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, cancer prevention, 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.
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- AI Cage Index
- AI Traps Detection Index
- Bias Detection and Mitigation Index
- AI Hostility Data Training Index
- Data Source Diversity Index
- Data Collection Practices Index
- Transparency in Data Usage Index
- Ethical Data Collection Practices Index
- Data Anonymization and De-identification Index
- Human Oversight and Review Index
AI Brick Computing: Scalable and Sustainable Green AI
The recent craze for Data Center Free AI stems from growing concerns over the massive energy consumption, carbon footprint, and centralization of AI models reliant on cloud infrastructure. As a result, researchers and industries across the world are shifting fast towards decentralized, edge-based, and sustainable AI solutions that can operate efficiently without data centers, leveraging low-power devices, federated learning, and renewable energy to ensure scalability and environmental responsibility.
This article introduces Mobile AI Brick Computing, a groundbreaking concept that envisions autonomous, portable AI computing units designed for large-scale deployment of Knowledge-Augmented Generation (KAG), Retrieval-Augmented Generation (RAG), Large Language Models (LLMs), and Mixture of Experts (MOE) AI architectures.
The increasing demand for artificial intelligence (AI) applications has led to unprecedented reliance on centralized cloud computing, resulting in high energy consumption, increased latency, and concerns over data privacy. As AI models grow in complexity, the need for sustainable, decentralized, and scalable AI processing has become more evident. One emerging concept aimed at addressing these challenges is AI Mobile Brick Computing—a novel approach that leverages modular, self-sufficient AI processing units capable of running AI workloads without the need for centralized data centers.
AI Mobile Brick Computing envisions autonomous, portable AI computing units, designed for local AI inference, decentralized model training, and renewable-powered AI processing. By moving away from the traditional cloud-AI dependency, these compact AI bricks—which can be embedded in mobile devices, IoT nodes, or edge computing stations—offer a sustainable alternative for real-time AI processing and energy-efficient deployment.
What is an AI Brick?
An AI Brick is a modular, self-sufficient AI processing unit designed to run AI workloads without relying on centralized data centers. It is a portable, energy-efficient, and decentralized AI computing module that can perform inference, training, and distributed learning using low-power hardware, federated learning, and renewable energy sources.
AI Bricks are envisioned as building blocks of decentralized AI ecosystems, capable of running Knowledge-Augmented Generation (KAG), Retrieval-Augmented Generation (RAG), Large Language Models (LLMs), and Mixture of Experts (MOE) architectures at the edge. Unlike traditional AI deployments that depend on cloud-based GPUs, AI Bricks are designed for scalability, mobility, and sustainability, enabling AI applications to function efficiently in off-grid, remote, and distributed environments.
AI Bricks are not just AI processors—they are self-sustaining, intelligent AI nodes capable of operating independently and collaboratively within decentralized AI ecosystems. Their six capabilities—autonomy, decentralized learning, knowledge efficiency, AI collaboration, real-time execution, and energy optimization—make them ideal for next-generation AI deployment.
As the demand for Data Center Free AI grows, AI Bricks provide a scalable, efficient, and sustainable alternative to traditional AI models, enabling AI to be faster, smarter, greener, and compassionate.
Read more ..Five Steps to Building AI Agents with Higher Vision and Values
Building reliable AI agents with higher values and safety is a challenge. It requires balancing advanced technological innovation with rigorous testing, transparency, and accountability. This article explores five key steps and the top ten ethical principles for developing reliable AI agents with higher values.
Developing AI agents is not merely a technological endeavor; it is an artistic process of harmonizing purpose, integrating human values, intelligence, and adaptability. At our Compassionate AI Lab, the deeper purpose of innovation is to bring clarity, harmony, compassion, and higher values to life. Future AI agents are envisioned not just as tools but as catalysts for transforming society toward greater compassion, care, and better society.
This article explores into the top ten ethical principles and five essential steps for creating an AI agent, blending technical innovation with a vision of enlightened progress.
The vision for future AI agents is not merely one of technological advancement but one of societal evolution, where compassion and intelligence collaborates to create a harmonious and enlightened world.
Read more ..The 10 Ethical AI Indexes for LLM Data Training and Responsible AI
In this article, we present an exploration of ten indispensable ethical AI indexes that are paramount for the responsible AI development and deployment of Large Language Models (LLMs) through the intricate processes of data training and modeling.
Within our compassionate AI Lab, we have diligently worked to create a series of AI indexes and measurement criteria with the objective of safeguarding the interests of future generations and empowering humanity. The names of the ten ethical AI indexes are as follows:
Ethical Responsibilities in Large Language AI Models: GPT-3, GPT-4, PaLM 2, LLaMA, Chinchilla, Gopher, and BLOOM
Large-language AI models like GPT-3, GPT-4, PaLM 2, LLaMA, Chinchilla, Gopher, and BLOOM have changed the field of artificial intelligence in a big way. However, ethical considerations are the biggest challenge for large-language AI models. These models are very good at generating language and have a huge amount of promise to serve humanity. But with a lot of power comes a lot of responsibility, and it's important to look into the social issues that come up when making and using these cutting-edge language models.
Ethical Responsibility in Large Language AI Models
In this article, we explore the ethical considerations surrounding large language AI models, specifically focusing on notable models like GPT-3, GPT-4, PaLM 2, LLaMA, Chinchilla, Gopher, and BLOOM. If not carefully addressed now, the immense power and influence of these types of models can inadvertently promote biases and other chaos in the human society.
By critically examining the ethical implications of large language AI models, we aim to shed light on the importance of addressing these concerns proactively. These models possess the ability to generate vast amounts of text, which can significantly impact society and shape public opinion. However, if not appropriately managed, this power can amplify biases, reinforce stereotypes, and contribute to the spread of misinformation.
Calling for a Compassionate AI Movement: Towards Compassionate Artificial Intelligence
This is a call for a Compassionate AI Movement that advocates and promotes the creation and use of AI systems that put human safety and values like compassion, equity, and the common good first.
Calling for a Compassionate AI Movement:
Join the Compassionate AI Movement, championing the advancement and implementation of AI systems that place utmost importance on empathy, fairness, and the betterment of society.
"The true measure of AI's greatness lies not in its intelligence alone, but its ability to combine intelligence with compassion." - Sri Amit Ray
The moment has come for a Compassionate AI Movement to reshape the course of AI development and deployment. We can build AI systems that accord with our collective values and contribute to a more compassionate and equitable society by prioritizing safety, empathy, fairness, and social benefit.
Read more ..From Data-Driven AI to Compassionate AI: Safeguarding Humanity and Empowering Future Generations
In this article, we explore the transition from data-driven AI to compassionate AI and how it holds the key to safeguarding humanity and empowering the next generation. We can unlock the full potential of AI while assuring fairness, encouraging well-being, and creating meaningful human-machine interactions by incorporating empathy, ethics, and societal values into AI systems. By embracing compassionate AI, which incorporates empathy and societal values, we can overcome these limitations, safeguard humanity, and empower future generations.
Safeguarding Humanity and Empowering Future Generations
Recently, artificial intelligence (AI) has made enormous advances, revolutionizing sectors and reshaping the way we live and work. Data-driven AI has been at the forefront of this AI revolution, with its capacity to handle massive volumes of data and extract valuable insights.
However, as we continue to harness the power of AI, we must understand and confront the limitations of a data-only strategy. The transition to compassionate AI has arisen as a necessary and ethical necessity, with the goal of protecting mankind and empowering future generations.
We can unlock the full potential of AI while assuring fairness, encouraging well-being, and creating meaningful human-machine interactions by incorporating empathy, ethics, and societal values into AI systems. By embracing compassionate AI, which incorporates empathy and societal values, we can overcome these limitations, safeguard humanity, and empower future generations.
Read more ..Artificial intelligence for Climate Change, Biodiversity and Earth System Models
How to make an accurate prediction of climate change and greenhouse carbon emissions with artificial intelligence? We are experimenting with several AI models for climate change and global warming. Sri Amit Ray explains the hybrid system of process-based earth system models and the deep learning networks of artificial intelligence.
As climate change and global warming have become the most urgent issues for human survival, researching ways to improve climate change and earth system models has become of the utmost importance.
Artificial intelligence (AI), specifically deep learning algorithms, has the ability to make decisive interpretations of large amounts of complex data. Moreover, modern machine learning techniques appear as the most effective tool for the analysis and understanding of ocean, earth, ice formations, CO2 emissions, biodiversity, and atmospheric data for situation and target specific dynamic prediction. One of the strengths of machine learning tools such as convolutional neural network is its capacity to combine a variety of methods.
Deep Transfer Learning Models for Biodiversity, Climate Change, and Global Warming
Quantum Machine Learning: The 10 Key Properties
In this article, we discussed the 10 properties and characteristics of hybrid classical-quantum machine learning approaches for our Compassionate AI Lab projects. Quantum computers with the power of machine learning will disrupt every industry. They will change the way we live in this world and the way we fight diseases, care for old people and blind people, invent new medicines and new materials, and solve health, climate and social issues. Similar to the 10 V's of big data we have identified 10 M's of quantum machine learning (QML). These 10 properties of quantum machine learning can be argued, debated and fine-tuned for further refinements.
Hybrid Classical Quantum Machine Learning
The compassionate AI lab is currently developing a hybrid classical-quantum machine learning (HQML) framework - a quantum computing virtual plugin to build a bridge between the available quantum computing facilities with the classical machine learning software like Tensor flow, Scikit-learn, Keras, XGBoost, LightGBM, and cuDNN.
Presently the hybrid classical-quantum machine learning (HQML) framework includes 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.
Five Key Benefits of Quantum Machine Learning
Five Key Benefits of Quantum Machine Learning
Here, Dr. Amit Ray discusses the five key benefits of quantum machine learning.
Quantum machine learning is evolving very fast and gaining enormous momentum due to its huge potential. Quantum machine learning is the key technology for future compassionate artificial intelligence. In our Compassionate AI Lab, we have conducted several experiments on quantum machine learning in the areas of drug-discovery, combating antibiotic resistance bacteria, and multi-omics data integration.
We have realized that in the area of drug design and multi-omics data integration, the power of deep learning is very much restricted in classical computer. Hence, with limited facilities, we have conducted many hybrid classical-quantum machine learning algorithm testing at our Compassionate AI Lab.
Five Benefits of Quantum Machine Learning
What's Holding Back Machine Learning in Healthcare
What is holding back the large scale implementation of machine learning systems in healthcare and precision medicine? In this article Dr. Amit Ray, explains the key obstacles and challenges of implementing large-scale machine learning systems in healthcare. Dr. Ray argued that lack of deeper integration, incomplete understanding of the underlying molecular processes of disease it is intended to treat, may limit the progress of implementing large-scale machine learning based reliable systems in healthcare. Here, nine obstacles of present day machine learning systems in healthcare are discussed.
Machine Learning in Healthcare
Recently, machine learning algorithms, especially deep learning has shown impressive performance in many areas of medical science, especially in classifying imaging data in different clinical domains. In academic environment, Deep learning and Reinforcement learning methods of Artificial Intelligence (AI) has shown tremendous success in numerous clinical areas such as: Omics data integration (such as genomics, proteomics or metabolomics), prediction of drug-disease correlation based on gene expression, and finding combinations of drugs that should not be taken together. Deep learning is very successful in predicting cancer outcome based on tumour tissue images. Machine learning are used for medical decision support systems for ICU and critical care. Artificial Intelligence in Healthcare Current Trends discusses the current status of AI in healthcare.
Read more ..Roadmap for 1000 Qubits Fault-tolerant Quantum Computers
How many qubits are needed to outperform conventional computers? How to protect a quantum computer from the effects of decoherence? And how to design more than 1,000 qubits fault-tolerant large-scale quantum computers? These are the three basic questions we want to deal in this article.
Qubit technologies, qubit quality, qubit count, qubit connectivity and qubit architectures are the five key areas of quantum computing. In this article, we explain the practical issues of designing large-scale quantum computers.
Quantum Computing with Many World Interpretation Scopes and Challenges
The Many-Worlds Interpretation (MWI) of quantum mechanics posits that all possible outcomes of quantum measurements are realized, each in a separate, non-communicating branch of the universe. This interpretation challenges the traditional Copenhagen view, which involves wave function collapse to a single outcome. In the context of quantum computing, MWI offers a framework for understanding quantum parallelism—the ability of quantum computers to process multiple computations simultaneously.
In this article, we explore how MWI aligns with quantum computing's principles, the opportunities it presents, and the challenges we must address to harness its full potential.
Many scientists believe that Many Worlds Interpretation (MWI) of quantum mechanics is self-evidently absurd for quantum computing. However, recently, there are many groups of scientists increasingly believing that MWI has the real future in quantum computing, because MWI can provide true quantum parallelism. Here, I briefly discuss the scopes and challenges of MWI for future quantum computing for exploration into the deeper aspects of qubits and quantum computing with MWI.
This tutorial is for the researchers, volunteers and students of the Compassionate AI Lab for understanding the deeper aspects of quantum computing for implementing large-scale compassionate artificial intelligence projects.
Navigation System for Blind People Using Artificial Intelligence
Machine Learning to Fight Antimicrobial Resistance in Health Care
This project focuses on the use of grid cell, place cell and path integration strategies with artificial intelligence for designing the navigation system for blind people.
Antimicrobial resistance is one of the key reasons for human sufferings in modern hospitals. We focused on seven most important antimicrobial resistance machine learning projects. Antibiotic resistance genes detection, microbial community classification, molecular basis for bacterial resistance, multi-drug resistance behavior of bacteria, bacterial behavior with phages, MRSA Strains for Hospital-Acquired Infections, and machine learning for culture-free bacterial identification are among them.
AI Based Vaccine Design for COVID-19 (SARS-CoV-2 Virus)
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.
Key Artificial Intelligence Projects to Fight Against COVID-19
Quantum Machine Learning and Deep Intelligence Frameworks
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.
AI to Combat Antibiotic Resistant Bacteria
AI for Balance Control of Elderly People
Antibiotic resistance bacteria is becoming world’s biggest health crisis. Here, we focus how deep machine learning techniques can be used to counter antibiotic resistance bacteria. Read more.
This project focuses on developing systems to eliminate the imbalance, fall and injury of elderly people or for physically challenged people. Read more on balance control of elderly people.
AI and Deep Transfer Learning for Biodiversity and Climate Change
What Holding Back Machine Learning in Healthcare
This project of Artificial Intelligence for Climate change, focuses on the applicability of Convolutional Neural Networks (CNN), Generative Adversarial Networks (GANs) for generating synthetic data related to climate change, biodiversity, and CO2 emissions.
This study focuses on eliminating the issues and difficulties in the applications of machine learning in healthcare.
Artificial Intelligence for Caring Blind People
Artificial Intelligence in Radiology for X-Ray and CT-Scan
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 ,.
AI for Brain Computer Interface for Mentally Challenged People
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.
Three Levels of Compassionate Artificial Intelligence: Scopes and Challenges
Here, Sri Amit Ray talks about how artificial intelligence, neural networks, deep learning, reinforcement learning and other machine learning technologies can be used for designing advance compassionate artificial intelligence systems. Dr. Ray discusses the scopes, issues and frameworks to include compassion, kindness and empathy in future AI systems. Read more to understand the three levels of Compassionate Artificial Intelligence.
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.
Roadmap for 1000 Qubits Fault-tolerant Quantum Computers
7 Core Qubit Technologies for Quantum Computing
Quantum Computer Algorithms for AI
Spin-orbit Coupling Qubits for Quantum Computing and AI
The 10 Key Properties of Future of Quantum Machine Learning
