Google's Gemini model surpasses GPT-4 in AI benchmarks due to several key factors:
- Gemini is built from the ground up to be multimodal, meaning it can process and understand different data types like text, images, and audio together. This allows it to perform tasks like image captioning, video question answering, and audio translation more effectively than GPT-4, which is primarily focused on text processing.
- Multimodal training data: Gemini is trained on a much larger and more diverse dataset of text, images, audio, and code than GPT-4. This gives it a broader range of knowledge and allows it to better understand the relationships between different types of information.
Superior performance on academic benchmarks:
- Gemini Ultra outperforms GPT-4 in 30 out of 32 widely used academic benchmarks used in large language model (LLM) research and development. This includes benchmarks for tasks like text summarization, question answering, and natural language inference.
- Gemini Ultra is the first model to outperform human experts on the MMLU (massive multitask language understanding) benchmark. This benchmark assesses both world knowledge and problem-solving abilities using a combination of 57 subjects such as math, physics, history, law, medicine, and ethics.
- Better reasoning abilities: Gemini uses a more advanced reasoning engine that allows it to follow complex instructions and solve multi-step problems more effectively than GPT-4.
- Stronger common sense: While GPT-4 has an edge in common sense reasoning for everyday situations, Gemini generally demonstrates better understanding of the real world and how things work.
- Fine-tuning capabilities: Google has fine-tuned a version of Gemini Pro for Bard, which allows it to perform more advanced tasks like summarization, generation of different creative text formats and writing different kinds of creative content.
Google's Gemini is a multimodal model with real-time response capability.
- It can recognize and respond to real-time video inputs, such as identifying objects in a video feed.
- The model can track ongoing activities in a video, like locating a hidden ball or connecting the dots.
Google's Gemini can generate music and perform logic and spatial reasoning
- It can create music from images, not just text to audio but image to audio.
- It can also assess aerodynamics and help civil engineers generate blueprints.
Google's Gemini offers three sizes for different applications
- Gemini Nano:
- This is the smallest and most efficient model of the three.
- It is designed for on-device tasks, such as running voice assistants, facial recognition, and natural language processing.
- It is already being used in the Pixel 8 Pro smartphone, where it powers features like Summarise in Recorder and Smart Reply in Gboard.
- Gemini Pro:
- This is a more powerful model that is designed for edge computing tasks, such as running smart home applications and industrial automation systems.
- It is not yet available, but it is expected to be released in the near future.
- Gemini Ultra:
- This is the largest and most powerful model of the three.
- It is designed for cloud computing tasks, such as running large language models and training AI models.
- It is also not yet available, but it is expected to be released in early 2024.
Here is a table that summarizes the uses for each size of Gemini:
|On-device tasks like voice assistants, facial recognition, and natural language processing.
|Edge computing tasks like smart home applications and industrial automation systems.
|Cloud computing tasks like large language models and training AI models.
Gemini Ultra outperforms GPT-4 in most situations
- Gemini Pro is not quite as good as GPT-4 Pro, but Gemini Ultra is better in almost every category
- Gemini Pro underperforms GPT-4 in benchmarks, but Gemini Ultra outperforms it in almost every single category
Gemini Ultra underperforms GPT-4 on the HellSwag Benchmark
- The HellSwag Benchmark assesses Common Sense natural language by having the AI complete ambiguous sentences
- This test is crucial for evaluating how human-like the AI's responses are
GPT-4's performance compared to Google's Gemini is concerning
- Google's Gemini uses newly unveiled version 5 tensor processing units in super PODS of 4,096 chips each.
- The super PODS have a dedicated Optical switch for quick data transfer, and can dynamically reconfigure into 3D torus topologies.
Google's Gemini uses vast training data set and reinforcement learning for quality control.
- The training data set includes web pages, YouTube videos, scientific papers, and books, filtered for quality.
- Gemini's Nano and Pro Models will be available on Google Cloud on December 13th, but the Gemini ultra Pro Max won't be available until next year due to safety tests.
For more info: https://deepmind.google/technologies/gemini/