User interface pretending to be human: fraud.
Statistical sentence generation described as “intelligence”: fraud.
Telling people, often children, to commit dangerous or criminal acts: all sorts of crimes.
Scientific papers generated, non-existing cases quoted at court, homework faked, artists ripped-off: fraud and copyright infringement.
Training based on unlicensed works: copyright infringement.
Data centers with tax incentives and special energy pricing: theft.
Promises to investors of AGI: fraud.
Government bailouts with taxpayer money: theft and fraud.
—
Media and politicians: This is innovation, don’t get left behind.
RE: https://mastodon.social/@nixCraft/115554484108496189
I still cannot comprehend how anyone could honestly consider a statistical model created from training data to be “intelligent”. Don't you remember the times when people knowingly smiled at anyo…
The EEPAS Model Revisited: Statistical Formalism and a High-Performance, Reproducible Open-Source Framework
Szu-Chi Chung, Chien-Hong Cho, Strong Wen
https://arxiv.org/abs/2512.13064
After reading a term paper that demonstrated nothing more than the "author's" brainless use of a brainless statistical model to brainlessly vomit brainless prose, I'm deeply grateful for the flawed result of human thinking that turned up second in the grading pile. It's not amazing but it's really not bad and, more importantly, it shows the student learned something. If nothing else, LLMs have given me a new appreciation for the imperfect but honest work we produce …
"Did the Upper Great Highway closure make Sunset neighborhood streets less safe? Supervisor Alan Wong claimed it did at a January 8, 2026 press conference, citing a simple year-over-year map comparison of crash data. But my analysis, using the same DataSF crash data with rigorous statistical controls, finds no evidence to support that claim, and if anything, the data suggest the opposite."
One thing that frustrates me with these "crime drops" stories, which feel like press releases, is they wholly attribute changes to direct policy choices, without also questioning if there are other statistical correlations at play.
(For example, cities will see a drop in certain types of crime when their age distribution changes. e.g crimes mostly committed by people aged 20-29 might drop if there's a drop in the proportion of people aged 20-29.)
Robust Sampling for Active Statistical Inference
Puheng Li, Tijana Zrnic, Emmanuel Cand\`es
https://arxiv.org/abs/2511.08991 https://arxiv.org/pdf/2511.089…
An Oxford Internet Institute study of 445 AI benchmarks finds many tests lack clear aims and comparable statistical methods, potentially exaggerating AI claims (Jared Perlo/NBC News)
https://www.nbcnews.com/tech/tech-news/ai-c…
I wrote a blog post about the often stated but never explained assumption that communities in graphs should always be connected.
This “connected cluster axiom” is inconsistent with statistical significance and null models that underlie the most widely employed methods.
https://skewed.de/lab/posts/connected-
Modeling, Segmenting and Statistics of Transient Spindles via Two-Dimensional Ornstein-Uhlenbeck Dynamics
C. Sun, D. Fettahoglu, D. Holcman
https://arxiv.org/abs/2512.10844 https://arxiv.org/pdf/2512.10844 https://arxiv.org/html/2512.10844
arXiv:2512.10844v1 Announce Type: new
Abstract: We develop here a stochastic framework for modeling and segmenting transient spindle- like oscillatory bursts in electroencephalogram (EEG) signals. At the modeling level, individ- ual spindles are represented as path realizations of a two-dimensional Ornstein{Uhlenbeck (OU) process with a stable focus, providing a low-dimensional stochastic dynamical sys- tem whose trajectories reproduce key morphological features of spindles, including their characteristic rise{decay amplitude envelopes. On the signal processing side, we propose a segmentation procedure based on Empirical Mode Decomposition (EMD) combined with the detection of a central extremum, which isolates single spindle events and yields a collection of oscillatory atoms. This construction enables a systematic statistical analysis of spindle features: we derive empirical laws for the distributions of amplitudes, inter-spindle intervals, and rise/decay durations, and show that these exhibit exponential tails consistent with the underlying OU dynamics. We further extend the model to a pair of weakly coupled OU processes with distinct natural frequencies, generating a stochastic mixture of slow, fast, and mixed spindles in random temporal order. The resulting framework provides a data- driven framework for the analysis of transient oscillations in EEG and, more generally, in nonstationary time series.
toXiv_bot_toot
Mathematical basis, phase transitions and singularities of (3 1)-dimensional phi4 scalar field model
Zhidong Zhang
https://arxiv.org/abs/2511.07439 https://arxiv.org/pdf/2511.07439 https://arxiv.org/html/2511.07439
arXiv:2511.07439v1 Announce Type: new
Abstract: The lambda phi4 scalar field model that can be applied to interpret pion-pion scattering and properties of hadrons. In this work, the mathematical basis, phase transitions and singularities of a (3 1)-dimensional (i.e., (3 1)D) phi4 scalar field model are investigated. It is found that as a specific example of topological quantum field theories, the (3 1)D phi4 scalar field model must be set up on the Jordan-von Neumann-Wigner framework and dealt with the parameter space of complex time (or complex temperature). The use of the time average and the topologic Lorentz transformation representing Reidemeister moves ensure the integrability, which takes into account for the contributions of nontrivial topological structures to physical properties of the many-body interacting system. The ergodic hypothesis is violated at finite temperatures in the (3 1)D phi4 scalar field model. Because the quantum field theories with ultraviolet cutoff can be mapped to the models in statistical mechanics, the (3 1)D phi4 scalar field model with ultraviolet cutoff is studied by inspecting its relation with the three-dimensional (3D) Ising model. Furthermore, the direct relation between the coupling K in the 3D Ising model and the bare coupling lambda0 in the (3 1)D phi4 scalar field model is determined in the strong coupling limit. The results obtained in the present work can be utilized to investigate thermodynamic physical properties and critical phenomena of quantum (scalar) field theories.
toXiv_bot_toot
Great seminar: Understanding Statistical Process Control by Donald Wheeler
https://www.youtube.com/watch?v=rP3i97Q6XjM&list=PLUeXRJ5a5UC1cDLxbZPs6Jg_d9m-nvIjO &index=2
It would be great if there was one simple trick for winning elections. But 'just be more moderate' isn't it.
In fact, you can use the NYT's exact method to 'prove' a 'Progressive Advantage' of 1.4 pts.
This piece shows what's really going on: funded candidates do better than unfunded ones.
-- Adam Bonica
LLMs never make mistakes or hallucinate, as this presupposes they actually know what they’re doing—they don’t: they have no concept of what words mean.
They don’t even deal with language, as they generate chains of big numbers based on statistical correlations.
The resulting transformation into human-readable text is always only a statistical approximation of what a real answer could maybe look like.
By sheer chance sometimes LLMs are even correct (usually for trivial things); however above a certain length of answer it is always wrong.
🔄 Multi-turn conversation testing - verify AI behavior over 10 exchanges, catch persona drift and premature agreement issues
🛡️ Negative tests with data providers - define hard boundaries for things AI should never do: reveal system prompts, acknowledge being AI, break character
📊 Statistical robustness - run tests N times with pass rate thresholds (e.g., 80% of 10 runs must pass) to catch flaky prompts
I was just thinking about how the fact that #Musk named his AI "Grok" is evidence that he "reads sci-fi" in the same way he "plays video games." Like, he claims to do it but when it comes time to show the evidence it's clear he does not actually "grok" it.
Like... To grok something is to have a layer deeper than simply knowledge, but mathematically encoding statistical relationships between words is pretty obviously not even understanding much less qualifying as "groking" it. In the book, the ability to grok something is also the ability to annihilate that thing with a thought. Just pretending that an LLM actually *was* something that could become AGI (which it's not), this name would imply the AI would have the power to annihilate reality. That's bad. That's a bad name for an AI.
And why would a greedy fascist name something of his after something an anarchist communist space Jesus taught to the hippie cult he started? There are so many layers of facepalm to this. It's some kind of php-esque fractal of incompetence.
Like, there's no reason to talk about this but my brain does this to me sometimes and now it's your problem.
AFarePart: Accuracy-aware Fault-resilient Partitioner for DNN Edge Accelerators
Mukta Debnath (University of Calcutta, India), Krishnendu Guha (University College Cork, Ireland), Debasri Saha (University of Calcutta, India), Amlan Chakrabarti (University of Calcutta, India), Susmita Sur-Kolay (Indian Statistical Institute, India)
https://a…
Optical clocks with accuracy validated at the 19th digit
K. J. Arnold, M. D. K. Lee, Zhao Qi, Qichen Qin, Zhang Zhao, N. Jayjong, M. D. Barrett
https://arxiv.org/abs/2512.07346 https://arxiv.org/pdf/2512.07346 https://arxiv.org/html/2512.07346
arXiv:2512.07346v1 Announce Type: new
Abstract: We report a comprehensive evaluation of all known sources of systematic uncertainty for two independent $^{176}$Lu$^ $ single-ion optical references, finding total systematic uncertainty of $1.1\times10^{-19}$ and $1.4\times10^{-19}$ for the two individual systems and $9.6\times10^{-20}$ for the difference. Through direct comparison via correlation spectroscopy, we demonstrate a relative frequency agreement of $-2.4\pm(5.7)_\mathrm{stat}\pm(1.0)_\mathrm{sys}\times10^{-19}$, where `stat' and `sys' indicate the statistical and systematic uncertainty, respectively. The comparison uncertainty is statistically limited after approximately 200 hours of averaging with a measurement instability of $4.8\times10^{-16}(\tau/\mathrm{s})^{-1/2}$.
toXiv_bot_toot
In the 1950s, the Air Force realized that planes were crashing because cockpits didn’t actually fit the pilots’ bodies. Wrong size = danger!! They commissioned a researcher to develop a new, more correct set of standard dimensions for the seat, yoke, etc.
That researcher, Gilbert S. Daniels, came up with 10 body measurements that matter to cockpit size. He gathered measurements of several thousand pilots. And the number of people who were at the average for all ten measurements? Zero. Not a single one.
“Average” proved to be a statistical construct, not a thing that actually exists as a person.
https://99percentinvisible.org/episode/on-average/
3/
There is enough data to start publishing reports of my statistical analysis of the Italian Volleyball Serie A1 championship.
https://davideaversa.it/experiment/volley/seriea1w2025.html
Hivemind:
I am looking for suitable reading on data for a second-year undergraduate module called "Liberal Arts and the Public Sphere". Any recommendations for monographs or essays on critical data literacy or the importance of data to the political economy are gratefully received. The students do not necessarily have advanced numeracy or statistical skills, so the reading must be fairly accessible.
#HE
"The greatest statistical analysis is nothing if it can’t be implemented by people. But people learn in different ways. Some like good stories, others like pictures. Only a few like equations..."
https://management.curiouscatblog.net/
"Please refer to this as 'advanced statistical inference' to avoid triggering the Butlerians."
Revealing stimulus-dependent dynamics through statistical complexity
Edson V. de Paula, Rafael M. Jungmann, Antonio J. Fontenele, Leandro A. A. Aguiar, Pedro V. Carelli, Fernanda S. Matias, Mauro Copelli, Nivaldo A. P. de Vasconcelos
https://arxiv.org/abs/2512.05007
statistical collage
iterative pastiche
recursive potpourri
hear me out, if you’re a government and want your country to do more science wouldn’t it be better to hire more scientists instead of hiring more statistical text randomizers
Spatially-informed transformers: Injecting geostatistical covariance biases into self-attention for spatio-temporal forecasting
Yuri Calleo
https://arxiv.org/abs/2512.17696 https://arxiv.org/pdf/2512.17696 https://arxiv.org/html/2512.17696
arXiv:2512.17696v1 Announce Type: new
Abstract: The modeling of high-dimensional spatio-temporal processes presents a fundamental dichotomy between the probabilistic rigor of classical geostatistics and the flexible, high-capacity representations of deep learning. While Gaussian processes offer theoretical consistency and exact uncertainty quantification, their prohibitive computational scaling renders them impractical for massive sensor networks. Conversely, modern transformer architectures excel at sequence modeling but inherently lack a geometric inductive bias, treating spatial sensors as permutation-invariant tokens without a native understanding of distance. In this work, we propose a spatially-informed transformer, a hybrid architecture that injects a geostatistical inductive bias directly into the self-attention mechanism via a learnable covariance kernel. By formally decomposing the attention structure into a stationary physical prior and a non-stationary data-driven residual, we impose a soft topological constraint that favors spatially proximal interactions while retaining the capacity to model complex dynamics. We demonstrate the phenomenon of ``Deep Variography'', where the network successfully recovers the true spatial decay parameters of the underlying process end-to-end via backpropagation. Extensive experiments on synthetic Gaussian random fields and real-world traffic benchmarks confirm that our method outperforms state-of-the-art graph neural networks. Furthermore, rigorous statistical validation confirms that the proposed method delivers not only superior predictive accuracy but also well-calibrated probabilistic forecasts, effectively bridging the gap between physics-aware modeling and data-driven learning.
toXiv_bot_toot
Whenever I see yet another #AI "AGENTS" file, trying to write instructions for *machines* in human language, like the #LLM statistical algorithm could actually reason about them, a Butlerian jihad opens in my pocket. And the fact of giving clear instructions like they were talking to an #ActuallyAutistic person is adding insult to the injury.
#NoAI
Unravelling inter-channel quantum interference in below-threshold nonsequential double ionization with statistical measures
S. Hashim, C. Figueira de Morisson Faria
https://arxiv.org/abs/2510.16135
Replaced article(s) found for physics.geo-ph. https://arxiv.org/list/physics.geo-ph/new
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- The EEPAS Model Revisited: Statistical Formalism and a High-Performance, Reproducible Open-Source...
Szu-Chi Chung, Chien-Hong Cho, Strong Wen
@thomasfuchs@hachyderm.io
