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The history of Generative AI (GenAI)

Date
August 28, 2023
Hot topics 🔥
AI & Data Science
Contributor
David Roman
The history of Generative AI (GenAI)

We’re willing to bet you’re already very familiar with ChatGPT and DALL-E. Who isn’t? These popular GenAI tools have shaken up our digital society like nothing before and turned scores of internet users into overnight digital creators.

But what is GenAI?

Generative AI, also known as GenAI, is a subset of artificial intelligence (AI) that is reshaping our collaboration with technology by the minute. Rather than simply recognising and following predetermined patterns and rules in existing data, GenAI possesses the unique ability to create.

Without explicit instructions, GenAI can generate new content such as images, music, and text by simply learning from datasets – transforming AI from executor to creator. This interesting branch of AI has significant potential to enhance creativity across various industries – and even automate it. 

Let’s take a look at the fascinating history of Generative AI, its pivotal developments, and its potential to see just how much of an impact this advanced technology is poised to have on humanity.

The birth of Generative AI

The genesis of Generative AI can be traced back to the mid-1950s when the concepts of artificial intelligence and machine learning (ML) were beginning to take shape. 

IT pioneers like Alan Turing and John McCarthy played a pivotal role in laying the foundation for GenAI when they proposed early models of computation based on the idea that machines could one day mimic human intelligence.

Early versions of GenAI were the Hidden Markov Models (HMMs) and Gaussian Mixture Models (GMMs). These statistical models were designed to generate new sequences of datasets based on manual input, such as speed and time.

Further developments resulted in the creation of models like Restricted Boltzmann Machines (RBMs) and Variational Autoencoders (VAEs) which laid the foundation for more sophisticated generative models.

The evolution of Generative AI

It wasn’t until the 2000s that GenAI began to gain momentum thanks to the advancements in machine learning and deep learning (DL) to create neural networks – interconnected layers of “neurons” that process and learn from data like the human brain. 

Trained to recognise patterns in datasets, neural networks are able to make predictions and decisions without being programmed to do so. 

But the creative power of GenAI is thanks to a specific type of neural network developed in 2014 by Ian Goodfellow and colleagues called a Generative Adversarial Network (GAN). GANs revolutionised image generation by combining two neural networks into the architecture – a generator and a discriminator – which compete to improve the quality of the generated data.

Added to this, further advancements such as Transformers (using natural language processing (NPL)), Variational Autoencoders (VAEs), and Recurrent Neural Networks (RNNs) began to demonstrate AI’s ability to generate new, creative content.

Significant implementations of GenAI

Generative AI has been used for many applications across various domains, demonstrating its powerful capabilities for generating creative and even life-like content. 

GenAI image style transfer tools such as DeepArt and DeepDream have demonstrated the potential of generative AI tools by making users capable of creating amazing artworks out of ordinary images.

Most recently, the Generative Pre-trained Transformer (GPT) series, particularly ChatGPT-3 has taken the world by an electrifying storm for its remarkable ability to generate human-like text from simple prompts, igniting global imagination about the creative potential of AI.

OpenAI, the company behind the ChatGPT series and co-funded by Elon Musk, has played a major role in advancing the capabilities and adoption of Generative AI. GPT-1, GPT-2, and GPT-3 displayed incredible language generation capabilities but none have come close to GPT-4. The latest release is more powerful and sophisticated than its predecessors and promises even better creative potential.

The ethical considerations of GenAI

As Generative AI inevitably advances, we must be aware of its current ethical and legal concerns in order to quell them from evolving together with the technology.

The AI control problem highlights the increasingly complex challenge of ensuring artificial intelligence continues to align with humanity’s values and work for us, not against us. The human biases hidden in the algorithms which power these pervasive technologies are concerning now – what will happen when the tech advances, furthering the already muddy waters of technology compliance?  

Another challenge to consider is the black box problem which explains the difficulty to understand how some generative models arrive at their outputs. How do we know if the information we receive is accurate and devoid of bias or discrimination?

This leads to legal concerns about the potential for GenAI models to generate harmful or misleading content, which raises questions about responsible AI usage and accountability. There are serious concerns surrounding the use of this technology in the political space, where viewers are to duped by deep fake technology showing political figures expressing political views that are not theirs. This has immense implications regarding the impact this can have on shaping the political sentiments of voters via fake content. 

Additionally, GenAI poses intellectual property and copyright issues. Currently, the legal implications of GenAI content generated by existing content from original creatives are unclear. In particular, regarding copyright infringement, unlicensed content in training data, and ownership. As these issues are still in their infancy, lawmakers are still in the process of establishing clear protocols on how to apply existing intellectual property laws to this new trend. 

The future of GenAI

The future of both Generative AI and our society appears to be more inextricably linked with each new release. With the overwhelming benefits these tools continue to provide major industries and sectors around the world, the future certainly holds great promise for both. 

Research in Generative AI will most certainly find new ways to develop more accurate and diverse models that could reshape our approach to creative content generation. These tools could burst open exciting possibilities in creative and conceptual areas like music, design, art, and film. 

However, a balance between innovation and regulation must be defined to ensure that like all artificial intelligence, GeneAI is used in ethical and responsible ways.

David Roman

David is one our marketing gurus. He loves working with content but has a good eye for marketing analytics as well. Creativity is what drives him, photography being one of his passions.

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