Google has unveiled Gemini, its latest general-purpose, multimodal, generative AI model, positioned as a more powerful alternative to OpenAI’s GPT-4. The model comes in three sizes—Nano, Pro, and Ultra—tailored to diverse user needs.
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Gemini’s Computational Power
Gemini boasts five times the computational power of GPT-4, accelerating training processes and potentially accommodating larger model sizes. Notably, it surpasses human experts on Massive Multitask Language Understanding (MMLU), a prominent benchmark for evaluating AI knowledge and problem-solving abilities.
Versatility in Modalities
Unlike traditional approaches, Gemini is natively multimodal, allowing simultaneous training on various modalities from the outset. It excels in understanding and reasoning across text, images, audio, and more, showcasing advanced capabilities in complex subjects like math and physics.
Breakthrough Applications
Gemini’s sophisticated multimodal reasoning proves invaluable in extracting insights from extensive document sets, contributing to breakthroughs in fields ranging from science to finance. Additionally, it emerges as a foundation model for global coding, understanding, explaining, and generating high-quality code in popular programming languages.
Infrastructure and Training
Google employed its AI-optimized infrastructure, leveraging Tensor Processing Units (TPUs) for Gemini’s training. This strategic choice mitigates dependency on GPUs, enhancing reliability and scalability during both training and deployment.
Rollout and Applications
Gemini is gradually integrating into various products and platforms, with Google’s chatbot, Bard, utilizing a fine-tuned version of Gemini Pro. The model’s deployment introduces new protections to address potential risks associated with its multimodal capabilities, emphasizing safety throughout development stages.
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