Hey everyone and welcome back for another deep dive.
Today we're gonna be looking at something really interesting,
something that could change how we think about AI.
Yeah, this is a really cool paper.
It's all about making AI act more human, more like us.
Exactly.
We're diving into simulating human-like daily activities
with desire-driven autonomy.
And it's got some pretty fascinating ideas.
It really does.
I mean, think about it.
Yeah.
We're so used to AI that's basically task oriented, right?
Like tell it what to do.
Yeah.
And it does it.
Yeah.
But this paper, this is different.
It's about AI that's driven by its own internal motivations.
Almost like it has its own desires and needs.
It's giving AI its own personality in a way.
Yeah, and that's a huge shift.
Huge.
Because if you think about how we humans work,
we don't just go through life checking off a to-do list.
Yeah.
Our actions are driven by all sorts of internal factors,
our need, our desires, our moods.
Even just how we're feeling that day.
Right.
I might decide to skip the gym if I'm feeling tired.
Exactly.
Maybe your desire to just relax on the couch
overrides your desire to exercise.
And that's what's missing in most AI today.
They're great at following instructions,
but they don't really want anything.
Exactly.
They lack that intrinsic motivation.
Yeah.
And this is where this paper, this desire-driven autonomy
thing comes in.
D2A.
Right.
So how do they actually give an AI wants and needs?
Well, they've come up with this framework.
It's called D2A, which stands for Desire-Driven Autonomous
Agent.
And get this, it's actually inspired
by Maslow's Hierarchy of Needs.
Oh, wow.
Maslow's Hierarchy of Needs.
That takes me back.
Right.
That pyramid that shows all the different levels
of human needs.
Yeah.
From basic survival stuff all the way up to like self-actualization.
Exactly.
So they're essentially giving AI a virtual version
of that pyramid.
It's like a hierarchy of needs that the AI has to fulfill.
OK.
So they're not programming the AI with specific goals.
They're giving it this set of internal desires
that it has to satisfy.
Precisely.
And they focused on 11 key desire dimensions,
things like hunger, thirst, sleepiness, cleanliness,
comfort, even social connection,
and spiritual satisfaction.
OK.
I see.
So it's kind of like the AI has these internal meters
for each of these desires.
Yeah.
Like little gauges.
And it's constantly trying to keep those meters
in a reasonable range, right?
Exactly.
So if the AI's hunger meter gets too low,
it's going to start looking for food just like a real person
would.
So it's making decisions based on its internal state,
not just reacting to commands.
That's the big idea here.
That's wild.
OK.
So walk me through how this actually works.
How does the AI know what to do to satisfy these desires?
So there are two core modules to D2A that work together.
The first one is called the value system.
And think of it like the AI's internal dashboard.
The AI's internal dashboard got it.
It basically keeps track of all those desire meters
and tells the AI how it's feeling about each one.
OK.
So the value system is keeping tabs
on all those needs and wants.
Right.
What about the second module?
The second module is called the desire-driven planner.
And this is where the magic happens.
OK.
The desire-driven planner tell me more.
This is where the AI actually makes decisions
about what to do based on the information from the value
system.
Right.
It's like the AI is constantly checking its internal dashboard
and saying, OK, I'm feeling a bit hungry,
I also need some social interaction.
What's the best way to satisfy both of those desires right now?
It's like a constant internal negotiation,
weighing different desires.
That is fascinating.
So they're basically giving the AI a way
to make decisions for itself.
Exactly.
It's all about autonomy.
But how do you test this in the real world,
or at least in a simulated world?
Well, they created this simulated environment.
It's kind of like a virtual dollhouse.
A virtual dollhouse.
I like it.
And they have this AI agent named Alice, who
goes about her daily life in this house.
I'm picturing a little AI character running around,
trying to figure out what to do.
Exactly.
And Alice has to take care of all the same needs
that a real person would.
She needs to eat, sleep, stay clean, you name it.
And all her actions are driven by those 11 desired dimensions.
You got it.
The house is set up with all sorts of different rooms
and objects that she can interact with.
A kitchen, with food, a living room, with the TV,
you get the idea.
It's like playing the Sims, but with an AI
that's calling the shots.
Yeah.
OK, so how do they know if the AI is actually
doing a good job of satisfying Alice's desires?
Like, how do you measure that?
They use something called a dissatisfaction metric.
It basically tracks how far off Alice is
from feeling completely satisfied in each of her 11
desired dimensions.
So the lower the dissatisfaction score,
the happier and more content she is.
Exactly.
The goal is to keep that dissatisfaction score
as low as possible.
Makes sense.
But they also wanted to see how human-like Alice's behavior
was.
Yeah, because that's the ultimate goal, right?
To make AI that acts more like us.
So how do they measure that?
Well, they compared Alice to other AI agent models
that use different approaches.
They had React, BabyAGI, and LLMob,
all with their own strengths and weaknesses.
So it's like an AI showdown.
Exactly.
And they wanted to see which agent could live the most
realistic and fulfilling virtual life.
OK, I am hooked.
So spill the beans.
How did Alice stack up against the competition?
To really put them to the test, they
designed some specific experiments.
But I think we should dive into those after a quick break.
Sounds good to me.
Let's take a break, and we'll be right back with more
on this fascinating deep dive into desire-driven autonomy.
OK, so we left off talking about these AI showdowns.
How did they actually test these different AI agents?
All right.
So they had these two main experiments.
The first one, they called it the random eight steps experiment.
Random eight steps, catchy name.
Yeah, basically what they did was they let each AI
loose in the simulation for eight steps, eight actions.
Eight steps.
But the catch was they randomized the starting values
for all of Alice's desires.
So it's like each AI was waking up in a different mood.
Maybe one was starving, and another one was like,
I got to talk to someone.
Exactly.
And they wanted to see how well each AI could adapt
to these random desires and figure out how to satisfy them.
OK, so how did they measure how well they were doing?
With the dissatisfaction metric.
Right, right.
The lower the score, the better.
Exactly.
So after those eight steps, they crunched the numbers
and compared those dissatisfaction scores
across all the different AIs.
And drumroll, please.
D2A1.
I knew it.
Yeah, it consistently outperformed all the other models.
So giving Alice that internal set of desires
really made a difference.
It seems like it, yeah.
She was much better at figuring out what she needed
and actually doing something about it.
It's like she had this intuition,
the sense of what would make her feel better,
even when her desires were all over the place.
It's really cool to see it in action.
So that was the random eight steps.
What about the second experiment?
OK, so for this one, they called it the fixed 12 steps
experiment.
So the cold scaps, OK.
And in this one, instead of randomizing
those starting desires, they gave all the AIs,
including Alice, the same moderate levels of desire
across all 11 dimensions.
So everyone started off on equal footing.
Exactly.
But they also added a new element to this experiment.
They brought in real humans to play the simulation.
Whoa, humans are in the mix now.
Yeah, so they gave the human players
those same starting desire settings as the AI agents.
And they just said, OK, go about your day.
Choose actions that feel natural to you.
I see.
So they're comparing the AIs to how actual humans would
behave in the same situation.
Exactly.
And you know, the human players, as you'd expect,
they were the best at reducing their dissatisfaction scores.
Well, yeah, they have that human instinct, you know.
Right.
But D2A came really close.
It didn't quite match human level performance.
But it was definitely closer than any of the other AI models.
That's still super impressive, right?
Even though it's just a simulation,
D2A is showing that it can understand and respond
to those internal desires in a way that's
similar to how we do it.
Yeah.
And that's a big step towards making AI that
feels more relatable, more understandable.
OK, so we've got Alice who's powered by D2A.
And then we've got these other AI models,
React, BabyAGI, and LLMob.
Can we talk a little bit about how those other models actually
work?
Like what are their approaches?
And why didn't they perform as well in these experiments?
Sure.
So each one has its own way of making decisions.
Let's start with React.
OK, React.
It's based on goal reasoning.
Goal reasoning.
So it's good at planning things out.
Yeah.
It's great at logically working through tasks.
OK.
But it's missing that intrinsic motivation piece.
Right, that internal drive that D2A has.
Exactly.
It's like React knows how to do things,
but it doesn't really want to do them.
It's like, I could do this, but why bother?
Yeah, it's just missing that spark.
Then you have BabyAGI.
OK, BabyAGI, what's this deal?
So it's got a system for prioritizing tasks,
but it's really focused on external goals and instructions.
So it's got a super efficient task manager.
Exactly.
But again, it doesn't have that same sense
of internal needs and desires.
OK.
And then finally, there's LLMob.
LLMob, right.
Bring on LLMob.
So it tries to incorporate planning and motivation,
but it's still very much driven by external factors,
not really internal desires.
It's almost like it's following a script,
rather than making its own choices.
It's like an actor who's really good at playing a role,
but doesn't really understand the character's inner
motivations.
A perfect analogy.
And I think that highlights the key difference with D2A.
It's not about telling the AI what to do.
It's about giving it the tools to decide for itself,
to be truly autonomous.
And that's huge.
But I know there are always limitations
with any new research.
Of course.
What are some things that D2A still needs to work on?
Well, one of the main things is that the way
they've modeled desires in D2A, it's still pretty simplified.
Human desires are so complex, they're influenced
by our personal history, our culture,
our interactions with others.
They're trying to capture the entire human experience
in a few lines of code.
Exactly.
And then there's also the number of desired dimensions
they use.
Right.
They have those 11.
11, yeah.
And it's a good start, but it definitely
doesn't cover everything that motivates us.
There's always more to explore.
OK, so we've talked about how D2A works,
the experiments they ran, the limitations.
But what about those specific case studies
that they mentioned in the paper?
Can we dive into those now and see how this all played out
in practice?
Absolutely.
Let's see how Alice navigated those different situations
and what we can learn from that.
OK, case studies, let's get into it.
So where did they put Alice to the test?
They actually looked at two different environments,
indoor and outdoor.
OK, two environments.
Let's start with the indoor one.
All right.
So they gave Alice some specific desires.
They said she was moderately gluttonous
and extremely sociable.
So basically, she was hungry and wanted to hang out with people.
Yeah, basically, they wanted to see how D2A would handle
that combination.
OK.
So they gave her some initial, you know,
like they made her a little bit hungry, a little bit thirsty,
and really craving social interaction.
OK.
And then they just let D2A take over.
So what did she do?
Did she go straight for the snacks?
Actually, her first move was to take a shower.
Yeah, remember, she also had that desire for cleanliness?
Right, right.
So D2A kind of weighed all those desires together.
OK.
And decided that hygiene was the priority at that moment.
Interesting.
So it wasn't just about fulfilling one desire at a time.
It was about finding the best balance.
Exactly.
And after her shower, you know, she did make some breakfast,
got rid of that hunger.
Of course, got to eat.
But then, here's the cool part.
She decided to call a friend.
She wanted to chat.
And think about this.
They didn't program her with any specific instructions
on how to use the phone.
Wait, really?
Yeah, she had to figure that out on her own.
So she just like intuitively knew how to use a phone?
Well, she had that desire to connect with someone.
And based on her knowledge of the environment,
she kind of put two and two together.
Wow, that's impressive.
That's problem solving right there.
It really is.
OK, so indoor, she handled basic needs,
social interaction.
What about outdoors?
What did they do with her there?
OK, for the outdoor one, they put her at a big party
in Central Park.
A party in the park.
Sounds fun.
And for this one, they focused on her desires
for recognition and sense of control.
They said she was extremely reputation conscious
and possessive.
OK, so now she's got some bigger goals in mind.
It's not just about basic needs anymore.
Exactly.
So how do you think she tried to gain recognition
and control at this party?
Well, if she's reputation conscious,
I'm guessing she tried to make a good impression on people.
You got it.
She started by socializing, chatting with other party
goers, trying to stand out.
Then she joined a group discussion,
maybe trying to show off her knowledge a little bit.
Show off those smarts.
Exactly.
She even spent some time journaling,
which in the simulation, that helped
her feel more in control of her thoughts and emotions.
So she was really actively trying
to achieve those goals, not just passively attending
the party.
Right.
It's all about that agency, that drive.
So did it work?
Did she become the life of the party?
Well, maybe not quite the life of the party,
but the researchers observed that her actions were definitely
aligned with those desires for recognition and control.
So she was on the right track.
Definitely.
It was really interesting to see how her behavior changed
based on the context, the environment,
and those specific desires.
It's like watching a virtual character come to life.
It really is.
And it's exciting to think about where this could lead.
Yeah.
Where could this go?
What are the possibilities here?
I mean, imagine video game characters
that feel more real, AI assistants that actually
understand our needs, or even virtual companions that
could provide real emotional support.
That's amazing.
It's like giving AI a heart in a way.
It's about moving beyond just intelligence.
It's about giving them that spark of motivation, that desire
to connect and engage with the world around them.
Well, that's an incredibly fascinating deep dive
into this whole world of desire-driven autonomy, D2A.
It's a really exciting area of research.
Yeah.
Huge thanks to our expert for walking us through this paper.
This really thought-provoking stuff.
And to all our listeners out there,
keep exploring, keep asking questions,
and keep pushing the boundaries of what's
possible in this amazing world of AI.
We'll see you next time.