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.