Generative AI for Beginners: Part 4 — Introduction to Generative AI
This blog is part of the series Generative AI for Beginners, where we are learning basics of Generative AI, one simple step at a time.
To make it easy to grasp, I have divided the entire series in small parts. Each blog requires maximum 15–20 minutes to learn. After finishing the series, you will get a clear idea on fundamentals of Generative AI and its various aspects.
Part 2 — Understanding Machine Learning
Part 3 — Deep Learning: The Fundamental Pillar of Generative AI Advancement
Part 4 — Introduction to Generative AI [current blog]
Part 5 — What is Large Language Model (LLM)?
Part 6 — Prompt Engineering: The Art of Communicating with AI
Part 7 — Ethical Considerations in Generative AI
Part 8 — Challenges and Limitations in Generative AI
This is the 4th blog in this series where we will explore what Generative AI is and it’s several aspects.
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What is Generative AI?
Generative AI is:
- A type of artificial intelligence
- that can create new things, for example artwork, music, or even realistic images.
- without being explicitly told what to create
While traditional AI focuses on specific tasks or solving a problem, Generative AI is distinguished by its ability to exhibit creativity similar to human creativity. Generative AI is capable of generating new, unique content, ideas, or solutions as we human do.
Let’s understand it better with an example!
Imagine I asked you to draw an animal you have never seen before. You need to use your imagination and draw a brand-new animal the world has never seen.
Since we human have imaginative power and creativity, you will be able to do that. Maybe you will draw an animal that has the body of a lion, face of a cow and the wings of a butterfly.
Now, what if a computer program could create new things all by itself! It can create new things, for example artwork, music, or even realistic images, without being explicitly told what to create.
The computer program has been given lots of pictures of lions, cow and butterflies. Now, with this knowledge, it can draw a completely new animal, say a “lion-cow-butterfly” combination. It doesn’t copy any existing image; instead, it uses its understanding of what makes lion, cow, and butterfly unique to create something entirely new something as below.
This is Generative AI — A machine (or computer) which has imagination and creativity to draw pictures, tell stories, or even make up new games without anyone showing it how.
Where Does Generative AI Fits into AI Hierarchy?
Generative AI is a subset of Deep Learning. Below diagram shows the relation between AI, Machine Learning, Deep Learning and Generative AI.
Generative AI leverages machine learning techniques, particularly deep learning and neural networks.
The main differentiator of Generative AI is the ability to generate new content.
AI, machine learning and even deep learning is mostly limited to predictive models. These are mainly used to observe and classify patterns in content or predict a new pattern or content. For example, a classic machine learning use-case is to identify image of a cat out of several given images or classify animals in different clusters based on various properties.
Generative AI is a breakthrough, because it has the ability to do something only humans were supposed to do — create an image of a cat or create an image of a totally new animal from it’s creativity.
The following image shows the evolution of AI with time. The evolution of AI from traditional rule-based systems to Generative AI has been driven by advancements in learning algorithms, computational power, and access to vast amounts of data.
Generative Models
Generative AI uses different types of machine learning models, called Generative Models.
The generative models:
- learns the underlying set of data and generates new data the closely mimics the original data
- are mainly used to create new content, such as images, text, or even music which looks exactly the same as what might be created by humans
- Usages unsupervised learning approach
Most common generative models are:
- Variational Autoencoders (VAEs),
- Generative Adversarial Networks (GANs)
- Limited Boltzmann Machines (RBMs)
- Transformer-based Language Models
In the next chapter, we will learn more about generative models.
Usages of Generative AI in Real-life
Here are some examples of how generative AI is being used to create real-life applications:
Text Generation
Most of us have used ChatGPT which is based on Generative AI. Similar to ChatGPT Generative AI based tools can be used to generate new content such as articles, reports, poetry, stories or any other text-based content.
One of the most common uses of generative AI is to build Virtual Assistants and Chatbots. Generative models are used to build advance chatbots which can interact with users mimicking human interaction.
Image Generation
Generative AI tools are used to generate new pictures even creative ones using various generative models. These models can learn from large sets of images and generate new unique images based on trained data. These models can even generate images with creativity based on input prompts similar to content generated by humans. There are various ways this can be used in real-life applications such as image-to-image translation, text-to-image translation, photograph editing, face generation, image quality enhancement etc.
One of the most common tools which usages generative AI to create realistic images and art is DALL-E, developed by OpenAI. It is a text-to-image model, which usages deep learning to generate digital images from natural language descriptions.
Video Generation
Generative models can be used to create whole videos from scratch. It stitches together scenes, characters, and actions to make a story. These videos can be used for entertainment, advertisements, or even training simulations. Video game development is one field which is heavily using generative AI.
Some generative models can be used to create new videos by learning from existing videos. This can be used for video prediction if an existing video such as security clip is damaged.
Voice Generation
Generative AI can also mimic voices or generate a whole new voice! It can learn how people talk by analysing audio data, and then generate voice in same style or create entirely new voices.
This is useful for making virtual assistants or audiobooks sound more natural.
Healthcare Applications
Generative AI models can be used to generate synthetic data samples that resemble real data. This can be very useful in medical field, where sometimes collecting real-world data is expensive or limited. For example, generative AI can be used to generating synthetic patient data for research purposes.
Drug Discovery
Generative AI is being used in drug discovery to generate new molecular structures with desired properties. This helps accelerate the process of drug development by exploring vast chemical spaces and identifying promising drug candidates.
Gaming
Generative AI has truly changed the world of gaming. It is increasingly being used in the gaming industry to accelerate game production and create unique experiences.
It helps game developers make games more exciting and immersive by creating entire worlds, characters, and stories.
Generative AI can also be used to make virtual worlds more realistic. It can be used to create unique creatures and characters, finetune each character’s personality and traits, making the game feel alive and full of surprises.
Art Generation
This is one major usage that distinguish generative AI from regular AI. Generative AI has the capability of creative thinking like we human do. Various generative models are used in generative artistic artifacts such as paintings, poetries, stories, and other multimedia-based arts.
Software Development
Generative AI has totally changed the way we write code and build software. With Generative AI tools such as GitHub Copilot, ChatGPT, AlphaCode, we can write code much faster with fine details.
Generative AI tools can assist developers by generating code snippets, enhancing software testing efficiency by identifying more defects, and suggesting optimal solutions to coding challenges. This results in faster development cycles and higher code quality, ultimately leading to improved software products and enhanced user experiences.
Finance
Financial institutions are using generative AI to analyse market trends, forecast stock movements with a high accuracy rate, and refine trading strategies. The technology also helps us having better risk assessment, fraud detection, and portfolio optimization, leading to increased efficiency, reduced costs, more profitability and better investment choices.
Example of Some Popular Generative AI Tools
In previous section, we talked about various use-cases of generative AI. Now, let’s have a look into some of popular generative AI tools available currently.
ChatGPT
ChatGPT is a conversational AI developed by OpenAI. It is designed to engage in natural language conversations with users, providing responses that are contextually relevant and coherent.
ChatGPT works by processing input text and generating responses based on the patterns and relationships it has learned from vast amounts of training data. It usages deep learning techniques, specifically transformers, which allow it to understand and generate human-like text.
GPT (Generative Pre-trained Transformer)
GPT is a transformer-based large language model, developed by OpenAI. This is the engine behind ChatGPT.
The free version of ChatGPT is based on GPT 3.5, while the more advanced GPT-4 based version, is provided to paid subscribers under the commercial name “ChatGPT Plus”.
AlphaCode
AlphaCode is a transformer-based language mode, developed by DeepMind. It is an AI-powered coding engine that generates computer programs. AlphaCode is more complex than many existing language models, with 41.4 billion parameters.
The tool leverages deep learning algorithms to analyse huge amounts of code and learn from patterns, enabling it to generate optimized code solutions. It supports a wide range of programming languages, including C#, Ruby, Python, Java, C++, and more.
GitHub Copilot
GitHub Copilot is an AI-powered code completion tool developed by GitHub in collaboration with OpenAI. It integrates directly into code editors like Visual Studio Code and provides real-time suggestions and completions for code as developers write.
It’s designed to assist developers by generating code snippets, suggesting entire lines or blocks of code, and providing contextual documentation. GitHub Copilot supports multiple programming languages such as Python, JavaScript, Java, C++, and more.
Bard
Bard is a conversational Generative AI chatbot developed by Google, as a direct response to the swift rise of OpenAI’s ChatGPT. Bard was initially based on LaMDA, a transformer-based model. Later it got upgraded to other models such as PaLM and Gemini.
Microsoft Copilot
Microsoft Copilot was initially launched by Microsoft in 2023 as an AI-powered assistant that can help to browse the web. Later it got rebranded to Microsoft Copilot.
Microsoft Copilot can be used to request summaries of articles, books, news etc., general text and images, reformat text, update images etc.
DALL-E
Developed by OpenAI, DALL-E (other versions are DALL-E2 and DALL-E3) is one of the best generative AI tools to generate images. It uses deep learning algorithms to generate images from texts.
StyleGAN
StyleGAN, developed by NVIDIA, is a generative model of type GAN (Generative Adversarial Network), which is used to generate high-quality synthetic images.
StyleGAN is extremely good in creation of realistic images of human faces and other visual content. It can generate images of human faces with a high degree of control over specific visual features such as facial attributes, pose, and background.
Below are some images generated by StyleGAN that looks like a real person. There is an interesting site https://this-person-does-not-exist.com which demonstrates how StyleGAN can be used to generate human faces which actually don’t exists.
Summary
In this blog, we got a clear idea on what generative AI is and how it is different from other AI types. We also touched upon various real-life use-cases of generative AI and some popular generative AI tools available.
If you still have any query, please let me know in comment or get in touch with me in LinkedIn!
Next Blog: Generative AI for Beginners: Part 5 — What is Large Language Model (LLM)?
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