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SDS PODCAST
EPISODE 307:
PROBLEM SOLVING
THROUGH BETTER
THINKING
Kirill Eremenko: This is episode number 307 with AI engineer Marc
Sarfati.
Kirill Eremenko: Welcome to the SuperDataScience podcast. My name
is Kirill Eremenko, Data Science Coach and Lifestyle
Entrepreneur. Each week we bring you inspiring
people and ideas to help you build your successful
career in data science. Thanks for being here today
and now let's make the complex simple.
Hadelin: This podcast is brought to you by Bluelife AI. Bluelife
AI is a company that empowers businesses to make
massive profits by leveraging artificial intelligence at
no upfront cost.
Kirill Eremenko: That's correct. You heard it right. We are so sure about
artificial intelligence that we will create a customized
AI solution for you and you won't need to pay unless it
actually adds massive value to your business.
Hadelin: So if you're interested to try out artificial intelligence in
your business, go to www.bluelife.ai, fill in the form
and we'll get back to you as quick as possible.
Kirill Eremenko: Once again, that's www.bluelife.ai and Hadelin and I
both look forward to working together with you.
Kirill Eremenko: Welcome back to the SuperDataScience podcast ladies
and gentlemen. We're super excited to have you back
here on the show today. Today's guest is super special.
Today we've got Marc on the podcast. Marc is an AI
engineer who's working with Bluelife and helping us
solve massive challenging projects. It just so happens
that today or this week we are together in Switzerland
and we decided to use this opportunity to record a
podcast and in the process I got to know Marc a bit
better and you will get to know him too. Here are a
couple of things that we discussed. First of all, we
talked about how the thoughts that you choose can
affect the way you live. Very interesting, deep
conversation there. Then we talked about university
education versus online education. Marc completed
one of the top schools on machine learning in the
world and I think you'll be interested to hear his
opinion on how online education compares to in
person university education.
Kirill Eremenko: We also talked about Marc's systematic approach to
solving problems; to solving machine learning
challenges and you'll find some valuable takeaways
there. Then we talked about why Marc quit Spotify.
Marc was actually building neural networks at Spotify,
had an amazing job there and in this podcast, you'll
find out why he gave it up. We also talked about
stepping out of your comfort zone, what it means and
what kind of manifestations that can have. Those are
just five examples of topics that are covered in this
podcast. I'm sure you will find plenty more useful
insights here. I'm super pumped for you to meet Marc.
Without further ado, I bring to you AI engineer Marc
Sarfati.
Kirill Eremenko: Welcome back to the SuperDataScience podcast ladies
and gentlemen. Super excited to have you back here
on the show. Today I've got Marc returning from the
previous episodes. Marc, how are you doing?
Marc Sarfati: I'm doing great.
Kirill Eremenko: For those who don't know, Mark is our AI engineer at
BlueLife AI and it's a company where we do artificial
intelligence consulting to help businesses make
massive profits with no upfront costs. Hadelin just left
to the airport, right?
Marc Sarfati: Yes, he just did it.
Kirill Eremenko: So it’s just you and me now?
Marc Sarfati: Mhm.
Kirill Eremenko: What I wanted to talk about today is a critical thing
you told me a few days ago.
Marc Sarfati: Yes.
Kirill Eremenko: It's not about the thing, it's about where it's coming
from.
Marc Sarfati: Yes, exactly.
Kirill Eremenko: What does that mean? Such an interesting quote.
Marc Sarfati: I'm very interested spiritually and I've been in a self-
development journey for quite a while and to me it's
very critical to understand the notion of differentiating
basically the thing from the place it's coming from. You
can do the main thing in two different paradigms
which are completely different. One very simple
example is when you are working on something that
really excites you. You can spend hours upon hours
upon hours working on it without feeling like its work.
It's just fun. Sometimes you have to do something that
someone forces you to do and you really don't want to
do it and every single second spent working on it feels
like a huge pain. Basically, you're doing the same
thing but the difference between these two examples is
the place it's coming from. It can either come from a
place of inspiration; when you have the will and the joy
and the happiness to do it or from a place of
desperation; which is something you do to escape
something.
Kirill Eremenko: Very interesting. Okay. So Tony Robbins would say
you're either coming from a place of fear or pain or a
place of pleasure.
Marc Sarfati: Yes, exactly.
Kirill Eremenko: You're either chasing something that you want or
you're running away from something. It's like a push-
pull. You can either be pushed to do something or it
can be like pulling.
Marc Sarfati: Exactly.
Kirill Eremenko: Which one do you think is better?
Marc Sarfati: It's obviously doing things from inspiration. It feels like
you have much more energy to spend. You have much
more joy doing the things. It's a no brainer.
Kirill Eremenko: That's interesting. Actually, yesterday we had this
conversation. We were at this client sites where they
were undergoing the digital transformation. We've
identified six use cases where we can add value and it
was very interesting for me to have that conversation.
Where you said, "Obviously I can't work on everything.
I want to work on the thing that I get inspired the
most." Right behind us on the wall there, right now,
they were all written out there. How do you decide
what inspires you?
Marc Sarfati: Usually you just know it. It's really obvious. One of my
main roles in life is to always follow my intuition
because I really believe you have a GPS inside you that
exactly knows where you want to go or where is best
for you to go. I tend to really listen to basically how
you feel. If you feel good about something, sometimes
you use the words, 'I have a good feeling doing this.' To
me, this is like a super powerful, guidance system that
you can just follow and it will give you ultimately the
best feeling and the best results.
Kirill Eremenko: Very strange hearing that from an AI engineer.
Marc Sarfati: Yes, I know.
Kirill Eremenko: How do you combine the two; the logic and the feeling?
Marc Sarfati: It’s hard to say. I come from a very scientific
background. I started my spiritual journey, I would
say, very recently so it's hard to combine both. I don't
feel the need to combine both. I like doing data
science. I like doing like the math, but I also enjoyed
the spiritual aspect of life and I enjoy them separately.
Kirill Eremenko: Do you ever come into situations where the two
contradict each other; your logic tells you to do one
thing, but your feeling tells you to do it another thing.
Marc Sarfati: Yes, of course.
Kirill Eremenko: Do you follow your feeling?
Marc Sarfati: Yes. I try to always follow my feeling. The thinking
mind is very strong so sometimes, no one will see why
you want to make a decision and all the evidence show
that you should do something, but the feeling tells you
to do something else. Even though it's hard, I try to
always follow my good feeling.
Kirill Eremenko: Do you have an example like that? A recent one where
all the evidence were just in one thing, but you decided
to follow your feeling?
Marc Sarfati: One example is recently I was working at Spotify
basically before doing consultancy and machine
learning more in a freelance kind of way; and the
situation there was great. I was doing a job I really
enjoyed. I had a lot of flexibility and freedom working
at Spotify. I've been passionate about music for a long
time. Basically it was for, it was a good salary, so for
everyone, this would be the dream job. There came a
point where it was obvious that I needed to do
something on my own.
Kirill Eremenko: Well, how was it obvious? It was like a feeling?
Marc Sarfati: Yes. It's almost like a fire burning inside you.
Kirill Eremenko: Interesting. Okay. Has this feeling ever been wrong?
Let's say it this way, have you ever had this feeling but
it comes from a place of fear? Sometimes we have
feelings that are pushing us to do something or not to
do something. Let's say somebody might have all the
evidence suggesting that he should quit his job or her
job, but then they have a really bad feeling about it. It
might be like an intuition that they need to follow or it
might be coming from a place of fear.
Marc Sarfati: That's a very interesting question. To me, I feel you
need to develop a radical honesty with yourself.
Ultimately deep down, you know the difference if it
comes from a place of fear or inspiration. You just
have to be honest enough with yourself to take the
information without judging it, which is a very famous
concept in spirituality, but basically observing what's
inside you without trying to label it. If you have this
clarity, you view yourself within the lens of pure
clarity. In my opinion, you will see the difference and
you'll see the answer.
Kirill Eremenko: How do you get that clarity?
Marc Sarfati: From watching yourself all the time; meditation
definitely helps. Basically, trying to understand
yourself without judgements.
Kirill Eremenko: Interesting. Okay. So you meditate a lot.
Marc Sarfati: I try to meditate. I have to say recently I was not as
serious as I was before, but I tried to meditate every
day for at least 15 minutes.
Kirill Eremenko: Is morning better or is evening better?
Marc Sarfati: I like in the evening before going to bed.
Kirill Eremenko: I would fall asleep.
Marc Sarfati: It's a good transition from your active day to going to
sleep.
Kirill Eremenko: Interesting. Actually this was supposed to be a
FiveMinuteFriday episode; like a five minute one, but
let's just keep going. This is a fun conversation. We
can make it into a big podcast. Since we're on this
really cool. So tell us a bit about yourself. Where you
mentioned in the previous FiveMinuteFriday that you
worked at Ecole Polytechnique, right? Is it a big
powerful school on machine learning in Paris?
Marc Sarfati: It's the best engineering school in France in general.
It's quite a general scientific school but then you have
specific tracks inside the school. I focused in data
science. The level in mathematics at Polytechnique is
super high and data science is becoming more and
more developed inside the school.
Kirill Eremenko: Why did you pick that field to study?
Marc Sarfati: My intuition. That's a domain I really enjoy. I tried
several courses in several fields. During the university,
I tried economics, I tried biology, mechanics and I
studied math and physics for a lot longer before. I
really liked computer science in general. I did a lot of
algorithmic too and graph theory. Computer science
and applied mathematics was what I enjoyed the most.
Kirill Eremenko: Okay. It's always interesting to talk to somebody who
actually studied this at a university because a lot of
our students and listeners on this podcast study this
themselves online and through courses. What would
you say are the main differences between studying
data science and machine learning at a university; one
of the top universities in France or in the world
actually versus studying it online?
Marc Sarfati: I think the main difference is the level of theory
compared to practice. I think in university, especially
in the one I did, we were very focused about
understanding the math behind the models and how
they work. Sometimes in many courses, we wouldn't
even code on a computer. You really understand the
mathematical principles upon which the machine
learning is based. This gives a truly in depth
understanding of basically what happens under the
hood and how it works. I think it's not necessarily
useful for everyone to understand this because 90% of
the cases when you want to do data science, you can
use the tools almost as in a plug and play fashion. You
need basically to understand how it works, what kind
of inputs you can give, what it can understand and
apply them as is.
Kirill Eremenko: Okay. What would you say about this theory, now
knowing this theory; has it changed your mindset? Is
there any benefit apart from the 2% cases where you
actually need to change an algorithm or do research in
that space or something like that where the theory
would come and apply? Has it changed your mindset?
Has it maybe made you look at problems in a different
way?
Marc Sarfati: Yes. I think it makes you tackle problems in a more
systematic approach. First I think if you have a deeper
understanding of how the models work, you have a
clearer idea and intuition on which models would work
and would not. Also, it's easier to understand when for
instance, a deep learning model doesn't train. Why
doesn't it train? It makes you question whether the
architecture you chose is the best fit for the problem.
Kirill Eremenko: Okay.
Marc Sarfati: I think it gives you a bit more clarity why things work
and why things don't work so you can use this as a
good signal to explore other possibilities for models.
Kirill Eremenko: Okay. What is your systematic approach to solving
problems?
Marc Sarfati: I always usually start with the most basic version of
the problem. For instance, I would try to predict
whether it's going to be sunny tomorrow or rainy or
cloudy or the precipitation etc., I would start with the
most simple use case like just taking my input data
and fitting linear regression on the data to predict
whether it's going to be sunny or cloudy. Then I would
try to add rainy, cloudy, like different outputs or
maybe other inputs that are a bit more complicated to
pre-process. Then I would try more complicated
models like random forest, gradient boosting or simple
deep learning models, etc. I would really start basically
with the most simple, usually like a toy example and
then I would build upon it with layers.
Kirill Eremenko: So not only would you use a very simple algorithm at
the start, but you'd actually simplify the problem itself.
Marc Sarfati: Yes, always.
Kirill Eremenko: Are you always able to do that? The weather example
is pretty straight forward. You would simplify weather
to sunny and not sunny. In business use cases, are
you always able to simplify?
Marc Sarfati: One easy way to simplify is basically you can use one
input feature instead of all the features you have. In
the features you can have time series, you can have
static features, etc. I would keep for instance all the
static features that wouldn't change over time. Just
keep these ones and try to first make a very simple
naive model of prediction and then add the features
throughout exploration and the project.
Kirill Eremenko: Okay. Also increase the complexity of the algorithm.
Marc Sarfati: Yes.
Kirill Eremenko: Along the way.
Marc Sarfati: Yes. Basically you have, I would say, three main
components, which are the input feature, the model,
and then what you're trying to predict. I would always
start with the most basic I can in all of them and then
I would try to improve, for instance, the features. Once
I have identified a set of features that seem to be
relevant and quite exhaustive, I would improve the
model, etc.
Kirill Eremenko: When do you start the feature engineering?
Marc Sarfati: Very soon. It's quite fast to have a very basic model.
Usually the first thing I do is working on the features.
Kirill Eremenko: Okay. After a simple regression, what do you proceed
towards next?
Marc Sarfati: It depends on the project but usually, I like using
random forest regressor quite quickly especially in
Python. You can almost use linear regression and
random forest interchangeably with scikit-learn so it's
quite easy to make it run. They give you quite a good
estimation of the importance of each feature in your
input space. For instance, I would train a random
forest and I will see basically, which is the importance,
the weight of each feature in the forest, so that you
have a clearer understanding of which feature affects
the results the most. It gives you, usually, a clear
understanding of the dynamics of your problem.
Kirill Eremenko: Okay, and then what'd you do? If the problem is not
solved yet or if you need more accuracy or more
sophisticated algorithms, which one do you choose
from there?
Marc Sarfati: Usually at that point, it's good to have some business
insights because usually the experts in the field
know... When you have something predict, they know
which are the things that can make good predictions of
this output. It's good to have this discussion now to
understand if something is not working at all; try to
figure out whether it is because you haven't used the
right features or you haven't treated them in the right
way or if the input data are just too noisy. Basically
straight to troubleshoot why it's not working as you
expect.
Kirill Eremenko: Okay.
Marc Sarfati: There's no general rule.
Kirill Eremenko: Interesting. It's segues into why we're here. That's the
reason why we're here. We were building this model for
months and we got to a point where we realize we need
more domain knowledge.
Marc Sarfati: Exactly.
Kirill Eremenko: We got on to planes, came here to Switzerland and this
is our fifth day here; spending time with the client and
getting domain knowledge. How do you feel? Do you
feel you've gotten new insights from being here?
Marc Sarfati: Yes. A lot. Mostly it gave us a clear idea of how things
work, basically the dynamic behind... Of course we
cannot disclose on the podcast; the dynamics that
basically rule the problem. This is of great help to
understanding why the predictions we made were good
but not excellent. It gives direction on which area you
can work on and improve your model.
Kirill Eremenko: Okay. Very cool. From your experience, because we
were walking around and talking to people, but I'm
just curious for you what was the best way to get the
domain knowledge? Was it like by listening, by asking
questions, by maybe reading communications? Do you
have any secret or any advice for somebody who's
going to be doing the same thing and looking for this
domain knowledge? What's the best approach to get it
as effectively as possible?
Marc Sarfati: Interesting question. If you want to teach a computer
to do something, you need to understand how you
would do it yourself.
Kirill Eremenko: Yeah.
Marc Sarfati: To me, this is what we were lacking as basically the
comprehension of the whole structure and the whole
dynamics. To me, you need to understand the problem
very well, almost as if you could do it by hand if you
had enough computational power in the brain. You
need to understand things very clearly so you can
teach the computer how to do it. Even if it's not, if
then statements. Even if you just use a deep learning
model that basically learns by itself, understanding
the problem gives you a lot more keys to understand
why your model is failing or why it is working and you
definitely have much clearer analysis of the model.
Kirill Eremenko: Okay. What's the secret? What's the advice?
Marc Sarfati: Understand the whole scope of the project as much as
possible. It's easier to troubleshoot basically where it
comes from. Then you need to explore, of course. If
you don't have a clear understanding of the project;
there are many multiple factors that can influence the
results and since you don't really understand them
you just put them aside. By understanding them very
clearly, you can test every assumption one by one until
you find which one is the bottleneck.
Kirill Eremenko: Speaking of putting aside, we had this situation where
one of the things that we were working on, we decided
to put it on pause simply because by obtaining more
domain knowledge, we realize that this is not the best
place where we can add value. There's other places we
can add more value; we'll come back to it later. That's
another form of insight that you can get.
Marc Sarfati: That's super powerful. Any insights on how to use
your time is always super helpful.
Kirill Eremenko: Just knowing what you don't know is important. Even
before you set out to get the domain knowledge, maybe
write that out. Do I know what I don't know or I don't
even know what I don't know. Interesting, isn't it?
Okay.
Marc Sarfati: One thing I'd like to add that's very powerful to me is
also to be very agnostic in your approach. Start a
project without any assumptions apriori. Sometimes
you see people; data scientists that will do some
prediction model and they will detect an outlier and
then they will say, "Oh yes, it's because of a bug in the
measure or it's a bug here and there."
Kirill Eremenko: Yes.
Marc Sarfati: Everything happens for a reason. When you actually
try to really understand what caused this that you
didn't expect, it gives you a clear understanding of the
whole project. Don't neglect the details because; I don't
know if it's a saying in English, but it's in France at
least; the devil's in the details.
Kirill Eremenko: Yes. In English as well. It's a good point. Were there
any instances like that recently for you?
Marc Sarfati: I would not necessarily be able to say it without
disclosing more information on the project; which is
confidential.
Kirill Eremenko: Okay. All right, now I want to ask you about how you
maintain your level of adequate skills. How long have
you been out of university now?
Marc Sarfati: Two years.
Kirill Eremenko: Two years. After leaving university for two years,
what's your go to method to make sure you are up to
date with the cutting edge technology and you know
these recent algorithms, because it sounds like a
university is really intense? It really pushed you hard
to be up there. It's very easy to lose ends. This also
applies to listeners who are learning through online
education, right? You might put in a lot of effort to
learn something and get good at it, but then your skills
are going to get outdated unless you are maintaining
them. What are your ways of keeping up?
Marc Sarfati: I would say practice. Practice consistently. I have a
mentor of mine that said to me, "refine, refine, refine
and all will be fine." I really liked the sentence. It's very
powerful. I even noticed when I was on holidays for
three weeks; when I came back I would open a Jupiter
notebook and I could feel I was not as sharp as I was a
month before. Of course it came back very quickly but
practice makes things so much easier and automatic.
During periods where I code a lot, I can almost start
the beginning of the file with my eyes closed.
Kirill Eremenko: If you're not working on a project, what do you
practice with?
Marc Sarfati: I really like what I do so sometimes I just do random
projects on my own. Sometimes even at 4:00 AM or
recently I was in a plane; I was going to the US with
friends of mine and one of them had a position starting
in September where he had to learn how to code and I
was like, "okay, let's do some coding in the plane. I'm
going to teach you a bit." My other friend was solving
Sudoku on a paper next to us and I was like, "okay,
let's code something that solves Sudoku." We just
spent an hour trying and making an algorithm that
solves Sudoku automatically.
Kirill Eremenko: Did you make it?
Marc Sarfati: Yes.
Kirill Eremenko: In an hour?
Marc Sarfati: 45 minutes maybe.
Kirill Eremenko: That's so fast. You are really fast. This is something
that you are quite notorious for. How did you get so
fast? Listeners, Marc once.. This is crazy. Marc once
did a prototype for a project, not for this client, for
another client, a web scraping thing. We were
expecting it to be done in a week, it was done by
morning. How do you get so fast?
Marc Sarfati: That's hard to say. It's hard to say. Of course a lot of
practice. To me, coding almost reached an
unconscious competence level so basically there is no
loss between me having an idea and me implementing
them. Basically, if I can think of the thing, I can code
it. This is why I can code that fast. I think of the thing
I want to do and I think for instance, 'Okay, I'll need to
sort this array and then do this and that and this and
this,' and then I just do it. There's no like, "How am I
going to do it? Should I do this? Should do that? Let
me pull up a tutorial. Of course, I don't know
everything I have to Google specific functions but when
I see the problem, when I have the clear plan of what I
want to do and I do it. It's hard to explain.
Kirill Eremenko: That's very cool. Did you do any touch typing courses
or something like that?
Marc Sarfati: No.
Kirill Eremenko: No?
Marc Sarfati: No.
Kirill Eremenko: Okay. Very cool. Very cool. All right. We spoke a bit
about that. I see you're reading The Magic of Thinking
Big. Are you doing it?
Marc Sarfati: Yes. It's a very super interesting.
Kirill Eremenko: I remember it as a book that mostly teaches you how
to be a good person.
Marc Sarfati: Yes. I'll say yes.
Kirill Eremenko: What did is the main take away? So far? Are you just
about well over halfway?
Marc Sarfati: Yes. About halfway. I've been like learning this kinds of
concepts for a while now so the concepts are not brand
new for me, he shines light on the details that I haven't
heard before but basically the main thing is the way
you think will totally either empower you or
disempower you, depending on the thoughts you
choose to maintain.
Kirill Eremenko: That's true. Interesting. It stems back to what do we
started with.
Marc Sarfati: Yes, exactly.
Kirill Eremenko: That it's not about the thing, it's about where it's
coming from.
Marc Sarfati: Exactly the same situation; you can view it in very
different angles that definitely change how you react to
it. Even the words you choose. When there is
something unexpected that happens, you can say,
"Oh, we have a big problem. It's terrible. It's the end of
the world, etc." Or you can say, "Oh, we have a
situation. It's interesting that that happens. We'll
figure it out and once we have figured it out, we'll have
a deeper understanding of how it works and we will be
able not to make this mistake again in the future. It's
like basically the same situation but so different angles
to tackle it. It really changes the way you act.
Kirill Eremenko: I totally agree. I'm reading a book called How Yoga
Works, given to me by a very dear friend of mine. I
knew this before, that yoga is not actually just about
the poses. Yoga, the word, actually translates as
union. It's like union of spirit, mind and body or union
of your left and right hemispheres, creative and
analytical and all these things. Actually in the book of
yoga, there's less than 10% about poses. This book,
How Yoga Works is more of like a novel about a lady
that saw a girl that's walking from Tibet and gets stuck
in a police station and teaches the captain there how
to do yoga. One of the quotes; and she explains these
quotes to them; one of the quotes says, "Things that
are not themselves often seem to us as if they are." It's
full of these quotes.
Marc Sarfati: [crosstalk 00:33:03] Mind boggling quotes.
Kirill Eremenko: If you stop reading for a second, you're like, "What is
that?" I think its purpose is to make you think a bit.
Then they explain. It was interesting how they
explained it or the girl explained it to this captain at
this police station. He had these pens that were out of
bamboo. It's a piece of bamboo which you dip into ink
then you can write with it. So she was asking him, "Is
this a pen?" He's like, "Yes." "Is it a pen on its own?"
"Yes." "By itself, is it a pen?" He said, "Yes, of course.
What are you talking about?" Then she looks out the
window and there's a cow there and she gives that pen
to the cow and the cow eats the pen. For the captain it
was a pen but for a cow it had found something to eat.
Similar to the concept you described, this one is that
our minds extend the meaning of things.
Marc Sarfati: Yes.
Kirill Eremenko: It's doesn't exist out there in the world in the way that
we think it exists. The item or even phrase or event
might have a completely different purpose. Therefore,
it's so powerful what meaning we give to it. It's exactly
what you said.
Marc Sarfati: Exactly. It's a philosophical debate. What is truth? If
what we perceive is only our perception of the reality,
what is reality?
Kirill Eremenko: Yes. Interesting. I heard a recent interesting thought
that we tend to equate ourselves to our faces. Like this
is me, Kirill, this is Marc. I recognize you. But in
reality, we're actually sitting behind our faces. It's this
wet where three and a half kilograms of bio chemical
connections and whatever else that is sitting behind
the face. You have these five or six senses. Six because
maybe gyroscopic can be counted as a super sense.
You have all these senses coming in and you creating
this model of the world. So we go all the way back to
the matrix and all these things.
Marc Sarfati: Exactly. Yes.
Kirill Eremenko: At the end of the day, it is what it is, right? Cool. Very
cool. Why did you quit Spotify?
Marc Sarfati: I always had in mind the idea of working on my own. I
really view life as a game and basically I learned a lot
working at Spotify. It was a great experience, but then
I was like, "Okay, I want to explore new stuff and just
play the business game; try new stuff." Some things
will workout, somethings will fail. We'll see. I like
playing and exploring the world.
Kirill Eremenko: So no regrets?
Marc Sarfati: Yes, no regrets.
Kirill Eremenko: Nobody must have understood that. It's hard to
understand.
Marc Sarfati: Yes.
Kirill Eremenko: It’s not just a cushy job, but a great job. Right?
Marc Sarfati: Yes, it was a great job. If you had asked me three years
ago, what would be your best job? I think I would say
AI for music.
Kirill Eremenko: Because you love music.
Marc Sarfati: I love music. I love AI. It's like the best of both worlds.
Kirill Eremenko: What you said today at lunch was really cool; that
Spotify is full of people who love music.
Marc Sarfati: Yes.
Kirill Eremenko: To give up something like that, you've got to have a lot
of courage in the face of uncertainty.
Marc Sarfati: Yes. I got that a few times actually when I left Spotify. I
also was living in London and I came back to Paris.
This happened very quickly, almost from one day to
the other. I sent my resignation letter. I moved back to
Paris, I had a one month notice and I moved back to
France. Many people told me, "You're very brave and
courageous to have left everything so quickly and
coming back to Paris." To me it didn't feel like
something extraordinary at all. It was just a next
logical step. I had something in mind. I was like,
"Okay, now it's time to do it." I had really this
sensation. Basically, when people told me, "Oh, it
must be a strange to come back, etc. It must be
difficult." I was like, "No, my two hands are still here
and my two legs are still here, my body; my mind is
still here and it’s all fine. I'm still here."
Kirill Eremenko: I love that. My two hands, my two legs, everything
basically.
Marc Sarfati: Things around me changed, but I was here.
Kirill Eremenko: Makes sense. That's, very cool. I read a quote recently
that 'life begins at the end of your comfort zone.'
Right? Even though maybe in your case, Spotify was
when you're a [inaudible 00:38:19] ahead was a, jump,
a leap forward. Such an exciting thing you doing new
projects and so on but within the two years, especially
at the speed at which you learn and code, you
probably got to a level where now it became part of
your comfort zone and to stay there would make you
stay within your comfort zone. Interesting. I had a
similar experience when I was leaving Deloitte. I did
two years at Deloitte and then I went to Sunsuper,
which is a pension fund in Australia; like an industry
type of job. I only did 11 months. I didn't even wait for
12 because I felt that's it. Comfort zone has expanded
and as you say, I would rather experiment and fail and
learn and do it again rather than just stay within my
comfort zone. Different people have different levels of
tolerance to uncertainty.
Marc Sarfati: Yes.
Kirill Eremenko: What would you say to those who want to make a leap
but feel some sort of hesitation?
Marc Sarfati: I would say it's very normal to have hesitation. I
always do, but I always try to basically take the first
action that towards the end goal. If you feel like you
want to... and sometimes this can be very extreme; if
you feel you want to move to another city or
something, just send your landlord a letter that you're
going to quit the apartments in three months. That
way, once you start to have this ball rolling and have
the momentum, you'll have to figure out what to do. If
you send, basically, to your landlord saying, "Okay.
You can stop my contract or my lease now." You'll
have to figure out another solution, right?
Kirill Eremenko: Yes. As a radical commitment.
Marc Sarfati: There was a training I wanted to go that happened in
the US right after my masters and I was hesitating a
lot going there; whether I should go there, whether I
should not go there. Of course I had a lot of doubts
and I was like, "Okay, I'm just going to pay for the
training then I will figure out all the rest later." But I
know I will do it eventually because I'll have to figure it
out and I paid.
Kirill Eremenko: And you went?
Marc Sarfati: Yes. I did.
Kirill Eremenko: Was it worth it?
Marc Sarfati: Definitely worth it.
Kirill Eremenko: Well, that's a cool story.
Marc Sarfati: Also, the more you go out of your comfort zone, the
easier it is. For instance, it can start with taking cold
showers in the morning. I know you do this every day.
You talked about this. I did this for a few months last
year. Well, even just like when you go to work, just use
a different way. You maybe cycle to work if you're used
to taking the bus or a walk in the different streets or
do different things and this will give you more diversity
in your thoughts. Basically, it extends your creativity
and your thought patterns and it allows you to think
more widely I would say.
Kirill Eremenko: Interesting. Stepping out of your comfort zone doesn't
necessarily always mean to be more ambitious, fast,
strong, brave, doing unexpected things that people will
be surprised at. Sometimes stepping outside of your
comfort zone and I'm just going to use myself; actually
means the opposite. It’s becoming more humble, more
caring, more soft with people. Something you don't do
before. That's one thing I definitely need to step out of
my comfort zone and work on. It’s like developing
closer, deeper connections and relationships with
people because otherwise I find myself rushing around
the world and doing lots of things. I forget that you can
connect with people on a deeper level. They are people
in my life that I connect with, but not everybody and
not that you have to connect with everyone. There are
certain relationships you can develop and I'm not used
to that, but that's also an example of stepping outside
our comfort zone.
Marc Sarfati: Yes, very similar. Sometimes you're afraid of saying to
people you appreciate that you appreciate them. It can
feel a bit scary; or saying, 'Thank you.' To me, it's
going to be out of your comfort zone, but super
positive and not as you mentioned, super ambitious,
adventurous; just allowing yourself to open up a bit
more. It's a great skill.
Kirill Eremenko: Great skill. There are a lot of examples. Whatever
makes you uncomfortable basically is maybe
something you could look into to try and step out of
your comfort zone. Interesting how in life you can
develop lots of different things; machine learning skills
in one hand, self-development on the other. What else
are you into? Are you into sports or anything like that?
Marc Sarfati: Not so much anymore. I used to play tennis and
basketball for a while [inaudible 00:44:09].
Kirill Eremenko: But you play guitar, right?
Marc Sarfati: Yes, I do play the guitar. That's very cool. Music is
great. It's a great passion too.
Kirill Eremenko: Cool. What's next for you? You getting in a plane in a
few hours?
Marc Sarfati: Yes. I'm flying back to Paris and have the dinner with
my family tonight.
Kirill Eremenko: Nice. Very nice. Very good weekend. And then maybe
looking into some different projects we picked up here.
Gotcha. Cool. Before we wrap up, what would your one
piece of advice be to those who are entering the field of
machine learning, deep learning? What's one big... If
you could give yourself three years ago, one piece of
advice, would it be?
Marc Sarfati: Enjoy the process. Enjoy, have fun. Learn new stuff,
enjoy learning it and if you have nice ideas of things
you want to implement, like toys you want to make
with AI, try this. This is, for me, the best way to learn
is having a project you have in mind which inspires
you and then work on it, work on it until it works. It's
a great source of motivation and learning and...
Kirill Eremenko: Fantastic. Fantastic. Well thanks a lot Marc for
coming.
Marc Sarfati: Thank you for the invitation.
Kirill Eremenko: Awesome. Have a safe flight today.
Marc Sarfati: Thank you.
Kirill Eremenko: Thank you ladies and gentlemen for being here today
on the SuperDataScience podcast. Thank you for
joining us today for our conversation with Marc. I hope
you enjoyed the valuable insights that Marc was
sharing and also our conversations on things like our
thoughts. The thoughts you choose can affect the way
you live. I found those very, very valuable. As usually
you can get the show notes at
www.SuperDataScience.com/307, that's
SuperDataScience.com/307. We will link to all the
materials mentioned on the show notes and of course
you can connect with Marc there as well.
Kirill Eremenko: If this episode sounded inspiring to you and your
business, your enterprise wants to work with Bluelife,
you can always find us at www.bluelife.ai. On that
note, thank you so much for being here once again
and I look forward to seeing you back here next time.
Until then, happy analyzing.