 Yes, please go ahead. Okay. So, yes, as I was saying, this has been. I've been developing this for a long time. And there's lots of different titles, but the general summary is that. I think if you're here, you probably agree that lack of diversity and computer related fields is something that we care about a lot. But, you know, there's lots of efforts to improve things, but are they really working or not. And after working with people in a field this field for many years now, I've seen many different factors that are come from the environment. And, um, yeah, so I'm basically here to discuss them and see how this official environment actually isn't as diversity promoting as we would like. So, you can start with value sensitive design, which is not something that I really know anything about I heard it in a conference once and then I realized it's something important. So every process is a reflection of some inner value of the designer of that process. So I've been supporting people and helping them do computational research for many years. And my conclusions about the values of the way universities and alto is set up are not really convenient for the support services. The value sensitive design relate to diversity. So we have people entering, and they may have different initial skill levels. If we are not promoting diversity, we have people who are a little bit ahead get even further ahead, and those that are a little bit behind, stay further behind. So if an environment promoted diversity and equality, then as people come in. Everyone can like the people that are slightly behind can also help to be helped to improve. The next task is this is zero some game like want to talk to some people they say no we should invest in the research that is the best, which is really close to investing in the researchers which are the best. And this sort of like the fundamental question here. So how do we do with these aspects. So there's some basics here. We learn informally. So I don't know where I've exactly seen this from but it's, I've heard before we learned 70% by doing things 20% through networks and 10% formally. And that means we have to promote these informal networks and come working in order to teach people well. There's this one. List by someone. Someone wants link to me. That was how to help someone use a computer. And one of the ideas there was the best way to learn is through apprenticeship. That is by doing some real tasks together with the person who has skills you don't have, which is something that you'll see come up a lot in the future. So let's talk about our backgrounds. So, we have lots of different backgrounds. Yeah, we assume that we have the same basic skills. So we promote this in a disciplinarity these days, especially a lot within computer science, we're a very diverse department. So, there's not just this traditional computer scientist there. We have people from, well, bioinformatics data science, all these kinds of things. And these different backgrounds have different strengths and weaknesses. So for any X that we assume people have, there's some people that don't learn that. And, well, this may be up for debate, but we don't expect every CS department PhD student to have a computer science bachelor's degree. People started different times of the year. So, for example, bachelor's students all start at the same time and have a clear induction program. As you go up the ladder to PhD student or postdoc, then people start at all sorts of random times. So that means the earlier that you're in the system, the more introduction you have and the more your basic skill set is what others expect you to have. There are also many different intake paths. So it used to be some decades ago at TKK that, you know, most of the more advanced academic rank people were the same people that were bachelor's students here so things were very consistent. Now that's not so much the case. So, we have even less uniform backgrounds. So do we design the university to recognize the above that people have these different skill sets coming in, and we want everyone to be able to advance. And something I haven't written here is the role of friends in your life. Like I got started as a computational people because I had friends all the way back in high school that were sort of into like Linux and programming and things like that so I learned a little bit from them. So how much of our inequality now is caused by the people we associated with even way back then. So let's see what's our reality. So academic skills is not equal to practical vocational skills. So, I don't really exactly like the word vocational, but it's the best I can think of so far. So academic skill might be a course in database theory, a vocational skill is being able to take your data and put it in a database and use it instead of writing all your own code to do the same thing. So these kind of vocational skills within a university like ours are usually learned by informal networks. If you aren't part of that network, then you fall more and more behind. So another classic example is shell scripting. So people who can shell script can do research much faster than those who can't yet where is this top these days. It's sort of not really you just either pick it up or don't. So I have a theory that computer science is worse because it's broad and the theory is not exactly equal to the practice, or maybe you could even say it's too close to the practice. So for example, in chemical engineering, you say, where I studied at first. So, there's a clear difference between the mathematics you need to know and then the stuff you're learning in your degree in your degree program. So shell scripting and things like that is that part of a CS degree program. No, because it's not academic stuff. Yeah, we assume that everyone will know it because I mean, obviously it's like what we do as computer scientists. It's sort of mismatch in what we're taught in programs and what is what people need to know to be successful. And this is especially through in this divide between, sorry, computer science, the traditional field and then data science, and our department combines both of these. So these days we have more pressure to leave because of government and university policies and that this means less long term knowledge investment. In the past, in Finland, people were often students forever just working at the same time the studying and the work compliments the studies and vice versa. Now we have all these pressures PhD students are told, okay, you have to finish your classes in the first one and a half years, or else you lose this money that you could be paid otherwise, which means that people are learning their academic skills in isolation and then they focus on applying it later after they're done with their courses. And in a certain number of years, again on penalty of finance or financial loss. So, non EU students have to graduate by a certain date or you have to pay more tuition. Once I was talking to someone said okay would you like to help me to teach you this and the person said okay that would be great but if I don't finish my thesis by the state I have to pay thousands more euros. So as much as I know that I need to learn this for the future. I'm not going to do it right now because I just can't. So as this quote by Chris said, so as we add these formal pressures, then the network aspect of learning gets weakened, and all we learn is what we're supposed to learn, and not all the other things that go around. My favorite things to complain about rooms are what are arranged to eliminate peer support. So if we learn by example and experience, then we want to discuss with people who are better than us, but our offices are so big that we say, don't talk to any people in here. So there's a quote from an email when we did our big rearrangement. Please avoid having conversations in open plan offices or corridors and do it in other places. And this partly comes because of the policies by the rest of the university like a great, which by the way my favorite meaning for that acronym is annoying campus research experience. So, like the world view of people who designed the rooms is different than the world view of people that need to work together in those rooms. People are isolated by rank. So if knowledge is best transferred vertically and informally. So, as someone pointed out once we take and we put all summer interns into these summer intern rooms with only other summer interns. Then who are they supposed to be learning from this learning happen once a week or once a day in a meeting that you book with your colleagues. That just doesn't work. And most of skit put in all these offices other than PhD students. So, um, yeah, there's different reasons for that and as far as I know this is something that we basically fight with them all the time but what can you do it's their job to bend us to their will and it's our job to do research and not to fight them. Okay, what about social factors. And social skills is not the same as research skills so we think people come in as digital natives, but there's a big difference between having a phone and using Facebook and being able to communicate with people from things like programming your Omega and assembly. So that means there's a bigger separation when people are coming in from these sort of hacker programmer negatives that have gotten involved in that in those that aren't. The point here is that we have more diversity and incoming students. So this is a little bit theoretical but these days when we're learning we go straight to the end state. So, the people that are older we saw the hard way of doing things. Well actually I wouldn't even put myself in that category. I'm not that old. But people say, 10 years older than me like you had to take and install your computer and then like scripted and do these other things things were hard but you learned how the inside of the computational tools work. Now people arrive and we're told okay here's your system. Just use it. So that goes on inside so it's more like a black box and it's harder to be some like hacker person that knows the inside and has the spirit of programming things yourself. And I guess you could say this also applies to things like, um, like the software. So these days if you do machine learning, you can just straight up use TensorFlow out of the box somehow. But maybe 10 years ago you had to was a little bit harder so you learned a bit more. Okay. You can't build everything yourself, you have to reuse and this isn't taught this wasn't really interesting point that I saw that someone brought up to me once. So in a class if you reuse what others have done its plagiarism and you get punished in real life. If you don't reuse what other people have done and you don't know how to reuse it, then you basically can't do your work very well. And this isn't taught in classes. And where do you learn the skill. It's a. Well, it's something that is not helping everyone to learn to be their best support is always what gets cut first. So here's I'm sort of biased. When there's budget cut who goes to PhD students, or the staff scientists who's been around forever, and the one that's actually been giving practical practical mentorship to every PhD student. So, this is something that we saw five or so years ago when there were budget cuts, and obviously I'm kind of biased here because I'm exactly that kind of person that tries to dedicate myself to mentoring other people. So it's easy to learn by example and hard to get started. So things are really complicated and just sitting down and reading an instruction manual and something. I mean, I guess it can be done but many people don't. The way I've learned almost everything these days is I've seen a good example from someone and I've started modifying it. I learn how it works and I see all the practical, like, workings of it. And then I can read the instruction manual, and then I can start using it myself. So, what happens when we don't talk to our colleagues and we don't see these examples and you're given your desk and said, and you're told do this and then you're on your own. Are you actually able to do work as well as other people. We support experts more than beginners. So, I often say we don't have time to give as much support these days as we'd like, although that's sort of changing with some of my recent initiatives. But then it was that's not actually true. If someone comes in with a really basic question. I'll do what I can to help them and then send them on their way. Or at least that's what I guess many people would do. But if someone comes in that has a really advanced and difficult project so these are one of the experts and they're trying to do something that no one else has ever done. Yeah, then this becomes interesting because it's worth our time because we're helping the most advanced research. So we give the expert more support than the beginner. And, um, yeah, so this is something where all of the support staff really have to be carefully watching what they do and consider this. And my extreme theory so not just about this but every problem and also comes from the fact that leadership grew up decades ago, where life was different. Most people advanced internally there weren't international people so they have a certain viewpoint about how people learn. Now we have this much more diverse environment very different ways of learning and very different community, and the policies just can't adapt to that kind of thing. Okay, I've got a few other quotes here and we're almost to the end. So, some people say I'm not sure how to ask for help so what am I expected to already know myself and I'm expected to continue reading it and until I figured it out, and when should I ask someone. This is a good reason why I'm good office arrangements are good. Imagine if you could ask your colleague okay I'm learning this, should I read about it myself or should I ask you. Now when you're alone. No one wants to go to another office and take someone's time and say oh should I learn this or that if they're not already there. When you don't know something there's lots of different reasons here. So, it could be your own issue that you don't understand that yourself, or it could be that what you're trying to learn from is actually not that good. We can say most of the time it's that the material you're learning from is not that good, which even more biases towards this working together. That's basically the end so I'd say the summary is that going back to the beginning what's the value of the university so I think that our whole setup is not really set up that well so people can learn from each other. So, and that's where people will learn most so when I was a young student I was put in a small office with one other mentor. We were pretty fortunate about that and we talked all the time. So it's not that this person taught me everything but basically by talking with this person, I can be guided in a good direction for my own learning. So all these days at an environment like this. If I had started working and got put in the large converted classroom with a bunch of other summer interns and told to do things and I met with someone infrequently. I just wouldn't be where I am now. And this is not like this is these are not things that are just about diverse like demographic diversity. This is intrinsic factors about how we see the skill diversity of our community. And these are hard questions, and most of these things are not done by us so they're either by learning services or campus facilities or IT services, things like that. So I can talk about what I try to work on in the area but maybe I should give a chance for other people to talk some.