Google Gemini

- Google Gemini is a generative AI tool that was developed by Google. It was designed to be multimodal, and it's built for various computational and application needs. It comes in three versions, Nano, Pro and Ultra, each with its own use cases. Nano is intended for on device usage. It's the smallest model. Pro, currently in public preview is designed for intermediate level projects. Ultra, the largest and most advanced is in private preview and not yet available to the public. Gemini is integrated with Google Cloud services such as Vertex AI. Gemini's ability to process multiple types of data simultaneously, such as images, text, and video is a compelling feature set. Gemini's versions cater to different needs. Nano is tailored for small scale applications, particularly mobile. Pro is more complex and Ultra is for large scale AI operations. Pro and Ultra come in two models each, a standard version for text only and a vision version for processing images, videos, and text. The multimodal capabilities of Gemini, particularly in Pro Vision and Ultra Vision, allow for innovative interactions with AI, such as generating code from visual inputs or performing multimodal Q and A. This opens up new possibilities for developers enhancing AI applications across various industries. Gemini offers advanced coding capabilities like automated code optimization and predictive coding suggestions. Its multimodal nature allows developers to generate code from images or videos in a single call, a significant advancement over the traditional models. This can lead to more intuitive and efficient development processes, such as UI to code or diagrams to code. Additionally, Gemini's function calling and multimodal RAG or retrieval augmented generation extend its utility enabling complex queries over multimodal data. This will fundamentally change how developers interact with AI encoding. Responsibility and trust in Gemini are insured through comprehensive safety measures, including safety ratings and filter thresholds for developers to control content. Gemini adheres to Google's ethical guidelines and AI principles, incorporating fairness and bias checking algorithms. It also includes features to address concerns like harassment, hate speech, sexuality, explicit statements, and dangerous content. These measures build trust among users and ensure responsible AI development and deployment. Learners can access a wealth of resources to learn about Gemini. Google's official documentation, interactive tutorials and GitHub repositories offer detailed insights. Additionally, YouTube hosts various playlists showcasing Gemini's capabilities and use cases. For more structured learning experience, I'll be creating a new course on LinkedIn Learning in 2024, focusing on developing, testing and deploying applications using Google Gemini.