Thank you everyone. Good afternoon. I'm a little nervous right now because I looked at all the various talks at this conference and mine is probably going to have the, you know, embarrassing misfortune of being the least technical talk, so I apologize for that. But this, the content of this conversation is based on all the various learnings that we've gone through at Flipkart and it's my very sincere hope that sharing this out allows you to rethink some of your data strategy, validate some of your data strategy in your various organizations. I strongly believe that some of these patterns that I'll be talking about are things where organizations will have to disproportionately focus on. So every pattern, I've got I think six to eight patterns. Every pattern in there is going to be a subject which any serious data minded organization should focus on. We ourselves are focusing on that and I'll be annotating, you know, these patterns with examples from Flipkart throughout the course of this talk. Another quick note, this is, I'm not a PPT guy. I'm basically an engineer. I'm horrible at PPT. So my slides are going to be very minimal. Don't focus as much on the slide. Just focus. Those are there to act as my memory pivots. Focus a lot on what I'm actually going to be talking about. All right. So the first thing that I, the first pattern that I actually want to talk about is data being a first class citizen in SDLC. All right. This is going to be, this is going to require a little more explanation.  How many of you, I'm presuming that most of you are engineers? Anybody who's not an engineer here? All right. Most of you are engineers then. How many of you, at some point in your career, had to, had to get data by scraping through logs? All right. All right. For those of you who have not yet done that, I'm presuming that you've not coded a lot or not built, you know, not done a lot of systems. I'm sorry. I'm just kidding. Most of us go through that experience, right? Any thoughts on why do we end up doing that? Don't know the code. All right. I'm specifically asking about where you had to, it's not troubleshooting that I'm actually talking about. I'm talking about getting access to data. I'll give you an example from Flipkart. We wanted to know what are people actually searching for? How do you go about doing that? Scrape through all the various web logs, standard activity, figure out the, excuse me, Jeff Dean. What are the pages that the user is visiting? Absolutely. So you'll figure out which are the URLs that actually look like the search pages, and you will extract that term from it. That's what the user is actually searching for. That's what we start out with. And one fine day, due to one feature request, we realized, we decided that all of our browse pages will actually be powered through search. All right? And all of a sudden, the structure of those URLs changed, and our data was completely wrong. I'll give you an even worse of example. We realized that, you know, so we started going big time on this mobile app thing, right? And we started prefetching all the data because that creates for a much better experience. And all of our searches and browse data, search was still, you know, actually that data was not particularly wrong. But browse data was very wrong because we were doing a lot of prefetching for a lot of the most browsed parts of the app. All right? So what's the issue here? Anybody? What's happening here is the guy who's consuming the data is very disconnected from the guy who's actually producing the data, not just in terms of roles, but very often in terms of the chronological order. We think about data very retrospectively. We think about data when the need arises. It's only then that you start groping for that particular data. If you're lucky, you find that in logs. If you're unlucky, you know, you find partial data in logs and you don't even know that it's actually partial data. Okay? And if you're particularly lucky, you know that you're not logging the data, so at least you don't have incorrect information. All right? We at Flipkart realized that things have got to be better than that. We, one of the beliefs that we actually hold very dear to ourselves is that for companies, particularly for internet companies, the only true intellectual property that we have is our data. It's not scale. It's not infrastructure. Those are very critical things, but those are not your most true IP. Your most true IP is the data that we have. Does that thought process reflect when we are building our systems? How many of you think about scale when you're designing your systems? All of you? I'm presuming. Otherwise, your managers would notice you're not raising your hand. How many of you think about quality when you are building your systems? Right? You think about unit as you think about all of those things, right? How many of you think about what data points my system should generate when you are actually building the system? All right. I see a few hands. Great. You guys are on the cutting edge of technology. So what happens is we tend to think about data retrospectively. Why am I saying this? Because we tend to extract data a lot lower in this stack, right? There's a database. We've got CDC and all of these things and change data capture and things like that. And you're continuously pulling data from your databases or from your log files and generating a warehouse and data lake and all of that kind of stuff, right? You are fundamentally thinking about data retrospectively. You're not thinking about data a lot more up in the stack where the workflows are happening. All right? Imagine if every time a search happened, the search system fires off an event saying that the user such and such did a search at this particular time and this many number of results were found. Okay? I'll give you another example. In a warehouse where we keep all of our various products, a particular good is moved from this shelf to that shelf is an activity that happened. Something mutated in our business process. It needs to be captured as such. Recognizing all of these a lot more upfront in your SDLC, thinking about it when designing your product roadmap, when designing your systems, and when doing production management. That's what I mean by data as a first-class citizen in SDLC. It would be a first-class citizen if you think about data alerts that the amount of data being generated by my system all of a sudden has dropped by 10%. Okay? You would be thinking about data as a first-class citizen if you've got unit tests around data. All right? So data being a first-class citizen in the thought process of building systems, this is going to be an increasingly important thing for organizations to do. Organizations that do this are the organizations that will be much better prepared for success than the others. The second piece, scale of data. All of us know that. Data is exploding, right? Data is exploding. We recognize that and we try to build for that scale. But as engineers, as good engineers actually, there's a good trade that I'm going to talk about. We fundamentally try to think about the right balance between time to market and the amount of investment that you make in making your product more mature, correct? It's a balance that we actually try to strike. Otherwise, you're doing over-engineering, correct? You guys, I'm hoping for good reasons, you guys get concerned about over-engineering, right? The trouble is that the parameters of that balancing act are fundamentally different for data. If your business is growing like this, your data is exploding like that. And engineers typically, you know, when we are building systems day in, day out, we are unable to differentiate between these two rates of growth. We apply the same parameters of incremental hacking, refactoring every three months and things like that in both the cases. Big mistake. Very, very quickly, you will realize that you would, you have hit a wall that is very different, that is very difficult to overcome. At Flipkart, at one point in time, you know, we had every system dealing with its data in its own different way and almost all of them hit a wall very, very quickly in about six months. That is all that it took. And of course, you know, Flipkart's growth scale is actually very quick itself. But that also meant that the scale of data growth was that much higher. All right. This is one area. Data systems is one area where I strongly believe that we must over index. We must think about scale a lot more than what people around you are actually talking about. If your manager tells you that we will see data growth by 10 times, imagine that, you know, that number is actually 100 times. Okay, and you guys will thank me for that. I can guarantee you. All right. Over engineer data systems. You know, I mean that with a pinch of salt, because over engineering is not exactly a good word. But think about data scale in a very disproportionate way as compared to your regular system scaling. So this is point number two, but very often missed. Pattern number three. Metadata, discovery and relationship inference. I think it was Yagnik in the first talk, right? He was talking about metadata. Beautiful concept. What we realized is, so we had all of these various systems at Flipkart pumping in data into our central data platform. And, you know, because of data being a first class citizen, not only we were actually capturing very rich data, but the number of data types, the number of schema had exploded. It had run into thousands. Okay. One fine day, a particular data scientist actually wanted to get access to, for, you know, a model that she was building, she wanted to get access to all the various page views that have lasted more than five seconds and have not yielded any conversion. In this thousands of schema points, how does one figure out what data stream to target? That's where we realized that as you start thinking about data a lot more richly, a lot more deeply, there's a huge proliferation of your data definitions. And thus, you need to invest in some sort of a dictionary, some sort of a metadata dictionary where you are capturing, you know, in the search space, what are the various kinds of data points that actually get generated? Okay. Around checkout, what are the various kinds of data points that actually get generated? Just purely from an organization standpoint. You start off with that and you very soon start realizing that there's so much more that you can do. You can figure out dependencies, right? That this particular, you know, if my search, number of searches drops, my number of checkout flows actually drops as well. So you can infer those causal relationships. That's what I mean by relationship inference. And you can do automated RCAs. If you know that what data stream is leading into what, these are some, you know, beautiful things that you can actually do on top of that. It's a matter, it's a subject matter of, you know, that I can go a lot, lot deeper into, but we are still in the process of building out some of these things. So maybe, you know, we'll share out some of this work, you know, at the next fifth elephant. But this is a very important issue. Start acting on it a lot earlier in the game. If you do it when you have a very large amount of proliferated data, you would find it a much harder thing to solve. Pattern number four. Primary users will be machines, not humans. You know, quite often we tend to start thinking about data in terms of this report, that report. Those things will continue, but the power of data will come because of systemic consumption of data. When it's the machines that are consuming the data because that's where a lot of the power of data will emerge. Very deep contextual decision making. When the machines are doing those, that's when you are actually making the true use of data. This pattern has large implications on your system design. Okay, you think about API contracts a lot more often. You think in terms of latencies and design for bounded latencies. It has huge implications on the architecture of your systems, which is one reason why I thought it would be very useful to share this out. The data systems of tomorrow are going to be, you know, 90% of their work is going to be towards feeding systems than people. Okay, so, you know, while innovations on reports etc will continue and in fact we have a few talks on a partial lens etc as well. So, innovations on the, you know, the human consumption, visualization etc will continue, but this will completely, you know, explode in size in terms of impact. Pattern number five. This is, you know, there's a natural derivation from pattern number four. When the machines start using, we realize that the more data that we had at Flipkart and the more systemic consumption we were allowing, that much more real-time processing, real-time needs started emerging, you know, in a very, very strong way. People started thinking about if this customer is sitting at the airport and opens up the Flipkart app, I want to recommend at that moment, here is a quick read for your flight. People started thinking about things like that. We started thinking about, you know, if, when in the future we are talking about three to four hour delivery times, how do we use real-time traffic information in order to determine whether can I make this promise to the customer or not. In a city like Bangalore, you know, you know that it's actually very difficult, right? All of this requires real-time processing. So start over-indexing on real-time processing today. So this is another pattern that's actually going to be very, very big. Data systems that are oriented towards real-time processing will have more, you know, edge over the other systems that don't. Pattern number six. This is very straightforward. There's a lot more about democratizing machine learning, all right? Everybody having access to machine learning constructs. It's like, you know, in languages we go from assembly language and exposing better, easier constructs to deal with. It's the same notion I'm going to actually, in the interest of some sort of a Q and A, I'm going to skip deeper focus on this. Lastly, deep learning will move out, actually has already started moving out of the research space into a lot more of day-to-day use. In fact, we've got a talk on this tomorrow morning on how we've applied deep learning in the NLP space to solve for some, you know, real-world issues at Flipkart. I strongly encourage, you know, attending that as well. Deep learning is no more a research subject alone.  It has real-world implications and if you are bothered about your data strategy, you need to start thinking about your investments in this area very deeply as well. So this is all that I really had. I don't know to what degree I was able to relate, you know, express this in a good way, but I'm very open, very, you know, happy to answer any questions on this case. I have a system-level question but a very different part of your system speaking as a customer. Do you have any plans to educate delivery people in how street numbers work? So the trouble is that street numbers do not work in India. I have a street number and it is no use telling it to a Flipkart delivery guy.  We navigate using landmarks. That's just because of the fact that in India, most of the numbering schemes are not topological. They are not actually, you know, defined with a certain topology in mind. Right, it's over the bar to shop or something. Yeah, yeah, something like that, right? But if you don't have a good language in common, giving that whole sequence of instructions can get very difficult. Canada's well-per-swell-per good too. Yeah, see, there are ways that we are actually trying to approach the problem. One of the things is that geolocation capabilities being available in smartphones is a big step, is a big help in terms of figuring that out. In fact, towards the same thing, one of the, you know, one of the realizations that we've had is we were trying to solve for, you know, solving the last mile problem in an automated way. We realized, and we started off with address classification as a mechanism of approaching that. We realized that address classification using the numbering schemes or those phrases in the address was a lot more worse off than identifying using landmarks and, you know, the pathways. It was a lot, lot easier. So I can say that, you know, it was almost like the data proved that you've got to figure out a lot more in a, in a, you know, in a geographical fashion than a numbering scheme fashion when thinking about efficiencies there. Cool. Any, any questions folks? Yeah, please. A point you made about metadata and relationship inference. Yeah. Metadata is a static thing compared to relationship inference. And whenever you infer relationships in one round, in the next round only you can think of, or maybe in a periodic map to change the metadata. You can't do both at the same time, because metadata is already there before you do the processing. Not entirely. See metadata is any description of the data itself. It's the meta part of the data, right? So some of the metadata would be static. Some of the metadata is going to be dynamic. Static metadata, I'll give you a few examples. Which part of the product ecosystem does this schema belong to? Another is who was the author of the schema? Who needs to be alerted when this particular schema is changed? These are static metadata, which, you know, you actually put in. The dynamic metadata examples would be that it's been inferred with a 90% probability that this data points against the schema have a causal effect on this other schema. This might be an inferred metadata. So you'll have both kinds of metadata, static as well as dynamic. It's not like it's getting overwritten. But the inference is always dynamic over a period of time. The inference is dynamic, yes. So to become metadata, it requires to be some kind of a proof or some stability in that inference. Otherwise, we can't go on changing metadata every time. Absolutely. What's the reason we need to categorize? Okay. Or why we need to think in this direction? Yeah, so the question is just to summarize it out for everybody. Why do we need to think about human consumption as distinct from systemic consumption? That's the question, right? Human consumption tends to be a lot more exploratory in nature. The kinds of workload patterns that it actually creates are very different from the kind of workload patterns that a systemic consumption actually creates. In case of systemic consumption, there's a model that you actually created and the system is pretty much reading from that model. Because you've built that system to read data from your data systems, you know what kind of patterns it will actually create and you can significantly optimize for that. And plus, because this systemic data consumption is, in a lot of cases, will have a customer touch point, you need to have very hard bound on latencies. Because if your data is not bounded on the latency, latency is not just in terms of time to first bite, but a lot more in terms of freshness. That how old is this data that I'm actually consuming. When systems are consuming that, they might need to make business critical decisions as in if this data is older than such and such time is older than five minutes, I'm not going to use this data because it's no more useful to me. Okay, so systemic consumption workload patterns are fundamentally different than the human. Human then maybe you need to give very summarized information. Yes, that's also true. That the amount, the volume of data and the diversity of data that a machine can process can be a lot, lot more than humans. And that also, I think, you know, I was implicitly talking about it when I was talking about workload patterns, but you're right. Yeah, because whether a data is old or not, it's applicable for both, whether it's a human or a machine, right? Sure, yeah, I agree. Okay, thanks. Yeah, sorry, we'll come back to you. I have one question. What are the factors that Flipkot has considered forcing the customers to use mobile apps only rather than desktop applications? I'll read this as a technical conference, not I'm going to pass on that question. I would love to discuss that, but offline, you know, over length. So are you discovering a innovative customer behavior using mobile apps? We believe so, yes. So with a lot of real-time systems coming up, like you mentioned some use cases. Yeah. So how do you differentiate between real-time processing or what to be processed real-time between what to be processed in batching? So how do you do that? What, come again, what's the real question here? So processing, so when to do the processing? Do it in batches? What needs to be done in batches and what needs to be processed in real-time? I think it's very use case dependent. There are use cases, say for example, when you're talking about personalizing something based on the actions that the user did in the past one minute, in that case, you're talking about a lot more real-time personalization. On the other hand, when you're talking about personalization on aspects, on demographic information, because the gender of a person doesn't change on a minute-by-minute basis, or their spend, propensity, does not change on a minute-by-minute basis, those kinds of personalization are done in a lot more offline fashion. So it's really on a use case by use case. Okay. Thank you. Hi, this is Sanket here. My part of my question was, this question was included in my question. So you said that we should more focus on real-time processing, right? So I just want to be more clear on this, like, and a little curious also, like, what Flipkart is looking into real-time processing. Okay. And how do you solve it? Okay. So very good question, actually, because it's very... So these two statements are very different. One is we are encountering a lot more, a lot of real-time use cases. Second is over-index on real-time capability. These are two very distinct statements, right? So we have anecdotally found that real-time use cases are growing in number. Okay. We have a strong belief that people's mindset, by people I'm talking about a product manager, an engineer, you know, all of the folks who are involved in building products, our mindset is way too prejudice by what we have seen in the past. So instead of waiting for those real-time use cases to emerge, build out this capability and then force people to think about real-time use cases. Because if you've done that, you know that the experience that the customers are actually going to go through is going to be significantly better, significantly more... You know, the customers will be significantly more wowed and competition will find it more difficult to react to this sort of a situation. That's our belief. You mentioned about the unit. You mentioned about writing unit tests about around data. Yeah. So can you please elaborate more on that? How should the thought process be when you are writing a unit test around data? So starting from... So the unit test that you are actually... So say for example, you change a feature. Okay. You change a particular feature in your product and you're validating that these data points that were supposed to be getting generated, are they getting generated or not? That's the unit test part. Then when you are actually productionizing this, the way you'll think about quality, that is there any metric that is regressing, that is degenerating, right? In the same fashion, you would be observing, you would be having alerts around, is your data stream volume actually seeing a dip or not? So it's the same notion. Yeah. I think real-time processing means real-time streaming you are talking about. Streaming is the most commonly used way of solving for real-time, but right now I'm actually specifically shying away from calling out any implementation detail. Tomorrow, if say for example, the compute capacity grows by such a huge amount that you don't have to rely on streaming and you can do real-time calculations on very, very huge data sets in a non-streaming way. So be it. So... Hello. Yeah. So what are you using for that? Are you using Kafka, Storm, or storing it on which database? So we actually have a talk coming up on... Today evening, Sid is... Today evening, right? Today afternoon. We've got a talk on joining streaming data sets. All of those questions are better answered there. Hello, Anul. I think you'll talk about over-engineering. Sorry, here. Here, here, here. Okay, yeah. You'll talk about over-engineering, over-instrumenting. I think it's very insightful. And coming from a traditional data-ware housing world, going back 10 years in time, we have been explicitly told the primary problem is give space for OLTP transactions and move the data into a different area. So I think it's a big cultural shift in a way. That's why you see organizations have horizontals which do data management. Yes. You don't have to move away from that mindset into a mainstream within the organization. I think that's a very radical point of... Oh, it is. It absolutely is. In fact, I'll actually remind you because you're talking about that era, you would remember that 70% of the time of that horizontal time team would go into what is called as data sanitization. Right. Yeah. Right. That time would get spent because the guy who's producing that data didn't give data that kind of a seriousness. Right. And one thing that you can help for organizations which want to leap is how do we balance transaction systems with the data? Because over-instrumenting data comes at a cost. So you obviously would have done something with in-memory grids and stuff like that. I'm not trying to take away this time on that, but something that you can allude to would help. You mean in terms of implementation? Yeah, yeah, yeah. So in terms of implementation, there are actually a whole bunch of talks. I think five talks from Flipkart, each one of them in some way or the other talks about what we've done. Those might be actually much better places and the context would be more relevant for everybody. Sure. Yeah. Thank you. All right, cool. Okay, so I see a few more hands. I really don't want to be standing between the lunch of people. No, we do have time. All right. Okay, here. I had a question on the machine learning part. I wanted to understand how Flipkart uses machine learning to reduce returns and control returns. To reduce returns, instead of me answering that, I will point you to the data scientist who's actually working on that problem. Why don't you meet me offline and I would be very happy to connect you with him. Great. Thank you. Are we thinking about using deep learning now at Flipkart? Where are you? Okay. Yeah. Come again. Are we thinking about using deep learning now for problem solving at Flipkart? We are already using actually. The talk tomorrow morning talks about some of the work that we've already done. Hello. Yeah, you mentioned one of the patterns saying data as IP, right? When you mentioned data as IP, you're saying the amount of data you're collecting that can be used as IP or more the process. That's my first question. Amount and richness as in the quality of that data. I can store petabytes of data which is worthless. So do you also think that in a general context, IP is more like little more something which you can use it over a long period of time? But do you think that is true for this kind of a context where you said data as IP? Because data itself is changing a lot, right? Data is changing a lot. See the point is that, okay, let me elaborate on what I mean by data as an IP. Today I understand better than a Samsung why a particular phone sells less or more as against its competition. Why am I able to do that? Because I have access to a lot more fine-grained data about customer behavior as in when they come on to this particular product page, what is the actual product that they end up buying?  At what point in time I start using that in order to make this IP useful is a separate matter, but doesn't take away the fact that data is really the true IP that we have. Yeah, one question here. So keeping privacy concerns apart, are you thinking of doing something like a credit score for each and every user? So every kind of use case becomes available, including that. Hey, hi, I think your talk was pretty insightful in certain points are very interesting. So I have a question around, you mentioned about scaling early, right? So, I want to understand it a little bit more because currently there DevOps processes and cluster management and all of those things have become pretty evolved. It's easier to auto scale and do all of these operations. But I'm wondering, that's not the kind of scale you're talking about. Are you talking about scale when thinking about over partitioning your data? Or I basically want to understand more when you mentioned the word scale. Do you mean, what exactly do you mean? Sure. See, auto scaling is going to be constrained by the architectural choices that you've made. Okay. There are very few architectures that actually scale indefinitely because there are trade-offs in every kind of an architectural decision that you make, right? DevOps is essentially taking care of production management for you in the constraints of the architectural decisions that you have taken. My point about scale was to force all of you to think about a lot bigger volume of data than what you might actually be thinking it would be and incorporating that in your architectural decisions. Sure. So let's say we are talking about distributed systems, right? So if a distributed system is architected well, so you're saying probably we are not even probably architecting a distributed system well enough and the problem is more coming to the fore because we are actually dealing with larger volumes of data. Yeah, actually we usually do not architect distributed systems well for the simple reason and this is particularly true for startups. The real reason is that there's a balance that you strike between your time to market and the maturity of your engineering system. Doing it the right way takes up more time because you think about all the various ways that things can go wrong, all the various kinds of workload patterns that might be made on my system. You think about all of those things. That time may not necessarily be available. That's the reason why I was talking about that balance between the two that you strike. In case of data, air in favor of doing it the right way than time to market. That's what I was saying. Last question please. You spoke about OLTP, OLAP, you know what may be useful there. What you spoke a little bit about what problems real time is useful. Can you give examples of when you would use real time over something that requires pre-processing? Okay, firstly I didn't use the words OLTP, OLAP. I think those are constructs and notions of an earlier era. Regarding the question about when real time, when not real time, it's really like I answered that in one of the previous questions. It's really use case dependent. But in general, the approach that we are taking is force people to think about real time. What would you do as a product manager? What use cases can you come up with if you had this capability? So we are actually flipping the problem there. It's not about a product manager coming and telling you that I had this use case in mind but it requires real time capability. I'm flipping it around and saying that this real time capability is available better than anybody else. What can you do with it? All right, thanks a lot folks. That was the last up to you. I have a question here. If you define here. Yeah. So we talked a lot about real time processing. Can you clarify more on the word real time? How fast or what do you mean by real time here? Real time is a slightly fuzzy word. I talk about it. I usually mean a sub minute latency. But at times the say real time system which I see is say for a nuclear control system that has to handle it within microsecond of latency. I agree. In a use case by use case basis you would not be using the word real time. You would be talking in terms of this is my time bound. You would be talking about the boundedness of that than the word real time. Real time I'm using this as a very broad umbrella thing. Okay. One last question. So just to take off on the last response you had which is you're sort of turning over to the product management team and saying, hey, we have all this data. We have this capability. Why don't you think about things you can do? Yeah. How are they approaching that? How are they thinking about it? So they're actually very excited and so there's a very, very recent step that we have actually taken in this direction. The excitement is huge and there are some very good ideas that have begun to emerge. There are things that we are thinking of how to amplify that. So we are thinking about data hackathons where you have this huge pool of data, this huge set of capabilities. What can you do with it? So in hackathon specifically focused on data. So those are the only things that we'll be using to nurture that orientation. Cool. Thanks a lot, folks. Yeah.


In the first video about structural equation models, I gave some background to what structural equation modeling is, the historical paths that led to its development, some of the key ideas and the ways that it can be applied in social science settings. In this video, I'm going to talk about some of the key ideas, terms and concepts in structural equation modeling. This is important because SEM is rather different to other areas of statistics. Some of the ideas that are important in understanding and applying SEMs are quite unfamiliar, and so it's important to have a grounding and a familiarity with these ideas before we move on to other applications. So in this video, I'll be talking about path diagrams, the way that we represent equations and theories in the form of diagrams in SEMs. I'll talk about the difference between exogenous and endogenous variables. I'll talk about the way that structural equation modeling analyzes not the raw data, but the covariance matrix of the variables that we're interested in. I'll talk a little bit about how parameters are estimated using maximum likelihood in structural equation modeling. I'll also go over how we apply what are called parameter constraints, how we don't always estimate every parameter in the model. Some of the parameters are fixed to values before we start fitting the model. I'll also talk about how we assess the overall fit of a model in structural equation models and the importance of the idea of what are called nested models for assessing model fit. I'll also talk a bit about identification of structural equation models. Again, that's something which is linked to model fit and something that we don't encounter so much in regression context that many people are familiar with. The first thing I'm going to talk about are path diagrams. Path diagrams are one of the reasons why structural equation modeling is very appealing to many social scientists in particular. This is because social scientists don't always have such a strong grounding in mathematics and are less comfortable with reading complex equations and so on. Path diagrams are another way of presenting the same information as we can get in an equation, but they do this visually. That's often a clearer way of seeing what is being presented in an equation compared to Greek letters and symbols and so on. If we write our path diagrams correctly, then we can read directly between an equation and a path diagram. They tell us exactly the same thing. In this example here, we could write a bivariate regression equation in the usual way where our dependent variable y is a function of our independent variable x. We are going to solve this equation using data and we're going to solve for the unknown parameter beta, what is the relationship between x and y. We can also write that same information down in the form of a path diagram, a simple path diagram in this case. So we have here y is now represented as a rectangle and x is also a rectangle. We have an arrow running from x to y in a single direction and we have a small circle pointing into y which represents the error term in the equation. You can see there that there is a b above the line to indicate that the parameter represented by the straight single arrow is a regression coefficient. This is quite clear visually in the sense that what the model is implying at least is that x causes y and that there is some coefficient beta which summarizes what that causal effect is and there is an error term in that equation. There are conventions for path diagrammatic notation so that we use it consistently. There are some variations in how different conventions are applied and so on but this is the general form where we have a latent variable is represented in the form of an ellipse. An observed variable, a variable that we've actually measured in our dataset directly would be represented as in the last slide using a rectangle. Error variances are small circles and this is similar to a measured latent variable. It's a circular form but it's actually a small circle now and this indicates that these error terms are also latent variables but we don't actually label them. These are kind of unknown or residual latent variables. We also indicate the relationships between variables using lines with arrows. A curved line with an arrow at each end indicates a covariance between two variables. We call this sometimes a non-directional path or an unanalyzed relationship because this is used to show that two variables are related to one another but our model does not specify anything about the direction of that relationship. It may be because it's not an important part of the theory but we know that the two variables are associated. Lastly, a straight line with a single arrow at one end indicates a directional path, a regression coefficient. We're saying if we use a single headed arrow then we are indicating the direction of the relationship between two variables in our model. We can put these basic symbols together to form more complex models but ones which have a clear meaning and which can indeed be translated back into the standard equation notation. Here are some examples of some quite simple path diagrams. Here we're just looking at measurement models. These are confirmatory factor models. We have here Eta1 which is a latent variable shown as an ellipse. Eta1 here is shown to cause three observed variables, X1 to X3. We can also think of that as Eta1, the latent variable being measured by the observed variables X1 to X3. At the top of the diagram we have three error variances E1 to E3. Those are the errors for each of those equations that Eta1 is predicting X1 with some error, it's predicting X2 with some error and so on. So that's a simple path diagram for a factor model and that could be written as an equation but we are in this instance using a path diagram. We can extend this to make a slightly more complicated path diagram. Now we have two latent variables Eta1 and Eta2. They're essentially the same diagram as we saw in the previous slide but now we have two latent variables. And we have six observed variables, six variables in rectangles each one of which has an error term. Now we've also added in here a curved line with an arrow at each end. This is to show that in our model the two latent variables are correlated with one another. We're not saying anything about the direction of the relationship between Eta1 and Eta2. We're just saying that we think that there is some kind of relationship between them. In this path diagram we've now introduced a theoretical statement about the direction of the relationship between Eta1 and Eta2. So we no longer have this curved arrow but we have a straight line with an arrow at one end. What we're saying here is that Eta2 is a cause of Eta1. And this again would be similar to the first diagram that we saw, a bivariate relationship, a bivariate regression, with Eta1 regressed on Eta2 and we would then solve for the unknown beta coefficient above the straight line with the arrow at the end. But, as I said, this is now a bivariate regression of a latent variable onto another latent variable. When we are building path diagrams and systems of equations, in structural equation modelling we need to distinguish between two important kinds of variables, exogenous variables and endogenous variables. Now an endogenous variable, as the name suggests, is something which is caused within the system. It's a variable that has, if you like, an arrow pointing into it. It is a dependent variable in one or more equations. An exogenous variable on the other hand is akin to an independent variable in that terminology. It's a variable that is not caused by anything within the system of equations that we are presenting as our SEM. That doesn't mean to say that we believe that exogenous variables are in some senses not caused by any other variables. It's simply that within our own model, the variables in the model, it doesn't have any direct cause. Now an important part of SEM is that variables can be both exogenous and endogenous. So we can have an arrow pointing into a variable, making it endogenous, and that variable itself can have an arrow pointing at another variable, making it an exogenous variable in that limited sense, although it's now a different kind of variable because it has an arrow both pointing into it and an arrow coming out of it. And that's important because that kind of variable is a mediating variable. It's a variable through which another variable has an effect on a third variable. In this path diagram, which we've already seen, but this path diagram now we can distinguish what kinds of variables these are. We've got two exogenous latent variables here. They're exogenous latent variables because there is no directional path pointing into either of them. Neither of them therefore has an error term. This is just a correlation that we're seeing here. So these are both exogenous in the model. Again, we've seen this path diagram. We've got a new distinction that we can apply to it now, though, that Etta 1 is endogenous and Etta 2 is exogenous. Etta 2 doesn't have any direct path going into it. It doesn't have an error term, whereas Etta 1 has an error term pointing into it because it's got a directional path running from Etta 2. So a fundamental advantage of using structural equation models is this ability to represent our theories as diagrams rather than using notation which many social scientists are less comfortable with. Another, if you like, unusual feature of CEM is that in the conventional practice anyway we don't analyse the raw data of the observed variables, but we analyse the covariance matrix, which will denote S, of those observed variables. This is kind of unusual and somewhat surprising to people when they first come across it that all the data that we need is just the set of covariances and variances of the observed variables. As we shall see in later videos, some structural equation models also use the means of the observed variables in addition to their variance-covariance matrix. So what are we doing with this covariance matrix? Well, in broad terms, we are trying to summarise S, the variance-covariance matrix of the observed variables, by specifying a simpler underlying structure. So we're going to specify a model which is in some ways simpler than simply reproducing S. And our model, our CEM, in this sense, the simpler underlying structure, will yield an implied variance-covariance matrix. What I mean there is that if our model is true, then the variance-covariance matrix that we observe should look like this. It should have these numbers in each of the cells. And again, as we'll see later, this implied matrix can be compared to the one that we've actually observed. And that comparison, if it's done properly, can tell us something useful about how well our model is accounting for the data. To the extent that the implied and the observed matrices differ, then our theory, our structural equation model, is not doing a very good job of telling us how this data were generated. So a covariance-covariance matrix, probably most people will be more familiar with a correlation matrix, but here we're dealing with unstandardised variables, and this matrix shows six observed variables, from X1 to X6, and they are in both the columns and in the rows of this table. And the diagonal, which is shown in bold, indicates the variance. So the covariance of a variable with itself, in this case maybe X1 and X1, that gives us the variance of that variable. So a covariance of a variance with itself is its variance, and those are shown in bold on the main diagonal. Then we see in the other cells the covariances, which can be negative or positive in the other cells of this matrix. And you'll observe that the top part of the matrix is redundant with the bottom part. So we actually only need the lower part of this matrix. Now, an important aspect of any model fitting, and structural equation modelling is no different, is the need to estimate what the unknown parameters in our models are, the betas. What is the relationship between eta1 and eta2 in the population? Now there are different ways of estimating these parameters in standard regression modelling we would use ordinary least squares. In structural equation modelling, practices mainly around using a technique called maximum likelihood. And maximum likelihood estimates the unknown model parameters by maximising the likelihood, which we can denote L, of a particular sample of data. Now L is the likelihood is a mathematical function which is based on the joint probability of continuous sample observations. So in essence, maximum likelihood finds what the maximum value of L is for a particular sample of data, and it does that by sort of iterating through using different values for the unknown parameters until it finds the maximum likelihood. Once that maximum has been found, then we have produced the maximum likelihood estimates for the unknown parameters. Now, maximum likelihood is appealing because it is unbiased and efficient. Now, what those terms mean are that if we have a large sample, then our estimates of the unknown parameters will be correct. They will converge upon the true values in the population. They're efficient in the sense that no other way of doing this will give us more precise estimates of those parameters. Now, those two assumptions of being unbiased and efficient do themselves hinge on some other assumptions. One important one is that the data come from a multivariate normal distribution. Essentially, that requires us to be using continuous variables. So, maximum likelihood is less good when we have variables in our data set that are not continuous and that we have arrows pointing into. In those situations, we need to use different estimators. But for now, I'll be focusing on the simpler case of multivariate normal data and maximum likelihood. Now, another way in which maximum likelihood is used in SEM is that not just in the estimation of the unknown parameters but in use of the likelihood, the maximum likelihood. If we take the log of the likelihood for the model, then we can use this to test how well our model fits compared to some more or less restrictive alternative. So, maximum likelihood is used in two ways in SEM. One is for estimation of the unknown parameters and linked to that is use of the log likelihood to assess how well the model fits the observed data. Most areas of statistics that social scientists are familiar with, the focus is very much on estimating the unknown parameters in the model. We want to know what the relationship between X and Y is in the population or possibly what the conditional association between two variables is. And so we focus on estimating those unknown parameters. This is also true, of course, in SEM, but in SEM we have an additional focus which is on fixing or constraining parameters to particular values before we estimate our model. And that's a bit unusual for many people. So, we can fix model parameters to any values, but it tends to be the case that we will be fixing parameters to be the value zero or the value one. Those are the most common parameter constraints that we make in SEM, and I'll come back to why we do that later. But we can also, in addition to fixing parameters to these values, we can also constrain model parameters to be equal to other model parameters. So, we will still estimate those equalized parameters, but they have to be estimated so that they are the same. The model applies that constraint on what the parameters are that are estimated. So, again, that's something which is quite unusual and we don't really see that in many other statistical techniques that we might use in social science. The main thing that we are using these parameter constraints for is for the purposes of model identification, and I will be saying some more about that soon. Now, I said that we can use the likelihood of our model to test how well it fits the data by comparing our model with another model. Now, when we do this, the two models that we compare have to be what is called nested, one within the other. So, what do we mean by nested? Well, it is precisely this that the one model is a subset of the other, or the parameters in one model are a subset of the parameters in the other model. Another way of saying this is that if we have two models, A and B, then model A is the same as model B, but it just adds some additional parameter restrictions. So, A is B plus parameter restrictions. To take an example here, then, if model B has the form y equals A plus beta 1 x1 plus beta 2 x2 plus E, then model A will be nested if it has that same structure, but it applies a parameter constraint to the two beta coefficients that they are equal. So, we now have this property that model A is the same as model B with an added parameter constraint. It is therefore nested within model B. If we consider a third model, model C, though, and we now remove x2 from the model and we add z2 instead, then model C is not nested within model B because it isn't just model B plus some parameter restrictions. It has a new variable, z, which is not in model B. So, these are, if you like, Apple and Pear models. We can't really, in any sensible way, make comparisons between the fit of model B and of model C because they include different variables. So, I've said something about model fit already and the fact that it's based on the log of the likelihood of the model that we've estimated and that we can do this comparison of model fit when the two models are nested. This is because if we take the log of the likelihood for model A from the likelihood for model B, or the log of the likelihood, then that is itself. That number is itself distributed as chi-square and the chi-square distribution then has a degree of freedom which is equal to the difference in the degrees of freedom for model A and model B. We can therefore use this chi-square distribution to test the fit of the first model to the second model. Now, if our value of chi-square has a p-value greater than 0.05, then we will prefer the more parsimonious model, model A. Because what we're saying here in this situation is that the models are not different with regard to one another's likelihood values. We say that the likelihoods are essentially the same. That means that we will prefer the model that is simpler and is estimating fewer parameters. So, where in this case that model B would be our observed data, the variance-covariance matrix, then we're saying that there is no difference between the observed and the implied matrices and our model therefore fits the data well. So, that's the essence of the assessment of model fit using chi-square in structural equation models. We can look at the difference in the likelihood for one model and compare it to the likelihood for a nested model and make a statistical test of whether one fits the data better than the other. So, the last thing I'm going to talk about in this video is model identification. This is all linked with the things I've talked about already to parameter constraints and fixing parameters to particular values and to assessing model fit and so on. So, what is model identification? Well, in conceptual terms, we need to have enough known pieces of information in an equation to produce unique estimates of unknown parameters. Now, we need unique estimates, otherwise we don't know which ones to prefer. So, to give an example of what we mean here by the balance between known and unknown pieces of information, if we look at these two equations, the first of these is unidentified. We have x plus 2y equals 7. So, what we would want to do is to find the unique value that satisfies that equation for y. Now, because we have a balance of knowns and unknowns where x and y could really take on many, many, many different values and they would all be true if you like in terms of the equation being correct, that equation is unidentified because it doesn't enable us to produce unique estimates. Now, if we change that equation slightly where we now are not having x as an unknown and we make that 3, then we can only have one value for y, which is 2, in a way that will satisfy that equation. So, that equation then is identified. Now, that is the essence of what we need to understand about identification is that it's to do with the balance between the number of known and unknown pieces of information in an equation. Now, there is something else to know about identification which is that it's a theoretical property of the model. It's not really linked to the data as such, so we can figure out what the identification status of a particular model is without having any data or estimating any parameters, but it's also true to say that a model can be theoretically identified but empirically unidentified given a particular set of data. So, we are looking at the balance between the known and the unknown pieces of information in our equations and in SEM, the known pieces of information are the variances and covariances and means, if we are using means in our model, of the observed variables. These are the known pieces of information. The unknown pieces of information are the parameters that we want to estimate in the model. Now, models can have different identification status. A model can have, as we saw a moment ago, more unknowns than knowns. That means that it's unidentified. We can't produce unique values for the unknown parameters, so that's an unidentified model. Other models can be just identified where the number of knowns is equal to the number of unknowns. We don't have any what we call over-identifying restrictions on the model, and therefore, for just-identified models, we don't have any likelihood for the model that we can use to assess its fit. Now, most of the models that people are familiar with using, again, ordinary least squares regression, those kinds of models are just-identified. The third level of identification status is over-identified models, and that's usually what we are trying to get to and deal with in SEM. That's where the number of knowns is greater than the number of unknown parameters in the model. That means that we can assess the fit of the model as well as estimating the unknown parameters. There are different ways that we can assess the identification status of a model. A very simple one these days with modern computers is simply to run our model, and most software will tell us what the identification status is of the model even before we fit the data. It's quite easy compared to how things were done in the past, but nonetheless it's still useful to have a consideration of the identification status of a model as it helps us to understand where things might be going wrong if we have a problem and our model is unidentified. Working through it in this way can help us to see why. Here's the accounting rule that can be used where if we have S as the number of observed variables in the model and the number of non-redundant parameters is given by this equation which is a half of S times S plus 1. Again, S is the number of observed variables and T is the number of parameters that we are going to estimate in the model, the number of unknown parameters. So if T is greater than the answer to this equation then our model is unidentified, we have more unknown parameters than we have non-redundant parameters and if it's less then we have an over-identified model. So to give an example of that here is the path diagram that we saw earlier where we have eta1, a latent variable which is measured by or causing three observed variables and each of those observed variables has an error variance. So if we want to find the number of non-redundant parameters we can use our half S times S plus 1 equation. Now we have S here is equal to 3 so S times S plus 1 is 3 times 4, that's 12, if we take half of that it gives us 6 as the number of non-redundant parameters. Now, how many parameters are we trying to estimate with this model? Well, three variances, one for each error term. We've got two factor loadings, one of them you'll see there is constrained to one so we're fixing that loading and that is for identification of the model. So we're not estimating that factor loading but we are estimating the other two. So we have two factor loadings and then lastly we have variance for the latent variable. So 3 plus 2 plus 1 is 6 parameters to be estimated which is the same as the number of non-redundant parameters. So we have with this model 0 degrees of freedom. The model is just identified so we can estimate the unknown parameters but we do not have any way of assessing the fit of this model because it's just identified, no degrees of freedom. Now something else that's important to understand about identification is that we as the analyst can control to some degree the identification status of our model. So we can do this for a model like the one that we just saw that's just identified or a model that's under identified by adding more known pieces of information to the equation or by removing some unknown pieces of information removing parameters that are to be estimated and adding constraints. So if we were to constrain two of the parameters in the model to be equal to one another, let's say we constrain two of the regression coefficients or the factor loadings to be equal now we're only estimating one parameter where previously we were estimating two. So we've removed one unknown and gained one degree of freedom. Now we can see this in this model here where we have added an additional observed variable to the previous path diagrams and now the model is essentially the same but we've got a fourth observed variable X4. We now are estimating an additional factor loading and an additional error variance but we have gained more in terms of our known parameters. So now if we use our half s times s plus one equation s is now four so four times s plus one four times five is 20 we take half of that now we have ten non-redundant parameters in this model and we have four plus three plus one parameters to be estimated eight so ten minus eight gives us two degrees of freedom so adding that fourth observed variable our model is now over identified and we can say something about the fit of that model to the variance covariance matrix that we've observed. So in that example we changed the identification status of our model by adding in another known piece of information another observed variable of changing the identification status is to remove unknown parameters. Now here in this example we are now not estimating the two factor loadings that we were in the first example so you can see there's a number one next to each of the arrows for the factor loadings so rather than estimating those we're saying these are all equal now this may not be a very theoretically meaningful thing to do this isn't the point at this particular juncture what we're showing here is that you can change the identification status of the model by removing unknown so we're not estimating these anymore so we still have six non-redundant parameters but we now are only estimating four unknown parameters because we are not estimating any of the factor loadings so now this model is over identified so in this video I have covered some of the important ideas and concepts that learners will need to take with them into later videos and applications these are focused around the use of path diagrams for representing our theories and our equations the fact that we analyse the covariance matrix of the observed variables rather than the raw data that we use for the most part maximum likelihood estimation which has some quite restrictive assumptions about multivariate normality but nonetheless is a very useful estimator it gives us consistent, unbiased and efficient estimates of the unknown model parameters and allows us to do global tests of the fit of the model to our data those kinds of fit tests are mainly applicable in the context where models are nested where we can say that one model is a subset of a second model that is the same as the second model with some additional parameter restrictions and I've talked about identification of models and models that can be under identified just identified and over identified and how we as the analyst can exert some control over the identification status of our model by removing unknown parameters or adding in more known parameters


Live from Austin, Texas, it's theCUBE. Covering South by Southwest 2017. Brought to you by Intel. Now, here's John Furrier. Okay, we're back live here at the South by Southwest Intel AI Lounge. This is theCUBE's exclusive coverage of South by Southwest with Intel, hashtag Intel AI. We're amazing stars with Intel. Our next guest is Dr. Don Napis, who's with Intel and you are a senior research scientist. Welcome to theCUBE. Thank you. So you've got a panel coming up but you also have a book, AI for Everything, and looking at a democratization of AI. And we had a quote yesterday that AI is the bulldozer for data. What bulldozers were in the real world is AI will be that bulldozer for data, surfacing new experiences. This is the subject of your book. Kind of, what's your take on this and what's your premise? Right, well the book actually takes a step way back. It's actually called self-tracking. The panel is AI for everyone but the book is on self-tracking. And it's really about actually getting some meaning out of data before we start talking about bulldozers. So right now we've got this situation where there's a lot of talk about AI is going to sort of solve all our problems in health and there's a lot that can actually get accomplished. But the fact of the matter is is that people are still struggling with Gs, like what does my Fitbit data actually mean? Right, so there's a real big gap. And I think probably part of what the industry has to do is not just sort of build new great technologies which we've got to do but also start to fill that gap and sort of data education, data literacy, all that sort of stuff. So we're kind of in this first generation of AI data. You mentioned wearables, Fitbits. So people are now getting used to this. So this sounds like this integration into lifestyle becomes kind of a dynamic. Yeah. How are people grotting with this? What's your research say about that? Well right now with wearables, frankly, we're in the classic trough of disillusionment, right? For those of you listening, I don't know if you have sort of wearables and drawers right now, right? But a lot of people do. And it turns out that folks tend to use it maybe about three or four weeks and either they've learned something really interesting and helpful or they haven't. And so there's actually a lot of people who do really interesting stuff to kind of combine it with symptoms tracking, location, right? Other sorts of things to actually really reveal the sorts of triggers for medical issues that you can't find in a clinical setting, right? It's all about being out in the real world and figuring out what's going on with you, right? So then when we start to think about adding more complexity into that, which is the thing that AI is good at, right? We've got this problem of there's only so many data sets that AI is actually any good at handling, right? And so I think there's going to have to be a moment where sort of people themselves actually start to say, okay, you know what, this is how I define my problem, right? This is what I'm going to choose to keep track of. And some of that's going to be on a sensor and some of it isn't, right? And sort of really being intervening a little bit more strongly in what this stuff's actually doing. You mentioned the Fitbit and you were seeing a lot of disruption in the areas, innovation and disruption, same thing. Good and bad potentially, but obviously autonomous vehicles is pretty clear. One knows of Tesla's tracking and their Hot Trend. But you mentioned Fitbit, that's a healthcare kind of thing.  Is that a low-hanging fruit for AI, healthcare? I don't know if there's any such thing as low-hanging fruit in the space. But certainly, if you're talking about actual human benefit, right? That absolutely comes to top of the list, right? And we can see that in both formal healthcare and clinical settings and sort of imaging for diagnosis. Again, I think there's areas to be cautious about, right? Making sure that there's also an appropriate human check and there's also mechanisms for transparency, right? So that doctors, when there is a discrepancy between what the doctor believes and what the machine says, you can actually go back and figure out what's actually going on. The other thing I'm particularly excited about is, and this is why I'm so interested in democratization, is that health is not just about what goes on in clinical care, right? There are right now environmental health groups who are looking at a slew of air quality data that they don't know what to do with, right? And a certain amount of machine assistance to sort of figure out, you know, signatures of sort of point source polluters, for example, is a really great use of AI, right? It's not gonna make anybody many money anytime soon, but that's the sorts of, you know, that's the kind of society that we want to live in, right? There was a social good angle for sure, but I'd like to get your thoughts because you mentioned democratization and it's kind of nuanced, depending upon what you're looking at, democratization with news and media is what you saw with social media. Now you got healthcare. So how do you define democratization in your context that you're excited about? Is that more of freedom of information and data? Is it getting around gatekeepers and siloed stacks? I mean, how do you look at democratization? All of the above. I'd say there are two real elements to that. The first is making sure that, you know, people who are going to use this for more than just business have the ability to actually do it, right? And have access to the right sorts of infrastructures to really do whether it's the environmental health case or there are actually artists now who use natural language processing to create artwork. And people ask them, why aren't you using deep learning? It's like, well, there's a real access issue, frankly. It's also on the side of, if you're not the person who's going to be directly using data, a kind of a sense of, you know, there are bigger, democratization to me means being able to ask questions of how the stuff's actually behaving, right? So that means building in mechanisms for transparency, building in mechanisms to allow journalists to do the work that they do. Sharing, potentially. I'm sorry? And sharing as well more data. Right, absolutely. I mean, frankly, we still have a problem right now in the wearable space of people even getting access to their own data, right? There's a guy I work with named Hugo Campos who has an arterial defibrillator and he's still fighting to get access to the very data that's coming out of his heart, right? Is it on SSD? I mean, is it in the cloud? I mean, where is it? It is in the cloud. It's going back to the manufacturer and there are very robust conversations about where it should be. That's super exciting. So this brings up the whole thing that we've been talking about yesterday and we've had many statements on theCUBE is that there are all these new societal use cases that are just springing up that we've never seen before. Self-driving cars with transportation, healthcare access to data, all these things. What are some of the things that you see emerging on that tools or approaches that could help either scientists or practitioners or citizens deal with these new critical problem solving that needs to apply technology to because whether it's, I mean, I was talking just last week at Stanford with folks that are looking at gender bias and algorithms. Something I would never would have thought of. That's an outlier, like, hey. Oh no, it's not. What? No, but it's one of those things where, okay, let's put that on the table. There's all this new stuff coming on the table. Yeah, yeah, absolutely. What do you see? How do we solve that with approaches? Yeah, there are a couple of mechanisms and I would encourage listeners and folks in the audience to have a look at a really great report that just came out from the Obama administration and New York NYU School of Law. I was called AI Now and they actually proposed a couple of pathways, right? To sort of making sure we get this right. So, you know, a couple of things. You know, one is frankly making sure that women and people of color are in the room when the stuff's getting built, right? That helps.  Like, it just will. We can't predict how these things are gonna work. And catching it after the fact and building in mechanisms to be able to do that really matters. There was a great effort by ProPublica to look at a system that was predicting criminal recidivism, right? And what they did was they said, look, you know, it is true that the thing has the same failure rate for both blacks and whites, but some hefty data journalism and data scraping and all the rest of it actually revealed that it was producing false positives for blacks and false negatives for whites. Meaning that black people were predicted to create more crime than white people, right? So, you know, we can catch that, right? And when we build in more systems of people who have the skills to do it, then, you know, we can build stuff that we can live with. This is exactly to your point of democratization, I think that fascinates me that I get excited about.  That there's all these new things that are emerging and the community has to work together because it's one of those things where there's no, there may be a board of governance out there. I mean, who is the board of governance when it's done? It really has to be community driven. And NYU's got one, and there are examples of communities that are out there that people can participate in or... Yeah, absolutely. So, I think that, you know, they're certainly collaborating on projects that you actually care about and sort of asking good questions about is this appropriate for AI or not, right? It's a great place to start or reaching out to people who have those technical skills. And there are also the Engineering Professional Association actually just came out a couple months ago with a set of guidelines for developers to be able to, you know, the kinds of things you have to think about if you're going to build an ethical AI system. So they came out with some very high level principles. Operationalizing those principles is going to be a real tough job and we're all going to have to pitch in and I'm certainly involved in that. But yeah, there are actually systems of governance that are co-hearing, but it's early days. It's a great way to get involved. So I got to ask you the personal question in your efforts with the research and the book and all your travels. What's some of the most amazing things that you've seen with AI that are out there that people may know about or may not know about that they should know about? Oh, gosh. I'm going to reserve judgment. I don't know yet. I think we're too early on the curve to be able to talk about sort of the magic of it. What I can say is that there is real power when ordinary people who have no coding skills whatsoever and frankly don't even know what the heck machine learning is, get their heads around data that is collected about them personally, right? That opens up, you can teach five-year-olds statistical concepts that are learned in college with a wearable because the data applies to them. So they know how it's been collected. It's personal. Yeah, they know what it is already. You don't have to tell them what an outlier effect is because they know, because they were that outlier. You know what I mean? They're immersed in the data. Absolutely. And I think that's where the real social change is going to come from. I love immersion. It's a great way to teach kids, but the data is key. So I got to ask you with the big pillars of change going on and at Mobile World Congress, I saw you Intel in particular talking about autonomous vehicles heavily, smart cities, media entertainment, and the smart home. I'm trying to get a peg, a comparable of how big this shift will be vis-a-vis, I mean, the 60s revolution when chip started coming out, the PC revolution and server revolution, and now we're kind of in this new wave. How big is it? I mean, in order of magnitude, is it super huge? All of the other shifts combined? Are we going to see radical configuration changes? You know, I'm an anthropologist, right? So everything changes and nothing changes at the same time, right? We're still going to wake up, we're still going to put on our shoes in the morning, right? We're still going to have a lot of the same values and social structures and all the rest of it that we've always had, right? So I don't think in terms of plonk, here's a bunch of technology, now that's a revolution, right? There's like a dialogue, right? And we are just at the very, very baby steps of having that dialogue. But when we do, right, the people in my field call it domestication, right? These things become tame, they become part of our lives, we shape them and they shape us, and that's not radical change, right? That's the change we always have. Let's have a solution. So I got to ask you a question because I have four kids and I have this conversation with my wife and friends all the time because we have kids, digital natives that are growing up, and we see a lot of also workplace domestication, people kind of getting domesticated with the new technologies. What's your advice for whether it's parents to their kids, kids that are growing up in this world, whether it's education, how should people approach the technology that's coming at them so heavily in an age of social media where all of voices are equal right now and yet more filters are coming out? So it's pretty intense. Yeah, yeah. I mean, I think it's an occasion where people have to think a lot more deliberately than they ever have about the sources of information that they want exposure to, right? The kinds of interactions and mechanisms that actually do and don't matter. I'm thinking very clearly about what's noise and what's not is a fine thing to do, right? So yeah, probably the filtering mechanisms has to get a bit stronger. I would say too, there's a whole set of practices. There are ways that you can scrutinize new devices for where the data goes, right? And often kind of the higher bar companies will give you access back, right? So if you can't get your data out again, I would start asking questions. All right, final two questions for you. What's your experiences like so far at South by Southwest? And where is the world going to take you next in terms of your research and your focus? Right, well, this is my second year at South by Southwest. It's hugely fun. I am so pleased to see just a rip-roaring crowd here at the Intel facility, which is just amazing. I think this is our first time as Intel proper. Having a really good time, the self-tracking book is in the bookshelf over in the convention center if you're interested. And what's next is we are gonna get real about how to make these ethical principles actually work at an engineering level. Yeah, computer science means social science happening right now, Intel powering amazing here at South by Southwest. I'm John Furrier, you're watching theCUBE. We've got a great set of people here on theCUBE. Also, great AI lounge experience, great demos, great technologists, all about AI for social change. I'm with Dr. Dawn Napest with Intel. We'll be right back with more coverage after this short break.


Hello everybody and welcome back to my vlog to my channel. I just wanted to give you a little bit of an update of the Long range well medium to long range is you know, what depends what you consider to be long range or not quad built Drone Now this is the last one on the hardware basically. I'm just going to show you how I've put this in I've gone for the 30.5 Scale you can get smaller 20 by 20 and I'm not using an all-in-one stack or anything Basically, I'm using what I've used before in other builds, which is the Caddx camera I find it to be pretty good with a ND filter on there Not so good without an ND filter The only thing I bought new for this is the frame a long range frame and the ESC's these I bought them because they're very narrow as you can see these are I think likes I can't remember what they are, but they're very very narrow. These are the talon sky star talon 48 40 amp, sorry and They've got a heat sink built on top Which is quite nice and they got LEDs. I've not actually seen these lit up yet because I've not powered any of this just yet It's not Set up in your software or anything. I just wanted to show you the sort of thing you you might be dealing with You know when it comes to the wiring and it can all start looking like a bit of a colossal mess and These is just one of the things, you know, yeah, as you as you build you'll be able to make these Pretty much smaller and smaller even though. This is pretty much on its limit Wow small I can get this imagine there look that's for the top for the GPS now I Set mine so my antenna is at the back the control and ten is at the back and video transmitter is at the four so four and the app and The reason why I've done it like that is because even though it's not a set rule and I'll show you Even though it's not a set rule, let me just push that up away because if you can see this little thing here This has the VTX and the receiver pretty much on top of each other down here Because the VTX is this bottom part In amongst just in the top of this space here is the actual receiver For this but what I don't like about it is that to me is just too close for long range for this the way This is is great. Just for flying around a bit of fun. So I'll see you bring this is a hundred and forty two grams all in Even with a 4k camera It might be an extra gram when the ND filters on there But even with a 4k camera is 142 grams. This is brilliant, but it's no good for long range. All right, there's The battery and everything the flight times of this is five minutes tops But so it's not as though it's a rule. Oops, there's my thing in number. I think you can do anything with that anyway I don't think it's a rule But I don't like doing it like that, especially if it's going to be something for you know Trying to get a bit of an extended distance So what I like to do is try to have them as far away from each other as possible the video transmitter No, that's gonna be on there. I'm not quite sure yet what I'm putting on there could be this this fat shark thing This is no good because there It doesn't have the pin on the middle and that means I have to put an adapter in there And there's a little bit of loss associated with that We're gonna try and keep as much gain as we can so it's probably gonna be one of these just because it will go everything's all black on here and Another thing about it as well is I've bummed my battery, so it's gonna swing underneath No, it's gonna be underneath because if I were to put the battery on the top where I believe that that's where they might You know, so have you put it given these lines near for your strap to get through? I'm not gonna be able to put the GPS and As this is gonna be a bit of a longer range thing and as I've got these GPS needs are very good these new blocks N8M NEOs Very good GPS It's got a bigger antenna and for me for a long range. I prefer to have a bigger antenna. I got a small one We could swap that on pop that on top, but again, it means that's gonna be somewhere where the battery has to be All would have been and so the battery on mine is gonna sling underneath Like I say my receiver antenna at the back here. This is my little receiver and I'll just use You know a zip tie or Whatever to keep that in place underneath there and this will actually You know, you just better push in to push it through there The actual video transmitter, but anything you can see you might just be able to see where it connects in Put a bit of light on that you might just be able to see where it connects in underneath there On the AMX XC or whatever it is connection I don't know if you cannot look trust me. It's it's underneath there and it Sits out to the back here a little bit. That's where the power cables come out there if you can see the board Just down there So it looks like it's all a bit of a cluster Effort and it's But you know that that should work reasonably well when I say long distance a lot of this is gonna be How far can your battery get you and back of course safe But it will be fun to play around with and it will be able to be you know Longer distance and you have a little while just showing you because the battery that's gonna go on here The batteries I've used before I Don't know how much juice they're gonna have left in them because I have three so I come quite a bit But these are just to be compacts 220 2200 or 2.2 amps 2200 million 25 C series just because hey I'm not gonna be pumping this and trying to do flips and rolls even though it will be capable of it But it will probably put some thing on the battery this one's got a slight So I have to watch that one. It's got a slight bubbling very slight the other ones I've got down So I expect there'd be better, but I may just get some refreshed batteries anyway by some new ones I Like to got a little caddocks on the front just for now. I don't know what to put in there I wanted to have enough space to be able to put that and maybe sticky 4k camera On here somehow. I do have a little fitting That came with that other little quad I showed you that may I may be able to put Fit it through here, you know and connect it through and just have it right on the front But maybe not I mean, I'm not quite sure I'm gonna do that I may have to move this around at some point in order to be able to do that Maybe see if I can get it squeezed into here. I don't really like putting super tight bends on the wires That's not so bad, but if you start, you know, you start crimping that wire up too much It's it's just not really good practice to do that Twizzling all these wires up is a good idea because of the way the magnetic fields work as you've got You know juice flying around it and it'll cancel cancel them out And it also means you've not got individual wires just sticking out because that can become a transmission or an antenna And depending on the frequencies that are used So that's it. Yeah, this is this is once it goes together and then these are the tails for the battery I'm not quite established. I'll get a battery strapped on it and work out what the best Length of this is gonna be and then get the XT60 put on there And that's it that that's basically what it's gonna be like it's gonna be like this with that sticking out here So we've got the active side here and there you can tell that with these antennas got one slightly longer in the other and That will fit in there This will be sat on the top of here Once I can get it to screw on there Set like that and just for now. It's gonna like I say it can have that camera I will be able to see the props through it. I Got some props here Oh So with that sat on there, it's quite a bit of distance actually if I put that on there Look, he's still quite a bit of distance. It may even be good for eight inch props But that's yeah, that would actually that would actually have had that's the right prop for the right motor So it'd be spinning inwards those ones be spinning outwards, but we'll settle that up in the software Like I say with ease, you know, if there's one not the right way around I don't need to cut that up I just adjust it in the software just reverse it It's not a big problem So there we go. That is the update with this nothing special nothing great here But it seems that once it's all on and this is all down and on and Everything else is connected up. I get the software I'll give you a little show it up and then we'll get it up in the air and see see what we got So we've got some that flies relatively stable Straight from default settings, which is what we want and normally that's the way it goes You did default settings will do that for you At least that's been in my experience And I'll sit there transmitter Receiver and receive. All right guys. Hey, thanks if you've watched this and so far be watching these videos it's been a while since I've built one of these and I Can't wait actually to get up in here So that should be by the end of the week just because I've got a whole bunch of things I've got to do this week doctors and bits and pieces and I will talk to you shortly. Bye for now


So guys it is finally here, the major power world update which introduces new content like the first ray boss and many many many many other things which are definitely a welcome deletion have been added into the game. Now as I make this video the Steam version is available to download for you PC players to play but the Xbox version is coming very very soon I'd probably save it in the next few days but today guys we go through the entire update patch notes and there's a lot to get through here guys so sit back relax smash that like button it really helps me out and let's get into this new power world content ok so let's go I'm going to start with the new content here so implement the first raid boss you can summon raid boss powers by using slabs at the new summoning altar now the summoning altar guys is something you have to put inside of your base from what I've seen so far so you need to make room for this and slap this thing inside of your base and they can go in some of the boss and fight against this dude pretty cool now raid boss powers summoned by slabs are very powerful and cannot be captured work alongside your base powers to take on these powerful foals pretty cool pal eggs can drop after defeating raid boss powers that's very very interesting that's probably going to be the way we can actually capture and use these raid bosses obviously the first one is Balanor so that's pretty cool the extreme version of the raid boss is incredibly powerful and difficult to defeat very very interesting for sure can't wait we love a good challenge here new training manual items have been added these manuals allow you to give experience points to pals wow new item ancient technical manual has been added this manual gives you ancient technology points and can be randomly found inside dungeon chess okay good addition new recovery meds item has been added to using these meds will slowly recover your HP over time awesome you won't need to rely on your pals anymore okay so new item homeward thunder cloud has been added when you use this item will instantly move you to your nearest base wow get out of jail free card that sounds new item ability glasses have been added when equipped you can see pal stats like what like what that was a modern arc what there that's amazing that's pretty cool so if you're out after certain stats uncertain pals I don't know to help with your breeding progress this is incredible that's an amazing addition okay so new stat boosting items have been added power fruit life fruit and stout fruit these items will boost pal stats when used really is that a permanent change we'll see you guys we got a lot to investigate with this new update okay so we also have it added a new passive mercy hit pals with this passive cannot reduce enemy HP below one when attacking oh man now upon first reading this I'm thinking why would you want this but actually it makes complete sense because if you have this on a pal and you want to capture a certain power out in the open world but you don't want it to be killed this now will allow you to take it down to a one HP and catch it much much easier that's an incredible inclusion it really is we also have new item ring of mercy has been added when wearing this ring you can not reduce enemy HP below one when attacking that's incredible too these are amazing for you going out and capturing those powers pretty cool okay so a new armor multi-climate undershirt has been added protect yourself against both the heat and the cold with just one slot incredible okay so a new building electric egg incubator has been added this incubator consumes electricity to automatically adjust the temperature to the optimum temperature for each egg that's a great great change or an inclusion it really is okay so a new building or mining site has been added this mining site allows you to reduce or from the comfort of your base wow no more or farming spots or no more or base spots where a lot of people have had these have built their bases around these all spots on the map and have their pals they're just working to farm that all for them this completely eliminates that wicked absolutely incredible change okay so now on to pals kelp see can now produce power fluids at the ranch oh my these changes are just getting better and better and better okay so do mud can now produce high-quality power oil at the ranch you can now reduce the weight of metal ore while riding surf and terror cool increase the amount of ore dropped while riding astagon geez if you guys haven't seen my astagon ore build he's got basically the lightning strike attack you boost up his stats so it's got maximum attack yeah absolutely wipes out or in a certain vicinity with one shot this just makes that even better awesome you can now raise pals rank to the maximum for a single synthesis using the power essence condenser condensation progress is now accumulated in the individual power wow negative power status will now be resolved after spending some time in the power box pretty cool okay so now on to UI while aiming a sphere it will now display how many of the target pal has already been captured geez you can now check the cooldowns on partner skills for all your pals on the main screen nice equipment and item stats are now visible on the technology screen even if you have not unlocked them first awesome the tutorial has been improved and renamed to journey you can now show slash hide the journey in the games options damage number display size can be changed in the game options to in raid boss battles the damage numbers tend to overlap a lot and it may be difficult to see so recommend adjusting the size here cool okay so unto you player items dropped by players after death on a dedicated server can now be picked up by anyone after 24 hours of real time has passed pretty cool guys well added new sleeping player emote okay so now onto base related you can now allow and disallow certain work for base powers at the money stand incredible change chest filters have been added nice select item types to allow or disallow inside chess pretty cool I guess this will stop certain pals around your base taking certain items to certain things that's what we like to see crafted items are now transported from crafted facilities selecting the allowed transport when crafting will result in pals transporting those items to chess when finished nice you can now edit your characters appearance at any time by using the antique dresser cool building and building piece placement rules have been relaxed nice you can now connect stairs facing upwards pretty cool probably the best change so far I'm joking but yeah so cool roof pieces can now directly connect to foundations man that's badass triangular walls can now be connected to stairs pretty badass too you can now force a power to work and cancel a break by picking them up and throwing them towards a facility pals recover they're saying we'll take a night break so be careful yes they do fix assignments remain fixed even after bad events occur previously some assignments would change due to certain conditions but now they will remain unless the power is placed inside the power box that's a great great change people hopefully they stay locked to that certain assignment after you lug off and lug back into that was a problem I always had so yeah okay so balance adjustment and minimum heat and cold resistance have been added to various armor you're no longer need to take off your heat resistant armor when it is cold at night in the starting areas we change reduce the button press time in the egg incubator change the pattern of the German Tide Ignis to something more unique okay added legendary blueprints for some firearms dropped from specific enemies nice corrected the selling price of diamonds in single player is no longer possible to select the initial spawn points for multiplayer a lot the back of the starting area to prevent players from getting lost or stuck increase the attack power multiplayer of partner skills that increase the players attack power riding has been unformally reduced from 2.0 to 1.2 okay so eggs now have a small chance to produce alpha pals say what really that's badass flying and floating pals are now immune to falling damage shut price adjustments okay so we have bug fixes here fix a bug where treasure chest would become empty when dying in a dungeon etc fix an issue where the effect that increases the players attack power while riding was duplicating and accumulating on the certain conditions okay fix an issue where players were not receiving loot when capturing pals while mounted adjusted the HP of the legendary power and fix an issue where the difference in HP between the captured legendary power and a better legendary power was too large fix an issue where power would eat while riding okay so lastly for this section fix an issue where spheres thrown close to what pals would not hit and be lost okay so others improved the various power models and textures nice added and adjusted some sand effects and many other minor bug fixes now onto a dedicated server here fix an issue where sawing did not work in the server list improve the server list to allow page transitions dedicate servers now support various log outputs and implemented the rest API and please check the tech guide for details the link there cheap prevented fix a vulnerability that allowed steam account spoofing and fix several and fix seven of our critical vulnerabilities soundtrack one new song has been added to the soundtrack you've already purchased soundtrack please update and enjoy the new song please note that the soundtrack may take a few days to update they also saying this is a big one we are planning a larger more content packed update for summer 2024 enjoy never before seeing scenery and filling adventures on a new island home to many new pals in addition we plan to add a large amount of new content including new buildings weapons and tower bosses thank you for your continued support that is absolutely incredible people a massive massive update for sure and again another big one is coming in the summer now there's also a another little inclusion they say a non-bug now this is obviously been added with the latest patch I'll keep this in mind after the update a version 0.2 point 0.6 it has been reported that there was an issue where eggs cannot be placed inside the incubator we apologize for these inconvenience the problem can be resolved by rebuilding the incubator so not too much of a problem right there but there we have the guys an absolute massive jam packed content update for power world absolutely unreal I cannot wait to jump back in guys about a week off now but this is going to pull me straight back in so expect many many more powerboard videos from me but guys if you enjoyed the video leave a like really helps out if you like what you see and want to see more be sure to subscribe and hopefully guys I will see you on that next one


The heads of the NCB, it's so useful because we're getting a lot of things from this conference. First of all, 150 states that come in the same hall. It's very, very useful for us. We can discuss about a lot of things. We are seeing a lot of mutual entrants and we coordinate by this. It's a very, very big and good event that we can do something like this. The main criminal activities that we have in Israel is, first of all, organized crime activities, national ones. We have, of course, drug trafficking, a lot of drugs import and things like this, and money laundering, human trafficking, all of the side effects of organized crime activities. And I think that the Interpol is the main platform that gives us the access to a lot of countries, even that we don't have a diplomatic relationship with them or a mutual entrance between the countries. On the police level, on the inter-level, we are getting a full cooperation. And this is a string that bounded all of the countries together, so I think it's a perfect issue.