地址

地址：Introducing Gemini: Google’s most capable AI model yet

時間

2023年12月6日

作者

Sundar Pichai

CEO of Google and Alphabet

Demis Hassabis

CEO and Co-Founder, Google DeepMind

A note from Google and Alphabet CEO Sundar Pichai:

Every technology shift is an opportunity to advance scientific discovery, accelerate human progress, and improve lives. I believe the transition we are seeing right now with AI will be the most profound in our lifetimes, far bigger than the shift to mobile or to the web before it. AI has the potential to create opportunities — from the everyday to the extraordinary — for people everywhere. It will bring new waves of innovation and economic progress and drive knowledge, learning, creativity and productivity on a scale we haven’t seen before.

That’s what excites me: the chance to make AI helpful for everyone, everywhere in the world.

谷歌和Alphabet首席執行官Sundar Pichai的一則聲明:

每一次技術變革都是推動科學發現、加速人類進步和改善生活的機會。我相信我們現在看到的人工智能的轉變將是我們一生中最深刻的，遠遠超過之前向移動或網絡的轉變。人工智能有潛力為世界各地的人們創造機會——從日常生活到非凡的生活。它將帶來新的創新浪潮和經濟進步，并以前所未有的規模推動知識、學習、創造力和生產力。

讓我興奮的是：有機會使人工智能對全球所有人都有幫助。

Nearly eight years into our journey as an AI-first company, the pace of progress is only accelerating: Millions of people are now using generative AI across our products to do things they couldn’t even a year ago, from finding answers to more complex questions to using new tools to collaborate and create. At the same time, developers are using our models and infrastructure to build new generative AI applications, and startups and enterprises around the world are growing with our AI tools.

This is incredible momentum, and yet, we’re only beginning to scratch the surface of what’s possible.

We’re approaching this work boldly and responsibly. That means being ambitious in our research and pursuing the capabilities that will bring enormous benefits to people and society, while building in safeguards and working collaboratively with governments and experts to address risks as AI becomes more capable. And we continue to invest in the very best tools, foundation models and infrastructure and bring them to our products and to others, guided by our AI Principles.

Now, we’re taking the next step on our journey with Gemini, our most capable and general model yet, with state-of-the-art performance across many leading benchmarks. Our first version, Gemini 1.0, is optimized for different sizes: Ultra, Pro and Nano. These are the first models of the Gemini era and the first realization of the vision we had when we formed Google DeepMind earlier this year. This new era of models represents one of the biggest science and engineering efforts we’ve undertaken as a company. I’m genuinely excited for what’s ahead, and for the opportunities Gemini will unlock for people everywhere.

– Sundar

作為一家以人工智能為先的公司，我們已經進行了近八年的探索，進展的速度只是在加快：數百萬人現在正在使用我們產品中的生成式人工智能，做一些他們一年前甚至無法做到的事情，從解答更復雜的問題到使用新工具進行協作和創造。同時，開發人員正在利用我們的模型和基礎設施構建新的生成式人工智能應用程序，全球范圍內的初創公司和企業正在借助我們的人工智能工具實現增長。

這是不可思議的動力，然而，我們只是剛剛開始觸及可能性的表面。

我們正在大膽而負責地開展這項工作。這意味著在研究中抱有雄心，并追求那些將為人們和社會帶來巨大利益的能力，同時建立防護措施，并與政府和專家合作，以應對隨著人工智能變得更加強大而出現的風險。我們繼續投資于最優秀的工具、基礎模型和基礎設施，并將它們引入我們的產品和其他產品，遵循我們的人工智能原則。

現在，我們正在Gemini的旅程中邁出下一步，這是我們迄今為止最強大且最通用的模型，在許多領先的基準測試中具有最先進的性能。我們的第一個版本Gemini 1.0針對不同的尺寸進行了優化：Ultra、Pro和Nano。這些是Gemini時代的第一批模型，也是我們今年早些時候成立Google DeepMind時的第一個愿景的首次實現。這一新時代的模型代表了公司迄今為止進行的最大的科學和工程努力之一。我為即將發生的事情感到真正興奮，也為Gemini將為全球人民開啟的機會感到興奮。

By Demis Hassabis, CEO and Co-Founder of Google DeepMind, on behalf of the Gemini team

AI has been the focus of my life's work, as for many of my research colleagues. Ever since programming AI for computer games as a teenager, and throughout my years as a neuroscience researcher trying to understand the workings of the brain, I’ve always believed that if we could build smarter machines, we could harness them to benefit humanity in incredible ways.

This promise of a world responsibly empowered by AI continues to drive our work at Google DeepMind. For a long time, we’ve wanted to build a new generation of AI models, inspired by the way people understand and interact with the world. AI that feels less like a smart piece of software and more like something useful and intuitive — an expert helper or assistant.

Today, we’re a step closer to this vision as we introduce Gemini, the most capable and general model we’ve ever built.

由Google DeepMind首席執行官兼聯合創始人Demis Hassabis代表Gemini團隊發表

人工智能一直是我畢生工作的焦點，也是我的許多研究同仁的焦點。自從十幾歲時為電腦游戲編寫人工智能程序以來，一直到我作為神經科學研究者試圖理解大腦工作的這些年，我一直相信，如果我們能構建更智能的機器，我們就能利用它們以令人難以置信的方式造福人類。

在Google DeepMind，我們繼續致力于這一由人工智能負責任地賦予世界權力的承諾。很長一段時間以來，我們一直想要構建一代新的人工智能模型，靈感來自人們理解和與世界互動的方式。這種人工智能感覺不像是一款聰明的軟件，更像是一種有用而直觀的東西 —— 一種專業的助手或專家。

Gemini is the result of large-scale collaborative efforts by teams across Google, including our colleagues at Google Research. It was built from the ground up to be multimodal, which means it can generalize and seamlessly understand, operate across and combine different types of information including text, code, audio, image and video.

今天，我們向這一愿景又邁進了一步，我們推出了Gemini，這是我們有史以來打造的最強大、最通用的模型。

Gemini是谷歌各個團隊大規模合作的結果，包括我們在谷歌研究部門的同事。它從頭開始構建，以多模態為特點，這意味著它可以泛化并無縫地理解、操作和組合不同類型的信息，包括文本、代碼、音頻、圖像和視頻。

Introducing Gemini: our largest and most capable AI model

Gemini is also our most flexible model yet — able to efficiently run on everything from data centers to mobile devices. Its state-of-the-art capabilities will significantly enhance the way developers and enterprise customers build and scale with AI.

We’ve optimized Gemini 1.0, our first version, for three different sizes:

>> Gemini Ultra — our largest and most capable model for highly complex tasks.

>> Gemini Pro — our best model for scaling across a wide range of tasks.

>> Gemini Nano — our most efficient model for on-device tasks.

Gemini:我們最大、最強大的人工智能模型

Gemini也是我們迄今為止最靈活的模型，能夠在從數據中心到移動設備的所有設備上高效運行。其最先進的功能將顯著增強開發人員和企業客戶使用人工智能構建和擴展的方式。

我們已經優化了Gemini 1.0，我們的第一個版本，有三種不同的尺寸：

>>GeminiUltra -用于高度復雜任務的最大最強大的模型。

>> Gemini Pro -在各種任務上擴展的最佳模型。

>> Gemini Nano?-在設備上任務中最有效的模型。

We've been rigorously testing our Gemini models and evaluating their performance on a wide variety of tasks. From natural image, audio and video understanding to mathematical reasoning, Gemini Ultra’s performance exceeds current state-of-the-art results on 30 of the 32 widely-used academic benchmarks used in large language model (LLM) research and development.

With a score of 90.0%, Gemini Ultra is the first model to outperform human experts on MMLU (massive multitask language understanding), which uses a combination of 57 subjects such as math, physics, history, law, medicine and ethics for testing both world knowledge and problem-solving abilities.

Our new benchmark approach to MMLU enables Gemini to use its reasoning capabilities to think more carefully before answering difficult questions, leading to significant improvements over just using its first impression.

我們已經對Gemini模型進行了嚴格的測試，并在各種任務上評估了它們的性能。從自然圖像、音頻和視頻理解到數學推理，Gemini Ultra的性能在32個廣泛使用的大語言模型（LLM）研究和開發中使用的學術基準中有30個超越了當前最先進的結果。

在MMLU（大規模多任務語言理解）中，Gemini Ultra以90.0%的得分首次超過人類專家，該任務使用57個主題（如數學、物理學、歷史、法律、醫學和倫理學）結合測試世界知識和解決問題的能力。

我們對MMLU的新基準方法使Gemini能夠利用其推理能力在回答困難問題之前更加謹慎思考，從而比僅使用第一印象有顯著改善。

Gemini Ultra also achieves a state-of-the-art score of 59.4% on the new MMMU benchmark, which consists of multimodal tasks spanning different domains requiring deliberate reasoning.

With the image benchmarks we tested, Gemini Ultra outperformed previous state-of-the-art models, without assistance from object character recognition (OCR) systems that extract text from images for further processing. These benchmarks highlight Gemini’s native multimodality and indicate early signs of Gemini's more complex reasoning abilities.

Gemini Ultra在新的MMM（多模態多任務）基準測試中也取得了59.4%的最先進得分，該基準測試包括涉及不同領域的多模態任務，需要深思熟慮的推理。

在我們測試的圖像基準測試中，Gemini Ultra在沒有目標字符識別（OCR）系統的輔助下，超越了以前最先進的模型。這些基準測試突顯了Gemini的本機多模態性，并表明Gemini具有更復雜推理能力的早期跡象。

Until now, the standard approach to creating multimodal models involved training separate components for different modalities and then stitching them together to roughly mimic some of this functionality. These models can sometimes be good at performing certain tasks, like describing images, but struggle with more conceptual and complex reasoning.

We designed Gemini to be natively multimodal, pre-trained from the start on different modalities. Then we fine-tuned it with additional multimodal data to further refine its effectiveness. This helps Gemini seamlessly understand and reason about all kinds of inputs from the ground up, far better than existing multimodal models — and its capabilities are state of the art in nearly every domain.

到目前為止，創建多模態模型的標準方法包括為不同的模態訓練單獨的組件，然后將它們拼接在一起，粗略地模仿一些功能。這些模型有時可以很好地執行某些任務，比如描述圖像，但在更概念性和復雜的推理方面會遇到困難。

我們設計Gemini是天生的多模態，從一開始就在不同的模態上進行了預訓練。然后我們用額外的多模態數據對其進行微調，以進一步改進其有效性。這有助于Gemini從一開始就無縫地理解和推理各種輸入，比現有的多模態模型要好得多，而且它的能力幾乎在每個領域都是最先進的。

Gemini 1.0’s sophisticated multimodal reasoning capabilities can help make sense of complex written and visual information. This makes it uniquely skilled at uncovering knowledge that can be difficult to discern amid vast amounts of data.

Its remarkable ability to extract insights from hundreds of thousands of documents through reading, filtering and understanding information will help deliver new breakthroughs at digital speeds in many fields from science to finance.

Gemini 1.0復雜的多模態推理能力有助于理解復雜的書面和視覺信息。這使得它在發現在大量數據中難以辨別的知識方面具有獨特的技能。

它通過閱讀、過濾和理解信息，從數十萬份文件中提取見解的非凡能力，將有助于在從科學到金融的許多領域以數字速度實現新的突破。

Gemini 1.0 was trained to recognize and understand text, images, audio and more at the same time, so it better understands nuanced information and can answer questions relating to complicated topics. This makes it especially good at explaining reasoning in complex subjects like math and physics.

Gemini1.0經過訓練，可以同時識別和理解文本、圖像、音頻等，因此它能更好地理解細微的信息，并能回答與復雜話題有關的問題。這使得它特別擅長解釋數學和物理等復雜學科的推理。

Our first version of Gemini can understand, explain and generate high-quality code in the world’s most popular programming languages, like Python, Java, C++, and Go. Its ability to work across languages and reason about complex information makes it one of the leading foundation models for coding in the world.

Gemini Ultra excels in several coding benchmarks, including HumanEval, an important industry-standard for evaluating performance on coding tasks, and Natural2Code, our internal held-out dataset, which uses author-generated sources instead of web-based information.

Gemini can also be used as the engine for more advanced coding systems. Two years ago we presented AlphaCode, the first AI code generation system to reach a competitive level of performance in programming competitions.

Using a specialized version of Gemini, we created a more advanced code generation system, AlphaCode 2, which excels at solving competitive programming problems that go beyond coding to involve complex math and theoretical computer science.

我們的第一個版本Gemini可以理解、解釋和生成世界上最流行的編程語言的高質量代碼，如Python、Java、c++和Go。它具有跨語言工作和對復雜信息進行推理的能力，使其成為世界上領先的編碼基礎模型之一。

Gemini Ultra在幾個編碼基準測試中表現出色，包括HumanEval(一個重要的行業標準，用于評估編碼任務的性能)和Natural2Code(我們的內部保留數據集)，它使用作者生成的來源而不是基于web的信息。

Gemini也可以用作更先進的編碼系統的引擎。兩年前，我們推出了AlphaCode，這是第一個在編程比賽中達到競技水平的人工智能代碼生成系統。

使用專門的Gemini版本，我們創建了一個更高級的代碼生成系統，AlphaCode 2，在解決涉及復雜數學和理論計算機科學的競爭性編程問題方面表現出色。

When evaluated on the same platform as the original AlphaCode, AlphaCode 2 shows massive improvements, solving nearly twice as many problems, and we estimate that it performs better than 85% of competition participants — up from nearly 50% for AlphaCode. When programmers collaborate with AlphaCode 2 by defining certain properties for the code samples to follow, it performs even better.

We’re excited for programmers to increasingly use highly capable AI models as collaborative tools that can help them reason about the problems, propose code designs and assist with implementation — so they can release apps and design better services, faster.

當在與原始AlphaCode相同的平臺上進行評估時，AlphaCode 2顯示出巨大的改進，解決了幾乎兩倍的問題，我們估計它的表現優于85%的比賽參與者——較AlphaCode的近50%有所提高。當程序員通過為代碼示例定義某些屬性與AlphaCode 2協作時，它的性能會更好。

我們很高興程序員越來越多地使用高性能的人工智能模型作為協作工具，幫助他們推理問題、提出代碼設計并協助實現——這樣他們就可以更快地發布應用程序和設計更好的服務。

We trained Gemini 1.0 at scale on our AI-optimized infrastructure using Google’s in-house designed Tensor Processing Units (TPUs) v4 and v5e. And we designed it to be our most reliable and scalable model to train, and our most efficient to serve.

On TPUs, Gemini runs significantly faster than earlier, smaller and less-capable models. These custom-designed AI accelerators have been at the heart of Google's AI-powered products that serve billions of users like Search, YouTube, Gmail, Google Maps, Google Play and Android. They’ve also enabled companies around the world to train large-scale AI models cost-efficiently.

Today, we’re announcing the most powerful, efficient and scalable TPU system to date, Cloud TPU v5p, designed for training cutting-edge AI models. This next generation TPU will accelerate Gemini’s development and help developers and enterprise customers train large-scale generative AI models faster, allowing new products and capabilities to reach customers sooner.

我們使用谷歌自家設計的Tensor Processing Units（TPUs）v4和v5e在我們的AI優化基礎設施上大規模訓練Gemini 1.0。我們把它設計成最可靠、最可擴展的培訓模式，也是最有效的服務模式。

在TPUs上，Gemini的運行速度明顯快于早期、較小和功能較差的機型。這些定制設計的人工智能加速器一直是谷歌人工智能產品的核心，這些服務為數十億用戶提供搜索、YouTube、Gmail、Google Maps、Google Play和Android等服務。它們還使世界各地的公司能夠以經濟高效的方式訓練大規模的AI模型。

今天，我們宣布了迄今為止最強大，最高效和可擴展的TPU系統，Cloud TPU v5p，專為訓練尖端的人工智能模型而設計。這款下一代TPU將加速Gemini的開發，并幫助開發人員和企業客戶更快地訓練大規模生成式人工智能模型，從而使新產品和功能更快地到達客戶手中。

At Google, we’re committed to advancing bold and responsible AI in everything we do. Building upon Google’s AI Principles and the robust safety policies across our products, we’re adding new protections to account for Gemini’s multimodal capabilities. At each stage of development, we’re considering potential risks and working to test and mitigate them.

Gemini has the most comprehensive safety evaluations of any Google AI model to date, including for bias and toxicity. We’ve conducted novel research into potential risk areas like cyber-offense, persuasion and autonomy, and have applied Google Research’s best-in-class adversarial testing techniques to help identify critical safety issues in advance of Gemini’s deployment.

To identify blindspots in our internal evaluation approach, we’re working with a diverse group of external experts and partners to stress-test our models across a range of issues.

To diagnose content safety issues during Gemini’s training phases and ensure its output follows our policies, we’re using benchmarks such as Real Toxicity Prompts, a set of 100,000 prompts with varying degrees of toxicity pulled from the web, developed by experts at the Allen Institute for AI. Further details on this work are coming soon.

在谷歌，我們致力于在我們所做的一切中推進大膽而負責任的人工智能。在谷歌的AI原則和我們產品各個領域的健全安全政策的基礎上，我們為Gemini的多模態能力增加了新的保護措施。在開發的每個階段，我們都考慮了潛在的風險，并努力測試和緩解這些風險。

Gemini擁有迄今為止谷歌所有人工智能模型中最全面的安全評估，包括偏見和毒性。我們進行了關于潛在風險領域的新穎研究，如網絡攻擊、說服和自治，并應用了谷歌研究最佳的對抗測試技術，以幫助在Gemini部署之前預先識別關鍵的安全問題。

為了在內部評估方法中識別盲點，我們與外部的多樣化的專家團隊和合作伙伴合作，以在一系列問題上對我們的模型進行壓力測試。

在Gemini的訓練階段診斷內容安全問題，并確保其輸出符合我們的政策，我們使用了真實毒性提示(Real toxic Prompts)等基準測試，這是一組從網絡中提取的具有不同程度毒性的10萬個提示，由艾倫人工智能研究所的專家開發。有關此工作的進一步細節即將發布。

To limit harm, we built dedicated safety classifiers to identify, label and sort out content involving violence or negative stereotypes, for example. Combined with robust filters, this layered approach is designed to make Gemini safer and more inclusive for everyone. Additionally, we’re continuing to address known challenges for models such as factuality, grounding, attribution and corroboration.

Responsibility and safety will always be central to the development and deployment of our models. This is a long-term commitment that requires building collaboratively, so we’re partnering with the industry and broader ecosystem on defining best practices and setting safety and security benchmarks through organizations like MLCommons, the Frontier Model Forum and its AI Safety Fund, and our Secure AI Framework (SAIF), which was designed to help mitigate security risks specific to AI systems across the public and private sectors. We’ll continue partnering with researchers, governments and civil society groups around the world as we develop Gemini.

為了減少傷害，我們構建了專用的安全分類器，用于識別、標記和分類涉及暴力或負面刻板印象的內容。結合強大的過濾器，這種分層方法旨在使Gemini更安全、更包容。此外，我們還在繼續解決模型的已知挑戰，如事實性、基礎、歸因和協同。

責任和安全將始終是我們模型開發和部署的核心。這是一項長期的承諾，需要協作建設，因此我們正在與行業和更廣泛的生態系統合作，共同制定最佳實踐，并通過MLCommons、Frontier Model Forum及其AI安全基金以及我們的安全AI框架（SAIF）等組織設定安全和安全標準，該框架旨在幫助緩解公共和私營部門中特定于AI系統的安全風險。在我們開發Gemini的過程中，我們將繼續與世界各地的研究人員、政府和公民社會團體合作。

Gemini 1.0 is now rolling out across a range of products and platforms:

Gemini 1.0現在正在逐步在一系列產品和平臺上推出:

We’re bringing Gemini to billions of people through Google products.

Starting today, Bard will use a fine-tuned version of Gemini Pro for more advanced reasoning, planning, understanding and more. This is the biggest upgrade to Bard since it launched. It will be available in English in more than 170 countries and territories, and we plan to expand to different modalities and support new languages and locations in the near future.

We’re also bringing Gemini to Pixel. Pixel 8 Pro is the first smartphone engineered to run Gemini Nano, which is powering new features like Summarize in the Recorder app and rolling out in Smart Reply in Gboard, starting with WhatsApp — with more messaging apps coming next year.

In the coming months, Gemini will be available in more of our products and services like Search, Ads, Chrome and Duet AI.

We’re already starting to experiment with Gemini in Search, where it's making our Search Generative Experience (SGE) faster for users, with a 40% reduction in latency in English in the U.S., alongside improvements in quality.

Gemini專業在谷歌產品

我們通過谷歌產品將Gemini帶給了數十億人。

從今天開始，Bard將使用Gemini Pro的微調版本進行更高級的推理、規劃、理解等操作。這是Bard自推出以來的最大升級。它將在超過170個國家和地區提供英文版本，并計劃在不久的將來擴展到不同的模態，并支持新的語言和地區。

我們還將Gemini引入Pixel。Pixel 8 Pro是首款運行Gemini Nano的智能手機，它支持一些新功能，比如在Recorder應用程序中進行總結，并在Gboard中推出智能回復功能，從WhatsApp開始，明年還會推出更多的即時通訊應用程序。

在未來幾個月內，Gemini將在我們的更多產品和服務中推出，如Search、Ads、Chrome和Duet AI。

我們已經開始在Search中嘗試Gemini，它使我們的搜索生成體驗（SGE）對用戶更加快速，在美國英語中的延遲減少了40%，同時提高了質量。

Starting on December 13, developers and enterprise customers can access Gemini Pro via the Gemini API in Google AI Studio or Google Cloud Vertex AI.

Google AI Studio is a free, web-based developer tool to prototype and launch apps quickly with an API key. When it's time for a fully-managed AI platform, Vertex AI allows customization of Gemini with full data control and benefits from additional Google Cloud features for enterprise security, safety, privacy and data governance and compliance.

Android developers will also be able to build with Gemini Nano, our most efficient model for on-device tasks, via AICore, a new system capability available in Android 14, starting on Pixel 8 Pro devices. Sign up for an early preview of AICore.

從12月13日開始，開發者和企業客戶可以通過Google AI Studio或Google Cloud Vertex AI中的Gemini API訪問Gemini Pro。

Google AI Studio是一款免費的基于web的開發者工具，可以通過API密鑰快速創建和發布應用。當一個完全托管的人工智能平臺到來時，Vertex AI允許Gemini的定制化，具有完全的數據控制，并受益于額外的谷歌云功能，包括企業安全、隱私、數據治理和合規性。

Android開發者還可以通過AICore (Android 14中的一項新系統功能，從Pixel 8 Pro設備開始)，使用Gemini Nano(我們最高效的設備上任務模型)進行構建。注冊獲得AICore的早期預覽版。

For Gemini Ultra, we’re currently completing extensive trust and safety checks, including red-teaming by trusted external parties, and further refining the model using fine-tuning and reinforcement learning from human feedback (RLHF) before making it broadly available.

As part of this process, we’ll make Gemini Ultra available to select customers, developers, partners and safety and responsibility experts for early experimentation and feedback before rolling it out to developers and enterprise customers early next year.

Early next year, we’ll also launch Bard Advanced, a new, cutting-edge AI experience that gives you access to our best models and capabilities, starting with Gemini Ultra.

對于Gemini Ultra，我們目前正在進行廣泛的信任和安全性檢查，包括由可信賴的外部團體進行的紅隊測試，并在廣泛推出之前使用來自人類反饋的微調和強化學習（RLHF）進一步完善模型。

作為這一過程的一部分，我們將向選定的客戶、開發人員、合作伙伴以及安全和責任專家提供Gemini Ultra，以便在明年年初向開發人員和企業客戶推出之前進行早期實驗和反饋。

明年年初，我們還將推出Bard Advanced，這是一種全新的尖端人工智能體驗，從Gemini Ultra開始，您可以使用我們最好的模型和功能。

This is a significant milestone in the development of AI, and the start of a new era for us at Google as we continue to rapidly innovate and responsibly advance the capabilities of our models.

We’ve made great progress on Gemini so far and we’re working hard to further extend its capabilities for future versions, including advances in planning and memory, and increasing the context window for processing even more information to give better responses.

We’re excited by the amazing possibilities of a world responsibly empowered by AI — a future of innovation that will enhance creativity, extend knowledge, advance science and transform the way billions of people live and work around the world.

這是人工智能發展的一個重要里程碑，也是我們谷歌一個新時代的開始，因為我們將繼續快速創新，負責任地提高我們模型的能力。

到目前為止，我們在Gemini上取得了很大的進展，并且我們正在努力進一步擴展其能力，包括在規劃和記憶方面的進步，以及增加上下文窗口以處理更多信息，以提供更好的響應。

我們對由人工智能負責任賦能的美好可能性感到興奮——這是一個通過創新來增強創造力、擴展知識、推動科學并改變全球數十億人生活和工作方式的未來。