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Generative AI UPSC NOTE


What is Generative AI?

  • Generative AI (GenAI) is a type of Artificial Intelligence that can create a wide variety of data, such as images, videos, audio, text, and 3D models.

  • It does this by learning patterns from existing data, then using this knowledge to generate new and unique outputs.

  • GenAI is capable of producing highly realistic and complex content that mimics human creativity, making it a valuable tool for many industries such as gaming, entertainment, and product design.

  • The rise of GAI can be attributed to the development of advanced generative models, such as Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs).

    • These models are trained on large amounts of data and are able to generate new outputs that are similar to the training data. 

    • For example, a GAN trained on images of faces can generate new, synthetic images of faces that look realistic.

  • Recent breakthroughs in the field, such as GPT (Generative Pre-trained Transformer) and Midjourney, have significantly advanced the capabilities of GenAI.

  • These advancements have opened up new possibilities for using GenAI to solve complex problems, create art, and even assist in scientific research.

Applications and Uses

Text Generation:

  • Text generation has numerous applications in the realm of natural language processing, chatbots, and content creation.

  • ChatGPT, developed by OpenAI, is a successful platform that uses Text Generation to generate human-like responses in chat conversations.

Video and Speech Generation:

  • Video Generation involves deep learning methods such as GANs and Video Diffusion to generate new videos by predicting frames based on previous frames.

  • Video Generation can be used in various fields, such as entertainment, sports analysis, and autonomous driving. 

Video and Speech Generation:

  • Video Generation can be often seen in use with Speech Generation. 

  • The models used for speech generation can be powered by Tranformers. 

  • Speech Generation can be used in text-to-speech conversion, virtual assistants, and voice cloning.

  • Platforms such as DeepBrain and Synthesia utilize Video and Speech Generation to create realistic video content, that appears as if a human was speaking on camera.


  • It can help musicians and music producers explore new sounds and styles, leading to more diverse and interesting music.

  • Amper Music - creates musical tracks from pre-recorded samples.

  • AIVA - uses AI algorithms to compose original music in various genres and styles.

Art and Creativity:

  • Image Generation using deep learning algorithms such as VAEs, GANs, and more recently Stable Diffusion, to create new images that are visually similar to real-world images.

Art and Creativity:

  • Image Generation can be used for data augmentation to improve the performance of machine learning models, as well as in creating art, generating product images, and more.

  • DeepDream Generator - An open-source platform that uses deep learning algorithms to create surrealistic, dream-like images.

  • DALL·E2 - This AI model from OpenAI generates new images from text descriptions.

Data augmentation:

  • Data augmentation is a process of generating new training data by applying various image transformations such as flipping, cropping, rotating, and color jittering. 

  • The goal is to increase the diversity of training data and avoid overfitting, which can lead to better performance of machine learning models.

  • Synthesis AI simplifies the process of building and optimizing machine learning models by providing a platform for creating AI models using automated machine learning techniques.

Computer Graphics:

  • It can generate new 3D models, animations, and special effects, helping movie studios and game developers create more realistic and engaging experiences.


  • By generating new medical images and simulations, improving the accuracy and efficiency of medical diagnoses and treatments.

  • In healthcare, generative AI can help generate synthetic medical data to train machine learning models, develop new drug candidates, and design clinical trials.

Manufacturing and Robotics:

  • It can help optimize manufacturing processes, improving the efficiency and quality of these processes.

Adverse effects

  • Ability to falsify data.

  • Dishonest actors wielding AI are one of many threats.

  • Inscrutability of the inner workings of AI models, their use of copyrighted data, regard for human dignity and privacy, and protections from falsifying information.

  • GAI models are trained on large amounts of data, and if that data is biased, the outputs generated by GAI may also be biased. 

    • This can lead to discrimination and reinforce existing societal biases.

  • There is a risk that this data could be used for unethical purposes, such as for targeted advertising or for political manipulation.

  • It may be used to generate fake news or other malicious content, without knowing who is responsible for the output. This could lead to ethical dilemmas over responsibility.

  • Automation and Lowering Job

India and AI

  • As per NASSCOM data, the overall AI employment in India is estimated at about 416,000 professionals.

  • The growth rate for the sector is estimated at about 20-25%. Further, AI is expected to contribute an additional USD 957 billion to India’s economy, by 2035.

Indian Initiatives:

  • National Strategy for Artificial Intelligence

  • National Mission on Interdisciplinary Cyber-Physical Systems - Technology Innovation Hubs (TIH) has been established on AI and Machine Learning at IIT Kharagpur.

Indian Initiatives:

  • Artificial Intelligence Research, Analytics and Knowledge Assimilation Platform.

Steps to be taken:

  • The Indian government should proactively launch and maintain: 

    • an open-source AI risk profile

    • set up sandboxed R&D environments to test potentially high-risk AI models, 

    • promote the development of explainable AI,

    • define scenarios of intervention,

    • keep a watchful eye.

  • Regulations and standards must be put in place to ensure that GAI is used in a responsible and ethical manner.

  • Collaboration between stakeholders



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Learnerz IAS | Concept oriented UPSC Classes in Malayalam: Generative AI UPSC NOTE
Generative AI UPSC NOTE
Learnerz IAS | Concept oriented UPSC Classes in Malayalam
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