Artificial Intelligence (AI) has rapidly evolved over the years, and Google is at the forefront of this technological revolution. With their latest innovation, Ultra Gemini AI, Google is set to redefine the boundaries of what AI can achieve.
It represents a significant milestone in the development of AI models, offering unparalleled capabilities and a level of sophistication that was previously unimaginable. Also, this era is for AI. AI technologies spreading around the world.
Human beings in our society, have five sensors, and the world we’ve built and the media we consume is in those different modalities. And this is excited to announce the launch of the Gemini era, a first step towards a truly universal AI model. The Gemini AI approach to multimodality is all the kinds of things you want an artificial intelligence system to be able to do. And these are capabilities that haven’t existed in computers before.
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What is the Potential of Ultra Gemini AI
Gemini AI is the culmination of years of research and development by Google’s DeepMind team. This state-of-the-art model is designed to be multimodal, meaning it can seamlessly understand and combine different types of information, including text, code, audio, image, and video. This multimodal capability sets it apart from its predecessors and opens up a world of possibilities for its applications.
Gemini AI is available in three different sizes: Ultra, Pro, and Nano. Each size is optimized for specific tasks and platforms, making Gemini versatile and adaptable to various user needs. Whether you require highly complex tasks, scaling across a wide range of applications, or on-device efficiency, Gemini has got you covered.
AI Gemini is the largest and most capable model. It means that Gemini can understand the world around us in the way that we do and absorb any type of input and output. So not just text like most models, but also code, audio, image, and video.
Three Variants of Gemini AI
Google created a family of models that can run on everything from mobile devices to data centers, each of which is best in class. Google Gemini AI will be available in three sizes. Gemini Ultra, the most capable and largest model for highly complex tasks.
Gemini Pro, is one of the best-performing models for a broad range of tasks. And Gemini Nano is the most efficient model for on-device tasks. Google wants to provide the best foundational building blocks, and then they know developers and enterprise customers are going to figure out creative ways to further refine Gemini foundational models and the potential is almost limitless.
Unparalleled Performance in Multimodal Benchmarks
| Capability | Benchmark Higher is better | Description | Gemini Ultra | GPT-4API numbers calculated where reported numbers were missing |
| General | MMLU | Representation of questions in 57 subjects (incl. STEM, humanities, and others) | 90.0%CoT@32* | 86.4%5-shot* (reported) |
| Reasoning | Big-Bench Hard | Diverse set of challenging tasks requiring multi-step reasoning | 83.6%3-shot | 83.1%3-shot (API) |
| DROP | Reading comprehension (F1 Score) | 82.4Variable shots | 80.93-shot (reported) | |
| HellaSwag | Commonsense reasoning for everyday tasks | 87.8%10-shot* | 95.3%10-shot* (reported) | |
| Math | GSM8K | Basic arithmetic manipulations (incl. Grade School math problems) | 94.4%maj1@32 | 92.0%5-shot CoT (reported) |
| MATH | Challenging math problems (incl. algebra, geometry, pre-calculus, and others) | 53.2%4-shot | 52.9%4-shot (API) | |
| Code | HumanEval | Python code generation | 74.4%0-shot (IT)* | 67.0%0-shot* (reported) |
| Natural2Code | Python code generation. New held out dataset HumanEval-like, not leaked on the web | 74.9%0-shot | 73.9%0-shot (API) |
A Multimodal Approach to Gemini AI
| Capability | Benchmark | Description Higher is better unless otherwise noted | Gemini | GPT-4VPrevious SOTA model listed when capability is not supported in GPT-4V |
| Image | MMMU | Multi-discipline college-level reasoning problems | 59.4%0-shot pass@1 Gemini Ultra (pixel only*) | 56.8%0-shot pass@1 GPT-4V |
| VQAv2 | Natural image understanding | 77.8%0-shot Gemini Ultra (pixel only*) | 77.2%0-shot GPT-4V | |
| TextVQA | OCR on natural images | 82.3%0-shot Gemini Ultra (pixel only*) | 78.0%0-shot GPT-4V | |
| DocVQA | Document understanding | 90.9%0-shot Gemini Ultra (pixel only*) | 88.4%0-shot GPT-4V (pixel only) | |
| Infographic VQA | Infographic understanding | 80.3%0-shot Gemini Ultra (pixel only*) | 75.1%0-shot GPT-4V (pixel only) | |
| MathVista | Mathematical reasoning in visual contexts | 53.0%0-shot Gemini Ultra (pixel only*) | 49.9%0-shot GPT-4V |
Empowering Developers with Advanced Coding Capabilities
Google’s Gemini not only excels in language understanding and multimodal tasks but also offers advanced coding capabilities. With its understanding, explanation, and generation of high-quality code, Gemini AI is poised to revolutionize the way developers write software. Supported languages include popular programming languages like Python, Java, C++, and Go.
To showcase the power of Gemini in coding, Google developed AlphaCode 2, a code generation system that outperformed its predecessor, AlphaCode, by solving nearly twice as many programming problems. With Gemini as its engine, AlphaCode 2 demonstrates the potential for highly capable AI models to collaborate with programmers, assisting in problem-solving, code design, and implementation.
Safety and Responsibility at the Core
As with any advanced AI model, safety and responsibility are top priorities for Google. With Gemini AI, comprehensive safety evaluations have been conducted, including assessments for bias and toxicity. Google has also conducted extensive research into potential risk areas like cyber offense, persuasion, and autonomy, employing adversarial testing techniques to identify critical safety issues.
To ensure content safety, Gemini AI incorporates dedicated safety classifiers and robust filters to identify and filter out content involving violence or negative stereotypes.
How to Use Gemini: Accessing Gemini AI
The Gemini AI release date is not announced yet but it is expected to arrive in the middle of 2024. Google is making Gemini AI accessible to users across various platforms and products. Bard, an expert helper and assistant powered by Gemini AI, is now available in English in over 170 countries and territories.
Additionally, Gemini AI is integrated into Pixel 8 Pro, Google’s flagship smartphone, offering new features like Summarize in the Recorder app and Smart Reply in Gboard.
Developers and enterprise customers can access Gemini AI through the Gemini API in Google AI Studio or Google Cloud Vertex AI. Google AI Studio provides a free, web-based developer tool for quick app prototyping, while Vertex AI offers a fully managed AI platform with customization options and additional enterprise features.
Conclusion of Gemini AI
Gemini AI will continue to evolve and expand its capabilities in future versions. With advancements in planning, memory, and processing capabilities, Gemini AI aims to provide even better responses and a deeper understanding of complex information. Google is excited about the possibilities that Gemini AI brings, envisioning a future of innovation and creativity empowered by responsible AI.
Gemini AI represents a significant leap forward in the field of artificial intelligence And I think Gemini continues that rich tradition. It’s been an enormous sort of monumental engineering task, which has been, you know, very challenging, but also very exciting. I have been at Google for quite a while, and the reason I’m here is I believe in the company’s mission.
