From the SiliconANGLE Media office in Boston, Massachusetts, it's theCUBE. Now, here's your host, Dave Vellante. In the early days of Big Data and Hadoop, the focus was really on operational efficiency, where ROI was largely centered on reduction of investment. Fast forward 10 years and you're seeing a plethora of activity around machine learning and deep learning and artificial intelligence deeper business integration as a function of machine intelligence. Welcome to this CUBE Conversation, the skinny on machine intelligence. I'm Dave Vellante and I'm excited to have Jim Cabilis here up from the district area. Jim, great to see you. Thanks for coming to the office today. Thanks a lot, Dave. Yes, great to be here in beautiful Marlboro, Massachusetts. Yeah, so, you know, Jim, when you think about all the buzzwords in this sort of Big Data business, I have to ask you, is this just sort of same wine, new bottle when we talk about all this AI and machine intelligence stuff? It's actually new wine. But, of course, there's various bottles and they have different vintages and much of that wine is still quite tasty and let me just break it up for you, the skinny on machine intelligence. AI, as a buzzword and as a set of practices, really goes back, of course, to the early post-World War II era, as we all know, Alan Turing, the imitation game and so forth. There were other developers, theorists, academics in the 40s and the 50s and 60s that pioneered in this field. So, we don't want to give Alan Turing too much credit, but he was clearly a mathematician who laid down the theoretical framework for much of what we now call artificial intelligence. When you look at artificial intelligence as an ever-evolving set of practices, where it began was in an area that focused on deterministic rules, rule-driven expert systems, and that was really the state of the art of AI for a long, long time. And so, you had expert systems in a variety of areas that became useful or used in business and science and government and so forth. Cut ahead to the turn of the millennium, we are now in the 21st century. And what's different, the new wine is big data, larger and larger data sets that can reveal great insights, patterns, correlations that might be highly useful if you have the right statistical modeling tools and approaches to be able to surface up these patterns in an automated or semi-automated fashion. So, one of the core areas is what we now call machine learning, which really is using statistical models to infer correlations, anomalies, trends, and so forth in the data itself. And machine learning, the core approach for machine learning is something called artificial neural networks, which is essentially modeling a statistical model along the lines of how, at a very high level, the nervous system is made up with neurons connected by synapses and so forth. It's an analog in statistical modeling called a perceptron. The whole theoretical framework of for perceptrons actually got started in the 1950s with the first flush of AI, but didn't become a practical reality until after the turn of this millennium, really after the turn of this particular decade, 2010, when we started to see not only very large big data sets emerge and new approaches for managing it all like Hadoop come to the fore, but we've seen artificial neural nuts get more sophisticated in terms of their capabilities. And a new approach for doing machine learning, artificial neural networks with deeper layers of perceptrons, neurons, called deep learning has come to the fore. With deep learning, you have new algorithms like convolutional neural networks for current neural networks, generative adversarial networks, these are different ways of surfacing them, higher level abstractions in the data. For example, for face recognition and object recognition, voice recognition and so forth. These all depend on this new state of the art for machine learning called deep learning. So what we have now in the year 2017 is we have quite a media for all things AI. Much of it is focused on deep learning. Much of it is focused on tools that your average data scientist or average developer increasingly can use and get very productive with and build these models and train and test them. And deploy them into working applications like going forward, things like autonomous vehicles would be impossible without this. So that's where we're at. And we'll get some of that. But so you're saying that machine learning is essentially math that infers patterns from data. Exactly. And I mean, math that's new math, math that's been around for a while. Yeah, inferring patterns from data has been done for a long time with software. And we have some established approaches that in many ways predate the current vogue for neural networks. We have support vector machines and decision trees and Bayesian logic and so forth. These are different ways of approaches statistical for inferring patterns, correlations in the data. They haven't gone away. They're a big part of the overall AI space, but it's a growing area that I've only skimmed the surface though. They've been around for many, many years. Yeah. Like SVM, for example. And describe further, that's of color to deep learning. You sort of painted a picture of the sort of deep layers of these machine learning algorithms and this network with some depth to it. But help us on better understand the difference between sort of machine learning and deep learning and then ultimately AI. Yeah, well with machine learning generally, inferring patterns from data, as I said, artificial neural networks of which the deep learning networks are one subset. Artificial neural networks can be basically two or more layers of perceptrons or neurons that have a relationship to each other in terms of their activation according to various mathematical functions. So when you look at an artificial neural network, it basically does very complex math equations through a combination of what they call scalar functions that like multiplication and so forth. And then you have these nonlinear functions like cosines and so forth tangent. Although, you know, all that kind of math playing together in these deep structures that are triggered by data, data input that's processed according to activation functions that set weights and reset the weights among all the various neural processing elements and ultimately output something, the insight or the intelligence that you're looking for. Like a yes or no, is this the face or not a face that these bits, these incoming bits are presenting or might present output in terms of categories. What category of face is this?  What I'm getting is that so deep learning is more layers of these neural processing elements that are specialized to various functions to be able to abstract higher level phenomena from the data. It's not just is this a face, but like if it's a scene recognition, deep learning network, it might recognize that this is a face that corresponds to a person named Dave who also happens to be the father in the particular family scene. And by the way, this is a family scene that this deep learning network is able to ascertain. What I'm getting is that those are the higher level of abstractions that deep learning algorithms of various sorts are built to identify. In the automated way. Okay, and these in your view all fit under the umbrella of artificial intelligence? Or is that sort of an Uber field that we should be thinking about? Yeah, artificial intelligence as the broad envelope is essentially refers to any number of approaches that help machines to think like humans, essentially. And that's, when you say think like humans, what does that mean actually? To do predictions like humans, to do to look for anomalies or outliers like a human might separate figure from ground, for example, in a scene, to do to identify sort of the correlations or trends in a given scene. Like I said, to do categorization or classification based on what they're seeing in a given frame or what they're hearing in a given speech sample. So all of these cognitive processes just skim the surface are what AI is all about. Automating to a greater degree when I say cognitive, but I'm also referring to effective like emotion detection. That's another set of processes that goes out in our heads or in our hearts that AI based on deep learning and so forth is able to do depending on different kinds of network, different types of artificial neural networks are specialized particular functions and they can only perform these functions if A, they've been built and optimized for those functions and B, they've been trained with actual data from the phenomenon of interest. Training the algorithms with the actual data to determine how effective the algorithms are is the key linchpin of the process because without training the algorithms, you don't know if the algorithm is effective for its intended purpose. So in Wikibon, what we're doing is in the whole development process DevOps cycle for all things AI training the models through a process called supervised learning is absolutely essential an essential component of ascertaining the quality of the network that you've built. So that's the calibration and the iteration to increase the accuracy and they can say the quality of the outcome. Okay, what are some of the practical applications that you're seeing for AI and ML and DL? Well, chat box, voice recognition in general Siri and Alexa and so forth without machine learning without deep learning to do speech recognition, those can't work, right? Okay, pretty much in every field now for example, IT service management tools of all sorts, when you have a large network with this logging data at the server level the application level and so forth those data logs are too large and too complex and changing too fast for humans to be able to identify the patterns that related to issues and faults and incidents. So AI and machine learning, deep learning is being used to fathom those anomalies and so forth in an automated fashion to alert a human to take action like an IT administrator or to be able to trigger a response workflow either human or automated. So AI within IT service management, hot hot topic and we're seeing a lot of vendors incorporate that capability into their tools. Like I said, in the broad world we live in in terms of face recognition in Facebook. You know, the fact is when I load a new picture of myself or my family or even with some friends or brothers in it Facebook knows lickety-split whether it's my brother Tom or it's like my wife or whoever because of face recognition which obviously depends, that's not obvious, it's not obvious to everybody depends on deep learning algorithms running inside Facebook's big data network big data infrastructure.  And we see this all around us now speech recognition, face recognition and we just take it for granted that it's done, but it's done through the magic of AI. I want to get to the development angle it's an area that you specialize in sort of part of the reason why you came to Wikibon is to really focus on that whole application development angle. But before we get there I think I want to follow the data for a bit because you mentioned that was really the catalyst for the resurgence in AI. And last week at the Wikibon research meeting we talked about this sort of three tiered model edge sort of edge piece and then something in the middle which is this aggregation point for all this edge data and then cloud which is where I guess the deep modeling occurs so sort of a three tier model for the data flow. So I wonder if you could comment on that in the context of it means more data more I guess opportunities for machine learning and digital twins and all this other cool stuff that's going on but I'm really interested in how that is going to affect the application development and the programming model John Furrier has a saying, the phrase that he says that data is the new development kit well if you get all this data that's distributed all over the place that changes the application development model at least you can think it does. So I wonder if you could comment on sort of that edge explosion the data explosion as a result and what it means for application development. Right so more and more deep learning algorithms are being pushed to edge devices by that I mean smartphones and smart appliances like the ones that incorporate Alexa and so forth. And so what we're talking about is the algorithms themselves are being put into CPUs and FPGAs and ASICs and GPUs all that stuff's getting embedded in everything that we're using increasing everything's got autonomous more and more devices have the ability not to be autonomous in terms of make decisions independent of us or simply to serve as augmentation vehicles for our own whatever we happen to be doing thanks to the power of deep learning at the client. Okay so when deep learning algorithms are embedded in say an internet of things edge device what the deep learning algorithms are doing is A, they're ingesting the data through the sensors of that device B, they're making inferences deep learning algorithmic driven inferences based on that data it might be speech recognition, face recognition and so on environmental sensing and be able to sense geospatially where you are and whether you're in a hospitable climate or whatever. And then the inferences might drive what we call actuation in the autonomous vehicle scenario the autonomous vehicle is equipped with all manner of sensors in terms of Lidar and Sonar and GPS and so forth and it's taking readings all the time is doing inferences that either autonomously or in conjunction with inferences are being made through deep learning and machine learning algorithms that are executing in those intermediary hubs like you described back in the cloud or in a combination of all of that but ultimately the results of all of all those analytics all those deep learning models feed the we call actuation of the car itself should it stop, should it put on the brakes because it's about to hit a wall should it turn right, should it turn left should it slow down because it happened to have entered a new speed zone or whatever. All of the decisions the actions that the edge device like a car would be an edge device in this scenario are being driven by ever more complex algorithms that are trained by data. Now the data that let's say with the autonomous vehicle because that's like an extreme case of a very powerful edge device to train an autonomous vehicle you need of course lots and lots of data that's acquired from possibly a prototype that you a Google or a Tesla, whoever you might be have deployed into the field or your customers are using B, proving grounds like there's one out near my stomping ground in Ann Arbor a proving ground for the auto industry for self-driving vehicles and gaining enough real training database and operation of these vehicles in various simulated scenarios and so forth. This data is used to build and iterate and refine the algorithms the machine deep learning models that are doing the various operations of not only the vehicles in isolation but the vehicles operating as a fleet within an entire end to end transportation system. So what I'm getting is if you look at the three-tier model then the edge device is the car it's running under its own algorithms. The middle tier of the hub might be a hub that's controlling a particular zone within a traffic system like in my neck of the woods it might be a hub that's controlling congestion management among self-driving vehicles in Eastern Fairfax County, Virginia and then the cloud itself might be managing an entire fleet of vehicles, let's say you might have an entire fleet of vehicles under the control of say an Uber or whatever is managing its own cars or from a cloud-based center. So when you look at the tiering model then analytics deep learning analytics is being performed increasingly will be for various not just self-driving vehicles through this tiered model because the edge device needs to make decisions based on local data. The hub needs to make decisions based on a wider view of data across a wider range of edge entities and then the cloud itself has responsibility or visibility for making deep learning driven determinations for some larger swath and it might be, the cloud might be managing both the deep learning driven edge devices as well as monitoring other related systems that self-driving network needs to coordinate with like the government or whatever. Okay, so envisioning that three tier model then how does the programming paradigm change and evolve as a result of that? Yeah, the programming paradigm is the modeling itself, the building and the training and the iterating of the models generally will be centralized will stay centralized meaning to do all these functions I mean to do modeling and training and iteration of these models. You need teams of data scientists and other developers who are both adept at statistical modeling who are adept at acquiring the training data at labeling it, labeling is an important function there and who are adept at basically developing and deploying one model after another in iterative fashion through DevOps through a standard release pipeline with version controls and so forth built in the governance built in and that's really it needs to be a centralized function and it's also very compute and data intensive so you need storage resources, you need large clouds full of high performance computing and so forth be able to handle these functions over and over now the edge devices themselves will feed in feed in the data, ingest the data that is fed into the centralized platform where the training and the modeling is done. So we're going to see as more and more centralized modeling and training with decentralized execution of the actual inferences that are driven by those models is the way it works in this distributed environment. The holy grail. All right, Jim, we're out of time but thanks very much for helping us unpack and give us the skinny on machine learning.  Great to have you in the office and to be continued. Thanks again. Sure. All right, thanks for watching everybody. This is Dave Vellante with Jim Kobielus and you're watching theCUBE at the Marlboro offices. See you next time.


Hello, I've just had a go at Unit 7 of my online Teflocourse. This one was about teaching new language. So moving away from sort of basic conversational stuff and teaching maybe a little bit more advanced kind of vocabulary. How you kind of deliver that was the main sort of focus of this unit I think in terms of whether you, how you actually teach it. So do you use certain materials to get them to respond? Do you use conversation? What kind of activities? Can you get them making up profiles for example? And in another unit that I did they used profiles about famous people which is really useful because then you've already got the knowledge and they already have the information, they just need to figure out how to explain it. So really quite useful. Again there's lots of stuff about suitable activities which you kind of overlook and when someone says oh you need to make sure you've got relevant activities you think yeah then that's really easy. But actually you really have to think about which activities you're using because if you're going to try and engage them and you need to get a good study session out of them and reinforce it you need to be using the right activities. So I did enjoy this one. I did access the online tutor and I did read all of the online notes and what I've actually found is useful is I've been printing off the online notes so you can sort of go back and refer to stuff. So just a little tip is if you print them all off, have them in a folder you can go back through so anything you think oh I'm not really sure about that or I don't know or you've had a good activity example before you can draw back to it and use it so it's a really good tool to keep using. But yeah really really good, really enjoyed it.


Hello. This is Mark Punsock, head of Product GitLab. I'm going to demonstrate going from idea to production faster with GitLab 9.0. I'm starting off with the new installation of GitLab Enterprise Edition, running on a Kubernetes cluster, running on Google Cloud Platform. I'm already logged in, so let's create a new group. I'll make it public. Now let's create a new project, starting from a really simple example app just to save myself some typing. Let's get our project connected to the built-in Mattermost. Mattermost is an open-source Slack alternative that comes bundled with GitLab. Now we're ready to configure GitLab AutoDeploy. Back to the project, let's click Setup AutoDeploy and choose the Kubernetes template. This is a great template to get us started, and we just need to edit the cube domain to use our own domain. Great, that completes our project setup. Let's go to our Mattermost client. Chat is where the team would discuss the project and come up with great ideas such as, let's improve the homepage. When a great idea comes along, it's such a waste to let it die in a chat room, so wouldn't it be great if we could create an issue for the project right from chat? Well, with the GitLab Chat command integration, we can do exactly that. Let's see how it works. On first use, the command will ask you to connect your GitLab account, which is as simple as clicking the provided link in the response. Great, now we can see what commands are available. Let's go ahead and create that issue. Great, now we can click through to see our first issue on our new project. As a team leader manager, I'd go to the issue board. Since this is our first time, we have to add a couple columns here to match our workflow. I'll just add the default to do and doing columns. Now we can just add the new issue to our board. Because we want to resolve this right away, I'll move it into the doing column. Great, now let's get coding.  Since we've set this project to deploy automatically to a staging environment, GitLab provides web terminal access to that environment. This is especially useful for debugging, but we can use it here for testing out small tweaks. By clicking the terminal button, we get a command prompt in the same container as our application. Let's edit the server.rb file. Now we've saved the changes. Let's restart the server. And now we can view the web page live to see how we like the changes. That looks pretty good for now, but we didn't commit anything, so this change will be lost the next time we deploy. So let's move on to committing changes into source control by using the web editor. I'm just going to add a header to it. Now, instead of committing directly to master, I'm going to create a new branch named with the issue number. GitLab knows by the branch name that it closes issue number one and adds that message automatically, so we don't have to do anything except hit submit. As soon as the merge request is created, we see it kicked off the CI CD pipeline that will test our contributed code. Here we see a simple pipeline that contains three stages for build, review, and cleanup. At this point, I'd ask another developer on the team to review our merge request, but I don't want to just trust reading the code. I want to see it live in a production-like environment. When a new change is pushed to our branch, this change will automatically deploy to our Kubernetes cluster in a special app called a review app created just for this branch. Right from the merge request, we see a new status telling us that it's been deployed and a convenient link to the actual app. This is what we just created, automatically deployed to Kubernetes to make our review easier. It looks great, so let's accept this merge request and merge it into the master branch. Taking a look at the pipelines tab, we see that we're rerunning CI CD on master to make sure the test still pass after the merge. We actually see the history of all CI CD pipeline runs, and if there are any failures, it'll quickly show you the stage where any runs fail. Going back to the merge request, we now see another status showing that this code has indeed been deployed to staging. We can see our changes running live on our staging server. Let's ship these changes to production. There's this thing called chat ops that encourages us to do these kinds of things in a common chat room so everyone can see important changes. Great, here we see the deployed to production happening live. As an alternative to chat ops, we could have also triggered the deployment from the GitLab interface. Now that it's done, let's go back to environments. Okay, great, we see that the production environment shows up. There we go, we've got our new text in it all the way from idea to production. Here's a new feature in 9.0. If I click on this icon, I see the deployed board for production. Right now, it's only showing a single pod, but if I scale that manually using the Kubernetes command line and then reopen the deploy board, I can see that it's now rolling out 10 instances of the application. Refreshing shows progress. It's now up to three instances. While that's going on, let's see how the app is performing. Clicking on the graph icon, I can see a graph of CPU and memory usage. There's not much to see right now, but this will show the last eight hours so you can monitor how your app is doing. One final thing, the cycle time of getting from idea to production is very important. So GitLab has built a dashboard that helps you track that. Here we can see some metrics on the overall health of our project, and then a breakdown of average times spent in each stage on the way from idea to production. So far, we're doing amazingly well by completing a release cycle in minutes. This is great for team managers and high level managers looking to better understand their company's release cycle time, which is key to staying competitive and responding to customers. So that's it. In 10 minutes, we took an idea through issue tracking, planning in an issue board, coding in the terminal, committing to the repo, testing with continuous integration, reviewing with merge request and review app, deploying to staging with continuous deployment, deploying to production with chat ops and monitoring with Prometheus, and closing the feedback loop with a cycle analytics dashboard. This is all on top of a container scheduler that allows GitLab to get lab runners for CI and the applications we deploy to scale. Welcome to GitLab, the only platform that connects every step of your software development life cycle, helping you bring modern applications from idea to production quickly and reliably.


This work gives an overview of the production, properties and industrial applications of metal phones. First, it classifies the most relevant manufacturing routes and methods. Then, it reviews the most important properties with special interest in the mechanical and functional aspects, but also taking into account costs and feasibility considerations. These properties are the motivation and basis of related applications. Finally, a summary of the most relevant applications showing a large number of actual examples is presented. Concluding, we can forecast a slow, but continuous growth of this industrial sector. This article was authored by Francisco Garcia Moreno.


Good morning, everyone. Thank you for joining us. I'm Nancy Gibbs. I'm the editor-in-chief of Time. And you're docent for this morning. And I think that this is a particularly good topic for us to be addressing in the round. And I would have you take seriously the geometry of this room. This is a subject that is constantly evolving, which touches all of us and the stakes of which could not be higher. And so I'm very eager to have such an interesting circle of people to help lead our conversation. But I really want this to be a conversation in which everyone feels invited to engage. Those of you in this room, those of you who are watching the live stream on Facebook, because there are few topics that touch each of us more deeply, individually, and collectively, institutionally, nationally, internationally than a topic like privacy. There was a time when we could assume that privacy meant, I assume that no one is looking. And we have very quickly come to a point where our default is to assume that someone is always looking, is always watching. We have created in very short amount of time an entirely new economy of privacy and economy of information in which billion-dollar corporations have risen up to monetize and take advantage of something that was once impossible to turn into a product. So to help us, I also think it's striking that we are here the day before the inauguration of a new American president who has talked about why he does not use email and that if you really want to have something be communicated securely, you should use a courier, perhaps a pigeon, some other technology that we have forgotten. This is a very interesting moment in this conversation. And so to help us explore it, I am joined by Andre Kudalski, who is the chairman and CEO of the Kudalski Group, which is a global leader in cybersecurity and convergent media systems with Michael Nidor, the chairman and CEO of Centine Corporation, a Fortune 500 company that provides health care services, in-home health, software, benefits management, telehealth services. Ken Roth, who is the executive director of Human Rights Watch and a former prosecutor and author on topics having to do with security, counterterrorism, the NSA, US spying in recent years. Evelyn Rupert, professor of Goldsmiths at the University of London, and an expert in the sociology of data, and the founder and editor of Big Data and Society. And Jean-Yves Charlier, the former head of France's SFR and now the CEO of Implecom, which is the world's sixth largest mobile carrier. So before each of them gets a chance to weigh in, I'd like for all of you to have a first stab at our presenting question. So get out your phones, because we're, or we could do this in the analog way with a show of hands, because I can't rotate quite that much. You get to vote at weft.ch forward slash vote on the question. By the year 2025, will we see a gap between the privacy rich and the privacy poor? By 2025, will we see a gap between the privacy rich and the privacy poor? We did a version of this poll on time.com in the days leading up to this session. And addressed a few other questions as well. And the response was remarkably strong for something that is this much a matter of public debate. We asked whether people agree that people are increasingly becoming products to be sold to advertisers. 69% of the respondents agreed with that prospect. When we asked whether the concept of privacy itself would gradually fade away because it required too much time or money to protect and preserve it, 77% of people agreed with that statement. When we asked whether your appliances, your refrigerator, or your baby monitor should be allowed to report criminal behavior or be subpoenaed, 72% thought that that would be a bad idea. And on this question about the prospect of the opening of a privacy gap between rich and poor, 91% believe that that is going to occur. So do we have the results from this room? So not quite as strong as the general public, but still a significant conviction that this gap is going to be opening up. So I would like to put this question to each of you because we have a great range of perspectives on this subject, so starting with you, Andre. Thank you. So first I would ask another question. What do we mean by privacy rich or poor? And I don't think that there is one single answer to that. You may have the economical part, you have the education, but you have also to take into account that if someone wants to have privacy, he can sometimes be out of a network that he really appreciates. Don't forget that, for example, some person use WhatsApp, use Facebook, not because they just love it, but because they want to be at the same place to communicate with the person that they appreciate. It's a question of community. Now, even if you have some positive and negative element, take another example, smoking. People know that smoking is maybe not the best behavior for your health, but it's not saying that everyone is stopping smoking because it may be also a question of community. Now, when we look at question of privacy, imagine that you have some person that invests to grant a maximum of their own privacy. Now I will take the other question in a different aspect.  You come to the conclusion that he must eat during nighttime. So fundamentally, if someone is trying to protect himself with some of the parameter of privacy, it's basically saying, oh, it's someone that is just for principle asking for having privacy, or something to hide. So basically, this behavior can even trigger more question for people that are interested to know about the profile. Take another example. If you have a very big house in the middle of, not Afghanistan, but Pakistan, and there is no traffic with mobile phone, no internet traffic, some people may ask who may be living here. So just to say that's the question of privacy with the evolution of technology is extremely difficult to imagine all the way to measure what the people are doing. So if someone is trying to look after one or two parameter, he may forget other element. For example, the refrigerator that is giving away some information that you are not thinking about that can give other information. Now, the last element I would say is that I'm sure that if there is a business model where you can have something, where you can get value out of really preserving privacy, I'm sure that some company will come here to fill the gap and come with something creative. Right. Michael. Thank you. I have a sign in my office that says the secrecy of my job prevents me from knowing what I'm doing. So when one thinks about privacy, it kind of comes into that. And I look at it, and it's going to vary by area. And when I first heard the topic, it seemed relatively simple. People are inside of privacy. But as you think about it, there are many aspects of it. You start off with those areas where there's sunshine laws and there's the transparency that's demanded. And that by definition, you give up privacy by being involved in something. There is that which is stolen, which is the more difficult area. And even when you think about trying to protect privacy, there are the two elements, and you probably know as well as anyone, when it's at rest and when it's in motion. And those are two different elements of protecting the information. And it's a question in my mind, is the technology going to grow faster that protects it or that makes it available? Now, I'm coming at it for a moment or two from the medical area, in which it's our area of the world. And in that particular place, there's serious financial consequences to how it's treated. Medical data, medical information, ID numbers and things have become more valuable than a credit card number. The individuals who get this data and get this information file false claims with fraud. They buy illicit drugs. There's a whole series of things. So I think when you start thinking about in the medical realm, I have difficulty thinking about the difference between one's wealth impacted it. In our field, everybody has to be entitled to the full privacy of their information. But there's also a corporate need, as I highlighted, to protect it from a fraudulent standpoint. Because once that data's out there, it becomes very difficult. So as we look at this, the other question that comes to mind is how easy is it to bifurcate information and data and privacy just based on wealth? Because for individuals, companies in any area, governments, to try and maintain two separate systems. One for those that can afford to pay more than the other. That in itself, I think has a lot of consequences that make it very difficult. So I'm not sure that wealth, I'll be a little contrarian, and say I'm not sure that one's wealth will necessarily provide them with a better opportunity, definitely not in a medical field. The question is in other areas, it becomes a matter of, and it's really interesting, it becomes a matter of what is one entitled to and what are they not entitled to? So I'm looking forward to hearing everyone's comments today. Thank you, Ken. Well, implicit in the question as I read it, it suggests that privacy is somehow a commodity and that if you're wealthy enough, you can buy the commodity. We shouldn't think of it that way. We should think of privacy as a right and it doesn't matter how much money you have, everybody deserves this right to privacy. The problem is that the right to privacy is incredibly poorly protected today. You know, Snowden showed that to us, but it's actually gotten worse, not better than after Snowden. I mean, there are some modest reforms. We've seen Europe make some efforts with respect to data protection, but at the same time, there's been huge efforts with respect to counterterrorism that have justified the mass sweeping up of data. We've seen private companies move toward encryption, partly by popular demand, but encryption only deals with the content of communications. It doesn't deal with the metadata. You can't encrypt the metadata because the metadata has to tell you where the communication goes. And that metadata can be very revealing. Basically, this is a tracking device and governments, companies, the police are able to follow me around and that can be very incriminating. Who you call, who you email tells a whole lot about your life. So I think we have an enormous need to have legal protections of privacy that are not there right now. So far the answer to this has been, oh, but you consented to this. When you subscribed to your internet service, you consented, you remember you read that form very carefully and then you signed at the bottom. But of course, there's no real choice here. There are a handful of companies in any given field. They all have the same gobbledygook with a consent form. And unless you're gonna opt out of private life, which isn't an option, there's no consent here. That's why we do need a regulatory regime, not a consent regime. And it's just gonna get worse. I mean, that's why I think it's important that we're having this session here. Because when you talk about privacy, people sometimes say, oh, I have nothing to hide. I don't do anything wrong.  And that's not even counting how the Chinese may be putting into your baby monitor some kind of virus that takes up things that we're not even intended. So we are all gonna be vulnerable to have personal, very personal information being sent out into some or multiple mass databases. And then what happens? It's not, did you violate the law? Did you see a psychiatrist? Maybe your next employer can go and buy this data and figure out you're not a good bet. Or were you seeing a doctor a bit too much? So maybe that life insurance company is gonna increase your premiums or perhaps not even give you a policy.  So there is an urgent need for laws and regulations here. Businesses should be doing the right thing, but you can't just trust business here. There's an urgent need for laws. I have so many questions. This is just great, so you don't need it. Well, to add to the mix, and I think it's great I'm following this perspective because I think about digital citizens and rights. But I think it's important to start with thinking about that privacy is not something possessed. It's something that involves an ongoing negotiation. It's not a settled thing that, like a commodity, as you suggested, assumes some kind of solidity. We know that technologies are vastly changing quickly and over time. Privacy today may change tomorrow. So we have to think of it in a processual kind of way. And one aspect to the question that's been posed, I think that's really important, that was suggested by Andre, which we in the social sciences think about in terms of the different forms of capital or resources that are necessary to secure something like privacy as a good. Not only does economic capital mean you can buy it, but also technical capital of understanding how these things operate and the kinds of whether it's cryptography or other resources that you can have access to to secure your privacy. Or even your social capital, the kind of networks you're part of and in which you can draw upon in terms of learning and also securing your privacy. So if we're thinking of the future, it's gonna be stratified along all of these different forms of capital. And there's not gonna be just about rich, poor, along one dimension. But as in society, more generally in terms of how societies are organized, it will be stratified across all these dimensions. There will also I think be really uneven effects about whether privacy is something that is secured or not. People who have fewer choices and perhaps are more marginalized will be more susceptible perhaps to the effects of not having privacy. That's also something to think about in terms of the kinds of data that we're talking about because it's very hard to generalize and generalizations generalize. So your search engine queries is that a matter of concern?  I mean, these are very different forms of data and have very different consequences. So when talking across them, privacy is not one thing. Then we have to sort of open up and discuss how it means different things across these various forms. I think the other thing to watch is that people are voting not just through voting as people have done today and through the time poll, but they're voting through their digital actions. They are encrypting their communications. They are opting out of certain platforms. They are creating alternatives. They're maybe not using email anymore. I think that we can also listen to and observe how people are opting out and voting already about this question by saying unless this can be resolved, I don't want to be part of it. So I think those are the kinds of questions that maybe echo a few of the comments already raised, but I think they raise more questions, I'm sure. Well, let me start by saying that this is probably an immensely complex topic and we're probably going to still be discussing the topic of privacy, I think, in 2025. I think there is no radical regulation or legislation on the matter. It's going to be ever evolving as the internet evolves and as innovation evolves. I think there are two scenarios that we need to think about when we think about privacy. I think one that's been raised is that privacy matters. But let me start with a contrarian view. Maybe privacy doesn't matter. And yes, while consumers around the world are concerned about privacy matters, when you look at what they're actually doing, in fact, they're trading off today privacy for convenience, right? And convenience is a big thing in this digital age and new services are a big thing in this digital age. And the new generations that are born today might not be that concerned about privacy, right? Because the trade-offs and the benefits are such that they're willing to give up privacy for new services and convenience, right? And maybe what we're discussing is an issue of the industrial age and not an issue of the digital age, right? So I think we've got to contemplate that scenario. Then the other scenario is obviously that privacy does matter, right? That ultimately there will be a backlash. There's obviously a major concern around the internet of things, right? Because as we've exposed, the internet of things is going to expose a lot more data than what social media is exposing today. And under that scenario, there's a high probability, in fact, that regulators, and most probably in Europe in the next three or five years, are gonna step in and are going to create an individual's bill of rights on the matter of privacy. And there's a high probability also under that scenario that new business models are going to emerge, companies that vault data, companies that create innovation that allow those consumers that want to regain a degree of privacy will emerge. And that innovation cycle is accelerating as we speak. So I think an important theme is certainly whether privacy matters or doesn't matters. But behind that, I think the theme of security is absolutely critical, right? Because if privacy doesn't matter that much, then security is at the heart of the debate, right? And so I think these two scenarios are gonna play out in very different matters. As it comes to the private sector, I think the private sector has a role to play today, right? And that role is ultimately about the issue of security, and certainly giving consumers choice. And I think that's an important dimension of the whole debate, ensuring that consumers across the world have equal choice, right? And so that the debate is not about those, a digital divide on luxury or non-luxury, but the debate is around security and choice. So you've touched on exactly, I think, an interesting fault line between right versus the notion that maybe we, this is a right we're willing to trade away, which normally with fundamental rights, we don't like to think about the idea that we might value something more like convenience or like my willingness to, tell me how you react to the way. No, let me jump in, because the, I mean, to be honest, I think it's an oversimplification, what you're saying. We, in limited senses, do sometimes give up our privacy for convenience, but at this point, it's very difficult to have a partial consent.  And I can, you know, click things off rather than having to find them and enter in again. But do I want the grocery company to then send that data to a life insurance who is gonna say, oh, you're eating unhealthy food, we're gonna raise your premiums, you know. And so you've got to look at the nature of the consent. And I may well be consenting for one particular retailer to use, you know, my preferences for convenience. But what are the protections from that spreading? You know, if I go and use a search engine, I like the fact that they remembered what I searched for last time, it just makes it a little bit quicker, a little bit easier. But should they then be sending that off to the government, because I've been spending too much time looking at up Isis or whatever it is. But isn't that you wanting to have it all, where you want the benefits, you want the convenience, but you don't want to have any of the things that make that a business for that grocery store? Well, for me, this shows the importance of regulation. In other words, just because you have consented for one company, for one purpose to have a bit of information, doesn't mean that it's fair game. And the current regime is, once you've disclosed anything to a company, it can sell it to anybody else. The government can buy it. You basically have no right whatsoever. You've given it up forever. And we've got to get past that. We've got to think of, you know, the partial consent that happens here as limited consent. And the presumption should be that information stays with the retailer for the purpose it was given, period. And there should be an explicit consent required for that to be sold for another purpose. Still, the government can come in with a formal warrant. If they think there's criminality there, they can make a probable cause case and argue for that data. That's fine. But to be able to scoop up all of our preferences or to have any company be able to sell those preferences to anybody else is an invasion of privacy. And I didn't consent to that when I, you know, agreed to shop there and click off what I bought last week. Andra, you? I would try to consider the element from a slightly different angle. Like for piracy, internet has no border. And one of the issues that we have is that the value in different countries or different continents are not the same. Take an example regarding the freedom of speech. Quite absolute in the US, much more limited in some countries in Europe. And in the US, the protection is granted through freedom of speech. In some continental European country, the freedom is guaranteed by privacy. And we have really a fault line between the two. And as internet is not limited to one country, if you have someone that have a regulation in one country and doing things in another country, it will be very difficult through regulation to address this issue. And now I think that there is a need to revisit some of the fundamental rights. We have until now the freedom of speech, but they think that through the internet age, we need to think about the freedom of thinking. Because one element that has not been said so far is through analyzing the personal data, you may now find not only what the people are saying, but what's how they think and what they think about. And if we are not granting the appropriate rights with such type of tracing, then we may face a real issue. How do you possibly come up with when you have so many different cultural attitudes, national attitudes, traditions around these? We're talking about competing values in many of these cases. So how do you, given how global this question is given, how global the internet is, how do you come up with a set of regulations that can operate in that kind of a way? Well, you certainly need some culturally specific possibilities and opportunities, but let me approach that first by responding to the point you made earlier about the young generation does not really worry about this. Well, I think we have responsibilities to the young generation. We have responsibilities to children and youth in other areas of life and social life. To teach them to care about something they don't care about? Well, also, we regulate lots of things that our children are exposed to, whether it's toys and the regulation of the toy industry so they can't be harmed by it. That's our obligation to protect children too. And so just that they don't see it as a problem, we do not leave that. As parents know too and are becoming more sensitive about posting images of their children on the internet because we know those images stay forever. And we have to be more cognizant of what kinds of rights for the future generation are we perhaps giving up through not attempting to protect them. So let's think about cultural differences too, not just across borders and boundaries of nation states, but within countries as well. These kinds of issues are extremely diverse and we cannot come up with just one solution, but we can come up with fundamental principles and rights. And I think that's where we need to start is at the sort of fundamental level which get negotiated and translated into specific national regulations or regulations that are specific to the particular kind of platforms and data that we're talking about. So we can't come up with one answer to that question and be inappropriate in a diverse world that we live in to try to do so. So I think that means we need to really worry about this also in the ways and I'm glad you raised this point about data sharing and consent which are really critical and key about thinking about privacy. So yes, convenience, I like the same things that you have mentioned, but I want the opportunity to consent to how that data is shared or not across different producers. So let me apply that point of view to health because you could argue that one of the most promising aspects of healthcare research now is the promise of big data that by looking at all of this data that we're now able to collect. I mean, this knows more about my health than any of my doctors ever did through my life until a year ago. The promise of big data and in analyzing health outcomes potentially has enormous benefits for research and development of treatment. So does the individuals right to keep their data private supersede the much broader public benefit of researchers having access to that health data? Well, I view the data and we were doing a lot with big data right now. We can look at in five minutes at a million files and identify that somebody's potassium is increasing and they're at risk for a heart attack and notify their doctor. So there's things that can be done at the analytical side of it. But I view the medical data that we collect as personal to that individual and we have an obligation to safeguard it. I think with a financial side if I can come back to that a little bit for you as we're talking about that is that it takes a given critical mass and size company to afford what it takes to protect it. And so you might find some companies charging more for their premiums or for their services because they have the scale and size to buy the protections and install the systems that are expensive. I mean, I've had our CIO come in at the drop of a hat and say, I need $30, $50 million to improve this aspect of the cybersecurity. And you say, yes, now not every company can do that. And so that's where the cost and the future in financials come in. But I definitely answer your first question. I believe medical data belongs to the individual and must be protected. So, Andre, you've written that companies need to become more security literate. What are you talking about there and what is the implication of that for this? But fundamentally through the digitalization, you have more and more critical function in our society that's our function of digital. Take an example, if you have self-driving cars, if you have a cyber attack, you can create a really big accident. So more and more you have digitalization, more you have capability to do new things. Take in the medicine, all the personalized medicine to treat cancer is something that gives you incredible possibilities. So fundamentally not allowing personal medicine will avoid that you can be treated in a very efficient way. So fundamentally to be able to do new things, you will need some data and use data in a way that is very personal, that is extremely high performance. Now, the only way you can reconcile the two is not only to protect much better the data and especially everything that is related to health, everything that is related to things that are extremely personal, must be extremely well protected. Having said that, one element that we have not maybe talked about, there is a big difference between keeping the information, how they say grabbing the information and what is the use of the information. If for example you collect some information about your DNA for example, and if it's used to give you a better treatment, I don't think that people will argue about that. They will argue if this information is used to adapt your insurance premium in a way that is unfair. So fundamentally we have to ask ourselves, is a question of just privacy as such or have we to think about how data can be used even the example of the information like DNA. If the information about you can be useful to improve the way you treat cancer. And for example someone has a cancer today that has been treated, it's not saying that he cannot have something bad happening 10 years from now as a consequence of the first cancer. And fundamentally the data used in an aggregated way may help to solve some issue and be beneficial for the same person. So we have really to think much more about what is the way to handle data in a unique way or aggregated way. And I think that we have really to think more about that rather than just consider privacy as a yes or no question. In August, Tom Wheeler the head of the FCC warned against the discount for data business model and said that if you provide discounts to customers who agree to share their data that he thinks that that is going to drive us in the direction of privacy becoming a luxury that people just can't afford. How would you answer? I think fundamentally what I was raising was that there's two scenarios that could play out. I think that there is a requirement for regulation as we've just talked about here, that there is a requirement for enhanced security. But a point that we haven't touched upon that I think also relates to this is the point of education and the point of consumer knowledge on the matters. And whether our education systems also need to step up to educate at the youngest age also are consumers as to their rights, as to regulations, but also as a matter of understanding what is potentially done with their data. So I think that to Ken's point there's no oversimplification. This is a complex matter on many points. I don't think that we can go to a model where we over-regulate because regulation will always be behind. So hence why I put on the agenda also the issue of education. Well, that feels like it touches on what you were saying, but I do think it's an interesting question about if the 20th century or pre-21st century view of privacy as something that we value as a fundamental human right as a good that deserves to be protected. If that notion were to fade away what would be lost? Well, I wouldn't assume it's gonna fade away, but I mean, what's lost? But it certainly is under, it's not in a certain way. I mean, one thing we've seen is that there's a direct relationship between the right to privacy and the right to free expression. That people are more willing to talk if they feel they have a degree of privacy because you don't always talk with a megaphone for the government to hear. You often just talk among a circle of friends. But if you fear that the government is gonna intrude and particularly if you live in a place where the government may not be so respectful of dissent, a lack of privacy means a lack of free expression. So I think privacy is a key element of self-actualization overall.   That's smart education. But that's very different from saying, education is a substitute for regulation because education ultimately is premised on a consent regime. We're gonna educate you to be a good consumer and then you consent to share all your data. But we can't really consent.  So you need protection of it. Now, we talked about big data and I think it's useful to distinguish between different types of big data. Big data, you know, anatomized can be very useful. You can get a sense of the public health of a broader population using anonymous data on health.  How do they know where there's a traffic jam? It's because the phones aren't moving. So, you know, that doesn't... And so, as long as it's anatomized, I'm fine with that. But what I have a problem with is if you look at what the U.S. government used to justify its surveillance, they said, we need a haystack to find the needle. You know, how are we gonna find the needle being, you know, some terrorist plot if we don't keep the haystack, which is all of your communications? So therefore, you know, we're gonna unanomized. You know, we're gonna know who is talking to whom, who is emailing who, and we're gonna keep all that data in our computer. And then that became problematic. And indeed, I mean, everybody should worry about the U.S. government's approach because at least the U.S. recognizes some nominal right to privacy for Americans or for non-Americans in the United States. But the official U.S. government view is that it owes non-Americans outside the United States zero right to privacy. It has complete power in its view to sweep up your phone calls, contents of them, to read your emails, and to share those with your governments. And that is the official U.S. government view. And of course, then everybody else takes the view. So there's this so-called five-eyes arrangement between the U.S., UK, Canada, Australia, and New Zealand. And so if the U.S. government can't snoop on its citizen, they just ask the Brits to do it, and then they share it back. You know, so the current law is abysmal when it comes to protecting our privacy. And there's an urgent need. You know, yes, it's hard maybe to do universal laws with real specificity, but we have to focus nation by nation on improving what we have because what we have right now is virtually nothing. You know, I think you said something that is so important. That is somebody's willingness to share information when they believe it's gonna be protected and held private, or if they think it's gonna end up in the public domain. And that's true of medical, that's true in talking with legal issues, so many things that, you know, the idea of what happens if you give it up to question, the single biggest issue is we will have less information available that can be very impactful whether it be health, or lifestyle, and so many other things. There is an importance in teaching education and educating people how to protect their privacy, how to protect their credit cards from being stolen, their identities from being stolen. There's things that have nature, that's also education. But you really touched on an important point too. Can I comment on that? Because I think on the education question, I agree it's very important, but I also feel that when we think about citizens and rights, we also have to have mechanisms for people to claim rights and not just know about what existing rights we have. If we look at the history of rights and rights claiming whether it's civil rights or political rights, it's a capacity for people to make demands and make claims on new rights, ones that don't currently exist. And to me, that's what citizenship is about. It's not just being educated to perform as a good citizen, but to have the right to claim rights. And how can we build that into the very workings of the technologies that make up the internet? I think that's really a critical challenge and that's not just for children, but it's also for us as adults. So it's beyond regulation, it's beyond education, all of those important. But what mechanisms exist within the current configuration of the internet that enables someone to make a claim or a demand that my data does not get shared or I consent only to this? Those are the mechanisms, the active ones that we need to incorporate into the workings of the internet. Again, back to my first point, it's a process. It's an ongoing thing. We cannot settle it through educating somebody today. What's the evidence that there's a demand? I mean, you touched on this at the beginning, but that there's a demand for this, that my cell phone contract should have terms of service that I actually can read and understand as opposed to that endless... How much demand are we seeing from, again, if this is something that citizens in countries are not pressing for, asking for, demanding from, whether it's from the government or from private companies, then how is that going to happen? I think on the production side, we can see, especially in the invention of new apps that secure privacy and allow one to communicate through encryption and cryptography and their take-up is one sign or one piece of evidence about this happening, or people who are opting out from consenting to cookies and saying, no, I don't consent to that, even if it's gonna make my platform work better. So I think this is a growing movement. As in all historical movements of claiming new rights, these things happen slowly, and we're still the very early days of people coming to understand this. So we need to look at these actions as the emergence of new claims, and that's how we start to study and start to think about ways that we can enlarge the possibilities of that. And I think that's a very important point, the timeline on all of this, because ultimately, the point I was making is that consumers today around the world are basically opting for convenience, right? And we're seeing that. And even if these new services are growing, they're still very limited, right? I think it's 10% of the world population is sort of downloading to block ads on their browsers, right? So it's still very limited. But I think as more revelations appear, more scandals appear, as we ask fundamentally these questions, regulators potentially step in, I think that there is going to be more and more concerned. This agenda is gonna evolve ultimately. And I think it's ultimately about finding the right balance between new services, convenience, but also the right levels of protections. And I think we've gotta be careful to ensure that that balance always exists. And I'm sure the pendulum will swing one way and then to the next, over-regulation could kill innovation, right? But obviously, a completely free internet with no privacy could create major problems for consumers down the road. So it's a balancing act that we're gonna have to achieve in all of this. And maybe the starting point is around the point that you raised, which is about first looking at key principles and for our governments to agree on key principles of privacy. Now we are mainly speaking about the balance between privacy and some economical value. Now I would just like to make a short flashback in security. Everyone was thinking about economical impacts or some terrorist action. Now if we look at the last US election, cybersecurity has been invited to the table in all the discussion. And now I'm just wondering if we should not also look at what is a trade-off between privacy and possibility to influence some votes and with the impact on democracy. So just to say that one element is to think about economic element because at the end you can opt in or out to buy something or not. But if you use the elements of private information to target elements to make people change in mind, let's imagine I'm just trying to make an analogy with a microwave oven. Basically you send some wave, microwave, it's the exact frequencies that do that the watcher is resonating and starting to hit. You may imagine that if you have some private data you can find the exact emotion that will do that individual will react in a certain way. So I'm just saying that we need also to consider this part of the equation in this question of privacy. What does that take you? I think you're talking about the importance of security, first of all. And I agree that every corporation that has data has a fundamental responsibility to secure it. So I think we can probably all agree on that and we can probably also agree that it's a neglected duty. So I'm in the same page as you there. I think that as we think about what are principles that there could be broad agreement on at the level of governments and at the level of corporations because you can't micro-regulate in a forum like this but you can come up with principles that would have an effect. And we should recognize that there is a trade-off between privacy and money because the lack of privacy is big money. The corporations are selling our data and whole businesses are being built around the data. So there is huge resistance to limiting access to that data. But principles that I would like to see would be that there's a presumption that you didn't consent unless your consent is explicitly given. I would like to see a presumption that if you do consent it's for the narrow purpose of the use of the data so I can do my shopping more conveniently but not to give it to a life insurance company. And then I think if you had presumptions that required explicit consent to broader uses of your personal data that would provide enormous protection. We should recognize there's gonna be big time corporate resistance to that. How would you feel about a sliding scale of if you charge me this much for the service you can have this much of my data but if you give me this much of a discount you can have this much more. Which is essentially letting people monetize their right and sell it consciously in return for whatever economic. Yeah, I mean you could make that argument about any right. Do you wanna pay for a trial or do you wanna just have the cheap version of taxation and if you get arrested we'll deal with you. There's certain things that shouldn't be bought and my inclination would be to say we should not make privacy something just for the rich. We should come up with standards that protect everybody without regard to money then you let companies build around that and if it's valuable to them they can advertise and encourage you to consent. But they shouldn't be taxing you in essence to be able to preserve your right to privacy. I think if you did that you might decrease the value of the data that they get. Cause then the next question is gonna be well what can you tell me about the person that won't let me have it? Versus the person that took the money to do it. So you know when you start bifurcating things one result impacts another. So if you did that all of a sudden I think you'd find nobody'd want the data. Cause they couldn't be sure. Cause they're interested in the law of large numbers. I think there's also beyond the economic value we should also put some attention to there's great public good and public value of data. I think health is in one really potential good area but a lot of governments see this as a new opportunity to better understand and know services and populations in ways that they could never have done before for the purposes of better public policy for the purposes of addressing inequities, et cetera. And I think we need to also recognize that it's just not for commercial good but it's for broader public service goods that a lot of this data could be of value. So in a consent model can we have a consent to allow for public authorities to be able to use some of that data for the purposes that it could be put to in terms of better transport services or better social welfare services. But a lot of that could be done I think through anonymous data. I'm much less concerned about anonymous data. And so if the question is building better roads and you wanna know where people are traveling you don't need to know the names of the people traveling. I completely agree and I agree it's gotta be anonymized and I think governments are completely clear on that but we have to also be careful that anonymized data does not necessarily mean it's protected in terms of against re-identification especially when data is joined up. But also there are pernicious problems of anonymized data being used to profile particular groups and then making them targets of particular interventions and they can make groups vulnerable too. And there needs to be mechanisms for addressing the ethics of that which just gets into a whole other problematic area. Even a bigger issue because fundamentally if you can extract a rule on the group of people and to consider that these people are at risk for one reason or another one it's even worse than a specific individual because a specific individual you have some probabilities that can be pretty low but if you look at the group then you may have something that is much more dangerous. Yes. I mean it seems to me that the social service provision problem is exactly one of the places where you go to a gap between rich and poor. If you want access in many communities to food stamps or transportation vouchers or housing vouchers from public service then you have to give away a huge amount of your personal information whether you have a criminal history or social security number all of that in order to have access to those things. Absolutely. That brings us back to who's privacy. The amount of digital traces that each individual leaves is highly stratified in terms of the kinds of interactions or in government transactions that they have no choice to engage in. And there are particular groups who become targeted ones of digital data collection. So we can't speak of this again as everybody has the same sort of data traces and volumes of data collected about them. So it's highly unequal. We are running very short on time and I don't want to miss the opportunity for people to ask questions. We don't have a lot of time for a lot but I welcome hearing from you. Hi I have a question surrounding the privacy rights around this new Alexa and Amazon Echo. In the United States in the last month there has been a murder trial going on where prosecutors are trying to get information that might have been recorded on the Amazon Echo because she's always listening. And the prosecutors are saying that the Amazon Echo may have information that will help them put someone behind bars for a murder. And there's been a lot of discussion about whether Amazon should be forced to give the information because it's in their cloud. And it raises tremendous privacy. Ken I'd love to hear you on that. I mean it recalls what Apple went through with unlocking iPhones. I mean this is now familiar territory. Where do you come down on that? Well I mean in principle I don't object to targeted government inquiries. If there's a showing of probable cause that a crime was committed and a reason to believe that evidence is gonna be here or there. You know that's classic search warrant areas. So I mean I think that scheme is less problematic. What this raises though is the question of how much data are companies gonna retain? Because if the company retains the data it does invite the government to snoop after it. And I think we've been too little focused on data retention. Companies like to retain data because it's valuable. They can learn more about who you are. But there's a cost to us in terms of our privacy because if the data is sitting there it invites somebody to come in and seek it. But what if Amazon argues look if Alexa can be subpoenaed then she becomes less valuable and this is damaging our business model. Does that? Well I mean obviously you say the same thing about your email is less valuable if it can be subpoenaed or your phone calls are less valuable. I mean I think it's just a modern day version of an old problem. I think if when somebody puts this in their home if they understand that could happen and they made that conscious decision then it's fine. But you have to let them know that that can happen. But if you disguise that and they get surprised by it. And the difficulty is a lot of us don't have a choice. I mean I think people don't realize that this is, this monitors everything happening in the room. And it can be turned off it doesn't matter.  Or if that's not possible you take out the battery but you can't take the battery out of these things. So the only way to ensure that this is not a listening device for whomever is tapped into it is to get rid of it. Well give it to somebody else telling them to walk around for. Yeah no, but I mean that is the difficulty of day to day life because most of us go home and you plop your phone down on the desk or in your bedside and you don't think about it but it still is monitoring everything in the room. And that's a problem. And we're gonna have more and more of these monitoring devices because it's not just issue with Amazon Alexa. Our smart TVs in our home are in fact listening devices. They're connected to the internet. So with the internet of things we're gonna have more and more of these devices that potentially are tracking exactly what we're doing. So I go back to the point there is still a fundamental issue about consumers understanding the benefits but also the issues potentially that these devices create for ourselves. And on the issue that we've debated on consents for example I think there really needs to be a close look at the structure of the consents that are being used by enterprises today because ultimately private businesses are producing consents of three, four pages that no consumer can understand. So I think that's a fundamental issue that will need to be addressed also. Consents need to be I think to Ken's point it's simple to understand by the average consumer anywhere around the world and they can't be these three or four, five page legalistic documents that protect the enterprises without an understanding of the consumers. Yes ma'am. Deborah Estrin, Cornell Tech. Do you think it would be helpful and viable if individuals are able to see the data that is held through their consumer devices that I have a personal data API to be able to see in some legible form in the same way that I can look up now in a credit report or on a credit card bill what is being charged to me. You'd be appalled. You know, but it would be. I wouldn't be, but it would be. But yes, it would be absolutely be interesting. It would be very instructive because I think it would also wake people up. I mean, that's part of the education. Let that be a light that we ask for. Yeah, I think there are proposals to have an app that can tell you what all your apps are doing and what all your apps are collecting. And one simple interface without the legalistic language of 23 pages, you could have an app that says this is what is happening with your phone. These are the possibilities of what can be collected as you are using it. That can be made through a very simple interface where one could say, oh, I'm turning that one off. Or I have a right to the data that's being collected and I should be able to request that in database format and analyze it for myself. I wanna see what's being collected about me. So I think these things are technologically really possible and available to us. And it should be a right that when you buy a phone that app is part of it. I mean, it does raise the question about whether the kinds of regulation and government role is likely to be more effective versus the technological solutions or ones that the private detector essentially makes available because there's a demand for it. I don't wanna miss people behind me and forgive me for having my backs to you this whole time. Yes. Yeah, my name is Laini Hamson. I'm an education advocate from the United States. And one of the things we're very concerned about is student privacy. Because right now the position of states is that once you put your child in a public school, you lose all right to their privacy and their data. So already we're seeing people pull out of the public school system to homeschool their children or put them in private schools in order to protect their privacy. So adults that may not feel very sensitive about protecting their data are passionate about protecting the privacy of their children, especially when it comes to disabilities, their health information, which goes straight into their education records. And now there's a push to overturn the ban on the federal government from creating a comprehensive student database. And we've already seen this in England where they have started creating this comprehensive database, which was promised to be only used for research. And now it turns out the home office has been asking for data for immigration control to be able to identify children who are undocumented to push them out of the country. And for the first time since the Trump election, there's concern that the federal government will ask for state data because some of the data that's being collected on children in public schools is their nationality and whether they're immigrants. So this is an ongoing concern because it turns out that student privacy is even less protected than medical privacy or health data. And yet health data goes straight into your education records. We are, unfortunately, we are out of time. And this is one of, in classic Davos tradition, one of those debates that I am confident is going to continue over your lunches and coffees and conversations through the day. But I appreciate the perspectives and the insight that each of you have brought. Thank you very much for joining us and thank you all of you. Thank you. Thank you. Thank you.


So, what I've noticed about a lot of these stocks, bro, is pre-market, they go crazy. They go crazy, crazy, crazy, pre-market, and then right when the market opens, boom, straight down. So, what ends up happening is oftentimes, you like nibble in a little bit pre-market, you like take a little bit of a feeler, and then you get destroyed as it goes up. So over the years, bro, I used to get destroyed on those moves, so I'm using all of my willpower to just wait for the market open, and if it looks like it's going to reverse at the market open, I'm going to short it. So that's pretty much what we're watching today.


Hey, it's Brent Smith from Attract Hotter Women and for 21 Studios and in this upcoming video I'm going to teach you how to let women know you're the friend with benefits and how to have that relationship It's one of the greatest relationships ever to be able to have the casual no strings attached Relationship where all the expectations are managed and it's much easier to have one than you think so watch this upcoming video And I'll tell you how to do it Hey, it's Brent with another question from Facebook and this one's about how to have a friends with benefits relationship With a girl. Okay, so it actually says how to communicate How do you communicate subtly to a woman that you want to friends with benefits relationship? Okay? So the best way to do that subtly is to give off that vibe Right, that's one way right the best way to do that is the first start with that vibe Which that which comes from your story. So your story is that? I'm the guy who's the friends with benefits guy and all Attractive women are all women who are exactly my type are all women. I'm attracted to Right, they understand that they understand it. They get it They support it and they take action on it. So that's the very beginning of this you have to see yourself in the mirror as friends with benefit friends with benefits guy rather than relationship guy Pursuer guy right provider guy See yourself as that first. That's number one. Okay, and then number two Suddenly you could not make advances that your former pursuer self might make or that you see other Over-pursuing creepy guys make right so you're not going to move in real close to them and go so You're the most beautiful eyes I've ever seen right like that's the thing that you don't want to do So suddenly this all comes from your vibe And of course part of your vibe is telling yourself that you're care-free and you're out to help and give and inspire and so on And so that will help make you relaxed But now when I say relaxed it doesn't mean you can't have high energy like this like I'm speaking right now because I've talked Like this to people all the time and I still suddenly right like Convey to a woman that I'm not relationship guy like they can tell just the way I'm talking right now And I'm not even talking about Anything provocative or controversial or sexual or whatever. I'm just talking See it's sort of like you know my conversations are short I'm interested and I'm connected the person I'm talking to but then I'm like, okay cool Well listen, it was great talking to you and I'm gonna move on and then I move on right? Okay, that suddenly tells them hey man. This guy's not gonna chase me This guy's not like and I don't I feel this incredible vibe from him and I want more Right, that's what suddenly tells them that right and if you don't of course You don't make girlfriend jokes and you know say you'd make a great girlfriend and gee We should go out sometime right so so if you're if you're refraining from pursuing behavior Then that will definitely help so the vibe the man who has pursued behavior rather than the over-pursuer behavior so make sure you're doing your your checking yourself on all of that and And then don't take the bait don't agree. This is the last thing don't agree to things That pursuers would agree to Right if you are on the non pursuing path and the only thing that women do is come home with you or ask you over to their place Then you need to tell yourself that's your vibe and that's what always happens to you And then don't agree to a date don't agree to go to the movies don't agree to be her date to a wedding Right, don't do pursuing things. Okay, so listen, I think that will help you start. There's much more, but hopefully that helps for now All right, I'll talk to you later Hey, it's Brent Smith again for 21 studios I hope you enjoyed the video and if you'd like more on that topic or more on attracting hotter women into your life effortlessly and consistently Then you're gonna want to click the button below and get my new attract hotter women program lots of bonuses in there I've been teaching guys just like you for years How to attract hotter women into your life every single day high quality women and have it be the easiest thing you've ever done I've also spent years and years in the trenches on my own experimenting with everything you can think of and now I'm laying it all out for you. So click the button below and get the program and I'll see you later


It's fun to come back. It really is. You get a little electricity, a little juice out of it. We were really a very close group, a very close class. It's almost like we just saw each other yesterday, which was great. Besides my wedding and my four daughters' birth, this is probably the greatest weekend I could have, right? And I mean that. Being at the altar is a great honor. Holding the chalice next to Father was really a special moment. There's not only a feeling of spirit, but also of love. Yeah, we had a great time at the dinner dance last night. I think what made it fun was we had two classmates provide entertainment for us. And a bunch of us started singing back up to one of the guys playing guitar, you know, thinking that we may be any good, but we worked. And it was just like old timesakes and so on. It was a lot of fun. And they'll look at help, too. I think the graduation was wonderful. It was very moving. To see just the beautiful day and the turnout that was here, it was just giving goosebumps. Fifty years ago I was here and all the people who were with you and so very special. Walking down there, people are cheering you. It's so wonderful to be at this age and have this honor in a way to us. It was a first-rate operation from the time we got here. It proves the fact that I went to a first-rate college at university. We just came here and reconnected and realized what Fairfield meant to us when we were here. Great to come back to the campus and see what's transformed and it feels like home. It feels like home. I loved this place. It made me a better man and a better father and a better husband.


Hello everyone and welcome back to another tutorial. In this video we're going to be looking at, well, in a short video, but very in-depth, pivot tables. So how to get started with your very first pivot table and maybe some tips and tricks to pick up on the way if you're already familiar with using them. But before we do that, do make sure you subscribe to the channel and hit that bell notification button because that way you'll be notified of all videos that come out in the future. Right, so jumping in with pivot tables, the first thing we need is some data. So you'll see on the screen we've got some sales data that we're going to use to public or populate our pivot table. We'll put a link to where we download the data from because it's just randomly generated data in the description for this video and hopefully the link will be on the screen as well. So you can download for yourself and give this a try at home. So the first thing you do is insert our pivot table and the quickest and easiest way of doing this is we're going to go to our desired location, which for us is a separate sheet called summary. And we're just going to go to cell reference A1 and then once that's selected we'll go to insert and we're going to select the pivot table option. You'll see there's a recommended pivot tables option here for us that we can select. If you were to select that Excel would give you some recommendations on how it feels best to present your data. So I'll give you one, two or three or four options on templates to use. But for us, by all means, please feel free to use that and you can see for yourself. But for us, we're going to use the pivot table option on the left. Once we've done that, all we need to do is now select our range of data. So you can either select this button here so you get the minimal view so you can see more what you're doing. Or if not, you just need to be selected in here to select the data. Just go to your whether data is stored and for us it's in this sales data sheet. And all we're going to do is obviously go to the last row or the last piece of data and highlight everything from the first row to the last row. You can just select the whole columns. So it could be like this as you see on the screen now. So A to G, but the thing will not the thing. But the problem with this is by doing so, you'll get blank references. And that's in reference to obviously the blank cells you see beneath this table. There are benefits to doing both because obviously if you select the whole column, then any new data that's added into your table, you can automatically refresh and it will be pulled through to your pivot table. But obviously by using our defined or set ranges we're doing here, if any new data was added, we would need to update our range to populate our pivot table. But we'll be covering another video on how you can get around that with using a dynamic range for your pivot table ranges. But again, this is why you need to subscribe to the channel to make sure you don't miss that video when it does come out. So we've selected our range. Once doing that, we can just double check to make sure we're happy of where the pivot table is going to be populated. So this is where we could select a new worksheet location or just another worksheet where we want or another location where we have. But we can we're happy of where that is. So we need to do is go OK. And we can tell the pivot table has been added in there because we've got our default area range here in column C to row 18. And we've also got, and again, this only happens when you select within this range here. As you'll see by me clicking out and then back in again, we'll get our pivot table fields down the right hand side. You'll notice that our pivot table in row two or three here has been titled pivot table five and you'll see it again at the top here. By default, when you add your first pivot table, it'll be called pivot table one. But obviously for us, this is not the first pivot table we put into this workbook. So that's why we will see in pivot table five and again, why you'll probably see pivot table number one and not five. So to get started and put some data into our pivot table, you can see over the right hand side, we've got our pivot table fields. The fields are at the top and beneath that is the four different sections to help populate our pivot table. So the first thing we're going to do is scroll to the bottom to our total and we can now drag that down to our bottom right hand corner that is for values. So the first thing we achieve by doing that is we can now see our total or our total sales, which is 19,627. What we can do just to make that or tidy up and do some formatting with that number is if you right click on the sum of total and go down to field settings. And then you can see we can now do a bit of formatting. So we can go into number. I was going to go into the number or go into currency. We'll stick with pounds. I'll get rid of the decimal places, select OK and go OK game. And you can see it's a bit more, a bit better there. We've got the comma, we've got the pound symbol and it's a bit more in a readable format. So we just todded off that sum there. The next thing we can do or want to do our pivot tables to start bringing some other fields to help give some more meaningful information to our pivot table. So what we want to do, the first thing we want to do is we want to know, OK, what reps have been doing some sales for us. So we can bring into our reps into the rows section and you can see how automatically the rows or column A is populated with all our different reps names. And we can see the total sales that they have achieved. So we've got Andrew did 438, Jill 1700 and so on and so forth. So we've got that first piece of useful information so we can see how our total sales has been broken down by each individual. The next thing we're going to look at is columns. So rather than just doing it by one piece of information being named, let's do it by two. Let's look at total sales by name and also what items they have sold. So in order to do that, if we just go back into our field list, we can find our item field and we're going to drag that into columns. So if I just drag that into there, you can now see how this breaks out. So we can now see we've got the name down the left and across the headers at the top there. We've got each separate item so we can see what the total sales per item were. So we could look at binder and we can see that for binder, the total sales are at the bottom here of 19 and a half thousand or not 19. Sorry, nine and a half thousand and obviously how that breaks down. So we could see who did the highest sales and it appears to be Jardine and who did the lower sales, which is Howard. So again, another interesting and useful piece of information for us to use. The last part of our four sections we have is the filters. So for this one, we'll use region. So we'll drag region down into our filters and you can see we've got this drop down now populated into our pivot table. What we can do is go to our drop down and we can see each of our regions and by default, all three are selected. But if we only wanted to look at central, we can select central and you can see how that breaks down there. Or if you wanted to look at east, we can then select east and remove central or we can have a combination of east and west or all three if you require. So again, that's a filter. What again is very useful. So the next and final piece actually before getting onto that another part just to mention and use is through lack of having not very many options or fields in our table available. I'm just going to get rid of region for my filters and to get rid of region or any one of the other options we have here, all you need to do is just deselect it from the fields list up here. What we're going to do now is I'm going to use region and I'm going to drag it down into our rows section. So you can now see that we're able to bring multiple fields into our row section to do grouping. So we can now see in row five here, we can see for central, we can see the region central, we can see all the totals. So we know there's a total sales of 11,000 from central as a whole. We can then see obviously what the total for central in binder sales was 5,700 and then we can then see how that 5,700 breaks down for each individual who sold binders. So we're now getting some extra dimensions into this pivot table to really understand the data we are looking at. So to get to that last point I was going to mention and we're going to look at dates because we know we've got dates available to us in our table. So to do so I'm just going to remove region and rip and get back to this default view we've got here. So we've got some of our sales obviously for each item. Because we've got order date available to us, we can now bring order date down into our rows and you can see that it's automatically grouped our dates into years, quarters, and then the actual order date itself. So at the moment we can now see how the total sales are per each year. If I was to expand one of those, so let's say 2018, we can now see that we've got broken down by quarter one, quarter two, three, and four, and then if we were to go down one more, we can now see our actual date summarized by month. So this is again a really useful way of summarizing our data and actually an even better way of maybe using dates can be to break this down to separate. So let's take years into our filter and let's maybe take, let's get rid of quarters, let's get rid of quarters so we'll drag that up that fields option. So we can now see January through to December, so our total sales, but we can now if we wanted to we could select just 2018 or we can select just 2019. So a bit bit messy way to do it, but just another way to show you how you can now use these dynamic fields throughout your pivot table. So we hope that video was very useful to and it taught you how to get started where you're very first pivot table. If you do have any questions at all, please just leave us a comment below this video and we'll get back to you as soon as we can. Alternatively, there's a couple of links in the description to this video. So we've got our Facebook, Instagram, even our website. So if you prefer to get in contact with us there, then by all means again, send us a message there and we can obviously get back to you and answers to your questions as soon as possible. One more time. If you haven't already, please do subscribe to the channel and hit that bell notifications. You're notified of all of our future videos. And lastly, if you haven't already and if you did find the video useful, please or well, if you hope you did find the video useful. But nonetheless, if you could give the video a like, it'd be greatly appreciated by us. Not only does it help show us what content you like seeing and what you want to see more of. It also helps us with the YouTube algorithm so that we can reach more people and show them these this Excel skills. So thank you very much for watching and we shall see you in the next video. Thank you for watching.