Generative AI refers to a technology that uses AI and machine learning algorithms to enable machines to create new digital videos, images, texts, audio, or code. Generative AI is driven by algorithms that have the potential to identify the underlying pattern of input and generate similar outputs and provide high-quality content. A more upgraded variety of generative AI goes further than just monitoring a real-life environment to generate content. It can also leverage mathematical emulation and function unknown patterns revealed through it. Conventionally, these types of mechanisms rely on the application of stress testing and sensitivity analysis.  Here we have mentioned the top five applications of generative AI.

Enhanced Identity Protection

Generative AI helps in creating avatars, thus concealing the real appearance of people who are not comfortable disclosing their identities for any reason while being interviewed or working online.

Better Comprehension of Abstract Theories

Machines are not astute enough yet to fathom some abstract concepts they can encounter in the real or simulated world, and generative AI is instrumental to address this challenge.

Improved Quality of Output

The shuttle pattern of operation utilized in self-learning GANs is useful in getting high-quality images, video, or audio, even if the input content is far from perfect.

Decreased Financial and Reputational Risks

Generative AI tools are capable to detect malicious or at least suspicious activities in no time and prevent all kinds of damage to a business or a person.

Upgraded Reinforcement Machine Learning

Reinforcement ML is based on rewarding desired actions and punishing unwanted ones. Although, detecting where a perticular step belongs is oftentimes biased. Generative AI techniques are helpful in removing or at least considerably curtailing this bias.