Hi, I'm Zor. Welcome to Unizor Education. This lecture is continuation of theory of probabilities. We will devote some time talking about normal distributions. This lecture is a part of advanced mathematics course for teenagers, which is presented on Unizor.com website. And that's where I suggest you to watch this lecture from, because the website contains notes which are basically like a textbook for you. So it's very beneficial for you to basically work with both the video presentation and the text. Now back to probability theory. Last lecture was about normal distribution. And I told you that I have a little problem describing the details of normal distribution, because on one hand this is, well, arguably the most important probability distribution in the theory of probabilities. And on another hand, it's a continuous distribution and the apparatus, the mathematical apparatus is basically calculus and relatively advanced calculus, which I don't want actually to touch right now for this course. It's basically, it belongs to higher education, to universities, etc. So I'm trying to present the normal distribution of probabilities in a qualitative light, basically through examples maybe and some maybe more detailed explanations without rigorous mathematical derivation of certain formulas. So the last lecture was a definition of what normal distribution is about. And again, I told you this is probably the most important distribution because, and that's very interesting, because, well, I can say because of the central limit theorem. So there is a central limit theory, which is very, very important in theory of probabilities, which basically states that if you will mix together many different random variables, their sum behaves as a normal distribution, basically, normal variable. More precisely, we are talking about averaging of a certain number of random variables. Then there are certain conditions when this theorem is actually true. A very simple and sufficient condition for the central limit theorem is if we are averaging and identically distributed independent variables, we are averaging them. And the new random variable behaves more or less approximately, and there are certain mathematical criteria, obviously, of this approximation, so to speak, behaves more or less like a normal random variable. The random variable is normal distribution of probabilities. So this is a sufficient condition. So all of them are identically distributed and independent variables. And the theorem states that their average is as close to normal distributed random variable as possible whenever the n increases to infinity. Well, this is a statement, and I couldn't really prove it because, as I was saying, the mathematical apparatus goes beyond the scope of this particular course. However, I would like to illustrate it. So what I'm going to do right now is I would like to illustrate this central limit theorem in one particular case. The case which I would like to consider is if all our random variables are very, very simple, maybe the simplest random variables, results of Bernoulli experiment with the probability of success and the failure, obviously, the same. So this is our random variable. So c, i, t is equal to zero with probability equal to one-half, and it's equal to one with probability equal to one-half. Now, in practical terms, let's say you're flipping the coin, the probability of heads and tails is one-half. So you are adding together all your, let's say you associate zero with tail and one with heads. So you're adding together basically all your results, and that means you're adding one for each head. You are averaging divided by the number of experiments you have conducted, the number of flips which you made, and this is your new random variable eta. And then you are basically experimenting either once or twice or a hundred times in a row, and the more times you experiment, let's say you experiment with million coins, right, in one shot. You flip the million coins and you have the result. Then you flip them another time and you have the result. You add all the heads and that's the result that you have the number. So the number for a million coins will be in the range of from zero when all million coins are on the tail, or a million when all of them are heads. Now, both extremes are very, very rare probabilities. Now, the something like half a million for this particular sound can occur much more frequently because it doesn't require a particular result on every experiment. For instance, the first half of the experiments can be tail and the second half can be heads, or vice versa, or every second should be head and every another second, every odd and even can be correspondingly tails and heads. And in all these cases, the result will be half a million, right, because you will have exactly half a million heads and half a million tails. And there are many other variations, obviously. And the more combinations you have to basically get that half a million, the greater the probability of that half a million will be. So I can say that the distribution of probability is some kind of a graph which has values from zero to a million with different probabilities. And that's what I'm going to draw right now for the first few cases. And I will show that this graph actually more and more resembles the bell curve, which is the characteristic property of normal random variable. So that's my purpose for today. Right, let's do it. Now, what about graphical representation? Graphical representation will be very simple. 0, 1, 2, 3, 4, 5, 6, etc. Now, so my variable eta, which is equal to sum of Bernoulli experiments and Bernoulli experiments divided by n. So let's consider n is equal to 1. So what are the values of my eta? Well, in this case, eta is c1 divided by 1. So it's c1. And the values are 0 and 1. The probabilities are 1 half and 1 half, right? So the graphical representation will be like this. So this is 1 half. And the graph will be actually, I don't really need this. It will be dotted line. Okay? So for the value of 0, the probability will be 1 half. For the value of 1, the probability will be also 1 half. And there are no other values this particular variable can take. It's only either 0 or 1. And I have agreed before to graphically represent it as this type of combination of rectangles, which have one length, one unit as a base, and the probability as a height. All right, so that's simple. Great. Let's move on. n is equal to 2. So my eta is equal to c1 plus c2 divided by 2, where c1 and c2 are either 0 or 1 with probability 1 half. So what can we have here as for values? Well, the values are, if both of them are equal to 0, then the value will be 0, right? And the probability of this is 0 and 0. So two independent variables. And I'm asking for the probability of 1 being equal to 0, which is 1 half, and another being equal to 0, which is also 1 half. And I need simultaneous. It's two different coins, simultaneously going, falling on tails, right? So it's 1 half times 1 half, because we need the event, which is a combination of two elementary events, which are independent of each other, so the probabilities are multiplying. So it's 1 half by 1 half. So it's 1 quarter. Then, next value is 1. One can be either when the first is equal to 1 and the second is equal to 0, or the first is equal to 0 and the second is equal to 1. So we have two different variations. We have 1 0 or 0 1. The probability of this is 1 half times 1 half, 1 quarter, probability of this is equal to 1 quarter. 1 half of this times 1 half of this. So together, when the sum is equal to 1, I have two elementary events, which are supposed to be summed up, and the probability will be 1 quarter plus 1 quarter. It will be two quarters, right? So again, it's not this, it's this. So this is two quarters. And finally, my value can be equal to 2. I'm talking about sum. Forget about dividing by 2. So my value can be equal to 2 when both of them are equal to 1. So it's 1 half for the 1 and 1 half for another. So it's again 1 quarter. So that would be this way. That's for 2. Now, the values are divided by 2 actually, which means that the whole graph should be squeezed by the factor of 2. But it doesn't really change the shape of the graph. That's why I decided just to go with the sum, c1 plus c2, rather than c1 plus c3 divided by 2. But I would like to illustrate that the shape is something like this already. Now, it doesn't really resemble the bell curve, bell curve yet. But you already see that there is something like higher, middle, and lower both ends, right? But let's continue on and let's increase the number of experiments. Let's go to m is equal to 3. Now, before I go any further, let me remind you that it's very easy to calculate the formula for eta is equal to k, where eta is equal to c1 plus c2 plus et cetera plus cn. Now, if these are Bernoulli experiments with values of 0 and 1, with certain probabilities, p and 1 minus p, which is q. Now, you should remember that to find the value, the probability of the value of eta to be equal to k, you basically have to understand that there are k 1s among them, and other n minus k are 0s, right? But that can be in any kind of a sequence. So, basically, it's a number of combinations from n by k that gives you certain concrete distribution of 1s among all these times. You have 1s k times. So, the probability should be p to the ks degree and q should be to the n minus k, power n minus k. So, that's the formula, which I have already derived when I was talking about Bernoulli experiments. So, I can use it actually in this particular case without more detailed description of different combinations, et cetera. I will do it one more time for n is equal to 3, and then I will use the formula for 4, 5 and 6 and whatever. And again, on this graph, I will just use integer 1, 2, 3, 4, 5. I will not divide by numerator because whenever you divide by numerator, the whole graph would be from 0 to 1, basically, right? So, it will look the same, but it will be more difficult to view it in the details. All right. So, let's consider the next case n is equal to 3. And we will see if our graph would be a little bit more bell type. Okay, n is equal to 3. So, let's consider c1 plus c2 plus c3. So, again, what kind of different values my variable eta can get? Well, it's obviously maximum is 3 when all 3 are 1, and minimum is 0 when all 3 are 0, right? And anything in between. So, from 0 to 3, 0, 1, 2 and 3. So, 0, how can we get 0? Well, 0 we can get only in one way. When this and this and this are equal to 0, the probability of each 1 is equal to 0 is 1 half, but we need all 3 of them, so we have to multiply 1 half times 1 half times 1 half. So, 1 over 8. Now, when I'm talking about 1, it means only one of them is equal to 1, and two others are 0s, right? It can be either this one or this one or this one. So, it's 3 different combinations. And, for each combination, the probability is 1 half. So, it's 3 eighths, right? So, it's 3 times 1 half times 1 half times 1 half. So, it's either 0, 0, 1 or 0, 1, 0 or 1, 0, 0. These 3 combinations are delivering the result equal to 1. Next is the value of 2. Now, when can we get 2? When we have 2 1s and 1 0. So, 0 can be either here or here or here, and the other 2 are 1s, right? So, again, we have 3 different combinations. So, again, the probability is this. And, finally, the value of 3, it can get only in one case when all 3 of them are equal to 1. So, it's 1 half times 1 half times 1 half. Probability is 1 eighth. Now, graphically, it would be 1 eighths, 3 eighths, again, 3 eighths and 1. Okay. I don't think this looks more like a bell curve than the previous case. But you understand that right now we are spreading the whole curve, which means that whenever we will squeeze it by the factor of 3 right now to be only from 0 to 1, we will get actually a curve here, so to speak, bell. Let me add one more. M is equal to 4. Okay. And now I will look, I will actually use this formula, c and k times. Now, p to the power of k and q to the power n minus k. But in our case, we have p and q are equal to 1 half. So, this is 1 half and this is 1 half. So, which means we can add the exponents and we will have 1 half to the nth degree, right? Because p and q are both 1 half. So, in our case, n is equal to 4 and I'm looking for value of eta equal to 0. Okay. Now, n is equal to 4, which means in the denominator I have 16. Now, the number of, number c of n by k is obviously 1. So, that's 1, 16. Okay. Number of cases, number of combinations from n by 1, from 4 by 1 actually, well it's 4, right? I hope everybody remembers this formula. So, this is 4, 16. Next is 2. Combinations from 4 by 2. This is 4, 2 and 2. 24. So, it's 6, right? 6, 16. Probability of eta is equal to 3. That's same as 1. That's 4, 16. And probability of eta equal to 4 is, well, 4 should be equal to 1. It's 1, 16. All right. Now, let's draw the graph. So, now I have numbers from 0 to 4. 5 is because the rectangle goes this way. So, for 1, I have 1, 16. For 0, I have 1, 16. For 1, I have 4, 16. So, it's like this. For 2, I have 6, 16. So, it's something like this. Then 4, 16 again and 1, 16 again. Okay. We have more rectangles, but together, you see they are, again, resemble this bell curve a little bit better than the previous case. So, every time I'm adding the number of variables here, my graph resembles in more and more details this bell curve which I was talking about. Next, n is equal to 5. I'm challenging your patience just to demonstrate how closely it comes to normal distribution. So, 5. So, what do we have? We have variables from 0 to 5 and my denominator would be 1, 32. 2 to the fifth degree, right? And I will have 6 here. So, for the value of 0, I will have 1, 32 for the probability. For the probability of 1, I have 5, 32. For the probability of 2, I have 10, 32. Probability of 3 equal to 10, 32. And the probability of 4 is equal to 5, 32. And the probability of 5 is equal to 1, 32. So, 1, 5, 10, 32. So, I will have something like this, then like this, then like this, then like this, then like this, then like this. Okay, I have more rectangles. So, whenever I will average it, which will be divided by 5, they will also be on the same segment from 0 to 1, but more fine distribution and it will be more closely, it's closer resemblance to a bell shape. Yeah. Okay. And the last one, just to try your patience, patience is n is equal to 6 and that would be my nth example. n is equal to 6. So, I have values of the sum from 0 to 6, right? So, all the values in this integer values in this interval are possible. Now, the probability of this equal to 0 is 1 over 2 to the 6th degree, which is 164. Probability to take the value of 1 would be 664. Probability to take the value of 2. So, it's combinations from 6 by 2, it's 6 times 5 divided by 2, it's 15. 64. Probability to have the value equal to 3, which means we have 3 1s and 3 0s. So, 6 from 6, 6 factorial divided by 3 factorial and divided by 3 factorial, this is 20, right? Yes. Yes. 4 is equal to same as 2 because it's symmetrical. 15 is 64. 5 is 664. And 6 is 164. All right. As far as the graph is concerned, distribution of probabilities. Now, I didn't use this term. This distribution of probabilities, which I'm describing right now, sometimes it's called the density of probabilities, but it's more applicable to continuous random variables. That's why I'm trying to avoid this term right now. So, I'm calling it distribution of probabilities among integer numbers basically. So, it looks like this. 1, 2, 3, 4, 5, 6. Well, I will do it approximately. So, this is 164. 1, 2, 3, 4, 5, 6, 7. So, once then I have 6, then I have 15, then I have 20, then I have 15 again, and 6 again, and 1 again. So, again, the more random variables we are averaging, the more bell-like shape it becomes. And if we will introduce some kind of a difference between the real bell shape and this set of rectangles basically, the difference being probably something like an area of these little things. Whenever I will squeeze it to the segment from 0 to 1, when I'm averaging, divide by n, right? So, if I will evaluate this difference between my graphical representation of this distribution of probabilities and the real bell curve, it will be going to 0 basically, as n increases to infinity. And that's basically the purpose of the central limit theorem. The more and more the average of the sum of random variables resemble the bell curve, the normal distribution of probabilities. Now, just think about this for a second. I took something which is completely unrelated to normal distribution. I took the Bernoulli random variables. They just take two values, 0 and 1. But then I start adding them together, mixing them together. And as a result, on average, I am getting closer and closer to the normal distribution. Now, I might or might not, I'm not sure, illustrated in some other distributions like geometric, for instance, distribution and some others. And it will be exactly the same thing. And the meaning of the central limit theorem of the theory of probabilities is that no matter what kind of initial distribution of probabilities you're having, no matter what kind of random variable you start with, if you will sufficiently mix them together, you will add them together, you will eventually add up them to a new, almost normally distributed random variable. And that's why the normal distribution plays such an important role in the theory of probabilities and in statistics. Especially in statistics, when you are making certain experiments on drugs or errors recording or something like this, you're basically mixing together many different factors. Each factor, you don't even know what kind of distribution each factor has. But sufficiently mixed together, maybe from different people, maybe from different experiments, from different timeframes, etc. If you are mixing them together, you can count on the result, on averaging the result of this will be close to normal. Because it's very important for scientists to find out the distribution of probabilities of random variables. And if they're mixing these random variables together, they don't have to guess. All they need to know is basically that this is almost normal. And any normal distribution is characterized by only two parameters, the mean, the expected value expectation and the variance. Now, we can statistically evaluate these two parameters. And that gives you the distribution, the normal distribution with these two parameters. And from there, you can find out what's the probability of my new random variable to be in this particular interval or in that particular interval, whether it's more or less concentrated around your mean, or it's significantly spread around, etc. That actually concludes my today's lecture, which supposed to illustrate that mixing together different random variables under certain conditions results in the random variable, which more and more resembles the normal variable, normal random variable. If the number of initial random variables which we are mixing is increasing to infinity. I do suggest you to read the notes to this lecture on unizord.com. And basically that's it for today. Thank you very much and good luck.


Hello, everyone. Thanks for joining the second meeting where we're going to be discussing chapter one. It's the active textbook group and it's May 12, 2022. You can click on my screen and that will show you the full screen. We updated the live meeting page a bit and it's just like all these other pages work in progress, but hopefully this will be a structure where we'll be able to hear everyone's perspective and leave the coda in a better place than we found it. So what we'll do in the live meetings, which from here on out or at least for here for the next two and a half months or so, are going to be like driven by the material in the chapters and in the appendices. We're going to go to the questions page and we're going to look at the questions that have been asked for that week for that theme. And we're just going to start with the most upvoted questions and then we're going to be taking notes in the answers in the discourse. And we'll go into a little bit more detail when we flip to that page. And then after addressing questions for probably most of the time and discussing that, we'll go pull back a little bit more broadly like ideas for that week. And then in the last minutes, we can just see if there's been any changes to the project ideas and start to see what people are excited about applying active inference to, and then we'll end at the top of the hour and take a quick break. And then there'll be a tools organizational unit meeting following for those who want to just like listen or participate in dot tools. So let's head over to questions and I'm going to hide the sidebar because we're going to be mostly here in questions. Okay. So it's awesome. And also control and plus minus or control and scroll is like a good way to resize how it looks. So this is our first week with this format. And anyone's welcome to like suggest a variant or a different way to carry this out. But the co organizers of the textbook group kind of thought we could do something along these lines. The questions we're going to be looking at are in chapter one. So that's the chapter we're discussing today. And we're going to start with the ones that have the most upvotes in the interesting and just spend a couple minutes on each of the questions. And at any point, there are the affordances of just listening and also of course rewatching this recording. There's also the affordance in gather to raise your hand if you'd like to share on that question that we're going to have up. And additionally, in the by clicking on the answers and discourse that will pull up like a focused view on that question. And everybody can collaboratively edit the answers and discourse. And so it's better to like add and be noisy. Even if it's a bullet point list or incomplete sentence or there's incoherence or it's contradictory. Those are all fine. It just like adds information and perspectives to the question. Yeah, we're going to go to each question. Open up the answers and discourse. Read the question. See what has already been contributed in the answers and discourse and then also like if people want to share other thoughts that they're having. That's awesome. And then also if anybody wants to be like taking other notes just things that are coming to mind that they don't want to share in a speaking way. Or just taking some notes on what other people are saying. So let's just go into it and I'll be just keeping an eye for the raised hand in gather for anybody who wants to speak. So starting with this first question, maybe we can have like a like addressed during recording or something like that. But so how do you understand the distinction between the high road and the low road to active inference? What do you think is interesting or useful about this distinction? So does anybody want to like just summarize how they were thinking about the high and the low road? Or we can look at what others have already written here. What is the difference between the high road and the low road? Are there other roads? Are there high and low roads to other theories? What is this metaphor with the roads? So let's check out. Yes. Ali and then Mallory. Well, I believe it's just another term for top down or bottom up approach in a kind of way. And well, the high road, I believe starts from the actualized mind itself and tries to derive the underlying mechanisms or underlying principles. But the bar map approach or the, in other words, low road tries to start from the elements, tries to start from the unorganized elements, let's say, and derive the higher mechanisms or higher order mechanisms actualizing the mind itself. Thank you. Awesome. Ali? Yeah, I was just going to recapitulate I guess what I wrote in the comments and I see now that I probably should have written straight in the answers and discourse section. So sorry about that. But I really appreciated this schematic. Because I've read papers that I think come at active inference from both of these angles and in trying to understand, I don't know, the framework. I think it wasn't super clear to me how all of these different terms that are like really nicely outlined in this figure, figure 1.2 are related to one another. So just to have it, I think, laid out like this, it was really very, very helpful. And I think still being kind of new to the concepts and such, I liked the way they framed it in terms of kind of the high road describing the kind of what and why and the low road being more about the how. I thought that was really very useful. So yeah, I really, I loved it and I saved it and will probably reference it quite often. Yeah, agreed. It's really useful. And it touches on Aristotle's multiple wise, where some of the wise are more like house or they're sort of proximate mechanisms or more ultimate mechanisms. And we can like look at figure 1.2. So active inference is in the middle and the high road I guess this is the starting train stop here with a free energy principle, which some later questions also might invoke. And then it kind of goes down to active inference, whereas starting with Bayes theorem also gets us to sentience behavior, sentient being like sensing and feeling and acting not as much on the qualia and experiential side. So what else do we have in the answers. So high road, what and why that is starting with sort of the organizational imperative or like Ali, I think, said there, but the actualized mind and then what are the underlying principles that make adaptive adaptive behavioral adaptive behavioral systems, working down, you know, the castle in the sky is the adaptive behavior and intelligent cognitive behavior, what is under it. And then the low road doesn't begin with that imperative, it starts with like the elements, like the low road, starting with Bayes, none of these necessarily have to be or at least maybe until you get to like Bayesian brain. These don't have to be adaptive. A generative model doesn't have to be resisting entropy. So it's sort of like here's where we see a lot of the elements in the machinery. And then on the top is more of the self organization resistance to dissipation auto poesis. How are you going to get there you have to be minimizing your surprise. What does that actually entail and then active inference is like this intermediate stop. And again, maybe there's other roads or there's other paths. Yeah, so any other thoughts on high road and low road or we'll continue to the next question. This is just being introduced in chapter one, because the following chapters two and three, which we're going to get to after the appendix a and b are going to detail the low road and the high road. So this is kind of like a map for some of the next steps that we're going to be engaging in next question. The concept of organisms minimizing surprise is introduced on page six. How are prior assumptions or beliefs established when the organism is in new or novel environments. So anyone can raise their hand or take a note here. Mallory or Ali with raise hands. One helpful approach might be to think about like the questions asking about new and novel environments. So are we dealing with categorical novelty or with like quantitative novelty like I was measuring the temperature and then I'm in a new environment where the temperature is different. That's one type of novelty but there might also be like this like novelty at an even higher level with like I was looking at previously I was looking at temperature and now I'm in a place where I'm having priors about smell. So that's like a sort of intermodal or like a different type of thing that's being tracked. So that's a categorical novelty as opposed to just updating the temperature parameter in a different temperature room, whether the person who asked it is here or not. Oh yes. Okay. Ali and Mike go for it. Yeah. Well, again, if we're talking about the human mind. I mean, if from the priors, we can infer that. Well, it's something that kind of innate in human minds is something wired and I mean, perhaps genetically or in any other way. Another biologically hardwired way. But well, again, yes, the definition of a novel situation varies according to what we're exactly referring to. And if a novel situation is something that the organism hasn't ever presented with, well, in that case, these priors or these innate tendencies won't help it very much. So in that case, it should probably go along the way of constructing posteriors more actively, I believe, than in any regular situation. Thank you Ali. Jessica and then Mike and then Steve. I think maybe like in this situation, even though the agent might be encountering something that has an experience before they might use something as proxy that they already know and sort of like project those understanding into that new situation. And then it's through action, etc. Then they're going to be updating like, OK, how true is it or isn't to what they understand, but they might be making some kind of projection based on what they already know and things that look similar to that new experience. Awesome. Thank you. Mike and Steven. Yeah, I was just going to comment in the case where the organism is brand new and has no experience. I think Ali's comments covered this a bit that it will have to be updating its priors fairly rapidly and presumably starting with sort of a wider distribution. Great. Steven. Yes, along the lines of what Jessica was saying, there has been some work in systematizing that as case based reasoning, not necessarily in a Bayesian framework here, but as a derivative of expert systems where when you get a new scenario, you try to match that against previous scenarios that you've already seen and adapt it however necessary to meet the new observations. So that might be a place to look for inspiration. Thanks. Yeah. And in the ontology work and in a lot of these questions, there's often like a narrow answer and then a broader answer. Like the narrow answer would be, well, again, if all your Bayesian model is doing is looking at the thermometer reading and then doing a Coleman filter and inferring the temperature in the room. Well, then the answer of what happens when you put it underwater or what happens when you ask it to predict scent, it's kind of like a trivial answer, which is it's just not a parameter in that script. Nothing happens. And then that's like the narrower answer, which is often very deflationary or like very trivial answers. And then there's also a broader question like in the ecological context or in the organismal context in the expert applied context, what happens when there are these different qualitative and quantitative types of novelty. And so sometimes it's like there we are in the middle again with the deflationary or the narrow being too obvious and too simple to be brought into action. And then the bigger question like how do experts in medicine or some other field, how do they handle different types of novelty is like that's its own research question and agenda. Awesome. Any other thoughts on this question about priors. And it's also very important note that surprise is always relative to expectations, which are priors in the Bayesian context and those are granted by the generative model. So surprise is not just free floating. It's in relationship to the outcomes as expected by an organism, for example, Jessica, your hands raised, otherwise we'll go to the next question. Awesome. Okay, this question, I think is pretty similar to the previous one. So if anybody wants to give a thought, otherwise, no worries.  Many slash all roads leading to it. Jeth. Yes, that's more of a general question. If, for example, if what you're trying to understand is race cars, right, and you develop a mathematical framework, given another number of measurements like the weight of the car, the length of the car, the torque of the engine, the adhesion of the tires, the friction and whatnot. You're able to predict how well the race car is going to perform in different conditions and whatnot. Does that mean that you're an expert in race cars? Does that explain the race car? Could you engineer a race car based on this knowledge? So talking about multiple different roads, one road through the free energy principle, I think is an analytical road that looks at the outcomes of the machinery of the living beings that try to maintain homeostasis. But how much does it say about the implementation of those organisms? It says many things, but does it say everything that we deem might be interesting? So what are the limitations of this framework, the active inference framework defined by the free energy principle? What are its limitations? On top of what, given its capabilities, which are, I think, enormous, but there are limitations. And if there are limitations, that means there must be other roads that can be taken to circumvent these limitations. Thank you. Great comment. Ali? Well, to lose and get heavy in a thousand plateaus has laid out the concept of rhizome. I don't know if you're familiar with the concept of rhizome as opposed to an arborescent structure. Well, basically, the concept of rhizome expounds a structure, a knowledge structure in which every concept can potentially be connected to every other concept without having to go through a principal trunk of a knowledge tree, let's say. But in an arborescent structure, an arborescent knowledge structure, everything branches off from a principal, let's say, base. And I believe that in active inference, a rhizomatic approach can also be explored and, I mean, not only by approaching to different concepts via the high road or the low road main or principal trunk. It can potentially be approached from seemingly unrelated concepts to awesome, like to maybe even approach a limitation or a seemingly unrelated concept. Both the high and the low road in this textbook are formal and they're both grounded in like mathematical symbols. So are there qualitative or non mathematical roads? Maybe some of the early roads are mathematical. But is it possible that a future road or path or tunnel or choose your travel metaphor might not be that case? And then, and then thanks for sharing that the resource there, because this is another interesting angle, which various people have traced this evolution of visual metaphor, where it's like things are ladder like and scalar. And then there's more of a bifurcating tree. And then that becomes critiqued or a complexified into more of a network, like with integration among species, for example. So there's sort of this movement from linear through a tree, like a directed acyclic graph towards something that's more networked. So that's a lot of interesting ideas. Anything else that people want to add or we'll continue on? On page 10, they write, Creatures engaging in active inference actively seek out salient sensory observations that resolve their uncertainty. So, what is the relationship between salience and the kind of observation that reduces uncertainty? Why is the relationship between salience, observation and uncertainty this way? How does active inference frame this relationship similarly or different from which other specific frameworks? So someone wrote, I like this question a lot, eager to get a better understanding of what constitutes salience. Is salience referencing information that is self evidence or information that is precise and surprising or dot dot dot? Yes, I think attention and salience is addressed more in papers on active inference than at least just from a preliminary pass of the book. So attention and metacognitive mechanisms are not going to be a core feature in the book. However, there's again been a lot of interesting work on like the neurobiology and the computational aspects of salience and attention. We can click on the word and see a definition, but this is not like the final definition. This is just what some have gotten to. Salience is the extent to which a cue or an observation commands the attention of an agent given their regime of attention. So potentially a little bit tautological. Like a salient observation is something that is grabbing attention. A salient cue is something that commands attention. Steven? Yes, I would say that especially if we're defining it in that way that there are two elements to salience. And one is what was pointed out in the question, I guess, that you are reducing uncertainty. But there is also the question of what uncertainty is worth reducing. And that gets into utility and where it makes sense to explore. I don't know if we want to choose to change the definition of salience to just be the second of those or leave it as having both of those elements. Thanks. Mallory? I think something I don't necessarily understand, which I would appreciate maybe somebody speaking to a little bit is. This is related actually to a question I put in earlier about the distinction between perception and learning, but something that grabs attention. Are we distinguishing so that would say that something is like the point that was just made something a salient if it reduces uncertainty. If we're also attending to things in order to actively construct a niche that is consistent with our model of the world, would we not consider that something like that object as attracting attention? As opposed to how do we distinguish between active sampling and attending? And how is that related to salient? So something salient either if it is surprising or if it is like serving the purpose of kind of like fulfilling a prediction. I'm not sure I understand that kind of distinction. I'm not sure that makes sense, but awesome question. And this is one reason why looking at the formalisms and how they're framed will help us because in the purely English linguistic realm, we could imagine sentences of any type. So in the active inference formalisms, what are being tagged with salience? Which variables are said to reflect salience and then are they or in what ways are they related to this concept of surprise, which we're going to get to in a question soon versus utility. And this is also speaking to the way that expectations and preferences in active inference are framed differently than in certain other frameworks. Can I jump in and just ask another question about this question rather than an answer, which is wouldn't some data be salient if it's even if it's sensible? Like we might have two levels of salience. So I just put this quote in about the notion of a umvelle being a more or less broad series of elements called carries of significance or marks, which are the only things that interest the animal. Now interest the animal is one thing, but even just perceiving those marks as existing. I mean active inference does have room for that in terms of each agent having some sensory data. So you could say, well, since I can't see infrared light, it's not salient to me when I'm out in a field of flowers, but a bee can see that. I think as you can see infrared or ultraviolet, I can't remember which one. So, you know, it may be a salient concept for me as a thinking agent, but it's not one that's salient for me when I'm navigating a field of flowers basically. Nice. Thanks for bringing in umvelle and a lot of ecological psychology terms do make their way to active.  For example, there's things that are outside of the wavelength that can be perceived. So photons are not going to be perceived at that wavelength, but then there might be within the visible range novel arrangements. Like there's something moving there or there might be something that is static yet commands attention like the Mona Lisa. So what is happening when somebody's regime of attention is fixated on static artifact versus something where they're being fixated on something that's rapidly changing like a movie. Mallory in the chat you wrote can folks recommend papers. So definitely people can recommend papers anywhere they see fit. And one place to check out is in the bottom of our quota in the live streams under the live stream tab. You'll find like 45 papers that we've relatively extensively discussed. And so these are are not like the only or the canonical papers or anything, but there's a lot of important ones in there. So especially if you're interested in looking at papers where like people in the lab might have familiarity or there's some things to listen to or watch about it. Then these are some good places to start. Awesome. Next question. Alright, this is a contextualization question. This is not a metadata contextualizing sentence. In page nine and 10 they talk about differences from alternative frameworks. For example, in contrast with other frameworks policy selection in active inference automatically balances exploration and exploitation. And later active inference extends the inferential approach to domains of cognition that are rarely considered. What are five to seven examples. Names in a sentence of other theories and frameworks that active inference improves upon or updates. Anyone can raise their hand. This is an excellent homework slash collaborative Stigmergy question. Because even if each person only thought of one novel answer here with a link and a name or somebody improved what somebody else had already brought very quickly. As a textbook group, we could have like quite a coherent and comprehensive list. The idea of balancing exploration and exploitation so called is going to be a key theme. And the way that active inference does handle this trade off. Not through the use of like an exploratory mode or exploitative mode or some parameter that is like governing the handoff there like some sort of, you know, X times exploration. Plus one minus X of exploitation. That is like one approach that's commonly used, but it would be a question of like where, you know, what citations use that type of an approach. And it's going to come to the epistemic and pragmatic value. Whereas when there's little epistemic value to be gleaned, little informational uncertainty result to resolve, then pragmatic value can be pursued, which is reducing the surprise about outcomes in relationship to preferences. And then on the other hand, when there's a lot of uncertainty, exploratory behavior might be engaged in, because there isn't enough certainty around how to pursue those pragmatic aims. And these are going to come into play with like the T-Maze mouse model that's going to be later in the book. But this is a great question. So anyone want to like just give an example of some other theory or framework? Or some other approach to thinking about exploration, exploitation? One starting reading, if anyone is interested there is a 2015 paper that is looking at exploration and exploitation trade-offs in different domains. Ali? Well, I don't know if it necessarily improves upon, but certainly an alternative approach to this theory can be Stephen Grossberg's art theory, the adaptive resonance theory that we talked about previous session. In that theory, the problem of learning and cognition is attacked from the viewpoint of solving the stability slash plasticity dilemma. And in this context, in the context of art or adaptive resonance theory, well, or let's say the difference between the active inference, one of the main differences between the active inference or any predictive coding schemes from adaptive resonance theory is that in Bayesian models, generally, the fully suppressive matching circuits, I mean, the circuits that can propagate the information, it cannot solve the stability slash plasticity dilemma as opposed to the art or adaptive resonance theory. So plasticity being more exploratory and openness to updates and stability being more like parametric lock-in, more exploitative within a certain realm? Yeah, the plasticity refers to a system that doesn't have a catastrophic forgetfulness. Cool. So we can just, you know, have this question up and check back and people can add things. We'll just look at a few more questions. So on page eight, they write, the high road perspective is useful to understand the normative nature of active inference. What the living organisms must do to face their fundamental existential challenges, minimize free energy, and why the what and the why in the high road? Why to vicariously minimize the surprise of their sensory observations? What does it mean to minimize surprise vicariously? So someone has written interesting choice of words. Here are two possible answers. Also the authors or the readers, participants may have something else in mind. Vicarious might refer to the point on page six that agents cannot tractably minimize surprise as the authors technically defined it in chapter two coming up. So instead, they're going to minimize an approximation to surprise, which is variational free energy. However, I'm not sure if they're referring to the two types of surprise that are going to be approached in chapter two, which is the marginal surprise, like informational surprise and then the Bayesian surprise, or both. So one option is surprise as defined one way or the other or both cannot be directly calculated or it's not tractable to pursue. And so the vicarious could mean like indirect or via a proxy. And in that case, something like variational free energy is being vicariously minimized, and then that is going to be used to get at the surprise minimization. And the second option the person has written vicarious might refer to the point that agents minimize free energy not only in response to real sensory observations, but in anticipation of them. Those anticipations could come from internal simulations by subroutines or other modules or memory from recall of past observed events. Page 32, which is probably in chapter two, the authors seem to be referring to engaging models vicariously in a counterfactual sense dealing with imagined surprise at presumed observations. So these are really important comments. We have the indirect minimization of surprise using a proxy. So some type of heuristic to minimizing surprise now. And so that is vicarious but contemporary. And then the second sense is whether we're using a proxy or not is referring to observations that haven't occurred so anticipated or expected observations. Some of these hopefully we'll come back to in chapter two. But these are important questions. And it's going to speak to like variational free energy and expected free energy and free energy of the expected future, a few other constructs. And yeah, then I'll leave. Yeah, I put that last comment and I was just thinking that should probably go under number two there. So I guess that would be evidence of that second interpretation. Thank you. Okay, so we'll maybe return to this in chapter two. Okay, let's just see if we can look up just a few of these questions.  And of course anyone can always read and contribute asynchronously. All right. The subtitle of the book is the free energy principle in mind, brain and behavior. What is the relevant distinction between mind and brain in the context of the free energy principle? Someone has written really appreciate how the researchers have started proposing biological implementations of active in the brain. But it's not clear to me how this maps onto related psychological concepts like colloquial surprise prediction salience arousal. Yeah, awesome. And there's been some recent work and some work that we're going to be looking at in live stream 46 on active inference and folk psychology about connecting some of the formalisms to to folk psychological concepts. So that's one place we'll be looking at that. But how does or does active approach this mind brain question, whether the Cartesian do list question, hard problem of consciousness intentional stance of Dennett. Why is the subtitle of the book that way, Jessica, and then anyone else with a recent. Yes, I mean, I don't know the answer, but I guess I make a guess. I think maybe the mind would be more the cognition and the brain looking at the physiology and and then, you know, looking at behaviors. That would be my guess. Yeah, awesome. Bacterium does not have a brain, but does it have a mind. Also, great questions. We'll just continue just these questions with fewer upvotes because there's a bunch just so we can get them in. So, generative model is used in a singular or is using the singular as in the generative model, or in the plural as in hierarchical generative models. Is a generative model composed of generative models, ie is it structurally fractal. Someone has written personally, I think the answer is yes, or or it's possible to answer yes, via referencing a paper that talks about blankets of blankets Markov blankets life. This paper definitely does think about the nesting of multi scale complex systems. In terms of like nested generative models that then are constituting. Yes, they are then constituting generative models at a higher level. So like there's people with their generative model than the team that has a generative model of a different type, jf. Yes, but even an individual, myself, yourself can be could be characterized as having a generative model composed of other general models dealing with less abstract belt, for example. So I think I think yes, I think almost trivially in the sense of a collective, but in the sense of an individual doesn't individual also can be said to possess a generative model that is itself composed of sub generative models. And we've in several live streams and settings discussed like there's one sense where nesting is used to refer to systems that are like potentially even physically nested. And then we also see the nesting architecture. For example, here it's live stream number 25. When we discussed it, but this is a nesting of attentional and cognitive models. So this is even of a so called individual. So what is it about model nesting when sometimes it can refer to physical enclosure. And other times it is something that enables counterfactual reasoning, attention and metacognition. So what is the nesting. And if someone is just seeing this kind of a notation or this kind of a graph for the first time we're going to totally go into it. But it's going to be one of the core formalisms. And also this paper is going to deal with nested models and other papers do the textbook is going to spend almost all the time on just one level. The just sort of the kernel or the essence or the archetype or the essential motif of this partially observable Markov decision process. And the textbook is going to focus on juxtaposing the discrete time partially observable Markov decision process with a continuous time. But it will deal less with hierarchical nesting, but it does bring it up, but it's not going to be focused on. Okay. What is the information content of a prediction. I'm just going to read these ones just so that those who are listening can see like the kinds of questions that they could be adding or addressing. This question has to do with the relationship between active inference and pleasant surprises, potentially even love languages, receiving money on the pavement. Page five is speaking to the author's rhetoric around theory proliferation and integration. Where basically they are saying, well, we could have a dedicated explanation for every neurotransmitter for every synaptic junction, every unique behavior, every cognitive phenomena. Or we could somehow utilize first principles, whatever those are to result in theory integration. And that gets mapped on to the scruffies and the needs. Is it really helpful to redefine terms such as probability and surprise to sometimes mean utility and diversions from utility? If that's what the authors are doing, there appears to be an unhealthy ambiguity in how they use these terms. This is where the formalisms are going to really come into play. And also the live stream on free energy, a user's guide 37 deals with this dual interpretation of expectations and preferences. And this is like one of the most essential differences between active inference and reward learning or reinforcement learning frameworks is a dual deployment of the same variable in formalisms. To represent preferred states, as well as expected states, like homeostatic range is expected and preferred. It's where one wants to be found, and it's where one expects to be found. And so one can realize those preferences by reducing their surprise about the observations, rather than saying, well, homeostatic states are valuable, and we're going to pursue maximum value. How might it be? How might an acceptable state space relate to the idea of a comfort zone? It seems the acceptable state space for an organism is necessary for it to persist. Whereas a comfort zone seems to be a less necessary state repertoire, which if exceeded may only indicate to an organism that it's at risk of leaving the acceptable state space. Interesting. And then the last written question here, looking ahead into part two, have people been looking in the computational complexity of the active inference model and how far are we in terms of making this computable? And the reference 29 team paper computational resource demands of the predictive Bayesian brain. Excellent and important question to understand, like the analytical computational complexity, like the big O notation, does nesting a model make it two? Or is it, you know, log the number of levels or is it number of levels to the sum exponent? And then also in practice, like using active blockference or pi MDP. What is the actual runtime of different algorithms? So this is awesome with the questions that everyone's added to one, like we're going to keep on returning to chapter one. Just like we're going to keep on returning to ideas, but people can fold it and continue to add to two and to the appendices, which we're going to go to next week. So just as a reminder, next week, we're going to be approaching appendix A and B, which are mathematical. And so just a few thoughts on that before we return back to the ideas on the book in appendix A, you can see that all the equations have already been added. So if anyone like wants to reference like what does equation a point 44 mean? And obviously, like there's 60 equations and you could get your PhD learning about some sub function of just one. So we're just trying to familiarize and get a first coat of paint. So for those with different mathematical backgrounds, if somebody can derive the proof amazing, let's take some notes on it. And if somebody is just going to be asking beginner questions, that's also very important. So that will be in next week's questions and discussions. But let's just look at what ideas people wrote and then see if there's any ideas percolating. So having addressed some specific uncertainties, I get with the questions. We can think about like, what are some impressions that people were having and like what was exciting ideas. So if Ben or Mike want to speak to this or if anybody else like wants to share something, JF and then anyone else.  No, my hand was not raised. Ben, go for it. Yeah, thanks. Can you hear me? Yep. Cool. Yeah, maybe I should have posed this as a question actually. I just thought it was it was interesting this connection between perception and learning as essentially the same process and folding over different time scales. Because on the one hand, this makes sense to me because one example of learning might be you can imagine an agent moving to a new city with which they're completely unfamiliar. And the agent could sit at a fixed point in the city or walk around the city and just perceive the city and they would learn over a longer time scale. They would observe certain regularities or certain occurrences. And clearly that's one form of learning. But then there's maybe another example where, you know, my mathematical backgrounds fairly weak. And so if I were to try and learn the kind of fundamentals of the equations that we were just looking at, no matter how, for how long I kind of perceive those equations, I'm not going to learn them. There's going to be that I have no access to kind of there's no information gain there. It seems like to learn certain things we need to scaffold perception in certain ways. And so I suppose my question would be when somebody teaches us something that we don't understand, is that merely a kind of scaffolded perception? Or is that kind of learning underwritten by distinct cognitive processing completely separate from perception? Great question. Thanks for unpacking that. And to give one thought, let's imagine a ball moving across the visual fields. It seems like a canonical example of perception. We're perceiving the ball, but it could also be understood as parameter updating or parameter learning of the location of the ball. And then that's one approach. But then also you pointed towards like this deeper sense of learning, referring to like the underlying structures, like in the ball movement examples, it seems like learning and perception are the same. But then learning might also refer to something deeper, like the tendencies of how balls move or one could, as probably many of us have, look at these symbols and perceive them visually. But because we wouldn't have learned the underlying generative model that gives rise to these pixels, we could continually be surprised. So what is happening when there's perception and learning seemingly happening hand in hand? And then other times where somebody speaking a language you don't understand, you certainly hear it, you may even be paying attention to it, yet the next phoneme may still continually surprise you. So it's a really important question. And then Mike wrote, yes, please go for it, Mike. Yeah, so I think these are straightforward, but foundational concepts that the dependence of sensory input on action and vice versa creates a framework where we can have feedback. And then the mathematical constructs, let's think about how we model that and simulate it. Thanks for this also essential point. In active inference, there isn't a perception and inference module that passes the perceptions to the learning module and the learning module passes it to the decision making module and the decision making module to the action model. The inference is conditioned upon policy selection and action. And that is going to approach this nexus of perception, cognition and action in an integrated way that may differ from other frameworks, which was also to the point of that previous question. So this was an awesome session, everyone. Really appreciated it. I'll upload the video recording following the meeting. Next week at the same time, we'll be looking at questions and ideas from appendix A and B. So just approach those however you'd like and annotate the code up. And also if anybody wants to like specify another time when they just want to discuss or co work. That can be added or in the active lab discord people can like join and we can have like a voice, like a study hour or just a time to talk about stuff or write down the questions we have. So awesome work everybody. Thanks a lot.


Mr. President, mixed bag of news, some bad, some good. Carlson, the missionary, appears to be dead. The rebels also seem to have broken into a hotel and conducted a miscellaneous shooting of white people with perhaps 50 casualties, or maybe even more, with 20 maybe dead. But the five official Americans are reported safe and on their way out. What we seem to have is very confused reports, but some reporting of one, this one violent outburst against Europeans by some kind of a band of rebels. We have no details on Carlson and no details on the five official Americans except that Carlson is dead and the official Americans are okay. Did we just have five official Americans? Five official Americans is what we have. Rest of them are missionaries and so forth. One consul and other, yes, five U.S. government people. And rest of them are missionaries and so forth. The rest would be missionaries and families. There may be another American family that have been shot, but we don't know. That would be probably among the Europeans that seem to have been in this one wild melee, but the reporting is not that precise. Okay, Mike. I think that really is all our body with tonight because I can't imagine anything that sharply will change the picture now. What time is it? It's now three o'clock your time, four o'clock our time. It's eleven o'clock in the morning out there. The operation is going on for five hours. Is there anything about the success of it? Well, overall, I think that we've had to put out one or two of these reports in order to prevent the newspapers from saying absolutely nothing happened and it was a bloodless triumph. But overall, the military side of the operation is entirely successful. The U.S. execution, which is the aircraft, appears to have had no difficulty whatsoever and there is no U.S. military casualty. There's no change in the report I gave you earlier that the paratroopers have had only two wounded in the process of landing, whether they've had any casualties in the town, in the process of carrying it, we don't get no. Neither do we yet know whether any substantial number of Europeans have been removed from the town, which was one of the things that was a great worry in the last day, in getting the five official Americans who were the ones that they kept threatening in their public statements, getting them safe and sound is a very important gain from our point of view. So overall, I would say good, but you can't call it all good when there's been this one tragic outburst of murder in a hotel. You don't know how many Americans were in that group? No, sir. There's no identification on that yet. There's a report that there may be one American, another report that there may be an American family, but now that I think about it, I think that second one is about something. Are we getting who's reporting this or is this news service? The Department of State is taking sifted reports under George Ball's guidance with Bill Brubeck from our office and putting out limited stuff to keep them up to date. What's the source of it? The source of it is primarily our reporting officer with the task force. We have an experienced ex-consul in Stanleyville, in with the flights, and he is reporting to the emcee in Mapleville and they're reporting to us, so that it's as solid as we can get it. Anything from the news services out there? I haven't seen any of this new stuff on the tickers. The first take on the new services are very good. Thank you, Mike. Thank you, ma'am.


Trust is so important in a blended family. And I think that was really what I leaned on so much in the early days of our relationship because Ryan and my husband had three kids. And at the time when we met, they were six, eight and nine. And I was really aware of building that foundation. And so the way it looked in practical terms was that obviously any discipline always came from Ryan. And even still to this day, we still kind of fall back on that because I'm always very aware that I don't have the same foundation that he has with the kids that dates back to their birth years. For me, it was much later. And so it's built slowly over time. And I really have to be aware of that. And getting to know each of them, they all have different personalities. And relationships change over the years. Our oldest is in college now. And I would say that even though she's not here, it's probably the best our relationship has ever been because she's getting into those years but I think all kids, regardless if you come from a blended family, once you start to get into your 20s, you start to realize all of the things that your parents did for you and you become more appreciative. So my relationships with each of the kids are different and they changed over time, but I definitely was looking for ways to connect with them early on based on their interests and based on their personalities. A lot of we talk about give to get and giving a little bit of information so that they'll talk more. And really listening is such an important skill set. Listening to what they wanna talk about and connecting with them on their interests as kids and really just being there for them. Something I talk a lot about and licensed a parent is this trust in a family and the words I promise. It's such a big deal in our family. We use that phrase sparingly because we take it so seriously. And by doing it that way, when we actually do say it with the kids, it's almost like it gives them a piece and whatever they've been asking that they're done, like they feel good because they know that when we say I promise we mean it and we follow through. And I think that that's something that I didn't really understand before meeting my husband and it was something he was doing with, we call them the bigs, but they were little at the time, but it was something he had been doing with them. And I was thinking of promising, was something like a pinky promise on the playground. It's like, oh yeah, I promise, yeah. But it's just so much more than that in our family. And I think that it's important for our kids, whether it's a blended family or not, to know that when we promise something that we follow through and that we say what we mean, I mean what we say and that we're gonna be there for them. And so I think that's really been the number one thing with my bigs who are my step kids is for them to know that I'm gonna be there for them. And I think that because Ryan and I've been together 10 years now that because I'm still around, I'm still here and that I've shown I'm dependable, I'm reliable, they can trust me. And that kind of grows over time.


All right, so before we get to our next use case, I just want to mention one quick thing about how we're helping each other in the OpenStack community. And in the particular case of big data, one of the things we've just launched this week is an update to the OpenStack Project Navigator. This is the URL. You can also find it just by going to openstack.org. This is a place in the website where you can learn a lot about different projects. What we've really added just this week are some reference architectures. And one of the first ones we have online is for big data. And so this is a big part of how we accelerate adoption of OpenStack is go beyond the code to actually the details of how people that are successful with OpenStack under different use cases are actually doing it and looking at the bigger picture. So go ahead and check that out. And you can learn about some of those reference architectures. Also, of course, it's open source, so contribute to this. If you have a reference architecture, you want to share, we've got a place to do that now on our website. And so next up, to talk about the media industry and their use case, I'm really excited to introduce from Sky UK, Matt Smith. Hi, I'm Matt Smith. I work at Sky UK. And over the past couple of years, it was a great pleasure to part of my job running the team, doing the architecture, design, implementation, support, and growth of the OpenStack platform at Sky. My presentation today is why Sky runs on OpenStack. So I'm going to run through a few things, give you a bit of background and context about what Sky does and multiple business units and a diverse architecture we have within Sky. And then go on to why we needed cloud and why we chose OpenStack. Moving on to a brief overview of what we deployed in our data centers and then go through some use cases of the applications running on our cloud and then finishing off with what we hope to do with OpenStack in the future. So Sky is Europe's leading entertainment company, about 30,000 employees across the UK, Germany, and Italy. We have something like 22 million subscribers. And we're primarily a satellite television company. And in the UK, we deliver something over 600 TV channels direct to our customers' homes. I say we've got lots of different products and businesses. We're also a telco, an internet service provider. So we have a product called Sky Talk, which is landline telephony for our customers. We have Sky Broadband, which delivers DSL and fiber broadband to our customers' homes. We have online TV products. So we have two of those, one called Sky Go, another one called Now TV. So Sky Go is free to Sky subscribers. And Now TV is a contract free, dip in, dip out online TV service. And both of these do video on demand and live TV. So video on demand, things like the latest films and TV programs, box sets like Game of Thrones, Walking Dead. And from live TV, we have things like Premiership Football and Formula One broadcast over the internet. Some of the products we have are public Wi-Fi. So Sky Wi-Fi is delivered free for Sky subscribers. And that's free Wi-Fi for the internet in pubs, shops, restaurants, cafes. And we have a product called Sky Store, which you can either buy and keep DVDs or rent them. And you can keep your DVD library in Sky's data centers. And then watch it on your iPad, iPhone, PC, et cetera. So why Cloud? Why OpenStack? So Sky's products are driven by technology. We have lots of applications, lots of developer teams. And all these applications need somewhere to run. And instead of having lots of cloud islands of different infrastructure, we decided that we'd need a sort of software-defined data center to deliver our applications on, including things like software-defined networking, software-defined storage. So we have a sort of a one-stop shop for delivering our infrastructure for our teams. And then that spans all the businesses. So it isn't just for one use case. It's for all parts of the business in Sky. And we're looking for flexibility, something that is highly scalable. And I think you'll hear later from the scientific chaps that they've got these massive, massive OpenStack installations, as is sort of quite small in comparison. And speed of delivery for applications so the end users can deploy their heat templates, their networks, and sort of self-serve on a single platform. And one of the primary drivers was cost. So total cost of ownership for the platform had to be private public cloud operators and also sort of enterprise vendor solutions. And the only solution we found was OpenStack that fit all this. And it sort of has over the past couple of years delivered a really good job for us. What we've deployed in Sky is we've got many data centers. We've deployed OpenStack in two of our main data centers that give both sort of corporate and internet presence for availability zones. So applications can be deployed across many day availability zones. And when we do updates or upgrades, we can sort of mitigate by spreading our applications across many data centers and availability zones. There's something like over 80 projects or OpenStack tenants on the platform using it for development stage and production apps. Every day there's sort of 400-plus users on the platform creating networks, routers, subnets, ports, instances, volumes, and connecting all together with heat. And from a data center footprint perspective, we've got something like 7,000 cores and 400 terabytes of Seth storage that's presented up through Cinder. And some of the applications that are hosted on the platform. So this is SkyQ, Sky's latest set-top box. And from our OpenStack platform, we can push out software updates. And we also receive user journey information back into our OpenStack platform. So all the SkyQ boxes report back what the users had the users interacted with the platform. And we analyze this data, refine the software, and push new software out. It's sort of made the delivery and receipt of all this information a bit of a seamless process and sort of self-service for the developer teams running the SkyQ boxes. From a public access perspective, so Sky branched out into selling concert, music concert tickets, music festival tickets, and supporting events tickets on a cycle Sky tickets. And this is open to the public. You can go on purchase some tickets for your favorite band. And this is an inter-business application. So it's a video-on-demand portal that we transfer video assets between SkyUK, SkyGermany, SkyItalia, and also between Sky and external studios like Sony Pictures and Paramount. The video assets are brought in through this portal and processed through our broadcasting facilities. And it's a very high-profile application. So this is called a CEO dashboard. And this collects information from across Sky's business. So you can see there's an OTT online. How many people are watching sort of premiership football? How many people are calling our call centers? How many people are purchasing things through our online shop? How many people are purchasing through Sky Store? And it's a full single pane of glass dashboard for the CEO and his executive team, how they engage, how the business is doing. So this is hosted on our OpenStack platform. It's a very high-profile, a really good use case of using OpenStack to do something really important. Some of the things we host, so enterprise monitoring, we collect all our data back into our OpenStack platform from servers, operating systems, databases, applications, et cetera. Video streaming analysis is where we, for SkyGo and Sky, or now TV, every streaming event that happens between where it leaves our broadcasting through CDNs and eventually to the end-users iPad, iPhone, all these streaming events are collated into a data warehouse on OpenStack. And at the moment, it's something like 5 billion streaming events hosted there. And that grows by about 10 billion streaming events a day. Lots of other applications, so developer tools, continuous integration, Confluence, Jira. There's a 10,000-user installation on our OpenStack platform and loads, loads more. So it's sort of spread across all types of business within Sky. For the future, we first installed, when we installed OpenStack, we went with IceHouse. We did an upgrade to Kilo earlier in the year. We've currently upgraded our development environment from Kilo through Liberty to Mitaka, and we were rolling that out into production very soon. We do lots of video transcoding in Sky. So when you get the video asset from the film company or from the studios, it's in one format, and we have to transcode that into formats for iPhone, iPad, iPad, Xbox, PlayStation, PC. And we see that doing video transcoding on OpenStack is a really good use case that we can scale out, do lots of video transcoding, use the platform for something to scale down. It's a really good use case. We've been trialling it. It's working very well. We'll use more applications, and from an OpenStack project perspective, we're looking at Iranics. Good to hear in Newton, we've got multi-tenant networking in Iranic. It's something that sort of held it back a bit. Magnum for container management, like the demo, and Sahara for doing big data. And I'd just like to say to thank you for a few people. So OpenStack Foundation, NASA and RexBase, because we think it's top software. It hasn't gone wrong at all. And it works just exactly what it says. The management team at Sky for backing us as a team. And there's lots of negativity from enterprise vendors saying you don't want to use OpenStack, use our product. The management team stuck with us. And the OpenStack team at Sky. So it isn't big. It's Alan, Colin, Martin and Ross. And finally, Canonical for giving us some really good support over the past couple of years. Thank you. All right. Thank you very much, Matt. Let's go ahead and shake hands. Cheers, Mark. Thank you so much, Matt. That was awesome. I don't know if you all caught that, but of all the amazing things he said, the one that jumped out at me is the CEO of his company looks at a dashboard every single day. It's powered by OpenStack. That's pretty damn cool.


Hello, everybody, welcome back to another AI tutorial. And this one I want to talk to you about a new image generator that's just coming out called Blue Willow. And why is this interesting? Because it's free. Mid-Journey, you have to pay for it after 25 or so runs, image generations. Dolly, you get a free one every month. It's stable diffusion, et cetera. This one's free, which makes it pretty cool. That said, if you want to look at the official term, it is a generating, image generating AI tool designed to help users create logos, graphics, and photo realistic scenes. And here's the thing, it pulls in from a wide range of different AI image generation models. So it could be using stable diffusion. It could be using Dolly. There are options. So anyways, keep that in the back of your head and remember that it's free. Now to join, just click on join the beta here.  I maybe I shouldn't have wrote it, set it that way. When you do that, you're going to see here blue willow inside your Discord. From there, what you want to do is you want to go into one of the rookie or you can, yeah, let's go into one of the branch ones. So I'm going to go into branch 19, which is the latest branch. And when you join one of these branch rooms, you can now start generating images. And as you can see here, you've got all sorts of different people making things. I did one here. Elvis Presley reading a cookbook. I just thought, hey, that'd be kind of funny. And Presto, here's what we got. Here are the four ideas, the four image generations that it created for me. So now again, we're in one of the branch rooms. Let me show you a few options. I'm going to go to do this. You just have to type in, imagine, this is exactly the same as what you would do in mid journey. And then I'm going to have, I don't know, what could we do? Let's do Elvis eating a pie. Okay, we got Elvis eating a pie. Photo realistic. We can add in a few other prompts. Again, this pulls in from multiple different AI generators. So it might, it's probably going to pull in from stable diffusion, but it might pull in from somewhere else, but we don't want to overload it. Let's hit something like that and see what we got. Now it's going to take a few seconds. So while I do that, keep in mind a couple of things. A, it pulls in from multiple sources. B, this is a free competitor. It's pretty wild. And C, if you're not sure what you want to do, you can always look at other people's prompts. So, you know, you can look at this and go, okay, well, this is in French, so that's no good to me. But you know, go on up, take a look. Jordan Peterson dressed like a king. Okay, fair enough. I look at that, it pulled this one in here. This is kind of cool. Whoa, what's going on there on the left side? All right, good enough. And we've got, you know, images. We've got a hippies full length shot, 35 millimeter lens, et cetera, et cetera. Here's what we got. Okay, that's a rough one. This one's not too bad. This one's kind of a mess and this one has some potential. And again, if you want to up, you know, you can click on U4, which makes a higher grade version of it. So it's going to upsample, it's full to speak. And if I want to create variations, I can click on one of the V4. So if I wanted to make a variation of that one, it will just do it. Maybe I want to do a variation of two. So it goes one, two, three, four. And maybe I want to upsample the second one as well. So now it's going to go ahead and, you know, chirp out those things. Okay, look at that. Okay, that's not so bad. It's kind of vectorized and stylized, but whatever. But there it is, guys. This is the free tool. It's a mid-journey competitor. It's free. It's by Blue Willow. It's not quite as good as the other ones. But hey, the price is right. Let me know if you tried it out. Look forward to your comments.


2014-2015 influenza vaccination campaign in Italy resulted in AIFA withdrawing two batches of fluid after three deaths were reported within 48 hours of vaccination, but investigations concluded there was no causality between the deaths and the vaccine. The media coverage impacted vaccine confidence and immunization coverage, highlighting the importance of effective communication on vaccine safety issues to restore trust in this tool for primary prevention. This article was offered by Miriam Levi, Ossilia Sinis Galli, Chiara Lorini, and others.


Gram-negative biofilm forming medical device associated pathogens such as Pseudomonas aeroginosa and Escherichia coli use a series of steps including adhesion, cellular aggregation and the production of an extracellular polymeric matrix to form biofilms. The biosynthesis and role of pylite and flagella in gram-negative adhesion and accumulation on surfaces are explored, as well as the process of biofilm maturation and the importance of quorum sensing systems in enabling gram-negative bacteria to adapt to their environments. The majority of gram-negative biofilms consist of polysaccharides of a simple sugar structure that provide an optimum environment for bacterial survival and maturation leading to increased resistance to antibiotics and predation. This article was authored by Gary Laverde, Sean P. Gorman and Brendan F. Gilmore. We are article.tv, links in the description below.


Have you ever looked at a rich person and wondered, what are they doing that I am not? Well, maybe it is time to ask yourself, what am I doing that is strange to the rich? In this video, I will be showing you 9 things that are strange to the rich but normal to poor people. 1. No birth control If you look around you, you will notice that the poorest people have the most kids. Statistically speaking, poor people tend to have children at an earlier age compared to the rich. Poor people begin having children in their teens and twenties. Kids need a good education, decent clothing, shelter and access to quality healthcare. These require a lot of money. But shockingly, while many poor people give birth without restraint, the rich are the ones who pay attention to having fewer kids. Many wealthy people have only one or two kids because they want to make sure they can afford to send them to college and provide for them whatever will make them comfortable. Because of poor education, poor people don't seem to mind that these kids share a bedroom, wear hand-me-downs and may not be able to afford to go to college when they get older. That is how poor people continue to recycle the generation of poverty. 2. Get rich quick schemes Poor people are far more likely to fall for get rich quick schemes because most times what they seek is money to gratify their instant pleasure. A rich person would never fall for this kind of system because they have educated themselves on finance and business. They would instead consistently increase their assets columns that put money in the hands of so-called money-dubblers. Their rule is that if it seems too good to be true, then it is too good to be true. Wealthy people understand that it takes a pretty long time to make money and that an overnight success scheme is a platform that is just out to ruin you. In the words of entrepreneur, New York Times best-selling author and sales expert, Grant Codone, once you earn and save, then you can start building wealth. I recommend multi-family real estate if you are a conservative like me. I never looked to get rich quick, but I did look to get rich. 3. Watching too much TV Warren Buffett, the third richest man in the world, still aims to read 500 pages every single day. He knows the knowledge gained is priceless. This attitude is typical among most successful individuals. However, poor people are usually so engrossed in watching TV programs and sport. They might come home from work and the first thing they do is turn on the television. For these poor people, watching television and movies is a way for them to escape their stress and reality. The rich people on the other hand, instead of looking for an escape, are trying to find solutions and keep moving forward. Although they also go to the movies, rich people often work more and realize that watching TV is simply a waste of time unless the information is somehow relevant to their lives. 4. Spend money they don't have One peculiar and funny trait about the poor is that they are good at calculating and spending money they have not earned. They're saying, cut your coat, according to your fabric, is not for the poor.  Many poor people also go as far as borrowing or purchasing items on credit. Hoping to pay from the money, someone else promises to give to them. Some even go as far as people immediately taking out loans as soon as they get a new job or promotion. To the rich, this behavior is strange, as they have mastered the art of saving and making reasonable budgets. Rich folks understand the adverse effects of bad debts, so they will wait until they have earned enough cash to make a purchase. 5. Always want to be helped Most people are plagued with an entitlement mindset. They believe that the society, government and other affluent individuals owe them something. Some of them assume that those who are rich were handed a big break by someone else. As they suppose, they deserve their handout. One reason why they quickly fall in the hands of scammers is that they presume that the person was sent to help them and changed their lives is finally here. The worst aspect of this mentality is that they never asked to be taught to fish, but they don't hesitate to request for fish to eat. Wealthy individuals, however, are quite skeptical with help. They believe that for you to get something, you must work towards earning it. The mentality of the rich is not of entitlement, instead, it is of dignity in earning. 6. Comfortable working 9-6 Unlike the wealthy individuals who look towards minding their businesses, poor people focus on working themselves out and fighting to get promoted. No doubt, even the wealthy individuals were once 9-6 workers. Still, instead of getting too relaxed and satisfied, they strive to break from the cycle of an employee to create an opportunities and become an employer of labour. The average poor person wants a job so they can pay bills and live comfortably. People who become wealthy work in other to learn from the post. After that, they use the knowledge to grow their businesses. 7. Blame game Once you see anyone defending his or a deplorable state, you know you are seen a poor person. They just do not take responsibilities.  They lament and blame every other person or situation by themselves about their miserable state. No matter the encounter you experience in life, you are responsible for your destiny. Maybe the decisions of your life were not made by you. Even if they turn out to be more harmful than positive, you are still in charge of your life. The blame game is an alien activity for those who are rich. Responsibility is the game for them. When they take responsibility, it is not because their life is any better, but because they have realized that blaming situations and their parents will do more harm than good. A practical example that not everyone was born rich is Chris Gardner, who grew up as an abused kid in foster care. 8. Not paying attention to their health Health is wealth and deserves utmost priority. When most poor people seldom take their health seriously, while the rich are very conscious of their health and visit the doctor regularly, the poor do not. They instead claim not to have money to do so. Health and wellness are incredibly important because without a healthy body, it is impossible to work and enjoy life. One of the things the rich people do, but the poor don't, is to give priority to their health. Buying medicines from hawkers eaten uncoordinatedly, delaying treatments and other examples is a shortcut they would rather take than health maintenance. 9. Oversleeping Research shows that young people who choose to wake up at 6 am, even when it was not required, are far more likely to succeed in life. The other people who are go-getters and willing to put in the work necessary to succeed. One attitude which the poor possesses that is alien to the rich is that poor people tend to oversleep and wake up late in the morning. The truth is, sleep is good. Very good. But you must sacrifice something to achieve what you want. If you sleep for 5 or 6 hours instead of 8 hours, you are still on track. Besides, some funny but real research has shown that people who sleep between 5 and 8 hours are most likely to live above 100 years old. The ones who sleep less or more tend to live less. Nearly all of the world's most successful people, including Bill Gates, Elon Musk and Warren Buffett, all wake up early. By waking up early, they can accomplish so much more compared to people who sleep their lives away. No doubt, there are certain choices and daily habits that are targeted towards wealth. But instead of only searching for the practice of the rich, learn about the things that poor people do that the rich don't do and avoid those things.


Russia does not want to discuss Ukrainian war with U.S. Contradiction between Russia and the United States over Ukraine is deepening Russian Deputy Foreign Minister Sergei Ryubkov confirmed this He told that Russia has nothing to discuss with the United States about Ukraine As for Ukraine, we have nothing to discuss with the Americans there Whenever we somehow touch upon the topic of Ukraine, all this communication boils down to sending mutual signals But there can be no dialogue or, moreover, talks since our views are polar opposites However, Russia does not rule out further high-level meetings and negotiations with the United States on strategic security issues According to Ryubkov, Russia will not refuse negotiations if the Americans show interest and readiness Strategic security is the term that Russia and the United States, their world's two largest nuclear powers, use to mean reducing the risk of nuclear war Russian and U.S. officials are expected to meet soon in the Egyptian capital Cairo from November 29 to December 6 to discuss a new Nuclear Arms Reduction Treaty