Google’s Gemini Ultra: Redefining AI with Multimodal Capabilities and Challenging OpenAI’s Dominance | by Sateesh Hegde

Google’s latest AI project, Gemini, is a milestone in artificial intelligence. Developed by DeepMind, Gemini is not just another type of AI; is a breakthrough that combines various aspects of machine learning, providing unparalleled performance in multiple domains.

Gemini Ultra: Pinnacle of Gemini Project

At the heart of the Gemini project is the Gemini Ultra, a model that has set new AI benchmarks. It has achieved the latest results in 30 out of 32 benchmarks, excelling in areas such as writing and reasoning, understanding images, understanding videos, recognizing and interpreting words. In particular, Gemini Ultra is the first model to reach humanities experts on the MMLU benchmark, a test that tests knowledge and reasoning.

Multimodal skills

One of Gemini’s most impressive features is its multimodal capabilities. It can process and understand different types of data, including text, images, videos, and speech. This ability allows Gemini to perform complex tasks that were previously difficult for AI, such as solving complex math problems and creating accurate arithmetic tables that are calculated.

Benchmarks and Performance

The performance of Gemini Ultra is not limited to one area. It excels in a variety of benchmarks, including familiar text and concepts, image comprehension benchmarks, and more. His versatility is reflected in his ability to work on a variety of tasks, from answering questions on natural images and mixed media to understanding infographics, charts, and scientific diagrams.

Google recently released Gemini Ultra, the largest and most advanced model in its Gemini family of Large Language Models (LLMs), which includes multimodal language models (MLLMs).

This model has outperformed OpenAI’s GPT-4 in 30 out of 32 popular benchmarks, marking a new era in AI technology.

The Gemini Family: Different Types of AI

The Gemini family consists of three models: Ultra, Pro, and Nano, each designed for different tasks and functions.

1. Gemini Ultra: Set to be released in Q1 2024, the Gemini Ultra is the ultimate model, excelling in the toughest and most demanding applications. It outperforms GPT-4 on various benchmarks, demonstrating its superior ability to handle and manage various types of data such as text, images, audio, and video.

2. Gemini Pro: This model is balanced between low cost and functionality, showing the ability to think strongly and multitask. It often outperforms ChatGPT-3.5 Turbo in benchmarks, showing its robustness in various tasks.

3. Gemini Nano: Designed for desktop use, the Nano comes in two versions for low-end and high-memory devices. Nano-1 has 1.8 billion partitions, which requires only 0.9 GB, while Nano-2 has 3.25 billion partitions, requiring 1.7 GB. These models are known for their efficiency and are suitable for use in many mobile phones.

Gemini’s Multimodal Approach

What sets Gemini apart is its multifaceted nature. Unlike other models that use linking to combine different types of data, Gemini is trained with text, images, audio, and video at the same time. This approach allows the model to develop a coherent understanding of concepts in different ways. For example, the word ‘coffee’ can be represented in the same way if it is described in words, shown in a picture, or heard in words.

Contradictions and Innovations

Despite their impressive powers, Gemini has been criticized. A promotional video showing its real-time communication capabilities was revealed to have been edited to work, prompting accusations of false advertising. However, Google has also shown real progress, especially in the area of ​​Process-Supervised Reward Models (PRMs). These models, in contrast to traditional models controlled by the results, analyze each step in the reasoning process, improving the accuracy of tasks such as mathematics.

Google’s Place in the AI ​​Race

Although Gemini has received mixed reviews, it shows that Google is entering the AI ​​race strongly. Previously, Google’s ability to compete with OpenAI was uncertain, but Gemini, as well as the release of AlphaCode2 at the same time, indicate a strategic shift. Google is not only capturing but also redefining the subject of AI by combining MLLMs with models and search algorithms, advancing System 2 thinking.

The future with AlphaCode 2

AlphaCode2, released in conjunction with Gemini, is a key part of Google’s AI strategy. It represents a shift to a combination of MLLMs and advanced algorithms, an area where Google currently leads, and OpenAI’s wind version of Q* to be validated.

Finally, Google’s Gemini Ultra represents an important step in the development of AI, challenging the dominance of OpenAI and introducing a new paradigm in multimodal AI capabilities. Despite the controversies, its advanced features and intelligent nature herald a new era in AI competition and creativity.