Explainable AI

There is no denying the fact that artificial intelligence is the future. From the security forces to the military applications, AI has spread out its wings to encompass our daily lives as well. However, AI comes with its own limitations. The machines may be made by humans but the processes they follow and the speed with which the machines go through the huge chunks of information is beyond human perception. This is the reason, we are working to make sure that the humans can understand the reasoning and the logic behind every decision these machines take and use the knowledge to develop better machines.

So What Do We Do?

We are a group of scientists and researchers trying to make AI machines understandable for human ease. We take into consideration the machine learning mechanism and would try to remodel it for better human understanding. The program will aim to create new and improved machine-learning techniques which would enable the machines to not only explain but also rationalize and predict their future behavioral pattern for better human understanding. This will help the future developers create better machines with these explainable graphs and better human-machine interface.

How does Explainable Artificial Intelligence work?

XAI program will incorporate new explanation techniques with the results produced by the machine in order to create more explainable models and results. Architectural layers, design data, loss functions, optimization techniques, and many other processes are used to experiment and develop interpretable models of the AI machines. Model induction would also take place to treat the machine processes like a black box and experiment with it to develop a better understanding of its processes.

We collaborate with the best researchers and scientists from all over the world who have excelled in the field of AI machines. Together we will work to create better understanding and learning techniques to provide a greater support to the machine-human relationship. The program will test and research various XAI prototypes to create more explainable models. Once the prototypes are approved, they would be available commercially as well create a more robust XAI solution.