There is much to be said about Google, but ultimately they know how to get it done. When the world woke up to the launch of Open AI, there was a moment when it seemed like Google was falling behind. However, Google doesn’t always need to be first, they just do it well. Here are the new features Google is adding to the AI world:
Unveils Geminin
A new and highly efficient model combined with tools and API interfaces, designed for future innovations such as memory and planning.
Geminin is still in training, but it is already showcasing multi-modal capabilities not seen in previous models.
It can be used to improve the accuracy of machine translation, automate tasks currently performed by humans, and create new forms of creative content.
Geminin is multi-modal, meaning it can process and understand information in various formats, such as text, code, and images.
It is highly efficient when combined with tools and API interfaces, allowing for the rapid and easy development and deployment of AI applications.
It is intended for future innovations such as memory and planning, meaning it can be used to create artificial intelligence systems that are more intelligent and capable than ever before.
PaLM 2
PaLM 2 is the second generation of Google’s Pathways Language Model, a large language model (LLM) trained on a vast array of text and code data. PaLM 2 is significantly larger and more powerful than its predecessor, PaLM, with 540 billion parameters compared to PaLM’s 137 billion parameters. This makes PaLM 2 capable of performing a wider range of tasks, including:
- Understanding and generating natural language
- Code generation and translation
- Answering questions
- Summarization
- Creative writing
- High capabilities in mathematics and sciences
So What’s the Actual Difference Between Unveils Geminin and PaLM 2
PaLM 2 and Gemini are both large language models (LLMs) developed by Google AI. However, they have different strengths and weaknesses.
PaLM 2 is a more powerful LLM than Gemini. It has 540 billion parameters, compared to Gemini’s 280 billion parameters. This means that PaLM 2 can process and understand more information than Geminin. PaLM 2 also performs better in tasks requiring deep language understanding, such as answering questions and summarizing.
However, PaLM 2 is less adept at multi-modal tasks like Gemini. Gemini can process and understand information in a variety of formats, such as text, code, and images. This makes Gemini better at tasks requiring a more holistic understanding. Better at multi-modal tasks.
These are the key differences between PaLM 2 and Gemini:
PaLM 2
Parameters 540 billion
Strengths Deep language understanding, good at answering questions and summarizing
Weaknesses Not good at multi-modal tasks
Gemini
Parameters 280 billion
Strengths Multi-modal, good at answering questions and visually and creating natural language
Weaknesses Not as strong as PaLM 2
Overall, PaLM 2 is a stronger LLM than Gemini. However, Gemini is better at multi-modal tasks. Which LLM is preferable for a specific task depends on the specific requirements of the task.
And with Google, a few more implementations you want to know
✔Google X Adobe
Google and Adobe have partnered to give Google’s AI capabilities through Adobe’s best visual creation tools.
✔Google Photos’ New “Magic Editor”
This new feature allows you to:
-Adjust the lighting of the image
-Remove elements you don’t like
-Change your location in the image
✔Upgrade to Google Maps, new options that help:
-Know the weather along your route
-Calculate the fastest route for you (not yet clear what the advantage of the feature over Waze is, time will tell)
In summary, Google is not lagging behind and it’s interesting to continue following the innovations and new capabilities on the horizon.
It’s also interesting to see how the different models learn differently, and in the future, we will learn more and more about the differences between them from the answers they provide.
In short, join us and stay updated >>
