2026-07-14 16:47:03
Applying the Turing test to the current “chatbot” generative AI does not show that AI is “intelligent”¹ but that humans are very bad in playing the imitation game as judges³.
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¹In the original paper “ I.—Computing machinery and intelligence”² Turing explicitly states that there is no useful definition for the meaning of the question ‘can machines think?’ and replaces it with: ‘can computers play the imitation game?’
²
AI models have passed the Turing test | The Week
https://theweek.com/tech/ai-llms-pass-turing-test
Is AI becoming more human, or are we discovering just how much of our "intelligence" is actually uncritical automated thought?
https://ocrampal.com/the-inverted-turing-test-what-ai-tells-us-about-human-intelligence/
from my link log —
Unicode's transliteration rules are Turing complete.
https://seriot.ch/computation/uts35/
saved 2026-07-08 https://dotat.at/…
RE: https://hachyderm.io/@thomasfuchs/116706024941055452
“Corollary 1 (AoE II is Turing-Complete). Let I be an instance of AoE II with two players p0,p1. Assume p0 has two markets, a town centre, a trade cart, six villagers, and five farms; while p1 has a scout unit and only attacks p0’s buildings. Then if I has no time or size limits and the terrain allows for buildings everywhere, the game session in I is Turing-complete.”
It’s astounding how the Turing Test* is still something that’s taken seriously.
Modern LLMs get wrongly rated as humans with much higher rates than actual humans get rated as humans.
The test quite literally tests for gullibility and hubris—in humans.
*simplified: a human is randomly assigned an other human or a chatbot and has 5 minutes of text chat to determine if they’re chatting with a human or a machine
from my link log —
C99 doesn't need function bodies: VLAs are Turing complete.
https://lemon.rip/w/c99-vla-tricks/
saved 2022-08-04 https://dota…
(I've said various versions of this before, but maybe this makes the point clearer:)
The Turing Test is **NOT** a benchmark.
It's a **warning**.
i.e., Turing *knew* that trying to define "intelligence" was, at best, way more difficult than people gave it credit for, likely completely futile. His proposed "test" is, "If you can be fooled into thinking there's something intelligent there, then there is (or at least there might as well b…
“Machines take me by surprise with great frequency.” – Alan Turing, Computing Machinery and Intelligence (1950)
Turing Test 2.0 - Existential Comics https://existentialcomics.com/comic/652
from my link log —
ARM LDM and STM are Turing complete.
https://kellanclark.github.io/2023/09/18/armfuck/
saved 2023-09-27 https://
from my link log —
Jira is Turing complete.
https://seriot.ch/computation/jira.html
saved 2026-05-23 https://dotat.at/:/1CFDO.html
LSR-Net: Long-Short-Range Operator Learning for Pattern Dynamics on Manifolds
Qian Serena Hou, Zecheng Gan
https://arxiv.org/abs/2607.00750 https://arxiv.org/pdf/2607.00750 https://arxiv.org/html/2607.00750
arXiv:2607.00750v1 Announce Type: new
Abstract: We propose the Long-Short-Range Neural Network (LSR-Net), an extensible operator-learning framework for predicting pattern dynamics on planar domains, spherical surfaces, and general manifolds. The method decomposes the forward evolution operator into a long-range component, represented by a compact Fourier multiplier constructed via the Sum-of-Exponentials (SOE) approximation, and a short-range component adapted to the underlying geometry and its intrinsic symmetries. For general manifolds represented by irregularly sampled point clouds, the long-range component is implemented by Gaussian gridding onto an auxiliary regular grid, where the Fourier multiplier is efficiently applied in k-space using FFT and the result is interpolated back to the original sample points. We evaluate LSR-Net on several benchmark systems, including the Allen-Cahn, Cahn-Hilliard, Schnakenberg, and Turing systems, over planar domains, spherical surfaces, and a blob-shaped manifold. Numerical results demonstrate that LSR-Net consistently achieves higher accuracy and improved stability compared with baseline operator-learning models. In particular, for Allen-Cahn dynamics on the sphere, the RMSE is reduced by approximately three orders of magnitude compared with the Spherical Fourier Neural Operator (SFNO). Rotation and reflection equivariance tests further confirm that the learned operator is consistent with these geometric transformations. These results indicate that LSR-Net provides an effective and robust approach for learning pattern dynamics on complex geometries.
toXiv_bot_toot
from my link log —
RP2040 DMA is Turing complete.
https://people.ece.cornell.edu/land/courses/ece4760/RP2040/C_SDK_DMA_machine/DMA_machine_rp2040.html
saved 2023-01-21
AI-generated 'actress' making feature film debut in 'Misaligned'
I wonder if Tilly knows how to make a pipe bomb¹
https://youtube.com/watch?v=R3UsDH0TVRk&si=ubIo4drFFQ5fpyWQ
¹ the new Turing Test "are you an AI" question