(Applause)
NEO: As a species,
humans have mastered energy
to the level where it is,
for all practical purposes,
completely abundant.
200 years ago,
no one could have imagined a world
where energy was so accessible
that most people
would take it for granted.
If you had asked the smartest
person on Earth
whether we could one day summon light
with the flip of a switch,
they would have said it was impossible.
Even if the brightest minds
worked on it together for an eternity.
But today, it's just that easy.
Energy is everywhere.
All around us, all of the time.
Now what if I told you that the same
is about to happen with labor?
We are standing at the gates of a future
where the work needed
to build the products we use,
the services we rely on
and even the chores in our homes
will be as effortlessly accessible
as energy is today,
enabling you to explore new frontiers
and focus on what makes you truly human.
Thank you.
(Applause)
Bernt Børnich: Thank you, NEO.
You're the best.
It's an amazing machine, right?
Audience: Yeah.
(Applause)
BB: So I spent the last decade of my life
working on building
humanoid robots like NEO.
Robots that will hopefully
soon be able to do
almost anything that we could imagine.
Now whether this is
helping you with the dishes,
helping you do your laundry
or whether this is helping
your aging grandma,
there's never really been a time
better for robots.
We have an aging population
in need of help,
and we have a large labor shortage
across most of the global economy.
And there's much, much more.
But even more importantly,
to me, these robots,
they promise something greater
than just the ability
to solve the problems of today.
They can solve things
that we cannot do today.
They can give us back things like time.
And as these systems and AIs
now become both physical and agentic,
we can start to work towards a future
where we actually have
an abundance of labor.
We can start towards lifting humanity
out of this constant battle
over scarcity of resources,
and create a world where
everyone has what they need.
And I think that will, to some extent,
actually redefine
what it means to be human.
But since I'd say, around year 1400,
when Leonardo da Vinci
made "The Mechanical Man,"
that to me is kind of like the first
example of a humanoid robot,
these things have been mainly a thing
of science fiction, not reality.
But this is changing.
The robots, they're actually here.
And when I say here,
I don't necessarily mean in videos.
They're actually here in our homes.
At least if you work at 1X, where I work,
where we now have them in quite
a few homes throughout the company.
And already later this year,
I hope some of you guys
will have it in your home
and join us on this journey.
So that means NEO is now part
of my daily routine.
So it does some of the chores
around the house.
Some of this is autonomous.
Some of this is done
through remote operation
as it's learning.
And I talk to it.
I treat it kind of like a butler,
like a companion.
It's part of the family.
And I think it's actually
incredibly interesting
to also see how this social
dynamic develops,
because this is, of course,
incredibly useful and fun
to have it do stuff
I don't want to do around my home.
But it's also really fun
to see the beginning of like,
what will this relationship be
between man and machine
as these AIs become physical.
Now like I said,
the hardware is actually here.
It took us about a decade
of very hard work,
but also many people that came before us,
a lot of time to do
the foundational research
for us to now finally
be able to build a machine
that can do almost anything
that a human can do.
But it begs the big question, of course:
When will they be fully autonomous?
When will they actually
become truly intelligent?
And what is the path
that will actually take us there?
And I think this will be very
obvious in [retrospect].
They need to live and learn among us.
We actually need to take these machines,
and we need to adopt them.
We need to put them into our society
and let them learn just as we do.
So the general convention has been,
or general wisdom, that robots,
they're going to first
happen in factories.
So we're going to put
these robots into factories,
they're going to do the dull, repetitive,
dangerous tasks that they're good at.
And as they do these repetitive tasks,
they get better and better, right?
They get more intelligent.
And after some time,
we can put them into our home.
They will be able to do our laundry,
they will build our skyscrapers.
But this is actually categorically wrong.
And we know because
we actually tried that.
So back in 2022,
we took our previous generation
wheeled humanoid, Eve,
and we put it in industry.
And it actually went really well.
We solved a lot of kind
of narrow, specific tasks,
and it got really good
at them really fast.
And then after about 20 to 50 hours,
the robots, they just stopped learning.
And if you think about it,
it's not really rocket science.
Because if you’re doing the same
task over and over every day,
and it's the only thing you're doing,
you're not going to get very intelligent.
There's no information there.
And also you're going to generally become
very narrow-minded, right?
We don't like being narrow-minded.
And if you think about like,
what is a factory?
It is essentially a process that we design
to reduce diversity and variance.
You want your factory worker
to need as little information as possible
to be able to do the job
and get a high-quality,
repeatable product out.
And this is kind of the opposite
of what you need for intelligence.
You need diversity,
you need to challenge yourself.
You need to do new tasks every day
that you don't know how to do.
And there's a great parallel here
to the early days
of large language models.
So when we use these models today,
and they're getting really good,
we kind of forget where they started.
They started with a lot of people
trying to make very narrow models.
So if I take an example,
if you wanted to make a very good
writing assistant to write poetry,
then you would, of course train
on all of the best poetry in the world.
Make sense.
And then it wouldn't really work.
And when we started training these models
on all of the internet, right,
the complete diversity
of all human knowledge,
they started working.
They became kind of smart.
They started being able to,
to a certain extent, to reason.
And I'd say like,
understand to a certain extent,
what is the question you’re asking
and how should I answer.
And this is also how we humans learn.
We need a large amount of diversity
for us to be able to develop
into intelligent beings.
So why should it be different for robots?
And it really begs the question then:
What is the equivalent of the internet?
How do we find this kind of like
internet-level diversity of information
for our robots?
Well we come to the conclusion
that this is probably the home.
Now the home is this
beautiful, chaotic thing.
It's kind of like the messiness
that is being human.
And I want to take a small example here.
So think about a cup.
Now of course, there's
many cups in the world,
and you want to be able to figure out
how all of them work.
But even if you look at one specific cup,
it can be so many things.
Is it dirty? Is it clean?
It's kind of in the middle?
Is it on the table,
in the cabinet, on the floor?
It can even have a social context.
Someone's using the cup.
Someone's waiting for the cup.
Like, why is the cup even there?
And this is just a cup.
Now think about expanding this
out into everything
and every object and everything
going on in your home.
That's the kind of diversity
that we're talking about
to get to proper machine intelligence.
So like any good scientist, right,
we had this hypothesis,
and now we have to test it.
So in 2023, we brought our robots home.
And I had Eve in my house
for quite a while.
And it was, of course,
doing the standard things
like emptying the dishwasher,
but also bringing me a cup of tea
when I was enjoying playing
board games with my friends
or serving cupcakes
at my daughter's birthday party.
And pretty quickly,
it actually became quite clear
that this hypothesis
actually was the ground truth.
The home is this incredible,
diverse source of data
that lets us continue
to progress intelligence.
So we thought originally
that it was going to be this,
but actually it was this.
And let me show you guys now
how this actually works in practice.
Oh thank you NEO,
you’re doing a good job.
It's a bit noisy,
but hopefully you can still hear me.
What you see here now,
of course, is just a subset
of tasks that NEO can do.
And this is a mix of autonomy,
for things the robot is good at,
and some remote operation
where someone's guiding the robot
to basically do expert demonstrations
on how to do these tasks.
And as we have an increasing
number of these robots
throughout homes,
living among us and learning,
more and more of this becomes autonomous
until hopefully,
one day, all of this
will be fully autonomous.
And if you kind of
follow along in the field,
a natural question to ask
at this point would be:
Why doesn’t everyone do this?
If it's so obvious.
Well it actually turns out,
it’s incredibly hard
to make a robot that is safe among people.
So robots are traditionally
these quite stiff, high-energy --
you’re doing great, NEO,
you’re doing great.
They're this --
careful, I don’t want to get watered --
stiff machines that are
high-energy and dangerous.
And this is very different
from how NEO works.
NEO actually has tendons
that [get] pulled,
very loosely inspired by human muscle.
And this makes NEO
into a robot that is quiet, soft,
compliant, lightweight, safe,
and really able to live
among us and learn among us.
Let's see if he figures it out.
It's a hard one.
You can do it, NEO.
(Applause)
I said he's the best, right?
OK.
So this is still,
of course, incredibly early.
We're all the way
in the beginning of this journey.
But I do hope that, in not so long,
just like we take energy
for granted around us,
we will be able to take
labor around us for granted.
And we might soon
not even remember the day
where there wasn't always like,
a helping hand available
for anything we wanted to do.
But as these machines go
around in our society and learn,
to me this journey is about a lot more
than just you not having
to do your laundry.
It's about creating a future
where we actually have time
to focus on what matters to us as humans,
and getting rid of these constraints.
But also, it's an opportunity
to really have these machines
help us solve some of the outstanding
questions that we still have.
Like, can we have robots build robots?
Can we have robots build
data centers to progress AI?
Can we have robots that build chip fabs
to help us accelerate adoption of AI?
And I think it's getting pretty clear
that we can have all of these things.
But it goes even further than that.
I hope we can get a future
where we have humanoid robots like Neo
that is actually building
particle accelerators,
that is building labs.
We will have millions of robots
around in the world doing high-quality,
repetitive experiments in labs
and helping us progress science
at a pace that we have never seen before.
And I hope that in the future,
through this kind of like a symbiosis
between man and machine,
we can start trying to answer
some of the remaining
big unanswered questions
about the universe and our role here.
And I think if we can do that,
that will to some extent redefine
what it means to be human.
Thank you.
(Applause)