Google recently introduced Gemini,
Its latest multimodal general AI model, aiming to compete with offerings from OpenAI, Microsoft, and Meta. Described as the company’s “largest and most capable” large language model (LLM), Gemini showcases cutting-edge performance across diverse benchmarks.
Crafted by the Google DeepMind AI unit, Gemini is a versatile AI model trained on Google’s Tensor Processing Units (TPU), ensuring faster operation compared to its predecessors. This adaptable model adeptly comprehends and manipulates various types of information, including text, code, audio, image, and video.
Gemini 1.0 is available in three sizes: Gemini Ultra, the largest and most powerful for complex tasks; Gemini Pro, ideal for a broad range of tasks; and Gemini Nano, designed for efficient on-device performance.
Expressing enthusiasm about the Gemini era, Sundar Pichai, CEO of Google and Alphabet, emphasized its significance as a major scientific and engineering effort for the company.
Gemini Ultra stands out by surpassing GPT-4 on 30 out of 32 widely used academic benchmarks. Particularly noteworthy is its exceptional 90% score on the MMLU (massive multitask language understanding), surpassing human experts. Gemini Ultra also excelled in the MMMU benchmark, scoring 59.4% in multimodal tasks without assistance from optical character recognition (OCR) systems.
Regarding availability, Gemini Pro is accessible in Bard chatbot, expanding to English in over 170 countries, with plans for broader modality support and additional languages. Starting December 13, developers and enterprises can access Gemini Pro through the Gemini API in Google AI Studio or Google Cloud Vertex AI.
Gemini Nano will be integrated into Pixel 8 Pro smartphones, with future plans for integration into Google’s Search, Ads, Chrome, and other services. Android developers can access Gemini Nano via AI Core on Android 14, starting with Pixel 8 Pro devices in early preview.
Google aims to release the advanced Gemini Ultra version through Bard Advanced in early 2024 for select customers, developers, partners, and safety experts to provide early experimentation and feedback before a broader rollout to developers and enterprises in the following year.
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