Physics World. Hello, and welcome to the Physics
World Stories podcast. I'm Andrew Glester. And in
the Physics World magazine and on physicsworld.com,
we've recently been covering the carbon footprint
of physics as a discipline. A feature article
from March
by Michael Allen considers the huge footprint of
large scale computing.
In this episode of the podcast, we'll talk
to 2 scientists
who, alongside the work they're doing, are trying
their best to make sure that the science
we do is also looking after the planet
as best as possible.
Unusually for the podcast, we'll hear from somebody
from another field of science. But first up,
here's an astrophysicist.
My name is Simon Poteghi Swart. I am
a professor on computational astrophysics at Leiden Observatory
at University of Leiden. I'm doing research on,
astrophysical phenomena
using computers mostly.
What what are you looking at? Well, I
I I guess that with the computer, the,
the entire universe is my playground.
And I I like
to think freely
as as like a free range thinking
about universe
and computing in, in both aspects.
So I also like other things in computing,
like the hardware, the software,
the mathematics which goes behind it, the algorithms
that, that you need, the writing, the software
itself,
and the astrophysics. So all these things combined
makes my job in a computational astrophysicist.
Okay. But what sort of thing might you
be looking at from an astrophysical point of
view? I I would I would say basically
everything. So,
I like the small scales,
like the formation of planets or the evolution
of asteroids
to,
the evolution of multiple stars or individual stars
even,
how stars interact with each other, how they
dynamically interact with the the role of gravity,
the role of of gas interacting with stars,
so in star clusters, in star cluster formation,
how that works together in the galaxy if
you have the interaction
with dark matter,
or something else,
how the stars interact with each other in
that environment, how you get something like Oumuamua,
the the object that was discovered a few
years ago.
That was a complete surprise to many astronomers
at least. And how galaxies form and interact
together. I guess 1 of the few things
that is not really part of my
academic palette
is,
chemical evolution and radiation and is interaction the
chemical evolution the the interaction between radiation and
chemistry.
I'm not a chemist. I don't know much
about it, and I think it's a fascinating
field, but it is just a little bit
too far from my, academic palette.
Now we'll return to the main topic of
this podcast very soon, but I hope that
you'll indulge me a small diversion
because I'm endlessly fascinated by Oumuamua
having written a feature about it for Physics
World several years ago. And I wondered what
the role is of a supercomputer
in trying to work out the origin
of something like Oumuamua,
this surprising
visitor from another star that entered our solar
system? Well, actually,
a few years ago, when Oumuamua was observed,
I I,
wrote a paper exactly about this topic,
being a surprise to me at least that
this object existed enough. If you think about
it And if you do simulations
of its existence and where you expect these
objects to move around in the galaxy,
it turns out as in many things that
it in the end, it isn't that surprising
that the object should have been discovered
if you look carefully at the sky.
And in in in this case, use of
supercomputers or the use of any computer
would be,
in my case, to to try to make
estimates of where does it come from,
what does it do, how does it appear
to be the way it is, and where
does it go.
And to be to be a bit more
explicit in that
is,
what we figured out is that
a is a type of object that naturally
follows
out of the planet formation process.
And if you make planets, you make a
lot of these,
pieces of junk,
like Oumuamua,
which are spreading around by the star making
the planets into the galactic,
potential. And therefore, any other star that moves
around in the galactic potential may need a
few of these objects. Now if you're anything
like me, that begs a question. And don't
worry. I do ask that question
later in the podcast.
Astrophysicists
use super computers to process the enormous amounts
of data collected by telescopes
and to carry out simulations
to understand cosmological
processes.
But should we be concerned about the amount
of energy that they use? No. I I
yeah. I think I think we should be
concerned. I don't think we should worry.
I mean, we should of course, we should
worry about the climate,
because we are using too many resources,
or natural resources in order to to keep
our, convenient healthy life.
But, computers take an awful lot of energy
to work. And,
the I think I think the underlying problem
so I think this is the fundamental problem.
Right? Supercomputers, in particular,
are extremely power hungry,
and, some people,
use them maybe in in not the ideal
way, which I would say something like,
mining Bitcoins,
which is extremely energy unfriendly. But also high
performance computing in science is extremely environmentally unfriendly
as long as these computers are not powered
by renewable resources.
So 1 solution, of course, is
to power these machines by renew renewable resources.
But the problem there is, of course, there
is a limited supply of renewable resources. And
therefore,
if supercomputers all use renewable resources, then the
people at their homes are doomed to use
oil and gas in order to heat their,
environment. I mean, it's it's it's always a
sort of a a 2 sides.
You you can be say, I'm I'm using
only green supercomputers, but then, of course, they
take away the the greeniness from other people.
So I think this is this is the
main issue.
Having said that,
supercomputing
in itself is not the worst thing, at
least for astronomers, is not the worst thing
or the most damaging for the climate. I
think the traveling around
is way more,
demanding on the climate, on on at least
the renewable resources.
And, of course, astronomers like, to have their
telescopes on the very high spots in the
mountains, which are very fragile
environmentally systems,
And
and, you know,
using a lot
of debris there,
is not good for the environment. We have
fairly recently done episodes on the impact of
the science that we do on our climate.
But I wanted to look specifically for this
1 at supercomputers.
How many supercomputers are there? Are you sort
of, vying for time on them the way
the way you would the Hubble Space Telescope
or James Webb? Yeah. Basically. So there there
are let's say there are 500 supercomputers. I
mean, there is there is something what we
call the top 500, which is the 500
fastest,
computers on the planet.
And they're all supercomputers. And there are more,
than this, but this is the the the
the most powerful machines. And after that, there
are, of course, the the the less powerful
the machines become,
the more there are.
There's only 1 fastest computer, of course, and
there are, you know, a lot of which
are hundred times slower than that.
And these computers, the the the fastest computers,
of course, they take the most energy.
And the amount of energy, if you use
the biggest supercomputer on the planet,
full force
would be comparable to, to launching a spacecraft
in the amount of energy it requires.
Or to put it in other terms,
in the city I'm I'm living in, in
Haarlem,
the the biggest supercomputers on the planet take
as much energy as basically a small city
like like Harlem.
So there's a considerable amount of energy going
into these machines, and, of course, they run
twenty four seven.
Do you have a concept of how much
of that time is taken up with astronomy?
Actually, it is only a small fraction. So
the astronomers, they they think themselves also always
as being very,
looking using a lot of computer power, which
is sort of a pride for some people.
Yeah. That counts in the fast computer on
the planet. But, actually, I mean, I think
that astronomy is maybe 5% to 4% of
the total,
supercomputer,
usage on the planet. And what would be
the other 95%?
Well, a lot goes up in chemistry, and
a lot goes up in in particle physics
and, and and and regular other types of
physics.
Is is there anything we can do? Like,
you have to use supercomputers
to do the kind of science that you're
wanting to do. Yes and no.
I think there is a tendency of using
bigger machines because they are available.
And, of course, they are faster, but you
have to invest a lot of time to
program them and program them efficiently.
And that's the other part. So there there
are 2 parts to this story. 1 is
there is a tendency to use bigger machines,
and the other thing is there is a
tendency to,
to to be a little bit sloppy in
optimization if you have a faster computer.
And these 2 things, I think, are not
really necessary in in many cases.
You can do very nice,
scientific calculation on smaller computers,
in which case you have to program a
little bit more efficiently. So you have to
be more efficient in or spend more time
in how you optimize your codes.
And, of course, there are problems which just
are
get better if you have the bigger machines.
So I think you should use the biggest
machines really for the biggest problems for where
you really need those machines, but those codes
should be, you know, as optimized as possible
in order to be as environmentally friendly as
possible. But I like, for example, to, to
to use a lot of,
computer time on, you know, cheap laptops or
well, not cheap laptops, but but, laptop computers,
which are
about the most environmentally friendly you can have
because these machines are built to run on
a battery rather than on, being plugged into
the the the wall.
But modern laptops are are so amazingly fast
already,
that many calculations
done actually on bigger machines could be done
on, more efficiently on on on laptops.
On the other side, I mean, at the
same time, using a supercomputer, of course, everything
goes fast. You don't have to run it,
for a week or so. But that's surely
not gonna make up for the for the
amount of power you're using compared to a
laptop. That's right. That's right. 1 of the
ways we might be able to reduce the
time on computers is more efficient coding. Of
course, many people have been talking about it
for very long times, and there there are
many aspects on efficient coding. First of all,
the choice of computer language is important.
The the choice of algorithm is important, and
the detail of of, what you need in
your calculation,
of course, is important too. Right? We cannot
simulate nature
the way nature is. We don't want that
because then we wouldn't be able to interpret
nature as,
as we do now. Then we have to
the the the simulations would be as complicated
as the observations, and we have the same
problem.
So the whole strength of simulation is to
leave everything out what is not necessary
in your opinion,
and that makes the calculations doable, faster, and
interpretable.
So the question is, can you leave out
more and still get the answer you got?
And the tendency in science is to include
as much as possible because, you know, everything
seems to be important.
But sometimes, you know, it depends on the
question.
Now in terms of of more efficient coding,
so you have the algorithms. You have what
can you leave out? But you also have
the optimization of the hardware like, using GPUs
instead of CPU.
There are and, you know, you you wouldn't
believe it almost, but there are still people
using CPUs for their daily calculations
where GPUs, the graphical processing units are a
thousand times more efficient
than CPU. It is like almost as I
if I see people using, you know, high
performance computing completely based on on CPU,
that they're taking,
a big truck, you know, a 30 ton
truck to go to the supermarket in order
to to buy
a dozen x.
They think, well, this is this is not
efficient.
So that is definitely part of
of a change in the community, I think.
More efficient programming, parallelization,
optimum use of the hardware,
the better algorithms,
and and and, and and the proper language,
which is associated with that. And is is
there particular
languages that we people should be using? Is
it about that? Well, that is this is
an extremely difficult discussion because if you really
want to make it as optimum as possible,
you should program in assembler probably.
And with all with all respect, but I
I I do not recommend most people to
use assembler, and I think everybody knows why.
I don't know why. Can you tell me
why? Okay. So there are several reasons why.
First of all, it's it's machine dependent. To
those of you who are listening who are
not so familiar with computer programming,
and as you heard, that included me, an
assembler is a program used to produce the
intermediate machine language that takes your computer code
and assembles it into binary machine instructions that
your computer processor understands.
That was a nice description that I heard
on Jacob Sorber's YouTube channel. It is a
tremendous pain to to write an assembler and
to change language every time you have a
new computer or a new chipset.
And the the languages are not built to,
communicate with a human. They're built to communicate
with a computer. So you really have a
a long baseline of learning in order to
to learn these languages. There are languages which
are much easier to program
a computer with, like Fortran or c or
c plus plus. And, you know, there are,
I don't know, 200, 2 hundred 50 languages,
to program a computer with.
So the best way probably for a human
and it's it's a balance between investing time
and optimization and the possibility to optimize certain
parts of your code.
So the best problem is having
a relatively quick language where you can rapidly
prototype your problem
like you can do in Python,
then optimize the parts which are,
take a lot of time on the computer
and paralyze them using other c or maybe
even assembler.
So having a sort of hybridization of of
languages.
The the difficulty here in is that that,
for example, for an astronomer, I mean, I'm
I'm trained as an astrophysicist.
So what astrophysics should I not learn
in order to learn to program?
Right? I mean, I would love to to
teach my students,
assembler and and the most optimum way of
using a computer, but, you know, I I
want them to know orbital mechanics too, and
I want them to know about, stellar evolution
and hydrodynamics.
So I have to somewhere, you have to
to,
to dilute the the knowledge. That's not the
only area where balancing of time comes into
play. The problem is,
human time
versus computer time versus environment. Right? So it
is a sort of a continuous
tension between these these 3 fields. I would
like to spend as least time as possible
programming in order to do my physics, and
I wanted to do as fast as possible.
So in some sense,
several of these aspects go together.
And I think 1 of the problems we
have at the moment is that we don't
really know very well
how efficient our coding is in terms of
energy.
And, of course, we can look up the
specs of a computer,
but it is very hard to determine,
how how bad you're doing if you're just
running a full computer, at full force.
And I think it will be good if
there is a little bit more research on
this. And and I was a bit surprised
by,
1 of the papers I I wrote recently
on on this aspect,
how little information was available on actually these,
actual measurements. We'll return to Simon later in
the podcast. But some scientists, this time from
outside the world of physics, have developed a
tool called green algorithms
that can help us to understand the impact
of the computations
of our work. My name is Loic Landelong.
I'm a research associate in biomedical data science
at the University of Cambridge.
And for the past two years, I've been,
studying the
carbon footprint of computational science in general, with
a slight focus on computational biology.
It all started actually with
the bushfires in Australia, so in at the
January 2020 because part of our lab is
based in Melbourne, so we have a lot
of collaborators there.
So, it was kind of like a striking,
you know, manifestation of climate change.
And at the same time, we came across
an article by MS Truble, which was 1
of the first article about the carbon footprint
of AI and natural language processing.
So you may have come across the headline
saying that, you know, 1 AI is as
bad as 5,
cars during their lifetime, and it got picked
up quite a lot. And so we just
wanted to know what's the carbon footprint of
what we're doing. And that was the main
question. We you know, very small question. We're
like, okay. Well, AI can have a large
carbon footprint,
but we you know, algorithms we use in,
computational
biology,
also, you know, they run for a long
time. They can they they we need a
lot of resources, a lot of memory, so
maybe there's a large carbon footprint there too.
And I thought it would be a two
days project. You know, we just look it
up, find a calculator somewhere. It would be
brilliant.
It turns out there was no way to
find it. So I poked around a little
bit and there was just no tool at
all and no one was looking at it.
So there was a little bit of work
done for deep learning in particular, but very
specific to the tools and the
hardware used by deep learning. And that was
it. So suddenly we're like, okay. Well, there's
clearly a need for that and it seems
a bit, you know, we especially in biology,
the big part of our focus is to
improve human health,
but also what we're doing also contribute to
climate change, which has a massive impact on,
well, on human health. So it sounds like
we should be acknowledging it,
to some extent.
And so that's really what started it, and
there was no tool to do it. So
I but, yeah, in collaboration with, Jason Greeley
from, the Bacon Institute and Michael Linway from
Cambridge, we we tried to
build 1. So that's what created the green
algorithms project. We have no calculator. And I
thought what was more important than just another
theoretical paper that, you know, not many people
would read rather rather was rather an online
tool so that scientists, in my case, who'd
wanted to do exactly what I wanted to
do,
could have a tool without spending, you know,
months trying to understand how each component works
and things like that. So you've been using
it. What have you found? Not a big
surprise, but we found that, yes, when an
algorithm runs for days or, like, or even
just hours by using a lot of processing
calls and a lot of memory, it has
a significant carbon footprint.
And by significance, it can,
be like, you know, depending on the task,
it can be equivalent to, many flights
between,
Europe and The US, for example.
So that's the orders of magnitudes we can
be talking about. Doesn't mean all tasks have
large carbon footprints. Lots of analysis, you know,
they run fairly quickly.
The kind of thing, you know, you you
you would, you take an image, you export
it in PDF, for example, it's a blink
of an eye. That doesn't have a huge
carbon footprint. Not at all. But, yeah, many
tasks do have large carbon footprints and especially
it like it's across all fields of science.
So, it's, the there's a lot of that
in physics, obviously, because lots of simulations, for
example. So in our initial work, we started
by trying to look a bit at everything.
So we looked at physics simulation, weather forecast,
this kind of thing. And, yeah, all these
all these tasks have carbon footprint.
1 thing that does seem to make a
difference is where the science is taking place.
To find the carbon the carbon footprint of
running a task, you know, running an algorithm,
so it's just based on the energy needed
to power the computer during that time.
And to do that,
it depends on 2 things, how much energy
you need
and what's the carbon footprint of producing this
energy. That's a very simple formula.
So obviously because it's such a, like, linear
relationship, if you multiply the carbon footprint of
producing energy by 2, you multiply the total
thing by
2. And what we found is the discrepancies
between count countries is is like huge. For
a simple example is if you run the
exact same tool, exact same analysis on the
exact same hardware in Switzerland
compared to Australia,
the task will emit 74 times more
greenhouse gases.
Wow. Because Australia,
the energy mix is massively based on gas.
Well, like Switzerland is based on hydro and
apart nuclear from France and things like that.
So, yeah, so it's just based on how
energy is produced.
We have to do science. Right? Because
it's the most important thing that we do
as humans. I mean, I'm biased, but we
have to do science. It it seems to
me that we're going to use energy.
So if we
find ways of making the energy more efficiently,
more sustainably,
and use those, use solar, use hydro, use
use wind, then then that's the answer, isn't
it? Or or can we look at what
we're doing as well? I think it's both.
I I think
well, I mean, we've seen with the, like,
the recent, like, IPCC
reports, really. It's just we can't afford to
just say
this part is the biggest chunk of the
emission so we can afford to not look
at everything else. That's the argument we've seen
for, like, not, you know, climate change deniers,
in all over the world saying, oh, my
country and and for example, I've heard that
a lot in France. My country is only
responsible for, like, 1.2%
of emissions. So, you know, there's no point
doing it. The US and China should be
doing all the work. But, actually, that's not
true. What we need is, like, across the
board, every single,
like, aspect needs to be tackled and reduced.
So, yes,
reduce like, having more efficient energy is
undoubtedly
like 1 thing to be done,
and it's it will have
orders of magnitude more impact than what we
can do by addressing the carbon footprint of
science,
because, you know, obviously, it will benefit housing,
you know, all aspects of energy usage. So
that's that's without a doubt.
But also as scientists, we have limited impact
on that,
you know, through, like, what we're doing. You
know, with there can be grassroot movements or
things like that. But, you know, through our
science, there's limited impact we can do about
the energy policy of a country.
So that's why it is definitely what would
be the more efficient way,
but that's not what we can do personally.
So that's why we looked at, like, what
can we do as scientists instead of, you
know, tackling that. But to answer to answer
your point about we need to do science,
I totally agree. I mean, I would be
out of a job, especially my background is
machine learning. So,
you know, if I say we can't do
heavy AI anymore, I I need to find
something else to do.
But
the the whole point of this project is
definitely not to do to say we shouldn't
do science and not even to say we
shouldn't do big analysis. Like, some of the
large models have, like, brilliant outcomes and, you
know, it it's just a cost benefit analysis.
It's just saying when if we don't acknowledge
at all the carbon cost, we can't decide
whether or not it's worth it.
So we're just saying if we if in
the first place we consider the carbon cost
the same way we consider the financial cost,
you know, it's not because it costs money
that we don't do things anymore, but instead
before doing it, we say, okay.
Is it still worth doing? Say I'm a
scientist and I go to your website. What
what what happens? What what can it what
how can it help? So the website is
very much is very much a tool.
So I'd say the the important things to
do as a scientist is before running a
large scale project again, I'm not I'm not
saying we should, like, burden all of us
with, like, all the possible
details and every and before right clicking every
button, you should estimate the carbon footprint. Not
at all. But, you know, before running something
significant that will
require hours or days of of computation and,
like, loads of memory, lots of,
cores,
just plug in what you think the time
will be. Just estimate the carbon footprint beforehand
and and look at what the carbon footprint
will be and say, okay. Do I think
it's worth it or am I just running
it for the sake of it and I
don't, you know, actually, I don't really need
it. It's not worth that waste of carbon.
So that's the first thing. Then
mitigate the carbon footprint as much as possible
and I'll I'll just come back to that
in a moment.
And then
afterwards, once, you know, the results are here,
I think it's important we acknowledge what the
carbon cost of that was.
The same way you acknowledge, you know, ethical
concerns after you've done a study with animals
or humans or, you know, just saying, okay,
this was the analysis,
brilliant results, we're really happy with it. And
that's what we've been trying to do and
encourage people to do is to just include
acknowledgements at the end of the publication saying,
running all this work or or this new
tool
cost, like, emitted that much carbon.
And it's not a bad thing. It's just
something so people are aware that this is
the cost of running such analysis.
And it means people who want to replicate
it or people who want to use the
tool, scientists who want to use the tool
later on, well, they can, you know, they
know what they're getting into.
So I think that's that's the the important
thing.
Mitigating obviously is is really important,
and there are many ways to do that.
I would suggest people can check out with
published
paper called paper called 10 simple rules to
make your research more environmentally sustainable.
And,
that's basically, yeah, a very short 10 simple
rules article. So very easy to read, a
bit more easy like, a bit easier than
a long method paper or anything. And and
that just list how what you can do.
But basically, it means,
making the code efficient when you can. I
mean, we've all been there saying I could
either spend half an hour being smart about
the code and reduce, you know, how much
memory I need
for, like, for example, to merge 2 tables
or things like that or say, oh, I
can't be bothered instead I'll just like request
a hundred gigabytes on the HPC server
and I'll just do it in five minute.
And that's, I mean, I've done it, I
I'm many of us have done it, I'm
sure. And and that's a completely valid point,
man. Sometimes it's not worth. But
sometimes it's worth just like plugging in thinking,
okay, well, maybe it's worth being efficient especially
if many people will be using my code
in the future and all things like that.
So just having that in mind,
using updated softwares,
is a painless way to do it. I
know I know everyone hates updating softwares because
it usually breaks the entire pipeline so so
you don't want to touch it. But,
sometimes we we did the example with genome
wide association studies that try to find association
between,
genomic variance and traits. And so they require,
like, large volume of data because lots of
participants and so they are they're quite computationally
intensive which try to find association between a
lot of things together.
And these, we find by just updating from
1 version of a tool to the next
1,
you you would like reduce the carbon footprint
by 50 or 60%,
just because and and that's entire you know,
just because the authors made the the package
a lot more efficient. So in many cases,
actually, you you would reduce run time so
it would make life easier life easier anyway.
It's not just about these huge computer processes,
though. There's a message in here for all
of us. The total life cycle environmental impact
of your laptop,
between 7080%
of it is only due to manufacturing.
So, actually, you know,
if you only charge your laptop every other
day instead of charging it every day, Okay.
But you're only acting on 20% of the
total share because the bulk of it is
extracting the raw material fabric like, making it,
shipping it to you, and things like that.
So
typically,
keeping your device, phone, tablet, laptop
for longer is much better
than, you know,
trying to not charge it as much or
things like that. So that's why they're, like,
keep, repair, reuse, moto is is quite a
good 1. So sort of updating your phone
every year is not an ideal scenario?
No. It's it it really it really isn't.
No. Regular listeners to the Physics World Stories
podcast will remember our Sept. 0 episode where
we explored the importance
of free and open source software. The code
for green algorithms
is freely available on GitHub. Well, the idea
from the start was
we can't have a black box and, you
know, scientists put on numbers and then they
have like an a carbon footprint popping out
without being able to check where it comes
from.
We thought it was really important people could
trust it, and it would be so that's
why everything is open source.
All the data is available
there. All the papers on the topic are,
are also open access,
because we thought, you know, if someone wants
to dive in and check
what, you know, how we made the calculation
exactly or what the code does or what
data we use, it's completely out there. It's
been I mean, it's been great. I know
some people, for example,
got in touch because they
they were like, oh, the calculator is great,
but we would like to, like, make a
local version for our own institution to have,
like, our own hardware on it. Can and
and on GitHub, it's really easy. You can
just fork it. You know? You just download
it and you have your own copy and
you can edit the code and it's your
own version. And that was brilliant. That's the
goal, you know. It doesn't necessarily has to
be our thing or I, you know, I
I'm really happy when people do these kind
of things. Been great to be able to
share all the code. I think it's quite
an important part of of this kind of
project. Simon Porsche Wiesbad worked with some other
scientists to calculate the impact of astronomy and
astrophysics,
not just looking at the computational side of
things. It was a sort of consortium,
effort,
by,
the environmentally committee of the Dutch, astronomical society
or the Dutch astronomical community,
led by NOVA and and what we call
the RAFA and the Astronomy
in Dutch.
And what what we try to do is
make an inventory of how much energy actually
are we using. What is actually our footprint,
if I can say it that way,
in terms of,
of of carbon production or in terms of
renewable or nonrenewable energy sources?
And it was a very interesting thing to
do because it it showed that,
computing is not a big part of that,
at least as far as we could determine.
The travel, which is not a complete surprise,
takes the biggest,
hits. And and after travel,
it is probably just the the the buildings
itself and the commuting
we are doing.
And I think the interesting thing for me
was that,
so there was a second study of,
the big European conference, the the European,
Astronomical Conference
held in 02/18
in Lyon
in France and in 02/20
in,
Leiden in in the city where my university
is
online because of, of of the COVID pandemic.
And the difference in energy of 1 conference
to the other was a factor of 30
just because of,
mostly of due to the travel.
So travel is way more environmentally unfriendly as
we know. Right? I mean, this is not
a complete surprise, of course. But what I
think what is a little bit of surprise
is that,
more than half of the,
total amount of,
c o 2 produced in travel
is from less than 10% of the people
attending the conference, which are the people who
travel from all the way around the world,
take a flight from Australia or from Chile,
in order to get to The Netherlands.
And the short trips are not that bad
even though they are much more frequent.
So, but is the solution to that that
you just have these conferences online instead? Well,
clearly not completely because if the the biggest
traveling people,
they are they are indeed contributing a lot,
but they they may still want to be
present.
So so I think I think the the
story is is twofold. Right? Yes. You you
maybe you should go
online for certain people
or for certain conferences or for part of
the conference, but you also like the sort
of life feel of a conference. I mean,
during the pandemic, I had a discussion with
a with a mathematician friend
once a week discussing a problem, and we
couldn't get any further
with the problem over the two years of
having Zoom conversations.
And then in this Jan. 0, we came
together for half an hour in a room
sitting together. We solved the problem, basically. And
maybe we needed that two years of of
discussions online in order to solve it. On
the other side, I have the feeling that
the effectiveness of doing science
where you just bang your hats together is,
is is amazingly,
more effective than than having Zoom conversations.
The other thing is that we we still
like this live interaction, this live conversations.
So I can imagine that instead of going
for a conference for three days or a
week or so, you you spent a day
a week before visiting a few people in
the neighborhood and maybe a week after that
visiting another few people in the neighborhood. So
you just do
working visits like you do you did a
hundred years ago. Right? When the moment you
went traveling to another continent by ship and
by by and maybe horse drawn carts,
you didn't go there for five hours to
give a talk and then, have hop on
on on the the the
the the ship again. Right? So you spend
much more time and and travel was a
part of
the endeavor.
And I think we should we we probably
should go back to that. There's certainly an
argument for that. Stepping away from physics and
thinking about the world as a whole, it's
tempting to think that perhaps it's other uses
of supercomputers, things like mining Bitcoins that should
be the focus of people trying to reduce
the impact. But there's probably a whole series
of podcasts we could do discussing the various
benefits of science versus finance. Let's leave that
for another time perhaps. But when it comes
to blockchain, the technology underpinning Bitcoin and other
cryptocurrencies,
its uses vary massively in their ethics and
environmental
impacts.
And you can hear a lot more about
that in an interview with computer scientist Suzanne
Koehler.
Suzanne Koehler in an episode of the Physics
World Weekly podcast
from April.
And there's no doubt from the research that's
been done and as highlighted in the recent
IPCC report
that every bit that we can do in
our own endeavors
to help fight climate change is something that
we should very, very seriously
consider.
After all, without a habitable planet to live
on, there's no point and nobody to point
telescopes up to the skies.
I promised you we'd return to the subject
of Oumuamua, and the question that I couldn't
help but ask was if Oumuamua
is the type of object that we should
be seeing fairly regularly,
why haven't we seen them more regularly?
Yeah. I think that's a very good question.
And and I I I wondered about that,
and I'm I'm not thinking too deeply about
it at the moment, but,
I definitely would think that
more would be seen. 1 other 1 was
was seen. Borisov was discovered a year later.
But I would expect that,
that a few more would have been seen
by now. I I I don't know the
reason. It could be statistics,
but I my hopes are on,
the Vera Rubin Telescope, so LSST,
which will, I hope, see, a daily,
Oumuamua
type object.
And,
and, of course, James Webb,
which doesn't have a big field of view,
but, you know, it's it's so
sharp and and,
you know,
effective that it will discover a lot of
these objects, I think. So I I think
it's just a matter of waiting.
And the typical thing that astronomers do, now
we have 2 objects, yeah, Oumu Oumu and
bodies of. They seem to be 2 classifications.
Right? 1 with gas and 1 without gas.
The third one will be a third class.
Of course, it will. And thank you so
much to Simon and Loic for talking to
me. If you'd like to know more about
the main topic of this podcast, then I
can highly recommend the feature by Michael Allen
on physics world dot com, the huge carbon
footprint of supercomputing.
And if you'd like to know more about
Oumuamua,
I highly recommend several articles
on physicsworld.com.
And we'll be back next month with something
else from this wonderful world of physics.
Thank you very much for listening.
Physics world.