The 7 Pillars of Compassionate AI Democracy

    In the modern world, democracy faces numerous challenges, including corruption, unequal representation, and inequities in access to essential resources like education, healthcare, and legal services. Sri Amit Ray’s concept of Compassionate AI Democracy offers a transformative vision to address these challenges.

    This article explores the seven pillars of AI democracy, examining how each one can contribute to building a compassionate society with an AI-driven democracy capable of fostering a fairer, more just future for all.

     

    Combining the principles of compassion, human rights, and advanced artificial intelligence (AI), this system envisions a fair and just society where everyone’s voice is heard, human rights are protected, and corruption is eliminated. The foundation of this compassionate AI democracy rests on seven key pillars that promote equality, transparency, and inclusion.

    "Compassionate AI Democracy aims to bridge the gap between the governed and the governing, ensuring every voice is heard and every right is protected." - Sri Amit Ray

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    Compassionate AI-Driven Democracy: Power and Challenges

    In this article, we introduce the concept of Compassionate AI-Driven Democracy, a transformative approach to governance that integrates the power of artificial intelligence with the core principles of democracy. This innovative system envisions a world where AI is not only used to enhance efficiency and decision-making but is also guided by ethical frameworks that prioritize compassion, fairness, and inclusion. Here, we introduce the concept of compassion driven AI Democracy.

    As modern democracies struggle with issues like corruption, political manipulation, and inequality, compassionate AI offers a potential solution by empowering citizens, increasing transparency, and promoting a more equitable distribution of power.

    In a world where technological advancement shapes every facet of life, from healthcare to education to communication, the influence of artificial intelligence (AI) on politics and governance is inevitable. The rapid evolution of AI brings with it the possibility of reshaping democratic systems, offering new avenues for inclusion, transparency, and efficiency.

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    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.

    Ethical AI

    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:

      1. AI Cage Index
      2. AI Traps Detection Index
      3. Bias Detection and Mitigation Index
      4. AI Hostility Data Training Index
      5. Data Source Diversity Index
      6. Data Collection Practices Index
      7. Transparency in Data Usage Index
      8. Ethical Data Collection Practices Index
      9. Data Anonymization and De-identification Index
      10. Human Oversight and Review Index

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    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

    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.

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    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.

    From Data-Driven AI to Compassion and  Value-Driven AI: Safeguarding Humanity  and Empowering 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.

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    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

    Deep Transfer Learning Models for Biodiversity, Climate Change, and Global Warming

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    Artificial Intelligence to Fight Against COVID-19

    Key Artificial Intelligence Projects to Fight Against COVID-19

    Dr. Amit Ray 
    Compassionate  AI Lab 

    Prevention and early healing are the primary requirements for the present COVID crisis. In our Compassionate AI Lab, we broadly classified our fight against COVID 19 Artificial intelligence (AI) based research projects into six groups. They are AI for COVID vaccine development, AI for COVID drug discovery, AI for COVID diagnosis, AI for COVID testing, AI for COVID growth rate forecasting, and AI for social robots.

    AI to Fight COVID 19 Researches Sri Amit Ray Compassionate AI Lab

    AI to Fight COVID 19 Researches Sri Amit Ray Compassionate AI Lab

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    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. 

    What Holding Back Machine Learning in Healthcare

    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. 

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    Artificial Intelligence for Balance Control and Fall Detection of Elderly People

    Balance control in elderly people is one of the key issues of old age. Artificial Intelligence can play a big role to solve this issue. In this research work, we demonstrate the application of machine learning techniques for posture alignments and the control of the body center of mass for disabled people.

    Designing automated balance control system for elderly people is one of the key projects of our Compassionate AI Lab. Here, Dr. Amit Ray discuses about one of the recent projects of AI using deep learning algorithms for automatic balance control of elderly people. He explains how machine learning algorithms can be used to study and improve the dynamical properties of postural stability of elderly people. The project focuses on how image recognition, human-body joint dynamics, and path navigation methods of artificial intelligence can be used  to eliminate the imbalance, fall and injury of elderly people or for physically challenged people.

    AI for Balance-Control Fall Detection of Elderly People

    Compassionate Artificial Intelligence can be used for helping elderly people in many ways. Here, we discuss about one of our recent project of using AI & deep learning techniques for automatic balance control. The machine learning algorithms are used to improve dynamical properties of postural stability. In this project AI based machine learning algorithms are used to find the insights into the person specific postural strategies for older adults in order to adapt to the postural challenges during sleeping, standing, turning and walking. To study the body movement behavior of elderly people accurately, it is necessary to observe and record their movement trajectory and joint movements quantitatively and precisely in three dimensions.

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    Artificial Intelligence to Combat Antibiotic Resistant Bacteria

    Artificial Intelligence to Combat Antibiotic Resistant Bacteria

    Artificial Intelligence to Combat Antibiotic Resistant Bacteria - tools, techniques, models, scopes and challenges are discussed. Antibiotic resistance bacteria are one of the key research area of our Compassionate AI Lab. Dr. Amit Ray explains how artificial intelligence can be used in combating these superbugs. Antibiotic resistance bacteria is becoming world’s biggest health crisis. We discussed here multi-agent deep reinforcement learning models for predicting behavior of bacteria and phages in multi-drug environments.  We call this model as DeepCombat.

    Artificial Intelligence to Combat Antibiotic Resistant Bacteria

    Antibiotic resistant bacteria are bacteria that are not controlled or killed by antibiotics. They are able to survive and even multiply in the presence of an antibiotic.  These bacteria currently kill an estimated 700,000 people globally each year – a death toll which could rise to 10 million a year by 2050 if we don’t act [1]. The main difficulty is that the bacteria are changing fast. They changing faster than we can change the drugs in response.

     Artificial intelligence is showing alternative means of fighting these deadly infections and killer bacteria. Multi-drug-resistant bacterial infections annually result in millions of hospital days, billions in healthcare costs, and, most importantly, thousands of lives lost. Artificial Intelligence for healthcare is progressing at an exponential rate.  We are evaluating here, the role of artificial intelligence in fighting these superbugs.  Especially, the use of AI for intelligent Phage therapy.

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    Navigation System for Blind People Using Artificial Intelligence

    Do you know according to WHO, there are about 39 million people in the world who are blind? Artificial Intelligence is one of our key research area to overcome that challenge. Here, we explain the use of AI based grid cell, place cell and path integration strategies to solve the problems.

    Dr. Amit Ray explains how grid cell, place cell and path integration strategies with artificial intelligence can be used  for designing the navigation system for blind people. Here, we discuss the use of AI techniques for automatic navigation.

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    Brain-Computer Interface and Compassionate Artificial Intelligence

    Brain-Computer Interface and Compassionate Artificial Intelligence

    Dr. Amit Ray

    The purpose of Compassionate AI is to remove the pain from the society and help humanity. We focused on developing AI based low cost BCI based interfaces for helping disable people.

    Artificial Intelligence with Brain-Computer Interface (BCI) or Brain Machine Interface (BMI) is a fast-growing emerging technology for removing pains from the society. Here,  Dr. Amit Ray explains how with the advancement of artificial intelligence and exploration of new mobile bio-monitoring  devices, earphones, neuroprosthetic, wireless  wearable sensors, it is possible to monitor  thoughts and activities of brain neurons  and serve humanity.

    This research is going to be immensely  beneficial for the physically and mentally challenged people as well as for the people who are suffering from post-traumatic stress disorder (PTSD), and other mental disorders or brain problems. Over the last 5 years, technologies for non-invasive transmission of information from brains to computers have developed considerably.

    Brain-Computer Interface and Compassionate AI

    Here, researchers focus to build a direct communication link between the human brain and the smartphones, earphone, computers or other devices. With BCI mind can speak silently with a smartphone or other devices.  Recent advancement of neuroprosthetic, linking the human nervous system to computers and providing unprecedented control of artificial limbs and restoring lost sensory function.

     BCI establishes two way communications between the brain and the machine.  One is brain-computer interface, and another is called computer-brain interfaces (CBI). BCI hopes to create new communication channels for disabled or elderly persons using their brain signals.

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    GPUs and Deep Learning for Compassionate Artificial Intelligence

    GPUs and Deep Learning for Compassionate Artificial Intelligence

    Dr. Amit Ray discusses how GPUs and deep learning can be used for developing complex modules of the compassionate artificial intelligence. GPUs and cloud TPUs made complex deep learning of artificial intelligence possible in laptop, PC and smartphone. They made complex deep learning possible for research labs and smaller companies across the world. They provided a lot more computing power and efficiency for complex matrix operations and parallel processing. 

    GPUs and TPUs for Deep Learning 

    One of AI's biggest potential benefits is to keep humanity healthy, happy and free from inequalities and slavery. The role of AI is gradually changing. It is shifting from typical object recognition or diagnosis tool, to complex human like powerful integrated compassionate care giving systems. Compassionate AI is one area where AI is beginning to take strong hold. Here, Dr. Ray explains the implementation of deep learning modules of integrated compassionate care giving systems with GPUs and TPUs . 

    Compassionate AI GPUs and Deep LearningTraining of complex compassionate artificial intelligence modules requires deep learning neural networks. Training of compassionate modules are more time‐consuming compared to shallow learning models of AI. They may take months, weeks, days or hours depending on the size of training set and model architecture. This is because the number of computational steps increases rapidly with the number of elements in the matrix. In every doubling of the matrix size increases the length of the calculation eight-fold. Training on GPUs can considerably reduce this training time. It can reduce the time to tenfold or more. Hence,  the use of GPUs are also crucial for evaluating multiple deep learning models efficiently.

    How GPUs Help Deep Learning

    The core of many machine learning applications is the repeated computation of a complex mathematical expression. Neural networks store weights in matrices. Deep learning is mostly comprised of matrix operations like matrix multiplication. Emotional Intelligence modules of compassionate AI requires matrix operations of feeling like; anger, fear, depression, pain and pleasure. This is what people want a machine to exhibit for them to be trusted.  Linear algebra makes matrix operations  of feelings and emotions fast and easy, especially when training on Graphical Processing Units (GPUs). GPUs were developed to handle lots of parallel computations using thousands of cores. Instead of processing pixels one-by-one, GPUs manipulate entire matrices of pixels in parallel.  GPUs have almost 200 times more processors per chip than a CPU. CPU optimized serial tasks where as GPU optimized parallel tasks. For complex problems training of a deep learning networks takes months and days but with the help of GPU training will take hours or minutes. If you want to learn deep learning models quickly GPU is must. 

    Deep learning is an emerging computer science field that seeks to mimic the human brain with hardware and software. The use of generalized  algorithm was crucial to the development of . The popularity of graphics cards for neural network training exploded after the advent of general purpose GPUs. Initially, GPUs were developed mainly for processing arrays of pixels for 3D video games.  The large memory bandwidth with parallel processing power, makes them ideal for implementing deep learning in compassionate AI.

    Modules  and Algorithms of Compassionate AI

    The core of developing compassionate AI algorithms is learning, growing, adding values and embracing challenges. They deals with possibilities, creativity, innovation, awe, and deep service to the humanity. The models like emotions, beliefs, values, imagination, long term memory, protective habits, critical thinking,  and caring man-machine behavior needs huge data processing and training.  Large parallel processing power and memory bandwidth of GPUs and quantum computing  are ideal for that.

    "Compassionate AI algorithms are focused to feel connected to all living beings and our planet, and make this universe a friendly place for both human and machine to thrive." -- Amit Ray

    Presently, machine learning tools integrated with the advancements in humanoid design are enabling machines to have compassionate conversations with human and having social interactions with people to keep aging minds sharp and happy. More details about the modules and the algorithms are explained in the book Compassionate Artificial Superintelligence AI 5.0.  

    Growth of AI with CPU GPU and Quantum-ComputingThe growth of GPU is exponential. In 2007, NVIDIA launched the CUDA programming platform, and opened up the general purpose parallel processing capabilities of the GPU.  The CUDA toolkit is deeply entrenched. Now, it works with all major deep learning frameworks  like Tensoflow, Pytorch, Caffe, CNTK, etc. Deep learning with NVIDIA-CUDA is quite efficient. 

    Google’s new cloud TPUv2 are powerful enough. They are designed for machine learning and tailored for TensorFlow. They are free TPU cluster for researchers, and a lightweight TensorFlow version for mobile devices. TPUs are provided as network addressable devices.

    The most important feature for deep learning performance is memory bandwidth. GPUs are optimized for memory bandwidth. Memory bandwidth is the rate at which data can be read from or stored into a semiconductor memory by a processor. Operations like like matrix multiplication requires high memory bandwidth. 

    Summary: 

    Training of deep learning neural networks for compassionate artificial intelligence requires large numbers of matrix multiplications, which can be parallelized efficiently on GPUs. All state‐of‐the‐art deep learning frameworks provide support to train models on either CPUs or GPUs without requiring any knowledge about GPU programming. On desktop machines, the local GPU card can often be used if the framework supports the specific brand. Alternatively, commercial providers provide GPU and TPU cloud compute clusters are the easy way of implementing complex deep learning models of compassionate AI. 

    References:

    1. Compassionate Artificial Superintelligence AI 5.0, Amit Ray, 2018
    2. Large-scale Deep Unsupervised Learning using Graphics Processors, Rajat Raina et al., 2009
    3. Deep Learning,  Yann LeCun et al., Nature, 2015
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