Welcome back everyone.
Ready for another deep dive?
This time we're looking at a paper titled,
Neurosymbolic Graph Enrichment for Grounded World Models.
Oh yeah, this one's really interesting.
It is, right?
It's tackling this big question of how to build AI
that actually understands the world the way we do.
You're not just processing information
but getting all the nuance and the complexity.
Yeah, it's really ambitious.
Totally.
And what I found really cool about this paper
is that it tries to bring together these two huge approaches
to AI that we usually think of as separate.
Right, like you've got generative AI on the one hand.
Like JET-TPT.
Exactly.
Which is amazing at producing creative text.
And then on the other hand, you have this whole world
of knowledge-based AI which relies on structured data
and logic.
And each one has its own limitations.
Really?
Like neither one on its own seems quite enough
to build those grounded world models that can truly understand
the world through human experience.
Like trying to bake a cake with only flour or only eggs.
Perfect analogy.
You need both.
Exactly.
So how do they try to bridge that gap in this paper?
Well, this is where the idea of neuro-symbolic AI comes in.
Neurosymbolic AI.
Yeah, it's all about combining the strengths of LLMs,
like your JET-TPT, with the power of knowledge graphs,
which are essentially these structured databases
of information.
OK, so instead of just spitting out text,
they're using LLMs to build up a knowledge base that's
organized and logical.
Exactly.
But it goes even deeper than that.
They're using LLMs to extract what's called implicit knowledge.
Implicit knowledge.
I'm not sure I follow.
It's the kind of understanding that we humans just
have naturally unspoken roles and stuff.
Think about how you can tell someone's mood
by their tone of voice.
Oh, right.
Or how you know not to interrupt someone
when they're telling a story.
Exactly.
It's all that unspoken stuff that we pick up on as humans.
OK, I see.
So they're trying to teach machines
to read between the lines, basically.
Exactly.
And to do that, they develop 11 heuristics.
Heuristics.
You can think of them as rules of thumb
that help guide the LLM in extracting implicit knowledge
from both text and images.
So these heuristics represent different facets
of human understanding.
Precisely.
And one that really stood out to me
was the idea of image schemas.
Image schemas?
What are those?
Well, they're these basic cognitive structures
that we derive from our physical experiences,
like container or path.
It's like in or out or through.
Exactly.
We use these schemas to understand
all sorts of abstract concepts.
Even though I'm not literally in trouble the way I'm in a box,
my brain uses that same spatial schema to make sense of it.
Wow.
I never thought of it that way.
So it's like these physical metaphors
shape our understanding of more abstract ideas.
Precisely.
And that's just one of the 11 heuristics they use.
Some of the others tackle even more complex aspects
of human cognition.
Like what?
Well, there's one called moral value-driven coercions,
which recognizes how our own values and social norms influence
how we interpret things.
So like if someone cuts in line,
we might judge them not just on the action itself,
but also on our own sense of fairness.
Exactly.
Or how we might view someone breaking a promise as not
just factually wrong, but also as a sign
that they're untrustworthy.
I see.
So they're building a system that tries to understand
not just what happened, but also the moral implications.
Exactly.
And they represent all this information
using knowledge graphs.
Knowledge graphs.
Yeah.
They're like these structured databases
that represent information as a network
of interconnected concepts.
So instead of just storing data as text,
it's like they're creating a map of how different ideas
relate to each other.
A map of knowledge.
That's a great way to put it.
And by adding all these layers of implicit knowledge,
they're making that map much richer, much more
like how we humans see the world.
So they're combining generative and knowledge-based AI
using LLMs to extract this implicit knowledge
guided by these heuristics and then representing it all
in this structured knowledge graph.
You got it.
It's pretty complex.
It is.
To really see how it works, though,
we need to look at an example from the paper.
OK, perfect.
Let's dive into that.
All right.
So they start with this picture of an Olympic athlete
celebrating a victory.
OK.
And they feed that picture to GPT-4.
And they ask it to describe what it sees.
Interesting.
So the LLM is creating the initial text description
that they'll use to build the knowledge graph from.
Exactly.
And GPT-4 generates this really detailed description.
You know, talking about the athlete's expression,
their uniform, the cheering crowd, all those details.
OK, so now they've got this rich text description.
What do they do with it?
They use a tool called Text2AMR2FRD
to transform that natural language
description into a structured knowledge graph.
Well, Olan, Text2AMR2FRA, that sounds intense.
What exactly does it do?
Think of it like a super sophisticated translator.
OK.
It takes natural language like English
and converts it into a format that machines can understand
and use for reasoning.
So it's like translating human language
into machine language so the computer can
build its knowledge graph.
You got it.
But that initial graph is still just based
on the literal content of the image description.
It needs to be fleshed out with all those layers
of implicit knowledge.
Right.
This is where those 11 heuristics come in.
Exactly.
They use those heuristics to guide the LLM
in adding those juicy details that make the knowledge graph
truly grounded in human understanding.
So they feed the base graph back to the LLM
and use specific prompts to activate
each of the 11 heuristics.
Exactly.
Like for the factual impact heuristic,
they might prompt the LLM to consider
what emotions the athlete might be experiencing.
So the LLM would add things like athlete is feeling joy
or athlete's heart rate is elevated.
Exactly.
And they do this for all 11 heuristics,
creating separate graphs that each capture
a different layer of implicit knowledge.
Wow.
So they end up with a whole stack of knowledge graphs.
Right.
And then they merge them all together
to create this final multi-layered representation
of the original image.
It's like taking a flat map and then
adding layers for terrain climate population density.
You get this much richer picture.
OK.
That makes sense.
But how do they know if this super detailed knowledge
graph is actually any good?
Are they just trusting the LLM to come up
with plausible stuff?
That's a great question.
And to answer it, we need to talk about their evaluation
process, which is actually pretty amazing.
OK, let's hear it.
How do they test whether this knowledge graph is truly
reflecting a human-like understanding of the world?
Well, they use a three-tiered evaluation process.
And each tier is designed to assess
a different aspect of the knowledge graph's quality.
Three tiers.
Sounds thorough.
It is.
The first tier focuses on something called logical
validation.
Basically, they're making sure that the knowledge graph
makes sense internally.
So no contradictions or nonsensical statements.
Exactly.
And making sure that any new information the LLM added
is properly linked to existing concepts.
Like imagine if it tried to tell you that an apple is
a type of fish.
It wouldn't make sense.
Right.
They're making sure the AI is building
on a solid foundation of knowledge.
Exactly.
And the second tier of the evaluation
dives into foundational ontology alignment.
OK, that sounds a bit complicated.
Break it down for me.
Ontology basically refers to a standardized way
of defining concepts and their relationships.
Think of it like a dictionary that the AI uses
to make sure it's speaking the same language as the existing
body of knowledge.
So it's like checking the AI's work
against a reference guide to make sure everything
is defined correctly and fits together neatly.
Precisely.
And then finally, we get to the third tier of the evaluation.
And this is where it gets really interesting.
Oh, tell me more.
They bring in human experts to evaluate
the plausibility of the knowledge that's been generated.
Ah, so real people get to judge whether the AI is
capturing those subtle nuances of human understanding.
That's crucial, because that's ultimately
what they're trying to replicate.
Absolutely.
And the results of this three tiered evaluation
were really revealing.
They shed light on both the AI's remarkable capabilities
and the areas where it still faces challenges.
OK, I'm dying to hear those results.
Which areas did the AI excel in and where did it struggle?
Give me the highlights.
All right, well, let's start with the AI's strengths,
because there were definitely some impressive achievements.
OK, let's hear about the AI's shining moments.
What impressed the human judges the most?
One area where the AI really excelled
was in capturing what we call factual impacts.
Factual impacts.
What does that mean, exactly?
It refers to the likely consequences of an event,
both physical and emotional.
For example, in the case of the Olympic athlete,
the AI was able to infer that the athlete was likely
feeling a surge of joy and excitement
after winning the race.
OK, that makes sense.
But did it go beyond just recognizing basic emotions?
It did.
It was able to infer more subtle details,
like the athlete's elevated heart rate
and even the possibility of receiving endorsements.
It was like the AI was thinking through the ripple effects
of the victory, almost like a human would.
Wow.
So it wasn't just recognizing the event itself.
It was able to understand the broader context
and anticipate potential consequences.
That's pretty impressive.
It is.
And it suggests that the AI is starting
to develop a real capacity for reasoning about cause
and effect, which is a fundamental aspect of human
intelligence.
Absolutely.
We don't just perceive the world
as a series of isolated events.
We understand how those events are interconnected,
how one action can lead to another.
And it sounds like this AI is starting
to develop that same ability to see the bigger picture.
Exactly.
But the AI's strengths weren't limited to factual impacts.
It also did surprisingly well in grasping
some of the more subtle aspects of human communication,
like implied meaning.
You're talking about those times when we say one thing
but mean something else, like when someone says,
oh, fantastic, after spilling coffee all over themselves.
Exactly.
We call those conversational implicatures.
And they're incredibly common in human language.
And the AI was actually able to pick up
on some of those implied meanings and incorporate
that information into the knowledge graph.
Wow.
So it was able to read between the lines
and understand that the literal words don't always
tell the whole story.
That's pretty remarkable.
Can you give me a specific example of how it did that?
Of course.
Remember that caption we talked about earlier
about the exhausted but ecstatic athlete collapsing
onto the track?
A human reading that would naturally infer
that the athlete had just finished a race,
even though it's not explicitly stated.
Right.
It's just something we understand based on the context.
Exactly.
And the AI, guided by the conversational implicatures
heuristic, was able to pick up on that implied meaning
and add it to the knowledge graph.
It was like the AI was filling in the gaps
using its understanding of how humans use language.
That's really cool.
It suggests that we're making real progress towards AI
that can genuinely understand the nuances of human communication.
But you mentioned earlier that the AI also faced some challenges
during the evaluation.
What were some of the areas where it struggled?
Well, as you might expect, some aspects of human cognition
are proving to be more difficult to replicate than others.
And one area where the AI encountered a significant hurdle
was in predicting future events.
Predicting the future.
That's a tough one even for us humans.
But isn't that a key element of human intelligence
being able to anticipate what might happen next,
based on what we know about the world?
It is.
And while the AI could make some educated guesses
about what might happen next, its predictions
were often limited in their accuracy and nuance.
It seems that capturing the full scope of our ability
to anticipate and plan for the future
is still a major challenge for AI.
I can see why that would be tricky.
There are just so many factors that can influence future events.
And even we humans get it wrong all the time.
So is this a sign that we're still a long way
from achieving truly human-like AI?
It's a great question.
And it highlights just how complex human reasoning really is.
Even with all the advancements we've made in AI,
there are still some fundamental aspects of human intelligence
that we haven't fully cracked.
It's both exciting and humbling to realize that.
Absolutely.
And it just makes this whole field of research
even more fascinating.
I agree.
So we've talked about the AI's strengths
in capturing factual impacts and even
those subtle conversational implicatures.
But you mentioned that there are other areas where it struggled.
What else did the evaluation reveal?
Well, another area where the AI faced difficulties
was in dealing with heuristics that
involve moral judgments and social norms.
That makes sense.
Morality and social norms can be so complex and context
dependent they can vary widely across cultures
and even within a single culture,
depending on the situation.
Exactly.
And while the AI had been trained
on this massive data set of text,
it hadn't yet developed that nuanced understanding
of social and cultural dynamics that
would allow it to consistently make accurate judgments
about what's considered right or wrong or appropriate
in different situations.
So it's like the AI was still learning
the ropes of human social interaction,
trying to figure out the often unspoken rules
that govern our behavior.
Precisely.
And that's an ongoing challenge for AI research,
how to build systems that are not only intelligent,
but also socially and culturally aware systems
that can understand the subtle cues and unspoken rules that
shape human interactions.
It sounds like we're still in the early stages of teaching AI
how to navigate the complexities of human society.
And it's definitely a challenge that
requires careful consideration of the ethical implications.
Absolutely.
But even with these challenges, the fact
that we're starting to see AI systems that can grasp
even some of these subtle aspects of human understanding
is incredibly exciting.
It really is.
This deep dive is giving me a whole new appreciation
for the incredible progress being made in AI
and the potential it holds for the future.
It's a field that's constantly evolving
and it's full of surprises.
It's amazing to see how researchers are pushing
the boundaries of what's possible.
And it's even more amazing to think
about what the future holds for AI and its role in our lives.
This has been a truly mind-expanding conversation.
Thank you so much for sharing your insights
and expertise on this cutting-edge research.
It's been my pleasure.
And thank you for joining us on this deep dive
into the fascinating world of nurse symbolic AI
and grounded world models.
We hope you've enjoyed the journey.
It's been a wild ride.
And to our listeners out there, we
encourage you to keep asking questions, keep exploring,
and stay curious about the ever-evolving world of AI.
So the first tier is all about making sure
that the knowledge graph is logically sound.
We're talking about checking for contradictions,
making sure the information flows in a way that
makes sense, and also ensuring that any new information
that the LLM added is properly linked back
to the original concepts in that base graph.
So it's like making sure the AI isn't building
a tower of knowledge on a foundation of sand.
It has to be solid and consistent for the ground up.
Exactly.
And then the second tier dives into this idea
of what they call foundational ontology alignment.
OK, ontology.
That sounds a little intimidating.
What exactly are we talking about here?
Yeah, it's one of those words that
sounds really complicated, but it's actually
a pretty simple idea.
Ontology basically refers to a structured way
of defining concepts in their relationships.
OK, so it's like a blueprint for knowledge,
a set of rules that make sure everything is categorized
and defined in a consistent way.
That's a great way to put it.
So they're essentially checking that the AI is using
the same blueprint as the existing body of knowledge,
making sure it doesn't go off creating its own wacky definitions
for things.
Right.
You wouldn't want the AI to decide that a cat is
a type of food or something.
It has to be grounded in the same reality
that we humans operate in.
Precisely.
And then comes the most interesting part,
the third tier of the evaluation process.
This is where they bring in the human factor.
The moment of truth.
So real people get to judge whether the AI has truly
captured those subtle unspoken layers of human understanding.
That's exactly it.
They have human experts in knowledge representation
evaluate the plausibility of the information
that the LLM has added to the graph.
So it's like having a panel of judges
assess whether the AI is truly getting it,
whether it's able to grasp those deeper layers of meaning
that we humans naturally pick up on.
Precisely.
And what's fascinating is that this human evaluation isn't just
a thumbs up or thumbs down.
It reveals this nuanced spectrum of successes and challenges.
I see.
So it gives us more realistic picture of where the AI is at
in terms of its ability to understand the world like we do.
Exactly.
It's not perfect, but it's definitely
showing some real progress.
OK.
I'm on the edge of my seat here.
How did the AI actually fare in this human evaluation?
Was it able to pass the test and convince these expert judges
that it was truly understanding the world like we do?
Well, the results were quite fascinating.
The AI actually did remarkably well in some areas.
But as you might expect, there were also
areas where it struggled.
So let's start by looking at the AI strengths,
you know, the things that really impressed the human judges.
OK.
Let's hear about the AI's shining moments.
What were some of its standout achievements?
One area where the AI really excelled
was in capturing what's called factual impacts.
Factual impacts.
We're not new at that means again.
Yeah, sure.
So factual impacts basically refer
to the likely consequences of an event,
both physical and emotional.
So for example, in the case of the Olympic athlete,
the AI was able to infer that the athlete was probably
feeling a surge of joy and excitement after winning the race.
OK.
That makes sense.
But did it go beyond just recognizing
those basic emotions?
It did.
It was able to infer more subtle details like the athletes
elevated a heart rate and even the possibility
of receiving endorsements.
You know, it's almost like the AI was thinking
through the ripple effects of the victory in a way
that's similar to how a human might.
Wow.
So it wasn't just recognizing the event itself.
It was able to understand the broader context
and even anticipate potential consequences.
That's pretty amazing.
It is.
And it suggests that the AI is developing
this real capacity for reasoning about cause and effect,
understanding that actions have consequences.
Absolutely.
We don't just perceive the world as this random series
of events.
We understand how those events are interconnected,
how one action can lead to another.
And it sounds like this AI is starting
to develop that same ability to see the bigger picture.
Exactly.
But the AI's strengths weren't limited to factual impacts.
It also did surprisingly well in grasping
some of the more subtle aspects of human communication.
Things like implied meaning.
Oh, you mean like those times when we say one thing,
but mean something else?
Like when someone says, oh, fantastic,
after spilling coffee all over themselves?
That kind of thing.
Exactly.
We call those conversational implicatures.
And they're super common in human language.
And the AI was actually able to pick up
on some of those implied meanings
and incorporate that information into the knowledge graph.
Wow.
So it was able to read between the lines
and understand that the literal words don't always
tell the whole story.
That's pretty remarkable.
Do you have a specific example of how it did that?
Of course.
Remember that caption we talked about earlier
about the exhausted but ecstatic athlete collapsing
on the track?
Yeah, I remember.
Well, human reading that would naturally
infer that the athlete had just finished a race,
even though it's not explicitly stated.
Right.
It's just something we understand based on the context.
Exactly.
And the AI, guided by the conversational implicatures
heuristic, was able to pick up on that implied meaning
and add it to the knowledge graph.
It was like the AI was filling in the gaps,
using its understanding of how humans use language.
That's really cool.
It suggests that we're making some real progress toward AI
that can genuinely understand the nuances
of human communication.
But you mentioned earlier that the AI also faced some challenges
during the evaluation.
What were some of the areas where it struggled?
Well, as you might expect, some aspects of human cognition
are proving to be more difficult to replicate than others.
And one area where the AI ran into a bit of a wall
was in predicting future events.
Predicting the future.
Yeah, that's a tough one, even for us humans.
But isn't that a key part of human intelligence,
being able to anticipate what might happen next
based on what we know about the world?
It is.
And while the AI could make some educated guesses
about what might happen, its predictions
were often limited in their accuracy and nuance.
It seems that capturing the full scope of our ability
to anticipate and plan for the future
is still a big challenge for AI.
I can see why that would be tricky.
There are just so many factors that can influence future events.
And even we humans get it wrong all the time.
So is this a sign that we're still a long way
from achieving truly human-like AI?
It's a great question.
And it really highlights just how complex human reasoning is.
Even with all the incredible advancements we've made in AI,
there are still some fundamental aspects
of human intelligence that we haven't quite figured out
how to replicate.
It's both exciting and a little bit humbling
to realize that we've come so far,
but there's still so much more to discover.
Exactly.
And it makes this whole field of research
even more fascinating.
I agree.
So we talked about the AI strengths
in capturing factual impacts and even those subtle
conversational implicatures.
But you mentioned that there were other areas
where it struggled.
What else did the evaluation reveal?
Well, another area where the AI faced some difficulties
was in dealing with those heuristics
that involved moral judgments and social norms.
Oh, that makes sense.
Morality and social norms can be so complex
and context-dependent.
They can vary so much between cultures
and even within the single culture,
depending on the situation.
Exactly.
And while the AI had been trained on this massive data
set of text, it hadn't yet developed
the kind of nuanced understanding of social and cultural
dynamics that would allow it to consistently make
accurate judgments about what's considered right or wrong
or what's appropriate behavior in different contexts.
So it's like the AI was still learning the ropes
of human social interaction, trying
to figure out those often unspoken rules that
govern our behavior.
Precisely.
And that's an ongoing challenge for AI research,
figuring out how to build systems that are not only intelligent
but also socially and culturally aware,
systems that can understand the subtle cues and unspoken rules
that shape human interactions.
It sounds like we're still in the early stages of teaching AI
how to navigate the complexities of human society.
And it's definitely a challenge that
requires us to think carefully about the ethical implications.
Absolutely.
But even with these challenges, the fact
that we're starting to see AI systems that
can grasp even some of these subtle aspects of human
understanding is incredibly exciting.
It really is.
This deep dive has given me a whole new appreciation
for the incredible progress that's
being made in AI and the potential it holds for the future.
It's a field that's constantly evolving
and it's full of surprises.
It's amazing to see how researchers are pushing
the boundaries of what's possible.
And it's even more amazing to think
about what the future holds for AI
and its role in our lives.
This has been a truly mind-expanding conversation.
Thank you so much for sharing your insights and expertise
on this cutting-edge research.
It's been my pleasure.
And thank you for joining us on this deep dive
into the fascinating world of neuro-symbolic AI
and grounded world models.
We hope you've enjoyed the journey.
It's been a wild ride.
And to our listeners out there, we encourage you
to keep asking questions, keep exploring,
and stay curious about the ever-evolving world of AI.
So we talked about the AI's strengths,
being able to capture those factual impacts
and even those subtle conversational implicatures.
But you mentioned there were also some areas
where it struggled.
Let's dive into that a bit.
What were some of the things that tripped the AI up
during the evaluation?
Yeah, well, one of the biggest hurdles for the AI
was predicting future events.
You know, it seems like replicating that human ability
to anticipate what might happen next
based on our understanding of the world.
That's still a really tough nut to crack for AI.
I can see why that would be so challenging.
Even for us, humans predicting the future
is notoriously difficult.
We can make educated guesses
and try to plan for different possibilities,
but there are always those unexpected curve balls
that come along and throw us off course.
Exactly.
And it seems the AI ran into similar difficulties.
You know, it could make some reasonable predictions
about what might happen next,
but its accuracy and its nuance were often limited.
So it's like the AI was still learning to connect the dots
between cause and effect.
It could understand that certain actions
might lead to certain outcomes,
but it wasn't quite able to grasp the full complexity
of how events unfold over time.
That's a great way to put it.
And it really highlights the fact
that even though we've made some incredible strides in AI,
there are still some fundamental aspects
of human intelligence
that we haven't quite figured out how to replicate.
It's kind of humbling to realize, that isn't it?
It is, you know, it makes you appreciate
just how complex and sophisticated
the human mind really is.
Absolutely.
Were there any other areas
where the AI encountered difficulties?
Yes.
Another area where the AI struggled
was in dealing with those heuristics
that involved moral judgments and social norms.
Ah, that makes sense.
Morality and social norms can be so subjective
and so context dependent.
They can vary so much between cultures.
And even within a single culture,
things can change depending on the situation.
Exactly.
And while the AI had been trained
on this massive data set of text,
it hadn't yet developed that kind of nuanced understanding
of social and cultural dynamics
that would allow it to make those really accurate judgments
about what's right or wrong
or what's considered appropriate behavior
in different contexts.
So it's like the AI was still learning
the unspoken rules of human interaction.
You know, trying to decipher those subtle cues
and unwritten codes of conduct
that we often take for granted.
That's a great analogy.
It's like the AI was trying to learn this whole new language,
but it wasn't just about the words themselves.
It was about the subtle inflections,
the unspoken rules of grammar.
And, you know, that whole cultural context
that gives those words meaning.
It's fascinating to think about
how we might even begin to teach a machine those things.
You know, it's not just about feeding it data.
It's about helping it develop a deeper understanding
of human values and beliefs and motivations.
Absolutely.
And that's where things start to get really interesting
because it forces us to ask
some pretty fundamental questions about ourselves.
What are the core values that shape our societies?
How do we form our moral judgments?
And how can we translate those often intangible concepts
into something that a machine can grasp?
It's like we're holding a mirror up to our own humanity in a way,
trying to define those qualities that make us unique
and then figuring out how to instill those same qualities
in our machines.
That's a beautiful way to put it.
And I think that's one of the most exciting aspects
of this research.
It's not just about building smarter machines.
It's also about deepening our own understanding
of what it means to be human.
This deep dive has been a real eye-opener for me.
It's incredible to see just how far AI has come.
And it's really exciting to think about how close we're getting
to creating systems that can truly understand the world
through a human lens.
It's an amazing field to be working in.
And the pace of innovation is just mind-blowing.
It's hard to even imagine what breakthroughs
might be just around the corner.
It's both thrilling and a little bit daunting
to think about that, isn't it?
It is.
But one thing's for sure.
This journey of understanding AI is just beginning.
And it's a journey that's going to continue to challenge
and inspire us for years to come.
Well said.
And on that note, we've reached the end of our deep dive
into the world of neurosymbolic AI and grounded world models.
We hope you've enjoyed exploring these concepts with us.
Thanks for joining us.
It's been a great conversation.
It has.
And to all our listeners out there, remember,
keep asking those questions.
Keep exploring.
And most importantly, stay curious about the ever-evolving
world of AI.