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
arXiv:2607. 04505v1 Announce Type: new Abstract: 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.
By Charanjit S. Jutla, Vimal Sharma
arXiv:2606. 17289v1 Announce Type: new Abstract: AI systems based on artificial neural networks are being developed with aspirations of pushing the boundary of human mathematical knowledge.
By Phoebe Zeng, Thomas L. Griffiths, Brenden M. Lake
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:2501. 17629v2 Announce Type: replace-cross Abstract: Several studies claim that large language models have passed the Turing Test and hence can "think", yet none follow Turing's original instructions precisely.
By Sharon Temtsin, Diane Proudfoot, David Kaber, Christoph Bartneck
arXiv:2610.14005v1 Announce Type: new
Abstract: Under the accelerated pace of AI development, any benchmark whose difficulty is calibrated with humans or current AI systems becomes circular, or satur...
By Jose Hernandez-Orallo
arXiv:2606. 06533v1 Announce Type: new Abstract: What would it mean to have a scientific understanding of AI?
By Stella Biderman, Mohammad Aflah Khan, Niloofar Mireshghallah, Catherine Arnett, Fazl Barez, Naomi Saphra
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
arXiv:2605.10851v2 Announce Type: replace
Abstract: We initiate the study of the Generalized Turing Test (GTT), a formal generalization of Turing's imitation game from humans to arbitrary interactive...
By Daniel Mitropolsky, Riccardo Neumarker, Emanuele Rimoldi, Susan S. Hong, Tomaso Poggio
arXiv:2606. 20231v1 Announce Type: new Abstract: Can intelligence be measured?
By Ishanu Chattopadhyay
arXiv:2606. 30481v1 Announce Type: cross Abstract: Current large language models are extraordinary statistical engines.
By Ziqin Yuan, Jaymari Chua
arXiv:2608. 14407v1 Announce Type: new Abstract: We present a survey of the past and future of AI Scientists: machines capable of automating science.
By Ross D. King