Hi, hi, so how many of you have worked with microservices, lot, how many of you are planning to move to microservices, almost everyone has heard of it, right, so lot has been, no discussed about microservices assets, right, so, but the rationale began this talk, right, why, how we should design microservices, let me give an example why that is needed, let me say I want to do like food ordering app, so if I am making food ordering app, developer or architect A will come in and say that, okay, let us make restaurants and menus a microservice and let us just make orders another microservice and then let us have a load balancer and issued traffic, right, then the architect B will come in and say, no, no, let us make restaurant a microservice and menu items are another microservice, right, and then the third person will come in and they will say, no, let us make each dish a microservice, panitika will be a microservice, kadai panir will be a microservice, thanks again, so you see the old spectrum of people who are having different viewpoints are what is a microservice, so this is somewhat analogous to normalization in DP that we do, right, so either you have completely normalized tables or you have like a flat structure of this thing, so we will look at that in the stock, right, quickly about me, I have primarily worked at startups and built systems from scratch, right, and I am interested in distributed computing and building scalable systems and I purely enjoy coding in Python, previously enjoyed in C, so that means I am very, very picky in what I want to do, right, the company I work for, I work for match sheet then, we have this retail brand called View.a, where we have this B2B products which are APIs which are hosted in Amazon web services and these are e-commerce companies or other brands who are consuming this for machine learning or personalization which so we deploy machine learning models at scale, we are hosted on AWS and we serve like 200 MS latency requests, right, these are some of our API numbers, we serve pretty huge traffic because we are amalgamating a lot of e-commerce traffic to us, this is very important because the use case I do is something very similar to what we do, right, yeah, yes, I am innovating, Microsoft is very well, so they solve a lot of problems, what are the problems they solve, so basically essentially when you do a Microsoft service you are building a graph, you are building a graph where there are nodes and relationships, nodes are each Microsoft services, the relationships are, what are the data flowing through the Microsoft services and how they talk to each other and once you start off with the graph of, if you think of your system as a graph, that is, graph will keep evolving over time and if you do not have initial set of graph design right, then the nodes become inflexible and you will not able to play around the pieces like Logo blocks that easily, right, so let us go ahead and design them, I did a small exercise, so I want to do this exercise, is there like a common literature to design Microsoft services, so I went to Google, I tried how to design Microsoft services, I took the top 10 pages and this is the word cloud I got, this is a bunch of generic terms thrown at us, right, so out of which two of the keywords made a lot of sense to me, data and database, so essentially in engineering or software systems we are only building data, systems that are either communicating some data somewhere or storing some data somewhere, you index in different ways, you query it in different ways, you send it via binary stream, you send it via JSON, it does not matter, it is data and another thing, so if you keep data at the core of your Microsoft services, let us see how the exercise comes in, so as, you know, follow up to that, primarily what are the ways in which we store databases, let us look at each of the ways we do that, right, at the top of the list we have OLTPR relational databases, these are like standard use cases, if you do not know what database to use, you just use a relational database, right, and you have multiple tables, you are doing joints across them, you essentially use them for any web app kind of use cases, but they are bottlenecked on writes, you can only have a single write, they are not inherently distributed systems, that means that it is not scale horizontally and your master is write bottlenecked, second you have key value or document stores, these are eventually consistent systems which are horizontally scalable, that means that you can store one, but you cannot do joints there, that means you can store a lot of data of one type and spread it across system, like users in social media or tweets in Twitter, right, then you have in memory data store, these are data stores like Redis or Memcache, which primarily store the database in RAM, they do not store the database in disk and RAM, do the memory management for you, where are they used, wherever high performance is required, they use it, because latency of Redis is a single Redis call is less than 1ms, that is very low in compared to what RDBMS or Cassandra can give to you, right, these are essentially used as caches and other queues which are niche below latency, then you have the OLAP and column non-databases, which are used for analytical querying purposes, where the same data, which are stored as single table is split as columns and stored across different machines, so that your querying and your analytical queries are very fast, your search indexes, which are inverted index of your data, that is each of the item in your data is tokenized and the term counts or frequencies of the entire data is counted and stored, you have object stores which are just flat files, but you have this huge infrastructure like S3, where you can throw any number of files at it and it will store it, this is a old array of way you can organize your data and store it, each of which has a different set of SLA and different set of promise it offers, so we have to choose what kind of data base that we have to use, right, and this is not exhaustive list, there are more adding to it, so let's take one use case, right, so this is a very simple use case, which is image classification, so given an image, classify a single attribute for me, the attribute that I have taken here, classify whether the person is wearing a full sleeve or off sleeve, how do we solve this problem, this is a business use case I want to solve, so how do I solve this problem is, I will have to build a neural network using computer vision, that means that I have to build, feed in a lot of images to the system and train a model and use that model to infer and serve results, right, if I have to build a like a simple architecture diagram for this use case, this is how it would be, right, so like I told there is a set of image data source, which is passing through ingestion data pipeline, which is a set of queues where someone like through a pipeline, does image classification modeling, pushes it to a model store and there are a bunch of metadata that are associated with the images, like which user has uploaded it, what is the title of the image and things like that, that goes into data query store, finally when someone, it's an API, it's a combination of which user served the image plus how the model is trained, the model inference service, serves a request and finally the API is served, if you take the data, how the data is flowing and stored, data query store is like a multi-object system, where you have multiple models like your objects like users, permissions and things like that, accounts, so you essentially choose a RDBMS kind of system for it, if you take a model store, a single neural network image take some space between about 200 MB to one gig or two gigs, depends on number of layers and architecture and things like that, so you obviously cannot use any of the RDBMS or key value system, you have to use something like S3, which is a file store or a block storage like EBS or EFS, right, so that store is very different and data ingestion is in a queue, which is very different, so this is the simple use case diagram, right, we will take another use case, this use case is product recommendation, so given an email product, recommend me some more products of the same or similar attributes, right, so this is essentially a search problem, here you see a person looking at a shirt or jacket and they are recommended something of the same color or similar attributes, right, so how this use case is achieved is by again catalog and inventory data, right, what is the catalog or the set of images that is in the catalog, someone has to know for them to search through it, so it is a similar process where someone takes the inventory data and extracts features out of it and puts to a feature search store, right, a feature search operation is essentially a distance function operation, that means that you need a search index kind of store and not like a rdbms kind of system, so that is a search index, data curry store is similar to the use case which you saw before, which is rdbms, then you have, you can also use the events information like which product someone viewed, what is the product that they bought and things like that, so if I have already bought something, that means that I will buy more of that, it gives us some signals, so that is like a structured event stream that flows into the system and events data modeling is done and those models are stored, right, an event store is like a number of tweets, there will be like millions, hundreds of millions of that data, right, so it makes sense to put that into a key value store, you can also choose rdbms, but if you want to scale horizontally, you can go for a key value store, so that is like a different system, right, so this is a model architecture where again the AP is eating a service and it is going to a different search store, there is a pipeline that is pushing data to different stores and the results are being served, what we will do now is, we will merge to the, like I want to build a platform, I want to build a platform where let's say the business will come like to, come up with two, three use cases, right, today, like tomorrow those use cases will change, so that means that Microsoft's graph cannot change with business use cases, let's say they, tomorrow they want to come up with like a completely different use cases, it is not image based and text based, that means that there needs to be a Microsoft which supports text, right, so I want to build a platform, so if I, you know, compress this into a simple architecture diagram, this is how it comes like, right, there is a bunch of data pipelines which is writing to a bunch of data stores which are actually purpose built, like a search index is purpose built for search operations, RDBMS is for specific type of filter operations, right, a key values, again a specific type of operations, I cannot do aggregations on top of key value, then a set of like pipeline services which gets derived data out of those stores, then write it back again, for example the feature is a derived data, it's not like coming as part of the incoming pipeline, finally what you see in green here are those services which are compute, so you want to compute something at runtime and then send back results, so these are like API facing service services, what you see at the below API aggregation is a composite service, so you want a single endpoint where when a bunch of request comes in, it routes to different services and it microservices and finally like assembles the results, right, but it's okay, I now come up with this exercise to analyze microservices, I see that I can derive architecture diagrams based on data and you know come up with this thematic structure, but will I put this in production? No, there are a bunch of questions that needs to be answered, how to take this to production, so this is an exercise we are doing from scratch to production, so let's answer those questions that we want to answer to take it to production, right, so this gives us a pattern actually, so we see that a microservices are actually grouped by their functionality that they perform or the category that they fall into, like the green one you saw like it's like a API compute service, the yellow one is like a composition service and a gateway service, you have two sets of pipeline services, one pipeline service is a batch, something you want to be processing like twice a day or thrice a day, you want to use like a Kafka kind of system, you will use Spark kind of system, right, so that essentially software that is powering it's completely different, a pipeline which is a stream, you use a streaming application, right, and then there are also data reader and microservices, these data reader microservices what they do is that they shield from users from misusing the database, like you want them to use it in a certain way, if multiple microservices are directly accessing the data stores, probably they won't use it in the most prudent way, so these are the classes that you identify and you then go into thinking how do they communicate with each other, then there is, in real world when you deploy, there is SLA requirements, there is latency throughput, those are some of the parameters that you want to support, right, so the essential thing that comes when you want to do support like a very high throughput latency is that your system has to have caching enabled, that was not there in our initial architecture, that means that we need to have like a in-memory system which acts as a cache and that flows through system, right, and other cases that let's say you are doing microservices chaining, there is one microservice which is calling multiple microservices, or microservice A is calling microservice B five times for each call and if microservice B latency is let's say 10ms and it bumps up by to 20ms, that means that microservice A's contract has gone by 50ms, right, so this kind of cascading effects will take place, so caching has to be handled each layer of microservice, so these are some of the considerations that we need to take into account, then you need to take into account geographical regions, so if you are starting your business in North America and you put up your data center, you take cloud provider of your choice and then you start, let's say start with AWS or GCP and then provision endpoint there, and you suddenly get a customer from Singapore, then the single basic op time from AWS North America to Singapore is one second, so all your assumptions goes for a task, right, your API went from 200ms to 1.2 seconds, just like that, and that cannot be your basic platform design, right, what are the ways you can mitigate it, let's say you are a read-a-view company, you can use CDNs, or you can have region specific deployments, right, or in some cases you can have, some cases where the data that is being sent, read by any part of the world should be the same and dynamic, you need to have globally distributed databases, like what Google spanners or summer system supports, right, so logging and monitoring, I think all of us know that logging and monitoring is very important and we have also figured out ways using Elk and to use that, but we have really not figured out feedback loops, right, that is one thing which is missed out, right, when an alert is raised, how is it being closed and what are failures, defining that is very important based on your logging infrastructure. We spoke about one specific type of data, which is the data is stored in data source, we will move on to others, other type of data, which is data which is being communicated between microservices, right, microservices needs to communicate with each user, right, and they need to communicate really fast, the operation of communication between them should be cheap. If you see here, then it takes us to the conversation of service registry, right, service registry is something where microservice registers themselves saying I'm here and you can send me any communication at this end point. So this is like a system, feature that we can envision for a service registry. At peak point, you can have like, let's say 20 to 30 production servers running at any time, right, and you want, I'm a big fan of feature flags, by the way, like any feature that we do, we want to like put feature flags and then push it out, right?  If it takes the data needs to be communicated across, if it takes redeploying all the 20 services, then it's a system failure, right? It cannot be. So if someone goes to service registry and changes the config for service, then it actually has to immediately flow into all the 20 instances and immediately it switches that feature flag, right? So the other use case here could be, we just are thinking about config in terms of each service managing their own config, but service A can send config changes to another config. An example here is that let's say service 2 has a feature flag called talk to cats. It can talk to cats and it's turned off. But for a specific user, service 1 wants to enable that feature. So service 1 sends a message to service registry and service 2 updates its flag immediately at the runtime for that specific user, right? So these are all ways in which communication could be made really cheap, right? So for this specific use case of how microservice is coming with each user, we created this service like our own microservice registry called vane. So this is a simple web app, right? And we had like really four very small design principles when we came up with the service. Like that actually secure config management for per client, per deployment, and per environment. So we have a globally distributed data center operations. That means that this is very essential that this happens. And I already spoke about the real time changes. The config push from one service to another needs to be really cheap. It needs to be 1MS, right? It cannot be often over. Then as a developer, I would be really reluctant to push changes. Resilient notification system. If a microservice is down, that means that the notification can never fail. And yeah, a microservice can communicate its desired config changes to other services, right? That was another feature. So this is like a very simple service we wrote. It's nothing fancy. It's just a PostgreSQL DB plus like a Python web app where there is a service. And it gets notified through Lambda through any of these channels. A service can register through SQLs or ALB or EC2 server. And it will get notified. So this is a very similar flow that I discussed where a config change comes in. Each microservice is assigned admin or APA key through which it sends its config change. And the notification Lambda does exponential decay. So even if service is down, it will eventually get its notification. Like it will bring a two seconds or four seconds or eight seconds. So it has exponential decay. So if service is down for like one minute, it doesn't matter. The notification will reach it. The way we have handled the instant notification update is that each server will have Redis running inside its machine, right? Redis being single threaded. It's very efficient where the config changes are not actually loaded into the memory of the process. There are like 20 to 30 processes which are running in each server, right? Each process will actually read its config from the Redis which is like a very low cost top. So the config will actually go and update the Redis and the server will instantaneously get the config changes, right? Very simple but very effective because all of this is powered because Redis is single threaded, right? There are no race conditions there. So this is another flow where service one is sending message to service two. Again, this is the same flow just that depending on the channel it will go and update service two, right? Okay, so we have considered a bunch of observations that we want for our production infrastructure. It was latency, geography and logging, things like that. So if I have to put like a single core platform architecture out of this, this would look something like this, right?  What are the new things here? There were a bunch of data pipelines which write to online data stores and there is like data lake or offline data stores. You have caching infra which is introduced new which will power all your low latency AP aggregation request. So everything you see above the dotted line is a platform. Everything you see below that or apps or products that can be built on top of the platform. Since every service, if every service is designed as per the function of the data that is handled there is a very little chance that that service will have like a very systemic change over the course of the product roadmap, right? So you can build multiple SaaS apps on top of it. In fact, that's what we actually did, right? At the right, we have the service registry and discovery, notification service, all the components that we discussed about, right? Which will tie together all these pieces and create this one infrastructure. Let me quickly summarize the learnings that we did by all these exercise, right? It is the same question of normalization, right? How you want to normalize your microservice graph to the optimum level. And what one end you have brittle and another end you have rigid. You need to find the balance. Don't have single point of failure. That means that don't have like completely decentralized system, right? You can have like considerable like multiple single points of failure which are failure itself or like less in impact. And master your data stores, right? Because a single data store takes some time to master and you have to respect it and carry along with your architecture. For the third learning for us is that every API needs to have a synchronous and asynchronous API, right? Because you have batch operations, you have every operation can take time, right? You can send results immediately or they can submit a batch and get it back or this thing. But if this is a paradigm that is enforced across your microservices, it becomes very simple because the developer is not coming back and saying, oh, I need to introduce a synchronous API for you. No, we felt that that needs to be like a mandated thing. And we spoke about real-time feedback loops. Like, how do I actually have alarms so that it adds as input to my system building, right? And communication between microservices, solving that on day one, especially since we are multi-region and multiple deployment kind of setup, that solved a lot of problems for us. Finally, we also declared microservices as end-of-life, right? You can try to maintain microservice till you think it has life. Otherwise, you have to like, let's say you have a Spark-based batch system and you have migrated all your systems to like real-time. You don't want to like really, you know, keep hanging onto the system. Just an example, right? So yeah, so that summarizes the learnings that we have had. And yeah, questions? What if the scalability has to be on a massive scale, right? As you said, one of the examples when we need to, say, put the whole config on a global level. So any other example which you can say on this? So we kind of contained our scale limits, right? Your app player will scale like horizontally, but then your DB layer will have its limits. So we benchmarked saying that each read replica will have, will support this much scale. So let's say if the answer is predicted, we'll add like 3D replicas and things like that. What about security related? So yeah, like the previous talk, security definitely was not the first focus when we started building it, right? Security remained at the perimeter level, right? But then we really started ordering our AWS instances like having golden AMAs and having it, you know, reduce the level of intuition. So we introduced ideas and things like that. But in terms of security itself, from the data point of view, for all data stores mandate encryption from first, right? For all communications and SCTPS, and there is no like non-encrypted communication that is happening. That was like the, from micro-service and data here that was the main thing. Question here. You given analogy of no, don't put all your X in one basket, but don't put one in each basket. Can you talk more about that? I mean, how does it relate to micro-services? Is it not too fine grained micro-services, is that? Yeah, like I said, like the example I gave, right? Where some companies like Bosch, they have like 500 plus 600 micro-services. But at the scale of things, if you see, that's decentralized things too much. And basically you need to have the workforce to support it. Like the actual ratio is that if you have like three, two, five, four members per micro-service, and there's a maintenance cadence to that team and they have ownership and they maintain it over time, right? But the ownership falls flat at some point because there's multiple code bases and they eventually get merged into one, right? Another example would be like, you really like find over time, right? You have one service which is taking from a database and then giving it to something and doing a compute, right? We have all seen that. That is kind of really unnecessary because someone says, this is IO operation, that is a compute operation, you have to keep it separate. But that distinction is kind of great where if you have the gut feeling like the ownership of the team actually lies with one micro-service, keep it one, right? Yeah, yeah, we do merge it, right? So it's the graph keeps evolving. We should try to make the best graph initially but evolving it shouldn't be difficult. That's the basic criteria, right? But if you get the database layer and the SLA's of that wrong, you have to do complete migrations which is like a different level of evolvement, right? So let's say you have like streams of data coming in like which will scale to 300 million, 300 million, 400 million, things like that and you decide to put it in a RDPMS. Eventually you will have to move out of the system because you cannot like scale out that system, right? If your choice of data store is wrong, you definitely will have to rewrite, yeah. What is your recommendation for error handling in terms of communication between micro-services? Sorry? Error handling between micro-services communication. A is calling B and that is calling C and something went wrong. Yes. So that is mainly one is distributed tracing. Like in the injection pipeline, every incoming request is assigned a unique ID, right? So first part is monitoring then the second is error handling. So monitoring we have dashboards where we clearly see that the injection has not completed for this many products, right? Because you have this unique ID and you take like a pivot on that ID, you will get that data on a dashboard, right? Second thing is you have fallback services, right? Which is, service A is failing and there is a lot of pressure because you are pushing even more traffic on the service. It creates a back pressure. So you have fallback services or fallback data stores. For example, we have a RDPMS system which is a fallback for a solar system but that's not the primary purpose of RDPMS. It's serving different function. In case that system starts failing, we route the traffic to solar RDPMS, yeah. Right, so some services are multi-tenant, different services are multi-tenant in different ways. A single service can process multiple clients data, right? That is like a typical SAS kind of multi-tenants where you put all your clients in one DB and kind of thing. But some services cannot be handled like that. For example, if I am putting a solar cluster for one client which has like a huge data, that data set will be so huge that I have to spin up one more cluster for the other thing. So the handling might be different for different customers there. So multi-tenancy is not like a one solution, like say we either put all the clients in one, all the service like that or you have like different services it is different, yeah. Architecture once, yeah. Engines are convention, perspective, all the services are enabled. And from your data pipeline, what, can you explain what technology are you using to deliver the data pipeline? Right, so date for, so I will like repeat what I said, right? So data pipeline, we have multiple solutions for data pipeline. Like I said, a data pipeline cannot be one. We have Spark-based data pipeline. We have our own Enos data pipeline which is based on Kinesis on AWS or SQS on AWS. So there are multiple data pipelines. In terms of data stores itself, essentially if your data is scaling out a lot, you cannot keep adding them into online stores. For example, a journal data, if this product was this much price, currently is what online data store will have. But offline system will have, at this date, the price was this, at this date, the price was this. So it will have like a time journal to data which will scale very differently, right? But you don't want to lose out on data. So you move all those data on offline data stores. The modeling service, what is that is, it needs a lot of data. Basically, for to generate features for machine learning and all that, it will look up, use offline data store. Modeling services are basically batch jobs which will look up those data and generate models and put it across again to online data stores, right? It will like transuse it and put it there. So the end services, essentially will query only from online data stores. Offline data stores, essentially access input to the modeling stores.


We still love you. Yeah, sure.  Look around you, these are my connections. But I don't have people here. So, if you have a partner or a friend that's going to help you, I'm going to put up my e-organising gap and take off my speaking gap and say that we can sit around here and hear people who are from the bottom. So, if you have a partner or a friend that's taking you up, or they're waiting for you home and you want to put up your speaking gap, this is what people want to say. Go ahead. Really, go for it. Cool. So, I can pretty much guarantee you that Bruce and I have prepared nothing. Of course. I can almost guarantee that. The reason we prepared nothing is because it's an exciting art story. A story about a partner waiting for a community to make a difference in our home. Do I need to be on the ESD on camera? Thanks, John. This is the story of how I got involved in a community. Okay. So, my name is Jonathan Foster. I'm a freelance developer. I speak for the term of my name, WordPress, and I have a big term of my name in my book. I also want to be an ESD. I work for ESD, and I'm going to organise it along with you. Last year, I was dealing with the work I cared about. You can see here, this is my slide, this is my note. So, I'm going to go ahead and see that. And work for ESD is how to make a new chain of art. It was the first work I ever heard of. It was the first time I discovered that WordPress is not just a login tool. It was the first time I discovered this thing called the WordPress community. And at the end of that second day, I made the decision that I was going to choose WordPress for you all and make WordPress what I do with tomorrow. And I realised that in the last few years, I've realised that purchasing is not enough for what you do. How hard do you do it? Or how much? It's just the fact that you do something. One small thing. Make it a little small. I remember sitting in a tall pen, working out how to use 2016. Constantly learning was a speedy digital condition. And his first contribution was fixing a double underscore in a translation So it was supposed to be a double underscore or a single underscore. So he added another double underscore. And a line, whatever it was. He just added one character. And he submitted that hash. And he was working on it. So it's working. Okay. I'm going to add another one. And what I discovered when I realized about WordPress is that you don't have to be an organizer of WordPress. You don't have to be a finance programmer. You don't have to do anything more than what you can have. And it boils down to making it a choice. Now, the main reason that RoosterEye is doing this talk is to try and convince those of you who are here today to join us tomorrow for a truth. You totally should. Gave us about the numbers. We have gays and 450 people. So I won't take my mouth again.  Pete's going to look at me, telling me there are 50 people for our lunchtime. We currently have 16 people side up. If you want some group, if you want some group, come and join us. Please. Because the most important thing is you don't have to be an amazing developer. You don't have to be a core controller. You don't have to do anything more than just one thing. If you arrive and join us tomorrow, you're going to get there at the end of it. And you spend an hour, and you want to do one support, and you help one person start from there for the last time, and you help one person solve a problem you have come through. And that's what it says. The rest of my talk is going to be about my journey, my story, but before I leave, I'm going to have to do the second sentence before I'm done. Christmas. So apparently, the one thing I'm going to do is I'm going to have to write it. So it's a car wash. So I'm going to have to write it. Of course, my idea was to study it and that my involvement with the workers community led to involvement with other communities, which led to a changing career. And I had the idea to start with a developer. I started when I was 14 as a developer. I worked on the TV system for a big events company, as well as one of the big seminars. And then after that, for two years, I went to Kent, and produced a lot of career. I decided to take a job also, but I couldn't do it. Then the delivery of a boat, I went back to work. Then I kind of changed what I translated and the benefits of it. I realized I like to do it with people, which is weird because I'm actually into work.  And I then kind of learned to be a manager when I learned about it. And I joined a small company called the Gates-Bottom as the project manager and consultant and eventually became the manager of it. And after that, I decided I'm kind of done working with one of the last one-year-long mayor. So I co-founded the work manager. And we knew nothing about it. We did massive corporate assistance, so custom stereo systems and so on. So after two years of doing that, realizing that it is terrible, we started doing more websites. And someone said, hey, come to work at. So I joined my work. I think the first one was in 2015. And to be honest, I couldn't get out of that world. It was because of a legacy in, where they were soon willing to help and teach you how to train and get involved. What's great about this community is that even as a PhD student, I had too much work. I needed that I could contact the guys in the room and say, hey, would you help me with this? They'd happily probably help us out. And at the same time, we had quite a great say, hey, I'm sorry, do you have anything we can do? They'll actually help us out. So I really wanted to know about that community. Because of that, and a huge job, we started getting involved in running the meetings. We volunteered at the workshop. And now I'm based on the organized community workshop for your two years. We've fallen out with the math. And then you started to know that you couldn't say no anymore. And I thought that, I think I know, if we were going to do the best we could, and everything else, they can't have taught me how to do it to school no matter what.  We didn't need one. We started to get out there and we were more ahead of travel. And they didn't know how old you could suddenly, I think that in the special five years, it was like 34 hours. Because that's what you need to offset. And then you need more people. And through these people with your life change, you learn new things to grow. And then through that, about seven months ago, I got a call from the vice president of the community to start a grant. And I don't know anything about that. But I was just talking to the organization. He called me and said, just by the half of the day, if we had a community, we had to drop a good job. And I had my latest skills running. It was like, I'm sure, I dropped everything in our community. And he said, okay, that's a good job. And he called in our lady and said, by the way, that job's yours. So that day, I called the whole community and I was like, listen, I love you guys, but I can't say no to this. And because of my work with the work with the community, I was now being asked to help run this massive local community of volunteers. And I had to take it, I could say no. So I put it out in Wordpress without going to their first Wordpress. Without people, without people. I never would have meant to start a grant route. I wouldn't have been ever going to step into a five-year land job interview, which is nothing like a job platform. I never would have dreamed of to help literally millions of volunteers around the world. So I had to take it with me. I mean, totally not that much. So thank you. So this is my story. And we're going to have to refer to my photos. It's a terrible event. My name's and I'm glad it works. I'm very excited about it. It's a term 259 to look at for the first time. I'll get to this thing quickly. I heard about it before. I tried to go to the previous year and I wasn't as best as I could go. I met some amazing people. I was practicing amazing people. And I've been living with one of those two days going, this is what I'm going to do for the rest of my life. I'm going to be moving forward. Doing something that I've learned all my life, which I was crazy. I was going to make hardly a drastic change in my situation. But I kind of just used the word, I don't know why. Soon after that, soon after that work out was a term 259. I can't get to you because I saw the news running in the head time unit. And they were usually in the internment on that work out. And I'd like to do it before and it's about a hard drive, a good drive. And I said, I'd like to get you all starting up a meetup in the north side. And he said, well, why don't you join the way for us to start and join a meetup group and then from long time meetup we could turn. Unfortunately that meetup group doesn't exist yet. Because what happened was that I got involved in the gig time unit. I'm Jerry. Jerry, the 2016 man at the week did a fireside chat and he'd done much like this. I still say I don't like that. But it was around another time that my youngest was about at the end of the year. I fell over for a year into your own chat and now I'm going to be there. And I finally went to my first meetup with Hugh 2016. I met the folks and got to know them at the end of the time. I was going to go to Bruce and I then started getting more involved in the community. For the very next work out I applied to be a speaker. I didn't get invited that would be awesome too. I applied to be a speaker because I realised that if I wanted to share where I wanted to share I had to step up and make a choice. Then I was invited by a local worker at a company called Cocoa to be interviewed by an academic worker. The reason that they sent me that I wanted to interview because they were interested in people like Jeremy Piers and Hugh Rashford and people who are veterans of the K-time worker space. Yeah, I was this new party. I just want to remember I think it was a matter of asking who is the star of the night. Because I think you are what's I wasn't even as involved in the community of them. And the reason they wanted to interview you because they found it so interesting that I went to my first movie in 2015 and I was speaking at least in a way but what is so amazing about it is that I'm speaking with an expert and I've said to them and I've been able to do nothing. I'm just a guy who likes to do things and this is a space where you can do things where they welcome people to do things. At the same time so from the point of choosing to repress at that time I was working as a volunteer there and I was I worked in the business of my life trying to manage one son who was living in you and contracting on site business and all that so I did and the first year was hot the first year was exceptionally hot and then I met a company called Wearing a t-shirt right now that I were in Christmas in my future before I was accepted one of the questions that they asked me was is how are you involved in the WordPress community? Well that's how I'm going to go well I'm visiting a meeting with a guy and I would like to leave because they're they're really up still in the language community that I was in the scope and part of the meeting was a community a community project I was involved in the community I was giving back to it I applied to leave for example 2017 because after you finished your 2015 there was another lead who was leading in 2016 2017 I applied now I'm 520 because I was 520 in October 2016 I was asked by you whether I really was the person who was leading that jazz family issues that I needed and without being paid to do it I don't know how I'm going to be more good so I'm going to make the series not enough and I'm going to finish going to say a jazz interview and the key thing that I want to again drive home is it was I decided to try these things so I am making I have written this amount of code towards winners I have not done any design I have not done any testing it's only convenience but because I just got involved in an area where I was comfortable and get involved outside of my job I get especially where I am literally and I each question literally in the library but I'm literally in two years double my reputation because I've made that choice to go away double I kid you not and that's where you can be okay I'm not a situation where my wife and I don't have to work in all the issues and I feel comfortable enough to say don't worry I've got this because I'm involved in the community because I need more amazing clients around the world and this I'm a little bit to the score for when you wish to explore and things outside of where you wish and learning new things and growing all that kind of thing but because I'm involved I still rest assessing so the biggest thing I really think of you as a gardener as you come and get involved and the thing that I started the we're best if you get all the great community people together whether you're right-handed or about together and we try and promote websites for our products and the city talking about this is not like yes I'm involved and the first year I joined as a project manager for the team and after that you know I have a woman and I've been here before and every year it's better there and we work with their organizations and again because of you it's a very strong international so we have working with the community do we websites for my brothers or because of starting this start here we should all be right from a personal to everything like I said especially if you speak to some of those people and I know that we came from the of years of space to even talk about hey we can make that much better if we're from more by doing smaller projects more readily and then there's been all of our resources of time to a year and a half more project that half way through the internet is going anyway so we made that decision to say hey we're going to stop and we got worried and because we weren't involved in the community we started making key partnerships for the agencies we worked on all of them because of our sponsorship at these events at all at these events and we wanted to know if this is part of working time and then literally we taught the agencies the marketing and the pricing of the agencies how to do proper breeze technical breeze and they just started taking us work and we made a little work again and we really took in a bunch of breeze we put them together we tried out how to start to be and to be fantastic and in doing that we started doing more work on such and the last thing is the work on guys so we started bringing back everyone else that we needed back then and then we started being asked to speak on cows and we started building sites and we were processing a company and we were doing a company and then this day we were talking about a bakery or it's a good bakery but a bakery that makes cupcakes that's a little little ranch with orders whatever it is like a month all of them makes them wood logs that's pretty down resident we also had furniture and then we sold couches during that Friday and then we said we had a pretty down resident so from our perspective getting involved helped make more business more business but we started doing cool things with cool companies and almost I don't ever have a reaction to anyone who doesn't know I used to eat that in my head I made all of that from customers that's kind of how it started for me and all the people that we work with are people that we met in the world of this community we don't hire outsiders because we know that they're passionate about caring unfortunately they're not here today but we really we don't hire the community but I think most of you are actually going to a company and they just do the same thing that we're going to consider about you know the other guys from YouTube to say or do a bit more you know that's not necessarily providing code meeting people helping people giving back in any way you can and I can guarantee you that we'll be returned to you at least therefore so for a very less signed group our aim is that we're just doing it okay we might as well do it that's how we work so the direct in plan contributing that to my life contributing late you to recommend me it's all about you then you can submit that the amazing show you know letting him to recommend me to the new owner of services and broadcasting which they in terms of two year positions so far has been that much you have not seen a line in my phone when he made that recommendation it was clearly less on my what to do with you know I think you know I think it's the talk quite a lot so yeah not a lot of you have to do the purely because I was involved in this when he sold the lady at the U.S. State who do you recommend my I've never been out of this country on my I've never been out of this country on my I've never been out of this country when I was at the U.S. is the I've never been out of this country a lot of you may have a lot of full of a lot of a lot of a lot of people you know I was in a company this year this year is I think maybe you to the devil and making up a tension. If you haven't learned about the concept of vital diffusion, Matt Mothery talks about giving you 5% of your time back to the rooms. And that 5% can be joining us as a volunteer for the two days at work. It can be being involved in what I do. When I start with it, I used to go on the front. Who do you know how to provide enough to do? I'm a forum carp. You don't know how to work in whatever room. Most of the time. So what I used to do, and I know there's a bunch of people that do other things, front after the forum carp, we're first of all such support. Find some teams that have been around, that haven't been answered, that have been asked to support us, that have been asked to support us. That's five support teams that somebody else doesn't have to do. That's perfectly fine. So whatever it is. And the point that I try to explain is that we all use rooms, and we all make a living from rooms. And we love rooms. But if you can start thinking about as much as you can, and I say this as much as you can, as much as you can, giving back to the rooms, it will have a dire risk of impact on what you're able to do and grow and achieve as we grow. My hands are short, but I'm really tired. So as I mentioned in part, it's because I'm kind of a subject and kind of an expert. And somebody who is thinking about how I was talking about things that two years ago we had in our day-to-day life. So I definitely think that was really cool, by the way. Thank you very much for the opportunity. This is very important to me. I made friends in our contacts. A contact when you ask them for help, they're not really going to do it. They're very important to me. And this community has made a difference. It's very important. They taught me to live in a digital lifestyle. And they said, I think 34 countries are quite a lot of ideas. This year alone I went to 13 countries, trying to give me again a couple weeks or two weeks. And... I don't even remember. And then, as a matter of fact, they taught me pretty well that I'm never even born. Community management is kind of my passion, because my destiny. I have no idea that anything exists as a group. So it's a big thing. Mr. Mayor, what's your name? I'm Mr. Mayor. So these are the five key things that I've noticed about contributing to the workforce. Contributing just reminds me of something I've said before. The world wants to make a change in the world of workforce. One of the questions that I've never been able to deal with is if you're making improvements, contributing to the workforce, yes. If you're not contributing to the workforce, you're not contributing to the country. So the first thing that I know is that countries are human beings. We're all human beings. Some of us are more human. They're crazy. We are jobs, but they're clients. If you're clients, do what you want to do in the street, which is their team or their team or their team, that's iron. We are all a tiering. So if anything, I need you to do that to the workers of our new free time. So people aren't going to expect you to have these crazy day-longs and sticks to them. If you follow automatic, whether the automatic freeze is communication that must be distributed to teams, and that applies to the market as well. If you sell somebody or other teams that you're going to finish this document by next week or Friday, and something happens that you can't, then you just tell the team, they throw something out of that cartonition and they'll do it for the next week and everybody gets it. So it's not like you're looking for a company and I think that, I know for me at least, when I first started working, we were all on that. In a volunteer environment, in a remote source for the sunlight environment, we all understand, we all get when I, this year, when I think of you, instead of being the normalizer that I entered in my job so that people can actually form a team to live in it, and we have teams that are ISD, who has us community, that teams are digitally set up. And I contacted them to set up the petition and that project to the project, I said to them, we're doing this team thing, we're recruiting teams. You manage the team, do you have any advice for me in this volunteer space? The two that you sent to me were number one, make sure that everybody understands that you give that they're doing this volunteer. Make sure they know that it's okay to miss that day one. It's not really a day one. You're not dealing with this, yeah. Also make sure that you check in with the teams. You notice that they're high there, what hospital are you expecting to use, check in with your high there, what can I do to help you. Because they keep our boy kind to say, I need help with this. So those are the two pieces of our schedule. Number three, small things can affect big changes, adding one underscore to at least a new line, adding one underscore. Adding one underscore by character, adding one car, adding one translation, that's probably not even going to be that bad by the way. Go back to the situation. The reason I haven't downloaded it is because there's no more things that I'm fixing for you. If I could get a whole bunch of people to come along and add those comments, they'll be amazing. And they'll all get props to the way. I'm telling you to come out, I'm just saying work is the call, whatever. If it's my plan, you're not doing it right. If you continue to work with me, come to the people, you're taking me too seriously and that's okay. And finally, don't get dragged to the next WP draw. There is always some drama going on because of how big our community is. It's 32 cent on the top 500 websites in the world. That's a huge number of people. Ten million. Ten million is next week. I want to show a very good job. That's a huge number of sites. That's a huge number of people using women with same-hand workers, relying on workers to earn their income. Some people are going to disagree with you. And that's okay. Somebody's going to sell the person to the US. So when the drama is happening, don't get sucked in. Take a step back. It's like a ladder to the truth. I'm telling you, system-on-miner, disagree to high-fielder workers and that's not getting all the way done. Take a step back. They're experiencing it in one way. They're trying to expand their life. They're trying to include them in the concert. And in one of your orders is asked why all the family followers are going to be? Family followers? Family followers? Thank you. If you're going to go further, and if you're going to do business and projects that you actually can, it's okay to do business. It's okay to do business. It's okay to not finish your job where you're supposed to be. The more you move on to your job, the more you can get more people to join. And not just to join because you want to get in the way of something else to workers, but joining because they're busy workers and I want to expand on how amazing community to workers is. They're first of all free to tomorrow. Not just for that, not just for the people. I don't want to take you so I don't want to take you to another time. It's the least you can do. It's like having a greater family, Let me just say, you've changed the class. Actually, not me. I'm not a staff member. Also, I do as well as I wish to inform you why I need to work with the community, and since I've worked with my friends, what has brought me, what you have told me, is to get involved in everything that I've actually done. So, the main thing I've learned to do again is to get involved with the community, not just for the work that's in care, but to have a massive impact on your life. And because we're passionate about it, there's a good chance that we're going to have a big impact on the rest of the community. Number one thing for me to make friends, my contacts, which is great. I've traveled so much, it's a very simple city I've been to. I've met someone in the community and that city that I've been to, had a lot to share with.  You can just get to a distance and you've got a little mutual passion. That's amazing. It's about giving, but I'm just taking it. And that's not much to know. It's about volunteering. It's about doing what's important. The main thing to look around yourself as well is if you go to bed and you don't have to do something wrong. Others, people you've got to start with are very similar, but especially, in my opinion, the single most important thing that we have to deal with is time. You can make more money, you can get more things, but you can't get to your time there. So to actually take some of your time to devote yourself to something you're used to on us, that's priceless. And this would be a big opening for some of the new teaching ideas, a lot of these experiences. I never thought I'd be managing community. Now, I never thought I'd understand anything about people. It's a project. This is the second time I've learned about it. And also, this is a big one I know I didn't believe in myself, but if you heard that, actually read it. And if you can't find it, you can actually go to people. You can actually go to them. And it's okay to be scared. Do it anyway. So, you know, we're all scared of something. You don't know how to contribute. That's okay. You don't know how to change one line of code. Write one line of code. It doesn't matter if it's called out. And then just because he showed no interest, I think this was the only day I was going to make breakfast in Athens. So, this was the last year. I mean, as you can see, we're all obsessed with most people, man. And this is the information queue because we left us. But I put a mark on him. I learned a couple of years from him here. Do you have any questions? Yeah, where is it tomorrow? So, I've got a few things happening in the old, possible room. Which is just around my area as well. It's even a normal office. I had no idea what we were going to have. I sent out an email along with the electricity support for the ICANN portal. Oh, oh gee. Thanks so much. I was just thinking about the ICANN portal. I left this one with a link to the post for details. If you didn't receive it, you can just check your spare. And then if you saw it, you can just click there now, join in with the post and you're in the team on that comment. And I'll send you the link. Or you can go out to the WCAB website and just search for 100 today. There is a form to fill in. You don't have to fill in. It's just so you know that you'd like to help. You can go in for a minute. I'll be more sorry for the form. As I said, that last check was 60. My, so, I wish this was not a major thing tomorrow. My goal is tomorrow. I think everybody pitches up and that's just one thing. Either they join the slack travel for the team they want to come here to. Or they read it out for the teams and they pick a team to come here to. Or they ask them one small question. Or they fix one bug. Or they set up their government environment and they can go open fixed bugs. Just one thing. That is the end goal. Because it's the first time we're doing it together. No idea how to figure it out. Okay? If you can't make it through time, but you're sitting at home and you want to join us, we will be more slack. I can't see you pizza through school, but I'll see you in a minute. So you can go. But if you want to join us on slack and you want to figure it out, we'll be more slack. Ask us questions. Feel free. If you don't know us slackers, you can come and jet to the E.M. You can be more inspirational. You can find out. You can download the app on your MacBook or that type of desktop of mine. And you'll also have a T-Zero on your machine here that you can use for a second. So please, you can do it. So as a patient, you can have an opportunity to add to the sliding of the line. So if you need it, then we'll come to you to work with you and share your overall. Okay? If I can convince all of you to join us tomorrow, this talk will make the official event more online fun. As I said, we're 16 people. So if you're wondering if you're going to go for it, or if it's a while, or if you're doing other stuff or something like that, go ahead and talk. And now I'm going to make another statement. I'm sure we'll be fine. What time? We're starting at 10 o'clock for this very first talk. We've got the spaces until 4 o'clock and I don't think we're going until 4 o'clock and that aspect is only the right time to start that 8 o'clock. We will be having lunch at about 12 or 15. The beginning of the day will literally be it's like, I don't know, but I'll give it a time. I'll give it a time. I'll give it a time. Okay. So, if you're going to go for it, I'll give it a time. So, the four-day after the day will be a really good interaction on more than a team basis and one of the people who are leading our experience around the relationship and our questions of it. We will then give you time to find a seat and get your laptop stored down and go on to the main block. If you've never been there, I'll forget that and we'll read about more than a team. Once you've got our team, we're going to ask a big team if you want to contribute to it.  If you're getting access to a community team or a national team or whatever they're doing. Then, how often do they want to start doing it or what do you want to start doing? What kind of help do you want to do? We'll recognize you to talk with you and then if you're at home at that point in time, if you want to hang around for a few minutes, if you do something and put something on there to help you, it's great. And that will be the basic moment where you're there. It's very much important just as I've raised it up. But if you can't do it, please do it. You're a little bit late if you're going to be there at 12 o'clock for lunch, and I need to open up a pizza and I'm going to have 50 or 60 people on the floor. Yes. Sorry. Where's the most quality? I didn't check out a little piece of paper. Hang on. Okay. Okay. I want to see you need to do that. Make. Make. Make. Make. Make. Make. Make. Make. Make. Make. Make. Make. Make. Make. Make. Make. Make. Make. Make. Make. Make. Make. Make. Make. Make. Make. Make. Make. Make. Make. Make. Make. Make. Make. Make. East is off the clock and Friday is looking at us. Good evening. Thank you so much.


Hello, everyone, and welcome to the 4.30 to 5.00 p.m. session of the 2023 Open Simulator Community Conference. In this session, we are pleased to introduce the presentation, How Generative AI Will Change Content Creation and Coding. Our speaker is Maria Korlov. If you were at the previous session, you know that Maria is a published author and the editor of HyperGrid Business since 2009. Please check out the website at conference.opensimulator.org for speaker bios, details of the sessions, and the full schedule of events. The session is being live streamed and recorded, so if you have questions or comments during the session, you may send tweets to atopensimcc with the hashtag pound OSCC23. Welcome, everyone. Let's begin the session. Thanks, Lear. Thank you for having me. I love being in front of everybody here, in case you guys didn't see me in my previous presentation. So, this is about the stuff that I do outside of OpenSim. So, I've been the editor of HyperGrid Business since 2009, covering OpenSim in virtual worlds, but before that, I've been a technology journalist. I started as a technology journalist during the early days of the dot com thing. I was working on staff for Computer World, and I was covering the digital transformation of the industry back in the days where we had all these AI magazines, not AI magazines, Internet magazines like Business 2.0 and E-Company Now that were several inches thick, and you learned about the Internet by reading print publications, golden age for technology journalism, and I've been covering enterprise technology ever since. So, I'm still writing for Computer World. I write about AI for Computer World now. I write for Network World. I write for CIO Magazine, which is Chief Information Officer, and I write for CSO Magazine, which is Chief Security Officer, and these are the biggest enterprise technology publications on the planet, and I've won the top awards in my industry for my coverage. In fact, I won a national award for my coverage of Whirlpool using self-driving robots in their factories. So, this is my shtick. This is what I cover, and for the last year, it's pretty much been generative AI all day, every day at my job, and I'm talking to CEOs. I'm talking to CIOs and CSOs of enterprises who are trying to figure out what's going on, and I'm talking to experts and the people who are inventing this stuff. I'm going to conferences. I'm going to be a keynote speaker at an AI conference in March. If you happen to be in Peru, I'm going to be speaking in Lima. I'm going to be the keynote speaker there, and I speak at online conferences and all that stuff. So, that is my day job. And it has been an insane, literally an insane year. The stuff that has happened this past year is incredible. It is the fastest pace of change I've ever seen. So, before I go into the AI myths that I'm hearing the most from creative types, I want to just give you a sense of how fast this is changing.  It was hard to get online. It was hard to use it. If you wanted to put up a website, you had to learn HTML. There were a lot of barriers to entry, and if you were living overseas, it was even harder to get online. Many, many countries, rural areas did not have internet access. Companies were slow to adopt the technology. Everybody knew they had to do something, but they didn't know what they were doing. They hired a teenage kid to be their webmaster, to set up a website. It was a very slow, gradual transformation. And the thing that the internet hit most was information. So, the transmission of information, the control of information, that was disrupted by the internet. So, if you are an encyclopedia business, you are severely, severely disrupted and you either went out of business or you figured out a way to adapt. Similarly, Genitive AI is going to revolutionize the sector of expertise. If you are an expert in something, if you know how to do something, if you can take the information that's out there and show you how to apply it to your own particular case, if you know how to draw, if you know how to paint, if you know how to write computer code, if you know how to write news articles or how to edit books, all this kind of knowledge the Genitive AI can do. It does it badly. It does it worse than humans in many, many respects. The early internet was also worse than what it replaced. The early internet was worse than encyclopedias. The early internet was worse than physical books. The early internet was worse than your white pages and your yellow pages. It was worse than all the technologies it soon came to replace because it evolved quickly. Genitive AI is also evolving quickly. But it doesn't have any of the barriers to entry that the internet had. When ChatGPT was released a year ago in 10 days on November 30th, 2022, it became the fastest growing app in history. 100 million people from around the world immediately started using it because as long as you have an internet connection, a smartphone, a computer, anything, you can get to ChatGPT. You can use it in pretty much any language, anywhere on the planet. And if you're in Russia and you can't have access to it, you immediately got yourself a VPN so you can log in. It was literally the next day after it came out, all the Russian websites were talking about how to get into it because the advantages of it were so clear for spam and hackers and everything else. It is really, really transformative because everyone on the planet at this point either has a smartphone or knows somebody who has a smartphone and they can access it or borrow it. And everybody can now, almost anybody can set up their own private AI. There are over 200 foundational models out there right now. Competitors to ChatGPT, many of them much smaller, many of them open source that are available. There's some small enough that can run on a phone. There's some small enough that can run on your computer. But with cloud computing, anybody can run even the biggest of these AI foundation models by just renting cloud service space and any company can do it. There are zero barriers to entry in AI. And it is also evolving insanely fast. So usually the technology doubles in capacity every 18 months to two years. I mean, that's been the Moore's Law historical trend. AI is doubling in capacity every few months, if not faster. The latest AI app that I'm using, Claude, can now process 200,000 tokens, which is about 150,000 words, which has two average length books or one epic fantasy book. So you can upload an entire book that you're writing and ask it to identify plot holes, identify your major themes, to identify lack of character motivations. It can do all this already. The pace of change that's happened over the past year has been absolutely insane. So let's talk about some myths that I see creators have. So I'm involved with a bunch of writing groups and there's artists. I've run a science fiction magazine, Metasteller, and we have hundreds of contributors to our publication. And so we have artists and other people on our board who are part of it. And some of the main misconceptions that I'm seeing is that AI is cut and paste. It takes stuff that's out there and just assembles it together like a little jigsaw puzzle. And so it can't produce anything that's original. It's just copying what's out there already. And this is absolutely not true. The way that the generative AIs work is they learn from what they see and then they extrapolate from it. They create new stuff based on the patterns that they've seen. They forget their actual training data. The actual training data is not stored. It's the patterns that they discover that are stored. And in fact, we just saw one of the court cases about copyright infringement. A big part of it was dismissed because the people suing open AI couldn't produce any cases where something was copied exactly from an original artwork or a piece of text. It generates something new. If it overlaps with something that's out there already, it's... That wasn't like the way it's intended. And if it happens anyway, that's one of the AIs that had a problem with this. It was printing actual lyrics to songs. They just added a filter to filter the actual copyrightable stuff out. People are saying it's just autocomplete on steroids that it remembers the relationships between words and just gives you the next word in the sentence that's most likely. And that isn't fact how it used to work. So the old machine learning that we've had for the past 10 years does work like that. You've bought shoes previously on Amazon. Maybe you'll also like to buy these socks. It's a simple straightforward prediction based on past events, predict the future events. The new generative AI takes those patterns between words and then looks at the patterns between those patterns between words. Then looks at the patterns between those patterns between those patterns and then on and on like that. And the final result is pretty close to actual reasonable thinking. And in fact, if you ask an AI a complicated question and you tell it to think it through step by step, it will give you a better response. And if you ask it to go back and reevaluate its thinking and see where it went wrong, it will go back and review its thinking and actually identify the problems where it went wrong. And if you still don't think that AI didn't have like original thought, upload a piece of work that's new that you just wrote. And like for example, myself, I have a whole bunch of unpublished novels. I've got like nine novels finished that are ready to be edited and published. And so I uploaded one of those models and I said, what are the plot holes in this model? And it went in this novel and it went through and found all the plot holes. It was not ought to completing this because that novel didn't exist anywhere on internet. I had just written it. I hadn't published it anywhere. It wasn't repeating what somebody else said about my novel. It was coming up with original criticism of my novel and this is a frightening, a very frightening thing because obviously I make my living writing and editing. So yeah, I'm like really super worried about this. So people are saying that it can't match humans because it's based on human models. And so it can only do as well as the average human. So there's a program called AlphaGo and this is like a couple of years ago now. Google created this. They were the original inventors of this whole generative AI although they didn't do anything with it. And AlphaGo was trained on all the human games and so it could play as good as well as humans could. And then they had AlphaGo play against itself. And the thing about Go is you can't brute force an answer. You can't just think about possible permutations and pick the best one because there's too many of them. It has to understand the patterns, how the game works and it played millions of games against itself and it invented new strategies that humans in their thousands of years of playing Go hadn't come up with. So yes, AI can definitely match and surpass humans in many areas. In fact, in a whole bunch of tests, including creativity tests, AI, these AI models have beaten the average human or even average professional in their field. So if you are in a creative profession and you've been dismissing AI as something that you don't need to worry about for these reasons, those reasons are already not true and they're gonna be even less true with every month as the models improve. All right, so I talked about AlphaGo. So it created training data by playing against itself. AIs can also create training data by interacting with humans. So every time you talk to chat GPT, it learns from that interaction. Every time you tweet something on Twitter, Twitter learns from that interaction and it's gonna train its grok on it. And I'm told that I have to speed up a little bit. Okay, and it could interact with the environment. So it can actually run tests and see what works and what doesn't. So if it wants to learn math, it could actually interact with a calculator and see how good it is at making calculations. So there's a lot of things that an AI can do where it's not limited by training data.  So when photography was first invented, portrait painters were freaking out. Photography isn't real art. All you do is press a button. Literally you point a camera and you press a button. You don't need years of training and how to draw and it makes a perfect picture. This is like the work of the devil. We shouldn't have it.  But we still have human portrait painters. The human portrait painters often use photography to create reference images so their clients don't have to sit still for hours. But photography also became its own art form and photography also gave us television and movies and all sorts of magazines and all sorts of other things that would not have been possible without photography.  So there's legal issues around AI. So on training data is the big one. If you're an artist, Adobe uses only licensed data for its training and it pays its artist. It sent out the first payment in September. It pays people when their artwork is used to generate new art and it's the only big company that does it. Get images also uses only licensed data. So if you're an artist, I recommend that you use Adobe Firefly. That's the one that we use at Metasteller. It's only uses licensed data and it has a free plan and the paid plan starts at five bucks a month. Then the copyright. You cannot copyright AI produced work. So that is currently the standard in the United States. If an AI produces it, you can't copyright it because it's considered not to have human involvement. But you do have to type in a prompt and then often you might give it like a sketch or something to get it started and then you interact with it and modify the results and make it go a little bit more like this or change this part. So there's a lot of human effort already involved. But also with photography, all you do is press a button and you can still own the copyright to that. And with photography, if two people take the same picture, both of them have the copyright to that picture. The Chinese courts have already ruled that you can copyright AI generated stuff. And I have a feeling that because of corporate pressure, we're going to be able to copyright AI generated work very, very quickly. So if you're an artist hoping that this will protect you that you can't copyright AI stuff so you don't have to worry about AI, I don't think it's going to last long. Will it take jobs? It will take jobs, but it will also create jobs. So there's an Ernst and Young study a month ago and they asked a couple of thousand CEOs about if they plan to adopt generative AI, 99% of them said that they would or are already doing it. And the reason they're doing it is because they're already seeing immediate business benefits. It's speeding up computer programming, it's speeding up creating marketing content, it's speeding up all sorts of stuff that corporations do. So they're already seeing business benefit from it. Generative AI is the single biggest business technology adoption that I've ever seen in the history of covering enterprise technology. So as I already mentioned, the way the internet changed access to data, generative AI will change access to expertise. So how do you survive as an artist? And I'm getting this from the consultants that I interviewed. So consultants are totally in the crosshairs, their business model is about to disappear. So all the consultants are freaking out. PricewaterhouseCoopers is spending billions of dollars to figure out what their mission is gonna be in the age of generative AI because they do accounting and other advice and generative AI is gonna be able to give that advice better and cheaper than they are. So the first thing is you educate yourself on what's happening out there. You experiment with what's happening out there and you figure out where you can use it and where you don't wanna use it. So for example, if you're a portrait painter, you might use photographs as reference images.   The same way that organic farmers lean into the fact that they're organic and how has its own Facebook page, you lean into that.  You go places where you can show that you are human and you make that a major, major selling point. Okay, so for OpenSim, if you are a content creator or OpenSim, the two big use cases are marketing and content. Marketing is absolutely the big one. It's the one that OpenSim grids have the hardest time with. You should be using it for marketing right away. And that means that I use Claude II for text generation. Claude II is run by Anthropic, founded by expats from OpenAI, based on constitutional AI. And it does not use your stuff for training data. The basic plan is free and you get all the features. You can upload 150,000 word book. That's like a huge, thick epic fantasy novel and you can work with it. It's as good as GPT-4. And again, it does not use your stuff for training data. And it's not being sued by everybody. An Adobe Firefly for art, it's not as good as Mid Journey, but it's getting up there and it's trained on fully licensed images and pays its artists. So those are the two right now that I recommend, but there's like hundreds of others. Use it to write website text, press releases, emails to your customers and clients and members. Use it to create illustrations for your marketing designs. It also suggests ideas for everything, for ideas for social media posts, ideas for like literally everything that you're having trouble with. It can give you advice and help you work through things. You are so good. You're almost at AI yourself. Look at that link. That's wonderful. More content for Open Sigma. So first of all, speed of development. This was March, 2022. This is what AI art looked like. This is the state of the art in AI art. This was April. So it looks a little bit more like a cat. This was July. This was when Mid Journey started winning art competitions. And this was November. This is the pace of change of AI and the way that image generation changed last year. Text generation is changing this year. Next year, we're gonna see video and 3D coming. But the same fast pace of changed. If you're not using it because it's not good enough, learn how to use it right now because it's about to get good enough like literally any second. In Open Sim, where did it go? In Open Sim, you can use it to create in-world stories, NPC dialogues, textures, 3D objects, scenes. You can use it to code interactive elements and scripts and debugging your code and to write documentation for code. It is really, really good at this and getting faster and better at it literally day by day. I've seen some estimates that it's doubling an ability every two months. It's an insane pace of change. And AI is designing its own chips and improving its own code. So it's like bootstrapping itself at this point. It is insane. This past year has been one of those insane periods in human technological inflection. People are saying it's the invention of electricity, the industrial revolution all over again. And I don't blame them. I've been covering this for decades.  So absolutely email me if you have any questions. And here is the way you can get the slides online. Well, thank you, Maria, for such an informative and interesting presentation. As a reminder to our audience, you will want to check out the conference that opensimulator.org to see what's coming up on the conference schedule. Our next session begins at 5 p.m. in this region and it's called Managing Student Emotions in the Metaverse. We also encourage you to visit the posters in the expo regions for the sponsor and crowd funder booths. Thank you again, Maria, and to you, the audience. Thank you for having me.