It's just a form of rhetoric.

To dismiss them, saying, "ah ha! they're making the same error as all failed prophets!" misses the point.

What they are really talking about is AI governance.

One popular idea is that these systems will asymptotically approximate human intelligence because they're trained on mostly human-written texts. Not only is that untrue, it's also directly contradicted by our experience with previous RL-systems, where they seem to breeze right by human ability without even the slightest hiccup.

They really do want to know the ins-and-outs of the HVAC service business, for example, because they hope their agents will be handling it in a few years.

It comes through even worse in this later sentence, which at the very least tells you the writer has never met a welder.

It's useful for estimating demographics in large datasets from limited information, i.e. just a name.

It's also fairly good at separating given name and family names across a wide variety of languages and contexts.

Contains a standalone binary, embeddable shared lib, and a python wrapper.

Examples

CLI:

user@box » ./build/name-classifier -j -c "Carlos Eduardo Fernando Salazar Montemayor" | jq .

{ "input": "Carlos Eduardo Fernando Salazar Montemayor", "script": "latin", "components": [ { "token": "Carlos", "role": "given", "index": 0, "surname_score": 0.009 }, { "token": "Eduardo", "role": "given", "index": 1, "surname_score": 0.001 }, { "token": "Fernando", "role": "given", "index": 2, "surname_score": 0.01 }, { "token": "Salazar", "role": "family", "index": 3, "surname_score": 0.998 }, { "token": "Montemayor", "role": "family", "index": 4, "surname_score": 0.975 } ], "attributes": { "gender": { "male": 0.9938, "female": 0.0062, "neutral": 0 }, "origin": { "english": 0, "french": 0, "germanic": 0, "nordic": 0, "iberian": 1, "italian": 0, "eastern_european": 0, "arabic": 0, "east_asian": 0, "south_asian": 0, "southeast_asian": 0 } }, "calibrated": true, "model_version": "embedded", "provenance": { "gender": { "lexicon": 0.598, "ngram": 0.302, "neural": 0.101 }, "origin": { "lexicon": 0, "ngram": 0, "neural": 0 } } }

Python:

from name_classifier import NameClassifier

nc = NameClassifier(args.model_dir)

nc.classify("Kateryna Olha Mykhailivna Shevchenko")

Comments URL: https://news.ycombinator.com/item?id=47198318

Points: 2

# Comments: 0

Incidentally, millenials also used the "no caps" style but mainly for "marginalia" (at most paragraph-length notes, observations), while for older generations it was almost always associated with a modernist aesthetic and thus appeared primarily in functional or environmental text (restaurant menus, signage, your business card, bloomingdales, etc.). It may be interesting to note that the inverse ALL CAPS style conveyed modernity in the last tech revolution (the evolution of the Microsoft logo, for example).

Special relativity however seems possible.

It does seem like in-person pen-and-paper exams would hold the line pretty firmly with respect to competence. It's a simple solution and I haven't heard any good arguments against it.

[1] https://en.wikipedia.org/wiki/Gell-Mann_amnesia_effect

What remains after is something like the social status games of the aristocratic class, which I suspect is why there's a race to accumulate as much as possible now before the means to do so evaporate.