SDS PODCAST

EPISODE 277:

THE NEW AGE

OF REASON

Kirill Eremenko: This is episode number 277 with Serial Entrepreneur,

Khai Pham.

Kirill Eremenko: Welcome to the SuperDataScience Podcast. My name

is Kirill Eremenko, Data Science Coach and Lifestyle

Entrepreneur. Each week, we bring 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.

Kirill Eremenko: This episode is brought to you by our very own data

science conference, DataScienceGO 2019. There are

plenty of data science conferences out there.

DataScienceGO is not your ordinary data science

event. This is a conference dedicated to career

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Kirill Eremenko: We're expecting 40 speakers, that’s four, zero, 40

speakers to join us for DataScienceGO 2019. And just

to give you a taste of what to expect, here are some of

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Creator of Makeover Monday Andy Kriebel, AI Thought

Leader Ben Taylor, Data Science Influencer Randy Lao,

Data Science Mentor Kristen Kehrer, Founder of Visual

Cinnamon Nadieh Bremer, Technology Futurist Pablos

Holman, and many, many more.

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So there will be plenty of networking opportunities

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Kirill Eremenko: So whether you're a beginner, practitioner, manager or

executive, DataScienceGO is for you. DataScienceGO

is happening on the 27th, 28th, 29th of September

2019 in San Diego. Don't miss out. You can get your

tickets at www.datasciencego.com. I would personally

love to see you there, network with you and help

inspire your career or progress your business into the

space of data science. Once again, the website is

www.datasciencego.com, and I'll see you there.

Kirill Eremenko: Welcome back to the SuperDataScience podcast, ladies

and gentlemen. Super excited to have you back here

on the show today and we've got an incredible guest

joining us, Khai Pham, who is a serial entrepreneur.

This is a person who has both an MD and a PhD in

artificial intelligence. Khai founded the company called

DataMind, which was in 2000 acquired by Epiphany

for, wait for it, $400 million. That's $400 million.

That's the second highest AI-based company

acquisition after DeepMind.

Kirill Eremenko: Currently Khai is working on a very cool, very exciting

project called ThinkingNode Life Sciences.ai. And lots

of knowledge bombs. Such an exciting podcast.

Literally just got off the phone. Can't wait for you to

check it out. Here's some previews of what you're going

to hear about.

Kirill Eremenko: Entrepreneurship and data science. Why data science

is an advantage in terms of mindset even to be an

entrepreneur. General artificial intelligence versus

super intelligence and what are the differences and

why you don't really need general artificial intelligence

to get to super intelligence. Democratization of

expertise. Questions are more important than

answers, and hence the reasoning engine versus a

search engine. Becoming a founder of companies and

what experience Khai got out of that. Why companies

need to move from data-driven and machine learning-

driven to reasoning-driven, and what is this whole idea

of reasoning?

Kirill Eremenko: Those are just some of the insights that you'll get from

this episode. It was such an amazing conversation. I'm

really excited for you to check it out. I personally

learned a ton and Khai is a very thought provoking

person with very philosophical ideas, so I think you'll

find this interesting. Without further ado, I bring to

you serial entrepreneur and founder and CEO of

ThinkingNode Life Sciences.ai, Khai Pham.

Kirill Eremenko: Welcome back to the SuperDataScience podcast, ladies

and gentlemen. Super excited to have you on the show

here today. We've got a very exciting guest joining me

for this episode, Khai Pham, calling in from San Diego.

Hi, how are you going today?

Khai Pham: Very good. Very good. I mean, how can you not be

good in San Diego with this weather?

Kirill Eremenko: That's awesome. How's the weather there?

Khai Pham: Fantastic as usual. Blue sky, perfect.

Kirill Eremenko: You live in San Diego, right?

Khai Pham: Yeah. Yeah. I live in San Diego. I moved here for about

seven years now.

Kirill Eremenko: Okay. Very cool. It was such a surprise. For our

listeners, I'm in Paris right now, in France, and I said

to Khai, "I'm in Paris." And you just started talking

French to me. That is so cool.

Khai Pham: Yeah. I mean, when people see me I don't look very a

French guy, but I grew up in France. Where I did all

my studies, my MD, PhD over there. It's my mother

language, I would say.

Kirill Eremenko: Oh, mother language. Wow. That is really cool. Maybe

we can have a podcast in French one day. I'm really

still improving my French, but it would be interesting.

Khai Pham: Well, the problem is, I was born in Vietnam, so I forget

my Vietnamese and I start to forget my French, and

my English will never be good, so I don't speak any

good language today.

Kirill Eremenko: Oh, wow. Wow. On the other hand you've traveled the

world and lived in so many countries, so that's exciting

I guess as well. Yeah, you have both an MD and a PhD

in AI. For our listeners, MD is like medical, in

medicine, a doctor of medicine. PhD is a PhD in AI.

That is such a rare combination. How did you end up

having those two degrees?

Khai Pham: Well, the reason is, because I have an Asian mum. As

you know, Asian mum, you have to be a doctor,

physician.

Kirill Eremenko: Very straight to the point.

Khai Pham: I didn't have a choice. Anyway, I started medicine, but

rapidly, I don't have a lot of memory, so it was tough. I

said to myself, "Yeah. Why computer cannot just

remember everything and just get the information I

need?" Each time I went questioning my chief of staff,

"How can you be sure that you make the best decision

for the patient? Were you able to explore all the

combination?" This is why, this kind of frustration

drive me to AI.

Kirill Eremenko: Very, very cool. Also, I was very impressed to find out

that you were the founder and CEO of DataMind, a

leading AI company that was sold later. It was

acquired in 2000 for $400 million. That is the second

largest AI acquisition after DeepMind, which was

bought by Google I think not that long ago, but that is

really cool. Congratulations on that. That's a massive

accomplishment and breakthrough or like a massive

way you've made an impact in the space of artificial

intelligence.

Khai Pham: Well, yeah. When I started, I didn't even have money to

buy a PC. I started not to make a great exit or

whatever. I just wanted to prove that the technology

idea really works. I wanted to go beyond the academic

environment to show that it can work in the real

world. So yeah. With passion and so on, you just

always find a new way to accomplish what your dream

is about.

Kirill Eremenko: That's fantastic. I find some of the most interesting

stories happen with people starting with nothing.

When you don't have, as you said in your example,

you didn't have enough money to buy the computer,

then you find the way, you breakthrough and you

create something incredible. I think, even though it's

hard at times, especially at the beginning, that's ... I

don't know, it creates some kind of hunger in you

when you want to really succeed and really make an

impact in the world, because you're seeing what

situation you're in and you want to improve that, not

just for yourself, but for others and make a difference

in this world.

Khai Pham: Well, at that time I was younger. I didn't picture in my

mind what kind of impact I can have today. At that

time, I just really believed in what I'm doing. That's it,

and just wanted to share it. This was the fundamental

engine for me to move on. It's not about, at that time

yet, okay, what kind of impact I can do with this or

that. I just believed in what I had and I think that for

everybody that has something that they believe in it,

then it become a passion.

Kirill Eremenko: Fantastic. I love that approach. What are you doing

these days? You sold or that company was acquired

back in 2000. What have you been up to and what is

your current passion?

Khai Pham: Yeah. Since the company has been acquired, it has

been renamed later on to Rightpoint and so on. I

decided to start something in social network, because

the idea is to gather information, data, so it can be

used for machine learning at that time. But then, if

you remember, there was the dotcom crash and then

there was the financial crash in 2008. It was a

rollercoaster. It was a tough time. After that, I decided

to really spend some time to think about, okay, what

really I care. I come back to my first love, which is AI,

and I work on this project for more than six years on

system reasoning, which have a business if you

consider that, it's going to be the next wave in the next

five years. I'm very excited to work on that and we

applied that for life science.

Kirill Eremenko: Got you. What is this concept? We chatted a bit about

this before the podcast that, we want or companies

need to consider moving from being data driven, which

is a very trendy topic right now and very impactful as

well, but according to you, companies need to consider

moving from being data-driven or being machine

learning-driven to reasoning-driven. What is this idea

of reasoning?

Khai Pham: Yeah. Actually, this is a very interesting question that

sometimes some people ask me. What is reasoning?

Actually, it's something we are doing every day without

realizing it. There is mainly two things that are

important first, pattern recognition and problem

solving. Pattern recognition is what human and

animals are doing, which means to recognize

something. We recognize a face, we recognize a piece of

music. It's everything we are doing in a second.

Problem solving is when we start to have some

assumption, hypothesis, deduction, tests back the

assumption to see if it can be true or not and have

plan. Problem solving is really what distinguishes more

from the animal kingdom, even though some animal

has some reasoning but not at the level that we have.

Khai Pham: Machine learning data-driven is a statistical approach

and provide a very, very efficient tool for pattern

recognition, but if you want to go beyond pattern

recognition, which means predicting things, if you

want to understand things, if you want to be able to

intervene, you need reasoning, because you need to

understand the causality of things and you need to be

able to have inference in your mind, which means,

how to deduce things and how to check back if it's

coherent with your knowledge. So reasoning is what

you do every day to solve problem. It's not about just

recognizing an existing situation, but it's about

generating a new idea about generating new

hypotheses and try to solve it.

Kirill Eremenko: As we know, correlation and causation are not the

same thing.

Khai Pham: Yeah, we repeat that all the time, but I'm sure if you go

into a lot of conference and you start to ask people,

actually you will be surprised that sometimes people

confuse about it and how many time on TV, because

they give you some data and it's very confusing. I have

a very funny story about it. In the '50s, there was a

perfect correlation between the sale of ice cream and

the polio outbreak.

Kirill Eremenko: Yeah, yeah. I remember you telling that one the last

time we met at DataScienceGO. I got you, yeah.

Khai Pham: Yeah. At that time people even advised people to eat

less ice cream. Yeah, just because ice cream, yeah,

you eat more ice cream in the summer, and in the

summer the temperature is higher, so it's why you

have a ... the virus is more virulent. This is kind of

example to do not confuse.

Kirill Eremenko: Basically, the correlation was that people are eating

more ice cream and they were getting more polio, but

the common denominator was that it's summer. It's

just hot and that's why ... there is correlation, but

there's no causation between eating ice cream and

getting polio, even though doctors or there was advice

not to eat ice cream so you would avoid polio, is that

right?

Khai Pham: Yeah. Correlation, you just observe that something is

happening at the same time than another thing. They

observed that the sale of ice cream increased at the

same time than the polio outbreak is increasing, but

it's not the cause of polio outbreak. One of the cause, I

mean, one of the factor that participate to the cause of

polio outbreak is high temperature. So yeah, you're in

the summer, the temperature is higher, so it's why

people eat ice cream. It's very important to think about

that when you go to so many AI machine learning

conference in particular for life science, how many

times people are going to focus on the ice cream

instead of on the real cause.

Kirill Eremenko: Got you. Totally agree. Tell us a bit about your recent

or current company. Well, you're the founder CEO at

ThinkingNode Life Science.ai. What is the mission of

the company? What is the vision? Why did you create

it?

Khai Pham: Yeah. ThinkingNode Life Science, our mission is really

to build a global library of reasoning network for life

science. What does that mean? Today you have a lot of

knowledge and every day you have scientists all over

the world working very hard to make new discovery.

Once they have the discovery, it goes to a publication.

Then, at some point, it's end up into a very big

database where you accumulate all these different

knowledge. What we do is, we crunch all this

knowledge and generate a reasoning network that can

either solve the problem directly or help dramatically

the scientists to solve it.

Khai Pham: Because today, knowledge is static, human use the

knowledge to make the reasoning and to solve a

problem. In this case, we want to use the machine to

help human to use this knowledge, because human

can only process five to nine concepts at the same

time. How to make knowledge directly reasoning

capable, if I may say. The idea is to build a library

where we have different reasoning network for different

kind of domain of problem, in immunology, in

microbiome and so on. This is the goal of the

company, so then companies, researchers, can tap

into that like thinking as a service to get the

knowledge to solve the problem they need.

Kirill Eremenko: Okay. How are you going to apply data science or

machine learning to create this?

Khai Pham: Yeah. At the beginning we do not apply machine

learning to do that, because today machine learning

start from scratch. It just used data to build a system

that can make some prediction based on pattern

recognition. For me, it doesn't make sense. You have

to start by building first the reasoning network, the

reasoning model. It's like in medicine, you go first to

medical school to build your mental model, your

reasoning model about medicine. Once you have this

reasoning model, then you practice medicine and you

can improve your reasoning model through

observation, through the different data and so on. So

we build first the reasoning model, or reasoning

network, and then we use data to improve this

reasoning model.

Kirill Eremenko: What will this reasoning network be based on?

Khai Pham: It's called system reasoning. It's completely proprietary

technology, but it's based on existing AI technology, in

particular intelligent agents, but the main thing is,

system reasoning is designed to have a human-like

reasoning. This is important for me because, if you

have a system that human cannot understand, its

limit a number of application. The second thing is, it

doesn't have a logic by itself. In addition to that, it's a

framework that can host different logic in it, because I

don't really that one logic can solve a very complex

problem. It's like human, we are using several logic to

solve a problem. We don't have just one logic in our

mind.

Kirill Eremenko: Okay. Basically, you're going to be aggregating all of

these different papers-

Khai Pham: Knowledge.

Kirill Eremenko: ... knowledge about the life sciences and allowing

researchers to ... helping or facilitating how they

navigate this research and put it together and get

insights for their specific applications or products or

further research that they're doing?

Khai Pham: Yeah. Well, we're kind of mimicking the way the

scientists will use this knowledge. For example, if you

are in synthetic biology and you want to genetically

modify an organism to produce something, what you

do is, you have to decide which organisms you have to

choose and then what kind of genes you are going to

put into this organism, and then to think about, what

can be the consequence of doing that? This take a lot

of time and a lot of experience, it take years for

somebody to master a different organism. In this case,

the system digest all the different organism into the

system so it can do the combination for you directly.

Kirill Eremenko: Okay, Got you. It speeds up the process, that makes it

a bit clear. The whole example situation.

Khai Pham: Another way to see it, it's like Excel for thinking. What

I mean by that is, you can still do accounting on the

paper or you want to throw everything into Excel and

then you can play with it. Like I said, we can only

process five to nine concepts at the same time, so it's

very difficult for us to combine all the criteria.

Khai Pham: Or if you take an example with the doctor, when you

come to see a doctor, you say, "Okay, you know what

doctor, I have this symptom, this symptom, this

symptom, and I take this medication and so on. And I

have in my family ..." At some point, "Okay, wait a

minute," because it go beyond five-nine concept.

Khai Pham: Beside that, the doctor is going to say, "Oh, you tell me

you take this medication for that, but it doesn't make

sense. Are you sure it's about this medication?"

Because the doctor has the reasoning network in his

or her mind, so can check back the consistency, the

coherency, of all these different knowledge to make

sense of it.

Kirill Eremenko: Okay. It can be applied in medicine as well?

Khai Pham: It can be applied in any domain where you have

reasoning.

Kirill Eremenko: Oh, it's not just life sciences? It can be in other

domains as well?

Khai Pham: Yes. Yes, but we want to focus on life science today.

Yeah.

Kirill Eremenko: San Diego is a great place to be for life sciences.

There's a lot of biomedical industry there.

Khai Pham: Yeah. I mean, it's a reason why I moved at that time

from Silicon Valley to San Diego, to be closer to the life

science community. For me, the big difference between

the two places, Silicon Valley is more technology and

San Diego is more science, if I may say.

Kirill Eremenko: I got you. Very interesting. At this stage of your

business, of this new company, you mentioned you're

... at the fundraising stage, tell us a bit about that.

This is very interesting, how much are you looking to

raise? You mentioned you are not interested in the

traditional venture capitalist approach, with the exit.

Can you provide a few comments about that? I found

that quite a interesting approach to raising money.

Khai Pham: Yeah. The thing is, we can either decide to grow the

company progressively through our customers and so

on, is one way to do it, or we can have enough money

to directly develop the major reasoning network that

we believe would be useful for the whole community.

For example, the immune system reasoning network or

maybe start to scratch a little bit more about the

microbiome. For that, we wanted to have a good

funding to just focus on developing that directly,

instead of growing progressively. VC are fantastic

engine for start up and growing up, but as you know,

most of them have the four or five years constraint,

because themself has to show the return at that time.

Khai Pham: We are not interested to have investment where you

are looking for an exit in the next three years or four

years. We really want to partner with investors that

are first looking for impact. For me, money is the

consequence. If you are looking for the right impact,

money will be way more than what you think. Impact-

driven visionary people who can understand that the

20th century was about information, it's why you have

a search engine. The 21st century is about knowledge,

it's why you're going to have a reasoning engine and

we want to be a leader in that. So we are looking for a,

yeah, investor that can see how this can impact any

industry, because it's about problem solving.

Kirill Eremenko: Wow. That is very admirable, at the same time, when

you said that the 20th century has a search engine,

and the 21st century has, should have, or will have a

reasoning engine, everything came together. What you

were talking about before about creating this

knowledge or reasoning network. Basically, what

you're saying is that, you are effectively creating, or

your goal is to create a Google, but not one that just

searches through information, one that helps you

reason. Is that what you're creating?

Khai Pham: You just summarized that. Yeah.

Kirill Eremenko: That is so cool. That is something, and I can totally see

myself doing that. If I have a question, for instance,

right now I'd go on Google. I don't know, how to make

a vegan lasagna. Then I get all these recipes and I have

to go through all this information myself. If on the

other hand there was some sort of other engine that

was a reasoning engine and I put in that question, it

wouldn't just give me information, it would actually, I

guess, tailor some answer to me. It would say, "You

need to take these following steps," or, "Based on your

preferences, Kirill, and based on what you've told us

about yourself, this is what you're going to enjoy the

most. How many people are coming? This is what

you're going to need," and blah, blah, blah. Something

like that. In a very rough description, is that the

difference?

Khai Pham: Yeah, absolutely. The thing is, a lot of time people are

talking about, "AI is going to take job," right?

Kirill Eremenko: Mm-hmm (affirmative).

Khai Pham: And change, the answer is yes. However, the role of

human will be very different. For me, human, we are

not designed to work. We are very weak. Until now the

thinking, the reasoning, apathy is the main thing, but

machine start to get better and better. Each time that

humanity we build machine, it's end up always better

than us. What I mean is, in the future, human, we are

no more there to solve problem. We are there to ask

the right question.

Kirill Eremenko: Wow.

Khai Pham: This is going to be a big shift, because even in

education today, everything is designed based on the

good answer you give, but now who cares about the

good answer? You can already start to see that with

Google, Alexa and so on, but later on it's about

problem solving. What will be important is, what is the

question we ask to the system to solve that really

matter? This is how I envision the future, and by doing

so, we are going to democratize expertise, make it way

cheaper, because the biggest asset that humanity we

have is not knowledge, it's expertise. How to use it, but

it's extremely rare and expensive and not everybody

can benefit from that. The consequence of putting that

into the machine in a digital way, we can really share

and scale all this expertise to help way more people

and solve major problem with environment and so on.

This is the mission and the dream for the company.

Kirill Eremenko: What an amazing dream. I can totally get behind that.

Love the dream. How far away are we from this? How

far are we away from where machines are so good at

answering questions, that it's no longer an occupation

or even an advantage for a human to be able to answer

questions? It all boils down to ask, oh, sorry, yeah, it

all boils down to asking the questions rather than

answering. For now, humans are still better than

machines, in my view, at answering sophisticated

questions involving multiple domains. How far are we

away from machines becoming the go-to for the

answers to the questions?

Khai Pham: Yeah. It's not black and white. If we talk about a

situation where it becomes systematically the machine

does it better, yeah, a lot of people talk about the

singularity. However, the singularity for me, we will get

there not with the machine learning only, we need the

reasoning. This would be, yeah, would make sense at

that time. Now, the other thing is, people talk a lot

about narrow AI, general AI and super intelligence. I

believe that we don't need general AI to reach super

intelligence.

Khai Pham: What I mean by that is, who cares about a system that

know how to go to the restaurant and understand the

menu and so on? Maybe what machine is better is to

have a network of very high skill expertise, connected

all these different expertise together to solve extremely

complex problem. I think that, if we talk about a

general way to solve any kind of problem, yeah,

singularity makes sense, but already today we can

apply in a number of application to solve very complex

problem, which sometimes people call that narrow AI.

But what if we make a network of narrow AI?

Kirill Eremenko: Okay. Very interesting. Could you summarize, what's

the difference between general AI and super

intelligence?

Khai Pham: Yeah. Usually, when people talk about general AI is a

machine that can understand at the human level and

solve problem at the human level. Super intelligence is

way beyond human level. The thing is, like I said, I

don't-

Kirill Eremenko: We don't really need general AI to get through super

human level.

Khai Pham: Yes, for a number of domain and then, why don't we

just, for example, connect all these super intelligence

in medical, in biology, in aerospace, in environment, in

agriculture? We combine them together, and maybe

the system understand nothing about how to behave

in the restaurant, but to get-

Kirill Eremenko: Yeah, or how to go bowling or how to have a picnic, the

human things.

Khai Pham: Yeah. Maybe we don't care much.

Kirill Eremenko: Very interesting. Okay. The cliche question, are you

afraid that a system like that would take over the

world?

Khai Pham: I believe that it's going to change it and it's going to ...

For me, technology is part of nature. It's just nature

that found a faster way to accelerate evolution. We

used to think that evolution is based on biology, well,

now there is technology, because technology come

from us and we are part of nature. The way I see it is

that, at some point, we are going to have a branch like

between the apes and human. We're going to have a

branch between human and machine.

Khai Pham: Machine is going to have its own evolution, because it

will be able to build better and better machine by itself

and human, we will be free from solving either "stupid"

problem or even complex problem. We will be free from

that. We will be able to develop something that we

were not able to develop until now, because we were

busy with our brain to remember things or to solve

problem. Maybe we are going to be able to, like I said,

just spend our time to think about, what is the best

next question?

Kirill Eremenko: Okay. Don't you think that, if everybody is thinking,

what's the best next question, then a lot of people will

be bored or just have not much to do and become

restless in their minds?

Khai Pham: Yeah. I think that, in your life, there are a lot of things

that you realize that ... How many times people say,

"Oh, at the end of day of my life, my family was the

most important and I didn't spend enough time," and

so on and so on? I don't think that, because we

associate too much human with intelligence, but

intelligence is just part of us. We have a bunch of

other dimension that maybe we don't develop enough,

because our society is so demanding on us to solve

problem, and we don't have time to develop the other

part of the human part.

Khai Pham: The other thing is, when you spend time to think

about the question, of course, you don't do stupid

things. What I mean by that is, you think about it. You

are not doing things without thinking about the

consequence of it. I think it will allow human being to

be deeper, to be wiser and to have more time to

develop the human dimension, because a lot of time I

think we are not human yet. We are pre-human and

we just take the title of human when you see what's

going on in the world. Some behavior is difficult, I

mean, it's difficult to be compatible with the human

definition.

Kirill Eremenko: Like what, for example?

Khai Pham: Well, the lack of compassion amaze me, because I

think that it's one of the major feature of human

being. Without that, we would not exist, because at the

beginning we were so weak. We help each other to

grow and so on. Our society today is doing more and

more things to get us more isolated and compassion

agnostic. Compassion is something that, I think, is

very interesting to think about.

Kirill Eremenko: Yeah. I see what you mean. I was just going to say that

it feels like we've actually moved in that sense from

human back to pre-human. I think it hasn't been this

way always, but I think there's been compassion

before, as you said, for us to survive previously

without technology. Without all these bottom layers of

the Maslow's hierarchy of needs taken care of by

automation and economies of scale and things like

that. Before, we had to have compassion, but it feels

like, I agree with you, some of the things that we see

happening in the world demonstrate a severe lack of

compassion or some like the race going towards a lack

of compassion and that's a bit of a shame as well. It

looks like we're moving backwards in that sense.

Khai Pham: Yeah.

Kirill Eremenko: What you're saying is, by having technology or AI take

over further of the answering of the questions, we'll

have more time for compassion and more time to

spend with our loved ones and families and actually be

humans, not pre-humans.

Khai Pham: Well, I am a extreme optimistic person. It's only my

personal opinion. Yes, I think that at least machine is

going to help us to not spend our time for things that

are not worth. When you think about, what is the

probability for you to exist? It's ridiculous. We

apparently have one life and we are going to spend our

life to go in the morning to work and to come back

doing things that we don't even like it, that take time

from our family or the loved ones or doing ... I think,

yeah, machine can help us to be more human.

Kirill Eremenko: Yeah. Totally agree. Do you happen to know Naval

Ravikant, who's the founder of AngelList?

Khai Pham: No. No, I don't know.

Kirill Eremenko: I think he will be very cool for you guys. I don't know

him personally, but if you ever get a chance to meet

him, he's really cool. I was listening to a podcast

recently and he's got interesting views as well on

technology and how things are going to progress, but

he gave this quote, he just said, "A man has," or a

person, "has one life." There's a quote by Confucius,

which I heard Naval quote that, every man or woman,

has two lives and the second one begins when he or

she realizes that they have just the one life. It's pretty

cool quote, yeah?

Khai Pham: Interesting.

Kirill Eremenko: Yeah. I love that personally.

Khai Pham: Thank you for sharing that with me.

Kirill Eremenko: No problem. No problem. I was very deeply inspired by

that. Once you realize you have one life, your attitude

towards life changes, and your second life starts. It's

pretty cool, cool meaningful thing. Khai, you

mentioned at [inaudible 00:41:49] podcast, I think we

talked about this a bit before, singularity. What is

singularity and how does it relate to general AI and

super intelligence? Just quickly, what do you

understand under or what should we see under

singularity, under that term?

Khai Pham: Yeah. I guess there're different definition of it, but I

guess the most common is when machines start to be

better than us in term of solving problem and so on.

For me, it has a different meaning, because this way of

saying singularity is mainly technology view of it, but

for me, singularity is the moment that really nature

will be able to use technology to accelerate evolution,

as I said. Then, it's maybe the beginning of the branch

that I was talking about between machine and human.

Now, how it's connected with super intelligence and so

on, so yeah, usually sometime people, singularity and

super intelligence are synonym and people use to put

in term of chronology, narrow AI, general AI and super

intelligence.

Khai Pham: Like I said to you, I'm not sure we need general AI to

get to super intelligence. It depend what we put into

this term. The other thing too is, even though if we

follow the same logic, the soon as we reach the general

AI, we have the super intelligence. Why? Because just

the machine can process more than five to nine

concepts at the same time. What I mean is, let's

suppose that today you have a doctor, biologist or

finance or whatever, that has the capacity to tap into

all available knowledge in his or her domain and be

able to process thousand and thousand and thousand

of criteria at the same time, don't you think that this

person would be a super intelligent person? What I

mean is, the intelligence is not based on how much

knowledge you have, it's based on how much

knowledge you can combine.

Kirill Eremenko: I see. Interesting. Okay. Got you. That's the whole part

where you were talking about the reasoning. That's

what it is.

Khai Pham: Exactly. It's why, in my presentation, I always talk

about the lady or tiger just to show that it's about

combining knowledge that we solve problem, not just

how much knowledge we have. Today, the world is

looking for to have more and more knowledge, which is

great, and it's why machine learning is there. We have

more and more knowledge, but it's not enough. It's

about how much knowledge we can combine together.

Kirill Eremenko: Fantastic. Fantastic. I love how all this came together.

Khai, you mentioned in your presentations that you

talk about a certain thing, that is a great segue. I want

to give a quick very exciting news just for a second,

news for our listeners that you are coming to

DataScienceGO to present in 2019, that you were in

2018 as a guest and we got to catch up and hang out.

It was really cool, we went to that dinner, it was a

fantastic time, but now in 2019, you're coming back to

be a presenter at DataScienceGO. Very excited. If

anybody doesn't know yet, it's end of September this

year in San Diego. Tell us a bit about that, how do you

feel of coming to DataScienceGO to present and what

will you be talking about?

Khai Pham: First of all, thank you very much for having me at your

event. Like I said before the podcast, I mean, I really

appreciate what you guys are doing, because you

really try to motivate and make people aware about

everything around data science, but like I said, data

science is just the beginning, but it's so important that

people understand how crucial is that. The goal of my

talk, and it's not just for data scientists, which of

course, is important, but it's for general public as well,

is to make people see that, like I said, data science is

just the beginning. You have to see the bigger picture.

Khai Pham: You have to see why we do data science. We do data

science for two main things. One is to have more

knowledge, and two, is to build predictive system,

pattern recognition, but to go to the next step, it's

about reasoning and problem solving. The talk is

about how these two things interact to each other so

both of them can benefit from each other, because if

you only think about data, you're going to miss the big

picture. The talk is about, is to understand which

based on the application the problem you try to solve,

then you know if you need only about machine

learning or you need only about system reasoning or

you will need both.

Kirill Eremenko: Very cool. I'm looking forward to that already. How to

combine, especially after listening to this first part of

the podcast where we learned about reasoning, how to

combine that and how these two pillars of data

science, more knowledge and building predictive

systems, how they can be combined, and reasoning,

what role reasoning plays in all that. Super excited

and I hope those of you who are listening and are

coming to DataScienceGO, are super pumped about

Khai's talk as well. I think you're going to have a whole

crowd of people attending your talk, Khai, very

pumped.

Kirill Eremenko: At this stage, I wanted to switch gears a little bit and

talk about something else that you're doing, which I

find very inspiring and very admirable. You are a

mentor. You are a part of this, I think is a network

called Connect or is it a group? I'd love for you to tell

us a bit more about that, but basically, you spend time

giving back to the community of entrepreneurs, things

that you have learned in your entrepreneurial journey.

Tell us a bit about that. Why do you do it and what are

some interesting highlights from there?

Khai Pham: Yeah. First of all, unfortunately, I have to slow that

down, because the company is in a very active mode

right now, so I had to stop for now. But the idea, as

you said, I mean, I learn a lot from, when I started I

really knew nothing about business. I even never

heard about business plan. A lot of people helped me

and give me advice, but advice, it's important you take

the advice that are positive advice, don't take advice

from experts that are telling you, "No, this you cannot.

No, this cannot." Only take the one that say you,

"Okay. Yeah, this you can." What I mean is, it was

helping me a lot.

Khai Pham: It's important for me to give it back and to see if I can

help some younger entrepreneur to go to the right

direction faster than have to experience things.

Connect is a very interesting organization. They've

been there for 30 years. The people over there are

fantastic. I have, actually today, a lot of people from

Connect working and ThinkingNode Life Science,

because as you know Connect now merge with SDVG

is another amazing organization for startup

community in San Diego. Sometime you just need to

ask the right question to help the entrepreneur to

realize something, and these can have some impact in

the way they see their business.

Kirill Eremenko: Helping somebody like mentoring or coaching is not

even about being smarter, it's about having a different

perspective, isn't it? It's like you see things from a

different way than they do and that might help them

open up their mind or see something new in their own

thinking or in their own product or process.

Khai Pham: Well, I think it's not just about throwing out there your

experience, because each of us, we have unique

experience and it's very important to take that into

account in term of context. I think the first thing is,

it's about really to understand the entrepreneur,

because each entrepreneur is different with the

personality, with the ambition, with the reason and so

on. So to help, first of all, the entrepreneur to ask the

right question, again, in this case.

Khai Pham: The second thing is to then try to put yourself into

their shoes and see, with the experience you have,

what would you do? It's not just about throwing to

them all your experience and that's it, it's more about

understanding who they are, in what situation they

are, and then try to think, "Okay, if I'm in your shoes,

this is what I would do, because of this and because of

that. It doesn't mean that it's the right way. It just

mean, based on my experience, this is what I would

do. Just think about it."

Kirill Eremenko: Yeah, yeah. No, I totally understand. You mentioned

you have a lot of people or quite a few people from

Connect working with you now. Are you at the moment

hiring for any more positions?

Khai Pham: Yeah, sure. We are hiring, even though we are in the

fundraising times, but what is important for me is to

know people. What I mean by that is, hiring is so

important. Having the right skill is one thing, but

having the right mindset is another thing. For

example, for me, human, we went to the moon, not

because of the technology, but because of the mindset.

Because at the time that Kennedy say, "Okay, we go to

the moon," we didn't have any idea how to get there.

Khai Pham: So, yes, we are looking forward to meet people, to

know these people, so when we get the full funding

then we can have the whole team together right away.

We start already the interview and meeting people. We

are looking for people who are really open minded,

people that are not afraid of trying something that they

don't know. You were talking about the quote of

Confucius. I have a quote that I really like from

Picasso. It say something like this, "I like to do things

that I don't know, so I have a chance to learn."

Kirill Eremenko: Very nice.

Khai Pham: "I have a chance to learn how to do it." Yeah, it's a

mindset that we are looking for, because what we're

doing, what we try to achieve, is ambitious, which

means that a lot of time, we are going to realize we are

wrong and we have to change it. It's not a problem. We

do it again and again and again. So persistent people,

of course, brilliant people with the lowest ego, if we

can, yeah. Yeah.

Kirill Eremenko: Yeah. Got you. A timeless approach. Persistent,

talented people with lowest ego. What are your

comments on, you've dealt with a lot of entrepreneurs.

You were and are an entrepreneur yourself. Any advice

for listeners who are into data science, who are data

scientists, and are considering maybe becoming

entrepreneurs? Does being a data scientist give you an

advantage at being an entrepreneur? What areas is

data science best positioned to disrupt in the coming

years?

Khai Pham: Interesting question. I think that the short answer is,

yes, it helps to be data scientist, not just because it's

about data science, but because, when you're a data

scientist, you have a certain way of thinking, which

means, okay, what do I have as a data? And based on

that, what can I deduce from there? If it's not right,

how I can improve it? It's a way of thinking that will

help you to build your company, because company, of

course, it's about ... You have different kind of

company. People always talk about in marketing the

red ocean or the blue ocean.

Khai Pham: The red ocean is where you try to do 10% better than

your competition and the blue ocean, when you create

a totally new market. Of course, it depends on your

personality, what you want to do, but still you need to

gather data, you need to then analyze them and think

about it and so on. Now, related to data science itself,

of course, today it's a very important skill. However,

it's important that people see that very rapidly a

number of tests that data scientists are doing will be

automated with more and more software, making it

easier and easier. So your value is not just about doing

data science, it's about thinking with data science. I

don't know if it makes sense what I'm saying.

Kirill Eremenko: Mm-hmm (affirmative).

Khai Pham: I try to say that, think about how to apply data

science, what is the consequence of applying it and

how you can apply it. Do you have enough data? What

kind of data, and so on. Does the competition can have

this data or not? The technique, as any technique,

evolve and become easier and easier, it will know more

the barrier of entry to entry. So don't take data science

just as an asset by itself, but use it as the way of

thinking and think about your business through it.

Kirill Eremenko: Very wise words. Couldn't agree with you more on

that. Data science, not just an asset. It's going to get

easier to do, therefore, it's going to become more

democratized.

Khai Pham: Yes.

Kirill Eremenko: Use the thinking approaches that you've developed,

the type of mindset, like you said, success is about

mindset as much as it is about mechanics. In fact,

Tony Robbins says that success is 80% psychology,

20% mechanics. It's all in your head, but having this

background in data science is a huge advantage,

specifically in terms of mindset, not just the doing.

Khai Pham: Well, no, absolutely. I would be even more extreme.

For me, everything is about mindset.

Kirill Eremenko: Totally, totally agree. Well, Khai, I just looked at the

clock. I cannot believe how fast this hour has gone by.

I feel like we're just getting started. We could keep

talking for at least another few hours about all of this,

but we need to wrap up.

Khai Pham: Sure.

Kirill Eremenko: We've approached the hour mark. I wanted, before I let

you go, please tell us, how can listeners find you and

follow you or learn more and get more of these

amazing knowledge bombs that you shared today on

the podcast?

Khai Pham: Well, first of all, I am on LinkedIn, so it's easy. Just

contact me there, and maybe putting like it's come

from the podcasts of DataScienceGO. Then I will

understand the context of it, because I try not to take

contact of people that I have no idea. They try to, just

marketing or something, but if the people mention that

it's from DataScienceGO, then it would be different. I

think this is the best way to contact me. Otherwise,

yeah, we have the website ThinkingNode Life

Science.ai, and you can find via email over there.

Kirill Eremenko: Fantastic. Of course, people can come and find you in

person at DataScienceGO in the 28th September of

this year.

Khai Pham: Sure.

Kirill Eremenko: I think that'd be really cool encounter. We'll share all

these links and URLs in the show notes for this

episode. One final question, Khai, for today. What's a

book that you can recommend to our listeners that

can impact their careers or their lives? Something that

you found useful for yourself.

Khai Pham: Yeah, this is a tough question and we talked about

that before the podcast. But I was thinking, there is a

recent book that can be interesting to start to think

about reasoning, is called, The Book Of Why, from

Judea Pearl. It's really explain very well the difference

between machine learning, reasoning, where you go

and so on. I would recommend this book.

Kirill Eremenko: Got you. Could you repeat the name please, again?

Khai Pham: The Book Of Why.

Kirill Eremenko: The Book Of Why, got you.

Khai Pham: From to Judea Pearl.

Kirill Eremenko: The Book of-

Khai Pham: Pearl, P-E-A-R-L, and Judea is J-U-D-E-A.

Kirill Eremenko: Thank you. The Book Of Why.

Khai Pham: Yes.

Kirill Eremenko: Well, on that note, it's thank you so much, Khai, for

joining me today for this chat and sharing these

amazing insights and philosophical things for people

to think on, and best of luck with your project. This

town's extremely exciting. The reasoning engine and if

that's going to be the new Google then that is going to

be so epic and is going to make so many lives easier

and more fun and can get some equal answers. Thank

you so much.

Khai Pham: Thank you very much, Kirill, for having me.

Kirill Eremenko: Thank you, dear friends, for tuning into the

SuperDataScience podcast and joining me and Khai

for this episode. What an amazing person Khai is and

what a fantastic conversation. All these insights that

he shared with us today. I am super pumped and

super humbled to have been part of this and to learn

these things. This whole idea about reasoning engines

and creating reasoning versus being just simply data-

driven or machine learning-driven. That is a brand

new idea, and you can see that it takes somebody who

really thinks about philosophy, who really considers

the future, has visions, has ideas, it really takes a

person like that to come up with something as

complex, and it takes a lot of courage to jump into

that, create a company around that and push the

world in that direction. Push the frontiers of

technology into the space of reasoning.

Kirill Eremenko: I really appreciated what Khai said about questions

versus answers. It'd be interesting to see if indeed

that's where the world will end up. It sounds like a

very exciting place to be in. On that note, you can get

all of the show notes for this episode at

www.superdatascience.com/277. As I mentioned on

the podcast, Khai will be joining us for DataScienceGO

2019, which is on the 27th, 28th and 29th of

September this year, in San Diego. So if you haven't

gotten your tickets yet, make sure to go get them

www.datasciencego.com. That's datasciencego.com, get

your tickets today while they're still on special

promotion, and you can meet Khai and many other

speakers and entrepreneurs and influencers and fellow

data scientists in person. We're looking forward to

hosting from 600 to 800 data scientists this year.

Can't wait to see you there and network with you

personally. Once again, that's datasciencego.com, and

I'll see you there.