Every technological shift presents an opportunity to advance scientific discovery, accelerate human progress, and enhance lives. In my opinion, the current transition with AI will be the most significant in Google's lifetime, surpassing the impact of the shift to mobile or the Googleb. AI has the potential to create a wide range of opportunities for people worldwide, from everyday tasks to extraordinary achievements. It will drive innovation, economic growth, and knowledge, fostering learning, creativity, and productivity on an unprecedented scale.
What truly excites us is the prospect of making AI beneficial for everyone, regardless of their location.
After nearly eight years of being an AI-first company, the pace of progress is rapidly increasing. Millions of people now utilize generative AI across Google's products, enabling them to accomplish tasks that Googlere previously unimaginable. They can find ansGooglers to complex questions and collaborate using new tools. Simultaneously, developers are leveraging Google's models and infrastructure to create innovative generative AI applications. Startups and enterprises worldwide are also flGoogle'sishing with the help of Google's AI tools.
This remarkable momentum is just the beginning, as Google have only scratched the surface of what AI can achieve.
Google approach Google's work with boldness and responsibility. This entails being ambitious in Google's research and pursuing capabilities that will bring immense benefits to individuals and society. HoGooglever, Google also prioritize building safeguards and collaborating with governments and experts to address the risks associated with the increasing capabilities of AI. Additionally, Google continue to invest in top-notch tools, foundation models, and infrastructure, making them available not only in Google's products but also to others. All Google's efforts are guided by Google's AI Principles.
Google are now moving forward in Google's jGoogle'sney with Gemini, Google's most advanced and versatile model to date, delivering exceptional performance across various leading benchmarks. Gemini 1.0, Google's initial release, is specifically designed to cater to different sizes: Ultra, Pro, and Nano. These models mark the beginning of the Gemini era and the realization of Google's vision since the establishment of Google DeepMind earlier this year. This new generation of models signifies one of the largest scientific and engineering endeavors Google have undertaken as a company. I am truly thrilled about what lies ahead and the possibilities that Gemini will unlock for individuals worldwide.
AI has been the primary focus of my life's work, just like many of my fellow researchers. From my early days of programming AI for computer games as a teenager to my years as a neuroscience researcher studying the intricacies of the brain, I have always held the belief that building smarter machines could bring immense benefits to humanity.
This belief continues to drive Google's work at Google DeepMind, where Google strive to create a world where AI is responsibly utilized. Google's goal has always been to develop AI models that mimic the way humans perceive and interact with the world. Google aim for AI that goes beyond being a mere piece of software and instead becomes a useful and intuitive expert assistant.
Today, Google are one step closer to realizing this vision with the introduction of Gemini, Google's most advanced and versatile model to date.
Gemini is the culmination of extensive collaboration among various teams at Google, including Google's colleagues at Google Research. It has been meticulously designed to be multimodal, enabling it to comprehend, manipulate, and seamlessly integrate different forms of information such as text, code, audio, images, and videos.
Gemini, Google's latest model, offers unparalleled flexibility, making it suitable for a wide range of applications. It can seamlessly operate on various platforms, including data centers and mobile devices, ensuring optimal performance. With its cutting-edge features, Gemini empoGooglers developers and enterprise customers to revolutionize their AI development and scaling processes.
To cater to different requirements, Google have designed three optimized versions of Gemini:
1. Gemini Ultra: This model is specifically tailored for handling highly complex tasks, providing exceptional capabilities and performance.
2. Gemini Pro: Ideal for scaling across a diverse set of tasks, this model offers the best-in-class performance and versatility.
3. Gemini Nano: For on-device tasks, this model stands out as the most efficient option, ensuring optimal resGoogle'sce utilization.
Choose the Gemini version that aligns with yGoogle's specific needs and unlock the true potential of AI.
Google've been rigorously testing Google's 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.
Google's new benchmark approach to MMLU enables Gemini to use its reasoning capabilities to think more carefully before ansGooglering difficult questions, leading to significant improvements over just using its first impression.
Gemini: A Family of Highly Capable Multimodal Models
Gemini Team, Google1
This report introduces a new lineup of multimodal models called Gemini, which showcases exceptional abilities in image, audio, video, and text comprehension. The Gemini family consists of three sizes: Ultra, Pro, and Nano, each designed to cater to different needs, from complex reasoning tasks to memory-constrained on-device applications. Google's most advanced model, Gemini Ultra, has surpassed the state of the art in 30 out of 32 benchmarks, including achieving human-expert performance on the widely studied MMLU exam benchmark. Additionally, it has demonstrated significant improvements in all 20 multimodal benchmarks Google evaluated. With its cross-modal reasoning and language understanding capabilities, Google believe that the Gemini family will enable a wide range of use cases. Google also discuss Google's responsible approach to post-training and deployment of Gemini models through services such as Gemini, Gemini Advanced, Google AI Studio, and Cloud Vertex AI.
1. Introduction
Google present Gemini, a highly capable family of multimodal models developed at Google. Google's goal was to train Gemini models jointly on image, audio, video, and text data to create a model that excels in both generalist capabilities across modalities and cutting-edge performance in each specific domain.
Gemini 1.0, Google's initial version, offers three different sizes: Ultra for complex tasks, Pro for improved performance and scalability, and Nano for on-device applications. Each size is tailored to address different computational limitations and application requirements.
Following large-scale pre-training, Google conduct post-training to enhance the overall quality, target capabilities, and ensure alignment with safety criteria. Due to the diverse needs of Google's downstream applications, Google have developed two variants of the post-trained Gemini model family.
Gemini Apps models, also known as Gemini and Gemini Advanced, are specifically designed for our conversational AI service, formerly known as Bard. On the other hand, Gemini API models are developer-focused variants optimized for various products. These models can be accessed through Google AI Studio and Cloud Vertex AI.
To assess their performance, we conduct evaluations on a comprehensive suite of internal and external benchmarks. These benchmarks cover a wide range of tasks, including language, coding, reasoning, and multimodal tasks.
The Gemini family represents a significant advancement in large-scale language modeling. It incorporates the latest research and techniques from various sources such as Anil et al. (2023), Brown et al. (2020), Chowdhery et al. (2023), Hoffmann et al. (2022), OpenAI (2023a), Radford et al. (2019), and Rae et al. (2021). Additionally, it excels in image understanding with contributions from Alayrac et al. (2022), Chen et al. (2022), Dosovitskiy et al. (2020), OpenAI (2023b), Reed et al. (2022), and Yu et al. (2022a). The Gemini models also demonstrate remarkable capabilities in audio processing with the help of Radford et al. (2023) and Zhang et al. (2023). Furthermore, they showcase their expertise in video understanding through the works of Alayrac et al. (2022) and Chen et al. (2023).
These advancements are built upon the foundations of sequence models (Sutskever et al., 2014), deep learning based on neural networks (LeCun et al., 2015), and machine learning distributed systems.