The Underlying Science of Generative AI: Demystifying Deep Learning Techniques

Last updated by Nahush Gowda on Sep 02, 2024 at 12:05 PM | Reading time: 11 minutes

If you’ve been scrolling through social media lately, you’ve probably seen some mind-boggling art, realistic chatbots, or maybe even a couple of songs generated by artificial intelligence. We can thank the surge in large language models and the growing field of generative AI for this. But what is generative AI? How does it even work? And why should you care? Don’t worry; we’re about to break down the science of generative AI in a clear way.

What Is Generative AI?

Before we get into the science of generative AI, let’s start with some basics. Generative AI refers to machines that are capable of creating: be it writing a story, painting a picture, composing a piece of music, or even inventing a new recipe. With generative AI, the content created is often on par with what a human can do unlike other forms of AI which may simply be trained to recognize a cat in an image or predict tomorrow’s weather.

You may have heard about GPT-4, ChatGPT, or Bard. These are all large language models, a specific type of generative AI that’s been in the news recently because it can write essays, and poetry, and hold conversations that feel eerily human-like. But generative AI is not limited to only text; it’s a whole research field that includes image generation, music composition, and much more.

Also read: Gen AI for Beginners: Understanding its Basics

Science of Generative AI: What Are Its Types?

Not all generative AI is the same. There are different types, each with its weaknesses, strengths, applications, etc. Here are the different types of generative AI.

Type of Generative AI	Description	Examples
Generative Adversarial Networks (GANs)	Uses two neural networks competing against each other to generate ultra-realistic images, videos, and more.	Deepfake technology, AI-generated art
Variational Autoencoders (VAEs)	Focuses on generating new data similar to a given dataset by learning the distribution of data.	Image generation, anomaly detection
Transformers (Large Language Models)	Uses attention mechanisms to understand and generate human-like text.	GPT-4, ChatGPT, Bard, AI-driven chatbots
Recurrent Neural Networks (RNNs)	Good at sequence prediction. Also good at generating text which seems like it could have been in the training data.	Text generation, language translation

Each of these types has its own way of generating content, but all of them use deep learning methodologies to generate new data.

Also read: Applied GenAI Explained, Benefits, Examples

The Underlying Science of Generative AI: How Does Generative AI Work?

Now let's dive in and see how generative AI works. If you ever wanted to know what happens under the hood when an AI writes a poem or creates an image, this is the section for you.

Underlying Science of Generative AI: Neural Networks

Underneath the hood, generative AI is based on a kind of neural network called the “generative model,” which is designed to pass data through multiple layers of nodes or neurons, similar to neurons in the human brain. Each layer of neurons performs mathematical functions on the data it receives and passes its answer along to other nodes in the next layer. In this way, each subsequent layer can make more abstract calculations based on the calculations of the previous one(s).

Underlying Science of Generative AI: Deep Learning

In today's context, the term 'Deep Learning' refers to neural networks that are deep or have multiple layers of neurons in the network. This is only one specific type of structure called deep neural networks, that find their application within generative AI scenarios. The principle of capacity growth with the number of layers is that a model with more layers can represent and recognize more complex data patterns.

Underlying Science of Generative AI: Data Feeding

Generative AI models are given a set of data, like thousands of images, texts or even pieces of music. They learn its patterns and structure. In the end, they can generate new data that is similar to the original input data. For instance, a massive language model such as GPT-3 had been fed with an enormous quantity of text data. It nailed the grammar, syntax, and even a bit of common sense. Thus, when you ask it to write a story, it is able to produce sentences not only logically but creatively also.

Underlying Science of Generative AI: GANs

Let’s dive a bit into arguably the most fascinating type of generative AI: Generative Adversarial Networks (GANs). GANs involve two neural networks pitted against each other: the generator and the discriminator.

Generator: Produces synthetic data (e.g. image, text) in order to mislead the discriminator that it is real.
Discriminator: The discriminator tries to decide whether the data it is observing is real or generated.

As time goes by, the generator improves in generating artificial data that look realistic and the discriminator improves in spotting fakes. This adversarial process is the one that leads to GANs having the ability to generate extremely real images, voices, or even videos.

Underlying Science of Generative AI: Transformers

Transformers are what powers large language models at a high level. They have this concept called an "attention mechanism." This means they can focus on different parts of the input data (like words in a sentence) as they produce new output. This is why transformers really excel at tasks that require context, like translating languages or having a dialogue.

Here’s a simplified diagram to help visualize how a transformer works.

Step	Description
1	The model takes in a sequence of words (or tokens).
2	This mechanism decides which parts of the sequence should be taken into account at each step in order to predict the next word.
3	The model uses this information for the purpose of determining the next word in the sequence.
4	The process is repeated, new word after new word, until the output string desired by the human operator (a sentence, a paragraph, or an article) exists.

Science of Generative AI: What Are the Applications?

So, now that we know the science of generative AI, what can it do? The applications are nearly endless, but here are a few areas where generative AI is making waves:

Application Area	Description	Examples
Art and Creativity	Art, music, and literature generated by AI go beyond what people can imagine in their creative works.	AI-generated paintings, AI-composed music
Content Creation	Automating articles, blog posts, and even marketing copy creation.	AI-written articles, social media posts, ad copy
Healthcare	Help in creating new drugs or letting us know how proteins will fold which shortens the duration of the discovery process.	Drug discovery, protein structure prediction
Gaming	Procedural content generation for creating infinite amounts of unique game levels and characters.	AI-generated game levels, NPC dialogues
Customer Service	Bots and virtual agents who can chat with customers in a way that is natural, and human-like.	AI-driven customer support, virtual assistants
Fashion and Design	AI tools that can generate new designs of clothes, patterns, or even whole fashion collections.	AI-designed fashion collections, interior design

The flexibility of the science of generative AI is such that it’s not just about the automation of tasks, it’s the creation of something new altogether.

Science of Generative AI: What Are the Benefits?

Generative AI is a revolution in many fields because of the benefits it provides. Let us outline the most important ones:

1. Creativity Unleashed

Generative AI can unlock the critical potential of an artist’s creative expression by iterating on previous work. It’s like a thought partner that provides new ideas based on your unique style and previous body of work.

2. Efficiency and Productivity

In the content creation space, generative AI can take over mundane and repetitive tasks, so creatives have more time for strategic thinking. Think of an AI that writes blog posts, social media updates, or even responds to basic customer queries.

3. Innovation in Healthcare

Generative AI is enabling breakthroughs in areas like pharmaceuticals by designing new drugs and predicting their properties, and we hope one day this will also be able to predict the structure of all proteins well enough to understand diseases and find cures.

4. Personalization

Generative AI has the ability to develop content that is individualized to each customer, such as personalized marketing messages or even new and unique products like personalized fashion designs, creating a level of personality that drives consumer preference and loyalty.

5. Cost-Effective Solutions

By automating some tasks and processes, generative AI can reduce the costs of businesses. An AI-generated marketing copy could mean you need fewer human writers on your books, reducing the costs associated with paying their salary.

Difference Between Generative AI vs. AI

It’s important to get a sense of how generative AI is different from the AI you’re used to because those differences are what make the capabilities and potential of generative models so interesting.

Traditional AI

Traditional AI is typically focused on analyzing data and making predictions or decisions based on that data. This type of AI is often used in applications like image recognition, fraud detection, and recommendation systems. Traditional AI models are trained to recognize patterns in data and make accurate predictions or classifications based on those patterns.

Generative AI

On the contrary, generative AI is focused on creating new content in particular. Instead of just analyzing and predicting, generative AI models do something different – they understand the patterns in data and use this understanding to produce new, original content. It is a creation that differentiates generative AI from the traditional type and gives rise to novel opportunities for innovation and creativity.

A Side-by-Side Comparison

To better understand the differences between generative AI and traditional AI, here’s a comparison:

Aspect	Generative AI	Traditional AI
Focus	Creating new content (text, images, music, etc.)	Analyzing existing data to make predictions or decisions.
Example	Generating a painting, writing a story, and composing music.	Recognizing faces in photos, predicting stock prices, and diagnosing diseases.
Complexity	A more simplistic form of ‘rewriting’ is used to generate content that makes sense.	Simple rule-based systems to complex deep learning models.
Data Dependency	Requires large datasets to learn how to generate new content.	Also requires data but focuses more on analyzing patterns within that data.
Real-World Impact	Expands creative possibilities and can automate content creation.	Optimizes processes, improves decision-making, and enhances efficiency.

Generative AI and traditional AI have different objectives but can be very powerful when combined. For example, a traditional AI model could analyze customer likes and dislikes, while a generative AI model would generate product recommendations or personalized content. By integrating those models we can bring efficiency and innovation in various domains.

Also read: Mastering Generative AI: Your Roadmap to Getting Started

Wrapping Up

Generative AI is here, and it’s not just changing industries. It’s challenging our own idea of what it means to be creative. Whether it’s the onset of large language models, the incredible potential of GANs and transformers, or just the underlying science of generative AI, there is a whole lot of exciting (and intimidating) machinery beneath that hood.

But the end result is simple: teaching machines to create. Whether you’re a tech-head, a genius artist, or just plain old curious about what’s coming next in our world- it’s important that we all begin to become familiar with the science of generative AI, especially since we may be seeing a lot more of it.

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FAQs: Science of Generative AI

1. What is the difference between generative AI and traditional AI?

Generative AI creates new content, like art or text, while traditional AI focuses on analyzing existing data to make predictions or decisions.

2. What are some popular examples of generative AI?

Popular examples include GPT-3 (a large language model), DALL-E (an AI that generates images from text prompts), and DeepArt (an AI that creates art).

3. How does generative AI learn to create new content?

Generative AI models are trained on large datasets. They learn patterns in the data and use that knowledge to generate new, similar content.

4. Can generative AI replace human creativity?

Generative AI is a tool that can enhance human creativity, but it doesn’t replace the unique perspectives and emotions that humans bring to creative work.

5. Is generative AI safe to use?

While generative AI is powerful, it’s important to use it responsibly. Concerns like deep fakes and biased content generation highlight the need for ethical considerations.

Related Reads:

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Nahush Gowda

Machine Learning

Generative AI

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The Underlying Science of Generative AI: Demystifying Deep Learning Techniques

Contents

Nahush Gowda

Attend our Free Webinar on How to Nail Your Next Technical Interview

How to Nail your next Technical Interview

Worried About Failing Tech Interviews?

Tools to Enhance Full Stack Development with AI

The Future of Data Science: Emerging Trends and Opportunities

Data-Driven Decision Making: Your Roadmap to Business Success

The Business Impact of Machine Learning: Real-world Case Studies

What is OpenAI? Everything You Need to Know

Mock Interviews for Generative AI: Essential Practice to Land Top AI Roles

Top Python Scripting Interview Questions and Answers You Should Practice

Complex SQL Interview Questions for Interview Preparation

Zoox Software Engineer Interview Questions to Crack Your Tech Interview

Rubrik Interview Questions for Software Engineers

Top Advanced SQL Interview Questions and Answers

Twilio Interview Questions

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