Principal Scientific Advisor, India will set up a “high powered committee” to explore the development of Large Language Models (LLMs), tools that harness Artificial Intelligence to create applications that can understand and process human language.
Large Language Models
- LLMs : LLMs are a specific class of generative AI models that are trained to understand and generate human-like text.
- These models are built using deep learning techniques, particularly using neural networks.
- They can generate coherent and contextually relevant text given a prompt or input.
- One of the most well-known examples of LLMs is OpenAI’s GPT (Generative Pre-trained Transformer).
Generative AI
- Generative AI refers to the subset of artificial intelligence that focuses on creating systems capable of generating content that is similar to what a human might produce.
- These systems learn from patterns in existing data and then use that knowledge to produce new, original content.
- This content can take various forms, such as text, images, music, and more.
US-India Collaboration
- India and the U.S. have a great relationship now, which is perfect for deep tech cooperation. India’s draft policy on deep tech says that Startup India’s database lists over 10,000 startups in different deep tech areas, which aligns well with the U.S.-India partnership.
Deep Tech
- Deep tech or deep technology refers to a class of startup businesses that develop new offerings based on tangible engineering innovation or scientific discoveries and advances.
- Usually, such startups operate on, but are not limited to, agriculture, life sciences, chemistry, aerospace and green energy.
- Deep tech fields like Artificial Intelligence, advanced materials, blockchain, biotechnology, robotics, drones, photonics, and quantum computing are moving more and more quickly from early research to market applications.
Characteristics of Deep Tech
- Impact: The deep tech innovations are very radical and disrupt an existing market or develop a new one. Innovations based on deep tech often change lives, economies, and societies.
- Time & Scale: The time required for deep technology to develop the technology and reach the market-ready maturity is way more than shallow technology development (like mobile apps and websites). It took decades for artificial intelligence to develop and it is still not perfect.
- Capital: Deep tech often requires a lot of early-stage funding for research and development, prototyping, validating hypotheses, and technology development.
Challenges Faced by Deep Tech
- For deep-tech startups, funding is one of the biggest challenges. Less than 20% of startups receive financing. Government funds are underutilized, and domestic capital is lacking for such startups.
- Talent and market access, research guidance, investors’ understanding of deep-tech, customer acquisition and cost for talent are the major challenges faced by them.