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[12/8/2023] Google just unveiled Gemini 1.0, its largest and most capable AI model. Gemini was trained using the company’s custom-designed AI accelerators, Cloud TPU v4 and v5e. Built natively to be multimodal, it’s the first step in the Gemini-era of models. Gemini is optimized in three sizes – Ultra, Pro, and Nano. In benchmark tests, Gemini outperforms OpenAI’s GPT-4 in 30 of 32 tests, particularly in multimodal understanding and Python code generation. 

Each model targets specific applications. Gemini Ultra, is able to perform complex tasks in data centers and enterprise applications, harnessing the full power of Google’s AI capabilities. Gemini Pro serves a wider array of AI services, integrating seamlessly with Google’s own AI service, Bard. Lastly, Gemini Nano has two versions: Nano-1 with 1.8 billion parameters and Nano-2 with 3.25 billion parameters. These models are specifically engineered for on-device operations, with a focus on optimizing performance in Android environments. For coding, Gemini uses AlphaCode 2, a code-generating system that shows the model’s proficiency in understanding and creating high-quality code in various languages.

Central to the Gemini models is an architecture built upon enhanced Transformer decoders, specifically tailored for Google’s own Tensor Processing Units (TPUs). This coupling of hardware and software enables the models to achieve efficient training and inference processes, setting them apart in terms of speed and cost-effectiveness compared to previous iterations like PaLM.

A key element of the Gemini suite is its multimodal nature – trained on a vast array of datasets including text, images, audio, and code. Gemini’s reportedly surpass OpenAI’s GPT-4 in various performance benchmarks, especially in multimodal understanding and Python code generation. The version just released, Gemini Pro, is a lighter variant of a more advanced model, Gemini Ultra, expected next year. Gemini Pro is now powering Bard, Google’s ChatGPT rival, and promises improved abilities in reasoning and understanding.

Gemini Ultra is said to be “natively multimodal,” processing a diverse range of data including text, images, audio, and videos. This capability surpasses OpenAI’s GPT-4 in vision problem domains, but the improvements are marginal in many aspects. In some benchmarks, for example, Gemini Ultra only slightly outperforms GPT-4.

A concerning aspect of Gemini is Google’s secrecy around the model’s training data. Questions about the data’s sources and creators’ rights were not answered. This is critical, as increasingly the AI industry is facing lawsuits over using copyrighted content without compensation and/or credit.

Gemini is getting a mixed reception after its big debut on Dec. 6, 2023, but users may have less confidence in the company’s multimodal technology and/or integrity after finding out that the most impressive demo of Gemini was pretty much faked. Parmy Olson at Bloomberg was the first to report the discrepancy. TechCrunch does a great job itemizing the issues with the video below.

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