$μλεδ$-Calculus: A Self Optimizing Language that Seems to Exhibit Paradoxical Transfinite Cognitive Capabilities
September 09, 2024 · Declared Dead · + Add venue
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Ronie Salgado
arXiv ID
2409.05351
Category
cs.PL: Programming Languages
Citations
0
Last Checked
4 months ago
Abstract
Formal mathematics and computer science proofs are formalized using Hilbert-Russell-style logical systems which are designed to not admit paradoxes and self-refencing reasoning. These logical systems are natural way to describe and reason syntactic about tree-like data structures. We found that Wittgenstein-style logic is an alternate system whose propositional elements are directed graphs (points and arrows) capable of performing paraconsistent self-referencing reasoning without exploding. Imperative programming language are typically compiled and optimized with SSA-based graphs whose most general representation is the Sea of Node. By restricting the Sea of Nodes to only the data dependencies nodes, we attempted to stablish syntactic-semantic correspondences with the Lambda-calculus optimization. Surprisingly, when we tested our optimizer of the lambda calculus we performed a natural extension onto the $μλ$ which is always terminating. This always terminating algorithm is an actual paradox whose resulting graphs are geometrical fractals, which seem to be isomorphic to original source program. These fractal structures looks like a perfect compressor of a program, which seem to resemble an actual physical black-hole with a naked singularity. In addition to these surprising results, we propose two additional extensions to the calculus to model the cognitive process of self-aware beings: 1) $ε$-expressions to model syntactic to semantic expansion as a general model of macros; 2) $δ$-functional expressions as a minimal model of input and output. We provide detailed step-by-step construction of our language interpreter, compiler and optimizer.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
📜 Similar Papers
In the same crypt — Programming Languages
R.I.P.
👻
Ghosted
R.I.P.
👻
Ghosted
Tensor Comprehensions: Framework-Agnostic High-Performance Machine Learning Abstractions
R.I.P.
👻
Ghosted
Glow: Graph Lowering Compiler Techniques for Neural Networks
R.I.P.
👻
Ghosted
Learnable Programming: Blocks and Beyond
R.I.P.
👻
Ghosted
Scenic: A Language for Scenario Specification and Scene Generation
R.I.P.
👻
Ghosted
Vandal: A Scalable Security Analysis Framework for Smart Contracts
Died the same way — 👻 Ghosted
R.I.P.
👻
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
👻
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
👻
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
👻
Ghosted