AI transcript
[MUSIC]
>> Hello, and welcome to the NVIDIA AI podcast.
I’m your host, Noah Kravitz.
One of the most fascinating,
dynamic, and visceral applications of deep learning,
machine learning, computer vision,
all these terms that we now put under
the AI umbrella is the world of robotics.
Robotics has been a vibrant space for years and years now,
but since the advent of large language models,
the interest in robotics,
the exposure it’s getting to the mainstream audience,
and certainly some of the ideas about how we can
communicate, train, and leverage robotics has expanded.
Here today to talk to us about the world of robotics,
specifically in the manufacturing industry is Ed Mer.
Ed is co-founder and CEO of Makina Labs,
and I am absolutely delighted to welcome Ed
onto the podcast.
This has been in the works for a little while now,
so I’m excited to hear all about what Makina Labs is up to,
and trust me, it’s some really, really cool stuff.
Ed, thank you so much for taking the time to
join the NVIDIA AI podcast, and welcome.
>> Thank you. Great to be here.
>> So let’s start at the beginning, if you will.
Tell us a little bit about what Makina Labs does,
maybe how it got started, and we’ll go from there.
>> Sounds good. So at Makina Labs,
we’re trying to, or we’re building the next generation
of manufacturing floors, and the main enabler is
artificial intelligence and robotics.
The main challenge that we’re trying to solve is
something that I learned in my past careers,
and it is that every time you have to build a physical part,
a part made out of the material,
a design that’s made out of physical material,
you pretty much have to build a factory around it.
There’s a lot of machinery and equipment that you need to
build that are specifically designed for that part,
for that geometry, for that material that part is
using that cannot easily change.
Every time you want to change the design and material,
they have to go change these machineries and
these toolings that you have to deploy in your shop.
So what we’re trying to do is,
can we build a truly software-defined factory?
A factory that can change its design,
can change its operation without having to change
its machinery, which is very expensive,
it takes a long time.
These challenges have been
prevalent in manufacturing for the past 100 years,
and I think right now we are at the point where
both robotics and AI are mature enough that we
can rethink this paradigm of how
manufacturing floors and shop floors have been built.
>> So when you mentioned learnings you picked up in
your previous jobs,
you were at SpaceX and also Relativity Space, is that right?
>> Great.
>> Were you working on manufacturing and
sort of the everything surrounding
the manufacturing process there as well?
>> Yes.
>> Got it.
>> Okay. So I have
little experience with manufacturing myself.
I’ve been living in the digital world as
a content creator for a while now,
but it makes sense to me that if you want to
create something physical,
you have to build the physical tooling
and such around it to create it,
and obviously, tearing that down,
reconfiguring would be an expensive process.
So when you talk about the idea of
a software-defined manufacturing floor,
what does that mean?
Because obviously, I would think,
we’re not able to turn zeros and
ones into physical output as such.
So how does that work? What does that mean?
>> Yeah, no, absolutely.
So I have a little bit of
a kind of an interesting, not interesting,
maybe a more hybrid background.
So my education-
>> I bet it’s interesting, but we’ll see.
>> Yeah. Appreciate it.
My education was mostly focused around robotics and software.
When I went to school,
academically, did computer engineering,
and then focused more specifically
toward machine learning and
protocol modeling and AI.
I spend early days in my careers at
companies like Google and Microsoft.
But always had it been for manufacturing since I was a kid.
I did a lot of welding and
carpentry when I was in school.
So I always wanted to be able to combine and bring
this world of software and robotics into manufacturing.
So when I went to SpaceX,
that’s when I started to learn how tough it is
to manufacture parts.
So to illustrate that,
I’ll give you an example from one of
the projects we were working on in SpaceX around
2010-2012 timeframe, which is Falcon 9.
Once we decided on the diameter of the Falcon 9 rocket,
a lot of tooling, a lot of equipment in
the factory were configured for that diameter, right?
Meaning that, for example,
the tooling that you used to build
the tanks for that rocket are all fixed at that diameter.
The moment you want to go get a fatter rocket,
then you want that because a fatter rocket means
you can put more fuel in it,
it can go to higher orbits,
but it’s just not an easy change.
It means hundreds of millions of dollars investment
in the factory to change that.
So actually, if you follow Falcon 9
throughout its life or just Falcon family,
you will see that the rocket kept getting
taller but could never get larger in diameter.
As a matter of fact,
if you look at SpaceX in the past 20-something years
that the company has existed,
they have two rocket families that they have developed,
actually one and a half.
So there’s a Falcon family,
and there is a Starship family,
which is significantly larger rocket
and still hasn’t become productionized yet.
So one and a half, that’s why I call it one and a half.
In order to build Starship,
they had to go start from scratch,
new facility, but a lot of new tooling out in Texas.
So that’s what I mean when we say
manufacturing is very hardware-specific.
Now, there are technologies that have been
developed in the past few decades
that allows us to slightly move away from that.
3D printing is one of them.
That’s why I’ve moved from SpaceX to Relativity Space,
and the goal at Relativity Space is,
can we 3D print as much of the rocket as we can
to get rid of this limitation,
a limitation where we cannot change
the design of the rocket if you want to.
So over there, I was in charge of a team
that was building a very large and low-metal 3D printing.
You could print structures that were like 15, 20 feet wide
and 20, 30 feet in length.
And this was basically a robotic arm
with a welder attached to it
that was welding layer by layer,
basically what 3D printing is,
layer by layer a very complex structure
that could be very large.
So for the first time,
techniques like 3D printing kind of give this promise of,
now you can actually turn a design
into something that incrementally can build a geometry,
and it’s not tied by a specific tooling.
The challenge with 3D printing
was that there’s a lot of geometries
that are still not accessible to it.
Either physically not possible to print it,
that’s a lot of physical challenges,
or economically it’s just not feasible, right?
And that’s kind of gave way toward the thinking around
how do we build a little bit more overarching automation
that can do different types of processes,
different types of material,
and it’s not a specific to just maybe 3D printing
or machining or one process.
It can do different types of manufacturing processes
that need it in the shop floors
without having to be tool specific.
And that was the genesis of Machina.
And the core idea is to answer your question,
like how is this gonna be possible?
The core idea is, we used to have this flexibility, right?
If you go a couple of centuries back,
manufacturing used to be arts and crafts,
people actually manufactured things.
And they could one day you would go to a blacksmith
and you say, okay, build me a sword,
and they will start from a raw material,
use their hammer and apply it incrementally
to that material.
And they had a creative mind,
so they could apply different set up steps
and be creative with it to get it to a sword.
And then the day after that, you could go say,
well, can you build me a sheet?
And they would start from a flat sheet of metal
and then they apply the same hammer,
but in a different way.
And it will give you a shield, right?
Out of a sheet metal.
So they were super flexible.
The challenge was they were not scalable.
So with the current manufacturing paradigm,
we kind of traded that flexibility for throughput.
But I think now if we can replicate what a craftsman does
in an automated fashion,
and then we can maybe get the best of both worlds.
We can get the flexibility that a craftsman had,
but also scale it.
And that’s basically what we’re doing at Machina.
We’re building what we call a robo craftsman.
And the core components is,
can you keep the replicate the dexterity of the craftsman,
which is the robotic comes into play?
But more importantly,
can you replicate what happens in the mind of the craftsman?
You know, how do they come up with a set of procedures
and steps using the similar, very simple tools
to get to a part that is accurate?
And then because it’s robotic system,
you can easily scale it.
– So I want to ask you to dig into that a little bit.
But before I do,
are there particular industries,
size and types of projects or other parameters
that define what Machina is working on,
and then also what you are not working on,
is it specific to, you know, are you building,
are you building robotic blacksmiths for that matter?
Or are they aviation industry, different industries?
What’s sort of that end of the approach?
And then maybe we can dig into some of the technical bits
about how you’re doing it.
– Yes, so the robotic craftsman cell that we build,
this is a robotic system, we call it a cell.
It’s actually industry-agnostic, right?
You could build parts for aerospace,
you can build part for automotive,
and that’s the beauty of it, right?
Like in the morning, you can do automotive parts,
in the afternoon, you can do airspace parts.
But in order to build a good business,
we always want to start from somewhere
that we are a very good fit,
based on the current economic conditions.
Develop it there,
and then from there expand to other verticals.
So the focus that we have today
is mostly on airspace defense.
We have some focus on automotive,
but dominantly our business comes from airspace defense.
And the reason for that is that, you know,
the traditional factories are very good
at producing the same thing over and over again.
So we don’t want to necessarily start competing with them
on making the same thing over and over again.
We want to compete in areas where design is constantly changing,
the parts are constantly changing,
and what we call is a very high-mix environment.
And that’s where airspace and defense is a very good fit.
And they’re also traditionally good adopters of new technology,
compared to maybe some of the other industries
that have lower margins and cannot necessarily risk,
you know, kind of testing a new technology.
So airspace defense is where we start,
because of kind of like market factors
and voter market kind of considerations.
But the goal is that this can apply
to any vertical in the future.
Yeah, it makes sense.
I should ask, when was Muck and Affounded?
Yeah, so we started the company in 2019.
Okay.
So the company is right now like four years old,
four or five years old.
But yeah, but the team comes from, you know,
manufacturing background, you know,
my co-founder Bob is a material scientist.
He comes from aerospace and automotive in the past.
Lots of space, six alumni,
some of the folks from relativity space.
So a lot of people who have been into agile manufacturing space
and robotic space.
And then we started in 2019.
And then I think we have our first manufacturing cell in 2020.
Cool.
And in the interest of full transparency,
this would be a good time for me to mention
that NVIDIA is actually an investor in Muck and Affounded Labs.
All right, so let’s dig into it a little bit
from whatever angle and to whatever extent you’d like to add.
I guess I have sort of two questions
and you can pick the one that makes more sense.
One is kind of how does it work in terms of
what does your manufacturing floor look like
or the best that you can describe that over the radio?
So to speak and kind of, you know,
how have you built the cells to be able to accommodate
this mix of different manufacturing tasks?
And then the other question I have, of course,
is how has and is AI playing a role in informing, you know,
and I’m hoping that we get into sort of the creative mind
of the, what was the term, robo-manufacturer?
No, robo-craftsman.
Robo-craftsman, thank you.
I knew it was a little warmer of a term than manufacturer.
So where should we start to dig in a little more here?
Yeah, no, I think starting from the kind of the layout
of the floor has a good start.
It’s because it is different
than traditional manufacturing floors, right?
We have a facility, we have a couple of facilities here
in LA around 110,000 square feet now.
And it’s very different than, you know,
what you would see, for example,
if you go to a automotive factory.
The traditional paradigm of manufacturing, like I said,
is very much has been focused
on making the same thing over and over again.
And the way we’ve been able to achieve that
is definitely because of a invention
that actually we did in America done by Henry Ford
in early 19th century called assembly line, right?
Where you have a product moving in a linear fashion
in the factory and each station or along its way,
people are just basically installing different things
or doing different manufacturing operations on it.
So if you go into a, you know,
high volume factories of today,
you will kind of see that assembly line flow
where, you know, material comes in from one end,
they start getting put together on the assembly line.
Each operation is getting done at each station.
And again, you get a, you get a call
or whatever product that you get coming out of it.
And for my sort of non-expert point of view,
the thing that I always think of is
each station is doing exactly the same thing
or more or less exactly the same thing
over and over and over again.
– Yes, and that’s how we’ve been,
we have been achieved to get very high throughput.
But then the Achilles heel is that, you know,
you cannot change.
The moment you want to change it,
basically you have to overhaul this whole factory, right?
So that’s why it becomes very expensive.
So our paradigm kind of like flips that a little bit
on its head.
And it says, okay, let’s have these, what we call cells.
And these cells are enabled by robots
and each robot can be programmed
to do a different operation.
And now we’re decoupling logistics also
from the manufacturing.
Meaning that now you have a facility
that actually more looks like a data center
for folks who are familiar with the compute world,
where you have basically the same way in a data center,
you have a whole bunch of computers
and you can program these computers
and these computers are connected together.
You can program these computers
to do different types of operations through software.
That’s the same kind of paradigm we have in our shop floor.
There is a ray of manufacturing cells.
These cells are robotic cells
and can be configured to do different operations
and different parts.
And then through a kind of centralized system,
you can basically program what each cell does.
Maybe one cell forms a hood for a car, another cell,
maybe it’s welding it, another cell is trimming it.
And the logistics is completely decoupled
because we want to get this flexibility.
Now there’s a whole bunch of other benefits as well, right?
Like not only you get flexibility
but you get kind of rolling updates.
You can kind of like, you can bring down a cell
and the whole manufacturing doesn’t need to get overhauled
as it is in the assembly line, right?
So there’s a whole bunch of other benefits
that come into play.
But yeah, our shop floors look very different
than what you would see in a, let’s say a car factory today.
When you’re talking about the robotic cells,
the robots themselves, are they arms?
Do they have different physical forms,
depending on the task and how much does
or doesn’t the physical form of the robot itself
to call it, the RoboCraftsman.
How much does that dictate?
I’m going back to your space example
about the diameter of the rock being fixed
and imagining, well, there’s got to be some constraint
on what one of these cells just physically can’t do.
Yeah, yeah.
So this has actually have been kind of like
very big subject to debate within our company
since early days, right?
Like how do we make these cells as agile
and as flexible as possible?
So there’s I think two considerations, right?
One is the size of the cell
and the other one is what are the components?
If you look at the industry today,
there are multiple options we have had.
Like, you know, we can go very simple automation.
A lot of people can think of like gantries and systems
that are kind of basically XYZ movements
that you can have in them.
So that’s very simple system.
And then you can go all the way
to a very complicated robotic system like humanoid, right?
Where they have a lot of degrees of freedom
and they can do a lot of things.
But then the downside is as a system gets more complicated,
it’s harder to manufacture.
The accuracies maybe is not as good.
That the amount of forces they can apply is not as good.
Versus on one end, you have very simple system,
can do a lot of accurate stuff on the gantry side,
can apply a lot of force.
And then you have on the other end,
very complex system, let’s say humanoid,
which companies like figure and others are trying to do.
And you get a lot of flexibility,
but maybe you lose precision, accuracy,
and the amount of load they can apply.
So we kind of chose to be somewhere in the middle.
So we use robotic arms.
We use industrial robotic arms
to kind of have the benefit of both worlds.
Get enough flexibility in our manufacturing selves,
because we can kind of replicate almost the same dexterity
as a human, I would say Saxex or seven axis robotic arms.
But then at the same time,
you’re using a kind of slightly commoditized system.
So we don’t have to build a wheel from scratch.
So robotic arms already have existed for a few decades.
There are multiple vendors that are offering the same thing.
So it’s more commoditized,
but then it still gets us that benefit of,
so it has flexibility,
but it has the benefit of applying a lot of force.
Still, it’s still very accurate.
And we can kind of get rid of a lot of its limitations
through our software stack.
So use robotic arms,
but ideally you might want to have multiple configuration
of these robotic arms, right?
The same way in a data center,
you have one system that has X amount of RAM
and this type of a CPU,
another system might have much larger RAM,
another system might have a GPU,
but hopefully we can only have like
few five, six different configurations.
And then what we do is five, six different configurations,
we basically can do all kinds of different types of parts.
Right now, for example, we have very strong robots
that can do very thicker material.
We have thinner robots that are slightly more accurate,
weaker robots that are slightly more accurate,
but then they cannot apply as much force.
So we have few different configurations,
but together pretty much can get,
do different types of geometries,
maybe 80, 90% of geometries out there is accessible to us
with different configuration, different size.
– I’m speaking with Ed Mer.
Ed is the co-founder and CEO of Machina Labs,
who as Ed has been detailing,
is revolutionizing the manufacturing industry
by combining robotics with AI and a new approach
to what the manufacturing floor itself looks like,
how it can be reconfigured, what it’s all about.
So let’s talk now about how you get the robotic cells
to do what you want them to do.
And maybe even past that,
I’m curious when you talk about the creativity aspect
and combining that sort of old world human world,
there’s still artisans out there making things,
putting that into this robotic process,
what that’s all about.
– Yeah, so ideally what is the system that you want?
And you want a system that actually works
like a robotic craftsman or a true craftsman,
right, you can go to the system and say,
“Hey, here’s a design.
“Here’s a design of a part that I want.”
Basically input design intent.
And then the system can come up
with how it will direct its kinematics,
basically robotic arms, to do the right set of operation,
pick up the right tool, apply the right way to the material,
and then get you the right part into it, right?
So that’s the ideal solution that we want.
Today, those steps are done with a lot of experts, right?
So there’s a expert designer, get that look at the cat,
maybe it modifies the cat or the design intent
to get to something that’s manufacturable.
It goes back and forth maybe with the person
who brought the design intent to figure out,
okay, what are compromises you can make
to make this manufacturable?
Then there’s another expert that turns the finalized,
the negotiated design into a set of steps
that the robotic system can do,
and then programs that robot to do those steps.
And at each point in time, if the robot is not doing well,
maybe we do a QC and say, okay,
the robot is not doing the right thing,
we have to iterate again,
and then kind of update the robotic instructions,
and then see if we can get to a better part.
And then in the end, you have to do some kind of a QC.
The part comes out, somebody looks at it and say,
okay, does it actually toward the specs that we want?
Now, these are the steps that are currently done by experts.
So what we want is kind of combine all of those
into an intelligent system.
Now, what you see has been done with chat GPs and others,
you would say, oh, okay, yeah, it seems like
we are very close to be able to do these things.
Like now we have these robotic system
that I can ask it to do something that’s very complex,
and it will give you an answer.
There are news now that latest iteration of training
of these models is as smart as maybe a PhD, right?
So we have these very expert systems
that can reason through with AI through these steps.
The main challenge we are facing is that,
these systems that you see like chat GPD and others
have been trained on the data
that has been publicly available, right?
You can use internet to train them.
With manufacturing data, the data has not
been traditionally available.
So we have that extra challenge is that, okay, yes,
seems like the models are capable enough
to be able to decipher and turn the design into steps
that the robot needs to do,
but we don’t have enough data or available data
to train things on them.
So the extra additional complexity for us was,
we need to build systems that can generate data
and incrementally get better.
So we need to start with systems
that use heuristic methods first, right?
Non-model is necessarily to generate a lot of data.
And then we use the data to kind of build models
and incrementally improve its capabilities over time
to get to a point where we have that robotic craftsman.
You can literally put the design intent,
input the design intent and comes out the other
and actual part that these robotic systems are building.
– So do you generate that data in a simulation?
How did you go about building the data sets?
– Very good question.
So were we started?
– It’s always the question and no matter who, right?
Talking to you, talking to somebody
about wildlife conservation and tracking animals,
it’s always about the data.
– Yes, yes.
So here’s the challenge, right?
Simulation is a good way to create the synthetic data, right?
The challenge is even our simulations today
are not fast enough for our specific processes.
So if you look at, for example,
in some of the other robotic tasks
where you do manipulation, like you move stuff around,
then you have simulations that are fast enough, right?
They call it in these robotic gyms, right?
Where the robots basically can do
a whole bunch of different things
and kind of explore the space and kind of get training data.
Because the physics, the kinematic physics
is actually pretty simple, right, to simulate.
The challenge with our process
is that we are simulating a physical phenomenon
that’s much more complicated.
You know, when we are forming these sheet of metals,
we have to simulate the deformation.
So plastic and elastic deformation,
we need to simulate friction.
We need to simulate material cohesion.
There’s a whole bunch of things.
We need to simulate tear in there.
– Yeah, yeah.
– So when we initially, we started to do some of this work,
for example, like try to simulate it.
We realized that actually,
if we properly want to simulate the environment
and our processes, it’s actually very expensive
and it takes a long time.
A part that would take for us to form with the robots
for 15 minutes at the time
on the differential equation-based simulations,
we call it finite element analysis models.
It would take one week on 27 cores of a CPU machine, right?
So it’s like, okay, it’s cheaper maybe
to just run it in real life.
– So just capture the data, yeah.
– Capture the data.
But we took both approach.
So we said, okay, let’s build systems
that we can do a lot of trials in real life
and make sure we build them in a way
that we can capture the data,
a state of this process every four milliseconds.
That’s what we’re doing.
So we captured terabytes and terabytes of data
with that over the past four, five years.
We’ve built thousands and thousands of parts.
But also we are working on making faster simulations
using GPUs, right?
Can we actually expedite the speed of simulation
so that we can also augment it with data
that comes from a synthetic world?
– So we have to do both.
But yes, it has been a huge challenge.
So these are the two challenges that are kind of ahead of us
that maybe other companies,
like the ones that are working on natural language models,
I don’t have because internet is giving them,
user-generated data for years.
– Yeah, no, you mentioned QC and it’s stuck in my head.
I wouldn’t want the robotic QC craftsman, so to speak,
hallucinating based off of at least the stuff
I read on the internet.
Let’s put it that way.
So you mentioned the data and the training
and then the speed of everything
being kind of two big hurdles
that you’ve been working to overcome.
– There are other either hurdles you’ve overcome
or perhaps just moments along the way
that were surprising or just kind of stand out
as kind of like these big milestones
in the development of everything Muckin’ The Labs is doing.
And then sort of the follow-up to that
is I guess beyond the training and the speed,
which obviously huge.
If you look ahead to the next couple of years
or whatever the best timeframe is,
maybe what are some of the things
that you and other robotics and manufacturing companies
are trying to clear to get to sort of the next stage
of your evolution?
– Yeah, so there’s an interesting challenge
when you want to generate your data,
which has been probably the biggest challenge
for a robotic company.
I remember there was a podcast from Ilya
talking about why open AI dropped a robotic arm
back in the day, right?
And the main challenge is that, yes, you say,
“Okay, I have to generate my own data.”
But you’re generating data with a very complex
physical system.
These robots can break and they might need maintenance.
So you almost have to figure out a way
to be very good at operation, right?
You need to be able to run a large fleet of robots
very smoothly to generate data.
And that actually has been one of the main challenges,
like I said, even open AI had at the time,
where we’re like, I don’t know if we are in it,
like we don’t have the right expertise
to create this operational rigor
to operate these robotic cells.
Now, they were funded by billions, actually,
of kind of they could go and dedicate themselves to do this.
We also don’t have the luxury of that.
We need to create, we’re venture-funded business,
obviously we have a good amount of funding,
but we need to also generate results for our customers.
So I think one of the biggest challenges for us was,
in order to enable this AI-powered world,
we need to actually become a very good operators
and create value for the customers fast.
So a lot of challenges that we had in early days
was like, how do we create a robotic facility
that can work around the clock and generate data
and kind of resolving a lot of those challenges?
So that’s been one big area of work for us.
And the other piece, I think,
kind of like maybe a little bit away from technical work
is because you are operationally heavy company,
you also need to deal with a lot of people.
One challenge that people–
How big is Makada?
How many people?
Mostly now 70 people, but across many skill sets, right?
We have technicians all the way to robotic engineers,
to AI engineers, to software engineers, material scientists.
And if you traditionally want to build
such a multidisciplinary team, you have to balloon up, right?
You have to create all these different expertise
that kind of work together.
So we have to also figure out a way,
how do we not balloon up
while we operate a huge operational facility
and then generate data while we also maintain
a rigor and expertise in the fields like AI and others.
So I think the biggest challenge for us
has been kind of navigating those waters.
As a technologist, you always underestimate
how important the people factor is.
And I think that’s something that I kind of,
I kind of have learned over the time
that it’s probably the most important factor, right?
A lot of technological developments are already there,
but having a team that is excited
and can operate efficiently
has been one of the biggest challenges here.
Absolutely, yeah.
So to put you on the spot here a little bit,
how do you see manufacturing changing,
because of AI and the things that we’re talking about?
And as we go forward, I mean, five years, 10 years,
20 years down the line,
is all the manufacturing gonna be,
look very different and have this sort of flexible,
scalable, reconfigurable type of design
that Makana has and is continuing to build.
Are we gonna have to sort of knock down or gut
the existing manufacturing facilities
and rebuild them with this in mind?
And then this may be out of your wheelhouse,
so please feel free to demure on this,
but do you see these types of things coming to,
sort of consumer and household robots going forward
with every a time where I’ll be able to have my home robot,
build me a new desk for that, whatever it may be.
So it’s an interesting time right now, right?
We are in a situation where,
I think there’s a lot of tailwinds
for changing manufacturing.
As a country, we develop a lot of previous manufacturing
methods in late 19th century and 20th century.
As we develop these technologies,
but then over time,
as the cost of labor went high in United States,
we started kinda moving these things into other countries.
So a lot of manufacturing moved out of United States,
but now we’re at the point where we’re realizing that,
oh maybe that was not such a great idea,
because now we have dependency
on these large centers of manufacturing
that might not necessarily align with our values, right?
There might be a little bit more authoritarian
or they might just not wanna have the same interest
as we do.
So now we’re thinking about,
okay, we need to maybe be a little bit more self-sufficient.
The challenge is that the core technologies
that are underlying these manufacturing techniques
does not allow them to come back to the United States.
We talked about a factory right now
needs to be built for every part.
So what does that mean?
That means that that factory needs to be building
a lot of the same part for the rest of the world
before it’s economical.
So it’s very hard to replicate that same factory
in a smaller version and still be competitive
in United States.
The nature of the technology
lends itself to centralization.
So we’re kind of finding the nature
of the technology at this point.
It’s not just like with gusto,
we can bring manufacturing back in the United States.
Maybe some of it we can,
but for the most part, unless the technology doesn’t change,
I don’t think we can be able to bring manufacturing back.
But that being said, like I said, there’s a lot of tailwind.
People wanna bring it back
because now it’s becoming a existential threat.
So I think because of these tailwinds,
there’s gonna be a lot of changes
that gonna happen in manufacturing.
And automation is one of those biggest things
that’s gonna change, right?
In order for manufacturing to come back to the United States,
it’s not gonna look like what we had in ’60s and ’50s.
It’s gonna be the new paradigm of manufacturing
that requires less labor
or maybe requires a more sophisticated labor
with higher productivity than we had before.
So automation, flexible manufacturing,
easy to reconfigure shop floors
are gonna be big part of bringing manufacturing
back to the United States.
You look at some of the towns in Midwest
that used to be manufacturing towns,
you go there as it goes downtown.
The whole economy around that factory died
because that factory,
the product it was making became obsolete
and it was not flexible enough to change, it died.
So this will change.
Now the question is how fast?
That’s a harder one to predict
because I think there’s a lot of geopolitical parameters there.
Interesting times, as you said.
You enter a conflict with China,
that will change very fast.
If you don’t, maybe it’s gonna be a little bit more paced,
but yes, it’ll definitely change.
Timeline is tougher to predict.
Now, will we have these systems in our homes?
I think that timeline is a slightly higher, longer.
There are people predicting at some point,
everybody’s gonna have a human or robots
that’s gonna help them with things.
I think the lower hanging fruit is getting robots
to use AI and automation in factories
because we already have figured out
a lot of other stuff around these robots
and we have a very good supply chain around them.
But I do see a future where we’re gonna have
human or robots in every home, everybody will own one.
Maybe a little bit longer.
It’s harder to predict anything beyond 10 years,
but I would put that in more 20, 25 years out there.
Fair enough.
For folks listening who might be aspiring entrepreneurs
in the manufacturing or robotics automation space
or maybe studying and interested in robotics and automation,
but not quite sure what path to take,
what advice would you give either
from sort of the technical side
or you obviously have an entrepreneurial background
and spirit yourself here.
So like the point you made earlier
about the people being at the core of it
and sometimes that being the harder thing
for technologists is a salient one,
but what would you give us kind of advice?
I think one thing that has changed since I came out of school
is we need way more multidisciplinary people.
When I came from a family that valued education a lot,
my mom was a teacher, my dad has his PhD,
so it was very focused on like go to school,
get expert in one area and that’s gonna be your thing.
I think that paradigm slightly is breaking
where we need people that can connect different components
more, we always gonna need experts,
but we also need more people
that maybe they know mechanical engineering,
but they also know software, they also know robotic,
they also maybe know a little bit of material science
because we have these kind of breakthrough technologies
like AI and we need people who can connect the dots
between multiple disciplines and build something new.
So that is becoming more and more important.
So I would suggest for people
who are going to school now, get exposure,
maybe become an expert in one area,
but get as much as exposure as you can
in other fields of science and engineering,
that makes you a much more attractive candidate
for the companies of next generation companies, right?
So that would be one main area of say advice I have
for people who wanna be in the technical field.
On the entrepreneurial side,
I think it is becoming more and more important
to build mission-driven companies.
There used to be a time where there was a lot of
kind of incremental improvement companies
that you can build, that the system’s already there.
You just wanna make some incremental improvement.
I think with AI, now the shifts
are gonna be more fundamental.
There’s gonna be always incremental thing,
but incremental things are just easier to build.
So I don’t know if companies are necessarily
gonna be well equipped, startups well equipped to do that.
As a startup, you wanna kind of go after
something more fundamental.
And for that, you need more kind of mission-driven companies.
So find something that you’re really excited about.
You become a big believer that that change needs to happen
and go after that.
And I think there gonna be less companies
that are just gonna optimize this and that
because it’s just easier to do those things
for the companies themselves,
for the larger companies themselves.
– Sounds like sage advice to me.
Edmere, for folks listening who want to learn more
about Machina Labs, your latest innovations,
what you’re up to, perhaps more technical-oriented
research papers and blogs and stuff like that,
where can listeners go online to learn more?
– Yeah, obviously our website,
MachinaLabs.ai is a good place,
but also we’re very active on social media.
LinkedIn, I think we put both even technical
and commercial milestones on it very often.
Twitter as well, and Instagram.
Yeah, so I think follow us on those channels.
I actually, very openly, one of the main kind of philosophies
we have on Machina is like build out into public.
So we actually share even our technical challenges online
and people respond to it and we are very open.
So if you follow, I think even for the technical folks,
you will see a lot of interesting kind of content
that we put out there that they can contribute.
– Fantastic.
Ed, thank you so much for taking the time to chat with us.
I could binge at your ear all day
or ask you questions all day.
I should say I’m listening to you talk about it.
It’s fascinating, fascinating stuff.
And as you said, interesting times we live in
and it’s probably only gonna evolve faster than it has been.
So I look forward to keeping an eye
on what Machina Labs is up to
and maybe we can talk again down the line.
– I would love that, thanks though.
– Thank you.
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>> Hello, and welcome to the NVIDIA AI podcast.
I’m your host, Noah Kravitz.
One of the most fascinating,
dynamic, and visceral applications of deep learning,
machine learning, computer vision,
all these terms that we now put under
the AI umbrella is the world of robotics.
Robotics has been a vibrant space for years and years now,
but since the advent of large language models,
the interest in robotics,
the exposure it’s getting to the mainstream audience,
and certainly some of the ideas about how we can
communicate, train, and leverage robotics has expanded.
Here today to talk to us about the world of robotics,
specifically in the manufacturing industry is Ed Mer.
Ed is co-founder and CEO of Makina Labs,
and I am absolutely delighted to welcome Ed
onto the podcast.
This has been in the works for a little while now,
so I’m excited to hear all about what Makina Labs is up to,
and trust me, it’s some really, really cool stuff.
Ed, thank you so much for taking the time to
join the NVIDIA AI podcast, and welcome.
>> Thank you. Great to be here.
>> So let’s start at the beginning, if you will.
Tell us a little bit about what Makina Labs does,
maybe how it got started, and we’ll go from there.
>> Sounds good. So at Makina Labs,
we’re trying to, or we’re building the next generation
of manufacturing floors, and the main enabler is
artificial intelligence and robotics.
The main challenge that we’re trying to solve is
something that I learned in my past careers,
and it is that every time you have to build a physical part,
a part made out of the material,
a design that’s made out of physical material,
you pretty much have to build a factory around it.
There’s a lot of machinery and equipment that you need to
build that are specifically designed for that part,
for that geometry, for that material that part is
using that cannot easily change.
Every time you want to change the design and material,
they have to go change these machineries and
these toolings that you have to deploy in your shop.
So what we’re trying to do is,
can we build a truly software-defined factory?
A factory that can change its design,
can change its operation without having to change
its machinery, which is very expensive,
it takes a long time.
These challenges have been
prevalent in manufacturing for the past 100 years,
and I think right now we are at the point where
both robotics and AI are mature enough that we
can rethink this paradigm of how
manufacturing floors and shop floors have been built.
>> So when you mentioned learnings you picked up in
your previous jobs,
you were at SpaceX and also Relativity Space, is that right?
>> Great.
>> Were you working on manufacturing and
sort of the everything surrounding
the manufacturing process there as well?
>> Yes.
>> Got it.
>> Okay. So I have
little experience with manufacturing myself.
I’ve been living in the digital world as
a content creator for a while now,
but it makes sense to me that if you want to
create something physical,
you have to build the physical tooling
and such around it to create it,
and obviously, tearing that down,
reconfiguring would be an expensive process.
So when you talk about the idea of
a software-defined manufacturing floor,
what does that mean?
Because obviously, I would think,
we’re not able to turn zeros and
ones into physical output as such.
So how does that work? What does that mean?
>> Yeah, no, absolutely.
So I have a little bit of
a kind of an interesting, not interesting,
maybe a more hybrid background.
So my education-
>> I bet it’s interesting, but we’ll see.
>> Yeah. Appreciate it.
My education was mostly focused around robotics and software.
When I went to school,
academically, did computer engineering,
and then focused more specifically
toward machine learning and
protocol modeling and AI.
I spend early days in my careers at
companies like Google and Microsoft.
But always had it been for manufacturing since I was a kid.
I did a lot of welding and
carpentry when I was in school.
So I always wanted to be able to combine and bring
this world of software and robotics into manufacturing.
So when I went to SpaceX,
that’s when I started to learn how tough it is
to manufacture parts.
So to illustrate that,
I’ll give you an example from one of
the projects we were working on in SpaceX around
2010-2012 timeframe, which is Falcon 9.
Once we decided on the diameter of the Falcon 9 rocket,
a lot of tooling, a lot of equipment in
the factory were configured for that diameter, right?
Meaning that, for example,
the tooling that you used to build
the tanks for that rocket are all fixed at that diameter.
The moment you want to go get a fatter rocket,
then you want that because a fatter rocket means
you can put more fuel in it,
it can go to higher orbits,
but it’s just not an easy change.
It means hundreds of millions of dollars investment
in the factory to change that.
So actually, if you follow Falcon 9
throughout its life or just Falcon family,
you will see that the rocket kept getting
taller but could never get larger in diameter.
As a matter of fact,
if you look at SpaceX in the past 20-something years
that the company has existed,
they have two rocket families that they have developed,
actually one and a half.
So there’s a Falcon family,
and there is a Starship family,
which is significantly larger rocket
and still hasn’t become productionized yet.
So one and a half, that’s why I call it one and a half.
In order to build Starship,
they had to go start from scratch,
new facility, but a lot of new tooling out in Texas.
So that’s what I mean when we say
manufacturing is very hardware-specific.
Now, there are technologies that have been
developed in the past few decades
that allows us to slightly move away from that.
3D printing is one of them.
That’s why I’ve moved from SpaceX to Relativity Space,
and the goal at Relativity Space is,
can we 3D print as much of the rocket as we can
to get rid of this limitation,
a limitation where we cannot change
the design of the rocket if you want to.
So over there, I was in charge of a team
that was building a very large and low-metal 3D printing.
You could print structures that were like 15, 20 feet wide
and 20, 30 feet in length.
And this was basically a robotic arm
with a welder attached to it
that was welding layer by layer,
basically what 3D printing is,
layer by layer a very complex structure
that could be very large.
So for the first time,
techniques like 3D printing kind of give this promise of,
now you can actually turn a design
into something that incrementally can build a geometry,
and it’s not tied by a specific tooling.
The challenge with 3D printing
was that there’s a lot of geometries
that are still not accessible to it.
Either physically not possible to print it,
that’s a lot of physical challenges,
or economically it’s just not feasible, right?
And that’s kind of gave way toward the thinking around
how do we build a little bit more overarching automation
that can do different types of processes,
different types of material,
and it’s not a specific to just maybe 3D printing
or machining or one process.
It can do different types of manufacturing processes
that need it in the shop floors
without having to be tool specific.
And that was the genesis of Machina.
And the core idea is to answer your question,
like how is this gonna be possible?
The core idea is, we used to have this flexibility, right?
If you go a couple of centuries back,
manufacturing used to be arts and crafts,
people actually manufactured things.
And they could one day you would go to a blacksmith
and you say, okay, build me a sword,
and they will start from a raw material,
use their hammer and apply it incrementally
to that material.
And they had a creative mind,
so they could apply different set up steps
and be creative with it to get it to a sword.
And then the day after that, you could go say,
well, can you build me a sheet?
And they would start from a flat sheet of metal
and then they apply the same hammer,
but in a different way.
And it will give you a shield, right?
Out of a sheet metal.
So they were super flexible.
The challenge was they were not scalable.
So with the current manufacturing paradigm,
we kind of traded that flexibility for throughput.
But I think now if we can replicate what a craftsman does
in an automated fashion,
and then we can maybe get the best of both worlds.
We can get the flexibility that a craftsman had,
but also scale it.
And that’s basically what we’re doing at Machina.
We’re building what we call a robo craftsman.
And the core components is,
can you keep the replicate the dexterity of the craftsman,
which is the robotic comes into play?
But more importantly,
can you replicate what happens in the mind of the craftsman?
You know, how do they come up with a set of procedures
and steps using the similar, very simple tools
to get to a part that is accurate?
And then because it’s robotic system,
you can easily scale it.
– So I want to ask you to dig into that a little bit.
But before I do,
are there particular industries,
size and types of projects or other parameters
that define what Machina is working on,
and then also what you are not working on,
is it specific to, you know, are you building,
are you building robotic blacksmiths for that matter?
Or are they aviation industry, different industries?
What’s sort of that end of the approach?
And then maybe we can dig into some of the technical bits
about how you’re doing it.
– Yes, so the robotic craftsman cell that we build,
this is a robotic system, we call it a cell.
It’s actually industry-agnostic, right?
You could build parts for aerospace,
you can build part for automotive,
and that’s the beauty of it, right?
Like in the morning, you can do automotive parts,
in the afternoon, you can do airspace parts.
But in order to build a good business,
we always want to start from somewhere
that we are a very good fit,
based on the current economic conditions.
Develop it there,
and then from there expand to other verticals.
So the focus that we have today
is mostly on airspace defense.
We have some focus on automotive,
but dominantly our business comes from airspace defense.
And the reason for that is that, you know,
the traditional factories are very good
at producing the same thing over and over again.
So we don’t want to necessarily start competing with them
on making the same thing over and over again.
We want to compete in areas where design is constantly changing,
the parts are constantly changing,
and what we call is a very high-mix environment.
And that’s where airspace and defense is a very good fit.
And they’re also traditionally good adopters of new technology,
compared to maybe some of the other industries
that have lower margins and cannot necessarily risk,
you know, kind of testing a new technology.
So airspace defense is where we start,
because of kind of like market factors
and voter market kind of considerations.
But the goal is that this can apply
to any vertical in the future.
Yeah, it makes sense.
I should ask, when was Muck and Affounded?
Yeah, so we started the company in 2019.
Okay.
So the company is right now like four years old,
four or five years old.
But yeah, but the team comes from, you know,
manufacturing background, you know,
my co-founder Bob is a material scientist.
He comes from aerospace and automotive in the past.
Lots of space, six alumni,
some of the folks from relativity space.
So a lot of people who have been into agile manufacturing space
and robotic space.
And then we started in 2019.
And then I think we have our first manufacturing cell in 2020.
Cool.
And in the interest of full transparency,
this would be a good time for me to mention
that NVIDIA is actually an investor in Muck and Affounded Labs.
All right, so let’s dig into it a little bit
from whatever angle and to whatever extent you’d like to add.
I guess I have sort of two questions
and you can pick the one that makes more sense.
One is kind of how does it work in terms of
what does your manufacturing floor look like
or the best that you can describe that over the radio?
So to speak and kind of, you know,
how have you built the cells to be able to accommodate
this mix of different manufacturing tasks?
And then the other question I have, of course,
is how has and is AI playing a role in informing, you know,
and I’m hoping that we get into sort of the creative mind
of the, what was the term, robo-manufacturer?
No, robo-craftsman.
Robo-craftsman, thank you.
I knew it was a little warmer of a term than manufacturer.
So where should we start to dig in a little more here?
Yeah, no, I think starting from the kind of the layout
of the floor has a good start.
It’s because it is different
than traditional manufacturing floors, right?
We have a facility, we have a couple of facilities here
in LA around 110,000 square feet now.
And it’s very different than, you know,
what you would see, for example,
if you go to a automotive factory.
The traditional paradigm of manufacturing, like I said,
is very much has been focused
on making the same thing over and over again.
And the way we’ve been able to achieve that
is definitely because of a invention
that actually we did in America done by Henry Ford
in early 19th century called assembly line, right?
Where you have a product moving in a linear fashion
in the factory and each station or along its way,
people are just basically installing different things
or doing different manufacturing operations on it.
So if you go into a, you know,
high volume factories of today,
you will kind of see that assembly line flow
where, you know, material comes in from one end,
they start getting put together on the assembly line.
Each operation is getting done at each station.
And again, you get a, you get a call
or whatever product that you get coming out of it.
And for my sort of non-expert point of view,
the thing that I always think of is
each station is doing exactly the same thing
or more or less exactly the same thing
over and over and over again.
– Yes, and that’s how we’ve been,
we have been achieved to get very high throughput.
But then the Achilles heel is that, you know,
you cannot change.
The moment you want to change it,
basically you have to overhaul this whole factory, right?
So that’s why it becomes very expensive.
So our paradigm kind of like flips that a little bit
on its head.
And it says, okay, let’s have these, what we call cells.
And these cells are enabled by robots
and each robot can be programmed
to do a different operation.
And now we’re decoupling logistics also
from the manufacturing.
Meaning that now you have a facility
that actually more looks like a data center
for folks who are familiar with the compute world,
where you have basically the same way in a data center,
you have a whole bunch of computers
and you can program these computers
and these computers are connected together.
You can program these computers
to do different types of operations through software.
That’s the same kind of paradigm we have in our shop floor.
There is a ray of manufacturing cells.
These cells are robotic cells
and can be configured to do different operations
and different parts.
And then through a kind of centralized system,
you can basically program what each cell does.
Maybe one cell forms a hood for a car, another cell,
maybe it’s welding it, another cell is trimming it.
And the logistics is completely decoupled
because we want to get this flexibility.
Now there’s a whole bunch of other benefits as well, right?
Like not only you get flexibility
but you get kind of rolling updates.
You can kind of like, you can bring down a cell
and the whole manufacturing doesn’t need to get overhauled
as it is in the assembly line, right?
So there’s a whole bunch of other benefits
that come into play.
But yeah, our shop floors look very different
than what you would see in a, let’s say a car factory today.
When you’re talking about the robotic cells,
the robots themselves, are they arms?
Do they have different physical forms,
depending on the task and how much does
or doesn’t the physical form of the robot itself
to call it, the RoboCraftsman.
How much does that dictate?
I’m going back to your space example
about the diameter of the rock being fixed
and imagining, well, there’s got to be some constraint
on what one of these cells just physically can’t do.
Yeah, yeah.
So this has actually have been kind of like
very big subject to debate within our company
since early days, right?
Like how do we make these cells as agile
and as flexible as possible?
So there’s I think two considerations, right?
One is the size of the cell
and the other one is what are the components?
If you look at the industry today,
there are multiple options we have had.
Like, you know, we can go very simple automation.
A lot of people can think of like gantries and systems
that are kind of basically XYZ movements
that you can have in them.
So that’s very simple system.
And then you can go all the way
to a very complicated robotic system like humanoid, right?
Where they have a lot of degrees of freedom
and they can do a lot of things.
But then the downside is as a system gets more complicated,
it’s harder to manufacture.
The accuracies maybe is not as good.
That the amount of forces they can apply is not as good.
Versus on one end, you have very simple system,
can do a lot of accurate stuff on the gantry side,
can apply a lot of force.
And then you have on the other end,
very complex system, let’s say humanoid,
which companies like figure and others are trying to do.
And you get a lot of flexibility,
but maybe you lose precision, accuracy,
and the amount of load they can apply.
So we kind of chose to be somewhere in the middle.
So we use robotic arms.
We use industrial robotic arms
to kind of have the benefit of both worlds.
Get enough flexibility in our manufacturing selves,
because we can kind of replicate almost the same dexterity
as a human, I would say Saxex or seven axis robotic arms.
But then at the same time,
you’re using a kind of slightly commoditized system.
So we don’t have to build a wheel from scratch.
So robotic arms already have existed for a few decades.
There are multiple vendors that are offering the same thing.
So it’s more commoditized,
but then it still gets us that benefit of,
so it has flexibility,
but it has the benefit of applying a lot of force.
Still, it’s still very accurate.
And we can kind of get rid of a lot of its limitations
through our software stack.
So use robotic arms,
but ideally you might want to have multiple configuration
of these robotic arms, right?
The same way in a data center,
you have one system that has X amount of RAM
and this type of a CPU,
another system might have much larger RAM,
another system might have a GPU,
but hopefully we can only have like
few five, six different configurations.
And then what we do is five, six different configurations,
we basically can do all kinds of different types of parts.
Right now, for example, we have very strong robots
that can do very thicker material.
We have thinner robots that are slightly more accurate,
weaker robots that are slightly more accurate,
but then they cannot apply as much force.
So we have few different configurations,
but together pretty much can get,
do different types of geometries,
maybe 80, 90% of geometries out there is accessible to us
with different configuration, different size.
– I’m speaking with Ed Mer.
Ed is the co-founder and CEO of Machina Labs,
who as Ed has been detailing,
is revolutionizing the manufacturing industry
by combining robotics with AI and a new approach
to what the manufacturing floor itself looks like,
how it can be reconfigured, what it’s all about.
So let’s talk now about how you get the robotic cells
to do what you want them to do.
And maybe even past that,
I’m curious when you talk about the creativity aspect
and combining that sort of old world human world,
there’s still artisans out there making things,
putting that into this robotic process,
what that’s all about.
– Yeah, so ideally what is the system that you want?
And you want a system that actually works
like a robotic craftsman or a true craftsman,
right, you can go to the system and say,
“Hey, here’s a design.
“Here’s a design of a part that I want.”
Basically input design intent.
And then the system can come up
with how it will direct its kinematics,
basically robotic arms, to do the right set of operation,
pick up the right tool, apply the right way to the material,
and then get you the right part into it, right?
So that’s the ideal solution that we want.
Today, those steps are done with a lot of experts, right?
So there’s a expert designer, get that look at the cat,
maybe it modifies the cat or the design intent
to get to something that’s manufacturable.
It goes back and forth maybe with the person
who brought the design intent to figure out,
okay, what are compromises you can make
to make this manufacturable?
Then there’s another expert that turns the finalized,
the negotiated design into a set of steps
that the robotic system can do,
and then programs that robot to do those steps.
And at each point in time, if the robot is not doing well,
maybe we do a QC and say, okay,
the robot is not doing the right thing,
we have to iterate again,
and then kind of update the robotic instructions,
and then see if we can get to a better part.
And then in the end, you have to do some kind of a QC.
The part comes out, somebody looks at it and say,
okay, does it actually toward the specs that we want?
Now, these are the steps that are currently done by experts.
So what we want is kind of combine all of those
into an intelligent system.
Now, what you see has been done with chat GPs and others,
you would say, oh, okay, yeah, it seems like
we are very close to be able to do these things.
Like now we have these robotic system
that I can ask it to do something that’s very complex,
and it will give you an answer.
There are news now that latest iteration of training
of these models is as smart as maybe a PhD, right?
So we have these very expert systems
that can reason through with AI through these steps.
The main challenge we are facing is that,
these systems that you see like chat GPD and others
have been trained on the data
that has been publicly available, right?
You can use internet to train them.
With manufacturing data, the data has not
been traditionally available.
So we have that extra challenge is that, okay, yes,
seems like the models are capable enough
to be able to decipher and turn the design into steps
that the robot needs to do,
but we don’t have enough data or available data
to train things on them.
So the extra additional complexity for us was,
we need to build systems that can generate data
and incrementally get better.
So we need to start with systems
that use heuristic methods first, right?
Non-model is necessarily to generate a lot of data.
And then we use the data to kind of build models
and incrementally improve its capabilities over time
to get to a point where we have that robotic craftsman.
You can literally put the design intent,
input the design intent and comes out the other
and actual part that these robotic systems are building.
– So do you generate that data in a simulation?
How did you go about building the data sets?
– Very good question.
So were we started?
– It’s always the question and no matter who, right?
Talking to you, talking to somebody
about wildlife conservation and tracking animals,
it’s always about the data.
– Yes, yes.
So here’s the challenge, right?
Simulation is a good way to create the synthetic data, right?
The challenge is even our simulations today
are not fast enough for our specific processes.
So if you look at, for example,
in some of the other robotic tasks
where you do manipulation, like you move stuff around,
then you have simulations that are fast enough, right?
They call it in these robotic gyms, right?
Where the robots basically can do
a whole bunch of different things
and kind of explore the space and kind of get training data.
Because the physics, the kinematic physics
is actually pretty simple, right, to simulate.
The challenge with our process
is that we are simulating a physical phenomenon
that’s much more complicated.
You know, when we are forming these sheet of metals,
we have to simulate the deformation.
So plastic and elastic deformation,
we need to simulate friction.
We need to simulate material cohesion.
There’s a whole bunch of things.
We need to simulate tear in there.
– Yeah, yeah.
– So when we initially, we started to do some of this work,
for example, like try to simulate it.
We realized that actually,
if we properly want to simulate the environment
and our processes, it’s actually very expensive
and it takes a long time.
A part that would take for us to form with the robots
for 15 minutes at the time
on the differential equation-based simulations,
we call it finite element analysis models.
It would take one week on 27 cores of a CPU machine, right?
So it’s like, okay, it’s cheaper maybe
to just run it in real life.
– So just capture the data, yeah.
– Capture the data.
But we took both approach.
So we said, okay, let’s build systems
that we can do a lot of trials in real life
and make sure we build them in a way
that we can capture the data,
a state of this process every four milliseconds.
That’s what we’re doing.
So we captured terabytes and terabytes of data
with that over the past four, five years.
We’ve built thousands and thousands of parts.
But also we are working on making faster simulations
using GPUs, right?
Can we actually expedite the speed of simulation
so that we can also augment it with data
that comes from a synthetic world?
– So we have to do both.
But yes, it has been a huge challenge.
So these are the two challenges that are kind of ahead of us
that maybe other companies,
like the ones that are working on natural language models,
I don’t have because internet is giving them,
user-generated data for years.
– Yeah, no, you mentioned QC and it’s stuck in my head.
I wouldn’t want the robotic QC craftsman, so to speak,
hallucinating based off of at least the stuff
I read on the internet.
Let’s put it that way.
So you mentioned the data and the training
and then the speed of everything
being kind of two big hurdles
that you’ve been working to overcome.
– There are other either hurdles you’ve overcome
or perhaps just moments along the way
that were surprising or just kind of stand out
as kind of like these big milestones
in the development of everything Muckin’ The Labs is doing.
And then sort of the follow-up to that
is I guess beyond the training and the speed,
which obviously huge.
If you look ahead to the next couple of years
or whatever the best timeframe is,
maybe what are some of the things
that you and other robotics and manufacturing companies
are trying to clear to get to sort of the next stage
of your evolution?
– Yeah, so there’s an interesting challenge
when you want to generate your data,
which has been probably the biggest challenge
for a robotic company.
I remember there was a podcast from Ilya
talking about why open AI dropped a robotic arm
back in the day, right?
And the main challenge is that, yes, you say,
“Okay, I have to generate my own data.”
But you’re generating data with a very complex
physical system.
These robots can break and they might need maintenance.
So you almost have to figure out a way
to be very good at operation, right?
You need to be able to run a large fleet of robots
very smoothly to generate data.
And that actually has been one of the main challenges,
like I said, even open AI had at the time,
where we’re like, I don’t know if we are in it,
like we don’t have the right expertise
to create this operational rigor
to operate these robotic cells.
Now, they were funded by billions, actually,
of kind of they could go and dedicate themselves to do this.
We also don’t have the luxury of that.
We need to create, we’re venture-funded business,
obviously we have a good amount of funding,
but we need to also generate results for our customers.
So I think one of the biggest challenges for us was,
in order to enable this AI-powered world,
we need to actually become a very good operators
and create value for the customers fast.
So a lot of challenges that we had in early days
was like, how do we create a robotic facility
that can work around the clock and generate data
and kind of resolving a lot of those challenges?
So that’s been one big area of work for us.
And the other piece, I think,
kind of like maybe a little bit away from technical work
is because you are operationally heavy company,
you also need to deal with a lot of people.
One challenge that people–
How big is Makada?
How many people?
Mostly now 70 people, but across many skill sets, right?
We have technicians all the way to robotic engineers,
to AI engineers, to software engineers, material scientists.
And if you traditionally want to build
such a multidisciplinary team, you have to balloon up, right?
You have to create all these different expertise
that kind of work together.
So we have to also figure out a way,
how do we not balloon up
while we operate a huge operational facility
and then generate data while we also maintain
a rigor and expertise in the fields like AI and others.
So I think the biggest challenge for us
has been kind of navigating those waters.
As a technologist, you always underestimate
how important the people factor is.
And I think that’s something that I kind of,
I kind of have learned over the time
that it’s probably the most important factor, right?
A lot of technological developments are already there,
but having a team that is excited
and can operate efficiently
has been one of the biggest challenges here.
Absolutely, yeah.
So to put you on the spot here a little bit,
how do you see manufacturing changing,
because of AI and the things that we’re talking about?
And as we go forward, I mean, five years, 10 years,
20 years down the line,
is all the manufacturing gonna be,
look very different and have this sort of flexible,
scalable, reconfigurable type of design
that Makana has and is continuing to build.
Are we gonna have to sort of knock down or gut
the existing manufacturing facilities
and rebuild them with this in mind?
And then this may be out of your wheelhouse,
so please feel free to demure on this,
but do you see these types of things coming to,
sort of consumer and household robots going forward
with every a time where I’ll be able to have my home robot,
build me a new desk for that, whatever it may be.
So it’s an interesting time right now, right?
We are in a situation where,
I think there’s a lot of tailwinds
for changing manufacturing.
As a country, we develop a lot of previous manufacturing
methods in late 19th century and 20th century.
As we develop these technologies,
but then over time,
as the cost of labor went high in United States,
we started kinda moving these things into other countries.
So a lot of manufacturing moved out of United States,
but now we’re at the point where we’re realizing that,
oh maybe that was not such a great idea,
because now we have dependency
on these large centers of manufacturing
that might not necessarily align with our values, right?
There might be a little bit more authoritarian
or they might just not wanna have the same interest
as we do.
So now we’re thinking about,
okay, we need to maybe be a little bit more self-sufficient.
The challenge is that the core technologies
that are underlying these manufacturing techniques
does not allow them to come back to the United States.
We talked about a factory right now
needs to be built for every part.
So what does that mean?
That means that that factory needs to be building
a lot of the same part for the rest of the world
before it’s economical.
So it’s very hard to replicate that same factory
in a smaller version and still be competitive
in United States.
The nature of the technology
lends itself to centralization.
So we’re kind of finding the nature
of the technology at this point.
It’s not just like with gusto,
we can bring manufacturing back in the United States.
Maybe some of it we can,
but for the most part, unless the technology doesn’t change,
I don’t think we can be able to bring manufacturing back.
But that being said, like I said, there’s a lot of tailwind.
People wanna bring it back
because now it’s becoming a existential threat.
So I think because of these tailwinds,
there’s gonna be a lot of changes
that gonna happen in manufacturing.
And automation is one of those biggest things
that’s gonna change, right?
In order for manufacturing to come back to the United States,
it’s not gonna look like what we had in ’60s and ’50s.
It’s gonna be the new paradigm of manufacturing
that requires less labor
or maybe requires a more sophisticated labor
with higher productivity than we had before.
So automation, flexible manufacturing,
easy to reconfigure shop floors
are gonna be big part of bringing manufacturing
back to the United States.
You look at some of the towns in Midwest
that used to be manufacturing towns,
you go there as it goes downtown.
The whole economy around that factory died
because that factory,
the product it was making became obsolete
and it was not flexible enough to change, it died.
So this will change.
Now the question is how fast?
That’s a harder one to predict
because I think there’s a lot of geopolitical parameters there.
Interesting times, as you said.
You enter a conflict with China,
that will change very fast.
If you don’t, maybe it’s gonna be a little bit more paced,
but yes, it’ll definitely change.
Timeline is tougher to predict.
Now, will we have these systems in our homes?
I think that timeline is a slightly higher, longer.
There are people predicting at some point,
everybody’s gonna have a human or robots
that’s gonna help them with things.
I think the lower hanging fruit is getting robots
to use AI and automation in factories
because we already have figured out
a lot of other stuff around these robots
and we have a very good supply chain around them.
But I do see a future where we’re gonna have
human or robots in every home, everybody will own one.
Maybe a little bit longer.
It’s harder to predict anything beyond 10 years,
but I would put that in more 20, 25 years out there.
Fair enough.
For folks listening who might be aspiring entrepreneurs
in the manufacturing or robotics automation space
or maybe studying and interested in robotics and automation,
but not quite sure what path to take,
what advice would you give either
from sort of the technical side
or you obviously have an entrepreneurial background
and spirit yourself here.
So like the point you made earlier
about the people being at the core of it
and sometimes that being the harder thing
for technologists is a salient one,
but what would you give us kind of advice?
I think one thing that has changed since I came out of school
is we need way more multidisciplinary people.
When I came from a family that valued education a lot,
my mom was a teacher, my dad has his PhD,
so it was very focused on like go to school,
get expert in one area and that’s gonna be your thing.
I think that paradigm slightly is breaking
where we need people that can connect different components
more, we always gonna need experts,
but we also need more people
that maybe they know mechanical engineering,
but they also know software, they also know robotic,
they also maybe know a little bit of material science
because we have these kind of breakthrough technologies
like AI and we need people who can connect the dots
between multiple disciplines and build something new.
So that is becoming more and more important.
So I would suggest for people
who are going to school now, get exposure,
maybe become an expert in one area,
but get as much as exposure as you can
in other fields of science and engineering,
that makes you a much more attractive candidate
for the companies of next generation companies, right?
So that would be one main area of say advice I have
for people who wanna be in the technical field.
On the entrepreneurial side,
I think it is becoming more and more important
to build mission-driven companies.
There used to be a time where there was a lot of
kind of incremental improvement companies
that you can build, that the system’s already there.
You just wanna make some incremental improvement.
I think with AI, now the shifts
are gonna be more fundamental.
There’s gonna be always incremental thing,
but incremental things are just easier to build.
So I don’t know if companies are necessarily
gonna be well equipped, startups well equipped to do that.
As a startup, you wanna kind of go after
something more fundamental.
And for that, you need more kind of mission-driven companies.
So find something that you’re really excited about.
You become a big believer that that change needs to happen
and go after that.
And I think there gonna be less companies
that are just gonna optimize this and that
because it’s just easier to do those things
for the companies themselves,
for the larger companies themselves.
– Sounds like sage advice to me.
Edmere, for folks listening who want to learn more
about Machina Labs, your latest innovations,
what you’re up to, perhaps more technical-oriented
research papers and blogs and stuff like that,
where can listeners go online to learn more?
– Yeah, obviously our website,
MachinaLabs.ai is a good place,
but also we’re very active on social media.
LinkedIn, I think we put both even technical
and commercial milestones on it very often.
Twitter as well, and Instagram.
Yeah, so I think follow us on those channels.
I actually, very openly, one of the main kind of philosophies
we have on Machina is like build out into public.
So we actually share even our technical challenges online
and people respond to it and we are very open.
So if you follow, I think even for the technical folks,
you will see a lot of interesting kind of content
that we put out there that they can contribute.
– Fantastic.
Ed, thank you so much for taking the time to chat with us.
I could binge at your ear all day
or ask you questions all day.
I should say I’m listening to you talk about it.
It’s fascinating, fascinating stuff.
And as you said, interesting times we live in
and it’s probably only gonna evolve faster than it has been.
So I look forward to keeping an eye
on what Machina Labs is up to
and maybe we can talk again down the line.
– I would love that, thanks though.
– Thank you.
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Edward Mehr works where AI meets the anvil. The company he cofounded, Machina Labs, blends the latest advancements in robotics and AI to form metal into countless shapes for use in defense, aerospace, and more. The company’s applications accelerate design and innovation, enabling rapid iteration and production in days instead of the months required by conventional processes. NVIDIA AI Podcast host Noah Kravitz speaks with Mehr, CEO of Machina Labs, on how the company uses AI to develop the first-ever robotic blacksmith. Its Robotic Craftsman platform integrates seven-axis robots that can shape, scan, trim and drill a wide range of materials — all capabilities made possible through AI.
