Google’s Gemini is a suite of generative AI models, apps, and services that aims to make a mark in the AI landscape. Developed by Google’s AI research labs DeepMind and Google Research, Gemini comes in three versions: Gemini Ultra, Gemini Pro, and Gemini Nano. These models are trained to be “natively multimodal,” meaning they can work with and utilize more than just text. They have been trained on various forms of data, including audio, images, videos, codebases, and text in different languages.
What sets Gemini apart from other AI models is its ability to understand and generate multimodal content. While models like Google’s LaMDA are trained exclusively on text data, Gemini models can work with and generate content across different modes. This makes Gemini a promising platform for tasks such as speech transcription, image and video captioning, and artwork generation.
However, it is important to note that Gemini is still a work in progress, and Google has faced some criticism for overpromising on its capabilities. The original launch of Bard, the precursor to Gemini, fell short of expectations. Additionally, a video demonstrating Gemini’s capabilities turned out to be heavily doctored and misleading.
Despite these setbacks, Google claims that once fully developed, Gemini models will have a wide range of capabilities. Gemini Ultra, for example, can be used to assist with physics homework, solving problems step-by-step, and identifying relevant scientific papers. It can also generate charts and formulas based on updated data.
Gemini Pro, on the other hand, excels in reasoning, planning, and understanding. In a study comparing Gemini Pro with OpenAI’s GPT-3.5, researchers found that Gemini Pro performed better in handling longer and more complex reasoning chains. However, the study also found that Gemini models, like other large language models, struggled with certain tasks, such as mathematics problems involving multiple digits.
Gemini Nano is a smaller version of the Gemini Pro and Ultra models that can run directly on mobile devices like the Pixel 8 Pro and Samsung Galaxy S24. It powers features like Summarize in Recorder and Smart Reply in Gboard. Summarize in Recorder provides users with Gemini-powered summaries of their recorded conversations, while Smart Reply suggests responses in messaging apps.
In terms of competition, Google claims that Gemini outperforms OpenAI’s models on various benchmarks. However, the differences in performance seem to be marginal. Some users and academics have also raised concerns about the accuracy and quality of Gemini models, citing examples of factual errors, translation issues, and poor coding suggestions.
As for pricing, Gemini 1.5 Pro is currently free to use in the Gemini apps, AI Studio, and Vertex AI during its preview period. Once it exits preview, the model will cost $0.0025 per character for input and $0.00005 per character for output. Ultra pricing has not been announced yet.
To try Gemini, users can access Gemini Pro and Ultra through the Gemini apps, API in Vertex AI, and AI Studio. Developers can customize Gemini Pro in Vertex AI for specific contexts and use cases and connect it to external APIs. Gemini models are also integrated into various Google development tools and platforms, such as Code Assist for code completion and generation and Google’s security products.
In conclusion, Gemini is Google’s flagship suite of generative AI models that aims to excel in multimodal tasks. While it shows promise, Gemini is still a work in progress and has faced criticism for overpromising on its capabilities. It remains to be seen how Gemini will perform compared to its competitors and how it will evolve as it continues to be developed.
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