arXiv AI

Why machines will still not rule the world

The article argues that artificial general intelligence is mathematically impossible because human intelligence arises from complex systems that cannot be captured by current models. It critiques two common defenses of machine intelligence: the theoretical reliance on universal approximation theorems and the Church‑Turing‑Deutsch principle, and the empirical reliance on benchmark performance. The authors point out flaws in benchmarks, such as data contamination and test construction issues, and conclude that models capable of true cognitive behavior in open‑ended, thermodynamically complex environments will never be achieved.

arXiv AI
Sep 2

Can machines think efficiently?

The article proposes an updated Turing Test that incorporates energy consumption as a key metric, arguing that the original test is insufficient for distinguishing human from machine intelligence in the context of modern AI. It suggests that by adding an energy constraint, the test evaluates intelligence through the lens of efficiency, linking abstract thinking to tangible resource limits. The new test also provides a measurable, practical endpoint, encouraging society to balance AI time savings against total resource costs.

By Adam Winchell
Hugging Face Trending Papers
Jul 5

Why Pure Reasoning is Not Enough: Nature as the Source of Mathematical Innovation

We advance the hypothesis that human mathematical reasoning, constrained by both the undecidability and the computational intractability of even modest logical fragments, relies fundamentally on pattern matching from domains external to pure deduction. The most prolific reservoir of such patterns is the natural world, whose physical laws and biological systems have undergone billions of years of ``pre-computation'' and already exhibit surprisingly innovative solutions.

arXiv AI
Oct 19

At the Limits of Computability: Steps Towards Autonomous AI Discovery on the Busy Beaver Frontier

The paper reports on an AI system that autonomously discovers and formally verifies new mathematical rules—called accelerations—for Turing machines in the Busy Beaver (BB) domain, specifically for eleven machines in BB(6). Using a Gemini-based pipeline that combines pattern recognition, conjecturing, informal reasoning, and Lean theorem proving, the system identified Collatz-like accelerations that provide compact descriptions of machine executions while leaving their halting behavior unresolved. These findings introduce a new class of machines, termed Cryptids, and push forward the analysis of the uncomputable BB frontier.

By Marco Dos Santos, Vivek Veeriah, Alex Havrilla, Andrew Bolt, Goran Zuzic, Satinder Singh, Tom Zahavy