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. Read More »Ethical Responsibilities in Large Language AI Models: GPT-3, GPT-4, PaLM 2, LLaMA, Chinchilla, Gopher, and BLOOM
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 »From Data-Driven AI to Compassionate AI: Safeguarding Humanity and Empowering Future Generations
Transfer learning is a framework for machine learning that has become increasingly popular over the past few years.
Though AI community primarily enjoy it for its unique power, efficiency, and easiness, evidence suggests it can provide higher performance benefits as well.
This article takes a look at transfer learning, including its benefits, steps, and how to implement it. For building deep learning frameworks for transfer learning, Python, TensorFlow, and Keras are the main tools that are used.
When it comes to deep machine learning, transfer learning refers to the process of first training a base network on a benchmark dataset (such as ImageNet), and then transferring the best-learned network features (the network’s weights and structures) to a second network so that it can be trained on a target dataset.
In other words, transfer learning is the process of transferring the best-learned network features from one network to another. Instead of starting from over with their training, one of the earliest ideas for applying transfer learning was to utilize already-trained models from other datasets, like ImageNet dataset.
This idea has been shown to improve deep neural network’s generalization capabilities significantly in many application areas. Recently, considering the lengthening timelines for deep machine learning the interest in transfer learning has grown significantly.… Read more..
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
Artificial Intelligence (AI) has provided remarkable capabilities and advances in image understanding, voice recognition, face recognition, pattern recognition, natural language processing, game planning, military applications, financial modeling, language translation, and search engine optimization. In medicine, deep learning is now one of the most powerful and promising tool of AI, which can enhance every stage of patient care —from research, omics data integration, combating antibiotic resistance bacteria, drug design and discovery to diagnosis and selection of appropriate therapy. It is also the key technology behind self-driving car.
However, deep learning algorithms of AI have several inbuilt limitations. To utilize the full power of artificial intelligence, we need to know its strength and weakness and the ways to overcome those limitations in near future.
Now, AI support messaging apps, and voice controlled chatbots are helping people for deep space communications, customer care, taking off the burden on medical professionals regarding easily diagnosable health concerns or quickly solvable health management issues and many other applications. However, there are many obstacles and number of issues remain unsolved.
Even with so many success and promising results its full application is limited. Mainly, because, present day AI has no common sense about the world and the human psychology. Presently, in complex application areas, one part is solved by the AI system and the other part is solved by human – often called as human-assisted AI system. The challenges are mostly in the large-scale application areas like drug discovery, multi-omics-data integration, assisting elderly people, new material design and modeling, computational chemistry, quantum simulation, and aerospace physics.
This article is focused to explain the power and challenges of current AI technologies and learning algorithms. It also provides the directions and lights to overcome the limits of AI technologies to achieve higher levels learning capabilities.
Quantum computing is the key technology for future artificial intelligence. In our Compassionate AI Lab, we are using AI based quantum computing algorithms for human emotion analysis, simulating human homeostasis with quantum reinforcement learning and other quantum compassionate AI projects. This tutorial is for the researchers, volunteers and students of the Compassionate AI Lab for understanding the deeper aspects of quantum computing for implementing compassionate artificial intelligence projects.
Building a quantum computer differs greatly from building a classical computer. The core of quantum computing is qubits. Qubits are made using single photons, trapped ions, and atoms in high finesse cavities. Superconducting materials and semiconductor quantum dots are promising hosts for qubits to build a quantum processor. When superconducting materials are cooled, they can carry a current with zero electrical resistance without losing any energy. These seven requirements refereed as DiVincenzo criteria for quantum computing [1].
The past present and future deep learning methods and approaches are reviewed. It covers both deep reinforcement learning and deep supervised and unsupervised learning techniques in both technical and historical perspective.
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 disable people.
Designing automated balance control system for elderly people is one of the key project of our Compassionate AI Lab. Here, Dr. Amit Ray discuses about one of the recent project 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.
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.Read More »Artificial Intelligence for Balance Control and Fall Detection of Elderly People
Quantum Computer with Superconductivity at Room Temperature
Quantum computer with superconductivity at room temperature is going to change the landscape of artificial intelligence. In the earlier article we have discussed quantum computing algorithms for artificial intelligence. In this article we reviewed the implication of superconductivity at room temperature on quantum computation and its impact on artificial intelligence.
Long coherence time (synchronized), low error rate and high scalability are the three prime requirements for quantum computing. To overcome these problems, presently, quantum computer needs complex infrastructure involving high-cooling and ultra-high vacuum. This is to keep atomic movement close to zero and contain the entangled particles, both of which reduce the likelihood of decoherence. The availability of superconductivity at room temperature will provide the quantum jump in quantum computer.
