Introduction to Generative AI

Generative AI

Given the recent advancements in the field of artificial intelligence, some large AI models have become sophisticated enough to execute multiple types of tasks. These tasks can range from conversational abilities to generating code in various programming languages and even creating images.

This gave rise to the term Generative AI, which refers to the use of AI to generate all sorts of stuff, such as speech, text, images, etc., in a smart manner. The content generated by AI has found many applications in content creation and application development. The main reason is that AI can help speed up the problem-solving process and potentially provide solutions that are as effective as or even superior to those conceived by humans.

Generative AI applications

Artificial intelligence is used for eliminating most repetitive tasks and speeding up the thinking process for most developers and common users. It can reduce the need for detailed planning by suggesting potential solutions directly. Some use cases are given below but are not limited to the ones presented:

• In most cases, large AI models are trained and used in setups that focus on a targeted audience. For example, models are trained on code documentation and are then used as assistants and helpers for developers facing issues with a certain programming language.

• For instance, models trained on documentation for specific tools, like Unity, can assist developers by generating code snippets and procedures tailored to those tools.

• Text and language models are used to enhance customer experience through support and search experiences. They play an important role in understanding user requirements and provide a solution accordingly, carefully catering to the customer needs.

In addition to the applications mentioned above, generative AI has found widespread use in various industries and domains, revolutionizing the way tasks are performed and solutions are created. Here are some additional examples of generative AI applications:

• Content generation: Generative AI models are being used to create content for different mediums. These include articles, blogs, stories and even music!. Generative AI models can understand context, and the content that they generate is based on it. This application is particularly used in industries like journalism, marketing, and entertainment.

• Design and creativity: Generative models, such as diffusion models, are used for image generation. The quality and realism of the generated images improve with more sophisticated training of these models. Moreover, generative AI is being leveraged in creative fields due to its ability to produce highly realistic images. Diffusion models are trained on different artworks to reproduce more art in their essence.

• Natural language understanding and generation: Recent advancements in this field have enabled generative AI models to be trained on huge sets of data. As a result, they understand and generate responses based on that data. These models are often also trained on subsets of data and are used as chatbots and virtual assistants.

What are we building?

We will use OpenAI’s latest language model, GPT-4, to develop an application that can autonomously create other applications based on user specifications. The user will give the application a prompt regarding the app they want to make and the application will do the grunt work and create the app on its own.