Chomsky on what ChatGPT is good for (2023)(chomsky.info) Artificial intelligence (AI) is sweeping the world. It is transforming every walk of life and raising in the process major ethical concerns for society and the future of humanity.
Marked decline in semicolons in English books(theguardian.com) A study suggests UK authors are taking Vonnegut’s advice to heart; the semicolon seems to be in terminal decline, with its usage in English books plummeting by almost half in two decades – from one appearing in every 205 words in 2000 to one use in every 390 words today.
132 points by bryanrasmussen 19 days ago | 138 comments
Why Arabic Is Terrific (2011)(idlewords.com) So I would like to stand up for the language nerds and give some reasons for studying Arabic that have nothing to do with politics.
Do language trees with sampled ancestors support a hybrid Indo-European origin?(nature.com) In this paper, we present a brief critical analysis of the data, methodology, and results of the most recent publication on the computational phylogeny of the Indo-European family (Heggarty et al. 2023), comparing them to previous efforts in this area carried out by (roughly) the same team of scholars (informally designated as the “New Zealand school”), as well as concurrent research by scholars belonging to the “Moscow school” of historical linguistics.
Accents in latent spaces: How AI hears accent strength in English(boldvoice.com) We work with accents a lot at BoldVoice, the AI-powered accent coaching app for non-native English speakers. Accents are subtle patterns in speech—vowel shape, timing, pitch, and more. Usually, you need a linguist to make sense of these qualities. However, our goal at BoldVoice is to get machines to understand accents, and machines don’t think like linguists. So, we ask: how does a machine learning model understand an accent, and specifically, how strong it is?
245 points by ilyausorov 32 days ago | 128 comments
Umarell(wikipedia.org) Umarell (Italian spelling of the Bolognese Emilian word umarèl, Emilian pronunciation: [umaˈrɛːl]; plural umarî) are men of retirement age who spend their time watching construction sites, especially roadworks – stereotypically with hands clasped behind their back and offering unwanted advice to the workers.[1] Its literal meaning is "little man" (also umaréin).[2] The term is employed as lighthearted mockery or self-deprecation.
Unparalleled Misalignments(rickiheicklen.com) This is where I maintain a list of Unparalleled Misalignments (formerly quadruple entendres), pairs of non-synonymous phrases where the words in one phrase are each synonyms of the words in the other.
16 points by dp-hackernews 36 days ago | 7 comments
Zipf's Law(wikipedia.org) Zipf's law (/zɪf/; German pronunciation: [tsɪpf]) is an empirical law stating that when a list of measured values is sorted in decreasing order, the value of the n-th entry is often approximately inversely proportional to n.
Why is English so weirdly different from other languages?(aeon.co) English speakers know that their language is odd. So do people saddled with learning it non-natively. The oddity that we all perceive most readily is its spelling, which is indeed a nightmare. In countries where English isn’t spoken, there is no such thing as a ‘spelling bee’ competition. For a normal language, spelling at least pretends a basic correspondence to the way people pronounce the words. But English is not normal.
Greek Particles (1990)(specgram.com) Two facts well-known to linguists for many years are that Ancient Greek orthography represented speech much more closely than does modern English orthography, or practically any other modern European orthography, and that speech, unlike writing, is full of hesitations, false starts, and meaningless expletive utterances which are not recorded in writing.
Do Large Language Models know who did what to whom?(arxiv.org) Large Language Models (LLMs) are commonly criticized for not understanding language. However, many critiques focus on cognitive abilities that, in humans, are distinct from language processing. Here, we instead study a kind of understanding tightly linked to language: inferring who did what to whom (thematic roles) in a sentence.