The Exchanges

Q So actually, Christopher, the way we want to just kick this off is I actually just want to talk to you starting with big data. I mean, that's a term that people throw around all the time. It's completely overloaded. It's buzzword. It means so many things to so many different people. Could you start by just sort of telling me what your definition and take on big data is?

A There are two ways you can think of the term big data. There is what I think most of the world thinks about when people talk about big data, they think of the Vs, start out as three Vs, volume, variety, velocity, and so on, and then I think it's now up to seven or eight different Vs, veracity, variance, and so on. I actually don't like that definition. I think that definition is, is, is functionally correct, but it focuses on the problems of big data, right? These are the, these are the challenges that you have to deal with when you deal with big data. But the definition skips or misses the part where it says you ask the question, why do you want to deal with these problems? Right. So it turns out the reason for big data is machine learning.

AI assessment note: “it turns out the reason for big data is machine learning.”

Q So the storage layer, the processing layer, and what's, what's, what is big compute?

A Uh, so big compute is the first example of the compute you can think of is MapReduce. And MapReduce, I don't mean in terms of the algorithm, but I mean the actual implementation with the Hadoop project, the Hadoop app MapReduce. So that's a, Parallelized computing system that can take all this data, do some computation with it, and then put it back. And then maybe an aggregation, for example, asking the same question, the example that I gave earlier, how much money did we make off of this widget out of Europe yesterday, is an aggregation question. And if you have a thousand such transactions, you can do it with one machine, but if you have somehow stored, you know, a hundred billion of these rows, and you want to ask the same question, maybe you have to parallelize it. And that's what MapReduce allows you to do. Unfortunately, MapReduce is actually not designed originally to handle queries.

AI assessment note: “so big compute is the first example of the compute you can think of is MapReduce”

Q So the storage layer, the processing layer, and what's, what's, what is big compute?

A Uh, so big compute is the first example of the compute you can think of is MapReduce. And MapReduce, I don't mean in terms of the algorithm, but I mean the actual implementation with the Hadoop project, the Hadoop app MapReduce. So that's a, Parallelized computing system that can take all this data, do some computation with it, and then put it back. And then maybe an aggregation, for example, asking the same question, the example that I gave earlier, how much money did we make off of this widget out of Europe yesterday, is an aggregation question. And if you have a thousand such transactions, you can do it with one machine, but if you have somehow stored, you know, a hundred billion of these rows, and you want to ask the same question, maybe you have to parallelize it. And that's what MapReduce allows you to do. Unfortunately, MapReduce is actually not designed originally to handle queries.

AI assessment note: “so big compute is the first example of the compute you can think of is MapReduce.”

Q make that possible? Because in the days of business intelligence, I think of stuff, you know, the products that SAP and similar companies put out, um, what's changed to make big data possible? I know the big, big obvious things are just more computing power, but more concretely, like, what's physically making this possible to be able to parse and get all this, get these insights out of this data?

A Right. Yeah, if you think about it, a lot of people have pointed out that big data has always existed, right? It's always been there. We just didn't collect it. And then the second insight that I, that I think about is that we don't necessarily get smarter over time. It's just that certain technologies get cheaper. They get, they become more available. Uh, so machine learning algorithms have always been around. The, the, the data that exists that you could collect has always been around. But it wasn't until the advent of things like the Hadoop project, right? And the launch of companies like Cloudera and MapR back in It, it, it made it affordable for many, many more companies to begin acquiring and storing a lot of this data.

AI assessment note: “it wasn't until the advent of things like the Hadoop project”

Q To navigate the world for the first time?

A That's exactly right. For example, let's turn, let's flip that around and imagine, would you like to have a child develop without any experiences? And, you know, after 20 years, what would that child, that person be like? And then why is it that we ascribe wisdom generally to older people than younger people? You know, our brain capacity essentially remains about constant after a certain age, 1618, 20, Whatever research you read, and yet wisdom continues to grow and accumulate, and, and, and that's, as the brain incorporates life experiences, it is taking in a lot of big data, just like what machine learning algorithms do with, with data, and as, uh, the, the opposite of that is sort of rule-based computing, right, or rule-based expert systems. You can come up with 10, 20, 30 rules, and so on, uh, but you can never come up with enough rules To handle the exceptions.

AI assessment note: “That's exactly right. For example, let's turn, let's flip that around”

Q make that possible? Because in the days of business intelligence, I think of stuff, you know, the products that SAP and similar companies put out, um, what's changed to make big data possible? I know the big, big obvious things are just more computing power, but more concretely, like, what's physically making this possible to be able to parse and get all this, get these insights out of this data?

A Right. Yeah, if you think about it, a lot of people have pointed out that big data has always existed, right? It's always been there. We just didn't collect it. And then the second insight that I, that I think about is that we don't necessarily get smarter over time. It's just that certain technologies get cheaper. They get, they become more available. Uh, so machine learning algorithms have always been around. The, the, the data that exists that you could collect has always been around. But it wasn't until the advent of things like the Hadoop project, right? And the launch of companies like Cloudera and MapR back in It, it, it made it affordable for many, many more companies to begin acquiring and storing a lot of this data.

AI assessment note: “it wasn't until the advent of things like the Hadoop project”

Q And when we take that to every application, though, because why, why is it not okay to have it be an isolated, standalone thing? Like, what do we get out of it when it becomes a part of every application?

A I think when it becomes part of every application, then every component of the application will be receiving data all the time. Right. Maybe the screen is receiving my gestures. Um, maybe my calendar is receiving appointments that I'm making. Uh, maybe even the location where I am at, and then they will be able to learn from all of this and make intelligent decisions about what, what calendar events to insert, what gestures to accept. And maybe I don't want to have to say that it'll just do that ahead of time for me. Uh, so, so in my view, in that world, things will happen a lot better for me. Right. It'll become a lot easier for me to move around. It'll become a lot easier for me to make decisions, and maybe a lot of decisions will also be suggested to me before I even have to think about it too much.

AI assessment note: “they will be able to learn from all of this and make intelligent decisions”

Q To navigate the world for the first time?

A That's exactly right. For example, let's turn, let's flip that around and imagine, would you like to have a child develop without any experiences? And, you know, after 20 years, what would that child, that person be like? And then why is it that we ascribe wisdom generally to older people than younger people? You know, our brain capacity essentially remains about constant after a certain age, 1618, 20, Whatever research you read, and yet wisdom continues to grow and accumulate, and, and, and that's, as the brain incorporates life experiences, it is taking in a lot of big data, just like what machine learning algorithms do with, with data, and as, uh, the, the opposite of that is sort of rule-based computing, right, or rule-based expert systems. You can come up with 10, 20, 30 rules, and so on, uh, but you can never come up with enough rules To handle the exceptions.

AI assessment note: “That's exactly right. For example, let's turn, let's flip that around”

Q Sorry, just to be cantankerous here for a second, why do we want things to be fast, actually? Like, why do we always want them to be fast? Like, what do we actually get out of that?

A Uh, fast is competitiveness. If you can get your answer five minutes before I can, Right? You make decisions, and then you, you, you make that purchase, you make that buy, the supply, whatever it is, that'll happen before I get there and you win. Sometimes it's implicitly, you know, obvious that we want everything faster, right? Because fast is competitive. But it turns out the difference between fast and slow is, is very, very critical. When you can get something in real time, or you can get something in five seconds, as opposed to five minutes, uh, you will actually change your workflow. You will actually do something. That's what I learned from you from the consumer perspective with things like Gmail and so on. We had a, a phrase we call the five second barrier. And if the user can't get something done, you know, within five seconds, they won't ever do it. It's not like they'll do it at, you know, at twice the, the, the latency. Uh, so fast enables new different use cases that may otherwise not happen.

AI assessment note: “fast is competitiveness. If you can get your answer five minutes before I can”

Q What does that mean? And I've heard Peter say that as well, you know, he makes the argument as well, that machine learning will be a property of every application as opposed to a standalone, isolated function. Like, what does that actually mean?

A Um, imagine a world where, let's say, you know, we, we work with a lot of People. And we expect our colleagues to remember what we say and learn from the interactions and so on and so forth. Can you imagine a world where your colleagues are just simple automatons and they don't understand what you're saying and, you know, you, you, you told them something and they don't remember it the next day and their, their actions don't change the result of that. Well, I claim that there will be a day very soon When then you will feel that about the machines you work with. In other words, you would expect that to be a property of all these machines.

AI assessment note: “you would expect that to be a property of all these machines.”

Q No, totally. Well, I mean, just one last question then. Concretely, what do we get out of that? I mean, it's interesting academically and clearly it's interesting beyond academically because companies are investing in it left and right. In fact, more so in the corporate sphere than even in the university sphere. But what do we get out of that deep learning? Like what concretely comes out of that?

A I'd like to think of it in two ways. And I, I think they're both concrete, but perhaps one is more concrete than the other to some people's views. Uh, certainly Companies are helped when they have more intelligence about their data. You know, people talk about in the past, you didn't even know what was going on in the company, let alone make decisions based out of it. We're coming to an age where you know what's going on, and the machines are also helping you make decisions, right? And so what you get out of it is competitiveness. Companies that invest in this and are good at this, that are data intelligent, that are data driven, will win. That's a competitive edge. That's inevitable. But I think the larger picture also is that as a species, we're explorers, right? It's, it's, it's, it's built into our genes, and you can count on that as being inevitable, right? Left alone, we'll figure out that these are exciting frontiers that we will explore. We will always want to build intelligence. We will always want to build images of ourselves, if you will, right? Maybe the, that intelligence that emerges would not be the same as human intelligence, But we will attempt all of this. And it's just like space exploration, right? This is exploration of, of the mind.

AI assessment note: “what you get out of it is competitiveness.”

Q A lot of what you're talking about is machines inferring and really aiding, you know, learning like humans and helping augmenting human intelligence. What happens next?

A I think that's a great question. Um, I think if you back up and think about human evolution, there's one variable that's been Inexorably increasing. You know, we may get taller, shorter, we may go from one continent to the next, and so on, but one thing that's been a single variable, that constant, or changing one direction, that's human intelligence, right? In fact, the species intelligence. There's absolutely no reason to think that we're at the end of that. I think we're just at the very beginning of that increasing intelligence. A lot of the things that we're learning about machine learning itself, I'm really excited about that, right? If you look at the, the research in, in deep learning, what's happening there, really just in the last 12 months, ah, 24 months, to me the exciting thing is that we're learning so much about how our brains might work. It's not just what the machines can do, but what they teach us about ourselves. And so if you think about it from that perspective, and think about how these algorithms are evolving, you actually see this very near future where Human intelligence is going to be boosted by all this machine intelligence. That will actually change how we think about evolution.

AI assessment note: “Human intelligence is going to be boosted by all this machine intelligence.”

Q Sorry, just to be cantankerous here for a second, why do we want things to be fast, actually? Like, why do we always want them to be fast? Like, what do we actually get out of that?

A Uh, fast is competitiveness. If you can get your answer five minutes before I can, Right? You make decisions, and then you, you, you make that purchase, you make that buy, the supply, whatever it is, that'll happen before I get there and you win. Sometimes it's implicitly, you know, obvious that we want everything faster, right? Because fast is competitive. But it turns out the difference between fast and slow is, is very, very critical. When you can get something in real time, or you can get something in five seconds, as opposed to five minutes, uh, you will actually change your workflow. You will actually do something. That's what I learned from you from the consumer perspective with things like Gmail and so on. We had a, a phrase we call the five second barrier. And if the user can't get something done, you know, within five seconds, they won't ever do it. It's not like they'll do it at, you know, at twice the, the, the latency. Uh, so fast enables new different use cases that may otherwise not happen.

AI assessment note: “fast enables new different use cases that may otherwise not happen.”

Q A lot of what you're talking about is machines inferring and really aiding, you know, learning like humans and helping augmenting human intelligence. What happens next?

A I think that's a great question. Um, I think if you back up and think about human evolution, there's one variable that's been Inexorably increasing. You know, we may get taller, shorter, we may go from one continent to the next, and so on, but one thing that's been a single variable, that constant, or changing one direction, that's human intelligence, right? In fact, the species intelligence. There's absolutely no reason to think that we're at the end of that. I think we're just at the very beginning of that increasing intelligence. A lot of the things that we're learning about machine learning itself, I'm really excited about that, right? If you look at the, the research in, in deep learning, what's happening there, really just in the last 12 months, ah, 24 months, to me the exciting thing is that we're learning so much about how our brains might work. It's not just what the machines can do, but what they teach us about ourselves. And so if you think about it from that perspective, and think about how these algorithms are evolving, you actually see this very near future where Human intelligence is going to be boosted by all this machine intelligence. That will actually change how we think about evolution.

AI assessment note: “Human intelligence is going to be boosted by all this machine intelligence.”

Q see the plumbing behind the scenes. That seems pretty obvious that we need that. So what does that actually get you in the big data world? I mean, sure, you can more easily read your data and get some insights from it, but I just feel like we throw that term around too much that we need a better interface to our data. Like, what does it really get us?

A So I think the way to, one way to understand it is to look back into the past, right? We went from the typewriter to the computer keyboard and to the mouse and now to touch screens and so on. You could ask the same question. What does touchscreens get us, right? And why did we do it before? The reason touchscreens and, and finger gestures and so on are valuable is because they're much more natural than using a keyboard. But the reason we didn't have that before is because the hardware and the software to make that happen was not available or too expensive to do so. So the same analogy applies with big data and machine learning. We could imagine all those capabilities before, but they were too expensive. We didn't have the storage. Capabilities for all of the data, and we didn't have the big compute capacity to do all of this. But now that we do, and they're affordable, what you will see is that all of this machine learning will be a property of every application.

AI assessment note: “what you will see is that all of this machine learning will be a property”

Q see the plumbing behind the scenes. That seems pretty obvious that we need that. So what does that actually get you in the big data world? I mean, sure, you can more easily read your data and get some insights from it, but I just feel like we throw that term around too much that we need a better interface to our data. Like, what does it really get us?

A So I think the way to, one way to understand it is to look back into the past, right? We went from the typewriter to the computer keyboard and to the mouse and now to touch screens and so on. You could ask the same question. What does touchscreens get us, right? And why did we do it before? The reason touchscreens and, and finger gestures and so on are valuable is because they're much more natural than using a keyboard. But the reason we didn't have that before is because the hardware and the software to make that happen was not available or too expensive to do so. So the same analogy applies with big data and machine learning. We could imagine all those capabilities before, but they were too expensive. We didn't have the storage. Capabilities for all of the data, and we didn't have the big compute capacity to do all of this. But now that we do, and they're affordable, what you will see is that all of this machine learning will be a property of every application.

AI assessment note: “all of this machine learning will be a property of every application”

Q But you're basically arguing for something different. Can you describe what that is and why?

A Sure. Um, so if you think about the, the, the V's definition of big data, they're all problematic, right? Uh, and so we tend not to want to have problems unless there's a reason for, you know, there's a, there's a greater benefit to pay that cost, right? And the benefit of, of big data is really because we can unleash algorithms at them, and these algorithms can automatically detect patterns and see these patterns. I want to sort of jump into that right away because a lot of us in machine learning Say this all the time. What does it mean to detect patterns and so on? And, and sort of people take that for granted, but then it's a little fuzzy. And the way I think about, about big data is when machines learn from big data is very much like human beings learn from life experiences.

AI assessment note: “the way I think about, about big data is when machines learn from big data”

Q What does that mean? And I've heard Peter say that as well, you know, he makes the argument as well, that machine learning will be a property of every application as opposed to a standalone, isolated function. Like, what does that actually mean?

A Um, imagine a world where, let's say, you know, we, we work with a lot of People. And we expect our colleagues to remember what we say and learn from the interactions and so on and so forth. Can you imagine a world where your colleagues are just simple automatons and they don't understand what you're saying and, you know, you, you, you told them something and they don't remember it the next day and their, their actions don't change the result of that. Well, I claim that there will be a day very soon When then you will feel that about the machines you work with. In other words, you would expect that to be a property of all these machines.

AI assessment note: “you would expect that to be a property of all these machines.”

Q definitely think you're right that we might already even be there in some ways, or that we need to be expecting that. But what does that really give us? Does it, I mean, cause when I think about big data, I think about in the abstract So it's still not clear to me what machine learning being a property of every application, like, so what does that do for us?

A Let me give you an example by a story from one of the, there's something called TGIF at Google, right? Which, which actually we do at our company at Atao today as well, which is every Friday, the execs basically come out and talk about almost every company secret possible to the whole company, and people can ask any kind of question that they want. I remember there was One time when at Google we were dealing with the problem of latency. Google cares a lot about speed. So Larry was pushing everyone to make their services a lot faster. And there was a question people asked and say, hey, Larry, we went from one second search delay to 500 millisecond and 300 millisecond, a hundred millisecond. What do you want? I mean, what happens when we get to zero? And then what Larry said was, why stop at zero? Why can't it be negative latency?

AI assessment note: “Why can't it be negative latency?”

Q But you're basically arguing for something different. Can you describe what that is and why?

A Sure. Um, so if you think about the, the, the V's definition of big data, they're all problematic, right? Uh, and so we tend not to want to have problems unless there's a reason for, you know, there's a, there's a greater benefit to pay that cost, right? And the benefit of, of big data is really because we can unleash algorithms at them, and these algorithms can automatically detect patterns and see these patterns. I want to sort of jump into that right away because a lot of us in machine learning Say this all the time. What does it mean to detect patterns and so on? And, and sort of people take that for granted, but then it's a little fuzzy. And the way I think about, about big data is when machines learn from big data is very much like human beings learn from life experiences.

AI assessment note: “the way I think about, about big data is when machines learn from big data”

Q definitely think you're right that we might already even be there in some ways, or that we need to be expecting that. But what does that really give us? Does it, I mean, cause when I think about big data, I think about in the abstract So it's still not clear to me what machine learning being a property of every application, like, so what does that do for us?

A Let me give you an example by a story from one of the, there's something called TGIF at Google, right? Which, which actually we do at our company at Atao today as well, which is every Friday, the execs basically come out and talk about almost every company secret possible to the whole company, and people can ask any kind of question that they want. I remember there was One time when at Google we were dealing with the problem of latency. Google cares a lot about speed. So Larry was pushing everyone to make their services a lot faster. And there was a question people asked and say, hey, Larry, we went from one second search delay to 500 millisecond and 300 millisecond, a hundred millisecond. What do you want? I mean, what happens when we get to zero? And then what Larry said was, why stop at zero? Why can't it be negative latency?

AI assessment note: “Let me give you an example by a story from one of the”