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DasAI envisions creating India’s own AI Foundational model on which multiple AI platforms like Multi-Lingual Chatbots, Image Generators, Video Generators, and several other tools can be created. The model is expected to be a go to solution for India’s vey own problems.
Goals For Building an India Centric AI Model
India is a vast country in terms of geographical size and also in terms of its 1.4 billion population, it would always need assistance from AI. The vastness and diversity of India, makes the application of foreign AI models impossible because they are trained on homogenized data from western countries.\
Hence, India must develop its own AI model to solve the crisis taking place in our country.
1. AI Solving Language Neutrality
India is a vast country with 22 Scheduled Languages, and thousands of regional languages, dialects and their variations. Each region in India follows a culture that is very close to that of their neighboring region but gradually changes as we move across the country.
Therefore, the foundational AI model of India not only needs to have zero bias but also should not let the assumptions of one region shadow that of the other.
The AI Model that we are building would not only be able to assist in solving the language barrier in the country but also present an opportunity for every other culture to understand everyone else’s opinions. Therefore, India’s own AI model DasAI attempts to bridge all the language gaps between fellow Indians.
2. AI Solving India’s Mineral Exploration
AI can play a huge role in India’s mineral exploration which remains a critical challenge for the country. Despite being a vast country and thus presumably rich in critical minerals like rare earths, monazite, pitch blend, and several others, the country faces a shortage of these due to lack of proper exploration techniques.
AI can easily understand the top soil data and predict which minerals could be present in the region.
3. AI-Based Predictive Healthcare
Being a country with a government funded healthcare setup, the country can save billions of rupees in predicting diseases even before they take a concrete form and harm any person. Predictive Healthcare is a cutting edge application of Artificial Intelligence and could helps Indians become aware of possible underlying conditions with just a selfie.
One example of Predictive Healthcare could be in the early diagnosis of Type-II Diabetes, one of the most critical growing epidemics in India. Visual signs like dark band around neck could help in its detection and early prevention. As a result, it would avoid a insulin crisis at a later stage in life.
4. AI-Based Crop Health
The largest part of India’s workforce is still in agriculture and with reducing individual land holdings, it becomes almost impossible to ensure high production rates and make space for failed crops. India being the food-bowl of the entire globe cannot afford a food crisis.
AI can easily help avoid any food crop failure in the country by identifying patterns that indicate a weak crop health which can lead to a crop failure. Crop failure not only leads to a fall in food production but also results in mounting crop insurance claims which
It would prevent two things at once: crop failure and mounting crop insurance claims.
Why Das AI Models Are Predicted To Have High Success?
There are three components where an AI model could go wrong: bad data, bad feature engineering, and bad fitting between features and labels.
1. Trained on Indian Datasets
Being trained on ethically sourced Indian data, the model is expected to avoid all the biasness of global models that stereotype Indians based on western beliefs.
Further, Indian data would enhance the cultural fitting of Indian models removing any bias against any Indian culture.
An Indian model would also detect India’s problems much faster than any other model, provide much more realistic solutions and actionable insights. This is not possible in global models because they forget to understand the unsaid rules of Indian society.
2. Zero Bias Feature Engineering
DasAI is ready to go to extreme lengths in ensuring that all the features used in developing its model removes any bias. This would ensure a neutral AI neural network which would then predict solutions without favoring anyone or anything.
3. Avoidance in Overfitting
Overfitting is the sole reason why models with good data, good features and good engineering fail. An overfitted solution does not only produce a wrong answer to any problem but also makes it difficult to assess which part of the AI model has encountered the error.
