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.
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.
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.
The 10 Ethical AI Indexes For LLM AI Sri Amit Ray Compassionate AI Lab
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.
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.
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.
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.
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.
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.
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.
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.
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.