Discovering Google DeepMind’s New Twins: What’s the Buzz All About?


In the world of Artificial Intelligence (AI), Google DeepMind’s latest product, Gemini, is creating a buzz. This innovative development aims to overcome the complex challenges of replicating human perception, particularly the ability to integrate diverse sensory inputs.

 Human perception, which is inherently multimodal, uses multiple channels simultaneously to understand the environment.

 Inspired by this complexity, multimodal artificial intelligence attempts to combine, comprehend and reason about information from different sources, reflecting human-like perception abilities.

The Complexity of Multimodal AI

While AI has made progress in managing individual sensory modes, achieving true multimodal AI remains a formidable challenge.

Existing methods involve training separate components for different methods and combining them together, but they often fall short on tasks that are complex and require conceptual reasoning.

The emergence of Gemini

In the quest to replicate human multimodal perception, Google Gemini has emerged as a promising development.

This creation offers a unique perspective on the potential of artificial intelligence to unravel the intricacies of human perception.

Gemini takes a different approach, being multi-modal in nature and pre-training on a variety of modalities. Gemini improves its effectiveness by further fine-tuning it with additional multimodal data and shows promise in understanding and reasoning across a variety of inputs.

What are twins?

Google Gemini is a family of multi-modal AI models introduced on December 6, 2023, developed by Alphabet’s Google DeepMind unit in collaboration with Google Research.

Gemini 1.0 is designed to create and understand content across a variety of data types, including text, audio, images and video.

The standout feature of Gemini is its inherent multimodality, which distinguishes it from traditional multimodal AI models.

This unique capability allows Gemini to seamlessly process and reason across various types of data, such as audio, images, and text.

Significantly, Gemini is capable of cross-modal reasoning, allowing him to interpret handwritten notes, graphs, and diagrams to tackle complex problems.

Its architecture supports direct acquisition of texts, images, audio waveforms, and video frames as spaced sequences.

Gemini Family

Gemini has a range of models tailored to specific use cases and deployment scenarios. Designed for highly complex tasks, the Ultra model is expected to be available in early 2024.

The Pro model, which prioritizes performance and scalability, is suitable for robust platforms such as Google Bard. In contrast, the Nano model is optimized for on-device use and comes in two versions:

Nano-1.8 with 1 billion parameters and Nano-3.25 with 2 billion parameters. These Nano models integrate seamlessly into devices including the Google Pixel 8 Pro smartphone.

Gemini vs ChatGPT

According to company sources, researchers extensively compared Gemini with ChatGPT variants that outperformed ChatGPT 3.5 in common tests.

Gemini Ultra excels in 32 of the 30 criteria commonly used in large language model research.

Scoring 90.0% on MMLU (multitask multitasking language understanding), Gemini Ultra demonstrates its prowess in multitasking language understanding, outperforming human experts. MMLU combines 57 subjects including mathematics, physics, history, law, medicine and ethics to test both world knowledge and problem-solving skills. Trained to be multi-modal, Gemini stands out in the competitive AI landscape by processing a variety of media types.


The emergence of Gemini has given rise to a number of use cases, some of which are as follows:

  • Advanced Multimodal Reasoning: Gemini excels at advanced multimodal reasoning, recognizing and comprehending text, images, audio, and more simultaneously. This comprehensive approach improves the ability to comprehend subtle information and excels in explanation and reasoning, especially in complex subjects such as mathematics and physics.
  • Computer Programming: Gemini specializes in understanding and creating high-quality computer programs in commonly used languages. It can also be used as an engine for more advanced coding systems, as demonstrated in solving competitive programming problems.
  • Medical Diagnostic Transformation: Gemini’s multi-modal data processing capabilities can mark a shift in medical diagnosis and potentially improve decision-making processes by providing access to diverse data sources.
  • Transforming Financial Forecasting: Gemini reshapes financial forecasts by interpreting various data from financial reports and market trends, providing rapid insights for informed decision-making.


While Google Gemini has made impressive progress in developing multimodal AI, it faces some challenges that need to be carefully evaluated.

Due to extensive data training, it is important to approach this issue carefully to ensure responsible use of user data and address privacy and copyright concerns.

Possible biases in training data also raise fairness issues, and ethical testing is required before public release to minimize such biases.

There are also concerns about the potential misuse of powerful AI models such as Gemini for cyber attacks; This underscores the importance of responsible deployment and ongoing oversight in the dynamic AI environment.

Future Development of Gemini

Google confirmed its commitment to improving Gemini by strengthening it for future releases with advances in scheduling and memory.

Additionally, the company aims to expand the context window, allowing Gemini to process more information and provide more detailed responses.

As we look forward to potential breakthroughs, Gemini’s distinctive capabilities offer promising prospects for the future of artificial intelligence.


Google DeepMind’s Gemini marks a paradigm shift in AI integration that transcends traditional models.

With local multimodality and cross-modal reasoning ability, Gemini excels at complex tasks. Despite the challenges, its advanced reasoning highlights its potential for applications in programming, diagnostics, and financial forecasting transformation.

As Google commits to its future development, Gemini’s profound impact is subtly reshaping the AI ​​landscape and marking the beginning of a new era of multi-modal capabilities.


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