In early years, AI performed functions such as classsification of data (whether the animal is a tiger or lion), grouping the data (say customers with similar income) or choosing actions ( whether a self-driving car should stop or speed up). This was a cumbersome job. You required large manpower of data scientists. You had to collect a lot of data. The data had to be labelled. There was training involved. Using AI then, a chatbot assisting home loan customer could be created in 6-8 months. The process was expensive. A chatbot assisting credit card users could be created if we start again from scratch. Thus AI’s adoption was slow.

Foundation Model

Traditional AI systems were trained for a specific purpose. The way out is to develop Foundation Model –an AI system with a broad set of capabilities, and which can be adapted to a variety of purposes.

Of course, Foundation Model are developed by big organisations, with resources and expertise.

Foundation Model uses an ML process in which the model trains itself to learn one part of the input from another part of the input. This is called self-supervised learning.

Thus if the Model is fed adequately, it will predict the word after ‘I love’ in all likelihood will be ‘You’. It is not necessary to label and train the data.

Once we have a collection of Foundation Models, these can be exploited to create derivative AI models and applications. This can be done economically. A single chatbot can answer queries related to housing loans, credit cards and many other banking services. To make it more effective, one can add a few more documents.

Large Language Model

When a foundation model is based on language model, it is called large language model (LLM). In fact, LLMs are a subset of Foundation Models. These models, in fact, automate language. They can generate dialogues, summarisation, text, translation and so many other things.

OpenAI’s LLM crawled over a lot of publicly available information on internet to create ChatGPT.

Of course, the publicly available information is not always accurate. ChatGPT, therefore, can give wrong answers. OpenAI’s GPT is fascinating and magical.

Enterprises need accurate information. They need trustworthy outputs. They need datasets on which they have rights. The data must be curated carefully — it should be free from hate and profanity. There should be simultaneous use of internal data and external data. Many Foundation Models can be created this way. IBM is working on this.

Generative AI is a new class of AI systems that can generate content in terms of text, images and video. Researchers are now working on models which have Artificial General Intelligence (AGI) which make them as smart as human beings and at time smarter,

Crowdsouraing of Data

India is a country of multiple languages. Under a project called Bhasha Daan, data is being crowd sourced. People can contribute to datasets in their own language. These are then subjected to LLMs to build tool sets for translation. These models are then published on API Setu Platform.

Government Contributing Data

Apart from crowdsourcing, the government can provide data from its various departments.

Translations for Data

English texts could be translated. Wikipedia could be translated into Hindi. However, translation itself requires LLM. It is a kind of chicken-and-egg situation.

Synthetic Data

Computers can be used to create data.

India has to build LLMs in the native languages.