Introducing Gemini: Google's Most Capable AI Model 

Introducing Gemini: Google's Most Capable AI Model 

On December 6th, 2023, Google unveiled its most capable AI model Gemini. This is grounding breaking promises to revolutionize the world of artificial intelligence.

What is very special about Gemini 

Gemini is not just a large language model(LLM). it’s multimodal meaning it can take input and process information from various sources like text, image, audio and code and especially video, in fact the one of the most impressive capabilities. Gemini can understand the content of videos, including the people , objects and action that are taking place . It can also generate transcripts of videos, translate them into different languages, and even create new videos.

Gemini’s Variants  

Google's Gemini represents a pivotal stride in the competitive field of AI, standing as a formidable contender against OpenAI's GPT-4 and Meta's Llama 2. Conceived from scratch, Gemini is distinguished by its 'multimodal' capabilities, enabling it to comprehend and engage with diverse data types, such as text, code, audio, visuals, and video, all at once.

The AI model is set to be offered in three distinct variants: Ultra, tailored for highly intricate tasks; Pro, designed for a broad spectrum of scalable tasks; and Nano, optimized for tasks executed on devices.

Gemini emerges as the inaugural model from the combined expertise of Google DeepMind, a result of the fusion between Google's premier AI research entities, DeepMind and Google Brain.

Key Highlights of Gemini : 

  • Multimodal Support: Text, vision, and audio are the many inputs and outputs that Gemini can handle. Because of its adaptability, it can work remarkably efficiently on tasks like image production and transcription. 
  • Cutting-edge Architecture: The model makes use of Multi Query Attention (MQA) to improve its processing power and has an amazing 32,000 context length decoder architecture.
  • Creative Image Encoder: Gemini's visual encoder, which is modeled after the Flamingo model, raises the bar for image processing. To put it simply, Flamingo is a visual language model (VLM) that can do a variety of multimodal tasks, including visual question answering, visual dialogue, captioning, and classification.
  • Comprehensive Training: The model has been trained on a wide range of data sources, including image, audio, and video data, books, and web papers. However, the precise number of tokens used is not made public.
  • Versatility in Sizes: To accommodate a range of use cases, Gemini is offered in three different sizes: Ultra, Pro, and Nano.
  • Cutting-Edge Training Hardware: To ensure excellent performance and efficiency, the model's training made use of TPUv5e and TPUv4 technology.
  • Mobile Integration: The Pixel 8 Pro is the first smartphone designed to operate the Gemini Nano model with ease, giving mobile devices access to cutting-edge AI capabilities.
  • Superior Performance: In areas like thinking, coding, and language understanding, in particular, Gemini has shown performance levels that are comparable to or marginally superior to those of GPT-4.

Reinforcement Learning from Human Feedback (RLHF) has been used to fine-tune the model, resulting in more accurate and dependable outputs. 

The technical report (Gemini vs GPT 4)

The benchmark tests conducted by Google to compare Gemini with GPT-4 reveal some interesting insights into the capabilities of both AI models. Here's a summary of the key points:

MMLU Test Scores:

  • Gemini: Achieved a 90 percent score on the Massive Multitask Language Understanding (MMLU) test.
  • Human Experts: Scored slightly lower at 89.8 percent.
  • GPT-4: Scored 86.4 percent on the same test.

Different Prompting Techniques:

  • GPT-4's score was achieved using the "5-shot" prompting technique, a standard in the industry.
  • Gemini Ultra's score was based on a "chain-of-thought with 32 samples" method, which differs from the 5-shot technique.

Comparison Using Outdated Version of GPT-4:

  • It's crucial to note that Google used an outdated version of GPT-4 for these tests, labeled as a "previous state-of-the-art" (SOTA) version.

Performance Using 5-Shot MMLU:

  • When both models were assessed using the 5-shot MMLU technique, GPT-4 scored 86.4 percent, while Gemini Ultra scored lower at 83.7 percent.

10-Shot HellaSwag Benchmark:

  • On the 10-shot HellaSwag benchmark, which measures commonsense reasoning, GPT-4 outperformed both versions of Gemini, scoring 95.3 percent compared to Gemini Ultra's 87.8 percent and Gemini Pro's 84.7 percent.

Understanding "Shot" in Machine Learning:

  • The term "shot" refers to the number of examples provided during training. For instance, "5-shot" learning means the model is trained with five instances of each class.

Chain of Thought (CoT) Prompting:

  • Chain of Thought (CoT) is a method where the AI model is guided to think through the steps logically before arriving at an answer.

These results highlight the importance of considering the methodologies and versions of AI models when comparing their performance. The tests demonstrate Gemini's strength in multimodal tasks and complex problem-solving, while also underscoring the robustness of GPT-4, especially in commonsense reasoning and tasks where fewer examples are provided for learning. The findings suggest that while Gemini shows promise, particularly in its multimodal approach, GPT-4 maintains strong capabilities in certain benchmark tests.

"What the quack?"

The launch of Google's Gemini AI model was accompanied by a promotional video titled "Hands-on with Gemini: Exploring Multimodal AI Interaction," which initially generated significant excitement due to its demonstration of Gemini's multimodal capabilities. However, this enthusiasm was tempered by subsequent revelations and critiques regarding the nature of the demonstration. Here are the key points of the situation:

Video Misrepresentation Concerns:

  • Reports and analyses suggested that the demo video might have misrepresented the actual performance capabilities of the Gemini AI model.

Bloomberg Opinion Piece:

  • Parmy Olson, a columnist for Bloomberg, noted in her opinion piece that the demonstration was not conducted in real-time or with live voice interaction. According to Olson, Google admitted to editing the video, and the voice in the demo was reading out human-made prompts, presenting them alongside still images. This raised questions about the video implying the possibility of smooth, real-time voice interactions with Gemini, which might not align with its current capabilities.

Video Description Disclaimer:

  • The description of the video did acknowledge certain modifications for the demo, stating that “latency has been reduced and Gemini outputs have been shortened for brevity." This disclaimer suggests that the presentation was optimized to showcase the potential of Gemini in a more streamlined manner.

Response from Google DeepMind’s VP of Research:

  • Oriol Vinyals, VP of Research at Google DeepMind and Gemini co-lead, addressed the interest in the video in a post on X. He affirmed that the prompts and outputs shown in the video were real but shortened for brevity. Vinyals emphasized that the video was intended to illustrate potential multimodal user experiences with Gemini and to inspire developers, rather than to demonstrate real-time capabilities.

The situation highlights the complexities and challenges in accurately representing the capabilities of advanced AI systems like Gemini. While the video aimed to demonstrate the potential applications and user experiences that could be built with Gemini, the subsequent clarifications and critiques underscore the importance of transparent and realistic portrayal of AI technologies, especially during their public introduction and demonstration.

Implications for the Future

The arrival of Gemini marks a significant advancement in the field of artificial intelligence. Its ability to process information from multiple modalities opens up a vast array of potential applications, including:

  • Personalized Education: Gemini can tailor educational experiences to individual learning styles and needs.
  • Enhanced Healthcare: Gemini can analyze medical data and images to assist healthcare professionals in diagnosis and treatment.
  • Improved Customer Service: Gemini can handle complex customer inquiries and provide personalized support.
  • Advanced Robotics: Gemini can power robots with improved perception and understanding of the world.
  • Creative Content Generation: Gemini can generate new forms of creative content, such as paintings, music, and even novels.

While Gemini is still in its early stages of development, its potential to reshape various industries and aspects of our lives is undeniable. As further research and development are conducted, we can expect to see even more remarkable applications emerge, paving the way for a future where humans and AI collaborate seamlessly to achieve extraordinary things.