Top Tweets for #Babelbit
🔥 SUBNET MOMENTUM · 24H
Net TAO · wallets · largest buy
1️⃣ SN51 #LiumIo +1,290 · 90 · 1,860
2️⃣ SN64 #Chutes +1,111 · 102 · 400
3️⃣ SN102 #Connitoai +1,041 · 146 · 250
4️⃣ SN59 #Babelbit +770 · 163 · 60
5️⃣ SN25 #Mainframe +544 · 62 · 391
#Bittensor #TAO #Subnet
��� The $100B real-time language market just got a serious challenger!
A Bittensor subnet, Babelbit (SN59), beat Google at real-time live speech-to-speech translation.
Decentralized miners > Big Tech APIs.
#Babelbit bittensor:native
[Article link in comments]
We're excited to announce that Japanese is now part of Babelbit's expansion.
French demonstrated what our incentive mechanism could achieve.
Spanish showed we could scale that success using the same breakthroughs across multiple languages.
Now we're bringing the same approach to Japanese as we continue building products for a global market.
$TAO
The Babelbit base miner has been updated!
Thanks to everyone that has participated and helped us collectively progress the boundaries of speech to speech interpretation.
There have been genuinely innovative ideas and solutions applied to your work - please keep up the efforts.
The more we move forward together, the deeper the Babelbit moat becomes, ultimately leading to wider business opportunities for the technology we’re creating together.
To date, we've paid out more than $400,000 for the innovations contributed so far.
Going forward expect more frequent base miner updates!
$TAO
$TAO Amazing things happening on @babelbit right now 🤯its the pace of innovation driven progress that is so astonishing #bittensor #sn59 #babelbit💪
$TAO Amazing things happening on @babelbit right now 🤯its the pace of innovation driven progress that is so astonishing #bittensor #sn59 #babelbit💪
The UK NHS currently spends around £75.5 million a year on translation and interpreting.
The spend represents just a tiny fraction of the booming global interpretation opportunity of $7.8b, where AI solutions like BabelBit can deliver major efficiency and scale.
This concept video gives context as to the problem that Doctors (and patients) experience on a daily basis, and how Babelbit’s technology can make a difference, not just on budget, but in saving lives.
For wider context read our full article.
bittensor:native
Great write up from @RvCrypto about @babelbit.
It will be one of those subnets that many sleep on because they haven’t taken the time to truly understand how big the market is and the value that super low latency speech translation tech can bring.
Yes, we already have tech that can do this well but @babelbit are looking to take it to a whole new level.
The team is stacked with people who have been in the field for decades, have brought multiple products to market and really know how to utilise Bittensor to fulfil their vision.
$TAO
Language devs are shipping - #Babelbit SN59 🔥
I absolutely love the idea, partly because I’m a language enthusiast. English is my second language. I’m Polish 🇵🇱, born and bred. Back when I was in primary school (I’m in my 40s now), foreign language education in my home country was honestly pretty rubbish. You had to learn the hard way: long hours hunched over textbooks, endless vocabulary memorisation, and repetitive grammar exercises, with very little focus on actually learning how to speak.
My English learning journey never really stopped, it just evolved. After finishing my master’s degree, I became fully immersed in the language. Looking back, it’s incredible how much impact that had on me, how it reshaped my view of the world, and how many doors it opened professionally. In hindsight, pushing my language education was probably one of the best decisions my parents made and later one of the best decisions I made myself.
Today, young people have opportunities we could only dream of back then. Language learning is far more accessible, with resources available almost everywhere online. On top of that, the use of artificial intelligence in language learning is a massive step forward. Tools like Babelbit show just how powerful this can become when AI is applied properly.
What excites me most is the practical side of it. Real time speech to speech translation, contextual understanding rather than literal word for word output, and the ability to adapt to accents, tone, and intent. That opens the door to genuinely useful applications, seamless conversations between people who do not share a common language, live interpretation in meetings or conferences, customer support without language barriers, education, travel, even collaboration between global teams without friction. If done right, this is not just a convenience feature, it is a real productivity unlock.
It is a brilliant learning tool for anyone, regardless of age or level and the fact that it is free makes it even more impressive. I honestly wish I had access to even a fraction of this technology when I was younger. It would have made my English education dramatically easier.
That said, while I fully appreciate the benefits of AI in this space, I cannot ignore the potential downside, the risk that people may eventually stop wanting to learn foreign languages altogether. Hopefully, tools like Babelbit end up doing the opposite inspiring curiosity, lowering the barrier to entry, and encouraging more people to engage with languages rather than replacing the desire to learn them.
We are about to move to the next stage of our prediction development, by adding both multilingual challenges (adding German and Chinese), and by adding an additional fundamental innovation which will revolutionise speech translation latency.
In order to take advantage of a prediction, the system needs to know which predictions are good and which are bad, and to measure that confidence level.
The multi-lingual challenges will have three separate dialogues (note these are not translations of each other). See image for the new JSON layout.
Obviously we are working on new releases for validators to score these as there will be some differences in our incentives as the results will be much richer, e.g.
- miners could excel in Chinese but not in German
- miners could excel in confidence calculation while taking a hit on accuracy
There's a lot of work to be done, and we are collaborating with @const_reborn's team at Affine #SN120, the Data Universe team #SN13 at Macrocosmos, and are working on new security models with Chutes #SN64 and have started working with my namesake @the_mattyk at Manifold with some of Targon's #SN4 VM encryption ideas and the Intel and Nvidia hardware attestation system, which (when we start adding real-time contests which measure actual speed), will ensure that miners and validators are using like-for-like hardware.
Exciting times.
$TAO #SN59 #babelbit
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