Later morning. You had at least a two-hour
nap this morning because of sleep interruptions due mostly to you both had
coughing fits last night. Carol is at the doctor’s office presently. – Amorella
1030
hours. I cannot remember when we were both down with the same illness at the same
time. Basically, I’m tired and still coughing, and so is she.
On another note the small bedroom
television’s picture is shrinking so it is time to be a new one as this is
about fifteen years old. It is a diversion you don’t mind when you are feeling up
to tackling it. – Amorella
1037
hours. Buying electronics and new cars is always a fun adventure in research.
The screen we have now is sixteen inches diagonal. It wouldn’t work at all if
we did not have a basic cable line attached. The problem is that there are few
flat screens of that size, particularly in a major brand. The closest I can
come to it is a 19” Samsung. We are not shopping at this time presently anyway.
You decided to go with Best Buy or Amazon
but that is almost a given in this day in age. Later, dude. – Amorella
Evening.
Earlier you and Carol watched Masterpiece Classic and Masterpiece Mystery from
last night’s PBS programming.
Tonight
you brought home a Papa John pizza and ate a fourth of it while watching NBC
News. Carol had another fourth. – Amorella
2059
hours. We are both still ‘under the weather’ so to speak. I found this article
from Edge in my email today. They don’t always strike my interest but this one
does because it suggests we will not continue to do things the way we now do
them. I have the read article twice and will read it a couple more times. It
looks like from Gershenfeld’s perspective that we are on the edge of a
renaissance that we cannot yet fully describe. This is what I get from the
article in my humble two readings. In any case I find it interesting enough to
read a couple more times for clarification or for admitting that I do not know
what this article is about at all or only a little. It can be shared on
Facebook so I suppose I can share it here too.
** **
Article below selected and
edited from Edge.org - 9 February 2015
NEIL GERSHENFELD directs
MIT's Center for Bits and Atoms. His unique research group investigates the
relationship between the content of information and its physical
representation, from molecular quantum computers to virtuosic musical
instruments. Technology from his laboratory has been seen and used in settings
including the Museum of Modern Art and rural Indian villages, the White
House/Smithsonian Millennium celebration and automobile safety systems, Las
Vegas shows and Sami reindeer herds.
He is the author of
numerous technical publications, patents. His is the author of the
best-selling When Things Start To Think, as well as The
Nature of Mathematical Modeling, The Physics of Information Technology,
and FAB, and he has been featured in media such as the New York Times, The
Economist, CNN, and the PBS.
To arrive at the edge of the world's
knowledge, seek out the most complex and sophisticated minds, put them in a
room together, and have them ask each other the questions they are asking
themselves.
*
Digital Reality
What interests me is how
bits and atoms relate—the boundary between digital and physical.
Scientifically, it's the most exciting thing I know. It has all sorts of
implications that are widely covered almost exactly backwards. Playing it out,
what I thought was hard technically is proving to be pretty easy. What I didn't
think was hard was the implications for the world, so a bigger piece of what I
do now is that. Let's start with digital.
Digital is everywhere; digital is everything.
There's a lot of hubbub about what's the next MIT, what's the next Silicon
Valley, and those were all the last war. Technology is leading to very
different answers. To explain that, let's go back to the science underneath it
and then look at what it leads to.
Digital is one of the most
widely misunderstood concepts. In computing there's a notion of a sign bit
error, where you calculate something and you get one bit wrong, so the sign is
the opposite of what it should be, which means everything you calculate is the
opposite of what it's supposed to be. There's a sense in which that's happening
right now in maybe three different areas
.
Claude Shannon wrote the
best master's thesis ever when he was at MIT, inventing digital. He went on to
Bell Labs and did two core things. The one that's most interesting for me is he
proved the first threshold theorem. What that means is I could send my voice to
you today as a wave, or I could send it to you as a symbol. What he showed is
if I send it to you as a symbol, for a linear increase in the resource used to
represent the symbol, there is an exponential reduction in the error of you
getting the symbol correctly as long as the noise is below a threshold. If the
noise is above the threshold, you're doomed. If it's below a threshold, a
linear increase in the symbol gives you an exponential reduction in error.
There are very few exponentials in engineering. That's the big one
What he showed is you can
communicate reliably even though the communication medium is unreliable; that's
what digital means. That's the essence of digital. It wasn't obvious, Claude
Shannon got that. When I was at Bell Labs, Bob Lucky was still around there and
could tell me stories. Claude Shannon had this idea that we should communicate
digitally. There was a real battle between analog communication and digital
communication.
The sobering lesson from
Bob Lucky is the resolution of the battle was death. The analog managers died
and a new generation of digital managers took over. Then we had digital
communication, and now the Internet. But the meaning of digital is this
threshold property, this exponential scaling.
Around that time,
computers were analog. Vannevar Bush at MIT made a differential analyzer, which
was a roomful of gears and pulleys, and the answer got worse with time. What
John von Neumann did was show you could compute reliably with an unreliable
computing device by computing with a symbol. It was exactly applying Shannon to
viewing a computation as a communication through the channel of the computer.
He showed that you can compute reliably with unreliable devices. The heart of
it isn't ones and zeroes, it's the threshold property—the exponential scaling,
the exponential reduction in error. Those are the digital revolutions in
communication and computation. We'll come back to von Neumann because what he
did in computation afterwards is, I'd say, relatively completely misunderstood
in computer architecture. We'll come back to that.
We have digital
communication; we have digital computation.
The thing I've been most
recently been involved in is digital fabrication. This is the first of the sign
bit errors I want to talk about. At the same time Shannon was digitizing
communication and von Neumann was digitizing computing, at MIT in 1952 the
first numerically-controlled milling machine was made. You could argue that's
digitizing fabrication. In Norbert Wiener's servomechanism laboratory, the
Whirlwind computer from Project SAGE, which was an early air defense computer,
was one of the first computers you could do anything with in real time rather
than batch. There was this idea that you could connect the computer to a
machine to turn the cranks on a milling machine and make aircraft parts. At the
time, this was a huge leap. It was connecting two alien realms: this new
computer thing and a milling machine. What it let you do was make aircraft
parts you couldn't make any other way.
Digital fabrication, in
that sense, dates back to 1952. Now there's lots and lots of attention about
the Maker movement and digital fabrication, but it misses the key point. In the
80s, Chuck Hull invented 3D printing. Now if you run a shop like mine at CBA
where we have one of every kind of computer-controlled manufacturing machine,
there's maybe twenty processes that can control a computer to make something:
cut with lasers, supersonic jets of water, EDM with wires, machining, plasmas,
fusing, bonding. For all the attention to 3D printing, we might use 3D printers
20 percent of the time. The other 80 percent of the time other machines are
faster, make higher performance parts, all of that. Maybe 20 percent of the
time 3D printing is best. 3D printing isn't a revolution; it's decades old.
It's a little bit like microwave ovens in the fifties. The kitchen of the
future was going to have one appliance and you push a button and make all your
food. Of course, we still have a stove. You wouldn't get rid of your stove even
though you have the microwave
oven.
What all of that misses
is, it's analog. The design is digital, but the process is smooshing material.
You might cut it or you might squirt it but it's smooshing material. The real invention
is 4 billion years old, that's the evolutionary age of the ribosome. To
understand the ribosome, think about a child playing with Lego bricks and
compare it to a state-of-the-art 3D printer. The child and the ribosome do much
the same thing.
When the child assembles
Lego bricks, the first attribute is metrology that comes from the parts. When
you snap the bricks together, you don't need a ruler to play Lego; the geometry
comes from the parts. What it means is that a child can make a Lego structure
bigger than her or his self. The same way in the ribosome—the Lego bricks are
amino acids, and the ribosome assembles amino acids to elongate a protein. You
can make an elephant one amino acid at a time because the geometry comes from
the parts. In a 3D printer today, what you can make is limited by the size of
the machine. The geometry is external.
The second difference—now
we come to the Shannon part—is the Lego tower is more accurate than the child
because the constraint of assembling the bricks lets you detect and correct
errors. The tower is more accurate than the motor control of the
child.
In a lab when you mix
chemicals the yield is maybe a part per 100. In the ribosome, making proteins,
the error rate is a part in 104, and when you replicate
DNA there's an extra step of error correction and the error rate is 1 in 108.
That 1 in 108 is the exponential. That's the
exponential scaling for working reliably with unreliable parts. Because the
parts have a discrete state, it means in joining them you can detect and
correct errors. That threshold property may sound like a technicality but it's
exactly the difference between an analog telephone and the Internet, or a
differential analyzer and a PC. The second difference is you can detect and
correct state to correct errors to get an exponential reduction in error, which
gives you an exponential increase in
complexity.
The next one is you can
join Lego bricks made out of dissimilar materials. In the ribosome there's
twenty amino acids that represent the basic properties of life. It's very hard
to 3D-print a conductor and an insulator and a semiconductor through the same
process.
The last one is when
you're done with Lego you don't put it in the trash; you take it apart and
reuse it because there's state in the materials. In a forest there's no trash;
you die and your parts get disassembled and you're made into new stuff. When
you make a 3D print or laser cut, when you're done there's recycling attempts
but there's no real notion of reusing the parts.
The metrology coming from
the parts, detecting and correcting errors, joining dissimilar materials,
disconnecting, reusing the components—those are all the things Shannon and von
Neumann taught us. They're digital fabrication. But the crucial distinction is
that the code isn't in the computer, it's in the materials themselves. It's
digitizing physical reality. There's an exact historical alignment between
going from analog to digital in communication and analog to digital in
computation, and now analog to digital in fabrication. That's the research
revolution: digitizing fabrication, coding construction.
Shannon and von Neumann
were very aware of the physical context in doing this. You can trace what
Shannon did back to, roughly, Maxwell's demon, the one molecule demon that
seems to violate the second law of thermodynamics. Leó Szilárd's analysis of
it—reducing it to a single molecule—and Rolf Landauer's explanation of the
erasure in the mind of the demon, Shannon was familiar with the history of all
of that. Late in life, Turing and von Neumann both started to think about
geometry and physics in computing. Turing, von Neumann, and Shannon were all
very aware of this physical context in what they did in communication and
computation. I've been attributed, and I'm happy to take claim for saying
computer science is one of the worst things to happen to computers or to
science because, unlike physics, it has arbitrarily segregated the notion that
computing happens in an alien world.
A year ago for the White
House Office of Science and Technology Policy, I ran a meeting because every
federal agency pretty much wanted to talk to me about their 3D printing
initiative. I was yelling at them that it's sort of shuffling deckchairs.
That's not the new opportunity. I got together all of these agencies, and then
I got together the people at the frontiers of this emerging field of the deep
sense of digital fabrication as coding construction. What's emerging from that
is in a whole bunch of areas we're discovering we can do things that were just
not considered remotely possible before
.
On the very smallest
scale, the most exciting work on digital fabrication is the creation of life
from scratch. The cell does everything we're talking about. We've had a great
collaboration with the Venter Institute on microfluidic machinery to load
designer genomes into cells. One step up from that we're developing tabletop
chip fab instead of a billion dollar fab, using discrete assembly of blocks of
electronic materials to build things like integrated circuits in a tabletop
process.
A step up from that, we
had a paper in Science last year showing we can make the world's highest
performance ultralight material for things like airplanes by digitizing
composites into little linked loops of carbon fiber instead of making giant
pieces. Now we're working with the aerospace industry on making printers of
jumbo jets. But the printers are really assemblers.
Bigger scale, we're
working with Homeland Security on geoprinting. Extreme events like Katrina or
Sandy do tens or hundreds of billions of dollars of damage. National technical
means to defend against them are bags of wet sand. We're now developing
machines that are like robotic ribosomes that link discrete parts to build
geological scale features to make landscape. We're working with NASA on doing
this in space, leading up to the idea of how you bootstrap a civilization.
There's a series of books by David Gingery on how to make a machine shop
starting with charcoal and iron ore. You make a furnace and you melt it, and
then you make hand tools, then slowly you bootstrap up to make a machine shop.
When people think about a notion like colonizing space and bootstrapping a
civilization, that's what they're thinking of
implicitly.
Now to come back to the
ribosome again. There are twenty amino acids. With those twenty amino acids you
make the motors in the molecular muscles in my arm, you make the light sensors
in my eye, you make my neural synapses. The way that works is the twenty amino
acids don't encode light sensors, or motors. They’re very basic properties like
hydrophobic or hydrophilic. With those twenty properties you can make you. In
the same sense, digitizing fabrication in the deep sense means that with about
twenty building blocks—conducting, insulating, semiconducting, magnetic,
dielectric—you can assemble them to create modern technology.
Digi-Key—the electronic
parts vendor—sells 500,000 different kinds of resistors but at heart there's
only three attributes: conducting, resistive, insulating. That's what we're
doing. By discretizing those three parts we can make all those 500,000
resistors, and with a few more parts everything
else.
That's the revolution. It
intellectually exactly aligns with digitizing communication and computation,
but now for fabrication. In turn, the alignment is even closer with the history
of computing. Where I realized this alignment was so close was, to do this
research CBA got a big NSF grant to buy machines. We wrote an ambitious
proposal to get one of anything to make anything, and that's luckily what we
got funded, which is an interesting story. But the problem we ran into was that
it would take too long to teach people to use all of those machines. I started
a class called How to Make Almost Anything and that wasn't meant to be
provocative. It was just aimed at ten or so research students to use the
machines to do that research. Something strange happened, which is hundreds of
students showed up to take a class for ten people, and they would say things
like, "This is too useful. Can you teach it at MIT?" Every year
hundreds of students try to take this class. Then in turn, the next surprise
was they weren't there for research, they weren't there for theses, they wanted
to make stuff. I taught additive, subtractive, 2D, 3D, form, function, circuits,
programming, all of these skills, not to do the research but just using the
existing machines
today.
Kelly Dobson, who’s run
Digital Media at RISD, made a device that saves up screams and plays it back
later when it's convenient. And Meejin Yoon, who runs Architecture now at MIT,
when she took the class she made a dress instrumented with sensors and spines
to defend your personal space. That happened year after year until, finally, I
realized that the students were answering what I hadn't asked, which is: what
is this good for? I was asking: can you do digital fabrication? It didn't even
occur to me to ask why. It was obviously just such an interesting question.
What they were answering was the killer app for digital fabrication is personal
fabrication, meaning, not making what you can buy at Walmart, it’s making what
you can't buy in Walmart, making things for a market of one
person.
Let's go back to the
history. From the Project SAGE Whirlwind, MIT developed the first
transistorized computers, the TX Series. The TX Series then got commercialized
as Digital Equipment’s PDPs. The PDPs gave us the Internet. The Internet, word
processing, videogames, just about everything you do on a computer today was
first done in that era. This was the time of mini-computers. At that time,
Route 128 in Boston had Wang, Prime, Data General, DEC, the whole computer
industry. Every single one of them failed. The organizational lesson is it
didn't matter how good you were at organizational change, they were just
doomed.
Ken Olson, the head of
DEC, famously said, "Nobody needs a computer in the home”. Personal
computers are a toy. They don't scale. Play with your toys, we'll make the real
machines. Obviously you have PCs in the home, and DEC is twice over bankrupt.
DEC was bought by Compaq, Compaq was bought by HP.
To play that forward, they
were the minicomputers. The minicomputer industry completely misread PCs. The
transitional stage between minicomputers and PCs were hobbyist computers. This
was the era of the Altair, and the Altair was life changing for people like me.
It was the first computer you could own as an individual. But it was almost
useless. The killer app was you could flip switches on a panel, load in a
binary program, start it running, and the lights would blink. This was late 70s
when that was beginning to happen. Minicomputer, hobbyist computer, then
PC. To understand why a 3D printer isn't analogous to the PC: When I was at
Bell Labs we used PDP 11/73s and there was a rack, and in the rack there was a
unit that's a processor, there's a unit that's storage, there's a unit that's
communication, there's a unit that's power, there's a unit that's I/O, there's
a unit that's graphics. There [are] all these systems and you have to plug them
all into each other. It was hard to use but it brought the cost from a million
dollars to 100,000 and the size from a warehouse down to a room. What that
meant is a workgroup could have one. When a workgroup can have one it meant Ken
Thompson and Dennis Ritchie at Bell Labs could invent UNIX—which all modern
operating systems descend from—because they didn't have to get permission from
a whole corporation to do it.
If you follow that history,
the minicomputer died, but the reality is every year the machines got faster,
better, smaller, better integrated. At the PC stage what happened is graphics,
storage, processing, IO, all of the subsystems got put in a box. The ten
subsystems of the PDP that were separate units all fit in one box.
To line that up with
fabrication, MIT's 1952 NC Mill is similar to the million-dollar machines in my
lab today. These are the mainframes of fab. You need a big organization to have
them. The fab labs I'll tell you about are exactly analogous to the cost and
complexity of minicomputers. The machines that make machines I'll tell you
about are exactly analogous to the cost and complexity of the hobbyist
computers. The research we're doing, which is leading up to the Star Trek
Replicator, is what leads to the personal fabricator, which is the integrated
unit that makes everything.
Now let's do that in
powers of ten. My research lab—think of it as ten one million dollar machines.
So, 10 million dollars to make molecular nano assemblers. Within that there's a
workshop—think of that as a million dollars in ten $100,000 machines, which is
things like high-speed mills, water-jet cutters, and very powerful lasers. But within
that there's a core set of tools that we used in things like the How To Make
class, and what happened was, starting about ten years ago, I had a big NSF
grant and a law was passed in Congress that government programs need to measure
social impact, called GPRA. NSF had no idea how to do it, so they turned to us
and said we had to measure social impact, and we had no idea how to do it. But
we had good NSF program managers. We that thought rather than tell people what
we're doing, we would give them the tools. We set up the first fab lab, and it
was about a 100K investment. Think of it as ten ten-thousand dollar
machines. It's the basic set of tools today to do digital fabrication. Now,
there's a casual sense, which means a computer controls something to make
something, and then there's the deep sense, which is coding the materials.
Intellectually, that difference is everything but now I'm going to explain why
it doesn't matter.
The fab lab is 2 tons, a
$100,000 investment. It fills a few thousand square feet, 3D scanning and
printing, precision machining, you can make circuit boards, molding and casting
tooling, computer controlled cutting with a knife, with a laser, large format
machining, composite layup, surface mount rework, sensors, actuators, embedded
programming— technology to make technology.
Think of it as a town
library scale. You wouldn't ask your town library, "Do you want to skip
literature or history?" There's a basic set of things for knowledge. The
fab lab is sort of like that for turning data to things, things to data. We set
up one of those for NSF and then they accidentally went viral. They've been
doubling every year and a half. There's 400 now, there's about 400 coming.
They're above the Arctic Circle. They're at the bottom tip of Africa, they're
in rural shantytowns, they're in big cities. We didn't plan that. We only set
up one but they just started
doubling.
Now comes the historical
alignment, which is the Internet didn't come after the iPhone. The Internet was
invented in the minicomputer era, and every year the computing got faster,
better, cheaper and better integrated. But you didn't have to wait twenty years
for minicomputers to start using computers. You could use it then. It's an
exponential. Socially it looked like a revolution but it was going from one to
two to four to eight. On a log-log plot it's just a straight
line.
In that same sense we're
ten years into the doubling of fab labs. Ten years you can just plot this
doubling. Today, you can send a design to a fab lab and you need ten different
machines to turn the data into something. Twenty years from now, all of that
will be in one machine that fits in your pocket. This is the sense in which it
doesn't matter. You can do it today. How it works today isn't how it's going to
work in the future but you don't need to wait twenty years for it. Anybody can
make almost anything almost anywhere.
Let's play that forward
with some of the implications and then relate it to computing and
communication. Around this point I began to realize that I was a victim of and
was fixing a mistake from the Renaissance, which is, in high school I
desperately wanted to go to the vocational school where you could weld and fix
cars and do cool stuff like that. I was told, "No, you're smart. You're
not allowed to." I had to sit in a room; it seemed punitive. At Bell Labs
I had union grievances because I would try to make things in a workshop and
they'd say, "No, you're smart, you have to tell somebody else what to
do." And it just didn't make
sense.
Finally, when I could own
all these machines I got that the Renaissance was when the liberal arts
emerged—liberal for liberation, humanism, the trivium and the quadrivium—and
those were a path to liberation, they were the means of expression. That's the
moment when art diverged from artisans. And there were the illiberal arts that
were for commercial
gain.
We've been living with
this notion that making stuff is an illiberal art for commercial gain and it's
not part of the means of expression. But, in fact, today, 3D printing,
micromachining, and microcontroller programming are as expressive as painting
paintings or writing sonnets but they're not means of expression from the
Renaissance. We can finally fix that boundary between art and
artisans.
Now, technically, the
roadmap we're going down is very clear. If you take this alignment between
mainframes, minicomputers, hobbyist computers, PCs, the research tools we're
using are like the mainframes, the fab labs are the minicomputers. They're
being used to do the equivalent of invent the Internet. The next step is we're
doing a lot of work on machines that make machines. You don't go to a fab lab
to get access to the machine; you go to the fab lab to make the machine. To do
that we've had to rip up CAD-CAM, machine control, motion control, all the ways
you make stuff, to make machines that make machines. That's the next step. Over
the next maybe five years we'll be transitioning from buying machines to using
machines to make machines. Self-reproducing machines. But they still have
consumables like the motors, and they still cut or squirt. Then the interesting
transition comes when we go from cutting or printing to assembling and
disassembling, to moving to discretely assembled materials. And that's when you
do tabletop chip fab or make airplanes. That's when technical trash goes away
because you can disassemble.
An early version of that
is Google's Project Ara. Ara was one of my students. That's based on modular
reconfigurable cell phones intentionally as the first step down this roadmap.
Instead of buying and throwing out a cell phone, it's made out of building blocks
you can reconfigure. The research will replace this bit by bit. We'll
reconfigure the blocks in the building blocks and then the blocks in the blocks
in the building blocks. That's maybe the twenty-year roadmap technically from
where we are today.
Now, the biggest surprise
for me in this is I thought the research was hard. It's leading to how to make
the Star Trek Replicator. The insight now is that's an exercise in embodied
computation—computation in materials, programming their construction. Lots of
work to come, but we know what to do. The thing that's been most surprising for
me is the consequences of this. The equivalent of inventing the Internet. As
the fab labs have been spreading, we've been working with heads of state, NGOs,
and tribal chiefs, and community activists, and generals—this amazing range,
because if anybody can make anything anywhere, it challenges
everything.
Start with education. I
love the maker movement and I also get irritated by the maker movement for the
failure in mentoring. At something like a Maker Faire, there's hall after hall
of repeated reinventions of bad 3D printers and there isn't an easy process to
take people from easy to hard. In the fab lab network we had this problem that
kids would come in all sorts of places all over the world, learn amazing skills
and then fall off a cliff. There'd be nowhere for them to go
educationally.
We started a project out
of desperation because we kept failing to succeed in working with existing
schools, called the Fab Academy. Now, to understand how that works, MIT is
based on scarcity. You assume books are scarce, so you have to go there for the
library; you assume tools are scarce, so you have to go there for the machines;
you assume people are scarce, so you have to go there to see them; and
geography is scarce. It adds up to we can fit a few thousand people at a time.
For those few thousand people it works really well. But the planet is a few
billion people. We're off by six orders of
magnitude.
In computing terms, MIT is
a mainframe. You go there and get processed. I don't like MOOCs—massive open
online classes—that trumpet: "Our class has a million people in it."
It's just not education as I understand it—a person sitting at a screen—it's
like time sharing. There's still a mainframe and you're a terminal plugged into
the mainframe. The way the Fab Academy works, in computing terms, it's like the
Internet. Students have peers in workgroups, with mentors, surrounded by
machines in labs locally. Then we connect them globally by video and content
sharing and all of that. It's an educational network. There are these critical
masses of groups locally and then we connect them globally.
We started teaching the
same digital fabrication class I teach at MIT, "How to Make Almost
Anything", but now instead of just teaching at MIT we're teaching it using
the whole planet as the campus. Amusingly, I went to my friends at Educause
about accrediting the Fab Academy and they said, "We love it. Where are
you located?" And I said, "Yes" and they said, "No."
Meaning, "We're all over the earth." And they said, "We have no
mechanism. We're not allowed to do that. There's no notion of global
accreditation." Then they said something really helpful:
"Pretend." What they meant was self-accredit with a skills-based
portfolio where people document skills and at some future date we'll figure out
how to accredit you. And that's been working great. Of all the questions we
get, the one question we never get is, "Who accredited us?" because
the content speaks for itself.
The interesting thing in
turn is we're now teaching this global version of a digital fabrication class
using the whole fab lab network. A lot of what we've done isn't tied
specifically to digital fabrication. Digital communication means we can talk to
each other at a distance. I run a giant video bridge where we have 100-site
video conferences where everybody talks to everybody, where each of those sites
is not just a person, it's a workgroup. Digital computing means you can store
the knowledge of the world and get access to that. The profound piece of
digital fabrication means you can bring the campus to the student, not the
student to the campus. Once you have a basic set of tools, you can make all the
rest of the tools.
Next year we're starting a
new class with George Church that we've called "How to Grow Almost
Anything", which is using fab labs to make bio labs and then teach biotech
in it. What we're doing is we're making a new global kind of university.
We're inventing this new
kind of global university and part of what I like about MIT is John Reed, who
was chairman of MIT's Corporation, came to see what this was all about. And
instead of in any way being threatened he was just delighted to see it. His
comment was, "This is how you change MIT. Change the world, MIT will catch
up to it." There's a core set of skills a place like MIT can do but it
alone doesn't scale to a billion people. This is taking the social
engineering—the character of MIT—but now doing it on this global scale.
This isn't my core
competence. I know how to invent the machines, but I can describe what's been
happening as they spread. To understand the economic and social implications,
look at software and look at music to understand what's happening now for
fabrication. Software at one time was Microsoft or IBM. A few exceptional
people could write it for themselves but for everyone else it was Microsoft or
IBM. Open Source came along. There's a brief spike of: "Yippee, it's free.
Nobody ever pays anything for anybody ever. You still have Microsoft or IBM now
but, with all respect to colleagues there, arguably that's the least
interesting part of software. If you think about app development, you can write
little scripts for yourself, you can write an app for ten people, or 100, or
1,000, or a million. In terms of writing software, there's powers of ten.
Mainframes didn't go away but what opened up is all these tiers of software
development that weren't economically
viable.
If you look at your phone
and look at the diversity of the apps you use and how you use them, how they're
being developed and sold, some are given away, some you pay a few dollars, some
you pay more, but they're being developed and sold into markets that just
weren't viable on the scale of Microsoft or IBM. A string of data becomes an
algorithm, becomes a program.
Now look at music. Music
was the labels or you'd play your piano. Napster comes along: "Yippee,
it's free. Nobody ever pays anybody for anything." In software, copy
protection failed—easily circumvented by dishonest people, irritating to honest
people. Copy protection doesn't work anymore. In music there was digital rights
management—easily circumvented by dishonest people, irritating for honest
people. Amazon now sells you tracks without protecting them, but they make it
easy to buy and sell. The labels fought it tooth and nail, now it's beginning
to finally turn around. If you look at music development, the most interesting
stuff in music isn't the big labels, it's all the tiers of music that weren't
viable before.
You can make music for
yourself, for one, ten, 100, 1,000, a million. If you look at the tracks on
your device, music is now in tiers that weren't economically viable before. In
that example it's a string of data and it becomes a sound. Now in digital fab,
it's a string of data and it becomes a thing. It doesn't replace mass
manufacturing but mass manufacturing becomes the least interesting stuff where
everybody needs the same thing. Instead, what you open up is all these tiers
that weren't viable before.
Now, in turn, what is it
good for? The answer to that in some ways is almost the opposite of what you
think, which is you can make all kinds of stuff but the real value we're seeing
in digital fabrication is one step removed—it's the benefits of having made it.
To understand that, remember Google doesn't sell search; they give away search,
and they sell the benefits of having searched, which is advertising. Facebook
doesn't sell talking to your friends; it gives away talking to your friends; it
sells the benefits of having talked to your friends. It took about ten years
for the dot com industry to realize pretty much across the board you don't
directly sell the thing. You sell the benefits of the
thing.
To understand what that
means for digital fab, the most obvious thing you can do is invent a widget and
sell it. You invent the widget and then you go to China and mass-manufacture
it. Now, it happens we're working very closely with Shenzhen. There's an annual
meeting of all the fab lab network. It's in Boston in 2015. 2016 it's in
Shenzhen because they're pivoting from mass manufacturing to enabling personal
fabrication. We've set Shenzhen as the goal in 2016 for Fab Lab 2.0, which is
fab labs making fab labs.
To rewind now, you can
send something to Shenzhen and mass manufacture it. There's a more interesting
thing you can do, which is you go to market by shipping data and you produce it
on demand locally, and so you produce it all around the world. There's a parallel
with HP and inkjet printing. HP's inkjet division is in Corvallis, Oregon
because they had to hide from Palo Alto because they were told that inkjet
printing would never scale, it would never be fast enough. But their point was
a lot of printers producing beautiful pages slowly scales if all the pages are
different. In the same sense it scales to fabricate globally by doing it
locally, not by shipping the products but shipping the data.
What is work? For the
average person—not the people who write for Edge, but just an average
person working—you leave home to go to a place you'd rather not be, doing a
repetitive operation you'd rather not do, making something designed by somebody
you don't know for somebody you'll never see, to get money to then go home and
buy something. But what if you could skip that and just make the thing?
Vicente Guallart was a
colleague who started the first fab lab in Barcelona. He's now the city
architect, the planner of the future of Barcelona. He's putting fab labs in
every district in the city as part of the urban infrastructure. There, they
consider IKEA the enemy because IKEA defines your taste. Far away they make
furniture and flat pack it and send it to a big box store. Great design sense
in Barcelona, but 50 percent youth unemployment. A whole generation can't work.
Limited jobs. But ships come in from the harbor, you buy stuff in a big box
store. And then after a while, trucks go off to a trash dump. They describe it
as products in, trash out. Ships come in with products, trash goes out.
What they want to do is
what they call DIDO: data in, data out. The bits come and go, globally
connected for knowledge, but the atoms stay in the city. The idea is you have
fab labs in every district in the city, then when you want furniture or
consumer goods or all of that, instead of working to get money to buy products
made somewhere else, you can make them locally. You might pay somebody else to
make it, or you might do it, but it all stays there. The cities provide
electricity and light and sewers. Now it's this new notion of infrastructure if
they provide the means to make stuff as part of the infrastructure of the city.
In Barcelona's case, the
attraction is whether or not you make anything any different from what you're
buying today, it means you can make many of the things you consume directly
rather than this very odd remote economic loop.
Let me give you more
examples. To talk about what you can make, again, today it requires ten
different machines in a fab lab. In twenty years it's all integrated in one
machine. But a good index is what people do make in fab labs or the How to Make
class and it's awfully close to the range of things today. One good example is
furniture. Anything IKEA makes you can make in a fab lab. The biggest tool is a
ShotBot 4'x8'x1' NC mill, and you can make beautiful furniture with it. That's
what furniture shops use. You can plot out custom furniture. Another example
has to do with mobility. People make bicycle frames. There are serious projects
making DIY cars. One step before that is super go-carts, and there are some
very serious project making cars. Boats are made in fab labs. Consumer
electronics—you can make antennas, radios. There's a couple of surprisingly
successful DIY phone projects, and the most interesting part of the DIY phone
projects is if you're making a do-it-yourself phone, you can also start to make
the things that the phones talk to. You can start to build your own telco
providers where the users provide the network rather than spending lots of
money on AT&T or whoever. To a surprising extent almost any of the things
you buy today you can make. There's consumables, but you can build with them
using the tools in the fab lab.
Let's keep playing through
the benefits of doing it. One is this economic one. We helped the White House
plan a White House Maker Faire and we set up a mobile fab lab literally outside
the Oval Office. This is one of the most sensitive places at the White House.
Even if you have a White House badge, you cannot stand outside the window of
the Oval Office because it's such a sensitive place. The White House guards
were going crazy because we had all our big lasers and machines there.
President Obama loved it. What was going on was the administration couldn't
directly say to American manufacturing, "You're Wang, and Prime, and Data
General," but they could demonstrate it. We had a fab lab at the World
Economic Forum last year for heads of state and CEOs and it's the same thing.
Traditional manufacturing is exactly replaying the script of the computer
companies saying, "That's a toy," and it's shining a light to say
this creates entirely new economic activity. The new jobs don't come back to
the old factories. The ability to make stuff on demand is creating entirely new
jobs.
There's one Danish fab lab
that's been focused on incubating businesses and they counted in ten years the
community lab made a thousand jobs and 300 million euros in turnover. Multiply
that by all of these labs. The new jobs just aren't coming back to the old
factories.
To keep playing that
forward, when I was in Barcelona for the meeting of all these labs hosted by
the city architect and the city, the mayor, Xavier Trias, pushed a button that
started a forty-year countdown to self-sufficiency. Not protectionism. Globally
connected for knowledge but the notion is Barcelona produces what it consumes.
Shenzhen is pivoting to help provide the technology for it. And that's what the
White House Maker Faire was about.
Why I am helping Shenzhen
as an American is two levels deep. My ability to do everything I'm describing
rests on a global supply chain that crucially passes through places like
Shenzhen. I need high-torque efficient motors with integrated lead screws at
low cost, custom-produced on demand. All sorts of the building blocks that let
us do what I'm doing currently rest on a global supply chain including China's
manufacturing agility. The short-term answer is you can't get rid of them
because we need them in the supply chain. But the long-term answer is Shenzhen
sees the future isn't mass producing for everybody. That's a transitional stage
to producing locally.
To be clear, we're not
telling people they should become part of this. Each of these doublings is
people opting to join. But they’re rich, poor, north, south, east, west, rural,
urban, and it's all the same person basically. This leads to the social
engineering. My description of MIT's core competence is it's a safe place for
strange people. These anomalously inventive people that wouldn't function in
normal society fit in a place like that.
The real thing ultimately
that's driving the fab labs ... the vacuum we filled is a technical one. The
means to make stuff. Nobody was providing that. But in turn, the spaces become
magnets. Everybody talks about innovation or knowledge economy, but then most
things that label that strangle it. The labs become vehicles for bright
inventive people who don't fit locally. You can think about the culture of MIT
but on this global scale.
My allegiance isn't to our
border versus anybody else's border, it's to the brainpower of the planet. I
don't know how far this goes. Bright, inventive people whose lives are being
transformed by this—call it one in a hundred—billions of people on the planet
means tens or hundreds of millions of these bright, inventive people that are
exactly the kind that keeps me happily based at MIT. We find them in Arctic
villages and African shantytowns. My allegiance isn't to any one border, it's
to the brainpower of the planet and this is building the infrastructure to
scale to that brainpower.
First let me pause to
relate digital fabrication to digital communication and digital computing
technically and then play out some of the implications. Our modern computer
architecture dates to von Neumann, and you can, in a sense, trace from von
Neumann back to Turing. Both of those were accidents. Turing's machine was
never meant to be an architecture, it was a theoretical construct for a proof.
Von Neumann wrote things he considered profound. He never really wrote about
his architecture. The most he did was he wrote a report on how to program the
EDVAC. Turing's machine and von Neumann's architecture are completely unphysical.
The best way to say it is the head of a Turing machine is distinct from a tape,
and the reason that's so important for being unphysical is a patch of reality
in nature takes time to transit, stores state, admits interaction and occupies
volume. All those resources are
coupled.
In computer science
there's a fiction that they're unrelated. Computing happens in a pretend world
that we then try to make work in a real world. A lot of what's hard now in
computing—programming multicore computing, cache concurrency, back-plane
bandwidth—is like the Matrix, going from the pretend world to the
physical world. There's a completely different parallel history of computing
where you make hardware look like software.
If you zoom from transistors
to microcode to object code to a program, they don't look like each other. But
if we take this room and go from city, state, country, it's hierarchical but
you preserve geometry. Computation violates geometry unlike most anything else
we do. There's an independent history of computing where you make hardware look
like software, and so computer science scales like physics because it's based
on physics. It turns out in many ways that's easier, not harder to do. The
reason that's so important for the digital fabrication piece is once we build
molecular assemblers to build arbitrary systems, you don't want to then paste a
few lines of code in it. You need to overlay computation with geometry. It's
leading to this complete do-over of computer science.
There's a lot of hype now
about the Internet of Things and that's a strange one. I did early work with a
number of the Internet architects on what became called Internet of Things. The
core architectural principle is the Internet succeeded over the Bitnet because
what it does is defined by what you connect to the network—the state is at the
edges of the network. A lot of what's called Internet of Things today is
actually Bitnet of things, meaning it's dumb devices connected to central sites
you can't control. What makes the Internet work is the state is pushed to the
edges so you don't need central control to invent new applications.
If you take digital fab,
plus the real sense of Internet of Things—not the garbled sense—plus the real
future of computing aligning hardware and software, it all adds up to this
ability to program reality. We're going to bring the programmability of the
digital world into the physical world, and it's going to be much bigger than
the earlier digital revolutions because it's out here where we
live.
Go back to Wang, and
Prime, and Data General, there's a whole bunch of incumbent entities across how
we live, learn, work, play, how we divide all of those. The next Silicon Valley
isn't a valley. There's this race for what is the next place going to be? When
you connect digital communication, computation, and fabrication, what you do is
you create networks. There are collaborating networks where you can see people,
you can interact with people, you can share content and crucially, bits become
atoms, atoms become bits. I can do something, I can put it into the computer,
it can come out on your side and become a thing again. I run a giant video
infrastructure and I have collaborators all over the world that I see more than
many of my colleagues at MIT because we're all too busy on campus. The next
Silicon Valley is a network, it's not a place. Invention happens in these
networks.
In Silicon Valley, one of
my students, Jason Taylor, who did a thesis with me on molecular quantum
control, is in charge of Facebook's infrastructure. He's building their data
centers and all the scaling of it. Another one of my students, Raffi Krikorian,
who did early work with a dear colleague, Danny Cohen, on the beginning of
Internet of Things, ran Twitter's infrastructure. A number of former students
are in these unexpected places in the tech revolution. It's not just a
historical accident. It has to do with being grounded in reality and thinking
deeply. I was at Bell Labs before deregulation, which was one of the most
wonderful research environments I was ever in, and it was merciless. People
would come and get mowed down and be challenged, but then they would push back.
You weren't meant to be there forever. It would forever turnover and there was
endless energy in it. MIT, if you add up businesses from it, it's the world's
eleventh economy. It's trillions of dollars of volume. Fewer single
billion-dollar companies but lots of $100 million companies.
You earn your way at MIT
from what you do each day. Your pedigree doesn't matter, and there's all kinds
of turnover and all kinds of energy. When Edwin Land was kicked out of
Polaroid, he made the Rowland Institute, which was making an ideal research
institute with the best facilities and the best people and they could do
whatever they want. But almost nothing came from it because there was no
turnover of the gene pool, there was no evolutionary
pressure.
John Bell's theorem was
published in the Journal of Physics, which was designed only for the
smartest people, ordinary people couldn't publish there. It expired because
there was no turnover and no evolutionary pressure. The way it's related to
this conversation is a lot of tech industry is recreating a failed history of
the wrong way to do research, which is to believe there's a privileged set of
people that know more than anybody else and to create a barrier that inhibits
communication from the inside to the outside rather than recognizing the
attributes: you need evolutionary pressure, you need traffic, you need to be
forced to deal with people you don't think you need to encounter, and you need
to recognize that to be disruptive it helps to know what people know. You do
your homework, you know what people know, then you can turn around and blow it
up, but against a background of having done your
homework.
Right now I think a lot of
the tech industry is in the process of getting that wrong. But the resolution
isn't going to be a better billion-dollar company. Business is going to move
into distributed networks and education is moving into distributed networks.
One person in one fab lab in one village can be a node in a network doing economic
activity and doing research and getting education. It turns on its side all of
our organizations.
For me the hardest thing
isn't the research. That's humming along nicely. It's that we're finding we
have to build a completely new kind of social order and that social
entrepreneurship—figuring out how you live, learn, work, play—is hard and
there's a very small set of people who can do that kind of organizational
creation.
Selected and edited from
EdgeDOTcom, 9 February 15
** **
You are many times interested in
what you do not know and understand, yet you follow through with it anyway –
gleaning what you can for your own purposes imaginary and otherwise. Post. –
Amorella
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