-I'm actually not going to talk
about almost anything that was
in that lovely introduction.
But thank you very much, Judy.
Instead I'm going to
talk only about what
you heard at the very end,
which is this project that I
and a number of
people in the room
have been working on
for a couple of years.
And it's called the
Prediction Project.
On HarvardX, it's
called PredictionX.
Those of you who
know, everything
on EdX and HarvardX--
almost everything--
ends with X. So sorry, but
it's called PredictionX.
And EdX, for those of
you who don't know,
is the online
learning platform that
was started by Harvard and MIT
in 2012, and now has many tens
of partner universities
around the world.
And it provides free
learning content.
And a lot of the
content that's created
takes the form of
standard courses,
like 12-week courses with
problem sets and lectures,
and things like that.
What I'm going to show you today
does not take that form at all.
So our goal in
PredictionX is not only
to bring together people
in the Harvard community
to have a really
interesting conversation
about the whole history of
how humanity has predicted
their own future, but also to
be able to make the material
that we're creating available
as very modular pieces
so that the whole world
can use it and repurpose it
however they like, including
people here at Harvard.
But we've already had
very good feedback
to the pieces we have released.
And I'll show some
of them to you today.
But just to end the
suspense, the whole project
won't be out online until at
least about a year from now
in terms of being finished.
And Judy was correct to say
that I was starting a book
project here to go with it.
I don't know how
long that will take.
I'm sure some of you who have
written books can tell me.
But anyway, so the one
last thing I should say
is that HarvardX,
for those of you who
don't know the local lingo,
is the Harvard arm of EdX.
And we have many,
many people here
from HarvardX today
who can tell you
more about the dozens of
other fantastic courses that
are being created by HarvardX.
But before I tell you
more about the project,
I'm just going to tell
you what I was thinking
when I was in Rome last week.
OK?
So I was in Rome.
And they're doing some
construction near the Coliseum.
And it's rather
bizarre, as you see.
So there's like these weird,
large construction fences.
And there's cranes everywhere.
But then there's
still the Coliseum
at the end of the road.
And then there's guys on
horses-- well, not on horses,
on horse and buggies--
with iPhones in their hand.
OK?
And on this iPhone,
you know, I really
don't think the guy
needs directions
to the Coliseum from there, but
he could have a predicted path
and it would tell
him it would take--
what does that say there-- four
minutes to get to the Coliseum.
And we're just so
used to this, right?
So in other words here you
are looking at something
from the ancient
world and thinking,
well, you know, I could
even do Ifa divination.
We have Jacob
Olupona here, who's
an expert on this tradition
of the Yoruba culture,
which is a kind of divination
that actually takes
15 years to someone to memorize
all the poems that are used
to interpret the outcome
of this shaking of a tray,
that I might talk about later.
But there's an iPhone
app that lets you
do divination on your phone.
I don't know, Jacob.
We'll have to talk
about that later.
If you want to see where
the planets are going
to be tonight in
three dimensions,
if you want to see
where anything is going
to be on the sky, we
understand celestial mechanics
so well now that we have
integrated all of that
into this World Wide
Telescope application
that Judy has talked about
that even runs on your iPhone.
This little image will come
up again later in the talk.
This is an app that we wrote
as part of this project
a long time ago called Take
a Sweater that actually--
and my mother is here.
That name is in her
honor, my Jewish mother.
Anyway, anyway, right.
So you want the weather
forecast with uncertainty.
OK?
So we don't just know how
to simulate the weather,
we know how to figure out how
sure we are about the weather
forecast.
And so that is a
weather forecast
that actually gives, for
the scientists in the room,
error bars associated
with the weather forecast.
And you can go get that.
That might be the most
useful thing you learn today.
takeasweater.com, download
it on the iPhone Apple store,
whatever, just app store.
OK.
You could also learn
to do Haruspicy,
which is divination by sheep
liver, that was practiced
by the ancient Mesopotamians
and the Etruscans
and many cultures over the
last many thousand years.
And there's a beautiful
exhibit at the Louvre
where you can plan your
visit and learn how
to do divination on your phone.
And of course, you could check
the projection of Apple's stock
on your iPhone, too.
Just in case you were concerned
about that while riding down
the street on a horse and
buggy toward the Coliseum.
OK.
So I found this kind of
a very interesting way
to think about why we're
making this course.
People have been interested
in all different questions,
whether they're global
questions or personal questions,
about what's going to happen in
the future since the beginning
of recorded history.
And some of the recordings are
quite difficult to interpret.
Rowan Flad is here
and he's going
to be able to show you how to
do divination by Chinese oracle
bones after, at one
of the demonstrations.
And the truth is even he
who's a big expert-- right,
Rowan-- doesn't exactly
know how they were used.
So we have a lot of
artifacts that we don't even
know exactly how they were used.
OK?
But we know what
they were used for.
And so, really, another
title for this course
could be the Past &
Present of the Future.
OK?
Now how did I come--
Judy and other people
have told me that a way
tt-- an important thing
to include in these talks is
a little bit of background
of how you came to be
interested in this project.
So let me just have
a few slides here
to tell you about the past.
OK?
So if we go back to 2008,
my friend Annie Valva,
where is she?
I'm going to
embarrass you, Annie.
So Annie Valva was at that
point working at WGBH.
And she invited me-- she
now works at HarvardX
and is responsible for
many of the courses
that I was talking about before.
Anyway, so she invited me there
to be a scholar in residence
and help with projects
that they had ongoing.
But she asked me what I
thought the public needed
to know about science that
they didn't already know.
And I said, how important
computer simulation is--
or computer modeling,
or forecasting,
or whatever you want
to call it-- in the way
that science is done.
And that is not
taught in school.
And a huge amount of modern
science and economics,
which we could consider
science, and we're not even
to talk about elections today.
OK?
That is not science.
Anyway, it relies on
computer modeling.
And people think, when you say
computer modeling, lay people
think like, do you mean making
a model with a 3-D printer
or something.
Like they don't even
know what that is.
Right?
And so I said, all
right, well, there
are three very important areas.
One is climate change.
That's the one I
care about the most.
One is the economy and
people's personal wealth.
And then there's their
personal health, sort
of epidemiology and genomics.
And, you know, why
don't we do something
that focuses on explaining
computer simulation.
And so, we thought
about that for a while,
and my friend Howard
Cutler who's either here
or going-- ah, there he is.
Howard, great.
So Howard's visiting.
Howard's retired now.
But at that time, Howard was
an executive producer at WGBH.
And he had this great
idea like, well,
that would be interesting.
But it would be much more
interesting to develop
this 15o-slide deck-- that
I'm not showing today,
Howard-- explaining how
to connect modern computer
simulation to old
fashioned prediction.
Because modern
computer simulation
is just how we predict
our future now.
That iPhone app telling you what
the future of the stock market
is is, just instead of
going out and doing augury
with birds for the Romans.
OK?
We're maybe a little bit
more accurate in most cases.
We care more about how
accurate and how well
we can check our predictions.
And I'll get to that in
a minute when I explain
the structure of the course.
And so let me just also explain
that-- you have a handout.
So I'm not sure if everybody
has one of these handouts.
But you, your neighbor
should have one.
And we're going to
get to this chart that
explains the various
parts of the course now.
But you'll see that
it goes a little bit
in chronological order in terms
of the first, second, and third
part of the course.
And I'll explain
that in a minute.
But I just want to give
credit where credit is due,
that I was really
originally only
interested in the third
part of the course.
But I was easily convinced that
the first two were just as,
if not more, interesting.
So what happened, the
economy collapsed in 2008,
if you didn't notice.
And WGBH was not really going
to add a lot of new projects.
And so, for all I know, this is
still on the books over there.
But anyway, we
didn't really make
much except we had this
idea to make an iPhone app.
Is John Alper here?
Anyway, there's
another person who
was involved at WGBH who said,
oh, there's this new iPhone
thing and we need a teaser app.
So the teaser app
for this project
was supposed to be this weather
forecast with uncertainty,
which it turns out is a
tremendously interesting story
about being unable
to get recorded data
about predictions.
Somebody asked me about
that in the questions.
It's a great story that I'm
not going to tell you now.
But it's why it took three
years to make this app.
And we wound up making it
in conjunction with my data
visualization class.
So then one day, Annie comes
to my data visualization class,
has this idea that we
should turn the Prediction
Project into a HarvardX course.
Because Rob [INAUDIBLE],
who's over there,
has been bothering me
for-- I don't know--
two years at that point to turn
my data visualization class
or something else into a
HarvardX class, which I didn't
want to do because I
wanted to do something even
weirder than that.
And so Annie tells Rob, and next
thing I know by the next day,
the Harvard Corporation is
in support of this effort.
And now here it
is 2016 and people
from all of these schools,
museums, and organizations
are now involved in this
campus wide effort, which
I have had the incredible
privilege of leading.
I have not done this alone.
There are many people
here from HarvardX
who I'll introduce later who
have made this all possible.
So now let me get-- now that
you've prepared your handout--
to the structure of
the actual course
and what we're doing now.
And I'm going to call
it a course or project
interchangeably.
Just know that I don't
really mean course
in the sense of course.
That's kind of a
HarvardX project,
is what they call a course.
OK.
So overall, the structure of
the course looks like this.
And we're going to talk about
the pieces a little bit,
one at a time.
But I already have explained
that today's version
of Prediction has to do with
modern computer simulation.
And what I mean by that is when
they talk about a hurricane
now, and you see a
map, and they show you
the projected path
of the hurricane,
so that's a simulation.
And then they show
you the uncertainty
in the path of the hurricane as
like a little cone coming out
along the path of the hurricane.
So that's the uncertainty
in the forecast of where
the hurricane is going to go.
Most people are familiar
with that kind of thing
when it comes to the weather.
But they don't really
think about how
that affects the
rest of their life,
how economic forecasts
and epidemiological
forecasts and the
climate forecasts
really combine to
determine our future--
the future of the whole world.
So that's why I was
so interested in this.
But if we go back
in time, things
were done very
differently in terms
of how people
thought about this,
how they collected data, how
they tested their predictions.
And the middle part of the
course-- so the first part
of course is really about
ancient predictive systems,
and I'll show you some
examples from that.
And we did start
at the beginning.
So I have a little
bit more about that
in my talk than the later stuff.
But anyway, in the middle,
we talk about the transition
from just collecting information
and making ideas, many of which
are supernatural, to
trying to actually make
predictive theories.
And getting all the way to
Newton's theory of gravity,
which as far as I can tell,
although we'll come back
to an example-- counterexample--
is the most systematic,
mathematical, successful,
predictive theory
that we as humans have today.
And then in
addition, we actually
have developed a number of
technological tools, only a few
of which I'll talk
about, and a number
of kind of pedagogical
frameworks for how
to think about all this
information together.
So the kind of reduced
version of this slide
is that, again, the name of this
is Prediction or PredictionX.
And that's the full
course and what
will become this book about the
past and present of the future.
It has three parts.
The little boxes
that you see in here
have different
outlines around them,
depending on
whether they're just
a topic that's covered in the
course or something that's
more self-contained that makes
a good sort of mini course,
we call it.
And then I mentioned already
the technological tools
and the pedagogical views.
So again, in the
ancient part, we're
talking about a subset of the
many, many traditions that we
could have talked about.
And you'll see later,
I'll explain why we
had to just be opportunistic.
And we couldn't really
cover everything.
And then, when you get to the
time of Aristotle and Ptolemy,
you get to this
transition where people
start thinking about
collecting more and more data
and being systematic about it.
So we'll talk about that.
We'll also use-- I'll mention
comets a couple of times
because comets are
something that now I'm
pretty sure almost everybody
here knows we can predict
the path of comets very well.
But comets appeared to be just
random, crazy, bright things
coming in the sky
at times that people
couldn't guess in the past.
And they were thought of as
messengers from the heavens.
So that's an interesting
example of something that's
now completely predictable
and mundane-- although pretty,
sometimes-- and not
the harbinger of doom
that it was once thought to be.
Another thing that not a lot
of people educated in the west
know about is the importance
of Islamic science
as this transition from the
kind of Greek Aristotelian views
to what we consider
the Renaissance
and what a lot of people call
the Scientific Revolution.
So in the course, we
talk a lot about that.
I won't talk about
that very much today.
But anyway, the main focus of
this rise of theory section
is this path to Newton that
I mentioned before, that I'll
come back to it later.
And then also the
story of the longitude
and how we managed
to figure out how
to navigate at sea by solving
this terrible problem of not
knowing where we were in
going around the Earth.
And then, as a bridge
between that kind
of 1700s, 1800s period
and the modern period,
we talk about the
origins of epidemiology.
And this is the example
I'm going to show you
in a couple of minutes.
I'm just curious.
Raise your hand if you know
what John Snow in cholera means.
Not Game of Thrones.
OK.
OK.
So it's about a third of
the people in the room.
So good.
OK.
Well, we'll talk about
that in a minute.
But anyway, we
use that as a lead
in to modern epidemiology
and personal genomics.
I don't know if
George Church is here.
But we're actually going be able
to feature the Personal Genome
Project in the course as well.
Finance, climate change,
earthquake prediction.
And then like I said
before-- I'll tell you what
the divination tent is later.
Ask me.
OK?
But anyway, like I said before,
these things combine to just
make the future of our world.
OK.
So I promised, I think, that
I would highlight the John
Snow and cholera story.
And I'm going to do something
incredibly dangerous, which
is a live demo.
OK?
So is the internet working here?
Hope so.
OK.
So anyway, just in case
the live demo doesn't work,
the way that this
is organized is
there's a main course page which
has some navigational layout.
And then as part of
the page, we have
things like video
interviews with experts.
And the two experts in
this little mini course
are Don Goldmann,
who you see here,
who is here at Harvard and
an expert on health care
outcomes and public health.
And then also with
this woman who
I'm not going to show you
today, but who's fantastic.
Her name is Rosalind
Stanwell Smith.
And she started something
called the John Snow
Society in London.
And she's-- we interviewed her
at the London School of Hygiene
and Tropical Medicine.
So if I go here to the course--
let's hide everything else.
Let's make this as big
as we possibly can.
We'll resume the course.
It'll ask me to sign in.
I'm giving a full demo
of how you use HarvardX.
OK?
I'll try to zoom
this in a little bit
so you can see what's going on.
So you just get a
little introduction.
And then if we wanted to go to
kind of the introductory video,
it would look like this.
OK?
But I really want to
show you a little bit
of the technology, too.
But kind of the normal
YouTube video format
would look like this.
And we'll hit--
-So we actually went to
London, and we traced out kind
of the footsteps of John Snow.
And to give you just an
overview, here's the story.
-I'm not going to tell
you the story on video.
I'm going to tell
you the story now.
OK?
So very, very
quickly what happened
was people didn't know-- there
was no germ theory of disease.
People did not know that cholera
was a waterborne disease.
And John Snow, the
physician in question,
did believe that cholera
was a waterborne disease.
And he basically traced
this one epidemic,
this one outbreak of
cholera in London,
to a contaminated water pump.
And the story is so
much more interesting
than that, which is why you
should go check out the course.
But today, I'm
going to just show
you a little bit about the
technology of the course.
So, I wanted to show you this.
OK.
So here's my love of data
visualization coming through.
And then I'm going to show
you the annotation in just
a minute.
But so here is-- this
is this famous map
that those of you who know
about Edward Tufte's work
have no doubt seen.
Because Edward Tufte
loves this map.
OK?
But then, if you would
rather see an interactive map
where you can actually
play with the data,
there's a video
demonstration of that.
Or one of the things that you
can do in the EdX platform
is actually include technologies
that run on the web elsewhere.
And so here is a bunch
of interactive maps
where I can turn on
and off various layers.
And so this is a different kind
of visualization showing you
where the density of
deaths was highest.
And by the way, the contaminated
water pump was here.
And this whole thing
is a really great story
that you should
really go check out.
And I can do things like turn
on a map of a different study
that he did before.
And all of this is
discussed in the video
and in the text on the website.
And so basically, the
site is a combination
of video interactives like that.
And one very
interesting feature,
that I meant to show you before
but that I'll show you now,
is you see this button that says
Check Out The Annotated Video.
OK.
So if we go here, and
this will please Howard.
I don't think Howard
has seen this.
This is the kind of thing
that media companies
have been trying to do
for a really long time.
So if I-- instead of running
the regular video-- I do this.
And I say, OK, I'd like to
see a list of annotations.
And then I would like to
search for interesting things
in the video.
And I'd like to just jump
to that part of the video.
And so that's all
searchable as text.
And then the video will
reload with whatever
it is that [INAUDIBLE].
So the whole idea is that
you can jump back and forth
between text, video,
conversations with experts,
tours of the actual place.
And you can have
this kind of rich,
do it the way that
you like experience.
But then if you
want to make sure
that you're understanding it,
there are actually questions
about whether or not you
got this right, including
even like fun little
matching questions where you
can drag the answers around.
And so it's really trying to
take advantage of the fact
that this is an online
platform, not a documentary.
OK?
So a lot of people do
want to just kind of sit
there and watch video,
and that's fine.
They can do that.
And they can do that on
their iPhone as well.
But if they really want a richer
experience, they can have it.
And one thing that
pleases me the most
is that actually the thing
that people have commented
about frequently in the last
few weeks that this has been out
is, we have a 10 minute
version of the video where
you learn this whole story.
But then we actually have
the 40 minute interviews
with each person,
where it's just
like the whole
conversation we had
and all the details that you
would never get on a Nova show
or something like that.
And people who are really
into it enjoy that very much.
OK.
So when we were
talking to Don Goldman,
he made the point that the
way studies are done now-- oh,
do you like my
PredictionX water bottle?
Anyway, the way
studies are done now
is very different than the way
that people collected data,
certainly in ancient times
and even relatively recently.
And so there's something called
the case controlled study.
And he accuses John Snow
in the video of not doing
a proper case controlled study.
And he says, how could
the father of epidemiology
have been such a bad
epidemiologist to not have
the right kind of controls.
Anyway, I don't have time to
tell you the entire story.
But what I want to tell you is
that the whole course is tagged
with these tags that have to do
with how you collect data, how
you design a study,
and how do you
set goals and analyze the data
in the context of the study
to see what you learned.
And so those annotations
that you saw before happen
to the text and the video.
And eventually, you'll be able
to search through the course
to try to understand how
studies have changed over time.
OK.
And then another thing
that we're working on
is this framework for
thinking about how
predictive systems work.
OK?
So if you think
about-- if you try
to make a flow chart almost-- it
doesn't look like a flow chart
anymore because
we've beautified it--
but if you're thinking about
how you would start making
a predictive system,
anybody-- even
in the most ancient,
not recorded times--
starts by observing
some phenomena.
OK?
Often they see this
a lot of times.
And they start to
notice patterns.
And then they try to figure
out if those patterns mean
anything.
And they come up with
some kind of system,
which they might refine later.
OK?
And we broke down the kind
of data that people use,
the kind of observations,
into four categories.
So one of them is what
we call deterministic.
So things like the
motion of planets.
Or once you've figured
out, where are comets
going to be in the sky.
That kind of thing.
OK?
Something where it's
very repeatable.
Newton's theories might apply.
It's very clear.
OK?
Then, kind of at the
opposite end of the spectrum
there, would be the
Oracle of Delphi.
OK?
Something which is completely
open to human influence.
OK?
So not even a priest with a
very well written down system
but really a priest trying
to systematically interpret
something that seems very
open to interpretation.
So anyway, that's what
we call nonrandom.
Then there's an interesting
other random-related choice
which we call randomized.
So things like throwing dice,
or the Ifa divination system
that I mentioned
before, where you're
doing something with a physical
object to randomize an outcome.
And then you have a
manual that says, OK,
if you get that
outcome it means this.
And then the means
this has to usually
be interpreted by a priest.
And then truly random phenomena,
which sometimes turn out
not to be random, like the
comet example I gave you before.
But the classic
example in antiquity
would be augury, which is
the flight of birds, studying
the flight of birds-- when they
appear, how they're flying,
how they sound.
And just to give you
some example, Emma Dench,
she's participating
in the course,
in one of the interviews
explained to us that no Roman
emperor would go to war
without first having
a careful augury done to make
sure that the birds said it was
an auspicious-- by the way
the word auspicious comes
from augery-- time to go to war.
So that kind of system has
been used for a long time.
Now here's the
interesting thing.
I didn't know this till
I started the course.
Well, today if we
make a prediction,
we check if we were
right, or at least we
do the best we can to
check if we were right.
Aristotle, who was the greatest
scientist who ever lived up--
according to Islamic
scientists, Renaissance
scientists, et cetera.
So for like 1,000 years
plus after he lived,
he was the definitive
reference source.
And he believed that you
could reason things out.
And so that if a prediction,
if a predictive system,
seemed right, it was right.
You didn't really have to check.
He did involve-- his
systems involved data,
they just didn't involve going
back and adjusting very much.
It was more about the
beauty of the prediction.
So as I mentioned before,
the Islamic scientists
looked to Aristotle
as their hero.
But after a few hundred years
of translating Greek to Arabic,
they decided that they
didn't necessarily
agree with everything
that he said.
And from my own
research, not necessarily
that all historians of
science would agree with this,
it seems to me that a lot
of the traditions that we've
follow today in
terms of measuring
the accuracy of a
prediction really
came from the 800 to 1,200
AD period of this heyday
of Islamic science.
And so, once you make
those measurements,
then you can start
improving your system.
And it does, unfortunately,
lead to things like epicycles
upon epicycles, and weird ideas
about how the solar system
works that do work but
it's not physically
how the solar system works.
And we'll come back to
that in a little while.
But anyway, then you wind up
with this kind of feedback loop
where you can keep taking more
data, checking out your system,
maybe adding something to it.
OK.
So again the kinds
of examples we're
talking about in the
deterministic system
would be like
celestial motion, Ifa
which is this Yoruba tradition
I mentioned before for something
that's randomized, the comets of
doom are a fun random example.
And I'll show you the first
little bit of fancy video
from HarvardX.
I've chosen one here that I
call the Egyptian bobblehead.
And I don't know if Peter
[? Emanuel ?] is here
and whether he would approve
of my calling it a bobblehead.
But there was an interview
where he showed me
what I'm about to show you with
his hands, not with the video
that you're going to
see, and explained
that this Egyptian doll kind of
nods its head according to what
the priests do to the doll.
And that it's not clear whether
the head was segmented or not.
And I said, oh,
like a bobblehead.
Sort of.
So anyway, here's what
is the real truth.
-And this is when individuals
might approach with a petition.
Yay or nay.
Am I in good shape?
Has someone stolen from me?
What about this
cow that I bought?
Have I paid on the
installment plan correctly?
Or is someone throwing
an accusation at me?
What should I do?
All kinds of
question would come.
So how would the god indicate
what the right thing to do was?
The hieroglyphs
talk about nodding
in approval or disapproval.
And there's some disagreement
about whether the statues
actually had some
moving parts that might
invoke their head a little bit.
That is probably unlikely.
More likely is that
the statue in its bark
would be brought by the
priests either forward
to say yes or
backwards to say no.
Or perhaps lean to
one side or another,
if there were two competing
petitions brought.
And this is how the god would
signal approval or disapproval.
-OK.
Don't you like the
bobblehead idea?
Yeah.
OK.
Anyway.
But the priests were clearly
in control of that system.
Now I'm going to say
several times that we've
had to be very opportunistic
about what people at Harvard
and people we know are
interested and expert
on in creating all the
material for this course.
Because I'll show you just how
many systems for divination
there might be.
And I'm not going to show you--
this is clips from about 10%
of the video that we have.
There's hundreds of videos.
And so I'm only going to
show you just a very few.
But I am going to give credit
to all of the people who
have so far participated, and
there's about this many again
who have promised to
participate in the more
modern pieces of the course.
And so, again, many of
these people are here today.
And you can meet a lot
of them at the reception.
So now let me just
show you a little piece
where I'm trying to highlight
different parts of Harvard
that we used in the course, too.
So I want to show you a piece
that we filmed in the art
museum where they went and got
a special little codex for Laura
Nasrallah, who's a Professor
at the Divinity School,
out of storage so
that she could tell us
how it was used in predictions.
Let me show you.
-The text is titled The
Gospel of the Lots of Mary.
And within it, it
contains multiple oracles.
And we're not really sure
how it would have been
used, to tell you the truth.
What's clear is that someone
held it in their hand.
And if you look
closely at the text,
you can see that it's
been well thumbed.
The middle as each page
is dark with the stains
of sweat and dirt
from a ritual expert
who would have
held it and thumbed
through the pages,
trying to find an answer
to the petitioner's question.
-Yeah.
So that's basically
open to a random page
and then the expert would
explain what that means.
So that's another one of
these randomizing systems.
So, if you remember,
I talked about how
we have these systems
for pedagogical view.
So one of the important
ones that I haven't
mentioned to you
yet is this concept
of so-called Golden Threads.
OK.
And now I'm not going
to read all of this now.
You can go take
the course online.
But I'm just going to
focus on one of them, which
is called our need to know.
And so it's this idea that
people just psychologically
have always wanted
to know their future.
And I love this
little section where
David Carrasco, who is a
professor at the Divinity
School, also explains
to Rowan Flad who's here
and Jacob Olupona who's here
how the Mayan culture thought
about this need
for humans to know.
So I'm just going to play
this little clip for you.
-What you have is a
mythology, some stories that
say, in the beginning, when
human beings were created,
they could see as
well as the deities.
They could all see everything.
And they had this vision to
penetrate great distances.
And then the gods, they didn't
like this about human beings.
So what they did
was they created
what is called-- like
breath on a mirror.
They breathed on
the eyes of humans
so that they could no longer
see in this penetrating way.
-I feel that way sometimes.
-Yeah.
And therefore, what
you need is people
who have been through this
training in order to do it.
Now, the other thing
that Jacob said that
was interesting, this
idea of opening the door.
This opening the door.
And the idea is the same.
It's that you open
a door of vision.
You open a door of
being able to see.
-OK.
So this-- I love this
idea that at first we
were just like the gods and
we could see everything.
And then the gods were, no, no.
This isn't fair.
I'm going to make it really
hard for them to see.
And so then the idea is that
all of these ancient systems
are sort of ways to
see past that fog.
And it turns out that that
idea exists in many cultures.
It's not all about gods
breathing on a mirror,
but it's very much like that.
A gentleman came up to me
just before I started talking
and asked if we had included
divination by patterns
in the sand on the beach.
And I said, no, I'm sorry.
We've really had to
be very opportunistic.
And if you go look at
this web page, which
may be my favorite
Wikipedia page,
called Methods Of Divination.
It goes on-- we'll
just wait here--
for like hundreds of
methods of divination.
And I don't have my glasses,
but let me try here.
What does that say here?
Divination by fish behavior.
OK.
Anyway there is every kind of
divination you could ever want.
And so I apologize again that
we are not covering all of them.
OK.
We are, however,
taking opportunities
to involve students at Harvard.
And in particularly, we have
involved a lot of freshman.
And so I've taught
two freshman seminars.
Some of the freshman seminar
students are here today.
You can ask them
how they liked it.
And we not only discussed
all of the concepts and ideas
and history in the
course, but we also
got to go on field trips.
And so we-- ask me again about
that tent, the divination tent.
We visited the collection
of historical scientific
instruments.
Sara Schechner has been
tremendously involved,
she's the curator
there, in this course.
And Emily Hartman hosted
us at the Houghton Library
to see all kinds of
crazy manuscripts
and beautiful drawings.
And maybe my personal favorite
is Timothy Leary's typewritten
manuscript explaining
the tarot cards
deck is an ancient formula
of neurogenetic symbols which
has been passed
down over centuries,
et cetera, et cetera.
And so Timothy Leary,
while taking LSD I believe,
was-- I'm serious-- was also
experimenting with tarot cards
and their value.
And then I'm actually going
to skip the video of Rowan
doing the oracle bones because
he's actually here to show you
the oracle bones later.
Although I don't believe
there's actual fire involved.
But Mike is here.
Yes, Mike, we did use the
Science Center lecture
hall to set bones on fire.
The lectures staff--
the lab staff
really liked that when I
said, can we make a fire.
Anyway.
Yeah.
So I should also mention
that this year, I
am incredibly privileged
not only to be a fellow here
at Radcliffe, but Radcliffe has
this thing called the Research
Partners Program.
And so I think some
of the students
from that are here too.
Those are all year
students at Harvard.
And they sign up for projects
that they're interested in.
So we have seven students
now working on this project,
on all the different aspects of
the project that you see here.
And I'm just going
to focus on one
because I want to talk about
philosophy for a minute.
So we have one student,
[? Philip ?] [? Chaudry, ?]
who's looking at the philosophy
of prediction over time.
And the moment
that did it for me
when I understood how bad
it is to impose 21st century
values on ancient times
was this moment right here
when I asked the
potentially stupid question
of a real historian, Emma
Dench, whether or not
people like Aristotle actually
believed in the Greek gods.
And this is a very
controversial question.
She gave me the answer
you're about to see.
But ask what answer Steven
Weinberg would give later
in the questions.
But I'll show you Emma's answer.
-In Greek and Roman antiquity,
not to believe that the gods--
that there are signs
from the gods--
-That is heresy.
- --would be-- no,
it's not heresy.
It's like-- it would
be about the same level
as saying that I don't believe--
I absolutely don't believe--
anything that science tells us.
It would be about the same.
-So we went on in
that conversation.
And I said, so you
mean like I would
say I don't believe in gravity.
She said yes.
OK.
And so the idea of believing
in something that was just
part of your culture
and part of your being
and your understanding
is something
that depends on the
cultural context.
And I really have learned
that in creating this course.
OK, so let's move
on a little bit
to the second piece and
talk about this path
to Newton and navigation.
Before I do that, and this
is with apologies to Owen,
I added this little
note here that says,
diagrams are not to scale.
The best historian of
science in the world
and of astronomy--
sorry, of astronomy,
I don't insult your colleagues,
Owen-- is here, Owen Gingerich.
And when I made this
slide, he said, oh no,
that's not to scale.
And indeed it's not.
OK.
But this is just this
for the non-astronomers
here to understand what
we mean by epicycles
and how the solar
system really works.
So this picture in the
middle here-- maybe we
can turn the lights down just
a little bit so people can see.
But the picture in the middle
is how the solar system really
works.
I'm sure you all know this,
but the sun is in the middle.
The earth and the other
planets go around the sun.
And the moons of the
planets go around them.
OK?
And Copernicus had that right.
And it wasn't until
Galileo, later,
that it was demonstrated
in a very interesting way
that I unfortunately don't
have to tell you about today.
But interestingly, before that
I mentioned epicycles before.
And from 150 AD-- from
the time of Ptolemy--
on, people believed that
the earth was-- well,
Aristotle also believe that
the Earth was in the center
of-- not just the solar system,
but the universe because that
was the same thing.
But then in order to get the
observations to work out,
yeah, you can have the
sun go around the Earth.
But then you have to do
all kinds of crazy things
with the other planets to have
epicycles-- little circles--
that they go in as they
go around the Earth
to make the projection of that
system work out on the sky.
So this is what I mean
that you collect data
and you can have
empirical theories, which
is math that lets
you make predictions,
but they're not based
on a physical theory.
OK?
They're just math.
And so this kind of
epicycles does work.
And then in order to get around
this problem of the Earth
philosophically being not at the
center of the universe, which
was very bad, Tycho
Brahe preferred
this-- in my opinion--
craziest system
where the Earth
is in the middle.
The moon goes around the Earth.
And then the sun and
all the other planets
go around the Earth.
No comment.
But anyway, so that's
as recently as 1587.
And then Galileo,
who I'm not really
going to talk about
very much, but who
we have a beautiful piece on,
put an end to that pretty much
in about 1610.
So that was just a little
cheat sheet for you.
And I'm going to show
you little bits of video.
And I'm not going to explain
this incredibly messy diagram
on the blackboard.
But I just want to show you
what we do with HarvardX.
So one day, it was
like, all right,
let me trace out the entire
path from the Babylonians
and the Greeks all the way
to how we solved navigation.
And, you know, what did
Copernicus and Newton do along
the way, and et cetera.
And again, we can talk
about that in the questions
because this would take two
hours to fully explain this.
So instead, I'm just going to
show you some clips of a dinner
party that we had
at my house where
I was very privileged to
have Owen and Sara, the two
local experts, over in
addition to John Hughes who's
also here somewhere, who's
a professor of physics, yes.
And also our science-- one of
the co-directors of science
here at Radcliffe.
They came over with
Dava Sobel, who
wrote this book
about the longitude
that we'll come to later when
we talk about navigation.
Some of you may have read that.
And also Curtis
Wang, my friend who
invented WorldWide Telescope.
And so let me just show you
a few clips from that dinner
party because I forgot to
mention that sometimes we
call this course an
academic reality show.
Because we're trying to
make it a little bit more
like real conversations
that people
who are interested in
each other's subjects
would have-- sort of
like what's supposed
to go on at Radcliffe-- than a
perfectly edited documentary.
So here's just a little
bit of it, edited.
Here you go.
We took out the part where the
cat walked across the table.
-When did people tried
to figure out how
to explain celestial motion?
-The Babylonians had a
predictive system of sorts,
but they couldn't
follow the planets
all the way through their
cycles in any theoretical base.
But what they discovered
was that if they picked
the right period-- maybe
45 years, maybe 60 years--
you could get, let us say
Mars, coming back and going
through the same general motions
that they had seen before.
-Isn't that their
kind of association,
that these good things
or bad things happen?
And they happen when they
notice that a planet is
in a particular position.
And is that any kind of
prediction point, to be able
say watch out for when Mars is
there in that position again?
-Certainly there are omens,
and things to look out for.
-And I'm not showing
you again, today,
that we let people reproduce--
all of the comments that
are made about astronomy,
they can reproduce them
in this WorldWide
Telescope program online.
OK.
So I gave an entire talk on
Radcliffe's behalf in a series
that John Hughes organized
called Navigation Lecture
Series.
And I'm not going to give
you that entire talk now.
I'm just going to tell you
that there's another issue that
comes right after Newton
where the British empire,
and other empires
mostly in Europe,
are trying to expand
around the world.
And what's stopping them
from being able to expand
is all these ships
getting lost at sea.
And the reason that the
ships get lost at sea
is because using the
stars, it turns out
to be pretty easy to
find your latitude--
so how far North, South you are.
But because the Earth
just spins on its axis
and there is no natural
reference point,
it's very hard to
find your longitude.
And so this is an
obscure problem
that not a lot of people
today even know was a problem.
But just trust me that at the
time that this was a problem,
it was like trying to
find a cure for cancer
in the early 1700s.
And there was something called
the Longitude prize which
was ultimately won
by a clock maker,
not by the astronomers,
who figured out
a method for finding
your longitude at sea
using the motion of the Earth
and very accurate clocks.
So again, we can talk
about that later.
But for now, let
me just show you
a little bit of what
that part of the course
will look like using
some highlights
from the original lecture
I gave last year about it.
So this is the book, by the way.
This is the famous book about
the longitude that Dava wrote,
which by the way has
an illustrated edition
with illustrations helped
out by Will Andrews, who
used to be the curator
of Historical Scientific
Instruments here.
And the whole story of this book
started at Harvard, as well.
And Dava will be
visiting Radcliffe again
later this year.
So more about that, too.
So here's the problem.
Let's say you're out at sea and
you want to know where you are.
No problem.
You have your trusty iPhone.
OK?
And you just turn it on.
You get the GPS.
I don't care that it's dark.
No problem.
Light out, dark
out, doesn't matter.
But, oh, wait a minute.
Oh.
Big problem.
What I'm going to do now?
Well, hm, when I'm
on land the way
most people navigate
is by landmarks.
The sort of natural way
that people navigate
is by knowing oh, those
mountains are over there.
I went around those mountains.
Now I have to go to the
next mountains, et cetera.
And a lot of people don't
even notice the movement
of stars on the sky.
But when you take away all the
landmarks and you're at sea,
you can only see the
motions of stars on the sky.
And you better know
a lot about that
if you want to use
them to navigate.
And like I said before,
the height at that point,
where you see all the
stars going around,
North Star, and the height that
stars reach on a given night,
can actually tell you where
you are, what latitude.
But you need something
else, something
that isn't so symmetric,
to tell you the longitude.
And that again is difficult.
But we have three demonstrations
that are listed on the
back here at the reception
after this talk where you
can talk to the experts
about how this
problem was solved.
And instead, I'm just going
to tell you a little bit more
about what we'll
be talking about.
And then I'll show a
little bit of sample video.
And I'll be done in
five or six minutes.
So anyway, in order to figure
out where you are at sea,
you need a combination of
these things-- you need math,
you need some way to
measure your speed,
you need some way to
very accurately measure
the positions of
stars, to keep time,
to know what direction
you're going,
and to know where
you are on a map.
OK?
And the problem was that a
very long time ago-- this
is a sort of schematic graph
of the uncertainty associated
with each of those kinds of
measurements, which by the way
is being improved
with accurate images.
Thank you, Sara.
But anyway, the uncertainty
was tremendously large.
And then it got
smaller and smaller.
But it was hard to take a
grandfather clock to sea,
so we'll talk about that later.
And I see Phil Sadler here, too.
He should contribute to our--
he's our expert on navigation,
teaches navigation at Harvard.
So Phil you're going
to the demo table.
Thank you, very much.
So Phil will
explain all of this,
along with our other experts.
And it got better and better.
This is John Harrison's
clock that he
made to solve the problem.
And the reason that we don't
understand this problem now is
because we basically have no
uncertainty in all of those
things, as long as the
[INTERPOSING VOICES] So
here's some old--
-And I'll start with this.
This is an octant.
It's made about 1800.
And it's made of wood
and has an ivory scale
here with [INAUDIBLE].
And my favorite part about
octants of this period
is they often come with this
handy little pencil that
sits in here, comes out.
And so you make
your observation,
and then you say, oh, OK.
That's 23 and 1/2 degrees.
And then on the back here,
you have this handy little pad
made of ivory where you
scribble down your observation.
Because on a deck of a ship
where it's windy and wet,
you're not going to have
a paper lying around.
It's just-- it's handy.
-So that is one of
thousands of instruments
that Sarah can show you in
our collection, many of which
are not even out on display.
They're in the basement of
the Science Center, which is
an incredible treasure trove.
And then let me show you
a little bit more here.
-To successfully
use the lunar orbit,
you had to solve the
so-called three body problem.
And Newton took a crack
at it, but his-- turns out
that his calculations were off.
It was recognized that if you
have a sort of full-on three
body problem, it's basically
impossible to solve.
-Just as the
physicists in the room,
let's just make sure
everybody knows why.
OK?
So we have this glass--
-We have two glasses.
-And then we have a tea cup.
All right.
And so, now you make your
glass orbit the other glass.
-So you can solve this
motion of the two glasses--
-Oh, no.
I'm coming through here, but
I'm affecting the other things,
and they're affecting me.
And I can't update
all of these things
at the same time [INAUDIBLE].
So I can't just write
down some equation.
-OK, so we're trying to
explain why that is not
a deterministic problem.
And if you keep
going in the video,
you get a little bit
more explanation.
And then, of course, they'll
be like a companion interactive
that has a little
three body simulation
and lets students play with
it, not glasses and teacups.
OK.
And so last, but
certainly not least,
I want to be sure
to thank HarvardX
and explain what this
has allowed us to do.
So I mentioned a little bit
about how we went to London
and I interviewed the person who
started the John Snow Society.
That's Rosalind Stanwell Smith.
We also got to go to all these
other places in conjunction
with the Isaac, path to
Newton, and the longitude
part of the course.
And I just have to tell you that
the reason I look so incredibly
happy-- how many of you
know what the Principia is?
OK.
For those of you, that half
of you who don't, that's
Newton's manuscript where he
laid out his theory of gravity.
This is the Principia, not
a copy of the Principia.
This is Newton's copy,
with [? Halle's ?] notes
scribbled in the
margins, which I
got to hold in the archives
of the Royal Society.
So this is like, for a
physicist, that's nirvana.
OK?
So this was very, very exciting.
This, by the way, is a
letter to [? Halle ?] asking
for some money for somebody.
And one of the letters
that I'm not showing you
is Newton sending a
proposal saying, you know,
I have this idea for a
new kind of telescope.
And the astronomers in the room
will think this is very funny.
And it's basically a proposal,
like I need 100 pounds
so I can build this thing.
Anyway, so I've
given you, hopefully,
some sense of most of
the pieces of the course.
And I just want to mention
one or possibly two,
but I think one
more thing, which
is we're actually-- in
conjunction with the course--
building a new standard for
time tagging information.
This is my interest in
data that Judy mentioned.
Turns out that in order to
automatically add information
to automated timelines
that students could build
and compare different,
say, regions of the world
here with each other and
populate the information
on there.
And filter it by all
kinds of different tags
like geographic
regions or topics,
and be able to see what was
going on in the Greek world
while such and such
was going on in China.
And I only want to know
about rulers and divination.
I don't care about
whatever food, you know.
You want to be able to
filter your timeline
in real time like that.
Turns out that could be done
in a crowdsourced kind of way
if there was a standard for
time tagging information.
And that's a technical
challenge, and to some extent
a sociopolitical
challenge, that again I
can talk about if
you want to ask me
about it in the questions.
But the good news is
that, as far as we know,
the Sloan Foundation is going to
fund this project, which we're
doing in collaboration with
most of the organizations
you see here-- the New York
Times, The New York Public
Library, Microsoft,
and MIT Press.
So the people, I've mentioned
most but not all of them who
are participated so far beyond
Harvard are listed here.
Here's Howard and Curtis, who
I mentioned at the beginning.
And this is Keith Moore, the
archivist at the Royal Society.
And then the people who
deserve the most credit here
are the people from HarvardX.
And maybe if you just
at least raise your hand
gently if you're from HarvardX.
OK.
That's the crew right there--
Drew and Colin and Jared--
who have made most of the
material that you see happen.
And so I really want to
thank them tremendously--
and you'll be able to meet
them at the reception--
because they work
very, very, very hard.
And I just would like
to wish you the best
from the sheep of destiny here.
This is a real coaster
I have in my office,
if you'd like to come visit it.
And if you'd like to ask
where our logo came from,
I'll be happy to leave
that up and explain.
Thank you very much.
