Generative AI is the new buzzword since late 2022. The likes of ChatGPT, Bard, etc. is taking the AI to the all new levels with wide variety of use-cases for consumers and enterprises.
I wanted to briefly understand the difference between traditional AI and generative AI. According to a recent report published in Deloitte, GenAI’s output is of a higher complexity while compared with traditional AI.
Typical AI models would generate output in the form of a value (Ex: predicting sales for next quarter), label (Ex: classifying a transaction as legitimate or fraud). GenAI models tend to generate a full page of composed text or other digital artifact. Applications like Midjourney, DALL-E produces images, for instance.
In the case of GenAI, there is no one possible correct answer. Deloitte study reports, this results in a large degree of freedom and variability, which can be interpreted as creativity.
The underlying GenAI models are usually large in terms of resources consumption, requiring TBs of high-quality data processed on large-scale, GPU-enabled, high-performance computing clusters. With OpenAI’s innovation being plugged into Microsoft Azure Services and Office suites, it would be interesting to see the dramatic changes in consumers’ productivity!
