SDS 277: The New Age of Reason

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 advancement. We have three days of immersive talks, panels and training sessions designed to teach, inspire, and guide you. There are three separate career tracks involved, so whether you’re a beginner, a practitioner or a manager you can find a career track for you and select the right talks to advance your career.

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 the speakers that we had in the previous years: 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.

Kirill Eremenko: This year we will have over 800 attendees from beginners to data scientists to managers and leaders. So there will be plenty of networking opportunities with our attendees and speakers, and you don’t want to miss out on that. That’s the best way to grow your data science network and grow your career. And as a bonus there will be a track for executives. So if you’re an executive listening to this, check this out. Last year at DataScienceGO X, which is our special track for executives, we had key business decision makers from Ellie Mae, Levi Strauss, Dell, Red Bull, and more.

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.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.