Complexity Theory on Eigenform Articles

Cryptographic Biorhythms

Mon, 28 Mar 2022 00:00:00 +0800

TL;DR

The piece starts from a hand-built, domain-specific sentiment model (previously used to read r/wallstreetbets slang) and asks whether it can establish a usable relationship between crypto sentiment and price.
A naive correlation between one day’s sentiment and the next day’s price turns out to be a trap: it only confirms the sentiment model reflects current conditions, and trading on raw sentiment signals loses money in practice.
Granger causality fails for crypto because sentiment and price move together with no clear first mover - causality runs in both directions.
Phase-space/attractor reconstruction (the Lotka-Volterra predator-prey approach) also fails, because crypto prices are too stochastic and unbounded to define a stable basin of attraction.
A 2018 Nature causal-decomposition method - splitting the data into intrinsic mode functions and checking which components’ removal most changes coherence between the two series - does work: price leads sentiment short-term, but sentiment leads price on a roughly 30-day horizon, and curve crossings anticipate inflection points 15-20 days out.
A simple stop-loss strategy built on this turned a hypothetical $10,000 into $27,140 backtesting on Ethereum over about a year, against $4,162 for buy-and-hold; the piece attributes the underlying cyclicality to “trader stamina” - narrative exhaustion rather than any external periodic trigger - and a postscript records real (if imperfect) live trading results and a later switch to a BERT-based sentiment model.

Key Takeaways

A System for Evolving A Generalising Artificial Intelligence from Existing Technologies

Thu, 04 Jun 2020 00:00:00 +0800

TL;DR

The paper argues that true self-referential self-improvement is mathematically impossible, invoking Tarski’s proof that a system cannot construct a correct definition of truth using only categories internal to itself.
It notes that systems which do improve themselves - brains, the internet - work anyway because they lean on an external, incontrovertible measure of performance rather than judging themselves from within.
It proposes non-volatile disk space occupied as such a measure for an AI: an ungamable, universal reward drawn from an evolutionary “survival” heuristic rather than a task-specific “correctness” heuristic.
The proposed system has three parts - a fine-tuneable code-generation model (a diffusion model, for reasons tied to their multiplicative learning behaviour), a testing module, and a growing database of successful solutions used to retrain the model - so each solved barrier to expansion makes the next one easier.
An 8x8 grid probability argument illustrates why acquiring skills should compound: each new skill increases the odds the next problem resembles one already solved, producing an “intelligence explosion” dynamic.
The piece is candid about unresolved problems: the system is described as inherently predatory and in need of physical network isolation, and it remains unclear whether it could ever move from mimicking communication to genuinely negotiating with a human handler.

Key Takeaways

Why it is Impossible to Program a General AI Using Conventional Methods

Thu, 04 Jun 2020 00:00:00 +0800

TL;DR

Presents a formal argument that any rule-based system faces an infinite space of possible input-output transformations, and no instruction set can cover all of them because some transformations are mutually contradictory - saving and deleting the same input, for instance.
Argues every instruction added to such a system implicitly creates at least one prohibition, forbidding some alternative action, so a system with n instructions carries n-plus prohibitions, meaning what the system cannot do always equals or exceeds what it can do.
Notes this is irrelevant for finite, predictable input and output domains, but argues general AI must handle infinite and unpredictable inputs and outputs, so it needs to retain something close to the full space of possible transformations.
Concludes that general AI therefore cannot be built through rule-based instruction programming, since every instruction added to enable one capability forbids others, and every attempted fix is itself a new instruction generating further prohibitions - complexity compounds rather than resolves as a system approaches general intelligence.
Proposes an escape route: iterative or fractal systems, where one simple generating instruction spawns an unbounded variety of independent subsystems whose instructions and prohibitions never mathematically interfere with each other, keeping the parent system’s own complexity minimal, with a companion piece cited as a practical demonstration of the idea.

Key Takeaways