Hello and welcome. My name is Shannon Kemp and I'm the Chief Digital Manager of Data Diversity. We would like to thank you for joining the latest installment of the monthly Data Diversity webinar series, Advanced Analytics with William Pignite, sponsored today by Chaos Search. Today William will be discussing 2022 trends in enterprise analytics. Just a couple of points to get us started. Due to the large number of people that attend these sessions, you will be muted during the webinar. For questions we will be collecting them by the Q&A section or if you'd like to tweak, we encourage you to share highlights or questions by Twitter using hashtag ADV analytics. And if you'd like to chat with us or with each other, we certainly encourage you to do so. To open the Q&A panel or the chat panel, you will find those icons in the bottom middle of your screen for those features. And just to note, the chat defaults ascended just the panelists, but you may absolutely change to chat with everyone to enable chat and networking with each other. And as always, we will send a follow-up email within two business days containing links to the slides, the recording of the session, and additional information requested throughout the webinar. Now let me turn it over to Courtney from Chaos Search for a brief word from our sponsor, Courtney. Hello and welcome. Thanks, Shannon. Nice to speak with you. It's a pleasure to be on today with both you and also with William and for everybody who's taken time to participate in the session. Chaos Search is a sponsor of today's program, so I'm just going to spend a few minutes at the top of this discussion before we get into the analytics trends that I'm sure many, many people here are tuned in to hear about, to just talk a little bit about how we at Chaos Search are trying to solve for those same trends and make analytics something that are totally accessible for the enterprise in 2022. So with that, hopefully my screen share is coming through, but who is Chaos Search and what do we do? We help modern organizations know better by activating the Data Lake for Analytics. I'm sure the Data Lake, which is a topic I know William will cover today on the call, is something that many people here have positive, negative, varying experiences with, and we are very, very focused on helping the modern enterprise activate the Data Lake. And then how do we do that? We have a Data Lake platform that indexes our customer's cloud data, making it fully searchable and enabling analytics at scale and result being huge reductions in time, cost, and complexity. And over the course of the next few minutes, I'll help this crew kind of see how that actually works for some of our customers. I'm sure this chart is something that many people look at and go, yes, this is true of my existing environment, but what is it? The data analytics challenge that we know organizations are facing, data-driven organizations are facing today, is that there is a tremendous demand on IT, infrastructure, DevOps teams to make data of all types accessible to users for purposes of analytics all the time. The promise has been that, oh my goodness, can we put all of that in a single repository and manage the data growth and get better insights? That's true. That's the promise. But the reality, and I think anybody here who's dealt with a more traditional Data Lake has seen that what actually happens, right? You have silos of information based on the sorts of users that need to access that information, based on the type of the information, based on the systems that the data is coming from, and the work, the pipelining, the migration, the toil as we call it at Chaos Search that ensues to make that data accessible, ends up precluding both the users and the organizations as a whole from really being able to tap into and derive insight from this volume and variety of data. What's the end result, right? A big gap in access as well as time to insight. At Chaos Search, we ask this question, what if you could analyze any and all of your data? I mean, is that not nirvana, right? When William talks about the trends and advanced analytics, they may be all focused on doing this, right? What if you could analyze any and all of your data? How? In an automated way and at massive scale and reducing the time to insight and also your cost by up to 80% without changing the tools and platforms that your users use to derive answers. You would know better, right? You would have insights at scale. You would have immediate time to insight. Your resources would be easier to manage and you can truly see the world the way you want it. At Chaos Search, quite simply, and we'll go through this in kind of rapid fashion, what are we actually doing for customers? For organizations who have placed their data in S3, Cloud Object Storage or Google Cloud Storage, we are operating right on top of S3, the data platform, enter the Chaos Search data platform, and we are allowing organizations to access that data directly from S3 for purposes of analytics at scale with no movement, no transformation, no work. So if you are and you are the end user, let's just say we have a lot of customers today who have chosen Chaos Search for serving their log analytics needs. Let's say you're using elastic search at scale, but you are constrained when it comes to retention. You are able to only look at a small subset of your data for purposes of, I don't know, containers analysis or understanding traffic on your website or any sort of heavy log data analytics that become so, so imperative for organizations who are heavily deployed in the cloud. With Chaos Search, introduce Chaos Search, sits right on top of your S3, you access Chaos Search, we directly connect through an elastic search, an elastic API, and DevOps, SecOps users are able to access their information at scale with no change to how they do their jobs every day. And what does it mean, right? You actually are able to benefit from one unified data lake. And at Chaos Search, not only do we do that for logs workloads, but we do that for SQL workloads. We do that, we will do that for machine learning workloads this year. So it is a single unified representation of your data that is pulling right from your object storage. You don't need to copy that, you don't need to store it elsewhere. Chaos Search doesn't own that data. We are virtualizing that data and representing it to the users in a way that really allows them to do more, more quickly. How do you do it? It's very, very simple. Insights at scale. Store, connect, analyze. We tell all of our customers the same thing. Store your data in S3. Many people have S3 or GCP. That's your baseline. If you are in S3 and you have your data or in Google Cloud Storage, you can connect to Chaos Search in less than five minutes. You connect it to your bucket, you click to index, you create a review. And then what is step three? Using whatever tool of choice, let's think about Kibana, for example, and being able to come in through that tool as an analytics offering, you can analyze on demand. So if you are sitting in Cloud Object Storage in either Google or S3, you can activate Chaos Search in five minutes or less. You could do that on your website if you choose to do that. But the point is really that it's meant to be very simple. But what does it mean from a benefits perspective? What we really care a lot about and what this question has been about the data lake for years now is the concept of a data lake is so everybody gets it. Of course, you want to place your data into a lake where multiple users can access it, but the reality has been hard. If you look at a traditional log scenario where you have multiple clusters of elastic search, which we're showing here on the left, you're limited in terms of retention. You're siloed in terms of representation of that data. You have downtime when you're thinking about how you can access any of this information and move it around at scale. And the silos themselves preclude you from having a singular view. It is as simple as what is shown on the right side here, which is with Chaos Search, you're getting rid of all of that complexity and you are actually able to benefit from a singular data lake where both scale and retention are unlimited. So clearly in that scenario, you can see how getting rid of a lot of that work that really happens today and also making this a much more streamlined experience for all of your users allows you to both save time and money. One of our customers, Blackboard, some people on this call may be very familiar with Blackboard, but they are either an online educator. And when COVID happened two years ago, what happened, the demands on their service just expanded dramatically and they needed something else to handle their logs. And when Joel talks about this, as he has discussed in webcasts and what you'll see here, the amount of time to manage the logs that were now everyday occurrences for their business for them to see and really understand what was happening across all of their product lines became impossible. And with Chaos Search, they are able to get that singular unified view of their logs without the work. So they're seeing more, they're getting answers more quickly, and the management has really, really gone away. And with that, I hope, oh my goodness, it's almost nine past. I would be happy to answer any questions at the conclusion of the call today, but we are incredibly excited about what William's going to share relative to trends and topics that are really relevant to this audience for purposes of advanced analytics because at Chaos Search, we are passionate about helping make analytics of all types at scale accessible to end users and really simplifying that experience. So with that, I'll turn it over to you, Shan, to turn it over to William. Courtney, thank you so much for kicking us off with this great presentation. And thanks to Chaos Search for sponsoring and help to make these webinars happen. And if you have any questions for Courtney, feel free to submit those questions in the Q&A section of your screen, as she will be joining us in the Q&A at the end of the webinar. And now let me introduce to you our speaker for the series, William McKnight. William has advised many of the world's best known organizations. His strategies form the information management plan for leading companies in numerous industries. He has a prolific author and popular keynote speaker and trainer. He has performed dozens of benchmarks on leading database data lakes, streaming and data integration products. And with that, I would give the floor to William to get his presentation started. Hello and welcome. Hello. And thank you, Shannon. Thank you, Courtney. Great hearing all about Chaos Search. And I think it does play into some of our trends here. It's time for the trend. So welcome to 2022. And I trust that the right slide is presenting right now. If not, just let me know. I can push the magic swap displays button if need be. But because you never know about that. But anyway, welcome to 2022. I'm excited. I hope you're excited as well that you've chosen wisely in your career to be here, to be here in data in some way, shape, or form and to know that it's growing and it's doing more innovative things. And as a matter of fact, in putting this presentation together this year and last year, I kind of noted to myself how even more progressive it is this year in terms of the things that are happening. In terms of the things that we need to keep up with. So in the spirit of keeping up and growing our enterprises appropriately, let's dive in here. I've been introduced. We have strategy, implementation and training options over here at McKnight Consulting Group. So I'm going to pull you in just a little bit more. Hope you don't mind. But why are trends important? Why should you pay attention to trends? I think it's imperative to see trends. They affect your business. Your users will eventually, if not real soon, care about these things in your offerings, in your services, in your products, in their products, in their services. And we're trying to help them, of course. So it's about planning and dealing with change, picking your winners. You know, I have my trends here. Others have their trends. My perspective is, I think most of you know, my perspective is data. My perspective is enterprise data now for 25 plus years. And it's about making that data into a significant corporate asset for you. So I'm looking at the trends that help that goal. And I want you to be a leader, not a follower. It's very important that organizations have data leaders and understand that data is important and how important it is and can continually articulate that to everybody that needs to hear it within the organization. And that's pretty much everybody.  I believe and I've said this many times that the great business ideas, the great initiatives are going to come from the perspective of data as we go forward and the perspective of analytics. And it's people like you and I that understand this and can be significant movers and shakers inside of companies as a result.  Leaders of tomorrow can advance maturity while also solving business issues. So pick and choose over the trends as you need to. And they're not all going to apply to everybody, of course, but you want to start working them into your initiatives. Everybody out there, every organization I should say is looking over initiatives now. Sometimes it's for what they're going to do in 2022. Sometimes it's multi-year initiatives, but either way, those initiatives are going to need to be architected. And the default easy thing to do or seemingly easy thing to do is to do it the way that we've always done it and forget about trends, forget about these new possibilities and just do it the way we've always done it. Nobody will know that we didn't move forward with things. Well, I think eventually if you've been around a while, you know that that does eventually get smoked out and the organization will like people that actually do think ahead and architect for efficiency, architect for functionality.  And that's very true with this year's trends. There's a lot of efficiency trends in there. And there's a lot of new capability trends that are in there. And every organization is going to need to call them over and get into their winning approaches. And don't worry if you're not 100% right about your winning approaches. Don't worry if you picked Hadoop for your day to warehouse a couple of years ago. And now you're kind of kind of stuck. Well, you think you're stuck. You're not really there's always a way out. It's just going to be inefficient to pick winner pick non winners here. So I'm going to give you my best shot at it. I want you to think about it as well. By the way, there's no budget for staying on trends. So if you're waiting for that, that's not going to happen. And by the way, you also don't get a whole lot of credit for staying on trends, right? In an enterprise, we get credit for meeting enterprise goals. Okay, quickly, last year's trends. Here they were. And I admit as I look over these, I didn't, I didn't phrase them all in terms of a good, a good, you know, letter grade applicable kind of trend, right? Like remote work continues. That was one of the easier trends, right? So let me just go through them quickly. See how I did. Some of these are going to carry forward in some way shape or form into 2022. Remote work certainly led by cloud capabilities. There was a strong tech rebound so far so good. Leading organizations are increasing a focus on AI and ML. Yes, absolutely. Model deployment takes center stage in those organizations. I believe that it did more edge AI. Yeah, more is kind of a fungible word, right? But I do think there was more. And I think we're in the process of seeing a lot more. So that's going to be, I think the first trend I'm going to share with you. So I'm going to revive, revise that one a little bit. Explainable AI. That didn't really, that didn't really go anywhere. I was thinking that some of these algorithm providers would be helping us understand what went into it. Not to a deep degree, but to the degree necessary to meet some of these requirements that are coming from outside entities in terms of being able to explain the AI that we're using inside the enterprise. So no bueno on that one. Strong Data Lake adoption. Yes. Oh, yes. Strong Data Lake adoption. Everybody wants a data lake. Sometimes they think they, they think they want a data lake and they really want a data warehouse. It's on S3. That's okay. That's okay. That's a data lake. That's okay. That's a type of data lake. New technology stacks shifting from only data warehouses, lakes and ETL to data fabrics, AI and pipelines. I'd say maybe a V-grade on that one because I think for new technology stacks that is true, but I don't see a lot of people going backwards and redoing their data warehouse ETL. It's full steam ahead and it's full steam ahead into pipelines and so on. I don't see a lot of, let's do this with ETL unless it's a kind of small departmental kind of thing. Strong DevOps adoption. Yes, there has been strong DevOps adoption at least in terms of people saying that they have strong DevOps and are using some of the science of DevOps, some of the, some of the things that are coming out of that domain, moving that into the organization. MLOps, not as strong, not as strong. We're still probably not there yet. Automation, certainly as a top AI mover and shaker at least at the beginning of your AI journey. Yes, it is that open source adoption. I'd say that was sort of flat, still strong, still strong, but it didn't really tick up any as far as I could tell. Kubernetes adoption, that's an easy one. Yes, that happened and we were at the start of general AI. That's just an opinion, not really something to be graded, but I believe that is true and I'll probably drop a few points about that into this year's trends. Speaking of this year's trends, let's get into them. The first one is I promised was about edge AI. Edge AI and edge computing will dominate architectures. Embedded databases have become a popular use of database technologies. Aggregating all the uses of databases would show that embedded databases are just as popular to the more extrinsic approach with all the connected devices that we have out there now projected to be 75 billion by 2025. What's happening is enterprise embedded databases can be found, for example, in mobile airline applications for online check-in, boarding pass retrieval, flight size checking and so on. It's all being empowered by improvement in chips at the edge. Improvement in chips means we don't have to just put data in files at the edge, just a little bit of data and a little bit of processing. We can put full databases, we can put graph databases potentially at the edge, and we can put not just light processing, but we can put artificial intelligence processing right there at the edge using some of the chips that are coming out from companies like SambaNova and Graphcore and so on. If you're in an edge computing system, the data and applications are brought close to the point where it's needed. However, rather than connecting that distributed system to an on-prem environment, it is often most pragmatic to connect it to a cloud infrastructure. All these distributed sites can be linked together with an enterprise computing environment. I think this is a strong trend for the year. Look forward in your architectures. As I mentioned, look for graph-shaped data to be out there at the edge of your architectures. All right, data scientists. Data scientists start doing more data science than data cultivation. There's a few reasons for this. I see this turning in this year. One reason is because our data environments are just that much more mature. A year more mature, we've largely gotten past our phase one data lakes. They're now up and running, meeting business goals, et cetera. Of course, we have our legacy everything that's still applicable and working like our data warehouses and our CRM and so on. The data scientists are starting to get more tools, and that means the data science is going to go up, because it used to be, of course, we all know that half of a data scientist's job was cleaning data, integrating disparate storage systems and finding the needed storage capability, and processing power to put AI and machine learning models into production. But in 2022, we expect some relief, because for one, the environments are more mature, as I mentioned. But also, more machine learning tasks are becoming automated. It's not becoming such hardcore hammer to the chisel kind of thing, where there's so much detail work to get to something that actually makes sense in an organization. I believe that this is where different tools are going to shine. I think Chaos Search is in here as well with their ability to index data lakes and so on. Something to look forward to. Data scientists are actually doing more data science in 2022. Now here's an easy one, I think. Wide adoption of containerized data. So we expect to see more Kubernetes-ready distributed database platforms that have addressed the challenge of stateful persistence. That's the issue that's been holding back containerization. It's that stateful applications need persistent storage, have been leading a legacy infrastructure in production, and I believe that this will abate in 2022. For example, Cockroach Labs offers this capability through a distributed SQL database solution architecture.  I also see advances in security around containerized data. All of these things are pointing up for all development in many shops, at least. It's going to be with containerized data. Also, it's incredible how many tools, and they're mostly open source that there are to empower Kubernetes developers and operators with powerful container orchestration. So this is becoming a little less big hammer and chisel kind of approach as well. So we're seeing some support there for building containerized applications, containerized data, and so on. So I expect to see that development is going to be containerized and it's going to be containerized largely with Kubernetes to a large extent. This one's obvious, right? It's giving rise to something less. So a tendency to spin up clusters, be they for big data, data warehousing and machine learning on a task-based basis. So all this quickly getting clusters up and running for something, testing something out, shutting them down, and so on. That's happening more and more. Call it serverlessness. The architecture is the very substance of the advanced, for example, cloud-era data platform. And it's also being leveraged by Google for Spark on Kubernetes on their Dataproc. Ultimately, it's enabling new workloads and it's making us more efficient. And it's making the architectures more efficient because you can port your development to new clouds or wherever you want to go in the future. Now, here's one that not everybody's even heard of, but I think it's going to take off in the New Year. Synthetic data. Synthetic data. We've been held back doing AI modeling because of the lack of data. And I believe going forward, synthetic data will be a requirement to build the enterprise. The enterprise cannot be built without this use of a lot of data for AI capabilities, which requires a tremendous amount of high-quality, labeled data. We all know this. They're connected to a high-degree AI and data, of course. And so, synthetic data they're starting to become a lot of startups that are emerging for structured data, unstructured data, etc. And this is all going to improve and enlarge data lakes out there. And this is where most of this data is going. So, for example, most self-driving cars are built with synthetic data produced in simulations. So, some of the startups, if you want to look them up, mostly AI, Y data. And there are a few others that I'm very hopeful about. Data fabric. I'm being careful with my words here. Data fabric is going to see an uptake. So, data fabric, let's start with that. It's a data management architecture that can optimize access to distributed data and intelligently curate and orchestrate it for self-service delivery. This is a strong trend. It's kind of like the old data virtualization, only instead of just database to database, it's architecture to architecture. And I think that we're seeing a lot of companies say, look, this is how we've grown up. We've grown up building silo architectures, but there's something we can do to make them work together at least to some level without bringing them all together in one big architecture, which we know will never happen. And that is data fabric. So, I think 2022 is going to see significant growth and interest in data fabric solutions. However, I also believe that it's going to become a pretty hollow term. I remember for quite a while, data warehouse was a pretty hollow term. Data Lake, it's just sort of coming out of that phase where it was a pretty hollow term. We could talk to people and talk about data warehousing or data lakes back in the day, and we could be on completely different planets in terms of what we were talking about, yet have a conversation and think we were meeting the minds. I think that's sort of what's happening with data fabric. It's something that all the vendors are glomming on to. A lot of people inside an enterprise are saying that's what they're doing, but they're not really moving the needle very much to make the goals of the data fabric happen, the orchestration, the access to all data, and so on. So, I know it's kind of hard to predict about something that's kind of nebulous like this, but true data fabric, I think it's going to see an uptake. Now, moving right along, AI-enabled applications. Not just AI tools, but AI applications. Businesses are going to expect vendors to deliver comprehensive AI-enabled solutions for line of business teams and departments instead of focusing on developer tools and technologies. Now, some people might say it was kind of early to see this industry making that turn, that change of direction, but I think it's time and I think we're going to start to see it. Start to see AI-enabled applications. So, these can impact all enterprises within an organization. So, remains to be seen where AI is going to focus on in terms of domain within an enterprise, but it will focus on automation and deep complex analysis of big data for immediate action. So, we're going to see organizations shifting their investments away from AI, moving into customized solutions that are built with AI. Data catalogs. Data catalogs are going to cross the chasm in the data stack. Well, what chasm am I talking about? I'm talking about the chasm of should we include them or should we not? Are they really part of the stack or are they not? I mean, we've got our ETL, we've got our data warehouse, we've got our BI tools. I mentioned before that the whole stack is changing and metamorphosizing into more pipelines and data catalogs are really important here. So, there's some players in the little box down there that I think are poised well for 2022 for the needs out there and I think data catalogs in general are starting to get accepted as part of the necessary stack for an enterprise for an important workload and you could also throw things like AWS glue in there as well. So, data catalogs. Data quality. Let me talk about data quality here for a minute. Data quality is probably as important as it ever was inside of organizations. Some organizations treat it with the importance that it deserves and some do not and I think you very clearly pay for that. I think I've had a talk in this series about data quality just like two talks ago or maybe it was last month. It's not important but there's more. Now that we're into pipelines, we're into data fabrics, some of us are doing data meshes. It's more complicated. We have data catalogs to try to track everything but what about taking action on what we're seeing out there inside of these new stacks and these new pipelines and that's data observability and I think that the people that are in data quality today are going to morph into people that are in data observability tomorrow. They're going to be responsible for data quality as well as the different things that we see here. Auto discovering and adapting data quality rules, proactive monitoring and anomaly detection, unified scoring and personal alerts, data reconciliation and masking, all these things that are done with data observability. Most likely with a tool of Calibra, in my opinion, position pretty well here for the future. So data observability, strong trend. Okay, here's another strong trend, streaming analytics. Streaming analytics in general, yes, but streaming analytics with IoT definite big upswing in that. So we've got our live device log data, it's going into Kafka, maybe a Pulsar, maybe a RabbitMQ type of thing and then it's going into your stream processing. A lot of us are using Spark but there's other alternatives there as well. Moving that into our data lake and eventually the data warehouse. I don't mean eventually like it's a long time. It's pretty rapid in terms of what needs to happen. The number of devices that are connected to the internet is growing at a rapid pace. This is known as IoT of course. IoT refers to all those physical objects or things that are connected and it's expected to grow exponentially this year and as more devices are connected to the internet, the volume of data generated will also increase and we absolutely need streaming analytics not old school ETL to handle this type of data as well as some other types of data but I'm putting a prediction here on IoT type data because it's a strong trend and streaming analytics is a definite must in terms of all that. You got real-time data and you have to take appropriate action on that data before a problem becomes critical. Some of this ties into my edge architecture trend back at the beginning of the talk. Moving right along. Sensors and automation will drive data volume so we all know data volume is growing. There doesn't seem to be any abatement of that that's going to happen. AI is going to dominate factories and begin to serve in many roles in society. Not necessarily 100% in 2022, it's not what I'm saying, but we're going to start to see more AI augmented teachers, cooks, pharmacists, law enforcement officers, athletes and other professionals. Universal translation, hundreds of sensors are going to be installed in our closed homes and overall environment and all these sensors and automation will be a top factor in driving overall data volume in this new year. Okay now I've tried to stay away from industry specific predictions but this was just a little too juicy. I had to put it out there and I think that it's something that we can all relate to even if we're not in healthcare and that is that medicine is going to jump the shark on neurological disorders leading to a DNA revolution. What might we do with that DNA? Will it be the next medium for information? Not in 2022 it won't, or will quantum computing be the next medium for information? Not in 2022, but down the road and we might start to see some early green shoots around that with DNA but more to the point. AI will treat neurological disorders like Alzheimer's, Parkinson's, spinal cord injury and blindness deafness robotic prosthetics may be becoming stronger and more advanced than our own biological ones. No I'm not going to chop my arm off for one of these things but I can know and we can all know that if it does happen in the future we might be relatively okay. No single psychological trait that does not show genetic that there is not one that does not show genetic influence. The ability to genetically modify cells in order to produce a therapeutic effect that is to add a corrected gene into the genome in order to treat disease that's what's going to really be happening in 2022. I'm not a big expert on this I'm not trying to say that I am but I do track CRISPR pretty closely and I'm just fascinated by the possibilities there. So look for that in a hospital near you and in the computing environments of the future. Now if you've been if you've been following my my presentations here month after month you know I talk about Sophia you know I like Sophia she's the young and accomplished humanoid robot from Hong Kong based Hanson robotics that already boasts a very impressive list of firsts and she just added a new one yeah she's the creator of the world's first AI generated piece of NFT art yeah I threw a lot in there didn't I she's a she's a she's a humanoid and she's doing NFT art yes and that's actually a kind of self-portrait you know it may not be my cup of tea this this art here but the video art and painting sold in an online auction for six hundred eighty eight thousand dollars as an NFT so there you go apparently there's there's something to it I think we're going to see more AI moving hard into design of things design of whiskey design of you know whiskey for example comes with a lot of variables right what casts are going to be used what the cast held before what the ingredients are how long it's going to sit there and so on there's actually 70 million different recipes that AI has created for whiskey and then it can curate that into what it thinks it's going to be the most popular and of the highest quality based upon you know the cast types that you may have on hand so it's getting into that it's getting into a lot of things it's getting into music design it's getting into art as you can see spices I I'm just thinking of different things that I know of that AI is moving hard into this is by the way as bad as the tech is ever going to be it's just going to be a whole lot better as time goes on this brings this brings up my opportunity to let you know that we're having an NFT here at advanced analytics we can get a picture of me giving this presentation I'm only kidding I'm only kidding they do seem to be everywhere there though don't they I'm going to stay away from it because that's not enterprise analytics but I think that's an interesting area as well I do think and this is kind of off topic but I do think that this will be the year of deep fakes we're going to start to see deep fakes not know what to do with them and be fooled be faked out if you will by many deep fakes and we'll see what kind of chaos that causes keep an eye out keep a skeptical eye out as you go forward in 2022 AI will also be conversational so we'll get to talk to talk to people that have maybe historical people just people for companionship and so on through AI I won't go too far there I'll actually come back on that point though in another minute or so and that design from AI is going to extend to tech and software what we do okay Google said that a chip that would take humans months to design can be dreamed up by its new AI in less than six hours the AI has already been used to develop the latest iteration of Google's tensor processing unit chips the tech giants engineers noted that the breakthrough could have major implications for the semiconductor sector so AI designing for things like that as well and I'll throw in another technology here and that's auto ML something I'm feeling like has a high markers here for the new year auto ML it's a machine learning model designed to create other machine learning models and helping you to automatically select the algorithm something that we're largely doing by choice and making a human decision about today so at some point in the future will AI be writing most of the computer code out there are we at the start of that I think we are we are at the start of that auto ML it's one of the technologies that's enabling that but there are others and it's it's able now you see the build code there able to explain code in English and suggest improvements and it can write code to the defense advanced project agency that's DARPA's probabilistic programming for advancing machine learning program is developing new technologies that improve machine learning for questions both deep coder and auto ML use machine learning to produce executable code based upon entire knowledge basis the basis that they use can not only generate the entire data hierarchy for a project but also the entire user interface in the middle layer I hope I haven't made any of you sad by saying that there's continual need for uh stem uh out there it's just going to it's going to morph it's going to grow it's going to morph auto ML moving right along here auto ML cements itself as the future of ML auto ML tools enable self-service data science using no code low code tools businesses can build train and deploy data models for deep analysis and insight generation some examples on that tableau has made this concept of business science business science a priority so business science that's like self-service data science enabled by augmented intelligence machine learning uh click acquired big squid for its auto ML capabilities altrex made auto ML center piece of its may 2021 platform update and we're just going to see more and more of this coming in 2022 it's not going to be as robust as a train data scientist in 2022 but it's so useful it's a great starting point and I think we're going to see more use of auto ML in 2022 now my last prediction here is a GPT-3 GPT-3 now maybe you've heard me talk about GPT-3 before and and call it a trend before and I'm calling it still a strong trend so so much so much to go on with GPT-3 it was trained on hundreds of billions of words and cookbooks so essentially it can do anything that you might think AI should do with with words with text so it can't do and can't do all of that maybe as well as it will well clearly it can't but it can do a lot of different things I talked about the training and calculates how likely one word is to appear in a text given the other words in in the text so this is known as the conditional probability of words so it's really good at that so for example if I'm going to the gym and I go to get my blank GPT-3 can intelligently fill in that word and it probably would not put the word piano in there right might put the word shoes in there or towel or something that's appropriate Microsoft has the license to the exclusive use of GPT-3 and I think that that is one of its strong assets the public can still use it to receive an output I'm actually in the beta program but only Microsoft can control the source code now as I yell from the mountaintops here about GPT-3 I do note that the Wu Dao 2.0 model was created by the Beijing Academy of Artificial Intelligence and developed with the help of over 100 scientists and what makes this pre-trained AI model so special is the fact that it uses 1.75 trillion parameters GPT-3 uses 175 billion so quite a difference there I know I haven't I haven't touched it I am tracking it and it's being used to simulate conversations understand pictures write poems and even create recipes a lot of the things that GPT-3 does as well so this is all in the area of natural language processing and LP you see that there it will continue to evolve understand speech rhythms along with all of our human idiosyncratic speech patterns ums us and words with mixed meanings it will continue to learn which ones apply making much more reflective of human speech and much more able to direct queries and resolve concerns we see NLP now implemented in our enterprises in terms of the help desk in terms of the call center in terms of contract management and a lot of different things like that anything really that has to do with text I think we're going to start to think about NLP and maybe specifically GPT-3 in 2022 okay so we've had a little journey through trends for 2022 you may agree or disagree that's fine I hope you give it some thought come up with your own list and go forward into those things and I think you'll find it really super exciting to do so there's more maturity and moving imperfectly than a merely perfectly defining shortcomings we can all point at things and say well that's wrong well there are reasons it is the way that it is let's be a part of the change right so build your credibility tout these or your preferred approaches for 2022 what you think is going to be necessary for your organizations to meet their goals don't talk yourself out of having a new beginning have an open mind no plateaus are comfortable for long and any resistance that you find it's probably not about being a leader it's probably not about getting to the end game it's probably about the journey so be sure you address the journey as you start to talk about these winning approaches in 2022 in your environments and just a quick recap here edge AI containerized data with Kubernetes synthetic data avoiding miscommunication like around the data fabric so that's a winning approach the data fabric itself you might consider a winning approach as well but my main thing there is let's be on the same page as to what we're talking about AI enabled applications data catalogs data observability streaming analytics AI design AI complimenting human design there auto ML and GPT-3 as an example of NLP that I think will be strong in 2022 and with that I will turn it back to Shannon and look forward to your questions William thank you so much for this great start to the 2022 2022 new year really appreciate it thanks again search if you have any questions for Courtney or William feel free to submit them in the Q&A portion of your screen and just answer the most commonly asked questions just a reminder I will send a follow-up email by end of day Monday for this webinar with links to the slides and links to the recording of the presentation along with anything else requested um so a question came in earlier is there a tool that helps to query or create multi-dimensional queries from a data lake I'm happy to take a pass at that Shannon yeah absolutely I guess the dimension Patricia maybe I am happy to follow up separately the dimension depending on the dimension of the query itself there could be other solutions but I would certainly be remiss if I didn't say that that is a big part of what chaos that you're trying to do and what we consider to be multi-model analytics right so we can help you run logs queries so text type style queries or SQL queries on this same data set in your lake in one singular platform so kind of like you could be using looker to run a SQL type query and represent that data as such and use Kibana to run a log query same data set no isolation of those sources singular result and if that helps great and if it doesn't feel free to follow up with me via email and I don't know William if there are other multi-dimensional query providers you wanted to talk about well I agree with everything you said there's there's obviously a host of tools that you can use to access data you know in a data lake but nobody goes as far as chaos search in terms of the capabilities that I'm aware of and I'll just add that I think that when you're sitting on the on the cusp of of architecting an environment bringing on a new tool bringing on new capabilities think about think about these trends that I just talked about think about how far things have come in the past couple years or even one year and you can you can guess that is probably going in a similar direction fueled by AI so next year we're going to be talking probably about some trends that are all about AI and all those capabilities that you're going to need is an organization so think beyond your immediate capabilities think beyond what that person was in your virtual office yesterday jumping up and down on the table about because you're going to need more once they get that they're going to need more right we've seen this pattern over and over again so be sure you get into enough capabilities that scale when you do things like need dimensional data in the data lake and so on and so forth I love it and everyone's so quiet today that was the only question that's come in I'll give you all a moment to type any additional questions that you have into the Q&A there you know William before the webinar we're talking about you know how we at data diversity are seeing you know a lot of jobs booming in for surrounding metadata and data modeling because of companies standing up machine learning and starting just starting those programs are you seeing anything similar in the companies you're working with or in terms of of a continued focus on metadata and data modeling yes in terms of the I say the oldies but goodies we're not all these all these trends by the way should not take away from the need for solid data quality work solid data modeling or solid data management work solid data governance all those things it's always it's always tricky to to give a presentation like this and and you know make it I want to make it not seem like I'm glossing over all the all the good things we've learned over the years which still remain true so I'm glad I had this opportunity to say data modeling is still important metadata is still important I mean we talked about we talked about the growth of data right we talked about how data is going to move with the data fabric you know and we talked about the data pipelines now it's moving data but it's moving to it's moving that data to places that need to have great modeling and metadata above it and we're I'm just sort of glossing over that today but we can get down into those weeds and we can see that that's as necessary as anything for the success of the overall project Courtney anything you want to end that no I always find these sorts of sessions to be eye-opening when you have a moment to just think about where with the variety of of sort of solves for the plethora of analytic challenges there are the sorts of really compelling things that are happening so William thanks for taking us through that and Shannon thanks for hosting us today we are very happy to be part of this data diversity community and we look forward to talking to you all again soon I love it while we were talking we had a few more questions come in which is great and I love this heavy hitter one so thoughts about the data ethics implications of self-service data science you know that I'll take a first pass at that I think there's been some pushback on well we just don't know what the what the algorithms are doing so I think we have to you know move a little more carefully I think different organizations are treating that treating that differently and some organizations if regulation ever stepped in would be in more or less a world of hurt when it comes to this stuff because they've really banked on on algorithms that they know nothing about and they really don't have the AI expertise in how to to deal with any kind of defense but you talk about ethics I'm talking about you know there I'm talking about regulations and and that's more more more business oriented but I think the biggest thing with ethics around AI that we're dealing with is bias data and this is where some of that some of that you know generated data can come in really handy and so I was talking about that is a strong trend and I think that that is a trend to also help out to be sure that we do not fall into any ethical holes when we do our artificial intelligence it is important to have representative data it's important to have a lot of data and we can define a lot you know we would need some time on that but you know what I mean a lot of data that represents your entire consist constituency and doesn't just gear itself to one part of it or the other based upon what it knows and we cannot let the majority majority data if you will which may represent the majority of our consumers out there you know their characteristics just be the only characteristics that we care about so anyway there's going to have to be some knowledge there that comes out of AI I love it and how AI machine learning will influence and enable data quality yeah I think as with a lot of things we're starting to starting to see that some things can be automated and we're not to the point where I'm comfortable saying that well data quality can completely be automated now not at all you still need data governance coming up with custom rules for the shop you still need to apply them in a in a what's going to give the the biggest bang for the buck kind of approach and grow that over time and score your data and hopefully get it better over time that's still the approach that's still the approach but but we see that machine learning can do some of the things some of the rather ordinary things I'll say that have to do with with data like I'm doing one right now correcting addresses to a third party that's not exactly AI but some of the matching is AI and I think that uh whenever data quality in the past has been relegated to a data governor that will actually have to manually get involved I think that is starting to abate because we're starting to understand what it is that they're doing to the data I don't don't tell me you're using your judgment tell me tell me how that judgment plays out and that's what I want to capture and get get automated but you know what if if I'm not told that but I can see the pattern of that activity over the course of time artificial intelligence can see that pattern and take it forward and that's that's just as good so I think that's one area that machine learning will definitely be helping data quality in 2022 awesome Courtney anything you want to add to that no not on that one I'm concurring with William love it all right some additional comments and they came in I love it well that does bring us to the end oh we got one more question coming in here I'm gonna throw it in we got just a couple of minutes from an ESG goals perspective environment social and governance any thoughts on trends that would facilitate an organization's ability to indicate and document such to meet scrutiny of regulators stakeholders and consumers that's that's similar to to the ethics question or at least how I addressed the ethics question we just got to see more explainable AI and I thought it was a trend last year I think it's I think it's still the trend but maybe not as strong of a trend as before I'm kind of gunchai about making that a trend didn't see it happen we'll see more of it for sure we'll definitely see a lot more of it if regulations take upward in that direction if you're in Europe you have more of this now already than we do here in North America so there you go it's going to be somewhat regional as well I love it well thank you both so much for this great presentation and thanks to our attendees for being engaged in this I really love it and love you guys as always and thanks to kia search for sponsoring today's webinar and helping kick off the series in the 2022 new year appreciate your help as always and enjoyment and engagement um so thanks everybody again I'll as a reminder I will send a follow-up email by end of day Monday for this webinar with links to the slides and links to the recording of this session hope you all have a great day thanks everyone


We have a really interesting topic, the mapping of surveillance cameras. The talk is given by Martin, who has done this project. And this is also about the tests of phase recognition in the train station in Berlin. And there was quite a lot of media coverage. And he's going to talk about the automatic mapping of surveillance cameras. And now, Martin, take it away. Thanks. I would invite you to look at this picture. It's by the London Transport Agency. It's no satire. It has a 1984 look, but sadly it's no satire or advertisement. And the British sometimes make it easy to talk about surveillance. Someone did a freedom of information request and a poster was designed by the Central Illustration Agency, which seems like a department of the Ministry of Truth. If you need a cover for a dystopian book, it costs 2,000 pounds at the Central Illustration Agency. The Brits with surveillance are far ahead when it comes to surveillance. This poster is almost 15 years old, I think. And slowly a video surveillance also became common hand in Germany. And for a previous Interior Ministry, and Seehofer have created this project in Berlin, Südkreuz, the train station. It was from August 2018 to 2018. Three companies were involved. They surveyed the escalators and the entrance. The targets were... The test people were given Amazon gift cards as a reward. You needed to carry a Bluetooth beacon and a person. The Berlin Südkreuz is an S train station and they tracked who was in the station. And the test persons have handed in pictures in high quality and they try to find them through the cameras. And someone asked how this project was designed. And I would invite you to read this quote. It says more or less that the project was successful, successfully concluded, independent of any success, actual measured success. And if you talk about these fundamental rights violations to connect us with camera surveillance, if you don't even care about the results, whether this helps police agencies. As we've mentioned, if you talk about 90,000 people at the train station, you would have at least about 6,000 mistakes every day, false alarms. And you'd have to sort those out. And I was quite angry. I thought, I try to do something against it. This was what it looked like in the past. If you didn't want it to get scanned, you had to go on the left. And if you were part of the project or you didn't care, you could go on the right. And this test project, so you could circumvent it. The British are ahead of us. Like I did a lot of times. If you go, if you look at the surveillance cameras and where they are and map them, it's a see it, say it's sorted at the check-in security. And you should report whether you see someone who tries to map cameras. So in Germany, at least have the determinant. But in England, you are suspicious. So in England, you can't choose. If you want, if you don't want to be recorded into the face, you shouldn't complain. You shouldn't scream at the police. There's other options. If you know where the cameras are, it's mentioned in the reports. You could just simply turn to the side, either 15 degrees up or down. The recognition rate is significantly lower there. The police have found a solution out of their perspective. You should advertisement and signs in this direction so that you can better recognize the people. So you can turn away. In minds, people complain that people walk down the stairs and that they move a lot. And that's not that good for a facial recognition. But if you want to turn away or prevent that you are being recorded, then you need to know where the cameras are. And this is the project. Now, to summarize, he gave us the data into the personal ID that are misused. Nothing stays on the ID card. All police agencies are allowed to look at the data on the ID card. His idea was we should turn away. We walk down the stairs. We have masks, hopefully. And on the right, this is a blanket with a certain pattern that gives a lot of... That makes the system misfire. We recognize 30 faces in a single person, but it's some form of a countermeasure. And you can look at the talk at 30C3. These were the problems. Art problems are more simple. If we want to look where the cameras are and we want to find them, we can look almost the same. A gray box with a lens. Or if we... We always look at the same thing. And only the... So if they look at us, only the background is different. At the pole, at the behind trees, at the roof. When we know what we have to look for and we need a database... OpenStreetMap is an option for this. I'm not really well-versed in that. There are several nodes and you can put them together to describe a house or a parking spot. On the left is a very popular OpenStreet editor. And these separate nodes make up a building. If you use them separately, they can put up a separate object. There's this project man-made surveillance. And as you can see on the right, which properties it has. And you can see an interesting project surveillance under surveillance. This data from surveillance cameras are in OpenStreetMap, but most maps aren't rendering them. And OpenStreetMap does that for you. This, for example, is the Hamburg inner city of Hamburg, the main trade station. This is what it looks like. These circles show a range of the cameras. It's difficult at the train station if you have several levels. And that's why it overlaps a little bit. It's an interesting project. You can look at it under this URL up here. The cameras are according to several countries. You can see Germany, France, USA. USA has 11,300 cameras. There's probably a lot more, but nobody inputted them into the database of OpenStreetMap. So there's a lot to do still. And that's what I try to do. Now let's talk about time, split according to time. This is for Berlin. You can see 2012 there has been a spike. This is just an estimation. And you can see that the data can be fairly old. There's also an API. You can read the data. And I'm also using them if I need them. Overpass API. There's documentation in the OpenStreetMap movie. There's also Overpass Turbo. It visualizes the results. You can get them as a JSON or an XML depending on what you need for private projects. The API is public and can do a couple of requests a day. Now we have an idea where we want to store the data. But let's get to the main part of the project that collects the data. Oh, and there are three options. You can do it manually. You can use the Spoochy, which is an editor for Android phones. You can create the cameras on the map and insert the individual values like angle, direction, and so on. And this data will later be shown. And now we come to the part of the project that is shown in the center, which is the Android app that I have developed, which includes a self-trained object recognition based on TensorFlow. This is all based on the TensorFlow example app from 2018 or 2019. And the central part of this app is self-trained object recognition based on the material that I have collected over time. So basically I would be the person who would be accused of doing shady things by the Brits taking photos of cameras at the train stations. And then I'm also, yes. I want to show you a demo. This is the Android app. You have to walk alongside the camera along the camera. It determines the position of the mobile phone and determines thereby the position of the camera. It tries to determine the type of camera. And there's also the option to do it manually or to change the position if it didn't work 100% correctly. You can create training images. And in an earlier version of the app you could also upload them. And this improves the object recognition. And you can filter based on camera types. And this is all based on object recognition. This is the newer editor that I have included. You can adjust the individual types, direction, angles and which zones being surveilled. And later you can export all the data as a CSV file and import it into an OpenStreet editor. And all of these parts of the project work without an internet connection. The object recognition is running on the phone. And you can download the OpenStreetMap data and save it on the phone so that the entire mapping and screening can happen without an internet connection. If you don't want to create any mobile phone location data while you're doing this. So it might be better to not leave any digital traces even though you're not doing anything evil. I also included support for group activities. So basically you can say the first person should do this quadrant and the second person should do this quadrant so that you can split up the work. And then you can meet at the end of the day and share the data and upload it. So it's also a bit of a protection if not everyone individually uploads things but you only upload the data at the end of the day in a café. Also if the software is not recognizing a camera you can also take a photo, export it and potentially send it to me. The other part of the project is the 360 degrees camera which is semi-professional hardware. It's about 300 euros. The next component is the coral dashboard mini which has a TPU so a tensor processing unit which is useful for doing machine learning. And you can basically compile your programs for object recognition for this device and then you can run all of this offline without internet access and you can mount the camera on a helmet and then use your e-scooter or whatever device vehicle you want. You can travel through the city and try to capture cameras. And there are two red parts at the top. They help you to find the position. This is what the app on Android does anyway but in this case it's without the risk of anyone else logging where you are. And these are connected through different pins with a GPS board and it locks when a photo is being taken of the cameras. And the idea behind that is you get these 360 degrees photos with those two fish eye lenses and you see the roof. Of course it's not that round in reality and that's why you have to do some image editing before you can start the object recognition and of 45 or 60 degrees you have to cut out images and walk them back into the original shape. I will show you this a bit quicker. And now you see that the roof becomes straight and then you also see the camera. And each image is processed by the deathport mini which has a really nice processor. This is a mobile net V1. It takes about 25-26 milliseconds to process an image. So you get roughly 40 frames per second with this camera to process all these images. But what costs time is the splitting of the images and transforming of the pictures because we only have small CPU scores with not so many gigahertz. This is something that could be improved. This project I have only worked on until this prototype. But in recent times I have not found that much time to work on this further. We have the possibility to get the data stored in OpenStreetMap. Now we also have to do something with the data and I have developed the following. This is a PCB with an ESP32 and a GPS chip which can determine the position. It takes a couple of minutes until it finds the position based on the satellites. And with just a few meters of accuracy it gives you the position. You can use a battery and you can also make it go to sleep if you don't move it a lot. And the nicest thing is the dynamic NFC tech which are these two golden parts that connect to the antenna. The NFC chip is interesting. You can provide a string into the function. And analyze the data with the NFC tech. The camera position is stored under SD card and compares the position with the position of cameras in your surroundings. You have to load them onto the SD card but then the device works without an active connection. And just with this near field communication just with your phone to talk to this device. I'll show you once again a video. This is the movement activation. If the GPS module has a satellite only needs 10 seconds to reconnect to the tech you can see how it's built up. You can communicate all kinds of things. And if you have at the end of the day the device it gives you a surveillance report, a kind of surveillance report. You can access your position and all the cameras in this area. And which cameras might have seen you on OpenStreetMap. You can click at them. And this once again on Android. And you can show which angle the cameras have if the data is correctly entered into OpenStreetMap. You can download the cameras in the area directly from OpenStreetMap. Here once again more screenshots. I've waited for the bus on the left side at the main train station in Mainz. And on the right side I was in the bus it even worked. So it's not really helpful to have a GPS within a large metal box. But yeah. This is almost it. These are the two parts of the project. There's some more. I have a browser, in-browser validation software. If you have been gone mapping it exports the ZSV file and in the browser you can validate that. It checks which data is already existent. And if not you can approve and it's exported again. And you can input it into the OpenStreetMap editor. And the backend where you can upload the training data. I don't have that in action anymore because I have more focus as a group to map, to export. And to upload it jointly with one account. That's what I've focused on. And now a couple of questions to you. Or requests to you how you can help. If you have access to 360 degree scanners. Lots of cities. As private carbon needs to drive through cities. To help whether they need to check. The building department can use the 360 degree scanners. Even with a laser distance meter. And this company can go through the city with a car. Berlin for example has done that. One company is doing that. You can look it up through a freedom of information act. And there's some contracts you might ask for. So the data is mostly only available for internal use. But if you work into a data protection agency. If they have an interest to map surveillance cameras. You might suggest to have that data access to that data. I would think they could get access if they would want to. You could still pay attention to cameras. And input them into OpenStreetMap. I can work with that. Anyone else who wants to do that. If you're interested. I'll try with others with other organizations. Like lame your face this summer. I'll do mapping parties. If that's possible. So without an infection risk. If that's given. You can follow the reclaim of your face on Twitter. If you want to do printed circuit board design. You can look at that maybe if you want to. And tell me what I did wrong. And if you're in the area of event management or other. And have access to a professional 360 degree camera. That would be interesting to put it onto the roof of a car. And try how it works and make a couple rounds through a city. And look and analyze the data afterwards. And do other things also for OpenStreetMap. This is these are the ways you could help me. And you could also look at the things on GitHub that I've used for my presentation. On Vimeo you can look at all the demonstration videos. There's also more current demonstrations for the current versions of the Android app. You can follow me on Twitter. If you want to have updates to this project. Or want to know where the mapping parties take place. If you look for... You can see this as crowdfunding. You can look at this board again. And if you have questions, I'm here. Yeah, thank you for your attention. Okay, thank you very much for this nice, very technical talk. And I'm always happy to join these. We have some questions from the audience. And I would like to start with them. You said that you stopped with uploading of training data. And did you look at the uploaded training data? How do you ensure that people didn't upload cat pictures? I thought about that. I thought on the server that does the upload. There's in the object recognition, there's differences in the performance. The object recognition on your phone. Or the performance differences. You can see them in the accuracy. On the server, I would have a higher performance object recognition. Or a classification of the picture. And I would have tried whether someone uploads a cat or videos. And the things that these object recognition would have been recognized as not being a camera. I would have looked at that. But if someone would have uploaded cat pictures, then I would have just looked at those. Is the answer app available on Aftroid? And is it also possible to run it without extra hardware? Without extra hardware, yes. With F, it's not on Aftroid, it's not on Google Play. This project that I built, I probably would rebuild the app entirely. Because the object recognition and the machine learning APIs have changed. They have become more simple in the last three years. I used the example app and threw everything out that I didn't need and put my own material in. But now the TensorFlow APIs have become a lot similar. And out of complexity reasons, I probably would do it completely again. Rebuild it completely again. And I would put it on regular Android. And you can also build it yourself from GitHub. That's also a possibility. Did you try to use public images? For example, photos with position data that is being uploaded to Google. Especially Google Street View or maybe pictures from other social networks. Because there are already many of those 360 degrees pictures. And these often are with location data. I looked at Google Street View, definitely. In the Google Server API, they only have low resolution. And the efficiency is not there for my own use case. The license, you cannot use it without approval of Google. Google has all the data in higher quality. But they limit public access. I think I don't know how it works with user uploaded 360 degree pictures. What license they are using, whether it's determined by user or by Google. The 360 degree images, I didn't look at them. But I think those are more about nice views. And not about the inner city or maybe a little bit about the train station. But I'm not really sure whether they are really relevant for surveillance cameras to find a position of those. But it might be worth looking into that about the licensing. And I'll probably do that. Yes, there are many different image sources. Yeah, that's a good attempt to use that and analyze that. That's a good idea. So the IFG requests, do we actually get any information about the duration of storage of data? The most are in the area of train stations. And I think they are marked as such there. But in private areas of restaurants, they might surveil their own area. But those are also mostly labeled properly. There's a lot of private use that's not properly labeled. And the second question was about the storage duration. You can see the contracts, they are censored a lot of times. I didn't do any freedom of information requests. Storage duration should be mentioned. They say that faces and license plates are censored before they are published before. If you want, if you do a lot of work, then probably you might find the people that you are targeting. But I think the protection is in place. Storage duration should be in the contracts. You should find it out. And there were questions about the links and where can we get the OpenStreetMap data and also the tools. So where can you get the stuff? So you can get everything from GitHub. All parts of the project. This is the Overpass API. You should look into the documentation of OpenStreetMap. It's well documented. And the tag that you're looking for is man-made surveillance. It should be on, you can look at it on OpenStreetMap. The man-lowcase-made surveillance. I didn't include them in the slides, but you can find them there. The data. I think the project Surveillance Under Surveillance also explains it really well how they extract the data. You can simply ask me again if you have problems. That's not an issue. The website is anserve.org. Yeah, that's right. It's not that nice, but you can look at it. Yeah. Or you just start with GitHub. This is the more important part. And that's a question. Someone asks what about frozen streets recognition that cameras are only used for frozen street recognition. Is this something that you also encountered? So have you found some cases where the reason for the surveillance was really, really specific like that? I think with traffic, you have to, in certain cases, if you just want to count cars and not just anything else, surface-ice recognition, I didn't see that. But it's probably not the worst use case where you can direct the camera straight onto the ground and then you won't even see anything else. So that wouldn't survey a public space. So this is a really interesting application. Okay, thanks. What can you do with the data? So what users can you make with the data besides evading the cameras? I thought of a research direction group of people that are representative of the population. You can split them the way you want according to income, whatever, that you could put these trackers into their pockets and just look who would be affected by surveillance measures disproportionately over-affected. And you can imagine what the result would be. So you could think about whether currently the political situation is good. I have hopes that biometric surveillance wouldn't be discussed Europe-wide. And that can change, of course, anytime. But if this would be rolled out everywhere, you could think about some routing and input how much cameras you want to see on your roots more or less with a slider, whether you want to see more or less. And I would build such a routing service to have a possibility to be caught by as few cameras as possible. And this might be really complicated to do that, but it's also a possibility that we could do. But I have not done it now, until now, but it's still discussed. Another question from the audience and also a remark, really nice project. And I'm really looking forward to the mappings, the sum of mappings. Gladly, if you want to take part. Usually we do this locally. We have a small open-street map communities that are responsible. Also that the local CCC organizations, there are probably also a good contact point, what they say. And then we could meet. That would be great. The hardware tracker. So you can also get the hardware tracker through crowdfunding, right? You can find this on the web page or on Twitter or if you Google, on surf. Many thanks. Those were all the questions from the audience. And I think we should now proceed to the extended Q&A. Many thanks again. Thank you for your attention.


ID Tech, I show here with Cooper Gray Robotics LLC. So hello, so who are you? Hi, I'm Paul Cooper with Cooper Gray Robotics. We're happy to be here at ID Tech and we've brought two of our robots here today. I call them robots because they are remote controlled vehicles. There are no operators that sit on board. The operator uses two joysticks to control our vehicles. We are developing our technology, which is, you're gonna edit this, our technology is the control architecture that drives the controls on board. So let's go, let's walk around here. So you have a smaller one here and it has what's it called? It has a plow blade on it. So it's a plowing? This little, we call this our little bot. This is a ground up vehicle. It has a sealed lead acid batteries on board. It's all electric remote control. This is used as a tug. It's also used to do plowing. We have a standard two inch tow hitch on the back where you can connect any number of appliances. We have here a 12,000 pound winch. So this is used to move other equipment around our yard. We have 24 robotic robots. They're all built on construction base equipment. Sometimes they need to be pushed or towed to get them back into the construction bay for repairs. And then right here, this is a big one. This is a big one. It's called Robo. Robo stands for remote operated electric bucket loader. This is a 1976 Mustang skid steer that we salvaged. We removed the diesel engine, the hydrostatic drive that used to move these wheels. And we installed all electric motors that drive the wheels. We have a 24 kilowatt hour lead acid sealed battery pack in here. And Cooper Graves technology is hidden here under the lids. And our technology takes the input signals and tells this machine to go left, right, forward, back and to raise and lower the boom. We also have a power take off in here where we can add other attachments, such as an auger or a pile driver or a lawn mower that skid steers are famous for. And this, again, is all electric drive, but our control architecture can convert any machine that's out there, whether it's diesel, propane, and doesn't matter on the size of the piece of equipment. So is any way you can do this autonomous so you can have a whole bunch of them like digging at this autonomously? That is our next generation of our control technology is to add in a sensor suite. What we call increase smart our intelligence for these vehicle. Ultimately, they can be made autonomous. Our current technology will allow an operator to control multiples of these at the same time. And our control architecture sends information back to the operator. We can bolt on LiDAR, Sonar, Radar, and GPS with our technology. And any developed sensor technology in the future can also mate and bolt in with our technology. So is there any hydraulics involved? You need a lot of power and strength to lift stuff. This machine started out with 70 gallons of hydraulic fluid on board. With the all electric drive, we've now reduced that hydraulic reservoir to five gallons to currently operate the boom and the bucket. We do have designs to convert these to electric linear actuators to remove all the hydraulics. This is especially interesting to organic farmers. The organic grape growers out there. We have a Kim Chi company interested in our vehicle because they want a non-polluting vehicle to do harvesting and be a tug for their harvest bins. So there's no pollution happening here. It's electric. It's all electric, no exhaust, no smoke, all work. And the full power that is needed for this kind of, it works? Yes. And how about the battery life? How far can it go? How much work can it do? This current battery pack in here is a lead acid pack and will work about four hours before needing charging. Our next generation of lithium pack in here, we fully anticipate this to work for eight hours. Typically a skid steer like this on a construction site works for about three or four hours and does accessory and cleanup work to the larger excavation and dump truck equipment on a job. So do you have lots of orders? Do you have lots of people? We have a lot of interest and yes, we have orders if I can give them how much and when can I deliver. I am currently standing here as a startup and we're looking for that seed money to develop our production model version for the marketplace. This is our working prototype. How much will the price be? We expect this to be the same price as the existing skid steer, maybe a little less. This machine today is running about $65,000 to $75,000 and the operating costs of an all-electric model is about one-tenth of that of a diesel brother. And have you been using it? What have you been doing with it? We have been using this. I can show you several videos on our screen. We've used it for grubbing services. We've hauled soil and dirt. We've moved and spread gravel with it. We've bailed water and a reservoir. We've built a dam with it to prevent flooding with this machine. Mostly moved a lot of dirt and gravel with it.


Cayenne court leader Paul McKenzie lived with hundreds of followers in makeshift homes of polythene shitting and fathched in a remote forest camp that he divided into areas with Biblical names like Jerusalem and Judea, relatives of his adherents say. He told them the world as they knew it was going to end on April 15 and Satan would rule for 1000 years according to the relatives and a senior police investigator. He ordered them to starve themselves and their children to death so they could meet Jesus in heaven ahead of that date they said. McKenzie 50 is police custody and has yet to be required to enter a plea to any charge related to the mass graves which has still been assumed. Two lawyers acting for him declined to comment. This is the moment Kenyan court leader Paul McKenzie was being escorted out of police station. The 50 year old former taxi driver has been accused of ordering his followers to starve themselves to death. Sources say he told his adherents the world was going to end on April 15 and Satan would rule for 1000 years. His court became the focus of national horror with the recent discovery of more than 100 bodies mostly children in mass graves in the Shakahola forest of southeast Kenya. From called records and interviews with relatives of the victims, medical workers and a police investigator, Reuters has pieced together a comprehensive picture of McKenzie and his cult. McKenzie grew up in Ruaquale County in southern eastern Kenya. In the early 1990s he moved to the coastal town of Malindi where he worked as a taxi driver. Fellow driver Jafeth Charo said McKenzie became increasingly focused on religion and started his own church in 2003. Charo said he and his family joined the church for two years until McKenzie's sermons became alarming. We had been there for about two years. Soon he started verbally attacking other religions like Muslims and Catholics. In March 2017 police searched McKenzie's compound in Malindi and found 43 children live in there without attending school. That's according to court documents at the time. McKenzie's brother says the court leader believed education was evil. As an individual he said he wouldn't take his own children to school because of various reasons. He said worldly education does not help anyone but when you give a child godly education they are safe not just on earth but in heaven as well and he said that he will give his children religious education. In 2019 the authorities ordered his church to shut down. That's when McKenzie relocated to the Shakohala Forest home to his Good News International church. There he lived with hundreds of his followers in makeshift homes of polythene sheeting and thatch. Stephen Neity says his wife joined the cult two years ago. My wife told me she'd found a church that would take her to heaven. She said it was wrong to go to school in hospital.  I asked her how she would have read the Bible.


Space rendezvous, a space rendezvous is an orbital maneuver during which to spacecraft, one of which is often a space station, arrive at the same orbit and approach to a very close distance e.g. within visual contact. Rendezvous requires a precise match of the orbital velocities and position vectors of the two spacecraft, allowing them to remain at a constant distance through orbital station keeping. Rendezvous may or may not be followed by docking or berthing, procedures which bring the spacecraft into physical contact and create a link between them. The same rendezvous technique can be used for spacecraft landing on natural objects with a weak gravitational field, e.g. landing on one of the Martian moons would require the same matching of orbital velocities, followed by a descent that shares some similarities with docking.


Hey, what's going on everybody? It's Coach Swift from Make Money Coaching Sports. I am your sports foreman's success coach. All right, so today is gonna be a very short message Because something that I had to remind myself recently. All right, so outside of training athletes I also do a lot of adult fitness programs online and My online fitness programs they with adults they Aren't always as you know successful as far as how much clients I have as I do with athletes athletes I'm sold out right now. Okay, probably like 99% I get the last few signing up and that's it Until spots open up with people taking a break You know, I have about 30 Online adult clients and I'm trying to get the 50 things work a little slower for me on this end because there's a lot more options out There for them online in person hybrid Do it yourself whatever. Okay, but something I have to remind myself and this is something that you guys all need to remind yourself is No matter how much you Love this job and it's great that you love it You want to train everybody because you love it and then you want to make more money. It's great money always remember in the process of getting more clients Using, you know, hopefully you sign up for our accelerated program and you use our system to get more clients get more paying clients Just remember before that happens and even as it does happen where the crowd starts coming to you more Never forget to take care of those who are already in your program. Don't be so money-hungry Client volume hungry that you forget to take care of those who are in the program. Okay I'm very hard and ambitious at the same time to get more adult clients and You know out of like 30 40 adult clients, you know, I probably have like a good hard-working 25. Okay Ones that are always in communication with me they do the work they don't make excuses They stand the program the longest. Okay, and they love it. I my best testimonies off of them. I Want more because I love helping more people and I know that I'm a great fit But I have to remember don't let that one take away from me helping my clients. Okay, so Me I make sure that I get that done.  Okay, so I have to remind myself to Never lose sight of those who are already your client in the biggest Compliant clients that you have and Then you know trust the process it may be a little slow at times When trying to get more clients, but don't let it drain you to where The service you give to those who are already in your rosters Stinks. Okay. I've done this in the past Because I saw other people, you know, online fitness and other sports trainers. They're getting like 20 30 40 50 people And I'm just like I need more. I want more. Okay. I love money. I love training I want to be that guy that everybody goes to right and I know a lot of you want to be that guy or girl Coach that everybody goes to but just remember When things are slow and you're Trusting the process you're putting in the systems and process Treat your people great. Okay, always see what can I do for you? How can I help you more? What are you doing great at? What do you need more for me and things will speak and that right there? We'll make you even more ready for when more people come into your program Okay, whether it's in person online a little bit of both a camp weekend Multiple camp clinic weekends one on two one on ten, whatever. Okay Do not forget about those you already serve and serve them even well This will also tie into you over delivering value not stop throughout the year and that means people will Want to pay you more that are already in your program So if y'all want to learn more about how to better serve and over deliver your current clients Get more paying clients on retention so that you can have a healthy financial Lee sound business Then if you look down descriptions, you can email me or text me. Let's set up a 20-minute phone call Let's see what's going great in your business. Let's see if we can find any holes in your business Give you a few tips plug those in beyond your way And we can also talk about more of the accelerator program where 1500 coaches have Found great success from nine different countries Training athletes for sports formants soccer lessons football Basketball baseball you name it. Okay


No. She never said that.  Okay, uh... Here, let's play on, let's play on easy. Because they said, they said normal and easy is this. Why? This is, this is weird. I have to leave. Kiki hurl how to seduce Asfin. What is seduce?  I'm not. Asfin, what the f- I'm not. You know what?  Instead, I'll just ask Barry to go to America. I think that would be better. Don't you think so? Guys, December is gonna be great. Chance to set things up for an easier shot at it. Let's see if we can do it. My B's here, said, then done. Game is over. Trout, let me ask you a question. Yeah. Have you ever eaten corn the long way? Sorry? Have you ever eaten corn the long way? Not now, Asfin. Don't do this to him. What does that mean? What do you mean? What does it mean? Corn the long way. I don't understand. Can you explain? Try to envision it. That way, the top of your teeth and the bottom teeth can attack the kernels equally at the same time. What this does is this allows you to double the efficiency of eating corn. What? Who's the picture of? Well, I was saving this for- I guess I'm gonna show you guys. But somebody is very close and, like, special to me. Dude, I just zoned out. He's moving it? Whoa. I wanna do that. You have to do bench press. Oh, my God. Yeah, that's how you do it. You just lift a lot of weight and then you get big strong muscles. That's right. I wanna do that. Can girls do it? Yeah. Yeah, I actually used to be able to do it. They can do it? Girls can do it, too. It's just muscles. Muscle flexing. Asfin uses lens of truth. No. Stop. Why is it so choppy? Oh, it's just like- No. Dude, come on. Seriously? Seriously? If this wasn't so funny. It was actually super funny. It was actually super funny, dude. The poster, the lights, the music. The fog machine didn't work. I was gonna come up with the fog. It was gonna be cool. It was gonna be, like, super cool. I was like, oh, look at all the fog. Wait, that's right under the fire. It's found happy. It's gonna be so bad. It's gonna be terrible. Dude. Love the stream, brother. Texas love. A little bit of splash, but hey. A little bit of splash. You don't like raw? You like it raw? That's disgusting, Ginny. Raw is gross. So you don't like sushi? Okay, I like sushi. I'll tell you what, memes aside. Memes aside, I'm on pretty good terms with everybody.  I cannot get over this. Every time I think about her, it's like Satan manifested itself. And just, I need to save the environment. I need to save the birds. So, like, oh my gosh, like, it blows my mind. Other than that, other than that, just don't burn bridges, right? They're left with a third and three. They'll get to the line here, but remember, it's also third down. That's a gain of seven. Back to throw. Three. Flush down right. No, catch it. Oh, my. It opens to the tail. Back to back receptions for him, and it's another first down. That's what they need right now. Get the push down. Get out of bounds. Stop the crowd. Just put up the guard here. Understanding the situation. Making the plays necessary. And making sure that clock stops at every opportunity. Mostly an end, dude. Oh, no. But here's the reality of the situation. Sebastian Fors is a menace. A menace to Twitch. A menace to everyone around him. And a menace to society. Okay. He should be kept off the streets because you never know what he's going to do. He should be kept out of farms because this guy is even a menace to horses. I'm going to be honest. Okay. That's the reality of the situation. Now, here's my issue. Here's my issue.  What is the best way to keep him off the streets? They should unban him, right? They unban him to keep him in his house. It's the best thing I've ever seen. Today, O.T.K. stands for one true king. But in reality, it stands for over the knee. No! No! Which is a well-established BDSM term for spanking. No! They even sell leather paddles on Amazon branded with O.T.K. That's Photoshop, Chad. I swear. No! Come on. Come on. Yes. God, we're bringing O.T.K. back, baby. Yeah. And here we are, almost through three corners of play in this passing game, so has not really found any kind of river. Put it mildly because Anderson loses the football and is picked up by the Cowboys. Oh, he's got a little left side. And this is gonna be brought back for a football return touchdown by Cowboys. What was that? This was a close game. They needed a little breathing room where they got it right there on their return for a touchdown. Did you IQ test return with negative results or something? I'm getting closer and closer to believing that your birth certificate is an apology letter from the condom factory. Oh, God. Jesus Christ. You just turned out my lights. Nice. That was an actual good one. Actual good one. What? What? That was not the pitch. Okay. That was garbage. What? He didn't even pitch it. Oh, no, no, no, no, no. Look what happened. He hit his own hand. He hit his own hand.


What is this? First controller lines for a date. Yeah, so we're on a date today. Go check out her channel for our video. And meanwhile, we're going to enjoy this nice, fatty lunch. Today, we're having a date. So one of the options was arcade or thrift shopping. And thank goodness, thrift shopping was chosen and I've been wanting to go thrifting for a while. We are going to head to Dongmyo Market, which is a very famous thrifting area on the streets. I'm going to take Soobinnie thrifting for the first time ever. Yeah, I've never been to a thrift shop. And I mean, this is only my second time at Dongmyo Market, so I'm not that much of an expert. But we're just going to thrift together, try to find something nice. She's been looking for a nice pair of sunglasses. I'm looking for like a vintage pair of Levi's jeans. Oh, I don't know anything nice I could find. So yeah, we're going to show you what thrifting in Dongmyo Market is going to look like. So, finally, the dust is a little bad. Yeah, I know, and it's out there, but it's okay. We're doing it for you guys.  For us to get there, there's just so many people. So this place is super crowded because it has people of all ages here. There's something for everybody. But yeah, there's like everything. I don't think there's nothing that like, yeah, I'm so overwhelmed right now because there's just so many people. They got books on one side. They got random miscellaneous things. They got kitchenware, socks, of course. And then on the streets, all these flea markets going on. Dude, this is like a zombie apocalypse right now. Oh my God. Ready? I want to get your reaction for this. This is the graveyard of Drip-Dip clothes. Yeah, there's just a bunch of clothes on the floor and everyone just has to go through them. I would at least try to go through a couple of things, but they open on the weekdays too. Yeah, yeah, they're open on the weekdays too.  Yeah, weekend is so crowded. But anyways, we're gonna be going to like some actual stores.  It's not bad. This is 20, it's like 25, 35. Did they say anything you like? They have some brand name stuff too. Yeah, I like them. Versace, Kenzo. They have like every animal here. These teachers are five dollars. I want to come up some glasses. I know, let's go. We got more exploring to do. Going to the little alleyways. Let's check this place out. Let me try the sock. Oh, hey, that's good. Hey, you should get these to match with me. Oh my, can you tie it off? It's like the gentle monster feeling. Yeah. Like what they've been going for these days. Can you put a little down? Like, yeah, a little bit. Only Jemez. You want to check yourself out in the mirror? I don't want to do it so much. Oh, dear Lord. Oh my God. That's so funny. Try it on. It's like super vintage. The fit is good too. Ask how much it is. How much is it? I'm going to try this blazer on. Looks very scholarly. Something Harry Potter would wear. And it's perfect because it's oversized. I'm wearing a sweater today, so. Yes. Oh, it's okay. I like it. I don't know, this is a bit too much for me. This is like a little too, you know. You don't have anything like this, huh? Yeah. I mean, I got blazers, but I also don't really like this because it's too high. You like a boxy fit, yeah. Can you just ask how much it is? Just to know how much of vintage Rolf R is. For a blazer, that's not bad. They look so heavy.  A peasant? A go-unlimited? Major throwback vibes. Wow, they have some really vintage cameras. Oh my gosh. Some vintage Rolex. Harry's supposed to find anything. Oh, Lord. I said I wanted to ask him to like, here you are. We've been here for like, how long now? 40 minutes? 40 minutes and still no luck yet. We haven't found anything that we liked, but yeah, we're just gonna look around a little more and try to find at least one thing today. Yes, that'll be our goal. You can literally find everything here, guys. Knee protectors, scissors, power cords, piggy banks. Golf balls. Oh my god. Daddy, you look good. I don't look good. That coat reminds me of Claudine. Claudine! I really like that brown one. Please let us just find one thing before today ends. This is a pretty background. Maybe. Thank you. Hello, Katie. I think I used to use this actual one with the flip cameras. Of course, you can't forget about the straight food.  Yeah, we thought so, too. Cheese. It's only 3,000 won. Christine would be looking at pottery. Look at his face. Elsa's face. We've been here for how long now? We have found nothing. Not that we're picky, but we really just... I can't believe we didn't find anything yet. Oh my god. This place is seriously endless. Christine? Can you see our facial expressions? Our enthusiasm is gone. DM vintage. I want this in a girl's size. Give me the red one. I like the red one. I feel like you can wear this more often. The back a lot. You can take a hype photo with that. This is $150. It's goodwill, Christine. Oh my god. Goodwill thrift stores can find some really cool gems for like $2, $8. Not a lot, but there were a lot of local thrift stores that I'm really sad that... Maybe you should do another one when you go to New York. Yeah, a year from now. In like three hours and a half since we've been here and I think we looked everywhere and we haven't found anything. I really wanted to come something and like show them though. Me too. I'm so sorry guys. This video is completely fail. We didn't get anything. I should have just not been a cheap bow and I should have just bought those sunglasses. It's because I used to like the like $2. Yeah. They were $20 the sunglasses. So I was like, hmm, no, no. But now I kind of regret not getting it since I couldn't find anything. How is it? This is definitely going to be like a honest, good-for-nothing video. Experience. I had such my hopes. I thought I was going to pick out like a whole outfit. Like I had accessory and sunglasses. To be honest, that's what we were going to do when we first... Yeah, we were going to do like let's like make an outfit for each other. But thank God we didn't die. That would have not gone out of my experience, right? Just to see how popular it is and how many people there are all the time. So if you guys want to go all on a weekday like early afternoon, like early afternoon. We're going to continue with our date for at least to be in these channels. You guys want to continue to watch our date? Go to our channel and make sure... Make sure you watch our video. But yeah, for now we are going to say goodbye. I'm sorry. It wasn't the drifting video that you guys expected. But I guess I'll just have to wait for New York. It's like the raw drifting video though. Yeah, I can for sure. Man, okay guys, bye. Thank you for watching this failed drift experience video. Yeah, thanks for watching.