One use case that has a lot of impact is applications with GitHub Co-Pilot. When you start coding, you start with a certain command, and then you start writing the command that you are going to code, but as you're writing the command, AI suggests the right piece of code or some standard functions that you may want to include. This application is getting quite good, which is bringing administrative coding time close to zero. As it generates the code, you just then accept what it is providing to you, or you continue with a different command and it provides you with something different that you can implement. This reduces development time significantly.
Generally speaking, I don't think companies have integrated generative AI much into their existing services or have created many new services with is. Frankly, many companies are not even aware of generative ai or looking at all of the potential applications in law, healthcare, banking, marketing, and education. There are countless possible applications, such as simplifying contracts, image generation for maybe some customized product packaging, confirming medical diagnoses, etc.
We are still in the early stages of adopting generative AI, which means there are still many unexplored possibilities for implementing generative AI to drive value for companies.
There are many potential legal gray areas, especially in copyright and intellectual property, in regard to what datasets companies use, as well as how they access and use those datasets to develop generative AI tools.
Generative AI has the ability to significantly reduce coding time, which will empower data scientists and ML engineers to develop new, more advanced tools and AI models faster and more efficiently.
What is Text Generation?
The Pros and Cons of Using LLMs in the Cloud Versus Running LLMs Locally
Abid Ali Awan
How to Learn AI From Scratch in 2023: A Complete Guide From the Experts
The Generative AI Tools Landscape
A Beginner's Guide to ChatGPT Prompt Engineering