All right, let's dive into some AI and video today.
Sounds good to me.
AI that can like watch and understand videos.
Yeah, you know, just like we do.
Yeah.
So we're looking at this paper called Apollo,
an exploration of video understanding
and large multimodal models.
It's all about teaching AI to make sense
of what it's seeing in a video.
So where do we even start with this?
Well, you know, it's pretty complex.
The researchers are working with these things called
large multimodal models or LMMs.
LMMs, OK.
Yeah, think of them like really powerful AI brains
that can process different types of information,
like text and images and video altogether.
So it's like teaching these AI brains
to watch and understand a movie.
Right.
But isn't that like a ton of information to process?
It is.
And that's one of the biggest challenges.
And you know, the researchers had
to tackle that sheer amount of data involved
in video processing.
Just imagine all the pixels and the movement,
the different objects, the actions.
It's a lot.
It's a computational beast.
It really is.
So how did they even begin to approach this?
How do you even start?
Well, one of the really cool things they discovered
is something called scaling consistency.
OK.
I like to think of it like a shortcut for AI research.
OK.
I'm intrigued.
Tell me more about this shortcut.
What is it?
So basically, they found that they
could test their ideas on smaller LMMs
and smaller data sets.
And the results they get will likely hold true,
even when they scale up to those massive, computationally
expensive models.
So they don't have to train these huge models every time
they want to try something new.
Exactly.
So it saves a lot of time and resources.
Yeah.
It's a really clever way to make research more efficient.
Yeah.
And it opens up a lot possibilities
for faster progress.
That's a pretty big win right out of the gate.
So what did they do with this newfound efficiency?
Did they start building like a super video understanding AI?
Well, not quite.
First, they realized that the standard tests used
to measure how good an AI is at understanding video
were kind of flawed.
The tests themselves were the problem.
Yeah.
They found that a lot of these tests
could be cheated by the AI.
Instead of actually understanding the video,
the AI could find workarounds by focusing only on the text that
was with the video.
Oh, interesting.
Or even just a single frame from the video.
So it was like the AI was acing the test
without really learning the material.
That's a great way to put it.
And that's why the researchers decided
to create their own benchmark, a new test called Apollo Bench,
which is much tougher, specifically designed
to measure true video understanding.
OK, so Apollo Bench is like the ultimate test
for a video savvy AI.
What kind of challenges does it throw at them?
Oh, it includes things like reading text that
appears in the video over time.
Oh, wow.
Understanding where objects are and how they move,
figuring out cause and effect relationships.
And even making sense of videos that
are taken from a first person perspective.
Really?
Yeah.
So they really raised the bar for what
it means to understand a video.
They did, absolutely.
And not only is it more challenging,
but it's also a lot faster to evaluate
than those older benchmarks, which means researchers
get quicker feedback and can make progress more rapidly.
So they streamlined the testing process, too.
It's like they're clearing all the hurdles
to make video understanding research more accessible.
I'm really curious, how did they actually
go about building this amazing video understanding AI?
Well, they dove into the nitty gritty of LMM design.
Systematically testing different approaches
to see what worked best.
It was a lot of trial and error.
But they uncovered some really insightful things along the way.
I bet.
So let's unpack some of those design decisions.
So first up, how does an AI even watch a video?
It doesn't have eyes, right?
Exactly.
That's a great question.
They had to figure out how to feed the video information
to the AI.
Should it look at every single frame?
Just a few.
Should it focus on the action-packed moments?
Or take a more even approach?
There's a lot of ways to go about it.
So what did they find was the best strategy?
So they found that sampling based on frames per second,
or FPS, was crucial for the AI to understand
the flow of time in a video.
FPS, like what we see in video games and movies.
Exactly.
Think about it.
If you want to understand how fast a car is going,
you need to see how its position changes.
From one frame to the next.
So FPS sampling gives the AI that kind of temporal information.
So it's not just about seeing what's in each frame.
It's about understanding how those frames connect over time
to tell a story.
Exactly.
You got it.
It's about giving the AI a sense of timing and sequence.
That makes a lot of sense.
Now, the paper also mentioned something about vision encoders.
Can you explain what those are and why they're important?
Sure.
So vision encoders are like translators for the AI.
They take the raw visual data from the video frames,
all those pixels and colors, and convert them
into a language that AI can understand.
So they're like turning light and shadows into something
the AI can actually process.
Exactly.
And the research is experimented with different types
of vision encoders.
Some that focus on spatial information,
we get the layout of a scene, and others that
are better at capturing temporal information,
like the movement of objects.
It seems like having both of those perspectives
would be important for understanding a video as a whole.
You're right.
And they found that combining a powerful image encoder called
Siglip with a video encoder called intern video 2
gave them the best results.
It was like having two different experts analyze the video
and then put their insights together.
Teamwork, even within the AI itself.
Exactly.
OK.
I'm really starting to see how much thought and experimentation
went into building this video understanding system.
But I have to wonder, videos generate a ton of data.
How do they manage to keep things manageable for the AI?
That's a good question.
Yeah.
That's where a clever technique called video token resampling
comes in.
OK.
It's all about compressing the visual information
without losing the important details.
So creating a super efficient summary of each frame.
Exactly.
OK.
And they found that using a perceiver resampler
was the most effective way to do this.
It's like a smart filter that helps the AI focus on what
matters most in each frame.
That sounds pretty high tech.
It is.
So I'm guessing they had to test a bunch of different resampling
methods to find the best one.
They did.
And this one really stood out for its ability
to condense the data while retaining
crucial visual information.
OK.
So they figure out how to streamline the data.
What about actually connecting the visual information
with text?
Yeah.
How does the AI link what it's seeing to the questions
that's being asked?
Yeah.
That's where video token integration comes into play.
OK.
It's all about how to weave the visual information
into the stream of text that the AI is processing.
Right.
Because ultimately, we want the AI
to be able to answer our questions about the video.
Exactly.
And something as simple as adding time stamps
between video clips.
OK.
It turned out to be a major improvement.
Really?
Yeah.
It's like giving the AI a roadmap of what's
happening in the video and when.
So even small design choices can have a big impact
on the AI's performance.
It's fascinating to see how all these pieces fit together.
It really is.
And they didn't stop there.
They also dug deep into the best ways
to train these video LMMs.
Oh, that's right.
Yeah.
The paper mentioned something about catastrophic forgetting.
Can you remind us what that is?
Yeah.
Catastrophic forgetting is when an AI,
while learning something new, starts
to forget things that it learned previously.
Oh, right.
So in this case, if you only train an AI on video data,
it might start forgetting its language skills.
So it's like it's becoming a video expert
but forgetting how to speak.
Exactly.
So they found that including a moderate amount of text data
in the training mix helps prevent this kind of forgetting
while still allowing the AI to benefit from that richness
of the video data.
So it's like a balancing act, making sure the AI develops
both its video and its language skills.
Exactly.
OK.
And they even found that leaning a little more heavily
on video data in the mix gives the best overall performance.
So they optimized the watching strategy, the representation
of visual information, the data flow,
and even the training process.
So what did all this effort lead to?
Did they actually build this amazing video understanding AI?
They did.
And they named their creation Apollo.
In fact, they created a whole family of Apollo models.
A whole family.
Yeah.
That's cool.
Ranging in size and complexity.
And in the paper, they talk about the number of parameters
in each model.
Can you remind us what those are and why they matter?
Parameters are like the knobs and dials
that the AI can adjust to learn from data.
The more parameters a model has, the more potential
it has to learn complex patterns and understand
nuanced information.
So more parameters means a more sophisticated, potentially
smarter AI.
Right.
But it also probably means more computing power is needed.
Exactly.
There's a trade-off there.
Yeah.
You want a powerful model, but you also
want it to be efficient.
And how did the Apollo models perform?
Like, did all that optimization pay off?
It did.
Big time.
Even their smaller Apollo 3B model with 3 billion parameters
often outperformed existing models that were much larger,
sometimes even twice the size.
So it's not just about brute force computing power.
It's about smart design choices and clever optimization.
Exactly.
And their largest model, Apollo 7B,
set a new state of the art for models of its size,
even surpassing some models with 30 billion parameters.
That shows just how powerful, well-designed, and optimized
models can be.
OK.
I'm officially impressed.
They tackled some really tough challenges,
came up with some ingenious solutions,
and created a family of AI models that
are pushing the limits of what's possible in video
understanding.
It's true.
And this is just the beginning.
I bet.
In the next part, we'll delve deeper
into the implications of this research.
What does it mean for the future of AI?
How could it change the way we interact with video?
Stay tuned.
All right, it sounds good.
I'm excited to find out.
So welcome back.
Back to marveling at the ingenuity of this Apollo research.
Yeah, it's truly groundbreaking work.
I mean, this paper isn't just about some cool new AI model.
It's like a window into the future of AI research itself.
Totally.
I agree.
It's fascinating to see how these researchers are pushing
the boundaries, not just of what AI can do,
but also how we go about developing and improving
AI systems.
OK, so before we get lost in all the possibilities,
can we take a step back and highlight the key takeaways
from this paper?
Sure.
What stood out to you is the most important aspects
of this research.
For me, one of the biggest takeaways
is this whole concept of scaling consistency.
We touched on it earlier, but it's
worth emphasizing just how revolutionary this idea is.
Yeah, so to recap, they discovered
that they could test their ideas on smaller, more manageable
models and data sets.
And those findings would reliably translate
to larger, more complex models.
Exactly.
That's a potential game changer for AI research,
because it means we don't always need to rely
on those massive, computationally expensive models
for every experiment.
That makes research more accessible.
Not everyone has access to supercomputers and stuff.
Absolutely.
Scaling consistency opens the door for more researchers
to contribute to the field, which could accelerate progress
significantly.
It's like democratizing AI research, which is amazing.
It really is.
And speaking of democratization, they also
tackled those flawed benchmarks, right?
Those tests that weren't really measuring
true video understanding.
They created Apollo Bench, which is, I mean, that's huge.
It is.
It's a huge contribution in itself.
It sets a much more rigorous and efficient way
to evaluate video understanding in AI, which
will lead to more meaningful progress.
It's like they said, a new standard.
They did.
A higher bar for what it means for AI to get video.
Exactly.
And another key takeaway for me was,
their focus on the temporal aspect of video,
like how they really emphasize the importance of understanding
the flow of time, the sequence of events.
It's key.
Yeah, I remember that aha moment when we discussed
the frames per second sampling.
It seems so intuitive now.
It does.
But it's such a crucial insight.
It reminds us that AI doesn't perceive the world the same way
we do.
We need to explicitly teach it how
to pay attention to the temporal dimension.
Exactly.
It's like giving the AI a sense of rhythm, right?
Yeah.
Like helping it see the beat of the video, so to speak.
That's a great analogy.
And that's essential for understanding actions,
relationships between events, cause and effect.
All those things that make a video more than just
a collection of static images.
It's about seeing the story unfold,
not just the individual frames.
Exactly.
And their work on combining different types
of vision encoders was also quite insightful.
Yes.
I was really intrigued by that whole idea
of having an AI with both a photographic memory
and a sense of timing.
It's a powerful combination.
It's like giving the AI a more balanced perspective,
allowing it to process both the spatial and the temporal
information in a video.
I mean, they really thought through every aspect of this,
didn't they?
They did.
From how the AI watches the video
to how it represents the information, how it compresses
the data, how it integrates text and visuals,
and even how it's trained.
They took a very systematic and thorough approach.
And that's reflected in those impressive results
they achieved with the Apollo models.
This whole discussion is giving me a new appreciation
for the complexity of video understanding.
It's complex.
It's something we humans do so effortlessly.
But teaching an AI to do the same
is a massive undertaking.
It is.
It makes you realize just how much is going on
beneath the surface when we watch a video.
Like we're processing so much information
without even realizing it.
Emotions, facial expressions, body language, context, story
lines, humor, symbolism.
It's mind-boggling.
It is.
And the researchers behind Apollo
are making remarkable strides towards building
AI that can grasp at least some of that complexity.
So now I can't help but wonder about the future.
What does all of this mean for how we interact with video?
How could this research change things for us
in the real world?
I feel like we're on the verge of something really big here.
Yeah, we are.
The potential applications of this research
are vast and incredibly exciting.
OK, so paint me a picture.
What kind of world could Apollo and its descendants create?
Well, imagine a world where you can search for videos,
not just by keywords, but by the actual content.
What's happening in the video?
Who's in it, what they're doing?
So instead of typing recipe lasagna,
I could search for videos where someone
is making lasagna from scratch.
Exactly.
Or videos where someone is making lasagna
and they're explaining the steps as they go.
Or even videos where someone's making lasagna
and it looks really delicious.
That'd be amazing.
No more waiting through endless search results
to find the exact video I'm looking for.
Right, AI that can understand video
could completely transform how we search for and discover
content.
It's like having a personal video librarian who
knows exactly what you want.
And it's not just about finding the right videos.
It's about understanding them on a deeper level too, right?
Yes.
What if the AI could help us learn from videos more
effectively?
Absolutely.
Think about educational videos.
AI could analyze the content and create interactive quizzes
or summaries or even personalized study guides.
It's like having a tutor built right
into every educational video, guiding you
through the material and making sure you're actually
learning and for entertainment.
I mean, imagine AI that can recommend movies or shows
based on your specific preferences.
Not just general categories like comedy or drama,
but on the actual themes and characters and story lines
that you enjoy.
So instead of suggesting another generic rom-com,
it might recommend a hidden gem with a similar emotional arc
to my favorite movie, even if it's
like a completely different genre.
The AI could become a true taste maker,
helping you discover content you might never
have found on your own.
It's like having a friend who knows your taste perfectly
and is always surprising you with new and interesting things.
Exactly.
And beyond entertainment and education,
think about the impact on accessibility.
Oh, right, right.
AI could generate real-time captions and descriptions
for people who are deaf or hard of hearing,
making video content much more inclusive.
And it could translate videos into different languages, right?
Yes.
Opening up a world of content.
It could.
All around the globe.
Absolutely.
It's exciting to think about how this technology could
break down barriers and make information more accessible.
It is.
Everyone.
And let's not forget about the potential
for our creative industry.
Oh, how so?
AI could assist filmmakers and editors
by automating tasks like scene detection or object tracking,
freeing them up to focus more on the creative side of things.
It's like giving them a super powered assistant
to handle the more tedious parts of their work.
Exactly.
And AI could even help generate new content.
Really?
Imagine AI-powered tools that can
create storyboards or animatics or even entire animated sequences.
Wow, that would be a game changer
for independent filmmakers or anyone with a story to tell.
Right.
But limited resources.
The creative possibilities are endless.
OK, I'm officially convinced the future of video is intelligent.
It is.
And it's going to be amazing.
But as with any powerful technology,
it's important to proceed thoughtfully
and consider the potential implications.
Right.
We need to make sure that these systems are developed and used
responsibly.
Absolutely.
We need to be mindful of things like bias in training data,
the potential for misuse.
And the impact on human creativity and employment.
It's like we're explorers setting foot on a new continent.
It is.
You know, we need to be aware of the potential dangers
as well as the incredible opportunities.
And one of the things that impressed me most about this Apollo
research was their honesty about the limitations
of their work, especially when it comes to conversational AI.
Right.
They acknowledge that existing benchmarks don't really
capture the nuances of like back and forth conversation.
It's one thing for an AI to answer a simple question
about a video.
But it's a whole other challenge to engage
in a natural fluid conversation about the content,
to understand intent and emotion,
and all the subtle cues we humans use when we communicate.
It's like the difference between a basic chatbot
and a truly intelligent conversational partner.
Exactly.
And that's where the real potential of video understanding
lies.
To truly unlock its power, we need AI
that can not only process information,
but also interact with us in a way that feels human,
intuitive, and engaging.
So instead of just asking what happened,
we could have deeper, more nuanced conversations
with the AI about the video.
Exactly.
We could ask, why did the character make that choice?
Yeah.
Or what do you think will happen next?
And the AI could respond in a way that
shows genuine understanding and even
offer its own interpretations or predictions.
It'd be like discussing a movie with a friend,
bouncing ideas off each other, and creating new insights.
That kind of interactive video understanding
is still in its early stages.
But the Apollo research lays a really strong foundation
for future work in this area.
It's exciting to think about, it's
like we're on the cusp of a new era where AI
becomes less of a tool and more of a companion,
helping us explore and understand the world
through the lens of video.
And as we continue down this path,
it's crucial that we involve diverse voices
and perspectives in the development and deployment
of these technologies to ensure that they benefit
all of humanity.
Well said.
This has been an incredible deep dive
into the world of video understanding
and the amazing work being done by the researchers
behind Apollo.
Truly groundbreaking stuff.
It is a field that's ripe with potential.
And I can't wait to see what the future holds.
Me neither.
Thanks for joining us.
And until next time, keep exploring, keep questioning.
Yes.
And keep diving deep.