hi sean really great to be back on computer file we are in the lab again i think a few or maybe
about a year ago you were down here i can't quite recall if the instrument was running at the time
but what we're actually going to talk about today is based very much on this paper i wish i could
say it's or a paper it's not our paper it's by a group led by this guy alex could turian sorry
alex if i've got your name for not pronounce your name incorrectly um at least you're not stuck with
moriarty for a surname interconnected single atoms could make a quantum brain and we're going to talk
about this there's an awful lot of physics we need to get through and sean hangs his head back
uh in pain the camera's not drooping oh sorry we'll try and condense our physics down
last video we did shows on neuromorphic computing which is about trying to take the
type of computing that happens in here rather than the sort of on neumann architecture which is what
happens in there and use that to do processing because there are certain things our brain does
incredibly well that silicon doesn't do so well and vice versa when this particular title popped
up in my physics world institute of physics feed it obviously repeat my interest directly
they've basically made neurons out of single atoms which is really really quite remarkable
not only have they done that they've got single atom neurons and then they've also controlled the
connections between those atoms and the way those atoms talk to each other so they've also built
artificial synapses again effectively using single atoms so this is really quite remarkable we've got
neuromorphic computing pushed all the way down to the atomic level and that work is is based around
an instrument called the scanning tunneling microscope now we've talked a little bit about
this before i bored brady harren's ears off over on 60 symbols about scanning tunneling microscopes
this is a scanning tunneling microscope all the stainless steel surrounding it is because it's in
an ultra high vacuum we're manipulating individual atoms we want to have this controlled environment
as possible we don't want lots of contamination we're actually working with silicon and if you
were to prepare silicon in air it will oxidize in a fraction of a fraction of a fraction of a second
so we have everything in ultra high vacuum and a pressure comparable to that you have on the moon
and actually in real time what you're seeing here these are atomic rows of silicon these
little holes are where atoms are are missing and we along with a number of other groups have been
embedding machine learning approaches like convolutional neural nets and there have been
lots and lots of videos on computer file about convolutional neural nets we've also actually had
a number of very helpful conversations with mike pound who i know many of you will be familiar with
on taking machine learning ideas embedding them into the scanning probe microscope and instead of
us sitting there laboriously preparing that probe we're training the instrument using reinforcement
learning and strategies along those lines to actually get the tip in a state where it gets the
best possible image without us having to sit there and babysit it for hours days weeks months on end
in a nutshell sharp probe atomically sharp you bring it in close to a surface and when
i say close i mean within an atomic diameter or so there is a means of doing that 60 symbols
you move it back and forth and you measure some type of interaction be that a current
and the tunneling in scanning tunneling microscope comes from the fact that it's a quantum mechanical
effect called tunneling that happens on the atomic scale or a force and we're now at the
point where you can measure the force and not just measure but exploit the force between single atoms
and that's what they've done here they've imaged and controlled right down to the single atom level
after that long introduction we're going to talk about this particular paper and what i'm
going to do is try and summarize it down to what the key ideas are so the paper itself is here
nature nanotechnology an atomic boltzmann machine capable of self adaptation
so what's a boltzmann machine well boltzmann is an incredibly important physicist
mostly because he thought about the statistics of physics he thought about
matter in terms of its component atoms and the dizzying bewildering array of gazillions upon
gazillions of atoms you you know try to count up the molecules in this room so that makes it a
statistical problem in terms of how those atoms interact because you know trying to follow the
trajectory of an individual atom or an individual molecule if you have to do that for 10 to the 23
some of you might know where that 10 to the 23 number comes from in terms of a mole of material
if you have to do it for that number of molle it's an impossible task so you think about it in terms
of statistics and that was boltzmann's genius was to think about the atomic draw comparisons between
i'm not sure comparisons raw parallels between statistics the atomic nature of matter and also
how energy is distributed and that's absolutely key his ideas were soundly rejected by some
of the key figures in physics at the time particularly boltzmann was one of the first to
really accept the reality as would wear of atoms we've been vindicated long before
those images appeared in terms of the evidence for atoms but you know he would be blown away
we're seeing individual atoms on the screen we're seeing rows of atoms um there so where does the
boltzmann machine's idea come from the boltzmann machine idea is really clever and physicists
really like it because it involves taking the ideas from physics in terms of the distribution
of energy and feeding that into a machine learning process in physics you have what we'd call an
energy landscape the simplest possible energy landscape is think of two hills with a valley
in between we have a ball it's at the top of the hill we let go of that ball what happens it
falls down the hill to get to its lowest energy state and actually there are very very few physics
questions that cannot be answered with well the system wants to reach its minimum energy state
if you want to use that at dinner party at some point or in the pub please feel free to do that
pretty well any question can be answered with that of course the devil is always in the details but
it's the system wants to get to its lowest energy or what we call the ground state you can think of
much more complicated landscapes energy landscapes with lots and lots of hills and valleys and it
doesn't just have to be gravitational potential energy if we're talking about atoms and molecules
for example it'll be a type of energy that describes how electrons behave which is very
much not gravitational energy and so what we do with a boltzmann machine is we take that idea of
minimizing the energy and we couple it across so we draw a parallel with trying to find the optimal
solution to a problem so we have a particular energy cost or an energy gain associated with
following some trajectory or other and what we're searching for in both cases is the minimum energy
state or the optimal solution and that's that's where the boltzmann machine idea comes from we're
borrowing from ideas from physics and importing those into machine learning what we want to do is
we want to model the brain what do physicists do physicists are very reductionist and for example
this is a big wet squishy thing with lots of very strange weird stuff happening so what we want to
do is reduce down so the question to ask yourself is what do we have in the brain we have neurons
and we have synapses could we have a neuron which is basically one atom so we want something whose
state we can change so how do you change the state of an atom though is an atom's an atom an atom is
an atom but what's inside that atom inside are the electrons so you can change for example a really
simple ways you could have a potassium atom or you could have a potassium ion where you've added or
subtracted a charge or you can rearrange how the electrons are organized in that atom you
can change the electronic configuration leave it neutral but change the electronic
configuration that's a brilliant question shown because that's exactly what they've done
in this paper they've changed the state of the atom by changing the electronic configuration now
so what we have is the possibility of having a one or a zero but also we can flicker back and forth
between those two we can go once it runs stay in one state go to the zero state for a little while
go to the one states do the zero state these are individual cobalt atoms we don't need to go into
the gory detail of this what we have here are individual cobalt atoms what we're imaging here
with our scanning probe microscope is actually effectively how the electrons are distributed so
although it's an atom and you might think of an atom as a little billiard ball actually what the
stm is sensitive to is the configuration of the electrons can you give us a two sentence what's
in an atom for people who haven't like me have forgotten so the the traditional model of the atom
two sentences is showing you asking me for two sentences on the atom physicists like to reduce
yeah um the typical model of the atom which is called the bohr model which is unbelievably
wrong but it's helpfully wrong is that you've got the nucleus at the center of the neutrons on the
protons and you've got the electrons spinning around outside it's not how it works quantum
mechanically we don't have these well-defined orbits but we certainly have that nucleus at the
center and then the best way of thinking about it is you've got some distribution of that electronic
charge right which is effectively like a cloud around that nucleus what they're doing here by
is by using the tip and changing the voltage on the tip they're changing that that cloud
with that electron cloud and effectively what you're imaging there is that electron cloud so
they basically have a two-state system they've left to its own devices it flips back and forth
between zero and one right so now with about equal probability our energy landscape in this case
is like that right so we've basically got the zero state and the one state in terms of what
the atom is and the configuration of the atom is bouncing back and forth between those two states
which is why you're getting high current low current high current low counter high
current logan and to use the physicist term it's stochastic or it's random it's bouncing
back and forth between those two states and so in one state let's see call this the zero state
it looks like this in the other state it looks like this and that's because the configuration
the electrons in the atom is changing so this is our basic model of a neuron right but that's
one so when you bring two of those atoms together like this what they then find is that you have a
larger number of different current values or basic unit our basic neuron is a single cobalt atom
and that cobalt atom can be in either a zero or a one state we're measuring and monitoring what
state that is in by looking at how current flows through it from the tip that's above it the zero
state we get a low current in the one state we get a high count it doesn't matter which way around
we label those now when you bring those you bring two of those atoms together they start to
to affect each other they affect the probabilities and now you've got instead of having just two
choices you've got two atoms you've got four choices they can both be in the zero state
this one can be in the zero state this can one can be in the one state this can be in the one
state this can be in the zeros here or it can both be in the one state you've got two bits right so
effectively you've got two bits and indeed that's what they see they now see that the current
has four distinct levels or a number of distinct levels yeah four distinct levels which is what
they've plotted out then you can add more and you can i build up the complexity of your system but
physicists love this idea because it links to boltzmann and b it's as reductionist as you get
it's we've broken it down to single atoms instead all that wet floppy stuff with gazillions of
bloody atoms widely flopping around in molecules we've got an atom on a surface in ultra high
vacuum that we can probe right down to you know the electronic configuration that's the type of
thing that physicists love and when that connects to computing it's really neat they've even gone
a step further so that's our neurons what they've then done is they've basically built in artificial
synapses which is nothing more than an additional atom and they bring that additional atom
in and that controls the linkages the weighting of the link between those those two neurons
and then they've even gone on further and they've actually got this thing to learn to some extent
in that it can detect or remember the voltage that's been applied to it by the tip because
that changes the weightings of the connections between the neurons it changes the synapses
so that the system can learn about its previous experiences
and so on we can't do a lot of interesting things with this image after just one set of convolutions
but we're getting there so this one is starting to be transformed
some of them are noisier than others purity atoms in that sample what happens in the real sample
is you have a voltage and that generates an electric
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