All right, so get ready because today we're going deep into a paper
Yeah.
that's making some serious waves in the AI world.
It is.
It's called Relational Neurosymbolic Markov Models.
Wow.
Yeah.
And I know it sounds a little intimidating.
It does.
Something on a Star Trek.
Yeah, a little bit, right?
But stick with us because the ideas here
are worth wrapping your head around.
I think what's exciting about this paper
is that it really gets at a fundamental challenge in AI,
which is how do we build systems that can both learn
from data, but also reason logically?
Yeah, and we've talked about symbolic AI a lot on this show.
And this idea of logic versus just brute force pattern
recognition.
Yeah, exactly.
So this is kind of getting into that.
Now, I know Markov models might sound a little scary.
Oh, yeah.
But you've probably encountered them
without even realizing it.
Yeah.
Think about things like weather prediction apps,
in short, or even your phone's auto correct.
Yeah.
They use Markov models to predict the next word based
on the current word.
That's right.
And that core idea predicting the future based on the present
is surprisingly powerful.
It underpins so many AI applications.
Yeah, it's really cool.
And I think that's why a lot of people
get a little intimidated by it because they're like, wait,
you could predict the future.
Yeah.
How does that even work?
Right, but in a limited sense, right?
Right, exactly.
Like we're not talking about like, you know.
And travel.
Yeah, exactly.
Yeah, but it's still.
It's still a very powerful idea.
And it's actually pretty intuitive when you think about it.
Yeah, and so what this paper is doing
is taking that simple idea and like turbo charging it.
With this neurosymbolic AI.
Yeah, this neurosymbolic AI.
Which is this really fascinating field
where we're trying to bring together.
Right.
These two really powerful approaches to AI.
Neural networks, which are all about learning from data.
Right.
And symbolic reasoning, which is about using logic and rules.
Yeah, so instead of just mimicking patterns,
they're actually trying to like understand
the why behind their actions.
Exactly, and that's where these TMMs,
Relational Neurosymbolic Markov Models come in.
Nesias.
Yeah.
I love it.
That's a mouthful.
It's catchy.
It is a mouthful.
So they're using this Nesi approach
to make Markov models even smarter.
But like, how does that benefit us?
So imagine a world where you go to the doctor.
OK.
And the doctor's using an AI diagnostic tool.
Right.
And that tool not only gives you a diagnosis,
but also explains its reasoning in clear logical steps.
Yeah.
That's the kind of trust and transparency
that these Nesi MMS could bring to AI.
And that makes a huge difference.
Because if you don't know why, the AI is doing what it's doing.
It's really hard to trust it.
Exactly.
Or think about self-driving cars.
Oh, yeah.
That can not only learn from driving data,
but also understand traffic laws at a logical level.
That leads to safer and more reliable autonomous vehicles.
OK, so this is already blowing my mind.
So let's break it down.
Right.
How do these Nesi MMS actually work?
So think of it as two interconnected parts.
OK.
First, you have these neural networks
that process raw data, like images,
sensor readings, things like that.
These networks act like a translator,
converting that complex data into a simplified symbolic
representation.
So they're kind of like taking this complex data
and turning it into a language that logic can work with.
Exactly.
And that's where the second part comes in.
These symbolic representations are then
fed into this logical reasoning engine
that can apply predefined rules and constraints.
So it's kind of like you're giving the AI a rulebook
and saying, OK, based on what you're seeing and the rules,
what's the most logical action to take?
That's a great way to put it.
So it's learning from experience
through those neural networks.
But it's also applying reason and logic
through that symbolic system.
And the beauty of this approach is that it's probabilistic,
meaning it can handle uncertainty,
make predictions based on probabilities.
Just like we do in the real world.
Just like we do.
All right.
So we've got the basics down.
Yeah.
But to really appreciate the power of these NSEMs,
we need to see them in action, right?
Absolutely.
The researchers put these NSEMs to the test
using a video game called Minnehack, which
is known for its complex rules and really unpredictable
scenarios.
So it's a good testing ground.
Yeah.
They wanted to see how well these models could learn,
adapt, and generalize their knowledge.
Video games and AI.
Always a good combination.
So what do they find?
Well, they did two main experiments,
focusing on two key capabilities of AI,
generation and discrimination.
OK.
So before we dive into that, mind me what those are again.
So generation is all about creating something new.
OK.
Think about composing a piece of music
based on what it's learned.
Yeah.
Discrimination is more about categorizing things.
OK.
Identifying if a picture has a cat or a dog in it.
Got it.
So they were basically testing if these NSEMMs could
create new game scenarios.
Mm-hmm.
That made sense.
Yeah.
And also accurately analyze existing scenarios.
Exactly.
OK.
And what they found was fascinating.
Well, don't leave me hanging.
OK.
What happened?
In the generative experiment, they
tasked the NSEMM with creating new game screens for Minnehack.
OK.
That actually followed the game's very complex rule.
So it's like giving the AI a paint brush and saying,
all right, paint me a plausible Minnehack scene.
Exactly.
And what surprised them was that the NSEMM wasn't just
mimicking what it had seen.
It was generating new game states
that were logically consistent.
That's a big deal.
Yeah, it is.
Because it's one thing to copy.
Right.
It's another to truly understand.
Yeah.
And create something new.
Exactly.
It suggests that the NSEMM was able to internalize
the logic of the game at a much deeper level.
Wow.
Now, the second experiment.
OK.
The discriminative task got even more interesting.
All right.
I'm on the edge of my seat.
OK.
Tell me more.
This time they wanted to see.
Yeah.
If the NSEMM could predict whether a player would die.
OK.
In a given Minnehack sequence.
Ooh, a life or death scenario.
Yeah.
So they just show the AI a bunch of gameplay.
Well, not exactly to make it more challenging.
They only gave the AI the symbolic representation
of the game state.
Hold on.
So they were asking the AI to predict the outcome?
Yeah.
Without even seeing the game.
Right.
That's like trying to figure out who's winning a chess match.
Yeah.
Just by looking at the notation.
Right.
And like the actual board.
OK.
That's tough.
Yeah.
And to make it even tougher.
Yeah.
They intentionally left out some information.
Oh, no.
About how the enemies in the game behaved.
So they're really putting it through the ringer.
Yeah.
Like an incomplete rule book.
Wow.
OK.
So how to do?
Remarkably well.
Really?
Even with this missing information.
OK.
The NSEMMs were able to combine the rules they did know.
Mm-hmm.
With the patterns they'd learned.
Right.
To pretty accurately predict.
Wow.
Whether the player would survive.
So it's like they were able to fill in.
Yeah.
The gaps in their knowledge.
Mm-hmm.
By thinking logically.
Exactly.
Not just relying on what they'd seen before.
That's right.
And to push it even further.
OK.
They tested these models on increasingly complex versions
of the game.
Right.
Changing the map size, the enemy count, the sequence length.
So they really wanted to see.
Yeah.
If it could handle anything they could throw at it.
Exactly.
So what was the verdict?
The NSEMMs consistently outperformed the other models.
OK.
Especially when things got really complex and unpredictable.
Yeah.
They showed this impressive ability
to generalize their knowledge.
Mm-hmm.
And adapt to new situations.
Which is really crucial.
Right.
For any AI system that's going to operate in the real world.
Yeah.
Because the real world is messy.
It is.
It's unpredictable.
It is.
Things change all the time.
That's right.
So you need an AI that can roll with the punches.
Exactly.
And I'm starting to see why this research is generating
so much buzz.
It's really exciting stuff.
It is.
But before we get too carried away with all the potential here.
Mm-hmm.
Let's kind of step back and consider
both the strengths and the limitations of this approach.
Of course.
Because no technology is perfect.
No technology is perfect.
Right.
You're right.
No technology is perfect.
Yeah.
But uh.
But this one's pretty cool.
It is pretty cool.
On the positive side.
Yeah.
These NECMs they've shown.
These remarkable generalization abilities.
Right.
They can handle incomplete information.
Yeah.
Really gracefully.
Which is important.
Which is huge.
In the real world.
Yeah.
And the use of these logical rules.
Mm-hmm.
Make some way more transparent.
Yeah.
Than your typical black box AI.
And that transparency is huge.
Yeah.
Especially if we're talking about AI making decisions.
Mm-hmm.
That affect our lives.
Right.
You know.
It's reassuring to know that we can understand
why the AI is doing what it's doing.
Exactly.
It's not just a magic black box.
Yeah.
No magic.
Yeah.
However.
But.
There are hurdles to overcome.
OK.
The paper itself acknowledges.
Mm-hmm.
This issue of scalability.
Right.
These NECMMs.
Yeah.
At least in their current form.
Mm-hmm.
Can be computationally expensive to train.
So it's kind of like.
Yeah.
Having a brilliant chef.
Oh, right.
Who can create these incredible dishes.
Mm-hmm.
But only in a tiny kitchen.
Yeah.
With limited equipment.
That's a great analogy.
They need a bigger space.
Mm-hmm.
To really shine.
Exactly.
And beyond just the computational challenges.
Yeah.
There's the ongoing task of finding.
Oh.
That sweet spot.
Right.
Between the neural network learning.
Yeah.
And the symbolic reasoning.
It's a balancing act.
It is a balancing act.
It's like finding the perfect recipe for AI.
Right.
Exactly.
But all in all.
It seems like these NECMs.
Mm-hmm.
Are a promising step forward.
Yeah.
In AI research.
I think.
It's not just about making AI smarter.
Right.
It's about making it.
It's about making it more reliable.
Yeah.
More understandable.
We're beaptable.
Yeah.
All the things that we want.
Exactly.
Yeah. That shift from pure pattern recognition.
To AI that can actually reason.
Right.
That's what excites me the most about this research.
No.
I can't help but wonder.
Yeah.
Like what are some real world applications.
Sure.
Beyond video games.
Mm-hmm.
Where these NECMMs could make a difference.
The potential is vast.
Yeah.
One area that's ripe for disruption is robotics.
Oh yeah.
Imagine robots in manufacturing.
Mm-hmm.
Or healthcare.
OK.
That can not only perform these complex tasks.
Right.
But also adapt to unexpected changes in their environment.
Right.
Because that's the thing.
Yeah.
The real world is not a controlled laboratory
where all things happen.
All the time.
You've got to be able to roll with it.
That's right.
And all while adhering.
Yeah.
To a set of like logical safety protocols.
Exactly.
So we have these robots that are not only more versatile.
Right.
More reliable but also safer.
Yeah.
We don't want robots going rogue.
No we don't.
And it's a far cry from those kind of rigid.
Right.
Pre-programmed robots we see today.
It's like going from Arumba to Rosie the Robot.
Exactly.
And speaking of collaboration.
Oh yeah.
What about education.
Oh that's a good one.
Could these NECMMs change how we learn.
I think so.
Absolutely.
We already have AI powered learning platforms.
Right.
But imagine a personalized AI tutor.
OK.
That can understand your unique learning style.
Yeah.
Taylor it's approach to your needs.
Explain complex concepts in a way that makes sense to you.
So it's like having a personal tutor.
Yeah.
Who's available 24 seven.
Exactly.
And never gets tired of explaining things.
And because these AI tutors.
Right.
Would be grounded in logical reasoning.
We could have more confidence that they're
teaching accurate information.
Yeah.
That's a big deal.
Consistent information.
Especially these days.
It is.
It could revolutionize education as we know it.
Personalized robots.
Yeah.
AI tutors.
It seems like the possibilities are endless.
They really are.
But there's one area we haven't touched on yet.
OK.
Transportation.
Yes.
Given all this talk about self-driving cars.
Right.
I'm curious how these NECMMs.
Yeah.
Could fit into that picture.
That's a great point.
Because that's a really big one.
It is.
With AI, right?
Yeah.
Self-driving cars are like the poster child for AI.
Right.
For good reason.
Yeah.
The stakes are high.
Yeah.
People's lives are at stake.
Yeah.
Exactly.
So by combining this data driven learning.
With logical reasoning about traffic rules.
OK.
Safety protocols.
Right.
We could create self-driving systems that
are not only more reliable.
Yeah.
But also more predictable.
And cautious.
Which I think is what a lot of people are worried about.
They are.
Is that the car is going to be too cautious.
Right.
And it's going to cause more problems than it's solved.
But the thing is.
Yeah.
If you can bake in that logical reasoning about safety.
Right.
Then you can actually create a system.
Yeah.
That understands why it needs to be cautious
in certain situations.
So it's not just reacting to what's happening on the road.
It's actually understanding.
Understanding the why.
Yeah.
Behind certain driving decisions.
Yeah.
So it can anticipate potential hazards.
Make informed decisions at intersections.
Yeah.
Even explain its actions to the passenger.
Exactly.
Which builds trust and confidence.
It does.
In the system.
Right.
And that trust is really what's going to make or break
the adoption of these technologies.
It sounds like NECI MMS.
Yeah.
Could be the key to unlocking a future of truly intelligent
transportation systems.
I think so.
And that's just scratching the surface.
We're talking about a technology that
could potentially revolutionize any failed.
Where AI needs to reason.
Adapt.
Interact with the world.
Yeah.
In a way that aligns with their expectations.
It's exciting.
It is exciting.
So the takeaway here is that while NECI MMS.
Yeah.
Are still.
And they're early stages.
Yeah, they're early stages.
The potential impact is enormous.
Right.
We're not saying.
Right.
This is the solution to everything.
The VL end all.
Right.
But it's definitely.
It's a step in the right direction.
It is a step in the right direction.
And the journey is only going to get more exciting from here.
I think so.
As researchers continue to refine these models.
And address those.
Right.
Scalability challenges.
Who knows what.
Right.
Round breaking applications.
We'll see.
We'll see in the years to come.
It's like we've opened a door.
Yeah.
To this.
To this whole new era of AI.
Whole new era of AI.
Where machines can not only learn from data.
But they can understand the world around them.
Yeah.
In a way that's closer.
Yeah.
To how we humans think.
And with that new understanding.
Mm-hmm.
Comes a greater potential.
Right.
For collaboration.
That's problem solving.
For problem solving.
And perhaps even.
Yeah.
A deeper understanding of ourselves.
That's a great point.
Mm.
Yeah.
It really is mind blowing to think about like.
It is.
All the ripple effects.
Yeah.
This research could have.
Mm-hmm.
We've talked about robots, education, transportation.
Right.
Like it feels like Nessiems could be like.
Mm-hmm.
The missing link.
Yeah.
To bridging that gap between AI as we know it.
Yeah.
And AI as we like imagine it.
Mm-hmm.
In science fiction.
It's certainly a powerful concept.
Yeah.
But it's important to remember that we're still.
Early and early stages.
We're not going to solve all the world's problems.
Not yet.
With Nessiems' tomorrow.
Not tomorrow.
But.
But it's hard not to get excited.
It is exciting.
About the possibilities.
Imagine a world where AI can not only diagnose diseases.
But explain its reasoning.
Yeah.
To doctors.
In a way that helps them make better decisions.
That would be huge.
Or imagine AI systems.
Yeah.
That can help scientists.
Mm-hmm.
Model complex systems like climate change.
Right.
With greater accuracy and predictability.
So we can make more informed decisions.
Working.
About our planet's future.
And let's not forget.
Yeah.
The potential for AI-powered personal assistants.
OK.
That can actually understand our needs.
Right.
Anticipate our requests.
Ooh.
Like a mind reader.
Almost.
Yeah.
And even help us solve creative problems.
OK.
So we're talking about AI.
Yeah.
That's not just like doing things for us.
Right.
But it's like helping us.
Helping us think better.
Think better be more creative.
Exactly.
Wow.
These are all exciting possibilities.
They are.
And I believe NECI-MMs represent a significant step.
OK.
Towards realizing that vision.
It's really cool to see.
It is.
How this field is progressing.
Yeah.
Because for a long time it felt like AI was kind of stuck.
Right.
In this.
In a rut.
Yeah.
Just like pattern recognition.
Brute force.
Yeah.
And now it's like we're starting to see AI that can actually
reason.
Right.
And understand.
And that's the game changer.
That is the game changer.
By combining the strengths of data driven learning.
Right.
With the power of logical reasoning.
We're opening up this whole new frontier.
And with any new frontier.
Right.
There are going to be challenges.
Oh absolutely.
Unexpected discoveries.
Yeah.
Setbacks.
Probably.
But that's what makes it so exciting.
Exactly.
It's a constant process.
Yeah.
Of exploration.
Experimentation.
Pushing the boundaries.
Yeah.
Of what's possible.
I think that's a great point to end on.
I think so too.
The future of AI is not just about creating machines.
Right.
That are smarter than us.
Right.
It's not about just outsmarting us.
It's about.
It's about creating machines that can help us understand.
Yeah.
Our selves and the world around us.
That's right.
In new and profound ways.
And I think that's a really beautiful vision.
It is a beautiful vision.
For the future of AI.
So to everyone listening.
Yeah.
Who wants to like dive deeper into this fascinating world
of neuro-symbolic AI.
We'll be sure to include a link.
Yeah.
We'll include links in the show notes.
To the full paper.
To all the resources.
All the resources.
So you can check it out for yourself.
And as always.
Yeah.
Keep those minds curious.
Stay curious.
And stay tuned for more exciting developments.
Because there are going to be more in the world of AI.
Thanks for joining us.
Yeah.
Thanks for going on this deep dive with us, everyone.
Until next time.