What is Generative AI?
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With the buzz of Generative AI all around, it is imperative to learn what enables this technology to create new content, such as images, music, text, and more, with such remarkable coherence. This remarkable ability opens up endless possibilities for real-world application — hence the general excitement from technologists. The goal of this post is to break down the fundamentals of Generative AI in simple terms and contrast it with Traditional AI. Furthermore, we will highlight where you can utilize Generative AI in your daily life to augment your capabilities. Read on if you're curious about what makes this latest AI advancement special.
What is Generative AI?
To understand Generative AI, we need to break down the entire hierarchy of AI sub-domains. Here's a Venn Diagram that summarizes the hierarchy followed by text explanations:
What is Artificial Intelligence?
As a field of Computer Science, Artificial intelligence (AI) enables computers and machines to simulate human intelligence to perform various tasks. AI under the hood spans from rule-based approaches to sophisticated mathematical algorithms that learn patterns from the observed data. We are surrounded by copious applications of AI in this digital age, like virtual assistants, recommendation systems, autonomous vehicles, and so on.
What is Machine Learning?
Machine Learning (ML) is a subfield of AI that aims to enable machines to learn meaningful patterns from data. Learning process improves by receiving directional feedback over time. ML-enabled systems use various algorithms to first learn patterns and then make predictions or decisions based on new or unseen inputs. Some examples of ML applications are spam email filters, personalized shopping recommendations, and predictive text suggestions.
What is Deep Learning?
Deep Learning (DL) is a specialized branch of Machine Learning that uses Neural Networks with many layers (alluding to "Deep" in Deep Learning) to learn even more complex patterns in vast datasets. These networks are designed to mimic the human brain's structure and function and can learn abstract concepts given a large amount of data. This enables machines to recognize specific categories of images, understand speech patterns, and even play games that require superhuman levels of intelligence. All the modern-day AI breakthroughs are powered by Deep Learning, prominently advanced image recognition, and natural language understanding.
What is Generative AI?
Generative AI (GenAI) is a subfield of Deep Learning where Neural Networks are trained specifically to create new content, such as images, text, music, and more, by learning fundamental properties of the data they were trained on. Unlike the general Deep Learning domain (Traditional AI), which focuses on analyzing data and predicting specific outcomes, Generative AI aims to produce new data that mostly resembles the property of the data it was trained on. Think of a Neural Network trained on existing people's images to generate realistic images of people nonexistent. This technology is behind innovations like AI-generated art, chatbots that can write essays, and virtual musicians that compose original songs. This has been fueled by the digital age now that a vast amount of data is available to consume on the internet.
Generative AI subsumes using Deep Learning to create new content, like text, images, music, audio, and videos.
Key differences between Traditional AI and Generative AI
Generative AI applications are different from Traditional AI (Deep Learning based) applications — let's dive deeper into specific aspects.
What does it do?
- Generative AI: It can understand fundamental properties of data to generate realistic and novel content (like text, code, music, audio, video, data, etc.)
- Traditional AI: It can predict specific outcomes based on historical patterns in data for different use cases (e.g., predict user ratings for movies)
Where is it applied?
- Generative AI: It can be applied to usecases that have open-ended outcomes (e.g., answer complex questions, create net-new images, audio, video, and so on)
- Traditional AI: It can applied to use cases that have narrowly defined outcomes (e.g., detect fraud, play chess, recognize an anomaly in an image, and so on)
What data is used to train the model?
- Generative AI: It uses vast data harvested from the internet, along with a small quantity of use case-specific data
- Traditional AI: It uses large quantity of carefully curated/labeled data for specific use cases
How is it Delivered?
- Generative AI: It is delivered via more human interfaces (e.g., chat interfaces via apps and web browsers)
- Traditional AI: It is built into use case-specific applications (e.g., BI reports, dashboards, streaming services, e-commerce websites, etc.)
Who can train it for their use cases?
- Generative AI: Anyone with basic language skills
- Traditional AI: People with specialized knowledge and AI skills
How Generative AI works?
Now that we understand what Generative AI is, let's briefly learn about how it works. Here's an easy-to-digest workflow, followed by a textual explanation:
The process begins with data pre-processing, where raw data is cleaned, normalized, and prepared to ensure it is suitable for model training. This is followed by training the GenAI model, enabling it to learn patterns and relationships within the data. Following this, these special models use the learned patterns to generate new data probabilistically, simulating potential variations and outcomes. This step is where Generative AI models deviate from Traditional AI models since the latter are not trained to generate data. The final step of incorporating feedback involves human reviewers who assess the quality, relevance, accuracy, and overall alignment of the generated data with the intended goals. Based on the collected feedback, models are adjusted by fine-tuning the parameters, incorporating new training data, or altering their algorithms to better align with human expectations. This cyclical process ensures high-quality data generation through the Generative AI models.
Generative AI model analyzes vast amounts of data, looking for patterns and relationships, and then uses these insights to create fresh, new content that mimics the original dataset — perfecting the process through human feedback.
Now, you may ask: what are these Generative AI models? They are Deep Learning or Neural Network based models (see the Venn Diagram above for domain overlap) under the hood. These networks, inspired by the human brain, ingest vast amounts of data through layers of interconnected nodes (neurons), which then process and decipher patterns in it. These networks can then be used to generate new data following the same patterns — allowing us to create diverse content, from graphics and multimedia to text and even music. Although details are out of scope for this post, these are the three popular architectures for implementing Generative AI:
- Generative Adversarial Networks (GANs)
- Variational Autoencoders (VAEs)
- Transformers
Please feel free to comment if you want us to provide intuitive explanations of these architectures in our future posts.
Where Generative AI can fit in?
Generative AI can aid in generating fresh content, streamline operations, and deliver tailored experiences by leveraging advanced algorithms. Following are some key non-exhaustive ways Generative AI can provide value for you:
- Enhancing Creativity: Generative AI can offer fresh ideas and content that can trigger new creative dimensions in humans. We can use these tools as a way to blend existing ideas seamlessly or trigger processes to create new ones — relieving creative blocks.
- Personalization: Generative AI excels at creating personalized user experiences by taking unique user attributes into account. It can closely mimic the content that people like to engage with to produce personalized recommendations, which can potentially enhance user satisfaction.
- Improve Efficiency and Productivity: Generative AI can automate mundane and repetitive generation tasks like writing summaries, generating skeleton code, handling basic customer queries, and so on. This helps businesses to bootstrap new products quickly and streamline existing operations to improve efficiency and productivity.
- Innovating and Problem Solving: Generative AI can contribute to innovation by following a complex set of prompts/processes in a relatively short amount of time, that may be beyond human capability. This could prove invaluable in domains like drug discovery and climate modeling.
- Large-Scale Data Analysis: Generative AI can quickly skim through vast amounts of data and generate insights in an easy-to-digest manner. It is capable of uncovering patterns and trends that might be missed by traditional analysis, providing a competitive edge.
Due to diverse applications, Generative AI can be applied to content creation, art and design, healthcare, entertainment, education, product development, and customer service to name a few sectors.
Bonus: Categorization of Generative AI Models
Now that we know what is Generative AI, how it works, and where it can be applied, we want to provide a quick peek at different types of Generative AI models, based on the input data they can ingest and output data they generate. We may all know the popular ChatGPT; however, there are many more Generative AI models that can be categorized by the use cases they serve. For example, DALL-E and MidJourney generate stunning images from textual descriptions, while models like Claude and Gemini are tailored for natural language processing tasks such as summarization and content generation. Additionally, models like MusicLM specialize in creating music, and Codex is designed for generating and understanding code, showcasing the diverse applications of Generative AI across different domains. Here's a quick overview:
If any X-to-Y application mentioned above intrigues you, feel free to try out the tools we have highlighted. We may cover specific application areas of Generative AI in our future posts.
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