Ethical Artificial Intelligence

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

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..