Artificial Intelligence

The 10 Ethical AI Indexes For LLM Data Training and Responsible 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.

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

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

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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. Read More »Ethical Responsibilities in Large Language AI Models: GPT-3, GPT-4, PaLM 2, LLaMA, Chinchilla, Gopher, and BLOOM

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

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 »Calling for a Compassionate AI Movement: Towards Compassionate Artificial Intelligence

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.Read More »From Data-Driven AI to Compassionate AI: Safeguarding Humanity and Empowering Future Generations

Artificial Intelligence Master Course Algorithms and Applications

Artificial Intelligence Master Course

Algorithms and Applications 

A 15-Weeks Guided Online Course 

Sri Amit Ray Compassionate AI Lab

In this 15-week online course, you will learn and understand the main algorithms and approaches to artificial intelligence and deep learning. You will learn the techniques to improve your AI and machine learning model building skills and algorithm development skills. It includes total 15 online one-on-one classes, for real-world case studies, and hands-on practices and exercises.

The aim of the course is to:

  • be able to apply the algorithms for different applications and interpret the results
  • be able to build models and algorithms and adjust parameters
  • understand the applicability of the algorithms to different types of data and problems along with their strengths and limitations

The case studies share real world stories from teams who have designed AI-driven products using human-centered AI based practices. Primarily you can use R programming or Python programming for practicing the examples and projects.

The Course Contents

The course structure varies, depending on individuals’ experiences, needs, and interests. However, there will be a total of 15 classes. The general content of the modules of the course are as follows:

Module 1: Artificial Intelligence Fundamentals

  1. Human Intelligence vs Artificial Intelligence
  2. Human brain vs Artificial Intelligence
  3. General Artificial Intelligence vs Ethical Artificial Intelligence
  4. Ethical Artificial Intelligence vs Compassionate Artificial Intelligence
  5. Difference between Artificial Intelligence, Machine Learning, and Deep Learning
  6. Recent trends of AI from robots to humanoids
  7. Machine Learning Frameworks
  8. Future challenges of Artificial Intelligence 

Module 2: Basic Machine Learning Algorithms and Practices

  1. Hypothesis Testing
  2. Linear Regression
  3. Logistic Regression
  4. Clustering
  5. Analysis of Variance (ANOVA)
  6. Principal Component Analysis
  7. Naive Bayes
  8. Decision Tree
  9. Random Forest
  10. Support Vector Machines
  11. K Nearest Neighbors
  12. Gradient Boosting algorithms 
  13. Neural Networks
  14. Ensemble Methods

Module 3: Feature Engineering and Model Building

  1. Data preparation
  2. Min-max Scaling, Standardization, Log Transformation, One hot Encoding, etc.
Read more..
Artificial intelligence for Climate Change and Earth System Models

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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Transfer Learning Basic Concept and the Building Blocks

Transfer Learning A Step by Step Easy Guide

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-19https://amitray.com/artificial-intelligence-to-fight-against-covid-19/

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

Read More »Artificial Intelligence to Fight Against COVID-19

Top 10 Limitations of Deep Learning

Top 10 Limitations of Artificial Intelligence and Deep Learning

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.

Top 10 Limitations of Artificial Intelligence and Deep LearningRead More »Top 10 Limitations of Artificial Intelligence and Deep Learning

10 Quantum Machine Learning Properties By Amit Ray

Quantum Machine Learning The 10 Key Properties 

The 10 Properties of Quantum Machine Learning

Dr. Amit Ray, Compassionate AI Lab

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. 

10 Quantum Machine Learning Properties By Amit Ray

Classical Quantum Hybrid

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.

Read More »Quantum Machine Learning The 10 Key Properties