So welcome everyone to the emerging trends in cloud engineering panel that will focus on platformers product, platform APIs, developer self-service and more. We have a fantastic lineup of panelists they genuinely privileged to be amongst these folks, including Alex Jones, Brian Graceley, Katie Gimangy and Victor Flasek. I'll let them introduce themselves in just a second. I'm your host Daniel Bryant. I'm currently director of DevRel and Ambassador Labs, where I focus on helping engineers build platforms and deliver applications on Kubernetes. So could we go around the room please and quick introduction of who you are. Katie, shall I start with you? Sure. Hello everyone. My name is Katie Gimangy and currently I am the ecosystem advocate at CNCF or Cloud Native Computing Foundation. I have been in this role for almost half a year, having my first anniversary there, well half anniversary. And my responsibility within this role is to pretty much lead the end user community. So these are the vendor mutual organizations that use cloud native tooling, but at the same time to bridge the gap between these practitioners and the projects within the ecosystem. And yeah, I'm going to finish off with this one. Perfect. Brian, you want to go next? Sure. Hi everybody. My name is Brian Graceley. I'm senior director of product strategy at Red Hat primarily for the OpenShift platform, so our Kubernetes platform. And on the side I host the Cloudcast podcast. Brilliant. Victor, you're next in my box lineup. Okay. So I work in Shippa. I'm a developer advocate there. I do not yet know what I'm going to do there because I'm about to join tomorrow. So something in Shippa. I have a YouTube channel and I don't know, I published a few books. That's all. Super. Alex, last, my name is Least. Hi, I'm Alex. I'm a principal engineer at Civo. Civo is a cloud computing provider built on K3s. I build hyperconverged infrastructure all the way up to operators, all the way up to tenant application architecture. I also work a little bit with SIG app delivery and observability. Excellent. I appreciate all that. That sounds perfect. So we'll start off by asking the question of what does a modern platform or I often say Paz platform as a service look like? You had the rise of infrastructure's code, containers, Kubernetes, and many of us are hanging back to the days of Heroku, of Cloud Foundry, where it was Git push master and we were good to go, right? What do you all think a modern Paz encompasses? I think we do not have it yet. We will soon. I believe that that's the next step in evolution of Kubernetes. I mean, we know that that modern platform is going to be Kubernetes based. That's certain today. And I think that we are just starting building platforms like that. And when I say platforms like that, I mean Heroku like, right? Because I think that there is some kind of misconception that developers will adopt Kubernetes. I think that that will never happen. It's too complex for vast majority of people. And 2021 will be probably a year where we will see emergence of platforms that layers on top of Kubernetes. Actually, I actually am over the same opinion here. I think there is always like an evolution cycle. We go around when it comes to deploying applications. We had this cycle when it came to the VMs. So pretty much how can we make best use of our compute at the time? We had the configuration as code that was heavily dominated by tools such as Ansible or sold at the time. And I think we kind of undergoing the same patterns, but with different technology. That's where we have Kubernetes coming around. And now, again, making the best usage of our space, but now we're using containers. And they pretty much the Linux primitives, the Linux kernel primitives with C groups and namespaces. We have different ways to describe our configuration from plain YAML. We transition to templating with Helm charts. Now we have CRDs to further customize the experience of our developers. And now I think we are at a moment where we have the ecosystem there. We have the technology, but now we're trying to make the best of it to really enrich the developer experience. I think how can we deploy easier to have that competitive edge towards like a technology or business? It's interesting just to follow from Katie's statement that Kelsey Hightower came out with a really great sort of sentence around that most people want a pass, but the requirement is it has to be built by them, right? So taking all these commodity tools and wrapping it into your own kind of flavor to give you your own pass. And I thought that was kind of a good take on it, right? I would build on top of that. I think that most companies do that. And after a couple of years, they realize that they failed. And then they're looking for a real pass. Yeah, we always kind of come back and forth between this, you know, I think James Governor the other day was joking on Twitter. He said everybody that's building platforms is trying to replicate Heroku. I think it's a little different. I think the spectrum is a little bigger. I think on one end, there is a set of developers or a set of work use cases that are looking for really highly opinionated and even maybe more so than Heroku, like what Netlify does or, you know, Cloud Run or Lambda or something. But then there's the other part of it, which is do we want to sort of proliferate opinionated platforms? And so we see things like, you know, with Kubernetes, we see operators let us run databases and AI and ML workloads. And so that to me is more the question is like, are you looking for highly opinionated sort of one-off platforms? And those will fit certain use cases. Where do you want something, you know, Kubernetes gives us a foundation to do a lot of things? Is that a better way to solve, you know, the breadth of problems that most companies have? I like that, Brian. And that actually leads nicely on. Thank you, everyone. But that leads nicely on to the next question is, we frequently now talk about platform as a product, it should be designed as a product, managed as a product and released and controlled as a product, which I totally buy into my day job. How does the implementation of this differ from the traditional approach as many of you sort of mentioned? I think from my own experience, there's a bias towards reuse and proliferation these days. Platform as a product is essentially a way of mandating and ensuring your survival within the ecosystem and that you are wrapping up all of the domain specific knowledge and the components that you need to be successful. And, you know, I think of some of these, you know, aforementioned products that are really popular in the ecosystem and Heroku really being kind of the nostalgic gold standard. But if you were to rebuild Heroku in the Cates based environment, it would look very, very different. And I think that it's really imperative that we understand that there is no panacea currently. And really, when we think about platform as a product currently, it's something that is extensible, right? So that there can be community reuse as well as building on top of. And we've seen many of those already today. The place where I think that many or one of the main reasons why many platforms failed in the past is I believe that they all tended to go to extremes, either be extremely opinionated and satisfy, let's say, developers and completely leave operators and CIS admins and SREs, you know, people who are making sure that systems are running properly unsatisfied or like Kubernetes is today that he satisfies one group and it doesn't satisfy the other, right? So it usually goes into extreme, extremely opinionated, doesn't work for CIS admins, works for CIS admins, developers cannot use it. And that's why I believe that we are now in a good spot that we have that we have Kubernetes, it does satisfy CIS admins, it does what it needs to do. And what we are missing is that layer on top that that's why I believe Heroku in a way failed or Docker's form failed as well. They started with from that other direction. Let's start with making it easy for people instead of making actually making it have all the all the levers that it needs to have before we build that that structure layer that that will satisfy the other group. And nobody did that in the past really or at least very few. Yeah, I think I think it's interesting as we get into thinking about it as a product that we've we've sort of moved past the idea of like, is the decision sort of by versus build something yourself, right, take a lot of the parts together. And now it's it's more a matter of like, if I have a platform, whether it's you consume it as a managed service or you consume it as software, it's does does that the way that that thing is delivered as a platform meet your needs. So like as an example, so there was a thread on on I think hacker news yesterday or the day before that was, you know, Kubernetes is really complicated because it comes out so frequently. How do I keep up with updates? And one side of the argument was, well, who cares, just spin up another cluster and move your stateless applications over there.  And I think we need to realize like delivering the platform is really important delivering updates and making it simple for people to consume. But but there's even parts of that that that are hard for people, right, like the three or four months cycle of Kubernetes is really hard for an organization that has an application they don't touch nearly as quickly, right? There is an aspect of that that's, you know, lift and shift or modernize. So again, I think there's there's sort of these these shades of gray in between stuff that if you make it all one kind of definition, it makes it hard to like, like Victor mentioned, you know, it doesn't apply to one side, but not the other necessarily. I think on that point, I'll skip a few of the questions I've initially talked about and look at the open application model, the OEM spec. One thing I've just heard you all talk about and one thing that really stood out to me in the OEM spec is this clear definition of personas. So it's gone through a few iterations, but now it's focused on components owners, which for me are kind of the developers, application operators, kind of SRE like, I think, so I rely on the engineering like and infrastructure operators, which for me are the kind of classic platform folks, the Sabin folks. Do you know, I do like the three distinct personas rather than the classic perhaps Dev and ops thing. Do you all see that in your day jobs? Do you see perhaps the three personas there? Do you like that part of the OEM spec? I'd love to get your thoughts on that. So I can speak a little from a financial perspective where there's a highly regulated set of protocols to which you have to follow, and it very much is reflected in there being a clear distinction between operational staff who can touch clusters, who can deal with infrastructure and debug, then the SRE team who may well be observing telemetry and making iterations on application code, and then you've got their counterparts with the development cycle team. And if you were to draw sort of a Venn diagram, these all touch slightly, you know, between the three groups. And so it is nice seeing a recognition there. And the thing that I really like about the spec is that it looks to sort of unite the users and the platform builders, right, within a single specification. So it's very much as reflective of what we're seeing in the industry. I can actually bring an example from my post roles as well. And I think this kind of segregation between different roles and different personas that happen organically for us, which was quite likely. But initially started as one team which provisioned infrastructure as a service. And that's if we're looking at the team topologies, that's going to be a type three. And at that point, we would just pretty much build for the demand we have from our developers straight away. However, there is not too much of upskilling going on. There is not too much of support that we could provide continuously. And that kind of transition into a team actually our team divided where we have our platform team, which just focus on the infrastructure provisioning. And then we had our cloud management team, which would focus on that ops kind of culture. And towards the end, we moved towards the SRE model, where we have an SRE team included as well. And then we have this kind of collaboration when it comes to, we don't just provision platform, we upscale, but at the same time, we can push back on future development. And based on the SLOs, we can bring things that would really empower our teams forever. Now, what I'm saying that is that all of these kind of free teams that we have, it was an organic kind of development of all of these personas that we see enforced by the open application model as well. And at the time, we had to provision all of these components, we had to create our own methodologies and pretty much build the tooling or bring in the tooling in-house, which will allow us to deliver this particular or to deploy our application using these models. Now with this back, it's already there. So it's kind of nice to see this further confirmed within the community. But at the same time, it brings a benchmark and a standard, which means that now we have the fundamentals, we just need to build on top of it. And this truly enables more extensibility and pretty much is a good model that every team can build on top of. Yeah. I think what Katie highlights in reality is that middle tier, that middle identity, is the one that's going to end up being the most fluid. At any given time, they may be more infrastructure oriented, especially if let's say you're moving an application from on-premises into the cloud, for example, or even cloud to cloud. And then at other times, they're going to have to be sort of more the application-centered SRE. So, but I think the thing I really love about it is, it is very distinct to say, it's not sort of one way or the other, there's some flexibility in there. And I think that's, but like you said, the fact that we're sort of writing it down, we're sort of making it a structured model that allows you to think within a certain framework and it's not just completely nebulous. What I believe is kind of missing from that description is describing who are users of each of those groups, which is often overlooked. They tend to be in a style or right, I'm in charge of infrastructure, I'm in charge of this or that. Well, I believe that the real move forward is that to acknowledge that SRE is application operators, infrastructure operators, their users are developers. They're supposed to build services that can be consumed by developers. They're not supposed to actually really create infrastructure. They're not supposed to deploy any applications. They're supposed to create tools, patterns, means for developers to be self-sufficient. I don't think, I don't see it as much right now in the industry, but I believe that's the direction we need to go. Very interesting, very interesting. So I jumped ahead and I probably should have introduced the OAM spec, but darling back a second, and I think if you've touched on these ideas as well, how important do you believe open standards are to creating a platform? I can definitely answer this one. It's mainly because I've been focused on this particular topic for a while now, and I have been trying to really pinpoint what was the impact of the emergence of interfaces within the Kubernetes ecosystem, but now I'm focused on the open standards when it comes not just to the application delivery, but to observability style because we have projects such as open matrix and open tracing, which really tries to bring that, again, fundamentals to how we exactly would collect metrics or how we deploy our application. And there are three main things that I usually identify when it comes to open standards or pretty much the fundamentals. They impact the vendors, the end users, and of course the community. For the vendors, it's going to be pretty much innovation because as a vendor, you don't have to concern yourself how can you merge your components with the platform. The standards are already going to be there. So as a vendor, you can focus on how to deliver value to the customers with minimal latency. So you really focus on your customers and what you can bring to them straightaway. For the end users on the other side, when you're talking about open standards, it means extensibility. Now, what it actually means here is that as an end user, you can choose multiple tools with the same, well, kind of trying to solve the same problem, but you can benchmark between them. And it's easier now because you have that one platform with the standards integrated or if the interfaces were available, you just inter-switch in between them. And of course, when you're looking into the entire ecosystem, that pretty much translates into interoperability, which means that we have an ecosystem which is quite colorful in terms of the technologies out there. But more importantly, again, we have multiple solutions for the same problem. And this is really kind of the driving force of pretty much the extensibility, kind of the growth, organic growth within the cloud native ecosystem we have now. Just to add to Katie's points there, around open tracing and open sensors, merging into open telemetry for end users like myself, it means that we can use the hotel collector and have a guarantee that there is a sort of a longevity to this project and that vendors are coming together to design a solution that will benefit the users and also allow us a degree of choice in that if vendors are compliant and they conform to these protocols and these specifications, then we know that we can have this path to migration, whatever should we choose to do in terms of our business direction. So it's wonderful to see that kind of collaboration and that's only made possible through these kind of standards that are being built out. Yeah, I think both of you guys, both of you point out something that's sort of indirectly important. We used to do standards, we'd have these I think standards bodies, the IEEE or WC3 or whatever. The nice thing about them being community based now and especially open source based is you not only get standards that come with code, but to a certain extent sort of the economic viability of those standards work themselves out. So like open telemetry is a great example, but there's been others and for people that are customers or users of it, they want to know that as much as they want to know that it's a standard, right? They're betting on a technology, they want to know is it going to be around for a while? Is there going to be people that support this? I don't want it to be fragmented because it's not good for Alex's business model, it's not good for anybody else's business model. So I think we've reached a point now where it's evolved, where it's not only the standard, which is great, but we get code, but there's also a certain amount of sort of economic Darwinism, if you will. So you know that the thing that you ultimately pick is going to be more viable than just paper specs like we used to have back in the day. Very nice. I've been from a Java background, I can definitely recommend the or definitely recognize the paper specs, HABs and so forth. So yeah, very good, very good. I think it's a sort of wrapping up question now. We've covered sort of some of the APIs and the benefits of specs. I'd love to get your thoughts on whether Kubernetes is somewhat becoming a centralized control plane. These days, it's almost a universal control plane, I guess, the rise of customer resources, operators, I think everybody mentioned, Brian, what do you think in relation to that? Do you think Kubernetes is becoming a universal control plane for modern platforms? You know, for some, yeah, having done this for a while with Kubernetes, I think it is, I think it's a, I think of it as more of sort of a foundation for what's going to build on those control planes. So whether that control plane is at the service mesh level, whether it's sort of this cross networking thing like Submariner and other projects, I think it's a really, really good foundation. And what's going to get interesting with it is, you know, does the control plane, is it multi-cloud? Is it sort of cloud to edge? That's where I think we've got a lot of flexibility, but the nice piece is we're not, we're not kind of shifting the underlying sand. So I feel really good about that. And I think, you know, like we're coming up into KubeCon, but KubeCon, we used to call it KubeCon because that was the dominant technology. Now, Kube is sort of the, the sort of safe and boring piece of the pieces and, you know, it's control plane con and what will that evolve to, which is really exciting. I can only echo that to be honest. I think the most powerful characteristic when it comes to Kubernetes is the fact that it's not opinionated. It has some assertiveness when it comes, for example, for networking model, like every single post you have an IP. However, it is not assertive when it comes to the underlying technology that Kubernetes run on top of. And this has been quite powerful because you have a methodology that allows you to lift and shift your application pretty much anywhere. And over the time, there has been this build integration where you can deploy this easily on the edge nowadays, actually now it's on the edge, but it came with the public cloud on-premise and more prominently now towards the edge, as I mentioned. And this, again, has been very, very, maybe empowered by the fact that Kubernetes allows a very good set of primitives. And based on top of that, you can have these building blocks principles where you have already components that are working that are stable and you can build on top of them. And I think it has been mentioned many times that I think Kubernetes, again, it's going to become the basic, it's going to be the boring as I mentioned. And this, again, has been seen by the fact that the Kubernetes source code has been changed for our time because at the beginning it would have had everything integrated within it. So for example, the runtime component would be very deeply integrated there, same with the storage, but now these are components which develop completely independently. They have their own landscape and they own pretty much vendors and community around it. So just based on that, we see that Kubernetes is getting slimmer, like the binaries for the Kubernetes. And I think this is the way to go forward. Becoming slimmer, it means it's out there, it's stable, it's reliable, and people can use it for anything that they can build on top of. And I think there is, I think Kiel said, I thought I mentioned this. Kubernetes is a platform to build other platforms. So I think this kind of encapsulates that quite nicely. And this also reminds me, Katie, of one of your keynotes around the interoperability of the components of Kubernetes and around the CNI, for example, where it's essentially like a train station where people can come and build out their ideas and figure out where they want to go from it. And I think that's the beauty of it, is that now we see, especially with the latest generation of operator mechanisms being brought into that ecosystem, people are doing all sorts of provisioning. And so yeah, it's a great meeting place to start building out that platform, as you said. Any other ideas for anyone there? Victor, you've been required on that one? Oh, that one. No, I'm quite mostly because I completely agree. I don't usually, people associate Kubernetes with the machine that runs containers. To me, that's definitely not the main benefit. It's all about its API. It's about the scheduler. And we see, I mean, we can see already that in action. People are running Mac farms based on Kubernetes API, right? Or control plane. We've seen with cross-plane managing your infrastructure with, again, with cross-plane, with API and so on and so forth. I mean, we've been scheduling VMs as if they're containers as well, right? So I would even make a prediction that Kubernetes control plane will outlive containers even, right? That's the real power of Kubernetes is in a control plane, not in the fact that it can run containers in your cluster. Great insight there. Great insight. So it definitely works in the show notes. The reference links to the OAM and cross-plane there. And there's KubeVail, which is a reference implementation of KubeVail. I'm not sure how we pronounce that one yet. But yeah, I'm definitely with you, Victor. I believe that the API is probably the good API often outlives the implementation, right? As a final outro question, we're going dangerously close to time now, I'd like to get like the tweet size version, right? So two eddy characters of where you think the most innovation in software delivery is going to happen in the next five years. Is it languages that we saw Dark Lang, things like that? Is it architectures, dreaded nano services, or is it platforms, things like serverless platforms built on top of, say, and other abstractions in terms of using the control plane, the Kubernetes control plane? Is it, yeah, perhaps platforms that are going to be the most exciting. So languages, architectures, or platforms? Where's the most innovation you're going to come from? Katie, I'm going to pick you. I was actually hoping you would, because I think like all of these areas are going to be quite in development within the next years. But I'm actually quite biased in this perspective. Because I've been following the platform space for a while now, I'll be following it quite closely. I think here is where there's a lot of dynamics. But again, I'm seeing this from my own kind of bubble of technologists around this area. So I think when it comes to the platforms, I think there's definitely an improvement when it comes to how we use the platforms, how we deliver that developer excellence, operational excellence, simplifying it. We still are like hanging around YAML manifests, which we shouldn't, and we even try to push this to our developers. It's still the case. I think moving away from that is definitely going to be a step forward. I'm personally quite excited to see how we can use some of these platforms to deliver these applications to the edge. This is something, again, which I'm following quite closely. But I would like to see how this can be achieved seamlessly, but at the same time to ensure that kind of obstructed propagation of applications to areas closer to the users pretty much at the edge. So yeah, from my perspective, is going to be the platforms. But again, my view is biased here. Brian, can I ask you? Yeah, I think I'm going to place my vote in the architecture space. I think the biggest changes I've seen in the Kubernetes space or even just the cloud native community of the last five years have all been people pushing the boundaries on what you can run on these environments, what you can distribute, how big or how small. And I think we're going to keep seeing that. So whether it's architectures to make apps simpler or architectures to sort of deliver applications across multiple environments, I think we're going to keep seeing that expand quickly. Super. Victor, you're next in the box. That's all right. I would go with platforms and languages over architecture. I somehow feel that architecture always follows those two. If you look at, let's say, microservices, nobody really, I mean, very few did them until we got containers that enabled it. So platforms and languages are usually enablers for different architectures. So I mean, we will definitely see improvements in all three of those. But if I would need to place them in order, I would say that architecture follows platforms. A bit of disagreement. Fascinating. I like that. This is good. It's been just a nice panel. We need some disagreement. Alex, I'd love to get your thoughts on this. I think that it is a quiet rebellion happening in the platform space where the price point of commodity infrastructure, you know, Keemew and KVM virtualization mixed with things like Kubevert with Kubernetes and other platforms means that people can now assemble their own cloud providers, combined with the fact that developer experience is, you know, really a hunger for offering. We're going to see in the next five years that there are competitors to Google and to AWS. And we're going to see that we're now starting to get developer centric platforms being built with Kate's just as a pure API and no more than that. So I think that it's going to be the community and the engineers that are guiding the next generation of innovation here. And that is a fantastic way to end the panel. I think thank you to all of you. It's been super interesting. We'll do some Q&A, hopefully in the live event as well. But thank you very much for your time. Thank you for coming guys. Thank you so much. Thank you for having us.


Hello everyone, welcome to this material characterization course. In the last class, we just started looking at some of the diffraction principles in a transmission electron microscopy. I started talking about a conventional selected area diffraction as well as the convergent beam electron diffraction and then I was saying that a SAT pattern or SAID pattern is obtained in a TEM using a parallel beam and a small probe or a micro probe or nano probe is obtained using the selected I mean convergent beam electron diffraction. So we will continue that discussion and if you use an incident beam as convergent, the spots becomes discs and their diameters depending upon the convergent angle 2 theta or 2 alpha something like that. You can form a convergent beam electron diffraction pattern in a TEM mode in any TEM that can produce a small probe which is less than 1 micron with convergence angle greater than 20 milli radians. So I will just play some of the rate diagram using schematic which will distinguish between a conventional selected area diffraction pattern and a convergent beam electron diffraction pattern. What you are seeing in this schematic A is the a conventional electron diffraction where you have the transmission transmitted beam that is also called a zero order spot and then you have the diffracted spot which is there. This is a conventional parallel beam elimination. Otherwise you have a convergent beam operation where you see that the zero order spot and you have the diffracted spot which appears as a disc as compared to the spot in a conventional SAD pattern. So this is an idea. We will talk about this and its application much more detail in the coming slides and this is one of the actual electron diffraction pattern taken from this textbook and what it shows is 4 different kind of patterns you get depending upon the angle your angle. So the first one is 1.4 into 10 to the power minus 3 and second one is obtained with 2.3 and third one is 4.9 and fourth one is 8.1 milli radians. So you can obtain a micro diffraction or Kossel molestant diffraction pattern depending upon the different converging angles. So this is first information about the diffraction using small probes. What is important here is always we talk about a probe size and probe diameter. What is the physical meaning of this? So in order to explain that I brought this slide. So the probe normally what we see in an electron microscope is nothing but a full width half maximum. So you see that you know the corresponding you see that corresponding profile intensity profiles are shown in this here and then you have the actual image in the microscope they will appear on the fluorescence state like this. So you have the different probe for example if you take a spot size of 6 which will have a probe diameter of this order that is 14 nanometers, 4 nanometers and 2 nanometers and so on. You have a nano probe or a stem mode or a normal probe you will have this kind of an intensity profile and if you go to the HR stem high resolution mode you see that the probe diameter is as small as possible nearly 10 nanometers. So which belongs to the spot size 4. So if you go to the microscope they will give the in a dial with the numbers which one each number will belong to particular probe diameter. The physical meaning of the probe diameter is a full width of maximum. So just you have to appreciate that fact that is why I brought this slide. What is the meaning of a probe diameter? The most important thing is camera length. Just now in the last class we have seen one of the operations I mentioned that we need to calibrate the camera length we will look at the details how do we get this relation with the camera length and the de-spacing of the specimen. So the similar ray diagram what we have gone through incident beam this is a sample and then you have the back focal plane transmitted beam a diffracted beam the distance between the specimen and the back focal plane is L and then you have the 2 theta diffraction angle the distance between the transmitted beam and the one of the diffracted beam is R. So the angle that the diffracted beam makes with the incident beam is 2 theta but from the Bragg's law we know that sin theta is equal to theta since theta is too small which is equal to lambda by 2D each set of diffracting plane spacing DHKL produces a spot in the diffraction pattern at the distance R from the center and the diffraction sorry and the direction perpendicular to the planes R by L is equal to tan 2 theta which is approximately equal to 2 theta because theta is small so you can write lambda is equal to 2 D sin theta is equal to 2 D theta therefore R D can be written as L lambda. So this relation comes from this approximation and the camera length what we are calibrating or using is a projected length and not a physical distance in TM. So you have to be very careful about this aspect it is always a projected length and not the physical distance in the TM. So you have to know this basic idea about this camera length. If lambda L is known the camera constant and relates the distance of a spot from the origin of the diffraction pattern to the D spacing of the set of planes from which it comes. The camera constant can be determined using a standard and hence the D spacing corresponding to a reflection can be found once R has been measured. So this is the experimentally measured quantity from the film or from the diffraction image you measure the distance R and if you use nanometer unit for D spacing then the unit of a camera constant is nanometer mm which is not a standard unit but it is convenient for determining the D values in the nanometer scale. So we will appreciate this a simple concept when we actually try to index some of the diffraction pattern using a camera length as well as the corresponding distance R from a given pattern. So this is the typical calibration again I am repeating this the D spacing in nanometer of the first 9 diffraction rings from a gold and aluminum both are you know face centered cubic lattice. So you can have this values for the reference and this is a calibration chart we will come back to this when we do an exercises then that will make more sense rather than I go through this values. Now I will come back to the reciprocal lattice concept which I talked about during the fundamentals of electron optics as well as the I mean fundamental of this course itself we discussed about this this is the second time you are seeing this image and as such the reciprocal lattice concept has been discussed again in X-ray diffraction here also it is the same just to have much more confidence let us go through this what you are seeing is an optical diffraction pattern taken from the grid. Grid is nothing but kind of mesh it could be a metallic mesh or it could be of any material made of any material it is like something related to something similar to grating it is a mesh. So the pattern A, B, C, D and E they are all optical a diffraction pattern obtained from the grating the pattern A from a grating with the spacing of 0.126 mm pattern B from a 100 mesh grid with the spacing of 0.25 mm pattern C from a 200 mesh grid with the spacing of 0.125 mm pattern D from a 400 mesh grid with the spacing of 0.0625 mm pattern E pattern from the 2D crystal shown in F. So what you have to appreciate here is the there is a relationship between the distance between the spot in a diffraction pattern to the grid spacing there is a relationship. So you can appreciate that the as the spacing in the grid decreases the distance between the spot increases. So this is a reciprocal relation here. So we can make some statement about based on this observation there is a definite relationship between a periodicity and orientation of the object and the spacing and orientation of the spots in the diffraction pattern. So the diffraction pattern is known as a reciprocal lattice because of the inverse relationship it bears to the direct lattice that is the object. So we have seen this already I will just play some animation. So what you are seeing in this animation is this is a undeviated beam from the specimen and this is a diffracted beam here and what is shown in this right hand head image is the intersection of 2 planes plane 1 and plane 2 and this is the electron beam and this is the screen on which you see the diffraction spots appearing here and I want you to look at the schematic much more carefully because you see that a plane 1 is drawn in one color and plane 2 is drawn in another green color and the diffracted spot corresponding to plane 1 and plane 2 are also indicated with the same color of the plane. So you see that a plane 2 you see the diffracted spot appearing in the this line which is 90 degree to the plane orientation you can see that it is 90 degree to the plane orientation either this side or that side. Similarly you look at the plane 1 the diffraction diffracted spot appears exactly on that space which is 90 degree to the plane 1 orientation. So this is very important and for a clarity only 2 planes are shown here in principle you can have n number of planes or it could be more planes also where a diffraction can occur and then you will see the corresponding diffracted spot in the screen like this. Now we will take you to another concept which is very important in analyzing the diffraction pattern called a stereographic projection. So like you have an atlas where all the you know countries regions are drawn in one scale they are all called you know area true projections here you have an angle true projections. So stereographic projections are called angle true projections similar to atlas where you have area true projections. So the idea of the stereographic projection is what you are seeing on the screen is like you assume that you assume that you are placing a small cubic crystal inside the centre of the sphere and you try to draw a normal to this cube and then which comes and intersects the surface of the sphere and each one is each one plane normal which comes out and hits the surface of the sphere from the centre is called a poles each one poles. So I will just show one very nice animation where you will appreciate what I am trying to say you imagine that I have kept one a cubicle crystal inside the sphere now the assume that the sphere is transparent and I have just put that into a 001 orientation from this from the I mean the plane of the screen the plane normal of the screen itself 001. So in that projection I have put the crystal inside the sphere now I will try to rotate this sphere you will appreciate what I am trying to say you see that the crystal each plane normal which comes and intersect the surface of the sphere you can see these are all poles individual poles. So the top one is 100 the bottom one is the opposite in fact it should be bar 1 00 here it is 001 and the opposite side is you can see that 00 bar 1 you can see that. So like that you have all opposite poles here and there the idea is if you represent your I mean a plane and directions with this we will be able to analyze the crystal crystallographic orientation much more easily. So I have just shown one simple thing you can look at these kind of a projection system and as I just mentioned all these poles and which will have a perpendicular I mean particular angle for example 100 and 010 will have a 90 degree and 110 and 010 will have about 45 degree. So like that you can you can just visualize the angle between two plane normals so that means you will be able to identify the angle between the planes and then you will be able to represent the crystal crystallography orientation much more easily and then we will use this concept to I mean explain some of the reciprocal lattice concept as well as some evolved sphere concepts we will use it and this is just for your introduction just you have to just imagine how this stereographic projection can be viewed in all these four five directions so you can see all the important poles are indexed and then how they rotate. So now after going through this I believe that you have some idea about this what is stereographic projection. So now what you are seeing is all a 2D projection what I have just shown is an animation is a 3D so this is the same thing what I have shown 0 0 1 projection I have shown it is represented in the 2D like this. So this is the stereographic projections for a cube crystal with 0 0 1 parallel to the south north direction the sphere of the figure stereographic projection is 0 1 0 is parallel to the east west so this is what it is shown here. So here how you can just look at the some of the poles are written here this is the some of the nomenclature which I am not going into the detail and the basic idea I want to show it here is using the stereogram you will be able to represent the crystallographic direction that is all I would like to say and if time permits we will solve some small problems using the stereographic relation and also try to plot some of the poles on a graph like this a wolf net divided into 2 degree divisions you can actually plot some of the poles and then try to do some kind of an exercise of rotating each pole what will happen what kind of stereographic projections you will be able to visualize that we can do it and I am introducing this technique as a tool which can be with this tool we can represent the orientation of the crystal much more clearly that is that is the information I want to give at this point of time and when we actually go through some of the exercises are solving the tutorial problems we will be able to appreciate how this tool can be used so this is just for one references where some of the poles are being rotated here and you can appreciate that how the angle true projections is very effectively represented in a 2d fashion that is the basic idea and this is a standard stereographic projection for a cubic structures 1 is 0 0 1 and 1 1 0 C 1 1 1 and D 1 1 2 so here is this there is only one projection is shown here but other projections I have not shown and what you are seeing here is this is for a 0 0 0 1 projection for cubic system where all this spot shows the parallel orientations of another system like this and suppose if you look at the stereo projection of both BCC and FCC their poles are indexed like that so you will be able to measure the angle between the 2 poles of the different crystal system here and for example some of the poles are very closely parallel that means 0 1 1 of BCC is parallel to 1 1 1 of FCC or 1 1 1 of BCC is parallel to 0 sorry 1 0 1 of FCC and 2 1 1 of BCC is parallel to 1 2 1 of FCC something like that you can you can see all these things are if they are exactly parallel both of them both the points will coincide and if it is not parallel then you can see that how far they are away from each plane that means the angle will tell you the orientation of each plane that means you can relate the plane which is suppose if you have a 2 phase system one is BCC and an FCC you will be able to relate the parallel planes of the 2 systems so when they are in parallel to each other then you will form you will obtain a diffraction pattern straight so that means because diffraction patterns are obtained only when the planes are parallel to each other so by looking at the stereogram you will be able to identify the poles which are parallel that means the plane normals are parallel then that in actual physical meaning it is a planes are parallel and then you will get a corresponding diffraction pattern in a back focal plane you will be able to analyze both the patterns and then you will be able to correlate the relationship between or the orientation relationship between a 2 crystals using this stereographic projection so that is the basic idea about this stereographic projection I do not want to get into the detail due to the lack of time but you should know why this is being used when a number of planes are parallel to a single direction they are set to constitute a zone and the common direction is the zone axis or a zone direction for example if you go to this particular zone for example 001 what are all the parallel directions you will be able to identify with this stereogram so when you say a zone axis so you will be able to identify number of planes that are parallel to the single direction which can be easily identified by using stereographic projection the indices hkl of all the spots in the pattern are related to the indices of the zone axis uvw to which the beam is parallel by the v's zone law so you have the very powerful relationship which is given by v's zone law you can identify the hkl and hkl of the spots in a pattern and it is zone axis they will have the relations like this hu plus kv plus lw is equal to 0 so using this relation you will be able to identify some of the planes which are parallel to this zone so this is very powerful law and we will be able to we will be using this relation when we index some of the diffraction pattern and finding out the parallel planes now what I will do is I will again come back to this reciprocal lattice what you are now seeing is a schematic animation where you have the real lattice and it is relation with the corresponding reciprocal lattice here you will be able to draw this physically because all that you need to know the information about for example it is 1 by d of 1 0 0 plane which is equal to 1 by a or 1 by d 2 0 0 type of plane is equal to 2 by a and then the angle between them so you will be able to draw physically when you have the the basic information from the real lattice for example this is a and this is b and this is d spacing you will be able to generate this kind of a reciprocal lattice of your own similarly you can do it in a 3d because we know the relation so this is just to give you an idea please remember in an electron diffraction experiment even in a TEM what you generate in a microscope is a 3 dimensional reciprocal lattice but you project it on a 2d screen so you get only the 2d information so because in a please understand in a transmission electron microscope it is that your crystal is transparent to the electron beam so you get a 3d information it is a 3 dimensional information but of course as I mentioned in the beginning it is an averaged information but you get the projection on the 3d information projected on a 2d screen and which is seen as a spot we will get into the details why it appearing like a spot what is the physical meaning of the spot and so on in the due course so this is again a reciprocal lattice construction for a monoculating crystal so you can try one of this systems because you know the basic relationship between the real crystal and reciprocal lattice system so this can be a good exercise if you do as a an exercise for few systems then your ability to analyze this reciprocal lattice or a diffraction pattern will be a manifold you will be more comfortable if you theoretically solve and then get into the selection rule and then see that which are the spots will be allowed and which are the spots will be not allowed you can mark them and then if you if you can have the a basic idea of calculating this theoretically then you will have much more confidence in analyzing the electron diffraction pattern which I will show with some few examples on a on a on a blackboard or a tutorial class then you will appreciate this so this is just for your information this is for a hexagonal crystal lattice you have the reciprocal lattice system and another important thing is when you index the reciprocal lattice then you have the kind of 180 degree ambiguity so you have what I what what is the schematic showing is suppose if you have a set of spots where which you have indexed with one type of an indices and if you rotate this like this then the indices should be different you can see that indices are opposite so you have to confirm this it is not that once you rotate this the indices are having different sign it is not completely wrong but then there is an ambiguity you have to fix this which particular indices is given we will talk about it when we do an indexing exercise so this is coming because of the symmetry and this is some of the single crystal electron diffraction pattern for a primitive cubic system these are all available in the literature in most of the books electron diffraction book you will find it you will be able to suppose if you you are able to find out the ratio from your electron diffraction pattern you can directly directly see this kind of an indexed pattern here then you will be able to transfer these indices to your system if it is if it is a cubic system and if you are able to match the angle between the each I mean the orientation and the spacing and the ratio everything matches then you will be able to use this kind of an indexing system nevertheless it is better to calculate of your own and index I will demonstrate in couple of exercises it is always better to do it do a calculation and do it but in if it is a well-known system like the cubic system there is nothing wrong in looking at this pattern and then look at the solutions I will skip this these are all basic information the the another important point I want to emphasize here is whatever we have just seen so far is a single crystal electron diffraction pattern what you are now seeing is a diffraction pattern from a polycrystalline material so what you are seeing instead of a spots you are you are able to see rings here so if a area of the specimen selected by the diffraction aperture contains a crystal in several orientations the diffraction pattern will consist of some of individual patterns in the case where the specimen consists of very many crystals of random orientation the spots are so close together that they fall on a series of continuous rings so what does it mean you you have in a single crystal pattern of different orientation whatever you have just seen some of the I just showed as for a cubic systems FCC, BCC and HCP systems is that they are all single crystal that means a diffraction is occurring from a single crystal or a single grain when you when you are aperture focusing a region which that region contains a lot more a crystals single crystals or the crystals oriented in much more random positions or orientations then your diffraction will be not a spot but a ring because the pattern will consist of a sum of all individual patterns suppose if you put all the patterns together then that will form fall in the the ring so all the individual or independent orientations that can be put are superimposed that is why it is called sum of individual patterns then you will find a ring pattern that means the physical meaning is all the you know the many crystals of random orientations will contribute to the a diffraction conditions that is all it means suppose if you look at the basic diffraction conditions that means you have all the orientations that means many many crystals are obeying the Bragg conditions and then contributing to the diffraction intensity that is all it means so now this is one simple example of how to use this camera constant to identify the single electron I mean single crystal electron diffraction pattern so this is a diffraction pattern from a fels bar which is a C phase centered triclinic crystal structure so the electron diffraction pattern appears like this the camera constant for this particular fels bar is lambda L is equal to 3.6 nanometer millimeter this is this how you have to do the measurement from the center that is a transmitted beam to the diffracted beam is r1 this is an r2 so r1 is about 9.5 mm and r2 is 6.35 mm using the relations you can lambda L is equal to rd d1 is equal to 0.379 nanometers and d2 is equal to 0.567 nanometers so like that we can readily index this electron diffraction pattern provided the camera constant is calibrated and well known otherwise this kind of indexing procedure is not valid I will show some of the other procedures of indexing the diffraction pattern in some of the tutorial class. So now we will look at the how the Ewald sphere is related to a reciprocal lattice so the Ewald sphere it links the reciprocal lattice to the Bragg law this we have already seen in an x-ray diffraction phenomenon just to recall you see that this is a Ewald sphere and here you have this sample incoming ray this is a transmitted beam and this is a diffracted beam for this geometry the radius is 1 by lambda and then you can write sin theta for this geometry OP by OX which is equal to 1 by D divided by 2 by lambda or 2D sin theta is equal to lambda this is simply a Bragg law derivation from this schematic so what it signifies the signification is so wherever the Ewald sphere cut through the lattice the where the reciprocal lattice point exactly intersects with the surface of the sphere then you will though only those spots will appear in the diffraction pattern what is the physical meaning only those spots will satisfy the Bragg law so that is that is why only those spots are appearing in the diffraction pattern not the other pattern. Now we can also look at this Bragg law using this vector equation so you have the Ewald sphere and this is an incident beam that is a k0 and this is a diffracted beam with vector k and this is the G vector it is a diffraction vector and G is equal to k-k0 so this is a vector equation alternative to the Bragg's law is shown like this this geometry is more appropriate for x-ray diffraction where the wavelength is of the same order of the magnitude as an atomic dimension that is the only difference and what you are now going to see an animation is very interesting animation you see that as I mentioned that in the beginning of the today's lecture or when I talked about the difference between an x-ray diffraction or electron diffraction the only difference is the wavelength one of the important aspect of the diffraction between x-ray and electron so here you see that I will play this animation again what you see you are now the Ewald sphere is so big that you know most of your diffracted spot are falling exactly on the periphery of this Ewald sphere so you are able to see the many number of spots in an electron diffraction pattern as compared to x-ray diffraction pattern in x-ray diffraction pattern you see only few peaks not so many number of peaks as you see in an electron diffraction pattern you have n number of spots around a transmitted beam so that is because the lambda is so small so that 1 by lambda is you know so big so your Ewald sphere become very big and then all this reciprocal lattice point are exactly intersecting the cutting through the or I would say that Ewald sphere is cutting through all the reciprocal lattice point so you are able to see so many spots around the transmitted beam so in electron diffraction the wavelength is 2 orders of magnitude smaller than x-ray diffraction as you can see a large number of reciprocal lattice points are close to the surface of the reflecting sphere because the radius of the sphere is so large so that we have seen so the other information about this reflecting sphere or Ewald sphere is you have to appreciate you have to see why we see a spot in a in an electron diffraction so what I really mean here is the sphere the diffraction spots are not really spots but are elongated perpendicular to the surface of the specimen if the specimen is of the thickness t the reciprocal lattice point is straight out to a length of 1 by t producing a rail rod that is a reciprocal lattice rod so since we are talking about a reciprocal relationship in the diffraction intensity so it is suppose if your specimen is having a thickness in one direction that is important suppose in the suppose this is the specimen direction I mean and this is your electron beam so this is the thickness of the sample then your diffraction intensities are going to be produced perpendicular to the I mean the orientation of the specimen so that is what it is shown here so in the 3 dimension what I just mentioned before when I showed a theoretical calculation of the reciprocal lattice in 3 dimension I just mentioned about the each spot in an electron microscope I mean the reciprocal lattice produced in electron microscope is in 3 dimension and then each spot intensity profile depending upon the specimen shape suppose if your specimen is a foil or a thin sheet of a thickness t then your intensity profile will be perpendicular to that orientation so similarly you have a cubic specimen then your intensity profile will be uniform in all the 3 directions so if your sample is oriented in this direction and your intensity profile will be in the opposite direction so it is a reciprocal relationship so similarly if you in most of the conventional TM analysis your sample is a thin foil and your electron beam is passing through the thin foil perpendicular to this so you will see that your reciprocal actual point will have intensity profile like a rod that is what it is shown here so you actually you produce a rod like this in 3D since you are I mean a wall sphere cuts through all the rail rod and when you cut the rod in a cross section you will see a circle so that is what you see as a circular point of intersection that is what is being projected on the fluorescent screen that is why you see as a very small small circular spot as a diffraction spot in a actual diffraction pattern so this is one of the concepts you will be able to appreciate in fact the very effective usefulness of the wall sphere concept is this where you are able to appreciate the actual what you are seeing on a screen why it is appearing in a circular spot so this is one of the usefulness so you can see that clearly in this schematic A where the wall sphere cuts through the rail rod and then you see that particular in fact it is not cutting in a line it is a complete it is a sphere so it is a surface so the whole surface cuts through the reciprocal rod in a 3 dimensions so you get a 2D surface like this what is shown in this schematic here and you can also appreciate that if the orientation of the you know your reciprocal lattice is slightly different you can see that this is a 0 order a diffraction spot and then you see the first order and second order and so on so another important aspect is so whatever we conventionally get a diffraction pattern is the 0 order diffraction pattern and as we talked about in a I mean conversion beam electron diffraction you will see that a higher order law by zones will be identified and you can see that how this higher order zones are being identified using this a wall sphere concept you can see that this is a 0 order this is a first order suppose if I have a reciprocal lattice here then it will appear like this first order second order and so on so this is another important aspect you have to remember what you are seeing is a cross section of I mean reciprocal lattice rod in 3 dimension what you see in a 2 dimension as a diffraction pattern so you can see that some of the examples this is a 0 order zone this is a first order in a TEM you can see the second order I mean this first order and second order higher order zones are visible using this concept you can see that the electron beam is parallel to the zone axis the crystal has been tilted slightly with the consequence that the first order law by zone is visible so this is a first order for that we need to just do the tilting experiment you will be able to appreciate this remember using a convergent beam electron diffraction this experiment can be done and we will be able to identify higher order law by zones which will enable as to characterize some of the symmetry elements in a material so we will see it when in the convergent beam electron diffraction when we take it up. So I will stop here and then I will continue this diffraction concept in a TEM and in the next class I will discuss the importance of kikuchi lines and how they are generated what to do with the kikuchi lines and so on and similarly we will look at little more detail about a convergent beam electron diffraction and its use in general very briefly we will look at it if not much more detail and then we will move on to some of the imaging aspects of TEM thank you.


So, in this lectures I am going to explain about the field of artificial intelligence and as you have seen that artificial intelligence is defined as machine making do things it require intelligence if done by human being and here we have to see how a artificial intelligence is related to philosophy of mind or contemporary issues in philosophy of mind. So, this is one of the important aspects that we have to develop here and not only the philosophy of mind but also to many other disciplines which are related to this artificial intelligence is a new research area of growing interdisciplinary interest and practical importance. And people with different background and professional knowledge are contributing new ideas and introducing new tools into this disciplines especially cognitive minded psychologist have developed new models of the mind based on the fundamental concepts of artificial intelligence symbol systems and information processing. Language is also interest in these basic notions while developing different models in computational linguistic and you see how the nature of artificial intelligence is interdisciplinary in nature, how the cognitive science, computer science, language science, philosophy and neuroscience and you can relate many other disciplines which are concerned on mind but on the philosopher in considering the progress problems and potential of this work towards non human intelligence have sometimes found solution to the age old problems of the nature of mind and knowledge how do you define knowledge, how do you define mind and how do you define the self and how do you define the ethics because many questions are related to this if you are defining mind in terms of machines then and rationality also to machines and many other mental capacities to machines then therefore, there is no distinction mind and machines then we have to redefine everything else because if there is no distinction between mind and machines the machines are also part of the human being and also part of the society and society is defined over in many way of social systems and the systems we have to redefine because there is a new agent who is entering into the society who is the part of the human being and if you are accepting the artificial intelligence way of defining mind at the same time philosophers are not going against the strong sense the artificial intelligence artificial intelligence in the sense that artificial intelligence is artificial intelligence if it is not like human beings if it is not substituting the human mind then there is no problems although it helps in many ways for the human existence for the human day to day life for scientific as well as many other things and in ordinary life you find the AI is necessary and that does not mean that AI will shift the human mind that we will see in the next some of the lectures now we have we know that artificial intelligence is a part of computer science in which are designed intelligent system that exhibit the characteristics we associate with intelligence in human behavior understanding, language learning, reasoning, problem solving and so on and it is believed that insight into the nature of mind can be gained by storing the operation of such systems therefore AI's artificial intelligence researchers have invented dozens of program techniques that support some sort of intelligent behavior now we have to see the question is is artificial intelligence a science or an art the activity of developing intelligent computers systems employ both proved mathematical principles empirical result of storing previous system and heuristic programming of programming technique therefore the way it is acting the way it is doing mathematics it is also science and it is also an art and you can see so how it is science that this artificial intelligence is both arts and science it is a science because it develops intelligent computer systems by employing proved mathematical principles it is an art also because it is design system by employing programming techniques inferences stored in rational data structure can be manipulated by well studied technique of computer science such as researching diagrams thus the field of artificial intelligence is fascinating because of this complementing of arts and science artificial intelligence research may have impact on science and technology in the following way it can solve difficult problems in chemistry, biology, geology, engineering and medicines it can manipulate robotic device to perform some useful repetitive sensory task sensory and many other motor tasks but besides artificial intelligence researchers investigate different kinds of computations and different ways of this kind computation in an effort not just to create intelligent artifact but also to understand what intelligence is and the basic tenets to create computers which can think thus expands the field of intelligent activities of human beings in various ways. Now the question is what would the world be like if we had intelligent machines what would the existence of such machines say about the nature of human beings and their relations to the world around them. These questions have raised profound philosophical issues which we will discuss in due course the hypothesis of artificial intelligence and its corollary a corollary or empirical nature whose truth or falsity is to be determined by experiment and empirical test. The method of testing the result of artificial intelligence is of the following types. In the narrow sense AI is a part of computer science and aimed at exploring the range of tasks over which computer can be programmed to behave intelligently. Thus it is the story of the way computers can be made to perform cognitive task which generally human beings undertake. In the wider sense artificial intelligence is aimed at program that simulates the actual process that human beings undergo into intelligent behavior. And these simulation programs are intent as theory describing and explaining human performance. But they are tested by comparing the computing output with the human behavior to determine whether the both results and the results are similar. And also the actual behavior of computers and persons are similar. Now we have to see a digital computer as we have seen is an example of physical symbol system. A system that is capable of inputting, outputting, storing, etc. following different courses of operations. These systems are capable of producing intelligent depending on the level of mechanical sophistication they are. The computer with these capabilities behave intelligently like human beings according to AI researcher. One of the important tenets in cognitive science is that it stresses the relationship between human and machine intelligence. It is interesting to see that the computer is capable of producing intelligent behavior. We are interested in using artificial intelligence technique to enlighten us about how human beings do intelligent task. For example, in getting a machine to solve geometric or integral calculation problems, we are also interested in learning more about the power and flexibility of the human capacity for problems. The AI research or artificial intelligence researchers attempts to develop and test computer programs that exhibit characteristics of human intelligence. The AI researchers always work with an artifact which is only inferentially related to the human intelligence. What is the artificial intelligence? I am not so sure my dear. Borden says that here I would like to point out Borden, the machine in questions are typical digital computers. But artificial intelligence is not the story of computers, but it is the story in thought and actions. Therefore, Burnett says that, artificial intelligence is the discipline that aims to understand the nature of human intelligence through the construction of computer program that imitate human behavior. AI is, artificial intelligence is concerned with the intelligent properties of the computational systems, which perform many different intelligent actions. These tasks can be of very varied nature, such as the understanding of spoken or written text in a natural language, playing chess, solving puzzles, writing a poem, making a medical diagnosis, finding one's way from Bombay to Delhi. Therefore, the AI program selects such activities include information reasoning processes, which are part of any intelligent systems. Therefore, AI is includes common sensical tasks such as understanding English language, recognizing scenes, finding a way to reach an object that is far overhead, heavy and frigid and making sense of the plot of mystery novel. In addition, artificial intelligence includes expert tasks such as diagnosing diseases, designing computer systems, locating many mineral deposits and planning scientific experiments. The technique that artificial intelligence applies to solve these problems are representations and inferences methods for handling the relevant knowledge and search based problem solving method for exploiting that knowledge. Although the task with which artificial intelligence is concerned may seems to form a very heterogeneous set. In fact, they are related through common reliance on technique of manipulation of knowledge and conducting search, which we will discuss in the next sections. Now, we have to see what a machine can do. Within the scientific discipline of artificial intelligence, there are several distinct area of research each with its own specific interest research technique and terminology that artificial intelligence is part of computer science, which is concerned with designing intelligent computer systems that is system that exhibit the characteristics we associate with intelligence in humans behavior such as understanding language, learning reasoning, solving problem and so on. Any artificial intelligence, this specialization includes research on language understanding, automatic programming and several others. The following discussion of the status of AI attempts to show the subfield of identifying some aspects of intelligent behavior and indeed indicating in the states of relevant research like the different subfields of artificial intelligence of the different behavior discussed here are not all the independent. What machines can do and which is one of the important aspects of artificial intelligence, within the scientific discipline of artificial intelligence, there are several distinct area of research each with its own specific interest research technique and technology. Therefore, artificial intelligence is part of computer science concerned with designing intelligent computer systems that is systems that exhibit the characteristics we associate intelligence in human behavior such as understanding language, learning, reasoning, solving problems and so on. In artificial intelligence, these specializations include research on language understanding, automatic programming and several others. The following discussion of the status of artificial intelligence attempts to show the subfield identifying some aspect of intelligence behavior and indicating the state of relevant research. Like the different subfields of artificial intelligence, the different behaviors discussed here are not at all dependent, separating them out is just a convenient way of indicating what current artificial intelligence persons can do. Now, we have to see that what a machines can do. Firstly, machine can do problem solving, natural language processing, machine visions and machine learning and these are the most important factors which a machine does and although machine does many other activities with the help of this technique in a many ways. Let us see first problem solving. What is this problem solving? There is a distinct way of problem solving the way we human being do problem solving, but in the case of machines it does in different ways, but the mediums are different. In the case of human being and the problem solving plays vital role and our mind functions in a holistic manner, but in the case of machines it is also little bit different way, but there are many artificial intelligence scientist, they concerned that the way even if a machine solving a problem, the same way human mind also solving the problem. First big success in artificial intelligence where programs that could solve puzzles and play games like chess and techniques like looking ahead several moves and dividing difficult problems into easier solve problems evolve in the fundamental artificial intelligence technique of search and reproductions. Another problem solving program that integrates mathematical formulas symbolically has attended very high level of performance and is being used by scientist and engineers. Humans brings offense solve a problem by finding a way of thinking about it that makes the solution easy, but so far artificial intelligence programs must be told how to think about the problems they solve as that which choose the best techniques and applies to them to the particular problems and another view is that a goal and a set of means of achieving the goal are called a problem solving. Before an agent can start searching for solutions it must formulate a goal and then use the goal to formulate a problem. A problem consist of four parts first the initial states a set of operators a goal test functions and a path cost functions. Any problems you see there are four states initial states a set of operators a goal test functions and a path cost functions. The environment of the problem is represented by a state space that means there is a gap whenever we are solving a problems from one step to another step there is a gap that is known as state space. A path through the state space from the initial state to a goal state is a solutions and in this path space there are many internal things are happening which is not visible to the outside world, but which is happening internally and a search can be judged on the basis of completeness, optimality, time complexity and space complexity that depends search path for an intelligent agent. The agent has two search which is the minimum path to reach the goal and this is the main aim of artificial intelligence in order to solve the problems and to solve a problem is the main task of the agent in artificial intelligence. Moreover one of the important searches in artificial intelligence is the technique of heuristic search. The word heuristic is derived from the Greek version's heuristicization meaning to find or to discover. Some people use heuristic as the opposite of algorithmic. According to Newell and Shaw and Simon process that may solve a given problem, but offers no guarantee of doing so called a heuristic for that problems. We know that heuristic techniques dominated early applications of artificial intelligence. Heuristic method is still used in problem solving. For example, game playing is also a form of problem solving. Games have engaged the intellectual faculties of the humans and game playing is also one of the oldest areas of India where in artificial intelligence.  Furthermore, the simplicity of the rules and their application in the program implies that it is easy to represent the game as a source through a space of possible game positions. The initial states, the operators and terminal test and a payoff functions can define a game. According to Carpenter, an intelligent machines can follow all these rules and play more efficiently than human beings. For example, in speed chess games computers have deferred the world champion Gary Kasparov. In both 5 minutes and 25 minutes games such a system would be a significant achievement not just for game playing research but also for artificial intelligence research in general because it would be much more like to be applied to the problem faced by a general intelligent agent. These are so many kinds of search in order to solve a problem but we are not explaining the entire search. We are mainly concerned with how artificial intelligence program solve different problems within a few seconds. Therefore, the way machines are solving a problem is one way of human beings are solving a problem. Now, we have to see the natural language processing. Natural language processing plays vital role in artificial intelligence and NLP and NLP because without the understanding of syntax and semantics of language it is very difficult to construct any kind of programs to solve any kind of problems. You know to solve a problems natural language processing is necessary because without linguistic implications without linguistic explanations and analysis it is impossible to explain the how artificial intelligence can solve problem. The most common way that people communicate is by speaking or writing in one of the natural languages like any languages like Hindi language, English language, French language, Chinese language or Oriya language or any other language. However, the computer programming language seems to be differed from human language. These artificial languages are designed so that sentences have a rigid format or a syntax making it easier for compliers to phrase the language the programs and convert them into the proper sequence of computer instructions. Besides being structurally simply than natural language, program language can easily express only those concepts that are important in programming and that is do this, then do that. See whether such and such is true. One of the important fact is that if computers could understand what people mean when people use English language, the system would be easier to use and would fit more naturally into people's lives. Therefore, the artificial intelligence researches hope that learning how to build computers that can communicate as people do would extend our understanding of human language and mind. The goal of cognitive linguistic is to specify a theory of language, comprehension and production to such a level of details that a person could write a computer program that could understand and produce natural language. Therefore, the intelligent machines have the capacity to understand natural languages. It also responds to question in an appropriate manner. Thus, if the question is where now is Rome the capital of France, the simply reply will be no would be appropriate, but no it is Paris or no Rome is the capital of Italy will be the more complex. This example shows that machine programs would also be able to explain the meaning of word in other terms and also to translate from one language to another language. Therefore, natural language processing plays vital role, explains what machines can do with the help of natural language processing. Let us see the machine vision. Machine vision is one is another important aspects of artificial intelligence. Machine vision is the information processing of understanding a scene from its projected images and image is a two dimensional functions obtained with sensing device that records the value of an image feature at all points. Image are converted into a digital form for processing with a computer. The task of a computer vision system is to understand the scene that an imaginary of pixels depicts. Generally, machine vision means the vision of a machine that can see or perceive the things in the world. Vision is the process of discovering from images what is present in the world and what it is. Thus vision is fast and foremost an important information processing task. Moreover, there is a distinction between human vision, human perceptions and machine vision, because human vision is defined as the eye is just as the as a sensor. The visual cortex of the human brain is our primary organ of vision. In the case of human vision, the sensory organs necessarily forms a representation or physical encoding of the received information that facilitates the answering of some questions about the environment, but make it extremely difficult to answer others. For example, an examination of the encoded information produced by the human eye reveals that an eye in state of time, the eye has sensed only a small part of the electromagnetic spectrum and has extended an image of the scene of the human vision from a particular viewpoint in the space. It possesses the visual information with the help of the lens fitted within it. Thus, machine vision works more or less like human vision. Learning is now perceived as a gateway to understanding the problem of intelligence, because seeing is also intelligence. Seeing is also learning and which is a key to the study of intelligence and biological vision. Visual neuroscience develops the method of understanding how human visual system works. Visual neuroscience is the beginning to focus on the mechanism that allows the cortex to adopt its circuitry and learn a new task. Machine vision as a part of the artificial intelligence programs, however, tries to develop systems that can simulate human vision. This is the main task of artificial intelligence that how to simulate human vision and how to get this human vision in robotic systems or in artificial intelligence machines. This is the main task of machine vision and also the main aim of machine vision to solve the also problems. In order to solve any kind of problems, machine vision is necessary, natural language processing is necessary. Lastly, we have to see machine learning. Machine learning is also one of the important factors in artificial intelligence. Learning for an intelligent agent is essential for dealing with unfamiliar environments. Learning a function from example of its inputs and output is called inductive reasoning. The ability to learn is one of the fundamental constitutions of intelligence. Learning being understood in its general sense as indicating the way in which the humans and animals can increase their stock of knowledge and improve their skills and reasoning powers. Very beginning of artificial intelligence. Researchers have sought to understand the process of learning and to create computer programs that show how learning behavior. Because this learning behavior plays important role how to learn and if you do not know in the case of human being to learning behavior it is very difficult to learn. And ordinary learning is a very general term denoting the way in which people increase their knowledge and improve their skills. There are two fundamental reasons for studying learning. One is to understand the process itself by developing computer models of learning. Psychologists have attempted to gain an understanding of the way human learn. Philosophers since Plato have also been interested in the process of learning because it might help them understand and how and known what knowledge is and how it grows. The second reason for learning research is provided by computers with the ability to learn. Therefore, the learning research has potential for extending the range of problems to which computers can be applied. In particular the work in machine learning is important expert system development, problem solving, computer vision, speech understanding, conceptual analysis of database and intelligent authorizing systems. In this way a computer can do more work than human beings. Therefore, computers can do arithmetic at an unbelievable speed. No human beings can do can compete with them. Besides computers can do many other odds of like this is like detecting a certain diseases from the available data. Thus artificial intelligence as a study of machine intelligence especially of computers and robots opens up a new vista of understanding mind and intelligence. Intelligence behavior can be studied in many dimensions. This is made possible by the invention of computers as high processing computing machines. The computers can do many things that seem to require intelligence and other mental abilities. In the last lectures in this lectures I have explained about artificial intelligence. I am not here concerned about the scientific view of artificial intelligence, but I am concerned with the philosophical aspect of artificial intelligence and how artificial intelligence defining mind consciousness and intentions and all other mental faculties. I am not going or arguing against the possibility of artificial intelligence, but my main argument against artificial intelligence is that how machines can simulate the human mind and how there is a possibility of mechanical mind and which is not distinguishable between the human mind. To ascribe the mind into a machines is one of the difficult task that is the main aim of this topic. In the traditional mind body problems if you see there was a lots of argument between mind and body whether the mind is primary or body is primary or the mind is explainable in terms of the body or body is explainable in terms of mind. Those things my colleague Prof. Anjan Ponda has explained in his talk, but I will be I am not going into details into that part of the course, but here I will be dealing with mainly with mind and machines how the machine is replacing the mind, how the machine is explaining the mind and there are some of the limitation, but I am not against many kind of help to the human society and to solve many other problems. There is no limitation into that area, but the problem is that whenever they are identifying with mind the problem arises can we ascribe consciousness to machines or any other mental faculties to machines. Many other things I will be discussing in the due courses. Thank you.


So we just talked about the velocity field of a fluid. Now what we're going to do, we're going to look at another property and that is acceleration, which is something that you get from the velocity field. So acceleration is also something that is very important because it's in Newton's second law, but let's take a look at acceleration. So we're writing out the velocity vector that we talked about in the last segment. Remember we said u is for the x component, little v is for the y component, and then w is for the z-axis or z component, which is multiplied by unit vector, little k. Now when we look at acceleration, it is expressed as being the derivative of the velocity vector. Remember the velocity vector is a function of four variables, the three spatial and time, with respect to time. And when we write this out, we get a formulation like this using the chain rule of calculus. And we're using partial derivatives here because velocity is a function of more than one variable. So with this, and that was by the chain rule that we're able to obtain that. Now when we look at this, we'll notice that we have these terms here, dx, dt, dy, dt, dz or dz, dt. Those are just the three velocity components.  So what we can do, we can rewrite the velocity, or sorry, the acceleration in two different components. We have the velocity vector with respect to time, and then we have this other section or component, which consists of the velocities. And we will refer to the first term here as being the local acceleration. That is at a given point. And then the second one is referred to as being the convective acceleration. Now it turns out if the flow is steady, if the flow is steady, that means that it is not changing with respect to time. And consequently in that case, we can write dv by dt is equal to zero. So actually the first term, the local acceleration term would disappear if we were dealing with a steady flow. So what we're going to do, we're going to take a look at rearranging this expression for the acceleration of a fluid. And so let's take a look at rearranging that mathematically. So this is referred to, let's take a look at the second term. That was the convective acceleration. And we are going to rewrite it using some of the different operators that you learn in your mathematics courses. And the one that we will use is the gradient operator. And if you recall the symbol for the gradient operator is an upside down triangle like that. So the gradient operator is defined as being partial by partial x in the i direction plus partial by partial y in the j direction plus partial by partial z in the k direction. So when we look at our convective acceleration, so remember the convective acceleration, let's go back and look at it. That was this term in our acceleration. This is our convective acceleration. We're going to rewrite that now using the gradient operator. So that was our convective acceleration. And we can then re-express that using the gradient operator as this. We take our velocity dotted into the gradient operators, that would be v dot del. And it is then multiplied by the velocity vector v. Therefore, we can rewrite the acceleration as being our local acceleration, which was dv by dt. So this would be for a generic flow. It could be steady, it could be unsteady. It would be unsteady and we would keep that term. Plus then what we just wrote using the gradient operator v dot del multiplied by the velocity vector v. So this is a form of the acceleration vector in a fluid. And it's kind of an important one. But what we'll do, we'll take a look at this one step further. So although we've written this for the acceleration, it turns out that we can use this for any fluid property, be it a vector or a scalar. And what we just looked at, we applied this to a vector and obtained the acceleration. But we can also use the same operator. And we can apply it to any of the properties scalar or vector. And so we will express this as a little d, let me change color, little d with respect to t. And I'm going to draw it with respect to write it out some arbitrary property so it could be a scalar or a vector. And that would be equal to the partial with respect to time plus v dot del multiplied by whatever we're dealing with, be it a vector or a scalar. And this is a very important derivative operator that we have within fluid mechanics. And it's important that we give it, in order to denote it, we denote it with this capital D. And this is referred to as being the substantial or material derivative. And written generically here, it could apply to either a scalar or a vector. So that is the substantial derivative or the material derivative. So, for example, let's say you're wanting to compute the value for pressure. And so if you're looking to compute the value for pressure, that would be applying the substantial or material derivative to the scalar field pressure. So the unique thing about the material derivative, so the unique thing about the material derivative that we just looked at, is that it follows a particle of fixed identity. And therefore, it makes it possible for us to apply the laws of particle mechanics in the fixed Eulerian flow field frame that we talked about earlier. And so material derivative is something quite important that we'll see in a lot of different equations, mainly Newton's second law, F equals ma, from which a lot of the analysis will derive. And so that is the material derivative and acceleration from velocity field, a very important property within fluid mechanics.