Generative AI is a form of Artificial Intelligence that can produce text, image, audio, video and varied form of content based on its training data. Generative AI refers to deep-learning models which learn from the training data and create new data instances that mimic the properties of input data.
Generative AI powered by large language models, have revolutionized the way we access knowledge. Gen AI’s technical capability to predict patterns in natural language and use it dynamically is having an impact on every sector of society.
Generative AI use cases (non-exhaustive): –
- Create a story, poem, novel based on the writing style of renowned author.
- Generate a realistic image of a person, item, nature etc.
- Compose a symphony in the style of a famous composer.
- Create a video clip based on the textual description.
- Generate script for a movie.
- Generate human-like text responses in chatbot.
- Generate personalized marketing content.
- Generate personalized recommendations and user interfaces.
- Generate realistic animation for gaming.
- Create sound for motion picture.
- Draft email.
- Write code.
- Generate 3-D objects, interior-design mockup.
- Create trainings, educational voiceover based on text to voice generation.
- Generate 3-D models to accelerate drug discovery.
- Create synthetic data for research and training purposes.
Sysfort is an invention-based enterprise to promote global economic growth. Innovation: it’s truly the key to accomplishing things in life.
Types of Generative AI Models
- Transformer based models: – Used for generating highly appropriate and coherent text.
- Generative adversarial networks (GAN): – Consist of two parts, generator and discriminator to generate high quality data instances.
- Variational autoencoders (VAEs): – Encodes input data and decodes to generate new data.
Challenges of Generative AI: –
Training generative AI models is computationally intensive, time-consuming and expensive. It requires significant resources and expertise, which can be a barrier for smaller organizations.
Ensuring authenticity, integrity, diversity of data to avoid bias in the generated output is a complex task. Having the right inputs, deployed with proper guardrails in place is the key to a successful AI strategy.
Inaccuracy, cybersecurity and intellectual property infringement are the biggest risks of Gen AI adoption.
Future of Generative AI: –
Generative AI will boost global GDP. Gen AI’s ability to automate grunt work and put information at fingertips will increase labor productivity across the economy. Professionals in the fields of education, law, technology and arts will see parts of their mundane work automated in the near future. It will deliver significant value when deployed across key use cases in technology, banking, healthcare, pharma, retail, manufacturing domains.