Okay, so imagine this AI that can understand language
without like chopping it up into pieces first.
Yeah, it's a pretty wild concept, right?
Yeah, it kind of blows my mind.
And that's exactly what we're gonna deep dive into today.
Exactly.
We have a fascinating paper all about
bite-latent transformers, BLT for short.
T-L-T, yeah.
And how it's kind of shaking up the AI world.
Yeah, so what's really interesting about this
is this model, it actually works directly
with the raw bites of information.
Oh, wow.
So those ones and zeros that make up the digital data.
Okay.
It basically skips over this whole process
of tokenization that's been a standard in AI for ages.
So for those of us who don't speak fluent AI,
what is tokenization and like,
why is ditching it such a big deal?
Yeah, that's a great question.
So think about tokenization,
like breaking down a sentence into individual words.
Okay.
AI models, they traditionally use this method
to try to understand the text.
But imagine you're trying to understand a new language
or maybe a technical document
that's filled with a bunch of jargon.
If your dictionary doesn't have those words,
you're kind of stuck, right?
Yeah, you're gonna have a bad time.
That's exactly the limitation that BLT is trying to solve.
So BLT is kind of like learning a language
by understanding the letters
instead of needing like a massive dictionary of words.
Exactly.
It's like going back to the basics.
Interesting.
And that's what makes it so much more adaptable.
It can handle new words and complex terms.
Wow.
Even those pesky typos without missing a beat.
Okay, cool, but how does it actually work?
That is the question.
And that's where this dynamic patching thing comes in.
Ooh, that sounds fancy.
What is dynamic patching?
So imagine you're packing a suitcase.
Okay.
You wouldn't pack a super delicate vase.
Right.
The same way you would pack like a T-shirt.
Of course not.
BLT does something similar with bytes.
It groups them into these little patches
based on how complex the information is.
So it's like it can tailor its focus
depending on what it's reading.
Yeah, like it's kind of reading the room.
Spending more effort on the parts that are tricky
and then just zipping through the stuff,
that's pretty simple.
It's all about efficiency.
So it's not just like blindly chewing through data.
It's actually being smart about it.
Exactly.
That's so cool.
But how does it actually turn these patches
into something that we can understand?
Well, that's where the magic happened.
The magic.
Yeah.
BLT, it uses this three-part system.
Okay, a three-part system.
Yeah, first you have the local encoder,
which basically takes those raw bytes
and maps them into these dynamic patches.
Okay.
Then we have the latent global transformer.
Okay.
Which is like the main powerhouse of the whole system.
This is where the actual processing happens,
understanding the relationships between those patches
and extracting the meaning.
So it's like the brains of the operation.
That's a great way to put it.
Okay, and then finally there's a decoder
to kind of put it all back together
so we can understand it.
Yeah, you got it.
The local decoder takes those process patches
and turns them back into bytes.
Okay.
But there's this other really cool trick that BLT uses.
Really get the hang of language.
And it's called hash and gram embeddings.
Hash and what?
I know it sounds a little intimidating.
Yeah, a little bit.
But it's not as scary as it sounds.
Okay.
Just think about it like recognizing patterns
in letters sequences.
Okay.
So for example, if you see the letter T in English,
Right.
there's a pretty good chance the next letter
is gonna be an H.
Yeah, A, yeah.
Exactly.
BLT uses these embeddings to kind of pick up
on those same types of patterns, but in the bytes.
Okay.
Which really helps to understand the structure
of the language at a deeper level.
So it's not just seeing individual letters.
It's like understanding how those letters
usually go together.
Exactly, it's all about context.
Okay, that makes a lot of sense.
But how does all this compare to the models
that we're already using?
That's a million dollar question.
Yeah, is it actually better?
Well, the research is pretty promising.
They actually compared BLT to the state of the art model
called LAME3.
Okay.
And they found that BLT could actually match
its performance using significantly less processing power.
Hold on, less power.
Yeah.
Same performance.
Yeah, pretty amazing, right?
That sounds like a win-win.
Yeah.
What does that mean for someone who's like building AI apps
or something?
So that means you could potentially achieve
the same level of sophistication.
Okay.
But with way less computational resources.
I like the sound of that.
Yeah.
It opens up a lot of possibilities,
especially if you're working with limited hardware or budget.
Right, or if you're just trying to be more efficient.
Exactly.
That's huge.
I could really democratize access
to these powerful AI models.
Yeah, exactly.
But hold on, comparing it to LAME3,
isn't that kind of like apples or oranges?
Yeah, that's a good point.
Because LAME3 uses those fixed tokens right.
While BLT is all about these dynamic patches.
You're exactly right.
And that's why the researchers,
they took it a step further.
They wanted to see how well each model could scale.
So basically how they perform as you increase the size
and the complexity.
And that's where that whole patches are better
than tokens thing really comes in.
I am all ears.
What did they find?
So with BLT, you can do something really unique.
You can increase the model size,
A&D, the patch size at the same time,
without needing more computational power.
That sounds like a game changer.
It is, it's a game changer,
because it means BLT can adapt to more complex tasks
and data sets without kind of hitting
that performance bottleneck.
So why can't traditional models do that?
Because they're stuck with that fixed vocabulary
and that fixed token size.
It's like trying to fit more and more clothes
into a suitcase that can't get any bigger.
That's not gonna work.
But BLT is like having this magical suitcase
that expands as you need it.
Okay, I love that analogy.
So more flexibility, less computing power.
What's the catch?
Well, there's always a catch, right?
So the research is still in its early stages.
While BLT shows this incredible potential,
there's a lot more to explore.
The existing tools and the infrastructure for AI,
they're mostly designed for those token-based models.
So we need new approaches to really optimize BLT
and to unlock its full power.
So it's like we have this super fast car,
but there's no race track design for it.
Exactly, that's a great analogy.
It reaches its top speed.
Yeah, but the potential is definitely there.
And the researchers, they're already looking into ways
to improve things like the training process
and make BLT even more efficient.
So BLT might not be ready to take over the world just yet.
But it's definitely a force to be reckoned with.
Absolutely.
This is getting me really excited about the future of AI.
What other cool things did they find out about BLT?
Oh, there's a lot more to unpack.
What kind of tasks is it really good at?
Let's dive into that.
So one of the really impressive things they found
is that BLT can handle messy real-world data, like a champ.
Think about social media posts with all that slang and typos,
or maybe technical documents filled with jargon.
Yeah, that stuff could be tough.
Yeah, those kinds of things can really
trick up those traditional models.
That makes sense if you're relying on a fixed dictionary.
Anything outside of that is going to throw you off.
Exactly.
But BLT's like bite-level approach seems
way more adaptable to those kind of curveballs.
Exactly.
It's like it can roll with the punches.
OK, cool.
So it's not just about efficiency then.
It's about fairness and accuracy too.
It's about robustness.
Yeah, that's a really important point.
It is.
But how did they actually test this?
So to test this, what kind of evidence did they find?
So they used this benchmark called CET.
C-E-T.
C-U-E?
Yeah, it's a cute name for a pretty tough test.
Oh, OK.
It tests how well a model understands
the building blocks of language, like those individual characters
and sounds.
OK, the nitty-gritty.
Yeah, the tiny little pieces.
OK.
And these tasks are surprisingly tricky for those token-based
models.
OK.
But BLT excelled.
So wait, it's not just understanding words.
It's understanding the individual letters.
Yeah.
And how they relate to each other.
Yeah, it's going deep.
That's pretty amazing.
What kind of tasks did they test specifically?
So they look at things like spelling correction.
Oh, wow.
Identifying words with similar spellings,
even figuring out how a word should be pronounced just
by looking at the letters.
That's wild.
Yeah, BLT achieved near perfect accuracy
on some of those tasks.
That's incredible.
It's like BLT is learning to read and understand language
the way humans do.
Yeah, it's learning like a kid.
By paying attention to all the smallest details.
It's really paying attention.
But if it's so good at these fundamental tasks,
what about more complex applications, like translation,
for example?
That's work.
It's really interesting.
OK.
They found that BLT was especially good at translating
languages with very little training data.
Oh, right.
So think about rare indigenous languages.
OK.
Or maybe dialects with limited written resources.
Right.
Where you don't have tons of data to work with.
Exactly.
And BLT was able to outperform those traditional models
by a significant margin.
Wow, that's a game changer.
Yeah, it is.
It could help preserve endangered languages
or like bridge communication gaps
between different cultures.
Yeah, exactly.
It has huge potential.
But how is it able to do that with so little data?
That's the question, right?
Yeah, is it like AI magic or something?
Well, researchers think it has to do with that direct engagement
with the bytes.
OK.
By looking at that raw data, it can pick up
on these subtle patterns and relationships
that those token-based models might miss.
OK.
It's like BLT is a more attentive student.
I like that.
It's really paying attention.
It's soaking it all up.
Exactly.
OK, that makes a lot of sense.
It's like the difference between trying
to learn a language from a phrase book versus like immersing
yourself in the culture and absorbing all the nuances
naturally.
That's a great analogy.
So all this talk about BLT's potential is really exciting.
Yeah.
What are the next steps?
What kind of research needs to be done to make this a reality?
Well, there's a few exciting avenues for future exploration.
Lay it on me.
One is optimizing that training process.
OK.
Right now, it takes a lot of time and computing power
to train these large language models.
But researchers are looking for ways
to make that faster and more efficient.
OK, because that's key.
If we want to make this tech accessible to more people.
Yeah, we got to make it practical.
OK.
What else have they worked on?
Another area is figuring out the best way
to biteify existing models.
OK, we talked about how BLT starts from scratch.
Right.
But what if we could take the knowledge that's
already stored in those massive token based models
and like convert it to a bite level format?
Yeah, what if we could just translate it?
That would be a huge time saver.
Yeah, it would be.
It's like giving BLT a head start.
A little cheat sheet.
Right.
Exactly.
Did the researchers experiment with that at all?
They did.
They took a pre-trained model called LAM-A 3.1.
OK.
And they used its parameters to basically initialize BLT.
Oh, wow.
And the results were pretty promising,
showing that this approach could lead to faster
and more efficient training.
So we could potentially build on the work that's already
been done.
Yeah.
Instead of having to reinvent the wheel every time.
Exactly.
We don't have to start from zero.
That's super cool.
Yeah.
But what about the practical side of things?
Yeah, the real world stuff.
Yeah, we talked earlier about the need for new tools
and infrastructure to support BLT.
Why?
All the behind the scenes stuff.
Yeah.
What does that actually look like?
So right now, most of the libraries and code bases for AI
are optimized for token based models.
OK.
So we need to develop new software and hardware
that can take advantage of BLT's unique capabilities.
OK.
Like that dynamic patching and byte level processing.
OK.
So it's a big challenge.
Yeah, it's a big undertaking.
But if we can overcome it.
I think we can pull it off.
The potential benefits seem huge.
Yeah.
More efficient models, better language understanding,
fairer and more accurate results.
Yeah.
It's a whole new world.
It's like opening up this whole new world of possibilities.
Exactly.
And it's important to remember this research is still going.
Right.
There's still so much more to learn and explore.
OK.
So what's the impact of BLT and its impact for the future of AI?
This whole deep dive into BLT has been absolutely fascinating.
I'm glad you're enjoying it.
Yeah.
It's made me think about AI in a completely different way.
Yeah.
It really challenges the status quo.
Yeah.
It's not just about processing words anymore.
It's about understanding the fundamental building blocks
of language.
A DNA of language.
Right.
But before we wrap up, I'm curious.
Yeah.
What are your thoughts on the bigger picture?
Ooh, the big picture.
What does this research tell us about the direction of AI
as a whole?
Well, I think BLT really highlights
the importance of thinking outside the box.
OK.
You know, for years, tokenization
has been the dominant approach.
Great.
The go-to method.
Yeah.
Everyone was doing it.
OK.
But this research shows that there
are other ways that might be even more effective
and aligned with how humans actually understand language.
That's a really powerful insight.
It is.
It seems like BLT is really pushing the boundaries
of what we thought was possible with AI.
Yeah, it's breaking down those barriers.
But what are the potential downsides?
Yeah.
Are there any ethical considerations or risks
that we need to be aware of?
Oh, that's a great question and a really important one too.
As this technology develops, yeah,
there's always that potential for misuse.
Right, because it's so powerful.
Exactly.
With any powerful technology, you
need to be really careful about how it's used as BLT
and other models.
Like, it get more sophisticated.
We need to think about how they're being used and developed.
OK.
Things like bias in the data that's used to train them
and the possibility that they could be used to generate
harmful content.
Oh, yeah, that's a big one.
And we have to make sure that they're being developed
responsibly.
So it's a lot of responsibility.
Yeah, it's a big responsibility.
Like, we're not just building a better engine.
We're building a whole new vehicle.
Exactly, a whole new way of thinking.
And we need to make sure it's safe and ethical to drive.
It's like giving AI a driver's license.
Right, exactly.
So focusing on the positive for a second.
What are you most excited about when
you think about the future of BLT?
OK.
And bite-level AI in general?
I'm really excited about the potential for this thing
called multimodal AI.
Multimodal AI.
Yeah, it's a mouthful.
But it basically means models that can understand,
not just text, but also images, audio, video.
Oh, wow.
So it's like AI that can experience the world
through all our senses.
Yeah, it's like giving AI all the senses.
That's amazing.
It is.
What would that look like in practice?
So imagine a model that can seamlessly analyze
the text of a news article.
OK.
And then look at the images that go with it.
Very.
And even listen to the tone of voice in a related audio clip.
Oh, wow.
And put all of that together into one unified understanding.
That's mind-blowing.
It would be like having an AI assistant that could really
grasp the world in all its complexity and richness.
That would be incredible.
That would be a game changer.
But it's not just about understanding, right?
No.
What about the creative potential?
Oh, that's the really exciting part.
Yeah.
Imagine being able to generate these brand new forms
of art and media.
Oh, wow.
Blending those different modalities.
OK.
In ways that we haven't even thought of yet.
So like AI-generated art, but on a whole other level?
Yeah, it's taking it to the next dimension.
A world where AI can help us tell stories, compose music,
create art in ways we haven't even imagined.
It's like opening up this whole new dimension
of artistic expression.
It is.
Yes.
Powered by AI.
But hold on.
If we're talking about AI models that can process all this data.
Yeah.
All that data.
Aren't we also talking about massive computational demands?
That's a good point.
Yeah.
Wouldn't that limit who could actually access and use
this technology?
Yeah.
That's a valid concern.
But that's where the efficiency of BLT
becomes super important.
OK.
If we can develop these models that can process information
at the byte level.
Right.
With the same efficiency and scalability as BLT.
OK.
We could potentially make those multimodal dreams a reality
without needing those massive supercomputers.
That's amazing.
It could democratize access to these tools.
Right.
And make them available to anyone.
Enabling innovation across different fields.
That's really cool.
So BLT isn't just pushing the boundaries of what AI can do.
No, it's not.
It's also potentially making it more accessible to everyone.
Exactly.
It's about making AI more inclusive.
That's pretty inspiring.
It is.
It's a really exciting time to be working in AI.
This whole deep dive into BLT has been incredible.
I'm glad you enjoyed it.
It really has.
It's made me realize that we're living
in this time of unprecedented advancement in AI.
Yeah.
Things are moving so fast.
And the potential for positive change is huge.
It's immense.
Yeah.
But as you said, it's also a time for careful consideration
and responsible development.
Absolutely.
We need to make sure that this powerful technology is
used for good.
We don't want to go down the wrong path.
Right.
And that it benefits all of humanity.
It's about using AI to make the world a better place.
That's a perfect note to end on.
Thank you so much for taking us on this deep dive
into the world of byte-latent transformers.
It's been my pleasure.
It's been an eye-opening and inspiring conversation
and to our listeners.
Yeah, to everyone out there.
Kept those imaginations running wild.
Don't be afraid to dream big.
And stay tuned for our next deep dive
into the ever-evolving world of AI.
We'll be back with more cutting-edge research.
Until then, keep exploring and keep questioning.
Keep learning.
Because that's how we unlock the true potential
of artificial intelligence.
That's how we build a better future.
And that's it for today's deep dive.
See you next time.
We'll see you next time.