What is Generative AI: History, Examples, Limitations

Generative artificial intelligence, also called generative AI, is the driving force behind the widely known ChatGPT. In this article, we’ll learn how generative AI works, its history, its examples as well as its limitations.

What is Generative AI

Generative artificial intelligence empowers us to generate any text, images, audio, video easily. So what does generative AI mean?

At its core, generative AI refers to algorithms or models (like ChatGPT) that can generate content based on patterns trained from training data. Take ChatGPT for example. Rather than just analyzing data, ChatGPT, which has being trained on massive text datasets,can generate brand new poems, articles, or other text content autonomously. For now, images are also being integrated into ChatGPT 4 or above so that you could generate images from text prompts.

Beyond just content generation, generative AI reinvents everything from marketing campaigns to scientific discoveries. These models can rapidly generate logos, web pages, entertainment videos, or other novel outputs once trained on the appropriate datasets. Rather than replacing human creativity, they augment our abilities, and improve our working efficiency at unprecedented scale and speed.


How does Generative AI Work

The inner workings of the Generative AI are still very much a mystery, but we can peg back some layers to understand the basic mechanisms that power these creative algorithms. It all starts with a large amount of training data, from which it can learn various patterns, with relationships and meaning.

When generating, the AI refers back to these patterns in its training data to make predictions that are statistically right

For example, it can predict that "earth" in a sentence follows "blue" more often than "man." This explains how a rhyme can be made. The token is processed through a multi-layer neural network, where each layer interprets the data separately, and passes it on to the next layer.

This layered process allows the AI to combine the broad information it has learned with concepts it has never been directly trained with. So while the original texts never mention "Chinese Spring Festival gifts for children",  AI can link "Chinese Spring Festival," "gifts," and "children" to their context and thus generative AI seems to successfully assemble entirely new concepts.


History of Generative AI 

The Genesis of AI

The pursuit of intelligent machines began in the 1940s-50s with pioneers like Alan Turing exploring machine intelligence and proposing his famous Turing test. The term "artificial intelligence" emerged at the 1956 Dartmouth workshop that sparked AI as an academic field.

The Early Decades

Progress continued in the 1960s-70s, including Arthur Samuel's 1959 machine learning checkers program. Frank Rosenblatt pioneered early neural networks called perceptrons. But funding declined in the 1970s, initiating the first "AI winter" of reduced innovation.

Revival of Neural Networks 

Key innovations in the 1980s-90s revived neural networks through multilayer models and backpropagation learning. But limitations led to a second AI winter in the 1990s. Persistent researchers like Hinton kept advancing neural nets.

The Deep Learning Revolution

By the 2000s, explosive growth in data and computing power enabled transformative deep neural networks in fields like computer vision. The rise of deep learning unleashed an explosion of AI capabilities.

The Birth of Generative AI

In the 1960s, Joseph Weizenbaum created ELIZA, an early natural language chatbot and landmark generative AI system. Ian Goodfellow's invention of generative adversarial networks in 2014 for generating new images propelled the field forward.


Generative AI Timeline

Generative AI Timeline

1956 - Introduction of AI as a science

1958 - First neural network called the perceptron

1964 - ELIZA chatbot, early generative AI system

1982 - Creation of recurrent neural networks (RNNs)

1997 - Long Short-Term Memory (LSTM) model for processing long data

2013 - Variational autoencoders (VAEs) generative model

2014 - Generative adversarial networks (GANs) for creating new images

2015 - Introduction of diffusion models for data generation

2017 - Transformer architecture for deep learning

2018 - Generative Pre-trained Transformer (GPT) models

2021 - DALL-E platform for generating photorealistic images

2022 - Stable Diffusion and Midjourney for image creation

2023 - Release of GPT-4 for long-form text generation and image generation from prompts is being integrated into ChatGPT-4


Recent Breakthroughs

1. GPT natural language models like GPT-4 in 2023 for generating 25,000 word texts

2. Stable Diffusion in 2022 for generating images from noise

3. Midjourney in 2022 for artistic image generation

4. DALL-E in 2021 for creating photorealistic images from text


Limitations of Generative AI 

Generative AI has emerged as a revolutionary technology with the potential to create new data, and revolutionize industries like computer vision or natural language processing. However, there are also some limitations. Now let's explore the various constraints faced by generative AI.

Limitations of Generative AI 

1. Limited Training and Range of Outputs: Generative AI heavily relies on pre-existing data to generate new content. Consequently, the scope of its outputs is limited by the depth and diversity of its training dataset. For example, if an AI model is trained solely on images of standard bicycles, it would struggle to generate images of unconventional bike designs.

2. Lack of Creativity : AI lacks the ability to generate new ideas or come up with innovative solutions. This constraint arises from AI's reliance on pre-existing rules and data. While it can generate multiple solutions to a puzzle like a Rubik's cube based on predetermined principles, it cannot propose completely novel approaches, such as smashing the cube and reassembling it.

3. Quality of Generated Outputs: Generative AI systems may produce outputs that contain errors, artifacts, or inaccuracies due to various factors like insufficient training data, poor training, or complex models. This can compromise the reliability and credibility of the generated content.

4. Control Over Output content: It can be challenging to exert precise control over the specific content of the output generated by generative AI. 

5. Computational Requirements: Generative AI demands substantial computational power and extensive training data, making it resource-intensive and time-consuming. This poses a barrier for organizations with limited resources or budgets to access the technology fully.

6. Bias and Fairness: Generative AI models can unintentionally propagate biases present in the training data, potentially leading to discriminatory or unfair outcomes. Bias detection and mitigation mechanisms are necessary to address this limitation effectively.

7. Lack of transparency: Generative AI models often lack transparency, making it challenging to comprehend the underlying processes and justifications for their predictions.

8. Safety and Security: Generative AI has the potential to generate realistic and deceptive fake content, including images, videos, and text. This raises concerns about the spread of misinformation, propaganda, and the need for robust safety measures to prevent malicious use.


While generative AI has demonstrated significant advancements, it is essential to recognize its limitations. The restricted range of outputs, lack of creativity, quality control challenges, computational requirements, potential bias, lack of transparency, and safety concerns must be addressed to ensure its reliability and ethical use. By acknowledging and addressing these limitations, we can maximize the potential benefits of generative AI while mitigating its risks.


Generative AI Examples and Tools

Applications of Generative AI include the following:

Content generation, music creation, 3D modeling, video creation and editing, game development, Chatbots and virtual assistants, image creation and editing, code generation, art creation, and voice generation etc..

Content Generation

Generative AI is revolutionizing content creation by generating human-like content. Popular examples include ChatGPT, Claude, and Google Bard. These advanced language models have transformed the field of content generation.

Generative AI Example - ChatGPT

Music Creation

Generative AI is also being used to create original music for various projects. Examples like Soundful, Amper Music, and AIVA offer unique tunes to help artists overcome creative blocks.

3D Modeling

Generative AI, particularly using GANs, is widely used in 3D modeling. It allows for the creation of digital models that closely resemble physical objects in texture, size, and shape. Alpha3D and 3DFY.ai are notable examples in this field.

Video Generating and Editing

Generative AI is revolutionizing video creation and editing by utilizing image and music generation algorithms as well as text generation models to create storylines. Tools like Descript, Xpression, and Synthesia offer flexible solutions for translating ideas into actual videos.

Game Development

Generative AI is helping game developers create diverse game content, including levels, objects, characters, and narratives. Unity Machine Learning Agents and Charisma AI are examples of generative AI use cases in the gaming field.

Chatbots and Virtual Assistants

Generative AI is used to develop chatbots and virtual assistants that efficiently provide relevant information and perform tasks. Siri and Google Assistant are examples of generative AI tools in this field.

Image Generation and Editing

Generative AI is transforming text into images with different settings, subjects, and styles, widely used in industries like education, media, and advertising.

Code Generation

Generative AI supports code generation for developers, providing code suggestions, identifying and resolving bugs, and promoting readability and consistency. Copilot, and Codex are examples of generative AI in code generation.

Art Creation

Generative AI allows for the creation of new and original artwork without human intervention. DALL-E 3 and Stable Diffusion are examples of generative AI tools for art creation.

Generative AI example - Dall-E

Voice Generation

Generative AI can create realistic audio speech,widely used in advertising, education, and marketing. Replica Studios, Lovo, and Synthesys are examples of generative AI for voice generation.


Final Thoughts

In this article, we've explained how generative AI works, its history,examples, and limitations. The underlying process involves breaking data into tokens, embedding information into vectors, and processing through layers in neural networks to make statistically likely predictions.

Leading applications today range from content generation, art and music generation, 3D modeling, video editing, and more. But there are still limitations around quality control, transparency, potential bias and other risks requiring ongoing improvements. Overall, generative AI is an extraordinary leap in using algorithms to enhance human creativity and productivity across industries,despite various constraints.