In my previous entry I showed you step-by-step instruction of how to develop a simple code for general text-based chat app. If you haven’t seen it yet, give it a try: Create Your first Chat App with PaLM 2. In this short entry I want to show you how you can test/run/showcase such application using Google Colaboratory
Quick introduction to colab
Google Colaboratory, commonly referred to as “Colab,” is a free cloud service hosted by Google to encourage Machine Learning and Artificial Intelligence research. It provides a versatile environment that combines executable code, rich text, and graphics to help users create, collaborate on, and share documents. Colab is based on the Jupyter notebook environment and supports Python 3 interpreters. It offers free access to computing resources including GPUs and TPUs which can be particularly beneficial for resource-intensive tasks. This platform is widely used for educational purposes, data analysis, and prototyping, as it allows users to write and execute code, save and share their analyses, and access powerful computing resources, all through their browsers without the need for any setup.
Sounds perfect for our use case, doesn’t it?
In the ever-evolving landscape of AI, Google’s PaLM 2 has emerged as a revolutionary force, unlocking new potentials in natural language processing. Imagine harnessing this cutting-edge technology to create something as interactive and engaging as a chat application. In this blog entry, I’m thrilled to show you exactly how straightforward it can be to develop your very first Python application—a “chat application” that interacts with the remarkable chat-bison model from the PaLM 2 family.
Fair warning: a very basic understanding of Python is required, but you definitely do not need to be a pro!
We’ll dive into the world of Generative AI Studio, a remarkable tool (set of tools really) that provides us with a baseline of code. From there, I’ll guide you through tweaking and customizing this foundation to fit your unique vision for the app. This isn’t just about coding; it’s about creativity and bringing your ideas to life.
In the rapidly evolving world of artificial intelligence, terms like Generative AI and Large Language Models are often tossed around and used interchangeably. However, this common misconception can lead to confusion and a lack of clarity when discussing AI technologies. In this entry, I will aim to demystify these terms and provide a clear understanding of where they fit within the broader AI domain.
Let’s start at the top. Artificial Intelligence (AI) is the overarching domain that involves creating machines capable of performing tasks that typically require human intelligence. Within AI, Machine Learning (ML) emerges as a significant subdomain, focusing on algorithms and statistical models that enable computers to learn from and make predictions or decisions based on data.