Compassionate AI Lab of Sri Amit Ray
Compassionate AI Lab of Sri Amit Ray is focused on research on compassion and building compassionate artificial intelligence systems for the benefits of humanity and all living beings. The objective is to eliminate the pain and sufferings of people by the use of emerging technologies. Compassionate AI is a multidisciplinary subject. This is a palace where you can exchange ideas within and across AI, neuroscience, omics science, meditation, quantum computing, compassion and other research groups.
Here, we focus on incorporating compassion, kindness and empathy in artificial intelligence systems. The lab conducts fundamental AI research, including theory and methods for serving humanity, helping blind people, old age support, cancer prevention, precision medicine as well as application-oriented human-centered AI research collaboration at a high international level.
Key Artificial Intelligence Projects
It includes the emerging research fields of AI such as compassionate care-giving, compassionate health care, precision medicine, Quantum Computing, compassionate weapon-defense system, compassionate teaching, machine learning, Internet of Things (IoT), drone, big data, blockchain, quantum computing, digital medicine, brain-computer interface, combating antibiotic resistant bacteria, Balance Control of Elderly People, computer aided drug design, etc. Our compassionate AI algorithms are designed to motivate the systems to go out of their way to help and eliminate the physical, mental or emotional pains of humanity and the world.
We analysed various competitive candidate vaccines to fight against COVID viruses. Finally, we designed the best vaccine construct, consisted of 563 amino acid residues derived from different peptide sequences.
We are currently developing a hybrid classical-quantum machine learning (HQML) framework to include the quantum learning algorithms like: Quantum Neural Networks, Quantum Boltzmann Machine, Quantum Principal Component Analysis, Quantum k-means algorithm, Quantum k-medians algorithm, Quantum Bayesian Networks and Quantum Support Vector Machines.
Designing the navigation system for blind people. This project focuses on how image recognition, voice recognition and path navigation methods of artificial intelligence can be used for automated navigation system for blind people. Read more ..
The main advantages of applying machine learning in radiology is providing accurate diagnostic results at an affordable cost. Currently we focus on the use of deep learning and deep reinforcement algorithms for analysis and interpretation of radiological images. Read more ,.
People who are suffering from post-traumatic stress disorder (PTSD), and other mental disorders or brain problems is facing a big challenge to cope with daily activities. This work focuses on technologies for non-invasive transmission of information from brain to computers and computers to brain. Read more.
Yoga and Om Chanting for Cell-Specific Nitric Oxide Regulation for Health Healing and Cancer Prevention
Nitric oxide regulation with Om chanting and yoga exercises is one of our key project. We have conducted various experiments on cell-specific nitric oxide regulation. Especially low frequency multi-stage Om chanting has given positive results.
Compassionate artificial intelligence systems are increasingly required for looking after those unable to care for themselves, especially sick, physically challenged persons, children or elderly people. How AI can help the emotional, social, and spiritual needs of poor, patients and elderly people are the scope of this research work. Our research projects are mostly self-sponsored and sustained by the contribution of common people.
Our Key Quantum Computing Projects
Our research lab is fully compatible with the current development of research on Quantum Computing. We focused our research activities on Quantum Artificial Intelligence. There are three approaches to quantum computing: Gate-based Quantum Computing, Quantum Annealing (QA) and the Adiabatic quantum computation (AQC). Here, we focus mostly on quantum annealing implementation of the algorithms for quantum deep neural learning and quantum deep reinforcement learning for our human-centered projects.