32 articles in Artificial Intelligence
How no-code and low-code platforms are democratizing software creation, their limitations, and what this means for professional developers and product teams.
Technology adoption failures are rarely about the technology itself; they stem from inadequate attention to user psychology, workflow disruption, and identity concerns. Research shows that the strongest predictor of digital adoption is whether employees perceive the new tool as enhancing rather than threatening their professional identity and competence. The article presents a human-centered approach to technology rollouts that addresses emotional resistance, provides adequate transition support, and celebrates early adopters as change agents.
Surveys the state of computer vision, from image classification and object detection to medical imaging and autonomous driving. Explains how convolutional neural networks (CNNs) process visual information and the practical challenges of deploying vision systems in the real world.
Chronicles DeepMind's AlphaFold breakthrough in protein structure prediction, a problem that stumped biologists for 50 years. Demonstrates how AI can accelerate scientific discovery and transform healthcare by enabling faster drug development and deeper understanding of disease mechanisms.
A practical framework for building ethical AI systems. Covers the key ethical concerns: bias and fairness, transparency and explainability, privacy, accountability, and safety. Proposes a three-step approach: identify ethical risks early, create diverse oversight committees, and implement technical guardrails. Increasingly part of computer science and business school curricula.
Traces the evolution of neural networks from the simple perceptron model to modern deep learning architectures. Explains key concepts—activation functions, backpropagation, gradient descent—in accessible terms, making it an ideal primer for anyone entering the AI field.
McKinsey research on why most AI initiatives fail to scale beyond pilot stage and what to do about it. Three key barriers: lack of a clear AI strategy, organizational resistance, and technical bottlenecks. Successful organizations treat AI as a business transformation (not just a technology project), invest in change management, and create cross-functional AI teams.
Introduces the fundamentals of natural language processing, from tokenization and embeddings to transformer architectures and language models. Uses GPT-2's development as a case study to illustrate how unsupervised learning on large text corpora produces surprisingly capable language understanding.
Explains reinforcement learning through the lens of AlphaGo Zero, which mastered the game of Go entirely through self-play without any human data. Covers the core concepts of agents, environments, rewards, and policies that underpin one of AI's most powerful paradigms.
The landmark 2017 paper introducing the Transformer architecture that powers modern AI. Replaced recurrence and convolutions with self-attention mechanisms, enabling massive parallelization and superior performance. This architecture is the foundation of GPT, BERT, and virtually every large language model. The most-cited AI paper of the decade.
Christensen clarifies and updates his theory of disruptive innovation. Disruption describes a process whereby a smaller company with fewer resources successfully challenges established incumbent businesses. Distinguishes disruption from sustaining innovation and explains why the theory is so widely misunderstood. Essential for technology strategy education.
Tim Urban's viral two-part explainer on artificial intelligence. Covers the three types of AI (narrow, general, super), why progress is exponential rather than linear, and why the jump from ANI to AGI to ASI may happen much faster than we expect. Uses accessible analogies to explain complex concepts. One of the most-read articles on AI ever published, widely shared in educational settings.