Generative AI in Creation

Generative AI is profoundly one of the most interesting sections of artificial intelligence that involves systems capable of generating new content, based on simple patterns ‘learned’ from large data collections. On the contrary, most systems are geared towards pattern recognition and decision-making using given data and cannot create new models/data. Generative AI models are dominant because they can create data such as images, videos, songs, and even texts.

Because of the peculiar nature of Generative AI, its uses can be felt in very many sectors:

• For instance, in the art sector, AI is helping artists to come up with new forms of paintings and other art forms.
• In medicine, AI models have been used to create synthetic medical images that can be used for research purposes making it possible for doctors to study rare diseases without real patient data.
• In the field of games, movies, and music production, video games, stories, and music composed are now filled with AI as the creative content amends the landscape of how such Original Work is produced.

Generative AI is not simply about remembering and reproducing what one has seen. Rather, it is an enabler of many concepts that cannot be readily generated; it creates potential events, cases, or things that were never present previously.

Why is it Necessary to Comprehend Generative AI?

Generative AI is of course important because it enhances the efficiency of what was previously considered as the domain of human imagination. By making creative and productive work processes more efficient, Generative AI can:

• Cope up quicker with artistically creative ventures by creating images and composing music and writing texts in much shorter time than humanity.
• Increase efficiency in advertising and mass media industries by generating AI content because of less time and cost due to content creation.
• Aid R&D by helping to create synthetic data that is useful in training AI models for medical imaging, drug discovery, and self-driving cars.

There are many forms of Generative AI, some of which include:

• Text generation: Taking input in some textual or other form and building a meaningful text (e.g. GPT).
• Image generation: Generation of pictures from expressions or texts (DALL-E) or rebuilding entire image from a few pixels.
• Video generation: Videos where the identity and expressions of individuals appear real can be modified using tools such as DeepFake.
• Music composition: Melodies and soundtracks generated by AI open new possibilities for musicians and music producers.

All these unique applications require comprehension of the very basics of Generative AI.

Course Instructor

Himanshu Garg Author

Experienced Engineering Mentor and Educator | Empowering Students to Excel. With a passion for guiding and empowering engineering students, I am dedicated to supporting their academic journey and fostering their success. With a strong background in Process Control (instrumentation), I completed my Mtech in 2020. Passionate about helping students excel, aims to introduce new modules addressing mental stress problems and other crucial areas in Engineer’s Planet. Connect with me to explore opportunities for collaboration and support in engineering education.