Generative AI refers to artificial intelligence algorithms that enable using existing content like text, audio files, or images to create new plausible content. In other words, it allows computers to abstract the underlying pattern related to the input, and then use that to generate similar content.[1]

Figure 1. the Generative Artificial Intelligence (AI)

Figure 1 shows Generative AI is a new buzzword that emerged with its novel applications like DeepFake. Generative AI leverages AI and machine learning algorithms to enable machines to generate artificial content such as text, images, audio and video content based on its training data, in a manner which tricks the user into believing the content is real. [3]

The benefits of Generative AI

Identity Protection: Generative AI avatars provide protection for people who do not want to disclose their identities while interviewing or working.

Robotics control: Generative modeling helps reinforcement machine learning models to be less biased and comprehend more abstract concepts in simulation and the real world.

Healthcare: Generative AI enables early identification of potential malice to create effective treatments. For example, GANs compute different angles of an x-ray image to visualize the possible expansion of the tumor. [3]

The challenges of Generative AI

Security: Some people can use Generative AI for fraudulent purposes like scamming people.

Overestimation of capabilities: Generative AI algorithms require an enormous amount of training data to perform tasks. Yet, GANs cannot create entirely new images or texts. They only combine what they know in different ways.

Unexpected outcomes: In some models of Generative AI like GANs, it is not easy to control their behavior. They perform unstably and generate an unexpected outcome.

Data privacy: Health-related applications involve privacy concerns on individual-level data.

Future of generative AI

There will be more generative AI applications in the future, that’s for sure. The cost of generating images, 3D environments and even proteins for simulations is much cheaper and faster than in the physical world.

This is the start of another disruption and even today companies are selling these photos. Modelling companies have started to feel the pressure and danger of becoming irrelevant.

As trust is becoming the most important value of today, fake videos, images and news will make it even more difficult to learn the truth about our world. There will be stronger regulations, penalties and improved fake detection algorithms.

Unfortunately, despite these and future efforts, fake videos and images seem to be an unavoidable price to pay for the benefits we are expected to get from generative AI in the near future.

There’s a lot your business can benefit from with the current AI technology. Our data science team is excited about bringing the latest in machine learning to our customers to help them with real life business problems.[2]

References:

1. https://www.analyticsinsight.net/what-is-generative-ai-its-impacts-and-limitations/
2. https://www.avenga.com/magazine/generative-ai/
3. https://research.aimultiple.com/generative-ai/

Cite this article:

Thanusri swetha J (2021), Generative Artificial Intelligence (AI), Anatechmaz, pp. 40