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Christoph Adami: Finding life we can't imagine

February 19, 2011

How do we search for alien life if it's nothing like the life that we know? Christoph Adami shows how he uses his research into artificial life -- self-replicating computer programs -- to find a signature, a 'biomarker,' that is free of our preconceptions of what life is.

Christoph Adami - Artificial life researcher
Christoph Adami works on the nature of life and evolution, trying to define life in a way that is as free as possible from our preconceptions. Full bio

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Double-click the English subtitles below to play the video.
So I have a strange career.
00:15
I know it because people come up to me, like colleagues,
00:17
and say, "Chris, you have a strange career."
00:20
(Laughter)
00:22
And I can see their point,
00:24
because I started my career
00:26
as a theoretical nuclear physicist.
00:28
And I was thinking about quarks and gluons
00:30
and heavy ion collisions,
00:32
and I was only 14 years old.
00:34
No, no, I wasn't 14 years old.
00:36
But after that,
00:40
I actually had my own lab
00:42
in the computational neuroscience department,
00:44
and I wasn't doing any neuroscience.
00:46
Later, I would work on evolutionary genetics,
00:48
and I would work on systems biology.
00:51
But I'm going to tell you about something else today.
00:53
I'm going to tell you
00:56
about how I learned something about life.
00:58
And I was actually a rocket scientist.
01:00
I wasn't really a rocket scientist,
01:04
but I was working
01:06
at the Jet Propulsion Laboratory
01:08
in sunny California where it's warm;
01:10
whereas now I'm in the mid-West,
01:13
and it's cold.
01:15
But it was an exciting experience.
01:17
One day a NASA manager comes into my office,
01:20
sits down and says,
01:23
"Can you please tell us,
01:26
how do we look for life outside Earth?"
01:28
And that came as a surprise to me,
01:30
because I was actually hired
01:32
to work on quantum computation.
01:34
Yet, I had a very good answer.
01:36
I said, "I have no idea."
01:38
And he told me, "Biosignatures,
01:41
we need to look for a biosignature."
01:44
And I said, "What is that?"
01:46
And he said, "It's any measurable phenomenon
01:48
that allows us to indicate
01:50
the presence of life."
01:52
And I said, "Really?
01:54
Because isn't that easy?
01:56
I mean, we have life.
01:58
Can't you apply a definition,
02:00
like for example, a Supreme Court-like definition of life?"
02:02
And then I thought about it a little bit, and I said,
02:06
"Well, is it really that easy?
02:08
Because, yes, if you see something like this,
02:10
then all right, fine, I'm going to call it life --
02:13
no doubt about it.
02:15
But here's something."
02:17
And he goes, "Right, that's life too. I know that."
02:19
Except, if you think life is also defined
02:22
by things that die,
02:24
you're not in luck with this thing,
02:26
because that's actually a very strange organism.
02:28
It grows up into the adult stage like that
02:30
and then goes through a Benjamin Button phase,
02:32
and actually goes backwards and backwards
02:35
until it's like a little embryo again,
02:37
and then actually grows back up, and back down and back up -- sort of yo-yo --
02:39
and it never dies.
02:42
So it's actually life,
02:44
but it's actually not
02:46
as we thought life would be.
02:48
And then you see something like that.
02:51
And he was like, "My God, what kind of a life form is that?"
02:53
Anyone know?
02:55
It's actually not life, it's a crystal.
02:57
So once you start looking and looking
03:00
at smaller and smaller things --
03:02
so this particular person
03:04
wrote a whole article and said, "Hey, these are bacteria."
03:06
Except, if you look a little bit closer,
03:09
you see, in fact, that this thing is way too small to be anything like that.
03:11
So he was convinced,
03:14
but, in fact, most people aren't.
03:16
And then, of course,
03:18
NASA also had a big announcement,
03:20
and President Clinton gave a press conference,
03:22
about this amazing discovery
03:24
of life in a Martian meteorite.
03:26
Except that nowadays, it's heavily disputed.
03:29
If you take the lesson of all these pictures,
03:33
then you realize, well actually maybe it's not that easy.
03:36
Maybe I do need
03:38
a definition of life
03:40
in order to make that kind of distinction.
03:42
So can life be defined?
03:44
Well how would you go about it?
03:46
Well of course,
03:48
you'd go to Encyclopedia Britannica and open at L.
03:50
No, of course you don't do that; you put it somewhere in Google.
03:52
And then you might get something.
03:55
And what you might get --
03:58
and anything that actually refers to things that we are used to,
04:00
you throw away.
04:02
And then you might come up with something like this.
04:04
And it says something complicated
04:06
with lots and lots of concepts.
04:08
Who on Earth would write something
04:10
as convoluted and complex
04:12
and inane?
04:14
Oh, it's actually a really, really, important set of concepts.
04:17
So I'm highlighting just a few words
04:21
and saying definitions like that
04:24
rely on things that are not based
04:26
on amino acids or leaves
04:28
or anything that we are used to,
04:31
but in fact on processes only.
04:33
And if you take a look at that,
04:35
this was actually in a book that I wrote that deals with artificial life.
04:37
And that explains why
04:40
that NASA manager was actually in my office to begin with.
04:42
Because the idea was that, with concepts like that,
04:45
maybe we can actually manufacture
04:48
a form of life.
04:50
And so if you go and ask yourself,
04:52
"What on Earth is artificial life?",
04:55
let me give you a whirlwind tour
04:57
of how all this stuff came about.
04:59
And it started out quite a while ago
05:01
when someone wrote
05:04
one of the first successful computer viruses.
05:06
And for those of you who aren't old enough,
05:08
you have no idea how this infection was working --
05:11
namely, through these floppy disks.
05:14
But the interesting thing about these computer virus infections
05:16
was that, if you look at the rate
05:19
at which the infection worked,
05:21
they show this spiky behavior
05:23
that you're used to from a flu virus.
05:25
And it is in fact due to this arms race
05:28
between hackers and operating system designers
05:30
that things go back and forth.
05:33
And the result is kind of a tree of life
05:35
of these viruses,
05:37
a phylogeny that looks very much
05:39
like the type of life that we're used to, at least on the viral level.
05:42
So is that life? Not as far as I'm concerned.
05:45
Why? Because these things don't evolve by themselves.
05:48
In fact, they have hackers writing them.
05:51
But the idea was taken very quickly a little bit further
05:53
when a scientist working at the Scientific Institute decided,
05:57
"Why don't we try to package these little viruses
06:00
in artificial worlds inside of the computer
06:03
and let them evolve?"
06:05
And this was Steen Rasmussen.
06:07
And he designed this system, but it really didn't work,
06:09
because his viruses were constantly destroying each other.
06:11
But there was another scientist who had been watching this, an ecologist.
06:14
And he went home and says, "I know how to fix this."
06:17
And he wrote the Tierra system,
06:20
and, in my book, is in fact one of the first
06:22
truly artificial living systems --
06:25
except for the fact that these programs didn't really grow in complexity.
06:27
So having seen this work, worked a little bit on this,
06:30
this is where I came in.
06:33
And I decided to create a system
06:35
that has all the properties that are necessary
06:37
to see the evolution of complexity,
06:39
more and more complex problems constantly evolving.
06:42
And of course, since I really don't know how to write code, I had help in this.
06:45
I had two undergraduate students
06:48
at California Institute of Technology that worked with me.
06:50
That's Charles Offria on the left, Titus Brown on the right.
06:53
They are now actually respectable professors
06:56
at Michigan State University,
06:59
but I can assure you, back in the day,
07:01
we were not a respectable team.
07:03
And I'm really happy that no photo survives
07:05
of the three of us anywhere close together.
07:07
But what is this system like?
07:10
Well I can't really go into the details,
07:12
but what you see here is some of the entrails.
07:15
But what I wanted to focus on
07:17
is this type of population structure.
07:19
There's about 10,000 programs sitting here.
07:21
And all different strains are colored in different colors.
07:24
And as you see here, there are groups that are growing on top of each other,
07:27
because they are spreading.
07:30
Any time there is a program
07:32
that's better at surviving in this world,
07:34
due to whatever mutation it has acquired,
07:36
it is going to spread over the others and drive the others to extinction.
07:38
So I'm going to show you a movie where you're going to see that kind of dynamic.
07:41
And these kinds of experiments are started
07:44
with programs that we wrote ourselves.
07:47
We write our own stuff, replicate it,
07:49
and are very proud of ourselves.
07:51
And we put them in, and what you see immediately
07:53
is that there are waves and waves of innovation.
07:56
By the way, this is highly accelerated,
07:59
so it's like a thousand generations a second.
08:01
But immediately the system goes like,
08:03
"What kind of dumb piece of code was this?
08:05
This can be improved upon in so many ways
08:07
so quickly."
08:09
So you see waves of new types
08:11
taking over the other types.
08:13
And this type of activity goes on for quite awhile,
08:15
until the main easy things have been acquired by these programs.
08:18
And then you see sort of like a stasis coming on
08:22
where the system essentially waits
08:26
for a new type of innovation, like this one,
08:28
which is going to spread
08:31
over all the other innovations that were before
08:33
and is erasing the genes that it had before,
08:35
until a new type of higher level of complexity has been achieved.
08:38
And this process goes on and on and on.
08:42
So what we see here
08:45
is a system that lives
08:47
in very much the way we're used to life [going.]
08:49
But what the NASA people had asked me really
08:51
was, "Do these guys
08:55
have a biosignature?
08:57
Can we measure this type of life?
08:59
Because if we can,
09:01
maybe we have a chance of actually discovering life somewhere else
09:03
without being biased
09:06
by things like amino acids."
09:08
So I said, "Well, perhaps we should construct
09:10
a biosignature
09:13
based on life as a universal process.
09:15
In fact, it should perhaps make use
09:18
of the concepts that I developed
09:20
just in order to sort of capture
09:22
what a simple living system might be."
09:24
And the thing I came up with --
09:26
I have to first give you an introduction about the idea,
09:28
and maybe that would be a meaning detector,
09:32
rather than a life detector.
09:35
And the way we would do that --
09:38
I would like to find out how I can distinguish
09:40
text that was written by a million monkeys,
09:42
as opposed to text that [is] in our books.
09:44
And I would like to do it in such a way
09:47
that I don't actually have to be able to read the language,
09:49
because I'm sure I won't be able to.
09:51
As long as I know that there's some sort of alphabet.
09:53
So here would be a frequency plot
09:55
of how often you find
09:58
each of the 26 letters of the alphabet
10:00
in a text written by random monkeys.
10:02
And obviously each of these letters
10:05
comes off about roughly equally frequent.
10:07
But if you now look at the same distribution in English texts,
10:09
it looks like that.
10:13
And I'm telling you, this is very robust across English texts.
10:15
And if I look at French texts, it looks a little bit different,
10:18
or Italian or German.
10:20
They all have their own type of frequency distribution,
10:22
but it's robust.
10:25
It doesn't matter whether it writes about politics or about science.
10:27
It doesn't matter whether it's a poem
10:30
or whether it's a mathematical text.
10:33
It's a robust signature,
10:36
and it's very stable.
10:38
As long as our books are written in English --
10:40
because people are rewriting them and recopying them --
10:42
it's going to be there.
10:45
So that inspired me to think about,
10:47
well, what if I try to use this idea
10:49
in order, not to detect random texts
10:52
from texts with meaning,
10:54
but rather detect the fact that there is meaning
10:56
in the biomolecules that make up life.
11:00
But first I have to ask:
11:02
what are these building blocks, like the alphabet, elements that I showed you?
11:04
Well it turns out, we have many different alternatives
11:07
for such a set of building blocks.
11:10
We could use amino acids,
11:12
we could use nucleic acids, carboxylic acids, fatty acids.
11:14
In fact, chemistry's extremely rich, and our body uses a lot of them.
11:17
So that we actually, to test this idea,
11:20
first took a look at amino acids and some other carboxylic acids.
11:23
And here's the result.
11:26
Here is, in fact, what you get
11:28
if you, for example, look at the distribution of amino acids
11:31
on a comet or in interstellar space
11:34
or, in fact, in a laboratory,
11:37
where you made very sure that in your primordial soup
11:39
that there is not living stuff in there.
11:41
What you find is mostly glycine and then alanine
11:43
and there's some trace elements of the other ones.
11:46
That is also very robust --
11:49
what you find in systems like Earth
11:52
where there are amino acids,
11:55
but there is no life.
11:57
But suppose you take some dirt
11:59
and dig through it
12:01
and then put it into these spectrometers,
12:03
because there's bacteria all over the place;
12:06
or you take water anywhere on Earth,
12:08
because it's teaming with life,
12:10
and you make the same analysis;
12:12
the spectrum looks completely different.
12:14
Of course, there is still glycine and alanine,
12:16
but in fact, there are these heavy elements, these heavy amino acids,
12:20
that are being produced
12:23
because these are valuable to the organism.
12:25
And some other ones
12:27
that are not used in the set of 20,
12:29
they will not appear at all
12:31
in any type of concentration.
12:33
So this also turns out to be extremely robust.
12:35
It doesn't matter what kind of sediment you're using to grind up,
12:37
whether it's bacteria or any other plants or animals.
12:40
Anywhere there's life,
12:43
you're going to have this distribution,
12:45
as opposed to that distribution.
12:47
And it is detectable not just in amino acids.
12:49
Now you could ask:
12:52
well, what about these Avidians?
12:54
The Avidians being the denizens of this computer world
12:56
where they are perfectly happy replicating and growing in complexity.
13:00
So this is the distribution that you get
13:03
if, in fact, there is no life.
13:06
They have about 28 of these instructions.
13:08
And if you have a system where they're being replaced one by the other,
13:11
it's like the monkeys writing on a typewriter.
13:14
Each of these instructions appears
13:16
with roughly the equal frequency.
13:19
But if you now take a set of replicating guys
13:22
like in the video that you saw,
13:26
it looks like this.
13:28
So there are some instructions
13:30
that are extremely valuable to these organisms,
13:32
and their frequency is going to be high.
13:34
And there's actually some instructions
13:37
that you only use once, if ever.
13:39
So they are either poisonous
13:41
or really should be used at less of a level than random.
13:43
In this case, the frequency is lower.
13:47
And so now we can see, is that really a robust signature?
13:50
I can tell you indeed it is,
13:53
because this type of spectrum, just like what you've seen in books,
13:55
and just like what you've seen in amino acids,
13:58
it doesn't really matter how you change the environment, it's very robust;
14:00
it's going to reflect the environment.
14:03
So I'm going to show you now a little experiment that we did.
14:05
And I have to explain to you,
14:07
the top of this graph
14:09
shows you that frequency distribution that I talked about.
14:11
Here, in fact, that's the lifeless environment
14:14
where each instruction occurs
14:17
at an equal frequency.
14:19
And below there, I show, in fact,
14:21
the mutation rate in the environment.
14:24
And I'm starting this at a mutation rate that is so high
14:27
that, even if you would drop
14:30
a replicating program
14:32
that would otherwise happily grow up
14:34
to fill the entire world,
14:36
if you drop it in, it gets mutated to death immediately.
14:38
So there is no life possible
14:42
at that type of mutation rate.
14:44
But then I'm going to slowly turn down the heat, so to speak,
14:47
and then there's this viability threshold
14:51
where now it would be possible
14:53
for a replicator to actually live.
14:55
And indeed, we're going to be dropping these guys
14:57
into that soup all the time.
15:00
So let's see what that looks like.
15:02
So first, nothing, nothing, nothing.
15:04
Too hot, too hot.
15:07
Now the viability threshold is reached,
15:09
and the frequency distribution
15:12
has dramatically changed and, in fact, stabilizes.
15:14
And now what I did there
15:17
is, I was being nasty, I just turned up the heat again and again.
15:19
And of course, it reaches the viability threshold.
15:22
And I'm just showing this to you again because it's so nice.
15:25
You hit the viability threshold.
15:28
The distribution changes to "alive!"
15:30
And then, once you hit the threshold
15:32
where the mutation rate is so high
15:35
that you cannot self-reproduce,
15:37
you cannot copy the information
15:39
forward to your offspring
15:42
without making so many mistakes
15:44
that your ability to replicate vanishes.
15:46
And then that signature is lost.
15:49
What do we learn from that?
15:52
Well, I think we learn a number of things from that.
15:54
One of them is,
15:58
if we are able to think about life
16:00
in abstract terms --
16:03
and we're not talking about things like plants,
16:05
and we're not talking about amino acids,
16:07
and we're not talking about bacteria,
16:09
but we think in terms of processes --
16:11
then we could start to think about life,
16:13
not as something that is so special to Earth,
16:16
but that, in fact, could exist anywhere.
16:18
Because it really only has to do
16:21
with these concepts of information,
16:23
of storing information
16:25
within physical substrates --
16:27
anything: bits, nucleic acids,
16:29
anything that's an alphabet --
16:31
and make sure that there's some process
16:33
so that this information can be stored
16:35
for much longer than you would expect
16:37
the time scales for the deterioration of information.
16:39
And if you can do that,
16:43
then you have life.
16:45
So the first thing that we learn
16:47
is that it is possible to define life
16:49
in terms of processes alone,
16:52
without referring at all
16:55
to the type of things that we hold dear,
16:57
as far as the type of life on Earth is.
16:59
And that in a sense removes us again,
17:02
like all of our scientific discoveries, or many of them --
17:05
it's this continuous dethroning of man --
17:08
of how we think we're special because we're alive.
17:10
Well we can make life. We can make life in the computer.
17:13
Granted, it's limited,
17:16
but we have learned what it takes
17:18
in order to actually construct it.
17:21
And once we have that,
17:23
then it is not such a difficult task anymore
17:26
to say, if we understand the fundamental processes
17:29
that do not refer to any particular substrate,
17:33
then we can go out
17:36
and try other worlds,
17:38
figure out what kind of chemical alphabets might there be,
17:40
figure enough about the normal chemistry,
17:44
the geochemistry of the planet,
17:46
so that we know what this distribution would look like
17:49
in the absence of life,
17:51
and then look for large deviations from this --
17:53
this thing sticking out, which says,
17:56
"This chemical really shouldn't be there."
17:59
Now we don't know that there's life then,
18:01
but we could say,
18:03
"Well at least I'm going to have to take a look very precisely at this chemical
18:05
and see where it comes from."
18:08
And that might be our chance
18:10
of actually discovering life
18:12
when we cannot visibly see it.
18:14
And so that's really the only take-home message
18:16
that I have for you.
18:19
Life can be less mysterious
18:21
than we make it out to be
18:23
when we try to think about how it would be on other planets.
18:25
And if we remove the mystery of life,
18:29
then I think it is a little bit easier
18:32
for us to think about how we live,
18:35
and how perhaps we're not as special as we always think we are.
18:37
And I'm going to leave you with that.
18:40
And thank you very much.
18:42
(Applause)
18:44

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Christoph Adami - Artificial life researcher
Christoph Adami works on the nature of life and evolution, trying to define life in a way that is as free as possible from our preconceptions.

Why you should listen

Christoph Adami researches the nature of living systems, using 'artificial life' -- small, self-replicating computer programs. His main research focus is Darwinian evolution, which he studies at different levels of organization (from simple molecules to brains). He has pioneered theapplication of methods from information theory to the study of evolution, and designed the "Avida" system that launched the use of digital life as a tool for investigating basic questions in evolutionary biology.

He is Professor of Applied Life Sciences at the Keck Graduate Institute in Claremont, CA, and a Visiting Professor at the BEACON Center for the Study of Evolution in Action at Michigan State University. He obtained his PhD in theoretical physics from the State University of New York at Stony Brook. 

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