All right, ready to jump into some really fascinating AI research.
Yeah, let's do it.
Today we're looking at how AI can be used to understand driving scenes.
Really understand, yeah.
Not just, you know, recognizing objects, like moving beyond just seeing a stop
sign and actually getting what it means.
Yeah, yeah, it's purpose, the context.
Exactly.
And the paper that we're going to be talking about today is called Knowledge
Graphs of Driving Scenes to empower the emerging capabilities of neurosymbolic AI.
Quite a mouthful, but a really interesting paper.
And it introduces this tool called DeCenica G.
It's a cool tool.
Yeah.
Yeah, it's basically like a map.
You can think of it that way.
But instead of streets and buildings, it maps out objects, events and the
relationships between them in driving scenarios.
So like building a deeper understanding of what's happening on the road.
Exactly, more connected.
Okay, so how does it go from just data to this like insightful map?
Well, it starts with massive data sets, right?
The kind that are used to train self-driving cars.
Okay.
So images from cameras, LiDAR scans, GPS data, all that stuff.
Okay.
And it takes all that raw data and turns it into a knowledge graph.
And what is a knowledge graph exactly?
I'm picturing like lots of lines and bubbles.
Is that right?
You know, that's not a bad visualization actually.
Basically, a knowledge graph connects different pieces of information and shows
how they relate to each other.
So yeah, you have your lines and your bubbles and they're all connected in a
way that makes sense.
So with TCNG, we're not just seeing a car and a pedestrian and a stop sign as
like separate things.
Right.
But we're understanding how they all interact within the scene.
Exactly.
That's the point.
Okay.
It's like taking a puzzle and actually putting it together to see the full picture.
And this is where the power of neural symbolic AI comes in, right?
Exactly.
That's what this paper is all about using TCNG to leverage this really interesting
approach.
Okay.
Now, Neurosymbolic AI, I've heard that term.
I'm not an AI expert though.
So can you break that down for me?
Sure.
Yeah.
It combines two different approaches to AI, you know, on the one hand, you have
neural networks, which are great at recognizing patterns, but not as good at
explaining how they do it, their logic.
And on the other hand, you've got symbolic systems, which are very logical and
good at reasoning, but they struggle with messy real world data.
So it's like having like a really intuitive detective who can spot all the clues,
Yeah.
but can't really explain how they got there.
Exactly.
And then you have like a super logical analyst who needs everything to be
perfect to solve the case.
That's a great analogy.
So Neurosymbolic AI brings these two together.
Okay.
So they can both learn from data and reason about it in a more, I guess, human
like way.
So how does that apply to DC and KG then?
How are they used together?
Well, DC and G with its knowledge graph, that provides the structure for
understanding relationships and rules, right?
Okay.
And then you can use machine learning, things like neural networks, to analyze
the patterns, make predictions within that structure, that framework.
So DC and G provides like the blueprint.
I like that.
Yeah, the blueprint.
And then the neural networks kind of fill in the detail.
I was thinking.
Make predictions based on that.
Yeah, that's a good way to put it.
And this leads to some really interesting, you know, developments in self-driving
car technology.
Yeah, absolutely.
Yeah.
So the paper highlights seven specific ways DC and G is being used to, you know,
kind of improve AI's understanding of driving scenes.
Let's start with the first three.
What can you tell us about those?
So the first three are really about enhancing machine perception, like how the AI sees
and interprets the world.
So giving self-driving cars a more human-like awareness?
Exactly.
That's a good way to put it.
Okay.
The first one is what they call knowledge-based entity prediction, or cake.
Cakey.
Okay.
So imagine a car driving down the street.
Okay.
It sees a ball roll out into the road.
Now, basic object detection would just be like, oh, there's a ball.
But with Keepee, it uses the context from the knowledge graph.
Okay.
So knowing that kids often play with balls, it might predict that a kid is about to run
into the street.
So it's not just reacting to what it sees, it's like thinking ahead.
Exactly.
Anticipating.
Wow.
That's impressive.
Yeah.
Based on a deeper understanding.
Okay.
What about the second one?
Second one is called explainable scene clustering typing.
Okay.
Think about all the different driving environments, right?
You got your busy intersection, you got your quiet neighborhood.
Exactly.
School zone, highway at night.
Yeah.
They all require different driving behaviors, right?
Mm-hmm.
So DC and IG can actually group similar scenes together based on what's there.
Yeah.
The objects, the events, the relationships.
So it's understanding not just what it's seeing, but what kind of environment it's in.
Right, exactly.
To inform the driving decisions.
Okay.
And it goes a step further.
It gives labels to these groups.
Oh, wow.
So instead of just saying urban environment, it might be like school zone during pickup time.
That's way more specific.
Much more useful information.
Yeah, it's like a driving instructor for the AI constantly analyzing.
That's a great way to think about it.
What about the third application?
This one is called computing semantic similarity.
Okay.
And this is about understanding how similar two driving scenes are.
Even if they look totally different.
Yeah, even if they look different on the surface.
Okay, so like what, give me an example.
So imagine like a car turning left from around about in a busy city.
Okay.
And then another scene where a car is doing the same maneuver, but in a quiet suburb.
Okay, yeah.
Totally different.
They look totally different.
Fertilitially.
But the core action is the same, right?
Right.
So DC and IG can actually see past those differences.
So it's recognizing that underlying similarity.
Exactly.
And use the knowledge from one to apply to the other.
So it can apply, yeah, can learn from different situations, even if they look different.
Exactly.
That's crucial for handling all sorts of situations, you know.
These are the first three already show how powerful this is.
What else can this knowledge graph do?
Oh, there's so much more.
Oh, there is?
But I think we should probably let this sink in for a little bit.
Okay.
And we can explore the rest in part two.
All right, looking forward to it.
So we've seen how DC and IG helps self-driving cars like really see what's around them.
Right.
Become more aware.
Yeah, but it goes beyond just perception.
Okay.
It can actually enhance the car's knowledge base.
So you're talking about those next two applications you mentioned?
Yeah, knowledge completion and augmentation.
What does those actually mean?
Okay, so think of it like this real world data is messy, right?
Like sensors miss things.
Yeah.
Or there are relationships that aren't explicitly captured.
Okay.
So knowledge completion is like filling in those gaps.
Using AI to make the knowledge graph more complete.
So if a car sensor only sees part of a pedestrian,
knowledge completion can help predict where they're going.
Yeah, based on typical pedestrian behavior or traffic rules.
So it's like having a detective on board.
Exactly.
She's seeing together the clues.
Yeah, to get the full story.
Okay.
And what about knowledge augmentation?
How is that different?
Augmentation is about adding new information to the graph.
Okay.
So this could be real-time stuff like weather reports, traffic updates,
or even insights that are derived from the existing data.
So constantly learning and expanding its understanding.
Exactly, making it more adaptable.
And I want to number five.
Semantic search.
What is that all about?
So imagine you have this huge library of driving scenarios all stored in the
decen-e-g knowledge graph.
Okay.
Semantic search lets you search through it using natural language.
So I could ask something like, find all the scenes where a car had to break hard to avoid
a pedestrian.
Exactly.
And it would understand the meaning.
Not just keywords.
Right, the context of your query.
Wow.
So we would find all those scenes even if they don't have those exact words.
Yeah.
It's like having a librarian who knows what you mean.
Okay.
Very cool.
And the last two applications.
These get a little complex.
Yeah, they sound pretty intense.
So the sixth one is about causality.
Causality.
Understanding cause and effect.
Yeah.
And driving scenarios.
Okay, but why is that important for self-driving cars?
Seems kind of philosophical.
It might seem that way, but it's crucial for safety.
Okay.
If we can teach AI to understand not just what happened, but why it happened, we can make them
much safer.
Okay, so if a car swerves.
Right.
Was it because another car cut it off?
Or was there like a mechanical failure?
Exactly.
Understanding the cause helps prevent accidents.
Makes sense.
And that last application, cross-modal retrieval of complex data.
Yeah, that was a mouthful.
Something intimidating.
It's actually simpler than it sounds.
It's about finding information in one format.
Okay.
Based on a query in a different format.
So like I could describe a scene using words.
Yeah.
And the system could find matching images or videos.
Exactly, from the DeSana Geo-Knowledge Graph.
So I could say, Shony all the times a car had to go through a roundabout in the rain.
And it would find all the visuals.
Wow, that's really cool.
So it's like a translator for data.
Exactly.
Helps the AI access and analyze information.
Now earlier you mentioned that DeSine KG uses data from a bunch of different driving data sets.
Yeah.
Can you talk about that a bit more?
Why is that important?
Sure.
So they use data from data sets like Pandaset New Scenes, Waymo Open, Dataset, Kit, TTI.
A lot of different ones.
Yeah, these data sets capture lots of different scenarios.
Okay.
Like a different weather, different locations.
It's about training on a variety of data.
Exactly.
Think of it like training an athlete.
You know, if they only practice in one environment.
They'll struggle when things change.
Right.
But if they train in lots of different places.
They're more adaptable.
Exactly, more resilient.
That makes sense.
So the AI isn't biased towards just certain types of scenarios.
Right.
Can handle the unexpected.
All right, so we've seen all seven applications of DeSine KG.
Yeah.
And it's clear this resource is really promising.
Yeah.
But beyond all the technical stuff, I'm curious about the bigger picture.
Okay.
What does DeSine K tell us about the future of AI?
So we spent this deep dive talking about DeSine K and how it can really transform
the way AI understands these driving situations.
Yeah, it's exciting stuff.
But it's not just about the cool tech, right?
No, definitely not.
About the impact it can have in the real world.
Exactly, real world impact.
This research could actually make self-driving cars safer.
Safer, more efficient.
More reliable.
And that's huge.
It really is.
It could change how we think about transportation.
Yeah.
I mean, when cars can navigate all those tricky situations with the same understanding as a human.
It opens up so many possibilities.
It really does.
And it helps with that black box problem.
It helps with the black box, yeah.
That people have with AI, like not knowing how it makes decisions.
Right, right.
That lack of transparency.
It can be scary.
Especially with something like driving, yeah.
You're handing over control.
Exactly.
But DeSine K, it lets us see the AI's reasoning.
We can trace it.
See what information led to a decision.
So it's not just about getting the right answer.
It's about the why.
Yeah, understanding the process.
The why behind the what.
And that builds trust, right?
Absolutely.
Both for the people using the cars.
And the people building them too.
Yeah, the engineers.
Yeah, that transparency is key.
It also makes it easier to catch errors, right?
Oh yeah, if you understand the reasoning.
You can make sure it's sound.
Fix any problems, make the systems more reliable.
So it's not just about making AI smarter.
It's about making it think more like us.
More human-like, yeah.
Understanding the world in a much deeper way.
With nuance, with context.
And that leads to a really interesting question.
Okay, what's that?
If AI can understand driving like this.
Yeah, with this level of detail.
What could it do?
I mean, the possibilities are huge.
What other fields could benefit?
Think about healthcare.
Healthcare, yeah.
Analyzing patient data, finding patterns,
personalized treatments.
Finance, predicting markets.
Exactly, or managing risk.
Environment, understanding climate change.
Oh, that's a big one.
Modeling ecosystems.
So many complex systems out there.
It seems like we're just scratching the surface.
I think so too.
With DCNEG and this neuro-symbolic AI.
It's a really exciting time to be in this field.
I can imagine.
Well, this has been an amazing deep dive.
I've enjoyed it.
Thank you so much for joining us and sharing your expertise.
It was my pleasure, really.
And to everyone listening, thanks for coming along on this journey.
Yeah, thanks for listening.
Into the world of knowledge graphs and self-driving cars.
I hope you learned something.
If you enjoyed it, be sure to subscribe and leave a review.
Yeah, we'd love to hear from you.
Until next time, keep exploring, keep learning, and keep diving deep.