An Ultimate History of Generative AI

An Ultimate History of Generative AI

Generative AI

Around 35% of all companies use artificial intelligence (AI) for their day-to-day business operations. Even though this technology has only scratched the surface of its potential, it’s already a part of the workflow for so many global companies out there.

Unlike other AI systems that operate under a strict set of rules, generative AI can create unique content from scratch without getting explicit instructions from a human operator.

The capabilities aren’t limited to text only; the tools can create visual graphics and audio.

The story of this technology is thrilling, so let’s take a look at the different steps that have culminated in the development of AI technology as we know it.

Where Does the Idea of Generative AI Originate?

Unlike popular belief, the theory and basic ideas of AI technology aren’t new–these were developed quite a while ago.

The concept of machine learning was introduced by scientists and researchers in the 1950s, and this branch of technology focused on using algorithms to create new data from scratch.

While the basis of AI has been out there for more than a century, advances were slow during the first few decades. Among the biggest challenges were:

  • Lack of computational power;
  • Lack of data resources;
  • And lack of technologies that could help researchers reduce the amount of manual labor.

It wasn’t until the late 1990s and 2000s that machine learning and artificial intelligence as a whole became available and grew in popularity.

The Early Days

The AI industry is now worth more than $50 billion, but the early days of this technology were uncertain.

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In the 1990s, researchers worked on defining the knowledge representation, knowledge acquisition, and rule-based reasoning concepts still used today.

During this same period, areas like neural networks also saw advancements, leading to innovative research on natural language processing.

After the turn of the century, generative AI started focusing more on information-driven techniques, like data mining or the analysis of huge data sets.

Not only this, but widespread internet adoption boosted this technology movement as community members could now share their approaches and findings with each other.

In the following decade and a half, researchers and scientists started focusing on neural networks once again while developing new yet necessary ideas like the ethics and regulations surrounding generative AI.

Big Milestones in Generative AI

From 2015 onwards, large breakthroughs that affected generative AI took place almost every year.

Let’s take a closer look.

2016 – WaveNet

A groundbreaking neural network architecture called WaveNet is presented to the world. The platform generated human-like speech, and neural networks were used to improve the quality of the output without human supervision.

2017 – Progressive GANs

NVIDIA proposes Progressive Generative Adversarial Networks (GANs), which were successfully used to create high-definition images with incredible realism.

2018 to 2020 – GPT, GPT-2, and GPT-3

In 2018, OpenAI published an article about Generative Pretrained Transformer (GPT) and introduced GPT-1, its best language model up to that date. GPT-2 was promptly introduced in 2019. Its follow-up, GPT-3, was released in 2020.

2022 to Present – DALL-E, ChatGPT, and GPT-4

OpenAI released DALL-E and ChatGPT in 2022. These two models are designed to help users generate images and text content using artificial intelligence.

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In 2023, OpenAI presented the most comprehensive iteration of their platform, which is called GPT-4, which brings a deeper understanding of context, among other benefits.

Other platforms also exist, like Google’s Bard. This AI platform, however, isn’t available everywhere unless you mask your connection.

You may be using a VPN and Wi-Fi to access some geo-restricted platforms, but don’t forget to use other protective measures to keep your sensitive data safe. Those include strong passwords, 2FA, and others.

The Impact and Future of Generative AI

From financial services to human resources, generative AI has already changed many fields, and it has the potential to optimize many more verticals.

Let’s take a look at some of the industries that generative AI has already altered.


  • Healthcare: AI is already used to generate and edit high-quality healthcare images, like those obtained through MRI and CT scans.
  • Entertainment: Maybe the most celebrated application so far, generative AI is already used by show, film, and video game producers to automate soundtrack production.
  • Finance: Traders and software developers already use AI to improve the algorithms used to power automated trading platforms.
  • Marketing: Chatbots and other generative AI tools help increase customer engagement and provide a great experience even when your team isn’t available.

Talking about the future of AI, there are many variables that impact its growth and evolution. We can expect to see more advancements in generative AI, as this technology is expected to grow in popularity over the next two decades.

In Conclusion

The very basics of AI that define the technology today have existed for over one hundred years.

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It’s crucial to learn the history of Generative AI to understand where it takes its roots from and to know how to apply it to your unique business needs.

While AI evolved slowly in its early days, the milestones that propel AI are occurring more and more often. WaveNet, Progressive GANs, and all the versions of GPT have and will continue to contribute individually to generative AI.

As long as you know the steps that led to the AI boom, you’ll be able to understand the different platforms, their purposes, and the features they give you access to.


Featured image provided by Pixabay; Pexels; Thanks!


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