What did the fish yell when it hit a wall?
Let me give you a few minutes to laugh before I reveal that AI created this joke!
What is generative AI (artificial intelligence)
Generative AI, also known as generative machine learning (ML), is a technology that creates new objects based on the patterns and structures learned from information. It can make things how you tell it to or differently, such as making videos or pictures out of pictures or words. ChatGPT is one of the most popular AI models and tools. Others include Bard, DALL-E, and Midjourney.
They use neural networks and computer systems to analyze information and discover hidden patterns. They can then create unique and original stuff. The cool thing about generative AI is that it can learn differently. It can do this by itself or even with some help. It’s flexible!
Generative AI can do many amazing things, from solving problems to art. It opens up exciting possibilities for working with machines and new ideas by using its power to create new things. Generative AI is constantly improving, pushing the boundaries of what can be done with AI.
This timeline shows how everything began.
In the years 2014-2023, generative artificial intelligence has improved significantly. We’ve seen some key models. From 2014 to 2017, Variational Adversarial Networks (GAN) and Variational autoencoders (VAE) were popular. They were improved by altering their appearance and learning methods. GANs are used to make images or music.
Transformer, a model released in 2018 and 2019, became very popular. Models such as GPT, which only has one part called a “decoder,” became popular. GPT-2, T5, and GPT-2 were improved language models.
The Big Model Era took place between 2020 and 2022. People combined ideas from various generative models. Models such as VQ GAN integrated GANs and something called VQ VAE. A model called Vision Transformer used Transformers to create images. They made incredible image models, like DDPM or DDIM, almost as good as GANs. Many organizations also created their language models, such as GPT-3.
In this period, people made models like DALL-E or Imagen that did multiple things simultaneously. These models were able to create both words and images. New ideas, such as Latent Diffusion or Stable Diffusion, improved the models.
These improvements have made generative AI more powerful and able to do various things.
Let’s now explore the business applications of generative AI.
Use cases for generative AI
In technology and software, it can be helpful in many ways. It can automate tasks, identify problems, improve performance, and provide reports. It can also assist with computer systems to keep everything running smoothly. It can detect bugs and recommend fixes. It can be used to test code, create it, and decide what to do.
Chatbots, such as ChatGPT, can use generative AI to understand the needs of users and can work with other computer systems. When there are problems with security, generative AI will help to find them and make suggestions, but the people making the final decisions still have to be involved.
Generative AI can help in software development by making suggestions, creating code fragments, and creating test cases. It can also detect problems and make suggestions for fixes. It can help decide how to implement code and where it should be placed.
ChatGPT and other AI-based tools can be helpful in many different fields. It can, for example, scan contracts to find problematic parts. It saves people time by not having to read every word. This can translate languages faster and more efficiently than by hand.
Generative AI makes emails more personal, using information about recipients and their friends. It makes people more interested in the subject, and things move faster. It can also give feedback on how well people are responding to customer service and make improvements.
Generative AI can help fix errors in large sets of data. It can find things others may not notice and improve the information.
It is also an excellent tool for healthcare. It can help manage patient referrals, improve healthcare, design medicines, take care of many people’s health, and make doctors better decisions.
It can help with many things, including improving healthcare and patient care. It can help with telehealth and other things, such as ensuring everything is organized, detecting cheating, and checking if someone is trying to cheat.
The power of Generative AI can be seen in many different fields. It improves technology and software, saves money and time, and helps people to stay healthy.
As tech enthusiasts continue developing and implementing generative AI examples, we must aim to maintain the balance between innovation and responsibility.
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What did the fish say as it swam against another wall?