How to pronounce "najjar"

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[Applause] this is a photograph by the artist

this is a photograph by the artist

this is a photograph by the artist Michael Najjar and it's real in the

Michael Najjar and it's real in the

Michael Najjar and it's real in the sense that he went there to Argentina to

sense that he went there to Argentina to

sense that he went there to Argentina to take the photo but it's also a fiction

take the photo but it's also a fiction

take the photo but it's also a fiction there's a lot of work that went into it

there's a lot of work that went into it

there's a lot of work that went into it after that and what he's done is he's

after that and what he's done is he's

after that and what he's done is he's actually reshaped digitally all of the

actually reshaped digitally all of the

actually reshaped digitally all of the contours of the mountains to follow the

contours of the mountains to follow the

contours of the mountains to follow the vicissitudes of the Dow Jones index so

vicissitudes of the Dow Jones index so

vicissitudes of the Dow Jones index so what you see that precipice that high

what you see that precipice that high

what you see that precipice that high precipice with the valley is the 2008

precipice with the valley is the 2008

precipice with the valley is the 2008 financial crisis the photo was made when

financial crisis the photo was made when

financial crisis the photo was made when we were deep in the valley over there I

we were deep in the valley over there I

we were deep in the valley over there I don't know where we are now this is the

don't know where we are now this is the

don't know where we are now this is the Hang Seng Index or Hong Kong and similar

Hang Seng Index or Hong Kong and similar

Hang Seng Index or Hong Kong and similar topography I wonder why and this is art

topography I wonder why and this is art

topography I wonder why and this is art right this is metaphor but I think the

right this is metaphor but I think the

right this is metaphor but I think the point is is that this is metaphor with

point is is that this is metaphor with

point is is that this is metaphor with teeth and it's with those teeth that I

teeth and it's with those teeth that I

teeth and it's with those teeth that I want to propose today that we rethink a

want to propose today that we rethink a

want to propose today that we rethink a little bit about the role of

little bit about the role of

little bit about the role of contemporary math not just financial

contemporary math not just financial

contemporary math not just financial math but math in general that it's

math but math in general that it's

math but math in general that it's transition from being something that we

transition from being something that we

transition from being something that we sort of extract and derive from the

sort of extract and derive from the

sort of extract and derive from the world to something that actually starts

world to something that actually starts

world to something that actually starts to shape it the world around us in the

to shape it the world around us in the

to shape it the world around us in the world inside us and it specifically

world inside us and it specifically

world inside us and it specifically algorithms which are basically the math

algorithms which are basically the math

algorithms which are basically the math that computers used to decide stuff they

that computers used to decide stuff they

that computers used to decide stuff they acquire the sensibility of truth because

acquire the sensibility of truth because

acquire the sensibility of truth because they repeat over and over again and they

they repeat over and over again and they

they repeat over and over again and they kind of ossify and calcify and they kind

kind of ossify and calcify and they kind

kind of ossify and calcify and they kind of become real and I was thinking about

of become real and I was thinking about

of become real and I was thinking about this of all places on a transatlantic

this of all places on a transatlantic

this of all places on a transatlantic flight a couple years ago because I

flight a couple years ago because I

flight a couple years ago because I happen to be seated next to a Hungarian

happen to be seated next to a Hungarian

happen to be seated next to a Hungarian physicist about my age and we were

physicist about my age and we were

physicist about my age and we were talking about what life was like during

talking about what life was like during

talking about what life was like during the Cold War for physicists in Hungary

the Cold War for physicists in Hungary

the Cold War for physicists in Hungary and I said so what were you doing and he

and I said so what were you doing and he

and I said so what were you doing and he said well we were mostly breaking

said well we were mostly breaking

said well we were mostly breaking stealth and I said that's a good job

stealth and I said that's a good job

stealth and I said that's a good job that's interesting how does that work

that's interesting how does that work

that's interesting how does that work and so to understand that you have to

and so to understand that you have to

and so to understand that you have to understand a little bit about how

understand a little bit about how

understand a little bit about how stealth works and so this is a

stealth works and so this is a

stealth works and so this is a oversimplification but basically it's

oversimplification but basically it's

oversimplification but basically it's not like you can just pass a radar

not like you can just pass a radar

not like you can just pass a radar signal right through

signal right through

signal right through 156 tons of steel in the sky it's not

156 tons of steel in the sky it's not

156 tons of steel in the sky it's not just going to disappear

just going to disappear

just going to disappear but if you can take this big massive

but if you can take this big massive

but if you can take this big massive thing and you could turn it into a

thing and you could turn it into a

thing and you could turn it into a million little things something like a

million little things something like a

million little things something like a flock of birds well then the radar

flock of birds well then the radar

flock of birds well then the radar that's looking for that has to be able

that's looking for that has to be able

that's looking for that has to be able to see every flock of birds in the sky

to see every flock of birds in the sky

to see every flock of birds in the sky and if you're a radar that's a really

and if you're a radar that's a really

and if you're a radar that's a really bad job

bad job

bad job and he said yeah he said but that's if

and he said yeah he said but that's if

and he said yeah he said but that's if you're a radar he said so we didn't use

you're a radar he said so we didn't use

you're a radar he said so we didn't use a radar we built a black box that was

a radar we built a black box that was

a radar we built a black box that was looking for electrical signals

looking for electrical signals

looking for electrical signals electronic communication and whenever we

electronic communication and whenever we

electronic communication and whenever we saw a flock of birds that had electronic

saw a flock of birds that had electronic

saw a flock of birds that had electronic communication we thought probably has

communication we thought probably has

communication we thought probably has something to do with the Americans and I

something to do with the Americans and I

something to do with the Americans and I said yeah that's that's good that's good

said yeah that's that's good that's good

said yeah that's that's good that's good so you've effectively negated 60 years

so you've effectively negated 60 years

so you've effectively negated 60 years of aeronautical research what's your act

of aeronautical research what's your act

of aeronautical research what's your act to you know like what do you do when you

to you know like what do you do when you

to you know like what do you do when you grow up and he said he said well you

grow up and he said he said well you

grow up and he said he said well you know financial services and I said oh

know financial services and I said oh

know financial services and I said oh because those have been in the news

because those have been in the news

because those have been in the news lately and I said I said how does that

lately and I said I said how does that

lately and I said I said how does that work and I said well there's 2,000

work and I said well there's 2,000

work and I said well there's 2,000 physicists on Wall Street now and I'm

physicists on Wall Street now and I'm

physicists on Wall Street now and I'm one of them and I said well so what's

one of them and I said well so what's

one of them and I said well so what's the black box for Wall Street and he

the black box for Wall Street and he

the black box for Wall Street and he said well it's funny that you asked that

said well it's funny that you asked that

said well it's funny that you asked that because it's actually called black box

because it's actually called black box

because it's actually called black box trading and it's also sometimes called

trading and it's also sometimes called

trading and it's also sometimes called algo trading algorithmic trading and

algo trading algorithmic trading and

algo trading algorithmic trading and algorithmic trading involved in part

algorithmic trading involved in part

algorithmic trading involved in part because institutional traders have the

because institutional traders have the

because institutional traders have the same problems that the United States

same problems that the United States

same problems that the United States airforce had which is that they're

airforce had which is that they're

airforce had which is that they're moving these positions whether it's

moving these positions whether it's

moving these positions whether it's Procter and Gamble or etc or whatever

Procter and Gamble or etc or whatever

Procter and Gamble or etc or whatever they're moving like a million shares of

they're moving like a million shares of

they're moving like a million shares of something through the market and if they

something through the market and if they

something through the market and if they do that all at once it's like playing

do that all at once it's like playing

do that all at once it's like playing poker and just going all-in right away

poker and just going all-in right away

poker and just going all-in right away right you just tip your hand and so they

right you just tip your hand and so they

right you just tip your hand and so they have to find a way and they use

have to find a way and they use

have to find a way and they use algorithms to do this to break up that

algorithms to do this to break up that

algorithms to do this to break up that big thing into a million little

big thing into a million little

big thing into a million little transactions and the magic and the

transactions and the magic and the

transactions and the magic and the horror of that is is that the same math

horror of that is is that the same math

horror of that is is that the same math that you use to break up the big thing

that you use to break up the big thing

that you use to break up the big thing into a million little things can be used

into a million little things can be used

into a million little things can be used to find a million little things and sew

to find a million little things and sew

to find a million little things and sew them back together and figure out what's

them back together and figure out what's

them back together and figure out what's actually happening in the market so if

actually happening in the market so if

actually happening in the market so if you need to have some image of what's

you need to have some image of what's

you need to have some image of what's happening in the stock market right now

happening in the stock market right now

happening in the stock market right now what you can picture is a bunch of

what you can picture is a bunch of

what you can picture is a bunch of algorithms that are basically programmed

algorithms that are basically programmed

algorithms that are basically programmed to hide

to hide

to hide and a bunch of algorithms that are

and a bunch of algorithms that are

and a bunch of algorithms that are programmed to go find them and act and

programmed to go find them and act and

programmed to go find them and act and all of that's great and it's fine and

all of that's great and it's fine and

all of that's great and it's fine and that's 70% of the United States stock

that's 70% of the United States stock

that's 70% of the United States stock where 70% of the operating system

where 70% of the operating system

where 70% of the operating system formerly known as your pension

formerly known as your pension

formerly known as your pension your your mortgage and what could go

your your mortgage and what could go

your your mortgage and what could go wrong right what could go wrong is is

wrong right what could go wrong is is

wrong right what could go wrong is is that a year ago

that a year ago

that a year ago 9% of the entire market just disappears

9% of the entire market just disappears

9% of the entire market just disappears in five minutes and they called it the

in five minutes and they called it the

in five minutes and they called it the flash crash of 2:45 right all of a

flash crash of 2:45 right all of a

flash crash of 2:45 right all of a sudden 9% just goes away and nobody to

sudden 9% just goes away and nobody to

sudden 9% just goes away and nobody to this day can even agree on what happened

this day can even agree on what happened

this day can even agree on what happened because nobody ordered it nobody asked

because nobody ordered it nobody asked

because nobody ordered it nobody asked for it nobody had any control over what

for it nobody had any control over what

for it nobody had any control over what was actually happening all they had was

was actually happening all they had was

was actually happening all they had was just a monitor in front of them that had

just a monitor in front of them that had

just a monitor in front of them that had the numbers on it and just a red button

the numbers on it and just a red button

the numbers on it and just a red button that said stop and that's the thing

that said stop and that's the thing

that said stop and that's the thing right is is that we're writing things

right is is that we're writing things

right is is that we're writing things we're writing these things that we can

we're writing these things that we can

we're writing these things that we can no longer read and it's we've we've

no longer read and it's we've we've

no longer read and it's we've we've rendered something kind of illegible and

rendered something kind of illegible and

rendered something kind of illegible and we've lost the sense of what's actually

we've lost the sense of what's actually

we've lost the sense of what's actually happening in this world that we've made

happening in this world that we've made

happening in this world that we've made and we're starting to make our way

and we're starting to make our way

and we're starting to make our way there's a company in Boston called

there's a company in Boston called

there's a company in Boston called Nanak's and they use math and magic and

Nanak's and they use math and magic and

Nanak's and they use math and magic and I don't know what and they reach in to

I don't know what and they reach in to

I don't know what and they reach in to all the market data and they find

all the market data and they find

all the market data and they find actually sometimes some of these

actually sometimes some of these

actually sometimes some of these algorithms and they when they find them

algorithms and they when they find them

algorithms and they when they find them they they pull them out and they pin

they they pull them out and they pin

they they pull them out and they pin them to the wall like butterflies and

them to the wall like butterflies and

them to the wall like butterflies and they do what we've always done when

they do what we've always done when

they do what we've always done when confronted with huge amounts of data

confronted with huge amounts of data

confronted with huge amounts of data that we don't understand which is that

that we don't understand which is that

that we don't understand which is that they give them a name and a story so

they give them a name and a story so

they give them a name and a story so this is one that they found they called

this is one that they found they called

this is one that they found they called the knife

the carnival

the carnival

the carnival the Boston shuffler

the Boston shuffler

the Boston shuffler Twilight and the

Twilight and the

Twilight and the gag is that of course these aren't just

gag is that of course these aren't just

gag is that of course these aren't just running through the market right you can

running through the market right you can

running through the market right you can find these kinds of things wherever you

find these kinds of things wherever you

find these kinds of things wherever you look once you learn how to look for them

look once you learn how to look for them

look once you learn how to look for them right you can find it here this book

right you can find it here this book

right you can find it here this book about flies that you may have been

about flies that you may have been

about flies that you may have been looking at on Amazon you may have

looking at on Amazon you may have

looking at on Amazon you may have noticed it when its price started at 1.7

noticed it when its price started at 1.7

noticed it when its price started at 1.7 million dollars it's out-of-print still

million dollars it's out-of-print still

million dollars it's out-of-print still if you had bought it at 1.7 it would

if you had bought it at 1.7 it would

if you had bought it at 1.7 it would have been a bargain a few hours later it

have been a bargain a few hours later it

have been a bargain a few hours later it had gone up to twenty three point six

had gone up to twenty three point six

had gone up to twenty three point six million dollars plus shipping and

million dollars plus shipping and

million dollars plus shipping and handling and the question is nobody was

handling and the question is nobody was

handling and the question is nobody was buying or selling anything what was

buying or selling anything what was

buying or selling anything what was happening and you see this behavior on

happening and you see this behavior on

happening and you see this behavior on Amazon as surely as you see it on Wall

Amazon as surely as you see it on Wall

Amazon as surely as you see it on Wall Street and when you see this kind of

Street and when you see this kind of

Street and when you see this kind of behavior what you see is the evidence of

behavior what you see is the evidence of

behavior what you see is the evidence of algorithms in conflict algorithms locked

algorithms in conflict algorithms locked

algorithms in conflict algorithms locked in loops with each other without any

in loops with each other without any

in loops with each other without any human oversight without any adult

human oversight without any adult

human oversight without any adult supervision to say actually 1.7 million

supervision to say actually 1.7 million

supervision to say actually 1.7 million is plenty you stick with it and as with

is plenty you stick with it and as with

is plenty you stick with it and as with Amazon so it is with Netflix and so

Amazon so it is with Netflix and so

Amazon so it is with Netflix and so Netflix has gone through several

Netflix has gone through several

Netflix has gone through several different algorithms over the years they

different algorithms over the years they

different algorithms over the years they started with cinema and they've they've

started with cinema and they've they've

started with cinema and they've they've tried a bunch of others there's dinosaur

tried a bunch of others there's dinosaur

tried a bunch of others there's dinosaur planet there's gravity they're using

planet there's gravity they're using

planet there's gravity they're using pragmatic chaos now pragmatic chaos is

pragmatic chaos now pragmatic chaos is

pragmatic chaos now pragmatic chaos is like all of Netflix algorithms trying to

like all of Netflix algorithms trying to

like all of Netflix algorithms trying to do the same thing it's trying to get a

do the same thing it's trying to get a

do the same thing it's trying to get a grasp on you on the firmware inside the

grasp on you on the firmware inside the

grasp on you on the firmware inside the human skull so that it can recommend

human skull so that it can recommend

human skull so that it can recommend what movie you might want to watch next

what movie you might want to watch next

what movie you might want to watch next which is a very very difficult problem

which is a very very difficult problem

which is a very very difficult problem but the difficulty of the problem and

but the difficulty of the problem and

but the difficulty of the problem and the fact that we don't really quite have

the fact that we don't really quite have

the fact that we don't really quite have it down it doesn't take away from the

it down it doesn't take away from the

it down it doesn't take away from the effects the pragmatic chaos has bring

effects the pragmatic chaos has bring

effects the pragmatic chaos has bring out a chaos like all Netflix algorithms

out a chaos like all Netflix algorithms

out a chaos like all Netflix algorithms determines in the end 60% of what movies

determines in the end 60% of what movies

determines in the end 60% of what movies end up being rented right so one piece

end up being rented right so one piece

end up being rented right so one piece of code with one idea about you is

of code with one idea about you is

of code with one idea about you is responsible for 60% of those movies but

responsible for 60% of those movies but

responsible for 60% of those movies but what if you could rate those movies

what if you could rate those movies

what if you could rate those movies before they get made right wouldn't that

before they get made right wouldn't that

before they get made right wouldn't that be handy well so a few data scientists

be handy well so a few data scientists

be handy well so a few data scientists from the UK or in Hollywood and they

from the UK or in Hollywood and they

from the UK or in Hollywood and they have story algorithms and company called

have story algorithms and company called

have story algorithms and company called epic oh jokes and you can run your

epic oh jokes and you can run your

epic oh jokes and you can run your script through there and they can tell

script through there and they can tell

script through there and they can tell you quantifiably that that's a 30

you quantifiably that that's a 30

you quantifiably that that's a 30 million dollar movie or a 200 million

million dollar movie or a 200 million

million dollar movie or a 200 million dollar movie and the thing is is that

dollar movie and the thing is is that

dollar movie and the thing is is that this isn't Google right this isn't

this isn't Google right this isn't

this isn't Google right this isn't information these aren't financial stats

information these aren't financial stats

information these aren't financial stats this is culture and what you see here or

this is culture and what you see here or

this is culture and what you see here or what you don't really see normally is is

what you don't really see normally is is

what you don't really see normally is is that these are the physics of culture

that these are the physics of culture

that these are the physics of culture and

and

and if these algorithms like the algorithms

if these algorithms like the algorithms

if these algorithms like the algorithms on Wall Street just crashed one day and

on Wall Street just crashed one day and

on Wall Street just crashed one day and went awry how would we know what would

went awry how would we know what would

went awry how would we know what would it look like and

it look like and

it look like and they're in your house right there in

they're in your house right there in

they're in your house right there in your house right these are two

your house right these are two

your house right these are two algorithms competing for your living

algorithms competing for your living

algorithms competing for your living room these are two different cleaning

room these are two different cleaning

room these are two different cleaning robots that have very different ideas

robots that have very different ideas

robots that have very different ideas about what clean means and you can see

about what clean means and you can see

about what clean means and you can see it if you slow it down and attach lights

it if you slow it down and attach lights

it if you slow it down and attach lights to them and there's sort of like secret

to them and there's sort of like secret

to them and there's sort of like secret architects in your bedroom yeah and the

architects in your bedroom yeah and the

architects in your bedroom yeah and the idea that architecture itself is somehow

idea that architecture itself is somehow

idea that architecture itself is somehow subject to algorithmic optimization is

subject to algorithmic optimization is

subject to algorithmic optimization is not far-fetched it's super real and it's

not far-fetched it's super real and it's

not far-fetched it's super real and it's happening around you you feel it most

happening around you you feel it most

happening around you you feel it most when you're in a sealed metal box a new

when you're in a sealed metal box a new

when you're in a sealed metal box a new style elevator they're called

style elevator they're called

style elevator they're called destination control elevators these are

destination control elevators these are

destination control elevators these are the ones where if to press what floor

the ones where if to press what floor

the ones where if to press what floor you're going to go to before you get in

you're going to go to before you get in

you're going to go to before you get in the elevator and it uses what's called a

the elevator and it uses what's called a

the elevator and it uses what's called a bin packing algorithm so none of this

bin packing algorithm so none of this

bin packing algorithm so none of this mishegoss of just letting everybody go

mishegoss of just letting everybody go

mishegoss of just letting everybody go into whatever car they want everybody

into whatever car they want everybody

into whatever car they want everybody wants to go the tenth floor goes into

wants to go the tenth floor goes into

wants to go the tenth floor goes into car two and everybody wants to go the

car two and everybody wants to go the

car two and everybody wants to go the third floor goes into car five and the

third floor goes into car five and the

third floor goes into car five and the problem with that is is that people

problem with that is is that people

problem with that is is that people freak out people panic and you see why

freak out people panic and you see why

freak out people panic and you see why right you see why it's because the

right you see why it's because the

right you see why it's because the elevator is missing some important

elevator is missing some important

elevator is missing some important instrumentation like the buttons right

instrumentation like the buttons right

instrumentation like the buttons right like the things that people use all it

like the things that people use all it

like the things that people use all it has is just the number that moves up or

has is just the number that moves up or

has is just the number that moves up or down and that red button that says stop

down and that red button that says stop

down and that red button that says stop and this is what we're designing for

and this is what we're designing for

and this is what we're designing for we're designing for this kind of machine

we're designing for this kind of machine

we're designing for this kind of machine dialect all right and how far can you

dialect all right and how far can you

dialect all right and how far can you take that how far can you take it you

take that how far can you take it you

take that how far can you take it you can take it really really far and so let

can take it really really far and so let

can take it really really far and so let me take it back to Wall Street okay

me take it back to Wall Street okay

me take it back to Wall Street okay because the algorithms of Wall Street

because the algorithms of Wall Street

because the algorithms of Wall Street are dependent on one quality above all

are dependent on one quality above all

are dependent on one quality above all else which is speed and they operate on

else which is speed and they operate on

else which is speed and they operate on milliseconds and microseconds and just

milliseconds and microseconds and just

milliseconds and microseconds and just to give you a sense of what microseconds

to give you a sense of what microseconds

to give you a sense of what microseconds are it takes you five hundred thousand

are it takes you five hundred thousand

are it takes you five hundred thousand microseconds just to click a mouse but

microseconds just to click a mouse but

microseconds just to click a mouse but if you're a Wall Street algorithm and

if you're a Wall Street algorithm and

if you're a Wall Street algorithm and you're five microseconds behind you're a

you're five microseconds behind you're a

you're five microseconds behind you're a loser

loser

loser so if you were an algorithm you'd look

so if you were an algorithm you'd look

so if you were an algorithm you'd look for an architect like the one that I met

for an architect like the one that I met

for an architect like the one that I met in Frankfurt who was hollowing out a

in Frankfurt who was hollowing out a

in Frankfurt who was hollowing out a skyscraper throwing out all the

skyscraper throwing out all the

skyscraper throwing out all the furniture all the infrastructure for

furniture all the infrastructure for

furniture all the infrastructure for human use and just running steel on the

human use and just running steel on the

human use and just running steel on the floors to get ready for the stacks of

floors to get ready for the stacks of

floors to get ready for the stacks of servers to go in all so that an

servers to go in all so that an

servers to go in all so that an algorithm could get close to the

algorithm could get close to the

algorithm could get close to the internet and

internet and

internet and you think of the Internet as this kind

you think of the Internet as this kind

you think of the Internet as this kind of distributed system and of course it

of distributed system and of course it

of distributed system and of course it is but it's distributed from places

is but it's distributed from places

is but it's distributed from places right in New York this is where it's

right in New York this is where it's

right in New York this is where it's distributed from its carrier hotel

distributed from its carrier hotel

distributed from its carrier hotel located on Hudson Street and this is

located on Hudson Street and this is

located on Hudson Street and this is really where the wires come right up

really where the wires come right up

really where the wires come right up into the city and the reality is is that

into the city and the reality is is that

into the city and the reality is is that the further away you are from that

the further away you are from that

the further away you are from that you're a few microseconds behind every

you're a few microseconds behind every

you're a few microseconds behind every time these guys down a Wall Street Marco

time these guys down a Wall Street Marco

time these guys down a Wall Street Marco Polo and Cherokee Nation they're eight

Polo and Cherokee Nation they're eight

Polo and Cherokee Nation they're eight microseconds behind all these guys going

microseconds behind all these guys going

microseconds behind all these guys going in to the empty buildings being hollowed

in to the empty buildings being hollowed

in to the empty buildings being hollowed out up around the carrier hotel right

out up around the carrier hotel right

out up around the carrier hotel right and that's going to keep happening we're

and that's going to keep happening we're

and that's going to keep happening we're going to keep hollowing them out because

going to keep hollowing them out because

going to keep hollowing them out because you inch for inch and pound for pound

you inch for inch and pound for pound

you inch for inch and pound for pound and dollar for dollar none of you could

and dollar for dollar none of you could

and dollar for dollar none of you could squeeze revenue out of that space like

squeeze revenue out of that space like

squeeze revenue out of that space like the Boston shuffler could

the Boston shuffler could

the Boston shuffler could but if you zoom out if you zoom out you

but if you zoom out if you zoom out you

but if you zoom out if you zoom out you would see an 825 mile trench between New

would see an 825 mile trench between New

would see an 825 mile trench between New York City and Chicago's been built over

York City and Chicago's been built over

York City and Chicago's been built over the last few years by a company called

the last few years by a company called

the last few years by a company called spread networks this is a fiber-optic

spread networks this is a fiber-optic

spread networks this is a fiber-optic cable that was laid between those two

cable that was laid between those two

cable that was laid between those two cities to just be able to traffic one

cities to just be able to traffic one

cities to just be able to traffic one signal 37 times faster than you can

signal 37 times faster than you can

signal 37 times faster than you can click a mouse just for these algorithms

click a mouse just for these algorithms

click a mouse just for these algorithms just for the carnival and the knife and

just for the carnival and the knife and

just for the carnival and the knife and when you think about this that we're

when you think about this that we're

when you think about this that we're running through the United States with

running through the United States with

running through the United States with dynamite and rock saws so that an

dynamite and rock saws so that an

dynamite and rock saws so that an algorithm can close the deal three

algorithm can close the deal three

algorithm can close the deal three microseconds faster all for a

microseconds faster all for a

microseconds faster all for a communications framework that no human

communications framework that no human

communications framework that no human will ever know

will ever know

will ever know that's a kind of manifest destiny and

that's a kind of manifest destiny and

that's a kind of manifest destiny and we'll always look for a new frontier and

we'll always look for a new frontier and

we'll always look for a new frontier and fortunately we have our work cut out for

fortunately we have our work cut out for

fortunately we have our work cut out for us this is just theoretical this is some

us this is just theoretical this is some

us this is just theoretical this is some mathematicians at MIT and the truth is I

mathematicians at MIT and the truth is I

mathematicians at MIT and the truth is I don't really understand a lot of what

don't really understand a lot of what

don't really understand a lot of what they're talking about it involves light

they're talking about it involves light

they're talking about it involves light cones and quantum entanglement and I

cones and quantum entanglement and I

cones and quantum entanglement and I don't really understand any of that but

don't really understand any of that but

don't really understand any of that but I can this map and what this map says is

I can this map and what this map says is

I can this map and what this map says is is that if you're trying to make money

is that if you're trying to make money

is that if you're trying to make money on the markets where the red dots are

on the markets where the red dots are

on the markets where the red dots are that's where people are where the cities

that's where people are where the cities

that's where people are where the cities are your going to have to put the

are your going to have to put the

are your going to have to put the servers where the blue dots are to do

servers where the blue dots are to do

servers where the blue dots are to do that most effectively and the thing that

that most effectively and the thing that

that most effectively and the thing that you might have noticed about those blue

you might have noticed about those blue

you might have noticed about those blue dots is that a lot of them are in the

dots is that a lot of them are in the

dots is that a lot of them are in the middle of the ocean

middle of the ocean

middle of the ocean so that's what we'll do we'll build

so that's what we'll do we'll build

so that's what we'll do we'll build bubbles or something or or platforms

bubbles or something or or platforms

bubbles or something or or platforms will actually part the water right to

will actually part the water right to

will actually part the water right to pull money out of the air because it's a

pull money out of the air because it's a

pull money out of the air because it's a bright future if you're an algorithm and

bright future if you're an algorithm and

bright future if you're an algorithm and it's not the money that's so interesting

it's not the money that's so interesting

it's not the money that's so interesting actually it's what the money motivates

actually it's what the money motivates

actually it's what the money motivates right that we're actually terraforming

right that we're actually terraforming

right that we're actually terraforming the earth itself with this kind of

the earth itself with this kind of

the earth itself with this kind of algorithmic efficiency and in that light

algorithmic efficiency and in that light

algorithmic efficiency and in that light you go back and you look at Michael no

you go back and you look at Michael no

you go back and you look at Michael no jars photographs and you realize that

jars photographs and you realize that

jars photographs and you realize that they're not metaphor they're prophecy

they're not metaphor they're prophecy

they're not metaphor they're prophecy right they're prophecy for the kind of

right they're prophecy for the kind of

right they're prophecy for the kind of seismic

seismic

seismic terrestrial effects of the math that

terrestrial effects of the math that

terrestrial effects of the math that we're making and the the landscape was

we're making and the the landscape was

we're making and the the landscape was always made by this sort of weird uneasy

always made by this sort of weird uneasy

always made by this sort of weird uneasy collaboration between nature and man but

collaboration between nature and man but

collaboration between nature and man but now there's this kind of third

now there's this kind of third

now there's this kind of third co-evolutionary force algorithms the

co-evolutionary force algorithms the

co-evolutionary force algorithms the Boston shuffler the carnival and we will

Boston shuffler the carnival and we will

Boston shuffler the carnival and we will have to understand those as nature and

have to understand those as nature and

have to understand those as nature and in a way they are thank

in a way they are thank

in a way they are thank [Applause]

[Applause]

[Applause] [Music]