Five worthy reads is a regular column on five noteworthy items we’ve discovered while researching trending and timeless topics. This week, we explore the concept of Generative AI.
Online shopping is becoming easier and more satisfactory. E-commerce vendors are now letting customers “try on” dresses, glasses, and even makeup on their platforms. The field of medicine is advancing—doctors and medical professionals can now depict the future developments of any disease. A photograph or a video captured years ago could be upscaled to a 4K quality media, making them sharp and clear. Behind all these applications is a technology that has been slowly and steadily transforming various industries and sectors: Generative AI.
Gartner explains that Generative AI refers to AI techniques that learn a representation of artifacts from the data, and use it to generate brand-new, completely original artifacts that preserve a likeness to original data. The Generative AI algorithms detect the underlying pattern in the source data (text, code, audio, images, etc.) and generate speedy, accurate, high-quality outputs (synthetic data). The data can be audio/visual, programming assets, designs, natural language, tactics, etc.
Generative AI is finding an increasing number of applications in the fields of software engineering, product development, pharmacology, marketing, media, and more. As this is still an emerging technology, it comes with certain limitations. One limitation is the associated concerns regarding the security and privacy of synthetic data. Additionally, the models themselves can be used for nefarious purposes, such as deepfakes, which will become increasingly difficult to identify and eliminate. However, rapid adoption of Generative AI will become inevitable as more benefits are unlocked.
Here are five interesting reads that discuss Generative AI, its applications and limitations in detail:
Synthetic data is generated through various techniques such as Generative Adversarial Networks (GANs), variational auto-encoders, and more. GANs are a semi-supervised learning framework that can help in eliminating human bias and model bias.
Generative AI offers many significant benefits besides catering for distinct purposes like identity protection or film restoration. It enables machines to use textual or visual data to create new content. However, the outputs are sometimes unexpected.
Deep generative models (DGMs) are the result of generative AI models and deep neural networks working together. DGMs are capable of handling particularly complex data sets and subjects. Yet, they lack explainability, and thereby creating a risk of a bias or an inaccuracy.
Generative AI is finding its way into numerous industries and sectors. From movie dubbing to improving the quality of a prosthetic limb, various manifestations of generative AI models are being used.
A novel application of Generative AI is in building the Metaverse. These models can help build codes and designs, even NFTs, faster and more accurate than human developers. Does this denote replacing humans with machines?
Generative AI models require less training since they self-learn from every set of input data. They can also reduce the biases from human decision-making and the risks associated with it. Generative AI promises new levels of automation and creativity. Making necessary regulations to control the usage and application of these models is crucial.
