Currently, AI is booming in the IT industry. Everyone is talking about AI and wants to get the most out of it as AI’s transformative potential simplifies human tasks and extends its influence across various sectors. In this introductory post, we’ll learn fundamental concepts behind GenAI, uncovering how machines learn to understand and generate data, and how it has revolutionized various industries like art, entertainment, healthcare, finance and many more.
Join us to explore GenAI capabilities, applications, and the exciting possibilities it holds for the future.
What is Generative AI?
GenAI, short for Generative Artificial Intelligence, represents a fascinating intersection of AI and creativity. Unlike traditional AI systems that focus on tasks like classification and prediction, genAI ventures into the area of imagination, producing content such as images, text, audio, videos.
Generative AI models are trained to understand the underlying patterns and structures of the data in order to generate new samples from it.
Generative AI focuses on creating systems capable of producing new content or data that resembles, and sometimes even extends beyond, what is found in the original dataset used for training.
How does Generative AI work?
GenAI Workflow can be divide in two major steps:
- Training
- Content Generation
Training: GenAI models are trained on huge datasets of text, code, images, or other types of unstructured data. This data can come from books, articles, code repositories, websites, and more.
Content Generation: Once trained, the model can use its knowledge of patterns to generate new content. It does this by predicting what should come next, based on the information it has been given in the prompt. This could be continuing a sentence, creating an image based on a description, composing a piece of music or generating a video.
Now let’s explore how users can simplify their work by using GenAI Applications.
GenAI : End-User’s Perspective
End-Users interact with Application Interface (ChatGPT/Gemini) and write appropriate prompts as per requirements. The applications are running on top of GenAI models to generate new content as shown in the figure below.
Now Let’s have a quick look at the GenAI application lifecycle and understand the whole process in detail.
GenAI Application Lifecycle
The first question arises in our mind is: How to start implementing a GenAI application? For that, we need to follow the steps as shown in the figure below.
- Define a Problem statement
- Find out the problem or task that generative AI will address.
- Define the objectives, requirements, and constraints of the project.
- Data Investigation / Data Collection
- Gather a diverse dataset that captures the underlying patterns of the target domain.
- Clean the data, handle missing values, and preprocess it as needed (e.g., normalization, dimensionality reduction)
- Architecture
- Choose an appropriate generative AI technique based on the problem requirements and available data.
- Develop the generative model architecture, including defining the network structure, loss functions, and optimization algorithms.
- Model Training
- Train the model on the prepared dataset using suitable training techniques (e.g., batch training, transfer learning).
- Model Evaluation
- Evaluate the trained generative model’s performance using appropriate metrics (e.g., fidelity, diversity, realism).
- Improve your Model
- Address any shortcomings or issues identified during evaluation and make necessary adjustments to the model.
- Deployment
- Deploy the trained generative model into production or integrate it into existing systems or workflows.
- Maintenance & Iteration
- Monitor the model’s performance in real-world settings and iterate on improvements as needed.
- Continuously monitor the deployed model’s performance and behavior for any deviations or issues.
Next, we will see the Use-Cases of Generative AI.
GenAI Use-Cases : Text, Images, Music/Audio, Video
Following are some real world use cases of GenAI in different areas/industries.
- Content Creation helps
- Writers
- Artist
- Musicians
- Art and Entertainment
- Animations
- Special effects
- Virtual character design
- Medical and science research
- Analyze diseases
- Drug discovery
- Genomics
- Data Augmentation
- To extract information from recent data or own pdf
- Marketing
- Create an effective strategy
- Social media analysis
- Education
- Prepare Quiz
- Study notes
- Advertising content creation
- Get new ideas and video creation
- Simulation and prediction
- Stock market prediction
- Weather forecast
- Finance
- GenAI algorithms to analyze portfolio
- Stock Market data
- Detect patterns
- Execute trades at high speeds and frequencies
- Portfolio Management and Optimization
- Customer Service
- Programming
- Generate code (Copilot)
- …(many more)
GenAI applications are widely used in many areas. So these applications should be responsible. It should prevent some data generation for kids.
Overview of Responsible AI
Responsible AI is not only an ethical imperative but also a foundation for building trust, fostering innovation, and ensuring the long-term sustainability of AI technologies.
We will explore other aspects of GenAI in the following section.
Other Aspects of GenAI
While working with the GenAI application we have to consider the following aspects of GenAI mentioned in the below figure.
GenAI applications run on top of LLM models. This models are facing some challenges like,
- Hallucinations and Misleading Outputs
- Computational Complexity
- Limited Data for Training
- Ethical and Legal Concerns
- Addressing Biases in LLM Outputs
- Struggle with Structured Data
Consider the above mentioned aspects while generating content to simplify your work.
Summary
In this post, We have seen the basics of Generative AI and how Generative AI represents a drastic change in human-machine interaction, empowering us to explore new boundaries of creativity and imagination. Please follow our recent post to understand how to craft GenAI applications.
