Generative AI

What is Generative AI?

Generative AI is an advanced AI technology that  can generate content like

text, audio, video, synthetic data, and images. It empowers AI systems to uncover the underlying input patterns in varied modalities to produce similar content. 

These applications can work with modalities such as text, images, music, code, and audio. Common techniques used in generative AI include Transformers, Generative Adversarial Networks (GANs), and Variational auto-encoders. Popular examples of Gen AI applications are GPT, Jasper, Dall E2, Stable Diffusion, GitHub Copilot, etc. 

To fully harness the potential of Gen AI, it's important to develop solutions tailored to your specific business needs. This is where transfer learning becomes crucial.

Let's clarify with an example. While GPT-3 is incredibly powerful, it might not be the most efficient solution for every NLP task. Transfer learning involves taking a pre-trained language model, like GPT-3, and fine-tuning it on a specific task or domain using a smaller dataset. This approach optimizes the model's performance and makes it more effective for the target task. So the overall generative AI workflow would look like this: 

Why Is Generative AI Important? 

Generative AI has emerged as a crucial technology with profound implications across various industries. Its importance lies in its ability to revolutionize content creation, streamline processes, personalize experiences, drive innovation, and foster efficiency. 

• Content Creation: Automate content creation in various forms, such as text, images, music, and more. 
• Automation: Automate repetitive and time-consuming tasks, including graphics and code generation. 
• Personalized recommendations: Analyze user data and preferences to generate personalized recommendations. E.g., product recommendations, personalized movie or music suggestions
• Research and Development: Generate synthetic data that mimics real-world scenarios, accelerate experiments and simulations, hypothesis testing, and scientific advancements 
• Innovation: Explore creative ideas, and generate novel designs, artwork, and music compositions 
• Personal Assistance: Use NLP to answer queries, provide recommendations, and offer support 

How To Build Generative AI Applications? 

Below we discuss the process of building generative AI applications and some of the best practices to follow.

Define the Use Case

Clearly articulate the problem you want your generative AI application to solve. It could be anything creative like text, image, audio, or similar output. 

Data Collection & Preparation

Gathering data from diverse sources and domains enables your models to generalize better and apply their extensive knowledge base. Data cleaning and preprocessing are vital as you deal with large volumes of unstructured and complex datasets. Some essential practices are tokenization, deduplication, outlier identification, and stopword removal. 

Select the Suitable Gen AI Model

The next step is to select a suitable algorithm. You can choose among options such as Generative Adversarial Networks (GANs), Transformers, and Variational Autoencoders (VAEs). Once the model is selected, it is fine-tuned by incorporating feedback, new features, and additional data points. 

Pre-training and Transfer Learning

As discussed above, transfer learning is crucial in building these applications. Model pre-training is a compute-intensive step demanding specialized hardware or cloud

resources. It involves feeding the data into the model and adjusting its internal parameters to minimize output differences. 

Application Fine-tuning

You can experiment with different hyperparameters, loss functions, and regularization techniques to get the desired output quality. 

Model Deployment

Finally, it's deployment time! You can integrate the model into your application and access it through API. Cloud-based or on-premise deployments are chosen for large language models based on data security and infrastructure. CI/CD pipelines help automate development, testing, and deployment. Current approaches include using tools like Jenkins, GitLab CI/CD, and GitHub Actions.

Model Monitoring

Model monitoring helps ensure that deployed models are not drifting and are performant over time. You can track model performance across metrics and strategize actions such as retraining whenever required. 

Best Practices That You Should Not Miss

• Clearly outline the objectives of your Gen AI application. You should know what you want the model to generate and how it aligns with your project and enterprise goals.
• Choose the robust and scalable model architecture to justice future requirements. 
• Gather a diverse and representative dataset that is relevant to your application. Properly preprocessed, cleaned, and labeled data helps reduce bias and inconsistencies. 
• Integrate observability tools to monitor the performance of deployed models and catch problems at their inception. 
• Place controls for bias mitigation and ethical compliance. Right disclosure policies help users be aware of AI model capabilities for publicly deployed models. 

Partnering With Attri For Shaping Something Truly Extraordinary

The future is generative AI, and together, we will shape it into something truly extraordinary.

Stay informed about the latest AI advancements by exploring our articles and if you need assistance with your generative AI solutions, you are just a click away! Our team of seasoned experts is ready to unleash their brilliance and collaborate with you to craft impeccable generative AI solutions.