Language Log

"Google Translate learns 24 new languages"
Isaac Caswell, Google blog (5/11/22)

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[This is a guest post by Jenny Chu]

It came up today when I was reading this somewhat viral post on Facebook. 

Now it's getting interesting:

"China’s internet police losing man-versus-machine duel on social media"

Stephen Chen, SCMP (11/14/21)

    Hordes of bot accounts using clever dodging tactics are causing burnout among human censors, police investigative paper finds
    Authorities may respond by raising a counter-army of automated accounts or even an AI-driven public opinion leader

There may well be a dogma out there stating that all languages are equally complex, but I don't believe it, especially not if it has to be "drummed" into our minds.  I have learned many languages.  Some of them are exceedingly hard (because of their complexity) and some of them are relatively easy (because they are comparatively simple).  I have often said that Mandarin is the easiest language I ever learned to speak, but the hardest to read and write in characters (though very easy in Romanization).  And remember these posts:

"Difficult languages and easy languages" (3/4/17)

"Difficult languages and easy languages, part 2" (5/28/19)

With this post, we are already acquainted with Inspur's Yuan 1.0, "one of the most advanced deep learning language models that can generate coherent Chinese texts."  Now, with the present article, we will delve more deeply into the potentials and pitfalls of Inspur's deep learning language model:

"Inspur unveils GPT-3 equivalent for Chinese language", by Wei Sheng, TechNode (1026/21)

The model is trained with 245.7 billion parameters—the number of weights in an artificial neural network, according to the company. This is more than the Elon Musk-backed GPT-3 language model for English, which has 175 billion parameters. Inspur said the Yuan model was trained with 5 terabytes of datasets.

…

New article in EnterpriseAI (October 21, 2021):

"Language Model Training Gets Another Player: Inspur AI Research Unveils Yuan 1.0",  by Todd R. Weiss

From Pranav Mulgund:

Given this:

"Measure words for robots" (9/4/21)

and this:

"Arigatō" (9/3/21),

I could not help but think of this:

Christian Horn was reading an article in Japanese Endgadget (8/11/21) about the introduction of a new kind of robot called a "Cyberdog".

Says Christian:

You don't need to know Japanese to understand the fascinating part:  in Japanese, when counting things, the type of "thing" you are counting is relevant.  So you count "flat things" differently than "long shaped" things.  Or machines, fish, or animals.

The article states that Cyberdog is aimed at developers, and is limited to "1000台(匹?)", showing hesitation over which measure word to use, dai 台 (counter for machines, including vehicles) or hiki 匹 (counter for small animals​; counter for rolls of cloth; counter for horses​).  If you use dai 台 as a measure word for counting Cyberdogs, it would indicate that you think of them as machines.  If you use hiki 匹 for counting them, it would indicate that you regard Cyberdogs as animals.

Article by Holly Else in Nature (8/5/21):

"‘Tortured phrases’ give away fabricated research papers

Analysis reveals that strange turns of phrase may indicate foul play in science"

Here are the beginning and a few other selected portions of the article:

In April 2021, a series of strange phrases in journal articles piqued the interest of a group of computer scientists. The researchers could not understand why researchers would use the terms ‘counterfeit consciousness’, ‘profound neural organization’ and ‘colossal information’ in place of the more widely recognized terms ‘artificial intelligence’, ‘deep neural network’ and ‘big data’.

Further investigation revealed that these strange terms — which they dub “tortured phrases” — are probably the result of automated translation or software that attempts to disguise plagiarism. And they seem to be rife in computer-science papers.

Research-integrity sleuths say that Cabanac* and his colleagues have uncovered a new type of fabricated research paper, and that their work, posted in a preprint on arXiv on 12 July¹, might expose only the tip of the iceberg when it comes to the literature affected.

[*VHM:  Guillaume Cabanac, a computer scientist at the University of Toulouse, France]

I've written several posts about unpredictable typing mistakes that are not the result of auto-correct or sloppiness, but are produced through phonological confusion in my own neuro-muscular hardware and software (see "Selected readings").  This morning I experienced another funny occurrence of such a mistake.

I had lost over 7,000 of the recent e-mails in my inbox, so I wrote to the excellent IT guys in Williams Hall:

Crisis

I'm making good progress moving things from inbox to archives, but I just had a disaster.  Everything in my inbox between these two e-mails is missing:

Margaret ********   today (6/18/21) 11:53 a.m.

MISSING

Jing ***  (11/18/20)  11:06 p.m.

There are thousands of important e-mails to me with all sorts of information, attachments, and so forth that I need to take care of, some of them very soon.

Can you somehow restore the missing items?

Have you tried the Google Translate app on your phone? It has a camera tool that automatically translates text that you point it to, but it looks like it needs some work for Mandarin…

I tried to translate a bag of chinese rice crackers using google translate and these are some of the ingredients it gave me pic.twitter.com/cWIghhrWOs

— Claire!!!!!!! (@awkwardfoxes) February 10, 2021

This has been making the rounds:

1. Go to Google Translate.
2. Set the input language to Spanish.
3. Paste in "soy milk"
4. Set the output language to English or X language.
5. Hilarity ensues.

The obligatory screen shot:

Sensational article by Hagar Hosny in Al-Monitor (7/23/20):

"Google presents new tool to decode hieroglyphics:  Google has created a new tool to translate hieroglyphics into English and Arabic at the stroke of a key."

It starts like this:

In a July 15 press release, Google announced the launch of a new tool that uses artificial intelligence to decipher Egyptian hieroglyphs and translate them into Arabic and English.

Google said that the tool, dubbed Fabricius, provides an interactive experience for people from all over the world to learn about hieroglyphics, in addition to supporting and facilitating the efforts of Egyptologists and raising awareness about the history and heritage of ancient Egyptian civilization.

“We are very excited to be launching this new tool that can make it easier to access and learn about the rich culture of ancient Egypt. For over a decade, Google has been capturing imagery of cultural and historical landmarks across the region,” Chance Coughenour, program manager at Google Arts and Culture, said in the statement.