An Introduction to Generative AI: How it Works and What it Can Do
Artificial intelligence (AI) is transforming industries, and generative AI is leading the way. From generating text, images, and audio to creating new data from scratch, generative AI has vast applications. But what exactly is generative AI? How does it differ from other forms of AI? In this blog, we’ll explore these concepts in detail and understand the underlying technology that powers generative AI.
What is AI, Machine Learning, and Generative AI?
At a high level, AI is a discipline of computer science focused on building intelligent agents that can think, learn, and act autonomously. One major subset of AI is machine learning (ML), where systems train on input data to learn and make predictions about new, unseen data. ML models can be categorized into two main types:
- Supervised models: These models use labeled data to predict an outcome. For example, predicting a restaurant tip based on the total bill and whether the order was delivered or picked up.
- Unsupervised models: These models analyze raw, unlabeled data to uncover patterns or groups. A business might use an unsupervised model to analyze employee tenure and income, identifying fast-track employees.
Now, let’s focus on generative AI, a type of AI that creates new content based on existing data. Generative AI is a subset of deep learning, which itself is a branch of machine learning. Deep learning uses artificial neural networks modeled after the human brain to process complex patterns and data.
Supervised, Unsupervised, and Deep Learning in Generative AI
Deep learning, with its multiple layers of interconnected nodes or neurons, powers generative AI. It uses labeled and unlabeled data in a process called semi-supervised learning. This allows the model to generalize from new examples after training on large datasets.
Generative AI falls under generative models—a type of model that produces new data by learning the probability distribution of existing data. These models can generate text, images, videos, and other types of content, unlike discriminative models, which classify or label data without creating new content.
For example:
- A discriminative model might predict whether an image is of a dog or cat.
- A generative model could learn from images of dogs and then generate a new, entirely unique image of a dog.
How Generative AI Works
Generative AI uses statistical models trained on large datasets to understand patterns in the data and generate new outputs. Large language models (LLMs), such as Google’s PaLM (Pathways Language Model) or LaMDA (Language Model for Dialogue Applications), use this technology. These models are trained on vast amounts of text data and can respond to user prompts with new text, images, or even code.
For instance, a generative model can complete a sentence like “I’m making a sandwich with peanut butter and…” with the word “jelly,” based on learned patterns from its training data.
The Power of Transformers in Generative AI
Transformers are a key component in modern generative AI models. They revolutionized natural language processing in 2018, and at their core, transformers consist of two parts: an encoder and a decoder. The encoder processes input sequences, and the decoder learns how to generate relevant output. This process enables models to generate coherent responses or even hallucinate new content, though sometimes these hallucinations can result in nonsensical outputs if the model’s data or context is inadequate.
Applications of Generative AI
Generative AI has practical applications across industries. For example:
- Text-to-text models can translate languages or generate natural language responses.
- Text-to-image models use input descriptions to create new images, a process often powered by methods like diffusion.
- Text-to-video and text-to-3D models generate multimedia content, which can be used in fields like gaming, virtual environments, and marketing.
- Text-to-task models perform specific actions based on input, such as executing tasks or navigating web interfaces.
Foundation Models and Industry Use Cases
A foundation model is a large AI model pre-trained on a massive dataset, enabling it to perform various downstream tasks, such as text generation, image captioning, or object recognition. Google’s Vertex AI offers pre-trained models in its Model Garden, including powerful tools for sentiment analysis, image generation, and occupancy analytics.
One exciting application is code generation. For instance, using Google’s Bard, developers can input a code file conversion problem and receive not only a solution but also the steps and code snippets required. This enables the automatic conversion of, say, a Python DataFrame to JSON, simplifying the workflow for developers.
Generative AI Studio and App Builder
Google Cloud provides several tools to simplify generative AI for businesses and developers:
- Generative AI Studio helps users create and customize AI models with pre-trained resources, fine-tuning capabilities, and deployment tools.
- Generative AI App Builder lets users create generative AI applications without any coding. With a simple drag-and-drop interface, businesses can build apps with conversational AI, custom search engines, and digital assistants.
- PaLM API offers developers access to Google’s large language models for experimentation and integration with MakerSuite—a suite of tools for training, deploying, and monitoring models.
The Future of Generative AI
Generative AI is rapidly becoming a key component of various industries, revolutionizing everything from customer support to fraud detection, creative content generation, and more. Its ability to understand and create content makes it one of the most versatile AI technologies available today.
Whether you’re generating new images from a description, translating languages, or building custom applications, generative AI is poised to unlock new possibilities for businesses and developers alike.
Conclusion
This blog has introduced the foundational concepts of generative AI and its broader role within artificial intelligence. As the field advances, its applications will continue to grow, reshaping industries and the way we interact with technology. Now, with tools like Generative AI Studio and PaLM API, even those without deep AI expertise can harness the power of generative AI to create innovative solutions.
