Animals which flourish in these ecosystems, they are the reptiles, the amphibians and different types of mammals. Decomposers as in all the ecosystems are part of this ecosystem and these are mainly the fungi and the bacteria. Let us have a look on a grassland food chain. In a grassland diagram shows that the again source of the energy is sunlight which is coming through producers inside the ecosystem. There are plants which gets energy from the sunlight and convert this energy into ATP, the chemical form of energy and into the carbohydrates to make their body and to make their life processes possible. Then you can see a mole or a rat, a rodent, the primary consumer. These are common in these ecosystems and they eat upon the producers. The grasses, the leguminous plants, they eat upon the nuts, they eat upon the leaves and other parts of the grasses. So we call them primary consumers. These are the herbivores. Then the primary consumers are eaten up by the snakes. We know that reptiles inhabit the grasslands. Snake is a reptile. Snakes eat upon these rodents. So we call the snakes secondary consumers. In grasslands snakes are very common and they eat upon and of course the moles and shrews they are also very common. The snakes eat upon these moles, shrews or the rats, other types of rodents. Then there are hawks present in these ecosystems or there are eagles present in these ecosystems which eat upon the snakes. So we call them tertiary consumers because these hawks or the eagles they are getting their energy by acquiring a snake from the environment and snake itself is a secondary consumer. So we call it the hawk a tertiary consumer. We also call it secondary carnivore because it is eating a primary carnivore. Snake is a primary carnivore because it is eating herbivore animal that is a mole or a rat. So secondary consumer is a primary carnivore and the tertiary consumer is a secondary carnivore. As you can see that all of these organisms, so this is a food chain that is producer, a grass, a grass plant, primary consumer, a rat maybe another rodent, the secondary consumer, snake and the tertiary consumer the hawk. The energy actually is flowing unidirectionally from sunlight to the plant in the producer, from producer to the primary consumer, the mole or the rat, from primary consumer to secondary consumer because it is eating the primary consumer and utilizing it then to the tertiary consumer the hawk. And all of these organisms when they die or plants for example they shed their some parts, they become part of the soil, the land and here comes the decomposers. There are different types of insects, worms and bacteria which decomposes all of these organisms, their dead bodies or their fallen parts and convert them into different inorganic or organic compounds and these inorganic and organic compounds are again taken up by as you see the diagram the arrow shows that these are taken up by the producers. For example ammonia, nitrates, other forms of like phosphorus, sulfur or others and all of these are then of course present inside the soil and plants the producers they absorb these ingredients from the soil through their roots and they become part of them. So this is a generalized food chain in a grassland ecosystem. Let us have a look on another example of a food chain we take in an aquatic ecosystem, an ecosystem present in water, a water body. Equated ecosystem first of all we look at some properties, aquatic present in water, it is marked by water which is a liquid medium which is quite good to support life because we know that temperature is a limiting factor and water changes its temperature slowly. So water is a good medium for supporting life. Water may be fresh water or marine water that is the saltish water, fresh water have less salts, less dissolved salts present in it, marine water or brackish water have more dissolved salts present in it or may be in other forms. A lake for example is a fresh water, a sea is a brackish water, saltish water then light and temperature penetration is important in aquatic ecosystems. Light is a very important factor in aquatic ecosystems because we know that the entry point of energy inside an ecosystem is always the sunlight. So the penetration of light inside the water in an aquatic ecosystem is extremely important. If the water body a lake, a sea, an ocean, a pound or a stream allows a good penetration of light then photosynthesis is possible and the producers can make energy. If there is lesser penetration of light then photosynthesis may be, may do not occur then the result will be like there will be two ways of handling it, one some organisms which are called chemotrophs they stay here, chemotrophs which make their energy and carbohydrates from in organic sources but they are fewer. So light penetration is a very important factor in the aquatic ecosystems and the temperature. Temperature stratification exist in water ecosystems. In the upper layers usually because they are exposed to sunlight they have higher temperatures. The lower layers, layers towards the bottom because they are quite far away from the sunlight because there is a top layer of water between them and the sunlight so they are comparatively colder. The life forms which are living in these water bodies depend upon all of these properties. Now we have, we go to a diagram and we look at the aquatic food chains. The organisms which are the producers in an aquatic ecosystem may be, they may be plant or they may be called some smaller organisms called phytoplankton, plant like organisms. Plants as you know they are producers, they convert sunlight into carbohydrates and energy. Phytoplankton are also plant like organisms, also some algae, some unicellular organisms they also have the capacity and the capability to carry out photosynthesis. Usually in the upper layers of water, the water body, a pound, a lake, an ocean, these organisms are present in abundance. On the shallow sides of the water, in those zones of the water body for example the lake where water is shallow, submerged and fully merged plants are present and these plants carry out photosynthesis. In more deeper regions where the lake is actually deep, these organisms, the phytoplankton are present in higher numbers. They have capacity, capability to carry out photosynthesis and they are the entry point of energy in an ecosystem. So there are lot many phytoplankton present in those parts in the upper layers of a lake in the deeper region. These phytoplankton are eaten up by small insects and crustaceans. There are lot many types of crustaceans, the shrimps, some water insects which are present in these water bodies, they eat upon the phytoplankton. These phytoplankton are the producers. These crustaceans and water insects, they are the primary consumers in a water ecosystem food chain. Then these crustaceans and insects, these are themselves consumed by the fish, the primary carnivore fish, different types of fishes. These fishes are themselves eaten up by the carnivore fishes. The large carnivore fishes which eat upon other fishes. So phytoplankton are producers which carry out photosynthesis, produces organic food. Crustaceans are the primary consumers that eat upon phytoplankton. Then fishes are secondary consumers that eat upon the crustaceans or the insects. Then other fishes which eat upon the carnivore fishes, they are the tertiary consumers in a water ecosystem. So this is how a water ecosystem food chain, it conducts the flow of energy from producers to consumers, the primary consumers. The crustaceans, the small insects, the water insects. Then comes the fishes and some other fishes. There could be another food chain that exists in the water ecosystem. For example phytoplankton produces the organic food. These are eaten up by the herbivore fishes. The fishes which eat upon the plant matter and then the herbivore fishes, they are eaten up by the primary carnivore fishes. Then some other fishes comes which eat upon those carnivore fishes which are eating the primary carnivores. They become the tertiary carnivore. So different types of food chains exist in different types of aquatic ecosystems. Now look at an Arctic ecosystem. You know that Arctic ecosystem is very, very cold. Arctic area is full of snow. Have a snowfall many times. Have very, very colder temperatures. Some specific life forms exist there. We look at a food chain in an Arctic ecosystem. On the base of the food chain you can see there are algae which are scattered and present in large numbers. Algae are the organisms which are also producers which have capability to convert the sunlight into food and into energy in the form of ATP. Then these algae are eaten up by the shrimps. Shrimps are the primary consumers or the herbivores. Shrimps are then eaten up by the cod fishes which are the secondary consumers or the primary carnivores of this ecosystem. Then the seals, seals eat upon the fishes, these carnivore fishes. So seals they make the secondary carnivores. Then comes the polar bears. Polar bears eat upon these seals and they make the tertiary consumer. So this is a food chain present in an Arctic ecosystem. To shrimp, to codfish, to seal and to a polar bear. Now we are going to talk about food webs. What are the food webs? We say food web. This is a network of eating and being eaten. Food chains are a straight relationship of eating and being eaten that is of energy flow. But in actuality, when there is an ecosystem and lot many organisms are living in that ecosystem, their relationships are actually much more complex than when exemplified by a simple food chain that is a straight relationship. For example, if there is a grass, a grass plant present in an ecosystem, this may be eaten by a goat, this may also be eaten by a dragon fly, this may also be eaten by a rodent and then the rodents, they make food for the snakes. Snakes eat the rodents, but rodents are also food of the owls, owls also eat upon the rodents, hawks sometimes may also eat the rodents. So the relationship, the feeding relationships between organisms are actually not as simple as a food chain. There are lot many food chains that do exist inside the ecosystem and then these food chains also interact with each other to make a web or a network of the relationship of the living organisms of feeding and that is of eating and being eaten by other organisms. So food web is actually a network of eating and being eaten. There are examples, we take certain examples of food webs from different types of ecosystems. From a grassland, forest, from aquatic ecosystem. The food web is basically a network of different organisms which have relationship of eating and being eaten with each other and these are, these food webs actually consist of interacting food chains. We take an example of a grassland ecosystem. We know that in a grassland ecosystem, there is a diverse, there is a diversity of life. There are diverse forms of animals present. There are amphibians, there are reptiles, there are mammals, there are different types of grasses but grass is eaten by most of the herbivores. There are different types of amphibians which also eat upon the grass. There are many reptiles like many lizards, they also eat upon the grass. So there are lot many organisms which are eating grass. Grass is a source of energy for all of these organisms. And these herbivore animals, all of these which are eating the grasses, they are themselves eaten up by more than one carnivores. For example, if there is an amphibian, a frog, it is not eaten up only by a snake, it may also be eaten up by an owl, by a fox. So the relationship becomes complex. Let us have a look on food web, an example. Look at this diagram. It shows that the grass which is present in an ecosystem is eaten up by a grasshopper.  The arrows shows the direction of energy flow. Then we see that grasshopper itself, so it means that the same grass, same grass plant is a source of food for a grasshopper, for a rabbit, for a mouse. Then we can see that the grasshopper itself is a source of food for a lizard because these lizards they eat upon these grasshopper. This grasshopper is also a source of food for a hawk. And we can see that the lizard which is eating upon the grasshopper, it is also a source of food for the hawk. So hawk is consuming both the grasshopper and the lizard. If we make two food genes, that is from grass to grasshopper to lizard to hawk, then we see producer, a primary carnivore, sorry a herbivore, then a primary carnivore the lizard and then a secondary carnivore the hawk. If we look at other way, then we can see that grass is eaten up by the grasshopper and grasshopper is directly eaten up by the hawk. The grasshopper is a herbivore and which is a primary consumer and the hawk makes then a secondary consumer and not the tertiary. Then we see that grass is eaten up by rabbit, herbivore and rabbit is eaten up by the hawk. So it is also a more simple food chain, then the mouse eats the grass and mouse is eaten up by the hawk, but there is another way of this food chain that the grass is eaten up by mouse, mouse is eaten up by the snake and snake is eaten up by the hawk. So all of these food chains in interaction with each other actually makes the food web, a complex relationship between the living organisms of being eaten and eating someone else. This is one example of a food web in a grassland ecosystem. We look at one more complex example here you can see in this diagram that there are producers and decomposers different types of and these producers if we look at start from producers then these producers the plants which are of various kinds these are eaten up by different types of organisms, a butterfly, a frog, a bird, a deer, a squirrel, lot money and then all of these organisms or we can say every one of these herbivores, these are all called will be called the herbivores, the primary consumers, every one of these primary consumers are eaten up by more than one secondary consumers, the carnivores. For example you can see that a raven is eating a butterfly, it is also eating a frog, it is also eating a bird. So secondary consumers they make various complex chains with the herbivores, the primary consumers and with the grasses. Then there are few tertiary consumers and these tertiary consumers as you can see like a bobcat, a big cat, a fox they are eating different types of secondary consumers and they are sometimes eating some primary consumers as well. This makes a more complex web of a diversified ecosystem. So if an ecosystem is more diversified in its life forms that is it have so many life forms, then the food webs or the feeding relationships present between its living community will be far more complex. So the complexity of a food web actually depends upon the complexity of life and the diversity of life. So more the diversity of life, more the complexity of the food webs. We look at an example of a food web from an aquatic ecosystem. Aquatic ecosystem we know as they are very suitable for life, they are suitable for different types of life forms. So aquatic ecosystems they have animals, they have plants, they have phytoplankton, they have soup plankton, they have decomposers. Their food webs are also usually very complex. Let us have a look on an example. As you can see in this diagram you can see two zones of water. One is the top zone, the other one is the bottom zone. In the top layer of water there is more penetration of light. There are different types of phytoplankton and the zoo plankton present. The phytoplankton are the producers which are utilizing the sunlight and the inorganic carbon, the carbon dioxide and converting it into organic form of carbon, the food. Then there are different types of fishes and the zoo plankton which are eating upon these different types of phytoplankton. For example, a fish is eating these phytoplankton and insect is eating the phytoplankton. And then we see that there are other fishes which are eating upon these fishes. We can see a bear which is present in the nearby areas and this is eating the fish as well because bears they also come to the waters and they also hunt the fishes. So you can see that there are different types of food chains that do exist in this ecosystem, the water ecosystem and they interact with each other and the same thing is eaten up by many other organisms and all those organisms they are eaten up by some other organisms. So in water also diverse types of food webs they do exist. Then we take another example of a desert ecosystem. Deserts as you know are hot dry climates. They are hot and they are dry because there is less rainfall. The deserts have few specific plants and few specific animals. Let us have a look on a food web. In this diagram you can see that there is sand and soil on the base. Then there are specific types of desert plants. These desert plants you can see are eaten up by different types of insects. These plants are also eaten up by the lizards, the reptiles. These are also eaten up by the rodents and these insects then themselves because these all of these insects the lizards and the rodents all of these makes the herbivores or the primary consumers. Then comes the secondary consumers the spiders, scorpions, different kinds of lizards they eat upon the insects. Then the lizard for example is eaten up by a hawk. The lizard is also eaten up by a fox. Then the herbivore lizards which eat upon plants they are eaten up by both fox and the hawk. The rodents different types of rodents which are herbivores which eat upon the plants they are eaten up by the snakes they are also eaten up by the hawks and the foxes directly and indirectly indirectly in this way that rodents eats upon plants snakes eat upon the rodents and then snakes are eaten up by the foxes and the hawks. So we can say that these rodents are directly eaten by the hawks and the foxes and they are indirectly through snakes eaten up by the foxes and the hawks. It means that complex relationship of food web food webs also does exist do exist in the desert ecosystems though desert ecosystems are comparatively less diverse in life because these are more hostile environments but you can see that even these environments lot many organisms can exist there is quite a good biodiversity and this diversity makes the complex relationship of eating and being eaten which is called a food web. This is another example of a food web in a desert ecosystem which shows again the producers at the base as usual the primary consumers different types of squirrels rodents and then secondary and some primary consumers then tertiary and some secondary consumers and then tertiary consumers some secondary consumers and some quaternary consumers. The food webs actually makes complex relationships between organisms for energy flow. The forest ecosystems are more rich in their biodiversity because they are more suitable for life. You can see in this diagram a relationship in the form of food web between organisms in a forest ecosystem there are trees which are eaten up by the deers by insects by rodents by birds rodents and birds are eaten up by the foxes and the owls. Deers are eaten up by the bears by large cats then the insects they are eaten up by the birds they are eaten up by the possum and so on. So, food webs are the complex networks which mark the relationship of different trophic levels at different trophic levels between the organisms of a specific area of a specific ecosystem. In today's lecture we talked about the food chains and the food webs we primarily covered the biotic components of an ecosystem then we talked about the food chains which are the straight relationships of energy flow between the organisms and we talked about food webs which are complex forms of energy flow and interaction of the food chains actually present in different types of ecosystem. We also talked about some examples from various ecosystems about their food chains and about their food webs.


Hello everybody, my name is Bente Lukac, I'm here with my colleague Benjamin Hoijlich. We are from the University of Passau in Germany and today we're going to talk a little bit about our project, the Road to Open University and it's a teacher education project that we're going to present to you kind of what we did during the project and our thoughts and research ideas behind the project or the connections to open education resources and open science. If you want to follow the presentation you can scan the QR code or you will find the slides in the connect platform or just put in this link and you get to the presentation. So if you want to scan it I'll leave it up a little bit, all right, all right, so where is Passau? Passau is at the border, it's in south of Germany in Bavaria and it's at the border to Austria so we are kind of maybe a little bit isolated when it comes to broader Germany because we're quite far away from say the bigger cities, Munich is the next closest big city so in that sense every project that we work on especially at the university works kind of by its own rules I guess also some Bavarian rules I guess we'll get to that later as well. So in case you're at a computer later if you want to open up the presentation if you click on the S button you get the presentation view where we put in a couple of more information, little bit more explanations about some things we talk about and also the navigation with our slides is a bit different so you can use arrow keys and go to the left and to the right so if you open up the presentation and you start to go down that's where our presentation starts and then we go to the next page and by escape I don't know if it works on mobile as well but on a computer if you click escape you see the whole presentation can navigate to the slides specifically. So what we're going to talk about today are basically two big things or give an overview about the work we do. One is the structures that we have to work with in teacher education in Germany then give an overview about the project itself and what kind of role it plays in the university and also how it sort of embeds in the open science framework I guess. We purposely use the open science recommendation here because in our thinking and in our work the open science idea I guess is the foundation for everything we like to do so OER is just basically on top of it. You will see later how OER comes into our work but our foundation is basically open science. So the structures I'm not going to go into detail too much because this is quite specific and it's also not super interesting to talk about the I guess the policy structures and how Germany is set up in the federal states and how the funding lines work. We linked a lot of the things here you can see in the slides that if you click on the various parts you get to further information about that. I guess the only important thing is that it's embedded in a funding line specifically that deals with quality in teacher education and also it's a federal structure in Germany so each federal state kind of plays by its own rules sort of. I guess what's interesting is that we have a lot of student teachers actually at the university which is quite nice because we can with this type of project we can kind of push the university towards change in the bigger picture. So the open structures also I'm going to just mention them briefly. We set up an OER policy in the beginning of the project to kind of function as a guiding document for the lectures that we have who designed various courses and work with students on OER so that kind of helped us all the way through. We started a cooperation with the virtual university in Bavaria. I will come to that later as well to publish OER material and I guess the main parts are our innovation labs which will also come up later and just the entire focus of digital literacy and information immediate literacy which was also kind of the basis of the project and obviously the way teacher education works at the university of Passau and the way the project is embedded is that lectures should or want to pass on basic ways of working with digital tools to the student teachers so it's kind of an entire cultural I guess education as well which will make sense later when we get to the innovation labs and how we use them. I'm going to give it all to Benjamin to talk about that. Yes thank you very much. This is now more into our topic talking about opening up a university. We just saw some maybe rigid rich structures and some open structures and when it comes to opening up a university which seems to be a really big and like really time consuming project which it is I want to focus on something specific which is cooperation and participation especially in our project and in teacher education in general. So the graphic on the bottom which will we see later maybe modified will guide us through this content we see here. So we want to establish cooperative structures and that's kind of difficult in Germany. We have seen some of these rigid structures in the beginning but also teacher education in general is always constructed by states in Germany and they are really strictly ruled by maybe slow paced policy decisions and so on. So we wanted to implement new formats of teaching as well learning and interplay of higher education and schools and that's where cooperation and collaboration comes in and we think that especially when we think of teacher education sometimes only goes one way which is from university especially in the first phase from university to society so we build teachers we educate teachers so they can educate students and other people. And we want to do it so we have maybe do it both ways so we can have some insights from society and we will see later in what structures we can do that. And that comes with some special OER materials we are trying to implement so we are on the conceptual level with OER materials which is seminars or lectures which can both be used in schools and the universities and on the deductical level so we just try to test some methods and some digital structures we will see later in our innovation labs. So for now keeping this in mind university uses science education to do something with society but of course as he mentioned we are using these recommendations for us some of you might have seen them already they the UNESCO recommendations they consist out of three main areas just skip them for now skip them for now we are focusing on as I said on the part above which is about collaborations and of course sharing information to society with society and back so our goal is to maybe implement in both phases of teacher education in Germany which is maybe there's this there's a third one which which comes later but it's we both mostly talk about both phases the first and the second. The first one is rooted in the university which is mostly just subjects and scientific knowledge and teacher education inside the university and the institutions university institutions opening up these structures would mean we have to observe society of course because we have to teach future teachers what they have to teach the students which I said the curricula are very strict so the subjects are always in place but there are some things in society happening all the time right so technology technological advancements political decision-making and some societal discrepancies for example so we have to implement these in phase one and opening up this phase would mean we need more eyes and ears to observe society right so talk to us inside this is the university to what can you do with new technology for example or how can we react to new developments or social norms that are changing all the time phase two is more practical oriented they teachers go to schools and have one or two years practical practical work in schools and they come back and share their experience with us with us in the university so opening up this phase would mean just don't close our university doors and just let them let them go they have these state exams and they can go on with their life they would come back from time to time share experiences and we would invite other social actors and social workers for example so we use structures inside the university and we will see them in a second the dialectical innovation labs where we meet collaborate talk but still we don't have a real method how we can do that and how we can achieve really the way back from society into university and that's why we think and we are convinced that it's necessary to find a common ground and we think that we should use the scientific method not only in the first phase but also in the second phase as a way of collaborate so participation of society on the basis of the imperative of the scientific method would be a good way to achieve a common ground and to use the data from society to work with the data to use the experiences from teachers and to work inside the university in these structures we see in a second and why we use this what we think this method would be the best thing to do similar to the SDGs we think we identified some discrepancies or even colliding values and principles when you think of the SDGs maybe there is number eight and number 13 which is economical growth and climate action basically which are always colliding right you can have it both ways and you have to work it out somehow and I think we identified some of these here as well like responsibility respect and accountability you can see there and the quality and integrity or collaboration so when we think about involving every stakeholder and the passive participation opening up everything maybe there is a lack of quality in the scientific research because then maybe normative the scientific objectivity would be deluded by normative principles or opinions or maybe too too much of talking and not researching so there's some colliding principles that that's what we think we should choose one method for everything build it as a solid base and work on that so promoting a common understanding of open science on the basis of the scientific method would associate benefits and challenges which is really the most important thing because in the recommendation the UNESCO said we all have to think of different paths to achieve open science and that's what we do and we chose the path which is mostly rooted in scientific research and apart from that not only have you have you have to choose different paths but we have to find incentives and rewards right for our professors for lecturers because our experience is it's always nice to talk about open science and open principles but all the professors they want to have something right they want to just okay that's my data that's my research I understand open science but I need some incentives for me for my students and for my people I work with so these things combined are in our from our perspectives in the light of university-based teacher education the things we want to achieve on the basis of the UNESCO recommendation and of course that's where we show where we do all these things and we have some of our digital the tactical innovation labs for you in our presentation we come back to this graphic at the at the end but for now we see something has changed already at I was talking about open university on the basis of the theory and method of scientific research and then we have collaboration with society but after a few pictures of our structures we can have that's that's fine we can come back to this graphic all right now a couple of the practical examples that we had in a project the open work hub was one of the I guess collaboration structures that we tried to implement which kind of goes against like traditional professional development structures in teacher education it was more of an open space where the goal was to build smaller groups based on topics that were interesting to the lecturers themselves and we just bring them together sort of with the infrastructure of certain rooms and then we let them work basically by themselves and maybe come back to the room and then create their materials as well like I mentioned the collaboration part especially with the sharing of the material was also quite interesting in Germany because there every federal state wants to build its own basically repository or any structure it has to be something that's unique to the federal state itself so what we did we didn't want to build anything new as a university we just said you know what let's try to find somebody in Bavaria who works with maybe online education more and let's collaborate with them and then we talked to this virtual university Bavaria as it happened they set up an OER repository for their own materials so we started collaborating with them so our material can be on this repository which is quite nice actually this is one of our innovation rooms you can maybe click through them by yourself because it's an interactive thing so you can click around and check what we have we have three of them basically it's a class room we have a teacher's lounge as well or a teacher's room of the future very innovative I think most universities have some kind of lab because it's kind of a cool thing to have nowadays also in the co-working space which promotes even more this open innovation and collaboration idea because this is not a traditional room that you kind of have to book and do something like do lectures in it or seminars in it it's mostly about meeting up brainstorming using the computers to create material and just get together and talk about whatever is on the mind of the lecture so that's where it kind of finishes with our basically goal is to build on the scientific method and research in these rooms how we can try new teaching methods that are digital and innovative but also obviously for us media pedagogy media that that takes is the also the foundation of everything and the last few parts we actually mentioned it's about cooperation and collaboration across the university as well in teacher education with schools and ideally obviously with everyone else as well and openness in practice is that we produce most of our whatever seminar concepts and ideas as we are so it's openly licensed and it can be accessed through various platforms and now to finish off the finish on that last note that slide here we see that we have maybe achieved the way back from society maybe back through the university when we meet in these kind of structures all together chair experiences work on the basis of the scientific methods that means using the data using the methods and be really strict with the method and then you invite every time we invite new teachers and new students to share their ideas not only teachers and students we talk about shareholders stakeholders and social actors because we think that maybe it sounds a little paradoxical but to be that strict with the method we can open up what university used to do just in one way and we can go the other way as well and work together on in the center on the scientific method and that because we know we are always a little bit short on time I want to end on this one we want to go into iteration with you as well so we have a little board for you you can visit when you click on this link you will end up here we have some questions prepared for you just grab a sticky note on the side and please give us some feedback you can see the questions over there questions are welcome now as well but if you have some feedback any feedback as well we are happy to talk to you and thank you very much for your attention


Okay, here we are. Serial jobs. Yes, so serial jobs are basically jobs that are not interactive. So this is basically the idea. So basically serial in this case means that you run something from beginning to end completely in a script. So you don't, you do the whole from beginning to end in one go, in one script. And this might involve many like separate steps to do this, or it might be like only one program call. But and also serial in this case means that it's not running parallel. So you're not doing multiple, well, you're not doing multiple different routes. You're just going from a start script to the end of the script. Yeah, like one script, one data set, one submission. Yes, something like that, which I guess is always the starting point to doing anything. You have to submit it once before you can have it do multiple things. Yes. So serial jobs are these, like how they are actually written. They are written into these scripts, these slurm scripts, which are in in reality, they are simple like shell scripts. So this is why shell programming is very important for for using these clusters that you need to be able to tell the machine what you're planning on doing when you're doing these serial jobs. So because everything needs to be in that same script. So the software module loading, going to a certain work directory, such forth, they need to be all written into the same script. So the, you need to be able to tell the computer what to do, and you need to tell it in the language that it understands. And this is the shell language. So basically go to this folder, open up this data file, run this program. So these kinds of commands, you will give them to the computer. So, and yeah. Should we scroll down and look at the first job script or? Yes, yes, let's let's do that. Here we go. If we look at this first job script here, you will see that there are, there are a couple of like the special lines, what differentiate this script from a normal SH or shell script. So the first line that that comes to mind is the is the actual first line, which is this she bang directive. So this tells the slurm, when slurm actually executes this script, it will tell it what program it should use to execute this script. You can use other interpreters, besides like normal shell programs, but they are sometimes a bit more complicated to use. So I can have slurm start Python directly, for example. Yes, yes, it is possible, but it's usually, because you usually need to do some preparatory steps, like loading modules, you need to do some something that involves the shell or the folders and such forth. Right. You usually need to use some shell and bash is the most common one. Right. And on what we recommend people to use. So basically the first line, you will always have it in your script. So the first line just tells, tells the slurm that when you are going to run this script, use this, use this shell, use this interpreter. Okay. The next lines are then what is basically the meat of the slurm script in a sense that if they are the resource requirements that your job will want. And these are given with these comments to the to the slurm scheduler. So written this hashtag as patch and and the after that you have some resource requirements. So these are completely the same as previously when we were running with s run these commands and we were giving these requirements via the command line with these comment line arguments. These are basically the same thing. So instead of reading them from the command line, it will read them from this file and it will read the arguments from these comments. So so basically these comments are not something that doesn't involve your program, but they are like hints or instructions to their scheduler that when this script will be run. What kind of resources should be allocated for this. Yeah, I can see that the hash is the standard comment character in shell scripts so as far as the shell knows these are comments but I guess slurm looks at them and processes them before it gets to the shell. Indeed. So basically, there are two steps to to each job submission law or job when you run a job. First you submit the job. So basically you give it to the scheduler and the scheduler looks at the job or the shell script and it will try to determine what are the resources that the job needs. So basically what it does is it checks the command line arguments, it checks the defaults and it also checks the script itself and it will look for these comments. And when it encounters these comments, it will like put the note to them and say that okay I will need for example in this case five minutes of time, 100 megabytes of memory per CPU. And it will notice that okay I will put my output to this specific file. I will run it in this test debug partition just to make it run fast so that we have this debug partition for running fast jobs. And those are like it will make a note of them and then it will store them for when the job is actually run. And when the job is actually run, when the reservation has been given resources basically, once the script has been read in, the job has been submitted, the resources are like, well you are asking for the resources. And when the resources are available, the job will actually run with the allocated resources and then it will execute whatever comes afterwards. Should we try in this? Yeah, let's do it. Okay, it will take me a few seconds to get ready. So here I am on Triton. I guess I should change to my work directory, which I will do with this variable. Let's make a directory for the project. So let's see. Looking at the file. Let's see how would you like for me to arrange the windows here. Is this good? Yeah. Okay. I will edit the file. Hello.sh. No, I will use nano. Hello.sh. Okay. And I will copy and paste all of this to here. I think there's a window at the bottom right. So that window probably shows the previous commands that you were running. Yes, this here. Yes, just for everybody watching. Yes, so anybody watching, those are the commands that Richard just run. So it's not there by mistake. So okay, anything I need to edit here. So I notice there's this output. I guess this says my username and then the job ID. Yes. So each job will get an ID. So basically it will get, I think you need to put the % here before. Yes, right. So it goes to your work directory. Okay. So my work directory, my, I made something called kickstart 2021. Hello.jobid.out is where it will be saved. Yes. Okay. I guess I'll save with control X and click Y and enter. And there we go. Let's LS to see what's here. Okay. Yes. So after this has been done, like you have written this kind of a script, like you can submit it with this S batch function. This S batch is basically slurms command that says that okay, submit my task. Should I do it? Yeah. I think bit of the shell is hidden behind the font might be too large. Hopefully it will scroll down and work. I'll try to keep an eye on that. Okay. Yeah. So basically, there we go. Once we submit the job, we see that the job has, we get this job ID and we see that the job has been submitted. Like Richard tried to catch the job actually in the, like in the running state by running a slurm queue afterwards. But it actually runs so fast that it wasn't catched. But with slurm history, one can see that it has actually finished. So there's a lot here. I'll do storm history one day, but still a lot of stuff. What about maybe one hour? One hour. Yeah. Okay. And I need a smaller font size here. Yeah. Yeah. I think it might be. But I can't easily make this font smaller. Okay. Or can I? Well, anyways, we see from the output that the, the job has run and we see these. There we go. Yeah. Yeah. Okay. We see from here that there is a job ID on the, on the left side and there's a job name. What was the submission script? You can also change these if you will, but by default it's the submission script. Yeah. And you will see that there's like these, these underlying steps. And these are basically like the slurm bad script. It will run the whole script, but it can also run these individual steps. If we look at the, if you reach the show in the browser, the slurm script, we had the S run statement before the echo. So we see in the output on the history, we see the echo there. So the slurm, when you have an S batch script, you can have this individual S run pieces inside of it. And these basically show as individual top steps in the, in the output then, in the history output. And this is not something you have to do, but sometimes it's very useful to do because, well, especially if you're running something like an MPI job that needs to, well, we will talk about that later, but if it needs information from the slurm, it might need the S run. But anyways, if you're running some job which has like, let's say pretty processing step, then it has an actual calculation step, and then it has some sort of a post processing step. You might want to have these as like individual steps. So you can monitor, for example, resource users per step basis. So what do you say there's two purposes here? One purpose is to record resource, resource usage separately. And the second somehow makes the parallelism work for MPI programs. Yes. So for the main program runs, like if you're running some main program, so not necessarily running something like CD to a folder or module load something. For these main load, main program calls, you should use the S run. So then it will record, well, it will get the resources it needs and it will record what were the resource requirements for each individual step. But I guess something like module load, it's not really necessary or... No, no. I guess actually module load, it wouldn't work at all, but other stuff like that, like copies or moves, not important. Yes. Unless your copy takes several minutes and then you might want to track it. Okay. So where's the output for the job? Basically... Oh, go ahead. Yeah. So the output, we had in the slogan script, we had the output statement that this describes where the output was written and Richard changed it a bit with adding the kickstart folder. But anyways, it should be there. Yeah. Yeah. Okay. So here, I guess if I look at the contents of this file, which I can do with cat, and I guess that's the job ID that got submitted. Yes. And it says, hello me, I'm on node CSL48. Okay. Yes. And this is the, like because we use the echo statement in the, in the SBAT script, this is basically just like a standard output of what happens in the, when the script is running. And this brings an important point with the SBAT scripts that the communication, how, how you're monitoring these grids is happening via the file system. So basically you are monitoring the scripts via standard output or the output of your code. So you're not, when you're submitting the code, you will submit it and then you will only get output via either the standard output of the, the program, if it brings some statements or if it saves some files in the folder that you can then monitor to check what is the output. If I don't specify, if I don't specify an output path, what happens? Is there some defect? Yes. It will, by default create this slurm dash job ID. If you are running an array job, it will add the array number and then put an out dot out at the end. So you will get these default outputs if you don't specify the output yourself. And basically, what you usually want to do is in your code add some verbosity or add some normal output so that you can, you can monitor the state of the code. What is, what is it doing? What like state is it currently running and so forth? Like you can monitor it non interactively. So basically if you are currently running, let's say a Python script or MATLAB script and you press play on your IDE or development environment and you will start running it and then it won't produce any output for two hours or something and you're just monitoring it, looking at like whether it's finished or not. It can be really complicated to monitor it in Triton because you won't get, you will only get the output whether it's finished or whether it didn't finish. And this is not usually what you want to do. You want to write something that the program will give you some hints of what step it's actually taking at this point, what kind of output it's creating and so forth. So basically because you will be working, like it's basically like a fire and forget type of a thing where you say the Slurm script that okay, run this thing and it will tell you that okay, I will run it and you can ask the Slurm whether it's still running it or whether it's just completed. But you will have to have some phone number basically to the worker itself if you want to get updates on what the worker is doing. And in this case, it's the standard output. Okay, good. So what comes next in our lesson? Should we continue? I think we've covered most of the things here. This wording is something I've seen a lot. Are you going to talk about that? Yes, so this is, yeah, it's good to make a distinction immediately in your head that you shouldn't use bash to submit the jobs but sbatch. So sbatch is the, like Slurm takes hold of your script and it will run the script. Bash means that run it now, basically run this script now. So if you run bash, yeah. Hello.sh is like this. Yes. So now we see it looked like it submitted but it was srun that was submitting. So this was actually submitting as an interactive job and it says it's me and I'm running on this login node and it knows nothing about these other Slurm parameters here. Yes. So the sbatch is the wrapper that basically takes the script, reads it to the Slurm queue and then runs it somewhere else. And bash is the interpreter that is then used to, well, run it when it comes to the actual place where it's supposed to run it. If you just run bash and the script, you will get, well, you won't get far from the login node. You're actually running on the login node and that's not what you want. So remember to use sbatch when you're submitting stuff to the queue. Yeah. Okay. So what else? So we looked at the status with Slurm queue although there was nothing there because it was done too fast. You might want to put it like our sleep to the code and we can demo it. Do you want to do that? Yeah, let's do a quick demo. Hello.sh. Let's add like a sleep. Yeah. We can sleep for one minute. So sleep 60 means just do nothing for 60 seconds. Okay. Yes. Control X. So now. Save, enter, and I will go ahead and decrease my terminal size because people say it's readable if it's smaller. Okay. So should I submit it? Yeah, let's do it. So I remember to use sbatch. And you will see that the top is submitted. We do slurm queue. And here we see the job ID which matches the submitted job ID where it's running the name, the time, and it's running and where it's going. There are various things you might see here. So quite common if you're running a job that actually has some requirements compared to this small job, you might see a pending in the state. So pending means that it's waiting for its place in the queue. So basically it's waiting for a good resource where it's going to be put. I guess maybe later we can talk about how it prioritizes what's running. Yes. Okay. One other stuff you might see is these bad constraints. And this is an internal thing. It basically means that it's still waiting for correct kind of a node. But that's not something to be scared about. Okay. Yeah. So let's see what's next. It asks about resource parameters. But I guess these are similar to the interactive job resource parameters. Indeed they are. So the slurm, well, when you submit a job, you will need to give it some resource parameters. They are the default values set, but you should always set these once you know what the job actually requires. Right. So the two parameters that you will most always need to set are the time and the memory requirements. And this is important because, well, like Richard said about how it prioritizes the jobs. The job size is calculated basically like the number of CPUs or the number of memory required to, well, amount of memory required times the time requirement. So basically it will choose this kind of a block of time. And then it, well, it will calculate based on those. I don't know if I explained very well, but basically what you need to do is to tell the slurm to give you a certain block of like resources. And to do those, you will need to give it a time and a memory constraint. And the memory like we talked yesterday, you can go a bit over the memory limit and there is, you can go a bit over the time limit as well.  You know, once I heard this description, you know, it used to be on clusters, every job fit into a rectangle, a certain amount of time and a certain amount of resources, and that's all it needed. But now with, for example, machine learning methods or all these advanced things, the resource usage may vary over time where there's a short time it needs more memory and then it shrinks some and that makes it a little bit harder to adjust the parameters. Yes, that's true. Yeah. So maybe that's something we can talk about later or is it? Yes. So there's, yeah. So basically what you should think about these are the ceilings like how big, well, if you think about like driving a truck through like underneath the bridge, you're not going to like say that, okay, if your truck has one place where it's really tall and otherwise it's really, really tiny, you're not going to say that, by average my truck should fit through that bridge because that's not what's going to happen. The truck will hit the bridge because there's going to be this huge spike. So what you should do is, similarly in your code, if you have these kinds of spikes of let's say memory usage, you should try to write your code in a way where you don't have, for example, you don't float the whole data set just to work on a part of the data set because then you will need enough memory for the whole data set. So instead you should write your code in a way where you can only load the part of the data set because then you can lower the average height of your, or the maximum height of your job. So you should really think about your job as this, well, basically this truck that's going, that needs to be certain amount wide, certain amount of height and you don't want it to be all these things are somehow, all these things are somehow interrelated and that's what makes the cluster interesting or fun. Okay, so we are at half an hour in, let's see what else there is to discuss here. So we talked about the resource parameters, monitoring your jobs, I guess we've already sort of looked at. Yes, there's very many of these tools that you can use in Slurm to see like detailed information what your code was actually doing like this S act that is underneath actually the Slurm command is just a wrapper for the S act. You can see various other, well, I don't know if you need it because it's a huge amount of information there. But we have many of these like very common things we have put into the Slurm command. So this S act will give you this sometimes pretty complicated information. So we have written this Slurm script so that it will give you information in much more manageable pieces. Okay, so Slurm is an auto-specific thing. Yes, yes. You can look at it, it's a percol code written years ago but it's very good so we're still using it. But if you look a bit below, we have this list of very useful commands that you might want to use yourself. So the first one, well, the most interesting ones for you are probably the Slurm partition like the Slurm commands that show the state of the whole cluster so you can basically see what's happening currently. So you can plan what you have. You're going to see a bit of an expectation of what's happening in the cluster. So Slurm politicians will show you that because we were talking yesterday that the cluster is heterogeneous so there's multiple different kinds of computers and then it's been split into these partitions based on what kind of computers they are. We have written it in a way the way you don't usually need to reserve any of these partitions manually, you don't have to think about it, you just need to give the resource requirements.  So if you want to see like a problem here, there's allocated idle, other and total. So basically there are some 42 idle nodes in the batch as well, partition for example. So there's plenty of resources to go around currently. Okay, but this is something that users don't normally need to look at. Yeah, they don't normally need to, but if you want to look for example, like what is the timing limit for, let's say GPU partition, you will see it from here. Or what are the... Partition time limit and GPU says five days. Yes, so by default it's five days. Other well important command that you probably need is the, well this isn't necessarily the most important, but one of the important commands is worm features, that's somehow missing from this actually. Okay, maybe someone can add it quickly. Yeah, we should add it to this list. So this list tells you what available types of machines they are available. So maybe you can make the font a bit smaller. I think I can't. This is... Okay. This terminal has only a certain fixed things. Yeah, well if you run it in Tratton you will see for yourself that you can see with this command what kind of like computers we have. So if your code, like yesterday there was a question of if you try to run TensorFlow on Cosh you will get these AVX errors because like the Cosh machine doesn't have like the Auto Shell server where you shouldn't run necessarily TensorFlow it doesn't have AVX to instructions. So from here you can see that some of the machines, the Ivy Bridge machines, they miss the AVX instructions. So let's say you want... AVX is a processor feature or processor, like hardware thing? Yes, it's this vector vector instruction. So you will here see that that some of these features are available on some machines, some GPU cards are available on some machines and so on. Okay. So let's see. We should probably work towards the exercises. What else is important? I think all these commands people can read if they need to. There's basically lots of different ways to get information about the system. Yes, the most common that you use are the Slurm Q, Slurm History and the S-Batch. Like those are 90% of what you will do anyway. Like you will use S-Batch to submit a job. You will use Slurm Q to look what's happening in the Q. And then you will use Slurm History to check what was the output. And that's like 90% of your use will be that. But you should note that there are plenty of other tools to see more information about your jobs, more information about your... One thing, well we will actually we can look at it later the SF command. But now we probably should do the exercises or do some exercises. Okay. So we can see what else is on the page. We already talked about partition some. And like Simo said, I think for most users we don't... So on some clusters you have to care about what partition you're submitting to. But on Triton it's mostly automatic. So just say what you need and it will run somewhere. Which is a nice little feature. Okay, full reference is just more lists of commands so... Yes, exercises. So I guess we will break up into the Zoom breakout rooms for doing the exercises. So how should we do this? I think we should probably have like at least 15, 20 minutes to run the exercises because these are like the meat of the cluster really. So you should get a hang of how to write these espac scripts and how to do it. And after that I think we should demo a few of these exercises here. So maybe we can have some time in the Zoom breakout room for questions but then we can come back shortly to actually do the exercises together here. I guess we can go to a short break you can discuss in the Zoom room and then us instructors will talk with the Zoom host and decide what to do. Does that sound good? Okay. So there are how many exercises here? One, two, three, four, five? And how many should people try to do? I think do you think we should give a lot of time here because it's the first one where people can really like if people can do these then everything else will be relatively straight forward. Yes. Should we give maybe 15 minutes for independent work and then come and demo it together? Yeah. So five, two, one or... Okay, yeah. Should we have 15 minutes to demo and then 10 minutes for break to continue? Yes. Okay. And please... So we're sort of combining the self-working time with the break time but don't forget to actually take the break and we will resume at 105. Does that sound good? Okay. Great. Do you... Then in HackMD it will clearly say what the exercises are.


The top tips I would recommend for someone who's applying to teach in the Queensland State School is to be prepared to open your mind to opportunities and adventures outside of the metropolitan area and to really be open to embracing a new community because there's quality schools all over Queensland that are ready and waiting.


If you were to post a survey and ask Ravens fans, what was the Ravens biggest issue last year? Of course, you're going to get some different responses. But one of those responses that you're sure to get is a lot of people saying pass rush. The pass rush last year was nothing serious. The pass rush last year, it was a problem and not really for opposing quarterbacks Ravens pass rush. They really failed to consistently get back there. QBs did have a lot of time. Now when the secondary got upgraded, it did help a lot. But the pass rush still could have been a lot better. So Ravens this off season, they did some things that did sort of help out in the pass rush. Bringing in a Kaleas Campbell who can help set the edge and provide interior and exterior pressure. Also bringing Derek Wolfen who can provide some interior pressure and is really good against the run. But other than that, they really didn't do much to address the pass rush situation. Of course, they did put the franchise tag on Matt Judon who still hasn't signed it yet.  So I'm sure that'll be some nice extra motivation for him to go off and depending on how much of a role he has. So with that being said, the draft came. The draft came and the draft went and they didn't draft any pass rushes. So a lot of people were thinking, oh, whoa, what was that all about? But of course we know that the draft is not the end all be all and we have had so many and heard so many success stories based off of undrafted rookie free agents. Because again, sometimes all you need is a chance. And that is what the Ravens provided this young man from Upper Marlboro. I didn't even know he was from Upper Marlboro. So he's from Maryland. So he ends up signing with the Baltimore Ravens who are about probably about 45 minutes from Upper Marlboro. So he ain't even got to go nowhere. He ain't even got to go nowhere. I love stories like this where because I'm somebody who loves the closer the job, the better. And that can make such a big difference. And I know a regular job for us regular people is not like for the football job. But still, if you convenience means the world, it means the world. If you got this super high paying job, making all this money, it can be cool. But if it's far, far away, you got to take like two hours to get there because I would say one hour. But for a South Florida people, one hour is a ways away. But for y'all up there, that's in Baltimore, DMV and all that one hour is like, oh man, that's nothing. Because I know traffic up there is insane. Right down here is nothing compared to traffic up there. But anyway, he's close. But convenience means everything. So for me, if I could have the convenience of not making all the money in the world, but being a lot closer and the travel time to my job is a lot closer, I would take that. But anyway, that's beside the point. He is from Upper Marlboro and he gets signed by a team. That's also in Maryland. But he is an undrafted free agent defensive lineman. So as always, oh, and he went to James Madison. My apologies. I didn't even mention the school that he went to. He went to James Madison. So again, smaller school. So the level of competition, not that it's worse, but it just doesn't get all that spotlight like some of the biggest schools would. Doesn't mean it's a bad player though. Because again, sometimes all somebody needs is an opportunity. That's all they need. So as we always do, the first thing we look at is the stats. And with this, it was kind of hard to find his stats because I was going to some different websites. I tried to Google it. I was looking all around and because some websites were saying that he only got three sacks this year. And I was like, what? When I was watching film, I saw him get way more than three sacks just based off of that alone. And I ain't watched film from every single game this past season. But I'm like, hold up, what's that about? Now I was looking and looking and looking and then I finally found something. I found a couple of things. One was a graphic. Now this graphic, it seems like it's inaccurate, but maybe it was talking about just his sacks for just the entire school year. I don't know what's going on with this graphic. It says it's from PFF and it said his overall grade was a 94.5. He got 97 pressures, 21 sacks, 15 hits and 61 hurries. But then I look at his stats from this year and it said he played in 16, no, they don't even play 16 games. Anyway, so he got 16.5 sacks this year. So as far as the stats, I was having a little trouble getting that. But anyway, see and then in another statistic is showing that, okay, so 16.5 sacks this year. That's what we're going to go with. This 21 sacks, I don't know where that came from. But anyway, so year by year, sorry for all that confusion, because I was confused because like I said, finding his stuff, it was kind of hard. But anyway, 2016, he ain't getting any sacks. Cool. 2017, he got one sack. That's cool. Okay. So there's improvement. There's 100% improvement. So then in 2018, he got 10 sacks. So that's, isn't that 1000% improvement? Because 100% improvement from one would have been two. But okay, but y'all let me know if I'm doing a math wrong. I feel like it's right, but y'all would know better. Anyway, and then so in this third year, he got 10 sacks. So then in last year, he got 16.5 sacks. So this dude just upped his game every single year. He just got better and better, more comfortable, more comfortable. And everything rolls up, man, everything. So that was a beautiful thing. So after all of that confusion, we're trying to look for his stats. And again, I'm telling y'all, it was tough. I don't know why it was so tough for him. It wasn't tough for anybody else, but for his, I don't know what the problem was. But anyway, one thing that a real quick little side note, one thing that actually works in his favor that his team was already wearing purple. So he's already used to purple. But anyway, and he had a, he had number seven. Who does? That's a nice number right there for past Russian college number seven. I like that. What? Um, so looking at the film, first thing that stood out to me about DACA, that guy is explosive. He is explosive. I know a lot of commentators and when they say it, it can sound kind of cheesy. But for him, it's true. That guy has a high motor. He has a high motor. He moves fast. He attacks fast. He gets after it fast. When I watched him, um, once, once he gets around that, uh, that tackle, that's it. If he gets past that tackle, that's it. He's getting to the quarterback. He, he provides some very quick pressure, fast pressure. That's something that the Ravens lacked big time last year, but he, he gets back there. If he get past that tackle, he's back there right away. Right away. It was like, oh, geez. Like explosive, man. Seriously. I don't know what he ran into 40. If I got a guess based off of what I saw on film, I'm going for him. I'm going for five. I'm going for five. And I know some people ain't, well, that's not really that fast, but he also is a defensive lineman. I'm going for five. Yeah, I'm going for five. But he just, he looked explosive regardless of what his 40 years, a lot of times that don't even matter half the time because football speed is different than 40 speed. But he just relentless pass Russia, man. And I watched him cause pressure on all these different quarterbacks and just make them uncomfortable. And he was back there because he just, he moves so quick, man.  I ain't seen no ball rush now. And that's something that we got to talk about too. This game is more so finesse to me. Again, this is all what I saw. His game is not, it didn't really seem like a, he was super powerful because I did see with when he went up against some stronger, bigger tackles, they were holding them up pretty good, man. So the ball rush, that, that wasn't his thing. But that swim of quick. All right. Now let me, don't, don't mess around and get a little pass rush now. Shout out to my guy, to my guy, JB Smoove and John Moore. Those two, they, they, they did some good pass rushes when we had a little flag football thing. And we would have had it this summer, but y'all know everything going on. So that's probably going to be canceled. It's not official yet, but we'll see how it goes. But shout out to them too. Those are the best pass rushes from, from the flag football thing. Oh my God. Thomas too. He did some good pass rushing too. But anyway, but yeah, he, he struggled when, when the offensive linemen, when they got the best of them, it just, it was just straight up strength, the strength versus strength.  But when he got around them, that was it. That was it. When he used his quickness, that's a wrap. You're done. You're done. Because there's some guys that they may be able to get around the tackle real quick, but the tackle can recover. He'll be able to recover, but with a, with DACA, no, ain't no recovery time with that because he's, because he was just too fast. But yeah, again, he did struggle with some of those tackles. So as far as him doing that with the NFL, again, that could be a thing where he gets some NFL coaching and he can still use that speed there. But they just work on his upper body strength. They work on sort of his bull rush. He could definitely learn that from Jalen Ferguson. Hey you. Um, and like they, they could actually teach each other. Hey, Jalen, this, this is how you, this is how you, that's how you do that. And Jalen Ferguson be like, Hey, this is how you do that. You just put him back. But no, it's all good, but it's all love. But um, he, something else that I saw, very interesting. Um, and it wasn't a good thing and anyway, let's just get into it. Something else that I saw was when the plays wouldn't, the plays wouldn't be over yet. But the play, if the play wasn't right in front of him, if the play wasn't right next to him, then I would see him just sort of just stand in there. And the play didn't even have to be super far on the other side of the field. But they would, it would seem like he just, if the play wasn't right there in front of him, if he wasn't a part of it, then he would just, it would almost seem like he was removed from it. Like he like sort of took himself out of it. Like he just, he didn't just keep going after it. And I feel like, cause we've seen it so many times, we've seen how crazy some plays can be and always say it ain't over till it's over. And that, that's, that's what it was. There was, um, sometimes there was just a lack of playing through the whistle. Cause there'll be times where he would just stop and these plays, they will still be going on, whether it's a running play, whether it's a passing play. And it just seemed like, oh, if, if he felt like he wasn't right there by the play, then he, he just, he would just stand there and I'd be like, oh, okay. Um, but those would be my, my only two knocks on them, uh, just that. And maybe that, that could be more of a mental thing where maybe he just feels like, okay, this play is a certain distance away from me or it's, it's not that close to me. So you know what, I'm, I'm just, there ain't no point. But again, when it was some of these plays, they would, they would keep going to where, if he would have still been running after the ball carrier, if he would have still been trying to get to the QB or whatnot, if he would have still been trying to attack to where the ball was at, then he could have still been a part of the play. Cause they, again, they were still going. And again, those are one of those things where you just never know.  That's why they always say play through the whistle. Don't stop until you hear that whistle. And of course I know some people, they don't want to waste, waste energy and so to speak, shout out there all the time. But they don't want to waste energy. But at the same time, I just, that's one thing that I was like, eww. But yeah, my, my only two knocks on them is just the, the ball rush.  They'll work on that with you. But they also going to try to capitalize on your strength now. They definitely going to try to capitalize on your strength. So with him, with him I'm telling you, quick, quick. Watch him now, watch him. And again, don't, don't just watch highlights. You will see him quick, quick. Just watch the games, man. Watch the, cause he got that explosiveness, man. Um, and I was like, wow, okay, geez. Like I was, I was like really, really, really impressed with that. Like a lot, if you couldn't tell. But, um, his, he, he got some competition, obviously.  You have an opportunity. You really do. Because again, that's where Ravens lacked that. That's where they lacked that. Pass rush. Come in again, you at the crib. So you ain't got to go travel nowhere. You at the crib. You from upper Marlboro, but I mean, he's a defensive lineman. Again, they got rid of Chris Wormley. They, they did. He does got some competition. They drafted more interior defense, like Madibuque. I said his name right. I'm confident that I said it right. I know y'all correct me if I mess it up. But, um, again, Kaleas Campbell, he's not a guy that's going to be there for the long term. He's on it, telling to his career, Derek Wolf. He's on a one year deal. He probably get extended because he's going to have a good year. But, um, Matt Jude on that's still up in the air. Uh, but he's an outside linebacker. I think, um, that is a defensive lineman. But still you had, you have a way on this team. You really do. You can impress and, and be explosive. Man, you can do some damage. So I'm going to be rooting for this guy, man. I'm really rooting for him, man, because I want to see him on his NFL level.  This is, this is the cream of the crop. Is that the same cream of the crop? I don't even know. Sometimes I just be saying stuff. But yeah, this is real deal. So hopefully it'll all work out for me, especially because he'll have that convenience being right at the crib. So we just got to wait and see. Anyway, team keep it clean. I appreciate y'all. I love y'all. Dacker, we are rooting for you, especially since you could be a hometown hero. We appreciate you. Appreciate y'all. Team keep it clean. Y'all be good. Y'all be easy. Y'all be safe and I'll be out.


Hello and welcome to the AI for Good Global Summit. Our guest today is Will Jackson, he's CEO and founder of Engineered Arts. Welcome. Thank you.  Yeah, that's right. So I was always very hands-on with materials. I think my parents gave me a workbench when I was four years old and so I spent my childhood making. As soon as microcomputers became available, I taught myself the code and I wanted to bring that code into the world. So it was embodying that code and I made a simple robot that was just two wheels. Tell me what you did today. What's your work involved? Today I run a company that's now around 50 people. We have three locations and we build humanoid robots and they're primarily designed for interaction with people and of course humanoid robots that interact with people are an embodiment for AI. So that's the connection with AI for good. Give me some examples of what you do. So we make robots for public spaces, so museums, science museums, theme parks, visitor attractions. So you can give an AI a character, a personality. It can talk to people. It can entertain people. It can be very informative. It's a lot of fun. We also make robots that are used for exploration for scientific research. So we supply a number of universities. There is a lot of activity in the AI area, but if you're interested in the human interface, the embodiment of that AI, then there are not many choices. So we provide our robots for research labs. The other area we work in is with corporates and it's generally communications within corporations and to their customers using AI-enabled robots to present information. Okay, thank you. And you've spent the last couple of days here at the Global Summit. What's the takeaway do you feel for you? It's interesting to see so much activity and so much attention in this space. And we all know that it's received a lot of media attention. There are very rapid developments. One sense I do get is there's quite a bit of confusion. I think we could have some more transparency and some more honesty. There are things that AI can do fantastically well. And there are things that AI cannot do yet. And there are things that may not be possible for 20 years. And it can get a little bit over, over-optimistic, I would say, the conversation. And I think that could be harmful. We need to kind of temper expectations. We need to look inside the black box. And it would be good if we were really talking about how the language models work, how does computer vision work, how do predictive models work, and try. The difficulty is it's a very complex subject. But I think if people could understand a little bit more, they would probably have less fear and make better use of the technology. Okay. Now you said to me earlier, I wouldn't be in AI if it wasn't for good. No, no. What are some of the benefits then? I think being able to interact with people. So what we're doing is building the interface to AI. So it's the embodiment of AI. So we're all used to using a screen and a keyboard. Now the keyboard, its heritage is a typewriter from 200 years ago. Screens go back to the 1940s and 50s. So we're using these very arcane interfaces. What if we could interface with technology in the way that you and I are talking now? What if that nod of the head that you made that an AI can understand it? So what we're interested in is using AI to improve the interface between us and our technology. To empower us to use our technology in a much more simple, creative way. So you wouldn't have to be a technical guru or a genius to use an AI-empowered robot. You could just talk to it in the way you would talk to another person. So I see enormous opportunities for good then. And in particular, you're interested in how it can promote quality education? Education is kind of the foundation of everything. So I think education is not just knowledge. Education is discovery. It's about finding out about the world, discovering physical phenomena within the world, exploring the world, seeing it for what it really is. And if AI can contribute to that, I think that's a huge positive. And I really think it can. Can you give me some examples of? A concrete example. AI is fantastically good at just retrieving knowledge. So if you want an encyclopedic knowledge of the world, this is what AI does very, very well at the moment. So it can hold more information than any other person can hold. So as a way of retrieving information, it's really great. Does that form a quality education in its own right? No, it doesn't. But it's the start. It can give you background. It can give you information. Are there problems? Yes, not everything that a language model will produce is true. I'll give you a very simple and funny example. So our robot is named Amica. And actually, it was a corruption of the Latin Amica friend. That's the origin. And we asked the robot itself, where did your name come from? No, we'd never trained it on this. And it made its own version. And it said it stands for autonomous, mechanical, emotional, cybernetic assistant, which sounded tremendously convincing. And we were, well, that's wonderful, but completely untrue. So this is called hallucination in the world of language models. So there are dangers. And we have to look at how can we make an AI that's truthful? How can we make an AI that's transparent? These are some of the challenges. Sure. And if I can just continue with that theme of the challenges of AI, how do you ensure that the technology that you're developing and others is ethically safe? That is a huge challenge. So if I give another concrete example, what do you do with the memory of an AI? So if we've been having an AI conversational interaction, and I've told the AI some personal information, or maybe not even very personal, it's just recognized who I am, where I was at a particular time. Now I could be sensitive about that information. It's a kind of GDPR problem as well. So where is that data stored? And how do we explicitly get permission? Does an AI agent have to say every sentence, do you remind if I remember that? Now that could be pretty tedious. So we're going to have to come up with some mechanisms, some transparency. What we've decided in the short term is best that all data from interactions remains on a local robot itself. So it never goes beyond, it doesn't go into the cloud, it doesn't get stored on a remote server. So in a very simplistic way, you could switch off the robot, take out its hard disk and destroy it and your data would be gone. So at least you have that simple mechanical recourse to solve the problem. That's not going to work as a scalable solution. How do we protect data? How do we have good governance over that data? These are big problems and they need to be solved. We could talk all day but we have to do it there. Thank you so much. Will Jackson see you all and find our engineer darts. Thank you for your time. And more to come from the AI for Good Global Summit coming up.