In this video, we ll discuss Gemini, which represents a significant leap
forward in AI.
Gemini is a family of highly capable
multimodal models developed by Google DeepMind, a research division within
Google dedicated to advancing the state of the art in artificial intelligence.
A multimodal model is an AI model that can process and understand information
from multiple sources such as text, audio and video.
This is in contrast to traditional AI models which are typically trained on a
single type of data, such as text or images.
Gemini was trained on a massive data set
of images, audio, video, text and code to create a model with both broad
capabilities across modalities and exceptional performance in each domain.
The training dataset was both multimodal and multilingual.
Google DeepMind optimized Gemini for three sizes, Gemini Ultra, the largest
and most capable model for highly complex tasks, Gemini Pro, the best model for
scaling across a wide range of tasks, and Gemini Nano, the most efficient model for
on -device developments.
Nano brings AI closer to the user.
There are two versions of Gemini Nano, Nano 1 with 1 .8 billion parameters and
Nano 2 with 3 .2 billion parameters respectively.
In this course, we ll use the API for
Gemini Pro.
Gemini is the most flexible model yet
being able to run on everything from data centers to mobile devices.
Gemini models are trained to accommodate textual input interweaved with a wide
variety of audio and visual inputs such as natural images, charts, screenshots,
PDFs and video and they can produce text and image outputs.
Take a look at this image from the Gemini paper.
Imagine a teacher drawing a physics
problem on a whiteboard depicting a skier sliding down a slope.
Now, think of a student attempting to solve the problem scribbling down his
calculations and steps.
With Gemini s multimodal reasoning
powers, this AI model can grasp the messy handwriting, decipher the problem,
translate both the problem and the solution into mathematical symbols,
pinpoint where the student made an error in his reasoning and then provide a step
-by -step accurate solution.
This breakthrough opens up a world of
possibilities for personalized education and I believe Gemini s multimodal and
reasoning capabilities hold an immense potential for transforming learning.
Now, take a look at the Gemini paper released by Gemini team.
Same as GPT models, Gemini models build on top of the transformer technology.
They are trained to support 32K context length employing efficient attention mechanisms.
Now, let s take a look at some benchmarks.
Google AI conducted a comprehensive
comparison of Gemini Pro and Ultra against a collection of external LLMs and
Google s previous flagship model PAL2 across a range of academic benchmarks
including reasoning, reading comprehension, scientific and technical
knowledge and coding.
The results clearly demonstrate the
superior performance of both Gemini Pro and Gemini Ultra placing them at the top
of multimodal AI capabilities.
They consistently outperform their
counterparts showcasing their exceptional ability to grasp and process information
from diverse sources.
Now, let s take a look at the language
understanding and generation performance of the Gemini model family across
different capabilities normalized by the Gemini Pro model.
We observe consistent quality gains with increased model size.
Note that at this moment developers can access Gemini Pro via the Gemini API in
Google AI Studio and Google Cloud Vertex AI.
Android developers will also be able to
build with Gemini Nano via AI Core a new system capability available in Android
starting on Pixel 8 Pro devices.