All right, so today we're going to be looking at this paper about
logicity. It's all about this virtual city and how they're using it to push
the limits of AI.
Yeah, it's pretty wild stuff. Like imagine a city where AI is not just dodging
things, but actually like getting the whole concept of traffic laws and
interacting safely and smoothly with everything around it.
Yeah, it's not just like self-driving cars, right? It's about AI. They can
figure out any complex environment where there are rules and it needs to work
with other things in that environment. Exactly. So what's this
Neurosynvolic AI all about?
So it's often called NECI AI. Okay. It's trying to combine the strengths of
deep learning, which is really good at recognizing patterns with symbolic
reasoning, which is great at logic and rules. It's like, uh, it's kind of like
how humans use both intuition and experience and logical thinking when
we make decisions.
That makes sense. The paper mentions first order logic.
First order logic, yeah.
Is that how they're building this virtual city and its rules?
Yeah. So first order logic or FOL is like a set of building blocks for
creating rules. So it lets the researchers define the rules of the
city in a way that's flexible and powerful. And the best part is it uses
concepts that apply no matter what the city looks like or what kinds of things
are in it.
Okay. So instead of programming a ton of if then statements, they're
creating a system that can understand and use general rules.
Yeah. And it doesn't matter what situation it's in. Like teaching an AI,
the idea of yield rather than saying, if you see a red octagon and stop,
so then the AI can use that yield rule, even if it runs into some weird new
sign it's never seen.
So it's all about teaching it the logic behind the rules, not just memorizing
commands. I see why this is so cool. How do they even test if the AI is
learning these rules?
So they've got two main testing grounds inside logicity.
Okay.
Safe path following or SPF and visual action prediction or VAP.
Okay. With SPF, the AI has to navigate a long path through the city trying to
keep costs down and most importantly not break any rules.
Oh, wow.
Yeah. It's like a driver's test for AI.
Okay.
But in an environment that's always changing.
That's a good analogy. What about VAP?
VAP. That's all about prediction.
Yeah.
So the AI gets a single snapshot of the city and it has to predict what each
thing in that scene will do next based on where they are now, the rules of the
road and any other important info.
Oh, so it's like if you pause a busy intersection and try to guess what will
happen next, does the AI get a clear view of everything?
Nope.
Really?
And that's what makes VAP so interesting.
They actually added realistic noise to what the AI sees, like what we would see
in the real world. Stuff like, you know, a person mostly hidden behind a tree or a
car in the shadows. It has to deal with not having all the information, just like
we do sometimes.
Oh, well, that's an extra layer of difficulty.
Right.
So it's like the AI has to make smart guesses using what it can see and what it
knows about the city's rules.
Yeah.
So we've got our AI learning in this city taking on these challenges.
What were the results? Did the neuro symbolic AI actually do well?
Well, that's what we'll get into next.
The findings are pretty interesting, especially when you compare Nessie AI to
other approaches.
Ooh, exciting. Let's get into that.
So the results showed that in both SBF and VAP, these neuro symbolic AI
approaches, they did better than just deep learning, especially when things are
more complex.
So like mixing logic and learning really gives AI an advantage, huh?
But I bet there's still some challenges, right? I mean, these are pretty complicated
tasks.
Oh, for sure. One of the biggest hurdles was something called compositional
generalization. It's basically that the AI struggled when it had to adapt to
totally new combinations of agents, even if it already knew the basic rules.
So like it could understand right of way, but got confused if like a new kind of
vehicle showed up.
Exactly. It shows how hard it is for AI to take what it knows and use it in
totally new situations.
Kind of like if you learned chess, but then had to play with pieces that moved
differently.
Oh, yeah.
The basics are the same, but using them in a new context that takes a whole
other level of understanding.
That's a great way to put it. It makes me wonder, how did the AI do with the visual
noise in that VAP task?
I feel like that real world messiness would really mess things up, even if it
gets the rules.
Yeah, you're right. That was another big challenge. Telling the difference between
similar looking things, like a police car versus a regular car that was tough for
the AI when the images were blurry or hidden. It really struggled to use what
it knew when the visual data was messy.
Makes sense. So it's like it aced the written test, but the practical exam was
hard when things got visually complicated.
What about that comparison to large language models in humans?
The paper talked about testing them on a simpler version of the VAP task.
Right. They wanted to see how different AI approaches could do logical reasoning
without the extra difficulty of visual input.
So they took away the visual noise and just gave the AI and the humans text
descriptions of what the city looked like, and then asked them to predict what a
specific thing would do next.
Ah, so it's like giving them a written driving test to see if they get the
theory before putting them behind the wheel what they find.
So the large language models, even the advanced ones like GPT-4, they showed some promise,
but they still had a harder time with the tougher logical puzzles compared to people.
They often relied on common sense or were super cautious in their predictions.
They didn't quite get all the little details of Ligocity's rules.
It's interesting that even AI that's so good at language and information can trip up
when faced with complex reasoning. What about the humans? How they do on this logic test?
Humans did a lot better overall. Not surprising, right?
But even they made mistakes, especially when the rules got super complicated
and you needed a bunch of steps to figure things out.
It just shows that logical induction, even for us, can be pretty tough.
Yeah. It makes you realize how much we do without even thinking about it when we're
navigating everyday stuff. Something like understand the rules of the road is
actually a pretty amazing mental thing.
It is. And this opens up some cool research avenues.
Imagine combining those large language models, which are awesome at processing info and coming
up with ideas with a formal logic engine that can double check and refine those ideas.
It'd be like having a super smart brainstorming partner who also happens to be a logic genius.
Now that's a powerful combo. It's like those AI systems that are chess champs.
But instead of a game, they're mastering real-world logic.
But before we go too far down that road, I'm curious, what are your big takeaways from this
whole deep dive into logicity? What are the key points we should remember?
So what does it all mean? Where does logicity fit into the bigger picture of AI research?
Well, I think logicity is a really useful tool for researchers who are studying this
neurosymbolic AI. It gives them this rich environment that they can change up and use
to test how well AI can learn and use complex rules.
And it's way closer to the real world than a lot of other testing methods.
So it sounds like this could be useful for way more than just self-driving cars.
Exactly. The potential uses are huge. Like, think about using this type of AI to make traffic flow
better in cities. Or design smarter transportation systems. Or even build robots that are safer
and more adaptable so they can work better with us.
What about AI that can understand those super complicated legal contract?
Oh, yeah.
Or medical guidelines. There are so many areas where understanding and applying rules is super
important. Absolutely. If we can teach AI to understand something as crazy complicated as a city,
then using that same tech for other complex systems is like, it has enormous potential.
It's like we're giving AI a crash course in how human society works. That could totally
change how we design and interact with AI in so many ways.
Yeah, for sure. And that brings us to a really important point. This research isn't just about
building smarter AI. It's about building AI that gets the systems we've already created and can
work within them. From traffic laws to social norms to legal stuff to economic models,
these systems control so much of our lives.
So it's not just about intelligence, but about AI that can actually function in our world,
working with people.
Exactly. And that leads us to a question for you to think about. If we can teach AI the logic of
a whole city, what other complex systems could we use this for? What are the good things and the
bad things that could happen?
Those are some really interesting questions to think about. I'm already imagining how this AI
could change healthcare, education, and even how we manage the environment. There's so many
possibilities.
It is really exciting. But we do need to remember that logicity is still like a simplified version
of the real world. It's a big step though. It gives us a powerful tool to explore what AI
can do and to figure out what roadblocks we need to clear to build AI systems that are
really reliable and adaptable.
It's pretty mind blowing to think about all the ways this research could change things. But for
now, we want to hear from you. What areas do you think this type of AI could have the biggest impact
on?
Yeah. Let us know down in the comments.
We're really interested to hear your thoughts on this groundbreaking research. After all,
figuring out the future of AI is a conversation we all need to be part of.
Well, that wraps up our deep dive into logicity. Thanks for joining us on this exploration of
Neurosymbolic AI. Until next time, keep those AI brains buzzing.