Black Box: In the Background of Scientific Discoveries
Anton: Could you tell us
what are you doing right now,
what are you researching or about some
of your projects and stuff. Jan: Yeah, sure. So, okay ... I'm Jan Kralj,
I work at Josef Stefan institute in Ljubljana, Slovenia,
I work at the department of knowledge technologies.
Particularly my
research focuses on machine learning and
data mining in a network setting which
means that I am interested in studying data
sets which have a network structure.
So I don't just have data instances, but
I have instances that are interlinked in some sort of network.
So, as I said, my area of
research is data which is
somehow connected in a network setting.
So, I try to extract knowledge by
both exploiting the data itself and
the way that the data instances are connected
with each other.
I don't want to go too much into detail.
I got into this research by studying
mathematics in University, so I have a
Masters in mathematics and during my
mathematics studies I drifted from the
more theoretical mathematics into more
applied numerical mathematics and then
it was just one step further to machine
learning and what I'm doing now. And the projects
I'm most involved in are ... for one, I have a
project of my own—I am a junior
researcher, so,
my mentor is receiving funding for my
PhD studies and next to that we're also
involved in the Human Brain
Project, which is
a European flagship project and
then several other projects, especially
with the Institute of biology which we consider to be
a nice source of data for our experiments.
And you, Anton, your research and
projects? Anton: I'm a PhD student in
philosophy of science in the New Bulgarian University
in Sofia. I'm now in my last
months of my second year of Phd and I
became interested in philosophy of
science through ... well I began with the
general interest in what's going on
around us, of course, that immediately implies
going in the science. Before
that I was kind of into the question:
How do we know from different hypotheses
or theories in science,
which is the most confirmed by the evidence that you've got?
And this is, as it turns out, a philosophical
question which brought me to philosophy
of science and in fact to, particularly,
the confirmation theory, which is the field in philosophy
of science which deals with exactly the
question what is the connection between
a scientific hypotheses and
theories and the evidence, the data that we have. And I
did the Masters thesis on the topic; so I
in my Master's thesis I defended the,
well, the pros of the Bayesian
approach in confirmation theory and I
compared it to other classical approaches
which were in the literature but I don't
want to get into more technical details
here.
And for PhD project I continue the trend,
as you might say, but I now explore the
connection between scientific
conformation—so the connection between
evidence and hypotheses and theories in
science as confirmator—but also the connection
between confirmation and explanation. So,
I'm also interested in how scientific
hypotheses and theories explain the data
that we have. And in particular, I'm
trying to build a Bayesian explication
of inference to the best explanation.
Inference to the best explanation
tells us that out of many hypotheses and
theories, we should choose the one
that best explains the evidence and it is the
most probable or the closest to the truth.
But inference to the best
explanation doesn't have a really good
formal explications up to that
point, so, there is a debate whether it's
compatible with Bayesianism and with
Bayesian confirmation theory or it isn't.
So, I'm on the side that it is and I'm
trying to to show how it is compatible.
Anton: What motivates your research? What
drives your research?
Maybe tell us a bit more about that.
Jan: Well, my origins are in mathematics. So
I think mathematics is probably
the flagship science, which does research
for research's sake, at least the more
theoretical part of mathematics.
In part, of course, it always has to
be curiosity which drives any research.
Curiosity, especially, not only
drives but it motivates the researcher. If I
wouldn't be curious, if I
would just get a new idea I wouldn't be
curious whether it works or not,
then I would never get the motivation to
actually do it. But as I am also working
on some projects, my specific field
of research is directed by what is
needed. My research
mainly is developing and improving algorithms
for machine learning or data
comprehension, let's say, and
which direction I will go depends
also on the data that I have at hand and the
data that I have to
analyze. But overall I would say that the
motivation for new research comes in
looking at what I already have and then
being curious about and reading
a lot about how this data was already
tackled, what was already done with it
and, well, going through my own personal
library of what I know that could be
done, trying to find new ways to look at
the data. I think most of the
scientific research can be in some way
or other explained by:
"This brilliant scientist looked at this
data in this new way and got a new
explanation." Um, I mean, I'm not saying
I'm a brilliant scientist, but all the time I
try to find some new way of looking
at things which will maybe be an easier
explanation, that being a simpler and more
effective explanation of the data.
So, my research is directed by
necessity and driven by curiosity. Anton: Okay,
thank you.
I completely agree with the point about
being interested in what's going on
around you, that's what brought me to philosophy in the first place,
that's really the
greatest motivation that actually
still drives my arts. I'm doing my
own project, so I don't need anyone -
their ideas and so on -
to get on board.
I'm completely driven by my interests, which
is very good and I'm glad of that.
I would say that I'm about, I don't know, um,
fifty-fifty would be like, just,
a simplification of the
motivation, but I would say that internal
motivation - so, I'm into philosophy of
science because I'm interested in the
questions that I research like scientific
confirmation, scientific explanation and so on, but I
would be dishonest if I'd say that it's
just the internal motivation and no
external motivation.
Of course, funding is more or
less important. I wouldn't be able to do
what I'm interested in doing if I
didn't have the financial means to do it.
I would be lying if I'd say that it's
just my internal motivation and I'm, you know,
completely unfazed any external matters.
Anton: Would you say that ... How would you define creativity?
It's a very interesting question for me.
Jan: Yeah, I absolutely agree. In fact my
department has also just finished a
series of projects which was on
computational creativity, which was in
the general sense, we have intelligence and
then we want to make artificial
intelligence, but humans also have
creativity and, so, can we also make
computers creative ... And there was an
ongoing debate which is still ongoing -
how can we make computers
creative if we don't actually can't even
agree between each other what
creative is? So, I remember a nice
presentation by professor Simon Colton who
made a computer program which was able
to paint paintings and he said he's not
focusing on what creativity is
but what it isn't. So, what he was
doing was, his program would always, uh, he
always explained exactly how the computer
comes to its ideas to make the paintings
it makes and then he asked people:
"Do you think this is creative?" And,
of course, because it was made by a computer,
people said "No" and then he just
waited and he said: "But why is it
not creative?" And with whichever
response he got he always addressed
that next complaint: "Why is it not
creative?" "Well it's only
doing something from inside." And then he
added an algorithm which was influenced by
outside events and was looking at ...
are people right now happy or not ...
So, is it
now creative? Well it's not. But why isn't
it, it now is affected by outside events.
So, uh,
overall I think it's hard to say what
creativity is apart from the nebulous
"trying to, coming up with ideas
which did not exist yet in the world
before." But I guess I don't know ... in my
personal philosophy I'd say creativity means adding new
lines in the network of knowledge ... new lines
or new nodes in the network of knowledge
that humans have. So, knowledge and/or
art, but in the role of
science, creativity means either
developing new ways to look at the
existing things or to discovering new
things to look at.
So, I would say in the example of the theory of
evolution, the creative step was trying
to think of a new way of how would
this abundance of species that exists come
about. So, we tried to find a new
explanation for an existing thing. Or in
the case of ... I'm trying to think of a ...
what would be the case of discovering a
new thing ... maybe theory of gravity but
this is again a new explanation of an
existing thing. I'm
not sure, maybe I'm now leaning more
towards new ways to look at things that ...
looking at things in a way that wasn't
done before. So, looking at connections
that did not exist. Again we have an
example that we always share in
some of our projects is
an explanation of how
bisociations were formed - we call them
bisociations - bisociations were formed from
two
distinct fields of research and one was
about something about chemistry and the
other was about health. So, uh,
there was a field of research which
covered the fact that, I think, low levels
of magnesium in the bloodstream can
cause migraines in people and
then there was a completely separate
field of research where they showed that
drinking some sort of fish oil or some
nutrient increases the amount of
magnesium in the bloodstream and the
creative research was trying to put
these two together and "Let's try to
mitigate the consequences of migraine
using some new medicine", which started
something else in the beginning. I don't
know if I'm rambling, sorry.
Anton: I was about to say that it's not only
about discovering new things but
discovering the connections between
things that are already there, but you
said it. So, it may be a lot of different
things. It could be, you know, discovering
things that we haven't known before, it
could be, well, learning new connections
between already existing things and it seems that
we have very different definitions of
creativity in different fields. Like in
cognitive science they have another
definition of creativity, which is quite
different from everyday understanding of
creativity, like coming up with a new idea,
and it seems that in computer science
there is a difference as well as you
just said. And, well, in philosophy
we can say one definition could be that
we follow one compatible method So, we follow a
logical method or a
probabilistic one which comes up with
results which are not in
the premises themselves. So, comes up with new
information, but not new information
about the logical connections, but
actually something new. And the
interesting question as well is, well,
the role of creativity in our own
research.
So, do you follow any kind of, I don't know,
heuristic or pattern when you do your
own research in order to be creative,
in order to come up with something new?
Either for a discovery or a connection between the stuff that we
already know? Jan: I think in my research,
uhm, I'm not actively trying to say "Okay,
let's be creative", because
that's the best way to stifle any
new idea, because you'll always just be under stress of
"I'm not thinking of anything new
right now." But I think the best way in
research is to be exposed to a lot of
ideas, a lot of existing ideas, because in
the knowledge network of humanity if you
want to draw a new line, you have to know
as many nodes as possible to get the
idea to draw a line between two things.
So, for example, the research I'm working
on now, I'm trying to connect the field
of more network analysis techniques in
data mining and something called
semantic data mining. My idea is to
look at the background biological
knowledge of genes in a different way. So,
I think this idea, if it works, is a nice
example of this
"Let's look at this thing that already
exists in a different way and try to
attack with methods that weren't
applicable on it before."
But this idea came about from by me
knowing both sides of the argument.
So, the creative part comes in when
you see the two
similar things and asking yourself
the question "Wait, are these things
somehow related?
Can I draw a connection between
them?" But you can only get this idea by
being exposed to as many
views already as possible. So, you want to
do something ... You have to be able to draw
a parallel between things that already
exist and things that other people have
done and then try to do something
similar in your own research.
So I'd say creativity is something that
pops up once you can have enough
experience. And I think that's also similar
in art. I can't just get up and
draw a Picasso.
If I would want to be a good painter I would
have to train myself to paint
well and to paint in different styles and to see the
world in different ways and after a
while I would maybe get the idea ... If you
have a lot of knowledge already I would
then be able to do something new about it.
I'm not exactly sure how creativity appears
but the necessary condition for it is
practice, knowledge, experience.
Anton: Absolutely. In fact, I read one of your
articles and you're not only connecting
things that already exist but this Hedwig
system is of your development?
Jan: No, but it is of our department's, so it's
developed by my colleague, by Anže Vavpetič. Anton: So back on the topic of
creativity. I would say that I'm not a
great believer of talent either.
I didn't hear you say anything
about talent and I'm not a believer
of talent either. I would say that talent plays
a very small part. Of course, I've seen
people do
very productive work in far less time
that I would imagine. So, I kind of
expected that this thing should take very much
longer and some people actually
manage to do it in short term. But I believe that
any kind of genius or talent doesn't go
successful without putting his or her
back into it. So, hard work is involved
either way. What you get by means of, you
know, how powerful brain, this
comes by nature, so we cannot change this.
But what you can change, actually, is how
much hard work you're pulling and you're
putting into your project. So, I would agree
again with you on that point that
creativity requires a certain level of
experience, meaning that you have to put
a lot of work before you can get any
ideas up. And with me it's more or less
the same. So, my so-called creative
process is I would say not very different
than the creative process of any or
most PhD students. So, we read a lot and
after you read a lot ... No, the first base, in
fact, is you choose the important things
that you should read. Then you read a lot. And then you try
to reconstruct what you read in order to
see whether you actually got it or not,
because when you try to reconstruct it,
some of the things that you thought you
got in fact come out as "You didn't get it. You
don't understand this, period." And in this
thought process it takes as long as it
takes, you hope that at some point you
connect the dots that were previously not
connected or see the position of another
something that no one else thought about.
So, that's creativity for me, personally.
Anton: Promotion of science ... Would you say that
promotion of, promoting science to the
general population is a good thing or not?
And how it should be done? Jan: I, overall, of course, I
think it's a good thing. I got to also say I'm a
mathematician by origin, so one thing that
really annoys me about people is
whenever I mention the fact that I
studied mathematics, the first reply is,
and any mathematician will tell this,
that the first reply is: "Oh, I always hated math,
oh, I was never good at it ..."
But, um, my reply to that is "Well
that's why you have us so you don't have
to bother with it." So, overall, I think
science has a bad rap in the fact that a lot
of people consider scientists
introverted people who are only
interested in doing what they do because
of their own curiosity and just doing
their thing without any outward
benefit or without any ... I guess
sucking up public funds for their
own amusement,
I guess. Promoting science, especially
educating the general public, is
something I consider more or less vital
for the Western civilization. We are
in the world where we are, good things or
bad all together, because of the
scientific breakthrough through the last 300
years. Science is what raised the average
life expectancy by a factor of 2 in the
last 450 years. It's what killed
tuberculosis, it's what annihilated child
paralysis, it's what got us to the moon, it's also
what got us Hiroshima, but, overall, if you
look at the state of the world now and
compare it to any other point in human
history, it's beyond compare.
So, uh, and the problem is that people
forget what brought us here and start to
mistrust science and even more than science
the scientific method and the rational
way of looking at the world.
This can all go down very quickly if
people stop to think rationally, I guess.
So, that would be an overall
statement. But in my general field,
with promotion of science I'm lucky enough
that what I'm doing is fairly easy to explain
in general terms. So, it's fairly easy
to explain to people what a network is
and it's more or less easy to explain to
them that I'm doing something with
these networks
by trying to learn something from the
network. But my field is machine learning
and machine learning is a subfield of
artificial intelligence and people have
watched Terminator before, so the field that
I'm working on is open to
misconceptions from the public. And without us
explaining what we're doing and how, what
we're doing, will not end of the world ... If
we don't do that, people will soon, I
guess, turn against us and
mistrust what we're doing. So,
the way I'm explaining it, it almost seems
like it's a necessary evil to ... If
my field doesn't explain to people what
we're doing,
people will start to mistrust us. But I
don't think it is just a necessary evil, you
know. I also think that people need to
know what we're doing,
so that people can get curious about
what we're doing and so that people can
start to also be interested in what
we're doing and maybe join us and maybe
help us.
We're not some elite clique which does things
that no other people can do. We are
people who are interested in things and
other people can also get interested in
these things. So, it's important that the
new generation of, also new generations of
parents know what artificial intelligence and
machine learning is, so that when their
children ask them what this is, they don't
just say "Oh it's some hocus pocus that some
people in the white lab coats are doing." But that
they explain to them that these are
methods and the way people are doing
things are improving our world and
maybe these children then get interested
in the same things.
So, it's, overall, vital for our society
that science has a ... that people have a
positive outlook on science. Because we need
people in science, and we need good people in science to do
good work. And we get that by people being
interested in what we do and people and
children being interested in what we do. Anton: In connection
with this topic
does your field have civilian
science projects? So, do you involve
civilians in any way? Because some
fields, in some scientific fields there are
projects which involve civilians. Like, in
astronomy we have, not we, of course, but
astronomers have civilians looking at
data, also looking at the photos from deep
space and classifying what they see. And, well,
what scientists found out that, in fact,
actual people doing it is a lot
faster and a lot more accurate and they
need them to do it. So, they involve actually
amateur astronomers and even
civilians into
this kind of scientific research. So,
I was wondering whether in computer
science there is a way for civilians to do
actual work in it, help a little bit, even
if they're not experts. Jan: I can't think of an example of
that, but I would say a sort of
related example would be the recent way
that Google, the company, is making all of
its machine learning and AI software
open source. So, it's ... what Google is doing
and what they're developing
is called Tensorflow and that's
basically the structure that allowed them
to win at Go a year and a half ago.
But it's possible for anyone to
just go on the particular website that
they set up and download all of the
software that they use and
run it on their own computers.
I don't know of a case where actually people
like that would actually join into a
project, but the thing is that it's not
hard for us to accept a new person
into some project. The knowledge that
we require, the hardware that we require is a
computer and a keyboard. So, it's not hard
for us to accept new members. For
example, astronomy is different, you need
specific equipment, you need a
telescope to do astronomy, probably.
But everyone has a computer, so, I think
people are already doing more machine learning than
they think about. Because, people just
say "Oh, I want to do something that Google
is already doing, let's try it" and then they
can get into machine
learning by being curious about what
some other cool company is doing.
But I don't know case where we would
be helped by civilians. Anton: On the topic of
promotion of science, I am for
promotion of science, of course, when done properly
and I don't suppose that there are
many people in either science or
philosophy of science who'd be against
promotion of science, that would be kind of strange.
But the idea is that too
much attention has been paid to the
topic of whether the media covers
scientific projects and the results in a
good way and I think that that's
connected to the, as well, very
important topic which is how can
we not train, but teach the general populous, not we,
of course, because I am a philosopher, but scientists and
the people who are doing science to
filter out results which are badly
covered or the yellow pages in the
media from actual scientific results.
Because it's not just the media's
responsibility to cover it well. It's also the
people's own responsibility to filter out good
information from the bad. Jan: It's getting increasingly
difficult due to fake news media
and social media and raged
propagation of news, I guess.
Anton: Of course, that's why maybe the
problem falls more heavily on the
people managing to filter out bad
information by themselves rather than
censoring the media and saying "Well, look,
you will hire experts and just the experts
will see what you can cover, that's it."
You're not allowed to speak, you know, rubbish, for instance.
And about philosophy of science, of
course, philosophy of science would be, as most parts in
philosophy, also
I believe has a bad image, a bad
public relations image. So, there are
questions like whether it is needed,
actually, whether it does something that's
productive, whether it's any good with
scientists or not. So, this could be,
in my view amended with even more
effort put in telling the general public,
in a meaningful and clear way what
we do and how it is connected with
science and why it's important and even
more, to hear scientists on the side of
science saying that, well, they found something
useful or something meaningful
in philosophy of science that would, actually, be even of more help.
Anton: Would you tell us a few words about your
future research plans? What are you inclined to do?
Jan: So, my research now is ... my plans for the
near future are ... I sort of have two
threads of research, both connected to
network analysis, going on. So, my
immediate plans are to wrap
both of them up and to wrap them up in a
nice package which would carry my
name in front. The more long-term plans are the ...
important dilemma of
researchers are industry versus academia,
right. So, I would, I fully
support that I think industry, especially
people in my field of research which is
machine learning, I think we
definitely need to be exposed to
some industrial environment, to
some environment where we need
results, we need them now, we need them
yesterday. So, I ... in my student years I
was already working on some programming,
so I already was exposed to the
industrial parts of my possible career.
But, recently, I was also ... last year was
my first time being able to teach a
course on - I'm being an assistant - on a
course on machine learning, and I was
always interested in ... I always found it fun to
teach, I always found it exciting to
explain things to people who didn't
know it and to see in their eyes that "Oh,
now you got it!" So, I would never ... I don't
want to completely abandon the
academic world. I want
to ... Once I have enough knowledge I
also want to pass it down. Passing down the
knowledge has to be in some sort of
academic way where in some
way where the bottom line is not
the only thing that counts. But I would also
also like to be ... I don't know yet where I'll be
in two years but I would like to be maybe even
exposed to some or maybe work on a
project with a company, which is interested
in the final result of the project, not
only the method, but also the results.
My plan would be to reach out, but
not sway completely out of the academic waters.
And yours, Anton? Anton: Well, mine are more or less the
same. My immediate plans for the future are
finishing my PhD, of course. And after that
and in the meantime, I hope that I have
publishable results which are, of course, a pretty good
thing to have in the beginning of an
academic career. And after that I always
thought that in philosophy of science
there are quite many questions which ...
for which it is possible to be tested empirically, but
they're not really formulated as empiric
questions. Of course, we cannot get the
answer to every question in an empiric way,
but I think that there is much bread in
going empirically into some question in philosophy of science.
So, if I'm given the freedom ... because
in my PhD thesis I'll probably
probably not be able to include empirical
research. But after that, if I stay in
academia, which I would very much like do,
I would be interested in looking into these
kind of questions which could be re-formulated
as empirical and then ...
Well, they may not be solved by empirical
research, but they could be informed by empirical research and this
in my view is quite okay for now. I, too, I plan to stay in academia,
if I'm able,
but with philosophy of science, it's a bit
difficult to find work in industry.
If you're not in academia, skills
that you have in philosophy of science, of
course, you have the analytical skills and
all that, but you don't have or most of
the time you don't necessarily have a
practically applicable skill. That's why you can pick up some
programming or something like that, like
a side project, which also can help
inform your research in philosophy of science. But
that's a project that I'd have to pursue.
Thank you for
this lovely talk, it was very pleasant,
and for discussion and I wish you good luck with
your research and I look towards more
collaboration between philosophy of science
and computer science. I think that there
is a venue worth pursuing. Jan: We have exactly
mathematics to join us. Mathematics is a
form of philosophy and computer science
is a form of mathematics. So, we are not that
far apart, I guess. Anton: Absolutely, I agree. Okay, thank you!
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