[MUSIC PLAYING]
SPEAKER 1: Hey, everyone.
Thanks for coming out.
We have a great talk,
because today, we
have with us Professor
Chris Lintott.
He's a professor of astrophysics
at the University of Oxford
and is also known as the
presenter of BBC's "The Sky
at Night."
Today, he's going to be talking
to us about his new book,
"The Crowd and
the Cosmos," which
details his creation of
the Zooniverse, which
is a crowdsourcing platform
that lets everyday science
enthusiasts take part in
cutting-edge research.
The Zooniverse Project
lets anyone with a computer
classify galaxies or keep
tabs on endangered giraffes
or investigate the history
of Australian prisoners.
So there's a lot of
really cool stuff,
and I recommend that
you check it out--
but probably after the talk.
So now, if you
don't mind, please
make sure that your
cellphones are silenced
and join me in a warm welcome
for Professor Chris Lintott.
CHRIS LINTOTT: Thank you.
[APPLAUSE]
Thank you very much.
I've turned the
microphone on, I hope.
Yes, thank you for being
here, taking time out
of your busy days.
I really want to
start with the fact
that we're in the middle of
a revolution in astronomy.
I grew up as a small
kid with a telescope--
more about which later.
But I thought I'd missed
out, because for me,
the golden age of Apollo, of
Neil Armstrong's one small
step, of space exploration--
crude exploration--
was over.
But I've slowly come
to realize that this
is the golden age of
astrophysics, of astronomy,
of understanding the universe.
And so when we look at
a night sky-- and this
is a randomly selected picture
of a random piece of the night
sky.
Yes, we see the same
stars we've always seen.
You can see the bright
nebulae, the gas there
from which new stars will form.
And if you know a
little bit more,
you know that some
of those dark patches
are places where there's dust,
grains of carbon and silicon
from which planets and, indeed,
stars might go on to form.
But now, for the
first time in history,
when we look at a
star field like this,
we now know that most of
those stars have planets.
And so we're looking not just
at the glorious universe,
but we're looking at
a map of the worlds
that we can imagine exploring.
So I think there's
something really profound
about that idea, that when
you go outside and look up,
if you spot a star
in the night sky,
it probably has planets
going around it.
I can even show you
some of those planets.
This is my favorite
astronomical observation.
This is a star called HR8799.
Astronomers should not be
allowed to name anything.
But HR8799.
Actually, most of the light
from the star is missing here.
So we subtracted it away.
The little yellow
thing in the middle
is not what the star
actually looks like.
That's been added back in.
But if I animate this over
the course of several years,
you can see there are four
blobs in orbit around the star.
And those are four planets
going around this star.
They're called HR8799
B, C, D, and E,
because astronomers shouldn't
be allowed to name anything.
They're a bit bigger
than our solar system.
So the innermost one goes
around once every 45 years,
the outermost one
once every 460 years.
And they're a few times bigger
than Jupiter, the largest,
and Neptune at the smallest.
So it's like an exploded
version of our solar system.
But really, my point is that
we've all seen this animation.
Every time Brian Cox goes on
television and pontificates
about planets, you see this
for our own solar system.
But this is real data.
And we can now watch planets
going around another star.
Now, for most of the stars
that we've found have planets,
we can't do that.
Normally, the glare of
the star is too bright.
It's not that the planets
are too faint to be seen,
it's that the stars
are too bright.
And picking out an Earth-sized
planet around a sun-like star,
even in our local
galactic neighborhood,
is rather like trying
to spot a firefly flying
in front of a stadium floodlight
from about 50 miles away.
So what we do instead is
come up with indirect ways
to discover planets.
This is an object
that you will have
remembered from the summer.
This is the Sun.
We haven't seen it for a while.
This is the Sun on a
particular day in 2012, when
Venus went in front of the sun.
It's called a transitive Venus.
Things like this--
Venus transits the sun
about twice a century.
The next one's about
100 years away.
But it's quite a
spectacular sight.
On the right, you've got the
exciting version of this,
never mind the video.
This is a graph made
with a school light
meter, which records the
total brightness of the sun.
And you can see that when
Venus is in front of the sun,
it appears slightly fainter.
Astronomy's a really
simple science.
We deduce that something must be
getting in the way of the sun.
And if you were in the right
place in the galaxy such
that this alignment
happened every time
Venus orbited the sun, you'd
see a repeated pattern of dips.
And from that, you could deduce
that there was a planet there.
You have its orbital period.
And you might even work out
the size of the planet based
on the structure of this dip.
So that's what we do.
We look at hundreds of
thousands of stars and we search
through the data to look for
repeated patterns of dips.
So here's an example.
This is a
randomly-selected star.
This is a month's worth of data
from a satellite called Kepler
which, for three years,
stared at 150,000 stars,
measuring their brightness
every 29 minutes.
This is a month's worth of data.
So you see there's a lot of
natural variation in the star.
It gets brighter and
fainter over time.
The camera is operating at
the edge of its performance.
But I've colored
in here what you
can see is that
it's a dip caused
by a Neptune-sized planet.
And so this is how
we've got the evidence
for the remarkable fact
that I started with--
for the idea that there are
planets around most stars.
Of course, we don't just have
to worry about single planets.
We can find solar
systems, as well.
My favorite is called K2128,
planets B, C, D, and F, luckily
in that order, which is nice.
But this is a remarkable system
of at least five planets, where
the planets are rather special.
They're all crammed
close to their star.
Even F, the outermost
planet, is closer to its star
than Mercury is to the sun.
So there's five planets
crammed around the star.
But they're in what's
called a resonant pattern.
So for every 3 times B goes
round, C goes round twice.
For every three orbits C
completes, D goes round twice.
For every three orbits D
completes, E goes round twice.
And for every three orbits E
completes, F goes round twice.
This is cool, because
we can use that to make
interesting animations.
So this is by my friend Matt
[? Russo. ?] What he's done
is, he's turned the system
into a musical instrument.
So each planet
will cause a chime.
The pitch of the
chime will depend
on the size of the orbit.
Let's see if we can
get this to play.
There we go.
Do that again.
So each node will have a-- each
planet will have a note based
on the size of its orbit.
Hopefully you can hear that.
[NOTES PLAYING]
And the point is really
that that sounds musical
because there's this
resonant pattern.
It's actually-- you had a
perfect fifth between each
of the--
in each of the notes.
And this resonant pattern
tells us, first of all,
that these planets
formed together.
So it tells us something
about how these planets formed
and evolved.
And it also holds up the
hope that by studying
the interaction of the
planets with each other,
we can learn a lot
more about the system.
There's a European
satellite called
CHEOPS which will launch
just in the next few weeks
which is going to
follow up on this system
and watch the transits
in more detail
and try and work out the
masses and perhaps even
the structures of
some of these planets.
But the reason I'm telling you
about this system in particular
is because it wasn't discovered
by professional astronomers.
It was found by a
bunch of volunteers
on a website called Planet
Hunters, led by this guy.
This is Andrew Gray,
who's a mechanic in Darwin
in Australia.
This is him being interviewed
after his discovery.
His boss gave him the
day off to do interviews,
as long as he did them at work,
with a suitable background,
and in the company uniform.
So--
[LAUGHTER]
--if you ever need a BMW
serviced in Darwin, then
you should let me know.
Andrew was just so excited
by having discovered planets.
He was asked on television
how he planned to celebrate.
He's an amateur astronomer.
He has a telescope.
And so he said he was going
to take his telescope out
that night and have a
beer with it-- which
I thought was rather wonderful.
But more to the point, we've
gone from the first exoplanets,
planets around other
stars, were found in 1995.
The discoveries of those
planets just won the Nobel Prize
for that discovery.
And so in about 20
years, we've gone
from Nobel Prize winning
science to something
you could do with a website.
And I think that's illustrative
of the speed with which things
are changing.
I got involved in
this because I grew up
as an amateur astronomer.
Most professional astronomers
are refugee mathematicians
or physicists who've
found their way
into the scientific problems
that astronomy presents.
I come from a
different background.
I grew up with a
small telescope,
looking at the stars,
trying to make a discovery.
And in fact, I'd
already decided that I
wanted to discover a
comet, in particular,
for the very good
reason that comets are
named after their discoverers.
If you discover an
asteroid, you have
to name it after somebody else.
If you find a comet,
it's all yours.
A comet Lintott, I think--
I still think-- has an
excellent ring to it.
I never found a comet.
I did spend ages with a small
telescope looking at something
called the Orion nebula.
And the Orion nebula is
our nearest stellar nursery
to the Sun.
So it's a place
rather like the nebula
in which the Sun would've formed
about five billion years ago.
I love it, because
when you look at it
through a small telescope, you
can really see structure in it.
You see this green, glowing
gas which wraps itself
around clusters of
young, newly-formed stars
that have just emerged from
the mist of the nebula.
I was looking at it one
evening when I was about 14,
and I nudged my telescope,
and I got this view.
The nebula's off to the
bottom of the screen here.
And there's this rather
nice little star cluster.
This is long enough
ago that the best
I could do to look up this star
cluster was pull out a book.
I don't need to explain
books to this group, I know,
but you get the idea.
They don't update.
And so I pulled
out my star atlas,
and this cluster wasn't there.
And I remember taking a pencil
and marking across and writing
Lintott 1 next to it.
[LAUGHTER]
And of course, when I got
to school the next day,
this is actually called NGC1981.
It's not as good a
name as Lintott 1,
but it was discovered
250 years earlier.
So I think that
was the point where
I realized that I was out
of date as to how astronomy
happened.
It wasn't people with
telescopes looking
for individual discoveries.
And in fact, as I moved on
and got my PhD and so on,
I realized astronomy had became
a science of big data, a survey
science.
And so instead of
using small telescopes,
we use things like this.
This is the Sloan
Digital Sky Survey.
It's a two and a
bit meter telescope
in an observatory in New
Mexico, where it's clear
about 325 nights a year.
And for eight years,
this telescope
did a remarkable thing.
It just stared at the sky,
and it allowed the sky
to pass over it.
It just took pictures
of whatever happened
across its field of view.
The only exception to that
was on the clearest nights,
when Sloan went back to the
million or so galaxies that it
had found-- systems
like our own Milky Way--
and it measured the
distance to them.
And so with all
of that data, that
meant that for
the first time, we
had a decent three-dimensional
map of our local universe.
So I'm going to show
you a bit of that map.
I've got about half a million
galaxies in this animation.
We're going to start with
the Milky Way, our own system
of a few hundred
billion stars, and we'll
travel outwards
into the universe
through the Sloan data.
And the first thing you notice
is that space is pretty big.
There are gaps
between the galaxies.
You get out to maybe about here
and you start to see something
else-- something
remarkable, which
is that there is structure.
Even on these very large scales,
the universe is sort of lumpy.
There are places where
there are loads of galaxies.
There are places where
there are very, very few.
If we're writing press releases,
we call this the cosmic web.
And you can really see it
when we get this far out.
We'll stop in a minute and
rotate the universe for you,
and you'll get a proper
sense of this sort
of honeycomb structure.
And this was somewhat unexpected
when astronomers found this.
And it kickstarted a revolution
in how we think about galaxies
like the Milky Way.
In particular, people
started wondering
whether where your galaxy
sits in this cosmic web
might make a difference to the
evolution of the galaxy itself.
So a galaxy that grows
up in the equivalent
of a city, a dense
cluster, may be
different from one that
grows up in the middle
of a void, an empty region.
Our Milky Way, by the
way, is sort of suburban.
We're neither in a big
cluster but nor right out
in the middle of nowhere.
So that was the
problem I was supposed
to work on when I moved
to Oxford in 2006.
Here are two
galaxies from Sloan.
They exist in
different environments.
They behave differently.
We wanted to understand exactly
how that process worked.
Astronomy's a simple science.
So we can take these
beautiful images
and reduce them to really
simple measurements.
These two galaxies, for
example, are different colors.
One is blue and one is red.
This is a highly sophisticated
scientific measurement
that tells us about--
actually, it tells us
about star formation.
So when you form stars, you
form red stars and yellow stars
and blue stars.
But the blue ones
are massive, and they
burn through their
fuel very quickly.
So they last for only about--
I don't know, a couple
of hundred million years
or something.
And so we know that this blue
galaxy on the left has formed
stars in the last few
hundred million years,
whereas the one on the
right probably hasn't.
But you can also see that these
galaxies are different shapes.
One's a spiral,
one's an elliptical--
a big ball of stars.
And we've known for
about eight years
that the shape of a galaxy
tells you about its history.
So the shape the galaxy--
if we're being technical,
it's the integrated
dynamical history.
But it's the orbits
of the stars that
create the shapes that we see.
And what that means is
that every time a galaxy
collides with another galaxy,
every time it absorbs material
from its surroundings, every
time it forms stars and those
stars interact, the shape
of the galaxy changes.
And so we realized that
what we needed to do
was label all of the million
galaxies in Sloan with a shape.
Now, in the '50s
and '60s, people
had done this sort of research.
Back then, there were a couple
of hundred good galaxies
available.
And so professors would
spend their careers
labeling galaxies and
arguing about the results.
By the '80s, new surveys--
new photographic surveys
had provided
thousands of galaxies.
And it was discovered
that PhD students
could classify galaxies.
Few thousand's a bit too
many for a professor,
so it's fine-- you get
your student to do that.
We had a million galaxies.
The student's name was
Kevin, Kevin [? Sharinsky. ?]
And we set him the
task of working
his way through these galaxies.
And Kevin, in a couple
of weeks, classified
50,000 of these
galaxies, splitting them
into spirals and ellipticals.
And the first result
of Kevin's thesis
was that that's about as many
as a PhD student will stand.
[LAUGHTER]
That's not quite how he put
it, but it's what he meant.
The second result was, he was
better than the neural networks
at the time that people
had used to attack
this problem automatically.
It really mattered to
have a person do this.
And so in desperation,
we launched this website,
GalaxyZoo.
You'll remember web
design from 2007.
Yes, this had frames,
that sort of thing.
And this gave you a
randomly-selected galaxy,
and it asked the
simplest question
that we thought would be useful.
We said, is it an
elliptical or a spiral?
If it's a spiral, tell us
which way the arms are turning.
And the idea is that this
would be a nice side project.
I thought I'd go and talk
to people like yourselves,
to amateur astronomy groups.
Maybe I'd talk to an
audience of 50 people.
Maybe 50 of them will go home
and do 50 classifications.
And we thought, in
five years, we'd
work our way through
a million galaxies.
The web doesn't work like that.
And instead, on day 1, we had
70,000 classifications an hour
coming in.
And GalaxyZoo has
now received hundreds
of millions of
classifications of galaxies
from well over a
million people have
contributed to this project.
The really exciting thing
is that, taken collectively,
those results are good.
They were better
than the machine
learning we had at the time.
They're actually
better than Kevin,
because a crowd will always
outperform a single, slightly
distractable expert.
And so we could sort our
galaxy into elliptical,
to clockwise spirals, and
anti-clockwise spirals.
And just for fun, I
want to make sure I--
I told you about the clockwise
and anti-clockwise spirals.
The shape there tells you which
way the galaxy is rotating.
So clockwise and
anti-clockwise is the direction
of spin of the galaxy.
And we put this
question in because we
were annoyed by a
particle physicist,
a guy called Michael
Longo, who had
looked at a couple
of thousand galaxies
and said that the universe
had more anti-clockwise
than clockwise galaxies.
And this is a result that
makes no sense for two reasons.
Firstly, there's a
rule in astronomy
called the Copernican Principle,
which sounds kind of grand.
But it's a fundamental
assumption,
which is that we assume
that our bit of the universe
isn't special.
So any observation you
make on large scales
shouldn't depend on where
you are in the universe.
And of course, if I'm
looking up at an array
of anti-clockwise galaxies, if
I fly to the other side of them,
I see an array of
clockwise galaxies.
So that's disturbing.
The other thing is
that Sloan covered
about this much of the sky.
And there would have been
no time for information
to travel from the galaxies
over here to over here--
not enough time
since the Big Bang.
And so the only
way to arrange this
would be a sort of
cosmic conspiracy
that made sure that all
the bits of the universe
knew that they were supposed
to produce slightly more
anti-clockwise galaxies.
But 2,000 galaxies isn't a lot.
That's like tossing a coin
twice, getting two heads,
and concluding that all
coins always produce heads.
And we had 250,000 galaxies.
No problem.
Easy statistical test.
We can tell Longo
that he's wrong.
Except that we found more
anti-clockwise than clockwise
galaxies as well--
to some insane level
of significance.
And so we worried
about this for a bit.
We teased our
theoretical colleagues
that they didn't
understand anything.
And then we thought
we better check
that this was a real result.
So without telling anyone, we
switched all of the galaxies
for their mirror images.
So people were now
seeing the opposite.
So we should then have
got more clockwise
than anti-clockwise galaxies.
But we still got more
anti-clockwise galaxies.
[LAUGHTER]
So it's not the
universe that's odd.
It's us.
Something about how
we see these images
makes it easier to
see an anti-clockwise
than a clockwise galaxy.
One suggestion, my
favorite, is something
to do with the rotating
figure illusion.
I don't know if
you know this one.
So imagine you're looking
down on the cat here.
How many people see the
cat rotating clockwise?
Few people.
Good exercise in peer
pressure, this, as well.
How many people see it
going anti-clockwise?
Slightly more.
How many people
have seen it change?
There's a few.
First of all, can we-- we need
to acknowledge that it's weird
that we don't agree, right?
That's slightly disturbing.
If you want it to
change, if you do this--
it makes no difference
at all, but it amuses me.
So that's--
[LAUGHTER]
Yeah, yeah, yeah.
My favorite thing about
this slide is, I could say--
I could go and have
lunch and come back,
and you'd all still
be sitting here.
So the point is that
this is-- what's
happening is that this is a
two-dimensional image projected
into a 3D world.
And your brain has
choices to make
about how to interpret that.
And there are different options,
and your brain will randomly
choose between them.
But over time, there's
a bias in any population
to one side or the other.
So maybe something about
how you see spiral galaxies
is similar to that in some way.
So we ended up doing applied
psychology by mistake.
Slightly uncomfortable
for an astronomer.
But we're able to account
for this bias, measure it.
And the results from
GalaxyZoo went on
to be immensely useful
and interesting.
I don't have time to tell
you about all of them.
I thought I'd just pick
out one piece of work which
I thought would be interesting.
At the center-- so that
was a map of our galaxy.
It's got a bar at the
center, which is good news.
Couple of spiral arms.
But right in the
middle of our galaxy,
we have a remarkable thing.
These are stars near the
center of our galaxy.
And this is data taken
over about the course
of a decade using
the Keck telescopes
on Mauna Kea in Hawaii.
And what you can
see is that we're
able to get sufficient
resolution that we
can see how these stars move.
They are, in fact, in
orbit around something
at the center of our galaxy.
And because we know how
massive the stars are--
we vaguely understand stars--
we can work out that the thing
at the center that they're
orbiting must weigh
a few million times
the mass of the Sun--
and that mass must be crammed
into a volume of space
that would fit within
the orbit of Neptune.
And our physics says that
that must be a black hole.
So this is the best
evidence we have
for the presence
of the black hole
at the center of our galaxy.
Now, our galaxy's black hole
is a perfectly friendly beast.
It's reasonably quiescent.
But if you're overfeed a black
hole, then things can happen.
This is the nearby galaxy M87.
It's a bigger galaxy.
It's got a much
larger black hole.
Material is falling
down onto it right now.
And you can see there's
a jet of material that's
had a very lucky escape.
It's ejected just
before it falls
into the black hole because
of some complex physics
that goes on right
at the center.
And in fact, there's
a general rule
that we've discovered
about galaxies.
It's very simple.
The more massive
the galaxy, the more
massive the black
hole at its center.
So the bigger the galaxy,
the bigger the black hole.
And this is interesting.
Physicists like things
that align like that.
That feels like we're
getting some insight.
But actually, it's
also confusing,
because our galaxy weighs a few
hundred billion times the mass
of the Sun.
And the black hole
at the center--
although we call it a
supermassive black hole,
it only weighs about
3 million times.
So million versus
hundreds of billions.
So the black hole should be
completely insignificant.
And yet somehow, whenever
we look at a galaxy,
we see how massive the
galaxy is, the black hole is
roughly the same mass.
And so this is a problem
that needs explaining.
And there's a
natural explanation
that people have
reached for, which
is through colliding galaxies.
So this is a computer simulation
of the future of our galaxy.
Now I said we live
in the suburbs.
We actually live in a little
local group of galaxies.
There's us and there's
the Andromeda Galaxy
and there's another
spiral called Triangulum.
The clock's ticking in the
bottom right of this image.
And in a few
billion years' time,
the Milky Way and Andromeda are
going to merge with each other.
So there'll be a
dramatic collision
between the two galaxies.
Now, it won't be as
dramatic as it sounds.
There's enough space
between the stars
that no two stars collide
in a collision like this.
But the two galaxies, in about
4, 5, 6 billion years' time,
will collide to form
a single galaxy--
which, I'm afraid say,
some of my colleagues
like to call Milkdromeda,
because astronomers
shouldn't name anything.
I'm realizing I don't
have any power here,
and this is about
to go to sleep.
So that's going
to be interesting.
Could somebody else come
and fiddle with the power?
Thanks.
So there's a natural explanation
here for what's going on.
If galaxies grow mostly by
merging with each other,
and if their black holes
combine after a merger,
then you can grow
the two in step.
Your galaxy gets bigger--
so when the Milky Way
collides with Andromeda,
it will double in mass.
If their black
holes collide, that
will be a doubling
in mass, as well.
And you can explain
this very naturally.
But we needed to test this idea.
And we used GalaxyZoo.
In fact, my colleagues Brooke
Simmons, now at Lancaster,
and Becky Smethurst, now
at Christchurch in Oxford,
and also a YouTube
star, used a set
of galaxies derived from
GalaxyZoo to test this.
They took a bunch
of spiral galaxies
that, because of their
shapes, we could tell never
had a merger.
And so these are guaranteed
merger-free galaxies.
And we could measure
their black hole mass.
And we expected them to have
much smaller black holes
than we would otherwise expect.
But it turns out they have
the same sized black hole
as a normal galaxy.
And so mergers are not the
answer to this problem.
We've done that very
scientifically joyful thing
as ruling out the only obvious
hypothesis, which is great fun.
But there's a
broader point here.
Brooke and Becky
and myself ended up
using a sample-- they're all
on screen-- of 120 galaxies.
But I started this talk
by arguing that astronomy
is about big data.
We started with a
million galaxies.
We ended up studying 120.
And that pattern
keeps happening.
You end-- start with these
large data sets, but actually,
what we're mostly trying to
do is find the right things
to look at within the data set
to simplify a problem and gain
insight that way--
which brings me on to penguins.
It turned out that
astronomers are not
the only people who struggle
with getting too much data.
This is an image from a camera
network run by my friend Tom
Hart, who used to go to the
Antarctic to count penguins.
He describes himself
as a penguinologist.
That's on BuzzFeed's list of
the top 10 job titles ever
and that sort of thing.
Tom used to go once a year
to each penguin colony
and count them.
He's interested in whether
climate change is affecting
them, or whether fishing in the
southern ocean is a problem,
or whether the increased
tourism in Antarctica
is causing a problem.
He used to go once a year
and count and compare
how they were doing.
But obviously, if it's raining
one year and not the other,
or if the spring had been sunny
and the penguins had nested
earlier, or if a
tourist ship had
been an hour before Tom turned
up, the data is pretty sketchy.
And so Tom realized
he could take
cheap, off-the-shelf
cameras and a car battery,
set up a network of cameras that
could take pictures like this
every hour, and he could
monitor the penguins
over the course of a year.
So this is a
typical image from--
it actually looks like
the penguin's taking
action against the camera.
But I don't think
that's what's going on.
A typical image.
And Tom has simple questions.
He comes back to Oxford with 8
million of these images a year,
and he needs to know how many
penguins are in this image.
So audience participation time--
how many penguins
are in the image?
AUDIENCE: Three.
AUDIENCE: Three.
CHRIS LINTOTT: There's
usually somebody who says 2.4,
but I like three.
Three's a good answer.
We'll do one more.
Just shout out when you've--
[LAUGHTER]
I heard-- somebody said
lots, which is good.
This is an excellent
demonstration
of the advantage of
having people rather
than a neural network
do this, because you're
able to think
outside the training
and reject the premise
of the question.
Lots is a good answer.
So we used the same software
that built GalaxyZoo
to build Penguin Watch.
And so now we have
people all over the world
counting penguins for us.
Every time I show
this slide, I'm
reminded the original
name of this project
was Penguin Hunters,
which I think
you can see in the design.
But apparently, that
wasn't acceptable.
And in fact, I have to
mention, as I'm here,
a Google Global
Impact Award let us
build not just individual
projects, but a tool
for building projects.
So researchers can now
log on and very quickly
put a project together to
ask the Zooniverse community
to engage with their data.
And we've built all
sorts of projects
with it, including this one.
This is the Planetary
Response Network,
which is a
partnership with a NGO
called Rescue Global, who
do quick response to things
like the hurricanes
in the Caribbean
the other year, where we do
rapid assessment from satellite
of which roads are
passable, which
buildings are
standing, and so on,
using the power of volunteers.
I think, at this
point-- hopefully,
I've convinced
you that this sort
of scientific
crowdsourcing is fun,
that there's an
audience for it, and it
produces useful and
interesting results,
whether it's for
penguins or galaxies.
I want to spend the
last 10 minutes, really,
as I spend the last
half of the book,
making two separate arguments.
The first one I call
the Zorilla Problem.
So this is-- these are images
from another camera trap
project.
This is Snapshot Serengeti.
These are motion
sensitive cameras
distributed across the
Serengeti National Park
which take pictures every
time an animal comes past.
So we've got a giraffe, a
lion, porcupine, stripy horse.
I don't know.
I'm an astronomer.
[LAUGHTER]
And some of these images
can be really beautiful.
And some of them,
you'll have realized,
present a pretty easy machine
learning task, especially
these days.
So for example,
in the top left--
that couldn't really be anything
else other than a giraffe.
We now have a set of, I think,
350,000 images of giraffes
from the camera network.
It's trivial to train
a straightforward CNN
or something like that
to recognize giraffes.
In fact, we've built the
world's best open-source giraffe
detector.
So if you're ever
worried and need
to check your environment for
giraffes, you could do that.
That's one of the things
the Zooniverse has produced.
But one of the animals
that appears in the network
occasionally is this thing.
This is a zorilla.
It's a small
skunk-like creature.
They appear in one in
every 3.4 million images
that the network takes.
And so building a
training set of zorilllas
is really difficult, or at
least very time consuming.
And in astronomy, we have
a lot of space zorillas.
This is not a real picture.
This is a montage.
[LAUGHTER]
But despite-- I
think we're in a--
I like to clarify these things.
What I mean is that
even though we're
dealing with very large
data sets, most of the time,
there's value in the very
unusual in our data set.
So you may have heard of things
like gravitational lenses,
where the light of
a distant galaxy
is bent by a nearby galaxy.
That allows you to
find out all sorts
of things about both lens and
the galaxy that's lensing.
But we only have a
few hundred of those.
And so machine learning
approaches are possible,
but they're difficult,
because we're stymied
by the lack of training data.
And one of the things that
we've found with Zooniverse
is that we should also be
aware of the very unexpected.
This green blob-- well,
imagine I trained an algorithm
to classify galaxies.
If I showed it this
picture, it might well
get the fact that this galaxy
at the top is a spiral galaxy.
But the rest of
you are wondering
why there's an evil version
of Kermit the Frog hanging
in space underneath it.
This thing was first spotted
by a Dutch schoolteacher called
Hanny van Arkel.
Hanny called it the Voorwerp,
which I-- we thought
was a technical word.
I think it means "thingy"
or "object" in Dutch.
But it's now known as Hanny's
Voorwerp in the literature.
And it's an immense, galaxy-size
echo reflecting light that
comes from activity around
the galaxy a few hundred--
sorry, a few tens of
thousands of years ago.
It's become an object
of immense interest.
We didn't know to
look for these things.
And yet, because Hanny found
it, because she was an advocate
for it-- she did the simple
scientific thing of asking what
this weird thing was--
not only do we have that object,
but a bunch of other volunteers
went and found lots of these
glowing gas clouds that you
can see in green
here and pointed out
that we should really
pay attention to them.
We've even had volunteers carry
out their own research studies.
This is a set of light curves.
So this brightness against
time for a particular star--
one of the 150,000 stars
that the Kepler satellite
was observing.
You can see they have this
sort of shark fin dip.
So this is the star.
It dips rapidly, and then
it recovers rather slowly.
These were discovered by a
volunteer called Tom Jacobs who
read an old paper, realized
that this is the kind of thing
that you'd expect if you have
not a planet going in front
of the star, but a comet.
And then the paper explains that
he therefore looked at the data
from 200,000 stars and
found the one example where
this is observed to happen.
And then he e-mailed some
professional astronomers
and handed them the discovery,
and they wrote a paper
together.
So looking for the
unexpected is important.
And I think, as data
sets get larger,
that means that we keep
open a place for this sort
of scientific crowdsourcing.
I think we're going
to have to do that,
because we're building
telescopes like this one.
This is a mountaintop in
Chile, with a building that
will be an observatory on it.
This is the home of what's going
to be called the Large Synoptic
Survey Telescope, LSST,
because astronomers shouldn't
name anything.
LLST is a remarkable thing.
It's as big as the biggest
telescopes in the world today,
but it's a survey telescope.
It's going to scan the whole
sky every three nights.
We think we'll get about
30 terabytes of image data
a night.
And if that's not enough,
LLST will broadcast an alert
every time it thinks
that something
has changed in the sky.
And so if you subscribe to
those alerts with your phone,
we think you're going to wake
up to about 15 million alerts
a night.
And that will include
new near-Earth asteroids,
distant Kuiper belt
objects out near Pluto,
the flickering of
stars-- which are almost
all variable when
you look at them
with a large enough telescope.
We'll even see the
centers of nearby galaxies
will flicker as material falls
down onto their black holes
at their center.
There'll be catastrophic
explosions, supernovae,
and gamma ray bursts and so on.
But in there, there will be
things that we haven't even
thought to look at.
And what our challenge,
at the minute,
as data scientists
doing astronomy,
is working out how
to find those things.
And we think the answer might
be combining the kind of citizen
science I've been talking about
with modern machine learning.
That's not a
particularly new idea.
We've been thinking about
it for a long while.
In fact, back in 2015, we
ran a Kaggle competition
using the GalaxyZoo data.
It was won by this guy.
This is a paper by Sander
Dieleman, who's now here.
Hey.
How are you?
SANDER DIELEMAN: Good.
CHRIS LINTOTT: Credit.
Thank you-- who taught
astronomers about CNNs,
basically, through
winning this competition.
Actually, the thing I
got from this competition
was that the astronomers who
entered did really poorly.
And we realized we needed
to collaborate with experts
in computer science and so on.
But we've also been
thinking about this in terms
of looking for supernovae.
So we've run Zooniverse
projects looking
for supernovae for exploding
stars, for changes in the sky.
We've ran one using data
from a telescope in Hawaii
called Pan-STARRS.
And for this project, we
had volunteers classify
every possible supernova.
But we also ran a trained CNN,
Convolutional Neural Network,
trained on expert
classifications.
And I'm going to show you--
I want to show you one
graph, I think, at this talk,
mainly because I like graphs.
So on the bottom here,
you've got the score
that the machine gave this.
So if something's on
the far left here,
the machine was sure that
it was not a supernova.
If it was on the
right, the machine
thought it was
definitely a supernova.
And then the human score,
the score from volunteers,
is here on the bottom
left to the top right.
So at the bottom,
definitely not a supernova.
At the top, definitely
a supernova.
And then the colors
on each point
as a possible supernova--
the colors are the expert
judgments.
So green are definitely
not supernovae.
Yellow and red are
possible supernovae
that we'd want to
go and follow up.
Blue are asteroids,
which for this purpose,
are the same as supernovae.
You discover they're
asteroids when they move.
So that's the secondary thing.
And so what the task
here is to divide
green from the other colors.
And you can see, I
think, that the way
to do that is to put a
diagonal line on this graph.
So there's no cut just
on the machine store
or on the human score
that outperforms
the combination of two.
And we've discovered
this as a general point,
a general principle,
in what we're
doing-- that for the
kind of messy data
sets with poor training
that we have available,
the combination of human
and machine working
together produces better results
than either human or machine
alone.
So we have this
beautiful future in which
humans and robots can coexist.
Now, we could be much more
sophisticated than that.
My student Mike [? Walmsley ?]
has built a neural network that
predicts not a classification--
it predicts the vote fraction--
it predicts what
a crowd of humans
would do when presented
with an image of a galaxy.
And it has a probabilistic
understanding
of how confident it is
about that classification.
So what we can do
is, we can take--
first of all, we can
take the galaxies
that the machine is
least confident about
and prioritize those being
seen by the volunteers
so that people are doing
the work that we exactly
need them to do,
and the machines
are doing the other stuff.
And so far, that's working well.
We've discovered
that, for example--
I can actually show you.
So this is a set of
recent galaxies--
Zoo galaxies.
On the left, these
are the galaxies
where the machine is
most confident it's
got its classification right.
And on the right those where
the machine is least confident.
And you could sort of--
I never quite know what
to say about this slide.
I think it's slightly intuitive.
On the left, they're mostly
simple systems, often spirals,
relatively nearby.
On the right, you've got
some unusual objects.
There's a nice
ring in the middle
of the bottom row, there,
some artifacts-- the red
and the yellow on the
right hand side there
are problems with the camera
or with the data processing.
But I'm not sure
you'd necessarily
guess which of these the
machine would get right.
But we're able to actively and
programmatically pass what we
need to do to the volunteers.
And in fact, we can
get better than that.
We can get the volunteers
to look at the things
that the machine thinks
will most likely improve
its classifications.
And so we have this loop
between human and machine,
with the volunteers
picking up on the ever more
unusual and interesting things.
And that's the combination
of technologies
that we think we'll need to
deal with the data set that's
coming.
And so in the end of
the book, I end up
arguing about the
three possible futures.
There's definitely a future in
which machine learning wins,
but I think we miss
out on our zorillas.
There's a future in which
we decide not to go that way
and we trick people
into classifying
because we think
it's good for them,
because it's good
science communication,
and don't tell them that the
off-the-shelf technologies,
the kind of things that
you here are developing,
could do most of the work.
But there's this
middle path where
people get to do the
interesting stuff,
and we get better
results out at the end.
Now, I've talked about this all
as if it's a new set of ideas,
as if we've gone boldly
into new territory
and thinking to involve
a large crowd of people
in scientific research.
But it's actually an old idea.
So I want to finish
with this guy.
This is Argelander.
He was a Prussian scientist in
the 19th century who invented
the study of variable stars.
He was the first to realize that
you could systematically study
how stars change in brightness.
So that's the branch
of science that
really led all the way to
the discovery of planets
that I started with.
He decided that he could not
make all of his observations
himself.
And so he put out a call to--
a call to everyone in the world
who had been observing stars.
And he said that
if they helped him,
they would perhaps,
in a few years,
be able to discover laws in
the apparent irregularities,
in the brightness of the
stars, and in a short time,
accomplish more
than in all the 60
years that have passed since the
discovery of this phenomenon.
"I've got one request,"
he said, "which is this--
that the observations
be made known each year,
because observations buried
in a desk are no observations.
Should they be entrusted
to me for reduction or even
publication, I will undertake
it with joy and thanks
and answer all questions with
care and with the greatest
of pleasure."
He's a wonderful
example of how one
collaborates with an
enormous group of people.
I'll be very happy to answer
any questions with care
and the greatest of pleasure.
Thank you very much.
[APPLAUSE]
SPEAKER 1: Thank you very much.
So if you have questions,
I'll come around
with the microphone.
AUDIENCE: Have you thought
about getting more people
to help with this
classification by using these
in CAPTCHAs, things like that?
CHRIS LINTOTT: That
would be amazing,
especially now that we've got--
we have, in many cases,
a machine hypothesis.
So the task is, is
this right or not?
So, yeah, we talked
to Luis von Ahn
about CAPTCHA years ago,
just before, I think, he
ended up working with Google.
And then we never followed up.
But it's an amazingly good idea.
So thank you for asking.
I love the idea that people
would be accidentally doing
science, as well.
We've talked about replacing
our home page with--
because at the minute, you
have to come and choose
a project to work on and so on.
We've talked about having
the first thing that
happens-- we show you
an image and we say,
how many penguins are there?
So that you can start the
conversation by saying,
actually, you just
did some science.
Maybe it could be a fun Google
Doodle or something, as well.
Just surprising--
I should have said,
really, I expected to be
talking to amateur astronomers
about doing this.
Actually, we find
most of our volunteers
don't have a
background in science,
don't have a prior hobby
interest in science.
They stumble into
and then become
people who can do science,
and they're interested in it.
And that's the-- from
a science communication
point of view, that's the
really exciting thing.
AUDIENCE: Hi.
What are the new challenges that
you've seen for the astronomer
and, in particular, let's
say, the SpaceX launches
and all the satellites?
CHRIS LINTOTT: We're busy
arguing about it right now.
So for those who
don't know the story,
the suggested number of large
constellations of satellites
are going to have a significant
impact on the kind of work
that I've been talking about,
particularly with LLST,
actually, because the reason
LLST is so powerful-- it's
been built to have a
very wide field of view
so that we can
survey the whole sky.
So that means you're
very sensitive to things
like large fleets of
satellites crossing over.
And the problem's not
as simple as knowing
where the satellites are,
because you see the satellites,
but you also see ghost
images of them because
of the reflections
and the optics
and because they're
moving and so on.
So at the minute, we're--
friends of mine
are in the middle
of an assessment
seeing whether SpaceX
has affected our science.
And the answer will be yes.
And the question is how much.
I think there's a
cost-benefit here.
Obviously, if you can provide--
I don't know, free internet and
YouTube for everybody forever
versus making it slightly more
difficult for astronomers,
we maybe make that trade-off.
But no one's doing that
sort of cost trade off.
I don't think people
have realized, as well--
I'm an astronomer because I
grew up looking at the stars.
People-- I think everyone should
have the cultural experience
of going and looking at
an unspoilt night sky.
And if you do that after sunset
now, about half the time,
you see the SpaceX constellation
of satellites fly over.
And even when they're
in operational mode,
they're visible
to the naked eye.
So you're going to see a
sky that we've effected.
And maybe that's OK.
But we don't have a mechanism
for having that conversation.
In general, though-- you asked
about general priorities.
I think my main worry
is how we keep open
a space to be surprised.
Whenever we've opened
new telescopes,
new ways of looking,
we've got most
of our science out of
things we didn't even
expect to be looking at.
I went and looked the other
day at the original proposal
for the Hubble Space Telescope,
and then the 10 science
priorities when it launched.
And if you made a list
of the 10 greatest
achievements of Hubble,
I think two of them
match out of the 10.
And so I think, as we become
more a data-driven science,
we need to find--
as I've said, need
to find ways to make
sure we can be surprised.
AUDIENCE: So you
say on GalaxyZoo,
the examples that are
most likely to be shown
to volunteers are
the ones the machine
will struggle to classify.
How do you factor, for example,
the galaxy being quite simple
for the machine to classify but
also has something completely
unexpected like the
voorwerp in the image,
as well, that would otherwise
be missed if a volunteer didn't
follow that?
CHRIS LINTOTT: That's
a really good question.
So at the minute, we're doing
the crude thing of all galaxies
are seen by some
people in the hope
that we'll keep up this
rate of the unexpected,
because the voorwerp wasn't
classified as a galaxy.
It's just in the background.
There are other good examples.
There's a set of galaxies
called the GalaxyZoo peas, which
are small, round, and green.
So the volunteers
called them peas.
But they're in the
background of the images.
They were investigated by a
bunch of volunteers who called
themselves the Peas Corps.
Took me ages to realize
that was a joke.
And, yeah, they turn out to be
the most efficient factories
of stars in the local universe.
So at the minute, we're
sort of backstopping
by having three people--
three or four people
look at every image in
the hope that we'll still
get the unusual.
Obviously, one could build--
optimize machine
learning attempts
to find the unusual stuff.
Finding the interesting
unusual stuff's
really hard as an ML problem.
But we're looking
at, for example, ways
of doing clustering or so on.
So you might end up with a--
you could easily cluster the
weird stuff together and then
have people go through that.
So we may end up with a
targeted search for the weird
rather than the classification
with accidental discovery
of the unusual.
But that's an
experimental question
right now as to
whether it's better
to separate those approaches
or work on them together.
We've also got to
worry, of course,
about what people want to do.
So far, the galaxies that
our galaxy classifying
machine wants people to look
at are appealing to people.
But you can equally
imagine that might not
be the case for all problems.
So if, for example--
we haven't done this yet,
but on Snapshot Serengeti,
you can imagine the machine
getting all the nice, beautiful
images right and all the fuzzy
things of a distant shape
in the dusk, which
may or may not
be a sort of antelope, being
presented to the public.
So then I think
you have to think
hard about what you're doing
and how to balance these things.
AUDIENCE: Thanks for your talk.
That was really
interesting when you
said you discovered
the bias of people
to classify anti-clockwise
more than clockwise.
Did you discover
anything similar?
Or what would you
expect-- where would you
look for similar bias?
CHRIS LINTOTT: I think that's
the most counter-intuitive one,
or the most surprising one.
In project-- the other thing
that springs to mind is,
we've had projects where
people have looked--
where we're looking for unusual,
but not that rare things.
So we did a project using
Hubble Space Telescope
data which made an amazing
map of the Andromeda Galaxy--
a project called PHAT,
P-H-A-T, because astronomers
shouldn't name things.
But you look at
these star fields,
and the task is to look
for clusters of stars.
And we found there,
there's what the research
team called a distraction bias.
So once you find one
cluster, you stop looking.
And so we had to very carefully
take that into account
when we were looking at
the density of clusters.
And so we spend a
lot more time making
sure we measure these things.
And one thing that I
think is interesting
is that these biases would
be apparent, I think,
whatever classification
method you used.
But we get to talk
to our black box,
because it's got people in it.
And we can ask them why
they didn't see things.
And people don't, obviously,
self-report their biases.
But that makes it easier
to unpick what's going on.
The biggest bias,
though, is that people
don't think they can do this.
People get very scared by the
fact that this is real science.
The idea that I can
give you a graph
and ask you whether there's
evidence for a planet in it.
If you think of it as a silly
website game, then fine.
But if I tell you that
NASA scientists are relying
on you to discover planets, then
it begins to be a bit scary.
And people's results
get worse the more
they believe that they're
really contributing.
And so we've spent a
lot of time working out
how to reinforce the
message that there
are other people around,
that it's not just
your classification.
We found that was
particularly problematic
when we did projects with
Cancer Research UK, where
the strapline on
the project was,
"you can help us cure cancer."
And it turns out that's not
inspiring, that's terrifying.
If what you do in
your lunch break
genuinely could
make a difference
to whether we cure
cancer or not,
then maybe that's not quite
such a relaxing activity.
And so a lot of the
biases are around how
we think about ourselves
in relation to science
and helping people understand
what we're asking them to do,
and that everyone could
do this whole thing
turns out to be
really important.
Thanks for the question.
SPEAKER 1: Any other questions?
I have one for you, actually.
So you mentioned earlier
that you've been getting--
you had a grant from Google
that helped support you.
Are there other things that you
need for the Zooniverse Project
right now as far
as resources, or is
it literally just
people to partake in--
CHRIS LINTOTT: Well, we have
a large project to keep going,
so we have a standing
army problem.
So we're entirely
grant-funded, but we
run this service where
researchers can build and run
these projects at no cost
to the research groups.
And we do that
really specifically,
because when I was a postdoc
setting up GalaxyZoo,
if I'd had to apply for money to
do it, I'd never have done it.
The people who are on the front
line of dealing with the data
aren't the people with access
to large grants or resources
or so on.
So we try and keep
that ourselves.
One of the most
interesting partnerships--
we've been working a
bit with your colleagues
in Boston to look at how we
can use Google ML tools that
are available on the web and
make it easy for Zooniverse
researchers to do the kind
of sophisticated combinations
of machine learning and
human classification.
So that's been a
great partnership,
and we've really
enjoyed doing that.
But yes, if anyone wants
to write a large check,
I will happily take it.
SPEAKER 1: So any
last questions?
Well, thank you--
CHRIS LINTOTT:
Thank you very much.
SPEAKER 1: --once more.
[APPLAUSE]
