Google’s Gemini: Next-Gen AI Model Family with Multimodal Capabilities
Google recently introduced its new generative AI platform, Gemini, which has garnered attention for its multimodal capabilities. While Gemini shows promise in certain aspects, it falls short in others. So, what exactly is Gemini, how can it be used, and how does it compare to other AI models?
To stay updated on the latest developments with Gemini, we have compiled a comprehensive guide that will be continuously updated as new models and features are released.
Developed by Google’s AI research labs, DeepMind and Google Research, Gemini is the long-awaited next-generation generative AI model family. It consists of three different flavors:
1. Gemini Ultra
2. Gemini Pro
3. Gemini Vision
What sets Gemini apart from other models like Google’s own LaMDA is its multimodal nature. Gemini models are trained to be natively multimodal, meaning they can effectively work with various forms of data such as audio, images, videos, codebases, and text in different languages.
Unlike LaMDA, which is solely trained on text data and limited to text-based tasks, Gemini models possess the additional capability to understand and generate content beyond just text. While their ability to comprehend images, audio, and other modalities is still somewhat limited, it is a significant step forward.
It’s important to note that Gemini and Bard are separate entities. Bard serves as an interface through which certain Gemini models can be accessed, acting as a client for Gemini and other generative AI models. Gemini, on the other hand, is a family of models and not a standalone app or frontend. To draw a comparison with OpenAI’s products, Bard is equivalent to ChatGPT, a popular conversational AI application, while Gemini corresponds to the underlying language model powering it, such as GPT-3.5 or GPT-4.
It’s worth mentioning that Gemini is entirely independent of Google’s Imagen-2, a text-to-image model that may or may not align with the company’s overall AI strategy. The distinction between these models can be confusing, and many share the same confusion.
The multimodal nature of Gemini models theoretically enables them to perform various tasks, including speech transcription, image and video captioning, and generating artwork. However, only a few of these capabilities have reached the product stage as of now. Google promises to deliver all these functionalities and more in the near future.
However, Google’s track record raises some skepticism. The initial Bard launch suffered from significant under-delivery, and a recent video showcasing Gemini’s capabilities was revealed to be heavily manipulated, leaving little faith in Google’s claims. Despite this, there is a limited availability of Gemini in its current form.
Assuming Google’s claims are trustworthy, here is an overview of what can be expected from different tiers of Gemini models upon their release:
1. Gemini Ultra:
– Assists with tasks like physics homework by providing step-by-step problem-solving, pointing out errors, and extracting relevant information from scientific papers.
– Technical image generation capabilities, though not included in the initial productized version.
2. Gemini Pro:
– Offers advancements in reasoning, planning, and understanding compared to LaMDA.
– Performs well in handling longer and more complex reasoning chains, surpassing OpenAI’s GPT-3.5, according to independent research.
– Struggles with multi-digit math problems and exhibits occasional factual errors.
– Available through API in Vertex AI for text processing and generating text outputs.
– Gemini Pro Vision endpoint processes both text and imagery, producing text-based results akin to OpenAI’s GPT-4 with Vision model.
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In early 2024, Vertex customers will be able to utilize Gemini Pro to power custom-built conversational voice and chat agents (chatbots), search summarization, and recommendation and answer generation features. These features will draw upon documents across different modalities and sources to cater to queries effectively.
Moreover, AI Studio, a web-based tool for developers, provides workflows for creating freeform, structured, and chat prompts using Gemini Pro. Developers have access to both Gemini Pro and Gemini Pro Vision endpoints, allowing for model customization, control over creative range, tone and style instructions, and safety settings.
Moving beyond the preview stage in Vertex, the pricing for Gemini Pro will be $0.0025 per character and $0.00005 per character for output. Vertex customers are billed per 1,000 characters, with Gemini Pro Vision charged per image at $0.0025.
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