Online decision-making in the context of reinforcement learning involves a fundamental choice: Exploitation: Make the best decision given current information Exploration: Gather more information The best long-term strategy may involve short-term sacrifices Gather enough information to make the best overall
Understanding AlphaGo Fundamentals
AlphaGo is the first computer program to defeat a professional human Go player, the first to defeat a Go world champion, and is arguably the strongest Go player in history. This is one the major feats of AI research in
Getting Started with LISP
Developed by John McCarthy, MIT, in the late 1950s the original idea was to implement a language based on mathematical functions that McCarthy was using to model problem solving. A grad student implemented the first interpreter, and it was quickly
What does the future of Education look like?
Over the past couple of decades, Education outcomes especially in areas like STEM, have been underwhelming to say the least. Now with the rise of LLMs that are capable of solving advanced (but say well-defined problems) universities are concerned about
Elaborating Scalability requirements of Large Scale Systems through Goal Obstacle Analysis
In 1993, The London Ambulance service faced a critical failure. The cause was increased call traffic load, that prevented the system from maintaining location information about the ambulance units triggering a large number of exception messages. LAS’s failure to
An introduction to Federated Learning of privacy-preserving Machine Learning
I complied following notes to understand the fundamentals of federated learning: Most current data mining and machine learning techniques (like process mining) work by gathering all datasets into a central site, then running various algorithms against that data. In most
Understanding Generalisation in Machine Learning
“A computer program is said to learn from experience E with respect to some class of tasks T and performance measureP, if its performance at tasks in T, as measured by P, improves with experience E.” In Supervised machine learning
Explainable AI and AI-Alignment
As AI systems become more integrated into our lives, one thing is becoming clear: they need to explain themselves. Whether it’s a recommendation, a prediction, or a life-altering decision, people want to know why. And in many cases, they