SDS PODCAST EPISODE 169 WITH TARRY SINGH€¦ · industry leaders constantly ask me, "Hey Tarry, we...
Transcript of SDS PODCAST EPISODE 169 WITH TARRY SINGH€¦ · industry leaders constantly ask me, "Hey Tarry, we...
Show Notes: http://www.superdatascience.com/169 1
SDS PODCAST
EPISODE 169
WITH
TARRY SINGH
Show Notes: http://www.superdatascience.com/169 2
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
Show Notes: http://www.superdatascience.com/169 3
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
Show Notes: http://www.superdatascience.com/169 4
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?
Show Notes: http://www.superdatascience.com/169 5
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]
Show Notes: http://www.superdatascience.com/169 6
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
Show Notes: http://www.superdatascience.com/169 7
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."
Show Notes: http://www.superdatascience.com/169 8
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,
Show Notes: http://www.superdatascience.com/169 9
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.
Show Notes: http://www.superdatascience.com/169 10
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
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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
Show Notes: http://www.superdatascience.com/169 12
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.
Show Notes: http://www.superdatascience.com/169 13
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.
Show Notes: http://www.superdatascience.com/169 14
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
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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
Show Notes: http://www.superdatascience.com/169 16
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
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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
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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,
Show Notes: http://www.superdatascience.com/169 19
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
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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.
Show Notes: http://www.superdatascience.com/169 21
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
Show Notes: http://www.superdatascience.com/169 22
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.
Show Notes: http://www.superdatascience.com/169 23
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
Show Notes: http://www.superdatascience.com/169 24
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
Show Notes: http://www.superdatascience.com/169 25
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
Show Notes: http://www.superdatascience.com/169 26
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
Show Notes: http://www.superdatascience.com/169 27
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,
Show Notes: http://www.superdatascience.com/169 28
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
Show Notes: http://www.superdatascience.com/169 29
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
Show Notes: http://www.superdatascience.com/169 30
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-
Show Notes: http://www.superdatascience.com/169 31
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
Show Notes: http://www.superdatascience.com/169 32
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
Show Notes: http://www.superdatascience.com/169 33
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
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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
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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
Show Notes: http://www.superdatascience.com/169 36
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.
Show Notes: http://www.superdatascience.com/169 37
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.
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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
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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
Show Notes: http://www.superdatascience.com/169 40
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
Show Notes: http://www.superdatascience.com/169 41
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.