Generative AI has been a hot topic of every tech discussion recently. It’s getting more interesting day by day so today let’s deep dive into the history of Generative AI and how with time it has evolved.
Generative AI is a branch of Artificial Intelligence that is getting popular. This can create content of a variety ranging from images, music, text, and even 3D models as well. But It’s not very new if we talk about its origin. So let’s deep dive into the history of generative AI. (If you are interested in reading this that means you are a tech lover, Hats off to you.).
Early Days of Generative AI
The root of generative AI can be found in the dawn of AI in the 1950’s. Those early attempts were rule-based systems for example, a system might use grammar rules to write a sentence or an essay. However, its use case was very limited in the early days.
The expert-based and knowledge-based system was produced in this time era of 1950-1980. This was the era of the first attempt at Generative AI even though with very little creativity and originality.
The Rise Of Machine Learning
In 1990-2000 the focus of AI was shifted toward Machine Learning. Where models were being trained on data rather than programming and this opened a lot of doors for generative AI. Models like Markov chains were introduced in this specific era. You wanna know the basics of the Markov rule:
“Markov process is a stochastic model describing a sequence of possible events in which the probability of each event depends only on the state attained in the previous event”
Deep Learning Revoloution
Before Deep Learning was just a minor gear shift in the evolution of Generative AI but the real shift was after deep learning. Deep learning algorithms, inspired by the human brain, use artificial neural networks to process data and information. This led to the development of a very powerful model called Generative Adversarial Networks which essentially is a deep learning architecture where two neural networks compete against each other to generate more new authentic data from a given data set.
Generative AI Today
<p>After the revolution of Deep learning, Generative AI has been found across various industries. Let’s see some glimpse of it’s current applications:
- Art and Design: The most common use case is creating stunning images from a text description, generating new product designs, and even composing music.
- Media and Entertainment: AI can create real and special effects in movies. personalize content experience and this is being widely used in science fiction movies across all industries.
- Science and Engineering: Generative models are being used to develop new materials, discover drugs, and accelerate scientific research.
The Future Of Generative AI
The future of Generative models is brimming with possibilities as these are being used in every industry right now. If I give you the example of tech, every tech product can be SAAS or can be a simple physical product website as well everyone is trying to use AI to polish or even sometimes just to look fancy on their website. You can see that Microsoft has embedded Microsoft CoPilot in their new browser Edge and it’s pretty powerful as well.
Even new phones are adopting this AI trends. You can have a look at the launch campaign of Google Pixel 9 Pro and they are marketing just on in-device AI. Same goes for the new Samsung Z Fold 6.
However, there can be some misleading use cases of Generative AI as well. This needs to be addressed with the massive increasing use.
These are just glimpses into the powerful world of Generative AI. But with its true potential, it will revolutionize various fields and is sure to play a vital role in shaping the future.
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