Many years ago, our smartphone’s predictive text feature made us wonder. Then Gmail made our lives easy with the Smart Reply feature which utilizes machine learning algorithms to provide us one one-sentence response. These basic innovations are examples of initial forms of generative AI. But, since then, with the advancements in generative AI we have come a long way.

The evolution of generative AI is a fascinating story. Let’s see how GenAI has evolved and transformed the landscape of AI −

Early AI Exploration (1950s – 1980)

1950s was the time when early ideas of AI emerged. In 1950, Alan Turing in his paper “Computer Machinery and Intelligence” explored the mathematics possibilities of AI and raised a question why machines can’t think like a human being.

Then in 1952, Christopher Strachey, a British computer scientist, created a program for the Manchester Mark 1 computer that generated a simulated love letter. This program was the first text-generated software.

In 1966, Joseph Weizenbaum, an MIT professor, creates the first chatbot named ELIZA. It was an early natural language processing program which simulates conversations with a psychotherapist.

In 1968, a Stanford university student Terry Winograd at MIT creates a natural language processing computer program named SHRDLU. It was actually a demonstration of a system capable of understanding and responding to commands in a restricted block world environment.

Michael Toy and Glenn Wichman developed a Unix-based video game called Rogue in 1980. It was one of the first games to implement procedural generation for dynamically generating new game levels.

Neural Network Resurgence (1980s – 2010)

In 1985, Judea Pearl, a renowned computer scientist and philosopher introduced Bayesian networks which are also known as belief networks of casual networks. Bayesian networks establish the modeling concept in Generative AI.

Michael Irwin Jordan, in 1986, with his publication “Serial order: A parallel distributed processing approach” lay the foundation for use of RNNs (Recurrent Neural Networks).

In 1989, Yann LeCun and Yosua Bengio demonstrate the use of CNN (Convolutional Neural Networks) for image recognition.

In the year 2003, Researchers from University of Montreal published a paper, “A Neural Probabilistic Language Model”. This paper suggests a technique for language modeling using feed-forward neural networks.

In 2006, Fei-Fei Li, a professor at Stanford University creates ImageNet database that provides the foundation for visual object recognition.

Deep Learning Dominance & Transformer Revolution (2010s – 2020)

In 2011, Apple released Siri, a text-to-speech voice based on deep learning technology.

In 2012, the AlexNet CNN architecture was introduced by Alex Krizhevsky. It was indeed an innovative approach to automatically train neural networks that take advantage of recent GPU advances.

Ian Goodfellow and his colleagues develops Generative Adversarial Networks (GANs) in the year 2014. In the same year, Max Welling and Diederick Kingma developed Variational Encoders (VAEs) to generate text, images, and videos.

In 2015, a group of researchers from Stanford University published a paper “Deep Unsupervised Learning using Nonequilibrium Thermodynamics”. They introduced a technique on diffusion model that provides a way to reverse engineer the process of adding noise to an image.

Google researchers, in the year 2017, introduce the concept of transformers. This technique automatically parses unlabeled text into large language models (LLMs).

In 2018, Google implemented transformers into BERT (Bidirectional Encoder Representations from Transformers). In the same year, GPT-1, a transformer-based language model was introduced by OpenAI.

Specialized Generative Models (2020s – Present)

In 2020, OpenAI released the third iteration of their Generative Pre-trained Transformer, i.e., GPT-3. It was one of the largest language models capable of generating human-like text.

Next year, in 2021, OpenAI launched Dall-E that can generate images from text prompts. On November 30, 2022, OpenAI unveiled the web preview of ChatGPT.

Open AI Unveiled GPT-4 in 2023. The AI company claims that “GPT-4 can solve challenging problems with greater accuracy, thanks to its broader general knowledge and advanced reasoning capabilities.” On August 20, 2023, OpenAI launched DALL-E3.

In March 2023, Google released the Bard chat service based on its LaMDA engine. But, on February 8, 2024, Google rebranded the Bard chatbot as Gemini.