#computational

graeme fawcettgraeme@tech.lgbt
2025-11-25

This guy here... this is a guy. I love this guy 🩷🧸🍷

How do you keep your little gooses on path? 🪿🦮

We're calling it Looking Glass Development. It's what happens when TDD and BDD have a wee bit too much wine when they're over for a visit with your teddy and what comes out has a wee bit more computational markdown in it than anyone wants to admit.

🧸🤷🏻‍♀️

You guys are already doing that thing where you use your user guide as the spec right?

Just add your unit tests at the bottom.

Living markdown.

🔴 ➡️ 🟢 ➡️ 🔄
♥️ 🏹 💚 🏹 🫶🏻

If the guide is accurate enough to direct accurate usage and the tests are passing then the garden has been well tended. 🌱

#ai #platform #living-documentation #computational-markdown

Everyone else had a C64 too right? 💾🦖It's lovely and green and red for Christmas 🎄All you have to do is really clearly state what you want.

And first know
Fabrizio Musacchiopixeltracker@sigmoid.social
2025-11-04

@axoaxonic @adredish Fully agree 👍 Horner's framework really begs for a formal dynamical model: defining trajectories, #attractors, and #manifolds within that 3D space. Something that could turn his conceptual #StateSpace into a genuine #computational theory of #memory dynamics.

I didn’t know Redish's book ("Beyond the Cognitive Map") before your comment! Sounds highly relevant and I’ll definitely put it on my reading list 👌

Nick Byrd, Ph.D.ByrdNick@nerdculture.de
2025-09-26

Proud of Vahid's use of #computational #CogSci to identify and compare #reasoning errors in #Reddit users and communities.

He's presenting it at an #AI + #decisionSci workshop at #CMU : cmu.edu/ai-sdm/research/human-

Follow him for alerts about this and more: researchgate.net/profile/Vahid

Beyond Fact-Checking: Empowering Flexible Human-AI Teams to Detect & Counter Online Misinformation

Vahid Ashrafi (in the photograph), Nick Byrd, and Jordan W. Suchow

Misinformation is persistent: From pandemic conspiracies to election denial, false claims spread faster and farther than facts, threatening
health and democracy.

Current defenses fall short: Fact-checking and moderation focus on sentiment but overlook the cognitive vulnerabilities that make cognitive errors drive spread. Biases like confirmation bias, emotional reasoning, and exaggerated thinking distort judgment and amplify viral narratives.

We detect and quantify 419 cognitive errors in Reddit users' text, building "cognitive fingerprints" that reveal reasoning flaws behind misinformation.

Impact: These insights enable flexible human-AI teams to anticipate, detect, and counteract misinformation.Beyond Fact-Checking: Empowering Flexible Human-AI Teams to Detect and Counter Online Misinformation

Vahid Ashrafi, Nick Byrd, and Jordan Suchow 

Abstract. [Vahid construced] a comprehensive knowledge base of 419 cognitive errors—including cognitive biases and logical fallacies—grounded in empirical findings from psychology and cognitive science. We then trained GPT-4.1, a state-of-the-art language model, to identify potential examples of these cognitive errors in users' posts. ... The robustness of AI-generated insights was rigorously validated by human annotators, achieving high reliability (Spearman’s ρ = 0.86). ... we propose actionable solutions such as targeted cognitive interventions to educate users about common reasoning errors, AI-driven moderation systems that identify problematic reasoning patterns rather than merely flagging false information, and personalized cognitive profiling tools to proactively identify at-risk individuals and communities.The NSF AI Institute for Societal Decision Making (NSF AI-SDM) sponsors the participation of selected speakers and students in an annual workshop of Human-AI Complementarity for Decision Making. Human-AI Complementarity, defined as the condition in which Humans + AI working together results in better decisions than humans or AI working alone, is a broad goal pursued in several projects of the NSF AI-SDM.

In 2025, we will focus on how to create flexible Human-AI teams to achieve complementarity. This theme refers to the interdisciplinary study of how to design and deploy AI systems in ways that are dynamically aligned with human values, robust to unexpected behavior, and safe even under failure modes. It encompasses short-term concerns about deployed systems (e.g., fairness, robustness, interpretability, and misuse) and long-term concerns about advanced general AI (AGI) that could have large-scale societal impacts if not aligned with human interests.
Md Ishtiak Rashidishtiakrashid
2025-09-18

Excited to share our latest work published in Nature Scientific Reports:

"Machine learning based characterization of high risk carriers of HTLV-1-associated myelopathy (HAM)"

This work represents a significant step forward in precision medicine for neglected viral infections — I am grateful to work with an incredible team of clinicians and Bioinformaticians.

biology

Further reading: 🔗 nature.com/articles/s41598-025

katch wreckkatchwreck
2025-09-07

rant: i know there are a lot of alternative (typically anti-) definitions of "AI" flying around, but i just thought of another one: Actual Insanity! anyone who has done serious work with finite precision , , , , or analysis, knows that minimizing the number of parameters is essential for building truly explanatory models. but establishment scientists have collectively signed onto the idea that large models are intelligent

2025-09-04

Via #LLRX #AI #slop and the #destruction of #knowledge – Prof. Iris van Rooij, #Computational #CognitiveScience at the School of #ArtificialIntelligence, shares thread of her email communications w #Elsevier Helpdesk detail with concerns about #AI generated definitions and links within scholarly articles, and the fact that authors cannot say ‘no’ to their work being used for #AItraining and #AIgenerated # texts. llrx.com/2025/08/ai-slop-and-t #education #learning #academia #teaching #knowledge

2025-08-27

Oxbow makes genomic data ready for high-performance analytics.
File formats are a major source of friction and headache in #computational #biology. Oxbow makes it easier to retrieve and manipulate data stored in conventional genomic formats using modern data tooling.
#bioinformatics
oxbow.readthedocs.io/en/latest/

❀𝓪𝓵𝓬𝓮𝓪𖤐alcea@alceawis.com
2025-08-09
If you think about it.
#Humans were never made with #computational task in mind.

Too much monkey brain and #evolutionary #baggage

And yet we think we are...

The study uses Random Forest regression to analyze data from Gaia and ALMA, uncovering hidden non-linear patterns in the distribution of matter and energy. #cosmic-code #Computational #Self-Organization www.researchgate.net/post/Towards...

Towards a New Mathematical Mod...

2025-06-26

[Loria Colloquium]

🗣️ We are pleased to welcome Alexander Koller, Professor of computational linguistics at Saarland University today, with a talk about Solving complex problems with large language models!

@cnrs
@inria

👉 More information: lnkd.in/eDX3NjP2

Colloquium Loria
Steven Saus [he/him]StevenSaus@faithcollapsing.com
2025-05-31

(29 May) Your next gaming dice could be shaped like a dragon or armadillo

Statistically, “the real behavior of a rolling object is largely a function of its geometry.”…

s.faithcollapsing.com/g6yqw
Archive: ais: archive.md/wip/HYNoa ia: s.faithcollapsing.com/oeaqj

#3d-printing #computational-science #computer-simulations #geometry #probability #science #shape-analysis

a selection of single 3D printed dice with weird shapes
Viðar Guðmundssonvidargudmundsson
2025-05-30

My early Linux user history

Here is a photo of the Linux distributions I used before the operating system was available through the internet. The first one was DLD (Deutsche Linux Distribution).

All through this time I only used happily Linux for all my activities. Linux was a real revolution for my work close to computational and condensed matter physics. It totally changed the access to computational infrastructure

Claus Jepsencvjepsen
2025-05-24

I fundamentally reject the notion that computational systems can be considered intelligent.

2025-05-15

UMA: A Family of Universal Models for Atoms
ai.meta.com/research/publicati

family of Universal Models for Atoms (UMA), designed to push the frontier of speed, accuracy, and
generalization. UMA models are trained on half a billion unique 3D atomic structures (the largest
training runs to date) by compiling data across multiple chemical domains, e.g. molecules, materials,
and catalysts.

#ml #deepLearning #compChem #computational #chemistry #AImodels

Client Info

Server: https://mastodon.social
Version: 2025.07
Repository: https://github.com/cyevgeniy/lmst