For many people the release of Threads, Instagram's text-based conversation app, represents an alternative to Twitter -- a platform which is widely considered to have become more toxic and problematic under Elon Musk. But for an even larger number of people, Threads will be their first step into this type of social media.
Switching from Twitter, Mastodon or Bluesky to Threads -- or using them in conjunction with each other -- is painless, but for anyone who has never used such a platform, the language surrounding it can be slightly mystifying. And this is why Meta has released a Threads Dictionary to bring users up to speed.
See also:
As Meta has launched Threads as a service that is tightly associated with Instagram -- to the extent that if you close your Threads account, you'll also lose your Instagram account -- it is little surprising to find that the Threads Dictionary was shared on the Threadsapp Instagram account.
While the language surrounding Twitter has become so widely used that even non-users are aware of what a tweet is and what it means to retweet something, the same does not yet apply to Threads. In its (currently very short) dictionary, Meta makes it clear that Threads’ equivalent of a tweet is a thread. And a thread is posted, reposted or quoted.
In comments on its own Instagram post, the Threads team makes it clear that there are not really any hard and fast rules. It says: "We've heard some people call posting "threading" or "stitching" -- that's cool, be creative, do your thing."
For readers of BetaNews, the almost laughably brief dictionary is likely to be entirely unnecessary but Threads is catering to Meta's massive Facebook and Instagram userbase -- a userbase whose technical expertise, knowledge of jargon as so on, ranges from complete novice to expert.
Dead languages are famously hard to decipher. It took 23 years to crack the Egyptian hieroglyphics on the Rosetta Stone. It took nearly two centuries to understand Mayan glyphs. And it took over 3,000 years to reveal Linear B, the earliest form of Greek. When techno-optimists talk about the game-changing potential of A.I., they cite difficult problems like this, and even for languages that have already been translated, challenges remain. Consider Akkadian cuneiform, one of the world’s oldest written languages. There are so few people who can read the extinct language that nearly a million Akkadian texts still haven't been translated to date—but now an A.I. tool can decode them within seconds.
An interdisciplinary group of computer science and history researchers published a journal article in May describing how they had created an A.I. model to instantly translate the ancient glyphs. The team, led by a Google software engineer and an Assyriologist from Ariel University, trained the model on existing cuneiform translations using the same technology that powers Google Translate.
A beacon to weary translation travelers
In translating dead languages, especially those with no descendant languages, piecing together meaning without a wealth of cultural context can be like traveling without a North Star. Akkadian is just such a language. The tongue of the Akkadian Empire, located in present-day Iraq during the 24th to 22nd centuries BCE, Akkadian existed as both a spoken and written language. Its cuneiform writing system used an alphabet of sharp, intersecting triangular figures. Akkadians typically wrote by marking a clay tablet with the wedge-shaped end of a reed (cuneiform literally means “wedge shaped” in Latin). Hundreds of thousands of these tablets, due to the durability of their material, have weathered the centuries and now populate the halls of various universities and museums.
Translation is often misunderstood as a one-to-one decryption of a foreign word or phrase. But many times, a statement in one language doesn’t have an exact or easy equivalent in another, accounting for cultural nuance and difference in the languages’ construction. High-quality translation requires a deep knowledge of both languages’ structures, their surrounding cultures, and the histories that anchor those cultures. Translating a text while preserving its original tone, cadence, and even humor is a delicate craft—and an incredibly difficult one when the language’s culture is largely unknown.
The number of existing cuneiform texts is overwhelming compared to the small number of linguists who are able to translate Akkadian. This means that troves of knowledge on the significant early civilization, sometimes considered the first empire in history, are completely untapped. Right now, the number of existing tablets and the rate of new tablets being excavated by archaeologists outpace linguists’ translation efforts. But that could change with the integration of A.I. into the cuneiform interpretation process.
“Hundreds of thousands of clay tablets inscribed in the cuneiform script document the political, social, economic, and scientific history of ancient Mesopotamia,” the team wrote. “Yet, most of these documents remain untranslated and inaccessible due to their sheer number and limited quantity of experts able to read them.”
The A.I. can perform two types of translation—translating cuneiform to English, and transliterating cuneiform (rewriting it phonetically). The A.I.’s skill at the two translation types of translation scored 36.52 and 37.47, respectively, on the Best Bilingual Evaluation Understudy 4 (BLEU4), a measure of translation quality. These scores were above the team’s target, and are both high enough to be considered high-quality translations. BLEU4 scores on a scale of 0 to 100 (or 0 to 1) with 70 being the highest that could be realistically achieved by a very skilled human translator.
For decades, computer-generated translations were brittle and unreliable, Tom McCoy, a computational linguist at Princeton University, said. Translation programs embedded with grammatical rules always missed the richness of meaning in idioms and nonliteral language that slip through the cracks of formal grammar. But recently, A.I. programs like the cuneiform translator have been able to get at the “fuzzier” areas of language. It heralds an exciting new period of A.I.-propelled computational linguistics.
“In recent A.I., the big new thing is statistical processing, which is another type of math but not the sort of rigid rules that people were working with before,” McCoy said. “Statistics got us kind of over the hump of previous methods. We're now working with machine learning and deep learning. Machines are able to learn all these idiosyncrasies, idioms, and exceptions to rules, which is what was missing in the previous generation of A.I.”
“You can never really trust the output”
The cuneiform A.I.’s translations still had mistakes—and had “hallucinations” as is common with A.I. In one example, it translated “Why should we (also) conduct the lawsuit before a man from Libbi-Ali?” as “They are in the Inner City in the Inner City.”
Despite occasional errors, the tool still saved huge amounts of time and human labor in its initial processing of the texts.
“A.I. currently is remarkable but unreliable. So it can do really amazing things, but you can never really trust the output it produces,” McCoy said of using A.I. for translation. “This means that the best case for using A.I. is something where it's very labor intensive, hard for humans to do, but once A.I. has given you some output, it's easy for humans to verify it.”
The model was most accurate when translating shorter sentences and formulaic texts like administrative records. It was also—surprisingly to the researchers—able to reproduce genre-specific nuances in translation. In the future, the A.I. will be trained on larger and larger samples of translations to further improve its accuracy, the researchers wrote.
For now, it can assist researchers by producing preliminary translations that humans can then check for accuracy and refine in nuance.
“A promising future scenario would have the [model] show the user a list of sources on which they based their translations, which would also be particularly useful for scholarly purposes,” the researchers wrote.
This story was originally featured on Fortune.com
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Pop-punk trio FRND CRCL have released their third studio album, Suburban Dictionary. The record is steeped in influence from the early-aughts, their genre’s golden days. It’s angsty and defiant, punctuated by jagged guitar riffs and sparklingly-polished vocals.
Suburban Dictionary is a deep-dive into the discontent of growing up through the lens of the suburban teenager, honing in on an emo-tinged view of entrapment and boredom. With melodic, hypnotizing centerpiece “Don’t Wait Up” paired with anthemic cuts like “No Bad Days,” “ADHD” and “Orange Tang,” FRND CRCL have taken an incredible leap in artistry on Suburban Dictionary.
Listen to “Don’t Wait Up” here and check out the artwork and tracklist for Suburban Dictionary below.
Suburban Dictionary Artwork:
Suburban Dictionary Tracklist:
7AM
No Bad Days
ADHD
Golden
Orange Tang
Clinically Insane
Fuck California
No Chill
47
Don’t Wait Up
Kids
Midnight
WYNWM
Alright
A Japanese railway company is introducing a simultaneous translation system to accommodate the rising number of foreign tourists.
Seibu Railway will start using the technology on a trial basis next week at its station in Tokyo's Shinjuku district.
It will help station staff communicate in 12 languages, including English, Vietnamese and Portuguese.
Seibu has been using tools such as translation apps.
It says the new method allows conversation while being able to watch people's expressions and show them pamphlets.
The company says the number of overseas customers has jumped, recovering up to 80 percent of pre-pandemic levels.
Seibu Railway representative Yajima Ayano says the company wants to try whatever makes foreign visitors feel safe and comfortable using its service.
The company plans to run the trial for about three months, before fully introducing the system this autumn.
Pop-punk trio FRND CRCL have released their third studio album, Suburban Dictionary. The record is steeped in influence from the early-aughts, their genre’s golden days. It’s angsty and defiant, punctuated by jagged guitar riffs and sparklingly-polished vocals.
Suburban Dictionary is a deep-dive into the discontent of growing up through the lens of the suburban teenager, honing in on an emo-tinged view of entrapment and boredom. With melodic, hypnotizing centerpiece “Don’t Wait Up” paired with anthemic cuts like “No Bad Days,” “ADHD” and “Orange Tang,” FRND CRCL have taken an incredible leap in artistry on Suburban Dictionary.
Listen to “Don’t Wait Up” here and check out the artwork and tracklist for Suburban Dictionary below.
Suburban Dictionary Artwork:
Suburban Dictionary Tracklist:
7AM
No Bad Days
ADHD
Golden
Orange Tang
Clinically Insane
Fuck California
No Chill
47
Don’t Wait Up
Kids
Midnight
WYNWM
Alright
Every part of your body defines beauty.
Every gesture of yours proclaims youth.
Gorgeous woman
Looking at your heavenly beauty
The world is spellbound.
You too left your mouth half-opened
Perhaps to explain the secrets
Of that celestial magnificence.
Your lips that lit fires
In so many hearts
That it never met
Burnt on the pyre.
Perhaps to show your heart
Age has sculpted curves and attractions
In every part of your body.
But could not take away
The innocence contained
In your childlike eyes and gentle heart.
Your serene heart suffered betrayal
Expecting a charming experience
From this decadent society, which is
Drowned in greed and belligerence.
You know,
This world grabs light produced by Niagara
But never considers how that fire was born.
Falling from such a high altar
Breaking the magnanimous heart of water.
Human psyche developed
From puppet play to running machines
But has not moved an inch
In understanding compassion.
Maybe that’s why you could not settle anywhere.
You radiated for a while
Just like lightning on the faces of bloated clouds.
Your presence for that moment
Is still raining gold.
But still
You have not yet become
A “sacred” subject to write about.
We grind our mouths till they tire
To gossip of rumours and slander against you.
Don’t they say that
Stones and pebbles and even shoes of Ram
Got life and gave rise to epics?
But you, a complete human being
A symbol of sex to boot
You are not worthy of poetry!
This society wants to see you nude
But detests your heart from
Appearing unclothed, poignant and unblemished.
This world has closed its eyes
To the splendour of your heart
Which enhanced the elegance of your body.
That’s why your sleepless eternal search
for peace of mind
Resulted in that beautiful long slumber . . .
Translated by N Venugopal.
Supernova (1987)
As the light of the sun or the moon
Fills the earth, how many histories
Of stars does the darkness mask
How many rays of light escape
The entrails of darkness, how many
Luminous streams fall prey to the
Cravings of a galaxy?
The story of a star’s
Explosion has to travel lakhs of light years
To reach us, and in the present
In the place of a star that has long died
All we see is a fledgling star being born.
Translated by Rohith.
No Classes Tomorrow (1988)
Those kids helplessly stand
At the zebra crossing on the road
The hurry to hang on to their moms’ necks filling their eyes.
The weight of homework on their backs pulls down their neck
Hair like fallen petals of withered flowers
Uniforms that drain all the colour in their face
Shoes that stop the mercuric feet running before time.
In the midst of an urban forest
Those kids are listless visions of
Fallen stars.
Vehicles stop only for the red signal
But not for the kids.
The hands that turn the wheel
That manage the handle and apply brakes
All the hands otherwise embrace those kids
But now, no one looks at them.
I waved at them with affection
But they looked at my hand strangely
As if thinking
What is this melody amidst this din?
Recognising the smile from within the police van
They sniffed a message in my handcuffed and raised fist
“Tomorrow there won’t be any classes.”
As they cross the road noisily
The vehicles stopped
Like stones in the stream.
The children ran with wild joy
Without looking back.
Translated by N Venugopal.
Human Being with a Voice (1997)
Hidden in thick mango foliage
The cuckoo sings of the coming
Of spring.
The peacock with its thousand-eyed feathers
Dances in pleasure at the onset of rain
In the darkness of the forest.
The blue jay vanishes in the sky
While people march, heralding
The arrival of the right time
For taking arms from the jammi tree.
Birds in the forest
Make agitated noises
To alert the grazing cattle and the jumping calf
About the pouncing tiger.
Waves inform the fish in water
About the imminent net.
Rough weather tells the pigeon in the nest
About the preying snare.
Who then will tell good and bad
To that person who does not have voice
Who only has two hands that work
And a stomach?
Translated by N Venugopal.
To Teach Kids 1 (2006)
Today’s little ones,
Are beaten up, shouted at
And lied to.
That’s how they are trained to be
Tomorrow’s citizens of this country.
When they grow up
They will repeat what they were taught
Some of them from positions of power
Most of them downtrodden.
Translated by Rohith.
To Teach Kids 2
Kids, when they are still little
Smudge their clothes as they
Play in the mud, like a worker
From the coal mine who digs up
And carries loads.
They are dragged back
To tailored uniforms, sent to school
And disciplined.
It is only then
They grow up to be
Army generals and
Receive medals for chivalry.
Translated by Rohith.
Excerpted with permission from Varavara Rao: A Life in Poetry, edited by Meena Kandasamy and N Venugopal, Penguin India.
