Artificial intelligence has endowed us with limitless possibilities. From intelligent marketing to fraud prevention and 24/7 customer support, artificial intelligence has transformed every aspect of businesses and lives.
Today, it can also enable machines to use textual or visual data to create new content via what we can refer to as Generative AI.
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.
–Ensuring the generation of higher quality outputs by self-learning from every set of data.
-Lowering the risks associated with a project
-Training reinforced machine learning models to be less biased
-Allowing robots to comprehend more abstract concepts both in simulation and the real world.
Identity Protection: Generative AI avatars have been used to protect the identity of interviewees in news reports about the persecution of LGBTQ people in Russia.
Image Processing: It helps in intelligent upscaling of low-resolution images to high-resolution images.
Film Restoration: It enhances old images and old movies by upscaling them to 4K and beyond, which generates 60 frames per second instead of 23 or less, and removes noise, adds colors and makes it sharp.
Audio Synthesis: Generative AI can render any computer-generated voice into one that truly sounds like human voice. Even I, the narrator of this video is a product of generative AI.
Healthcare: Generative AI can be employed for rendering prosthetic limbs, organic molecules, and other items from scratch when actuated through 3D printing, CRISPR, and other technologies. It can also enable early identification of potential malignancy to more effective treatment plans. IBM is currently using this technology to research antimicrobial peptide (AMP) to find drugs for COVID-19.
As Generative AI makes it possible for machines to create new content effectively, it also comes with a set of limitations.
Hard to Control: Some models of Generative AI like GANs are unstable as well as it is hard to control their behavior, they sometimes do not generate the expected outputs, and it’s hard to figure out why.
Pseudo Imagination: Generative AI algorithms still need a vast amount of training data to perform tasks. GANs cannot create entirely new things. They only combine what they know in new ways.
Security: Malicious actors can use Generative AI for deceitful purposes like scamming people, fraudulent activities, and create fake spammy news.