When the six merry murderesses of the Cook County Jail climbed the stage in the Chicago musical, they were aligned on the message: They had it coming, they had it coming all along. I didn’t do it. But if I’d done it, how could you tell me that I was wrong? And the part of the song […]
Regression Discontinuity Design: How It Works and When to Use It You’re an avid data scientist and experimenter. You know that randomisation is the summit of Mount Evidence Credibility, and you also know that when you can’t randomise, you resort to observational data and Causal Inference techniques. At your disposal are various methods for spinning
Regression Discontinuity Design: How It Works and When to Use It Read More »
Artificial Intelligence has become a mainstay of our daily lives, revolutionizing industries, accelerating scientific discoveries, and reshaping how we communicate. Yet, alongside its undeniable benefits, AI has also ignited a range of ethical and social dilemmas that our existing regulatory frameworks have struggled to address. Two tragic incidents from late 2024 serve as grim reminders
We Need a Fourth Law of Robotics in the Age of AI Read More »
Text Classification stands as one of the most basic yet most important applications of natural language processing. It has a vital role in many real-world applications that go from filtering unwanted emails like spam, detecting product categories or classifying user intent in a chat-bot application. The default way of building text classifiers is to gather
AI agents and automation are no longer just a trend — they are transforming how companies operate. In a previous article, I shared several case studies of AI Agents supporting the sustainability roadmaps of small, medium and large companies. AI Agents for Sustainability — (Image by Samir Saci) This is part of a 4-month exploration of how agentic AI can
How I Built Business-Automating Workflows with AI Agents Read More »
This article uses concepts from this brilliant paper. For a deeper understanding of the mathematics please refer to the paper. Here we try to present the math in a more intuitive and explicit way, with some important nuances highlighted. 1 Introduction Discussions about Backpropagation often say we use the ‘chain rule’ to derive the gradient
The Total Derivative: Correcting the Misconception of Backpropagation’s Chain Rule Read More »
In the previous posts, we explored Part I of the seminal book Reinforcement Learning by Sutton and Barto [1] (*). In that section, we delved into the three fundamental techniques underlying nearly every modern Reinforcement Learning (RL) algorithm: Dynamic Programming (DP), Monte Carlo methods (MC), and Temporal Difference Learning (TD). We not only discussed algorithms
Benchmarking Tabular Reinforcement Learning Algorithms Read More »
“It is not the strongest of the species that survives, nor the most intelligent, but the one most responsive to change.” – Charles Darwin, Originator of Evolutionary Theory Not long ago, I came across an article about a CEO, who was visibly frustrated with their company’s new AI assistant. The system could draft nice emails
Think. Know. Act. How AI’s Core Capabilities Will Shape the Future of Work Read More »
Introduction The invention of ViT (Vision Transformer) causes us to think that CNNs are obsolete. But is this really true? It is widely believed that the impressive performance of ViT comes primarily from its transformer-based architecture. However, researchers from Meta argued that it’s not entirely true. If we take a closer look at the architectural design,
The CNN That Challenges ViT Read More »
Introduction Generative AI is one of the most popular terms we hear today. Recently, there has been a surge in generative AI applications involving text, image, audio, and video generation. When it comes to image creation, Diffusion models have emerged as a state-of-the-art technique for content generation. Although they were first introduced in 2015, they have
Diffusion Models, Explained Simply Read More »
