Welcome to the world of Google's Gemini, a suite of generative AI models that's making waves in the tech world. This guide will explore what Gemini is, how you can use it, and how it compares to its competition.
Gemini is the brainchild of Google's AI research labs, DeepMind and Google Research. It's a next-gen GenAI model family that comes in three flavours: Gemini Ultra, Gemini Pro, and Gemini Nano. The models are "natively multimodal", meaning they can work with more than just words. They've been trained on a variety of data, including audio, images, videos, codebases, and text in different languages.
The Gemini apps and models are distinct entities. The apps are an interface through which certain Gemini models can be accessed, while the models themselves are the AI that powers the apps.
The Gemini models can perform a range of tasks, from transcribing speech to generating artwork. However, few of these capabilities have reached the product stage yet, but Google promises they're on their way.
Gemini Ultra can help with tasks like physics homework, identifying relevant scientific papers, and generating formulas to update charts. Gemini Pro improves on its predecessor, LaMDA, in reasoning, planning, and understanding capabilities. Gemini Nano, the smaller version, is efficient enough to run directly on some phones.
Despite Google's claims of Gemini's superiority, early impressions have been mixed. Some users and academics have found that Gemini Pro often gets basic facts wrong, struggles with translations, and gives poor coding suggestions.
As for cost, Gemini Pro is free to use in the Gemini apps and, for now, AI Studio and Vertex AI. Once Gemini Pro exits preview in Vertex, the model will cost $0.0025 per character while output will cost $0.00005 per character.
In conclusion, Google's Gemini is a promising suite of AI models with a range of potential applications. However, it's clear that there are still some kinks to be ironed out before it can truly outshine its competition.
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