Google Gemini: Meaning, How Does it Work?


A new family of Google AI models is called Google Gemini. Even though Google developed the transformer architecture, one of the essential technologies in large language models (LLMs), and was a leader in AI research for nearly ten years, OpenAI and its GPT models are stealing the show. Google is making an effort to catch up with the Gemini Nano, Gemini Pro, and Gemini Ultra devices. Because all three versions are multimodal, they can comprehend and operate with code, images, audio, and videos in addition to text. Let’s take a closer look to see if Google can win the AI race again.

What is Google Gemini?

Google Gemini, formerly known as Bard, is an artificial intelligence (AI) chatbot tool that uses machine learning and natural language processing (NLP) to mimic human conversations. Gemini can be integrated into websites, messaging apps, or other applications to give users accurate, natural language answers to their queries, in addition to serving as an addition to Google Search.

A family of multimodal AI large language models (LLMs) called Google Gemini is capable of understanding language, audio, code, and video. Built by Alphabet’s Google DeepMind business unit, which is dedicated to cutting-edge AI research and development, Gemini 1.0 was unveiled on December 6, 2023. Together with other Google employees, co-founder Sergey Brin is acknowledged for having contributed to the development of the Gemini LLMs.

How does Google Gemini work?

Google claims that before Gemini, the majority of multimodal AI models were created by fusing various AI models that had been trained independently. For instance, the processing of text and images would be trained independently before being integrated into a single model that could roughly mimic the characteristics of an actual multimodal model.

Their goal with Gemini was to develop a natively multimodal model. From the beginning, it was trained on a dataset that included trillions of text tokens in addition to images (combined with text descriptions), videos, and audio. The model was then further adjusted using methods like reinforcement learning with human feedback (RLHF) to make it produce safer and more accurate responses.

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Although Google does not disclose the source of all this training data, it most likely comes from image-text databases (like LAOIN-5B), archives of websites (like Common Crawl), and proprietary sources (like Google Books in its entirety).

Google says that Gemini can “seamlessly understand and reason about all kinds of inputs from the ground up” because it has trained all of its modalities simultaneously. For instance, it can read text from signs, comprehend charts and the captions that go with them, and combine data from several modalities in other ways. (Incidentally, GPT-4V, an unreleased variant of GPT-4, appears to have been trained similarly, albeit with limited exposure to text and images.) 

All this does is enable the Gemini models to react to prompts with generatively generated images as well as text, just as ChatGPT can do by combining DALL·E and GPT.

When was Google Bard first released?

On February 6, 2023, Google first revealed Bard, its AI-powered chatbot, along with an ambiguous release date. On March 21, 2023, it invited users to join a waitlist and opened access to Bard. Google made Bard available in more than 180 countries and territories on May 10, 2023, after removing the waitlist. Bard was renamed Gemini, almost exactly one year after it was first announced.

Many thought that Google rushed Bard out before it was ready because of pressure from ChatGPT’s success and favorable press. For instance, during a live demonstration led by Alphabet CEO Sundar Pichai, Google gave an incorrect response to a question.

How to access Google Gemini?

Some users can access a specially trained version of Gemini Pro via Google’s Gemini chatbot. You might understand it better than I have. The most powerful model, Gemini Ultra, won’t be available to everyone until next year, though developers will be able to access it via the Gemini chatbot (formerly Bard).

Developers can currently use Vertex AI or Google AI Studio to test Google Gemini Pro. Additionally, Zapier’s integrations with Google Vertex AI and Google AI Studio allow you to access Gemini from any app you use at work. To get you going, consider these few instances.

Is Gemini free to use?

Google made no mention of a fee to use Bard when it first became available. Except for enterprise use of Google Cloud, Google has never charged users for services. It was assumed that the chatbot would be free to use since it would be incorporated into Google’s standard search engine.

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On February 8, 2024, Google rebranded Bard as Gemini and added a premium tier to go along with the free online app. Currently, Pro and Nano can be used for free with registration. However, users must pay $20 a month for the Gemini Advanced option to access Ultra. Users who subscribe to Google One AI Premium, which also includes 2 terabytes of storage and Google Workspace features, can sign up for Gemini Advanced.

In which languages is Gemini available?

Gemini is available in over 45 languages. Its accuracy in translating text-based inputs into various languages approaches that of a human. Google intends to increase Gemini’s linguistic comprehension and spread it widely. But there are other crucial things to think about, like laws that forbid LLM-generated material or ongoing regulatory initiatives in different nations that might restrict or outlaw the use of Gemini in the future.

Gemini vs. GPT-3 and GPT-4

GeminiGPT-3 and GPT-4
DeveloperGoogle DeepMindOpenAI
Chatbot interfaceGemini; formerly BardChatGPT
ModalityMultimodal; trained on text, images, audio, and videoOriginally built as a text-only language model.
Model variationsSize-based variations, including Ultra, Pro and NanoOptimizations for size, including GPT-3.5 Turbo and GPT-4 Turbo
Context window length32,000 tokens32,000 tokens

What are Gemini’s limitations?

  • Training data: Gemini, like any AI chatbot, needs to be trained to provide accurate responses. The models must be trained on accurate data that isn’t deceptive or inaccurate to accomplish this. They must, however, also be able to spot false or misleading information when it is presented to them.
  • Bias and potential harm: Because there is always new data to learn, AI training is a never-ending, computationally demanding process. Google asserts that it has adhered to responsible development practices for all Gemini models, including thorough evaluation to help reduce the possibility of bias and potential harm.
  • Originality and creativity: The level of creativity and originality that Gemini can achieve in its content is limited. This is especially true of the free version, which has experienced difficulties handling complex prompts involving several steps and subtleties as well as generating sufficient output. The paid versions of the platform provide access to more sophisticated features, while the free version is based on the Gemini Pro LLM, which has more restricted capabilities.

Use Gemini for Cases and Applications

  • Use cases
  1. Text summarization: Gemini models are capable of condensing content from various data kinds.
  2. Audio processing: Gemini supports audio translation tasks and speech recognition in over 100 languages.
  3. Text generation: Gemini can produce text by asking the user questions. A chatbot interface that looks like a Q&A format can also control that text.
  4. Video understanding: To respond to queries and provide descriptions, Gemini can interpret and process video clip frames.
  5. Text translation: With their extensive multilingual capabilities, the Gemini models can comprehend and translate over 100 different languages.
  6. Image understanding: Complex visuals like charts, figures, and diagrams can be parsed by Gemini without the need for additional OCR software. It can be applied to visual question-and-answer features and image captioning.
  • Applications
  1. Google AI Studio: With Gemini, developers can create apps and prototypes using the web-based Google AI Studio tool.
  2. AlphaCode 2: The code generation tool AlphaCode 2 from Google DeepMind uses a modified version of Gemini Pro.
  3. Android 14: The first Android smartphone to take advantage of Gemini is the Pixel 8 Pro. Gemini Nano is available to Android developers via the AICore system capability.
  4. Vertex AI: Gemini Pro is also accessible through Google Cloud’s Vertex AI service, which offers foundation models that programmers can use to create applications.
  5. Search: To lower latency and boost quality, Google is experimenting with the use of Gemini in its Search Generative Experience.
  6. Google Pixel: The first gadget designed to run Gemini Nano is the smartphone, the Pixel 8 Pro, manufactured by Google. New features in Google apps that already exist, like Gboard’s Smart Reply for messaging apps and Recorder’s summarization, are powered by Gemini.
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Is Image Generation Available in Gemini?

Google bragged about Gemini’s ability to produce images in the same manner as other generative AI tools, like Dall-E, Midjourney, and Stable Diffusion when it first launched. Currently, Google’s Imagen 2 text-to-image model is used by Gemini, allowing the tool to generate images.

However, after generated images were shown to depict factual inaccuracies, Gemini’s image generation feature was halted to undergo retooling in late February 2024. Google plans to make changes to the feature so that Gemini will always be multimodal.

Gemini’s outputs varied in complexity based on end-user inputs before Google suspended access to the image creation feature. To evoke particular images, users could prompt with descriptions. To view the image that Gemini generated, edit it, and save it for later use, a user had to follow a straightforward, step-by-step process.


Google Gemini’s multimodal AI models represent a major advancement in digital advertising. Gemini seeks to compete with top AI platforms by seamlessly combining text, image, audio, and video comprehension. Google’s dedication to improvement, despite early difficulties, points to a promising development in the AI race.

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