lightcap은 말합니다.

Axel Springer is a media and technology company active in more than 40 countries. By providing information across its diverse media brands (among others BILD, WELT, INSIDER, POLITICO) and classifieds portals (StepStone Group and AVIV Group) Axel Springer SE empowers people to make free decisions for their lives. Today, the transformation from a traditional print media company to Europe’s leading digital publisher has been successfully accomplished. The next goal has been identified: Axel Springer wants to become global market leader in digital content and digital classifieds through accelerated growth. The company is headquartered in Berlin and employs more than 18,000 people worldwide.

On behalf of the OpenAI Board, I want to express our gratitude to the entire OpenAI community, especially all the OpenAI employees, who came together to help find a path forward for the company over the past week. Your efforts helped enable this incredible organization to continue to serve its mission to ensure that artificial general intelligence benefits all of humanity. We are thrilled that Sam, Mira and Greg are back together leading the company and driving it forward. We look forward to working with them and all of you. 

OpenAI 이사회를 대신하여 저는 전체 OpenAI 커뮤니티, 특히 지난 주 동안 회사가 나아갈 길을 찾기 위해 함께 모인 모든 OpenAI 직원들에게 감사의 말씀을 전하고 싶습니다. 귀하의 노력은 이 놀라운 조직이 인공 일반 지능이 모든 인류에게 이익이 되도록 보장하는 임무를 계속 수행할 수 있도록 도왔습니다. Sam, Mira, Greg가 다시 함께 회사를 이끌고 발전시켜 나가게 되어 매우 기쁩니다. 우리는 그들과 여러분 모두와 함께 일할 수 있기를 기대합니다.

As a Board, we are focused on strengthening OpenAI’s corporate governance. Here’s how we plan to do it:

이사회로서 우리는 OpenAI의 기업 지배구조를 강화하는 데 중점을 두고 있습니다. 우리가 계획하는 방법은 다음과 같습니다.

• We will build a qualified, diverse Board of exceptional individuals whose collective experience represents the breadth of OpenAI’s mission – from technology to safety to policy. We are pleased that this Board will include a non-voting observer for Microsoft.
• 우리는 기술에서 안전, 정책에 이르기까지 OpenAI의 사명을 폭넓게 대표하는 집단 경험을 갖춘 뛰어난 개인들로 구성된 자격을 갖춘 다양한 이사회를 구성할 것입니다. 이 이사회에 투표권이 없는 Microsoft 참관인이 포함되어 기쁘게 생각합니다.
• We will further stabilize the OpenAI organization so that we can continue to serve our mission.  This will include convening an independent committee of the Board to oversee a review of the recent events.
• 우리는 계속해서 사명을 수행할 수 있도록 OpenAI 조직을 더욱 안정화할 것입니다. 여기에는 최근 사건에 대한 검토를 감독하기 위해 이사회의 독립 위원회를 소집하는 것이 포함됩니다.
• We will enhance the governance structure of OpenAI so that all stakeholders – users, customers, employees, partners, and community members – can trust that OpenAI will continue to thrive.
• 사용자, 고객, 직원, 파트너, 커뮤니티 구성원 등 모든 이해관계자가 OpenAI가 계속해서 성장할 것이라고 신뢰할 수 있도록 OpenAI의 거버넌스 구조를 강화할 것입니다.

OpenAI is a more important institution than ever before. ChatGPT has made artificial intelligence a part of daily life for hundreds of millions of people. Its popularity has made AI – its benefits and its risks – central to virtually every conversation about the future of governments, business, and society.

OpenAI는 그 어느 때보다 중요한 기관입니다. ChatGPT는 인공지능을 수억 명의 일상생활의 일부로 만들었습니다. AI의 인기로 인해 AI(AI의 이점과 위험)는 정부, 기업 및 사회의 미래에 관한 거의 모든 대화의 중심이 되었습니다.

We understand the gravity of these discussions and the central role of OpenAI in the development and safety of these awe-inspiring new technologies. Each of you plays a critical part in ensuring that we effectively meet these challenges.  We are committed to listening and learning from you, and I hope to speak with you all very soon.

우리는 이러한 논의의 중요성과 경외심을 불러일으키는 신기술의 개발 및 안전에 있어 OpenAI의 중심 역할을 이해하고 있습니다. 여러분 각자는 우리가 이러한 과제를 효과적으로 해결하는 데 중요한 역할을 합니다. 우리는 여러분의 의견을 듣고 배우기 위해 최선을 다하고 있으며 곧 여러분과 이야기를 나눌 수 있기를 바랍니다.

We are grateful to be a part of OpenAI, and excited to work with all of you.

OpenAI의 일원이 된 것을 감사하게 생각하며 여러분 모두와 함께 일하게 되어 기쁩니다.

Thank you,

Bret Taylor

Chair, OpenAI

We’re interested in large-scale datasets that reflect human society and that are not already easily accessible online to the public today. We can work with any modality, including text, images, audio, or video. We’re particularly looking for data that expresses human intention (e.g. long-form writing or conversations rather than disconnected snippets), across any language, topic, and format. 

우리는 인간 사회를 반영하고 오늘날 대중이 온라인으로 쉽게 접근할 수 없는 대규모 데이터 세트에 관심이 있습니다. 텍스트, 이미지, 오디오, 비디오 등 모든 형식으로 작업할 수 있습니다. 우리는 특히 모든 언어, 주제, 형식에 걸쳐 인간의 의도를 표현하는 데이터(예: 단절된 단편이 아닌 긴 형식의 글쓰기 또는 대화)를 찾고 있습니다.

We can work with data in almost any form and can use our next-generation in-house AI technology to help you digitize and structure your data. For example, we have world-class optical character recognition (OCR) technology to digitize files like PDFs, and automatic speech recognition (ASR) to transcribe spoken words. If the data needs cleaning (e.g. has lots of auto-generated artifacts or transcription errors), we can work with your team to process it into the most useful form. We are not seeking datasets with sensitive or personal information, or information that belongs to a third party; we can work with you to remove this information if you need help.

우리는 거의 모든 형태의 데이터로 작업할 수 있으며 차세대 사내 AI 기술을 사용하여 데이터를 디지털화하고 구조화할 수 있습니다. 예를 들어, 우리는 PDF와 같은 파일을 디지털화하는 세계 최고 수준의 광학 문자 인식(OCR) 기술과 음성을 텍스트로 변환하는 자동 음성 인식(ASR) 기술을 보유하고 있습니다. 데이터를 정리해야 하는 경우(예: 자동 생성된 아티팩트 또는 전사 오류가 많은 경우) 팀과 협력하여 가장 유용한 형식으로 처리할 수 있습니다. 우리는 민감한 개인정보 또는 제3자 소유의 정보가 포함된 데이터 세트를 찾고 있지 않습니다. 도움이 필요한 경우 당사는 귀하와 협력하여 이 정보를 제거할 수 있습니다.