Generative AI- Exploring the world of possibilities
Introduction
Artificial intelligence is no longer just a random phrase; it has found premium applications across various industries. Generative AI is an advanced version of artificial intelligence. It offers limitless possibilities coupled with convenience for all users.
The technology is transforming businesses through fraud prevention, personalized customer support, and intelligent marketing tactics. Implementing AI is not only restricted to automation; rather, it is now driving data-powered decision making. Machines are now processing more data than ever and are creating newer content at a higher pace.
Future of AI
AI is shifting from interpreting existing data to generating novel content at scale. The main areas where it is redefining AI implementation are:
– Unsupervised Learning: Overcoming the limitations to labeled and segregated data used by conventional AI models. Advanced AI algorithms are now being trained through unstructured data without any human assistance.
– Federated Learning: Going beyond conventional machine learning from a unified dataset, AI models are trained locally on data subsets.
– Transformers: Recurrent neural networks perform data processing sequentially. They may be soon replaced with Transformers, which work on parallelized language processing.
– Neural Network Compression: Typical deep learning models are massive and must be reduced. Compressing them would make the neural networks smaller, faster, and power-efficient.
– Generative AI: Moving on from recognizing data sets to creating new ones.
What is Generative AI?
Generative AI refers to the capability of artificial intelligence-enabled machines to use existing text, audio files, or images to create new content. In other words, it runs on algorithms that identify the underlying pattern of an input to generate similar plausible
content.
Generative AI is supported by two key technologies – Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs).
Benefits of Generative AI
- Generative AI offers the following benefits for businesses:
- Improved quality of outputs
- Lower risks
- Reduced bias
- Localization of content
- Comprehend abstract concepts
Applications of Generative AI
- Identity Protection: Generative AI is helping maintain the anonymity of individuals through avatars. It helps organizations maintain unbiased recruitment processes and researchers to provide neutral results.
- Image Processing: AI is being extensively used in the intelligent processing of low-resolution images to create clearer and more detailed images. The process helps in the restoration of old images and old movies.
- Audio Synthesis: Generative AI is useful in creating human-like voices. This computer-generated voice is useful for creating video voice over, narrations, and other audible solutions for organizations.
- Client Segmentation: AI helps identify and segment target groups for your marketing campaigns. It studies your data to predict their response to your promotions and advertisements. Generative AI drives customer relationships through data.
- Sentiment Analysis: Machine Learning is using text, image, and voice analysis to comprehend customer sentiment. AI algorithms study web activity and user data to decipher customer opinion towards your products and services.
- Fraud Detection: Automating fraud detection processes has helped identify illegal and suspicious activities. AI is detecting illicit transactions using predefined algorithms and rules.
- Trend Analysis: AI and machine learning (ML) techniques help predict the future. They help you deep-dive into your data to study trends easily and beyond traditional mathematical analysis.
Limitations of Generative AI
- Hard to control: Generative AI models like GANs do not always generate desired results. Some outputs are hard to interpret and as the result is derived from an ML model, it is hard to change it.
- Pseudo Imagination: Generative AI algorithms perform tasks based on their training data only and cannot create anything new on their own.
The technology only allows models to combine things it already knows and present it in new ways. - Security: Fraudsters may use Generative AI for scamming and other fraudulent activities, which may be difficult to track.
Way forward
Generative AI is set to disrupt more industries than we can imagine. It is finding applications in crucial fields such as healthcare and defense security. As the technology evolves, it will find more advanced applications.
AI models are viable options to be used by organizations as it makes image generation, film restoration, and setting up 3D environments quicker and less expensive. The regulators, however, should focus on strengthening regulations and imposing penalties. As the algorithm improves, it will work on detecting fake and repetitive information.
Madhav Bissa
Supriya Samuel
Raj Shekhar
Tarun Kumar
