SDS PODCAST

EPISODE 2

WITH

HADELIN DE

PONTEVES

Kirill: This is session number two with machine learning expert

and entrepreneur, Hadelin de Ponteves.

(background music plays)

Welcome to the SuperDataScience podcast. My name is Kirill

Eremenko. Data science coach and lifestyle entrepreneur.

And 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.

(background music plays)

Welcome everyone to a very special edition of the

SuperDataScience podcast. And you might say this is only

the second episode Kirill why is it so special? Well, that is

because today I’m bringing you a very good friend of mine,

Hadelin de Ponteves.

And in fact, we only met a couple of months ago over a work-

related project but we instantly bonded. You know how you

get that feeling sometimes when you meet somebody whose

values are very similar to yours..their aspirations and

ambitions are very similar to yours and you instantly

develop this strong connection and understanding of each

other and that’s exactly what happened and I’m super

excited to invite Hadelin to this podcast and we had a great

chat. Hadelin - you should know about it. This guy is a

machine. So for the past three years, Hadelin has been

sleeping only three hours a night. And then once a week he

sleeps for eight hours to catch up.

For us normally like myself I sleep eight hours everyday

pretty much. This guy is so driven, so passionate about

achieving his goals, about working, about learning about

data science, about improving his skill set and also he’s an

entrepreneur so about building businesses and about giving

back to communities and creating value for the world. He

doesn’t even have enough time to sleep.

How cool is that? How interesting of a person do you have to

be to sacrifice your sleep to build things and create things

and improve. He’s also a very very inspirational guy.

So, Hadelin got two masters degrees because that’s the only

way he could actually achieve them, by sleeping three hours

a day. I was about to say a week but that would be an

overkill.

So, three hours a day sleeping and therefore you got two

masters degrees. One of it is Machine Learning so that’s a

great and interesting field of data science and we talked a lot

about machine learning in fact we go deep into machine

learning. In this podcast you will learn about regressions,

classifications, clustering, association, rule learning,

reinforcement learning, deep learning, national language

processing and much more. So, lots of stuff.

Hadelin it’s not a surprise that he’s worked at companies

like Canal Plus which is a Canadian competitor of Netflix.

So, you’ll see how Hadelin went about developing some or

recommend models – models that recommend customers

what movie or what show to watch next based on their

previous experiences.

Also, after hearing all these you won’t be surprised to learn

that Hadelin of course worked on Google and there he

worked with terabytes and terabytes of data.

Also performing sophisticated machine learning and creating

algorithms and resolving challenges so maybe some of the

stuffs that you do. With Google, some of the products you

use with Google actually were created and worked on by

him.

In this episode with super exciting. I just can’t hold back my

excitement about this. For the first time ever, we reveal and

exciting and brand new machine learning project that we are

working on together with Hadelin.

So, it’s really cool because I am here in Brisbane, Hadelin is

in Paris but over the internet, over the miles and miles and

miles of space, we’ve been working on this massive project

we’re going to bring to the world very, very soon.

So, you’ll be excited to learn about it, you’ll be one of the

first people to hear about it in this podcast. Definitely check

it out. Something that we hold very dear to our hearts.

Also in this podcast, you will hear some very interesting and

philosophical discussions about the future of machine

learning. Because Hadelin had so much exposure to do

machine learning and AI and things like that, he’s got his

own views about what’s going to happen to the world. In

fact, he’s actually writing a book. It’s going to be a fiction

book but about how he sees the future of the world in the

next twenty to fifty years and I’m pretty excited to see that

come out. Hopefully that’ll be published sometime in the

near future but otherwise we talk about how robots and AI

are on one hand very positive but on the other hand can be

dangerous and something that I’ve never discussed before –

what kind of career implications that can have for data

scientist? What kind of new jobs that can create in the

future? So, controlling AI, AI security, AI oversight and

things like that. So, very interesting discussions and that

space as well.

And at the of this podcast, we talked about some

aspirations, what inspires Hadelin to move forward, who he

follows, some movies that were both very interesting and

exciting about so definitely check out. I think some of the

movies that we’ve talked about were very interesting and

actually life-changing for a data scientist and finally, Hadelin

recommend his favorite book.

Call away to get started and without further ado, I bring to

you Hadelin de Ponteves.

(background music plays)

Kirill: Hey, everybody! Welcome to the Super Data Science

Podcast and today we’ve got an incredible guest, Hadelin De

Ponteves.

Welcome Hadelin! Thank you for joining me on the show.

Hadelin: Thank you very much. I’m very happy to be here.

Kirill: Awesome! And right off the bat Hadelin, tell us what do you

do or where have you worked before?

Hadelin: Today I would characterize myself as both a data scientist

and an entrepreneur and in the past, I’ve tried many things

before finding my own way, before finding my passions.

I tried with finance in the past and I didn’t like it because it

was all about analysis and I needed to create stuff and I

found out about data science, and I found out that there was

a huge potential for creation. You can let your imagination

go very far and we can use data science to create these

things. So I started to be passionate about data science and I

became a data scientist.

So, I first had an experience at Canal Plus which is a French

company, competitor of Netflix. I actually had a big chance

there which was to build the recommender system based on

data science and machine learning. So that was my first

experience as a data scientist.

And then I had a second work experience at Google where I

worked as a data scientist as well but rather on the business

side. I was implementing some machine learning algorithms

or data mining models for business to create added values for

business.

Kirill: Awesome!

Hadelin: That was my second experience. And then, I became an

entrepreneur.

Kirill: Awesome!

Hadelin: I had this thing to become an entrepreneur and that’s what I

am today.

Kirill: Beautiful! So, everybody who’s listening, you can tell why I’m

so excited about this interview. Hadelin actually has so much

experience in data science and we’ll get to his background in

just a bit. And also, this is a person that worked with Google

so this is a person that has actually seen the frontier of data

science, what is exactly happening on the brand new front of

data science and the most exciting stuff that is happening in

the world right now. So, that’s what we’re going to be digging

in today.

Before we continue, I’m super excited to talk about your

entrepreneurial quest because we’re actually doing some

work together in that space right now. So that would be

really fun to share with the listeners.

Can you tell us a bit more about your background before we

get more into exactly what you did at Canal Plus and Google?

Just tell us a bit more about your background. Where did

you come from? Where were you raised? What kind of

education did you participate in?

Hadelin: Okay. So let’s start from scratch. Let’s start from the

beginning.

I was born in Paris. I was raised in Paris as well. I’ve been

living nearly all my life in Paris.

I found out that I was good in Mathematics in high school so

I decided to pursue my education with a Bachelor in

Mathematics. Then once I got the Bachelor, I started in

engineering school in France.

So, French engineering school are quite particular because

it’s very general. You study everything. I’ve studied

everything from Mathematics to Philosophy, passing by

Economy, Finance.

Kirill: Wow! That’s a lot.

Hadelin: Yeah. It’s quite particular. It’s not like you specialize at the

beginning like in other countries. But, in the final year you

choose a specialization.

As I said, first I was interested in Finance so I’ve tried

Finance but then I quickly realized that I needed more than

that, more than just analysis. I found out about data science.

Then, I finally chose to specialize in data science. That’s my

final year of Engineering, specialize in data science.

Kirill: Sorry, if I’m going to interrupt you quickly. Because data

science is such a new field did you actually have the data

science faculty or like a course on data science or did you

put courses together yourself to create this?

Hadelin: No, actually I was very lucky on this one because my school

just started a program in data science for the first year. I was

the first student of this program. I was very lucky on this

one.

Kirill: Awesome! How did it feel like being the first student? Was

there a lot of things that they could have done better or was

it a good program right away?

Hadelin: It was a good program but I think that some things could

have been done better but that’s totally normal. It’s the first

year. it’s like a try. We’re part of this program as a person

who help to build this program. So yeah, I was very happy to

have this year.

Besides, I did this year in parallel of another program in

business. It was quite a challenge as well.

I have to say I’m an autodidact. So I learned a lot by myself. I

self-teach a lot.

Kirill: What is that called? Autodidact.

Hadelin: Yeah, autodidact. Is that what we say, right?

Kirill: That’s awesome! I haven’t heard of that ever before.

Hadelin: Yeah. I do a lot of learning by myself. So, in parallel of those

two programs, I also did a lot of books, read a lot of books

about data science because it was still about discovery.

The program was good but I found a lot of different stuff

outside that was very interesting as well. It was very new.

There was like this new wave that it was awesome to take it.

Kirill: Wonderful! After completing that degree, did you right away

were you able to find a job or did it take some time?

Hadelin: Yes. Right away I was able to find a job because there was

this new wave of data science in France. And in France,

everybody is looking for data scientists.

Every company is starting their department of data science.

Even consulting firms, for example, I was called by the

Boston Consulting Group. They were starting a new

department in their company about data science.

Kirill: Yeah, that’s lovely. I actually have two friends working for

BCG. One of them was in the data science division. I don’t

know what the other one is doing, actually.

Hadelin: Okay. Every company is starting a data science department

or data science team. So, there’s a high demand in data

scientist so I had no trouble finding a job.

My first job was in Canal Plus, the competitor of Netflix. And

it was very machine learning oriented because I have to build

a recommender system. It was a great experience. And then,

Google happened.

Kirill: Google happened.

Hadelin: Yes. Google happened. It was a great experience as well

because it was very challenging. The team was great. The

mission was great.

Kirill: What did you do at Google?

Hadelin: I was a data scientist in the business team. I was using the

data to help make good decisions. I was analyzing all

Google’s data and there was a huge amount of data. We can

really call big data. We have a lots and lots of data. We were

working on Hadoop, big data systems. And I was trying to

find some patterns in the data. So I have to use machine

learning and data mining models to find some insights, to

draw some insights and help the business teams and

managers to make the good decisions about Google strategy

and their product, that sort of things.

Kirill: When you say Google because Google is so broad – there’s

Gmail, there’s Google Drive, there’s Google Search. Are you

talking about a specific service of Google or something more

broad?

Hadelin: Yeah, that’s a good question. I was working for anything

related to mobile.

Kirill: Mobile.

Hadelin: For example, I was working on the click through rate on

mobile. We had all these metrics comparing desktop to

mobile. I was actually implementing a machine learning

system to optimize the click through rates on mobile. That

was my mission most specifically.

Kirill: Okay. This is where we get to the fun stuff because the

project that we’re working on right now, you and I together is

really involved around machine learning and personally I’m

learning a lot from you about machine learning.

So, can you tell us a bit more? What is machine learning and

what is this science all about?

Hadelin: Machine learning is very broad. You can use machine

learning to do a lot of stuff.

First, you can use it to make some predictions. You can

predict the future. You can predict some behaviors. You can

even predict some future that is going to happen based on

the past, based on the information you have on the past.

And you can also find some unknown information. Like,

you’re looking for something and thanks to machine learning,

you can discover some logic into a field that you’re studying.

So that’s called clustering. When you don’t know what you’re

looking for, you don’t know the answers, you don’t know the

categories of the segment that you’re looking for.

There’s also interactive machine learning. That’s what I did in

Google. I was actually trying to find an algorithm that

chooses the best ads to place at the right moment so that the

customers are more likely to click on the ads. That’s a

reinforcement learning.

And there’s this huge field of machinery called deep learning

that’s very powerful and that you can use it to do facial

recognition and you can use it to build the lying detectors for

example.

Machine learning is very broad. You can use it to many

things and many applications.

Kirill: In that case, like somebody just off the street who is not into

data science, who is hearing about these things for the first

time, machine learning sounds very sophisticated. Sounds

like machines are learning something.

How is that different to just the normal trend line or just a

normal like a pattern recognition system or is that a sub-

clause of machine learning?

Hadelin: Actually, machine learning englobes everything. Machine

learning englobes pattern recognition. It englobes deep

learning. Well, to my definition.

According to my definition, machine learning is like a big

science that englobes many sub fields of machine learning

and pattern recognition is one of them. Clustering is one of

them. Deep learning is one of them.

But then, I heard a lot of many different definitions about

machine learning and some even say, “artificial intelligence”

instead of machine learning.

So, I guess there is not a unique definition. I guess that for

some person in the street, I would tell him about artificial

intelligence because it’s more popular term. The machine

learning is more scientific term.

Kirill: Okay, I see. But in your view, in your more knowledgeable

view from the things that you have studied and learned, how

would you distinguish between artificial intelligence and

machine learning?

Hadelin: For me it’s the same. For me artificial intelligence and

machine learning is the same because artificial intelligence is

a machine that is intelligent enough to do things and by

doing things, it has to learn how to do it and that’s exactly

machine learning. Machine learning, it’s a machine learning

to do stuff by itself.

For me it’s the same. But if I had to precise the difference, I

would say that artificial intelligence is the cool term.

Learning is the scientific term.

Kirill: Yeah, totally. And so just hearing about your definition of

machine learning and your experience with it, it sounds like

data science is going in the direction of machine learning or

that for somebody to be successful in data science, inevitably

they’re going to encounter machine learning. Would you say

that’s…

Hadelin: Oh yes. Oh yeah, absolutely yeah.

For me a data scientist is also machine learning scientist,

Because anyway, machine learning includes linear

aggression, logistic regression; which are the basic models in

data science.

So yeah. From the moment you know about linear

regression, logistic regression and clustering, well you know

about machine learning. So, yeah. There is a lot of similarity.

And a data scientist for me is a machine learning scientist

and vice versa.

Kirill: Yeah, totally makes sense. And on that project that we’re

working with together, actually for those our listeners who

haven’t yet learned about this project; what we’re doing

together Hadelin is we’re creating a massive course about

machine learning. So everything, everything you can possibly

imagine of machine learning that exist today is going to be

covered. Or if you’re listening to this a few weeks from now,

he’s already covered in that machine learning course. So

that’s a very exciting project that we’re working on.

And just going through it because a lot of the input actually

came from Hadelin. I’m just there putting in like my spin on

things, but I was surprised to see at how many different

areas of machine learning exist.

If you don’t mind Hadelin, could you please walk us through

maybe just give us a list of the ones that on the top of your

head or are the most important areas of machine learning.

Hadelin: So, we started by the most simple algorithm like most simple

subfields of machine learning which are: regression, so

anything to predict a real value; then, classification to predict

the category, like doing some customer segmentations; and,

clustering. So that’s the three basic models of machine

learning: regression, classification and clustering.

And the, once we master this, we start with more

sophisticated machine learning models. So, then there is

association rule learning and actually this is going to be a

very popular subject very soon because a lot of companies

are already working on this subject to find some

associations, some logic in their business to create value. So,

that’s a very important subject.

Then we have reinforcement learning which is what I did in

Google; so to solve interactive problems. So, that will be a

very interesting subject. But actually it’s not very recent, it

exist for quite a long time. Actually there was some books

covering reinforcement learning already in the beginning of

the 2000.

Then we finished with some very, very, very advanced subject

that will make sure to explain very clearly so that everybody

can understand. It’s natural language processing that is

applying machine learning to text. For example; analyzing a

sentiments and feelings and their corpus of text; and deep

learning. Deep learning is one of the most powerful field of

machine learning because you can do amazing stuff.

With deep learning you can do a facial recognition, voice

recognition. You can spot some disease in a picture. You can

recognize anything in a picture. We will do this and this will

be a lot of fun.

And then we will even study some advanced subject of

machine learning like dimensional reduction; that will allow

people to kept the important variants in their data and

actually be able to visualize their data if there’s too many

dimensions.

So basically, we will study all the field of machine learning.

We will make it simple for everybody. Everybody will be able

to understand and use it for both personal life and their

businesses. It’s going to be a lot of fun.

Kirill: Yeah. It’s definitely a massive undertaking.

Hadelin: It’s part of challenge.

Kirill: Yeah. I was a bit concerned if we’ll be able to lift this off the

ground. But, actually this brings me to a very interesting

topic that I want to discuss and I hope you don’t mind

Hadelin.

I was surprised to learn just recently that the way Hadelin

structures his day - his day to day activities, what sleeping

patterns he involves because I think that’s a very important

part of our lives today. We live in such hectic world and

especially if you’re working in data science, you have to find

the time to both learn and also perform the analysis and also

present your findings and also learn again and there’s lot of

stuff going on.

So, I found that I think part of Hadelin’s success is the way

he approaches his daily routine. Hadelin, if you don’t mind,

can you share a little bit about that with us?

Hadelin: Sure, absolutely. Okay. This is quite particular and I hope I

won’t sound too weird.

But, I think a lot outside of the box. That means that I don’t

do things like everybody else. I’ll give an example.

I knew I was going to be an entrepreneur. My first investment

was in a bed, in a bed that would allow me to sleep less

because the bed is highly sophisticated. It’s an amazing bed.

I go on to my bed and I sleep immediately. So that’s amazing.

I fall asleep immediately. And I actually need three hours of

sleep everyday. And why did I choose to do that? Because, I

have a lot of projects in my life. I want to do a lot of things

and I want to be able to complete all these projects. And so I

thought about trying to sleep less to work more and be able

to complete all my projects.

So, yeah. That’s true. I only sleep three hours a day and I

take some naps from time to time to hold on. It’s actually

going very well for me because I’ve been doing this for quite

awhile and I feel I’m in a very good shape. I do some sports

regularly to have a balanced life.

Yes, I never worked so efficiently before in my life.

I complete all my project efficiently and I’m on a good way

about that.

Kirill: Yeah. So there you go folks. Hadelin, it was of course an

extreme example of sacrificing sleep for education and for

working on a person’s projects. But it’s also a great example

of how important it is to remember about these things. And if

you want to progress because as a data scientist, a lot of the

time, others are catching up to others, they’re stepping on

your toes and there’s lots that you need to grasp and taking

into account. Every minute of your life count.

Hadelin: Yes

Kirill: Again, you don’t have to go into the extreme like that’s

sleeping three hours a day. Like, I personally can’t imagine

doing that. But, for me it inspired me to kind of think about

the times in my day where I waste time. Always kind of like

now, taking into account that I can be doing more.

So, that’s a great inspiration. Thank you for that, even just a

personal thank you to you for that.

Hadelin: Well, you’re welcome.

Kirill: Alright. And now, moving on. We’ve talked about your

background, we’ve talked about some things you’ve done at

Google which is very interesting and we’ve talked more in

depth about machine learning and the different fields that

exist there.

What are some of the tools that you use on a daily basis? You

used to use at Google or maybe even now in some of the

analytics that you keep doing. What are the tools that you

use on a daily basis?

Hadelin: Okay. So, I use many tools. But the two tools that I used the

most are R and Python because they have amazing libraries

and amazing packages to do great stuff efficiently and in a

simple way and in a fast way. So that’s the two tools I used.

Besides these, I use Julia, I use Docker, I use that sort of

things. But, really I would highly recommend R or Python to

do some machine learning because for me it’s the best.

Kirill: That’s really great. And we always have this interesting topic

of discussion; R versus Python. What are your thoughts? And

I know that you know both but if you were to choose one,

which one would you choose and also why did you choose to

learn both?

Hadelin: So, that’s a very tough question. I think that there is no

answer to this question. I think it depends on what you want

to do. Because, Python is going be better for some subfields

of machine learning and better than R and R is going to be

better for some other subfields.

Perhaps, then I would count the number of subfields and the

one where I prefer R or Python. But it really depends on what

I have to do. It really depends on my mission, the business

problem.

I know that for example, for deep learning, I will use more

Python than R. But then, for visualization, for simple

machinery, I would rather use R. But maybe, I have a slight

preference for Python.

Kirill: Why would you say you have slight preference for python?

Hadelin: Perhaps it’s because that’s what I’ve been mainly using in

Google. In Google, I work most of the time on Python. Maybe

I have a better level at Python which make it more pleasant

to work on Python because I know more shortcuts. I know

more the libraries. Maybe it’s for this.

But, I really like them both. I use them both equally. But if I

have to say an answer, I would say Python.

Kirill: Okay, Awesome. You mentioned as well Julia, that you

worked for Julia. Actually this is funny because literally 15

hours ago, I’m just checking my messages right now. Literally

15 hours ago, I got a message from one of my students,

Ravender Ram. Ravender, if you’re listening to this, great big

shout out to you.

And he asked if learning Julia is worth it. He’s completed my

R programming course and my R advanced course. And he

was asking about Julia. So, can you tell us a bit more about

Julia and if it’s worth learning Julia.

Hadelin: Well, I don’t think it’s worth learning Julia because Julia is

all about libraries. I use Julia because it has very good

libraries.

I wouldn’t say that I’m a master at Julia because using

libraries is quite simple. There are some tutorials on the net

or there are some frameworks on the net. You just have to

know what inputs to input and then you have the output by

using the libraries.

It’s true that Julia has good machine learning libraries and

that’s why I use it. But, I wouldn’t start a new course all

about Julia to learn about the syntax, the complicated stuff

about Julia. Maybe we can introduce that a little bit in our

course, mention the good libraries to use with Julia. Once

you know what to input, it’s quite simple.

Kirill: Okay, yeah. Definitely that’s something that we could

consider for our project as well. What about some of the

other tools like apart from R, Python, is there anything else?

For instance, you mentioned you worked with Hadoop at

Google.

Hadelin: Yes.

Kirill: Did you worked with PostgreSQL or did you work with

certain types of Hadoop or any other database related tools

that you can mention?

Hadelin: I said Hadoop but actually Google is so protective of its data

that Google has its own database system. It was actually

called F1 and Dremel but it’s kind of like Hadoop. It’s based

on My Production which is actually introduced by Google as

well.

It’s like SQL. It’s like working on big data system and

managing the data inside of it. I was also building some

workflow. For example, in your data science course, Data

Science AZ, I was doing a lot of stuff that you explained in

Part 3 Preparation by making some workflow, by making

some templates, that sort of thing but it wasn’t on Google’s

own database system.

Kirill: That sounds good. Back at Canal Plus, where there any

specific tools that you use there?

Hadelin: Well, actually on Canal Plus I was mostly working on R and I

was building the recommender system on R. Actually just

on R and that’s where I improved a lot in my level in R.

Actually, I would like to say something. The best way to

improve skills in programming is by actually doing an

application by building something. And by building this

recommender system in R, I considerably improved my level

in R.

Kirill: It’s funny that you say that because a lot of students come to

me and they ask for more exercises, more challenges.

Because like you say, it’s the best way to learn, to actually

solve challenges based on real world data and real world

problems or even business problems.

Hadelin: Exactly, yeah.

Kirill: That’s something that we’re really looking into and in our

platform of Super Data Science. That’s going to be a big

focus. We’re going to have a heavy focus on actually not just

presenting the tutorials and lectures but also presenting

exercises on a monthly basis that people can come in and

refresh their skills. For instance, if somebody wants to learn

R or Python, they just do an exercise on that or on something

else. I think that’s an important part of learning.

Hadelin: That’s very important, yes.

Kirill: Definitely. Okay. And so, the next question is quite an

interesting one. If being a data scientist and learning a lot –

and by the way, people probably notice that today, I’m saying

data and data interchanging. That’s funny because I learned

recently that we only say “data” here in Australia, data

science. I’m trying to see if data science is going to be a good

option as well. How do you say it?

Hadelin: Data.

Kirill: Data Science, yeah. Coming from Google, probably you

would.

The question was, what was the biggest challenge you’ve ever

had as a data scientist?

Hadelin: I can think of two. The first challenge was actually to build

this recommender system. Because Canal Plus is a French

company that exists for a long time and Netflix just entered

the French Market in everything – in TV and shows and

everything and their Netflix program.

Netflix has a huge recommender system and that’s actually

their strength. It’s their weapon.

And the challenge was to build a recommender system for

Canal Plus that will be Netflix level. That was quite a

challenge because the Netflix recommender system was

based on a competition across several countries. And the

competition that last actually for several months. So, I have

to build it by myself. I was actually with somebody else and a

manager but we’re just a team of three. That was quite a

challenge to make a recommender system that would be the

same level as the Netflix one that was based on competition

with the best data scientists in the world. That was very

challenging.

So, I built a recommender system. I don’t know if it’s Netflix

level but I know for sure that I improved Canal Plus

recommender system so that’s a good thing. I wouldn’t say

it’s Netflix level but it was quite a challenge to make it

anyway.

The second challenge, well I didn’t mention it but I love

writing. I actually write a lot of stuff. Right now I’m writing a

book. This book is about data science in the future. It’s a

novel so it’s purely imaginative. So, I’m imagining a story in

the future where machines are really developing. I’m

imagining the world in 20 years how it’s going to be. And

that’s quite a challenge to imagine that and try to be logic

and try to predict what’s going to happen in terms of data

science and machinery. That’s the second challenge I would

mention about that.

Kirill: That’s so cool. That’s awesome. So, everybody who’s

listening, definitely check out the bookstores for Hadelin de

Ponteves’ book. It sounds like an exciting – I’ll definitely buy

that book coming from somebody who’s in the industry.

Hadelin: Thanks.

Kirill: Just on that second challenge, are you using Moore’s Law to

predict what’s going to happen in 20 years?

Hadelin: Well, I’m using mainly my imagination. Well, for example I

will tell you about my idea. I will tell you about what I believe

in.

I believe in the not too far future, there are only going to be

two jobs actually. There’s going to be the engineer and the

artist because machines are going to develop so much that

they are going to replace a lot of jobs. And, I think that in

the not too far future, the only jobs that are only going to

remain will be engineer and artist.

Because I don’t think a machine can replace an artist and we

will need engineer to continue building the machines and

improve them and mostly control them because we don’t

want have machine declaring a war to us, right?

So, that’s the kind of things I’m trying to picture in my book

and build in my book. So, that’s quite a challenge because

it’s not easy to predict in the future. But having that quite a

while, I think I’m on the right path.

Kirill: That’s really cool. I recently saw an infographic. I think it

was from Futurist.com which describes the predictions of

Ray Kurzweil for the next hundred years. Ray Kurzweil,

you’ve heard of him, yeah?

Hadelin: Yes, a little bit.

Kirill: He’s an American futurist who’s made predictions about the

future since 1980 and he predicted things like the iPhone,

the iPad, the modern machine, computing power of machines

and the way we live and self-driving cars and so on.

He just doesn’t predict it. He predicts with the exact date,

the exact year and so far, his predictions have been 80%

correct. Definitely check out that infographic and we’ll

include it in the show notes as well.

He predicts crazy things like by the year 2019, machines will

be voting for humans to recognize that they’re conscious

beings and give them rights to vote and things like that. It’s

really crazy as well.

But going back to your first challenge where you created the

recommender system, that by the way, that’s a huge

accomplishment. The recommender systems are like – the

Amazon has a very powerful recommender system.

Hadelin: Yeah, Spotify, Amazon, Netflix even Udemy actually.

Kirill: Yeah. Exactly, right. That’s the stuff that the ads or the next

recommended items that you should purchase that the

system comes up based on your previous experiences.

Amazon even took it to the next level. They introduced this

one-day shipping or same day shipping based on the

recommender system. Based on your previous purchases,

before they ask you to recommend you to buy something,

they’ll take that item and they’ll ship it to a warehouse which

is next to you. And then when you buy it, you’ll get in the

same day because they knew you will buy it before you even

knew you will buy it.

Hadelin: That’s great.

Kirill: That’s machine learning, isn’t that?

Hadelin: Yeah, that’s machine learning. That’s the power of machine

learning.

Kirill: So true.

Hadelin: That’s why it’s so exciting.

Kirill: Exactly. And so, two questions here. First one was about this

big challenge at Canal Plus. What were the main kind or how

long did it take to build a recommender system and what

were the main challenges there?

And the second question will be, how is it working in a team?

I know you said it’s a small team – a manager and you and

another data scientist. How is it working in a team of data

scientists building a project? Because a lot of the times data

scientists actually work on their own thing. Especially if

you’re freelancing, you’re just working on some project by

yourself.

Can you tell us a bit more, as a second part of this question,

what is it like to work in a team of data scientists?

Hadelin: Okay. So, the first part of the question was how long did it

take? It took six months because they were actually part of

my school program so I was doing that actually twice a

week in parallel of my engineering program. By working

two days a week in six months, I have to build this

recommender system .

And so that leads me to the second question. You have to

be very organized. You have to use most of the resources of

the team to be able to build this recommender system at

such a high level in six months. So, you have to

understand what’s the best skill in each person in the team.

You have to allocate the best resource from each person in

the team to organize in such a way that you can build a

recommender system faster.

Actually, I was better on the research part because I love

about machine learning theories so I was working a lot on

the research papers and trying to find the best models and

how to combine them.

The other guy was actually very good at coding. He was

more the developer. I was the researcher, he was the

developer. By doing this, we managed to build this

recommender system in due time.

Kirill: Fantastic. And what is your manager doing then?

Hadelin: He was supervising us. He was checking if we were going in

the right direction but he wasn’t part of the building

process. We were all by ourselves.

He also helped us at the beginning. He explained what was

Canal Plus recommender system at that time, what needed

to be improved. He really explained the context and the

goals, not much more than that. We did all the work by

ourselves.

Kirill: That’s lovely. I love it. I love how you described the two

parts.

And in this scenario, you split the work in a way that it

doesn’t interfere with each other. I think it’s always a better

approach when people are working, even within a team,

they’re working on individual parts of the project rather

than coding in the same code. That can lead to lots of

confusion and has to be managed differently.

Hadelin: Exactly, yeah.

Kirill: And you mentioned you love research. This is a great segue

way to my question. What is your most favorite thing

about data science?

Hadelin: My favorite thing about data science is creation. That’s the

first thing I said actually. I said that I chose data science

because you can create a lot of stuff. I also talked about all

the projects that I have in mind that I want to complete in

my life. A lot of these projects are related to creation. I want

to create a lot of stuff that is related to data science. That’s

what I love about it.

And you know, creating these stuff implies doing a lot of

research because for example if you want to build

something revolutionary, you have to spend quite some time

in research to know how to implement it, to see if it’s

possible. You have to read some research articles, to

understand which algorithm to use, how you can develop

them. So, that’s quite exciting. But for me, when I will be

doing some research, it will always be linked to creation, to

creating something. I will not do some research to invent a

new theory. It will be to create something.

Kirill: Okay, yeah. That’s very cool and that’s very in line with

what you said for your book that there will be artists and

engineers. You use software as more for an engineer.

Hadelin: Absolutely, yeah.

Kirill: This whole concept of robots in the future where this is all

going, aren’t you afraid? This is kind of like veering off to

the side here a little bit. Aren’t you afraid that by creating

so many things especially using data science, you’re going

to teach the machines to be very pragmatic, rational and

think on their own. And then at some point, they’ll decide

humans don’t have a place on this planet, that we’re just

making this planet worse. How do you see that going on?

Hadelin: I actually talked about that in my book. That’s a very true

subject. Actually, some movies in cinema have talked about

this subject. I can think of iRobot for example, Ex Machina.

There are a lot of movies discussing about this subject.

So, that’s why I think that engineering is going to be a very

important job in the future because it’s not only about

improving the machines and developing new machines. It’s

also about controlling the machines so that we can avoid

this to happen. We will need to improve the security

systems, to improve all the controls that there are in the

machines to avoid this.

I think that will be new subfield of machine learning, if it

doesn’t exist already. Actually, I think it already exists like

machine learning security or something like that. There will

be a lot of that.

Yes, I’m scared but I’m sure we’ll do the necessary to

prevent this on time and to predict what’s going to happen

and try to prevent this.

Kirill: Okay, so that is really cool. I love that you brought this up

because a lot of our listeners are always curious about

where everything is going and what career paths might

exist.

It is a fact that in the next top 10 jobs that are going to exist

in 10 years from now don’t even exist at the moment. It’s

all evolving so quickly. And what you brought up right now

is a very, very profound pathway that might develop which

is controlling machines, being a data scientist but in the

field of controlling machines so that we are always in charge

of them. That’s a great one. I’ve never heard that before.

Hadelin: Actually, you’re right. The world is going so fast. There is

going to be a lot of new jobs but I’m sure 100% that one of

these new jobs will be to control the machines and

guarantee the security of people against the machines.

Kirill: That’s really cool. That’s very interesting. What kind of

skill sets do you think will be required for that? Of course,

data science but what would you mix data science with to

get somebody who would be an expert in that field?

Hadelin: Well, I actually read an article not a long time ago about the

new skills that we would need to have in 2020. Actually,

the first one was creativity. I’m very happy to have a need

for creativity because it’s apparently a required skill.

There is creativity. There is also adaptation. You need to

adapt very fast because the world is going so fast that you

need to be able to learn fast, understand fast what’s going

to happen and actually predict.

It’s not only the machines that have to predict things, it’s

also you. You have to predict what’s going to happen. You

have to anticipate.

I think anticipation, adaptation, creativity are very

important besides having the skills of data science and

machine learning.

Kirill: Wonderful. I actually love to read that article if you can

share it, if possible.

Hadelin: Yeah, okay.

Kirill: We will include that in the show notes for our listeners as

well.

Hadelin: Okay, sure.

Kirill: It sounds like a great read. By the way, you mentioned a

movie, Ex Machina. If you’re listening to this podcast and

you haven’t seen Ex Machina, you have to see it. It is so

good.

Hadelin: It is very good.

Kirill: It is good. It’s got three or four actors in there total and at

the same time it blows your mind. It’s totally about

machine. It’s all about machine learning, about data

science, about robots, about where the future is going. I

could watch that movie every day.

Hadelin: And it’s about something that could happen and that’s

scary but that’s exactly what we’ve been talking about.

Kirill: It might be happening already.

Hadelin: Yes, maybe somewhere in the mountain.

Kirill: How they conveniently replaced Google with Blue Book.

The creator of Blue Book created this machine. It’s just

fantastic.

Hadelin: Yes, you have to watch it.

Kirill: You have to watch it, totally. We’re slowly coming up to the

end of our conversation.

So, just a couple of finish up questions in terms to give our

listeners some ideas of how to get inspired, how to go

forward in this career, what would you say was the biggest

inspiration or aspiration currently for you in data science

that has pushed you forward or something that is pushing

you forward every day to become a better data scientist?

Hadelin: Well, I have several aspirations, actually. I actually have

several mentors. I inspire from several mentors’ ideas.

I will start with Nelson Mandela who actually said that

education is the most powerful weapon which you can use

to change the world. I absolutely love this. That’s why I also

want to be an educator in everything that I’m doing. I’m

inspired from that a lot.

Then, I’m inspired from Larry Pages, CEO of Google not

because I worked in Google. That absolutely has nothing to

do about it. It’s because when he started Google, he started

his print and that’s exactly what I’m doing right now. I’m in

a print that I started not a long time ago. Now, I will do this

print until I realize all my projects that I have in mind. I’m

on this print and I will continue.

And I also have inspiration from Mark Zuckerberg who

started with Revenge Energy. I think Revenge Energy is one

of the most powerful energy to complete something to be

able to achieve something very, very difficult to achieve.

Revenge Energy can take it from all the people that put you

down. That’s called Revenge Energy. From these people,

you can use this energy to transform it into something very

powerful and very creative. That’s the third thing, Revenge

Energy from Mark Zuckerberg.

The fourth mentor I would mention about is Elon Musk who

is very imaginative, very creative. And I love his masterplan.

I actually will inspire from this because I think it’s great. I

love his ambition. And I want to have and ambition like

that too. I’m ambitious.

So, that’s all the inspirations that I use.

Kirill: Well, fantastic. I love it. We really meet on that, Elon Musk

and aspiration and inspiration. I also follow Elon Musk. I’ve

read his biography. By the way, listeners of this podcast if

you haven’t read it yet it’s by Ashlee Vance. It was released,

I think start of 2015. Great biography.

Elon Musk, definitely one of the pioneers of everything that

we’re doing right now that’s innovative – rockets, self-

driving cars and solar.

And I also loved Mark Zuckerberg. Revenge Energy - I

haven’t heard about that but it sounds like a great thing to

turn negative energy that’s coming towards you from others

which still happens even in this world. Unfortunately, it

happens.

Hadelin: It happens every day to everyone.

Kirill: It happens all the time.

Hadelin: All the time. And in every context. You can use this energy

to transform it into some very powerful energy that can

guide you and that can make you achieve some great

things.

Kirill: Yeah. The concept reminds of Judo or Aikido or some sports

used in martial arts where somebody is running at you and

instead of blocking them and fighting them back, you use

their weight and their energy to throw them down or in this

case, to create something beautiful.

Hadelin: I did a little bit of martial arts and I know exactly what

you’re talking about.

Kirill: That’s some inspirations. That was great for inspirational

characters in your life.

If our listeners want to learn about you, contact you in any

way, get in touch, follow you maybe, what would be the best

way to get in touch with you?

Hadelin: I think LinkedIn is a good way and Google Plus as well. I

don’t have a Twitter account yet. I should actually work on

that. But, I think so far it’s Google Plus, Gmail and

LinkedIn.

My LinkedIn is Hadelin de Ponteves. It’s my first name and

my last name. We’ll probably write it somewhere because

it’s not very simple for a non-French person. It’s Hadelin de

Ponteves on LinkedIn and Google as well.

Kirill: We’ll definitely include those in the show notes and as soon

as Hadelin includes that Twitter account or something else.

We’ll also include that and update it.

And final question, what is the one book that you would

recommend to our listeners that can help them become

better data scientists?

Hadelin: Okay. The one book, so my favorite book in data science is

called Data Science for Business but it’s actually not to

start data science. It’s a very good book once you have the

basics of data science. You must read it in anyway because

it’s really, really amazing. It talks about how you can use

data science to create added value in your business. That’s

great.

But If I had to mention a book to start from scratch, it’s

actually Data Science from Scratch with Python. Because

when you really start from scratch, you use Python which is

the best tool for data science. It’s a very complete book. It

tells you about the basics. So, I would say that yes, Data

Science from Scratch with Python.

Kirill: Wonderful. Thank you very much. Great recommendation

and I really appreciate you coming on the show. I’m sure

everybody’s going to get a lot of value out of this interview.

Thank you very much.

Hadelin: I hope so. Thank you.

Kirill: Take care. Bye.

So there you go guys. That was Hadelin de Ponteves. I hope

you enjoyed this inspiring and entertaining interview. I

definitely had a few good laughs while we were chatting and

at the same time, I did learned a lot. Hadelin is a very

impactful person and so I learned how to make myself work

harder, that I need to push myself further, and of course

I’ve learned a lot about machine learning as well. So,

speaking of which if you haven’t yet checked out our

course, it’s upcoming literally in a few days. It’s going to be

released and once it’s released or if you’re watching and

listening to this later further down the track, then it’s live,

definitely check out the Machine Learning A-Z course,

where Hadelin and I put our experience and our expertise

together to bring you the knowledge of machine learning.

And make sure to share this episode with your friends,

family, colleagues, whoever you think is interested in

machine learning and the future of data science and the

future of the world for that matter.

Also, you can get the show notes at

www.superdatascience.com/2 . So that’s just the number 2

for episode 2 and please leave us comments for Hadelin and

I at the bottom in the comment section under the episode.

We’d love to hear what you thought, what your ideas about

the future are and how you use machine learning in your

day to day role.

And I look forward to seeing you next time. Until then,

happy analyzing.