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Timeline of Generative AI
Generative AI has been getting better and better over the years. Scientists have been working hard to improve
the technology using things like neural networks and deep learning. They've been trying out different ideas and
making big discoveries in how to make it work better. Now, Generative AI can do lots of cool stuff like writing
text, making pictures, and creating new things. It's been a long process of learning and making things better, but
now we can see all the amazing things it can do!
Timeline of Generative AI:
1956 John McCarthy coined the term "Artificial Intelligence"
1958 Frank Rosenblatt proposed the perceptron, the world's first neural network
1964 First functioning generative AI—ELIZA chatbot was created
RNNs are designed to recognise patterns in sequences of data by maintaining a memory of
1982
previous inputs
Complex architecture LSTM—allows efficient processing of long sequences of data and
1997
identifies patterns
2011 IBM’s Watson was introduced
2013 Variational autoencoders (VAE) were created
2014 Creation of Generative Adversarial Networks (GAN), which were a breakthrough in generative AI
Diffusion models were introduced, representing a novel approach to Generative Modelling,
2015
example: Tensorflow
2016 Google DeepMind's AlphaGo was introduced
2017 Deep learning architecture—transformer was proposed
2018 OpenAI introduced Generative Pre-trained Transformers (GPT)
2019 GPT2 was introduced
2020 GPT3 was introduced
2021 Open AI launched DALL-E, an AI platform designed to generate images from textual descriptions
Two notable AI image-generating tools, the open-source Stable Diffusion and the proprietary
2022
Midjourney were introduced and ChatGPT was introduced
OpenAI released GPT-4, an advanced version of its GPT series. Also Microsoft Copilot (previously
2023
Bing Chat), Google Gemini (previously Google Bard), Adobe Firefly, Meta Llama were introduced
Generative AI vs Conventional AI
Generative AI and Conventional AI represent two different approaches in the field of artificial intelligence. The
difference between them is given in the following table:
Generative AI Conventional AI
Goal Generative AI creates new content that Conventional AI analyses, processes, and
mimics the original content. This content classifies data. It works to improve the
includes images, text, music, or other forms accuracy, precision, recall, and speed within
of media. the scope of the defined task.
Introduction to Generative AI 201

