First of all, thank you all for being here, and I'm very glad to be back and I feel like an old customer in an old pub, even though the surroundings are much more beautiful than the pubs, which I usually go to. Thank you, Shantar, thank you, Jess, for inviting me and I must warn you that it has happened in the past once more, when I started putting together my thought in order to prepare the presentation, I realized that it had too many things to say, so I tried to squeeze as much as I could in the presentation, but I think that we need to have a second part of this conference in the coming future. So, the, I was writing last year, I spoke about big data, data science, and I feel obliged to explain a little bit of the background. As many of you know, I used to be an astrophysicist, and almost 20 years ago now, together with George Argoski, we slowly drifted toward, could you say George, slowly drifted toward the data science, because the amount of data which were collected by the new instruments was so large and so impressive that there was no way to end it with the traditional tools, and we began to explore the possibilities to use innovative software based on machine learning to easy the reduction analysis and understanding of these data sets. So, I became more and more involved in data science, and since two years I am chairing the data science initiative on my university, and I've changed profession, I'm no longer an astrophysicist, and I frankly don't know what I am now. I'm somewhere in the middle between different disciplines. Mainly, I'm interacting with engineers, companies, and building a new know-how on what's happening around us. And therefore, even with respect to my last presentation here in Second Life, much has changed, at least in my understanding of the topic and of the field. And this presentation, and if we succeed in organizing it also, the second one will be about taking away a little of the drama which has been mounting in the last two years around the topic of artificial intelligence. There is a hype around us about artificial intelligence.  I mean you go to a supermarket and you find that the items were selected for you by an artificial intelligence algorithm.  You go out with, I mean you take a plane and they tell you that the CIT assignment is done with artificial intelligence and so on. Artificial intelligence, in my opinion, is just a bad word. You know, one of those words which periodically are used by community, by journalists, in order to signal that there is something incredibly new happening. I'm not saying that there is nothing new happening. I mean, actually we are in the epicenter of a revolution which deeply affects the way we live and the way we work, the way we organize our lives. But from this to artificial intelligence, I mean, there is still a lot to come. So I mean, there is a lot. There are many considerations. At the moment I'm writing a book on the topic. So, you know, there is, my thoughts have not yet reached the sort of crystallized form. So, there is still a magma of ideas which come back and forward and therefore I'm quite sure that in the second part of my talk I will say something which will be in slight disagreement with what I'm saying now. But I mean, this is science. This is, you know, evolution of understanding and evolution of thought. Data science, as shown on the slide, is something very simple. It's a set of the principles, problems, definitions, algorithms and the processes which focus on extracting non-obvious and useful patterns from large datasets. Data science extracts patterns, not knowledge. Knowledge which is, say, can be assimilated to understanding is something different. I mean, at the moment we are still far from having an understanding performed by some artificial device.   So it's what I'm going to say for data science also for artificial intelligence. Our wisdom, Ariane, our battery wisdom, I mean, yeah. But the problem is that wisdom is a problem for natural intelligence, not a future problem for artificial intelligence. I mean, so basically the layman understanding of artificial intelligence comes out of science fiction books and science fiction movies. And this is also why I think this buzzword has become so popular because the artificial intelligence cares, commits, carries with it, you know, bad feelings.   I mean, this is a common understanding. If you ask other people in the streets, 95% of them will tell you that artificial intelligence will mean the end of the human race. Also, because the idea of artificial intelligence makes humans feel relevant in some sense, because if you think about it, I mean, so far, all the progress in the, in our technology has been, as concerned, only one aspect of the human being, the homophobes, the Latin Romans will say. The man as maker of tools, the man as maker of stuff. The industrial revolution was a revolution about capability to make things. The revolution, which we had experienced at the beginning of this century, was also saying technological revolution, industrial revolution, everything was about the capability of humans, of humans to make handicrafts, to make new tools, new instruments. For the first time, with artificial intelligence, we are facing an evolution which affects the homo sapiens, so the capability of human beings think and understand. And this is something very scary. This is something we have never dealt with before, and for which we have no antibodies. Basically, we don't know how to react, how to adjust ourselves. And therefore, there are huge social, religious, philosophical topics, which are under discussion, I mean, even legal topics. And there is a huge discussion going on everywhere on the, more or less in every community, this was a pleasant discovery. Before starting the large amount of research, I thought that artificial intelligence was a topic discussed mainly by computer scientists and mathematicians and by data scientists. This is not true at all. There is a huge community covering all aspects of human thought, which is discussing the topic from lawyers to judges to theologians, to experts of human science, psychologists, and so on. There is a huge literature far larger than any human being will ever be able to grasp in normal life. It's a very active, very vibrant, very exciting field in this moment, but basically we're discussing the sex of the angels, but people are so scared by what it could be that there is a lot of discussion about how to deal with artificial intelligence machines, which are not yet here. And there is a lot of confusion, and I think it's important to clarify this confusion in all the groups of people I've mentioned before, because they mess up between artificial intelligence and the wrong use of big data and of algorithmic capability to use this data in order to predict trends, to control trends and to manipulate the society around us. The real meaning, therefore, is that artificial intelligence is not in girls that a collection of different programs which try to address specific, simple, specific problems. So basically what the approach is, I isolate a specific process hidden within human thinking, like for instance, how do I visualize a vertical structure, how do I visualize a cat, and how do I recognize that it is a cat rather than a dog and so on. Then first you isolate the process, then you transform it into algorithms, and this is usually the most difficult part, and then you run it on computers. And then if you are successful, you start using these algorithms to substitute humans who perform repetitive tasks of the same type. For instance, already now in the United States, after the success of the automatic driving car, automatic driving cars and trucks, now there is already a community which is scared of losing relevance, like for instance, truck drivers. We have understood that within, let's say, five years, ten years, most of them will be out of the job just because automatic driven trucks will do the same job more effectively, less costly and way. So I mean, I'm not saying that this algorithmic approach to problem solution pattern finding is not impacting our society, it is impacting it a lot, and it will even more impact on our way of life in the coming future. The most unexpected way. Also because once you learn how to do the trick on one task, to export it to other tasks is much easier, you already know how to do it, you just need to do some bit of fine tuning. The only change is light and you always get confused with your controls. I apologize for that. Okay, should work. Yeah. One thing which I found very refreshing is that at some point I decided to, you know, to understand why suddenly two, three years ago artificial intelligence seemed to emerge outside of nowhere, just popped out suddenly. This word, if you look to a statistics of the usage of the word of artificial intelligence in non-science fiction literature, you'll find that there is really a discontinuity, before 2016, after 2015 and immediately after, because the number of times these words appeared increased by factor 100. And this is always very suspicious.  In little we say it doesn't smell right. So the first thing I want to understand is whether this thing was really something new or was just a new cover on something very much older. And I did as I always do when I am interested in new problem. I went back to the literature and tried to reconstruct a little history and I realized that actually there's nothing very new. It's what we call nowadays artificial intelligence, which for me is more or less a synonymous of data science is something very old. It appeared very, very long time ago from the fusion of two trains, which have been around since the very early stage of human evolution, data collection and that analysis. If you go to, because basically we are doing just with computers, something which has always been done. It's not in you. Data collection is as old as human civilization and the type of data are always the same. Already in the third millennium before Christ, there were transactional data, which means basically data about economic transaction, SLU, a camel, and they get so many cows and these type of things were already stored in some form, as well as non-transactional data. So data which were not really related to economic transaction. For instance, the Roman and the Gypsum sensors were no longer, and it was always a sort of transaction and they wanted to put, to tax people in a more effective way, so it always money was involved. But these sensors were built collecting words, collecting permissions about words, sex and this type of things. All this data were collected in common storage. So the collection of data is a very old thing. And the next step, the capability to retrieve data in an organized way, what we would call today structured data, is at least as old as the library of Alexandria in Christ. So I mean, basically the analogic version of a database. And this thing, therefore, since data collection is old, you know, it's very likely that also that analysis is a little younger. I mean, algorithms and simple tools to analyze and extract information from the data are at least as old. Here you have an example where you have the data analyst of the Egyptian time, which were the scribes, you know, checking the census information, applying a mathematical formula say, you know, in order to derive taxes. I mean, these things have been around for a long, long time and their relevance increased as the society became more and more complex. One big difference, this is a slide from my old presentation, is that in the past, to collect data was a quite expensive and quite lengthy procedure. Therefore, the boat rate, the number of data which you could collect per unit of time was very, very low. While nowadays, we are surrounded by digital sensor, by every aspect of our life is subject. Every time we use a card, every time we file an email, we send a Twitter, we post the submitting of the social networks, but, you know, every time we buy something, this information is automatically stored. So basically, data collection has become a very inexpensive thing. And the amount of data which is produced is just very, very large, incredibly large. The turning point, which were everything which we today, nowadays called data science, started this in 1991, which is quite a long time ago. It's when this guy here, Edgar Cobb, he was born in 1923, 2003, you know, put laid down the foundation of the so-called, what we call nowadays the relational database. This guy was a quite amazing scientist. He won the famous touring prize, which because it's a sort of a Nobel for computer science. He is also the man who invented the term online analytical processing, which is well known as all up now. And what is more important, he codified the analytical processing, which taught them of everything we are doing nowadays, which is 12 laws for, which must be followed in order to perform online data processing. It is nice to say that at the time he was a researcher for IBM, and when he published this paper, Relational Model of Data for Large Shared Data Banks, IBM didn't consider it at all. I mean, it just didn't take into account, and Cobb published a data to a third party, let's say, basically a paper which was sponsored by Arbor Software, later became Ipillian, and now it's a part of Oracle. And this is one of the main mistakes which IBM has done, despite all the many good things they've done, but IBM has done a lot of mistakes, because the guy who instead understood the potentiality of the codes, the paper was Alison, he's a founder of Oracle, and now he's the eighth richest man in the world. He basically, at the time the paper by Cobb came out, he was working for CIA, building the first database on codes principle for A8, but when he understood the potentialities, he just went out, started from CIA, and established the company, which was after the laboratory, which in 1982 became Oracle, which actually, in this moment is the monopolist of relational database all over the world. Also, they established the standard query language, which is data, relational database. I mean, this is important because it begins to tell you that what matters is who controls the data and how they control it, but we'll go back to this later on. The same part of the story, this was that election is data statistics, and the statistics, I mean, I found always very curious to remind people that statistics, the word statistics come out from state, from nation, from state, because at the beginning it was conceived as a way to collect and analyze data about the state in order to tax people. Once more, taxes are always at the heart of our life, for many years at the beginning, it was merely a summary or descriptive statistics, there was not much behind it because it was a way to characterize large distributed data using average properties, variants, some of the things which is very nice. The real revolution, which laid almost immediately to the foundation of what nowadays we call machine learning took place in the 17th-18th century, when the probability theory transformed the statistics into statistical pattern recognition, and there are many, many players in this story. So the first player I always like to put to this guy is Hieronymus Cardanus, Gerolimo Cardanus, one of the most incredible people ever existed. He was born in Pavia in Italy in 1501, he was a polymath, he was a physician, he was a philologist, an astronomer, a biologist, also an astrologer, at that time it was possible to be both an astronomer and an astrologer, but mainly he was a mathematician and inventor and look at this, he had also some problems, he was a gendu, he first believed in witches, he was a great supporter of witch hunts and farming witches on the stake, which is not very nice, and he was also a gambler, he was an inventor, he has invented the combination lock which you use nowadays on your trolleys, he invented the Jimmel, which is that suspension you see here in the middle, which is the three degree of freedom suspension which is used by gyroscope or mobilized surface, and even invented the Cardan shaft, which was the car, so quite a general guy, he was the first one to systematically use negative numbers, at that time they were quite new and he made public, he was not the discoverer, but he advertised very much the solution of other mathematicians for cubic and quartic equations, he also understood the need for imaginary numbers, and this is really quite an extraordinary achievement, but what is very important, in order to make good prediction for his gamble, he invented probability theorem, he's the guy who basically invented the binomial coefficient and the binomial theorem, which are a little bit, you know, the foundation of probability, and also he parted with the first theorem of probability, so it's nice to see, you know, that everything starts either from taxes or from gambling, not really positive, but I find it quite fun, it's the next guy, I mean, then there is a long, long succession of scientists established this new field, which we nowadays, since in the modern world, we like to put a label and we have a sort of a schizophrenic attitude towards things, I mean, we like to fragment our knowledge, you know, in domains, and the narrower they are, the better it is, because this satisfies our need for control, but actually, I mean, I find very difficult to establish boundaries between, you know, any aspect of human thinking, but so nowadays we call these things statistical learning, the statistical pattern, the cognition, the machine learning, or, you know, data mining, at the end, basically, everything goes back to these people. We had Blaise Pascal, who made some important theorems about probability theory, then the Swiss jackal Bernoulli, then Abraham de Moivre, who was one of the most unknown, he was one of the most unknown mathematicians, but he also is the one who established some very important mathematical theorem of probability theory, which enabled the estimate probability distributions. And then Thomas Bayes, I think that most of you have heard at least once Bayesian approach, Bayesian statistic, Bayesian bias theorem, and so on, is a very accurate way to infer knowledge out of data, a good way to extract information from data in presence of partial or not information. Actually, Thomas, I never understood why the bias theorem is called bias theorem because he had the very, if you read this paper, it's, he has a very big bad understanding of what he wants to say. Then this guy here, Richard Prince was the one who began to put his, Thomas Bayes ideas in a more formal way, but the real inventor of the statistic was an astronomer, Thomas Bayes, over there, Simon de Laplace, one of the most influential astronomers of his time. He took the intuition, the very rough intuition by Bayes and developed it in, but nowadays he is known as the bias theorem and he established the Bayesian statistic. With the foundation of Bayesian statistics, we are basically beginning, so it's already the beginning of the 19th century, it was in 1805, we have all the bricks which are needed to do statistical pattern recognition, which is one of the main aspects of machine learning and data science. So these tools, these algorithms, these mathematics have been around for a long, long time. Then what has changed is the way we have implemented it into computer, but we go back to this in a quite short time. Now, another good guy is Carl Friedrich Gauss, who contributed a very total piece of information, you know, because basically he is the one who established the theory behind the regression, the logistic regression, which for everyone works in machine learning are very too familiar terms, but the mathematics behind them was established by Gauss in the attempt to find the elements of the orbit of Ceres. Ceres had been discovered by Piazzi in the 1st of January 1801 and Carl Friedrich Gauss solved a very difficult problem of finding the orbital elements of a new planet using only three observations. And in doing so, he invented the least square method, the method of the minimi quadrati, which is most common tool nowadays for replational data. And in doing so, he established the theorems behind linear regression logistic regression. From the beginning of the 19th century, all the bricks were in place. Then there was a long time of quiescence. In the meanwhile, there were significant advancement. For instance, nowadays, there is a lot of talking about data visualization, advance of the data visualization, which is correct. But again, the problem is a new one. It will start, everything started actually with this guy here, almost unknown to most young players. He was an English engineer and was working for Ray companies. And he developed the statistical visualization. He is a guy who invented the area charts, the line charts, the time of Instagram shown in the bar charts, the pie charts, all the tools which he used nowadays and which is somehow related to the field of mathematical visualization, which is moving to the advanced visualization we use nowadays in computers. Obviously, things have changed a lot thanks to the introduction of computers or monitors. Here you can see modern visualization of complex data, which would have been impossible over time. But remember that in most cases, more complex does not mean deeper. It's just that you are using old tricks to solve new problems. And you have a much larger computer power, which allows you to do much more complex tasks. But not much new under the sun, I will say. Then there are exceptions. For instance, you can visualize things in many different ways. And I think Jess remembers our first experiment in second life, George Raffaele, Gino, myself, were using the second life to visualize complex data. I had a sample, but you know, no, there is not. And I must tell you that this is something which should go into the curriculum of second life is that Ciro Donald and George experimented in second life and kept working on it. But basically using the same philosophy which was behind the debt with Jess's life. And Ciro Donald has started his own companies, which is nowadays one of the leaders of the visualization in the world, which is in politics. If you Google it, you're impressed by what you can do. Basically, it combines what he tested in second life with the virtual reality, augmented reality, uses HoloLens, Oculus Rift, and so on. You know, to allow you to interact with the complex environment. And this is, I would, I like to say that this is a spin-off of research done in second life, 10 years ago or more. Okay. Third piece of the bridge of the world is everything. Basically, the mathematics was in place. There has been not too much innovation in it. Also, the emphasis which has been recently put on deep learning, deep learning has been around for the last 20 years. Overcalled ensemble learning and so on. The only thing is that now these networks, which were all fully slow when you were running them on single processors of the previous generation, now can be running the cloud on a huge amount of data. And obviously they lead in many results. But in deep learning, there is nothing new. This is something which is seldom spoken out loudly. Google has invented nothing. All the theory, all the, all the mathematics behind deep learning was already in the machine, in the data mining literature 20 years ago. They just, you know, optimised the algorithm they invented, specific sub parts like adversarial network, which are, you know, but so specific problem. But behind deep learning, there is nothing new with the exception of a huge amount of computational power. So the revolution started, this is just for historical reason, with Alan Turing who invented the first computer and things, you know, exploded after that. This is again a slide from previous presentation. Nowadays, it is estimated that there is at least one processor every, we have a huge amount of processors and we have a huge amount of computing company, especially if you connect this processor with the network cloud computing and things like that. Ah, two other people, which I really think are always overlooked. I mean, who are really instrumental to what is happening today are the old Shannon, who really is the father of modern information theory and also is it the AORM or the Shannon theorem is it the central or it was in American cities and to publish a terminal paper and mathematical theory, one of the forces or whatever happened after. And then this lady, which is, who is really very unknown to most people, Erwin Ficks and their student, Joseph Forbes. Well, Erwin Ficks in already 1951, we are talking about 68 years ago, invented the AMN, the nearest neighbor method, which nowadays is one of most used things in data science. And one thing which I really am sorry for is that nowadays the way science works, which I mean, I'm an old guy, I'm 62 and I've been in business for 41 years now. And when I was young, people read literature nowadays with the attitude of modern science, which is publish or perish, basically you have to publish. It doesn't matter whether you publish good thing or bad thing, you have to publish because it is based on the number of kilos of papers you're publishing. Even if that paper is worthless, it doesn't matter. But basically people publish, they are in hurry to publish, they basically, they don't spend the time in reading, they don't spend time in thinking very often. And what happens is that, for instance, PhD students or young researchers do not read any literature which is older than five years at most, not usually three years. So you find nowadays that nearest neighbors is attributed to some young, not so young, but to some American researchers who republish 10 years ago. And the name of all of the fixes is virtually unknown, but I invite you to read the papers for those who like these things. It is the nearest neighbor, she invited it completely, and she has never been a knowledge for it. And she's rightly to be considered one of the founders of modern science, or modern data science. The time of traditional literature as from a scientific point of view, intelligent robots have been in the science fiction since 1852. But comes out of a meeting, like, you know, another thing which is surprising, this innovative word, these bad words always come out from meetings. For instance, the famous Drake equation, which is used very often to estimate the number of extraterrestrial civilians, the probability that some extraterrestrial civilization is two years ago. Drake equation and artificial intelligence come out of workshop. Of the planning for workshop or out of, in the case of the Drake equation, it comes out of the name of the session of the first meeting on the first for extraterrestrial. There is nothing simple. It's just, you know, a sort of summary of the session or workshop. This guy, John McCarty, one of the amazing minds of this century is a post-seconding medalist and mentor of the least. In 1955, proposed to the short side foundation, a workshop to analyze the possibility to teach computers or to program computers to performance tasks, which at that time were considered to be an exclusive prerogative of the human. And in that paper, for the first time, appeared the word artificial intelligence. And with co-authors, co-authors of the proposal were Kuroschans, and Marminiski, and Pater Moshesler. If you read his motivation is quite impressive. This guy then moved ahead. I mean, he invented the first language for this type of application, which is LISP. Then he contributed to Al Gore. I mean, he died a few years ago, but he has been active for the last minute. It's life. Quite impressive guy. Okay, so let's go back to the beginning. I mean, it's a long history. There is, as it always happened, most of human endeavors, there is no discontinuity. There is a slow evolution a better understanding of what's going on. And then suddenly in 2015, as I said before, artificial intelligence emerged from the science fiction. A very common phrase, a very common word. So why? Well, I think that now it brings back to the fact that for the first time, all things which have no intelligence whatsoever together, I mean, big data, no intelligence, no instinct intelligence, computer networks, well, less than less. Computing intelligence is just a set of algorithms, which are doing what I've been telling you so far. And they are also not intelligent. My dog is a magnet. And then the intelligent human machine interfaces. All these things together conspired to make the world artificial intelligence credible. Now it's important for me, and I am not a solution. I mean, this is an old slide. I am not a solution for this one. To understand who really is behind the wheel. I am my suspicion, but I'm not saying that we are not going to work in a far future toward an artificial machine, artificial intelligent machine. It is possible. It's feasible. It's just matter of complexity. It will take long, probably, because at the moment we are beginning to understand, it will take the convergence of many science, biology, biotechnologies, machine learning and computing and computer science there. But it's still far to come. So why suddenly artificial intelligence has become so popular? The data. It is a huge amount of data. And I know that now I'm going to repeat something you just said last year, but I want to say it in a different way this time. Because I have a different perception of the whole scenario. Artificial intelligence is, in some sense, emulated in specific tasks, which still have some algorithmic complexity behind it. But they are still in some way deterministic. They are still algorithmically defined by algorithms, by world-defined sequences of commands. For instance, computers now usually beat humans in chess, in chess playing, or even in television quits like Geopardy. They drive cars, like they land planes, they land probes. But if they have very complex problems, with some amount of determination, but everything is coded through algorithms, through a very smart exploitation of data collected by humans. And the algorithms are written by humans to emulate data-specific behavior or their brains. So it's something very different from what we all envision as artificial intelligence. The problem is that this revolution, for the first time in history, is not controlled by government. For anarchists, this can be good news. But for humans who believe in democracy, this is very good news. And they think that also the sudden increase in the use of genoterm artificial intelligence comes out, and also the emphasis on the fact that artificial intelligence is coming, artificial intelligence is behind the corner, comes by reasons which are very little to do with science. Another investment in the infosphere, where the infosphere is a name invented by the director of the digital ethics lab, Oxford, Luciano Floridi, is a complex of net plus infrastructure plus software. Basically, it is one thousand giga, one trillion dollars in less than ten years. A column of one hundred dollar bills which goes from the earth to the moon, given the idea of the money which has been invested by government and states. Then you find that the largest company existing in this moment in the world, the three companies which have gone beyond the one trillion dollar of corporate value are people, Google, and recently the Chinese giant Baidu. They are all related to the data. If you see, this is the Google galaxy. At some point Google is, I need to change my position because I cannot see something, just a second and it will work. I cannot read the number because my label falls, okay. It is 1.65 billion dollars for YouTube and it pays 19 billion dollars every year to control the traffic on the apps on Android. Now, one thing which is a very often let me go ahead for a second, then I go back to some consideration. They look for instance at Facebook. Two thousand two hundred billion users, so basically two, no sorry, not billion, million users, so basically 2.2 billion users, which the same Zoukemberg calls the use of a video. This means something and as a value of 700 billion US dollars and there's both for Instagram for 741 million dollars with only 20 million users, while I say it will stop 19 billion dollars with 1.5 billion users. Microsoft, 800 billion USD values, 1.5 billion users buys Skype by paying 8.5 billion dollars for 633 million users. If you look at it, you see that there is a direct correlation between the number of users and the value of society and also the fact that these people are trying to create conglomerates which monopolize the data.  Where does this value come from?   But for Facebook, for Zoukemberg, all users are useful idiots because basically they provide the data for free without realizing the huge value which is associated to this data, which they are providing for free. And this is also why these people rule the world. I am getting close to the end because I see that they have run for a long time. It's in five minutes and done. So basically, what is happening in this day is that at the beginning, I mean, there was a network. The network had many, the web, the only web had information in it. This information had to be found. In order to find it, a few companies built the first browsers. These browsers were exploring the network continuously and indexing the contents of the network. So, you know, the first Safari before that, Netscape and many other browsers, then Google came among these many players. In order to store the information, you need to build infrastructure. In parallel, in order to index this information, you need efficient algorithms for machine learning, you know, which basically go through the information that has been found on the network, you know, a code and convert it in keywords, which can be indexed and then searched by the browser. And in order to do this indexation, you need computing power. But again, what happened is that by chance, the people who started the first browser almost as a game found themselves in a virtual loop, basically in a spiral. More nodes in the network, because there were no users, there was more information, more information to be indexed, needed more storage for the indexation and more efficient algorithms for the understanding and classification. In order to dissolve, these companies needed to acquire larger infrastructure and so before the governments could be realized, could realize what was happening, these people become at the monopoly of the game. They basically became the owner of the infrastructure. Now, where does their huge richness come?  Nowadays, they own the world, please. They are by far more powerful than any company, than any large international conglomerate or companies. They really own the world. Data comes from the data man, not because as many people mistakenly believe, you know, the data used to sell advertisement, not at all. The advertisement for this company is basically peanuts.  You pay one million dollars, it's noise for these companies. Where does the richness come from? The value of this company, by the fact that they can use the information they have in their data set to exploit the society around themselves. So basically, they can sell this information to companies in order to improve their market. They can sell services for specific topics and so on. And this situation cannot be resolved. Now, since these companies are behind this machine learning and artificial intelligence, you realize that the main drive behind the sudden success of this artificial intelligence warning is related to the role which the data provides and conquer it and very careful not to lose in the normal world. I mean, well, I'm sorry that it took longer than foreseen. There are many other things which I would like to see, but as a lapse, that's all I would like to finish here. If there is any question, any plan for the second part of this talk, I'll be very happy. So, let's finish here. I don't see any reaction from the public. Here we are, the shot. Okay. Thank you very much. Thank you very much. I'm always glad that we're such an audience. You have an equation. You know, I always found that the three levels of robotics are not really believable because I think that let me put things like that. Asimov was writing this thing in a pre, in a racist and era, basically. When there was still the understanding that some types of humans or some races were better than the others, I'm not saying that he was a racist, but basically he was not. But the culture of that time was permeated by this behavior. So basically he created this sort of artificial intelligence, which has no freedom of choice. Basically he created the slaves. The rabbits by Asimov are are intelligence with the limited range of activity. And this was basically these three laws are dictated by the fear that humans have toward people who are different. The same reason why we have racism, you know, again, the people coming from other countries in the United States against Mexicans or, you know, they are different. Everything which is different creates fear. And we need to build the walls. In the case of Trump, in Italy, we need to keep them away from the coasts due to Salvini or, you know, in the case of Asimov, you need the three laws who are needed to protect humans from Robo. I think that an artificial intelligence will be exactly what the name says. It will be an artificial intelligence. She will not hear at all about any laws we can put in her brain just because it will be intelligent, it will be free, it will be able to decide by itself. But this is just my personal belief, you know, belief. This is a long question and I have to read it. Just a second. You know my sincere answer. I think that unless we do something, we do it at the governmental level and unless we recover a sort of popular control, state control on big data and on the usage of this big data, we are bound to lose our freedoms, our civil rights, and basically we are going toward one of the worst forms of dictatorship, which human being has always experienced. Also, because remember states right or wrong, dictators, non-dictators somehow want the good of their subjects. Private companies know. McZuggenberg doesn't care about your well-being, doesn't care about your freedom of society. Only thing he is interested in is profit for his company. So for sure if we leave a big data and the only revolution in the hands of big data providers, we are bound to form all the tyrants. I cannot read, sorry, yeah. I will go prospect, I'll give you a video, yeah, definitely. But I mean it's, you know, usually I'm a very anti-government person, but in this case I would say that government have no, no responsibility for it. I mean governments are things which react very slowly and the evolution of this field was very quick. Basically they were found unprepared, they had no time to react. And now the situation is rather big because of the control of the data, the control of their distribution of their usages in the end of a few tycoons or not subject to any formal control. And this is scary. I lost the food, so I can comment on that. I mean there is a cool nursing later. Sorry, I'm in problems with this computer. Okay.  Now to tell you the truth, I have no personal idea about that. I mean the thing is still far away in the future, but people are just to everything. And so I really have no answer. I think there is a problem behind it, but really no answer about that. And so basically no answer. I don't know. A comment to Taglin. Taglin, I don't know Taglin with you because the motivations behind the government, you can, you know, I am a democrat.  I mean, the equivalent in Italy. So in Italy I am on the left side of the world, rather than on the right side. I don't like the right side of the world. But history teaches us that government, even one government can always be corrected. We are the fascists and we took it down. There has been communism and we took it down when it didn't work. The problem is that when we put things, I mean, why there's a scenario, but now we go away. Well, I know you're from the middle of science, the opinion. I think that one of the worst problems nowadays is that the advent of a big data artificial intelligence is taking place in a vacuum or ideologies. In the past, basically for the last two centuries, human beings have been driven by three dominant ideologies, fascism, communism, and liberalism. Fascism was basically taken care of by the Second World War and we were left with communism and liberalism. Communism has been taken care of like it or not in, by the world when the Berlin world came down and liberalism has been taken care of by the crisis in 2008. So for the first time, we are facing an apical change in society, an apical revolution in society without any ideology. And this is terrible. Because if you don't have ideology, you don't have a path to follow, you don't have goals. You don't know what you want as a society, where you want to go and now to achieve it. And therefore, we are living in worse conditions. So maybe it's very different to have a bad usage of technology or things from a government or from a corporation. Governments can be somehow controlled and corrected. Corporation not. Thank you. I also must go because for me, it's dinner time. For me, it's, it's very late actually. It's 8 20 p.m. In any case, I mean, if you wish, I can give the second part of this talk in, let's say, near future. I leave to Chantal and Jess to decide. Thank you very much to all of you. It was really a pleasure to be here. Have a good night or a good day, according to your time zone. And most importantly, a good weekend. Shout out to everybody.


Let's be real guys.  We make a lot of stupid mistakes We waste a lot of time and more than anything what happens is that we end up not leaving our lives deliberately Consciously in a direction we want to go in and then we reach the end of our 20s and all of a sudden we're like crap I don't really know why I'm here and what I'm doing and what's going on. So in this video I want to share five more mistakes. I made my 20s that now upon further reflection. I think will help you the most What's up guys Alex Hine author of the book master the day now one of the things that made the biggest difference in my 20s that I used Massively was journaling.  What did I want in the kind of women that I dated? What did I want from my career? What did I want from my life? So I've included actually some of the core journaling exercises that have changed my life. It's an e-course It's the first link in the description. You can click to download that free So that'll give you some of the exercises. I worked on myself now The first mistake in my mind is the mistake of drifting and I see this more than anything Being a big flaw in the way that we tend to live our lives Especially in our 20s when we think I'm young. I have time. I've got all the time in the world It's not true when I moved back from China. I was 24. I think and when I got back. I honestly was really depressed I was super miserable. So I didn't really care like what I got in other words I got the first job that I could get that was a good job. I Didn't really care when I was dating a girl if it was going anywhere because right now I just wanted something kind of the fill the time. I was 23 I wasn't even sure if I wanted a real relationship. I was bummed out about life anyway So I had all these things that I just wanted to fill my day And that's fine if you're super happy and fulfilled and all the quadrants of your life are where you want them to be But in reality, I was letting myself drift I took a job and I ended up in jobs for three years that were wasting my time The money wasn't even worth it because what I was missing was the opportunity Discover what I truly was interested in what I was truly passionate about the direction I truly wanted to go in that was the downside I also missed the opportunity to really reflect on all the other parts of my life like what I wanted What did I actually want in a girl that I dated what are the flaws? I had to work on and so for me the main thing I needed to remedy that was to be more deliberate and think and reflect on what I really wanted and let that inform My choices the second mistake is valuing money over growth, especially of skills Now when I was 22, I got a decent job at a college and it was pretty good pay I was making like 45 grand. I was only working nine to three because it was a teaching job It was easy half the day. I didn't even have class. So I was like nine to three great pay I lived at home. I mean I saved $15,000 cash. That's what I moved to Asia with and The problem though is that at the same time I had a lot of friends that were getting these really high-paying jobs because they picked money first and so they were making 6070 80k Before 25 and I was super jealous But the thing was they very often did not choose work They were interested in or work They really wanted to get good at because they didn't really care that much and so now almost 10 years later I've been out of college and now I've gone through 15 20 different jobs Many of them are in the same job or on their second job and maybe they earned a lot in the early 20s And maybe it's gone up a little bit but maybe they went from like 60k to 72k and now in the meantime not only am I doing my absolute passion Where there isn't a billion dollars that would fall in my lap and I would just retire Not only do I love my work and it makes an impact on the world like to see from you guys every day But my income has gone up every single year and it's gone up dramatically especially as I've been an entrepreneur and so I see so many people playing the short run the short game and Then they max out at 30 or in their early 30s, but more importantly They're not happy and they're not fulfilled So don't make the mistake of choosing pay in a job Over growth and learning because either way if you're getting a job, but you are interested in you will enjoy the work And if you focus on growth and learning you will far out earn all your friends get in high-paying jobs But it may take you five or ten years The third mistake is something I think will inspire you and it's the mistake of thinking that life will never get better You know my early 20s I was always the dude that got dumped right always and I remember the first time I got dumped by a girl. I really liked I think I was in my late teens the first time I was like really into a girl and for at least like a moment in time. She was into me and When it ended and when I got dumped I was in this awful mood for a while and I remember thinking consistently I'll never find someone that good again a girl that attractive that smart that fun whatever But sure enough when I first got into my real Mutually loving long-term relationship at 25 that girl was much better and the same thing happened You know when I got laid off my first job I had all this time all this money and then when they're like hey What are your plans this summer and I was moving to China and they were like great because you wouldn't have a job anyway You know I was like oh man Maybe I'm never gonna find a job that easy and enjoyable again with pretty good pay But sure enough here. I am as a youtuber and an author and My pay is much better and much more fulfilling and I would do this even if it were not paying me And so we think With people with breakups with work with a parent dies or a friend dies and we think life will never get better I'll never find that again. It's I cannot ever make that happen It was like a once-in-a-lifetime experience, but it's usually not true Everything's gonna be okay, and that's the thing that's hard to always see in the moment But it almost always has been true in my life because if you constantly reflect you self improve And you figure out how to make your life better. It will almost always get better guaranteed The fourth mistake is the mistake of making the same mistakes So I don't for a second believe that with age comes wisdom because if that were true Then no one over the age of 70 would have issues with their health their weight their relationships their financial life And so we have these things that are recurring themes in our life. We struggle with our health or our weight for decades Which means maybe we haven't really learned enough about ourself and then put that into action We struggle with overspending and debt for decades Which means we have not learned the lesson and then changed our behavior We struggle with dating the same people with red flags that ruin our lives and yet If that's happening five times we haven't learned the lesson we haven't reflected we haven't changed our behavior and our habits So for me the solution has always been to make sure I'm not making the same mistakes more than a few times That doomsday document I talked to you guys about where if I have the problem in my life twice To try and make sure it doesn't happen a third time. I write down everything about it Including stories and anecdotes from my life, you know, I did in a really messed up girl once and from that experience I put together a long-ass document on red flags and dating These are the things that you saw but you did not know what they were your intuition felt this if you see these Run just teleport in the opposite direction, but you can do that for your finances. What were my financial flaws? Oh, I leased an expensive car when I didn't have that much money I took on business that I whatever Put those put your actual mistakes in one document related to that aspect of your life dating Finances or fitness so that you have something for the rest of your life to reflect upon now Finally, the last mistake is the mistake of praying and wishing and wanting without doing anything about it We have this This is a weird era in the world of law of attraction and manifesting Which all people have known about since the dawn of time, but I don't think until the secret did people really think that? Wishing would help you lose 30 pounds or praying would cause you to get a raise in your job while still doing shitty level work You know, there is no world where you can wish without doing and lose 50 pounds Then you can pray without doing and get a thirty thousand dollar raise while still doing C level work thirty thousand dollar a year work This is delusion and I think it's so tempting to want to wish and pray and hope it goes away But it doesn't by itself. It's the dual edge nature. You have to want those things, but you also have to do Good luck praying and using the law of attraction to get a raise while still doing work that does not deserve a raise Right. Yes magic happens. And yes, these amazing opportunities may just show up But don't fool yourself into thinking that I just do the belief stuff Nothing changes externally and my life will all be a paradise I don't want you to have to get stuck in the same situations I did where I thought that just by praying and wishing and wanting alone everything would be good Always try to change your habits specifically first So I hope those five reflections help you guys again for me The way that I helped reflect the most was by going through journaling exercises and again I've included that first link right there in the description. You can also check out my last two videos right there and right there You know, I'm often asked what was the number one thing that I used to change my life What was the thing I used to figure my life out and get my crap together What was the thing that I used to organize my goals or to study personal development and get in the best shape of my life And stay in the best shape of my life What was it that I used to help me clarify what I wanted in my career or what my passion was what my calling in life was and What was the thing that allowed me to build a business? That's now my full-time living with hundreds of thousands of followers and millions of views Well, that one thing that practice is journaling You know journaling is what helped me clarify not only what I wanted from life but turning that into core clear action steps to find my passion and write books and Create this kind of incredible audience. It was the thing that helped me clarify Specifically what I wanted and the crystal clear roadmap in order to get there I've put together a free download and a journaling email series that you can get by going to the first link right in the description or success journaling course comm


And welcome to the Brown Bag Concert in the Park series. This is the first of the summer series, 2016. So thank you very much for coming out. We want to thank the sponsors of this event. Ellis Mills Public Affairs is the sponsor of the whole series throughout the summer. Today's event is brought to you by Hunger Mountain Co-op. So please support those organizations. Thanks to Christchurch for allowing us to put these events on in this wonderful little park. Thanks to Montpelier Alive to helping coordinate all of this. Thanks to everybody from the Learning Network over here who are among the biggest fans of this series. They come every Thursday, 12 to 1, and weather permitting. If it rains, we don't do this, but as long as it's nice, we put on the music. It's free. Spread the word. I will tell you the lineup for the whole season because today is the first of the series. Today it's Bit Squadron and the Freelancers. July 21st will be Patty Casey. July 28th is Dana and Susan Robinson at 12 noon, and on that date we also have a show at 6 p.m. with the East Bay Jazz. On August 4th, it will be Chris Robertson and the Socket Rockets. That's Chris right over there. This is sort of the sockets right here, the Socket Rockets. And they will be here at 12 noon on August 4th, and at 6 p.m. on August 4th, we will have Green Mountain Swing. On August 11th is Christina Michelson. We're back to 12 noon events. August 18th is Big Hat, No Cattle. And August 25th, which is the last concert of the series, is Dave Keller. So without further ado, please give a warm welcome to Bit Squadron and the Freelancers. Which is Pittsburgh. It's really hard to get a steak and cheese, not cutting it. We're talking, give us the real deal, Philly cheesesteak, and also we miss tasty cakes, which is like the ultimate junk food from the Philly area. The showhouse in Berlin has them, discounted items, they put them there. So I usually stock up when that happens. But anyway, we came up with this tune. It's called Cheesesteaks, I think within six hours. But this is the exploratory version.


So you want to make a video game, you want to have cool 8-bit sound effects, and of course you can go to a website where they have tons of free sound effects that you can search through and they may have licenses that are available for whatever project you're working on. But why? When you can make your own. Let's go ahead and do that. So you're going to want to use whatever package manager you have for your distribution and you want to search for SFXR. In my case on Debian, I'm going to install SFXR-QT. I'm going to be using app, so I'm going to sudo app that. As it's installed, we're going to run it SFXR-QT at the shell or find it in your app menu. This is what the interface looks like. It's super simple. So up here on the left, you have generators, pickups or coins, lasers, shooters, explosions, power ups, all these things have similar attributes. So clicking these will set presets that generate a random sound but with those attributes. So for example, I can click this and I get a coin. Let me turn my volume up here. So each time I do it, it's a different sound but it's that similar sound of a coin or pickup. You're collecting something. You can get lasers and explosions, getting hit or hurt and of course jump sounds. And you'll notice over here on the left you have sounds as you generate them, as you're going through and you click on these. If you hear when you like but you've already clicked on the button again, you can see the previous ones here on the left so you can go back to a previous one by clicking on it here. And once you've found one, you can also use tools. You can play the sound again down here and on the left you can mutate that. So it's going to take that sound but just tweak it a little bit. So you can modify it. And of course you can adjust all the settings by yourself here in the middle and of course you can save that as a wave file. You did it.  And yeah, that's it. Thanks for watching.


Brianna Christie, soccer. After a standout freshman season, Christie combined for seven points over her sophomore and junior seasons, before recording 13 as a senior. A team captain in first team all conference selection as a senior, Christie netted three game-winning goals, leading the team to three straight conference victories. Keenan Frans, football. After recording just seven catches for 14 yards in his first two seasons, Frans totaled 66 catches for 998 yards and nine touchdowns as a senior. He had a career-high 13 catches for 166 yards in the Max Centennial Bowl game. Frans also totaled 10 catches for 148 yards and two touchdowns versus Bridgewater. Teya Ferreira, ice hockey. After recording just one point her freshman season, Ferreira finished her sophomore season with 14 points on eight goals and six assists. She led the team with seven power play goals and totaled two multi-point games. Ferreira recorded a career-best three points on two goals and one assist in a win at Kinks.


What's going on guys welcome back to the channel critical overlord here We'll be talking about Halloween ends in this video here today has been a while since I touched on Halloween ends I just wanted to let some weeks pass for I even did a video like this because it's more I sit on it the more I'm convinced that what I'm saying is something that many of you probably agree with There's probably some videos out there that I've already touched on this I've revisited the movie a couple times and I still hold on to what I say I do not believe that Halloween ends is a poorly made movie. I don't believe it's a bad movie It has bad elements to it just like any other movie But across the board in comparison to some other things I've seen this year Especially after going through that travesty that is jeepers creepers reborn Halloween ends is not a bad movie You might be able to label it. I you can say it's a bad Halloween movie, but in general Movie-making this is not a bad movie. It's a movie that has some structuring issues more than anything to me the placement of this story is Not correct. And when I say it's not correct I mean that by there's something in this trilogy that could have come off a lot better Had you done it in reverse order? Because while watching Halloween ends something to me would honestly think that they would have been better off doing this as Halloween 2018 you're introducing us to the character of Rohan Campbell's character Corey Cunningham Who is not really part of the marketing at all whatsoever for Halloween ends. It was literally Michael versus Laurie And as if we're being honest, that's really all it was. Yes There are some remnants of other things, but they marketed heavily around Michael versus Laurie That's what they marketed around Had you have made this story Halloween 2018 and not done that as much doubt that back We're also letting Rohan Campbell talk about his role talk about the character of Corey a little bit get us excited Versus us kind of just being seen him mentioned in the plot synopsis and other stuff like that This could have worked better as a 2018 movie. Now, obviously, you would need to tweak some things one Corey would not die in that movie He would not be becoming Michael Myers either in that movie. He would not be a straight-up copycat killer I would take that out of there, too and He would be dating Allison very early on I would have just had him as Cameron He would have been Cameron's character instead of camera. You have Corey Because then what you could do is you could have something play out in that first movie if you want to center it on Corey where you could explore how The town of Haddonfield has maybe had Some negative negativity Just lurking within it for the past 40 years ever since what happened to Laurie Strode and her friends all that time ago Also, how Laurie is presented in Halloween ends I've seen people argue that it makes no sense to them because of the fact that from a logical perspective how you've written it why is she Why is she so concerned about his escape when he's locked up? But now that he's out in the open She decides to let all of her walls down and she's just happy go lucky Living her best life and it's nice to see It's nice to see But when you write it like this and you already have this that existed prior Many people would argue you need to flip that because what you could have done is again with Corey Corey could have been someone that that same tragedy happened Jeremy still dies and the town of Haddonfield who again is just riddled with folks who can't let go of what Michael Myers has done Even though Laurie herself as we meet her in the 2018 movie in my head has let that go There are some things that traumatize her But she's not as Sheltered as we see her in downrodden and just in the dumps as she is in that 2018 movie They've actually got you take that put that into a reverse order of how she is in ends and then have Corey's Narratives still play out have bodies start dropping around town And people think it's Corey, but it's actually the return of Michael Myers Michael Myers is back. He's someone who people thought was dead uh Or he was actually yeah, you'd have to also tweak the fact that he wasn't locked up for 40 years And I'm I'm willing to let that slide. I'm willing to let that slide just to make this trilogy structured better He returns after 40 years And he starts killing people. They think it's Corey, but it's actually the return of Michael Myers That's the 2018 movie with Corey already being here Then you have halloween kills that can play out the same way play out the same way you have mob mentality mob rules Michael's going on a rampage of The ending of the 2018 movie though in my head would not have included that fire You would save that for halloween ends The mob mentality though and all that stuff plays out how it would end the kills movie Corey is prevalent there too. He also does not die there. Then you get to halloween ends where You have A different narrative play out I don't have that really mapped out in my head But what you would do is you would have it end how the 2018 movie ended You would have it in by maybe having Corey present for that Or maybe Corey has sacrificed himself to protect allison and lorry from michael mires at this point, but uh At the end of the day the movie would end basically how the 2018 movie ended lorry has set up a trap for michael or michael gets Trapped somewhere else and they just set his ass on fire and watch him burn And then we don't even have to see him burn the camera just Though the movie just ends with his breathing to signify that maybe there's a chance that he lived And survived that fire We won't get the answer because that would be the end of this team's iteration of halloween I just think honestly the problem with halloween ends isn't that it is a overly bad movie You can argue. Yes, it is a bad halloween movie I think it's structured incorrectly In terms of you could take so many elements in that movie that work for me Switch them around place them in the 2018 movie instead keep certain people present from the 2018 movie more specifically Corey Have him exist all the way through to the end. You can still kill him But at least he was here from the beginning that way when people watch halloween 2018 and kills They don't turn on ends and feel like it's so detached from the rest of the first two movies You were just working on Heck you could even still have karen die in halloween kills because then now would at least give give lorry Some more interesting material to focus on going into my version of halloween ends Where you have the same outcome with michael being locked up in some location setting his ass on fire watching him burn lorry gets to final laugh if Corey is alive he gets to final laugh because the town of haddonfield thought that he was some big bad boogie man That they just couldn't let go of because of their trauma and all of those themes that we know this trilogy was Exploring about evil and placing that blame on corey of all of that stuff could have still been explored It's just that a lot of people weren't buying what you were selling because of the way your narrative is structured You can defend the narrative as much as you want to I know I basically am kind of doing that by saying it probably should have been reworked But people had gripes with the story I honestly feel like it's because of how it was placed Corey could have been received very positively if he was introduced in the very first movie at the start of your trilogy Not at the end because by the end and how you had already structured your first two movies People were wanting to see that final battle between lorry and michael they got it But the marketing also they make things better either when that was all you marketed But you can guys let me know what you think about that down in the comment section below Do you think a restructure would have worked if you haven't already? Of course make sure you subscribe turn on post notification at never miss a video in the description I will have links to my social media accounts. I'm on facebook twitter and instagram. You can message me there


acing did true that banks have a low current and expected profits, and that's a problem also for us as supervisors, because banks which are not profitable, are not generating capital, and they have low price-to-book ratios, would have difficulties in raising capital in the markets. So what can be done? If you ask the banks, they would argue that we need to water down the capital requirements. I think that would not be the right answer. I think that banks need to focus on cost efficiency, becoming, investing technologies which will provide better strategic interior. So focus on the thing that they make better and the bring them to profitability. Apart from adding pressure on the business model senične讓iti.. We also acknowledge that there are structural impediments in the markets which are dragging the profitability in the banks down. We would need more consolidation, there is excess capacity in the system and we would need greater integration in the banking union. Tudi nekaj bilo, da bomo vse predstavili, da se začeli, da je bolj profetabilit. Však je však však tudi, da se je však v tem, da je to glasba političnja, ali, da se se vse, da se vse začeli, da se vse začeli, da se je začeli, da se je začeli.


Hey guys, it's Jen from iCreateCrafts. In today's video, I will be showing you how to create these cute little paper Easter egg holders. They are so cute and so much fun to create. Make sure to hit that subscribe button if you haven't already as I will be creating more step-by-step craft tutorials. Now let's get started. All right, so to make these cute little guys, you have to start in design space, but I want to show you where I purchased these from. So I purchased the files from design bundles here. It was so cute. It was originally $8. They put it down to $5.20, so if you're interested in this, I would definitely grab it now before the price goes back up, but I just wanted to show you these really quick. It's so cute. You get the duck, the rabbit, the penguin, and the sheep. I absolutely love these. I actually made a different one like the duck here, and it was so easy to create. So you can see it's just a few pieces of paper and a little bit of glue, and then you just put in this cute little egg here. So I thought this was the cutest thing. It's so easy to put together. So I figured I'd show you a tutorial on how to do it. It takes just a few minutes. So I have them all put in design space right here. I did not change the size. I'm going to try it the way it is, and I'll let you guys know if you need to make it bigger or smaller. But the last one I did was perfectly the perfect size. So I'm going to show you something in here really quick that I noticed. So when you have all these guys, obviously, there's different colors, right? You have the yellow, the orange, and then you have yellow, black, so on and so forth. So if we were to cut this out right now, I'll show you what it looks like. So if I click at the Make It button, look on the side here how many colors there are to cut out. This is absolutely crazy. So if you can see here, there's like four different colors of this grayish color, whiteish color. Then we go down further and there's all these yellow colors. So instead of wasting your time and trying to find different colors like this, or maybe moving them individually, I'll show you the easier way. So I'm going to cancel this and I'm going to start with the first one. So I'm going to start with the duck. So as you can see here, there's several different colors on the side here. So we have the yellow, right? But as we saw when I click the Make It button, there was different colors, like three or four different colors of yellow. So the way I do this is I'm going to start over here. I'm going to click on this first one and you could see that it made it a little bit grayer. And I'm going to hold my Command button down. Yours might be the Shift key or the Control key. Every computer is different. I have a Mac, so I'm holding down the Command button. So once I have this one selected, I'm going to hold the Command button down and I'm going to click on the next one. And I'm still holding the Command button down and I'm going to click on the next one. So I have all the yellows, right? So now I'm going to go up here and I'm just going to find we'll use this yellow. It's a little bit darker here so we can remember which one we use. So now we changed all of these to the same color yellow. I'm going to do the same thing with this guy. So as I can see already, this is a little bit lighter than these guys. I'm going to do the same thing because I clicked on this image. It shows me it right here. So I'm going to do the same thing. So it's all selected here. All I want is the yellow pieces. So I clicked on this first one and I'm going to hold the Command button and I'm going to hold and select this next one. So now we have both pieces here. So if you can see up here, there's a little question mark. So it's like saying there's more than one color here. So I'm going to click that and I'm going to do the same dark color here as the duck. So when it cuts out, I'm still just doing a wonderful color of the yellow. So I'll show you really quick what I mean. So if I go to make it, I'll go down all the way at the bottom. And now there's just one yellow piece here. So here's all the yellow that's going to cut out. So I'm going to go ahead and do the same thing with the white part. This you don't have to do this if you don't want to, but it just makes it so much easier when I cut things out. So I'm going to do the same thing. So I'm selecting the little penguin guy again. So he hasn't selected on the side here. And I'm going to do the white because I can already see that these, this color and this one are separate here. So I'm going to take the first one, click on that, hold the command button and get the next one, hold the command button, get the next one. So now I have all three pieces selected right here. I'm going to go up here. Oh, but looks like it's already all white, but I'm going to change it to white. So now that will all cut the same. This one, I know that the little feet here are a little bit grayer. I'm okay if it's white. So I'm going to do the same thing and then I'm going to do the same thing with the bunny here, but I'm going to show you really quick and see if we have any more that we can change up. But now it should definitely take those down. So we only have one white. We have one black one. We have one brown or one for the bunny itself. And here's the pink pieces for the ears that we definitely cut it down much more. And then here's the feet color. So depending on how many colors you have, you can do this or you can leave it the way it is, but I'm going to cancel one more time and I'm going to leave it the way it is, but I just wanted to talk about really quick for each piece you need. OK, so this is going to make one per one little duck. This is going to make one little penguin and so on and so forth. So if you want to make more of these, you just want to select it and hit the duplicate button, but I'm just going to do one now just for the purpose of the video. So I just wanted to show you that really quick. I'm not changing anything on it. I'm not changing the size or anything else or the colors I like how it is. So I'm just going to click make it and then I'm going to go ahead and do each one of these. So this is all the white that it's going to cut out. So I'm going to go ahead and click continue. And then for this one for my base material, you really have to watch here. So I have this copy paper here that's 20 pounds. I have this heavy card stock here, which was 100 pounds. So I'm using two different kinds of paper. So one card stock that I have is 65 pounds. And then another one that I have is 110 pounds. So you need to watch out how you cut this out. So if you have the thinner paper, you can use the 20 pound here. Or if you have the heavier one, you do the 100 pound here. So depending on what kind of paper you have, I have thicker card stock. So I'm going to click this heavy card stock. I already have my fine point bladed and that's all you need because when you go to do the creases, let's see, on one of these, you can't really see it here. Let's see if I can find another one that might be able to show it. But when you do the creases on these, OK, like this, I thought you would have to use a stylus tool, but it actually just cuts it out. So the only thing you need for this is a really good blade in your machine. And I'm going to talk about this in just a second. I'm going to get off of here and I'm going to show you how I put this on my mat and which tools I use for this. All right, guys. So before I get started, I'm just going to show you a few things here. So this was the paper that I was talking about. I bought this from I think it was Hobby Lobby for 40 percent off. So six dollars and minus 40 percent wasn't too bad. So this is the 65 pound card stock. Then I have this other stuff that I bought from I think Hobby Lobby as well. I don't know the price of it, but there's multiple colors in here. This one is the heavier card stock at 110 pounds. And then I also have this other one. It's more of a Christmas color, but I bought it from Goodwill for five dollars. I didn't think it was that bad of a deal. I see these at Hobby Lobby and Joann fabrics for like over 20 dollars. So there's a few different colors in here as well that I'm going to be using. But besides that, I just wanted to talk to you really quick about what I'm using. So if you guys know me and you know my channel, I don't buy a lot of cricket brands. So I bought this huge bag of blades from Amazon. I'll leave the link below in case you're interested. This bag costs less than one of the cricket brand blades. So I got, I think there was 36 of them in here and I believe it was under 20 dollars. But I mean, that's crazy. There's different ones on here for card stock and for fabric and whatever else you're going to be cutting. So if you're interested in getting these, I'll leave the link below. Another thing I want to talk about is this mat that I bought. So I had a lot of cricket brand mats and I was washing them and washing them. I have a video on that in case you're interested in your new and you don't know how to wash your mats. It's so simple. I have a video on that. I'll leave that in the link below as well. But for those of you who don't know, you don't have to buy the cricket brand. I don't know. Oh, here's the brand name. Real like if I'm saying it right, but I bought it from Amazon. I don't remember offhand how much it was, but it was definitely a lot cheaper than the cricket brand and it's just a sticky. And I've had this and I've always bought that off. I've always bought the off brand one and it seems to work so much better than the cricket ones. So I just wanted to do a quick interruption with this. I hope this is helping you. If you guys like this, that I'm kind of teaching you how to do things. Give me a thumbs up and hit that subscribe button. Leave a comment below. I love reading your guys comments. I love it when you guys tell me I'm doing a good job. It makes me feel better inside and I just want me to keep going. So I hope you're liking these little tips that I'm leaving you here in there. And I hope it helps you. But just continuing, I'm just going to go ahead and put the card stock on here. This is just the regular standard grip. It is still kind of sticky, but it has a little bit less stick to it because you don't want your paper to completely stick to this. So you want it a little bit less sticky. So I'm using one that I have used a couple of times already and it works really well. So just the hard part of this is once it cuts out, you just have to keep things in different piles. But I'm going to cut everything out. I'll pull it off of the mat and then I'll show you what it looks like when it's finished. But I just wanted to give you a quick update and a tip on that. So I hope it helps. One more little tip. So I have some of the paper still stuck on the paper here, but for the ones that don't come off very easy and that are stuck on your mat, I just use this. It's just a little scraper. Believe it or not, I have this from my first cricket. I think it was the first cricket that they ever made. Like years ago, I have this tool and I never really used it until now. But it just makes it so much easier to be able to go underneath and grab it like that instead of using your fingernails and kind of like ripping it off. It just makes it so much easier just to grab this piece and pull it off. So it's just another little tip for you. If you have these little tiny pieces that don't want to come off like these ones right here, instead of me using my fingernail and trying to go under there and break the piece, it just makes it so much easier just to use it like a cookie and just pull it off here. Just like that, just makes it so much easier and you're not breaking your piece at all and you know where they're all going. So that's just another quick tip to get this off. If they're still stuck in your paper, that's fine.  And you just want to do it gently that you're not breaking anything. But just to show you, I have just a few more pieces in here, but you got to be careful when you're doing these that you're not losing any of these little pieces. So just another quick tip. Oh, my gosh, these guys are so stinking cute. So I did go ahead and make two of them really quick. I put an egg in here to show you what it looks like. So this is the duck. I missed the little piece here. I have to find it or recut it, but I am missing one, but I wanted to show you guys what they look like. So that is the front and then this is the back and then it has a little hole on the bottom so your egg can fit there. And then I also did the penguin here, which doesn't have anything in it, but I just want to show you what it looks like. Here's the inside of it and then here's the backside. So this was really simple to put together. I just put this together so I could show you what it looks like already made.  I would recommend having a weeder or something sharp with you. I just have this one here. I can buy these from Harbor Freight for a couple bucks. So it would definitely help out. And this is definitely a lifesaver. I bought this from Walmart. I just happened to see it. So one end has a little squeegee on it and then the other end has the regular glue head. So I was just using the top part of this because it's so tiny, but this stuff works amazing. So I would highly recommend something like this rather than the glue stick and trying to rub it on there. So this is pretty much self-explanatory. You could probably figure this out yourself, but with these you just want to bend them in one way and then just carefully do it, but you just want to bend them both in the same way. So it's just like that. So this is the bottom part, so that's it. So I'm going to do both of them really quick. So this is just the feet. Just bend them in the same way. And then those pieces are done.  And I'll show you what I mean in just a minute. So starting with this first piece, it's not glued down, obviously. I wanted to show you guys how to do it and just go ahead and glue it on. So depending on how you want to glue it or what you have to glue it with, like I said, this really is helping me out, especially with these little tiny pieces. And just put it on there. And I honestly don't even remember where these pieces go. I'm just kind of putting it wherever I think it would be kind of cute for the ear pieces. I don't have my computer in front of me, but I'm just taking it and gluing it right there, wherever I think it would be kind of cute. And then take the eyes. So the eyes are two parts.  So I'm going to put the white part right down here. Just a little bit of glue and then push it on there. And then this is where this weeder would come in handy. So I'm just going to put a little dot right in the center. Like I said, this is more self-explanatory, but I just want to show you guys how I do it. So just take that and put it on there as well. This is so cute. Oh my gosh. The pieces are a little bit bigger. It'd be easier to do with kids, but it's a little bit more difficult because it's such tiny little pieces. So there's the first eye. And I'm just going to do the same thing with the next one. So this glue, I found out, takes a little bit longer to adhere and stick. So I'm just going to leave that stuff there for a minute and just continue working my way down. So I'm going to work on the nose next. And obviously, like I said, this is self-explanatory. Feel free to skip ahead to see what it looks like when it's all finished, but it's just kind of fun watching somebody put something together, I think at least. Got a piece of glue stuck on there. There, isn't that cute? So the last part to do is the little feet down here. Actually, not the last part. The last part to glue here is just the little feet. So I'm just going to put little dots of glue down here. You know, I'm thinking about it. I'm thinking maybe glue dots would even work. It would kind of give it like a 3D look to it if you can find little tiny glue dots. I'm just using my weeder to kind of help me put it on the spot. But this little pen, I have never seen anything like this before, but it works amazing. I saw it and I'm like, I gotta have that. I think that would be really helpful. And it sure is. So I'm almost done with this foot. Oh my gosh, that is so adorable. So I'm just going to finish this one up. Do the same thing. Just kind of sitting here talking to you guys. How you guys doing? You having a good time? You like in this video? I really love how this looks. And I really appreciate all the thumbs up and the likes and everything everybody has been doing. I love reading your comments, so please keep them coming. Really means a lot to me. I'm getting to my goal to 10,000 subscribers. So please, if you're not subscribed already, hit that subscribe button as I work very hard on these videos. And then try to show you step by step how to do everything rather than just rush everything. So while this is gonna dry, I'll do the next one. You guys don't have to stick around and watch this all fast forward this part, but it's just the same thing. Self-explanatory, where things go.  The duck and the penguin were the same way, but I have to admit they were a little bit easier. It's not as many pieces as this. So I'm gonna fast forward this part so you guys don't have to watch me, but then once it dries, I'll show you the next step. Okay, so I'm gonna let this one dry as well, but look at how cute that is. I think it's really cute. And work on this one. So basically the next step you have to do is just glue these next pieces together, but you wanna make sure that your actual piece goes in front of the bottom piece, just like that and like match it up with the circles up there. So I'm just gonna take a little bit more glue and then just hold it there. And you wanna have it up just a little bit higher here so it's not touching, but it's just like that. Then you wanna do the same thing with the other side. And this is where I said it comes important where you put it. So obviously you wanna make sure the ears are the same way. If it was going like this, it would be, if it was going this way, it would be the wrong way. See, like this doesn't work. So you have to make sure you put it on the correct way. So this would be the right way. So I'm just gonna take a little bit of glue. I think that would be the only way that you would mess it up, so to speak. So then just take your glue, put it on, and then just hold it there for a couple of seconds. And then the last part to this is you just wanna glue the head together, but I like to let this sit for a minute before I start working with the next step. So here is the front part. Isn't that cute? And then the same thing with the little lamb here. I just wanna take a little bit of glue and put it directly on the front of this, just where the feet are. So you're gonna just match it up here. You wanna match it all up and then just hold it there tightly. Obviously these are like a one-time use, but I thought these were really cute and I wanted to share with you guys. I did actually do a couple of the other ducks that I had and they turned out so cute. And then I found this file and I made those. So I just wanted to share with you guys how to make these. I'm just gonna take again. This one I don't think matters too much. I think it's gonna be the same thing with the way that the image is. So you can take a little bit of glue, stick it on here. And then once it dries, you just wanna glue the top part together so you wanna match them up and then glue up the tops. I'll do the backside with glue, just like that, and then you just wanna match them up really well. And you just wanna glue the head part because you wanna be able to open it up on the bottom here and put the eggs inside. So once this is completely dry, then I will go ahead and put the eggs in here. But I really love how this turned out. I think it's so cute. So just like that. So here's the bunny, here's the inside of it, and then there's the backside. You could even go more and use one of your Cricut markers and write a name on it if you want while you're in design space, but I just left it the way it was. I thought it was super cute. So then I'm just gonna take the lamb here for the sheep and then just do the same thing. But I think this is super cute. I think it would be fun to do with kids if you kinda help them along with the glue, or maybe they do the glue part and then you work on the little tiny pieces, but I think this is super cute. It was a really easy, fun project to do. I think it'd be really cute to hand out to classmates at school or something or give to teachers or just anybody, I think would really appreciate something like this because there's little eggs that come in it. These are my brother's favorite. He loves these Cadbury eggs, but it's just like this. I just bought the Cadbury eggs. I think it came in a package of five and it was like $4, so it wasn't too bad. So you just wanna, whoops, you just wanna put those right in the inside of here. Once it's dried, so that's what it looks like. Isn't that cute? I love how these turn out. You just gotta make sure that the guy stands up properly. Just like that, but it's all done. I love how these turned out. I had a lot of fun creating them. I hope you enjoyed it.  But give me a thumbs up and hit the subscribe button if you haven't already as I do more videos and more how-to tutorials. Happy crafting everyone.


I think it is very easy to adapt to live in Australia because the weather is very good, the people are very friendly, the accommodation is relatively easy to access. At first it's a challenge, but eventually when you get adjusted to the new services it's good. The first barrier is the language, but once you overcome this barrier everything will become easier. We miss our families and we are in a totally new country, but there are support systems within the universities that help us and enable us to undertake our studies and we make new friends here in Australia. Australian culture and everything, it's very welcoming and it's very easy in terms of accommodation, food, religion, perspective. Everything is very easy for Pakistani and all other international students. Initially it was a bit difficult because I was homesick, but the support provided by the university and two different friends and organization made it very easy to live in Australia. It will be a wonderful journey that you will always remember.


Tumalo Matlala describes his wedding as a dream. Generations, the legacy actor Tumalo Matlala recently tied the knot with his longtime girlfriend Zifaleli Beku in a fairytale celebration. The traditional Khosa ceremony took place in King William's town, in the eastern cave, and was attended by their loved ones. Tumalo described the day on Instagram as a dream come true. God is amazing. We had a dream and on the December 15, 2019, God made it a reality and we are grateful. We also like to thank everyone who came to experience and celebrate with us as we began a journey of grace and favor. I pronounced to you MR and Mrs Matlala, he said. Tumalo gushed over his queen in a separate post, saying he had said I do till the end of time.


I was really struck this year and the thing I think which is going to impact the brand ecosystem is the modular screens. Samsung had a great display this year with their wall, Sony had it last year, but the one that really impressed me was LG's tunnel of screens, on curved screens, just the mathematics of keeping the rendering alright across these giant curved screens is amazing, but it unlocks unbelievable potential for experiential stuff, for events stuff, even just cinema. The convergence of all these technologies and IoT in an automation in the home is going to make people's lives a lot more convenient and a lot better. For brands it's going to create a whole new operating model or paradigm about how you get these recommendation engines, how you can get these products to actually recommend your product, your brand. You may have spent 20 years building a relationship with consumer and now they have a new fridge and that fridge is telling them to buy product X instead of your product which you spent years and years building. One of the things I'm really struck every time I come to CES is how fast technology is moving and how it's moving much faster than us as consumers are really adopting and absorbing. We're here in the middle of the world's most advanced consumer electronics show and we had a little bit of rain and the entire show got fried. I see that writ large as almost a metaphor for how we're struggling to cope with the pace of change with what consumers are actually doing. From a specific marketing perspective I think what teams really need to do to cope with the pace of change is invest in teams which help them navigate that and connect their data sources. So I'm not sure about which technology is going to be particularly big in the next year. One thing I really noticed and it's sort of an ongoing trend which we sort of stopped talking about. It was a bigger story a couple years ago but it's really scaling for me is the size of the Chinese brands, the Huawei's, the Hisense's, their impact at CES, their product mix, what they're doing. It's got bigger and bigger and bigger and interesting from a marketing perspective they are investing in big brand ways. Hisense is now the global World Cup sponsor. That's an expensive big property they've latched on to. Huawei announced this week that Gal Gadot has to be their global brand ambassador. These are big brand plays from big companies and they're taking over the world or seeking to. My takeaway sentence for CES is twofold. One is I think you need to be here. This has become a global stopping point in the business tech world and you have to be here in person. So they missed out for not attending. Actually encourage them to come to CES.


Putin orders army to prepare for nuclear tests. Russian President Vladimir Putin has instructed the Ministry of Defense and Rosatom, the National Nuclear Regulator, to ensure readiness for nuclear tests, as he believes that the United States can conduct tests and create new types of nuclear munitions. Putin told it during the address to the Federal Assembly on the 21st of February. He said, Of course, we will not be the first to do this. But if the USA conducts tests of nuclear weapons, then we will also conduct them. No one should have dangerous illusions that global strategic parity can be destroyed. The Russian President said that he had ordered measures to be taken because it is known for sure that the USA is considering the possibility of testing its nuclear weapons. According to him, the warranty periods for combat use of certain types of American nuclear munitions are expiring. In addition, Putin claims that the US is already developing new types of nuclear munitions. It should be noted before this, there was information Putin is set to demonstrate his willingness to use weapons of mass destruction with a nuclear test on Ukraine's borders. The Kremlin has been signaling its readiness for a significant escalation as Russia loses ground on the battlefield.