Are words on the way out?
circmoji - Bluesky Emoji Circles
Emojify โ the right emoji for a bit of text
Pick the most relevant emoji for a small length of text. Self-hostable, with a CLI and an XRPC endpoint.
Emoji Stats for Bluesky ๐ฆ
Live counters and stats on emoji usage on Bluesky, broken down by language.
Emoji Stats for Bluesky ๐ฆ
Live counters and stats on emoji usage on Bluesky, broken down by language.
Ascii Heart Emojis | โกโค๏ธโฅ๏ธ๐นญ | Copy & Paste
We've searched our database for all the emojis that are somehow related to Ascii Heart. Here they are! There are more than 20 of them, but the most relevant ones appear first.
FrankMoji
A bold emoji set. Pixel-perfect, even on low-resolution screens. Installable color font. Supports Unicode 18 emoji.
Model Misalignment and Language Change: Traces of AI-Associated Language in Unscripted Spoken English
In recent years, written language, particularly in science and education, has undergone remarkable shifts in word usage. These changes are widely attributed to the growing influence of Large Language Models (LLMs), which frequently rely on a distinct lexical style. Divergences between model output and target audience norms can be viewed as a form of misalignment. While these shifts are often linked to using Artificial Intelligence (AI) directly as a tool to generate text, it remains unclear whether the changes reflect broader changes in the human language system itself. To explore this question, we constructed a dataset of 22.1 million words from unscripted spoken language drawn from conversational science and technology podcasts. We analyzed lexical trends before and after ChatGPT's release in 2022, focusing on commonly LLM-associated words. Our results show a moderate yet significant increase in the usage of these words post-2022, suggesting a convergence between human word choices and LLM-associated patterns. In contrast, baseline synonym words exhibit no significant directional shift. Given the short time frame and the number of words affected, this may indicate the onset of a remarkable shift in language use. Whether this represents natural language change or a novel shift driven by AI exposure remains an open question. Similarly, although the shifts may stem from broader adoption patterns, it may also be that upstream training misalignments ultimately contribute to changes in human language use. These findings parallel ethical concerns that misaligned models may shape social and moral beliefs.
Model Misalignment and Language Change: Traces of AI-Associated Language in Unscripted Spoken English
In recent years, written language, particularly in science and education, has undergone remarkable shifts in word usage. These changes are widely attributed to the growing influence of Large Language Models (LLMs), which frequently rely on a distinct lexical style. Divergences between model output and target audience norms can be viewed as a form of misalignment. While these shifts are often linked to using Artificial Intelligence (AI) directly as a tool to generate text, it remains unclear whether the changes reflect broader changes in the human language system itself. To explore this question, we constructed a dataset of 22.1 million words from unscripted spoken language drawn from conversational science and technology podcasts. We analyzed lexical trends before and after ChatGPT's release in 2022, focusing on commonly LLM-associated words. Our results show a moderate yet significant increase in the usage of these words post-2022, suggesting a convergence between human word choices and LLM-associated patterns. In contrast, baseline synonym words exhibit no significant directional shift. Given the short time frame and the number of words affected, this may indicate the onset of a remarkable shift in language use. Whether this represents natural language change or a novel shift driven by AI exposure remains an open question. Similarly, although the shifts may stem from broader adoption patterns, it may also be that upstream training misalignments ultimately contribute to changes in human language use. These findings parallel ethical concerns that misaligned models may shape social and moral beliefs.
Empirical evidence of Large Language Model's influence on human spoken communication
From the printing press to social media, innovations in communication technology have repeatedly reshaped how ideas spread through human culture. Chatbots powered by generative artificial intelligence constitute a new medium, encoding cultural patterns in their neural representations and disseminating them in conversations with hundreds of millions of people. Whether these patterns transmit into human language, and ultimately shape human culture, is a fundamental question. While fully quantifying the causal impact of a chatbot like ChatGPT on human culture is challenging, lexical shifts in human spoken communication may offer an early indicator. Here we show that words preferentially generated by ChatGPT, such as delve, showcase, boast, intricacies and meticulous, increased abruptly in spontaneous human speech. A synthetic-control analysis of 737,083 hours of conversation from 824,634 podcast episodes, screened for unscripted speech, causally links this shift to ChatGPT's release. The measurable influence on spontaneous speech suggests that humans internalize the lexical choices of large language models (LLMs). A preregistered experiment (N = 496) confirms they do, as a brief chatbot interaction led participants to adopt its words as their own, persisting past a distractor task and confirmed in forced lexical choice, indicating entrenchment in the active vocabulary. Together these results show that machines trained on human data now feed their own traits back into human language, integrating LLMs into the ongoing processes of cultural evolution.. This coupling raises concerns about linguistic homogenization and the capacity of a few major AI providers for latent cultural influence at scale.
We need to come up with some fun interactive experiments with the firehose. Seriously, back in 2013 Emojitracker was big news. Today, I asked Opus to create a clone and a couple minutes later: artifacts.vza.net/emojihose
emojihose โ realtime emoji on Bluesky
artifacts.vza.net
Ronen Tamari
The AI community is SLEEPING on atproto's potential. With all the "on-tap intelligence" we have these days you'd think they'd be faster to figure that out
"The limits of my emojis mean the limits of my world"
Orion Reed
Emoji is neoliberal, not working class. We have ๐ฎ police, ๐ฆ banks, ๐น stocks go up, ๐ office necktie, ๐๏ธ government, โ๏ธ law, ๐คด๐ธ royalty but no wages stub, no union card, no "on strike" sign, no pink slip. there's a levitating businessman emoji ๐ด๏ธ but no picket sign.
Emoji is neoliberal, not working class. We have ๐ฎ police, ๐ฆ banks, ๐น stocks go up, ๐ office necktie, ๐๏ธ government, โ๏ธ law, ๐คด๐ธ royalty but no wages stub, no union card, no "on strike" sign, no pink slip. there's a levitating businessman emoji ๐ด๏ธ but no picket sign.
