- One of the more interesting problems we grapple with,
it's not just how to deal with individual robots and humans,
but how to think of them on another scale.
To have a shift in perspective,
so that we're actually thinking about the humans
as a group and the robots as a group.
And we're teaching the robots to think
of themselves as a group.
- You got trained humans and less trained humans
and untrained humans, right?
And they all are going to behave differently
around these autonomous systems.
And so to me, it's a really fascinating case
of like really trying to put these people
and robots together to do good work, right?
And that is a major challenge.
That is an unsolved problem.
- It is definitely an unsolved problem.
- It is so unsolved. (laughs)
Yeah.
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- I'm Leila Takayama
and I'm a human robot interaction researcher.
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- I'm Chris Nicholson.
And I lead a team of reinforcement learning scientists
and software engineers at an AI company called Pathmind.
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You know, a lot of the wider conversation about this
poses kind of a false dichotomy.
It's either machines or it's humans.
When really the machines are an extension of us,
they are a force multiplier.
They simply allow us to do more work, get more done
and hopefully produce surpluses for society.
So that's the larger vision.
Robots need a lot of help.
Sometimes humans need help,
robots need a lot of help.
- Because they can't deal
with every single possible environment
that we stick them in.
They're not going to know all the SKU's
that are going to be on those shelves, right.
They're going to need to learn over time.
It's like learning on the job.
And that takes mentorship.
I mean, as you know, there's so much excitement
about autonomous vehicles these days, right?
Cars, I'm a lot more excited
about robots getting out in the human populated world.
So maybe even on a sidewalk, which is a little crazy, right?
'Cause the rules of the road are much more well-defined
than the rules of the sidewalk.
- I totally agree with that.
When I think about autonomous vehicles,
I think one of the hardest problems to solve
is what I call the parking lot problem.
Because a parking lot is the place where
you have the densest interaction with social signals.
- We've just started this new research project where
we're trying to get robots to navigate
through crowds of people.
And you can model the heck out of human crowds, right.
And try to predict how your robot should navigate
through those bodies of people.
But when you actually put a robot in a crowd,
what really happens, is it gets surrounded
and everyone wants to take selfies with it,
or they want to kick it.
We were very reactive to seeing these things in our spaces,
especially when they're new.
At some point they're not going to be novel, right.
They're going to be a little more mundane
and that would actually be a good thing, right.
'Cause we're just trying to get work done.
These are not entertainment robots and figuring
out how to make them more invisible
in use more mundane and more boring, more unremarkable.
That would be great.
- [Chris] And also I think the form factor
of the robots themselves will have a big impact
on how people perceive them, right.
Does it look like a cone with wheels that was sent
by the police department to surveil us?
Right?
Or does it look like somebody who might answer questions
if we have them?
- Yeah, that social and cultural context
matters so much, right.
And what kind of authority does the robot inherit?
All those things convey information
to the end users who haven't necessarily been involved
in deciding whether or not they want
that robot in their space.
- There's an interesting thing just going on
with the Roomba, where they're really small and low.
And if you know much about like relating to livestock
or wild animals, you know, that like one way to signal
that you're safe is to lay down on the ground.
Don't be large.
So, some of these low flying or low rolling robots
are already successfully con-
Especially Roombas, right.
Successfully conveying, I'm safe.
- And what's been really interesting
is seeing people dislike the newer ones
because the old ones are just bumbled around
have more personality.
But now that they're doing this much, you know,
cleaner more rigid motion,
we're attributing a different personality to them.
- It's the irony of optimization,
that you thought you wanted optimized
for efficiency and you ended
up like, making them less friendly in a way,
because they're no longer perceived as vulnerable.
That's interesting.
And it gets, it actually gets bad.
We see that in software development too.
Like, if you answer a seemingly hard question too quickly,
it raises suspicions, right.
With software.
So it can actually be helpful to sandbag a response
by a few seconds so that people have the perception,
that you had to think about it.
- That was brilliant.
Actually yes, the phone bots too, right.
And now there's these little like (imitates robot) sounds
that they'll play when the phone bot is trying to come
up with an answer for you, about say,
the flight that you're trying to rebook.
And it's, 'cause it's like trying, it's trying really
hard to help you, right? (laughs)
And that effort needs to be communicated.
You have to stand on the shoulders
of giants to get anywhere in this field, right.
It's pretty much impossible for any single person
to do everything.
And so I think open source has a critical role to play
in just learning as a society and as an industry.
- We're aiming for something hybrid.
We're aiming for something cooperative, where
those robots are a force multiplier
neither a drag on the organization
nor a menace to their human counterparts.
- You know a lot of the media that gets produced
around robots, tends to like to pit us against each other,
or people versus machines.
And robots are going to take over the world.
But in reality, right, when you spend time
with robots and AI systems, right.
They're tools.
And I think using them to empower people
to do better work, is a noble goal.
And one that we're still struggling to try to achieve.
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