WEBVTT

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So, I have a strange career.

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I know it because people come up to me,
like colleagues, and say,

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"Chris, you have a strange career."

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(Laughter)

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And I can see their point,

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because I started my career
as a theoretical nuclear physicist.

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And I was thinking about quarks
and gluons and heavy ion collisions,

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and I was only 14 years old...

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No, no, I wasn't 14 years old.

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But after that,

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I actually had my own lab

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in the Computational
Neuroscience department,

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and I wasn't doing any neuroscience.

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Later, I would work
on evolutionary genetics,

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and I would work on systems biology.

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But I'm going to tell you
about something else today.

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I'm going to tell you
about how I learned something about life.

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And I was actually a rocket scientist.

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I wasn't really a rocket scientist,

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but I was working
at the Jet Propulsion Laboratory

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in sunny California, where it's warm;

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whereas now I am
in the mid-West, and it's cold.

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But it was an exciting experience.

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One day, a NASA manager
comes into my office,

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sits down and says

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that's my office, right there,

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and I had a nice office
with the sun shining... anyway -

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He said, "Chris,

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can you please tell us,
how do we look for life outside Earth?"

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And that came as a surprise to me,

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because I was actually hired
to work on quantum computation.

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Yet, I had a very good answer.

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I said, "I have no idea."

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(Laughter)

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And he told me, "Biosignatures,
we need to look for a biosignature."

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And I said, "What is that?"

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And he said, "It's any
measurable phenomenon

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that allows us to indicate
the presence of life."

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And I said, "Really?

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Because isn't that easy?

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I mean, we have life.

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Can't you apply a definition,

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for example, a Supreme Court-like
definition of life?"

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And then I thought about it
a little bit, and I said,

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"Well, is it really that easy?

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Because, yes, if you see
something like this,

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then all right, fine,
I'm going to call it life...

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No doubt about it.

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But here's something."

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And he goes, "Right,
that's life too. I know that."

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Except, if you think that life
is also defined by things that die,

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you're not in luck with this thing,

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because that's actually
a very strange organism.

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It grows up into the adult stage like that

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and then goes through
a Benjamin Button phase,

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and actually goes backwards and backwards
until it's like a little embryo again,

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and then actually grows back up,
and back down and back up...

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Sort of yo-yo... and it never dies.

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So it's actually life,

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but it's actually not
as we thought life would be.

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And then you see something like that.

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And he was like, "My God,
what kind of a life form is that?"

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Anyone know?

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It's actually not life, it's a crystal.

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So once you start looking and looking
at smaller and smaller things...

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So this particular person wrote
a whole article and said,

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"Hey, these are bacteria."

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Except, if you look a little bit closer,

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you see, in fact, that this thing
is way too small to be anything like that.

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So he was convinced,
but, in fact, most people aren't.

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And then, of course,
NASA also had a big announcement,

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and President Clinton
gave a press conference,

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about this amazing discovery
of life in a Martian meteorite.

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Except that nowadays,
it's heavily disputed.

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If you take the lesson
of all these pictures,

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then you realize, well, actually,
maybe it's not that easy.

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Maybe I do need a definition of life

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in order to make that kind of distinction.

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So can life be defined?

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Well how would you go about it?

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Well of course, you'd go
to Encyclopedia Britannica and open at L.

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No, of course you don't do that;
you put it somewhere in Google.

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And then you might get something.

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(Laughter)

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And what you might get...

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And anything that actually refers
to things that we are used to,

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you throw away.

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And then you might come up
with something like this.

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And it says something longwinded

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complicated,
with lots and lots of concepts.

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Who on Earth would write something
as convoluted and complex and inane?

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Oh, it's actually a really, really,
important set of concepts.

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So I'm highlighting just a few words

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and saying definitions
like that rely on things

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that are not based on amino acids
or leaves or anything that we are used to,

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but in fact on processes only.

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And if you take a look at that,

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this was actually in a book that I wrote
that deals with artificial life.

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And that explains why that NASA manager
was actually in my office to begin with.

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Because the idea was that,
with concepts like that,

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maybe we can actually
manufacture a form of life.

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And so if you go and ask yourself,
"What on Earth is artificial life?",

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let me give you a whirlwind tour
of how all this stuff came about.

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And it started out quite a while ago,

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when someone wrote one of the first
successful computer viruses.

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And for those of you
who aren't old enough,

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you have no idea
how this infection was working...

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Namely, through these floppy disks.

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But the interesting thing
about these computer virus infections

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was that, if you look at the rate
at which the infection worked,

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they show this spiky behavior
that you're used to from a flu virus.

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And it is in fact due to this arms race

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between hackers
and operating system designers

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that things go back and forth.

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And the result is kind of
a tree of life of these viruses,

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a phylogeny that looks very much
like the type of life

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that we're used to,
at least on the viral level.

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So is that life?

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Not as far as I'm concerned.

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Why? Because these things
don't evolve by themselves.

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In fact, they have hackers writing them.

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But the idea was taken
very quickly a little bit further,

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when a scientist working
at the Santa Fe Institute decided,

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"Why don't we try to package
these little viruses

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in artificial worlds
inside of the computer

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and let them evolve?"

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And this was Steen Rasmussen.

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And he designed this system,
but it really didn't work,

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because his viruses
were constantly destroying each other.

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But there was another scientist
who had been watching this, an ecologist.

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And he went home and says,
"I know how to fix this."

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And he wrote the Tierra system,

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and, in my book,

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is in fact one of the first
truly artificial living systems...

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Except for the fact that these programs
didn't really grow in complexity.

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So having seen this work,
worked a little bit on this,

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this is where I came in.

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And I decided to create a system
that has all the properties

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that are necessary to see, in fact,
the evolution of complexity,

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more and more complex
problems constantly evolving.

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And of course, since I really don't know
how to write code, I had help in this.

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I had two undergraduate students

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at California Institute of Technology
that worked with me.

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That's Charles Ofria on the left,
Titus Brown on the right.

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They are now, actually,
respectable professors

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at Michigan State University,

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but I can assure you, back in the day,
we were not a respectable team.

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And I'm really happy
that no photo survives

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of the three of us
anywhere close together.

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But what is this system like?

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Well I can't really go into the details,

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but what you see here
is some of the entrails.

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But what I wanted to focus on
is this type of population structure.

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There's about 10,000
programs sitting here.

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And all different strains
are colored in different colors.

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And as you see here, there are groups
that are growing on top of each other,

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because they are spreading.

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Any time there is a program
that's better at surviving in this world,

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due to whatever mutation it has acquired,

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it is going to spread over the others
and drive the others to extinction.

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So I'm going to show you a movie

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where you're going to see
that kind of dynamic.

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And these kinds of experiments are started
with programs that we wrote ourselves.

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We write our own stuff, replicate it,
and are very proud of ourselves.

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And we put them in,
and what you see immediately

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is that there are waves
and waves of innovation.

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By the way, this is highly accelerated,

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so it's like a 1000 generations a second.

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But immediately, the system goes like,
"What kind of dumb piece of code was this?

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This can be improved upon
in so many ways, so quickly."

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So you see waves of new types
taking over the other types.

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And this type of activity
goes on for quite a while,

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until the main easy things
have been acquired by these programs.

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And then, you see
sort of like a stasis coming on

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where the system essentially waits

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for a new type of innovation,
like this one,

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which is going to spread over
all the other innovations that were before

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and is erasing the genes
that it had before,

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until a new type of higher level
of complexity has been achieved.

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And this process goes on and on and on.

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So what we see here

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is a system that lives in very much
the way we're used to how life goes.

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But what the NASA people
had asked me really was,

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"Do these guys have a biosignature?

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Can we measure this type of life?

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Because if we can,

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maybe we have a chance of actually
discovering life somewhere else

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without being biased
by things like amino acids."

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So I sat down a little bit,

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and I said, "Well, if we do this,

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perhaps we should construct a biosignature

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that is based on life
as a universal process.

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In fact, it should perhaps make use
of the concepts that I developed

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just in order to sort of capture
what a simple living system might be."

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And the thing I came up with...

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I have to first give you
an introduction about the idea,

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and maybe that would be
a meaning detector,

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rather than a life detector.

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And the way we would do that...

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I would like to find out
how I can distinguish text

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that was written by a million monkeys,
as opposed to text that is in our books.

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And I would like to do it in such a way

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that I don't actually have to be able
to read the language,

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because I'm sure I won't be able to.

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As long as I know
that there's some sort of alphabet.

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So here would be a frequency plot

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of how often you find
each of the 26 letters of the alphabet

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in a text written by random monkeys.

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And obviously, each of these letters
comes off about roughly equally frequent.

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But if you now look at the same
distribution in English texts,

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it looks like that.

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And I'm telling you,
this is very robust across English texts.

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And if I look at French texts,
it looks a little bit different,

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or Italian or German.

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They all have their own type
of frequency distribution,

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but it's robust.

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It doesn't matter whether it writes
about politics or about science.

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It doesn't matter whether it's a poem
or whether it's a mathematical text.

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It's a robust signature,

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and it's very stable.

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As long as our books
are written in English...

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Because people are rewriting them
and recopying them...

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It's going to be there.

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So that inspired me to think about,
well, what if I try to use this idea

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in order, not to detect random texts
from texts with meaning,

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but rather detect the fact
that there is meaning

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in the biomolecules that make up life.

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But first I have to ask:

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what are these building blocks,

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like the alphabet, elements
that I showed you?

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Well it turns out, we have
many different alternatives

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for such a set of building blocks.

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We could use amino acids,

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we could use nucleic acids,
carboxylic acids, fatty acids.

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In fact, chemistry's extremely rich,
and our body uses a lot of them.

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So that we actually, to test this idea,

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first took a look at amino acids
and some other carboxylic acids.

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And here's the result.

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Here is, in fact, what you get

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if you, for example, look
at the distribution of amino acids

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on a comet or in interstellar space
or, in fact, in a laboratory,

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where you made very sure
that in your primordial soup,

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there is no living stuff in there.

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What you find is mostly
glycine and then alanine

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and there's some trace elements
of the other ones.

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That is also very robust...

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What you find in systems like Earth

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where there are amino acids,
but there is no life.

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But suppose you take some dirt
and dig through it

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and then put it into these spectrometers,

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because there's bacteria
all over the place;

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or you take water anywhere on Earth,

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because it's teaming with life,

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and you make the same analysis;

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the spectrum looks completely different.

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Of course, there is still
glycine and alanine,

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but in fact, there are these heavy
elements, these heavy amino acids,

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that are being produced
because they are valuable to the organism.

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And some other ones
that are not used in the set of 20,

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they will not appear at all
in any type of concentration.

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So this also turns out
to be extremely robust.

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It doesn't matter what kind of sediment
you're using to grind up,

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whether it's bacteria
or any other plants or animals.

00:13:30.725 --> 00:13:32.149
Anywhere there's life,

00:13:32.173 --> 00:13:34.124
you're going to have this distribution,

00:13:34.148 --> 00:13:35.965
as opposed to that distribution.

00:13:35.989 --> 00:13:39.226
And it is detectable
not just in amino acids.

00:13:40.050 --> 00:13:41.267
Now you could ask:

00:13:41.291 --> 00:13:44.450
Well, what about these Avidians?

00:13:44.474 --> 00:13:47.525
The Avidians being the denizens
of this computer world

00:13:47.549 --> 00:13:50.994
where they are perfectly happy
replicating and growing in complexity.

00:13:51.018 --> 00:13:56.035
So this is the distribution that you get
if, in fact, there is no life.

00:13:56.059 --> 00:13:58.777
They have about 28 of these instructions.

00:13:58.801 --> 00:14:02.153
And if you have a system where
they're being replaced one by the other,

00:14:02.177 --> 00:14:04.362
it's like the monkeys
writing on a typewriter.

00:14:04.386 --> 00:14:08.606
Each of these instructions
appears with roughly the equal frequency.

00:14:09.515 --> 00:14:14.295
But if you now take
a set of replicating guys

00:14:14.319 --> 00:14:16.269
like in the video that you saw,

00:14:16.293 --> 00:14:17.812
it looks like this.

00:14:18.599 --> 00:14:20.072
So there are some instructions

00:14:20.096 --> 00:14:22.529
that are extremely valuable
to these organisms,

00:14:22.553 --> 00:14:24.523
and their frequency is going to be high.

00:14:24.547 --> 00:14:28.588
And there's actually some instructions
that you only use once, if ever.

00:14:28.612 --> 00:14:30.135
So they are either poisonous

00:14:30.159 --> 00:14:34.664
or really should be used
at less of a level than random.

00:14:34.688 --> 00:14:37.376
In this case, the frequency is lower.

00:14:38.332 --> 00:14:41.003
And so now we can see,
is that really a robust signature?

00:14:41.027 --> 00:14:42.384
I can tell you indeed it is,

00:14:42.408 --> 00:14:45.656
because this type of spectrum,
just like what you've seen in books,

00:14:45.680 --> 00:14:47.833
and just like what you've seen
in amino acids,

00:14:47.857 --> 00:14:50.499
it doesn't really matter
how you change the environment,

00:14:50.523 --> 00:14:53.147
it's very robust, it's going
to reflect the environment.

00:14:53.171 --> 00:14:56.120
So I'm going to show you now
a little experiment that we did.

00:14:56.144 --> 00:14:57.528
And I have to explain to you,

00:14:57.552 --> 00:14:58.734
the top of this graph

00:14:58.758 --> 00:15:01.502
shows you that frequency
distribution that I talked about.

00:15:02.426 --> 00:15:07.073
Here, that's the lifeless environment

00:15:07.457 --> 00:15:10.869
where each instruction occurs
at an equal frequency.

00:15:11.804 --> 00:15:16.797
And below there, I show, in fact,
the mutation rate in the environment.

00:15:16.821 --> 00:15:20.124
And I'm starting this
at a mutation rate that is so high

00:15:20.148 --> 00:15:24.114
that even if you would drop
a replicating program

00:15:24.138 --> 00:15:28.263
that would otherwise happily grow up
to fill the entire world,

00:15:28.287 --> 00:15:31.297
if you drop it in, it gets mutated
to death immediately.

00:15:31.321 --> 00:15:36.667
So there is no life possible
at that type of mutation rate.

00:15:36.691 --> 00:15:40.727
But then I'm going to slowly
turn down the heat, so to speak,

00:15:40.751 --> 00:15:42.936
and then there's this viability threshold

00:15:42.960 --> 00:15:46.852
where now it would be possible
for a replicator to actually live.

00:15:46.876 --> 00:15:52.221
And indeed, we're going to be dropping
these guys into that soup all the time.

00:15:52.659 --> 00:15:54.295
So let's see what that looks like.

00:15:54.319 --> 00:15:57.317
So first, nothing, nothing, nothing.

00:15:57.341 --> 00:15:59.156
Too hot, too hot.

00:15:59.180 --> 00:16:01.476
Now the viability threshold is reached,

00:16:01.500 --> 00:16:05.992
and the frequency distribution
has dramatically changed

00:16:06.016 --> 00:16:07.492
and, in fact, stabilizes.

00:16:07.516 --> 00:16:09.026
And now what I did there

00:16:09.050 --> 00:16:12.648
is, I was being nasty,
I just turned up the heat again and again.

00:16:12.672 --> 00:16:15.018
And of course, it reaches
the viability threshold.

00:16:15.042 --> 00:16:17.910
And I'm just showing this to you
again because it's so nice.

00:16:17.934 --> 00:16:19.476
You hit the viability threshold.

00:16:19.500 --> 00:16:22.015
The distribution changes to "alive!"

00:16:22.431 --> 00:16:25.648
And then, once you hit the threshold

00:16:25.672 --> 00:16:29.721
where the mutation rate is so high
that you cannot self-reproduce,

00:16:29.745 --> 00:16:35.765
you cannot copy the information
forward to your offspring

00:16:35.790 --> 00:16:40.520
without making so many mistakes
that your ability to replicate vanishes.

00:16:40.544 --> 00:16:42.403
And then, that signature is lost.

00:16:44.156 --> 00:16:45.862
What do we learn from that?

00:16:46.186 --> 00:16:49.982
Well, I think we learn
a number of things from that.

00:16:50.006 --> 00:16:51.476
One of them is,

00:16:51.500 --> 00:16:56.724
if we are able to think about life
in abstract terms...

00:16:56.748 --> 00:16:59.379
And we're not talking
about things like plants,

00:16:59.403 --> 00:17:01.328
and we're not talking about amino acids,

00:17:01.352 --> 00:17:03.116
and we're not talking about bacteria,

00:17:03.140 --> 00:17:05.250
but we think in terms of processes...

00:17:05.273 --> 00:17:07.476
Then we could start to think about life

00:17:07.500 --> 00:17:10.118
not as something
that is so special to Earth,

00:17:10.143 --> 00:17:12.653
but that, in fact, could exist anywhere.

00:17:12.676 --> 00:17:16.990
Because it really only has to do
with these concepts of information,

00:17:17.013 --> 00:17:21.071
of storing information
within physical substrates...

00:17:21.096 --> 00:17:25.112
Anything: bits, nucleic acids,
anything that's an alphabet...

00:17:25.136 --> 00:17:27.015
And make sure that there's some process

00:17:27.039 --> 00:17:30.754
so that this information can be stored
for much longer than you would expect...

00:17:31.316 --> 00:17:35.652
The time scales for
the deterioration of information.

00:17:35.676 --> 00:17:38.844
And if you can do that,
then you have life.

00:17:38.868 --> 00:17:41.122
So the first thing that we learn

00:17:41.146 --> 00:17:46.358
is that it is possible to define life
in terms of processes alone,

00:17:46.382 --> 00:17:51.359
without referring at all
to the type of things that we hold dear,

00:17:51.383 --> 00:17:54.054
as far as the type of life on Earth is.

00:17:54.078 --> 00:17:56.719
And that, in a sense, removes us again,

00:17:56.743 --> 00:17:59.574
like all of our scientific discoveries,
or many of them...

00:17:59.598 --> 00:18:02.369
It leads to s continuous
dethroning of man...

00:18:02.393 --> 00:18:05.120
Of how we think we're special
because we're alive.

00:18:05.144 --> 00:18:08.200
Well, we can make life;
we can make life in the computer.

00:18:08.224 --> 00:18:10.540
Granted, it's limited,

00:18:10.565 --> 00:18:15.682
but we have learned what it takes
in order to actually construct it.

00:18:15.706 --> 00:18:18.494
And once we have that,

00:18:18.518 --> 00:18:21.165
then it is not such
a difficult task anymore

00:18:21.189 --> 00:18:25.341
to say, if we understand
the fundamental processes

00:18:25.365 --> 00:18:28.707
that do not refer
to any particular substrate,

00:18:28.731 --> 00:18:32.499
then we can go out and try other worlds,

00:18:32.523 --> 00:18:36.304
figure out what kind of chemical
alphabets might there be,

00:18:37.033 --> 00:18:41.758
figure enough about the normal chemistry,
the geochemistry of the planet,

00:18:41.782 --> 00:18:45.556
so that we know what this distribution
would look like in the absence of life,

00:18:45.580 --> 00:18:48.551
and then look for large
deviations from this...

00:18:48.575 --> 00:18:53.687
This thing sticking out, which says,
"This chemical really shouldn't be there."

00:18:53.711 --> 00:18:55.666
Now we don't know that there's life then,

00:18:55.690 --> 00:18:56.897
but we could say,

00:18:56.921 --> 00:19:00.690
"Well at least I'm going to have to take
a look very precisely at this chemical

00:19:00.714 --> 00:19:02.759
and see where it comes from."

00:19:02.783 --> 00:19:06.494
And that might be our chance
of actually discovering life

00:19:06.518 --> 00:19:08.637
when we cannot visibly see it,

00:19:09.270 --> 00:19:10.873
as I've shown you.

00:19:11.861 --> 00:19:16.425
And so that's really the only
take-home message that I have for you.

00:19:16.449 --> 00:19:20.680
Life can be less mysterious
than we make it out to be

00:19:20.704 --> 00:19:23.909
when we try to think
about how it would be on other planets.

00:19:24.480 --> 00:19:27.867
And if we remove the mystery of life,

00:19:27.891 --> 00:19:32.576
then I think it is a little bit easier
for us to think about how we live,

00:19:32.600 --> 00:19:35.658
and how perhaps we're not as special
as we always think we are.

00:19:36.082 --> 00:19:38.328
And I'm going to leave you with that.

00:19:38.352 --> 00:19:39.576
And thank you very much.

00:19:39.600 --> 00:19:41.774
(Applause)