use as benchmark, trairi is considered to be base and it is considered to be a risk-free, default rate is used because the US government is not sure that it is a risk-free, whereas in the case of other investments where there is a chance of default, so the rates of the US government are higher than the trairi, so the perception that is being built in the rates. a perceived risk increase so does the swap spread. the more the risk perception is, the higher the rate of the swap will be. in this way, the swap spreads can be used to assess the credit wordiness of the participant. when the US trairi is not there and there are local participants, that could be an institution or an individual, so according to their credit wordiness, according to the risk perception, the rate of the spread will be determined. basics of the spread, perhaps a contract that allows people to manage their risk in which two parties agree to exchange cash flow between fixed and floating rates. generally speaking, the party that receive fixed rate, close on swap increase the risk that rates will rise. now, the fixed rate that needs to be met, basically there is an element of risk there. at the same time, if rates fall, there is a risk that the original owner of the fixed rate flow will range on his promise. to pay the fixed rate, like if the rates drop a lot, then you have to get fixed from the other party. but sometimes it happens that you have to pay the fixed rate of the floating rate, so he says that he has defaulted or he has backed out from his promise. so this is the meaning of this talk, this is his credit wordiness and the impact is that if the party defaults, so the impact of that is coming in the spread. to compensate for these risks, the receiver of the fixed rate requires P on top of the fixed rate flows. so basically, that is the same swap spread that if the other party defaults, then where we go on, then we will come to our floating again. so the default risk of capturing that is being done in the swap rate spread. to generate the greater the risk of breaking that promise, if the other party is not very good or not renowned or has a history of defaults, then the swap spread will also increase. the better the party will be, if we talk about Pakistan, then if there are Nishad Mills, if there are Sapphire, then definitely the swap rate will be very low. but if there is a vulnerability company, that means its risk is high, then the swap rate will be high. so the spread of the swap will increase. swap spread correlates closely with the credit spreads as they reflect perceived risk. credit spread, you know the word is that if a bank or institute gives credit to a party, then its risk gauge is that how much is the risk. so according to that, they are being charged, so we call it credit risk. so the spread of the swap is also correlated with that only, because their perception of creditworthiness is linked, so this is also a type of the same element, and it is based on the default of a counterparty. swap spreads are usually large corporation to government to fund their operation. when institutes come, they come to build their earning levels, they come into display and they make good money out of it. typically private entities pay more or have positive swap spread as compared to US government. definitely when there is any transaction on the government level, then the level of risk perception will be very low. when private parties come, then the risk perception will increase, then the swap rate will also increase. we have already established this. swap spreads are essentially an indicator of desire to hedge risk. the cost of hedge and overall liquidity of the market. so this is our entire hedging desire, our appetite is linked to that, and again also with respect to the liquidity in the market. so how many players are there, how many transactions are there, that will be determining the swap project. the more people who want to swap out of their risk exposure, the more they must be willing to pay to induce the others to accept that risk. but this is a demand supply rule. if only 2-3 parties come to hedge their risk, then maybe no other player can ask for such a spread. but if a lot of demand comes that a lot of people are coming to capture their risk, then the institutes who take this risk, then they will be charging a higher premium for that. therefore larger swap spread means there is a higher general level of risk aversion. when the rates are too high, then what does that mean, that more people are going towards risk avers, towards safety, then they are going to pay a higher rate to get that coverage. so this is also a gauge systematic risk, that how these markets are moving, and how things are being run in the market. let's see an example of this. if a 10-year swap has a fixed rate of 4%, and a 10-year triary note with the same maturity, maturity is the same because otherwise there won't be any logic between them. as a fixed rate of 3%. so the swap rate is 4%, the triary rate is 3%. the swap spread is a 100 basis point, i.e. 1% spread which is a higher number, so it's an indicator that the market is at risk avers, if the market is a risk taker, if the market is so high that we don't need a lot of hedging, or if there is not a lot of participant, then the 1% spread can be reduced to 0.5 or 50 basis points. so this is a sentiment of the market which reflects in the swap spread. thank you.


I'm Danielle. I'm Danielle Cook. I work for Fairwinds. We are a security and governance software company and had put together the Kubernetes maturity model, which is why I'm on this panel now talking about this book. Hi, I'm Robbie Glenn. I work with John at Accenture and we individually also put together something of a maturity model and we have worked with Simon and Danielle to combine them. And I'm John Forman from Accenture. I'm a director in the Google practice, and I'm the global lead for Anthos and Kubernetes. So today we wanted to introduce our new book, Admiral Bash's Island Adventure with Phippian Friends. So why did we create another book? Right. We wanted to, just like the other books in the series, we really wanted to convey a complex topic as simply as possible to really be able to be accessible by children. If it can be read by a child, then it can be read by a C-suite, right? We hope. We hope. That's the goal. That's the goal. So so we actually developed a framework for understanding the maturity model, or understanding the landscape. The CNCF landscape has exploded over the past couple of years. How do we navigate it, right? How do we bring our organizations, large multinational organizations, to really achieve the landscape, right? So we wanted to divide up actually the tools and technologies and experience, the journey, into five phases, which we are calling our maturity model. And if you wanted to get involved, we have some slides later about how to do that. But you can check out our maturity model on GitHub. We have released it and we are looking for some help to improve it. And we are looking for feedback on how we can grow it and maybe how we can make it more, even more accessible to you and to your teams. All right. So how did we get here? Good question. Can I ask a question? So over the last five years or so, there's been a journey to many different things. First, if you guys remember, there was a big journey to the cloud. Everybody was flocking to the cloud. But when they got to the cloud, they realized we need to develop stuff as well. So then the whole DevSecOps journey began. How do we do DevSecOps the best way in a cloud environment? And by the way, that's what complex because now it's multi-cloud. How do I build a DevSecOps pipeline in a multi-cloud world? It's challenging. It's difficult. So it ended everything. Then there's Kubernetes Big Bang. Kubernetes became the operating system of the cloud. So now you have three journeys going on in different, three different work streams, where people weren't going to the cloud. They're trying to figure out DevSecOps in the cloud. And then this is another thing called Kubernetes. How do we do all this? How do we make a true multi-cloud world with these different tools? Well, that's where the world changed and shifted to cloud native. So today it's not a journey to the cloud. It's not a journey to Kubernetes. It's not a journey for DevSecOps. Today it's journey to cloud native. Next slide, please. All right. So the aim of the kind of graphics working group, I said it correctly this time, is that we want to be able to build a framework for the world. When you look at DevSecOps, going to cloud and Kubernetes, how do we take the trail map and the eyesore of CNCSIO, where you see a thousand icons of all these amazing little icons, Prometheus and others, right? But how do you map that to something real? That's what the framework was created. This way the average user and developer on the street can take all this information and do something with it. So I speak with many clients on a daily basis and I'm constantly reinventing the wheel for every migration that we do, every journey that we do. I'm like, why are we doing this? So that's when Robby and I got together and we began to map out a framework to build on, to mature. And over time it became very mature to the point where we met our great friends over here and created for you this working group. The aim that we want to achieve is we want to work not only with the CNCF, but with you also. So it's working with inside the CNCF in the ecosystem with Syra and other friends out there, along with clients and customers, to help us achieve this goal. This is something that's early stages. Over the last 10 months, we built what you see today and it's a lot of blood, sweat and tears, both of love. But the main point is that on a journey here, and we're not going anywhere, in this, you know, the long haul, if we want to work with you to make this real. I think everybody in this room and across the world and internet sees the value here. Let's hope you do. Because we definitely see a strong value to do this. Yeah. So as John mentioned, we had three work streams kind of simultaneously working on these maturity models and this framework in which to break up this journey, right? So what we did over the past eight months or so, we have combined the models into a single framework. And one of the really great things that came out of this was we actually focused, instead of just focusing on tools, on tooling, tool sets and just the landscape, we wanted to focus on things like the people, who's actually going to be developing this stuff, who's actually going to be planning this stuff, right? Again, the C-suite, the VPs, the sales people. How do the sales people talk Kubernetes, right? We need them to. So the goal here is to be accessible from a lot of different audiences within the organization to really convey this complex topic and break it up again into multiple years so that it can be achievable. It's not, you know, how do you eat an elephant? You have to do it bite by bite, right? So what we did to actually come up with this, each one of us had worked on a maturity model. So as I said before, we had the Fairwinds, Kubernetes maturity model, which looked at everything from starting with Kubernetes all the way through to what, you know, the ultimate goal was. And then Simon, who is presenting there, he had worked on one that looked at what's the journey from your legacy systems all the way to Cloud Native. And then Robbie and John had put together a maturity model as well. So we took all of these maturity models, worked on them, went line by line through them and came up with kind of the five stages that we saw of your Cloud Native maturity. So the first is, hey, you're building something, you're not in production, but you're playing around. Then you move to when you're actually going to operationalize your Cloud Native infrastructure and your applications, you move on to scaling, which is level three, which is when, hey, it's time we need to do more with this, we need to make sure that the organization across can use all the Cloud Native tools and process we put in place. Then you move to improving it and optimizing it. So there's five levels of the Cloud Native maturity model. And then as Robbie mentioned, we divided it when we were going through it, we looked at it and said there's some themes coming out here. So we have people and what happens with people because it isn't just about the technology, it's about how do you change as an organization? We looked at the process that you have to put in place, policies you have to put in place, like how does that look from level one all the way to maturity? And then finally we did look at the technology. So we have that throughout the maturity model. And then we have, but we do have a number of resources, so as I said on GitHub, or as one of us said on GitHub, you can look and you can see exactly how we did it, what we did. We have a prologue, we have each definition of each stage. And we also have a master spreadsheet, so you can look through all of our workings and see how it came to be. Now we have the bit that we really wanted everybody to be here for, which is the book. And so we're going to, Simon's going to read the book to you and then we're going to go through what it actually means. So we've talked about the Cloud Native maturity model right now. The book represents the model, so we're going to do some interpretation of the book for you afterwards. And everybody has to copy the book on the chair. And the book will be worth a lot of money because it's signed by the authors. So make sure you keep it and frame it. Okay. Well, hello everybody and welcome to Admiral Bash's Island Adventure with Fithian Friends by Simon Forster, John Foreman, Robert Gleane and Danielle Cook. Great. So it's brought to you by the Cloud Native Computing Foundation. And welcome to our adventure. The Good Ship SS Legacy was sailing the high seas, crewed by Admiral Bash and his team of penguins, looking to settle an uninhabited island. Suddenly Admiral Bash came under attack by pirates. Weighed down by his heavy mainframe, Admiral Bash could not manoeuvre away from danger. So the lifeboats shouted Admiral Bash as the SS Legacy started to take on water. Captain Cube, a fellow explorer, was able to evade the pirates and watched in safety from submarine. He followed the lifeboats to shore. After the SS Legacy sunk, Admiral Bash and his crew were washed up on shore where they were met by Captain Cube and his friends. We'd better start settling the island. We need shelter and water and we need to replace our mainframe to run the island. Let's draw up full plans and then we'll start building. If we wait to draw up the plans before building, we'll waste time. We must get shelter and water quickly. Let's start, inspect, and adapt as we go. We'll cover the basics and change direction quickly and when needed. Let's use our experts across teams. Admiral Bash agreed with Captain Cube and the team went to work. Admiral Bash's crew had written all their software for their mainframe computer. But after the pirate attack, it was lying on the bottom of the ocean. Goldie and Captain Cube were there to help. Goldie knew that the regulator Ravens used their eagle eyes to keep order and would wave their police badges if they spotted little gaps in security. Assemble the basics, shelter and water supplies, and get the computer available. And Admiral, we need to think about security. We need to make sure we avoid more pirate attacks. You've already been attacked once and we need a protective barrier. Security should be woven into everything. I'm tired of the regulator Ravens swooping down on me and we're all responsible for security. Goldie helped some of Admiral Bash's Penguin crew set up a basic installation of Kubernetes with Cube ADM. To get familiar with Kubernetes, the crew deployed a mapping application by hand with help from a friendly cube cuttlefish. The mapping app was small and scaled well and showed how Kubernetes could help the penguins build the settlement. Admiral Bash's busy crew got into the swing of things quickly. Goldie taught the development penguins how to work with Kubernetes and they took to it by penguins to water. The crew built an application to control the settlement's water supply and even distribute water to some of the houses. New island settlers came to help. Beavers and moles joined forces using the penguins applications to excavate and build a new dam to improve the water supply, getting it into even more homes. Settling the island was definitely a two track activity. The Beavers and moles were creating its infrastructure and the penguins were building the applications to support them. The build was happening so fast that the regulator Ravens started swooping down on the team. The Beavers and moles complained to Admiral Bash that the regulator Ravens were getting in the way. So Admiral Bash decided to write a set of policies and bylaws to keep everyone on track. The island settlement was working out brilliantly. It was so successful that the penguins were creating new applications like crazy as the Beavers and moles demanded more and more. With so much going on, Admiral Bash needed a better way to track usage. So Goldie introduced the penguins to Prometheus and quickly built a dashboard for Admiral Bash and his crew to monitor. Prometheus to the rescue. One of the nice things about the settlement's new applications was that they'd been turned into microservices. Goldie showed the penguins how to release new components without having to take them down. Admiral Bash loved this new way of working. In the past, every time he wanted to upgrade the legacy mainframe or the programs running on it, he had needed to take it down. The penguins were eager to learn even more. So Fippy and Z found themselves busy teaching Admiral Bash's crew new lessons. Fippy taught them how to write YAML and Z ran classes on security and policy management. While the development of water and shelter was coming along nicely, the island lacked communication. The penguins decided to set up a basic phone system made of lengths of rope and cans to allow clear communication. Fippy was very invested in this. Fippy's long neck made him the only animal able to hang the phone lines. But unbeknown to Admiral Bash, the pirates who attacked the SS legacy had discovered the island. Never missing an opportunity, the pirates started phone freaking using high-pitched whistles. They were an ever-present threat that no one was paying attention to. Now the penguins were confident with the new technology. They built more and more applications. But Admiral Bash and his crew started struggling with repetition. The penguins had even reinvented the wheel. Hazel, the helm hedgehog, noticed they keep repeating steps, which was very time consuming. So Hazel helped them to simplify everything. She created charts listing each component for the penguins. These templates mapped out exactly what was required for an application deployment. I've got penguins going off and reinventing many of the same tools again and again. What can I do? Community focused cows, Esmeralda and Margarita, saw an opportunity to sell the different components that penguins needed. They opened a store that sold wrenches, pliers, sores, get and even some standard applications and policies. To make life even easier, Hazel's charts were available at no more wheels. Esmeralda was very happy with herself and everyone flopped to no more wheels because it saved so much time and effort. It was a no-brainer. Welcome to our store, no more wheels, we stock everything. Flying high above the settlement, one of the regulator ravens spotted an eavesdropping pirate and swooped down to warn Admiral Bash. Luckily, Linky the Lobster had a plan. Linky had their team of lobsters write messages on shells and encrypted code. Then a Saharan silver ant collected the messages and transported them to another lobster who decoded them. That stopped the regulator ravens from complaining and stopped the pirates from eavesdropping. These cans don't work. Linky was so happy with the results that they continued to help. When a beaver wanted some messages to be diverted from the blue lake to the green forest, where the moles were starting to dig, Fitney helped Linky's team of lobsters and ants string a new line and divert the messages. Linky's mesh allowed Admiral Bash to test new lines and connect new homes and buildings easily. The penguins loved Linky's approach and decided to use the same technique with their Kubernetes deployments. As the settlement grew, Goldie created a machine to help the penguins build well-crafted applications. The penguins could put their code and dependencies in one side to be built, tested and scanned. Now the other side came little containers that were delivered to separate Kubernetes clusters by conveyor belt, with each cluster having a different role for development and testing. At the end, there was a production cluster that hosted all applications used by the beavers and moles. It worked beautifully. Goldie added a scorpion gatekeeper to control admission to each cluster and taught the regulator ravens to write policy. If a package didn't comply with the policy, the scorpion rejected it. A curious thing started to happen with Admiral Bash's penguins. Rather than lining up in ranks and moving together, sometimes after delay, there was always one laggard. The penguins now worked in little groups. They'd stand together in front of whiteboards for 15 minutes a day and then work in their own little groups. The penguins loved it. They worked on one thing, yet as a whole, they completed big projects. Captain Cube's model for the crew was definitely working. Goldie helped the penguins engineer flux spots to deploy their applications. They really helped speed up getting the applications into Kubernetes and meant the Cube couple fish was able to have a rest. Also, the bots were a lot more accurate. Z clocked the time it took for the penguins to release software, counting the number of successful releases. The faster, the better. The beavers were very close to continuously building houses, save for the foreman sign-off. And on the code side, now that all the application tests were automated, all FIPI had to do was approve each code release. Everything was continuously delivered up to that point. The flux spots were very popular. The penguins used them to speed development by quickly testing their applications, including their deployments, simply by checking and manifesting to get. The penguins watched as the flux spots deployed them and looked to use them for production. The regulator ravens were happy as it meant the Cube couple fish couldn't make ad hoc changes whenever asked by a penguin. The ravens were always taking a hard line. The feedback loop got stronger. The penguins got feedback out of their deployment pipelines and put it straight back into their code. The beavers and moles found small building defects and logged the issues as they repaired them, learning on the job. The regulator ravens refined the policies based on what the penguins, beavers and moles found. The ravens made sure the policies were available at no more wheels. This made it really easy for everyone to get hold of new policies for both software and buildings. Reduced defects and guardrails were in place, increasing the quality of everything. Many of the items at no more wheels were free for everyone to use, and improvements were welcomed. Admiral Bash noticed the crew was experimenting a lot more and had some new ideas for the beavers. Everyone has become much more comfortable experimenting. The community is actively contributing, said Captain Cube. Esmeralda and Margarita started an online marketplace for no more wheels to make it stock even more accessible. The teams were working so smoothly that when a penguin left to visit family, the team continued to work effectively.  Suddenly, the ever-present threat returned. A pirate attempted to sneak an unauthorized package onto the conveyor belt. The penguins used Goldie's machine learning algorithms to predict when load would increase or decrease. Admiral Bash's existing clusters scaled easily. Excitingly, new clusters were created automatically when needed. Admiral Bash was in awe. The machine handled the deployment of packages with lots of measurement. The regulator ravens took close note of how to tighten policy and paid special attention to what was deployed in Kubernetes, including documentation on exactly what was in each package, its dependencies, and where it came from. Each package was signed off against its software bill of materials, like the beavers' own lists of materials for their buildings. There was a lot of scanning security was universal. Because the pirate package wasn't signed off, it was rejected. The sneaky pirate plot was foiled. Goldie was pleased. Our applications match our platforms. The old mainframe monoliths had been broken up, and the applications were now loosely coupled. The crew was able to update parts of the applications much more easily. Admiral Bash exclaimed, this is what we wanted. We've come so far since the pirates sunk our mainframe, and we settled this uninhabited island. With that, the chair went up. Hooray! Thank you, Simon. So, there are some meanings behind all that. Obviously, it's just a perfect book to read to children before bed. They will understand all of it. First up, we talked earlier about how people were a big part of the theme throughout the maturity model. Really, it's about the organizational change. When you adopt Cloud Native, it's not just, hey, here's some technology. It's how are we going to work? How are the people going to do it? What skills do we need in-house? We represented this in the book with Admiral Bash talking to Captain Cube. We're going to plan. We're going to do this. We're going to do that. We're going to take a long time to put it together. Then Captain Cube was like, no, no, no. We're just going to start doing it. We're going to change as we go. That was our representation of that one. Okay. Well, at KubeCon, it's safe to say that open source is dear to everyone's heart. It's also close to the hearts of our community-focused cows, Esmeralda and Margarita. They are our most obvious symbol of community and open source in the book, but fair shot, no more wheels. But look a little deeper and we can pick out other things that map to people, process, policy and technology. First off with people you'll have noticed early in the book that there's a focus on working together with our friends Captain Cube, Fippy, Zee and Goldie, all lending a hand to Admiral Bash and his penguin development team. We see that everyone can contribute no matter their skill, location or situation.  And policy, at a high level, is the company decision whether to go with open source, either adopting projects or allowing your community contributions into your own products. We saw Admiral Bash adopt CNCF projects and Hazel the Helm Hedgehog give a chance to the nearest cow. Then there are the technical policies you can implement with Oprah and Coverno and publish those policies on GitHub, Artifact Hub or send them to normal wheels. And then we have process and think carefully about how you can avoid reinventing the wheel and what practices you can learn from or contribute to the community. GitOps is a great example of a delivery process that's grown within the cloud native community and it has the technical tooling to match. And we saw Admiral Bash and Goldie adopt Flux. Finally, and most obviously we've got technology, you can adopt CNCF projects whether directly or through another product or of course contributing. And all the technology and Admiral Bash's adventure is open source except perhaps for the mainframe at the bottom of the sea. Okay, security. Security was weaving through the entire book as well as the maturity path model. And if you notice from the book, security is mentioned in the early part of the book. Just like in real life, security is always a first thought, right? Well, maybe not, but it should be. And so we want to show that within the maturity path model as well as the book, how critical that is. So last year, I worked with the Linux foundation when I created the Kubernetes certification program for security. So all the lessons learned that we learned from that is we've the framework as well. Security is throughout the entire life cycle of a maturity path model as well as the book you just read. We just read for you. So within Kubernetes, making security first class citizen is critical. When you think about attack vectors in Kubernetes, there are 10 attack vectors in any cluster, right from the container down to the network. You got to make sure each one of those 10 layers is fully secured. Otherwise, guess what? There's a gap in security and somebody will get an XED and if they are, game's over for the cluster and they could blow it up. So we want to protect that entire vector as much as possible. We do that by using hygiene, right? Security hygiene, what is that? Well, it's a good question. It's how do I properly hygiene my security model within my containers to probably secure them by shifting left as much as possible, right, to that entire life cycle. Also images, images can be very bloated and very fat and very large. An image will only be a couple kilobytes, but I see them in megabytes. I've even seen in gigabytes, believe it or not. They're so bloated with drives and libraries you don't need. If I'm creating an application, the applications containers should only have within it the libraries and jobs that I need to run that specific task and nothing else. I've seen containers with HP3 drivers in them and HP3 printer driver in the container. Why? That's a tech vector for a hacker to get into and expose. So when you develop a container, you want to have minimum images. Anything you don't need, take it out. Do not use it. Have it shrunk as much as possible. If you do that, your tech vector is reduced and that brings you proper computer hygiene. Also, for God's sake, please, your developers ever download anything from Docker Hub. That's a big no-no.  Because you know they're hardened, they're unloaded because you took out all the drives and necessary things out of them.  I've seen this too many times where people get attacked. Why? Because they let their developers just go wild wild west. Develop whatever you want. Go crazy. We have to stop doing that. We have to really be smart now. Rensomware is on the rise. Why? This is why. Now we can build an anti-pattern for ransomware very easily with CNCF, open source. It's very easy to build an anti-pattern against these things. But you've got to be smart. We've got to stop doing the old way of doing things. We've got to do the new way of doing things. Next slide, please. So we talk about Service Mesh. One thing you'll notice in the book is that we wanted to really focus on, again, the journey. Like you're not going to be throwing away old infrastructure, right? So we built our lines. We built our network, right? Fippy hung the lines. They have cans on each end of them. They can talk to each other. That's great. Well, how do we repurpose that, make it more secure? We have little Silver Saharan ants carrying these shells that have encrypted packages that they can run along really fast using their little sneaker net technology to get it to where they want it to go. Then we introduced things like blue-green deployments using the very same infrastructure that we set up earlier in the book. That's exactly how these kind of journeys work, right? You're not throwing things away. You always want to be adding to it, building on to it. So you have to start with that secure foundation. Otherwise, you are going to be throwing it away, right? You are going to have to re-engineer it. So, oh, it's me again. So another thing we see, actually we call it very specifically as helm. We want the ability to not just build something once, maybe have all these one-off things, right? We're maybe creating networks all over the place. Why don't we just create a single resource, CRD or something that we can apply in all of our environments, right? So we created our shop, no more wheels, so that we wouldn't reinvent the wheel every time, right? We can just go to the store, buy the wheel that we need, put it on the vehicle or whatever. So in terms of policy management, so when you start off using on this cloud native journey, you're not going to have a lot of policies in place specifically for cloud native. Now, you'll have a ton of policies for other legacy systems and whatnot. So it's like, how do you get policy throughout your journey to maturity? So in the book, we represented this by showing the beavers and the raven trying to, this is what we want, this is what we don't want. We have the conveyor belts that showed like, hey, you have a package, it's moving through and oh, it's rejected because the policy says no. And then we have policies that are being donated to the store so that people can use those in the community. And that's really all about showing how you're going to start off with limited policies, they're going to evolve, you're going to keep working on it. And the goal is to achieve full policy maturity. And that's where you're actually not just putting policies on a piece of paper, but taking the policy, making sure that it's included in your CI CD pipeline all the way through production, you're enforcing it automatically, your developers know what they need to be doing. And if something doesn't comply, it's not going to get into production, and you're going to be able to tell your developer, hey, here's your remediation, this is what you need to do. And the little beavers on this have hats because my daughter wanted hats on the beavers. So that's a little fun nugget for all of you. And my wife wants scarves on the penguins in case you're wondering about that as well. So another thing along the line of building on top of infrastructure that we have, we have these admission controllers which allow us to apply these policies or rules or whatever we want at runtime and throughout the process. So it's actually the end of our deployment, it's admitting it to the cluster, but it also is continuously checking against these things. So in this case, our admission controllers are these represented as the scorpions who see a malformed package or something dangerous and throws them away. Supply chain, CI CD, DevSecOps, my favorite topic, right? So if you read the book and if you read, I'm sure everybody read it by now five times, right? Awesome. And if you go through the maturity model that we built, right, secured again, shifting left, first thought, the process here that we built a consistent workload, a system container that's consistent.  That's correct when you do these things. As you go through the life cycle with this, it's about a pattern. So when you go through the maturity fifth model, we give you a pattern on what to do. We don't tell you what tools to use, okay? But we tell you the pattern to use. So this is about choosing your own adventure, which is kind of the theme, underlying thing in this book is choosing your own adventure. So what tools do I want to use to build this adventure? That's up to you guys. So what you do with the right way, the smart way, you think about it quite a bit. Whether you want to be pure open source or mixed with enterprise open source, it's your turn to do that. But you must follow, but I recommend the pattern we have here is the glue. That's the magic source, the secret source, if you will, of the training model is how we do the pattern, right? There's many tools in the market that do similar things, similar patterns, so it's just picking and choosing the best ones you have to build it out. At the same time, there are gaps. There are many security gaps in every tool you use.  We work my gaps. So one tool, for example, we do everything, but one time security. Well, if I don't have one time security, that's very bad. So maybe not any two front top security. If I do that, now all my gaps are filled in. Like I talk to clients again very often and we do this assessment, we see gaps everywhere because nobody does this. It's just, don't think about doing this. Just because you buy a tool and they tell you it does everything you need, don't believe them. Check for yourself and go through it, you will find out you have gaps and those are the gaps hackers will find. I promise they do it every day. So it's your job, you know, to find those gaps and fill them in at every single segment of the pipeline. Also, when you build the pipeline, I recommend six quality gates to the entire process as you're building it out. Each quality gate, I'm scanning again. I'm doing my smoke screen checking. I'm checking for my vulnerabilities at every step of the way. Each quality gate. Like many, many people I speak to. John, guess what? I'm full of security, man. I scan my images. Okay. What else do you do? What about the security? They have no idea what the event is. All right. Also, sign your images. Please sign your images. Nobody does that. I don't know why. We know how to do that. Do it. If you don't sign your image, guess what happens? Somebody will hack that image and you won't know it's being hacked. I spoke to one customer. They had a Bitcoin operation going on for six months in one of the clusters. They had no idea it was happening. All right. So we've got to get some more of a security, please. And I'm hoping this Petroleum 5th Model will help you get there further and faster. So we've had a blast putting this model and the book together. Our imaginations have really run wild, but yet there's so much more for us to do. A repo is on GitHub under the CNCF organization. So please do check it out and come join our meetings. We meet every second week on a Tuesday and we would love to have you come and see us and join us. So what we're working on, now we're socializing the maturity model amongst the tags, projects, and end user groups of the CNCF. But the charter of the group means that we can work on more than just the cloud native maturity model. It allows us to put together tools to help the community navigate the ever-changing cloud native landscape. So right now we're collaborating on a landscape guide with the business value subcommittee and we're really interested in new ideas, new artifacts. So come along and see us and join the Categoricals working group. Thank you all for coming and attending our session. And if you all have the book to take a copy and there's signed copies there. So if you have any question for us we're happy to answer it. Otherwise, thank you for coming. And I want to see everybody on the next work group session please sign up. Thank you.


This is stock number 21J52. We are here at Summit Automotive in Fond du Lac, Wisconsin. You're new and use Jeep and Jeep Cherokee headquarters. Today we are checking out this brand new 2021 Jeep Cherokee Limited. This one has the elite package. It also has the 3.2 liter v6 pentastar motor, which is going to get you 27 miles per gallon on the highway, 19 city, for a combined total of 22. Sting gray clear coat is the color and we shoot all of our videos in 1080p, 60 frames per second. So if you have HD capabilities on your computer tablet or smartphone device, turn them on right now because it is definitely your best way to check out the quality and condition of the vehicle before seeing it in person. And if you'd like to check out all the photos on this Jeep in the upper right hand part of your screen is a link to our website. Click that and check us out there. This one comes with the painted and polished aluminum 18-inch rims and it has continental pro contact tires, kind of have an eye-way trend pattern to them. Four-wheel disc brakes on the Cherokees. Sting gray, really good looking color. So this one's going to have the front bumper parking sensors. It is also going to have the LED headlamps, the LED running lights, and the LED fog lamps. The limited package gives you the chrome trim around the fog light bezels, the chrome trimmed tow hooks, as well as the chrome trim grille and Jeep lettering. Sting gray, one of the most popular colors among the Jeep fan base. We'll take a quick look at the original Windows sticker. I'm not going to go through everything, but feel free to pause it and check out anything you may want to look at. I'm just going to go over the basics. So it's a limited 4x4, 3.2 liter, 9-speed automatic transmission. Kind of something cool to point out is that you get the Jeep wave membership with pretty much every Jeep now, which is pretty cool, gives you oil changes and such. Interior features for the limited, you get the dual pane panoramic sunroof standard, get the 8.4 standard Apple CarPlay, Google Android Auto, which will go over in a little bit, Sirius XM. This one has the premium Alpine speaker system standard with the limited package, and then the exterior features, you get those 18-inch painted and polished aluminum rims. Sting gray clear coats 245, the Elite package is $1,295, you get the cooled ventilated seats, four and aft adjusting second row seat, carpeted cargo area, trim panels, hands-free power lift gate, heated second row seats, off-road information pages, HD radio, the factory navigation system, you get Sirius XM, Guardian for a year, and Sirius Travel Link and Traffic Plus for five years. You also get the 4G LTE Wi-Fi hotspot and the Uconnect 4C NAV with the 8.4-inch display. And the chrome tow hooks are part of the Elite package, so I apologize for what I said before. 38,585 is the MSRP. If you want to get all the rebates and incentives on this vehicle in the upper right-hand part of the screen is a link to our website to get all those rebates and incentives and get your summit price. It has the five-year 60 powertrain and the three-year 36 bumper-to-bumper. 27 miles per gallon highway, 19 city and an average of 22 miles per gallon thing. I love most about the Cherokees, is the safety ratings four and five stars up and down the board. Very safe vehicles for transporting your family. As we come around to the back here, you'll notice that it does have the capitalist fuel fill, which is really nice. You get the four-wheel disc brakes, the rear parking sensors as well. You get the dual exhaust with the chrome tips part of that V6 package. Has the power liftgate, storage area. This one comes with the Alpine sound system with the factory subwoofer back here.  Those seats do fold down for extra storage. And to get this gate down, you press that button. It'll beep at you three times and then begin its descent. You also get LED license plate lights and down the rest of the vehicle. Very good-looking. You have the entering-go system, so you just have to have the key in your pocket. Put your hand in there. It'll automatically unlock. You have blind spot monitoring, heated mirrors, and built-in directional signals. Inside, you get the premium leather bucket seats. Both these seats are power-heated and cooled. Driver side lumbar factory floor mats throughout, auto headlamps, tilt, and telescopic steering wheel, power windows, power locks, power mirrors, and you do get memory driver seat. As we hop inside here, we'll turn the ignition on so you can check out the instrument cluster and everything. So you get the seven-inch digital display up here, digital speedometer, get range to empty. You can put time, compass, pretty much anything you want in those corners. It comes with the heated leather-wrapped steering wheel. So this one does have your forward collision warning. It also has your adaptive cruise control, which is what those buttons are right there. Normal cruise control, Bluetooth audio, and information center controls right there. This one does have the 8.4 with the factory navigation system. You also have your parallel and perpendicular park assist and your lane departure warning there. That's for your parking sensors. You can turn those off and on. AM FM Serious, you get the factory nav. You also have your heated and cooled seat buttons. You can check out your backup camera here as well. And that centerline lines right up with the middle of the vehicle. And then you do get the off-road pages, which we saw on the window sticker there. They take a second to load. And then you have your drivetrain. So you got your steering angle there. So as you turn the steering wheel, you can see those change. And then you have your power transfer unit there. You also have latitude, longitude, and your altitude there. You have your accessory gauges with your coolant, battery, trans temp, and oil temp. You have your pitch and roll. Good to see that we're nice and flat in our brand new picture studio. And the select terrain. You select that with your active drive 1 system down here. So as you turn that, it'll actually change in the instrument cluster right there. Actually, it changes up there now too. So you can change it to snow, sport mode, and then sand and mud. Pretty cool there. And that all just changes your shift points and how much power goes to either the front or rear wheels.  You have your keyless entry with your remote start and power gate button. And then the last thing I wanted to show you up here is your projection manager. So you can project your cell phone through Android Auto or Apple CarPlay. And if you have Google Maps or Waze or something like that, you can use that through the cell phone onto the screen. And then you have a different navigation program if you like that better. Up here you have your SOS and assist buttons, your map lights, and then your sunroof and sunshade controls, home link buttons for your garage door security systems and lighting systems. You do get the power sunroof, and then that second real fixed glass window. We'll take a quick look at the back seats, and then we'll take one more final walk around here. So the back seats, these are heated on the outboard side. You have the latch-child safety system for any child car seat needs you may have. And then you also have the factory floor mats back here. Down here you get two USBs, two USB C's, your heated seat buttons, and then a 115 volt, 150 watt plug-in. Really nice that they put the heated seats back there. You get the child safety locks on the back doors. And we'll take one more final walk around here, and I would personally like to thank you for checking out the video today. And hopefully from this HD video you will have gotten a good idea of the overall looks and styling and the color of the vehicle. Also learned a little bit about the limited package and this particular vehicle, the options on it. You can see those LED headlamps, fog lamps, and running lights are really good looking. Sting gray, like I said, one of the most popular colors on the Jeeps, especially Wranglers, but Cherokee's, they're pretty popular too. And to see more pictures of this Jeep or one of our other 450 new and used cars, trucks, SUVs, minivans, Wranglers, you name it, we got it. Go to our website right there www.summitauto.com. Full pictures and descriptions of every single vehicle from two locations all at Summit Auto.com. And if you'd like to check out more HD videos, you can go to youtube.com slash Summit Auto. Remember to like, subscribe, and share on this video and all the videos that you see there. In fact, in a second you will see links to subscribe to our YouTube channel on your left. It links more Jeep Cherokee videos like this one on right. If you have not been to our website on the bottom, I'll link to this vehicle on our website. Click those, check us out. And we really look forward to helping you with this super clean, really good looking, brand new 2021 Jeep Cherokee Limited with the Elite Package and Sting Gray Clear Coat. Thanks again for checking out the video.


Mae'r llesor yng Nghyrch gyda Gavyn Brown, y Llywodraeth Maesmae Llywodraeth yma yn y Cymru Ddullianeth. Mae'r llesor yng Nghyrch gyda'r llesor, mae'r llesor yng Nghyrch gyda'r llesor gyda'r llesor. Mae'n gwybod i ddisgu llesor yng nghyrch gyda'r llesor gyda'r llesor yng Nghyrch gyda'r llesor, i'n gavenadau, ac rhaid i'r maesin arall. Rhyw oedd wedi'i gwrthodaeth o'r sefydlu yn y Ffaptiw Ffaptiw, ac mae'n ymddangos i'r rhan o gwybodaeth yn y rhan o'r newid. A rhaid i'n rhan o'r rhan o'r newid. Mae'n gwrthod yma o'r Rhan o Gavenadau. A i'r rhan o'r newid. Dwi'n gweld ei ffordd. Rwy'n cael ei wneud i fynd yn ddweud, rydych chi'n gweld ei ffordd yn y mynd yn rhanol. Rydyn ni'n gydw i'n gweithio'r wneud. Dyna yw'r pari yw'r cyflwyno gwaith. Gareth yn ymgyrch i fynd i'n gweithio'r ffordd. So, Gareth yn ymgyrch yn ymddiadu'r eich Cymru o'r ddau cymryd ar ysgolodau ar gyfer ymgylchedd University of Birmingham, starting in 1998, also did his PhD with Jeremy White, again in artificial intelligence, graduating in 2004. He won the BCS Distinguished Dissipatial Award for his assignment of things to come. He arrived here in 2004 as a temporary Java lecturer, and then got a Career Development Fellowship in 2005, a Lectureship in 2010, Senior Lectureship in 2012, Reaver in 2015 and Professor in 2017. So a rather splendid progression, I think. So, during his time as a lecturer, academic here, Gavin has put himself with full spirit into teaching, a very enthusiastic teacher, working with undergraduates. He's established a really nice culture in this school of undergraduates entering into competitions, and this has now become essentially a self-sustaining culture where our students self-organise and enter into national and international coding and hackathon competitions, serially winning them as well, and that culture is all down to Gavin. Also, he's established our current context for how we do our recruitment of new undergraduates and rapidly revised that. Research-wise, a stream of PhD students, notable, is another one of his students who won the BCS Distinguished Dissipatial Award, making Gavin the only person of both ranks and supervised a winning student. But again, I tend to call rather splendid. Over his 20 years, the machine learning has done a lot of work on unifying various fields, particularly getting a unified understanding of machine learning, a feature selection side, unifying certain of the whole of machine learning, rather good if you just pull that off next week. But he's also branching out from looking at fundamentals of machine learning into application areas, particularly recently with AstraZeneca looking at some very quite impactful stuff in looking at clinical trials and even more recently working with the School of Law looking at predicting and understanding domestic violence reports. So, you know, from fundamentals to impact is all exceedingly good. So today Gavin's going to give us his inaugural lecture on new ways of thinking. Thank you very much Gavin. Thank you Robert. I'm just going to turn the lights down if nobody minds a little bit. You can see the slides. How's that? Wow. Thank you all for coming. You've come from all sorts of places all over the country, so I really appreciate it. OK, yeah, 20 years it seems like it was just yesterday right here in Manchester. So, when I was young, I used to really enjoy playing this game of looking up into the summer sky and seeing the clouds and seeing shapes in clouds and seeing you play that game of trying to see if you could see a shape like a car or an elephant or your favourite superhero or something like that. And if you look really closely and you have the right amount of imagination and you think in the right way, you might see something really exciting in the amount of the clouds. But two people won't see the same cloud in the same way. You might see something else if you have a different background and different perspective. And I think the same thing applies to scientific ideas. So, if you look up into the sky, out into the academic literature and you see one scientific idea, maybe your favourite scientific artefact that you've been working on recently. If you look at it from one perspective, you might see something familiar, you might see something you've seen before. If you see two ideas, if you think about them in the right way, you ignore some details, just look at the big picture. You might be able to build some bridges between them, focusing on the similarities between the ideas instead of the differences. And from there, you might build bridges going on to even more interesting places. So, this talk is inspired by my favourite quote, one of my two favourite quotes, the second one will come to the end. The most important thing in science is not so much to obtain new facts, but to discover new ways of thinking about them. William Henry Bragg, Nobel Prize in Physics, 1915. So, I have structured this talk around three ways of thinking, three strategies that me and my team, who are on the front row here this evening, which is very nice, focused on over the past 20 years or so, I guess, opening black boxes, similarities versus differences, and building bridges. And I'll come into what I mean by them in due course. But first of all, I have to focus on, give you some background into the area where I've been looking for the last 20 years, really longer than that, artificial intelligence. So, this is an area that has become so embroiled in science fiction and media press that it deserves this rather ominous white, glowing white font with a black background. This scary idea that everyone thinks is this terminator future. So, what is that subject? What is it really about? Well, before we get into what artificial intelligence is, let's just focus on what intelligence might be. Now, you know what that is, or at least you can characterize it from your own perspective. Someone is intelligent if they can do one or more of these things. They can see, hear, if you're lucky, both. You can think and reason about the world around you. You can listen and you can move around intelligently without bumping into things. The field of artificial intelligence can be characterised by taking each of these things and noting that somewhere in the world right now, there's a group of scientists and engineers trying to give computers the ability to do these things. The idea of computers seeing the world around them is called computer vision and there are a load of computer vision researchers in the field in the audience here today. Automated reasoning is a field where people try to prove things about the way in which the world works. Reasoning about interaction with the world. Natural language processing. Learning, the idea of computers learning from examples that you might provide to is machine learning and that's the area where I've focused. So I could go into all sorts of interesting technical details about this, but you have to admit there's still a few of us still thinking like this. It's still thinking that Gavin's got a terminator behind the electric terminal. It's impossible to separate these two because Hollywood likes making disaster movies around AI. And me, as a child of the 80s, I grew up on thoughts like this. You know, Star Trek and this quantum leap and movies like War Games and Short Circuit with science fiction, artificial intelligence characters. And I thought it was fascinating, but the reality that has come about in the 21st century and that it's going to continue is equally interesting but just not quite as scary. The most likely place you'll find AI applications nowadays is here on your phone. You have phones organising your photos for you, organising your news, suggesting news stories, suggesting modifications to your behaviour in order to get healthier. This is quite intelligent behaviour coming from your phone. There's a future as well in potentially self-driving cars. These are going to impress a lot at the moment. It's a few years away still. But these are the most visible aspects of AI that are out in the press right now. There are less visible things that are equally important like AI for personalised medicine. That's an area where I have focused over the past few years, where we have AI systems that suggest, predict what is the right medicine for the right person to help them get better and fastest the best possible way. The particular area where I focus, as I said, is this topic called machine learning. Before I get into some details about my ways to think, I want to go into a couple of details about what this subject is and why I find it interesting. Machine learning starts with a computer, obviously. We start with examples of what we want this computer to do. This is the first big assumption, the first myth about AI. You provide this thing with examples of what you want it to do, and then it kind of does that sort of thing. It's not the case that computers and AI systems don't wake up in the morning and decide what they're going to do with their day. Computers don't wake up and go, you know, I'm not going to be a self-driving car today. I think I'll be a terminator and take over the world or something like that. You train a system, you provide examples of what you want it to do, and then it adapts itself towards what you want it to do. Inside this computer is a thing that we might call a mathematical model. To go into a bit more technical detail, we call it a big fat equation. That's pretty much all it is. Probabilities, algebra, calculus, things you will know from school. This is the make-up of artificial intelligence and machine learning in particular now. It's just things, matrix algebra, calculus. It's all that sort of machinery working away. Inside this mathematical model, the thing can adapt itself in response to human feedback. A human supervisor will interact with this and provide a thumbs up or a thumbs down saying, you're doing well, you're doing not so well, that's correct, that's not so correct. This might be from historical data from humans, but that's the basic mechanism. Once the human is satisfied that the computer is doing what I want it to do, the system can spit out a mathematical model and deploy it onto your favourite mobile phone. That's pretty much the mechanism behind most of machine learning ideas, but it's only one of many sub-fields of artificial intelligence. The whole field is just ballooning at the moment. It's absolutely crazy how much it's in the press. You have games that previously required absolutely genius experts and level players that are now being beaten by computers. You have the major companies, Google, Facebook, et cetera, all investing absolutely millions into this topic. I think for academics, the most visible part is this. The number of attendees at conferences is almost doubling every single year at the moment. 8,000 or 9,000 people at the last gathering, whereas five years ago it was under 1,000. It's crazy. With that as a brief overview, I want to go into my three ways of thinking. The very first one is opening black boxes. I had the good fortune to start my career here at the University of Birmingham. I started my undergraduate in 1995, went on to my PhD in 1998, and met lots of amazing people who really changed the course of my life and influenced me in different ways. Some of them are here today, some of them not, unfortunately, so I thank you all. But chief amongst them was one person. When I rocked up at his office, not this person, this is data from Star Trek, but chief amongst them was one person. And when I rocked up at his office in 1998 saying, oh, robots, AI, oh, yeah, it's amazing, brilliant. Did you see that TV show last night? Oh, I'm going to build that. He said this, think harder, open the black box. Jeremy was my PhD supervisor, he sat over there, tore Menton later on if you want it. And he really set me this challenge of getting over my own self-obsession with AI and this magical thing. And he set me these questions, why, when, how, where, why does the thing work? Why are you fascinated with this thing? Instead of looking at the surface, the behavioural properties of this thing, delve into the guts of it. And this is really something every good academic does. It's not special to my particular career. Every good academic delves into the details. So I want to tell you about my first black box that I encountered. So we start with this mathematical model where we just look inside it. Let's imagine we set a challenge for this mathematical model of asking a very difficult question, such as some sort of advanced medical diagnosis or some sort of self-driving car system, set a really hard challenge for it. So one option here is to make one very clever model. I'm using the analogy of one very clever person, Stephen here. Unfortunately people like Stephen Hawking are very rare. And it's very difficult to build really impressive, big mathematical models. So an alternative is, if we keep with the human analogy, to build a committee of models, a committee of models that would vote on what they think is the right answer, the right direction to go. And so overall you might hope that you'll end up going the right direction with this group of people. But the thing about a committee is that it's only as good as the people that make it up, only as good as the components. And so we know that we don't want all the components to be identical. We know we don't want components to be like this. By Boris Johnson's is a rather terrifying prospect. And it's likely a wrong decision will be taken. There's no benefit of having multiple things for all the same thing. We also know we don't want this differences for the sake of differences. There's no point in finding those differences. The right amount of diversity as it were comes from good communication within a group. And this metaphor, this way of thinking about committees as communicating is effectively what we focused on during my PhD time. So obviously we didn't make committees of people like this, but we instead focused on neural networks, or if you're born from 2000 onwards, deep learning. This fascinating new field everyone thinks is amazing and new, and in fact it's been around for quite a while. But before it was cool we were playing with it and we made these groups of learners who interacted with each other. So going a little bit technical now, the algorithm that we were working with was this negative correlation learning thing. And it had this loss function, this cost function that defined the way it worked. And it has another component. So this is the prediction of the first network and the target aiming to approximate and then the average prediction here. And there are two components to this algorithm. Forgive the technical stuff for now. But the system tries to minimise this first term and also this second term. And by minimising this first term it tries to get the individual network to be as accurate as possible, as close as possible to the target D. And by maximising this second term, because we've got a minus sign there, it tries to force the individual components of the committee to be as different from each other as possible. So what we were left with was this algorithm that worked very, very well. But we didn't know why. And the people that had published it, one of whom was actually one of my most supervisors at Birmingham, we just didn't know why the thing worked. So Jeremy sent me the challenge of why does this thing work. So we opened the black box, we dug inside and we did lots of really nice few, really lovely stuff linking it to previous statistical theories that were out there in the community, including the 1952 Nobel Prize in Economics, weirdly, managed to get citation into that. Bounds on the parameters and ways it works and new theories generalising it. And, as Robert said, we won the 2004 Distinguished Distantional Award in the British Computer Society, which is very nice. We went down to the Royal Society and had this nice party in 2004. But shortly after this, I had the good fortune to move to Manchester, recruited by Graham and a few other people round up there and I, and a few other people to be a temporary lecturer in Java programming. It was fun. But then shortly after that, I was further recruited to become a permanent, I was only here about two months before Ian Watson over there started prompting me saying, has anyone spoken to you about permanent position? And then Steve and some other people got me to stay on permanent. Which is very nice. By coming here, it turned out to have some good positioning for me. I met lots of amazing people who really have again changed the course of my life. So here's some of you, thank you very much. And here's a whole bunch of us at graduation ceremony. It's very nice. I also, as a result of coming here, I hit my all-time career high when I was interviewed on Children's BBC. That was a serious event. That was so much fun. Being asked to judge children's pictures of robots and saying whether I thought they were plausible or not was genuinely fun. So, yeah, that was great. But another thing about coming to Manchester is fantastic PhD students. So I had some great ones. Here's some who have gone in years gone by who helped develop new methods on this same line of thought that we were doing. Really deep new insights which fuelled later work. Manuela here produced some work that fuelled stuff that I'll come on to later on. Richard here, who does something very impressive, he's around here somewhere. Do torment him later as well. He took this concept that I had been assuming was one thing. And I thought I had answered all the questions about it with my PhD. And then he opened the black box even further and showed that it was in fact equivalent to something else. Margin theory that appeared in the literature many years before. So it was even deeper stuff going deeper into that black box. I've also got some PhDs in progress following up on these ideas further. So Henry, who's around here somewhere as well, who I've just donated to the University of Birmingham. So I feel I'm repaying my debts by sending him back there. He's got a fellowship going there starting next week. And Georgiana and Charlie, who are around here somewhere over there, doing fantastic new work looking at biological plausibility for these ideas. New directions in modular learning, which is what Charlie's doing. All in the context of some nice funding which the UPSRC decided to throw us last year. So we're also hiring so if anyone wants a job. Come and join Andrew Webb, who's over there, and develop some new stuff with us. But this brings me onto my second way of thinking. We talked about this idea of just delving deep an opening black box. So now I want to talk about this second point. Similarities versus differences. And what I mean by this can be really characterised by a phenomenon every academic in the audience will know. This thing of publish or perish. There's this overwhelming variety of papers and huge numbers of papers that just keep appearing. And it's really difficult to keep up. And it's very intimidating, especially for new PhD students to find their place amongst this jungle. And it's very, very hard to distinguish yourself in some sort of way. So one approach to doing this is to look for so-called differences from some existing paper that you find out there. And you could ask yourself what's the minimum possible change that we could make to this scientific idea that would get us a new paper. Attacked it known as salami slicing, sometimes, or the minimum publishable unit. It's useful for getting started, but it doesn't often yield the deepest of insights. It can sometimes fuel radical new directions, but I guess they're quite rare. They require quite special people. I feel, at least, that it's more rewarding to look deeper and think about similarities between ideas. So I like to take two seemingly disparate parts of the literature. And instead of trying to extend this one, I like to see what are the bridges between them, what are the links, what are the similarities between them, how can we build, how can we interpolate between these ideas. And my first exposure to this concept kind of came in a meta-level fashion when, in 2005 or 2006, I can't remember exactly when, I was encouraged, forced to share an office with this chap here, who's around here somewhere, where are you? There he is, hiding. So thank you to Hilary Karm and Steve Herber again for putting us here in an office together. It was a long shot at the time, encouraging new staff to share offices. But he used to call me his second wife because he saw me more often than he saw his own wife. We were new academics, really trying to make our mark, and we spent many, many hours in that office together. But he was, and is, a specialist in this area called multi-core computing, high-performance computing systems. And he used to sit on the other side of the office saying stuff like multi-core, speculative threading, cash coherence and protocols to me. And I'd sit on the other side of the office going base theorem, gradient descent, feature selection and machine learning words. And we really had to focus on our similarities rather than our differences. We really had to think about how could we work together rather than hating each other. But it turned out quite well. We worked together for a while and we applied and EPSRC worked kind enough to shower us with money. So we got a number of grants including, we were included in a very early grant that Steve led, the so-called platform grant. And that gave us a really good platform, a launch pad into our careers, a kind of security. So thanks again to Steve for that. But this project here that we started on, I want to go into a little more detail about that. And it was a number of different areas, but one topic in particular we ended up focusing on. And it can be summarised with this metaphor. So have I asked you to guess the price of a car? What's relevant in that particular task? What matters? You might think, or you might be able to say quite straightforwardly, what matters in guessing the price of a car? You can say the engine size, the make or the model of the thing, the top speed of it. Lots of different things are relevant. What's irrelevant? What doesn't matter? You might think the colour, whether it has some furry dice in the window. Some stickers, all those things are probably irrelevant. But there's a third class of things around this task. If you take the age of a car and the mileage on the clock, it's probably the case that you don't need both at the same time. You can probably guess one from the other. So they're kind of contextual or redundant in the context of each other. If you have the age of the car, you can probably have a guess as to what the mileage would be and in turn guess what the price of the car would be. If you have the mileage, you probably don't need the age and you can probably guess what the age would be, what the other one would be and what the price would be. So with these three classes of features of a car, you can probably pick out these relevant ones, pick out one from this contextual set and tell me what your guess would be for the price of the car. Maybe there's one on the left. But you can do this because you know what a car is. You know how cars work, you know the system. What about if I ask you what is relevant, irrelevant and contextual or redundant for diagnosing the recurrence of a lung cancer? That's going to require a lot of medical training and expertise to pick out from this massive measurements that could be taken the small set of relevant things that really make a difference in diagnosing this. Which bits of your genes and lifestyle matter the most? What's the irrelevant, relevant contextual? The particular task that Michele was interested in was a bit more technical. Some speculative threading, this word coming back. Basically given a big complicated computer, what matters for this task of speculative threading? You don't need to know what it is but it's something to do with multi-core computing and what matters, what is relevant, what is irrelevant and obviously if you're not an expert in this particular area you just won't know and there's an overwhelming variety of things you could measure that make it more complicated. I had a student Adam here who worked on this topic between the two of us. We used to enjoy tormenting him. We used to pin him from opposite sides of the lab, come in and ask him questions on both topics, machine learning and computer systems. But the topic we focused on was this generic scientific problem of what's relevant, what's irrelevant and what's contextual. Let's give each one of these features a score to answer this question. We could characterise the score of the feature that is how useful it's going to be to us as how relevant it is, measure some sort of relevancy and then subtract away this redundancy, this contextual bit. Therefore if the contextual redundancy bit is really big it's going to subtract off from this and the overall score is going to be quite low. You could do it another way and you could say relevancy divided by redundancy and if this denominator is really big then overall this number is going to be really small so if something is highly redundant it's going to make the score very low. Third way is you could plug together bits and pieces like this and think about compatibility of features. So you could plug together different equations that come up with giving scores for features in which overall would tell you which ones you need, which ones are relevant. Obviously we didn't just think about the high level stuff. We used a mathematical framework known as information theory to quantify this. That was fun getting into that and that has turned out to be an incredibly fruitful area in my career looking at this type of theory. But it turns out that we weren't the first people to start thinking about how to plug these things together. It turns out that over the last 20, 30 years or so loads of people have suggested different scoring measures for this problem and people have done it in all sorts of machine learning conferences throughout the early 2000s. They've proposed new scoring measures in network security, in electronics, in bioinformatics in medicine. We're left with this question of which of these should we believe? Which ones are useful in which context? How do they relate to each other? There was very little cross comparison at the time. So what we did is write them on a whiteboard and this is our whiteboard in the office in July 2009 and that was pretty much me thinking very hard about these equations and this is really what we did. Wrote them on the whiteboard. Michael will recall me sitting staring at that whiteboard for hours on end just looking at them. And what we were trying to do is identify from all of these equations that are up on the board what was the commonality? What were the similarities between them? What was the root method that could explain all of these methods that were out there? Eventually, after a little while, we managed to do it and we published a nice paper. It turns out that this thing, this probabilistic model as it's called and a new proof explained exactly where all these things came from and we published a nice paper eventually after a low correction in 2012. So that was fun and indeed Adam won that same Distinguished Distation Award in 2013 which is very nice. Another book for the Society. But all of this came because we were thinking about the similarity between the ideas looking at what was out there and thinking how could we build upon that by linking ideas. And this turned out to be a very, very good foundation. It inspired a whole new generation of research, cost us here has generated a tsunami of new papers. He's down here, so I'll format him later. Extending this in lots of different directions and in particular, he's worked over the last couple of years with AstraZeneca and he's got himself a Data Science Fellowship funded by them. We're working with Jim Weatherall here in the head of the Marx Analytics for AZ and we are looking at the challenge of extending this whole framework looking at personalised medicine but in particular looking at two types of features. Ones that are useful for assigning treatment, saying what pill you should get and one for assigning care protocols. Never mind what treatments you give, how should the person be looked after long term, what's their prognosis, what's going to happen even without treatment. And this in turn has fueled new collaborations. Caroline Jay here unfortunately couldn't come along and Alan Davies and cost us a few days down there somewhere are all working on really fascinating areas around clinical trials and working very closely with AstraZeneca. It's very, very fruitful, I like it a lot. So this was kind of one arm of the research I've been doing for the last few years and I'll carry on doing it. But then there's been another arm of research that concluded very recently but this same strategy of looking for the similarities. Nikos who's down there has been working on an idea called boosting. It's a mathematical framework within machine learning. You'll recall earlier on we talked about having some examples of what we want this machine learning system to do and providing it to this mathematical model where it adapts itself. Now the idea of boosting recognises that this mathematical model is not perfect. So it will make some mistakes. So we can check where this system makes some mistakes and the logical thing to do next is to build another model that corrects the mistakes of the one that came before. And you can continue this chain, each model correcting the mistakes of the one that comes before it and end up with a set of models. And then you can use those as a committee to combine their predictions and go further from there. And you can see that obviously the first model there might make some mistakes that will be corrected by these letters. But what was left out of this framework in the relatively original form was the idea of so-called costly mistakes. So if we imagine we deploy some sort of artificial intelligence system in the future and it makes a prediction about the world in front of it and it makes a prediction that says maybe it predicts that this thing is a pussycat. I don't know if you can see it from back there but that's kind of a very very furry dog. And so the consequences of that, what are the consequences?  That's not a problem. However if you made the prediction here the consequences are going to be a bit bigger. Getting the head bit off the robot. This might be a jokey way to explain the idea of costly mistakes but they happen in real life. If you have a medical scenario and you predict a disease is going to come back and you are wrong well there will be some treatments and the person might have to go on to go a period of time in hospital and it will cost a lot of money, fair enough. But if you predict a disease will not come back that's going to cost a lot more. It will cost the person they love potentially. So you want to avoid this second. And so the question arose as to how you should do this challenge of cost sensitive boosting. How you should build this chain to do this. And it turned out that there were a lot of people that have suggested modifications to the original algorithm. They had attacked different parts of this pipeline modifying in different ways saying that we should change this bit and this bit and each had their own suggestions their own direction from which they approached it. But what was quite surprising is that there was very little if any cross comparison between the methods published from 99 till 2015 or 16 on this. And it turned out by looking deeper that several of these were exactly equivalent to each other. I mean not even just they look similar. They are the same algorithm but just written in different notation and nobody realised. And by looking deeper we saw that. So the question arises again. Which of these should we trust in what situation? How do they relate to each other? So the really nice thing about boosting as a framework is that it's a very, very elegant and deep rich theory and it has a lot of explanations as to why it works. And each of these mathematical frameworks around the edge different areas of maths and machine learning you can start from any one of these mathematical frameworks independently of the others and arrive at the same place this in the middle which are the equations that define the boosting algorithm. So you start from any direction you want and you follow the rules of this framework and you end up in the same place all roadly to road as it were. And that's really surprising but that is one of the most powerful parts about this algorithm. So what we thought is given that each of these frameworks can be used to derive the algorithm if we're going to modify this thing in the middle if we're going to change it such that it is not the original boosting but it's some kind of cost sensitive thing we should maintain consistency between the frameworks. Now that's just a theory we didn't know whether that would be true or not so what we did is check whether each of the theoretical frameworks still held for some sort of new algorithm if we modify it here will it still hold for these four frameworks and we expect to get four tips in the boxes. And so we checked the original algorithm whether all yes of course it satisfies its own properties but then we looked at the rest of the literature and found that that original was the only one that stuck to the rules that still stuck to the frameworks that explain it and all the others given the modifications effectively broke consistency with these theories. Now that's not necessarily a bad thing it might be that there's some deeper theory behind it that explains the cost sensitive thing and the non cost sensitive thing we didn't know but we took this as a hypothesis and did a whole bunch of experiments testing all these algorithms on loaded different data sets and found out that the winner overall was the original 1997 algorithm so it was just a theory but turned out to be quite nice and we published this rather controversial sounding paper cost sensitive boosting algorithms but the editors actually encouraged us to use that title and the spoiler if you haven't already figured it out is that no but that was quite a nice conclusion to come to and Nick Oscar a nice PhD after that and we worked with Peter Millis and Nara here Nara, former postdoc and Peter from the University of Bristol and then came Sarah down here same sort of tactic completely different area this challenge of feature selection that we talked about figuring out given a whole pile of data what matters and what doesn't matter what's relevant and what's irrelevant for a given problem so if I have 27 let's say patient health records this is quite a small number but health records are difficult to get hold of people don't often donate but if you have such a small sample and you ask the question what's relevant for predicting a particular disease and then you run your algorithm whatever it may be and it says this thing this particular measurement that is relevant and all this stuff is irrelevant that's quite a strong conclusion to make from such a small sample so let's say we perturb the original sample a little bit we hypothesize in an alternative universe that 26 patients turned up on the day we just delete one person from the pile or maybe we perturbed them in other ways now we run the whole system again and maybe it says oh no no wait a minute that thing is now relevant changes its mind completely now that could tell us something quite important in that maybe that one patient was critical and so should be investigated further but it could also tell us something else about the algorithm in the middle that's detecting what matters if we repeat this many many many times and it keeps changing its mind no matter what and you keep changing this original sample dropping people out changing them in subtle ways that really shouldn't matter and it keeps changing its mind saying what's relevant changing its mind all the time then it's probably not very reliable but this is the area that Sarah focused on how should you measure how should you quantify this stability so it turned out that a lot of people have proposed measurements that should be used in this area lots of different perspectives some conflicted opinions on what was useful in what scenarios but very interesting literature to delve into and there was one thing paper in particular that stood out from the crowd this one by Concheva 2007 and Lucy is over up there somewhere and this for us looking at the literature was really a breath of fresh air in the way she approached the problem very elegant so she posed the question should we invent another arbitrary new measure of stability no instead we should state the desirable properties axioms that this thing should have that should follow and derive one and so that's what she did state some properties and derive whatever followed from those very nice but there were some cases where this didn't always work so we took this analysed very deeply and we found so a very simple time which is always the case in certain algorithms and some of these properties turn out to conflict a little bit in rare but important cases it was a fantastic platform start from for Sarah and she met Lucy at a very early conference in her career and I can say that she definitely was inspired by that meeting Lucy Lucy was kind enough to come over last year and be the external examiner where she proposed a new set of properties which generalized Lucy's work out to cope with this variation in the number of features and we checked again the literature for what measurements had these properties and found that nothing had everything we wanted so we derived a new one which had everything that we needed and it followed it turned out to be a generalisation and had some new and interesting properties and we have published a nice paper you can find it on the interwebs next month so this brings me with this thought to the last of my three ways of thinking the opening black boxes was really something that every good academic does take something that was complicated that amazes you on the surface the shiny magic thing and rip it to pieces delve inside there ask why it works everybody should be doing that everybody does that the second is similarities versus differences that is everything's connected in some way or another and if you squint and look at things in just the right way you can build ridges between areas if you focus and so here's my final building ridges again by my second favourite quote opportunity favours to prepared in mind by Luke Pasteur really like this quote and here are two opportunities that came along because my team was prepared another good collaboration that is just about to launch a choice already happening really there's this really nice piece of work that Michele's team have done recently on something called memory caching protocols policies making effectively your computer memory more efficient the computer effectively predicts what you want from the memory before you even realise you want it yourself I like to think about it as a super efficient super library if you imagine you go to the library and the library has already got the book you want in the hand before you walk in the door that's effectively what this does and it did it in a very particular way the hardware overhead that is the amount of electronics you have to build into the thing by five times which is very impressive but again they didn't know exactly why it was so successful there was some real gut instinct in engineering intuition that went into it and it works amazingly well but sometime towards the end of Mohsons PhD they asked us why does this thing work so what we've started doing recently is building bridges a framework that Nikos was doing and asking why does this thing work given that other framework and Nikos has been working on this thing a new theory for why this method called happy works very well a machine learning perspective and it seems to explain it so far and it's suggesting some new directions which it's going another opportunity that has come along when I was over there and we've been working we've got this very nice funded ESRC project working with Greater Manchester Police and the challenge here is really quite serious one and I never imagined that I would be working with a school of law but it turns out that these sort of chances opportunities do come up it turns out that there are 63,000 domestic abuse incidents in Greater Manchester every single year for the past five or six years and this is a very big problem obviously this is just Manchester as well and the police and social services have a number of processes for risk assessment where they try to say is there a risk in this household is there a risk in that household on the basis of that makes social service interventions or something like that to perhaps offset potential problems further down the line one of the mechanisms they use for this is a thing called the dash questionnaire domestic abuse stalking and on a base violence and a number of questions are asked of the victim and of the perpetrator when on the site where the incident has occurred and obviously some of these questions 27 questions are very very serious and there are there are very distressing situations and it's not always possible to know the truth about perhaps someone's background and it's not always perhaps desirable for a person in a distressed situation to tell the truth so there might be an incentive to not tell the police officer the truth about someone you love someone you love and that's the awful situation to be in but when these when this data is recorded questions like this one become what are called under reported questions in the true incidents of the answer yes for this particular question is likely to be higher than is actually reported in the data so what we started doing is looking at the data and let's say we have a data set so we measure the correlation between has someone has this person ever hurt the children and is there a recurrence of violence in that particular household we measure that correlation and it comes out from the data the answer is no but this is against other evidence that may be available so what can you do go back after victims the truth did you tell the truth no you can't do that so what we've done is build bridges out towards work that Costas has been doing recently developed for the medical scenario the biomedical all the work with AstraZeneca and that fueled from all the work on feature selection which came out of the work with McKell many years ago this has built bridges and we're asking how can we correct the data but without ever going back to individuals and asking them another question correcting at the population level incorporating knowledge from other sources and it turns out it's possible to do that and if we ask that same question again at the data we get something that is more expected and on the basis of this perhaps policy decisions might be taken on the way in which resources are distributed within police forces so it's a very important problem to solve so this brings me to the end my conclusions and my three ways of thinking black boxes why does that thing work instead of thinking it's nice and shiny delve into it similarities and differences everything's connected and the last thing building bridges opportunity phase of the paired mind think deeply enough about these first two things and we're really finding the opportunities keep coming along and the important thing in science in my last two or three minutes is not so much to obtain new facts but to discover new ways of thinking about them and to thank those who help you do it I've said a few thank yous on the way because you're in this last 55 minutes that you've been sitting patiently before your champagne upside but there is a team that I've mentioned a few times that I want to say a special thank you to I've had some amazing interviews here a few of the very first ones I had I mean a Manuela Richard and Adam the others can be here Richard is down here some later ones Postdocs, Naracostus, Sarah and Nicos are all here as you can see I've got into a habit of taking photos of them in distressing situations in front of whiteboards I like this one in particular like all that math is just pouring out of her brain and yeah I mean I think some of these really take photos of people in the most distressing situations often to prove something on a whiteboard it's fun there's also the current generation haven't managed to capture them in front of a whiteboard yet but I'm sure it will do some time quite soon and of course we are part of the wider machine learning group here we are at one of our Christmas doos and here's another one we have a lot of fun to get to thank my wider colleagues around the world who've got me into some interesting situations over the years so finally I want to say one final thank you since it's my inaugural I get to make a fool of myself and I want to present to you my career heroes people who have taught me five lessons five people who taught me five lessons and without whom I wouldn't be here first is my parents thank you my mum said to me years ago do your love and I'll support you unconditionally and that made a difference that set me on this path the second person James he's round here somewhere I spent many years when I was about 16, 17 years old hiking around the school playground we've known each other that long talking about neural networks with him at that age and that was fine third person you've met him already Jeremy he taught me that very simple lesson think harder open that black box the fourth is really two people Lucy and Fabio now these two people taught me something very important something I hope every young PhD student will get a chance to learn about collegiality if you have someone that will open the doors for you encourage and support you the final person you've met him already again Michele he taught me something very simple a ruthless almost blind optimism yes we can the end


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