Research Archive

The absolute foundation of computer science. It introduces the theoretical "Turing Machine", proving what computers can and cannot calculate.

The birth of the digital age. It defines "entropy" in data, proving mathematically how information can be perfectly compressed and transmitted.

The first attempt at creating an artificial brain. It introduces a simple, biologically-inspired mathematical model that can "learn" from data.

A devastating critique proving early neural networks couldn't solve simple logic problems (like XOR), which famously triggered the first "AI Winter".

A crucial paper for modern internet infrastructure. It explains how decentralized computers can agree on the order of events without a central clock.

The breakthrough that saved neural networks. It proved "backpropagation" could be used to train deep networks, making modern AI possible.

The original blueprint for Google. It completely revolutionized web search by ranking pages based on how many other important pages link to them.

The paper that triggered the modern AI boom. It proved that deep neural networks running on GPUs could recognize images better than anything else.

The architecture behind ChatGPT. It introduced the "Transformer", proving that AI doesn't need to read text sequentially if it simply pays "attention" to context.

Proved that if you scale up a language model massively (GPT-3), it spontaneously learns to perform entirely new tasks with just a few examples.

Proves mathematically why putting all your eggs in one basket is gambling, establishing diversification as the only "free lunch" in investing.

Explains that while some risks can be diversified away, market-wide risk cannot. Introduces "beta" as the price tag for bearing that unavoidable risk.

An honest reassessment of the idea that markets perfectly price all information, acknowledging the limits and cracks in his own famous theory.

Shows that a stock's correlation to the market explains almost nothing about its returns, revealing that company size and value are the true hidden drivers.

The first time a biological neuron was modeled as a mathematical logic gate, laying the absolute groundwork for modern artificial intelligence.

Introduced the most famous rule in neuroscience: "Cells that fire together, wire together", explaining how the brain physically changes to learn.

A landmark study proving that the eye doesn't just act like a dumb camera. Visual processing and computation actually begin directly inside the retina.

Discovered that the brain processes vision in a hierarchy of simple and complex shapes. This directly inspired how we build AI vision models today.

Discovered "place cells" in the brain, proving that the hippocampus acts as a physical, biological GPS system for spatial navigation.

Overturned the idea that pure logic is best. It explains how bodily emotions and "gut feelings" are actually mathematically essential for making rational decisions.

Introduced "predictive coding", suggesting the brain doesn't just passively process what it sees, but constantly predicts what it will see next to save energy.

Expanded our understanding of the brain's GPS by discovering "grid cells", which map environments using an elegant hexagonal coordinate system.

A highly complex, grand unifying theory of the brain. It argues that all biological systems are driven by a single goal: minimizing surprise (free energy).