SDS PODCAST EPISODE 169 WITH TARRY SINGH€¦ · industry leaders constantly ask me, "Hey Tarry, we wanna set up and AI lab, we wanna set up, do this, do that. How can I do this?"

Show Notes: http://www.superdatascience.com/169 1

SDS PODCAST

EPISODE 169

WITH

TARRY SINGH

http://www.superdatascience.com/169


Kirill Eremenko: This is episode number 169 with Data Science

Thought-Leader, Tarry Singh.

Welcome to the Super Data Science 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.

Welcome back to the Super Data Science Podcast,

ladies and gentlemen. Today I've got a very special,

very exciting guest, Tarry Singh, who is a founder, a

CEO, and AI researcher, a Data Science Executive, a

philanthropist, a speaker, and just a very, very nice

person who gives back so much, so, so much back to

the data science community. Who educates, who helps

people, and I was very honored, very grateful to have

Tarry on the show today.

We had a lovely conversation, and we just let it go

where it went. We had no idea what was gonna come

out of it. We talked about things like philanthropy, we

talked about data science education, helping people

out, where the World is going in terms of getting third

World countries on the Tech radar, and helping people

in less privileged societies get up to speed with data

science. And what contributions we as individuals can

make towards those causes.

We also talked about Jeffrey Dentons recent capsule

network and capsule theory. So if you're interested in

that, then this podcast is for you. We also talked about



the research that Tarry himself is doing through the

research arm of his business. We talked about

advising executives and enterprises on data science,

and how all of those components come together. We

also talked about Tarry's recent major infographic hit

that is very popular on LinkedIn. He'll actually walk us

through it, it's called Climbing the Hill of Deep

Learning. But it's actually not just about deep

learning. It's about the whole process of building your

career in data science and exploring different

opportunities, and those five different plateaus at

which you can build your career. So you'll get Tarry's

advice straight from himself, from his experience, and

from his expertise in working with thousands of data

science students and data science professionals in

person.

So there we go, that's what today's podcast is all

about. A very lovely conversation, exciting journey.

Can't wait for you to join us, let's dive straight into it.

Without further ado, I bring to you Tarry Singh, a data

science thought-leader.

Welcome ladies and gentlemen to the Super Data

Science Podcast. Today I've got a very exciting guest

with me on the show, Tarry Singh. Welcome Tarry,

how are you today?

Tarry Singh: Thank you Kirill, thank you for having me. It's great

weather here in Amsterdam. And I'm super excited to

be part of your podcast show. Thank you once again

for the work that you have been doing tirelessly in the

last couple of years for data scientists. I think we all



know who you are, and I'm very thankful and grateful

to be part of this podcast.

Kirill Eremenko:: Thank you, and same here. Just recently it's been

interesting how your name as been popping up, Tarry,

Tarry, Tarry. And I am also a very big fan of all the

contributions you've given to the world of data science.

All the wonderful materials you've created, all the

advice, and insights that you've shared back to the

community. So, very excited about this chat. As we

discussed at the start, we don't have any predefined

agenda that we wanna talk about like plan how we're

gonna go through this. Just let it flow and see where it

takes us, right?

Tarry Singh: Absolutely. I mean we are all in the same field. Data

science is expanding actually in all directions. And I

think in the similar way the conversation will also lead

to our intuitions, which I hope the audience will be

able to enjoy as well. So let's keep it free-flow, yes.

Kirill Eremenko:: Sounds fantastic. All right, let's maybe start with your

company. So you're the CEO and AI researcher at

deepkapha.ai, and I'm happy I pronounced that

correctly from the first time, as you said.

Tarry Singh: Absolutely, yeah. You're one of the very few people who

has no problem at all in pronouncing.

Kirill Eremenko:: Yes.

Tarry Singh: Thank you.

Kirill Eremenko:: Yes. All right. Tell us a little bit about deepkapha, what

is the company all about?



Tarry Singh: Cool. I've been in this industry for like 25 odd years.

About a year and a half back, almost nearly two years

back, I decided that I did not want to be part of a

consulting world in which I sort of pretended that I

knew what I told my customers. I just needed to take a

break to get deeper into deep learning. I'm being very

honest here.

The reason why, is not taking a jab at the profession

that I've been previously in. It's just a field that is

expanding dramatically, in giving back to sort of what

you guys have been doing. Your podcast series, and

your educational series may have been very

educational, but they also opened up a huge new

world of data science. Now, when I look back about

almost two years ago, I said, "Okay, let's stop and let's

go deeper into it."

I had already established two companies in the past.

My first start up was a management consulting start-

up in which we wanted to sort of break the bank and

do some amazing things. My second start-up was an

NLP Social Analytics back in 2012. So I was already

playing around with this, but didn't realize that I

needed to explore myself and explain to the World

myself.

So I traveled around the World last year, in the

beginning of the last year. I met some global world

leaders who've been running some billion dollar

companies, tech companies, and also met and also

interacted with people in Montreal, in Toronto. And

also, [crosstalk 00:07:07]



Kirill Eremenko:: We all know who you're talking about in Montreal and

Toronto at University of Montreal, Geoffrey Hinton and

company. That whole ...

Tarry Singh: Yeah. So just kind of disclaimer ... So these are the

kind of interesting conversations we've been having.

For example, Geof Hintons paper which they released

in capsule. SO many conversations, some

conversations are very intense, internal. But also

industry leaders, guys who've been running big

companies, internet companies in China, also here in

Europe. What I realized was that, I think there was

two things I realized. One was that there is a huge

shortage of engineers, and I foresaw a huge shortage of

engineers. We were obviously aware of this trend that

Google, Facebook, and all these other companies are

constantly getting the best talent from Europe and all

over the World. All the Masters and PhD students in

different areas in healthcare, or bioinformatics, they're

all moving into these big companies.

It's leading to a huge problem in the industry. I knew

this because I come from the industry, I've been there

for a long time. And the second thing which I've

realized, when I was traveling and making these

travels around the World, I was giving speeches and

conferences and key-noting ... that there is talent

available, but it's not being connected to the industry.

So what's going wrong? I decided that I would create a

silk route ... I'm calling it an AI silk route, that's also

part of my pitch to the investors ... That I will work

with these people, I will start giving workshops, and

bring these people to the industry. Because the



industry leaders constantly ask me, "Hey Tarry, we

wanna set up and AI lab, we wanna set up, do this, do

that. How can I do this?"

It's very hard to get the right talent to get started, So

by the end of last year, I was already getting some

offers from a chairman of a large 25+ billion dollar

company. He reached out, and I started this project.

Then I realized, oh God, I don't have an entity. I was

incubating this idea so we incorporated the firm,

calling it deepkapha. Deep for Deep Learning. When I

say deep learning it's .... Deep learning is deep reading

and deep understanding. I didn't go into this

technology concept which is so popular right now. And

Kapha is more about harmony. How do you bring

these two together in a harmonious way so the World

can learn together?

Long story short, since January when we incorporated,

until now, I decided to ... I said, "Okay so I am starting

a company, why not do it the way I always wanted to

do it since I was a kid." I wanted to learn and play. So I

said, "Okay, then I'll set up a research arm." I wanted

to continue to stay in touch with the reality, which is

the business World out there. Because these are the

guys who need AI people right now, right?

Kirill Eremenko:: Yup.

Tarry Singh: I mean, we cannot just keep promising our people, our

young engineers, that there's a place for you in Google

or Facebook. These companies cannot continue to

keep taking hundreds of thousands of people. They

also have sort of a stop sign somewhere, saying,

"Okay, no more."



So I said, "Okay..." Since I have worked with

enterprises and advised chief executives of large

companies for quite a while, I said, "Okay, so this is a

nice conversation I can have with them." So I decided

to set up an enterprise advisory for AI as one business

unit. The other is research, and the third, which is far

more ... sort of appeals to me as a human, is to really

do it selflessly. How can I do this from philanthropy

perspective? Because there are many people, smart

people who don't have money. These are very bright

people ... kids even, very young kids, 12, 13 year olds

who are planning a future, who read a lot of books but

somehow don't have funds.

I also reached out and I was also approached by

companies like Think.iT in Tunisia, amazing group of

people there. A company called Recoded, which is a

humanitarian firm working in Iraq, in Syria, in Turkey.

So I just started traveling, going to these places

together with them ... Obviously these were our

partnerships, and also we had full advice and

guidance from United Nations, it's still going on.

So this way, it was giving me a lot of satisfaction to do

my job. Because normally happiness is a difficult thing

when you start on your mission, and you have to deal

with the hardcore world which is either enterprise. So

this way it gives me energy, but also keeps helping me

bring more and more people into this world. Which is

great, because that's the mission we have, right? You

also have the same. How do we bring people, and more

people, so we create these ... It's almost like saying ...

you go in front of this big castle, and you say, "Okay,



so you know, hey big castle you advised that this AI is

going to be shaping the new industry, and here, I have

a few millIon people standing with me. And we want to

enter, and we want to explore, and we want to make It

much bigger."

So that's the way it feels. I'm not the only one

fortunately. You guys are also in this game. It only

helps us expand this ecosystem more and more. So

Enterprise advisory to bring these guys some advice

and get them to hire smart people. Research has been

writing breakthrough research to write new activation

functions, to improve capsule theory into much more

detail, I can explain maybe later. So we are publishing

papers that are going to improve the deep learning

ecosystem literally, from algorithm perspective. And

third is philanthropy, which is ... My heart totally

warms up every time I have this mission, I have to go

somewhere. So I said, "Okay, let's do it." You know?

Kirill Eremenko: Mm-hmm (affirmative)-

Tarry Singh: So let's say in a nutshell what deepkapha intends to

do.

Kirill Eremenko: Fantastic. That's so interesting. I'm listening to your

story, and you broke it down into these three

components, and I'm actually seeing myself so much

in that. So you mentioned Enterprise Advisor to help

companies get these talented people on board.

Research arm, to improve the ecosystem, and the

philanthropy, because that's the ultimate mission,

that's what gives you fulfillment.



For me, so similar. I'm actually so surprised. We

started with this philanthropy component. I'm not

going to go out there and say I'm doing this all just for

philanthropy reasons. Of course it's a business. It has

to grow, it has people that work in it. But at the same

time, if you look at our courses, people studying,

learning, can get these courses at such low prices,

that's why we have hundreds of thousands ... We just

crossed half a million students. And that stands to

show that people really do want to grow and expand in

this area.

I would say that component was our starting one. And

then, funny enough, the research arm and Enterprise

Advisor, we just launched two new businesses. One is

a research business called bluelife.ai, where we do

research on new algorithms in artificial intelligence to

help ... also expand the space and empower

businesses-

Tarry Singh: Amazing.

Kirill Eremenko: ... to do more. And the other one, Data Driven

Executives, is to help executives understand better

how to become data driven and build these different

companies. So, also Enterprise Advisor, it's like, your

three points, I just check them off as well, so

interesting.

Tarry Singh: Yeah. It's beautiful. The more enterprises and firms

like yourselves, the more of these are in the industry,

the better. I think it's really great because we need to

go to Africa, where you are. I've been getting a lot of

requests already from Uganda. I've done a project in

Uganda a few years ago, that was 10 years ago. So I



think Africa is a huge continent where we can have

hundreds of thousands of people trained, maybe

millions. I think we need more guys and more outfits

like yourselves, so we can create this ecosystem and

make it much bigger. So amazing, I'm really happy to

hear that you're doing this as well. Amazing.

Kirill Eremenko: Thank you, thank you. And just on that point ... cuz

listeners might be a little bit confused, I am in Africa.

I'm just here on an island in Comoros. To your point,

it's a very far away place from everything. And its kind

of in the middle of the ocean, and there's a lot of

poverty, it's a very poor place. It's one of those place

that ... It only exists because of a certain industry, in

this case it's Chanel No. 5 that export this plant called

the Ylang Ylang.

But amidst all this poverty ... Like today morning I

went jogging on the beach, and I saw one of the local

kids, or maybe my age so I won't say kids, maybe

young adult, and he was also jogging. And he had a

phone, like an iPhone, and he was listening to music.

So even though there is so much poverty, they have

access to internet. The World is so different to what it

was 20 years ago, even 10 years ago. They have this

access. And by empowering people with online

education, sharing online knowledge and these things,

you can really change their lives drastically. It just

gives them a little bit of inspiration and they will

embrace it, and they will soak up all this knowledge

and change their lives.

Tarry Singh: Yeah, absolutely. I can just add one thing to it. I was

interviewed, I think two years ago, or was it three



years ago, by a journalist who used to work for Al

Jazeera back then. He was looking for a story ... We

had this conversation, it was published by a start-up,

a french start-up, I forgot the name. It was like a news

aggregator kind of a start-up in which they aggregate

news and make some interesting stories out of it. So

he asked me, "So what do you mean about technology

to get pervasive?" And I gave a ... from policy and from

migration perspective, which I still very strongly

believe in. I think the problem that we are having

today in Europe is it's .... essentially crisis for

European Union. You have boats floating all over the

place and Italy doesn't want it. Spain, for example,

yesterday you had this problem with hundreds of

young people.

I look at the boat, and I'm seeing those young men

struggling. These are like 15 to 25, young African men

and kids. No one in this World Kirill, wants to sit in a

boat and go to some country which is strange, no

matter how wealthy it looks, and eventually end up on

street. Or never be able to get that job which you

actually really deserve.

I spent four years in Uganda doing a project through

Dutch Ministry. I don't talk much about it, but I'm

very proud of that project which I did to bring

awareness, but also spread technology. I believe that if

we start bringing technology where people can start

building businesses and start doing things, they would

be so great. They would set up their own economical ...

Their economical reality is gonna change dramatically.



They are not gonna look a those boats and make those

horrible and dangerous passes to come to Europe.

I think it's a win-win situation if you bring deep

learning and artificial intelligence in its own beautiful

way to other parts of Africa. For instance, Kenya,

Uganda and even Rwanda, is really improving. As you

know ... you may have heard yesterday, day before

yesterday, the reason why they announced that they

wanna sponsor arsenal football club with donation is

because, they say, "We wanna get rid of the money

that we get from all these other richer countries."

Because it's a stigma. All these countries, even from

Netherlands, it's like 45 million or something, or

maybe more, that goes into Rwanda. So these guys are

saying, "We don't want your money. I want to build my

own nation."

From a policy perspective, it's great to give people tools

and techniques. And I think Africa is going to be the

huge, huge continent the World should be looking at,

really. From expanding this knowledge.

Kirill Eremenko: Yeah. Exactly. Have you heard of Peter Diamandis' X

Prize for education? The one for Africa?

Tarry Singh: No I have not. We are working with Think.iT, and I

know Obama, Barrack Obama, the U.S. President, he's

also launching a fund for Africa. And we are in

conversations with the founders of ... the CEO of

Think.iT. They're amazing people. So there are some

conversations going on to start that. But Peter

Diamandis, I know he's invested in a company of a

gentleman I know in Boston. But I haven't heard of

this initiative, no.



Kirill Eremenko: This one is very similar to what you're describing.

There's a prize, I think it's maybe a couple hundred

thousand dollars, maybe up to a million, I'm not sure

the exact amount. But it's about ... Or maybe it's

actually already finished, cuz last time I checked on

this was about a year ago. But anyway, it's about

creating an application for iPads in such a way that

anybody can pick up this iPad and learn basic

schooling things like Mathematics or English, or

Geometry and things like that, without any guidance.

And so basically, the plan is, as soon as that app is

developed, and tested, and it's verified, what they're

planning to do is to drop several thousand, or

hundreds of thousands of these iPads throughout

Africa. And just leave them in different places so any

child can pick it up. And by clicking, without any

guidance, without understanding the language, can

actually learn new stuff. How cool is that?

Tarry Singh: Yeah. I remember in 2006, when I started this project

in Uganda. There was also an initiative called OLPC, or

One Laptop Per Child. I'm sure you've heard of it as

well.

Kirill Eremenko: No actually, I haven't heard of it.

Tarry Singh: So it was ... I know I carried this as well, in fact I used

to bring it also to Europe for conferences here, back

then. So I think in a way it's similar, it's interesting

actually. The thing is these things need to start rolling.

So that is one. What I think [inaudible 00:23:07] from

experience I can give a word of caution is, it is people

like yourselves, and myself and others who need to go

there and bring this education in the classical way. We



should not forget that in European or other modern

economies, young people can sit behind a computer

and learn. While in Africa ... but also other Asian ...

Really you are from Australia right? So you know in

Asian cultures, people like to sit together and

understand it from a community perspective, and also

physical and classical perspective. Someone standing

and teaching me.

I think culturally anyways, but I think we have to take

some bold steps to set things up and maybe work with

governments if necessary. And that's what we are

exploring now in Africa, from a country governmental

perspective, to bring it in a more holistic way. And

expanding it in a way that people not only listen about

it, but they think it, and they can then expand it. And

I think that is needed.

I realize in those four years I spent at Uganda,

bringing in technology, starting prizes has a spiking

effect. Not like a neural spiking. But it's interesting

when it's there. But the minute it's gone, people go

back doing the same things which they were doing. So

that's the danger we should be careful about.

Kirill Eremenko: Gotcha. Thank you for that discussion. I'm sure

there's lots that we can all do in that space. Now, let's

move back a little bit and talk about deep learning and

some of the recent developments in that space.

Specifically I think a good place to start would be

capsule networks. So, I don't know much about

Capsule Theory, which Geoffrey Hinton released

recently. I know there's this one medium blog post

which is pretty popular on that space. Could you give



us an overview? What is Capsule Theory, and how is it

different to traditional deep learning?

Tarry Singh: Cool. So capsule definitely is a hub for many

researchers. Geoffrey Hinton in fact wrote a paper

back in 1981 in which ... In fact a few wordings which

we see from capsule's paper, there are some quite

similarities with what Sarah Ward, one of the authors,

has written about it.

First of all, it's not really that new, the whole concept

of poles, rotation, and basically trying to understand

the sparse or limited data about us let's say in a

certain manifold space. Meaning if I look at Kirill from

side, I just see part of his nose, or eye, or things like

that, then I understand that it's Kirill, I don't need the

MSCoe code ... huge dataset to [inaudible 00:26:13] to

figure out it's Kirill.

The rotation of your head, even the back of your head,

I can very quickly say, "I think it's Kirill."

Kirill Eremenko: Yeah. Whereas A-

Tarry Singh: I think this is what we are trying-

Kirill Eremenko: AI can't do that at this stage, right? Deep learning-

Tarry Singh: No.

Kirill Eremenko: ... can't look at the back of somebody's head and say

it's Kirill.

Tarry Singh: Exactly. So it's almost like you have this, maybe year

and a half year old little kid, that kind of sort of

cognitive capability we have helped AI achieve. But it's

not moved beyond that one and a half year-old kids



cognitive capability, who is maybe a little bit drunk

and not being able to see things at once. Things look

different, or tilted. So for example, if there is a tilt, if

there is a pose and change of rotation, texture,

warmth, and different things that are attributes to who

we are, and we put the three-dimension into it ... from

the three dimensional perspective, and then also start

adding different attributes to the same in which I see,

for example, Kirill, then I should still be able to make

... So for example, you're in Africa right now, there

might be a gazelle flying on top of your head. As a

human, even as someone who has not seen that data,

for example. This is a new data that has actually been

created in my brain, I'm able to make full sense out of

it.

So capsule theory, basically what it tries to do is, it's

trying to mimic more in a away in which how

neuroscientists have tried to understand how the

neurons are firing inside of brains, how they are

grouping together. So this whole idea of routing by

agreement is more about ... sort of, that's the

algorithm, part of the algorithm which tells the

network that, "Okay, so we agree as a group of

neurons that this is what it is, irrespective of

everything else that I see around Kirill's environment

which is strange, is this huge, weird marshland. It's

not a hack, it's real because I know he's there, and

then this honey Gazelle which is two meters in the

air." It's something which I can correlate to a certain

extent and say, "Well it looks strange, but we agree."

And then the neurons basically ... you take those



neurons and you pass it in the apex, and eventually

try to make more sense out of it.

Having said that, I think this is the beginning of what

AI should become as we move forward improving this

network. It's very relatively new. It takes about two

years, if you look at the experience with the

convolutional network also, before the accuracies are

improving in other ... Let's say improvements are being

brought to the network, which we are working on as

well.

I'm very happy to share with you in brief, because we

haven't published those papers yet. But we are trying

to bring more automated and more intelligent

algorithms into the network, into the neural network

ecosystem. But all in all, basically it means is trying to

understand from three dimensional, trying to ...

hopefully with as limited data as possible to make

approximations which essentially, as you've seen,

you've heard of the pixel attack and all those things,

the convolutional neural network ... Hopefully we can

move into more intelligent and more human-like and

neural network.

Kirill Eremenko: Gotcha, gotcha. And please do share, I'd love to hear

some of the ... whatever you can, some of those

research papers that you're working on. What's the

most exciting thing that's happening right now for you

Tarry Singh: So right now we're writing three or four papers. One of

the papers is actually being released, two papers. So

my goal is through a research arm, I have a head of

research, she is a neuroscientist, she's completing a

PhD here in Berlin, we just published a paper in ICSE,



it's called ICSE, 40th software engineering conference

in Gothenburg in Sweden. So [crosstalk 00:30:26]-

Kirill Eremenko: Gothenburg sounds like back then, right? Didn't know

that was a thing. All right, sorry, yeah. [crosstalk

00:30:35]

Tarry Singh: So we presented our paper, Neural deals. We're calling

it the Neural deal, meaning trying to use as much of

neural science data collection which passes through

from our retina to our neocortex. What we are trying to

explain is that there is a lot of data and a lot of data

manipulation that happens between these two

junctions, meaning your retina and the back of your

head. And then how can we use this data to basically

start maybe creating new different algorithms, for

example, back propagation probably is still rather

immature. However great it is in making

approximations today, it's still not the realistic way of

how we, let's say, deduce information about the world.

What we are writing is ... We're improving a squashing

function. Which is the activation function which

capsule networks has. We are calling it an in-squash.

So we are trying to, introduce a second order norm to

it. We are still right now testing vigorously on our

servers. The second which we are adding, which may

not necessarily have anything to do with capsule, but

obviously we want to include it into the capsule

framework, is trying to bring a deep switch. We are

calling it a deep switch internally right now. What it

means is that we should be able to switch across

various optimizers that are there while you are

running your network. So you don't have to babysit 10



different networks with 10 different models. And you

can just have it switched based on certain parameters

and certain sets of conditions.

So that is one, and then we're trying to combine also

other simple [inaudible 00:32:29]. It's very interesting,

my researchers are setting it up right now. For

example, even learning with hyper-parameters, which

essentially we either just run our network or train it

for weeks. But right now we are saying, "Hey, hang on.

Let's kind of [inaudible 00:32:44], this whole learning

rate." Be more adaptive. Make it more auto learn while

it's running in the network, and make it more

interesting.

And then there is obviously in capsules, we have

already done some research applying manifold

learning and unsupervised learning. And right now we

are currently experimenting heavily on PGM, so

probabilistic graph models. We are basically trying to

force this whole unsupervised learning, as I just

explained, Kirill in this strange, funny grassland, a

gazelle flying on his head, over his head. In fact, I had

a picture also on a research paper in neuroscience,

Neurodeal, in which there is this car flying in a jungle.

Very weird. So it's there in that illustration, and that

paper should be going into archive very soon.

So those are the kinds of papers we are writing. And

we keep talking to each other, because as you know,

writing research sounds interesting from far, but

there's a lot of research that fails as well. We have to

accept that and move on and keep trying new models.



So there are four or five papers we are writing. We've

already ... For example, one of my researchers has

written a paper on an activation function which

improves on ReLU and factors better than ReLU. So

that has already been published, it's on archive.

[crosstalk 00:34:13]

The research is really interesting. It's like kids coming

together, and we start Playing Lego with each other.

Kirill Eremenko: Yeah. Well congratulations. All of those sound like very

interesting, pushing the envelope type of undertakings.

So excited to see what comes out of that.

I wanted to move a bit to the side here, and talk a little

bit about ... More for our listeners who are just getting

into this space of deep learning. So you have this

wonderful, fantastic infographic which you shared. At

least I've seen it on LinkedIn, probably other places.

Gotten tons of comments, tons of likes, and I'm sure

many people have been impacted by it. It's, How

should I start in Deep Learning and Artificial

Intelligence? Got five main steps, I'm looking at it right

now, and we'll share it in the show notes. If you don't

mind, could you walk us through these five main

steps, and maybe give us your comments so that

somebody who is a bit lost in the world of deep

learning, but wants to get into it will have a very clear

pathway?

Tarry Singh: Yeah. So basically, I called it hill climbing, and this

was part of ... This was a result of the workshops and

the training that I've been giving to enterprises and

groups. Hundreds ... I think I've trained already eight

and a half to, I think it's probably already nine



thousand people.[crosstalk 00:35:32] These are all

classical. It's not online, I go to places.

Kirill Eremenko: That's insane. Where'd you find the time?

Tarry Singh: Yeah that's a huge number actually. If I look back

after, it's almost a year now. In fact, it is a year. In

June I really started doing this, last year. So I've

already touched almost close to nine thousand people.

These are in-house. People are asking how do I do this.

So I started sketching it, and I sketched it for over a

few months, because it was also my own journey. I

said, "How can you just throw information in peoples

face and expect them to learn?" It's very hard for a lot

of people. If you have ... The couple of basic things is

that if you have intuitions in physics and mathematics

... I studied physics first in University and then

Nodical Astronomy. So basically, I already was very

curious about this World as a physicist, and as an

astronomer, thinking the Universe, the World. So

basically very observant, and at least very curious ...

Observant, I don't know if I was observant enough as a

young guy. You have other things to do when you're

having fun, but still observing.

But not everybody is coming from that background.

People may have business commerce background.

People may have some other intuitions which do not

help them see the light. So then I started sketching it,

and the first which are called, I called it plateaus ...

When you should climb a hill, you have plateaus,

almost like climbing Mt. Everest.



The first plateau is the fundamentals. I started

revising, collecting information and data, and also easy

to understand stuff. For example, there is a beautiful

book written by a gentleman, and I just forget his

name, but he has written two beautiful books on

physics and mathematics, and he calls it No Bullshit

Linear Algebra, or something like that. Also on

physics. So I started giving people those kinds of books

that help people seamlessly climb into that plateau,

without being intimidated. Because back in high

school you have ... It's a priority, as in you have

information and you're just thinking, "Oh my God, so I

don't think I can ever do it."

I sucked at [inaudible 00:37:51] in high school. So that

is a difficult step. Then I tell people, "Okay, don't worry

too much about it." You're in plateau one. Plateau two

is trying to understand visualization skills. You are not

maybe an analytics person, you don't feel like it, you

don't think you can write and algorithm and share it.

Don't worry about it right now, let's start visualizing,

you're a visual person. So the plateau two I started

calling visualization, and then I give them introduction

into all this visualization libraries. And slowly, in very

seamless and easy to understand way, we start writing

code together.

When people get comfortable, I said, "Go back to

plateau one and try to see what you understood there.

And make some changes, come back to plateau two,

which is your visualization thing, and then let's move

on to the third one." And machine learning becomes

the third plateau. And there you have a ton of those



series yourself which you guys created. So I point

those, I point to several other areas, I said, "Look

there, look there, look there." And make combination

which suits the best, and try to keep your learning

curve measured. Be honest with yourself. If you don't

understand, go back and read it. If you don't

understand go back to the plateau two, plateau one,

come back again. So it's almost like going back and

forth.

Once you master parts of machine learning, you don't

have to do everything. So people start thinking, "I have

to boil this whole ocean." So then I said, "Okay, just do

parts of it." Maybe if you're moving into unsupervised,

do support vector machine understanding, how Apne

created. Get the historical perspective. Read why

people made those things, why people wrote those

things. That will help you remember these things

longer than if you just remembered as a formula or

some kind of algorithm.

So when people go back and say, "Okay yeah, this

Russian guy, he created this and it happened this and

he did that." And then they remember longer, then

their intuitions start developing. When these things

start happening, then I say, "Then you are actually

ready for deep learning." Although I keep saying, "If

you can already jump from plateau two to plateau

four, which is deep learning, what is it then?" Then

start showing them, explaining in a sort of easy to

learn sort of way ... Going back to all intuitions,

historical perspective. What was Boltzmann machines,

and who was Ludwig Boltzmann, and what was his



intuitions? What is the role of statistical mechanics?

How does this apply to your activation functions that

you're creating? And all these things.

Those perspectives start making ... It's almost like a

story telling if you will. I think the fifth plateau is

applied AI, which you need to eventually apply.

Because people say, "Okay now I have every theory, I

ran every darn dataset on Kaggle, and everybody's

done the same. So I'm still ... It feels as if I'm part of

the network in which everybody is saying the same

thing. So what? What is my differentiation? What do I

do?" And I said, "Okay." So that's the step in which

you start looking at datasets. So go talk to your

community. And then people say, "Well, it's easier to

say." I said, "Hang on. It's not easy to say. You're right,

it's not easy. So when you're going there, when you're

meeting ... when you go into hospital, you have

someone who is in the hospital network." Believe me,

in hospitals, even in India and Bhutan, we're also ...

I'm helping a researcher doing a project there. So there

are people who are collecting data, all you have to do is

just start going. People have data.

When you start going, then you start learning this

whole art of data collection, pre-processing, creating

balanced datasets. When you are starting to do that

then you'll really feel like you're building something.

You're almost like this guy who's building this brick

house and used to go brick by brick, and I said, "You

know this is the journey you have to go through and

then you can reach that summit with applied AI." It

could be anything, it could applying policy changes, it



could be trying to change the way the world ... Income

inequality, you can get statistical datasets from your

country, and try to start making sense out of it.

I think there's a whole lot of things, and then you can

start applying coronal network into some time series or

something else. These things start helping. There are a

lot of people who came back and are doing some really

amazing stuff actually. So in a way, those five plateaus

really helps you to really become a master in an area

that differentiates you from your other peers. And this

differentiation is eventually the trigger, or a catalyst,

for us, for you and I to seize satisfactorily and say,

"Well this is the network effect which we want to

achieve when we mean that this ecosystem has to

expand."

Because if we don't do this, I think the risk is that we

will continue to train people in theory, and in toy

datasets, and these toys are not going to make them

real men. They are going to remain boys and girls. We

have to make them men and women. Deep learning.

Kirill Eremenko: Yeah. Yeah, true.

Tarry Singh: That's my story, that's my opinion I guess. I have a bit

of experience.

Kirill Eremenko: Yeah. Very clear. And I definitely agree with that.

When I was creating the course on our programming, I

remember I looked around the place and did some

research of the existing courses. And one of the things

that I noticed is that every single course out there uses

the virginica setosa dataset. I was like, "Oh my God."

It's so repetitive, right? Those flowers and the whole



fisher iris dataset. And it's like, "Come on guys, we can

do better than that." And I made it one of the core

values of the course creation process, that I look for

datasets that are current, relevant, interesting, from

industries, real business challenges and so on. So that

people learn through ... they can see that it's not just

theoretic application to a dataset that was discovered

100 years ago. But it's actually something that is

happening now. Something like, I don't know, some

machines in a mining plant and you can predict their

maintenance requirements. Or there's a consulting

firm that is trying to help a bank differentiate or do

something with its customers and segment them

better.

You're right. By putting it into perspective like that, it

helps people see that this is not just a theoretic

exercise, I can actually make an impact. I can actually

help businesses, people, charities, friends,

organizations, myself, analyze and understand better.

It inspires people to actually look at stuff. You can get

your own Fitbit, or iPhone and measure how many

steps you took and analyze that. That's already

something cool.

Tarry Singh: Totally agree with you. I mean, make it real, make it

practical, and make it stick in your head. Because it's

not going to stick. Setosas and all these leaves or the

MNIST and all these guys are not going to stick in your

head because ... It's a great way to benchmark, so

MNIST is a great way to benchmark if you've written a

beautiful algorithm. But don't start using it as

something to prove if you have to do 3D lung cancer,



you need something different if you have that. I think

we need also more advanced datasets that are

normalized. That are presented to us in a way, where

you have healthcare data, agricultural data,

manufacturing data. There should be some interesting

data sets coming which will help.

But I think that's a next way which we should be

seeing in the next five years. You will have datasets for

specific, all verticals that will help us get even better

with our algorithms. So I totally agree with you. Yes.

Kirill Eremenko: Yep. Tarry, let's start a new business. Let's start a

repository of all datasets.

Tarry Singh: I can tell you Kirill, seriously this is no joke. In fact

this is one of the things also ... we are working on a

patent as well. My mentor actually advised me that

you need to go and file a patent. And it's all about

datasets. Today we are looking at datasets and people

are not making sense out of it Kirill. I didn't either, I

was also like, "Oh yeah, yeah." Because you're focused

on a mission, you're not looking around the world.

So, in their my mentor is amazing, he's almost like a

second dad to me. He said, "Okay, hey listen, let's take

a break. Let's go to a sauna, and you're not gonna talk

about anything. I don't want you to start visiting up

this big sequoia forest." I said, "Okay. So why..." I said,

"No, no, no, I want to go there." And I had a keynote

there in San Francisco with a bunch of people, Google,

LinkedIn and all these guys.

So he says, "Let's go away." And we went. We spent the

whole day doing nothing, and this is when the idea



started coming to us. He says, "you know, you have all

these datasets. For example, Google is releasing all

this audio and video and all that stuff." And I said,

"This is the new economy. The new economy is going

to be based on the manipulations and even

extrapolations and interpolations of these datasets.

Because essentially this is what your brain does,

right?" So I said, "Yeah."

Because I translate information in front of me which is

visual, which is text, which is audio. And it constantly

is transposing and interpolating, and that gives me

intuitions. He says, "This is what the new economy

has got to be. It's not just going to be in its own silo."

The danger is that you will have companies like Google

and Facebook< they will focus on their own silos,

because that's where the business is. And there has to

be someone who comes and starts looking from a

horizontal perspective, and how do you create a

cognitive layer from this ... This master algorithm

thing, right, which Pedro Domingos wrote.

What he meant was that how do we bring these five

tribes together? But this whole idea of creating a

master or supervisory algorithm, would be to

essentially take advantage of mature datasets, which

start teaching industries and verticals about their

systems. And obviously you need an algorithm to run

this, because the algorithm is the engine. But I think

datasets is ... More people should be thinking about it.

When I feel that I'm alone, I either apply Peter Thiel's

formula that ... If there are just a few people who

believe in it, and everybody else disagrees, you have a



great idea. But my intuition says that I have a great

idea, because I'm working on a patent, which I'm going

crazy thinking about it.

So yeah, why not? I mean let's have a chat. I believe

we're also definitely going to be meeting in San Diego.

That's something I spoke to your colleague ...

Kirill Eremenko: Yeah, yeah. So for our listeners, I'm very excited to

announce that Tarry is going to be joining us for Data

Science Go 2018 in October, this year in San Diego.

Super pumped about it, can't wait to meet you in

person. How are you feeling about coming there and

giving us a little bit of ... sharing some of your insights

with our audience?

Tarry Singh: Amazing, I'm so excited. Once again very thankful and

grateful for everything that happens. So excited to

meet you Kirill in person. I'm sure we'll exchange great

ideas. I think it will be a great, great show. I spoke to

Boe, Boe is a very good friend of mine. He is a kind

soul, and I know it's such a successful thing. I'm very

happy to help you expand. Because-

Kirill Eremenko: Thank you.

Tarry Singh: ... it's our common goal.

Kirill Eremenko: Thank you, thank you. We have-

Tarry Singh: Very, very excited. Yaye! Super excited.

Kirill Eremenko: Boe definitely added a lot of value to our conference

last year, and this time we've got 400 people coming

over. So it'll be really cool-



Tarry Singh: Waw. Huge, that's massive man. I mean yeah, it's

great. You know?

Kirill Eremenko: Yeah.

Tarry Singh: You know the reason why I think ... I'll just add

something to it Kirill. The reason why I think you, and

even guys at MIT, guys like Andre Karpathy, who's

right now at Tesla ... All these people are ... I think it's

important to create this ecosystem with the

community, and continue to work the community.

We stay away from all these world summits and all

these CogX, this X, and that X ... No offense but

there's so much air, so much hot balloons flying

around. I think the real work is done when you're

walking on the floor and talking with ... In fact, I know

every person that is .... The community member that

walks into all the conference that I've been, the ones

which I like to go to, like yours, is they're all walking

around with a problem. They're asking questions, they

have notes written, I wanna be there. I hate to go to

conferences, and that's why we stopped totally. We

said we don't wanna be near the World of AI, or World

Summit AI, where some business leaders are hanging

around sharing presentations.

I think, community building is probably the best thing

that is there in this. And I hope you keep doing this.

Kirill Eremenko: Yeah. I can't wait for you to come, because at our

event, we really focus on the, what's it called, inner

drive of people. These personal relations, for instance,

at some point we just all stand up and we have a



dance crew and we're all dancing, jumping, and then

after that everybody-

Tarry Singh: Nice.

Kirill Eremenko: ... you see you get five hugs, five high fives, and really

builds these connections between people. After literally

two hours after the events start, you can't recognize ....

everybody is so friendly with each other. I love it. I love

how everybody gets connected very quickly. So that's

[crosstalk 00:51:56].

Tarry Singh: Amazing. Yeah. I think ... I really look forward to this.

Amazing. Thank you so much. You really got me

excited.

Kirill Eremenko: Thank you, thank you very much. Okay, I guess we're

coming close to the wrap up. Time flies, this is

amazing. I just want ... I had this one question while

you were explaining the infographic of climbing the

mountain of deep learning. If you don't mind, if you

have a few minutes, how would a person know ...

You've got these five plateaus, which I think ar every

descriptive, so first one is statistics-mathematics

programming, second one data analysis visualization

skills, third is machine learning, fourth is deep

learning, and fifth is applied artificial intelligence. So,

the question would be, how would somebody know

when they are good with the plateau that they're on?

When they're confident and that they're ready to move

on to the next one?

Because sometimes I find it's very easy to be like, "Oh,

okay, so I did some ... you know I learned some stats

programming. I'm really excited about machine



learning, I want to move on forward." And they move

on forward, but because they lack that necessary

grounding in the ... whether it's stats, or whether it's

the programming part of things, they can get very

discouraged when they get to the plateau of machine

learning, because it's exciting and you can apply it,

and they dabble and they get some good results. But

because they're neglecting going back and refreshing,

as you said correctly, going back and up-scaling

yourself in the previous plateau as well, they neglect

that part. And they feel discouraged, and they feel like

it's not for them, it's not the right thing, when it's

really not the case.

Tarry Singh: Yeah. I think it's ... And you've trained hundreds of

thousands of people yourselves, so I'm sure you must

have got so many questions like these. But my

personal experience is that, yes, it's very hard to keep

a track of all the plateaus when you're climbing the

summit. So I say that you don't become and expert if

you have climbed the Mt. Everest the first time.

Because the first time, you take all the aids, and

you're there and you come back, because there's a lot

of hand holding going on, there's a lot of ropes. I'm not

a hill-climber by the way, but I've heard from people

who have done this, some good friends. And then you

start pushing the limits and start going without

oxygen, right? Many people have done that already, it's

proven that it's possible. And so it's almost like

building your fitness function, if you will.

It's kind of an auto-learn function in which you should

intuitively be able to go back. So my advice to people



who are, let's say, half way in machine learning, and

just thinks ... and even I have that by the way. So it's

very normal. First of all, one, it's very normal. If I had

to go and look back at the icing formula from physics,

and how it applies to the activation function, I have to

go back, and sometimes I write them down on a piece

of paper myself. Because hey, I mean come on, 24

hours a day, if I'm sitting six, or seven, or sometimes

even twelve hour flight from Amsterdam to China, I

can do that. I have my laptop, I have all those books in

my repository, all of them.

So I write it down, and then it helps me. Of course

there is a cognitive capacity beyond which you get

tired. So I would say, just be selective. Don't worry

about an area which you haven't explored yet. You

don't have to explain to yourself that you don't

understand it. That's okay, you can always come back

to it later. Keep almost like little flags, like wait points,

you say, "I will visit them later." And going back to the

step one is probably the most important, which I

realize from my experience, that statistical mechanics

and getting deeper and deeper into statistical

understanding needs to know how and why it is that

way. And then going back into the other ... sort of

jumping back from plateau one to plateau four would

become easier.

So sometimes you have to make big leaps. And

sometimes you have to just step down a bit, and take a

look at it. The other thing which I want to say is also

what I've observed in many people, it's also okay to be

at plateau two, for example. A lot of people say, "Hey



listen, I understand what it is. I don't need to be in

[inaudible 00:56:20] or an expert. Actually I'm great at

visualization." And they start exploring like 20

different types of visualization libraries, just as an

example. For example, if you're ... and I work with so

many bioinformaticians, and molecular biologists, and

cardiologists, and pathologists right now. We start

looking at the visualization libraries, so all we do is ...

The basic stuff is, you show map outlay, you have

seabourn, you have cuff links, you have a couple of

other stuff.

Now I have started seeing that people are looking into

the visualization toolkits and libraries that apply to

cardiologists that are massive big guys, the VTK and

MayaVi, and several other of those which are so

sophisticated that they start becoming experts in ...

sort of visualization experts. So you're basically a data

scientist with that specialization. Sometimes it's okay

to also be comfortable with what you're comfortable

with. There's no need to start climbing ... not

everybody has to do this whole pass of these five

plateaus. So choose which really appeals to you,

because eventually you will shine there, believe me.

Because you will find more things than anybody else

would have found.

There are many avenues which we ... I'm ignoring

them. Although, I jumped into the 3D-ling cancer

because I explored, explored, and found more stuff.

But then I left it. I hope that someone else takes it and

starts [inaudible 00:57:50]. I know you showed

something to me six months ago. I improved on it, and



take a look what I made. And I've seen one or two

people do that, and its was amazing.

So it's a two way process. You don't have to boil the

whole ocean. Feel comfortable to go down and pick up

some more cherries from what you may have picked or

may not have picked. But also be comfortable and find

with where you are, if this is what you're good at. So I

would say don't worry too much about it really. A lot of

young people especially get very nervous and anxious,

because they think the whole thing about this learning

is part of my Masters and my PhD, and beyond that I

will not be able to learn.

I've been I this industry longer, and you know yourself

Kirill, we keep learning everyday so we should

[crosstalk 00:58:43]

Kirill Eremenko: For sure.

Tarry Singh: It's very hard to tell young people that, "Don't worry,

it'll come later." They're like, "AH no, I want it now."

Kirill Eremenko: Yeah.

Tarry Singh: "I want everything packed into my brain right now."

But I say, "Well then, you have to wait til the algorithm

which in the matrix movie..." Remember in the movie

Trinity had to quickly learn how to fly the helicopter. I

said, "Wait, we're still working on that algorithm."

Kirill Eremenko: Yeah. Elon Musk with his Neuralink.

Tarry Singh: Yeah, for instance, yeah. That is an interesting area as

well. Sort of patching up the brain and getting ...

Literally patching it up, and some `firmware update to

get all that information in your head.



Kirill Eremenko: Yeah. Crazy world.

Tarry Singh: Yeah. You never know.

Kirill Eremenko: Thank you. Thank you very much for that excurse and

the additional comments on the infographic. We'll

definitely include in the show notes. We're coming to

an end, running out of time. Tarry I want to thank you

so much for coming on the show. Where can our

listeners get in touch with you, contact you, follow

you, follow your career, and all these amazing things

that you're doing?

Tarry Singh: Thank you so much Kirill. Obviously it's an honor to

be with you. You're one of the shining beacons in this

industry.

Kirill Eremenko: Thank you, thank you.

Tarry Singh: It is true. I've followed some of your trainings myself. I

continue to talk about your trainings in every class

that I teach. On LinkedIn I'm there, quite active.

Fortunately, LinkedIn's algorithms, they've been

improved since I think July. Things are working very

well for LinkedIn, and also for us.

I'm on Quora quite often. Lately again jumping actively

to Quora trying to answer questions as well. I think

these are the two platforms. I'm also on Twitter,

although I occasionally respond only to my Silicon

Valley Networks, and all the researchers form U.S. or

Canada. But otherwise, I think LinkedIn and Quora

are the great place to be, but Twitter is also a place.

These are the three places.

Kirill Eremenko: Thank you.



Tarry Singh: And please reach out to me. I personally like to talk to

people. There's a lot of time that goes in to do that. But

send me a note, you will always get a response. I'm

just a normal guy, I'm not sitting on a high-horse,

some elite researcher who works at Facebook or

something. Honestly, I just like to talk to you guys. So

please, let's just be normal human beings and have

fun learning-

Kirill Eremenko: That's so cool.

Tarry Singh: ... deep learning.

Kirill Eremenko: That's so lovely, thank you. And I'm just gonna add to

that, Tarry has a blog, so Tarry Singh, T-A-R-R-Y S-I-

N-G-H.com. Some very interesting topics are discussed

there. And of course, if you don't mind me sharing,

deepkapha.ai, D-E-E-P-K-A-P-H-A.ai. Something

exciting is going on there. You've got a countdown

timer, what's that all about? 18 days, 16 hours, 16

minutes, and 22 seconds.

Tarry Singh: Yeah, yeah. So the platform, Oh my God. The platform

in which we have like a hundred and fifty applicants,

or probably a lot more. I know there are thousands, I

looked at the landing page, it was like, "Oh my God."

So people wanna go for research, they're applying for

research and philanthropy. I forgot to mention, maybe

it's worthwhile mentioning-

Kirill Eremenko: Sure, of course.

Tarry Singh: ... that we are going to be collaborating with Hult

Foundation, that's H-U-L-T. And Hult Foundation is ...

so Bill Clinton and Hilary Clinton are also ... they

contribute to the foundation, in fact, they also inject



cash in it. Hult is a very wealthy Swedish family, who

also set up the Hult Business School a long time ago

in U.S. So we're going to be collaborating with them,

and I will be in London I think sometime in August to

be coaching and mentoring. It's a week long program,

40 start-ups in AI. Hult is something which you will

see more announcements coming as we try to launch a

prize. Something like you mentioned Peter Diamandis'.

But I have an idea how to do it, sort of ... we are

calling it a deep [inaudible 01:03:02] shield. So getting

more and more people into AI.

So that is a philanthropy initiative. So there's a lot of

stuff coming Kirill. There's a lot of work. I know people

are really upset at me that I put so many things in

parallel, so everybody has to work. But yeah, we are

full of energy. And we'll stay healthy. So I hope we can

achieve the mission that I think deserves achieving.

But I'm looking at you, I mean you've done it. So I

think it should be possible for me to do.

Kirill Eremenko: And I'm looking at you, and I'm like "Yeah, If you..."-

Tarry Singh: You learn from me too.

Kirill Eremenko: Yeah. For sure, for sure. And I'm sure it's all going to

go well. Can't wait for the timer to hit zero and for you

guys to unleash the power of AI to solve worlds

problems, and bring the word out there [crosstalk

01:03:49].

Tarry Singh: I hope so. The developers are working hard for the

platform. Let's hope it... Thanks once again Kirill.

Amazing, thank you very much. This is the first time

we've spoken, but I have followed your work for such a



few number of years. Great job for doing this. Without

you guys ... every interaction leads to some expansion

and idea, and thought. And thank you for this great

talk. My day is now full of talking to another

gentleman who's done this for so long. It's a thankful

day, it's a grateful day for me.

Kirill Eremenko: Thank you. Thank you so much for coming on the

show, and I had a wonderful conversation, and I'm

sure our listeners enjoyed all the insights you shared,

and all the inspiration that you just convey with your

energy. I can feel it from over here even though we're

on opposite hemispheres in the world right now.

Tarry Singh: Amazing. I'll talk to you soon, and see you in San

Diego.

Kirill Eremenko: See you in San Diego.

So there you have it my friends. That was Tarry Singh,

founder, executive, philanthropist, researcher, and as

you'll probably agree with me after today's session,

just a very, very nice guy who gives back so much to

the data science community.

And my personal favorite part of today's episode was

when Tarry described the three different components

of his strategy, when he talked about the enterprise

advisor, the research arm of his business, and the

philanthropy component. So when you put all those

three together, it becomes pretty clear on how he has

been able to make such an impact on the world, and

empower so many individuals in becoming data

scientists, becoming more data literate, becoming data



advocates. And I'm sure he's going to continue this

mission going forward.

If you'd like to get in touch with Tarry or follow him

and his career then make sure to check his LinkedIn

and Twitter, we'll be sharing those in the show notes

at www.superdatascience.com/169. We also

mentioned quite a few of his websites and different

undertakings. Those links will also be available there.

And as Tarry mentioned, he'll be coming to Data

Science Go 2018, October 12th, 13th, 14th. If you

haven't gotten your tickets yet, you can get them at

www.datasciencego.com. We've still got the early bird

prices available. They're increasing this week, so make

sure to jump on board and you'll see there're plenty of

wonderful, amazing speakers, just like Tarry. We've got

twenty speakers coming to Data Science Go, and we

can't wait to see you there. Hope to see you in October,

and until then, happy analyzing.


Documents

SDS PODCAST EPISODE 169 WITH TARRY SINGH€¦ · industry leaders constantly ask me, "Hey Tarry, we wanna set up and AI lab, we wanna set up, do this, do that. How can I do this?"