What is Generative AI? Exploring a Creative New World

What did the fish say when it swam into a wall?

Dam!

I’ll give you some time to chuckle before I share that AI has crafted this joke!

What is generative AI?

Generative AI is a special technology that can make new things based on patterns and structures it learns from existing information. It can create things in the same way it is told or even in different ways, like making pictures from words or videos from pictures. Some popular generative AI tools and models are ChatGPT, Bard, DALL-E, Midjourney, and DeepMind.

These models use something called neural networks to study information and find hidden patterns. This helps them create original and unique stuff. What’s cool about generative AI is that it can learn in different ways, like by itself or with a little help. It’s flexible!

Generative AI can do lots of amazing things in different areas, from art to problem-solving. By using its power to make new things, it opens exciting opportunities for new ideas and working together with machines. As it keeps getting better, generative AI keeps pushing the limits of what we can do with artificial intelligence and being creative.

Here’s how it all started… Look at this timeline…

Fig 1: Generative AI Over the Years

From 2014 to 2023, generative AI has gotten much better, and we’ve seen some important models. In the years 2014 to 2017, the Variational Autoencoder (VAE) and the Generative Adversarial Network (GAN) became popular. People made them better by changing how they look and how they learn. GANs were used to change images or make music.

In 2018 and 2019, a special model called the Transformer became famous. Models like GPT (which only has a part called a decoder) got big. People made language models that were even better, like GPT-2 and T5.

From 2020 to 2022, something called the Big Model Era happened. People combined ideas from different generative models. Models like VQ-GAN mixed GANs with something called VQ-VAE. There was also a model called the Vision Transformer that used Transformers for images. People made amazing image models like DDPM and DDIM that were almost as good as GANs. Big language models like GPT-3 came out too, and many organizations made their own models to make language better.

During this time, people also made models that could do many things at once, like DALL-E and Imagen. These models could make both words and pictures together. Some new ideas like Latent Diffusion and Stable Diffusion made the models even better.

All these improvements made generative AI grow and become able to make lots of different things well.

Now, let’s explore business use cases of generative AI.

Use cases of generative AI

Generative AI can be used in lots of helpful ways in technology and software development. In technology, it can do tasks automatically, find problems and risks, make things work better, and give reports. It can also help with computer systems that keep everything working smoothly. It can find bugs and suggest fixes. People can use it to make code, test things, and decide where to put work.

Generative AI, like ChatGPT, can also be used with chatbots to understand what people want and work well with other computer systems. When there are security problems, generative AI can help find them and give suggestions, but people still make the final decisions.

In software development, generative AI helps by giving suggestions for code, making parts of code, and creating tests. It can also find problems and suggest fixes. It helps with putting code into action and deciding where it should go.

Generative AI, like ChatGPT, is also helpful in other areas. For instance, it can look at contracts and find parts that might cause problems. This saves time because people don’t have to read everything themselves. It can also help with translating languages, making it faster and cheaper than doing it by hand.

Generative AI can make emails more personal by using information about people and their friends. This makes people feel more interested and makes things go faster. It can also look at how good people’s responses are in customer support and give feedback to make them better.

In customer relationship management (CRM) systems, generative AI helps fix mistakes in big sets of information. It finds things that people might not see and makes the information better.

Generative AI is also great for healthcare. It can help with things like managing patient referrals, making healthcare better, designing medicines, taking care of a lot of people’s health, helping doctors make good decisions, watching out for problems with medicines, making supply chains better, and more.

It helps with many things to make healthcare better and take care of patients. It can even help with things like telehealth, checking if people are trying to cheat, and making sure everything is organized.

Generative AI is powerful and can do a lot of things in different areas. It helps make technology and software better, saves time and money, and improves healthcare to help people stay healthy.

Well, as tech enthusiasts continue to develop and implement generative AI examples, we must keep aiming to strike the golden balance between innovation and responsibility.

Write to us with your opinions and visit us at Nitor Infotech to learn about how we explore the generative AI world!

What did the fish say when it swam into another wall?

Dam again!

No prizes for guessing what crafted this joke! I couldn’t resist! 😀