Generative Artificial Intelligence (AI)

Generative Artificial Intelligence (AI) has become a prominent trend in recent times, particularly emphasized by our enthusiastic Sales folks ??. Are you familiar with the concept of Synthetic Data? Surely, it's not new to you ??. Additionally, for those with a knack for technology, the term "Generative AI" might trigger thoughts of Generative Models or Statistical Models, amusingly so. Indeed, the application of Generative and Statistical Models has been pervasive for decades. Surprisingly, Generative AI has been in the research sphere since as early as 1950 ??.

In the current AI era, Generative AI offers the capability to produce or simulate predictions for various media forms such as videos, audios, and images.

To grasp the concept of Generative AI, it's essential to comprehend the notion of Generated Models, which in turn necessitates a grasp of Generative Models and Statistical Models. Similarly, delving into AI requires an understanding of Data Science modeling, encompassing machine learning and deep learning models.

Generative:

? A Statistical Model is a mathematical representation embodying a set of statistical assumptions concerning the generation of sample data (and analogous data from a broader population) [Wikipedia].

? In Statistical Classification, two primary approaches exist—Generative and Discriminative Approaches—to compute probabilities [Wikipedia].

Artificial Intelligence (AI):

When machine learning and deep learning models mature to the point where they autonomously operate, learn, rectify, analyze, and forecast, they manifest as Artificial Intelligence (AI). [Note: A separate topic, "AI VS DATA SCIENCE," is available for more detailed comparison.]

Generative AI:

Hopefully, it's now evident that Generative AI centers around the automated generation of sample output or predicted data using automated data science models fueled by extensive datasets. This time, the data is not confined to structured formats only i.e., Synthetic data; it encompasses semi-structured and unstructured data, employing both statistical and generative models.

'Synthetic Data produces structured data, while Generative AI creates structured, semi-structured, and unstructured data.'

For instance, feeding videos, audio, images, text, etc., into Generative AI (which, as we know, is essentially automated data science models) results in the generation of anticipated outputs in desired formats—videos, audio, images, text, and more.

Granted, the execution is not as straightforward as the description suggests; the complexities arise during implementation. Nonetheless, in reality, Generative AI isn't foreign territory. As mentioned earlier, the roots of Generative AI date back to the 1950s ??. So, there's no need for intimidation, nor is there a necessity to expend vast sums of money. In the words of someone wise, 'All Models are Wrong.' Remember, the outputs of models are just one of numerous inputs guiding your decision-making process, rather than the sole determinant.

Cheers.