Gemini: Intro & Use Cases

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*** NEWS: Google Cloud is introducing Gemini 1.5, the next-generation model prioritizing safety and efficiency, offering significant enhancements and processing capabilities. Gemini 1.5 Pro matches the 1.0 Ultra’s quality with lower compute use. Additionally, this model sets a new standard in long-context understanding, processing up to 1 million tokens, the longest for any large-scale foundation model. ***

Google has always wanted to build a generation of AI models closest to how humans understand and interact with the world. With the recent release of Google Cloud Gemini, it has never been closer to this vision.

What is Gemini?

Gemini is Google’s newest natively multimodal AI model. It is the largest, most capable, and most general AI model that Google has built so far. Gemini combines multimodal capabilities, complex reasoning, and generative capabilities in a way never done before. Consequently, Google believes that Gemini is a huge leap forward in its work with AI as it will affect just about all of Google’s products. 

Background: How does Gemini work?

A multimodal model is a type of AI model capable of understanding and processing information from multiple modalities or sources (text, images, video, and audio) at the same time. 

For instance, you can give it an image as input and ask it by voice or via text to identify the items in the picture. Here it takes visual (image) and textual/audio information as inputs at the same time and generates a textual response. 

Conventionally, multimodal models are trained on different modalities separately in the early stages of development, and then stitched together in the later stages. These types of models might excel at unimodal use cases, for example, generating text from images. However, they will likely struggle with use cases that require them to combine different modalities effectively to solve. 

Unlike conventional multimodal AI models, Gemini has been trained and finetuned on multimodal data from the ground up. As a result, Gemini excels at combining information from different modalities, not just processing it. This allows it to get a cohesive understanding of the overall context of the task at hand. 

Gemini works well for complex tasks, especially those dealing with real-world data, because it looks across modalities when analyzing a task, instead of focusing on each modality in isolation. This is what gives Gemini state-of-the-art multimodal reasoning capabilities, better than existing AI models (more on this later. 

Gemini sizing

Besides being Google’s most capable AI model, it is also its most flexible. It comes in three different sizes each with its own capabilities and use cases. These include:

  • Gemini Ultra – The most capable model that delivers state-of-the-art performance across a wide range of highly complex tasks, including reasoning and multimodal tasks. It is efficiently serveable at scale on TPU accelerators due to the Gemini architecture.

  • Gemini Pro – A performance-optimized model in terms of cost as well as latency that delivers significant performance across a wide range of tasks. This model exhibits strong reasoning performance and broad multimodal capabilities.

  • Gemini Nano – Our most efficient model, designed to run on-device. We trained two versions of Nano, with 1.8B (Nano-1) and 3.25B (Nano-2) parameters, targeting low and high memory devices respectively. It is trained by distilling from larger Gemini models. It is 4-bit quantized for deployment and provides best-in-class performance. 

What can Gemini do? 

As Google’s most capable AI model, here are some of Gemini’s capabilities:

Outstanding multi-modal understanding and processing

Gemini can handle text and visual data such as code, images, and video simultaneously, making it possible to understand nuanced information. Gemini Ultra has been exhaustively tested on several multi-modal benchmarks and found to edge out GPT 4 in all video, image, and audio benchmarks. Here is a technical report detailing the complete results. 

Complex general-purpose language understanding

Gemini has advanced natural language processing capabilities, allowing it to understand the use of language in different ways. It can:

  • Answer complex questions even if they are not directly stated in the piece of text. 

  • Accurately translate sentences from one language to another while retaining context and meaning. 

  • Use its knowledge of the world to understand simple scenarios and answer questions about them. 

Gemini can also understand humor, cultural references, puzzles, and more. After thorough testing, it has been found to outperform human experts in massive multitask language understanding, a benchmark that tests how well a model understands the different uses of language. Here is a technical report of the same. 

Sophisticated reasoning to extract information

Gemini’s multimodal reasoning capabilities come into play in making sense of complex written information. This model can scour through large documents, reading, understanding, and filtering information. This will be useful in discovering insights quickly and efficiently. 

High-quality code generation

Gemini can understand, explain, and generate high-quality code in several popular programming languages like C++, Go, and Python. Google has also released AlphaCode 2, an advanced code-generation system built from a specialized version of Gemini. This solution will help competitive programmers solve problems that go beyond coding to involve complex Math and Computer Science concepts. More on this in the Alpha Code2 technical report. 

This is only a tiny subset of what Gemini can do. Remember, Gemini Ultra is still under development, and its capabilities are constantly evolving. So, we expect even more diverse capabilities in the future as the model continues to learn and grow. That said, Gemini’s impressive capabilities open up the model to several real-world use cases. 

What can you use Gemini for?

Gemini is built to empower enterprises, developers, and other end users to do more faster and more efficiently. Here are the different ways in which users can leverage Google’s most capable AI model. 

Empower developers to build transformative applications

Developers can utilize Gemini Pro in the Vertex AI Studio to build applications containing Gemini’s capabilities via a simple API call. The Gemini API gives developers access to Gemini Pro and Pro-vision models. Developers can fine-tune these models with enterprise data and integrate them into their applications. This way, they will be able to create apps like smart search engines or virtual agents that excel at a wide variety of multimodal use cases. 

Driving scientific discovery

Collecting relevant information is essential for meaningful scientific discoveries. But this can be difficult when you have tens of thousands of documents to analyze and extract information from. With Gemini, this process can be quick and efficient. You can make use of its complex reasoning capabilities to look through numerous documents and extract meaningful insights from them in a fraction of the time you would do it manually. 

Improving the performance of competitive programmers

Competitive programmers can leverage AlphaCode 2 (built from Gemini) to solve complex programming problems much faster. AlphaCode 2 can understand these problems quickly and accurately, and implement optimum approaches to solve them. Technical reports reveal that it can do this better than 85% of competitive programming participants. 

Is Gemini secure?

Google has and will continue building Gemini with its responsible AI practices. All the potential risks at each stage of development are constantly being identified and mitigated. Currently, it has the most comprehensive safety evaluations of any AI model from Google including those for toxicity, bias, persuasion, and cyber-security. 

Summary

Gemini is Google’s most capable AI model yet. It is natively multimodal and able to work with text and visual information to generate accurate responses. Its natively multimodal nature equips it with the ability to combine information in different formats to solve complex reasoning problems. 

With this ability alone, it has been found to edge out previous models and human experts in language understanding, multi-modal tasks, and solving advanced problems in STEM. It comes as a huge step change for anyone looking to innovate with Google’s AI products. 

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