AI for Social Good

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..
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

Machine Learning to Fight Antimicrobial Resistance

Machine Learning to Fight Antimicrobial Resistance

The seven top machine learning projects to fight against antimicrobial resistance are explained. Antimicrobial resistance  is one of the key reasons of human sufferings in modern hospitals. Preventing microbes from developing resistance to drugs has become as important issue for treating illnesses across the world. Artificial Intelligence, machine learning, genomics and multi-omics data integration are the fast-growing emerging technologies to counter antimicrobial resistance problems. Here,  Dr. Amit Ray explains how these technologies can be used in seven key areas to counter antimicrobial resistance issues.

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. Read More »What’s Holding Back Machine Learning in Healthcare

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. Read More »Navigation System for Blind People Using Artificial Intelligence