In this episode, Byron and Deep talk about the nervous system, AGI, the Turing Test, Watson, Alexa, security, and privacy.
Deep manages data engineering functions across the Trulia business. This includes the vital acquisition of listings and public records, the consumer search experience and API, email/push, efforts to enhance personalization, industry leading location services such as geo coding, as well as data science, data warehouse, and reporting.
Byron Reese: This is Voices in AI, brought to you by Gigaom. I’m Byron Reese. Today our guest is Deep Varma, he is the VP of Data Engineering and Science over at Trulia. He holds a Bachelor’s of Science in Computer Science. He has a Master’s degree in Management Information Systems, and he even has an MBA from Berkeley to top all of that off. Welcome to the show, Deep.
Deep Varma: Thank you. Thanks, Byron, for having me here.
I’d like to start with my Rorschach test question, which is, what is artificial intelligence?
Awesome. Yeah, so as I define artificial intelligence, this is an intelligence created by machines based on human wisdom, to augment a human’s lifestyle to help them make the smarter choices. So that’s how I define artificial intelligence in a very simple and the layman terms.
But you just kind of used the word, “smart” and “intelligent” in the definition. What actually is intelligence?
Yeah, I think the intelligence part, what we need to understand is, when you think about human beings, most of the time, they are making decisions, they are making choices. And AI, artificially, is helping us to make smarter choices and decisions.
A very clear-cut example, which sometimes what we don’t see, is, I still remember in the old days I used to have this conventional thermostat at my home, which turns on and off manually. Then, suddenly, here comes artificial intelligence, which gave us Nest. Now as soon as I put the Nest there, it’s an intelligence. It is sensing that someone is there in the home, or not, so there’s motion sensing. Then it is seeing what kind of temperature do I like during summer time, during winter time. And so, artificially, the software, which is the brain that we have put on this device, is doing this intelligence, and saying, “great, this is what I’m going to do.” So, in one way it augmented my lifestyle—rather than me making those decisions, it is helping me make the smart choices. So, that’s what I meant by this intelligence piece here.
Well, let me take a different tack, in what sense is it artificial? Is that Nest thermostat, is it actually intelligent, or is it just mimicking intelligence, or are those the same thing?
What we are doing is, we are putting some sensors there on those devices—think about the central nervous system, what human beings have, it is a small piece of a software which is embedded within that device, which is making decisions for you—so it is trying to mimic, it is trying to make some predictions based on some of the data it is collecting. So, in one way, if you step back, that’s what human beings are doing on a day-to-day basis. There is a piece of it where you can go with a hybrid approach. It is mimicking as well as trying to learn, also.
Do you think we learn a lot about artificial intelligence by studying how humans learn things? Is that the first step when you want to do computer vision or translation, do you start by saying, “Ok, how do I do it?” Or, do you start by saying, “Forget how a human does it, what would be the way a machine would do it?”
Yes, I think it is very tough to compare the two entities, because the way human brains, or the central nervous system, the speed that they process the data, machines are still not there at the same pace. So, I think the difference here is, when I grew up my parents started telling me, “Hey, this is Taj Mahal. The sky is blue,” and I started taking this data, and I started inferring and then I started passing this information to others.
It’s the same way with machines, the only difference here is that we are feeding information to machines. We are saying, “Computer vision: here is a photograph of a cat, here is a photograph of a cat, too,” and we keep on feeding this information—the same way we are feeding information to our brains—so the machines get trained. Then, over a period of time, when we show another image of a cat, we don’t need to say, “This is a cat, Machine.” The machine will say, “Oh, I found out that this is a cat.”
So, I think this is the difference between a machine and a human being, where, in the case of machine, we are feeding the information to them, in one form or another, using devices; but in the case of human beings, you have conscious learning, you have the physical aspects around you that affect how you’re learning. So that’s, I think, where we are with artificial intelligence, which is still in the infancy stage.
Humans are really good at transfer learning, right, like I can show you a picture of a miniature version of the Statue of Liberty, and then I can show you a bunch of photos and you can tell when it’s upside down, or half in water, or obscured by light and all that. We do that really well.
How close are we to being able to feed computers a bunch of photos of cats, and the computer nails the cat thing, but then we only feed it three or four images of mice, and it takes all that stuff it knows about different cats, and it is able to figure out all about different mice?
So, is your question, do we think these machines are going to be at the same level as human beings at doing this?
No, I guess the question is, if we have to teach, “Here’s a cat, here’s a thimble, here’s ten thousand thimbles, here’s a pin cushion, here’s ten thousand more pin cushions…” If we have to do one thing at a time, we’re never going to get there. What we’ve got to do is, like, learn how to abstract up a level, and say, “Here’s a manatee,” and it should be able to spot a manatee in any situation.
Yeah, and I think this is where we start moving into the general intelligence area. This is where it is becoming a little interesting and challenging, because human beings falls under more of the general intelligence, and machines are still falling under the artificial intelligence framework.
And the example you were giving, I have two boys, and when my boys were young, I’d tell them, “Hey, this is milk,” and I’d show them milk two times and they knew, “Awesome, this is milk.” And here come the machines, and you keep feeding them the big data with the hope that they will learn and they will say, “This is basically a picture of a mouse or this is a picture of a cat.”
This is where, I think, this artificial general intelligence which is shaping up—that we are going to abstract a level up, and start conditioning—but I feel we haven’t cracked the code for one level down yet. So, I think it’s going to take us time to get to the next level, I believe, at this time.
Believe me, I understand that. It’s funny, when you chat with people who spend their days working on these problems, they’re worried about, “How am I going to solve this problem I have tomorrow?” They’re not as concerned about that. That being said, everybody kind of likes to think about an AGI.
AI is, what, six decades old and we’ve been making progress, do you believe that that is something that is going to evolve into an AGI? Like, we’re on that path already, and we’re just one percent of the way there? Or, is an AGI is something completely different? It’s not just a better narrow AI, it’s not just a bunch of narrow AI’s bolted together, it’s a completely different thing. What do you say?
Yes, so what I will say, it is like in the software development of computer systems—we call this as an object, and then we do inheritance of a couple of objects, and the encapsulation of the objects. When you think about what is happening in artificial intelligence, there are companies, like Trulia, who are investing in building the computer vision for real estate. There are companies investing in building the computer vision for cars, and all those things. We are in this state where all these dysfunctional, disassociated investments in our system are happening, and there are pieces that are going to come out of that which will go towards AGI.
Where I tend to disagree, I believe AI is complimenting us and AGI is replicating us. And this is where I tend to believe that the day the AGI comes—that means it’s a singularity that they are reaching wisdom or the processing power of human beings—that, to me, seems like doomsday, right? Because that those machines are going to be smarter than us, and they will control us.
And the reason I believe that, and there is a scientific reason for my belief; it’s because we know that in the central nervous system the core tool is the neurons, and we know neurons carry two signals—chemical and electrical. Machines can carry the electrical signals, but the chemical signals are the ones which generate these sensory signals—you touch something, you feel it. And this is where I tend to believe that AGI is not going to happen, I’m close to confident. Thinking machines are going to come—IBM Watson, as an example—so that’s how I’m differentiating it at this time.
So, to be clear, you said you don’t believe we’ll ever make an AGI?
I will be the one on the extreme end, but I will say yes.
That’s fascinating. Why is that? The normal argument is a reductionist argument. It says, you are some number of trillions of cells that come together, and there’s an emergent “you” that comes out of that. And, hypothetically, if we made a synthetic copy of every one of those cells, and connected them, and did all that, there would be another Deep Varma. So where do you think the flaw in that logic is?
I think the flaw in that logic is that the general intelligence that humans have is also driven by the emotional side, and the emotional side—basically, I call it a chemical soup—is, I feel, the part of the DNA which is not going to be possible to replicate in these machines. These machines will learn by themselves—we recently saw what happened with Facebook, where Facebook machines were talking to each other and they start inventing their own language, over a period of time—but I believe the chemical mix of humans is what is next to impossible to produce it.
I mean—and I don’t want to take a stand because we have seen proven, over the decades, what people used to believe in the seventies has been proven to be right—I think the day we are able to find the chemical soup, it means we have found the Nirvana; and we have found out how human beings have been born and how they have been built over a period of time, and it took us, we all know, millions and millions of years to come to this stage. So that’s the part which is putting me on the other extreme end, to say, “Is there really going to another Deep Varma,” and if yes, then where is this emotional aspect, where are those things that are going to fit into the bigger picture which drives human beings onto the next level?
Well, I mean there’s a hundred questions rushing for the door right now. I’ll start with the first one. What do you think is the limit of what we’ll be able to do without the chemical part? So, for instance, let me ask a straight forward question—will we be able to build a machine that passes the Turing test?
Can we build that machine? I think, potentially, yes, we can.
So, you can carry on a conversation with it, and not be able to figure out that it’s a machine? So, in that case, it’s artificial intelligence in the sense that it really is artificial. It’s just running a program, saying some words, it’s running a program, saying some words, but there’s nobody home.
Yes, we have IBM Watson, which can go a level up as compared to Alexa. I think we will build machines which, behind the scenes, are trying to understand your intent and trying to have those conversations—like Alexa and Siri. And I believe they are going to eventually start becoming more like your virtual assistants, helping you make decisions, and complimenting you to make your lifestyle better. I think that’s definitely the direction we’re going to keep seeing investments going on.
I read a paper of yours where you made a passing reference to Westworld.
Putting aside the last several episodes, and what happened in them—I won’t give any spoilers—take just the first episode, do you think that we will be able to build machines that can interact with people like that?
I think, yes, we will.
But they won’t be truly creative and intelligent like we are?
So, there seem to be these two very different camps about artificial intelligence. You have Elon Musk who says it’s an existential threat, you have Bill Gates who’s worried about it, you have Stephen Hawking who’s worried about it, and then there’s this other group of people that think that’s distracting.
I saw that Elon Musk spoke at the governor’s convention and said something and then Pedro Domingos, who wrote The Master Algorithm, retweeted that article, and his whole tweet was, “One word: sigh.” So, there’s this whole other group of people that think that’s just really distracting, really not going to happen, and they’re really put off by that kind of talk.
Why do you think there’s such a gap between those two groups of people?
The gap is that there is one camp who is very curious, and they believe that millions of years of how human beings evolved can immediately be taken by AGI, and the other camp is more concerned with controlling that, asking are those machines going to become smarter than us, are they going to control us, are we going to become their slaves?
And I think those two camps are the extremes. There is a fear of losing control, because humans—if you look into the food chain, human beings are the only ones in the food chain, as of now, who control everything—fear that if those machines get to our level of wisdom, or smarter than us, we are going to lose control. And that’s where I think those two camps are basically coming to the extreme ends and taking their stands.
Let’s switch gears a little bit. Aside from the robot uprising, there’s a lot of fear wrapped up in the kind of AI we already know how to build, and it’s related to automation. Just to set up the question for the listener, there’s generally three camps. One camp says we’re going to have all this narrow AI, and it’s going to put a bunch of people out of work, people with less skills, and they’re not going to be able to get new work and we’re going to have, kind of, the Great Depression going on forever. Then there’s a second group that says, no, no, it’s worse than that, computers can do anything a person can do, we’re all going to be replaced. And then there’s a third camp that says, that’s ridiculous, every time something comes along, like steam or electricity, people just take that technology, and use it to increase their own productivity, and that’s how progress happens. So, which of those three camps, or fourth one, perhaps, do you believe?
I fall into, mostly, the last camp, which is, we are going to increase the productivity of human beings; it means we will be able to deliver more and faster. A few months back, I was in Berkeley and we were having discussions around this same topic, about automation and how jobs are going to go away. The Obama administration even published a paper around this topic. One example which always comes in my mind is, last year I did a remodel of my house. And when I did the remodeling there were electrical wires, there are these water pipelines going inside my house and we had to replace them with copper pipelines, and I was thinking, can machines replace those job? I keep coming back to the answer that, those skill level jobs are going to be tougher and tougher to replace, but there are going to be productivity gains. Machines can help to cut those pipeline pieces much faster and in a much more accurate way. They can measure how much wire you’ll need to replace those things. So, I think those things are going to help us to make the smarter choices. I continue to believe it is going to be mostly the third camp, where machines will keep complementing us, helping to improve our lifestyles and to improve our productivity to make the smarter choices.
So, you would say that there are, in most jobs, there are elements that automation cannot replace, but it can augment, like a plumber, or so forth. What would you say to somebody who’s worried that they’re going to be unemployable in the future? What would you advise them to do?
Yeah, and the example I gave is a physical job, but think about an example of a business consultants, right? Companies hire business consultants to come, collect all the data, then prepare PowerPoints on what you should do, and what you should not do. I think those are the areas where artificial intelligence is going to come, and if you have tons of the data, then you don’t need a hundred consultants. For those people, I say go and start learning about what can be done to scale them to the next level. So, in the example I’ve just given, the business consultants, if they are doing an audit of a company with the financial books, look into the tools to help so that an audit that used to take thirty days now takes ten days. Improve how fast and how accurate you can make those predictions and assumptions using machines, so that those businesses can move on. So, I would tell them to start looking into, and partnering into, those areas early on, so that you are not caught by surprise when one day some industry comes and disrupts you, and you say, “Ouch, I never thought about it, and my job is no longer there.”
It sounds like you’re saying, figure out how to use more technology? That’s your best defense against it, is you just start using it to increase your own productivity.
Yeah, it’s interesting, because machine translation is getting comparable to a human, and yet generally people are bullish that we’re going to need more translators, because this is going to cause people to want to do more deals, and then they’re going to need to have contracts negotiated, and know about customs in other countries and all of that, so that actually being a translator you get more business out of this, not less, so do you think things like that are kind of the road map forward?
Yeah, that’s true.
So, what are some challenges with the technology? In Europe, there’s a movement—I think it’s already adopted in some places, but the EU is considering it—this idea that if an AI makes a decision about you, like do you get the loan, that you have the right to know why it made it. In other words, no black boxes. You have to have transparency and say it was made for this reason. Do you think a) that’s possible, and b) do you think it’s a good policy?
Yes, I definitely believe it’s possible, and it’s a good policy, because this is what consumers wants to know, right? In our real estate industry, if I’m trying to refinance my home, the appraiser is going to come, he will look into it, he will sit with me, then he will send me, “Deep, your house is worth $1.5 million dollar.” He will provide me the data that he used to come to that decision—he used the neighborhood information, he used the recent sold data.
And that, at the end of the day, gives confidence back to the consumer, and also it shows that this is not because this appraiser who came to my home didn’t like me for XYZ reason, and he end up giving me something wrong; so, I completely agree that we need to be transparent. We need to share why a decision has been made, and at the same time we should allow people to come and understand it better, and make those decisions better. So, I think those guidelines need to be put into place, because humans tend to be much more biased in their decision-making process, and the machines take the bias out, and bring more unbiased decision making.
Right, I guess the other side of that coin, though, is that you take a world of information about who defaulted on their loan, and then you take you every bit of information about, who paid their loan off, and you just pour it all in into some gigantic database, and then you mine it and you try to figure out, “How could I have spotted these people who didn’t pay their loan?” And then you come up with some conclusion that may or may not make any sense to a human, right? Isn’t that the case that it’s weighing hundreds of factors with various weights and, how do you tease out, “Oh it was this”? Life isn’t quite that simple, is it?
No, it is not, and demystifying this whole black box has never been simple. Trust us, we face those challenges in the real estate industry on a day-to-day basis—we have Trulia’s estimates—and it’s not easy. At the end, we just can’t rely totally on those algorithms to make the decisions for us.
I will give one simple example, of how this can go wrong. When we were training our computer vision system, and, you know, what we were doing was saying, “This is a window, this is a window.” Then the day came when we said, “Wow, our computer vision can say I will look at any image, and known this is a window.” And one fine day we got an image where there is a mirror, and there is a reflection of a window on the mirror, and our computer said, “Oh, Deep, this is a window.” So, this is where big data and small data come into a place, where small data can make all these predictions and goes wrong completely.
This is where—when you’re talking about all this data we are taking in to see who’s on default and who’s not on default—I think we need to abstract, and we need to at least make sure that with this aggregated data, this computational data, we know what the reference points are for them, what the references are that we’re checking, and make sure that we have the right checks and balances so that machines are not ultimately making all the calls for us.
You’re a positive guy. You’re like, “We’re not going to build an AGI, it’s not going to take over the world, people are going to be able to use narrow AI to grow their productivity, we’re not going to have unemployment.” So, what are some of the pitfalls, challenges, or potential problems with the technology?
I agree with you, it’s being positive. Realistically, looking into the data—and I’m not saying that I have the best data in front of me—I think what is the most important is we need to look into history, and we need to see how we evolved, and then the Internet came and what happened.
The challenge for us is going to be that there are businesses and groups who believe that artificial intelligence is something that they don’t have to worry about, and over a period of time artificial intelligence is going to start becoming more and more a part of business, and those who are not able to catch up with this, they’re going to see the unemployment rate increase. They’re going to see company losses increase because some of the decisions they’re not making in the right way.
You’re going to see companies, like Lehman Brothers, who are making all these data decisions for their clients by not using machines but relying on humans, and these big companies fail because of them. So, I think, that’s an area where we are going to see problems, and bankruptcies, and unemployment increases, because of they think that artificial intelligence is not for them or their business, that it’s never going to impact them—this is where I think we are going to get the most trouble.
The second area of trouble is going to be security and privacy, because all this data is now floating around us. We use the Internet. I use my credit card. Every month we hear about a new hack—Target being hacked, Citibank being hacked—all this data physically-stored in the system and it’s getting hacked. And now we’ll have all this data wirelessly transmitting, machines talking to each of their devices, IoT devices talking to each other—how are you we going to make sure that there is not a security threat? How are we going to make sure that no one is storing my data, and trying to make assumptions, and enter into my bank account? Those are the two areas where I feel we are going to see, in coming years, more and more challenges.
So, you said privacy and security are the two areas?
Denial of accepting AI is the one, and security and privacy is the second one—those are the two areas.
So, in the first one, are there any industries that don’t need to worry about it, or are you saying, “No, if you make bubble-gum you had better start using AI?”
I will say every industry. I think every industry needs to worry about it. Some industries may adapt the technologies faster, some may go slower, but I’m pretty confident that the shift is going to happen so fast that, those businesses will be blindsided—be it small businesses or mom and pop shops or big corporations, it’s going to touch everything.
Well with regard to security, if the threat is artificial intelligence, I guess it stands to reason that the remedy is AI as well, is that true?
The remedy is there, yes. We are seeing so many companies coming and saying, “Hey, we can help you see the DNS attacks. When you have hackers trying to attack your site, use our technology to predict that this IP address or this user agent is wrong.” And we see that to tackle the remedy, we are building an artificial intelligence.
But, this is where I think the battle between big data and small data is colliding, and companies are still struggling. Like, phishing, which is a big problem. There are so many companies who are trying to solve the phishing problem of the emails, but we have seen technologies not able to solve it. So, I think AI is a remedy, but if we stay just focused on the big data, that’s, I think, completely wrong, because my fear is, a small data set can completely destroy the predictions built by a big data set, and this is where those security threats can bring more of an issue to us.
Explain that last bit again, the small data set can destroy…?
So, I gave the example of computer vision, right? There was research we did in Berkeley where we trained machines to look at pictures of cats, and then suddenly we saw the computer start predicting, “Oh, this is this kind of a cat, this is cat one, cat two, this is a cat with white fur.” Then we took just one image where we put the overlay of a dog on the body of a cat, and the machines ended up predicting, “That’s a dog,” not seeing that it’s the body of a cat. So, all the big data that we used to train our computer vision, just collapsed with one photo of a dog. And this is where I feel that if we are emphasizing so much on using the big data set, big data set, big data set, are there smaller data sets which we also need to worry about to make sure that we are bridging the gap enough to making sure that our securities are not compromised?
Do you think that the system as a whole is brittle? Like, could there be an attack of such magnitude that it impacts the whole digital ecosystem, or are you worried more about, this company gets hacked and then that one gets hacked and they’re nuisances, but at least we can survive them?
No, I’m more worried about the holistic view. We saw recently, how those attacks on the UK hospital systems happened. We saw some attacks—which we are not talking about—on our power stations. I’m more concerned about those. Is there going to be a day when we have built massive infrastructures that are reliant on computers—our generation of power and the supply of power and telecommunications—and suddenly there is a whole outage which can take the world to a standstill, because there is a small hole which we never thought about. That, to me, is the bigger threat than the stand alone individual things which are happening now.
That’s a hard problem to solve, there’s a small hole on the internet that we’ve not thought about that can bring the whole thing down, that would be a tricky thing to find, wouldn’t it?
It is a tricky thing, and I think that’s what I’m trying to say, that most of the time we fail because of those smaller things. If I go back, Byron, and bring the artificial general intelligence back into a picture, as human beings it’s those small, small decisions we make—like, I make a fast decision when an animal is approaching very close to me, so close that my senses and my emotions are telling me I’m going to die—and this is where I think sometimes we tend to ignore those small data sets.
I was in a big debate around those self-driven cars which are shaping up around us, and people were asking me when will we see those self-driven cars on a San Francisco street. And I said, “I see people doing crazy jaywalking every day,” and accidents are happening with human drivers, no doubt, but the scale can increase so fast if those machines fail. If they have one simple sensor which is not working at that moment in time and not able to get one signal, it can kill human beings much faster as compared to what human beings are killing, so that’s the rational which I’m trying to put here.
So, one of my questions that I was going to ask you, is, do you think AI is a mania? Like it’s everywhere but it seems like, you’re a person who says every industry needs to adopt it, so if anything, you would say that we need more focus on it, not less, is that true?
There was a man in the ‘60s named Weizenbaum who made a program called ELIZA, which was a simple program that you would ask a question, say something like, “I’m having a bad day,” and then it would say, “Why are you having a bad day?” And then you would say, “I’m having a bad day because I had a fight with my spouse,” and then would ask, “Why did you have a fight?” And so, it’s really simple, but Weizenbaum got really concerned because he saw people pouring out their heart to it, even though they knew it was a program. It really disturbed him that people developed emotional attachment to ELIZA, and he said that when a computer says, “I understand,” that it’s a lie, that there’s no “I,” there’s nothing that understands anything.
Do you worry that if we build machines that can imitate human emotions, maybe the care for people or whatever, that we will end up having an emotional attachment to them, or that that is in some way unhealthy?
You know, Byron, it’s a very great question. I think, also pick out a great example. So, I have Alexa at my home, right, and I have two boys, and when we are in a kitchen—because Alexa is in our kitchen—my older son comes home and says, “Alexa, what’s the temperature look like today?” Alexa says, “Temperature is this,” and then he says, “Okay, shut up,” to Alexa. My wife is standing there saying “Hey, don’t be rude, just say, ‘Alexa stop.’” You see that connection? The connection is you’ve already started treating this machine as a respectful device, right?
I think, yes, there is that emotional connection there, and that’s getting you used to seeing it as part of your life in an emotional connection. So, I think, yes, you’re right, that’s a danger.
But, more than Alexa and all those devices, I’m more concerned about the social media sites, which can have much more impact on our society than those devices. Because those devices are still physical in shape, and we know that if the Internet is down, then they’re not talking and all those things. I’m more concerned about these virtual things where people are getting more emotionally attached, “Oh, let me go and check what my friends been doing today, what movie they watched,” and how they’re trying to fill that emotional gap, but not meeting individuals, just seeing the photos to make them happy. But, yes, just to answer your question, I’m concerned about that emotional connection with the devices.
You know, it’s interesting, I know somebody who lives on a farm and he has young children, and, of course, he’s raising animals to slaughter, and he says the rule is you just never name them, because if you name them then that’s it, they become a pet. And, of course, Amazon chose to name Alexa, and give it a human voice; and that had to be a deliberate decision. And you just wonder, kind of, what all went into it. Interestingly, Google did not name theirs, it’s just the Google Assistant.
How do you think that’s going to shake out? Are we just provincial, and the next generation isn’t going to think anything of it? What do you think will happen?
So, is your question what’s going to happen with all those devices and with all those AI’s and all those things?
As of now, those devices are all just operating in their own silo. There are too many silos happening. Like in my home, I have Alexa, I have a Nest, those plug-ins. I love, you know, where Alexa is talking to Nest, “Hey Nest, turn it off, turn it on.” I think what we are going to see over the next five years is that those devices are communicating with each other more, and sending signals, like, “Hey, I just saw that Deep left home, and the garage door is open, close the garage door.”
IoT is popping up pretty fast, and I think people are thinking about it, but they’re not so much worried about that connectivity yet. But I feel that where we are heading is more of the connectivity with those devices, which will help us, again, compliment and make the smart choices, and our reliance on those assistants is going to increase.
Another example here, I get up in the morning and the first thing I do is come to the kitchen and say Alexa, “Put on the music, Alexa, put on the music, Alexa, and what’s the weather going to look like?” With the reply, “Oh, Deep, San Francisco is going to be 75,” then Deep knows Deep is going to wear a t-shirt today. Here comes my coffee machine, my coffee machine has already learned that I want eight ounces of coffee, so it just makes it.
I think all those connections, “Oh, Deep just woke up, it is six in the morning, Deep is going to go to office because it’s a working day, Deep just came to kitchen, play this music, tell Deep that the temperature is this, make coffee for Deep,” this is where we are heading in next few years. All these movies that we used to watch where people were sitting there, and watching everything happen in the real time, that’s what I think the next five years is going to look like for us.
So, talk to me about Trulia, how do you deploy AI at your company? Both customer facing and internally?
That’s such an awesome question, because I’m so excited and passionate because this brings me home. So, I think in artificial intelligence, as you said, there are two aspects to it, one is for a consumer and one is internal, and I think for us AI helps us to better understand what our consumers are looking for in a home. How can we help move them faster in their search—that’s the consumer facing tagline. And an example is, “Byron is looking at two bedroom, two bath houses in a quiet neighborhood, in good school district,” and basically using artificial intelligence, we can surface things in much faster ways so that you don’t have to spend five hours surfing. That’s more consumer facing.
Now when it comes to the internal facing, internal facing is what I call “data-driven decision making.” We launch a product, right? How do we see the usage of our product? How do we predict whether this usage is going to scale? Are consumers going to like this? Should we invest more in this product feature? That’s the internal facing we are using artificial intelligence.
I don’t know if you have read some of my blogs, but I call it data-driven companies—there are two aspects of the data driven, one is the data-driven decision making, this is more of an analyst, and that’s the internal reference to your point, and the external is to the consumer-facing data-driven product company, which focuses on how do we understand the unique criteria and unique intent of you as a buyer—and that’s how we use artificial intelligence in the spectrum of Trulia.
When you say, “Let’s try to solve this problem with data,” is it speculative, like do you swing for the fences and miss a lot? Or, do you look for easy incremental wins? Or, are you doing anything that would look like pure science, like, “Let’s just experiment and see what happens with this”? Is the science so nascent that you, kind of, just have to get in there and start poking around and see what you can do?
I think it’s both. The science helps you understand those patterns much faster and better and in a much more accurate way, that’s how science helps you. And then, basically, there’s trial and error, or what we call an, “A/B testing” framework, which helps you to validate whether what science is telling you is working or not. I’m happy to share an example with you here if you want.
So, the example here is, we have invested in our computer vision which is, we train our machines and our machines basically say, “Hey, this is a photo of a bathroom, this is a photo of a kitchen,” and we even have trained that they can say, “This is a kitchen with a wide granite counter-top.” Now we have built this massive database. When a consumer comes to the Trulia site, what they do is share their intent, they say, “I want two bedrooms in Noe Valley,” and the first thing that they do when those listings show up is click on the images, because they want to see what that house looks like.
What we saw was that there were times when those images were blurred, there were times when those images did not match up with the intent of a consumer. So, what we did with our computer vision, we invested in something called “the most attractive image,” which basically takes the three attributes—it looks into the quality of an image, it looks into the appropriateness of an image, and it looks into the relevancy of an image—and based on these three things we use our conventional neural network models to rank the images and we say, “Great, this is the best image.” So now when a consumer comes and looks at that listing we show the most attractive photo first. And that way, the consumer gets more engaged with that listing. And what we have seen— using the science, which is machine learning, deep learning, CNM models, and doing the A/B testing—is that this project increased our enquiries for the listing by double digits, so that’s one of the examples which I just want to share with you.
That’s fantastic. What is your next challenge? If you could wave a magic wand, what would be the thing you would love to be able to do that, maybe, you don’t have the tools or data to do yet?
I think, what we haven’t talked about here and I will use just a minute to tell you, that what we have done is we’ve built this amazing personalization platform, which is capturing Byron’s unique preferences and search criteria, we have built machine learning systems like computer vision recommender systems and the user engagement prediction model, and I think our next challenge will be to keep optimizing the consumer intent, right? Because the biggest thing that we want to understand is, “What exactly is Byron looking into?” So, if Byron visits a particular neighborhood because he’s travelling to Phoenix, Arizona, does that mean you want to buy a home there, or Byron is in San Francisco and you live here in San Francisco, how do we understand?
So, we need to keep optimizing that personalization platform—I won’t call it a challenge because we have already built this, but it is the optimization—and make sure that our consumers get what they’re searching for, keep surfacing the relevant data to them in a timely manner. I think we are not there yet, but we have made major inroads into our big data and machine learning technologies. One specific example, is Deep, basically, is looking into Noe Valley or San Francisco, and email and push notifications are the two channels, for us, where we know that Deep is going to consume the content. Now, the day we learn that Deep is not interested in Noe Valley, we stop sending those things to Deep that day, because we don’t want our consumers to be overwhelmed in their journey. So, I think that this is where we are going to keep optimizing on our consumer’s intent, and we’ll keep giving them the right content.
Alright, well that is fantastic, you write on these topics so, if people want to keep up with you Deep how can they follow you?
So, when you said “people” it’s other businesses and all those things, right? That’s what you mean?
Well I was just referring to your blog like I was reading some of your posts.
Yeah, so we have our tech blog, http://www.trulia.com/tech, and it’s not only me; I have an amazing team of engineers—those who are way smarter than me to be very candid—my data scientist team, and all those things. So, we write our blogs there, so I definitely ask people to follow us on those blogs. When I go and speak at conferences, we publish that on our tech blog, and I publish things on my LinkedIn profile. So, yeah, those are the channels which people can follow. Trulia, we also host data science meetups here in Trulia, San Francisco on the seventh floor of our building, that’s another way people can come, and join, and learn from us.
Alright, well I want to thank you for a fascinating hour of conversation, Deep.
Thank you, Byron.
Byron explores issues around artificial intelligence and conscious computers in his upcoming book The Fourth Age, to be published in April by Atria, an imprint of Simon & Schuster. Pre-order a copy here.