Tootfinder

🎶 TweetyBERT parses canary songs to better understand how brains learn language
#birds

TweetyBERT parses canary songs to better understand how brains learn language
A new machine learning model, TweetyBERT, automatically segments and classifies canary vocalizations with expert-level accuracy, offering a scalable platform for neuroscience, providing insights into the neural basis of how the brain learns and produces language, and offering potential applications for understanding animal vocalization more broadly. The study by University of Oregon researchers appears in the journal Patterns.

A ceaseless urge beneath all appearance; an impulse not merely to persist but to intensify; life as duration, as movement, as an unbroken unfolding in time. As a technology of becoming. Life as sequence - procedural quests of forward momentum.
A language underwrites the motion; lines on lines inscribing themselves across a dark field. Pulses like pixels, moving in canon. Constructions that branch, loop, and repeat. Patterns generate patterns, as though the law of existence were simply…

I wonder if consciously avoiding the style and patterns frequently applied by generative AI might be triggering some kind of forced language evolution. Previously considered best practices that are now shunned to not sound like a machine.
#language #linguistics

Last keynote of #DGfS2026 🤓😄
Annette Gerstenberg on longitudinal patterns #linguistics

https://www.instagram.com/p/DUNFbR2AbG9/?utm_source=ig_web_copy_link&igsh=NTc4MTIwNjQ2YQ==

VT on Instagram: "In Japan, a new kind of bar is drawing attention for flipping workplace culture on its head. The bar is designed specifically for people who are considering leaving their jobs. Drinks are free, and the focus is not on alcohol sales but conversation. Patrons are invited to talk openly about burnout, frustration, and career doubts, while staff are trained to listen without judgement or pressure. The idea taps into a growing reckoning in Japan around long working hours, rigid hierarchies, and the mental health toll of intense job expectations. Conversations about quitting or dissatisfaction have traditionally been stigmatised, making this kind of space unusual. By removing the cost barrier and offering a supportive environment, the bar aims to normalise discussions about career dissatisfaction and help people reflect on what they actually want from work, even if that means starting over."
34K likes, 169 comments - vt on January 31, 2026: "In Japan, a new kind of bar is drawing attention for flipping workplace culture on its head. The bar is designed specifically for people who are considering leaving their jobs. Drinks are free, and the focus is not on alcohol sales but conversation. Patrons are invited to talk openly about burnout, frustration, and career doubts, while staff are trained to listen without judgement or pressure. The idea taps into a growing reckoning in Japan a…

Anthropic details the "Assistant Axis", a pattern of neural activity in language models that governs their default identity and helpful behavior (Anthropic)
https://www.anthropic.com/research/assistant-axis

The assistant axis: situating and stabilizing the character of large language models
Anthropic is an AI safety and research company that's working to build reliable, interpretable, and steerable AI systems.

Pattern matching in #csharp is probably the most impactful addition to the language since LINQ. #dotnet

"producing the feeling of knowing without the labor of judgement"
https://arxiv.org/abs/2512.19466
oh what a great line

Epistemological Fault Lines Between Human and Artificial Intelligence
Large language models (LLMs) are widely described as artificial intelligence, yet their epistemic profile diverges sharply from human cognition. Here we show that the apparent alignment between human and machine outputs conceals a deeper structural mismatch in how judgments are produced. Tracing the historical shift from symbolic AI and information filtering systems to large-scale generative transformers, we argue that LLMs are not epistemic agents but stochastic pattern-completion systems, for…

LLMs are masters of the "Understood"—the vast library of human output. But they lack "Understanding"—the creative spark that generates those concepts in the first place.
https://www.ocrampal.com/the-understood-understanding-gap-why-llms-hit-…

The Understood-Understanding Gap: Why LLMs Hit a Wall
Large Language Models have become ubiquitous in knowledge work, yet a curious pattern has emerged. Talk to consultants at McKinsey, BCG, Bain, and you'll hear the same refrain: "I don't really know how I can leverage AI to boost my productivity beyond research and brainstorming." The same applies to programmers,