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Research & Model Capabilities

AI vs Machine Learning vs Deep Learning vs LLMs — the actual differences

Four layers everyone conflates — AI, Machine Learning, Deep Learning, and Large Language Models. What defines each, what each can and cannot do, where production systems use them in 2026, and how to tell which layer a system actually is. With a paired lab.

Human vs machine intelligence: where they actually differ

How humans and machines actually differ in 2026 across five axes — data efficiency, embodiment, transfer, common sense, energy — where they're surprisingly similar, and why the productive frame is complement, not replicate. With a 4-challenge lab.

What is intelligence? The definition AI inherits

Four serious, mutually inconsistent definitions of intelligence — psychometric, biological, generalisation-efficiency, and multiple-intelligences — and why which one you accept silently decides which AI you build. With an interactive yardstick lab.

What is attention? The mechanism behind every modern LLM

The mechanism every modern LLM is built on, explained from scratch. Q/K/V, the scaled dot-product formula walked through term by term, multi-head attention, positional encoding, and the engineering variants that show up in 2026. With a live interactive lab.

What is mixture-of-experts? The architecture behind cheaper trillion-parameter AI

Mixture-of-experts is how a 671-billion-parameter model can cost less per token than a 70-billion-parameter one. A plain-English guide to MoE, active vs total parameters, routers and experts, and why open-weights frontier labs picked the architecture first.

What is synthetic data? The training material AI is starting to make for itself

Synthetic data is generated training material. It can teach models faster, protect privacy, and also cause model collapse when used badly. Here is the practical version.

What is dataset poisoning? The supply-chain risk inside every model

Dataset poisoning happens when AI training data is deliberately contaminated. Here's what the research proves, what may be happening in practice, and what remains uncertain.