Let us dive right into some of the cool projects that I created and it is my privilege to put these in open source so that we togather can realize its benefits, improvize upon what it offers, and help improve our lives.

Thermocloud

Heating, Ventillation and Cooling (HVAC) operation constitutes 45-55% of house energy, due to inefficient conventional thermostats. Goal of the project is to create a cloud-based collaborative learning thermostat that optimizes HVAC operation and maximizes comfort and energy savings using machine learning.

Unlike the other thermostats which attempt to conserve energy by reducing the time you run the HVAC system while you are away, this thermostat uses a patented technology that also learns how best to operate the HVAC system so that it can conserve energy while HVAC runs when you are home.

It automatically learns the patterns of operations that offer maximum savings and comfort, and collaborates with other thermostats with similar house profiles to further maximize savings.

Checkout this cool project in its entirety here.

Autonomous Learning

This project implements a novel autonomous approach to knowledge refinement wherein the system automatically determines what can be improved and trains itself to do so. It enables solutions with smartness that goes beyond initial training, when domain is expansive and high quality training may not be feasible for entire expanse of the domain, or when spontaneous mutations alter various aspects of the domain.

A novel collaborative approach with automatically built dynamic ensembles of models target improvisations, such as improvements based on past knowledge or trends reflected in the results, and overshadows sub-optimal performance of existing model in a portion of input feature space. A model selector automatically identifies an optimal model for a query using reinforcement learning. This alleviates need for complex monolithic models requiring extensive training, automatically adapt to changes in the domain without destabilizing the models, offer better control over query performance, and can guide exploration when exploitation becomes inadequate.

The efficacy of this approach was demonstrated esp. for areas where deep-learning failed to perform for following applications:

1. Doc-Aide: DocAide is a general purpose AI-based medical guidance system to guide doctors for surgeries and new diseases like Covid-19. A medical expert initially trains the system to provide this guidance, and then the system uses autonomous learning to refine its knowledge automatically far beyond the human expert
2. American Football coach-sidekick: The coach-sidekick predicts play-calls to maximize points for an offensive drive. The system uses autonomous learning to refine its knowledge of the expansive domain comprising of game strategies based on capabilities of players and numerous intricate rules. It also automatically adapts to spontaneous mutations due to change in playing conditions or new rules.

There are numerous additional potential applications for this paradigm such as coaching sidekick for personalized training for student, athlete, astronauts; combat sidekick to adapt to a soldier/special ops needs; smart cities to adapt to unforeseen weather/social phenomenons and space exploration satellites that adapt during long journey in unknown conditions.

Checkout this cool project in its entirety here.

Learn AI

This is an education project where my goal is to give an honest comprehensive overview of the many prominent fields under Artificial Intelligence and then go a bit deeper in some of these fields to explore the field using some sample applications.

Checkout this cool project in its entirety here.