Kind: captions
Language: en
the ribosome who I mentioned a little
while back can make an elephant one
molecule at a time ribosomes are slow
they run at about one molecule a second
but ribosomes make ribosomes so you have
trillions of them and that makes an
elephant in the same way these little
assembly robots I'm describing can make
giant structures
at heart because of the robot can make
the robot so more recently to my
students Amira and Miana had a nature
communication paper showing how this
robot can be made out of the parts it's
making so the robots can make the robot
so you build up the capacity of robotic
assembly
the following is a conversation with
Neil gershenfeld the director of MIT is
Center for bits and atoms an amazing
laboratory that is breaking down
boundaries between the digital and
physical worlds fabricating objects and
machines at all scales of reality
including robots and automata that can
build copies of themselves and
self-assemble into complex structures
his work inspires Millions across the
world as part of the maker movement to
build cool stuff
to create the very act that makes life
so beautiful and fun
this is Alex Friedman podcast to support
it please check out our sponsors in the
description and now dear friends here's
Neil gershenfeld
you have spent your life working at the
boundary between bits and atoms so the
digital and the physical what have you
learned about engineering and about
nature reality from uh working at this
divide trying to bridge this divide I
learned why Von Neumann and Turing made
fundamental mistakes
um it's I learned the secret of life
yeah
um I I learned how to solve many of the
world's most important problems which
all sound presumptuous but all of those
are things I learned at that boundary
okay so uh touring and Von Neumann let's
start there some of the most impactful
important humans who have ever lived in
Computing why were they wrong so I
worked with Andy Gleason who is
touring's counterparts so just just for
background if anybody doesn't know
Turing is credited with the modern
architecture of computing
among many other things Andy Gleason was
his U.S counterpart and you might not
have heard of Andy Gleason but you might
have heard of the Hilbert problems and
Andy Gleason solved the fifth one
so he was a really notable mathematician
during the war he was throwing his
counterpart then van Neumann is credited
with the modern architecture of
computing and one of his students was
Marvin Minsky so I could ask Marvin what
Johnny was thinking and I could ask Andy
what Alan was thinking
and what came out from that what I came
to appreciate
as background I never understood the
difference between computer science and
physical science but
turing's machine that's the foundation
of modern Computing has a simple physics
mistake
which is the head is distinct from the
tape so in the turing machine there's a
head that programmatically moves and
reads and writes a tape the head is
distinct from the tape which means
Persistence of information is separate
from interaction with information yeah
then van Neumann wrote deeply and
beautifully about many things but not
Computing he wrote a horrible men memo
called the first draft of a report in
the edvac which is how you program a
very early computer in it he essentially
roughly took turing's architecture and
built it into a machine
so the legacy of that is the computer
somebody's using to watch this is
spending much of its effort moving
information from Storage Transit
transistors to processing transistors
even though they have the same
computational complexity so in computer
science when you learn about Computing
there's a ridiculous taxonomy of about a
hundred different models of computation
but they're all fictions in physics a
patch of space occupies space
it stores state it takes time to Transit
and you can interact that is the only
model of computation that's physical
everything else is a fiction
so I I really came to appreciate that a
few years back when I did a keynote for
the annual meeting of the supercomputer
industry and then went into the halls
and spent time with the supercomputer
Builders and came to appreciate
see if you're familiar with the movie
The Metropolis uh people would Frolic
upstairs in the gardens and down in the
basement people would move levers and
that's how Computing exists today that
we pretend software is not physical it's
separate from hardware and the whole
Canon of Computer Science is based on
this fiction that bits aren't
constrained by atoms but all sorts of
scaling issues and Computing come from
that boundary but all sorts of
opportunities come from that boundary
and so you can trace it all the way back
to turing's machine making this mistake
between the head and the tape Von
Neumann in in
um create he never called it vinomen's
architecture he wrote about it in this
Dreadful memo and then he wrote
beautifully about other things we'll
talk about now to end a long answer
Turing and Von Neumann both knew this so
all of the Canon of computer scientists
credits them for what was never meant to
be a computer architecture both Turing
and Von Neumann ended their life
studying exactly how software becomes
Hardware so van Neumann studied
self-reproducing automata how a machine
communicates its own construction a
touring studied morphogenesis how genes
give rise to form they ended their life
studying the embodiment of computation
something that's been forgotten by the
Canon of computing but developed sort of
off to the sides by a really interesting
lineage
so there's no distinction between the
head and the tape between the computer
and the computation it is all
computation right so I never understood
the difference between computer science
and physical science and working at that
boundary helped lead to things like my
lab was part of doing with a number of
interesting collaborators the first
faster than classical Quantum
computations we were part of a
collaboration creating the minimal
synthetic organism where you design life
in a computer those both involve
domains where you just can't separate
Hardware from software the embodiment of
computation is embodied in these really
profound ways
so the first quantum computations
synthetic life so in the space of
biology
so space of physics at the lowest level
in the space of biology at the lowest
level
so uh let's talk about CBA Center of
bits and atoms what's the origin story
of this MIT legendary MIT Center that
you're a part of creating
in high school I really wanted to go to
vocational school where you learned to
weld and fix cars and build houses
and I was told no you're smart you have
to sit in a room and nobody could
explain to me why I couldn't
go to Vocational School
uh I then worked at Bell labs this
wonderful place uh before deregulation
legendary place and I would get Union
grievances because I would go into the
workshop and try to make something and
they would say no you're smart you have
to tell somebody what to do
and it wasn't until MIT and I'll explain
how CBA started but I could create CBA
that I came to understand this is a
mistake that dates back to the
Renaissance so in the Renaissance the
liberal arts emerged and liberal doesn't
mean politically liberal this was the
path to Liberation birth of humanism and
so the liberal arts with the Trivium
quadrivium roughly language Natural
Science and
at that moment what emerged was this
Dreadful concept of the ill liberal arts
so anything that wasn't the liberal arts
was for commercial gain and was just
making stuff and wasn't valid for
serious study and so that's why we're
left with learning to weld wasn't a
subject for serious study
um but the means of expression of
changed since the Renaissance so micro
Machining or embedded coding is every
bit as expressive as painting a painting
or writing a sonnet so uh never
understanding this difference between
computer science and physical science
uh the path that led me to create CBA
with colleagues was
I was what's called a junior fellow at
Harvard I was visiting MIT through
Marvin because I was interested in the
physics of musical instruments I
uh this will be another slight
aggression I uh and Cornell I would
study Physics and and then I would cross
the street and go to the music
department where I played the bassoon
and I would trim reads and play the
reads right and they'd be beautiful but
then they'd get soggy and then I
discovered in the basement of the music
department at Cornell was David Borden
uh who you might not have heard of but
it's legendary electronic music because
he was really the first electronic
musician so Bob Moog who invented um
Moog synthesizers was a physics student
at Cornell like me crossing the street
and eventually he was kicked out and
invented electronic music David Borden
was the first musician who created
electronic music so he's legendary for
people like Phil glass and Steve Reich
and so that got me thinking about I
would behave as a scientist in the music
department but not in in the physics
department but not in the music
department got me thinking about what's
the computational capacity of a musical
instrument
and through Marvin he introduced me to
Todd mackover at the media lab who was
just about to start a project with Yo-Yo
Ma
um that led to a collaboration uh to
instrumenticello to to extract yoyo's
data and bring it out into computational
environments what is the computational
capacity of a musical instrument as we
continue on this tangent and when we
shall return to CBA yeah so
one part of that is to understand the
Computing and if you look at like the
finest time scale and length scale you
need to model the physics it's not
heroic you know a a good GPU can do
teraflops today that used to be a
national class supercomputer now it's
just a GPU and that's about if you take
the time scales and length scales
relevant for the physics that's about
the scale of the physics Computing for
yoyo it was really driving it was he's
completely unsentimental about the strad
it's not that it makes some magical
Wiggles in the sound wave it's its
performance as a controller how he can
manipulate it as an interface device
interface between one and one exactly
human sound okay and so so what it led
to was I had started by thinking about
Ops per second but the yoyo's question
was really
um resolution and bandwidth it's
um how fast can you measure what he does
and
um uh the the the bandwidth and the
resolution of detecting his controls and
then mapping them into sounds and what
what we found what he found was if you
instrument everything he does and
connect it to almost anything it sounds
like yo-yo that that the magic is in the
control not in ineffable details in how
the wood Wiggles and so with yo-yo and
Todd that led to a piece and towards the
end I asked yo-yo what what it would
take for him to get rid of his Strat and
use our stuff and his answer was just
Logistics it was at that time our stuff
was like a rack of electronics and lots
of cables and some grad students to to
make it work once the technology becomes
as invisible as the strad then sure
absolutely he would take it and by the
way as a footnote on the footnote an
accident in the sensing of yoyo's cello
led to a hundred million dollar a year
Auto Safety business to control airbags
and cars how did that work I had to
instrument the bow without interfering
with it so I um set up
um local electromagnetic fields where I
would um detect
um how those fields interact with the
bow he's playing but we had a problem
that his hand whenever his hand got near
these sensing Fields I would start
sensing his hand rather than the
materials on the bow
and I didn't quite understand what was
going on with those that that
interference so my very first grad
student ever Josh Smith
did a thesis on tomography with electric
Fields how to see in 3d with electric
fields
then through Todd and at that point
research scientists my lab Joe Paradiso
it led to a collaboration with uh Penn
and Teller who
um where we did a magic trick in Las
Vegas to contact Houdini and sort of
these fields are sort of like you know
contacting spirits
so we did a magic trick in Las Vegas and
then the the crazy thing that happened
after that was uh Phil ritmuller came
running into my lab he worked with um
this became with Honda and NEC airbags
were killing infants and rear-facing
child seats
um cars need to distinguish
a front-facing adult where you'd save
the life versus a bag of groceries where
you don't need to fire the airbag versus
the rear-facing infant where you would
kill it and so the the the seat need to
in effect see in 3d to understand the
occupants and so we took the pen and
Teller magic trick derived from Josh's
thesis from yo-yo's Cello to an auto
show and all the card companies said
great when can we buy it and so that
became ellisis and it was 100 million
dollar a year business making sensors
there wasn't a lot of publicity because
it was in the car so the car didn't kill
you
so they didn't sort of advertise we have
nice sensors so the car doesn't kill you
but it became a leading Auto Safety
sensor and that started from the cello
and the question of the computational
capacity musical instrument right so now
to get back to
MIT I was spending a lot of outside time
at IBM research that had gods of the
foundations of computing
um this is amazing people there and I'd
always expected to go to IBM to take
over a lab but at the last minute
pivoted and came to MIT to take a
position
in the media lab and start what became
the predecessor to CBA media lab is well
known for Nicholas negroponte what's
less well known is the role of Jerry
Wiesner so Jerry was mit's president
before that Kennedy science advisor
grand old man of science at the end of
his life he was frustrated by how
knowledge was segregated
and so he wanted to create a department
of none of the above a department for
work that didn't fit in departments
and the media lab in a sense was a cover
story for him to hide a department it as
mit's president towards the end of his
tenure if he said I'm going to make a
department for things that don't fit in
departments the Departments would have
screamed but everybody was sort of
paying attention to Nicholas creating
the media lab and Jerry kind of hid in
in it a department called Media Arts and
Sciences it's really the department of
none of the above
and Jerry explaining that and Nicholas
then confirming it is really why I
pivoted and went to MIT
um because my students who helped create
Quantum Computing or synthetic life get
degrees from Media Arts and Sciences
this department of none of the above
so that led to coming to MIT yeah with
um uh Todd and Joe Paradiso and my
colleague we started a Consortium called
things that think and this was around
the birth of Internet of things and
um RFID but then we started doing things
like work we can discuss that became the
beginnings of quantum Computing and
cryptography and materials and logic and
microfluidics and those needed
uh much more significant infrastructure
and were much longer research arcs so
with a bigger team of about 20 people we
wrote a proposal to the NSF to assemble
one of every tool to make anything of
any size was roughly the proposal one of
any tool to make anything of any size
yeah so they're usually nanometers
micrometers millimeters meters are
segregated input and output is
segregated we wanted to look just very
literally how digital becomes physical
and physical becomes digital and
fortunately we got NSF on a good day and
they funded this facility of one of
almost every tool to make anything and
so uh with
um a group of core colleagues
um that included Joe Jacobson like
trying Scott minnellis we launched CBA
and so you're talking about nanoscale
micro scale nanostructures
microstructures macro structures
electron microscopes and focused on beam
probes for nanostructures laser micro
Machining and x-ray microtomography for
microstructures multi-axis Machining and
3D printing for macro structures just
some examples what are we talking about
in terms of scale how can we build tiny
things and big things all in one place
yeah so a well-equipped research lab has
the sort of tools we're talking about
but they're segregated in different
places they're typically also run by
technicians where you then have an
account and a project and you charge all
of these tools are essentially
when you don't know what you're doing
not when you do know what you're doing
in that they're they're when you need to
work across length scales where we don't
once projects are running in this
facility we don't charge for time you
don't make a formal proposal to schedule
and the users really run the tools and
it's for work that's kind of in Kuwait
that needs to span these disciplines and
length scales
um and so you know uh
work in the project today work in CBA
today ranges from
developing zeptidual electronics for the
lowest power Computing to micro
Machining Diamond to take million 10
million RPM bearings for molecular
spectroscopy studies up to exploring
robots to build 100 meter structures in
space
okay can we the three things you just
mentioned let's start with the biggest
what are some of the biggest stuff you
attempted to explore how to build in a
lab sure so viewed from One Direction
what we're talking about is a crazy
random seeming of almost unrelated
projects but if you rotate 90 degrees
it's really just a core thought over and
over again just very literally how bits
and atoms relate how digital and just
going from digital to physical in many
different domains but it's really just
the same idea over and over again
so to understand the biggest things
let me go back to uh bring in now
Shannon as well as Von Neumann yeah so
what is digital
the Casual obvious answer is digital in
one and zero but that's wrong there's a
much deeper answer which is
Claude Shannon at MIT wrote the best
Master's thesis ever in his master's
thesis he invented our modern notion of
digital logic
where it came from was Van ever Bush uh
was a grand old man at MIT uh he created
the post-war research establishment that
led to the National Science Foundation
and he made an important mistake which
we can talk about
but he also made the let the
differential analyzer which was the last
great analog computer so it was a room
full of gears and pulleys and the longer
it ran the worse the answer was
and Shannon worked on it as a student
and he got so annoyed in his master's
thesis he invented digital logic
um but he then went on to Bell labs and
what he did there was
communication was beginning to expand
there is more demand for phone lines and
so there's a question about how much how
many phone lines you could phone
messages you could send down a wire
and you could try to just make it better
and better he asked a question nobody
had asked which is rather than make it
better and better what's the limit to
how good it can be and he proved a
couple things but one of the main things
he proved was a threshold theorem for
channel capacity and so what he showed
was my voice to you right now is coming
as a wave through sound and the further
you get the worse it sounds but people
watching this are getting it as as in
from packets of data in a network
um when they get when the computer
they're watching this gets the packet of
information
um it it can detect and correct an error
and what Shannon showed is if the noise
in in the cable to the people watching
this is above a threshold they're doomed
but if the noise is below a threshold
for a linear increase in the energy
representing our conversation the error
rate goes down exponentially
exponentials are fast there's very few
of them in engineering and the
exponential reduction of error below a
threshold if you restore state is called
a threshold theorem
that's what led to digital that that
means unreliable things can work
reliably so Shannon did that for
communication then van Neumann was
inspired by that and applied it to
computation and he showed how an
unreliable computer can operate reliably
by using the same threshold property of
restoring state it was then forgotten
many years we had to ReDiscover it in
effect in the quantum Computing era when
things are very unreliable again
but now to go back to how does this
relate to the biggest things I've made
so
in fabrication MIT
invented computer-controlled
Manufacturing in 1952 jet aircraft were
just emerging there is a limit to
Turning cranks on a machine on a milling
machine to make parts for jet aircraft
now this is a messy story MIT actually
stole computer controlled Machining from
an inventor who brought it to MIT wanted
to do a joint project with the Air Force
and MIT effectively stole it from him so
it's kind of a messy history but
that sounds like the birth of
computer-controlled Machining 1952.
there are a number of inventors of 3D
printing one of the companies spun off
my lab by Max lebowsky's form Labs which
is now a billion dollar 3D printing
company that's the modern version
but all of that's analog meaning the
information is in the control computer
there's no information in the materials
and so it goes back to Van ever Bush's
analog computer if you mistake make a
mistake in printing or Machining just
the mistake accumulates
the real birth of computerized digital
manufacturing is four billion years ago
that's the evolutionary age of the
ribosome
so the way you're manufactured is
there's a code that describes you
the genetic code it goes to a micro
machine the ribosome which is this
molecular Factory that builds the
molecules that that are you
the key thing to know about that is it
there are about 20 amino acids that get
assembled and in that Machinery it does
everything Shannon and vanyman taught us
you detect and correct errors so if you
mix chemicals the error rate is about a
part in a hundred
when you make elongate a protein in the
ribosome it's about a part in 10 to the
four when you replicate DNA there's an
extra level of error correction it's a
part in 10 to the eight and so in the
molecules that make you
you can detect and correct errors and
you don't need a ruler to make you the
geometry comes from your parts
so now
compare a child playing with Lego and a
state-of-the-art 3D printer or
computerized milling machine
the Tower made by a child is more
accurate than their motor control
because the act of snapping the bricks
together gives you a constraint on the
joints
you can join bricks made out of
dissimilar materials you don't need a
ruler for Lego because the geometry
locally gives you the global parts and
there's no Lego trash the parts have
enough information to disassemble them
those are exactly the properties of a
digital code the unreliable is made
reliable yes absolutely so what the
ribosome figured out four billion years
ago is how to embody these problems
these digital properties but not for
communication or computation in effect
but for construction
so a number of projects in my lab have
been studying the idea of digital
materials and think of a digital
material just as Lego bricks the precise
meaning is a degree discrete set of
Parts reversibly joined
um with global geometry determined from
local constraints and so it's digitizing
the materials and so I'm coming back to
what are the biggest things I've made my
lab was working with the Aerospace
industry so Spirit era was Boeing's
factories
they asked us for how to join Composites
when you make a composite airplane you
make these giant wing and fuselage parts
and they asked us for a better way to
stick them together because the joints
were a place of failure and what we
discovered was instead of making a few
big Parts if you make little Loops of
carbon fiber
and you reversibly link them in joints
and you do it in a special geometry that
balances being under constrained and
over constrained with just the right
degrees of freedom we set the world
record for the highest modulus
ultralight material just by if in effect
making carbon fiber Lego
so so lightweight materials are crucial
for Energy Efficiency this let us make
that the lightest weight High modulus
material we then showed that with just
just a few part types we can tune the
material properties and then you can
create really wild robots that instead
of having a tool the size of a jumbo jet
to make a jumbo jet you can make little
robots that walk on these cellular
structures to build the structures where
they error correct their position on the
structure and they navigate on the
structure and so using all of that with
um NASA we made more airplanes a former
student Kenny
Chung and benjinette made a morphing
airplane the size of NASA Langley's
biggest wind tunnel with Toyota we've
made super efficiency race cars we're
right now looking at projects with NASA
to build these for things like space
telescopes and space habitats where the
ribosome who I mentioned a little while
back can make an elephant one molecule
at a time ribosomes are slow they run at
about one molecule a second but
ribosomes make ribosomes so you have
thousands of them trillions of them and
that makes an elephant in the same way
these little assembly robots I'm
describing can make giant structures
uh at heart because of the robot can
make the robot so more recently to my
students Amira and Miana had a nature
communication paper showing how this
robot can be made out of the parts it's
making so the robots can make the robots
so you build up the capacity of robotic
assembly you can self-replicate can you
Linger on what that robot looks like
what is a robot it can walk along and do
error correction and what is a robot
that can self-replicate uh from the
materials that is given what does that
look like what are we talking so um this
is fascinating yeah the answer is
different at different length scales so
so to explain that in biology primary
structure is the code in the messenger
RNA that says what the ribosome should
build yeah
um secondary structure or geometrical
motifs they're things like helices or
sheets tertiary structures are
functional elements like electron donors
or acceptors quaternary structure is
things like molecular Motors that are
moving my mouth or making the synapses
work in my brain so there's that
hierarchy of primary secondary tertiary
quaternary
now what's interesting is
if you want to buy Electronics today
from a vendor there are hundreds of
thousands of types of resistors or
capacitors or transistors huge inventory
all of biology is just made from this
inventory of 20 Parts amino acids and by
composing them you can create all of
life
and so
as part of this digitization of
materials
we're in effect trying to create
something like amino acids for
engineering creating all of Technology
from 20 Parts I
um I see as another discretion I helped
start an office for science in Hollywood
and
um there was a fun thing for the movie
The Martian where I did a program with
Bill Nye and a few others on how to
actually build a civilization on Mars
that they described in a way that I like
as I was talking about how to go to Mars
without luggage and the at heart it's
sort of how to create life in non-living
materials so if if you think about this
primary secondary tertiary quaternary
structure
um in my lab we're doing that but on
different length scales for different
purposes so we're making micro robots
out of like Nano bricks and to make the
robots to build large-scale structures
in Space the elements of the robots now
are centimeters rather than micrometers
and so the assembly robots for the
bigger structures are
uh there are the cells that make up the
structure but then we have functional
cells and so cells that can process and
actuate each cell can like move one
degree of Freedom or attach or disk
detach or process now those elements I
just described we can make out of the
still smaller parts So eventually
there's the hierarchy of the little
Parts make little robots that make
bigger parts of bigger robots that up
through that hierarchy in that way you
can move up the line scale right early
on I tried to go in a straight line from
the bottom to the top and that ended up
being a bad idea instead we're kind of
doing all of these in parallel and then
they're growing together and so to make
the larger scale structures we um like
there's a lot of a hype right now about
3D printing houses where you have a
printer the size of the house we're
right now working on using swarms of
these you know table scale robots that
walk on the structures to place the
parts much more efficiently that's
amazing but you're saying you can't for
now go from the very small to the very
large that'll come
um that'll come in stages can we just
Linger on this idea starting from
vinelman's uh self-replicating automata
that you mentioned
it's just a beautiful idea so that's at
the heart of all of this in the stack I
described so one student will Langford
made these micro robots out of little
parts that then we're using for miana's
bigger robots up through this hierarchy
and it's really realizing this idea of
the self-reproducing automata so van
Neumann when I complained about the
weinerman architecture it's not fair
Devon Neumann because he never claimed
it as his architecture he really wrote
about it in this one fairly Dreadful
memo that led to all sorts of lawsuits
and fights and about the early days of
computing he did beautiful work on
reliable computation and unreliable
devices and towards the end of his life
what he studied was how and I have to
say this precisely how a computation
communicates its own construction
so beautiful so a computation can store
a description of how to build itself but
now there's a really hard problem which
is
how if you have that in your mind how do
you transfer it and wake up a thing that
then can contain it
um so how do you give birth to a thing
that knows how to make itself and so um
with Stan ulam he invented cellular
automata as a way to simulate these uh
but that was theoretical now the work
I'm describing in my lab is is
fundamentally how to realize it how to
re um realize self-reproducing uh
automata and so you know this is
something van Neumann thought very
deeply and very beautiful of beautifully
about theoretically and it's right at
this intersection it it's not
communication or computation or
fabrication
it's right at this intersection where
communication and computation meets
fabrication
now the reason self-reproducing automata
intellectually is so important because
this is the foundation of life this is
really just understanding the essence of
how to life and in effect we're trying
to create life and non-living material
the reason it's so important
technologically is because that's how
you scale capacity that's how you can
make an elephant from a ribosome because
the assemblers make assemblers so simple
building blocks yeah that inside
themselves contain the information how
to build more building blocks and so uh
between each other construct arbitrarily
complex objects right now let me give
you the numbers so let me relate this to
right now we're living in AI Mania
explosion time
let me relate that to what we're talking
about
a hundred petaflop computer
which is a current generation uh
supercomputer not quite the biggest ones
does 10 to the 17 Ops per second
your brain does 10 to the 17 Ops per
second it has about 10 to the 15
synapses and they run at about 100 Hertz
so as of a year or two ago
the compute the performance of a big
computer matched a brain so you could
view AI as a breakthrough but the real
story is
um within about a year or two ago and
let's see that that the super computer
has about 10 to the 15 transistors in
the processors 10 to the 15 transistors
in the memory which is the synapses in
your brain so the real breakthrough was
the computers match the computational
capacity of a brain and so we'd be sort
of derelict if they couldn't do about
the same thing but now the reason I'm
mentioning that is
the
chip Fab making the supercomputer is
placing about 10 to the 10 transistors a
second
while you're digesting your lunch right
now you're make you're placing about 10
to the 18 parts per second
um there's an eight order of magnitude
difference not so in computational
capacity it's done we've caught up
but there's eight orders of magnitude
difference in the rate at which biology
can build versus state-of-the-art
manufacturing can build
and that distinction is what we're
talking about that distinction is not
analog but this deep sense of digital
fabrication of embodying codes in
construction so a description doesn't
describe a thing but the description
becomes the thing so you're saying I
mean this is one of the cases you're
making and that this is this third
Revolution we've seen the Moore's law in
communication we've seen the Moore's Law
like type of growth in uh computation
and you're anticipating we're going to
see that in digital fabrication can you
actually first of all describe what you
mean by this term digital fabrication so
the Casual meaning is the computer
controls the tool to make something and
that was invented when MIT stole it in
1952. yeah um there's the deep meaning
of what the ribosome does of a
computation of a dis a digital
description doesn't describe a thing a
digital description becomes the thing
yeah that's where the that's that's the
path to the Star Trek replicator
and that's the thing that doesn't exist
yet
now I think the the best way to
understand what this roadmap looks like
is to now bring in Fab labs and how they
relate to all of this what are Fab Labs
so here here's a sequence
um with colleagues I accidentally
started a network of what's now 2500
digital fabrication Community Labs
called Fab Labs right now in 125
countries and they double every year and
a half that's called lassa's law after
Sherry Lasseter who I'll explain so
here's the sequence
uh we started Center for bits and atoms
to do the kind of research we're talking
about we had all of these machines and
then had a problem it would take a
lifetime of classes to learn to use all
the machines
so with
you know colleagues who helped start CBA
we began a class modestly called how to
make almost anything yeah and there's no
big agenda it was just it was aimed at a
few research students to use the
machines and it were completely
unprepared for the first time we taught
it we were swamped by every year since
hundreds of students try to take the
class it's one of the most over
subscribed classes at MIT
um students would say things like can
you teach this at MIT it seems too
useful it's just how to work these
machines and the students in the class I
would teach them all the skills to use
all these tools and then they would do
projects integrating them and they were
amazing so Kelly was a sculptor no
engineering background uh her project
was she made a device that saves up
screams when you're mad and placed them
back later
and saves up screams when you're mad and
plays them back later you scream into
this device and it it it deadens The
Sound records it and then when it's
convenient releases your screen can we
just just like pause on the Brilliance
of that invention creation the art
I don't know the Brilliance who is this
that created Kelly Dobson going on to do
a number of interesting things uh me Jin
who's gone on to do a number of
interesting things uh made a dress
instrumented with sensors and spines and
when somebody creepy comes close it
would defend your personal space they're
also very easy um another project early
on was a web browser for parrots which
have the cognitive ability of a young
child and lets parrots surf the Internet
an alarm clock you wrestle with and
prove you're awake and what connects all
of these is
so MIT made the first real-time computer
the Whirlwind that was transistorized as
the TX the TX was spun off from MIT as
the PDP pdp's
where the mini computers that created
the internet
so outside MIT was deck Prime Wang data
General the whole mini computer industry
the whole Computing industry was there
and it all failed when Computing became
personal
Ken Olsen the head of digital famously
said you don't need a computer at home
there's a little background to that but
but deck you know completely missed
Computing became personal so I mentioned
all of that because
I was asking how to do digital
fabrication but not really why the
students in this how to make class were
showing me that the killer app of
digital fabrication is personal
fabrication yeah how do you jump to the
personal fabrication so Kelly didn't
make the screen body because it was for
a thesis she wasn't writing a research
paper it wasn't a business model she
wanted it was because she wanted one
yeah it was personal expression going
back to me and vocational schools
personal expression in these new means
of expression so that's happened every
year since it literally is called the
course is literally called how to make
almost anything yep a legendary course
at MIT yep yep every year
um and it's grown to multiple Labs
um at MIT with as many people involved
in teaching is taking it and there's
even a Harvard lab for the MIT class
what what have you learned about humans
colliding with the Fab Lab about what
the capacity experience to be creative
and to build I I mentioned Marvin
another Mentor at MIT sadly no longer
living is Seymour pepper so pepper
studied with Piaget he came to MIT to
get access to the early compute Piaget
was a Pioneer in how kids learn
um papert came to MIT to get access to
the early computers with the goal of
letting kids play with them Piaget
helped show kids are like scientists
they they learn as scientists and it
gets kind of throttled out of them
Seymour wanted to let kids have a
broader landscape to play Seymour's work
LED with Mitch Resnick to Lego logo
Mindstorms all of that stuff as Fab Lab
spread and we started creating
educational programs for kids in them
Seymour said something really
interesting he made a gesture he said it
was a thorn in his side
that they invented What's called the
turtle a robot kids could early robot
kids could program to connect it to a
Mainframe computer Seymour said
the goal was not for the kids to program
the robot it was for the kids to create
the robot
and so in that sense the Fab Labs which
for me were just this accident he
described as sort of this fulfillment of
the Arc of kids learn by experimenting
it was to give them the tools to create
not just assemble things and program
things but actually create so come into
your question
what I've learned
is
MIT a few years back somebody added
added up businesses from spun off from
MIT and it's the world's 10th economy it
falls between India and Russia and I
view that in a way as a bad number
because it's only a few thousand people
and these aren't uniquely the four
thousand brightest people it's just a
productive environment for them and what
we found is in rural Indian villages in
African Shanty towns and Arctic
um Hamlet I find exactly precisely that
profile so
um link cited a few hours above Trump so
way above the Arctic circles it's so far
north the satellite dishes look at the
ground not the sky
um Hans Christian in the lab was
considered a problem in the local school
because they couldn't teach him anything
I showed him a few projects next time I
came back he was designing and building
Little Robot vehicles and in
um South Africa in I mentioned social
Govi in this apartheid Township the
local Technical Institute taught kids
how to make bricks and fold sheets it
was it was punitive but to piso in the
Fab Lab was actually doing all the work
of my MIT classes and so over and over
we found precisely the same kind of
bright invent of
um creativity
uh and historically the answer was
go you're smart go away it's sort of
like me and vocational school but in
this lab Network what we could then do
is in effect bring the world to them now
let's look at the scaling of all of this
so there's one Earth a thousand cities a
million towns a billion people a
trillion things
there was one Whirlwind computer and my
teammate uh the first real-time computer
there were thousands of pdps there were
millions of hobbyist computers that came
from that billions of personal computers
trillions of Internet of things so now
if we look at this Fab Lab story 1952
was the NC Mill
there are now thousands of Fab labs and
the Fab Lab costs exactly the same cost
and complexity of the mini computer so
on the mini computer it it didn't fit in
your pocket it filled a room but video
games email word processing really
anything you do with the internet
anything you do with a computer today
happened at that era because it got on
the scale of a work group not a
corporation
in the same way Fab labs are like the
mini computers inventing how does the
world work if anybody can make anything
then if you look at that scaling
Fab Labs today are transitioning from
buying a machine to make machines making
machines so we're transitioning to you
can go to a Fab Lab not to make a
project to make but to make a new
machine
so we talked about the Deep sense of
self-replication there's a very
practical sense of Fab Lab machines
making Fab Lab machines
and so that's the equivalent of the uh
hobbyist computer era what it's called
the Altair historically then the work we
spent a while talking about about
assemblers and self-assemblers that's
the equivalent of smartphones and
internet of things that's when so the
the assemblers are like the smartphone
where a smartphone today has the
capacity of what used to be a
supercomputer in your pocket and then
the smart thermostat on your wall has
the power of the original PDP computer
not metaphorically but literally and now
there's trillions of those in the same
sense that when we finally merge
materials with the machines in the
self-assembly that's like the Internet
of Things stage but here's the important
lesson
if you look at the Computing analogy
Computing expanded exponentially but it
really didn't fundamentally change the
the core things happened in in that
transition in the mini computer era so
in the same sense the research now I'm
we spent a while talking about is how we
get to the replicator
today you can do all of that if you
close your eyes and view the whole Fab
Lab as a machine in that room you can
make almost anything but you need a lot
of inputs bit by bit the inputs will go
down and the size of the room will go
down as we go through each of these
stages
so how difficult is it to create a
self-replicating assembler
self-replicating machine that builds
copies of itself or builds more
complicated version of itself which is
kind of the dream towards which you're
pushing in a generic arbitrary sense I
had a student Nadia Peak with Jonathan
Ward who who for me started this idea of
how do we use the tools in my lab to
make the tools in the lab yes in a very
clear sense they are making
self-reproducing machines so one of the
really cool things that's happened is
there's a whole network of machine
Builders around the world so there's
Danielle and now in Germany and yens in
Norway and
um each of these people is has learned
the skills to go into a Fab Lab and make
a machine and so we've started creating
a network of superfap so the Fab Lab can
make a machine but it can't make a
number of the Precision parts of the
machine so in places like Bhutan or
Carol in the south of India we started
creating super Fab Labs that have more
advanced tools to make the parts of the
machines so that the machines themselves
become even cheaper
so
that that is self-reproducing machines
but you need to feed it things like
bearings or microcontrollers they can't
make those parts but other than that
they're making their own things and I
should note as a footnote the stack I
described of computers controlling
machines to machine making machines to
assemblers to self-assemblers view that
as fab1234
so we're transitioning from fab 1 to Fab
two and the research in the lab is three
and four at this Fab two stage a big
component of this is uh sustainability
in the material feedstocks so Alicia
colleague in Chile is leading a great
effort looking at how you take Forest
Products and coffee grounds and
seashells and a range of locally
available materials and produce the
high-tech materials that go into the lab
so all of that is machine building today
then
back in the lab what we can do today is
we have robots that can build structures
and can assemble more robots that build
structures
we have finer resolution robots that can
build micro mechanical systems so robots
that can build robots that can walk and
manipulate and we're just now we have a
project
at the layer below that where there's
endless attention today to billion
dollar chip Fab Investments uh but a
really interesting thing we passed
through is today the smallest
transistors you can buy as a single
transistor just commercially for
electronics is actually the size of an
early transistor in an integrated
circuit
so we're using these machines making
machines making assemblers to place
those parts to not use a billion dollar
chip Fab to make integrated circuits but
actually assemble little electronic
components so I have a fine enough
precise enough actuators and
manipulators that allow you to place
these transistors right that's a
research project in my lab
on called dice on discrete assembly of
integrated electronics and we're just at
the point to really start to take
seriously this notion of not having a
chip Fab make integrated Electronics but
having not a 3D printer but a thing
that's a cross between a pick and place
makes circuit boards in 2D the 3D
printer extrudes in 3D we're making sort
of a micro manipulator that acts like a
printer but it's placing to build
Electronics in 3D but this micro
manipulator is distributed so there's a
bunch of them or is this one centralized
thing so that's why that's a great
question so um I have a prize that's
almost but not been claimed for the
students whose thesis can walk out of
the printer oh nice so you have to print
the thesis
with the means to to exit the printer
and it has to contain its description of
the thesis that says how to do that
it's a really good uh I mean it's a it's
a it's a fun example of exactly the
thing we're talking about and I've had a
few students almost
get to that
um and so
um in what I'm describing there's this
stack where we're getting closer but
it's still quite a few years to really
go from us so there's a layer below the
transistors where we assemble the base
materials that become the transistor
we're now just at the edge of assembling
the transistors to make the circuits
we can assemble the micro parts to make
the micro robots we can assemble the
bigger robots and in the coming years
we'll be patching together all of those
uh scales so do you see a vision of just
endless billions of robots at the
different scales self-assembling uh
self-replicating and building the
complicated structures yes
yes and the butt to the yes but is let
me clarify two things one is that
immediately
raises King Charles fear of gray goo of
runaway mutant self-reproducing things
the reason why there are many things I
can tell you to worry about but that's
not one of them
is if you want things to autonomously
self-reproduce and take over the world
that means they need to compete with
nature on using the resources of nature
of water and sunlight and in light of
everything I'm describing biology knows
everything I told you every single thing
I explain biology already knows how to
do
um uh what I'm describing isn't new for
biology it's new for non-biological
systems so in the digital era the
economic win ended up being centralized
the big platforms
in this world of machines that can make
machines I'm I'm asked for example
um you know what what's the killer
opportunity you know who's going to make
all the money
um who to invest in but if the machine
can make the machine it's not a great
business to invest in the machine
um in the same way that if you can
produce if you can think globally but
produce locally then the way the
technology goes out into society isn't a
function of central control but is
fundamentally distributed now that
raises an obvious kind of concern which
is well doesn't this mean you could make
bombs and guns and all of that
the reason that's much less of a problem
than you would think is making bombs and
guns and all of that is a very well met
Market need anywhere we go there's a
fine supply chain for weapons now
hobbyists have been making guns for ages
and guns are available just about
anywhere so you could go into the lab
and make a gun today it's not a very
good gun and guns are easily available
and so generally we run these lab in war
zones what we find is
people don't go to them to make weapons
which you can already do anyway it's an
alternative to making weapons it coming
back to your question I'd say the single
most important thing I've learned is
the greatest natural resource of the
planet is this amazing density of
Brighton event of people whose brains
are underused and
um you could view the the social
engineering of this lab work as creating
the capacity for them and so it you know
in the end the way this is going to
impact Society isn't going to be command
and control it's how the world uses it
and it's been really gratifying for me
to see just how it does yeah but what
are the different ways uh the evolution
of the exponential scaling of digital
fabrication can evolve so you said uh
yeah self-replicating Nanobots right
this is the the gray goo
fear it's the caricature of a fear but
nevertheless there's interesting just
like you said spam and all these kinds
of things that came with the scaling of
communication and computation what are
the different ways that malevolent
actors will use this technology yeah
well first let me start with a
benevolent story