Creating Generative AI – A Complete Guide

Generative AI is a subset of narrow AI that uses machine learning to create new content based on training data. This topic is new. Let’s learn together!

What happens when you don’t walk with time? Your sense of time speeds with age. There will be a time when you will lose track of what is happening. Life wasn’t as smooth but it still existed before computers, smartphones, the internet, and Google search. The discovery of AI, promises a leap in the overall productivity of businesses, automating mundane tasks, reducing error, generating insights, and ideas, and innovating new ways of doing things.

Google softly pushed Generative AI, as a counter-response to Chat GPT. It is still experimental. Once this setting is enabled from the web browser, it answers users’ queries in the form of text, images, videos, audio, and music (by making use of tools like Boomy, AIVA, Suno, and Udio). Like every AI tool, it learns from the writing pattern and generates responses. It picks its answers from the information prevalent on the internet, and may often be plagiarized, or duplicateed. So picking the information, and using it as such, is not advisable. Generative AI comes in various forms. They are sometimes called Generative Adversarial Networks, Variational Autoencoders, Autoregressive models, Multimodality, Neural Radiance Fields, Stable Diffusion, and DALL-E.

Where is this information being used?

• – Generative AI helps medical specialists with drug discovery, personalized treatment plans, and predictive images for disease progression. It enhances medical images like X-rays or MRIs, synthesizes images, or reconstructs images.
• – It assists business teams in transforming and labeling third-party data for more sophisticated risk assessments and opportunity analysis.
• – It creates photorealistic images from scratch, which has applications in industries like fashion, interior design, and gaming.
• – Generative AI comes out with images, text, and videos based on the prompts by users. Natural Language Processing models like Generative Pre-trained Transformers generate contextually relevant text.
• – Generative AI composes music by replicating existing styles to create entirely new compositions that blend genres and eras. It uses AI algorithms to train massive datasets of existing music, learning the patterns, relationships, and styles present in the data. After that it generates new music by extrapolating the learning.
• – Generative AI automates tasks like responding to RFPs, localizing marketing content, and checking contracts.
• – Generative AI optimizes business processes across all lines of business by identifying trends and suggesting insights.
• – Generative AI automatically generates reports, summaries, and projections, saving time and reducing errors
• – It produces code without manual coding, and suggests code completions as developers type
• – It predicts future frames in a video, such as objects or characters moving in a scene
• – Generative AI produces a new video that adheres to another video’s style or a reference image
• – Risk mitigation: Generative AI analyzes data to identify potential risks to the enterprise more quickly
• – It is used to create diverse and realistic scenarios for training purposes, such as driver training or medical scenarios

How is it better than normal Google search and Chat GPT?

Around 67,200 companies are working towards being Generative AI startups. It Creates new images, videos, audio, and text that are indistinguishable from human-made content. Improves the accuracy and efficiency of AI systems like natural language processing and computer vision. It helps Generative AI Companies find patterns and trends in data that might not be obvious on their own. Automates the creation of media, reducing the need for human labor and increasing productivity.

Building Generative AI App

• Creating a generative AI app involves several stages and requires a range of considerations, from data collection and model selection to deployment and performance monitoring.
• – First step – Development Stage involves the collection and preprocessing data, focusing on cleaning, normalization, and tokenization. It requires choosing a suitable model based on modality, size, and cost.
• – Second step – Prompt design involves author prompt and response pairs to provide context and instructions to the language model.
• – Third step – prototype building involves building a prototype for testing and refining it for efficiency and reliability.
• – Fourth step – customization and extension involves customizing the model or using extensions to expand its capabilities.
• – Fifth step – Deployment Stage involves creating a pipeline for deployment and configuring the model for security.
• – Sixth step – Performance Monitoring involves monitoring the model’s performance and making updates based on user feedback.

What to consider before making such an app?

• – Zero-Shot or Few-Shot Learning approach is considered as the simplest way to build a generative AI app. It requires services for a foundation model, its interface, an ML platform, compute, custom code for prompts, and a front-end app.
• – The use of image processing tools involves image generation or manipulation, tools like OpenCV and PIL are valuable.
• – Graphics Processing Units speed up the training process for deep learning models.
• – Machine Learning Pipelines plan routing logic, responses, permissions, and content migration to smoothly integrate evolving machine learning pipelines.
• – The model should capture minority modes in its data distribution without sacrificing generation quality.
• – Fast generation is essential for interactive applications like real-time image editing.

Tech Stack

Generative AI app development tech stack is made up of (3) infrastructure layer, (2) model layer, (1) application layer and are integrated based on Generative AI models (types) discussed above. Major enterprises and budding startups are recognizing the need to bring their AI initiatives into cost-effective production. They choose technologies diligently to make it happen.

• – Deep learning frameworks like adversarial networks (GANs), recurrent neural networks (RNNs), and variational autoencoders (VAEs);
• – Data preprocessing tools like Apache Spark and Apache Hadoop;
• – Programming languages like Python;
• – Amazon Web Services (AWS), Google Cloud Platform (GCP), and Microsoft Azure, Docker, Flask, and Kubernetes for deployment;
• – Machine learning frameworks: TensorFlow, PyTorch, Keras
• – Programming languages: Python, Julia, R
• – Data Preprocessing: NumPy, Pandas, OpenCV
• – Visualization: Matplotlib, Seaborn, Plotly
• – Other tools: Anaconda, Git, Jupyter Notebook
• – Generative AI Models: (Discussed above)
• – Cloud services: Azure, GCP, AWS

Conclusive

Generative AI in app development also presents some potential ethical concerns, such as bias, misuse, and job displacement. Responsible use of technology is important so that it positively transforms industries. Building a generative AI app is complex, but by following the steps discussed above and considering the key requirements, the chances of success will increase.

Generative AI models cannot handle suboptimal or inaccurate information. So they are good only till the point you ask the correct thing. If your input prompts are accurate, the result will be accurate. So Generative AI still needs to learn how to handle such situations.