Philipp Scheerer

Language itself has a few shocking properties that are similar to a real virus: Self-replication: it spreads from host to host. Symbiosis: one could argue that the language virus is so deeply embedded in our minds that we mistake it for our consciousness. Like a virus, language is a non-living pattern of information. Prof. Elan Barenholtz compares LLMs and humans and makes a few assumptions: ...

Traditional machine learning models are built to generalize. And while that works well in many cases, it’s also their Achilles’ heel—they often miss the sharp edges, the anomalies, the rare patterns that matter most. But transformers changed the game. ...

When most people see a volatile time series, they think it’s random. But chaos and randomness are not the same. Chaos has structure – sensitive to initial conditions, and governed by deterministic laws. What if we could map time series data onto a chaotic system and let its internal dynamics do the forecasting? ...

Fuzzy logic has long offered an elegant framework for reasoning under uncertainty – but designing good fuzzy systems has remained more of an art than a science. What if we could automate the design of fuzzy systems, make their internals differentiable, and even train them via gradient descent? ...

Fuzzy Cognitive Maps (FCMs) are powerful tools for modeling systems where variables influence each other in complex, uncertain ways. Traditionally, building them requires domain experts, people with deep knowledge of how concepts interact. But what if we could replace these experts with language model agents? ...

My journey to automatically generate fuzzy rules for a fuzzy engine led me to frequency-based algorithms like Apriori and FP-Growth. As I tried to understand how they work, I began to wonder if it would be possible to write a simple classifier based purely on support values. When all features in a dataset are one-hot encoded, it’s possible to calculate the support value of each feature for a given classification target. ...

TL;DR This post explores how principles of holography, quantum computing, and neural networks might share a common foundation in interference patterns. Could these parallels help us better understand AI, data storage, and brain functions? ...