Okay, our final lecture this morning is going to be given by Professor Hans Hermann Hoppe, and the title of this lecture is Praxeology, The Method of Economics. Hans? I forgot my fur coat. I'm sure all the greens among you would have loved to see me in the fur coat, but you will have to do without it this time, I guess. My lecture is, in a way, a recapitulation and the continuation of last night's lecture. The question that I want to address is, what is the status of economic propositions? Is economics some axiomatic deductive science, or is it what is nowadays called an empirical science, or to formulate it somewhat differently, are economic propositions logically derived from some firmly established starting point, and hence provided that no flaw occurs in the course of logical deductions, statements that are absolutely true, or as Mises said, apodictically true, or are economic propositions hypothetical statements that require some sort of empirical testing? And I want to show that, contrary to widespread beliefs, these types of methodological questions are of utmost importance and have fundamental implications depending on what the answer to this riddle is. If we look at the present situation, then we can say that all economists, except those who belong to the Austrian school, believe that economics is an empirical science, in the same sense as physics and chemistry is an empirical science. Now, this does not mean that all economists actually practice what they preach. In fact, we can say that the better economists among the mainstream economists are people who don't really practice what they preach. That is, they frequently act as if economic propositions are not testable propositions, but when you ask them are they hypothetical propositions that need to be tested, then they say, yes, of course, they need to be tested.  The view that the Austrians hold that economics is more akin to logic or mathematics in the sense that their propositions do not require to be tested, this view represented until maybe about the 1950s or so the mainstream position. Until the 1950s, the most famous book dealing with the methodology of economics was a book by Lionel Robbins, The Nature and Significance of Economic Science. You can get that here in the bookstore, a very, very good book. And Lionel Robbins had been heavily influenced by Ludwig von Mises. He frequently went to Vienna and if you look in his book, Ludwig von Mises is more frequently cited in this Nature and Significance of Economic Science book than anybody else. And this view that economics is more like logic was also the position held by many famous 19th century economists such as Jean-Baptiste Sey and Nassau Senior and John Kearns and so forth. The adoption of the new view held nowadays by almost everyone except for the Austrians that economics is an empirical science that formulates hypothesis that require continuous testing. This view was adopted mainly because of the influence of modern positivism. Modern positivism is the modern air of the empiricism in particular as espoused by David Yew. The most influential group of people promoting the positivist view, the empiricist view, is the so-called Vienna School or more precisely the so-called Schlickkreis. They existed in Vienna at the time various groups of intellectuals. They existed in Mises Kreis for instance where he assembled his students and there existed also Schlickkreis where the logical positivists came together. Ludwig von Mises' brother Richard von Mises, a famous mathematician, was also a leading member of this Schlickkreis because of that the two brothers had a somewhat complicated, not always friendly relationship. If I give you some famous names of logical positivists, many of them are nowadays forgotten but once upon a time they were very influential, Moritz Schlick, Rudolf Karnab, Hans Reichenbach, Gustav Hempel, Otto Neuerath and also Karl Popper who did not refer to himself as a logic positivist. He used a different term called himself a critical rationalist but that was some sort of a misnomer, very much like liberals nowadays in the United States are about the opposite of liberals were in the old days and still are considered to be in Europe. So he was not really a rationalist but he was a logical positivist too. He just adopted a different type of name. This school was not very influential in Europe at the time but became influential in the United States because most of them happened to be Jewish and emigrated to the United States and acquired quite prestigious positions at some leading universities in the United States. Now I want to give to you their basic views and use Karl Popper as the main representative of this group. So his view and by implication the view of most of these people is the following. There exist two types of scientific propositions. The first one we call empirical propositions. Empirical propositions say something about the real world. And empirical propositions must be either verifiable, that is we must be able to find out that they are true, or they must be falsifiable, that is a particular Popperian view, falsifiable by experience. We must be able by referring to experience whether they are false or what makes them false. Statements that are not falsifiable are not scientific statements. Nothing can be known about reality with certainty. Anything that we know about reality is hypothetical, must be testable. And on the other hand we have statements that are called analytical statements. And analytical statements are statements that are true by definition, such as bachelor means unmarried men. They do not say anything about reality, they just say something about how we use certain words, how words are defined. And their view was that logic and mathematics, for instance, are analytical statements. They do not say anything about real things, they just say something about how we use certain symbols. Anything about reality at all, that is a very unusual view. Most people before saw that logic and mathematics does have something to do with reality. And of course, as I mentioned also last night, normative propositions about what is good and what is bad are not cognitive propositions at all. They are just expressions of emotions. More particularly, they saw that the task of science is to give explanations and explanations have the same structure as predictions. They are always of the kind, if, a, then, b. If then statements are, so to speak, the characteristic of science and these if-then statements need to be testable statements, they must in principle be falsifiable by experience. These if-then statements are statements that have universal terms, that is if a refers to all sorts of things that fall under the category of a, then b, b is also a term that refers to all things that fall into the class of b objects. Now if we find that a hypothesis is confirmed, that is our hypothesis if a, then b, if we observe an a and a b indeed follows, then we would say the statement is confirmed. But a confirmation of a hypothesis is not a verification, that is we do not know that this statement is really true because the hypothesis refers, of course, to an infinite number of examples. All that a confirmation does is so far the hypothesis has not been falsified. So far we have to, we can accept it but we do not know whether it is true. It might be that in the future we find instances where a was the case but b did not follow. And on the other hand if we observe a and b does not follow, then we would say the hypothesis has been falsified. But a falsification also does not mean that a and b are not related at all. All it means is they are not related as the initial hypothesis stated, that is it might well be that a is the cause of b, except we might have to control some other variables as well. So if c and d are also fulfilled, then we can observe that a is indeed the cause of b. So a falsification does not mean a and b have nothing to do with each other. The hypothesis just has to be rephrased. We can always find excuses, so to speak, why a was in particular cases not followed by b. And the view of the positivist was then about the progress of science is we formulate hypothesis, then we try to falsify them. And if we falsify them, then we reformulate the hypothesis, formulate a new hypothesis and thereby we gradually approach the truth and scientific progress results. They also had a specific view on definitions. According to them there exist two types of definitions. These are ostensive definitions, that is by pointing to something, this is green or this is a tree. Or they were stipulative definitions, that is such and such is defined as such and such, bachelor is defined as unmarried men. Those are the two types of definitions that they allowed. Now before I come to a refutation of this empiricist logical positivist research program, so to speak, I want to make you aware of the relativistic implications that this research program involves. To make you aware of this is not the same as refuting it, but it might be good to make you somewhat skeptical about it. And I want to show you what the relativistic implications are. The first one is obvious, that is simply we can say nothing about what is right and what is wrong. Ethical statements are not scientific statements at all according to their view. But there are more important relativistic implications as well. The implication of these empiricist views is that we in the social sciences in particular have to engage in some sort of piecemeal social engineering in order to find out what is right and is wrong. What is wrong? That is, they imply that we just act as if we were social engineers. Now to make this clear what I have in mind with this, I want to give you first a number of examples where we would clearly say these are hypothetical statements. What are these hypotheses? And I agree that these are hypotheses. Children prefer McDonald's over Burger King. Worldwide beef consumption to pork consumption is 2 to 1. Germans prefer Spain over Greece as vacation destinations. Consumer education leads to higher wage rates. Consumer spending before Christmas is higher than after Christmas. Catholics vote predominantly democratic parties. Japanese save a quarter of their disposable income. Germans drink more beer than Frenchmen. US produces more computers than other countries. American inhabitants in the United States are white and of European descent. Now if you hear these statements you'll realize that we can in each case also just formulate the opposite. We could say for instance that children prefer Burger King over McDonald's or worldwide beef consumption to pork consumption is 1 to 2. Your longer education leads to lower wage rates. Consumer spending before Christmas is lower than after Christmas. If we would negate these statements we are obviously not saying nonsense. Which one of these, the original one or the negation of the original one is right and which one is wrong we can only find out by making observations, looking at data and then we find out it is true that children prefer McDonald's over Burger King or it is not true that children prefer McDonald's over Burger King. These are clearly hypothesis and what we must do is we must collect data so to speak to determine is this right or is that right. Now consider however some economic propositions. Consider this for instance, human action is an actor's purposeful pursuit of valued ends with scarce means or no one can purposefully not act or every action is aimed at an improvement over what otherwise would have occurred or a larger quantity of the good is preferred over a smaller quantity or what is consumed now cannot be consumed again later or if the price is lowered either the same quantity or more is bought or prices fixed below market clearing prices lead to shortages or without private property in production factors there can be no prices and without prices cost accounting is impossible or interpersonal conflict is possible only if things are scarce or no thing or part of a thing can be owned exclusively by more than one person at a time or property and property titles are distinct entities and an increase of property titles without a corresponding increase in real property does not raise social wealth but leads to a redistribution of existing wealths or if the minimum wage is increased let's say to one thousand dollars per hour then massive unemployment will result or every voluntary exchange benefits both exchange partners otherwise it would not occur or every coercive exchange involves a loser and a winner one who gains in utility in another one who loses in utility or if we increase the amount of money without increasing the quantity of non-money goods social wealths will not be higher but only prices will rise now ask yourself are these second the second second set of examples that I gave the same type of statements as the first set of examples that I gave that is would we accept the position these letter examples that I gave are also hypothetical this that we have to go out and test them to find out whether they are true or false in the same way as we would have to go out and test whether people indeed prefer McDonald's over Burger King or the other way around now the astonishing thing is that the logical positivists say claim that there is essentially no difference between the first set of examples that I gave and the second set of examples that I gave that is the second set of examples such as minimum wages of 100 of $1000 in our causes unemployment is also a hypothesis that is if it is a hypothesis then we would be able to negate these sentences and they might also be true we do not know so if I say for instance a minimum wage of $1000 per hour if enforced to the hilt might increase employment a coercive exchange benefits both exchange partners a voluntary exchange benefits one at the expense of another increasing the amount of money without increasing the quantity of real goods increases the general standard of living now I trust that you immediately recognize that there is something fundamentally wrong to think that these statements might be true that is that it might be true that a coercive exchange benefits both exchange partners that the voluntary exchange might just benefit one at the expense of harming someone else I trust that you realize that the negation of the second set of examples that I gave strikes you as absurd how can we possibly test a statement like this but according to the positivist they are hypothesis they must be tested if they are not hypothesis then according to them these statements do not say anything about anything real at all so now back to what I said what are the relativistic implications of this so if we say these are hypothesis then in order to find out whether a minimum wage of $1000 per hour increases employment or decreases employment what is the only way that we can find out which one is true we would have to try it out first because we don't know the answer a priori we do not know in advance what the answer will be socialism mesus' argument you will hear more about that in the course of this week I'm sure economic calculation under socialism is impossible because if there is no private property in factors of production then there exists no prices for factors of production if there exists no prices of factors of production then not then we cannot compare the input prices and the output prices and cannot determine whether we produced efficiently or inefficiently whether we make profits or losses if this is a hypothesis then what need what do we need to do in order to find out whether this is true or not is we have to introduce socialism first and then we find out whether it is true or not and if we find out that it is not true that standards of living let's say go down then we have remember falsification does not mean that we have proved that there is no connection between the variables that we try to associate with each other it only means the hypothesis is not quite right the way we have currently formulated we can just say oh that might be different if we also control the weather or if Stalin puts on a hat which he did not put on the year before or if we murder a few more Ukrainians then it might work out perfectly all right so there is an endless way of finding excuses whenever things do not turn out the way that we initially predicted based on our hypothesis what would what would come what would come through um take take an example that this currently of importance doing monetary easing printing ever more money in order to allegedly create ever more wealths in society obviously all economists except the Austrians are of this opinion that you if you print additional pieces of paper social wealths will somehow increase now what if it doesn't increase then you might just say oh the quantity of money that we have added to the existing quantity of money was not increased drastically enough but if you increase it even more drastically then of course you will see that the result will come out will come out right if you have an endless line of excuses whenever things do not go the way that you want them to go now i want to come to a more rigorous refutation of this claim of the positivist i'm i'm almost convinced that you realize by now that there is something fishy about this this whole idea now Mises proceeded in the following way he's just said look if this distinction that the positivists make about there are either empirical statements that must be testable or there are analytical statements that do not say anything about reality what what is the status of this distinction between empirical statements and analytical statements obviously if we apply the argument to itself then this distinction between there are only empirical statements and there are analytical statements must be either an empirical statement that is it is empiric is a hypothesis there are empirical statements and the analytical statements and nothing else but if it is a hypothesis it might be just wrong so why should we accept this distinction if it just the hypothesis itself or that's the second alternative is or it is an analytical statement and if it is an analytical statement then we know it doesn't say anything about the reality at all so again why should i then accept this type of proposition either it's a hypothesis and i can just formulate the opposite or it is an analytical statement it says nothing at all or and that is of course the third alternative but the alternative that the positivists cannot admit that this distinction between there are only analytical statements and their empirical statements is itself a statement that is none hypothetically true but that is something that they do not admit as possible um the same we can apply to their view that all of science is consists of explanations and explanations have the same structure as predictions that is they consist of if then statements now what is the status of this explanation of an explanation is this explanation of what explanations are an empirical statement is a hypothesis and then again we could say so what we can just formulate the opposite just as well or it is an analytical statement then we have no reason to believe it that is simply a verbal convention that we made we could just as well make some other conventions the same also applies to definitions definitions are either stipulative definitions one word is defined by another or they are ostensive definitions were a point to something but what is the definition what is the status of the definition of definitions is it again just a hypothesis about what definitions are or is it simply a verbal convention that we accepted we define definition in this way or is it something else and again all students will say yes there is something else that is what we call real definitions definitions that accord to the nature of things now i want to proceed further and show that whatever we might think about this research program it certainly can't apply to human actions let me for this go high tech and explain again how according to positivists the process of science works so we begin with a hypothesis and we test the hypothesis and we can either falsify the hypothesis or we can confirm the hypothesis because confirmation does not mean it's true it means just so far it has not been shown to be false so we have to continuously retry a new test again that can test can falsify or it can confirm if it falsifies you revise the hypothesis and then the revised hypothesis is again tested and it can be either falsified or it can be confirmed and so forth and so forth now ask yourself this when we have a hypothesis and the hypothesis is tested and we said it is confirmed we must make an assumption and the assumption is why don't we for instance just say first i found this result and then i tested it and i found the same result again just a repetition why would we say this confirms it or i have a hypothesis then i falsified why can we say that it is falsified why can't we just say first i found out this and the next time i found out something else now the assumption that we must make in order to say the second observation second set of observation is a confirmation or the second set of observation is a falsification of what we had observed before the assumption that we must make is that the nature of things did not change that is that the nature of things is constant otherwise if things sometimes work out this way and sometimes things work out that way we would could not could not say this is a confirmation this confirms what i thought before otherwise would be just a repetition if we think that things work out sometimes this way and sometimes that way we would not say it is a falsification we would just say okay first we observe that and then we observe that but that doesn't mean that any consequences follow from that that any consequences follow from it that is that we say this confirms this makes us believe the same thing will happen again in the future or this falsifies this makes us believe that the next time we have to revise our hypothesis requires that we assume the nature of things did not change nature of things is constant now the question is can we make this constancy assumption when it comes to human action and the answer is no the hypothesis now refers to some hypothesis about human actions not the hypothesis about whatever the behavior of material objects but hypothesis refers to people did such and such or do such and such if these and these conditions exist before now but can we make this constancy assumption when the hypothesis refers to human action the answer is as i said is no for the following reason is obviously the person that engages in this research process is part of the realm of of the objects about which the hypothesis has been made but what what happens to the scientist or the person who does regression analysis in order to test certain hypothesis what happens to the person depending on the outcome of his tests obviously if we falsify our hypothesis the person knows something that he did not know before he is in a way a different person than he was before he did not know the outcome of the test before he actually conducted the test the same as to if the hypothesis is confirmed after all that is the purpose of the entire enterprise we learned something that we did not learn did not know before so the process of engaging in this scientific endeavor is precisely a process that leads to a result of the kind that i'm different now after the test than i was before the test the purpose of the test was precisely to inform us about something that we did not know before so what we realize here is that it is impossible to predict so to speak what the outcome of the research process will be that's why we engage in research so people are different after each test after each test they know something that they did not know before and since all of our knowledge influences how we act what we do we reach the conclusion that it is impossible to predict our own future states of knowledge until we actually have it and since we cannot predict our own future states of knowledge and our states of knowledge influence how we act we also cannot predict in any scientific way how we will act in the future in various circumstances so predicting human action is not a science predicting human action is what entrepreneurs do this is an art but nothing for which we can formulate so to speak formulas that allow us to make correct predictions formulas that other people could apply in the same way as we as we applied we applied them but there is also an implicit admission of something else so while it is impossible to predict human actions in the same way as we predict whatever the behavior of material bodies because we learn after all and the learn our learning influences how we will act in the future and we cannot predict what our future states of knowledge are until we actually have them we have also gained an insight that is true about every one of our actions about all human actions namely that there are no constants no empirical constants allowing us to predict what what we will do in the future so we also have what we can say is a priori knowledge about action namely that this type of prediction that most mainstream economists think they are doing and they should do that this thing is not possible so we have also a priori knowledge of action it might not be telling us a lot of things but it does tell us something of utmost importance applying to each and every one of our actions and to each and every actor now let me explain this by using one example let's take the law of marginal utility that is in a way the most important of all economic laws the law states that if the supply of a homogeneous good increases by a unit then the marginal utility decreases or if the supply of a homogeneous good decreases we have less of a good the marginal utility the marginal utility increases now how do we come up with this with this law what we realize is the first unit of a good will always be employed in order to satisfy the most highly ranking of all desires that can be satisfied with this good that is a logically true statement the first unit of any good by any actor by every actor will be used in order to satisfy what this person considers to be the most important use that can be made of this good if i have a second unit of the same good then again by definition the second unit will be used in order to satisfy the second most highly ranking goal that can be satisfied with the help of this particular good if i have three units then the third unit will be used in order to satisfy the third most highly ranking good goal if one unit is taken away from our supply it doesn't matter which unit is taken away because they are homogeneous we regard each good as equally serviceable as every other but we have to do without one set with one satisfaction less which satisfaction will we give up and the answers of course we will give up the least important of the previously satisfied satisfactions in giving up one unit now you realize that this statement is an a priori statement there is no way that we can ever think of how could this statement possibly be falsified on the other hand what it does not do it does not tell us of course anything about what is my most highly ranking desire so again use an example so i have an orange i can apodictically say the first unit of the first orange will be used in order to satisfy my most highly ranking desire of all desires that can be satisfied with an orange but i cannot predict what that desire will be will it be eating the orange squeezing the orange using the orange as a baseball or throwing the orange away giving it to somebody as a present none of that we can predict that depends on on knowledge that we have on changing ideas that we might have what can be done about an orange let's say i find out that oranges are are no good for anything then i might throw it away the next day i find out oranges are really good because they have lots of vitamin c then i might ingest them well i find out that that oranges can be used for this purpose as this purpose as well it can be squeezed i didn't know that they could be squeezed before and and now i think my most important use of an orange is to squeeze them none of that i know um the second unit will be used in order to satisfy my second most highly ranking desire that can be satisfied by means of an orange but i do not know which my second one will be or what your second most important use of an orange will be it might also be that that changes today you saw the first most important use of an orange is to do such and such which the orange and tomorrow you might think that it is far more important to use the first the first orange for an entirely different purpose none of those things all the things that can be influenced by changing knowledge by changing values all those things cannot be predicted in a scientific way i'm not saying that they cannot be predicted in a non scientific way in an entrepreneurial way yes of course entrepreneurs make constant predictions in terms of people will use this for such and such people will use it for such and such people would value this more than they value this and so forth but this is not nothing scientific because if it would be scientific it could be just imitated everyone would be just equally good as an entrepreneur and we all know that that is obviously not the case but what we do know is that whatever our various uses our various rankings of goals are the first unit will always be employed in order to satisfy the most highly ranking desire the second one will be used to satisfy the second most highly ranking desire and the third one will be used in order to satisfy the third most highly ranking desire it isn't the same as what i indicated last night logic does not tell us much about reality but what it does tell us about reality is of utmost fundamental importance economics might not tell us much about what humans will do here and there in in this situation in that situation but what this what economics tells us is of utmost of utmost importance so if we want to compare the austrians again to mainstream economists we can say the austrians are in some ways more dogmatic let me say certain things we know apodictically it cannot be different than this way and we do not need to test this at all and to test it is a sign of intellectual confusion it is as if somebody thinks that he has to test the law of pisagoras let's say by measuring triangles and then if he finds out such and such results i have gained in the united states that he has to travel to australia and to find out whether the same laws of pisagoras hold in australia as well we would just say that is absolutely stupid why do you do something like this on the other hand we are far more humble than the mainstream economists because mainstream economists think they can predict what my most highly ranking goal of using an orange will be and what my second most highly ranking goal of using an orange will be there the austrians would say no that's that sort of enterprise is a silly enterprise this is something that entrepreneurs have to do and if you are so smart you economists why aren't you as rich as the entrepreneurs are are so austrians are dogmatic in a small area and they are very humble in most other areas and mainstream economists are all around confused thank you


Hi everybody, good afternoon. Thanks for coming to my talk. Apology that I couldn't be there physically, but I'm still glad that you chose to spend your time listening to my talk. So just to make sure that you are in the right place. In this talk, I'll be covering a few things. The first thing is to explain AI Singapore. Who are we and what are we trying to do? The second thing is to share with you how we create open source solutions using the experience and knowledge we have by doing real life industrial projects. And the third thing, of course, is to share with you our available pre-built solutions and a quick overview of them. So hopefully by the end of this talk, some of you might find some inspirations or potential use cases for them. And if you have any questions regarding our open source solutions, feel free to let us know through our website or forum. We'd be more than happy to share a few more details about the open source solutions that we have built. So a bit about myself. I'm currently a principal AI consultant at AI Singapore. What I do on a daily basis is I help companies to embark on AI journeys. So it could be companies of any size, doesn't have to be SME or AMCs. So any corporations that wish to begin their AI journeys, be it defining a problem statements or building a simple AI model to solve a very specific needs. This is where I will come in with my colleagues. So that's my role at AI Singapore. At the same time, I've been second to the National AI Office of Singapore. So under the National AI Office of Singapore, I'm handling the healthcare portfolio. So I'm providing my AI technical expertise there. If you guys do some national healthcare projects. At the same time, also a visiting mentor at 500 startups. So this is where I provide some mentorships to the CTOs of startups. So in the evening, I'm pursuing my part-time master of computing at NUS. So you can see I'm quite a busy person. And in the past, I was from finance. So I used to be a training trader plus a Pokemon manager. So nice meeting everybody. And so be a copyright and let's get started. So what is AI Singapore or who are we and what are we trying to do? So my mission is to build deep national capabilities in AI and thereby creating social and economic impacts, grow local talent and build AI ecosystem in Singapore. So let me walk you through the overall statements from the left to the right through the graph. So we are funded by NRF National Research Foundation and we are hosted by NUS, right? And AI Singapore started in 2017 around June, July period. And what we do is that we have to coordinate scarce resources of AI because right now AI is very hard and talent series is very scarce. So we coordinate across different agencies and different universities and research institutions. So we have to coordinate efforts to do AI development activities gathered by real life needs. So what we do is that we go out to talk to the industries and find out what's the needs. And from there we do some coordination activities and help to build AI models for them. So at the same time, right? Due to the scarcity of AI talents, we also have our own in-house talent development program such as the AI apprenticeship program, AIP, AI for AI for everybody. This is a free three hours talk online which you can join with AI for AI, which is AI for industry. So we give you access to data cam for you to try out some of the coding exercises. And we have AI for students and AI for kids, right? So we go all the way down to the primary school level to train people in using AI. So under AI Singapore, we have three different pillars. We have the AI research, AI technology and AI innovations. So under AI research, what we do is that we invest resources in inventing a next generation AI techniques and algorithms. So this is where the fundamental research takes place, right?  So under AI technology, so this armed with soft national challenges. So I think that right now, one of the challenges you're trying to solve is on healthcare in tackling the three H, right? The hypertension, hypoglycemia and one other H. So AI innovation is where I am from. So AI innovation is small industry facing. So under AI innovations, we have three different teams. The first team is Learn AI. So under learning AI, we have various talent development programs that we have to teach people about AI. And I'll go deeper into AI AP later on, which is our fact sheet program. And there's also a program called Do AI. So under Do AI, this time we will do AI with the industry. So our 100 experiments and AI AP goes hand in hand. I will elaborate more on them later on. We have the AI bricks, which is the open source solutions that we have developed and we're trying to promote to the industry to use because they're free and open source. We have the AI clinic and AI discovery. So these two are more on the consulting arm. So this is where we help the companies to define a problem statements on AI, like out of the 100 problems that they have, which are the exact problems that they should focus on to solve. And we have data readiness as well. So one of the things that we notice is that in a lot of companies that we engage, they told us that they have data, but they are not ready for ML. So what we do is that we have the in-house program to help them do some data readiness check and do some data radius preparation so that once that is done, their data can be fed into whichever AM orders that they want to do. And lastly, we have the shared AI. So under shared AI, we have the forum, blogs and community. We are trying to build our ecosystem in Singapore where AI practitioners can share ideas with each other. So let me go in depth into the 100 experiments at AI apprenticeship, which I briefly described earlier. So under our 100 experiments, what we do is that we will work with any organization in Singapore of any size. As soon as they have problem statements and they have a data set, we can do something together. So under the 100 experiments, we have two tracks, the 100 experiments for research. So under the research research at the top part, the duration is typically 18 months. So this is where we will reach out to a network to get a professor to join the team to do the AI modelling together. So for a project to qualify under AI research, it must be something that is fundamentally challenging. It could be that the company needs a brand new algorithm to do certain things that can be done using out-of-the-box TensorFlow PyTorch modules. So they have to develop a brand new algorithm to solve specific business needs. So we also have this track called AI for Industry, or 100E for AI. So in this project, typically the duration is nine months and it's more translation in nature, meaning that, let me give you an example. Let's say if a bank come to us and they want to do a customer chain predictions. So typically these kind of problems could be solved using out-of-the-box solutions, right? SQL and PyTorch and so forth, you name it. So for this kind of problem statements, this is where we will work together over nine months period to do the AI models for them. And it is also under this 100E for AI, we will assign apprentices to the projects to work on this together. So this is where we match the demand and the supply together. So under the demand side, a company could have a demand for specific solutions to address their business needs. On the supply side, what we do is that we will have this AI apprenticeship training program where we require people to quit their job, to join our programs over nine months periods. So during these nine months, that apprentices usually two to three will be tacked onto the project. So along in that project team, there'll be like AI engineer, it could be me or my colleagues and a project managers. So these four to five men team will be assigned to this project, the work with the sponsor, project sponsor over the next nine months to solve the models, all the business problems that they are trying to address. So in overview, this is how the fact sheet programs of ours, the hundred experiments and AP works. So now I may be wondering, so what does this has to do with the open source solutions that we have, right? Through our engagement with different industry, we have worked on different sectors across different use cases. So let's go through a few of the projects I've done, which is pretty interesting. So on the top left, we have done a project with Sabara Jurong, to do a predictive lift maintenance, right? To predict which lift will break down in advance so that they can send attention style to do some predictive maintenance checks, right? We also done a project with agents, a company on credit scoring. So this is typically for the unbanked. So for example, if a mate come to you, we find a banking history, credit history, and want to borrow $50 from you, how can you access that percent risk, right? Without a proper credit score in place. So you have done that. So during my apprenticeship days, I have done a project with a kidney dialysis company to predict the hospitalization risk of a kidney patient, which is the project on our top right, right? So this is one of the projects I have done over the nine mile periods. So I was from the batch to apprenticeship program. I graduated, thankfully, Singapore likes me and I like them as well. So decided to join them as an engineer, subsequently becoming an AI consultant here. We also done a project on chronic room management. So this is chronic care. So what I have done is that they, we have developed this AI model to look at the wounds and to able to identify whether the wound is healing or not, right? So typically if you have a chronic wound, what you have to do is that every few weeks, you have to go to a polyclinic or any like doctors and nurses, right? They will open up the wound, the bandages and look through the wound and do some poking to see whether the wound is healing, right? So this is long, expensive and painful. So we have developed an AI model that is running on a mobile phone that can look at the wounds itself to assess whether the patient needs to go for further checkup or not. We have also our own NLP code switching on the right hand side, which is the speech-to-text, which I will do a quick demo later on. We also have done a abnormal detection with a security agency. So based on the movement of the vessel on radar, can detect whether this is normal or abnormal traffic. And I have done a project with a manufacturer as well, an electronic manufacturer to look at the, using committed visions to look at the equipment to see how likely that a customer is likely to return these things that is being produced. And lastly, we have done a project with Expedia. So Expedia has done a project with us on name-edited recognitions, right? Because like the way that people search in the text in Japan, right? There are many different scripts of Japanese scripts. So they all could refer to the same thing. So how do you improve the search results of the customers, right? So some of our projects that I mentioned earlier are announced in the news, right? That's why I can share their names. For most of the project that I've done, we have signed an NDA, non-discussion agreement for the companies. So for some of the projects, I can't share their company's names, right? If you do a quick search on Google, you'll see that a lot of our projects that we have done with big companies, right? It's all on the news, right? You can see that Expedia, AS, Singapore here, Cisabara, Juno, over here, Piotena, this is on cell therapies and on FHDB to do some construction safety. Using AI, right? So now I come to the main part of today's presentation, right? AS, Singapore Bricks. How do I link the hundred experiments that I talked about to the AS, Singapore Bricks? So let me explain using this graph here. So AS, Singapore is in a very unique position because we work across different industry of companies with different sizes. So we have a lot of engagement with the industry and sometimes if a project doesn't fit into the hundred experiments, because in hundred experiments, AS, Singapore will co-fund the project together with the company. So for projects under hundred experiments, it is to go through two stages of approval internally before they get the funding from AS, Singapore to co-build the model together, right? So for some other projects, right, which are more simpler in nature, we will take them on under our consulting arms and we'll do it. So usually for these kind of projects, for consulting projects, it's usually three to four months period. So we work with big projects, 80 months long under the hundred E for research, nine months long, a hundred E for industry and short term project under consulting. So we have projects ranging for super complex to quite manageable and easy to do. And at the same time, through all these project engagement, we spoke with the management of the companies, right? We got CEOs, the management directors on what the problems are facing. So what we then do is that from all the learnings and experience that we have, we take the common denominator out of them, not the data, not the proprietary algorithm, but our knowledge and experience. We pull them up together and we develop it into a bricks that can solve most use cases in the industry. So let me give you an example. So the first bricks are open source solutions. So let me clarify by bricks, we mean open source solutions, right? So bricks is just our acronyms, right? So the first brick that we have developed is for robotic process automation, right? Automating your keyboard and mouse to spare your productivity. So one of the bricks that we have is Tech UI. So Tech UI is open source RPA tool. You can see on the right hand side is a demonstration of how we can download the first racks, the FX rates from DBS and send it through email to whoever you want it to run. So this is one of our open source software. I can take a look at our website, which I will share with you later on if you wish to use this internally in-house or if you want to build a product out of it, please go ahead. The next interesting bricks that we have is called golden retriever. We name all our NLP tools, natural language processing tools under docs. So one of it is golden retriever. So this is like a FAQ retriever. So under this, what we do is that, right? In a typical chatbot, you need to prepare the data by listing the questions and the answer itself. So the unit use case of our golden retriever is that you don't need to specify which answer are tagged to which question. If you have a document of FAQ by loading it into the golden retriever, it can automatically pass out the semantic meaning of each paragraph. So whenever you type a question into the golden retriever, it can give you like the top three most relevant paragraph in that documents that you loaded inside. So the use case is, let's say for example, you are working as a salesman, your company has a lot of policies for the follow. So when a customer asks you, hey, can I have a payment extension? And you say, okay, let me check. You can type your questions right into the engine and it'll give you the relevant paragraph in the policy. The good thing about this is that it's not dependent on like control fine, right? The exact keywords match. It's dependent on semantic meaning. So let's say for example, light and love, even though they are not exactly the same, but symmetrically they are the same. So the engine will be able to recognize this, right? You can try out the demo on the website. Next, we have this another dog. It's called Korgi. So it's auto-labeler. So the use case is that it helps to accelerate your data labeling process, right? There's an issue here is that you might have, let's say you're in a call center or you are running a e-commerce store. You have a lot of consumer reviews, right? The issue is for you to train customer segmentation, customer sentiment analysis model, you need to manually tag each of the paragraph, right? So by using these tools, right, it uses a non-negative factorization behind it. You can read up more on the website, but what it does is that it helps to speed up the way you tag your data. And one more thing about NLP is that NLP is very complex, especially in Singapore context. Why is that so? Let's take a look at this sentence, right? They, we always mark one. So in these very short sentences, you have various languages combined together. You have Tamil, Mandarin, Cantonese, English, Hokkien, Malay, and English at one, right at the back. If you are to say this sentence into Google translate, it will give you, the transition result won't be that good to transcription because Google requires you to speak in that language that you select and in perfect English, right? But for most Singaporeans like me, we can't speak proper English, right? So we need our own speech-to-text engine that can handle our language. So this is where we have, we have this like, we have this NLP tools. Sorry, let me go back here. So we have this NLP tools that can handle different languages when you speak to it. So this is our own speech lab, right? So let me give you a quick demonstration because this is a very common, I'm not sure if you can hear it, but this, all the demonstration is available on my site. So let's hear it. Now, I hope the audio is available to you. So essentially what the guy is saying is that, he said, hey, hello. What I'm about to say means that I wanna make a police report in Chinese, right? So this guy is testing the engine by using both English and Chinese at the same time. And our engine is able to transcribe the languages in real time for two languages, right? So this is something that Google Translate is not able to do, right? We have a demo for our speech lab on our website. Please feel free to give it a try, right? So what is next, right? So at AI Singapore, we have, just recap again, we have the learning AI, share AI and do AI, right? So you can go to our website. Let me just give you a quick demonstration now on our website.  And under do AI, this is where all our bricks are. So for example, right, let me give you an example. So this is an example on golden retriever, which I mentioned to you earlier. So what we have done is that we have loaded in the COVID-19 database as well, right? So this is interesting. So right now, for example, if you can type in, what is coronavirus, right? We click fetch. So these are all the databases, FAQ that load up from the Ministry of Health in Singapore. So it gives you the top three results. So you can say, gonna buy us a large family of viruses, conic unicers, yeah. So this is what I meant by giving you the top three answers that you have. So let me give you a try. So is coronavirus dangerous? Okay, let me give you a try and you'll see whether it works or not, right? So the answer is, okay. So you can see one of the top two answer is many characteristics of the virus is still unclear. How it might affect people, severe disease, death in 23%. So the engine is able to roughly understand the symmetric meaning behind dangerous and give you back the answer the other way, right? So I have come to the end of my presentation. So let me just do a quick recap for you again. At AI Singapore, our mission is to help the companies in Singapore adopt AI and accelerate that AI journey, right? And what we are doing right now is that through these AI breaks or open source solutions, we are taking our industrial experience, combine it with the multiple projects that we have done. We distill our learnings and knowledge from doing this project and turn them into, find the common denominator and turn them into open source solutions which anybody can use. If you're interested in using our engine for going to receiver, all our source code is hosted on GitHub. So feel free to go use it to build products for the general good, especially nowadays with the coronavirus, there's a lot of false news, fake news spreading around. So it'd be good if there's a search engine which can have all the relevant authorized database answers that is within this engine that everybody can have access to. Won't that be great? So thanks again. Thanks for your time in listening to my presentation. I hope that you have learned something about AI Singapore and our machines. If you wish to work with us on our Henry E projects or to hire our apprentices who are graduating for our programs, feel free to go to just Google AI Singapore. It should be in the top few links. Come to our website, you can contact us and we can discuss whether, how it can work together. Thank you for attending my talk again. Stay safe, stay healthy, all the best. Thank you. Goodbye.


Regardless of whether you are a fan of Apple or not, you have to give your props to them, as they make some of the best devices out there, no matter the form factor. Their laptops, desktops, phones, and tablets are always in the running as some of the most sought-after machines and that is not simply because of their brand. Apple's combination of software and hardware as one of the best and many others have tried to replicate their success, but as of right now, their quality and ecosystem are still the greatest. Let's see how their newest iPad Pro does at continuing on the already well-established legacy and if it is good enough to be a suitable laptop replacement. Today we are presenting you our top 5 picks about the Apple iPad Pro 11 2020. As with any Apple device, performance is one of its main attributes. The Apple Bionic A12Z system on a chip has in itself an 8-core CPU and GPU. The CPU uses the popular among mobile chips big little architecture, with 4 high performance cores and 4 power efficient cores. It is built on the 7nm process and is paired with 8MB of cache, and 6GB of LPDDR4X RAM. The 8-core GPU is an upgrade over last year's model, which had a 7-core GPU. The 2020 iPad Pro has a 120Hz 11-inch IPS touchscreen display with a very high resolution of 2388x1668 pixels and an aspect ratio of 10-7. Additionally, it has comfortable viewing angles, a high maximum brightness of around 600 nits, and a very good contrast ratio. What is more, is that it covers 97% of the sRGB color gamut and has a non-flickering backlight. Please remember, it would be of great help to us if you just hit the like button and subscribe to our channel. That would motivate us to make even more and better videos for you. The Apple iPad Pro has a great quad speaker setup, that is plenty loud and produces crisp sound with very good quality. Moreover, the low, mid, and high tones are clear of deviations. This is a bit of a mixed bag for us since the build itself uses premium quality materials, but the device is so thin that even the aluminum body can't keep it from bending, shall you make the mistake of unintentionally sitting on it. Otherwise, on the front, you get a scratch-resistant glass, and while the bezels aren't the thinnest, they actually help with the usability, eliminating the risk of accidental touches while holding the device. The lack of a microSD card reader means that there is no way to upgrade the internal storage. Good thing is that the device comes with up to a terabyte of storage so you should be fine. The iO is one of the poorest that we have seen with only a single USB type C3.1 port at the bottom.


So, let us look at another example ok. So, I took the same Lyapunov function as you can see that was my aim as I can keep the same Lyapunov function and come up with a different example that was the plan alright great. What is this and we already know this is continuous positive definite and radial and bounded and all the nice things not decrescent. Now v dot as usual is x 1 x 1 dot 1 plus t x 2 x 2 dot and x 2 squared by 2 here I have just use the chain rule or the product rule, product rule not the chain rule the product rule I always get confused between the product this is not the chain rule it is the product rule ok. Chain rule is for chain of functions and then as always I substitute the dynamics this remains the same yeah here 1 plus t x 2 is as it is then I have substituted for x 2 dot right here and because of this wicked choice of system that I have made the first term will cancel this right if you see 1 plus t 1 plus t cancels out and then the first term will cancel this ok alright that was the plan yeah and then the second term again by my wicked choice of system will cancel this term alright that is exactly how I chose it this guy multiplied by this is minus x 2 squared by 2 and this is plus x 2 squared by 2 this was what was remaining last time so I made sure I cancelled it yeah ok is that clear notice this is all that was remaining here so I just introduce the term here to cancel this ok cancelled now I have v dot to be exactly 0 ok v dot to be exactly 0 yeah very ridiculous looking system but it is stable at least alright it is stable it is not v is not decrescent so can't claim uniform stability again I am saying not uniformly stable but it is more appropriate to say can't claim uniform stability with this v ok if you don't get a property from a v does not mean that the system does not have the property that you have to conclude in different ways I am most certain that this is not uniformly stable but but it is not obvious just by choosing only upon our function and doing one v dot and claim no that's not enough yeah in fact if you do 100 even then you can't yeah it's just a deficiency in your choice of v and not in you know your you know in terms of the system itself yeah ok great any questions alright so it would be very illustrative to see the difference between these two systems ok if you look at this guy and if you look at this guy ok forget how I modified it and whatever did whatever just to cancel terms this was just a modified harmonic oscillator right because if this term was not there or for small time just like we discussed it's a pure harmonic oscillator and this system is actually a damped harmonic oscillator it's a modified damped harmonic oscillator ok why damped because of this term this term is acting as a damping right if you all of you folks who do PID control know that the derivative term is the all connected so derivative term is the damping term yeah so this is the derivative term this is the proportional term this is the derivative term there is no integral term here but honestly speaking in typical nonlinear control design you don't get a integral term it would be rather unusual yeah but there is a PID control if you think about it yeah but with a time varying modification yeah so this is a modified damped harmonic oscillator ok if time was small or if this term did not exist or it was one then this system is actually what you mean I mean in Lyapunov terms what is this system in terms of Lyapunov stability terms uniform what what do you know about this system about a damped harmonic oscillator what is the example of a damped harmonic oscillator spring mass damper alright ok see if I leave a spring mass damper from any arbitrary initial condition what happens come to rest at origin unless you are springer poor quality and they start stretching and all yeah which is the case in general but yeah they come to rest at the origin so what does it mean what does it mean about stability property of the system asymptotic stability it is asymptotic stability yeah in fact uniform because of no time dependence yeah so if this was not there this system is in fact uniform asymptotic stable alright so very strong property in fact it's exponentially stable why why do I claim it's exponentially stable yes yeah I am looking at you why am I claiming it's exponentially stable without doing any analysis all I talked about was asymptotic stability by giving an example of leaving a mass in a spring mass damper why is it exponentially stable yes we discussed this for a linear system asymptotic stability exponential stability is the same we proved it we proved it ok well we didn't prove it did you prove it exactly yeah we proved the stability one but from that you can see that the exponential stability one is also basically any convergence for a linear system is always exponential a linear system will never converge at any other rate ok than exponent linear time invariant system ok so but all as soon as I introduce this time dependence I drastically reduce what I got I am now only stable not even uniformly stable I am quite certain it's not uniformly stable ok so significant drop in what I can do now it's very simple to see why things go wrong as time becomes large this stabilizing term the so-called PD controller if you put a PD controller on a system and after a certain time you stop the controller control you make the control 0 ok then the system is not going to be stable anymore right ok here it works in the absence of the time dependence it works because it's perpetually acting as soon as you introduce this time the effect of the proportional derivative term is dropping at a linear rate ok so which means that your proportional derivative term which is what is stabilizing the system is dying ok so the proportional term gives you stability the derivative term schemes you asymptotic stability yeah so these terms are the contribution of these terms are dying as time increases and therefore the system will not be stable anymore so you lose all these properties ok so at best what you get is stability in fact you are lucky that you at least get stability ok alright any questions yeah if you think about the control gains this is the gain right 1 over 1 plus t right k p k d whatever if you think about it the gains are going down right if you ever design a PD controller a PID control with your gains going down you will see the system will not work it's obvious ok alright great so let's go to this argai yeah when the time dependence is removed I already said that this is exponentially stable ok because for linear systems asymptotic and exponential is the same in fact in this case you can even solve this system right this is not difficult to get a solution for the solution will have exponential decay term so it's exponentially stable but if I wanted to do the very very hard work it turns out to be very hard work of proving exponential stability via Lyapunov functions and then it's very complicated that's what I've sort of illustrated here now but then sometimes you do need to do this kind of an exercise because your system may not be linear always ok so I cannot choose something as simple as x 1 square plus x 2 square by 2 as my Lyapunov function that does not work I can promise you it will not work it will not work so I have to choose something like this ok yeah later on we will go into the motivation of choosing something like this and why something like this might make sense and so on when we do design yeah so I am not telling you anything about why and how I choose it ok I am just choosing it yeah so this is a little bit complicated but the idea it's it's basically based on the notion of back stepping ok but we are not we have not done back stepping yet so don't worry about it this is just think about this as just an example alright alright so what is this half k x 1 plus x 2 squared plus 1 over 2 alpha x x 1 squared so it's not just a combination of 2 terms I have basically introduced some random constants also like this k and alpha are positive constant ok I know that this is positive definiteness and definite in fact radially unbounded do you understand why why do you think this is positive definite and radially unbounded see all I need to verify for positive definiteness is what that for non-zero values of the state it is strictly positive so what about do you think this is k and alpha are also positive so is it that easy ok yeah but if you remember we had discussed terms v terms like x 1 plus x 2 whole square and I said it is not positive definite right because this is also 0 when k x 1 equal to x 2 is minus k x 1 right so there is a problem right because you guys immediately said yeah everything is positive this is positive then what does it do these are not good arguments no no no no can't be so vague no no no don't do that will be more precise you tell me a little bit will be this is where I am I may not ask you to design these things but you have to be very precise but why this is positive definite we have already discussed this wait I am going to write this again this now what are you saying both terms are positive you mean non-negative then ok so when when is the when are the 0 which means that individual terms are 0 when x 2 is minus k x 1 and this is x 1 equal to 0 and this is an and condition which implies x 2 is also 0 so the only place where this so things change because I added this x 1 square if I did not add this did not have this then this is not positive definite anymore because it is 0 on a line and that is not positive definite ok so only because I added this so there are now two conditions need to be satisfied this has to be 0 and this has to be 0 because they can't cancel each other these are all non-negative terms therefore this and this both have to hold which means this happens ok so be very precise when I ask you do not just say positive positive positive ok otherwise why did we do all these definitions there was a very good reason ok of doing these definitions carefully so please be careful when you state that something is positive definite or not all right fine sounds good that is what I have said so anyway the k is missing here it does not matter I will put a k ok all right great we take the derivatives yeah just like we are doing pretty straight forward right lot of bookkeeping in this case it look complicated yeah but eventually I am not doing anything very fancy I am just taking derivatives and substituting for the derivatives so here I will get k x 1 plus x 2 times k x 1 plus x 2 derivative I will get x 1 and x 1 derivative and the half will go away this half will go away ok that is it all right now I substitute for the dynamics here k x 1 dot is k x 2 x 2 dot is minus x 1 minus x 2 similarly this is x 1 x 2 all right now we have to do a lot of manipulation yeah k x 1 plus x 2 k x 2 minus x 1 minus x 2 and then I write this as x 1 times k x 1 plus x 2 minus k x 1 square by alpha ok so this is just equal to this do you believe me yes I have just added the k x 1 term and why did I do this again these are manipulations that you will have to do these are the kind of manipulations you will have to do in any Lyapunov analysis so better be comfortable if you are not asking why did I do this why do you think I did this see in the harmonic oscillator case this guy was getting cancelled directly ok now I know I cannot cancel it directly I have a problem so what do I do they do the next best thing I try to club it with the term I have already and I see that here I have a k x 1 plus x 2 term so I try to write it as k x 1 plus x 2 so what did I do I took the x 2 I wrote it as k x 1 plus x 2 minus k x 1 so I got from this one term these two terms now I know that this guy can be clubbed with this term ok alright I cannot cancel so I try to club them together this is the only few manipulations you can do ok there is one or two more which we will get too soon but right now remember this is the only thing I can cancel or I can club terms so I cannot cancel so I try to club it with the term I already have so this is the only term I have it does not make sense to club it with this is way more complicated anyway why would I do that I will just club it I will just introduce this term here ok and the cool thing that happens when I do this this is again a product of the back stepping method which we have not discussed by introducing the k x 1 and a minus k x I actually got a negative term in x 1 yeah both k and alpha are positive so this term is actually a nice negative term it is a good term it is a helpful term ok now this guy gets combined with this and what do I have k x 1 plus x 2 k x 2 minus x 1 minus x 2 and x 1 by alpha right this is coming from here alright make sense ok this first term is coming just by the clubbing of the terms alright and I already have a good term here which I keep as is yeah I love the good terms as soon as I get negative quadratic terms I keep them as it is never touched them they are what will help me eventually so I never touch them this gets carried on until the end of the analysis now I have only two variables actually x 1 and x 2 yeah so I just club all the terms in terms of x 1 and x 2 ok alright so I get something like minus 1 over 1 minus 1 by alpha minus 1 minus k and all this mess but I get some terms ok so now what do I do I take this 1 minus k common I pull it out yeah these are all variables that I choose that is why I kept these handles or knobs ok so that I can play with them this is for me to gives me freedom to play with this yeah otherwise I will not be able to choose a good V yeah when I was starting to choose a V I did not have any idea what is going to be k and alpha but one once you conclude the analysis you will see that the choices of k and alpha will become obvious alright so I have I have deliberately written everything as negative terms why because V dot needs to be negative definite so writing it as positive terms is ridiculous makes no sense so I have written everything as negative terms how will I get V dot to be negative definite I want this term and this term to look identical this should also look like k x 1 plus x 2 yeah I am doing this carefully just follow the steps ok these are things we do often huh so I take 1 minus minus 1 minus k common outside now the first term is the same the second term has 1 minus 1 over alpha divided by 1 minus k plus x 2 and this is of course my favorite term remains as it is now what will I say I will say that I want my k to be equal to this guy yeah and if it is suppose it is yeah just to wit it is equal to k then these two terms together become k x 1 plus x 2 whole square right and this is some nice negative term this is nice negative term I have negative definite V dot ok now all I need is this has to be equal to k ok and of course I want k to be less than 1 that is the other requirement otherwise k has to be strictly less than 1 all good it is my choice it is just a V right it is system function I use for analysis it is not changing anything alright now you just have to see if this is feasible or not you just have to check the feasibility of this guy huh so k is between 0 and 1 alpha is positive I want to check the feasibility of this this will give me a quadratic equation in k ok which is going to solve this I have to choose one of them yeah I can choose any one of them right they will both satisfy so if I choose k as this guy I am fine yeah I can choose actually any one of them apparently alright so I think I took an example or what let us see yeah so first I am trying to ensure that this because that is what is going to give me the feasibility right I want to check that the discriminant is going to be positive or not so that I get a real outcome here so that is all I need to do I get alpha less than 4 by 3 ok because this is a linear requirement yeah so alpha is positive but less than 4 by 3 anything less than 4 by 3 is good ok now if I assume that this is equal to half some value less than 4 by 3 then I get whatever then I get alpha is 8 by 7 which is fine no problem yeah and I can choose k as any one of actually it has to be less than 1 so I will choose the it is preferable to choose the let us see negative one oops I cannot erase this can I I will just choose the negative sign right yeah I will just choose the negative sign because whatever appropriate value of alpha I choose less than 4 by 3 you see that the quantity inside is going to be less than 1 because this 1 minus some quantity less than 1 it is something less than 1 so it is square root of that is also less than 1 so it is better to choose the 1 minus because if I choose the 1 plus 1 plus will also work for some time because I have a divided by 2 and all that but this will be an easier choice yeah this is guaranteed to work yeah so basically I have given you a choice of a k and a choice of an alpha yeah so alpha is exactly this and and so on and so forth I mean whatever alpha is exactly this in if you want something like this and k comes from here with this choice of alpha alright so with this very very complicated construction I have proven exponential stability of this system which is very easy yeah see in this case it does take a lot of work because the first the because exponential stability requires the same order of magnitude functions and so on and so forth alright so if you look at this guy and you look at the V dot these are exactly same looking functions right they are the same order of magnitude functions they both have the same quadratic terms k x1 plus x2 square and x1 square yeah so I actually got same order of magnitude functions yeah and so by my Lyapunov theorem it is exponentially stable yeah so this is not too easy to do for nonlinear systems in general okay


is up Marvel Squad and welcome back to our channel. So I didn't film anything this morning because we'll actually explain to you guys more about this later but if you guys don't know we recently got banned on TikTok for literally absolutely no reason. We will go more in detail later but that is why I didn't film this video but basically today I'm at the salon as you can see and I'm doing something a little different with my hair. I'm not doing anything super crazy but it is a drastic change. I don't know if you guys can see I have my old extensions right here. I took those off for the summer because when I was at the beach the other day it was just like so much hair. I really just wanted to change it up and switch it up so I am taking my extensions out but I'm not gonna tell you guys anything else yet.  I'm gonna surprise you guys and then when I'm gonna go home I'm gonna surprise Bill and then we'll talk more about what happened on TikTok and just everything that went down with that but yeah I'm super excited for you guys to see my hair and now we just wait for it to be done processing. Alright guys so I just got out of the salon and I'm about to go home and surprise Bill but I actually wanted to give you guys a little sneak peek. Actually you know what I want to surprise you guys at the same time that I surprised Bill. You guys can see I didn't really change much besides you guys will just have to see I'm gonna go home. I'm gonna go surprise Bill. I'm gonna go show him what I did. He has no idea what I did. He just thinks I'm going in and doing what I normally do but I changed it up today so I guess you guys stay tuned and I'm so in love. Shout out to my girl Samantha. She is literally the best and I love her so much. She has literally done so much to my hair that is just like absolutely crushed it and gave it to my like absolute goals, hair goals. So I'm just so blessed to have found such an incredible hair stylist but I'm excited to go home and it's raining. It's actually, we have a little bit of a tropical storm because there's actually a hurricane in Mexico so we're getting the aftermath of it all. Luckily we didn't get hit with it but we're getting the aftermath with rain. So I got to hurry home before it's absolutely flooded so let's go. Alright guys so I don't want to show anything yet. Are a lot allowed to come out yet? Yes you're allowed to come out. You can open your eyes open because I have my hair pushed to the back. Wait have them open? Yeah you can keep them closed I guess. Okay. Alright I'm showing you and the model squad. So they didn't see it yet right? No. Okay I'm not looking. You ready? Yep. Three, two, one. Okay you ready? Yeah. Dang. Oh you got that. Oh that's. Tinsel and pink. Can you see the tinsel in it? That looks good. Look at this girl. Wait why is there a little like pink in it? I got pink. Tinsel too. Oh it makes your hair look pink. Oh it does. It makes it look like a tin of pink because I think the light was reflecting off of it. What do you think? I think you look like almost the exact same just with tinsel. Yeah but look how long it is still. So this is more your natural? I'm confused. What did you have before? What do you have now? Because this looks good. You guys want to see what I had before? This is how much hair. Look at all that hair. Oh my. See I'm telling you you probably weigh like 10 pounds less now. I think I do. You probably do. Yeah I probably do. I just feel like I can balance like. Wait this is so much. What do you have in your house? Okay let me explain. Let me see. So I have a. You might see a bead because of the tinsel. Oh yeah I see this. That's a tinsel bead. I see that. You won't. I did an IBE method which is invisible bead method. Wow. So if I pull my hair up. Look at it. You can't see the bead. You can't see any of it. Wow.   I'm not fully fake but I do have a row a really thin row of extensions in my hair just to add more fullness just because I love extensions. I just feel like I don't know they just. But you look good. I actually I love it. Yeah I love your natural more hair. This I like it because it's more natural and it's not as big. Babe you have a small body. A petite body. I know I know.  I know it kind of over. You didn't look bad but this looks way better than what she had before. Not saying you look bad before. Oh 10 out of 10. You're always 10 out of 10. She calls me. She's like I really hope you like it. I'm like why wouldn't I like it. I still have to do my makeup so make sure you stay tuned for Instagram. I'll be posting on Instagram my hair. We do have to talk to you though. I love your hair but about our tiktok account of 14 million followers getting banned today. You guys. Or last night. You guys will see this was filmed on Friday but we're posting this on Monday. So last night we actually yesterday at like one o'clock I go on tiktok after we post our tiktok and I was like oh our views are like weird. Our tiktok that we posted was going viral right. It was awesome. We were having a good day. It had like a hundred thousand views and only like 50 minutes. I think not even like 45 minutes. Okay. It was at like a hundred thousand views and then all of a sudden our tiktok just stopped getting views stopped getting likes and our whole account every video besides eight were under review. So every single video besides eight videos note all the public could see it was eight videos and we posted hundreds and hundreds and hundreds.  We got nothing. There was no notification. We were still able to go into our account at that time but when we got no notification tiktok that our videos were going under review were like always just a glitch. So you guys can always see like eight videos when we could see hundreds and hundreds of our videos.  All these videos were from 2019 2020. So years ago like videos that were not even posted any time soon like the nearest one was like still eight months ago. Guys it was insane. It was the most scariest thing. So then that's not even the worst part. That's when we were going to bed. We were like okay hopefully it resolves itself. Hopefully we wake up and our videos are back normal and then I wake up at four forty five in the morning.  So I was thinking I was like alright I have a weird feeling our tiktok like I'm gonna open it up and it's just gonna be gone or it's gonna be fixed and I was trying to go back to bed and I was like no I need to look now because I knew if it was banned it would ruin our whole night not think it was actually going to be banned. I go on my phone we got a notification you've been signed out your tiktok account has been banned permanently and I tell Mariah this we start freaking out. We start bawling my eyes out four in the morning. She hasn't slept since four forty five. I haven't. This is what our account looked like before it was banned. 14.2 million followers all of our recent videos and my drafts and this is what my account looks like after it was banned. I took a screenshot it literally said not even our name it just said our username all of our videos are gone all of our followers we had no access for the entire day. And you know what sucked you know what sucked is we were up from four to eight o'clock knowing that we had couldn't do anything because nobody's awake. Our manager wasn't up nobody was up. Can't email tiktok at four a.m nobody's gonna answer. We could not sleep we literally tried we're actually having a tropical storm right now which I wish this was going on and we were trying to go back to sleep. We literally tried that. Yeah on Alexa because we couldn't sleep we were too in our heads. I made it go off when I said that. We were too in our heads we couldn't go back to bed it was absolutely terrible worst night ever. Because you guys don't understand like me and Bill were like one of the first couples on tiktok when it first came out. Which means we've been doing this for literally almost like four years since 2019. And we grew such a huge following we've worked so hard we've always tried to stay you know in the community guidelines like positive yeah our whole main thing is to entertain like and we never want to do anything bad so the fact that our account got banned for no reason and we couldn't even appeal it we weren't even notified before that it could get banned it all it just went away it was just it was just a very scary scary situation and we thought it was gonna be like this forever maybe days months yeah because literally I am just so thankful they answered us within a couple hours. We reached out to a customer support on tiktok luckily they answered we also had have you follow us on instagram we made a bunch of stories to tell all of you guys to go and message tiktok and go tell them that we are wrongfully banned and all this stuff and you guys helped so much. So we just want to say thank you guys for our love and support you guys are a really strong army and it's actually insane like stuff like this actually shows me how much you guys truly do care and love us and it means a lot. Now that we have our comeback we literally are so freaking happy it honestly would have ruined like my whole year would have been like ruined if we had to start fresh guys we were depressed I would have been so I was depressed I was depressed too and it's crazy how easily something so big 14 million dollars that we earned ourselves without any really help from anyone except like our friends that we post with like we literally lost that all in a second and a lot of you guys don't know this is this is our career like we're full-time social media influencers like we're full-time like on here that's like so it's like getting your job just ripped away no imagine going to college for four years getting your degree and then one day someone just takes your degree and rips it up and then you all that four years of work is gone that's exactly how it would feel like imagine that yeah it's honestly it's just crazy but we're gonna have a fun night tonight make sure to follow us on tiktok watch our videos we're gonna be posting a bunch we're having fun follow us on instagram because we do message you guys a lot and we also like incorporate our day-to-day life in our stories so you'll see more of us than you'll see on here a lot of the times besides the fact so you got my comeback I love her hair it's not even that much shorter it's like three inches shorter yeah it is but it's like not as thick like it's like it honestly looks more natural I love it you don't look like you're like way down I know honestly you had so much hair looking back at it it's like ill how did you even like that I don't know especially in Florida heat like I was talking to my hair girl she was even saying like I feel like you gotta do it once to realize if you like it or not some people like that thick of hair yeah you like it like it for you I like it like this but so we're gonna get ready we have some friends coming over some family that are gonna be hanging out the rest of the night and we're trying to enjoy the rest of the day and just be positive we literally we've been like so toasted she's been up I at least slept for a little bit you slept for a good hour after me yeah she's been up since four five and I'm just like I'm fine you've been up for almost 12 hours I know it's crazy and we literally have like I'm lively now once I get up on my face I'm good I'm Gucci well thank you guys so much for watching this video I hope you enjoy her hair as much as I love it she looks like a freaking queen and I can't wait to kiss the crap out of you and have a good night your hair smells good too I know why can't it always smell like a video it always smells good okay whatever you say all right guys I will see you in the next video but until then peace love you guys


I would like to bring up our director of community, James McLeod, who has done I think a fantastic job of growing the community here in EMEA. These are the community awards for EMEA. We will have the Americas one in the US in case you're interested to see us next month. James? That's brilliant. Thank you very much, Gav. So Gav is going to be my assistant for today. So firstly, what I would like to say, so as you can see our first award that we nominated is the newcomer award. But before we actually get on to announcing that, number one, thank you everybody for being here until the end. It's really great to see so many dedicated people at OSSF this year and it's also good to meet people in person. Some people who I've known for a while and some people who I've also met for the first time but have also met over Zoom for a while as well. I'm also privileged to be working within the community full time and like what Gav was saying, I also feel like an 18 year old because I get to do something that I absolutely love, which is building communities and building the ability to engineer. So I see that passion from absolutely everybody within Finos, everybody who's contributing equally has massive value and impact. I also have the privilege of being able to look across the Finos community at all of the work that's happening. So I also get like notifications on my mobile, but we also have LF analytics now, which gives us a real good insight into what's happening. And this is how we've been able to actually look at the community awards this year and also last year through other mechanisms. But it's really great to see the new people who are actually coming into the foundation and how the foundation is growing. So with that background, I'd like to announce the newcomer award for 2021 in Amir. And that is going to Maurizio Yigari from Goldman Sachs. So if there's somebody here who can actually- Anything Maurizio's here, but anyone that can come? So maybe Fee? Thank you. Welcome Maurizio. Make sure you give it to him though. And there's also a Finos store discount code that will come with it and will be emailed to Maurizio. So that will send it to him. Trust but verify. Absolutely. So it's really good to be able to chart back through all of our analytics and be able to see who the newcomers are. And so it was really good to see Maurizio kind of like joining within the last year. But now we actually move on to another award, which is the coaching award. So this goes to somebody who I know has been an absolute team coach on a lot of calls that we've actually had within cloud service certification, making sure that people have been unblocked and moving forward and really rolling his sleeves up whilst the team needed contribution on the ground. And so this year's coaching award goes to Abdullah Garcia from JB Morgan. Thank you. Thank you man. Cheers. Great job. Thank you. And now we're moving on to outstanding leadership. And so talking amongst the community and also seeing how people are contributing and bringing people into projects, I'm pleased to announce that this person, Vijay Chandel, from Goldman Sachs, is actually winning this year's award for all of the work done on legend and also currency reference data. So congratulations. Thank you. Well done. Congratulations. And now the shining star award. This is going to a project which is risen in the ranks. Everybody's heard of it. You can see how many people are contributing into it through all of the stars, the forks, the contributions through our analytics. And this award this year is going to the legend. I'm playing. Congratulations. That's the beauty of transparent metrics that I'm biased. And the most active individual. So the person who is absolutely plowing commits into the foundation is also resolving issues and doing a lot of merging, working within the community and also bringing other banks to collaborate within projects. This year the most active individual goes to David Watkins from Deutsche Bank. I don't think we have David, do we? All right. Come on. He's a star. Thank you. Well, we can edit the picture afterwards. I'll put it over. Yeah. Nice one. But you'll send us a picture with him. Great. Thank you. Say thank you. Thank you everybody. I don't know if you have any closing gap. No, no closing. Maybe there is a slide. Yes. Okay. Yes, I do have a closing. Again, we can begin to thank you for being here. I'm not sure how many of you will be able to make the trip to New York also because it is unclear when the US is going to reopen to Europeans. Sounds like it could be beginning of November. We're all keeping our fingers crossed. But as I said before, please help us spread the word. I know there are several colleagues of yours which we know and we hope to meet many that we don't know already. So thank you so much for a great day and I hope to see you all and many of your colleagues in New York in a month from now. Thank you so much.