India plans to establish a “high powered committee” dedicated to exploring the development of Large Language Models (LLMs). These models are integral in harnessing Artificial Intelligence to create applications capable of comprehending and processing human language. This step signifies a forward-looking initiative by India in advancing technological capabilities for language processing using AI.
Understanding Large Language Models (LLMs):
- Overview: LLMs are a specific class of generative AI models crafted through deep learning techniques, primarily utilizing neural networks. These models are proficient in comprehending and generating human-like text.
- Example: Among the prominent LLMs, OpenAI’s GPT (Generative Pre-trained Transformer) stands as one of the most well-known examples, designed to produce coherent and contextually relevant text when given a prompt or input.
Generative AI and US-India Collaboration:
- Generative AI: This subset of artificial intelligence concentrates on creating systems capable of generating content similar to human production. These systems, informed by existing data patterns, generate new, original content in various forms, such as text, images, and music.
- US-India Partnership: The collaboration between India and the United States in the realm of deep technology is experiencing a fruitful phase. India’s draft policy on deep tech aligns well with the existing partnership, listing over 10,000 startups across different deep tech areas, fostering prospects for cooperation.
Deep Tech and Its Characteristics:
- Definition: Deep tech or deep technology refers to a class of startup businesses that focus on innovations derived from substantial engineering breakthroughs or scientific discoveries.
- Fields: These startups operate in diverse areas like agriculture, life sciences, chemistry, aerospace, and green energy, while other growing areas include AI, advanced materials, blockchain, biotechnology, robotics, drones, photonics, and quantum computing.
- Characteristics: Deep tech innovations are radical, capable of disrupting existing markets or creating new ones. However, their development timeline is considerably longer than shallow technology, with implications for requiring significant capital for research and development.
Challenges Faced by Deep Tech Startups:
- Funding: Securing funding remains a primary challenge for deep-tech startups, with less than 20% receiving financial backing. Government funds are underutilized, and there’s a dearth of domestic capital for these startups.
- Talent and Market Access: Deep tech startups grapple with challenges in accessing talent, understanding from investors, acquiring customers, and facing high talent costs.
Draft National Deep Tech Startup Policy (NDTSP), 2023:
- Objective: The policy aims to bolster research and development in deep tech startups, providing financial support at critical stages and simplifying the intellectual property regime. It emphasizes easing market entry barriers for Indian startups in foreign markets and recommends the creation of an “Inter-Ministerial Deep Tech Committee” to enhance the ecosystem.
Office of the Principal Scientific Adviser (PSA):
- Role: The PSA’s office aims to offer pragmatic and objective advice to the Prime Minister and the cabinet in matters concerning Science and Technology.
- PM-STIAC: The Prime Minister’s Science, Technology and Innovation Advisory Council facilitates the PSA’s office to assess technology domains, formulate interventions, and devise a forward-looking roadmap for the Prime Minister’s consideration.
The effort signifies India’s commitment to technological advancement and scientific innovation, promising a collaborative ecosystem for the development of cutting-edge technologies.
