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Google’s Gemini: A Deep Dive into the Next-Gen AI Suite


Google, a perennial pioneer in the realm of artificial intelligence, is once again making waves with its latest offering: Gemini.

This flagship suite encompasses a spectrum of generative AI models, applications, and services poised to redefine the landscape of AI technology.

While Gemini showcases remarkable potential in several facets, our informal review has unearthed areas where it falls short, prompting a nuanced exploration.

Exploring Gemini: Unveiling Google’s Next-Gen AI Marvel

What Exactly is Gemini?

Gemini emerges as Google’s much-anticipated next-generation GenAI model family, the brainchild of the tech giant’s AI research labs, DeepMind, and Google Research. It comprises three distinctive iterations:

  1. Gemini Ultra: The pinnacle of the Gemini lineup, boasting unparalleled capabilities.
  2. Gemini Pro: A streamlined variant of the Gemini model, offering a lighter alternative.
  3. Gemini Nano: A compact iteration tailored for mobile devices such as the Pixel 8 Pro.

These Gemini models are inherently multimodal, transcending traditional text-based AI by seamlessly integrating audio, images, videos, and diverse codebases into their repertoire.

Unlike Google’s LaMDA, confined to text-only interactions, Gemini models exhibit a multifaceted understanding and generation prowess, encompassing a broader array of data types and languages.

Distinguishing Gemini Apps from Gemini Models

One aspect that may cause confusion is the distinction between Gemini apps and Gemini models. Google’s branding strategy, often criticized for its lack of clarity, muddies the waters.

Essentially, the Gemini apps serve as a gateway to accessing specific Gemini models, functioning as a client interface for Google’s GenAI.

This demarcation is crucial, as it clarifies the relationship between the Gemini suite and its accompanying applications.

Moreover, it’s imperative to note that Gemini apps and models operate independently of Imagen 2, Google’s text-to-image model present in select development tools and environments.

This disentanglement dispels any perplexity surrounding the interplay between these distinct AI frameworks.

Exploring Gemini’s Potential: A Multimodal Marvel

Gemini, Google’s latest foray into the realm of artificial intelligence, holds the promise of revolutionizing multimodal tasks.

With its diverse range of applications, from transcribing speech to generating artwork, Gemini is poised to redefine the boundaries of AI functionality.

While some of these capabilities are still in development, Google’s ambitious vision hints at a future where Gemini becomes an indispensable tool in various domains.

Diving Deeper into Gemini’s Multimodal Features

Gemini Ultra: Unveiling the Flagship Model

At the forefront of the Gemini lineup is Gemini Ultra, touted for its unparalleled multimodality. Google asserts that Gemini Ultra possesses the potential to assist with tasks ranging from solving physics problems step-by-step to identifying relevant scientific papers for research endeavors.

Moreover, it can extract pertinent information from these papers and update charts with the latest data by generating the requisite formulas.

While Gemini Ultra technically supports image generation, this functionality has yet to be integrated into the finalized version of the model.

The intricacies of native image generation pose challenges, unlike conventional methods employed by applications like ChatGPT’s image generation feature, which relies on intermediary steps.

Despite this, Gemini Ultra remains a formidable tool for various tasks.

Accessing Gemini Ultra: The Gateway to Multimodal Intelligence

Google has made Gemini Ultra accessible through its Vertex AI platform and AI Studio, catering to developers and platform enthusiasts.

However, access to Gemini Ultra comes at a cost, as it is bundled within the Google One AI Premium Plan, priced at $20 per month.

Subscribers to this plan gain exclusive access to Gemini’s advanced capabilities, seamlessly integrated with their Google Workspace accounts.

Gemini Pro vs. GPT-3.5: A Comparative Study

A study conducted by researchers from Carnegie Mellon and BerriAI revealed that Gemini Pro surpasses OpenAI’s GPT-3.5 in managing longer and more intricate reasoning chains.

This finding underscores Gemini Pro’s potential as a powerful tool for tasks requiring sophisticated language understanding.

Despite its strengths, the study highlighted areas where Gemini Pro, like other large language models, struggles, such as with complex math problems and occasional inaccuracies in reasoning.

ALSO READ: What is ChatGPT? Here’s everything you need to know about ChatGPT


Gemini 1.5 Pro: Enhancements and Features

Google has introduced Gemini 1.5 Pro as an upgrade to its predecessor, offering several improvements. One of the most significant enhancements is the model’s increased data processing capacity.

Gemini 1.5 Pro can handle approximately 700,000 words or 30,000 lines of code, a substantial improvement over Gemini 1.0 Pro.

Moreover, as a multimodal model, Gemini 1.5 Pro can analyze up to 11 hours of audio or an hour of video in various languages, albeit at a slower pace.

API Integration and Customization

Gemini Pro is accessible via API in Vertex AI, allowing developers to integrate the model into their applications seamlessly.

The Gemini Pro Vision endpoint enables text and imagery processing, akin to OpenAI’s GPT-4 with Vision model. Developers can customize Gemini Pro to specific contexts and use cases within Vertex AI, using a fine-tuning process to enhance its performance.

Gemini Nano: Powering Mobile Features

Gemini Nano, a compact version of Gemini Pro and Ultra, is designed to run directly on select mobile devices, such as the Pixel 8 Pro.

It powers features like Summarize in Recorder and Smart Reply in Gboard. The Recorder app provides users with Gemini-powered summaries of recorded audio, even offline, without compromising data privacy.

Additionally, Smart Reply in Gboard offers contextual suggestions for messaging apps, enhancing user experience and productivity.

Gemini vs. GPT-4: A Performance Comparison

Google asserts that Gemini Ultra outperforms OpenAI’s GPT-4 on 30 out of 32 widely used academic benchmarks in large language model research and development.

Similarly, Gemini Pro is said to excel at tasks such as content summarization, brainstorming, and writing when compared to GPT-3.5.

However, despite Google’s claims, early impressions suggest that Gemini Pro’s performance may not be as flawless as advertised.

Users and academics have reported instances of incorrect facts, translation difficulties, and subpar coding suggestions, raising questions about the model’s overall reliability and effectiveness.

Cost of Using Gemini

Currently, Gemini Pro is free to use in the Gemini apps, AI Studio, and Vertex AI during the preview period.

However, once Gemini Pro exits preview in Vertex AI, users can expect to pay $0.0025 per character for the model and $0.00005 per character for output.

This pricing model applies to 1,000 characters, roughly equivalent to 140 to 250 words. Additionally, models like Gemini Pro Vision will incur an additional cost per image.

To illustrate, summarizing a 500-word article with Gemini Pro would cost approximately $5, while generating a similar-length article would cost around $0.1. Pricing for Gemini Ultra has not yet been announced.

Where to Experience Gemini

Gemini Pro and Ultra are accessible in the Gemini apps, providing users with the opportunity to interact with the models in various languages.

Additionally, both models can be accessed in preview mode in Vertex AI via an API. The API is currently free to use within specified limits and supports features like chat functionality and filtering in select regions, including Europe.

Developers can also utilize Gemini Pro and Ultra in AI Studio to create prompts and chatbots, with the option to export the code to other development environments.

Furthermore, Gemini models are integrated into Google’s Duet AI for Developers, offering assistance with code completion and generation.

Gemini Nano, designed for mobile devices like the Pixel 8 Pro, will be available on other devices in the future, with developers able to sign up for a sneak peek to incorporate the model into their Android apps.


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