Camilla Pang:
It's okay to link things
that don't make sense.
And if you find the links between them and
they make sense to you, even if you like.
Well, is that even useful?
Yes, probably. It's been able to
not judge yourself by thinking weirdly.
That's probably one of the things, because when
I was little, I made these notes.
They made sense me. They made
no sense to everyone else.
But when you get older or when you as
time goes, you might not make sense to me.
So it's doing, I mean, cheesy, but staying true
to yourself and to what you what your
vision is.
Harpreet Sahota:
What's up, everyone? Welcome to another
episode of the artist Data Science.
Be sure to follow the show on
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hours. I'll be hosting for the community.
I'm your host Harpreet Sahota.
Let's ride this beat out
into another awesome episode.
And don't forget to subscribe,
rate, and review the show.
Our guest today is a postdoctoral
scientist specializing in translational bioinformatics.
She's earned a bachelors in biochemistry from the
University of Bristol in a PHD in
structural, chemical and computational biology from
the University College of London.
At the age of eight, she was diagnosed
with autism spectrum disorder and struggled to
understand the world around her
and the way people were.
Desperate for a solution, She asked her mother
if there isn't a structural manual for
humans that she can consult and upon learning,
there is no such blueprint to life she
began to create her own.
This Blueprint, culminated in a book where
she dismantles our obscure social customs and
identifies what it really means to be a
human, using her unique expertise in a language
she knows best science.
Her book is an original and incisive exploration
of human nature and the strangeness of
our social norms. Written from the outside,
looking in her unique perspective of the
world teaches us so much about ourselves, about who
we are and why we do the things we
do. And there's a fascinating guide on how
to lead a more connected, happier life.
So please help me in welcoming our guest
today, author of Explaining Humans: What Science
Can Teach US About Life, Dr.
Camilla Pang.
Dr. Pang thank you so much for taking
time at your schedule to be here today.
I really, really, really
appreciate having you here.
Camilla Pang:
Yeah. Thank you for having me on here.
I'm excited to discuss this.
I really enjoyed this podcast, so.
Yeah.
Harpreet Sahota:
Thank you. Thank you. I'm glad you enjoy it.
So I'd love to hear more about your journey.
So if you could just talk to us about
your journey, how'd you develop and cultivate this
interest in science?
What were some of the struggles you had to
overcome on your path to getting where you are
today? And how did you deal with them?
How did you overcome them?
Camilla Pang:
So basically, there's too many
questions in one question.
And so ironically, that is open ended
questions for I find really hard.
And I have this at
the start of every podcast.
And it's you know, I'm not even going to
hide it, I think we should include this, because
when I try and process something, I find it
really hard to pick the details, which I need
to say. And it's always
been like that, like fun.
I was little. You have no filter when
you have autism, and that means basically taking
everything literally and you don't
know where to start.
You're kind of marooned in the middle of
this probabilistic landscape and you just don't
really know to do.
And so then when you try and communicate
with people, you somehow don't know how.
It's really weird. And for me, I've tried to
make sense of human behavior and how to
connect even from such a young age as four.
And I needed something tangible and
concrete to hold on to.
And I read books and the things that I
kind of, you know, clasped onto was science and
math, because when I read it,
I thought, yeah, that makes sense.
And when nothing else makes sense, you're like,
well, this is the substrate that I'm
going to form my life around.
And that came to me writing even highlighting
science books with pens, copying bits out.
And then when the science books in question
can describe everything I used to stitch
notes together and that formed the
basis of the book Explaining humans.
Harpreet Sahota:
So that's pretty interesting because I really
enjoyed going through your book and you've
got some awesome notes and
drawings in there as well.
So I cant wait to dig deeper in and talk
about that but before we get into that, I was
wondering if maybe you could pick your brain a
little bit on what you think the next big
thing in machine learning is going to be, you
know, in the next two to five years,
Camilla Pang:
Two to five years?
That's quite interesting. I think I mean, to put
a timeframe on it, to be honest, I feel
like the attitudes we have generally in machine
that is to replicate, you know, the kind
of logic within the human brain and that's going
to get more accurate we're going to have
more data. And I think there's going to be
a point where we realized that this element of
precision that we're all aspiring to have even as
humans, is not going to be as effective
as we think it's going to be.
So, for example, there needs to be more nuanced
and there's going to be question in what
we know, because everything about A.I.
is based on what we know and
what we want to kind of predict.
Whereas the human mind is a
lot that we don't know.
And that still catches us off guard.
And that's the same for everything else
in life, new complex adaptive systems.
And so the thing that I'd like to be reading
about or be looking at is more things like
an agent based modeling and modeling these
complex adaptive systems, not assuming that
one rule fits all or one logic fits all.
So I talk about it for quite a while.
I think we're definitely going to appreciate
the limitations of very rigid algorithms and
try and incorporate more flexibility in absorbing and
making the most of chaos as opposed
to try and kind of spread it to a side.
So that's what I'm hoping, I know
that's quite a vague answer but.
Harpreet Sahota:
Thats definitely , very, very interesting.
So, you know, with this kind of vision you have
for what it looks like in the next two to
five years. What do you think would
be the biggest positive impact on society?
Camilla Pang:
Oh, its like many things.
It depends how is used, isn't it?
So you could make this amazing tool.
Then it's like me saying, oh, yeah, I'm gonna
make a clone of a super human, which, quite
frankly, is what we're kind of
trying to do with A.I .
But then. What is a superhuman do?
So I'd like to feel.
I'd like to think that A.I.
is used in a way which can make
more sustainable solutions and not kind of accelerate
this whole capitalism that we're seeing.
It's more for the greater good.
Basically, you know, I could say I'd like
to see applied to things like climate change.
I'd like for it to be able to predict
things are more to do with mental health.
That there's my subjective causes.
But I'm sure there are many other things.
But before we get on to making the most, of
A.I we first need to make the most out of
human minds.
Harpreet Sahota:
Very, very interesting point.
And I 100 percent agree with that.
Yeah. So just kind of continuing on this wave
here, trying to pick your brain about the
future. What do you think would be scariest
applications of machine learning in the next
two to five years?
Camilla Pang:
I feel like of a similar point the
things that scare me most about this emerging
technology is, you know, humans started to see
it as the kind of almighty consciousness.
They try and believe A.I.
more than they do with, you know, a human.
So I feel like it's how we
interpret it and knowing its limitations.
The thing that scares me a little bit
is people assume that because it's not human,
therefore it is the, you know,
the right answer to everything.
You know, at number 42,
Hitchhiker's Guide to the Galaxy.
So basically, the thing I'm scared
of is people trusting A.I.
more than they do humans.
Harpreet Sahota:
So I'm wondering, in the vision of the future
that you have or what do you people separate
the great Data scientists from
the merely good ones?
Camilla Pang:
Well, the thing is, we have lots
of different shapes of data scientists.
And I've actually noticed that there's actually not
one type of data scientist that is
correct. And it's acknowledging this and making the
most of the soft skills that you
have. I mean, I could say that I need
to code and do an all nighter every night.
But then what would you achieve?
You'll achieve you might you know, there's
many different things that make a data
scientist other than coding.
And this is something only I've recently
learned because I've only been in bioinformatics
for I don't know I say two or three
years, a little more than that many five now.
So I'm still quite new to it.
And I did a personality test at work and I
was actually quite scared to show my boss the
results because it was
not Data scientist shaped.
It wasn't as technical or logical or
organizational as one would, you know, stereotype.
And then you're like, yeah, that's fine,
because we need different types of data
scientists. So it is really
nice to hear you know.
So mine was more emotional and conceptual.
And I feel like acknowledging the
different shapes and to improve A.I.
we need to appreciate the nuances in people.
For a start, and not everyone is to
be tunnel vision and doing one skill.
So it's been adaptable and
knowing what you can offer.
Harpreet Sahota:
I absolutely love that response.
I think that's 100 percent true, that the
ability to just have different shapes of data
science. Sorry, I go on
go through your example.
Camilla Pang:
I know. I've just seen about my strengths.
I'm learning how to code like
I'm not a python wizard.
I try and learn it so that I can try and
apply it to the problems that I'm faced with at
work. And just for
general, you know, funnzies.
But what I'm really good at and I think
this requires an element of competence, is being
able to envision and look at how algorithms
are simulated in real life, but also in
practice.
Harpreet Sahota:
And if you could talk to us
about the terms neurotypical and neurodiverse.
Would you mind defining these
terms for our audience?
Camilla Pang:
Yes, of course. I've had this question a couple of
times, and I think every time I say it,
the answer's only slightly bit different.
And I think it's because
it's an evolving term.
So I guess there's two definitions of it.
If someone is neurodiverse or neurodivergent,
you I mean, everyone's neurodivergent.
I mean, it's like we genetically divergent
because we're not all exactly the same.
But when it comes to neurodiversity and having
attributed, I guess, diagnosis, so you are
so different enough to not be
able to function in the normal.
I guess everyday life, it
hinders everyday life, basically.
For me, I have a form of autism.
I have autism spectrum disorder.
I also have ADHD. So Attention
Deficit Hyperactivity Disorder and generalized anxiety
disorder. I know that sounds like a lot of
acronyms, but what I really want to highlight
is that everyone is nerodiverse to the
point in which it hinders everyday life.
You will have different parameters of your brain
which are altered to such an extent that
you can't even focus on tiny little sayings.
Or when you say something is outside, is
that outside what is expected of you?
And I think to be able to be neurodivergent and
own it and know your own shape is very
brave because a lot of the time when
you are neurodiverse diagnosis or not, because it's
actually very hard to get a diagnosis,
especially in adulthood, you will feel squished
and you can't be yourself.
So tha is also something that I would
attribute to someone who is neurodivergent is one
they like. I don't understand my
shape, even if I did.
I know no one else would.
Harpreet Sahota:
Yeah, definitely. Very interesting.
And thank you for defining that for us.
Camilla Pang:
I tried, I'm sure someone will be
like oh no or something else.
But this is the thing is divergent.
Harpreet Sahota:
Yeah, that's the thing. Right.
Like some of these terms, like it's how
you internalize them and how you've come to
understand them. I mean, there's always more
than one right answer, I think.
I'd like to get deeper into your book.
So I think it's fascinating in your book
how you draw parallels between machine learning
and human cognition, especially in
terms of decision making.
You talk about two things that I
thought were pretty fascinating, among many other
things, and they were thinking in
boxes and thinking in trees.
Can you talked to us about what that means.
What does it mean to think in boxes and
what does it mean to think in trees?
Camilla Pang:
So basically, simply put, I tried to attribute
these different types of I guess I call
Data structures or like these categories vs.
an evolving, branched stream of thoughts as
two opposite ends of the spectrum.
And we all have them. We all do both.
But to be boxing computer box thinking
is there's no room for error.
You're like ticking to the
time intervals you like.
I got 22 minutes to do this and it's
great because it means that you can be very
efficient and you are the fidelity of thought of
you doing X, Y, Z is, you know, very
definite. It's very good for
acting things there and then.
But sometimes what happens is I used to be
like this through and through because that was
how I implement the structure of my life.
I didn't know what else to do.
I felt that there was only one way.
And I thought, OK. Does that mean I
just do this can i understand it?
And most of the time it's knowing what you
like and liking what you know and with box
thinking. It's categorical in that regard.
And that is there's only a few alternatives
and you can't really see the solutions in
between. You get quite stuck with to
think in trees just like all it.
And it's more of a
branched, I guess, probabilistic landscape.
And I talk about this more and later chapters
in the book, but primarily it's about being
able to acknowledge the different branches of fates
can arise from moment in time and be
able to reassure yourself that there are, you don't
have to put all your eggs in one
branch and you can separate
them out and be like.
I could do this. We could do this.
And if this doesn't happen,
then let's do this.
So it's kind of a reassurance based on
experience that was helps because in, you know,
the eventualities. But sometimes you get stuck in a
rut and think so much in trees that
you don't end up doing anything because every
movement that you make could be a wrong
move. So this is why boxing can also comes
in handy, because it helps you to coalesce
your streams of thought into action.
And I think this is one thing that we're
missing in lockdown is these boxes that we're
used to having. I certainly am, anyway.
Harpreet Sahota:
So sounds like there's not really one mode
of thinking that's better than the other.
Does it really depend on what it
is you're trying to solve, problem wise.
What it is that you're thinking about.
Camilla Pang:
Yes, a bit of both.
And this is ironically, this is by
acknowledging that we can do both.
Is tree thinking in itself?
Because it does depend.
I've tried to assimilate
the extremes of both.
This quarantine, I've not done box thinking at
all because I actually find it hard without
my normal environment.
And so I've actually been less productive because
I'm like, well, I could do this.
And then you're stuck in the middle and
you're looking at your knee for two hours.
That's not what you want. You want to be
able to make a decision there and then.
And this is where you need
to notice what you're doing.
And then how can you go to
the other one to get things done?
So it does depend on who you're dealing with.
For example, if you're trying to think of
flexible solutions and it's good to know what
you know, but to kind of connect the boxes and
to feel like you're not on a cliff edge of
decision thinking in this branch like none, I
can offer a lot of reassurance and also
alternative solutions that you
wouldn't otherwise thought of.
If you just stepped back a little bit.
Harpreet Sahota:
And you mentioned that people tend to be stuck in
a box thinking type of mode, why do you
think that is? That most people are stuck
in a box thinking type of mode.
Camilla Pang:
It's a creation that they
built for them to survive.
And for them to function
almost like an algorithmic module.
OK. This is the module. This is the function.
Also emotionally what that means for a human,
is you learn something and you're like,
well, it worked so far.
The model might be wrong.
You start to question other alternatives and try
and squeeze them into the vision of what
the world should be. I mean, that
is one of two ways really.
But it can be quite limiting and
you can get very bored easily.
So it's good that you get bored easily because
it means that naturally you are quite a
tree thinker. You know that
there's always more to do.
But it's based on fear ultimately, because
obviously you've invested in this box like
vision or box is like visually pixels.
And you're like well, how else
do I see the world?
Harpreet Sahota:
And for those of us who are able to think in
trees, what can we do to get beyond the first
branch of that tree?
Camilla Pang:
For me, much like...We're going to refer
to Data science now, because, you know.
This is a term that I
recently learned could make jittering.
So basically, when you do a visualization,
you've got many points for one point,
basically, and you want to see them all and
so you kind of just do this random scattering
about points that you can kind of
acknowledge that they're in the same space.
So what I try and do is do that.
Everything that I'm doing, if I haven't got routine,
set up, I don't know where to start.
Then why do it?
Okay. Do I write, Okay. I could pick up
a pen or I can do this to this.
Maybe I could do this.
What's related to this?
And I think that randomization
is something that I find.
Useful in defining what I actually
need to do there and then.
That's because and it cushions me around what I'm
trying to do and what I want to get
from it. It's hard to know where to start.
Which is why I guess procrastination does help me
in that regard to help centers me where
I need to be. But honestly, make
sure you put your phone away.
That's another rabbit hole.
Harpreet Sahota:
Yeah. So you talk about learning to embrace
errors and why it's super important to do
that. So what can we do to
start embracing errors in our own lives?
Camilla Pang:
I think it depends what
people mean by errors.
So for example, error and uselessness are merely
just a byproduct of not attributing to a
you know, an orchestrated utility
is not good for this.
Therefore, it's an error.
But actually reshaping how what we think of
what error is and can be quite useful,
because an error in one context is a solution
in the next it is depends on what your
viewpoint is and when it comes to
being neurodivergent you would have multiple different
viewpoints in the space of a minute, which is
why you A you always see the ultimate
solution. But when that's useful enough, that
takes you zooming in on it.
And that can take confidence.
So with errors, I know as many
different ways of doing doing one thing.
But to acknowledge errors are to acknowledged
spaces in between the tree like thinking
what bunches them together.
I think by acknowledging what we define as
noise isn't completely useless, but could be
used in other contexts as
being flexible with that.
And I think back to your question earlier, to
see the signal from the noise and to see
the opportunities that can arise from it will take
what is a very good feature of a data
scientist, not have the
tunnel vision So yeah.
Harpreet Sahota:
And I thought it was really interesting how
you mentioned it, like the knee jerk response
to error is like a downfall of box
thinking, where you kind of categorizing everything
so if it's like, oh, that's not right.
Immediately, just kind of given automatic response
to that - did I understand that
correctly.
Camilla Pang:
Yeah. Basically, it's highlighting.
Yeah. That resourcefulness is not
useful now in this context.
But why would it be useful in the next.
We don't know dis it that we won't
acknowledge it's there and question why it's there.
We'll get on with this. But it's
not been like oh that's wrong.
It's like saying to a person like people say
to me, oh no, your, you know, your, your
existence is wrong. Huh!
Cheers. No, it's not. It's
just different to yours.
So I feel like I don't want to extrapolate
from actually from a data point to a human.
That's the same attitude.
Harpreet Sahota:
It's really interesting. Are you
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But it's really interesting how we're
making connections between proteins and personality
and interpersonal relationships.
So what do proteins have to
do with personality and interpersonal relationships?
Camilla Pang:
So basically, just to bring it back to the book
is why I used proteins as a means to model
humans. Whereas when I was little, a lot
of people use attribute personalities and to
learn from the personalities of the teddy
bears and what that means for humans.
And they made characters out of the
things I knew that resonated with them.
And they connected with I didn't reconnect with
those or people and I knew that I
connected with science. And back to the chapter
I mention that, quote, Proteins are like
humans or humans are like proteins.
It was a way of me being able to model
this dynamic behavior I saw on a football match,
as one does. And I realized that
this dynamic behavior could be modelled.
So I knew that a lot of people that
they moved in cliques and they moved independently.
But then one person was different
in one context to another.
And I really love this disparity between what
it meant to function in one environment and
then be able to adapt to another.
So, for example, it provides a good model
of human behavior because it enables humans to
be considered in different adaptive contexts
as opposed just having one function.
That's why I liked it.
Harpreet Sahota:
So how could we use this understanding of
proteins to be better colleagues and better
teammates at work?
Camilla Pang:
Ok. So for example, when I am so when
wrote the chapter, it was quite...I wrote all this
book before I was 20.
So at the moment I
hadn't actually been in work.
But now thinking of it, what I did is
try to tribute these different types of proteins
and their personalities or their time to
function and their responsiveness in the cell
and what they did. You got the receptor
proteins, you adaptor proteins, you kinase and
your nuclear protein - I'm
sure there's many others.
But what I like most about this protein model
was to do with the fact that you have
different layers of structure.
You got the sequence and you've got the
primary structure with the sequence of amino
acids and then you've got it folds in on
itself to create this module of evolution that
is then into wound with other units of
the evolution to kind of help characterize the
different modes of function.
And when it came to humans, I thought,
well, what's basically the same as us?
We're ultimately determined by our genetic
sequence, along with our environment, among
many other factors that make us human
and make us behave differently in different
contexts. So for me, I was
like, well, I saw this.
I was like, this is great. I was to try
and make it a bit more accessible to people in
the book. I like to kind of parallel the
worlds of proteins to a well known psychological
tests. Called the Myers Briggs classification and just
make it a little bit more human?
Because loosely, when people see
proteins, you don't see personalities.
So I tried to parallel that.
But I think one of the limitations of the Myers
Briggs is that you limited to a 4D, you
know, four letter metric and then thats it
where the protein could be many different
things in many different contexts.
I mean, you could have a tertiary structure, which
is basically like a blob of, you know,
can coded by one genetic sequence.
That can then interact with another kind of
protein and tertiary structure where the blob
to have a different function.
So therefore, it depends a little bit
more on what the cell needs.
And so this is something that thankfully I'm in a
job that I think that makes the most of
the different sides of people.
And for example, if I'm not
Data scientist shaped that, that's OK.
So it's not just getting to know people.
It's about knowing the shape, the different sides of
people and what they can offer in a
team. And also, it goes both ways.
Is being able to open up and be
like, actually, I'm good at this also.
And speaking up so that
you can shine, basically.
Harpreet Sahota:
What tips do you have for, let's say a
Data scientist in a team environment who might be
scared of looking like they don't know something,
or maybe scared to admit that they
don't know everything, but they do want
to openly communicate that to their teammates.
Do you have any tips for them on
how to overcome this type of fear?
Camilla Pang:
That's a really good question.
And this is something that we
often sometimes ask myself as well.
Some of my friends ask me, and it's knowing
your own shape and admitting that because you
don't feel like you are on top of the main
skill that kind of pinned you down to the job
and be like, yes, I coded this
and I'm going to deliver this.
That doesn't mean that I mean it means you've
done the job, but not all of it.
So I feel like I'm hoping that in the
next two to five years, there's gonna be an
increased awareness that there's gonna be
many different Data science shapes.
And from that, people are going to really that
they have more to offer and that they can
be that shape. So when you are feeling like you
have more to offer but you don't want to
open up, ask yourself.
Okay. Brilliant if I'm great at this
how is this gonna be useful.
It is not useful then.
Where is it useful for.
And then before you know it, you can kind of
route down to how it is useful and work and
might even just be a
collaboration with other labs.
Well, that doesn't involve coding.
No. But it does involve being able to
communicate between different sides of science and
bringing it altogether. And surely that's the
philosophy of Data science anyway, isn't
just being at the computer. So there's many
different sides of data scientists that cover
the basic fundamentals of being human in my eyes,
because you have to be a scientist and
an artist to have a vision
and see how things connect.
And also to communicate with experimentalists and
higher order structures, for us to
integrate the main question to actually this is
something I tell myself and I feel
reassured because even though I probably want to
spend more time coding and I try.
I know that there are other sides to my
personality that also make me a great data
scientists such as, you know, it's what energizes you
if you if you're really not a good
fit for the job and
something you have to question.
But ultimately, the more we get to know Data
science, the more we realize that we're all
scientists at the end of the day because
it requires a science and an art.
Harpreet Sahota:
I love that response about the
science and art being blended together.
I like how you talked about the principles
of gradient descent in order to identify and
prioritize our goals.
So for, you know, the non Data scientists,
listeners in our audience, would you mind just
quickly describing what gradient descent
is in layman's terms.
Camilla Pang:
In layman's terms, yeah.
For me, it means trial and error.
And the main bit of trial and error is
knowing which bits of the kind of landscape of
solutions is better for the short term.
So local kind of solutions or local optimum.
And then you've got the kind of bigger picture,
which is a little bit vague, but we kind
of aspire to it.
And we know that there's
a better solution out there.
I can't quite touch it yet.
So we kind of keep exploring.
And then we kind of come to a point where
like hold on a minute, we can't get much better
than this point of convergence.
And so this is the global
kind of solution of this scenario.
And we could even argue that
this is the case for lockdown.
We're trying to find these global
solutions on every day local actions.
And which one's optimal for today might
not be optimal for two months time.
And so we're in this constant
battle of local versus global solution.
And to be able to navigate this landscape, you
have to iterate and block this work and
then you kind of be able to kind
of be oh, no, that wasn't good.
I want to backtrack. And it's been able to execute
trial and error in a way to find these
global solutions. So I hope
that's kind of clear enough.
Harpreet Sahota:
Yeah, that's great, because we definitely need to
have, like, the way you interpret it,
the way you understand it now in order for us
to kind of explore how we use this to solve
or rather prioritize and identify goals.
So I'd love this analogy.
So given, you know, the definition
of gradient descent has provided us.
How can we use that to or how can we
use that framework, that mental model in order to
help us find our path to
prioritize and identify our goals?
Camilla Pang:
I feel like people do this anyway.
So this is something that I'm not proposing
a new algorithm for humans to implement.
I mean, people do this anyway.
We're just trying to
make machines implement them.
So on a scale which isn't judged, because
if we were to humanize algorithm, it's already
on a computer, we'd call it an anxiety
attack because quite frankly, what we're trying to
do is simulate all these different solutions and for
it to go up and down all these
different, you know, highs and lows
and to find a solution.
So this is where I realized I was doing
a gradient descent whenever I have a meltdown,
because it was like oh no no she's
being silly and, you know, I'm like crying.
And my head, my head is really hurt
and I've got my hands on my ears.
I'm doing a gradient descent, but accelerated to
the point where I need to find a
solution of convergence because there are times
and where your head is spinning.
And this can often happen in a meltdown.
This is where I don't mind having meltdowns.
I know it often enables me to reach a
point of convergence in a trial and error just
through, you know, dynamic
simulation to be okay.
I need to do this now.
So it's basically something that everyone can
use, but it's discriminated against on a
kind of accelerated scale because it's basically
you are just doing it everyday.
Trial and error or you're doing it
in the form of an anxiety attack.
But people don't realize how powerful anxiety
attacks are, because whenever I have this,
I call them storms in my
head.I feel a lot clearer after.
Because I'm like okay. I know
whats most important to me.
Harpreet Sahota:
Yes. Very interesting. Really laid
it out in your book.
No hope in people go and get the book after
this because they can make a lot of great
analogies using, you know, math and statistics
to pretty much the human mind.
I think it's really fascinating.
One of them i really liked was you
talked about probability and empathy using Bayes
Theorem. There is a method
that's pretty well put.
So I'll take it for
granted that my audience knows.
The difference between Bayesians and frequentists
and they understand what Bayes Theorem
is. So how can we use Bayes Theorem
for empathy and managing the relationships that we
have with ourselves?
Camilla Pang:
So basically, I originally used Bayes Theorem as a
way of being able to not just take
things at face value, because when you were
literally that is when you have autism and
especially in my form of Autism especially I
take, things literally which is as they are.
And that is great.
But also it means that not
many things are in context.
And so you're trying to
make this context around them.
Okay. They're angry at me.
And I don't know why they're angry at me,
should I be angry or should I be happy?
Why they upset? Oh, no, I got
them this, I to do this.
So Bayes Theorem, I use it as a way
of being able to simulate or kind of contextualize
the words, the actions and the characters of peoples
that I know how to respond best and
to make them happy. So if, you know, obviously
it takes a while to get to know someone.
And what I also described in this chapter that
use Bayes Theorem is that getting to know
a person in situations
is like cellular evolution.
Your a stem cell at first when you kind
of could be anything, and so therefore you don't
necessarily specialize. But as you go, you
see this kind of outward hierarchal structure
of cells that are a little
bit more specialized each time.
And it's an absolute beautiful diagram, it's
actually one of my favorites actually.
It's hemapheresis the kind of differentiation of
blood cells and the immune system cells.
And from this, you can see the
parents cell of which each cell.
So it's like, oh, you keep back in track.
You kind of come to the starting point.
And what I tried to do from this
is I saw Bayes Theorem in this.
This wouldn't have happened if
this cell didn't occur before.
And so I use both cellular evolution and Bayes
Theorem as a way of being able to simulate
the events that happened beforehand.
And the data gathered, you know, of this
person, for example, to know more about what
makes them tick and more about what
makes them happy or when they're upset.
How can I make them feel
better based on what's happened before?
Harpreet Sahota:
Yeah, it's definitely very interesting the way
you laid it out in the book.
so, again, guys if your listening to
this you have to get this book.
It is super cool. So what is a neural network
and how can it teach us about ourselves or
what can it teach us about ourselves?
Camilla Pang:
So that's an open ended question.
And my mind is spinning because I'm like,
well, people do neural networks the time they
basically are a neural network.
But massive and interlinked.
And it's about this process of combining all
these inputs and assembling them in all
these different combinations so that you
can kind of make a decision.
And if it doesn't work,
like, oh, no, go back.
So it's ability to assemble the input information
to come up with all these different
combinations of which ones work best.
And then from that have response
leaps of feedback loop that goes.
Whether it's good or bad over time, there's
lots of different brands which you can do
this particular event or situation.
Humans do this all the time.
It's our ability to take in and process information
and then make it and make a decision
on how to act. And from this act is then
feed back into the original, I guess, data that
you sense. So for me, that's what it is.
I think there are other ways which people
describe as a Perceptron, which mean that it's
not this small neural network, but that's the
basis foundation of being able to - you
know, that's the parallel
between human and computer.
You sense. You process.
You output. Then you feedback.
Harpreet Sahota:
That's what I really enjoyed about the book was
the fact that actually, you know, I think
at a fundamental level, like machine learning,
these decision systems, they are created
to model kind of, in a
way, human decision making processes.
I just like the way that your book really
makes it explicit and then provide some real
world examples from, you know, your own
life the way you think about things.
So I thank you for that. So, you know,
you're somebody who is a practitioner and you
understand the subject of machine
learning and data science.
I'm wondering how you view
Data science and machine learning.
Do you view as art or as a science?
Camilla Pang:
A bit both, just to put it bluntly.
I think people learn it as a science, but
then they realize that things go wrong and then
they're like, oh, no, why is it go wrong?
Because we assume that the machine has some
more nuanced than anticipated and then trying
to get the code right. It's also like talking
to a human that doesn't give you any
feedback. You're trying to decipher
why the code's going wrong.
So when it comes to the everyday grind of
Data science, it literally is an art that takes
a lot of patience, still trying to work on.
But also to be able to curate what reality is
on the computer so that you can model it.
I mean, that takes an artist.
but you have to see things in between the lines
and the know how to model them on the
lines effectively both.
Harpreet Sahota:
And how does the creative process manifest itself
in Data science or in science in
general, rather.
Camilla Pang:
I'm basically writing another book on this?
So at the moment my head is spinning.
But thats cool because I was trying to
distill it down to a very small sentence.
I'm actually reading about that now.
But as scientists, we're not.
We're stereotypically, we're not meant to be
creative we're meant to be rational.
But actually, one of then I guess the mistake
in stereotypes is that to be a scientist,
you need to be very creative, need to think
between the lines need to be able to envision
and be resourceful in different contexts and
make things and make theory turn into
practice. And from that theory, that doesn't
mean anything if it doesn't model anything
in real life. Both art and science are just
an appreciation of the world and being able
to feel like how can I model
it in the most effective way possible.
And effective? And that's where it
mainly differs in science and art.
Effective in science means
objective, representative, logical.
Art is everything else in between.
It's the noise. It's the nuance everything that
you probably wouldn't want to put down
because you worry that it's subjective.
But actually, this is where they're very
much complementary, but also it's about the
attention to detail.
So what level of granularity are you able to
pick up on to make up your picture?
And from that. This is something that I
worry about when I'm doing my own simulations.
I worry they're not going to be good enough.
I don't know what that means.
All I know is that.
Does my intuition of a Data
scientist with this specific, detailed profile.
Is this going to be what they envision?
It might be.
It might not be. So there's
no one solution to the problem.
And I feel like that's one of the things
that a lot of scientists need to know.
Also, myself included, because we can have
imposter syndrome that we know that everyone's
got a different type of vision.
Harpreet Sahota:
Thats definitely very important to keep mind
- that different Data scientist approaching a
particular problem statement will have their own
unique way of making progress in solving
that problem statement. But as long as they're
able to justify every step they're doing
along the way, I think that methodology, the
methodological part of it is like the
science. Everything else, like you mentioned, like
the noise, the grey stuff in between.
That's the art and play.
So on throughout the book and on your
Instagram as well, I see this post.
You've got your books and your notes on
Instagram, and then you have this drawing
structure book explaining humans and I
really like that note-taking style.
I think a lot of our audience would benefit
from you kind of taking us through a process
for taking and making notes.
Would you mind sharing your
note, taking process with us?
Camilla Pang:
Yes, of course.
Whenever I make note, I've noticed that having
books that are OK, these my notes today
and they're not very structured and to be
structured, you need everything to be make
sense in your head. I write to make sense.
So you've got chaos.
You've got all these
different weird scribble marks.
You like a map or an atlas of the problem.
It's me trying to boil down the
different elements of dimensionality into something that
is more coherent, such as a paragraph that
much like Data science takes a lot of
wrangling and a lot of processing to make
sense of what you envision in your head.
So for me, I like to -
and this is a form of artwork.
You see the links and the bonds between
the words and each angle of the paper.
In my pile means something.
It's a message much like the terms and DNA.
You have all these different encodings of
which paper, and what angle means what?
And for me, obviously, if the wind blows,
then, OK, I'm going to reassemble it.
It's a narrative that I'm very sensitive to.
I like to physically see everything
laid out in front of me.
So, for example, it's its own message.
And even when I'm at work, whenever I make
to do lists, you see them and they're really
messy. My boss is like maybe
we should type them up.
And I'm like, yeah, maybe but
it just doesn't feel the same.
So I'm constantly trying to be neater.
But then the information I put
out doesn't feel the same.
So it's good to do a bit of both.
And if it's messy, then if it
makes sense to you, then that's fine.
But yeah, it's basically like an artwork.
It's very messy.
Harpreet Sahota:
Yeah. Really. I'm a big fan of like that, almost
kind of like mind maps in a sense has
kind of reminded me most of.
But it really does like just looking at the
drawings that you have for the book, its
inspiration for me and how I
want to think about taking notes.
But also just it distilled
everything down to a picture.
It's really cool. So I think it's a
lot to be learned from that, too.
Thanks for sharing. Thank you.
So a lot of data scientists, whether they're
in their actual jobs or whether they're
trying to break into the field.
Projects are a part of what they do, and
they may be feeling some type of hesitation or
fear because they're wanting to make their
project absolutely perfect before releasing it
to the world, before releasing it to
their boss or what have you.
Do you have any tips for anyone
who is stuck in this kind of.
It must be perfect. Before
I release it mindset.
Camilla Pang:
Yeah. So I'm a bit of a
perfectionist and so I completely get it.
It's good to question and humor yourself.
What does perfect actually mean?
And then your like actually what does it mean,
does it mean it has to be this color.
And then you be like nah
they can't be that specific.
And then you start to kind of reason with
yourself as to what your definition of perfect
means. And for example, it might be, you
know, when you're working with, you know,
experimentalists, working up scientists, everyone's vision
of perfect is very different.
And so it's knowing a bit more
about your team and what they want.
And even though it might not feel like the
perfect solution for you, if it does the job,
then that's fine. So it's yeah, I definitely
feel like it's something that a lot of
people battle with.
I think communicating more of the expectations are,
you know, that upon your role, what
you need to do. If you know what the wiggle
room is and you can kind of work around that.
But to make it of a quality that your
work justice, that is not a personal endeavor isn't
that? So, it's not to
get rid of perfectionism.
Just making sure that when you
do it, you're doing it yourself.
Also for the needs of the team.
Harpreet Sahota:
Thank you for that. I think that's really
valuable advice that our audience is going to
benefit hearing.
Thank you so much for that. So how
are soft skills for a minute here?
What are some soft skills that you think
Data scientists are missing that are really
going to help them excel in their
careers and in their interpersonal relationships?
Camilla Pang:
So whoever coined the term soft skills
they're wrong because soft skills are actually
really hard work.
Because the nuanced and
they're context dependent.
And no matter how friendly you are, you're
always going to end up annoying someone.
I think that's just a given.
And that's actually quite nice to know because you,
might end up catching on a bad day.
But when it comes to soft skills, I feel like
from my experience so far, for me it's a
bit different because me, it's mainly being able
to ease anxiety in myself so I can
communicate effectively with my team.
And it's also being able to communicate and making
your team feel that they can come to
you and ask you questions.
So that's one thing I've learned is that
people that don't have as many soft skills,
people don't want to come and ask for help.
Because they feel like they can't.
Or they're on the box thinking,
they're cliff edged by someone's judgment.
But if you feel like, yes, OK.
If you open with them and being able to
not judge them based on them not knowing
something, I think it's
having that friendly banter.
And to see them as a human, as a friend.
And I'd like to do that with all my
colleagues because I really I think that's really
beneficial, because when you're an off day, you can
kind of like talk to them and you
can. I think that's the soft skill for me
that's been most important is even now and most
the time everyone's got their
headphones in, which is great.
But underneath all, you have no idea
how anxious they're feeling when they're coding.
So I think that's a soft skill that your
need to teach yourself is being able to reassure
yourself that even though everyone looks like
they know what they're doing, they might
not. They might be as stuck as you.
So it's been open enough people to you
can help each other, basically not judge each
other because there's this
whole kind of stigma.
You have to be rational.
You can't be emotional.
You know, emotions are weak.
They're a soft skill. We don't need them.
But this is a form of toxic masculinity.
We need to be able to make the most
of the different sides of people to work effectively.
Harpreet Sahota:
What's up, artists?
Be sure to join the free, open,
Mastermind slack community by going to
bitly.com/artistsofDatascience. It's a great environment for
us to talk all things Data
science, to learn together, to grow together.
And I'll also keep you updated on the open
biweekly office hours I'll be hosting for our
community. Check out the show
on Instagram at @theArtistsOfDataScience.
Follow us on Twitter at @ArtistsOfData.
Look forward to seeing you all there.
100 percent agree with you that soft skills are
a bit of a misnomer because they are
really the hardest skills.
And I think at least from my perspective, they
are the hard skills because they can't be
taught. You learn through experience.
Camilla Pang:
You learn from experience.
Yeah. And just to put in that further, in
Chapter eleven, I talk about how to be polite
or mainly about the kind of nuances
of etiquette and how to model them.
So there is there is some ways you can
kind of benchmark whether you're doing it right or
wrong. But ultimately, they can't be taught, which
is my one of the conclusions of the
chapter. But not all of them.
Harpreet Sahota:
And to your point, I think, like vulnerability
is definitely a very important soft skill.
I think once I sort of embrace that in
my professional life, when I was just open about
like, yeah, I don't fucking know what
the answer to this thing is man.
Give me time to look it up and research.
And once I became okay
with not knowing everything.
Things just became so much more easier.
Camilla Pang:
Yeah, definitely. Which is where the protein
model comes in because they're always
evolving. Everyone's not just
this one dimensional being.
We were constantly evolving.
Harpreet Sahota:
So I mean, I.N.F.J.
personality type on that Myers Brigg scale.
What what protein would you
say would best describe me.
Camilla Pang:
ooo you've got.
I guess it's a nuclear protein, isn't it.
So it's one with a guest
on it or the nuclear membrane.
If you're talking about that.
It's someone -  so I.N.F.J.
So what I've tried to do, even though you
like, what's the point in using the protein
model if you just map it to Myers Briggs, which
is a very good question and one I ask
myself many times throughout this podcasts.
For example, if you were to equate the two
would be a nuclear protein because you get
along with it. I mean, you're receptive to it,
but that new kind of doing your own thing
quietly, but then you care about other, you know
who you affect and how you make people
feel. This is what cause I'm
an INFJ as well so snap.
This is the thing. It depends on the context.
And I don't really like talk about it too much
because it's I don't feel a lot like has
much to offer. And people are
ohh what protein am I.
there's actually a quiz.
I am saw back up now like ten
years ago maybe about what protein am I.
And for some reason I got Kinase, which
I thought her hilarious, which because kinase
would be a very extroverted, dynamic kind of
like party animal and like, you know, spoiler
alert, I'm not that.
So when it comes INFJ, such as she
knew it would be a nuclear protein.
Harpreet Sahota:
Thank you. Definitely. I'll look into
that a little bit more.
Never, never saw myself
as being very nuclear.
But I like the way you spelled it
out in the book with the different mappings.
That was really interesting.
Camilla Pang:
It depends what you call what context.
I mean, it could be a
Kinase when you feel really comfortable.
So this is why I - this is why it changes.
Harpreet Sahota:
Yeah. So I was wondering if you could speak
to your experience being a woman in STEM and
if you have any advice or words of encouragement
for the women in our audience who are
breaking into STEM or maybe they're currently in
STEM, might be facing, you know, any
manner of adversities.
Do you have any words of
encouragement or advice for them?
Camilla Pang:
Do not try and hide your femininity because you
feel like it will make you more logical.
Doesn't work. I feel a lot of women
try to mask their femininity because they're worried
that they're going to be frowned upon or they're
going to be silenced because ah yeah, I
know thingy said that, you know, cause she's,
you know, she's you know, that people will
judge more. They're worried that people are going
to judge us more because we wear
lipstick, because we, you know, we like to wear
perfume or we like to have all these
things that make us feel good because it's
from an emotional place and therefore we're
less logical. I feel like a lot of women
try and silence is part of themselves because it
makes them feel less of a Data scientists, even
though they feel better as a woman, that
it shouldn't be mutually exclusive.
You're just a person who feels who has
their way about them and shouldn't have to
sacrifice a part of themselves in order
for them to be listened to.
But this goes for other
people in Data science [inaudible].
It's a two way thing. It's not just, oh, we
need we can give women a voice and we can
empower them all we like, but we can
scream and shout as a per say.
But what it takes is for people to listen,
not judge based on what we look like.
Based on what if we're having anxiety attack?
Ah yeah she's having an
anxiety attack yeah she's unreliable.
That that's something I feel that
could be helped a lot.
I mean, I'm very, very lucky.
I'm in a work environment where people know
that's my nature, mainly attributed to the
fact that I'm, you know, I'm autistic.
A lot of women go for this
inside and a lot of men, actually.
But I think to be able to show it is
something that takes alot of bravery and it requires
a person to be empowered.
Also that the work environment to be receptive
and supportive and not silence based on
the fact that someone has certain shape.
Harpreet Sahota:
And what can the Data community do to foster
the inclusion of women in Data science and AI
and STEM?
Camilla Pang:
I think it relates a bit more back to
vulnerability, being able to talk a bit more about
what you find difficult and open it up.
It's actually quite well in my eyes its
quite simple solution because is to be human.
For me, I don't I mean, I shouldn't
say this, but I find gender something.
I see human as a human.
I don't see them. Oh, yeah. You're a woman.
Oh, you're a man. I'm not. Okay.
You're person. Cool. You know?
So I feel like a lot of
people should go with that shit.
I think a lot of them do.
But it's just being self-conscious about what
you portray and not knowing what people
think of you.
Harpreet Sahota:
Last formal question before jumping to
a quick lightning round here.
And that is what's the one thing you
want people to learn from this story?
Camilla Pang:
It's okay to link things
that don't make sense.
And if you find the links between them and they
make sense to you, even if you like what,
is that even useful?
Yes, probably. It's been able to
not judge yourself by thinking weirdly.
And that's partly one of the things, because
when I was little, I made these notes.
They made sense to me. They
made no sense to everyone else.
But when you get older or when you as time
goes, you, like, yeah that makes sense to me.
So it's staying I mean its cheesy, but staying
true to yourself and to what you what your
vision is. So that's yeah.
That's probably one of them.
From the top of my head.
Harpreet Sahota:
I love that, that's absolutely an
amazing way to put it.
I think that's really the basis for creativity
is taking two things that maybe on the
surface of it don't look like they belong here
or don't relate to each other, but then
combining them in new ways
to produce something completely different.
That's kind of.
Camilla Pang:
Yeah, exactly.
That's a great way of seeing it.
And also, sorry, just want to not
judge yourself for being obsessed about something.
For example, if you obsess about this book
or this or this link, go for it.
Be obsessed about it, because this
is how you get stuff done.
And this is how you get to solutions.
I think a lot people, especially when
they're adults, they see obsession as something
that is bad or chaotic or, I don't
know, unreliable, I dont know what they think.
I'm not an adult.
And they try to silence themselves
because they want to feel regulated.
That's actually one of the
important messages as well.
So which is combined just to put that in.
Harpreet Sahota:
I like that. So let's jump into a
quick lightning round here, starting with the first
question. What is your
Data science superpower?
Camilla Pang:
Right. Okay. I'm not very good with numbers, but
I am very good at being able to simulate
different models in my head simultaneously and
know how they link back to algorithmic
logic.
Harpreet Sahota:
That is one hell of a superpower.
So if you could put up a billboard anywhere
in the world, what would it say and why?
Camilla Pang:
I know I don't like that one.
I'm not good with advertising.
Harpreet Sahota:
Yeah,
Camilla Pang:
Sure. Yeah. You know what?
There it is. I'm not good at advertising
Harpreet Sahota:
Thats perfect. I love it.
So what's something you believe that
other people think is crazy?
Camilla Pang:
A lot of people discourage reaction either
from themselves or from other people.
I don't know why I feel like this is due
to the fact that we just we think we judge
people based on this one reaction as
opposed to seeing how a person evolves.
So basically, it's a signal,
but it's a positive signal.
A negative signal. If I was really excited, she'd
be like, oh, calm down, Millie, if I'm
crying. Ah millie is being dramatic again.
So I feel like a lot of people get
scared of this intensity of reaction that we naturally
hardness as humans because it's instinctive,
which ironically is something that we're
trying to get the computer to do.
So that's the one thing I find really
weird is that we're trying to suppress our
instincts, to react, to look
like we are logical.
Harpreet Sahota:
So what would you say is the most
bizarre aspect or quality of human nature?
Camilla Pang:
That kind of.
You know, we always seek conformity.
I have no idea why, because, I mean,
if we were to look at evolution.
Cancer doesn't believe in,
you know, conformity.
If anything, it's a branched evolution.
And this is something that I feel like
humans naturally have that we naturally try and
oppressed so that everything is coherent.
That and making meetings about meetings.
I've never understood.
Harpreet Sahota:
I like that. Meetings of our meetings.
Yes. So another point about conformity.
Do you think conformity is distinctly different from
wanting to belong to the tribe or be
a part of the tribe?
Are those two kind of the same thing?
Camilla Pang:
I feel that we try and make conformity; its
a critical mass effect than a bystander effect
for a common cause.
And that's fine. But when it comes to
the point of exclusion, will you feel excluded?
That's something else. If you feel like you don't
fit or people like you need to be the
shape, that's when it gets a bit sticky
because you're like, well, I don't completely
agree with that. And if you think about
all these different categories of different boxes
that humans live by not
wanting, people are congruent.
So to be able to be a conformist or
completely in the middle of everyone, you probably
don't exist. And if you did, you'd be on
your own because, you know, it's no one's no
one's normal.
Harpreet Sahota:
And you talk about this new book.
So those who are interested to pick up the
book, you go into crowds and individuality as
well. So that's really interesting, really.
So what is an academic topic outside of
Data science that you think every Data scientist
should spend some time researching?
Camilla Pang:
I'd say it depends on
the Data scientists in question.
For people like me who haven't started out
as coders and are wanting to and hopefully
learning a more about code, but
also how Data science is.
As an art and also what Data on its
can do beyond what you think you can do.
So I feel like it's a
bit more the philosophy as well.
If I were to get eggy about it.
It's to read around the subject.
And that isn't just like,
oh, read another language.
It's like no read philosophy.
What are you to learn philosophy?
Well, to be able to simulate psychology of
different types of areas and different types
of high order structures and how they localized
down to the different people, then surely
you're going to need to know
the structures of hierarchies that exists.
I see hierarchy. I mean, levels
of abstraction, but also the interpretation.
So actually, I think learning things that
are unrelated, such as philosophy, psychology
and art is something that is very fun to do.
It's very inspiring. And it can also make
you look at your work a lot differently.
Harpreet Sahota:
To quote Marcus Aurelius -
What could guide us?
Only philosophy.
I 100 percent believe that.
Philosophy is definitely one subject I
am very deep into right now.
Camilla Pang:
Yes great isn't it.
Harpreet Sahota:
It is. Yeah. So what's the number one book
fiction, nonfiction or maybe even one from each
that you would recommend our audience read.
And what was your most
impactful takeaway from it.
Camilla Pang:
Nonfiction book.
Is it really hard? Because
I actually love reading.
And you learn something from every book.
If I had to have one, I remember
it's called Critical Mass By Philip Mass.
It came out quite a few years ago,
I think in 2004 or something like that.
And I read it in my
first year of uni in 2010.
And I actually loved it.
It gave me the confidence to be able to realize
I can link things and I can link things.
But that me linking things makes
sense and is also desirable.
Oh, so I can link science with psychology
and psychology with physics and and then
politics. Yes, you can.
And so this book, I read it quite I
mean, it's quite chunky thing, but it's definitely
worth it. It discusses physics, politics and
biology, graph theory and in such an
accessible way.
And also, agent based modeling is great.
It's a really good book.
Harpreet Sahota:
I'll add that to the show notes and.
Camilla Pang:
I'm not disagreeing with you
was like, yes, it is.
Harpreet Sahota:
Pick up pretty much every book
that my guests recommend to me.
And I've got something like 40 unread
books sitting on my shelf right now.
But luckily, I tend to read a
little bit faster, blast through them.
But I'm looking forward to this. And something that's
kind of on a related note to it,
you just mentioned there's a couple of books.
One is called Range.
And one is called the
self-made billionaire, in fact.
And essentially the premise of those two books
is that being able to take two unrelated
ideas, put them together into something new, combining
them in a new way, is what drives
progress forward in premature every field.
Camilla Pang:
Definitely. Definitely.
And I think that's where a lot of
the new information evolution comes from is joining
things unrelated for you
to create something bigger.
So, yeah, fiction.
I can't answer that one. I'm afraid
of fiction, but I've started reading it.
But at the moment, I know I haven't read enough
for me to feel like I am informed, but I
actually love Normal People by Sally
Rooney but that's my personal preference.
Harpreet Sahota:
So I'll definitely add those the show notes.
Camilla Pang:
It might not be your cup
of tea, but you know.
Harpreet Sahota:
I don't have too much
fiction in my bookshelf.
I think the only fiction book I have is The
Virtue of War, and that is a book written
from the perspective of Alexander
the Great through his conquest.
So it's like a historical fiction.
I guess in some sense.
Camilla Pang:
Yeah
Harpreet Sahota:
So if we can get a magical telephone that
allowed you to contact 18 year old Camilla, what
would you tell her?
Camilla Pang:
Keep doing what you're doing.
It'll come handy later on.
People call you crazy now, but in our guess,
because you don't fit in a system, that
doesn't mean you weren't born
to make a new one.
And I feel to have that confidence is something
that I wish I had when I was little.
But once you can see that with every child.
I was actually quite confident
teenager in my own way.
But it's just carry on memory.
I don't regret doing anything other than I.
I wouldn't be who I
am now, you know, Bayesian.
So just to reassure her that
everything's gonna be OK basically.
Harpreet Sahota:
So what's the best advice
you have ever received?
Camilla Pang:
I've got two bits that I feel like is that
I can repeat myself on a daily basis, really.
Nothing changes if nothing changes,
which is really simple.
But then you're like, oh yeah.
Been doing the same thing.
Wow. Why aren't I getting different results?
Well, it's actually the
definition of insanity.
But when you're writing or communicating.
Often people get writer's block, so to speak.
And the very thing you are afraid to write
is actually the very thing you should be
writing because other people are
going to be feeling it.
But to have the bravery to communicate it is
another thing that is to relate to things a
lot people are scared of but
don't have the guts to articulate.
Now, that's great.
Harpreet Sahota:
Now I love that. Yeah. If you are feeling
fear about something, that is a good indication
that that is the direction
that you should go towards.
So what song do you have on repeat right now?
Camilla Pang:
Currently I really like Massive Attack
and Moby, currently at the moment.
I'd say Teardrops by Massive Attack in is
quite retro, but always does the job.
Harpreet Sahota:
It's a good track. It's also the theme
song for a TV show House M.D.
House M.D is about.
He's a doctor. It's
essentially he's a doctor.
He's modeled after Sherlock Holmes.
Camilla Pang:
Oh does it got Hugh Laurie in.
Harpreet Sahota:
Yeah that guy. Yeah.
Camilla Pang:
Yeah. Oh, yeah.
Harpreet Sahota:
So what's next on the horizon for you?
Any new projects?
Any new books?
Camilla Pang:
Yes. So basically, I'm
currently writing another book.
But when I say that it
might not turn into a book.
It might turn into three books.
I don't know yet. So I'm
writing and assembling bits together.
How I've always done.
And from that decipher when they're going
to go, when things crystallize more.
I'm also speaking to TV production
companies to see what we can.
It is really exciting, actually.
I mean, nothing's finalized yet.
Nothing's approved yet. But it's great to have
communication to see what can be possible
to outreach. The message of not just your
diversity, but also, I guess, a philosophy of
science in everyday life.
And just being able to get there.
Harpreet Sahota:
Definitely some exciting things, exciting things
on the horizon for sure.
And if people wanted to pick up
your book, where could they find that?
Camilla Pang:
I guess you could find it online.
I just tell people.
Amazon or Waterstones.
I mean. Yeah, and just Google it.
Really? You find it.
Harpreet Sahota:
If people wanted to connect with you and
find you online, where could they do that?
Camilla Pang:
Look at my Instagram. That's kind of
where I kind of post stuff.
Instagram. Twitter at LinkedIn.
Yeah. Those are the three that I
am mainly used to be honest.
And you want to shout
out your handles on those.
Yeah. My Twitter is @millzymai.
And my Instagram is Millie_Moonface.
Dr. Camilla Pang on LinkedIn, I think.
Harpreet Sahota:
Yeah. So definitely add those to the
show notes as well to guest profile.
We've talked about.
Sure. Social media profiles and stuff, but
how could people actually connect with you.
Camilla Pang:
By being real.
Humor and curiosity go a long way, but the
most important factor is for them to not be
afraid to react.
Or for me to react because.
And also, indifference is quite
literally a flat liner.
React and be human.
Harpreet Sahota:
I love it. Dr. Pang, thank you so much for
taking time with your schedule to come on the
show to talk about yourself in your book.
I really enjoyed it. I really
appreciate you staying up late.
I think it's quite late for you in England.
So.
Camilla Pang:
Thank you for having me on
here. I really enjoyed it.
