A record for understanding AI.
The Shift September 2026: AI Commercialization Keeps Accelerating
Warnings about AI risk grew louder in September, but capital and commercialization did not slow down. A look at the month's biggest shifts and what they reveal about the AI industry.
The Shift September 2026: Who Decides AI Safety?
Who decides how safe AI should be: researchers, governments, courts, or the market? In September, AI safety moved beyond technical debate into politics, regulation, cybersecurity, and legal responsibility.
The Lab Does a Bigger Model Always Mean a Smarter AI?
How Scaling Laws — and later Chinchilla — changed the way we think about scaling AI
The Lab How Did Computers Learn to Understand the Meaning of Words?
A paper review of word2vec and how learning distributed word representations changed the way machines could capture relationships between words.
The Lab What BERT Changed Wasn't the Architecture — It Was the Direction of Reading
A paper review of BERT and how bidirectional pretraining changed the way language models understood context.
Understanding AI What Does It Really Mean for AI to Learn?
What does it really mean for AI to learn? Explore how models adjust their parameters, how human feedback shapes their behavior, and how AI learning differs from human learning.
The Lab Language Models are Few-Shot Learners
A paper review of GPT-3 and how scaling up language models enabled strong few-shot learning and in-context learning.
Understanding AI When AI Gets It Wrong, Is It a Mistake or a Choice?
Why does AI sometimes give convincing but incorrect answers? A look at how language models generate responses, what causes errors, and how prompts and context can shape better results.
Understanding AI Does AI Really Understand What We Say?
Can AI truly understanding meaning? A closer look at how language models work, the debate over understanding, and the limits of their capabilities.
Understanding AI Are AI Parameters Basically Brain Cells?
What 'parameters' actually mean in an AI model, and where the brain-cell analogy breaks down.
The Lab Improving Language Understanding by Generative Pre-Training
A paper review of GPT-1 and how generative pre-training followed by supervised fine-tuning became a foundation for modern language models.
The Lab Attention is All You Need
A paper review of Attention Is All You Need and how the Transformer replaced recurrence with self-attention as a new foundation for sequence modeling.