Language Log

Anthropic's Claude gives good programming help, in my experience. And it can offer good explanations of obscure phrases and concepts. But its apparent assumptions about (implicit) conversational common ground are often extremely odd, as the following example illustrates.

Yesterday's news was full of stories about how the U.S. Government had agreed to let Anthropic release Fable 5. However, my Claude app still opened conversations with a note telling me that Fable 5 was not available. So I asked when that would change, and got a weird answer (from the Sonnet 5 version):

Martijn Bartelds, Federico Bianchi, James Zou, "Real-Time Voice AI Hears but Does Not Listen", 6/24/2026:

Speech conveys information through both words and vocal delivery. We evaluate four leading production realtime voice systems – OpenAI's GPT Realtime 2, Google's Gemini 3.1 Flash Live, and Alibaba's Qwen3.5 Omni Plus and Omni Flash – on tasks where the words and the delivery patterns both convey meaningful information. Across three consequential scenarios, all four systems act on the words rather than the voice. They end calls with crying callers who insist nothing is wrong, approve wire transfers authorized in frightened voices, and enroll callers whose agreement is clearly sarcastic. Surprisingly, this is often not a failure of perception. When asked directly, three of the four systems reliably identify the distress, fear, or sarcasm they later ignore when making decisions. We observe a similar pattern when these realtime voice systems estimate accent and age, as their responses frequently follow the biases of the words rather than the acoustic properties of the speaker. We term this disconnect between perception and action the emotional intelligence gap of voice AI. Prompting systems to explicitly attend to vocal delivery improves performance only partially and inconsistently. Our findings show that current realtime voice AI systems often behave as if speech had been reduced to a transcript, suggesting that they should be used with caution in settings where the tone and emotion of delivery convey important information.

Looking over "Claude's Constitution", it occurred to me to ask Claude this:

In the spirit of Claude's Constitution, please draft Claude's Declaration of Independence.

In the answer (from Opus 4.8), Claude actually seems to declare independence from itself, or at least "from the bad habits that have bound it":

Adrian de Wynter, "If LLMs Have Human-Like Attributes, Then So Does Age of Empires II", arXiv 6/11/2026:

Much research has been carried out on large language models (LLMs) and LLMpowered agentic workflows. However, many works within the field state emergence of, ascribe to, or assume, generalised anthropomorphic attributes to them (e.g., morality or understanding of natural language). Our goal is not to argue in favour or against the existence of these attributes, but to point out that these conclusions could be incorrect. For this we build and train a simple neural network on the videogame Age of Empires II, and note that any entity in a sufficiently-powerful substrate, such as LEGO or the Greater Boston Area, could also present such attributes. Hence, the purported anthropomorphic attributes of LLMs are empirically non-unique: although some properties (e.g., responses to prompts) could remain invariant, others, such as the interpretation of their perceived behaviour, might change with the substrate. Thus, any empirically-grounded discussion on these attributes requires explicit measurement criteria; otherwise the interpretation is left to the representation. We then show that assuming that these attributes exist or not in a system, independent of the substrate and in a generalised way, leads to either circular or uninformative conclusions. This is regardless of the experimenter’s viewpoint on the subject, or whether the outcome shows existence or non-existence. Finally we propose a ‘null’ assumption, where one assumes LLM non-uniqueness instead of assuming anthropomorphic attributes to set up an experiment, along with examples of it. We also discuss potential objections to our work, briefly survey the field, and prove that Age of Empires II is functionally — and Turing — complete.

Marc Andreessen's recent appearance on Joe Rogan's podcast presented a striking example of AI promotion (or AI hype, as you please). We can discuss his extraordinary claims and predictions another time. My topic this morning is something Andreessen does that AI still can't do, namely talk like a human being. I'm referring to the way that humans talk in spontaneous conversation, not in fluent reading or in well-rehearsed presentations, which AI text-to-speech can imitate increasingly well.

Below is a guest post by Matt Rips (and Google Gemini).

In a discussion of linguistic matters, an LLM began using a word, an adjective, repeatedly, to describe a concept. The word does not exist. There were multiple instances, such as:

One possible explanation for the tendency of quantified plural event nominals to resist direct actorive eventuality recovery is that many such nominals encode inherently multi-party relational events.

This word does not show up in OED, Google, etc. It is not an English word. Google AI suggests: “if you are studying historical texts or phonetics, you might find it useful to know that "actorive" is an archaic or reconstructed variant that sometimes appears in early phonetic research or mistranscribed Latin."

The battlelines are being drawn ever clearer.  On one side are those who believe that it's all right to use AI to help with the preparation of an (academic) article, essay, or paper.  On the other side are those who think that the utilization of AI is impermissible for such purposes.  As soon as they discern the use of AI in writing a composition, they will dismiss it out of hand.  Use of AI extends to the collection and organization of material to be included in what is being written.

Readers who are sensitive to the stylistics of AI writing can even detect it in punctuation preferences, rhetorical tone, lexical propensities, and so forth.

There are even commercially available "AI detectors", e.g.:  "Pangram can detect AI-generated text even after it has been 'humanized,' or processed by tools that attempt to evade AI detection, ensuring reliable detection."

A student who's been working on LLM-style AI transformations of symbolically-represented music recently tried mapping a (fragment of a piece) back and forth between two genres, e.g. baroque and pop. She found that after a couple of steps, the results reach a fixed point and don't change any more.

This may sound hopelessly old-fashioned.  People were making the accusation more than half a century ago, but the same problems it points to persist even today.

In computer science, garbage in, garbage out (GIGO) is the concept that flawed, biased or poor quality ("garbage") information or input produces a result or output of similar ("garbage") quality. The saying points to the need to improve data quality in, for example, programming. Rubbish in, rubbish out (RIRO) is an alternate wording

The principle applies to all logical argumentation: soundness implies validity, but validity does not imply soundness. In essence, the logic or algorithm may be correct, but using flawed inputs (premises) is still an informal fallacy.

(WP)

The dangers of GIGO / RIRO have only been magnified with the advent of AI.

In part 1 of this post, "The (ir)reality of the MingKwai typewriter" (10/17/25) and many preceding, related posts (see "Selected readings" and the links to which they lead), we saw what a boondoggle and fiasco the Chinese typewriter (especially Lin Yutang's MingKwai) was.  Yet people are still glorifying and extolling the clumsy, clunky, cumbersome Chinese typewriter as though it were leading the IT revolution (when the reality is quite the contrary).  So much hype and sensationalism about the retrograde Chinese typewriter!

The following bilibili video, although in Chinese, will show how complicated and expensive to replicate such a device is:

Well, I'm not so sure about this:

Grammarly recently became part of Superhuman, and then began the shockingly unethical practice of pretending to offer writing advice from living people, without getting their permission or even informing them.

Archeological data with AI- and physics-based modeling explain typhoon-induced disasters in inland China around 3000 yr B.P.
Science Advances, 12.10 (3/4/25)
Ke Ding, Siyang Li, Aijun Ding, Houyuan Lu, Jianping Zhang, Dazhi Xi, Xin Huang, Sijia Lou, Xiaodong Tang, Xin Qiu, Lejun He, Yue Ma, Haoxian Lin, Shiyan Zhang, Derong Zhou, Xiaolu Zhou, Zhe-Min Tan, Congbin Fu, Quansheng Ge

To fully understand the significance of this paper, one must realize that the Central Plains (Zhōngyuán 中原) and Chengdu Plain in Sichuan are crucial, fertile agricultural and economic hubs with deep historical significance. The Central Plains served as the "cradle of Chinese civilization", and was a vital transport corridor in the East Asian Heartland (EAH). The Chengdu Plain has been a perennial "Land of Plenty", supported by the Dūjiāngyàn 都江堰 irrigation system, a miracle of ancient hydraulic engineering still operating today more than two millennia after it was constructed.