All right, no pressure then, that's what I like. Apparently, intellectual abseiling is now our destiny for the next hour. Listen, I too want to acknowledge that we meet on Ngunnawal Country and I wanna thank Wally for that incredibly gracious welcome to country. I've spent 30 years living in the United States and one of the great pleasures for me is to come home and get to be part of that ritual again. Whilst America may also be a country that is previously occupied, it is never acknowledged. And there's something really powerful about the fact that we get to sit here and remember that every time we open an event. So I know sometimes it feels like a ritual but I actually think it's a really important one so I wanna thank Wally for that. I also wanna thank Brian for his gracious and pointed introduction. Most of you don't know quite how much is sitting underneath Brian's notion of saying we had a few hiccups. Can I actually have a quick show of hands of who was here last year when we kicked this off? Excellent, recidivists, that's what I like. Good, that I have things to tell you for the rest of you. It's a little bit of a new adventure and a new journey. As Brian said, a year ago on Monday we met in a building just across the road to kick off the first of the ANU's Innovation Institutes. These were designed to be ideas incubators and a place that we could build new sorts of knowledge, create new kinds of experiences and find new ways of connecting those experiences up. And Brian's right, it's been an extraordinary adventure. It is certainly the case that were I to write the comparative ethnography of my 20 years in Silicon Valley and my two years at the ANU. I fear it would be a bestseller for all the wrong reasons. So let us imagine that potted ethnography is waiting. That's not this talk. What I thought I'd do instead was reflect on where we've been, where we are going and the moving pieces that are involved in it. And in fact, I wanted to start where I left off. So those of you in the room may remember a year ago, my very last PowerPoint slide was this. I said, we're launching the 3A Institute and we need everyone's help. And I want to stop and say thank you here because it turns out so many of you came and found me. I want to thank all the kind of the people that have been incredibly supportive to me. I have to thank my Canberra family and my mother, Diane Bell, who was in the audience again for all the ways in which they have made it easier to come home than it should have been. I need to thank Brian and the rest of the administration for being supportive even when I think they worried. I might burn the building down. Brian may not remember this, but when he interviewed me for this job at the very end of the interview, I said to him, listen Brian, I'm capable of being a good girl. I scrub up nicely, but there will be a moment when I'm gonna come into your office and I'm gonna say, Brian, I have to burn the building down. And Brian looked at me and very sweetly said, I keep kerosene in the bottom drawer. And I thought this is a man I may have to move to. And I turned up. So I want to thank the administration and Brian in particular for all the ways in which he has been incredibly good. I also want to thank the rest of the academic staff at the ANU who I know have found me to be a slightly query awesome creature and not what they were expecting, but who have been very, very good to me nonetheless in both public and private ways. I have to thank my colleagues in the College of Engineering and Computer Science and in particular my dean who is, as my team calls her, a minor marvel. I was the minor because she is tiny, not because she should be underestimated in any other way. She is in fact a force of nature and it is both my pleasure and my privilege to get to work beside her every day. And I need to thank all of you who weren't in any of those places, but found me anyway.  There are people in this room who have turned up in my office and offered help. There are people who sent me pronunciation guides because apparently my Australian English is not what it should be. I thank you for that. Also, although I don't know what to do with it, I will always say data badly. So I wanted to just say thank you because I know that it is something to get all of that help from everyone. But it turns out we're not done. So I'd like to just to get to say thank you and stop, but the reality is that's not the case. Because it turns out a year ago for those of you who weren't here, I started with this chart. So this chart was published by the World Economic Forum at the beginning of 2016. It was the World Economic Forum's attempt to make sense of the last 250 years and to stabilize the future. Is their attempt to basically say, listen, the world is really quite neat and tidy. It's had three really clear waves, that first one with steam engines, that second one with electricity and mass production, that third one with computers, oh, and there's a new one coming. Now, most of us in the room can look at that chart and see some immediate deficits. For me as a cultural anthropologist, deficit number one, no people, just technology. For those of us who are associated with universities, deficit number two, each one of those waves of technology necessitated the creation of a generation of scholars and practitioners to manage the technologies that arose there. Whether that's engineers and engineering in the first wave, frankly, both the branching to electrical engineering but also business and management schools in the second wave, whether it's computer scientists in the third. Each one of those waves of technology created an interesting set of challenges because it turned out to go from being an individual piece of technology to something that operated at scale and operated at scale safely. Required people that didn't exist when the technology came into being. If you just pick that very first wave and you think about the moment when Newcomen Watt are building atmospheric and steam engines in England, there were no engineers. There were blacksmiths and wheelwrights and guys who knew how to bend steel and people who were experimenting with water under pressure. But there was no one who actually knew how to think about the whole system, let alone imagine what would happen when that whole system went mobile and would require train tracks and thinking about adverse camber and then imagining speed and safety and braking distances and thinking about how you managed time in order to have shared train tracks. There was no one who did all of those things. They were just steam engines. And the reality is each one of these waves have not occurred, of course, in neat chronological order. Sometimes they've been mapped on top of each other. And for me, the impulse that keeps driving the institute is looking at that thing that the World Economic Forum says we're entering into now. What they call the fourth wave of industrialization or the fourth industrial revolution. And they've always said it was characterized by cyber-physical systems, always, as in for two years, they have said that. And at the time when I first saw this chart, I didn't know what that meant. They've subsequently rebranded this as the age of intelligence. I'm not sure that helps for several reasons. It raises questions about what came before it, just as a starting point. Nonetheless, I think it is safe to think about cyber-physical systems here as meaning an entire class of objects. And really, I think of this as the challenge here is about how do we get artificial intelligence to scale safely? Where the best way I can imagine this is to think about artificial intelligence is remarkably like that steam engine from 250 years ago. It means one thing when it's stationary on top of a mine, it is something completely different to imagine it is now going to be inside objects that are running around doing things. And really, for better or worse, that's one way to think about cyber-physical systems. A year ago, I stood here and said, so listen, here's the thing, we built engineers. We built electrical engineers and computer scientists.  Like, what will that necessitate and who's gonna do it? And I put a stake in the ground and said, I think it's gonna be us right here. Oh, and by the way, I think what we're managing here if before we were managing, well, basically mechanized in industrial systems, managing capital and managing computers, I thought what we were managing was data. I think that we were wrong about that or I was wrong and that in fact what we are talking about here is indeed managing cyber-physical systems. So what do I mean by that? Well, Brian just tipped his hand to owning one. Brian drives to work every day in a Tesla. A Tesla is an early version of a cyber-physical system. It is a physical object with a computational engine that doesn't drive it necessarily, but certainly can control it. And when Brian's Tesla is in autonomous mode, Brian is not in control of that vehicle. It is controlled by a set of software and a set of algorithms and frankly a little bit of early artificial intelligence. And when you think about cyber-physical systems, what you should think here is an emerging class of technologies where we are talking about a number of things that at the moment we wouldn't think were all the same thing, but one day will be.  Those are all early instances of cyber-physical systems. And frankly, it was my contention a year ago and it remains my contention today that those systems require something new.  We need both of those. It's not enough to say that computer science will need ethics, because it does and that's not sufficient here. And it's not enough to say we need to add some design thinking to engineering. That would be good, but still isn't the answer. The reality is this class of objects require something new, something that doesn't exist yet. And so I stood on a stage not far from here a year ago and said, right, so we're gonna build something new. And I said, there were four things that I needed to do at that moment in time. I said, I can't do it by myself because that's depressing. Also, you don't scale that way. Also, I didn't wanna do it by myself. So I said, I need to find some people. And at the time I thought that would be an interesting challenge in no small part because I needed to convince them to move to Canberra, which was mean. And that was the response you all had last year. Just why wouldn't want people wanna move to Canberra? It's great. I'm thinking it's been a long, cold winter. So problem number one was find people who wanna work on the problem and who would those people be? The second bit was, well, it's all very well and good for you to say, cyber physical systems, but like, do you have one more click down on that? Like, do you have anything more you can say there? And then I said, and it's not just enough to identify what the questions are, you're gonna have to find ways to test and verify them and what would that look like? And then, oh, by the way, because in fact this is happening at the Australian National University, not at a company in Silicon Valley, it's not just enough to build the knowledge, you actually have to train people, create the next generation of scholars and practitioners, find other people who wanna take that knowledge out into the world. Brian has been talking a little bit recently about the fact that the role of the Australian National University is about intellectual leadership, not just academic excellence. And I look at that and I think this is a place where that is true. It's actually about how do we create something that's a little bit different and a little bit new. So I gave this myself as my running list and I thought I'd use this to architect the rest of this evening to show you what we did. So step one was find people. So a year ago I went, I'm gonna need to find some people. And then I had the problem of going, if you're building a new discipline, you can't very well say, I'd like the people that do cyber physical systems because they didn't exist. And I was lucky enough that already a year ago there were two people in the room who had found me and a third who wouldn't leave my office and read every book I gave him. And I kept giving him more books because I hoped one day he would go away and he didn't and I'm deeply grateful for that. So we put out a call, those of us that were then together to find new people. And it's an interesting process at a university to put out a call for academic jobs in a college of engineering and computer science where you don't require engineering and computer science as the degrees, where you suggest that you're more interested in people's attributes than their disciplinary excellence. And so when I said I wanted people who were team players that were collaborative and wanted to build something, we did have an interesting argument about whether that was counterintuitive to academic excellence. I, of course, believed it wasn't. And so as of today, astonishingly, there are 10 of us and they're all in this room so I want them all to stand up because you don't get to sit down, Rob, Zach, Liz, Caitlin, all of you here. So what you need to know is this group of people represents what the next generation of university work would be because standing amongst you here, you have a nuclear physicist, you have someone who's expert, it's teases in strategic foresight, you have at least one person who has an undergraduate and a graduate degree in civil engineering as he puts it to me, concrete one, two, and three, as well as a PhD in sociology. You have someone whose undergraduate degree is in computer science and whose PhD is in human geography. You have people whose backgrounds are in interdisciplinary studies, in the law, in biomedicine and in anthropology. Oh, and that's not me. You have people in the room who can juggle. You have people in the room who do theater and improv. You have someone in the room who has spent five years doing scientific diplomacy. Obviously not for the Australian or American governments. And you have at least one person here whose background is in feminist theory, just like me. And it turns out if you wanna build a new world, you want all of that. You want people who range in age from 25 to 51. You want people who were born in Australia and born in all other parts of the world. You want people who have had careers that aren't even started yet. And I'm really lucky because all these people said yes to me.  So will you join me in thanking them? And also, we're ritually embarrassing them. You can sit back down again now. So that's my people. So the rest of this talk, I now get to say we not I. And that makes me more happy than you can possibly begin to imagine. Because once we got together, we realized that the next question was, well, okay, what's your body of knowledge? And Brian will remember, as will Eleanor, that a year ago, about now, because we're about 72 hours out from announcing the Institute, Brian's like, do you have a name for that applied discipline yet?   What's the name? I'm like, Brian, I have no idea what to call it. Brian's like, that's not helpful. What are you gonna call the Institute? I said, well, I'm gonna name it after the three questions I think I have. And Brian took a very deep breath and went, okay, and he's learned those three questions really well, autonomy, agency, and assurance. And I thought those were the questions. Here's the problem. A year in, two things are still true. I don't know what to call the discipline. Despite Michael Walsh, if you were in the room, your best efforts here, when the people, the babies at you as a child turn up and go, I think I can help you name that, you wish it were true. So far, Michael and I haven't got to an answer either. So I still don't know what it's called. We're taking bets. Brian and I think it might be cybernetic engineering. And I kind of like that. It gets to celebrate cybernetics, which has a delightful history. It has engineering in it, which honors my dean who likes to tell me two things. One, that I'm building a new school of engineering, and two, that she's always right. So if it were just cybernetic engineering, she'd be very happy. I'm not convinced about that. As Brian and I know, it's an aim already taken, but it is taken in Norway. So, you know, we may take it back. So, cybernetic engineering. What I also happen to know is it turns out that three As weren't enough. And I've got two more letters, Brian. I'm really sorry. I promise not to stick them in the institute name, but there are two more questions. So, remember when I said, cyberphysical systems. So imagine a class of objects that physical, powered by computing, that computing has an element of artificial intelligent capacity, which means it doesn't always have pre-written rules, which means there is some capacity for those objects to move by themselves. Which means in some ways the very first question that we came up with the very first A was the obvious one. It was the A for autonomy. And I think what is clearer to all of us now than it was to me a year ago is that what we say when we mean, when we say autonomy is really messy. Autonomy is one of those words that has extraordinary semantic slippage in English.  It basically goes from self-aware to sentient to killing all of us in the room. That's something that happens in English. It doesn't happen in all other languages, but we can't hear autonomy without putting it into this much larger context. That's one problem. You might argue that autonomy is a term well-rehearsed in at least one discipline that is not my own. That would be philosophy. It's also the case that in tech companies in Silicon Valley and elsewhere, everyone has an autonomous project of some description and every single one of them is using that word differently. They have architected autonomy differently. They are implementing it differently. They are securing it differently. Autonomy is a word that now covers many technical solutions. Dr.  It is the case that everyone now talks about autonomous vehicles. The Tesla solution that Brian has is one data is collected locally. It's edited at a central node. Anything that is interesting or useful is pushed back down into the system. Those systems operate semi-autonomously locally. The Volvo solution is that all vehicles communicate to each other without a central hub, but only certain kinds of information. Both of those are called autonomous vehicles. They're implemented completely differently. They have different challenges for how we would think about the network, how we think about the compute, also how we think about we might regulate those objects and who gets to. And frankly, it is clearly the case that what it means for objects to be autonomous is different in different parts of the world. So now we have different versions of autonomy lurking inside technical objects. So I still think the first question you have to ask of cyber-physical systems and the first question that this new applied science needs to answer is what does it mean to talk of autonomy and how would we build systems that embodied that? And if you think in the vehicle space, this is really about how would a vehicle function without pre-written rules and what would that look like? If you imagine you can resolve that question, the second problem really becomes one about what I would call agency or you might want to think about as controls and limits. If the system is autonomous, i.e. it can act without reference to some other agent, what are the rules that it's operating on? And who gets to determine those? Do they sit inside the object? Do they sit outside the object? Brian's Tesla isn't the best example of this, but if this were an autonomous vehicle, and it were here in Australia and it were allowed to drive on our roads, it would need to have at least two different modes, a mode for Melbourne and a mode for everywhere else. For those of you who grew up in Melbourne, you know one of the things that characterizes Melbourne, particularly in the CBD, is a willingness to fecklessly do something called a hook turn.  If you had a vehicle that knew how to do hook turns, it shouldn't do them anywhere else. A hook turn in Sydney would create chaos. I'm willing to bet a hook turn in Wagga would be a subject of inordinate scorn. I think it is safe to say, imagining how you build multiple sets of rules into objects and how those rules are negotiated, isn't just a matter of location sensitivity, but also context. And if you were to think about, again, autonomous vehicles in the case of Australia, it is certainly the case that there are institutions who might want to be able to control all vehicles on the road outside of those vehicles. Think of the country fire service or the SES who might want to say, we need all vehicles off the road so we can get that fire truck through. Who gets to decide who gets to take all vehicles off the road and under what circumstances? Oh, and by the way, how do all of those rules get made visible to the people who are encountering those systems? Do they need to be made visible? But if so, how would they be made visible? What is the moral equivalent here of the P plates and the L plates that we see on cars to warn us, do not get behind that person while they try to parallel park? What will that look like here, right? Third set of questions are still the same questions we had a year ago, Brian, which I think are the questions about, if you are building cyber physical systems, how do we think about the bundle of things here that we label under assurance because otherwise the list includes privacy, security, trust, risk, liability, ethics, manageability and explicability. Because it turns out if you start to imagine systems that act with a set of rules internal to those systems, how do we now think about what makes those systems safe and what makes us safe? Who gets to decide that? Is this just a matter of implementing an actuarial table or is there something more to it? What does it mean to think about machine coding ethics if we could do that? What does it mean to think about how you might need to reimagine liability? Who is liable for these objects? Who decides that? How are these objects scrutinized? What does it mean to think about a series of objects that are in some ways in physical spaces where the safety is not obvious? How do all of those things get negotiated? Where does the role of government sit? Where is the role of public regulators sit and where is the role of society? These again are questions that sound like social questions. They're also technical questions. It turns out as we start to think about cyber physical systems, particularly using artificial intelligent technologies, we start to think about learning techniques. How will these systems learn over time? That implicates two things. One, the stream of data that goes into those objects, but also the learning techniques those objects are using. At the moment, most machine learning is really just repackaged statistics. So if you know how to do statistics, machine learning is actually not that mysterious. There is, however, a class of techniques inside machine learning of which deep learning is the first, so basically unsupervised learning. Whilst they offer an ordinate potential, one of the challenges is it's actually very hard to know how the computational object has gotten to the decision it has gotten to. And more importantly, it's very hard to have that object get to that same decision twice. And more importantly still, it's very hard for that computational object to explain the point at which it changed state and the point at which a decision was rendered. So the idea of traceability, of backtracing, of algorithms that can crassly put unbox themselves is actually a technical challenge. It's not just an ethical challenge. It is a challenge for computer science.  And so having algorithms that can explain how a decision was rendered is the only, in some ways, option here. Because heaven for fend, we be in 2025 at the next Royal Commission into the banking in Australia and the CEOs of the Australian banks to a woman, I say hopefully. Find themselves saying, I don't actually know what happened to you super, the algorithm did it. I think we would find that as unsatisfactory is we have found the current set of answers. And this is a set where there are technical questions that have outwork. So the first questions remain the same. When you talk about cyber physical systems, we need to resolve the issue of, are these objects autonomous? If so, what do we mean by that? How do we plan to implement it and secure it? The second set of questions remain the same too, which is what are the rule sets? Who gets to determine them? Who will update them, modify them, make them public, scrutinize them? And the third set of questions too, how will we think about safety, security, ethics? There's two more questions that have become really clear to us as we have spent the year in some ways talking these things through. The first one is about the necessity for metrics. So if you think back to that chart from the World Economic Forum of the four waves of industrialization, steam engines, mass production lines, electricity, computers, one of the ways all of those systems were measured was by productivity and efficiency. Those were in some ways the dominant metrics of the last 250 years. Did the thing do it faster, cheaper, for less, in some way, right? They were dominant metrics. I think there is an argument here to pause briefly and consider what other metrics we wish to see at work here. Part of the reason I think that has to do with the fact that having spent 20 years in a manufacturing company, I know you make what you measure. And so thinking about what the measurements are drives what the behaviors are, or at least that is one way of thinking about it. In this instance, I think if we look at some of the early examples of cyber physical systems, autonomous vehicles, for instance, the language that we hear there from manufacturers and regulators isn't one about efficiency, it's one about safety. These objects will be safer than humans, not necessarily more efficient than humans. Certainly the case if you listen to some of the conversations about drones, whether they are in the air or on the water or indeed under the water, it's about can we see more of the world than the human can see? So can we extend our capacity to know things? Can we get more data? I'm not sure again that's about more efficiency, it's not necessarily more efficient data, it isn't just volume. It's also the case if you think back to that prior example about certain kinds of machine learning techniques, there are things we already know emerging in the artificial intelligence space that are issues in terms of a different kind of metric. So according to one of my colleagues in this year, so in 2018, about 10% of the world's electricity budget is spent in server farms. So 10% of the entire world's energy is spent in server farms. That's an extraordinary number. It's more remarkable if you imagine that we are moving into a world of greater technological density and those devices at the moment, even if you could solve the energy constraints of them, the density and energy trade off is quite high. It is also the case that some of the algorithms that are already at use, I'm thinking of Bitcoin mining and my other things and also deep learning are techniques that are actually really energy intensive. So they require a great deal of energy to use those techniques. It is interesting to contemplate what would happen if we decided for instance that a particular set of artificial intelligent technologies could only be used if they sat inside a particular energy budget. What it would mean to think about sustainability or energy requirements as a metric here is fascinating to contemplate. We didn't get to thinking about efficiency with steam engines and railway trains until there were very few trees left that you could chop down. And after they had moved away from a steady source of coal, we actually know that there were an inordinate number of problems that had efficiency in terms of environment spend being the metric. You might have developed a different solution much more quickly. So starting here and saying, what is it that we want from these systems? Maybe one of the things we want isn't just about efficiency, it's actually about energy, would be an interesting starting point. Likewise, would it be to say that we are interested in a better quality of data, we're interested in different kinds of outcomes, this is about safety. All those are conversations we should be rehearsing and I think thinking about what the metrics are for the systems as they are being built is an interesting fourth set of questions that at least allow us to interrogate the existing objects and new objects from a different kind of lens. And then last but by no means least, I think what's been most striking to me and I hope to the rest of my team is that it's one thing to talk about systems and the computation that sits within them, but these are also physical systems. So they are systems that we will encounter and that we'll encounter each other. For computing over the last 60 years, the interface to all of those systems was incredibly thin, was a keyboard, piece of glass, maybe a stack of punch cards, recently maybe voice, maybe a little bit of gesture, but it's been very, very in some ways narrow. Imagine now we are talking about cyber-physical systems that bring you to work every day, you sit inside them. They are cyber-physical systems that take us from the ground floor to the 30th floor. They may be cyber-physical systems that are in the air we don't even see but that are seeing us. They may be systems that are talking to each other about us. Not quite your ancestors Wally, but certainly an interesting world to contemplate when a building like this might be talking to the local power authority to talk about how much energy is being expanded in the building in order to drive a different energy budget. So imagining that all these systems are going to have interfaces, where the interface isn't as simple as the human-computer interaction, but might be something bigger and more complicated. And here it's also a matter of not dragging 20th and 21st century metaphors for interaction with us into the new world. I do not want to contemplate a world in which every encounter with a cyber-physical system commences with me attempting to remember what my 12 digit password is that has an uppercase and a lowercase, a hexadecimal and a number. That would be bad. And yet we know that when we think about security, that is in fact the dominant notion we have is that we should have hard passwords. I don't want a world of hard passwords. I also know from having spent time in the security space in the United States that the notion of using your biometric ID as an authentication isn't very helpful either. The United States government as of two years ago had this most single significant breach in their data layer, and what they lost was 20 million records of individuals. That was one thing. The second thing they lost much more importantly was a million fingerprints, or more to the point, 10 million fingerprints of one million individuals. And whilst it is the case that we can change our passwords or update them every 90 days, it's a completely different thing to think about how we might update our fingerprints. So imagining how it is that we want to engage with these systems, complicated too. So what we know a year in is that there are at least five questions about cyber-physical systems, not three, because I really am quite fond of Brian. I'm not gonna rename the institute Amy, which is I think what this gets us to, because that, whilst attractive, is not helpful. So we will still be the three A's plus two. So remember I said after you build a body of knowledge you should verify and test it. So that's the second thing we've been doing. Over the last three to six months is thinking about how do you test and verify those questions, right? What would it mean to start to say, well that makes sense to me, that it makes sense to anyone else. So part of what you do is go and spend a lot of time talking to people and going here are the questions, do they resonate? And once people get past the fact that I keep putting a picture of Furby up there, because why not, they are willing to start to say yeah maybe. So part of what we decided in addition to having conversations with, as Brian listed, our peers around the university, our peers in the broader community, people in both the tech field in Australia and the US and across other sectors, in addition to having those kind of conversations we also thought it would be really good to conduct qualitative research. And so to that end, starting about two and a half months ago we've been kicking off slowly an initiative to conduct qualitative research in five field sites around the world. We wanted to find people that were thinking about cyber physical systems. Maybe they were building them, maybe they were regulating them, maybe they were imagining what they could do with them, but we were interested in thinking about beyond autonomous vehicles and drones in those senses, what were people doing and how might spending time with people who were building, working through and using those systems help inform our questions better. And while I don't wanna get into the details of who the partners are, suffice it to say it is global. It includes scientific research organizations, it includes at least one arts and creative organization because I think it's really important to get beyond imagining this is all just about productivity tools. Imagine it includes technical systems that are in the air, underwater, happily for Brian and I in space. Turns out building semi-autonomous and autonomous things for space is much harder than almost anything else for obvious reasons. You can't pick it up, smash it around and reboot it. It's too far away. So we have the interesting challenge of now spending time in those organizations of getting a sense of what people's imaginings of these objects are about what they hope they're gonna do with them, about what they're already doing with them, about what the challenges that they are already encountering. And some of the earliest conversations here are kind of the obvious ones, right? Everyone is grappling with the, what set of rules do you use? In both pragmatic sense and a technical sense.  Everyone is grappling with the, that thing didn't quite work the way I expected it to. And those are the right kinds of places, right? So I hope a year from now, in a different building somewhere on campus, we'll get to talk about what that set of research has produced. But for me, it was a way of starting to say, rather than trying to build a system and build an applied science and isolation, we should build it in conversation and in dialogue. That was hugely important to me. And then you remember, after testing and verifying, there was training a whole new generation of practitioners. And you'll be happy to know, Dean Huntington, it is past five PM. So we are training a new generation of practitioners. In order to do that, we decided to do a couple of things. We got together all sorts of people from all over the university and went, come and hang around with us. Also, please come and make things with us. Also, let's see if any of those ideas scale. Good news is they did. And we had a whole series of extraordinary moments led by a number of wonderful people in this room where we actually spent time trying to work out whether there were ways that we could talk about those questions and talk about those objects and talk about the consequences in a way that started to get us closer to an academic encounter or an academic endeavor. When Brian and Eleanor and I kicked off the institute a year ago, there was a white paper. In the appendix to that white paper, I said we wouldn't get around to educational activity until 2022. That seemed like a really long time from now. And so 10 weeks ago, I went to the vice chancellor and said, I think I can do it quicker. And Brian, to his everlasting credit, went, yeah, okay.  That is not what's going to happen, but go your hardest. And go our hardest, my team, and I did. So over the last 10 weeks, my team and I have built out a series of courses that we hope we can use as a way to test the applied science. Why wait till it's perfect? When the clearest lesson I have over the last 20 years is, iterate, learn, fail, pick yourself up, and do it again. And so we built out courses. We built out courses that we think have something distinctive, unique, and wonderful in them. That for me is about how do we move from problem solving to question framing? How do we think about teaching in a slightly different way? And how might we create that first generation of practitioners? And so in a manner that is deeply complicated, the arcana of which will sit within the rules and walls of this institution, what I can tell you is that, starting on Monday, the 3A Institute will be taking applications for its first cohort of students in 2019. And the mic died. No, that didn't, good. That would be mean. Can't kill it now. It's on a slide. Maya's gonna put it on the internet. Exactly, right. So as of Monday, we will be taking students. We'll be taking them in a complicated manner, but we will be taking 10 students as our first, hopefully, experiment in education. We're hoping people will come and spend a year with us and co-design the courses with us. We've built them, but we want people to, well, break them with us and make them better. So Monday morning, we will be live. We'll be accepting applications. Please send everyone you know. I know it's Canberra. That's the deal. They will have to come here. But I'm incredibly excited. And I know that my colleagues in the academy here are excited and I know my colleagues in the administration are excited too. And it should have been in 2022 that we said this, but I like that I get to say it in 2018 instead. So we're gonna start making a new generation of practitioners and scholars go. The best thing is my team only found out about this three hours ago. And the vice chancellor only found out about it. An hour and 10 minutes ago. And Eleanor made me promise I wouldn't get to this slide until 5 p.m. when the system refreshed. Because now it will be live in the tool, but it wasn't until 5 p.m. So I've stalled almost enough now and I can go on to the next slide. So last but by no means least. For me and my team, I think what we need to get done is really clear. When I look out over the next year and beyond, I know it's really just about three things. And it's about the same three things it's always been about. How do we create a new body of knowledge that's about both knowledge and praxis? Because I think it isn't just about research. It's actually about the doing as well. It's hugely important to my team and I that this doesn't happen in isolation. I don't wanna be sitting in a room somewhere talking to ourselves. I desperately want this to be a broader conversation with everyone. And last but no means least, I don't want the knowledge that we're making to be locked up inside the institution. I started thinking the other day, Brian, that universities were a bit like seed libraries. We got to hold seeds, but really what we should be doing was sending them out into the world so other people can cultivate them. And that's what my team and I want. We wanna build something. That's why we all came here. We wanna build something. We wanna build something that matters and we wanna build it at scale. And that means exactly like a year ago, I still need everyone's help because I scaled safely in a year. I've gone from one to 10, but we're gonna need a little bit more to get it really done. So I asked a year ago for the help of everyone in the room and everyone out there on the internet. I don't need help with my pronunciation anymore. It's kind of a foregone conclusion. I do need help with all of this work and this heavy lifting. If there are people you think we should be talking to, if there are ideas you think are interesting, if you just wanna come and spend time with us, find us, connect to us, we have a mailing list, we have a website, by Monday we'll be taking applications. I'm just gonna keep saying that so that that makes it true. On Monday we'll be taking students. So please, all I know is that a year ago this was the right place to do it and it was the right time and a year later those things are both still true. I sometimes get asked 18 months in why I would possibly come back to Australia and leave Silicon Valley and why in some ways the inference being I would be foolish enough to do this on the edge of the universe. Here's what I know. The only way you could build this is not to be in Silicon Valley. I have a great deal of respect for my colleagues and my friends there and I miss a great deal about my life there. I also know that the time scales there are very, very short and short term there is oh maybe this quarter and I need an academic short term of about a year for the next piece of work to get done. But I hope I won't be doing it by myself. I know there are at least 10 people in the room who are doing it with me and I hope there will be a whole lot more. So with that I'm gonna stop and say thank you.


We are going into this conference with a lot of confidence. There have been many united struggles and victorious struggles, especially the Rajasthan and Maharashtra struggle, is something which has caught the imagination of the peasantry across the country. And it is in 92 that we had the 27th conference at Hisar itself. And that conference had made a correct evaluation that the neoliberal economic policies are going to lead to a serious crisis for the farmers in our country. While most other organizations at that time were hoping that it would bring better prices and better quality seeds and inputs and so on, we made a correct assessment. Now it is 25 years after that and we are all seeing what kind of a crisis that has led to. This conference will be a milestone because it is in the backdrop of a lot of struggles and we have been able to build a network with a lot of organizations with which may not be on every issue we agree, but we are able to build an issue-based unity. That is, first we began with the Bhoomi Adhikar Andolan which was against the land acquisition ordinance and that unity has further, we have been able to expand it to many more organizations after the Bhoomi Adhikar Andolan. We have the All India Kisan-Sanghar coordination committee which has come, more than 175 organizations in that. And also the more recent initiative of the Jan Ekta Jan Adhikar Andolan which is there that is a broad unity of left-hand democratic forces, Kisan agriculture workers, the working class, Dalit, Adivasi. So this is the coming days are going to be days of intense struggles. And this anti-people, anti-farmers, Modi government is going to face a lot of struggles in coming days. And we have given a call that Kisan Sabha in every village, every Kisan in Kisan Sabha. This is in line with this united struggles that are going on. Earlier Kisan Sabha when it was formed, it was an organization representing Kisans and the interests of the Kisans with people from different political leanings also being there. So we are trying to get all Kisans into the Kisan Sabha and emerge as the true representative of their struggles to stand with them, to be a part of their sufferings, to give confidence to them and struggle for their rights in coming days.


Hi, my name is Ezra Cotton. I'm from Delmont, New York, USA, and I'm the developer of climate catastrophe. The game starts out in a polluted city and you go through a simulation to learn more about climate change so that you can save your city. I incorporated some facts in my game from a project for school about climate change that I did in the spring. This isn't the first game that I've created. I've used Cove.org to create projects in the past and to create this game, I use GameStone Mechanics. I like to play Massage Zombies, Nitro Type, Mario Kart, and Overcooked, and some of my favorite hobbies are reading, drawing, bike riding, and playing Ultimate Frisbee.


All right guys, welcome to the channel. In today's video, we're gonna do a full walkthrough of Anki, exactly how to use it. Also, I'll be sharing with you a unique method that I specifically use to improve my grades, my retention, while decreasing my time. Let's get into it. All right guys, welcome. In case you're new to the channel, my name is Lakshman, internal medicine physician and resident in training. I'm really excited to make this video because Anki was one of my favorite tools to use as a medical student, but also one of the things I still use now as a doctor. Now I know there's plenty of you guys watching this video that may be in medical school, on your medical journey or are just trying to use Anki for your particular academic or learning purposes. So depending on how familiar you are with the platform, feel free to just check out some of the links down below to the various chapters in the video, so that way you can find the parts that are most important to you. And if you do enjoy this video, I've made tons of videos on Anki and how I use it as a medical student, all throughout this channel and those will also be linked down below as well as a playlist. So first let's quickly just talk about exactly what Anki is. Now, there's plenty of flashcard systems out there, but the way I would define Anki is that it's an intelligent flashcard system that's designed around space repetition. You may be familiar with other popular flashcard systems like Quizlet, which are amazing and I definitely use them in my college days. So the beauty of Anki is that as you're doing your flashcard it starts to recognize which topics you're good at, which topics you're kind of average at and which topics you're amazing at. And in the future, whenever you're reviewing your flashcards it's going to have an increased frequency of those garbage topics, the things that you're really weak on that it's going to show you first and it's going to intermittently kind of sprinkle in the topics you're averaging and even less so of the topics you're good at. And so overall it becomes a fantastic and free resource that you can use to improve your active learning depending on if you're on your medical journey, you're trying to learn a class for college or if you're trying to, for example, learn a new language, it fits all of those modes perfectly. So next let's talk about how to set up Anki. Now, the beauty of Anki is that it's free and you can use it on both PC as well as a Mac. And for the demonstrations of this video we'll be doing it on a PC. So for all of you Mac lovers it's essentially the same thing. And you can also use it on your phone, Android or iOS. Now for the app version for Android it's absolutely free and for iOS unfortunately it does have a little bit of a cost but it's totally worth it because you can sync to your multiple devices and have your flashcards on your computer and then immediately to your phone. So for the purposes of this video I'm just gonna demonstrate on my laptop. So the first thing you're gonna do is to go to the Anki website and I'll link that down below as well. And again it's free so you can just go ahead and click download and then find the version that is best suitable for you. And I already have it kind of downloaded already but as soon as you do on your desktop you'll go ahead and see the Anki desktop icon. And you click it the first time it's basically going to ask you to kind of create a username and a profile. This is basically so your email can then be linked to all the flashcards you create. So in case then if you try to open up on your phone or your iPad or any other device you can easily sync it based off of that free account you created. Now in addition when you create Anki it's going to give you the option of creating a profile. Now most people just have one profile so that's kind of like your whole kind of portfolio of flashcards. You can also kind of get really advanced with this. So example if you're studying for a board exam like if you're studying for your MCAT when you're in medical school trying to get into med school and you can have just one profile for all the flashcards that are related to the MCAT and then you can have a profile for just your college classes or if you're studying in medical school you can have just one for your lectures and then some for your board exams that you're studying on the side. So as you guys can see I have various different profiles that I've created just for demos. So one of them is the Step 1 Academy. So in case you're studying for Step 1 that'll also be linked down below but we can just go ahead and log in. So now that we're done setting up Anki we can go ahead and start creating your own flashcards.  And later in this video we'll talk about creating your own flashcards as well as using flashcards that other people have already made. And before we get into the flashcards I want to talk about creating decks because this may give you an idea of how to structure your flashcards and then also give you an idea of the power that Anki has. So for example let's say that you're about to study for your cardiology block. Now you kind of have one so I'm just going to say cardiology class. So this can be a deck that you can essentially put all of your cardiology related topics so that's where you're learning in class right now. But let's say you instead want to get a little bit more macular and you want to go ahead and actually split it up based off of lectures which is what I recommend. And you can say lecture one say you're learning about EKGs that day, lecture two let's say you're learning about CHF or heart failure. Now the beauty is is that what you can do is you can essentially go ahead and move that into your perspective. So now as you can see under cardiology class the deck that I created, I have all of my lectures and then as I continue to do more classes I can go ahead and add it in. And if I wanted to I can do flashcards from the whole cardiology block or specifically from that lecture depending on how I wanted to review. So the idea of having these kind of decks and then mini decks and then mini mini decks you have a lot of control of how you want to review your material. But now let's go ahead and talk about the fun stuff which is how to create the flashcards themselves. Now we're going to go ahead and break down some of the most common types of flashcards and give you an idea of what kind of things you can do with Anki. And then later in the video I'm also be sharing with you some of the advanced techniques that you should be using to ideally increase your retention while decreasing your time. So the first thing we're just going to go ahead and do is create an Anki tutorial for this YouTube video. And here is the deck I have. So right now I have no flashcards but what you can do is you can just go ahead and click add and then you can go ahead and click to the various types of flashcards. Now this may look overwhelming but the most basic flashcard that you can create is as a basic flashcard. That's what it's called. It's a simple front and back with the question that you would put as well as the answer. And I'm just going to put question one because it recognizes that I already have a flashcard like that. So if I click add and I click close now when I'm in my Anki deck I can click study and it's just going to ask me that question and I need to know that the answer is answered. And then you get to kind of play with the beauty of Anki itself which is telling yourself how well do you know this information. So if I don't know the answer really well I can click show to me again in a minute. If I do know it pretty well then I can say show it to me in 10 minutes. And if it's something I really, really can master then four days. And so it's a nice way to just press the button based off your immediate comfort level and then Anki will do everything in the background. So that is one type of flashcard that you can create but let's talk about a few other ones. Some of the ones that I like are the ability where you can create multiple flashcards with one effort. And so some things you can do is a basic and a reverse flashcard. And basically this is great for things like language translations or memorizing structures. So let's just say for example you're trying to learn the language you can have both the English version of the word as well as the translation and that respective language. And so you can go ahead and have the English and then translation whatever language you're trying to learn and click add. But now the beauty is that it's actually added not just one card but it's actually added two cards. As you can see now I have three flashcards. So the first one that's gonna show me is just the one that I did. But now I have English and then translation but I also have translation to the English. So it's a nice way to flip flop cards when it doesn't really matter which side you see first you can actually quiz yourself interchangeably. Now those are some of the most basic flashcards you can create but you can get really powerful with Anki and so this is where I started to geek out. But the next flashcard that I definitely recommend you guys kind of learning about and how to use is the close function. So close flashcards they basically imagine you have a phrase or a long thing that you have to memorize within it. So you know process A equals process B plus C. Let's just say you have to memorize an equation. So what you can actually do is just let's say I'm gonna go ahead and take something from this Anki script that I actually created for this video. So I'm just gonna copy it in there and let's just say I wanted to memorize this part of the flashcard for my respective class. You can just click these three dots right here or go control shift C if you're doing this on a PC. And then what it's gonna go ahead and do is if I click and actually review that flashcard it's gonna go and then block off that respective word and I have to memorize what went in there. So in this case it was an intelligent flashcard system. And since I got it right I can go ahead and click four days. Now again you can get even more advanced with this. So again if I go ahead and just paste that phrase in what I can do is I can now choose that respective word that I wanted but I can also add in other things from that phrase that I also wanted to quiz me on. So again the beauty of creating multiple flashcards from one phrase or one question. So in case you wanted to memorize different things in a phrase but you wanted to have one flashcard to quiz you on one part and another flashcard to quiz you on another that's also great. So here I'm just gonna go show you this example. If I click show the answers I'm just gonna click through. So as you guys can see this is the flashcard number one that I created. And then now I actually have another flashcard that's quizzing me on the latter part of the thing that I clicked. And then to finally close out this closed function that is definitely a tongue twister. One thing you can do as a little bit more of an advanced strategy is you can just go ahead and do the same thing. But let's say you wanted to get quizzed on both flashcards at the same time. Then what you can actually do is you can instead of clicking C2 and C1 you can just make both of these C1s. And so now when I see this flashcard it's actually going to force me to actually be asked both of the information at the same time instead of one at itself. So if you have a really long kind of chain of commands or if you have bullet points and you want to memorize a few of them together or all the bullet points as part of your flashcard then you can just click C1 or the same number and then that card will quiz you on all of them. Now the last type of flashcard that I want to show you is really where Anki gets really cool especially if you're trying to memorize diagrams and figures and like maps is the image occlusion tool. So the image occlusion tool basically gives you the function of blocking off different things on a picture or an image and then making that into a flashcard. Now this is an add-on, it's a free add-on and so let me just show you quickly how you add this on to your Anki portfolio. So one thing you're just gonna quickly do is you're gonna just go ahead and Google image occlusion Anki. So we're just gonna click on one of these for the example. So we're just gonna go down here and it's gonna kind of show you the ability of what that add-on can do. And then to actually install this add-on all you have to do is just go down and find the number that I'll give you. So in this case it's right here so I'm just gonna control copy that and go back into my Anki and then you can just click on tools and click on add-ons. And I already had this installed but just for your all purposes go ahead and click get add-on and paste that code in. It's gonna go ahead and download it and install it and then all you have to do is just restart your Anki before you can actually use that tool. So now let me show you exactly what the image occlusion tool can do and what type of cool things you can do with this especially if you're learning for things like anatomy. So let's click add and we're gonna click image occlusion and click choose. Now a lot of stuff can go here but all you have to do is just click on this last button that says add image occlusion and then just find a picture. So I have a picture of the heart here and now the beauty of it is essentially it's default it's where all you have to do is just kind of click over all the structures that you really need to know. So and you don't have to click on everything so let's say in this situation I didn't really care to memorize the septum but everything else was kind of fair game then you can just quickly in the matter just a few seconds click over everything which is good for this example. And then you get to decide essentially how you want this flash card to be presented. Do you want it to look like this and then basically quiz you on one thing at a time but the rest of them are still kind of blocked off or do you want everything to be exposed except the one piece of information you need to know. So I'm just gonna say, you know, hide one, guess one meaning it can show the rest of them. It doesn't really matter you can play with however you like but as you can see I've added 11 flash cards now in a matter of just a few seconds. So if I go ahead and click show answer then this is kind of the beauty of the image occlusion it's gonna ask me to basically say what the structure is I'm hoping it's the right atrium, there we go. Otherwise it basically fails as a doctor but you can now go ahead and just keep clicking through and it's gonna ask you a different structure. Now the first time you create your flash card it's gonna ask you based off of the order that you created them. But over time what you'll find is as you say this topic is easy and this topic is hard it's gonna interchange, you know, the order of them and kind of shuffle them for a natural learning process. So that's basically how to create your own flash cards. Now before we get into some of the advanced strategies I just wanna quickly talk about how to use the work other people have done to essentially get your learning. So let's say you are trying to learn for a board exam or a language and you don't really wanna make your own flash cards you just wanna take the hard work somebody else has already done and use it for your own learning. So one of the great examples is when I'm in medical school one of the board exams we have to study for is step one and there's plenty of flash cards that people have made and some more popular ones that people use. So Zonky for example is a great one that I kind of include for my Step One Academy students which is why it's here but to get those kinds of flash cards all you have to really do is kind of Google what you really want and see if there's an Onky deck. So if you're just trying to learn Spanish I'm sure if you Google Spanish and like Onky into Google you'll find a flash card deck for that. If you're trying to learn for the MCAT you can find respective flash cards there as well. And so essentially what you can do is just try to find something on Reddit which is a good option or sometimes the Onky website actually has it. So in this example it does and all you have to do is just click download and then once it's finished downloading you can just go to File and Import and it'll actually just take a bit but it will import into that profile that you created. So as you guys can see this is a pre-made deck that somebody else has made for a medical board exam and it has all of the decks as well as mini deck and it essentially can get as big or detail as I want into the topic. So now let's get to the part of the video that's gonna be probably the most exciting and where I get to geek out the most and that's talking about advanced strategies that you can use to really increase your retention. Now one of the biggest downfalls in really any flash card system is that sometimes it just takes you forever to make flash cards and then you really don't have enough time to actually review them. So when I was in medical school I tried to find as many techniques as possible to minimize that first part which is creating my flash cards and making them efficient. Now when you use a close and an image occlusion again you can make multiple flash cards and essentially one effort but some of the other things that I really enjoy doing is something I call my screenshot method. So essentially what you can do is you can on one side have a PowerPoint of your lectures or a PDF of your syllabus, really any material and then you can have anki and you can use either a snipping tool on Windows and PC or you can use the actual screenshot tools that the max allow you to do. Then you can essentially go ahead and screenshot the whole PowerPoint. So for example, I'll just say I was trying to create a flash card so I can click add and I'm just gonna create a basic one for the purposes of this video. Instead of having to type out various things from this flash card to try to memorize my main thing is I wanna be able to quiz myself as quickly as possible on this PowerPoint. And so what I like to do is actually make the heading of the PowerPoint, the question and then I actually will take a screenshot of just the overall PowerPoint and you can essentially just quiz yourself there. So you can then have the PowerPoint and then slowly as you see the flash card just quiz yourself on one thing at a time. I talk about other things on how to actually do this method on various videos so I'll link those down below but this will really definitely speed up your process of creating flash cards so you can get to that learning part which is the most important. Now the Anki method or the screenshot method by itself just works fantastically. I've had students that have just been able to boost their grade and improve their free time by itself. If you wanna get to a little bit more of an advanced stage what you can do is you can again make this into an image occlusion. So to use this combination of the image occlusion as well as the Anki method one of the cool things you can go ahead and do is actually create a folder of all the screenshots that you want and collect them or you can do this one at a time. So Anki has the beauty of collecting the screenshots that you just do last. So for example, if I took a screenshot of this again and then I go into my image occlusion it's gonna memorize that that's basically what saved. And so now you can essentially look at this flash card and ask yourself like what parts do I want to memorize? So I'll just say the basic things that I wanna do for the workup is X, Y, and Z. So I wanna quiz myself. I wanna quiz myself on what this EKG was and then also this one. And then I'm just gonna select the features of the flash card that are most important and I'm going to just kind of blank out the rest. And now I just wanted to basically say I wanna hide one and guess one. So it's created six flash cards from this PowerPoint really quickly. So essentially without having to type anything up I've essentially been able to create that flash card. So for example, let me just show you what that would look like. So as you guys can see now the flash card shows up and it's really just gonna quiz me on the flash card itself without me having to get crazy with the question. And the first part is gonna be what is the basic workup or some of the A5s? I really need to know that I need to take their history of chess, x-ray, or electrolytes. If I don't get all of it, then I can just show it to me again in a minute, Anki. As you guys can see using this image occlusion tool plus the screenshot tool can essentially get away without having to type anything actually into Anki itself and just using that PowerPoint or the syllabus lecture you wanna memorize and then using image occlusion tool to make multiple flash cards and forcing yourself to just memorize the things of the PowerPoint that you actually ended up having to do for the exam. Now hopefully you guys enjoyed those first two techniques and I have one more advanced strategies to really share with you the power that Anki can do and really kind of hone in, especially when you're studying for exams. One of the most difficult things that I had, especially when I started was his ability of saying, okay, like the test is coming up. I know there's hard topics and the easy topics. Anki's gonna start to quiz me on everything and I really just wanna focus on the hard things now because I know that's gonna cost me my grade if I don't get these mastered. So one that you can do is you can actually have use the function of tags. So if you click on browse, basically what a tag is, is essentially you can label a card for multiple reasons. So if you wanna say one card is cardiology, another card is an EKG, and another card is from this lecture. You have a lot of control. But one thing I like to do is actually like to add different tags in terms of the difficulty level of a flashcard. So let's say we're going through the browse menu. I found the flashcards I created. This, by the way, is a great option for you to kind of add notes. So if I'm in this lecture and I'm learning something about AFib, then I add my notes here and it'll show my flashcards in the future. But really where I wanna focus on is the tags. So let's just say, for example, as I'm going through this flashcard over time, I realize that it's a hard flashcard. I go to the next one and I realize this is an easy flashcard. And then the next one, I'm gonna call this a hard flashcard. And we'll just do one more in this example. We'll just say, hey, for some reason, this flashcard is difficult. Now, if I click out one option you can use and Anki is actually creating a filtered deck.  So what we can actually do is say, Anki, show me tags that are labeled as hard. And if you do this correctly, what you'll find is it'll say filter deck one or whatever. Like, study now, it's gonna show you those flashcards that you labeled at one point or another with that tag. And so as you guys can see, these are all of the tags that we added below and it'll just kind of continue. Now, the beauty of this method is that if you were studying for these flashcards in general, you can just go to edit and add tags. So this flashcard already had a tag for hard and then now it's suddenly easy. You can just type in easy or if it never had a tag at all, you can go ahead and label it based off your difficulty. So as you're getting closer to exam time, you can start labeling topics based off of how important and difficult they are for you because again, when test time is getting close, you really just want to say, Anki, I know you wanted to use space repetition, but right now I need to focus on difficult topics. Please show me those. So this is a great way to kind of get around that. And hopefully those last few methods really gave you some ideas of how to use Anki and the power of Anki. And again, all of this is free, which is just absolutely remarkable, but I want to give you some kind of remaining tips that I use to really kind of speed up my review process and make them really effective. So let's just say, for example, we're gonna go ahead and study it for step one. So one thing you can do is as you're going through the flashcard, you want to keep in mind things that slow you down. Now, often when we see a brand new flashcard, the first kind of method that I like to use to make sure that I'm always giving all of the topics importance and priority is I have kind of a mental timer. And sometimes I'll make it as low as 10 seconds, which means you have 10 seconds to answer the question and 10 seconds to review the actual answer that comes up. So this prevents me from getting too into a flashcard. It's not gonna just simply accept, you know, I don't know it, show it to me again in a minute. So let's say this is a flashcard and to avoid me kind of getting too much into the weeds of this figure, I can just say you have 10 seconds and then you have to click one minute or 10 minutes. And so having this timer method, especially if flashcards are hard for you to review, works really well. Now, again, the amount of time that you focus per flashcard doesn't really matter, but just having a timer just keeps you going and that way one topic is no more important than the topics of the very end of your flashcard deck. And that's my timer method, but number two is to really break down a large flashcard into tiny bits of information you want to memorize. So again, the beauty of Anki is that I can show it to you in a schedule. So there's no pressure of trying to memorize everything at once. And I feel like medical students and just students in general will do this a lot. So let's just say, for example, I'm trying to memorize the various structures of the skin in this flashcard. You know, somebody's first approach is gonna say, let me try to memorize everything at the same time. But again, you're not giving equal priority to other flashcards because by the time you get to the end of your flashcard review, you're likely gonna have low energy less focused. So what I like to do is instead say, okay, you have about 20 seconds. And let's just memorize the first two layers of this and then I'm gonna see this flashcard again. No big deal. So I can answer the question, so get that correct. But if there's something in the figure or the PowerPoint, if I was using the screenshot method that I wanted to do, I'm just gonna say, let's memorize these two. So the corneum and the granulosa. And then if I get those right, perfect. I can move on to the next three to four structures that it's labeling. And I can click show it to me again in a minute. Now I see that flashcard next time. The first thing I have to ask myself is did I memorize those two things? If so, cool. Let's add one more thing and show it to me again in a minute. This way you're constantly moving. Topics are all having equal priority and the learning process just has so much more of a flow and it's just not a stress. But that guys is my broad overview on how to exactly use Anki. Hopefully you guys really enjoyed this video. Put a lot of effort to kind of show the different things that I personally use, the things that I found medical students and other students using. So let me know in the comments down below what you guys think, what other questions you may have for Anki. Now remember, I've created tons of Anki related videos here on the YouTube channel. So make sure you check out that playlist down below. And while you're down there, probably just go ahead and smash that like button because if you made it to the end of the video, that means the video hopefully gave you enough value and some ideas on how to use it for school. So if you did enjoy this video, I'd really appreciate it at least for YouTube to kind of show this video to as many students as possible. Go ahead and smash that like button. And if you do enjoy the kind of advanced strategies that I've shared in this video, there's tons more across the YouTube channel and plenty more coming your way. So go ahead and hit that subscribe button and notification bell. If you enjoyed this video, I know you're gonna enjoy this video of how you can boost your grades and retention by just using one tip. And in this video, basically how I show you a full walkthrough of how you use that Anki screenshot method. If you are on your medical journey, then check out some of the programs that have for you down below. Basically showing you more step-by-step videos just like this on how to improve your retention, get better grades, and become a less stressful man student. But with that being said, guys, thank you so much for watching. Thanks for being a part of my journey. Hopefully I have this little help to you on yours and I'll see you guys in the next one. Peace.


Thank you Normally, I just give workshops around the libraries that I contribute to or something else related to machine learning, but today it's a bit of an Emotional talk because of the topic as you can see So I was introduced very well. So I'm going to move on to the next slide So in February there was a very big earthquake that had a magnitude of 7.8 and it was in the South Eastern Turkey and I think north of Syria and Many lives were just ruined that day and in In in like few days there was like 40,000 40,000 million 40,000 people died and so many were injured in just a few days and at the end it was like an area as big as Germany and 16 million people were affected because of it like an entire some of the cities were completely just destroyed because of it and what happened so That day We saw that there's a common pattern with the survivors survivors are posting a writing of a screenshot and posting it on Instagram saying hey, I'm I'm here This is my address. Can you come and rescue me and then they take the screenshot of that writing and then they post it or They they try to send messages to their relatives and They also post to Twitter or there are several telegram channels that take these take these notifications and We were a bunch of voluntary hackers We were in a discord group and we were like there is this survivor guilt that you need to do something about it and It's We just try to come up with ways that we could somehow make use of this data and Actually rescue people Because it would get lost and like the area was like so big the The civilians had to help a lot. There was a very big voluntary effort. So we were like, how can we contribute to this? and We were first asked to build an OCR application if you don't know OCR is optical character recognition and what it essentially does is that you have like type text or handwritten text In an image and you can extract the text that is inside it and just pass it. So your input is a screenshot of writing and your output is the text and this Application should return the parsed address not not exactly the text with the parsed version of it into streets apartment number and more So we built one Initially and this is how it looked like so you would you would in here you would input the screenshot and you could get directly The parsed address or you could also input the text if you wish and then you could get the address so we could actually crowdsource this data collection or Because this UI can be used as an API you could send a batch requests to get your addresses parsed and this was This was used a lot like you could actually send a request with the screenshot the It's base 64 encoded or you could send the text directly and you would get the parsed address if you want to and For OCR we tried many libraries. We tried pedal OCR easy OCR and tesseract But at the end we decided to go with easy OCR because of some of the characters not being parsed properly and This this whole address parsing is called named entity recognition and you can parse Streets apartments and so on you can get the names with named entity recognition this is actually the abstracted name of the problem that we were solving and Initially we used the open AI API, but we easily ran out of credit. So this application would crash a lot and We later decided to train our model Which brings me to today's talk which is about the disaster map application that we have developed so disaster map is essentially a front-end with With points or data points on it visualized on the addresses and you can filter what people need and You can just go to the data points which are matched with the longitude and the latitude of the person posting the tweet or Something and you can provide them with what they need and This was the amount of the track the amount of traffic it got in In only one month it was used a lot and we have received so many messages That people were actually saved thanks to this application and What's what's happening was that we were first asked To make a meaning like this distill the tweets and the messages into following information Overnors post owners needs post owners address but in a structured manner so we can pass to the Google's GIS API and The post-offner's name and phone number So there was a couple of problems with this or because we are a bunch of hackers There was a problem around the anonymization and storing the tweets We later learned that even getting these These information and putting it inside the plain CSV is actually processing it Like just collecting tweets and this might be in breach of you know, GDPR and so on and There was many challenges around the data drift as well, which I will Later come to today So before I move on to that I would like to Explain our methodology a bit So in here we have we have solved mainly natural language understanding problems And the in natural language understanding where you are doing text classification token classification what you want and not generation like GPT stuff The state of the art is the transformers and transfer learning transformer is the architecture and Transfer learning is to transfer information from a very big pre-trained date Pre-trained model so you can imagine for instance GPT GPT's are very big models or like there is for computer vision. There is ResNet and other things You can have that pre-trained model and then you can just adapt that model to Be useful in your own use case This is called fine-tuning and this is currently the state of the art I will explain the how it's done later on but We directly picked Transfer transfer learning for this use case and it's we also later compared it to NXG boost trained from scratch and it was again outperforming it. We also Compared it to OpenAI as API for generation But it was like a Restricted generation and it was again outperforming it because we were actually Fine-tuning a model for our use case So this is the BERT model in case you don't know BERT model It's it's the biggest one of the biggest changes in the paradigm of natural language processing. So Today's models like GPT, Lama and others are originating from the Transformers architecture and this is one of the first Transformers architectures used for natural language processing and Can I just move? Yeah, so What you can do with BERT is that and this was released by Google back in the day what you can do with BERT is that you have this pre-trained model and You put a classifier head on top of it and it can be about text classification for instance and if you have a text classification If you want to train for text classification with three outputs You just put a classifier layer on top of it with three out three three nodes and then you can just train that part and you will be transferring the knowledge you have from BERT To your use case. So you only train the classifier layer or you can also Unfreeze the previous layers. It's up to you, but it only takes three minutes on I think we used T4 T4 GPU three minutes on T4, so it's very simple and Yeah, that's why we picked this and on top of this this also comes with position lambeddings which improve the performance That's why I think it performed way better than XGBoost or any other architecture and Transformers is the library that makes a lot of obstructions over the whole transfer learning process because it's actually quite a handful, but you can just You can just abstract things away. Thanks to transformers and like it takes very little Change in the code if you would like to use it It has something called trainer API which you can directly pass your data and some hyperparameters and Fine-tune a model and it will just work fine So initially for intent classification, we were asked to do intent classification so that we can classify Shelter food water and more we have used something called Natural language inference models and essentially what it does is that it does zero-shot text classification And zero-shot means you haven't trained these models specifically for text classification But you provide the text itself and the candidate labels It can be like shelter food Water and then it will just return in here. You can see I think it will return the class Probabilities of what this text might be so it's like a large model that you can use out of the box without Training that's why we first opted for this and this was performing as good as GPT like models and this is completely open source by the way and Yeah, like for intent classification later the model that we trained also this one is a Bert's like model It's an it's the architecture is the same. It's called encoder only models, but it's a Bert variant and for address parsing we tried the GPT 3 but again, it's we ran out of it and The it would occasionally return wrong results and Confused name entities like you can have like an apartment name with a person's name and it would confuse it But if you are to train like it's often that my intuition is that and what I observed was that you shouldn't use large generative language models on Understanding tasks. It's like it's like cutting the bread with a katana. It's too overkill and you don't need that you can actually Just train your models in like three three minutes and it will eventually outperform That's case where you are paying a lot of money and The inference is not as bad And the thing is like we alongside with the named entity recognition model They've that's where parsing the address and the names that I just talked about we use regex and It's it's it's very often that the simpler solutions actually results better and The how we work together. So basically hugging face hub is like the GitHub of machine learning in a way it has repositories specifically faith made for machine learning and like it also has collaborative features like pull requests and so on so it was easier to work on that and like people could try our models right away and This also later helped us With the inference part because the inference for inference it would take a lot of work Which I will explain later So this is the first open source address parsing model that we have trained So in here You can see the inference widget and it parses the the streets the apartment the the municipality and everything and Thank you, and then we have passed this output We processed and parsed this output and passed it to the Google's GIS API and then later it's really it would return Longitude and latitude and that would be marked on the on the map directly and This was our multi-label intense classification model The the thing is people might state Multiple needs in one place like it can be I need blanket and shelter and food so you don't really classify into You know one class So we had to train multi-label intent classification and Yeah, there was a very small change Compared to text classification so on and For model serving we use the inference API of hugging face Because the thing is like if we were to get the model itself We to make it usable We would have to dockerize it and then put it to a cloud instance and so on and we were racing with time so This was very convenient because once you push the model to hugging face up. This is automatically enabled and Thanks to this We gained a lot of time and we could swap the models very quickly in the production and they charge per Per-token as far as I remember back in the day. So it's not it wasn't much at all and The problem was that after these model was deployed these models were deployed The problem was that there was a lot of data drift. So what was happening was that as The time passes Like the effects of the earthquakes actually stayed for one month or something as the time passes People's needs were changing a lot like in the first week You would act people would ask to be rescued under the building and Then in the second week you would have people asking for shelter and food because it was very cold over there And like even if people were rescued they would actually freeze to death or They were they couldn't find water and food and that was that was quite a problem and Later on this evolved into sanitary needs of people and other things So the tweets and the posts and the messages that we were receiving were constantly evolving and it wasn't it it's it's it was a challenging task and the problem another problem was that We weren't allowed to see the data that was coming to coming care from production they just weren't going to store it for privacy reasons and because of this also like they were on us like They were saying that we need to remove our data or anonymize it and store that instead of Keeping it because in this data you have phone numbers Names of people addresses, which is unimaginable within EU but acceptable within Turkey and And for this we use the faker library to anonymize and store the data without hurting the model performance and For the for the parts where we would have to adapt to the data drift We would have to crowdsource this process so people like we have the tweets with the timestamps and everything so we built a UI using our Gila and radio radio is like stream that if you are using stream that it's like you are building a UI for your machine learning model and The we had like multiple of these but this one for instance you input the data and then it's in the back end There is a model that classifies the The addresses and then you can say hey, this is this is true This is false or this is ambiguous and then it drops to a database where we collect The where we collect the incorrect stuff and we can later adapt our model and This was hosted open to everyone. So everyone could contribute to the To this but the problem is that data labeling itself is very hard like I remember We got a very big generous grants from Microsoft Asia of 150,000 I don't know the currency. I think it was in dollars and We also use this for labeling and everything and the initial initial thing will initial data set that we labeled was Very bad because there is a rush and everything and the guidelines weren't as clear and If you don't really have this then you cannot come up with a standardized thing and in labeling in data Labeling what you should do is like you should have people approving things Approving the labeled data and like it's if it passes from like six people or something then it goes on to your data set and we didn't have time for that that was very awful and Yeah, after a while we have improved our data set as well because we had clearer guidelines and everything but Then you know as you race with the time the problem is that you Begin with the people being rescued from refugees and then and then you need to first get them and That is where you need to race the hardest with the time and then the importance decreases eventually and That was the problem with the labeling and everything and Thanks to the hugging face repositories. We were able to host tensor board instances and We also had that built a leaderboard for the For the models because multiple people were training models. It's like a very big team effort We evaluated them and then we would write into the Model repositories themselves, so it would automatically get the leaderboard would automatically get them and then just Ranked them so we would use the best model that was available and In here we cared about recall for the multi-label intent because you would You you should be caring about false false positive false negatives Because if a person has a need and if you are not attending to it, then this is a problem That's why we opted for recall and F1 score And so it was the completely open source ML OPS pipeline So we would clean and version the data and then experiment using transformers and Version our models on hugging face hub and do active learning There were a bunch of other machine learning projects as well this one is determining road and building damage with remote sensing and This is about the fact that like the buildings were crushed and the roads were completely some of them were completely useless and because of this so many Like even if you were to deliver help over there, you wouldn't be able to do that because that road is just cracked and It wasn't usable so we had a bunch of computer vision engineers that Developed a model to determine do segmentation over the buildings and the roads and others so that we could inform the authorities that you know this road is useless and You should opt for another one and this was pretty cool application and This is my significant others blog he let the front-end part and if you would like to read about the front-end the back-end and the other challenges about this process you can go to his Devto profile and People are still struggling unfortunately ever since their homes were lost some of them were never able to move to another place and some of them are still living with shelters and Still they still need food and water so you can donate at abap.org and I can take questions now. It's a bit earlier. I'm sorry, but I guess So thank you very much for the talk If there was questions you could raise a hand and then go up to the microphone and ask them Let's have a quick look. Yes, please stand up and go to the microphone. Thank you. Thanks for the great talk I have a question. I don't know if you already heard maybe about initiatives like Maybe not for only for Turkey or others seismically active regions maybe can be Useful to have an application when I was listening to you. I was thinking about Kind of lack of an application where you can just press a button and specify your needs and stuff like that and Then it's gonna be sent to to authorities and I know if you if you already start maybe at government's side or maybe some Private initiatives if you start thinking about creating some kind of Application I agree that will be very useful But like the problem was that the fact that this didn't exist at all, you know, like there is a need to actually build something Why would we need to build something? Why like there are so many critiques actually why the buildings weren't Properly audited, you know, some of the buildings were not as good and that's why they crumbled and There is so many questions and what you say makes sense and I will just forward it to the discord server that we developed this on and we later on we also developed stuff for the voting processes in Turkey with OCR and everything and This discord is still active. So we might do something like that. Thank you Do you have any other questions? Yeah, it's loud. Thanks for the inspirational talk really shows you what even a small group or rather small group of hackers can Can accomplish I have a non-technical question. What after you gain the information we got Information what their people needed? How did you actually help get the? food or shelter or water so this This is the this is the map and like anyone can actually go to that website and See the address and the need and it's more to reflect this reflect this information and We have heard that people actually went through this website and they rescued other people and so they basically see the address and see the need and just go and just But those were mainly private persons helping each other not like The military or other authorities, but it was so it was it was and so NGOs were using it civilians were using it Later governments was like, yeah, we can use this but what about the data? What about the privacy and Yeah, like the Main search and rescue teams of Turkey were using yet. Okay. Yeah, thank you. You're welcome Another question, please Thanks for the talk. I have more of a technical question Okay You mentioned in your talk fine-tuning all these language models and I'm just curious how much data would you need to fine-tune a model? I guess the answer is it depends Maybe maybe you could answer By maybe talking about some factors that are important to consider for example in the case of classification a cool question Essentially the thing with the transfer learning is that You you do not need that much data anymore because you already have the features extracted you can only Focus on the data specific things. I would say The data quality should be better and you don't need as much data I would say like maybe like 5,000 samples or something should be enough It really depends on your model size if you have something like birthlike model But like models are often very small even if you have like something like distilled births, which is like a smaller version of birth You need you don't you just don't need that much, but if you are going to For instance, do a generative have a if you if you are fine-tuning a generative model, maybe T5 or something like that I think you would need more data for classification You don't need as much because it's a very simple problem to solve compared to generation where It's a bit tricky. Okay. Thank you. You're welcome. I Also work at the hugging face. By the way, if you have like hugging face related questions or transformers or Transfer learning related questions you can ask any other questions Seeing none. Okay at this point. I see no other questions But there's one will you be around tomorrow for the rest of the conference if other people want to talk to you? Yeah, of course You can just look for her tomorrow around the conference and again check the schedule which has the information also about your blog Yeah, so if you want to know more just look there. So let's have another round of applause from us. Thanks for the talk


You probably all have already used some virtual backgrounds during your Teams and Zoom meetings, where you can pretend to be somewhere else, like here, or here, or here.  Now, I've seen that apparently this feature already works on Zoom and Teams under circumstances, if you have the correct version of PowerPoint, et cetera. In the meanwhile, we can also use video setting software to create this effect in our educational videos. Now, the first thing you need is actually a green screen. It doesn't actually have to be a green screen. It could be, this worked for me in the past, some kind of sheet that is a very bright blue, or, as in this case now, a little upgrade, a $19 very bright green sheet, which should be evenly illuminated. And you should take care to take out as many wrinkles as you can to make it work better. In case you do not have a perfect green screen, a little trick that worked for me in the past is to actually do a Zoom meeting with a virtual background and upload a picture of a green background in your Zoom meeting, then record a meeting with yourself, save it, and then import that video in the software using for video editing. OK, so how do we combine a video of a presentation with my green screen overlay? So here I'm in iMovie. I already created a new movie, and I have the video of my presentation in here. Now all I'm going to do, I'm going to take my green screen video and I'm dragging it on top of it. Here is my green screen video. I click on the video, so it's yellow highlighted, and then I go here. And instead of cut away, I say green, blue screen. And magically, the whole thing already worked. And now I have my little presentation with me on top of it. So here you can do your voiceover of your presentation, your talking, maybe you want to go in some corner to not really be in the way, so you can talk and pretend that your presentation is on the screen right now. You record the video, you save to recording, and then use that in your editing later on. Those of you that have seen my other videos know that I do have a lightboard to do videos. So let's see if we can combine those two technology, the green screen and the lightboard. OK, so let's see how this looks with the lightboard. So I can write here as usual. So how does this look like with your original green screen? Or if I change the background to black, if I insert a random image, or if I make a dark blue background, or if I put this on top of a presentation, would this work?