The Attack That Exploits AI’s Core Design Every serious technology platform eventually acquires its signature vulnerability class. For web applications it was Cross-Site Scripting. For databases it was SQL injection — an attack so consequential that it shaped two decades of application security practice. For large language models, that defining vulnerability has a name: prompt injection. Ranked LLM01 by OWASP — the #1 threat on the OWASP Top 10 for LLM Applications — prompt injection exploits a fundamental architectural weakness: LLMs cannot reliably distinguish between trusted instructions and untrusted data. The comparison to SQL injection is more than rhetorical. SQL injection worked because databases executed user-supplied strings as code, blurring…

Three Terms, One Persistent Confusion Artificial Intelligence, Machine Learning, and Data Science are three of the most frequently used, and three of the most consistently conflated, terms in technology today. Job descriptions blend them interchangeably. News articles use them as synonyms. Even within organisations, teams labelled differently are sometimes doing work that is functionally indistinguishable. The confusion is understandable. The three fields overlap substantially, share tooling and mathematical foundations, and have converged further in recent years as data-driven methods have come to dominate AI research. But they are not the same thing, and treating them as such leads to poor hiring decisions, misdirected research investment, and architectural choices that do…

The term “Machine Intelligence” (MI), often used interchangeably with Artificial Intelligence (AI) and Machine Learning (ML), has graduated from speculative science fiction into the core infrastructure of modern computing. However, cutting through the industry marketing requires looking at MI not as a singular “thinking mind,” but as a highly sophisticated convergence of statistical learning, algorithmic optimization, and distributed computational systems. This post explores what machine intelligence truly is under the hood, how it evolved structurally, the core tools that power it today, and its high-stakes applications. Defining Machine Intelligence: What It Is (and Isn’t) We define Machine Intelligence as the capacity of a computational system to execute tasks by constructing…

This is the final part of a three-part series on the history of AI. Part 1 covered AI’s philosophical origins and the Turing Test, Strong AI vs. Weak AI, and Searle’s Chinese Room argument. Part 2 traced the rise of machine learning and deep learning through the founding of OpenAI in 2015. Part 3 picks up the story with the breakthrough that made ChatGPT possible. The Paper That Changed Everything By 2017, deep learning had already transformed computer vision and game-playing AI, as Part 2 described. But language remained stubbornly difficult. Earlier neural network architectures processed text sequentially, word by word, which made them slow to train and bad at…

This is Part 2 of a three-part series tracing the history of artificial intelligence. Part 1 covered AI’s philosophical origins, the Dartmouth Conference, and the AI winters. Part 2 picks up with the shift toward machine learning and traces the path to the founding of OpenAI. A New Approach: Learning From Data Instead of Rules By the end of Part 1, AI research had hit a wall. The symbolic, rule-based approaches of expert systems, sometimes retroactively called “Good Old Fashioned AI”, involved pre-programming knowledge and rules directly into a system. These approaches were brittle and difficult to scale. A quieter alternative had existed since the field’s earliest days but had…

This is Part 1 of a three-part series tracing the history of artificial intelligence, from its philosophical roots to the creation of OpenAI and ChatGPT. Part 1 covers the early foundations of AI through the AI winters of the 1970s and 80s. Before the Machines: A Question, Not a Technology Long before computers existed, humans imagined artificial beings capable of thought — from mechanical automatons in ancient myth to philosophical debates about the nature of mind. But the scientific story of AI begins not with a machine, but with a question. In the 1950s, researchers started exploring whether intelligence could be formalized, tested, and eventually built into machines. The foundations…

Introduction Artificial Intelligence (AI) is one of the most transformative and talked-about technologies of our time, and for good reason. In just a few decades, AI has moved from the pages of science fiction novels into our smartphones, hospitals, classrooms, and workplaces. It powers the recommendations that appear when you open Netflix, helps doctors detect diseases earlier than ever before, and enables cars to navigate roads with minimal human input. What makes AI remarkable is not just what it can do today, but how rapidly it continues to evolve. New breakthroughs in language understanding, image generation, and autonomous reasoning are being announced almost every month. Industries that once seemed immune…