[Music] we’ve all been there you’re walking
we’ve all been there you’re walking
we’ve all been there you’re walking around during the night coming from a
around during the night coming from a
around during the night coming from a party when suddenly you look up and you
party when suddenly you look up and you
party when suddenly you look up and you notice how wonderful clear the night sky
notice how wonderful clear the night sky
notice how wonderful clear the night sky is maybe some of you even remember an
is maybe some of you even remember an
is maybe some of you even remember an anecdote from a teacher or relative
anecdote from a teacher or relative
anecdote from a teacher or relative telling you that the light from these
telling you that the light from these
telling you that the light from these stars has often traveled many many years
stars has often traveled many many years
stars has often traveled many many years before it found its way on through our
before it found its way on through our
before it found its way on through our eyes or it could be that you just
eyes or it could be that you just
eyes or it could be that you just remembered from Carol’s talk like two
remembered from Carol’s talk like two
remembered from Carol’s talk like two hours ago I mean whatever works because
hours ago I mean whatever works because
hours ago I mean whatever works because stars are so distant studying them can
stars are so distant studying them can
stars are so distant studying them can be a very challenging task it’s one of
be a very challenging task it’s one of
be a very challenging task it’s one of the reasons why I studied physics and
the reasons why I studied physics and
the reasons why I studied physics and astronomy because how could we learn so
astronomy because how could we learn so
astronomy because how could we learn so much when there’s so little to work with
much when there’s so little to work with
much when there’s so little to work with astronomers don’t have a control lab
astronomers don’t have a control lab
astronomers don’t have a control lab environment where they can carefully
environment where they can carefully
environment where they can carefully monitor all aspects of their object of
monitor all aspects of their object of
monitor all aspects of their object of study instead the galaxies are often in
study instead the galaxies are often in
study instead the galaxies are often in a very chaotic and turbulent environment
a very chaotic and turbulent environment
a very chaotic and turbulent environment and because they’re so far away it’s not
and because they’re so far away it’s not
and because they’re so far away it’s not like we can physically go there to get
like we can physically go there to get
like we can physically go there to get some samples on top of that we’re stuck
some samples on top of that we’re stuck
some samples on top of that we’re stuck looking at these things from one side
looking at these things from one side
looking at these things from one side where we basically only get a
where we basically only get a
where we basically only get a two-dimensional projection of something
two-dimensional projection of something
two-dimensional projection of something that’s intrinsically three dimensions
that’s intrinsically three dimensions
that’s intrinsically three dimensions it’s like you’re trying to recreate an
it’s like you’re trying to recreate an
it’s like you’re trying to recreate an entire house when the only thing you get
entire house when the only thing you get
entire house when the only thing you get is a picture from one side so how do we
is a picture from one side so how do we
is a picture from one side so how do we even start solving that problem
even start solving that problem
even start solving that problem luckily lights can carry a lot of
luckily lights can carry a lot of
luckily lights can carry a lot of information just look at the world
information just look at the world
information just look at the world around you and notice how much we can
around you and notice how much we can
around you and notice how much we can learn just by looking at things the
learn just by looking at things the
learn just by looking at things the color the intensity the way objects
color the intensity the way objects
color the intensity the way objects reflect light can tell us whether
reflect light can tell us whether
reflect light can tell us whether they’re smooth or coarse whether they’re
they’re smooth or coarse whether they’re
they’re smooth or coarse whether they’re hot or cold whether they’re made from
hot or cold whether they’re made from
hot or cold whether they’re made from metal or wood in a similar way
metal or wood in a similar way
metal or wood in a similar way astronomers can extract information from
astronomers can extract information from
astronomers can extract information from light coming from stars to learn more
light coming from stars to learn more
light coming from stars to learn more about their temperature their movements
about their temperature their movements
about their temperature their movements their composition but because we have
their composition but because we have
their composition but because we have vast amounts of data of bill
vast amounts of data of bill
vast amounts of data of bill of galaxies there is no way that we can
of galaxies there is no way that we can
of galaxies there is no way that we can do all of this manually and we need
do all of this manually and we need
do all of this manually and we need artificial intelligence programs to help
artificial intelligence programs to help
artificial intelligence programs to help us understand this data for us and it’s
us understand this data for us and it’s
us understand this data for us and it’s something that they’re they’re really
something that they’re they’re really
something that they’re they’re really good at I mean and it’s really not the
good at I mean and it’s really not the
good at I mean and it’s really not the boring part of the job to see an
boring part of the job to see an
boring part of the job to see an algorithm that’s self learning that you
algorithm that’s self learning that you
algorithm that’s self learning that you can see picking up new rules in front of
can see picking up new rules in front of
can see picking up new rules in front of your eyes can be very self fulfilling
your eyes can be very self fulfilling
your eyes can be very self fulfilling and rewarding it’s a bit like becoming a
and rewarding it’s a bit like becoming a
and rewarding it’s a bit like becoming a parent every single time and without the
parent every single time and without the
parent every single time and without the horror of actual child births it’s not
horror of actual child births it’s not
horror of actual child births it’s not like these self probing self learning
like these self probing self learning
like these self probing self learning programs grow on trees unfortunately
programs grow on trees unfortunately
programs grow on trees unfortunately although maybe they do a lot of these
although maybe they do a lot of these
although maybe they do a lot of these programs founder nature have actually
programs founder nature have actually
programs founder nature have actually their root in nature like for example
their root in nature like for example
their root in nature like for example let’s have a look at how a giraffe is
let’s have a look at how a giraffe is
let’s have a look at how a giraffe is just that so well to its environment
just that so well to its environment
just that so well to its environment with the taller neck and legs over the
with the taller neck and legs over the
with the taller neck and legs over the years there has been a selection where
years there has been a selection where
years there has been a selection where giraffes that were not tall enough would
giraffes that were not tall enough would
giraffes that were not tall enough would be stuck eating leaves closer to the
be stuck eating leaves closer to the
be stuck eating leaves closer to the ground therefore unable to reach this
ground therefore unable to reach this
ground therefore unable to reach this large pool of nutrition that the taller
large pool of nutrition that the taller
large pool of nutrition that the taller ones were able to get to because of this
ones were able to get to because of this
ones were able to get to because of this disadvantage over time they disappeared
disadvantage over time they disappeared
disadvantage over time they disappeared because their chance of surviving and
because their chance of surviving and
because their chance of surviving and creating offspring are smaller so it’s
creating offspring are smaller so it’s
creating offspring are smaller so it’s because of this harsh filtering where we
because of this harsh filtering where we
because of this harsh filtering where we add random mutations to introduce new
add random mutations to introduce new
add random mutations to introduce new characteristics that the giraffe as an
characteristics that the giraffe as an
characteristics that the giraffe as an animal has become how we know it with
animal has become how we know it with
animal has become how we know it with the large neck and legs it’s this
the large neck and legs it’s this
the large neck and legs it’s this systematically favoring the fittest and
systematically favoring the fittest and
systematically favoring the fittest and only using those to create offspring
only using those to create offspring
only using those to create offspring that creates such a powerful mechanism
that creates such a powerful mechanism
that creates such a powerful mechanism which we can translate into a program we
which we can translate into a program we
which we can translate into a program we could translate it into an algorithm
could translate it into an algorithm
could translate it into an algorithm which is essentially a set of procedures
which is essentially a set of procedures
which is essentially a set of procedures and instructions a computer can
and instructions a computer can
and instructions a computer can understand the genetic algorithm is an
understand the genetic algorithm is an
understand the genetic algorithm is an algorithm that captures the essence of
algorithm that captures the essence of
algorithm that captures the essence of evolution theory it does so by creating
evolution theory it does so by creating
evolution theory it does so by creating a lot of random
a lot of random
a lot of random models and adding a lot of randomization
models and adding a lot of randomization
models and adding a lot of randomization and then filtering out those that are
and then filtering out those that are
and then filtering out those that are best fitted to solve a given problem to
best fitted to solve a given problem to
best fitted to solve a given problem to see how this is used in a real-life
see how this is used in a real-life
see how this is used in a real-life example let’s have a look at this the
example let’s have a look at this the
example let’s have a look at this the top image is a real observed galaxy that
top image is a real observed galaxy that
top image is a real observed galaxy that we see in two dimensions the bottom
we see in two dimensions the bottom
we see in two dimensions the bottom image is a genetic algorithm trying to
image is a genetic algorithm trying to
image is a genetic algorithm trying to recreate that image and we’ll see it
recreate that image and we’ll see it
recreate that image and we’ll see it starts off quite randomly making wild
starts off quite randomly making wild
starts off quite randomly making wild guesses but over time you can see it
guesses but over time you can see it
guesses but over time you can see it starts picking up new rules and more
starts picking up new rules and more
starts picking up new rules and more information out of this image until it
information out of this image until it
information out of this image until it ends up with something that’s actually
ends up with something that’s actually
ends up with something that’s actually pretty close to the original one
pretty close to the original one
pretty close to the original one and I can hear a lot of you people think
and I can hear a lot of you people think
and I can hear a lot of you people think like okay yet great stuff but if you
like okay yet great stuff but if you
like okay yet great stuff but if you just wanted to kind of recreate that
just wanted to kind of recreate that
just wanted to kind of recreate that image you could have just copy pasted it
image you could have just copy pasted it
image you could have just copy pasted it right so why bother the difference is
right so why bother the difference is
right so why bother the difference is that from the lower image we have the
that from the lower image we have the
that from the lower image we have the exact three dimensional distribution so
exact three dimensional distribution so
exact three dimensional distribution so we know where the stars are how they’re
we know where the stars are how they’re
we know where the stars are how they’re distributed how old they are how the
distributed how old they are how the
distributed how old they are how the dust is distributed so in the analogy of
dust is distributed so in the analogy of
dust is distributed so in the analogy of the house that we made before it’s like
the house that we made before it’s like
the house that we made before it’s like we’re creating a lot of random houses
we’re creating a lot of random houses
we’re creating a lot of random houses I’m taking pictures from all sides until
I’m taking pictures from all sides until
I’m taking pictures from all sides until we have that one picture that we were
we have that one picture that we were
we have that one picture that we were looking for genetic algorithms help us
looking for genetic algorithms help us
looking for genetic algorithms help us quickly find those that are up for the
quickly find those that are up for the
quickly find those that are up for the case and remove the ones that are
case and remove the ones that are
case and remove the ones that are clearly not doing a good job but we can
clearly not doing a good job but we can
clearly not doing a good job but we can even do better than this our human eyes
even do better than this our human eyes
even do better than this our human eyes only see a limited amount of colours and
only see a limited amount of colours and
only see a limited amount of colours and wavelengths but these galaxies emit on
wavelengths but these galaxies emit on
wavelengths but these galaxies emit on way more than just this
way more than just this
way more than just this they emit from the very short x-rays
they emit from the very short x-rays
they emit from the very short x-rays that the doctor for example uses to
that the doctor for example uses to
that the doctor for example uses to check whether your bones or fractures
check whether your bones or fractures
check whether your bones or fractures all the way up to the very long radio
all the way up to the very long radio
all the way up to the very long radio waves and they can take all of his
waves and they can take all of his
waves and they can take all of his information together to scan this object
information together to scan this object
information together to scan this object in a much smarter way and extract
in a much smarter way and extract
in a much smarter way and extract information from data that no human eye
information from data that no human eye
information from data that no human eye has ever directly seen but genetic
has ever directly seen but genetic
has ever directly seen but genetic elements are not the only
elements are not the only
elements are not the only nature-inspired program swarm
nature-inspired program swarm
nature-inspired program swarm intelligence is the intelligence we see
intelligence is the intelligence we see
intelligence is the intelligence we see large groups of insects and birds that
large groups of insects and birds that
large groups of insects and birds that seemingly move as one large organism
seemingly move as one large organism
seemingly move as one large organism they do so without ever colliding
they do so without ever colliding
they do so without ever colliding without ever communicating and without
without ever communicating and without
without ever communicating and without even having a leader that orchestrates
even having a leader that orchestrates
even having a leader that orchestrates the entire scenery swarm intelligence
the entire scenery swarm intelligence
the entire scenery swarm intelligence will be used a lot to solve optimization
will be used a lot to solve optimization
will be used a lot to solve optimization problems guide drones and manipulate
problems guide drones and manipulate
problems guide drones and manipulate satellites for years to come but it’s
satellites for years to come but it’s
satellites for years to come but it’s not just science fiction swarm
not just science fiction swarm
not just science fiction swarm intelligence already solves quite a lot
intelligence already solves quite a lot
intelligence already solves quite a lot of problems today with a lot of GPS
of problems today with a lot of GPS
of problems today with a lot of GPS systems having algorithms that are based
systems having algorithms that are based
systems having algorithms that are based on ant colonies because hands have
on ant colonies because hands have
on ant colonies because hands have perfected the art of finding the optimal
perfected the art of finding the optimal
perfected the art of finding the optimal routes between their colony and a source
routes between their colony and a source
routes between their colony and a source of nutrition they do so by first
of nutrition they do so by first
of nutrition they do so by first spreading out randomly until they found
spreading out randomly until they found
spreading out randomly until they found some foods at which point the ant that
some foods at which point the ant that
some foods at which point the ant that found the food starts spreading out some
found the food starts spreading out some
found the food starts spreading out some pheromones these pheromones are then
pheromones these pheromones are then
pheromones these pheromones are then picked up by the other ants nearby who
picked up by the other ants nearby who
picked up by the other ants nearby who are now more likely to follow that path
are now more likely to follow that path
are now more likely to follow that path than the random path before so over time
than the random path before so over time
than the random path before so over time more and more ants start following that
more and more ants start following that
more and more ants start following that bad and even reinforce it by spreading
bad and even reinforce it by spreading
bad and even reinforce it by spreading out pheromones themselves so ants have
out pheromones themselves so ants have
out pheromones themselves so ants have optimized their travel time in a very
optimized their travel time in a very
optimized their travel time in a very clever way and they can do so very
clever way and they can do so very
clever way and they can do so very flexibly very flexible in whenever
flexibly very flexible in whenever
flexibly very flexible in whenever sudden changes in the environment take
sudden changes in the environment take
sudden changes in the environment take place but there’s an even more common
place but there’s an even more common
place but there’s an even more common example water has the remarkable feature
example water has the remarkable feature
example water has the remarkable feature that it always finds the pad of least
that it always finds the pad of least
that it always finds the pad of least resistance it never runs uphill so by
resistance it never runs uphill so by
resistance it never runs uphill so by carefully simulating these water drops
carefully simulating these water drops
carefully simulating these water drops you can create a very powerful mechanism
you can create a very powerful mechanism
you can create a very powerful mechanism that can be used to solve optimization
that can be used to solve optimization
that can be used to solve optimization problems and potentially our future
problems and potentially our future
problems and potentially our future traffic jams so it seems that wherever
traffic jams so it seems that wherever
traffic jams so it seems that wherever we look in nature we always find some
we look in nature we always find some
we look in nature we always find some sort of intelligence and this should not
sort of intelligence and this should not
sort of intelligence and this should not come as a total surprise because a lot
come as a total surprise because a lot
come as a total surprise because a lot of these laws and mechanisms have
of these laws and mechanisms have
of these laws and mechanisms have existed for millions of years and have
existed for millions of years and have
existed for millions of years and have been fine-tuned to perfect
been fine-tuned to perfect
been fine-tuned to perfect and we as humans should learn how to
and we as humans should learn how to
and we as humans should learn how to exploit this knowledge and take nature’s
exploit this knowledge and take nature’s
exploit this knowledge and take nature’s expertise on this if you want to advance
expertise on this if you want to advance
expertise on this if you want to advance and learn more about our universe as we
and learn more about our universe as we
and learn more about our universe as we speak
speak
speak artificial intelligence help us
artificial intelligence help us
artificial intelligence help us understand how galaxies are formed and
understand how galaxies are formed and
understand how galaxies are formed and evolved from the Big Bang up until today
evolved from the Big Bang up until today
evolved from the Big Bang up until today they extract information out of these
they extract information out of these
they extract information out of these vasts amounts of data and learn more
vasts amounts of data and learn more
vasts amounts of data and learn more about our universe just as we do their
about our universe just as we do their
about our universe just as we do their goal being the same as ours to watch and
goal being the same as ours to watch and
goal being the same as ours to watch and learn and while we still have quite a
learn and while we still have quite a
learn and while we still have quite a few problems to solve up in the night
few problems to solve up in the night
few problems to solve up in the night sky I bet that there might still be a
sky I bet that there might still be a
sky I bet that there might still be a few solutions laying right here at our
few solutions laying right here at our
few solutions laying right here at our feet
”
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