Okay, hello. Welcome back. Please take your seats. As always, I'll start with questions from last time. Yes? Yes? I'm going to ask them to repeat your question now. Yes? Yes? I would like to know what happens if the shoe, bionic, and the likelihood are two completely different factors, for example. I have a uniform bionic glove, and I have a Gaussian likelihood. After, I mean, does it happen that after some updates the posterior sense to a Gaussian starting from a uniform? And is it possible for the prior to be changed shape? Yes. It is. It is. So the special case where the shape, the family or, yeah, the family of distributions does not change is called conjugate prior. So when the posterior is like the prior, then you've got a conjugate prior. And there are whole long tables of what likelihood distributions take, what conjugate priors. So for a Gaussian likelihood, a Gaussian prior is conjugate. For a Bernoulli likelihood, a beta prior is conjugate, and so on. So these tables are widespread. You can look them up on Wikipedia. But as a side remark, I have to say that these conjugate priors are often not unique. You get a range of choices of conjugate priors you can choose. And this can have consequences. So your update equations may look much more straightforward, much better interpretable depending on what conjugate priors you choose. Yes. Yes. Yeah. My question is, is there a way to understand the value of the conjugate priors? Mm-hmm.  I mean, if I calculate the conjugate priors, I see that this is much. A lot. Or little. I mean, can I infer from the number that I get that these distributions are a lot like one another or very different? Basically, I would say no. But it's a question I've never asked myself. So I've never gone and started interpreting the actual numbers I get. Yes. So relatively, you can, yeah. Yes. Here we have a suggestion. Yes, of course. I mean, maybe. But the question is, would one mean anything? Is one sort of a. What does a callback variable divergence of one mean? We say, why compare it to one and not to two or to pi or two? Yes, yeah. So I would say that when we are much bigger than one, they are very different. And if you are much smaller than one, I mean, that's a very. Well, yes, yeah.  So, you know, you would have to look at what values in KL divergences are very common and sort of where you are in the kind of distribution you want to look at. So the answer is basically, perhaps somebody can. I cannot. Further questions? Good. Then the topic for today is to go into variational or plus in detail. So there will be basically four parts to this. The first will be a little rehash of the basic concepts on the blackboard. Then we shall look at the free energy bound on the log model evidence again. And then we'll go into the thick of it by looking at the variational optimization of the free energy under the mean field approximation. I don't know whether we'll finish this today and otherwise we shall continue on Thursday when you will get a big dose of me, a four hour marathon from nine to one. So at least then we should get through all of this. We will do this on the blackboard. So I hope those who were longing for the blackboard will be happy with this. So first part, this is basically a rehash of the basic concepts. So what we're dealing with is always a generative model, p of y and theta given m. This is the model. And it is also the joint distribution y and theta. According to the product rule of probability theory, you can take this apart into the conditional probability of y given theta. And everything is always conditioned on a particular model, the marginal probability theta. Now these two things have names. What are the names of these things? What's the name of the first one? Likelier. First one is the likelihood. And the second one is the fetus. The distinction between states and parameters becomes important when we look at time series where we infer on hidden states of the environment. As I already briefly mentioned, states change with time. So from one time to the next, the states are going to be different, but parameters are constant. For the purpose of what we're doing today, this distinction does not matter. We just take this together in one big set theta. Those are our observations. Data measurements. So these are the basic quantities involved. Yes. I should write where should I write? Not below the line here. So I shall redraw this line. That's what the line was for. What I wrote here is observations, data measurements, just three words belonging to this arrow. These are the basic quantities we're dealing with. And then we have Bayes' theorem. This is the foundation of everything. Conditional distribution of theta on the observations y and the model n is simply the inverse of the likelihood times the prior divided by the model evidence. Posterior, this is new. We didn't have this on the left side. And this is the model evidence for marginal likelihood. It's also sometimes called the marginal likelihood. To be confused with the likelihood proper up here. And now let's look how we got this. What is this? This is again the product rule that gives you this. If you multiply this to the other side, then you simply see the product rule of probability theory. But if we apply the sum rule, we know how to get this. So the model evidence is the integral theta of the term up here. So this is for a specific theta. And if we just integrate over all thetas in this expression, we get the denominator here. So we take the likelihood times the prior, put a prime on theta because it's our integration variable. So this is the model evidence. And by the product rule here, you can see that this is a simple marginalization over theta. So these are the basic concepts that we're going to deal with. So it's already time. Y is part error. Free energy bound. The ingredients we're going to use is an arbitrary density. This is the Q of theta that you saw in the slides. We're going to write it like this. This is Q of theta. And then this is a semicolon. And then this is a lambda. And this means Q of theta parameterized by lambda. Just imagine the simple case again would be Gaussian, where Q of theta is, for instance, for example, taken to be Gaussian. And then lambda are the two sufficient statistics, mu and sigma, mean and variance that parameterize our Gaussian. So this is an arbitrary density parameterized. For example, in this case, yes, that's an assumption we're going to make that this... No, in general, no, in general, no. In the most general case, no. But we're going to use... we're going to want to use this because we want to make assumptions about our Q so that we can control it. So we are going to... As I said in very general here, we are going to wiggle around our Q of theta in order to reduce this KL divergence between the Q and the true posterior because that gets us closer to the true posterior and the variational energy closer to the true free energy. Variational free energy, I should say. So, for instance, Q Gaussian, lambda equals mu and sigma. So this is one of the ingredients that we're going to use and a model, plus a model as before. These are the ingredients we're going to use. No, m is basically the only model we're going to deal with here. So we're going to choose one m. That's going to be our model m. We choose a model m and a Q of theta parameterized by lambda. P of y theta... This is the model evidence. This here... I understand what you mean. Yes. So another way to write this... In some sense, I'm going to ask you to live with this degeneracy. But another way to write this would be P m of y and theta.  So what I mean is I have a particular likelihood and a particular prior. And those are probability distributions. And my way of saying these refer to a particular model m is simply to say conditional on me having the model m. Yes. Theta is the set of states and parameters we want to infer. So we have no control over them. The lambda is what parameterizes our approximate posterior. No. We will adjust them so that the goal is to... Let me write this down. Go, adjust lambda, prize by lambda is approximately... Sorry. The posterior here. So we make observations y. We have our model m. Theta are the parameters we're inferring. And lambda is what we're shifting around in order for this to be as like this as possible. Yes. We will go through examples. Many more blackboards will be filled with examples. Mu and sigma are lambda here. Lambda is just generic placeholder for the set of sufficient statistics that our particular queue has. Well, a one-dimensional Gaussian always has two sufficient statistics. They're separate. Theta is what we're trying to infer. This is what defines the world that we want to understand. Theta in some sense generates y. We observe y. We take y. And we infer back on theta. So it's this picture that we had in the slides. So we have basically the brain here. Let me try to... Yeah. This is a brain here. And now this is the word outside. So let me... Maybe this is North America and then South America. Like this. Yeah, maybe like this. And then with Europe and Africa it's more difficult. So this is the word. And we have a forward model that tells us how the world, this is theta, generates y. So this is our model. Probability of seeing y given a particular theta. And then we also have a prior on. This is the observation we make. But now what we're interested in is knowing what the world outside is like. And in order to do that we need to perform Bayesian inference in order to get the posterior given the observations that we have. This is the inverse problem. This is what we're trying to solve. And this is the forward model. The short answer is no. Because theta is the reality out there. So this distribution over theta is a probability distribution. And this distribution is different from the prior distribution. But theta is theta is theta. Theta is the external reality. So to go back to this example that we'll come back to again, if I'm out at sea and I'm measuring the angle between the lighthouse I'm seeing at north, theta is that angle. And there is one true angle I'm trying to infer that. And before I make my observation I have a certain belief about theta. And this is my prior. After I make my observation I have a new belief about theta. But theta is theta is theta. Slide number 21 in this example. Yes. So this is a particular example. So this is generic, what you see now. And then I take particular values for the quantities here. I fill them in and I get a Gaussian. But this is only, okay, I see you say, okay, theta is, this theta is another thing here. What is theta here is lambda there. I may have to adjust that. So, no, no, no, no. It's a different one. It's a different one. Yes. Yeah. So I should be more consistent in the notation. So here, here, though X is Y, X is Y. And the theta that we use to infer, so we have an added layer of complexity here. So theta here is the external reality that we're trying to infer. So there is a consistency but there's an added level of complexity because now in this whole scheme here, which is actually much simpler than the one we're looking at here, we don't have a queue. Yes, exactly. But now we're taking the, exactly, exactly. So the external reality that we're trying to infer is these parameters in this setting. So conceptually, this theta and the theta here are the same. But we've added a level of complexity now. So we have sort of interposed this queue between theta and ourselves. So we are mediating with theta on the basis of lambda. So we're using an approximate posterior. We are describing theta by lambda. We're learning a description of our universe and there we're sort of directly inferring on the parameters of the distributions generating our data. There's an added layer of complexity. The added layer of complexity is that we are not directly inferring the posterior. So what we're doing here is we're solving Bayes' theorem, doing this here directly. We're solving the inverse problem directly without ever going through an approximate posterior queue. What this will give us here is an approximate solution and what this gives us is an exact solution. So we are parametrically describing reality using our queue here. So here we're sort of directly going to the parameters generating our input and here we're using a description, a parametric description of that and we're going to adjust that so that it comes close to this. So we're not going to directly find theta. We are going to find the best lambda to describe our theta. That's the added layer of complexity that we have. And we do this because that allows us to solve the problem which would otherwise be impossible to solve. So we are doing all of this because in all interesting cases Bayes' theorem that you had here ten minutes ago cannot be solved analytically. There is in principle no solution to that equation. So you cannot derive an analytic solution to it that you can write down. So we have to find a way around this and the way around this is to, our way here, there are several ways but our way here to get around this is to use the free energy bound on the log model evidence as the title says. That's what we're doing now. I hope this will become clear in the course of the lectures as we also do examples. So we will get to, well, a whole class of examples where we apply this strategy to determine what is going on outside and what the states and parameters of our systems are. You can just ask so we can discuss it. Yes, okay. This is, yeah, the brain makes observations, has sensations. Yes. Theta is what I have. I can show you another slide that may be helpful here. Okay, okay, okay. This one here, I like this slide. Let me just update this to say, this is our agent, this is our brain or the robot we're building or whatever. This is the world outside. There are two interfaces between these two separate little worlds, the outside world and the inside world. One interface is the sensory input and the other interface is the action our agent takes on the world. So theta is basically out there. It's the state of the outside world and the lambdas are what describes that. So keep this in mind. I'll leave it up. We continue by introducing the mean field approximation. We'll do this over here. Mean field approximation originally from physics. Q of theta, and now for clarity, I'm going to stop writing this M. The M is always N, right? So let's forget about it. Q of theta is the product of all the elements in a partition of index sets Q of theta j. So we have our theta. So this is, all of our thetas are in here. Partition of j. So this is normal j and this is capital J. Little j, big j. Well, how can we, well, so there is improbability. There is the sort of bad habit which is at variance with all the rest of mathematics that when I write P of x and I write P of y, this is not the same function. So it just means probability distribution on y and probability distribution on x. This is unfortunate, but it's ubiquitous in probability. Yes? I know. So of course, hardcore mathematicians cannot tolerate this and they then write something like fx on x, on capital X, something like that. We're not going to do this because in much of the literature that we deal with in probability machine learning and so on, this is not done. So it's not just me who's doing this and you'll have to get used to it. P just means probability distribution and this distribution can be different from this and these Q's are going to be different from each other. So what counts in some sense is the variable inside. So the argument tells you what function this is. This is the distribution on x, this is the distribution on y. It's unfortunate but it's a fact that's historically grown. So we're partitioning up our thetas into different groups of thetas and so here we have little j equals one, little j equals two, little j equals three and so on. It's a set of basically indices. The whole set of states and parameters that we want to infer and big j is an index set and this is big j. That's what we're doing. So we're going to do some notation. Theta without i. So this is a backslash. You know from set theory you have a without b. So that's if you have a and b but I'm not going to say minus i. I'm going to say without i. This is defined as the set of theta j's without where j is not equal. It's all thetas in here except the ones that are in the i-th subset. You'll be happy about this. We're just going to write qi as a short term for q of theta i and q without i is going to be of all theta j with j unequal i. That's q without i. And in all of this we have i and j elements of big j. Free energy. This will be used as soon as we have looked at the free energy functional. Free energy functional. A functional is a function of a function. Not a function of a variable but a function of a function. So we are going to look at the free energy as a function of q and q itself is a function. That's why it's called a function. We take the log model evidence. The log model evidence. The marginal integral. The joint. Given m d theta. And then we're going to do some algebra. We're going to keep the log outside here. But then inside the integral we're going to do some very simple trick. We're going to multiply by q of theta parameterized by lambda. And then we're going to divide by the same thing again. So effectively we've done nothing. We've done nothing. We've multiplied by q of theta given l and we've divided by it again. So this equality holds. And now we take the log inside. We take the log in here. Why we get this inequality. This is called Jensen's inequality. It's itself easy to prove. You can look it up on Wikipedia. There's a proof on Wikipedia. Long divided by and integrated over theta. And then this thing continues. We take this part simply again using the rules logarithms. So we now have two integrals q of theta parameterized by lambda times the numerator here. That's the log joint minus q of theta parameterized by lambda times the log of the same quantity theta parameterized by lambda d theta. This is how the internal energy is defined because this is the expectation under q. That's what this notation means is the expectation under q. And this is again a functional and I'm going to write functionals with square brackets. Of the log plus the entropy, which is also a function of a function. Sorry, you see this is a function of a function. So I can just write q here. And this in turn defines our free energy function. So to be consistent with the slides and we call it a. This is a and this will be a function now of lambda of the observations and of whatever we put into our model. So our prior and our likelihood. So M here is just a placeholder for definition of prior and likelihood. This is what the free energy, the variational free energy is functional. This is the variational. This is the expected log joint. The physical analogy to it is the internal energy. We saw that in the slides and this is the entropy. To be exact, this is the expected long doing. This is the negative internal energy. And here in this notation, we need a minus sign here to get the variational free energy here. So because energies are always negative log probabilities, energies correspond to negative log probabilities. We want to have our optimal l, optimal lambda. And I will write down this goal that we have. Yes. Why is there no theta here? Because q is parameterized by lambda. So if you have your q, you have your distribution on the theta. But this is not a function of theta. It is a function of lambda. If you look at this purely as a function, so you don't... In this here, you have a distribution on theta, but it is parameterized by lambda. So what you put into the function q here is the lambda. Imagine a Gaussian again. So in order to define your Gaussian, you give it a mean and a variance. And the variable whose distribution is described by that Gaussian is not what you put in. So the theta is what this Gaussian describes, but it is a function of the mean and the variance you put in. So it only depends on this here. So what we want is basically lambda star, which is the arc min variational free energy. Let's... Sorry about this. I shouldn't have done it like this. Let's call this F. And then this is the negative variational free energy. And to be consistent with the slides, this is minus AB. So this is the quantity you have in all the slides. This is F and this is AB. So we want the lambda that minimizes variational free energy. So arg min of AB, which is a function of lambda y and n. Like this. This is what we want. We want the optimal lambda, the one that makes our Q as similar as possible to the true posterior. Yes. It's not just the mean. So it's the optimal mean and the optimal variance of the Gaussian that describes theta. So it is your posterior belief because some uncertainty about theta will remain. So you will still have a variance in your belief. So under Bayes theorem, if you could solve it exactly, you would have a posterior distribution with a certain mean and variance. The exact distribution may not be a Gaussian. So it may be any kind of distribution, but it will be a distribution. So if you approximate that distribution using a Gaussian, you will have a certain mean and a certain variance. And that will be the mean star and the variance star, for which lambda star is a short term. Let's just boldly move on. So what we do is we have a constraint on our Qs. And that is that all of the Qs have to be probability distribution. The Qi to be probability distributions. And that means in mathematical terms, the integral over each of the Qi's have to be one for all i's. So we want to minimize Av under this constraint. We have to observe this constraint. We're going to wiggle around with all of our Qs. We're going to change the lambdas parameterizing our Qs in order to minimize Av. What is the way to do that? From your calculus classes. If you minimize something under a certain constraint, what do you do? What do you employ? Exactly. So we're going to solve this using Lagrange multipliers. So we introduce Lagrange multipliers. So we're going to use f tilde, the functional f tilde, which is defined as, like the functional we have before, plus all of these Lagrange multipliers, lambda j, these are the multipliers here. And inside we have this constraint, that Q of feet to j d feet to j minus one b equal to zero. That's how you use Lagrange multiplier. You solve the constraint for zero. You multiply it and add it to the functional you want to minimize. And now we do the following. We take this apart, or we fill in the definitions, integral, the whole Q of theta. This is now our whole Q of theta. Not divided up into the theta's. And then the log probability, the joint minus the entropy. Also the whole Q of theta, log the whole Q of theta plus our Lagrange multiplier terms, lambda j, and in here, again, the Q of theta j d theta j minus one. I'll clean this up here. And now we're going to separate this out into different articles. So we're going to separate out one of the Q i's. So we're going to say this is the integral over Q theta i times the integral over Q theta without i. d theta without i d theta i. So this is exactly the same as here, but now we've taken this apart. So we have an inner integral. Overall, the theta j's where j is not i. And then an outer integral over Q theta i is still the same. And we do the exact same with this term here, too. So Q of theta i integral Q of theta without i and log Q. And then this is the log that applies to both of them. Q theta not i times Q theta i. And this is the end of the log. So all of this is in the argument of the log. d theta not i d theta i. Plus, and now here, it's easy to separate them. The lambda i times the integral simply over Q of theta i d theta i minus one plus a sum over all the j's that are not i. So I'm simply going to write not i here. This is what we're summing over. And this is the integral of Q theta not i d theta. And now we're going to write this as the functional f tilde argument. And the first of them is going to be Q i. And the second argument is going to be Q not i. And now we're going to take the function. Yes, I was waiting for somebody to spot this. You're right. We don't need the sum. We don't need the sum. So actually, yeah, let's be totally anal. And let's do it like this. I think then it's the clearest possible. Let's put in indices j here. And let's explicitly write j not equal i like this. Do you agree? This is an i. Sorry. Ah, so here we're summing over all of the lambda j's. It should be. Oh, OK. Sorry, I didn't see that. Yes, absolutely. Thank you. So we are going to take the functional derivative of this with respect to Q i. Who in here is not familiar with variational calculus? Everybody familiar with variational calculus? So you're fine with taking the functional derivative. So it's basically like taking the derivative with respect to a variable. If you have a function you take, and it is a function of x and y, you can take the derivative, the partial derivative, with respect to x, or you can take the partial derivative with respect to y. Now here we're dealing with functions, functions of functions. And in exactly the same way, we can now take the functional derivative here with respect to Q i. So this f tilde has a certain value. And we want to find out whether we can find a minimum with respect to Q i. And this minimum will have as a necessary condition that when we vary Q i at the point of this minimum, we will have a derivative of 0. So what we're going to do is we take the functional derivative with respect to Q i and set that to 0 and solve the whole thing for this optimal Q, which will be parameterized by lambda. And then we have found the optimal Q i with respect to our free energy and can use this in actual applications to models. So we shall, I will at first give you the definition of the functional derivative again, and then we shall actually start doing the functional derivative. We'll probably finish with it tomorrow, but we shall at least start now the functional derivative of f tilde with respect Q i. So this is the derivative with respect to a variable epsilon. Evaluated where epsilon is 0 of f tilde, which is a functional of Q i, plus epsilon times phi i, where phi i is a test function. And the second argument, which we're not worrying about at this stage, is Q without i. This is the definition of the functional derivative. We're just using this definition here. Basically, as badly behaved as allowable, so it has to vanish at the ends, basically. So it is in a thing called what space, which means it cannot grow to infinity at the ends, basically. It certainly has to be continuous, and it has to be differentiable. I don't know how many times, but I can, yeah. So it has to be reasonably well behaved, but we don't want to be too restrictive either. You can find pathological fies where this doesn't work anymore.  So we're going to take the derivative. Respect to epsilon evaluated at epsilon equals zero. And we're just going to put in... I'm going to give you the first step, and then basically I will leave out a few steps and we'll let you fill in this rest during a tutorial. So basically, when are your tutorials due or set, or how are they organized? When can I make you or let you fill in the gaps in the whole derivation? Is there time allotted for that? Or do we do it all here? All here, you're saying all here. You don't want homework? Okay, I will not do every little step, but I'm happy to take questions about the missing steps that may still be there. So times... the second integral here, because much of the whole thing is copying from a bob. Log joint by theta given m and d theta not i d. And this is then minus the derivative with respect to epsilon. And okay, this is a little algebraic trick. You don't have to give you this, otherwise you may get stuck. We subtract this here, plus epsilon phi theta i q of theta i plus epsilon phi theta q of not i and then close the parenthesis and add d theta not y d theta i. And then we continue. No, we need two more terms here.  Again, derivative with respect to epsilon. Evaluate the epsilon zero. And then we do the sum again. This is the j unequal i sum times lambda j theta d theta j minus 1. So this is the energy term. And then entropy term. And then the same for the... So the four terms are energy and entropy for theta i and theta not i. That gives you four terms. Take out the phi i's. That's the next step we do. We're going to come out before the integral. So I'm going to write the first line. I hope everybody's finished with copying here. Everybody finished with copying? No, no, no. Okay, okay, sorry, sorry. So I'm going to slow down a bit. Okay, we're just going to do the next step and there is going to be a radical simplification already now because we're going to take all of these derivatives there. So after we take the derivative in the first term, all that we're left with is phi of theta i, sorry, the integral over phi of theta i, I shouldn't forget the integral, q of theta not i and not i d theta i. So that's all that's remained of our first term. Second term, integral over phi theta i times integral q of theta not i log these two q's multiply together. q of theta i times q of theta not i d theta not i d theta i. That's our second term. Third term, that's going to look a bit more complicated. q of theta i, q of theta not i, and now a big fraction of phi theta without i and q of theta not i. So you see we're doing our old trick again. We're multiplying, yeah, we're just multiplying by one theta not i plus our last term which is very nicely simple. Okay, and from here it's uphill in the third term. Is it a, let me first check whether I did everything correctly. Yes. Now your question is about which exactly? The third, the whole third term. So let me check. No. It was somewhat misleading to relate the previous four terms directly to these four terms. Because if you look at the previous four terms we had, then the last of them does not contain epsilon. So if you take the derivative to that, it falls away. And this one. Exactly, yes. All right. Yes, you were absolutely right. Thank you for spotting this. Yes. Shouldn't we have phi in both terms. So here and here. On the right, what do you mean? No, no. Phi times q, q times phi. Yes? Yes. This can slide, yes. So we're going to need this in the next step. Yes. Because we want to end up with interoperable terms like entropy and a partition function so that everything will be very, very simple. So I will give you, maybe in the last few minutes, a preview, a sneak peek at the end result. So just to see you, just to show you that the effort will be worth it. We will end up with a q of theta i. This will be our optimal q of theta i. That is 1 over z i. This is just a normalization constant. It's the exponential of what we're going to call the variational energy, capital i theta i. This term, i of theta i, will turn out to be simply the expectation, I'm going to use angle brackets for that, of the log joint under not i. So all we need to do to find our optimal q is this. We take the log joint. This is simply our model. This is our likelihood and our prior. And we take the expectation of this, so basically we integrate over all the thetas that are not all the theta j's where j is not i, so that these remain. We take the expectation with respect to that and this gives us a function which is only a function of theta i because all the other thetas have been integrated out. Only a function of theta i. We exponentiate that function and this is our q finished. So exceedingly simple. All that we still need to do is normalize it, but that's easy. All you need to do to find your optimal q here in this mean field approximation is take your log joint, take the expectation of your log joint with respect to all of the q's that are not i, exponentiate this and you've got your q. Then you multiply all of your q's together and you've got the whole q. The whole approximate posterior which is going to be optimal under your assumptions about the parametric form of q. We're going to finish these algebraic steps on Thursday and then we will look at the Laplace approximation. So in the remaining three minutes or so any questions right now? I suggest you digest this at home. You find any more sign errors I could have made. I hope there aren't anymore and then we'll complete this on Thursday. If there aren't any immediate questions. Thanks for bearing with me. See you. Yes, I have this. You can have this written down at some point. A direct interpretation. We call this the variational energy because this is the log joint. So this is the physical analogon to this is the internal energy. So it's the internal energy.


What's going on guys? Got Kyle and Donnie here with Layton Sports Cards and we're ripping 22-23 select hybrid five box break number 12. We did a randomize for the break. Guys had Eric B with the Mavs down to Andrew L with the T-Wolves. Good luck here guys. You can always find those results on our website LaytonSportsCards.com. Yeah guys, still 17 to go on Crown breaking next guys, breaking next. Let's fill it up. It's a chat home green rookie. Start us off. Got Caleb Houston. This one is Kevin Porter Jr. on the Red Disco number to 49. Kevin Porter. Wiggins. Jeremy Sohan rookie. This one is O'Shea Bajie on the Light Blue Disco for the Jazz there number to 99. Quartzside, Quartzside not field level. Silver rookie O'Shea Bajie. Double Abajie pack there. Kai Branham rookie. This is Jabari Smith rookie there for the Rockets. Played Thompson and this one is White Disco. Anthony Edwards there on the Quartzside to 75. Nice Ant-Man T-Wolves and Yoakich. Blue rookie Andrew Nimhard for the Pacers number to 25. Nice low number there. Pacers on the Nimhard. KD Silver rookie Keegan Murray. Disco Chris Middleton and Trey Young. There's a White Brad Beale. White Disco is the 75 and Desmond Bain on the Silver. It's Ben Mathern rookie. Disco Jalen Green. Fred Van Vleet. Come on now. Yoakich. It's this way. It is Gold Zach Levine going to the Bulls. Nice one there. Gold Disco to 10. Nice one there. Zach Levine and a David Roddy Silver rookie. Hey, nice Salvatore. That's sick. That's my boy, man. I'm a magic fan. I like some Paolo. Haven't gotten too many of his cards though, man. He's been expensive since he came out. It's been tough. Haven't really been able to get a lot of his cards yet. Don't know that that's going to change with how he plays. Jalen Williams there, J-Dub. There's a Blue Caleb Houston for the Magic on the Blue Disco to 99. James Harden. Rookie of Bajie. We've got White Dame to 75. Dame Lillard and Silver. Nice Paolo. Josh Giddy on the court side and Jalen Brown on the red to 49. Damian Lillard. Hey, it'll work though. We were asking for less. How did that happen? How did that work out, Salvatore? Got Shaden Sharp rookie. There's a Blue Disco RJ Barrett to 99 and a Zach Levine Silver. Righty guys, two more. Hey, he will Salvatore. He is a beast, man. He is a beast. There's a J-Dub rookie. There's Blue Ant-Man. Blue Disco is to 25 and Pat Baldwin on the rookie disco. Nice Ant-Man there. A couple nice Ant-Man so far. Green. Come on now, green to five. It is Spicy Pea. Pascal Siakam on the court side, green to five. Nice one Raptors. Pascal Siakam, three of five. It's a nice one there. That dude can light it up, man. He was out in like 20 plus last season. There's a Ben Mathern rookie. And Light Blue is Tyrese Halberton to 99. Wiggins, sold on Red-A. Really? I haven't heard of selling cards on Red-A much. Jaden Hardy there on the Blue Disco to 99. Court side, nice one there. Maps. Darius Garland in the last one. Box five. I think Herman taking a spot. Nelson taking a spot as well. And some in tee for later. Down to 16 to go, guys. On Crown, team all breaks next. So, Disco, Marcus Smart. Ty Ty, gold again. Come on. Another gold. This time it is Chris Paul. Nice one there, Suns. CP three on the gold to 10. That one is three out of 10. Nice Jersey number, CP three. Gold to 10. Nice one there, Spurs. Her Suns. And the Obagi. Jazz. CP three, Jersey number. Keegan Murray, rookie base. There's Red. Jimmy Butler for the heat there. Court side to 49. Nice Jimmy as well. Tari Eason. Last pack. Williams. This one is Tyrese Halberton, White to 75. And a Silver, Jabari Smith on the back. Nice one there. Alrighty, guys. Won't do a hit recap on this one. It was pretty short. Appreciate you guys. We'll get these cards right out to you. That was Select Hybrid Five Box number 12.


In this video, let's learn how to write the formula for some ionic compounds. Now, for the first one, we have to think about the formula of zinc chloride. And the main idea of writing the formula of an ionic compound is that we need to keep in mind that the total charge on the compound of the final ionic compound, it has to be zero. The total charge is always zero. And what is total charge? Well, total charge is the charge that comes from cations plus the charge from anions. In this ionic compound, we have zinc chloride. So zinc would be the cation because it's the metal and chloride would be the anionine because it's the non-metal. Now, if we think about the charge that a zinc ion has, we can have a look at the Roman numerals which is written inside the brackets. So this is two. So the zinc ion really looks, it's Zn plus two. And the chloride ion, we can have a look at the periodic table. So if we bring, if we have a look at the periodic table, we can see that chloride lies right over here. This is group 17. And that means that it has to gain one more electron to form a complete octet. The chloride ion would be Cl. It would be Cl minus. This would be Cl minus. Now, the total charge has to be zero. So if we have a look at the two ions in the picture, if we take one Zn ion, so this is one into plus two. And if we take two chloride ions, two into minus one, the charge on the chloride ion, this becomes equal to zero. So this means that one ion of Zn plus two ions of chloride will be required to make the total charge of this ionic compound to be zero. So the formula becomes ZnCl2. Okay, let's have a look at some more. Now we have sodium sulfate. So again, the total charge on the ionic compound has to be zero. Total charge is charged from cations plus charge from anions. So here the cation would be sodium. What is the charge that a sodium ion has? Well, we can have a look at the periodic table again. So sodium really lies over here. This is group one, which means the sodium ion is Na plus. And sulfate ion is SO4 minus two. It's a polyatomic ion with a charge of negative two. And we can see that sodium has one plus one, sulfate has minus two. So if we take two sodium and one sulfate, the charge should come out to be zero. So the charge from cation, if we take two cations with plus one charge and if we add it to one and nine with a minus two charge, this will come out to be zero. So that means the formula would be Na2SO4 because we are taking two sodium ions. Okay, let's have a look at one more. Here we have ammonium nitrate. Now both of these are polyatomic ions. And the cation for ammonium, it is NH4 plus. It has plus one charge. And nitrate is Na3 minus. It has a negative one charge. Now the total charge on the ionic compound has to be zero. And in this case, the charge on cation is plus one. Charge on the anion is minus one. So we can take one of each. So this would be just one. If we take one of plus one and if we add it to just taking one of minus one, this will come out to be equal to zero. So the formula becomes NH4NO3.


Αυτή η Πόλη είναι η μόνη Πόλη στην οποία όλοι μπορούν να πούν το όνομα μου. Η μοναδική Πόλη. Σε όλες τις άλλες Πόλεις, αν το πανός, αν το πωλής, αν το πούλους, τα να κατεύουν. Εδώ πέρα όχι μόνο μπορούν να πούν όλοι το όνομα μου, αλλά μπορεί να είχε κανάς άλλο σαν τον όπουλος, δηλαδή δεν είναι και σπάνη ο όνομα. Σατός είναι ακαμόπουλος. Σατός είναι ακαμόπουλος. Ευχαριστώ πάρα πολύ που ήρθατε. Την πρώτη φορά που έκανα παρουσία στη Ελλάδα, το 2013, στο Hacker Space, ήταν 8 άνθρωποι. Την δεύτερη φορά που έκανα παρουσία στη Ελλάδα, το 2013, στο Hacker Space, ήταν 8 άνθρωποι. Την δεύτερη φορά που έκανα παρουσία στη Ελλάδα, στο συνέδιο της Disrupt, ήταν περίπου 800, από τις οποίους για Bitcoin δεν ήξερε κανένας. Την τρίτη φορά που έκανα παρουσία στη Ελλάδα, στην Κυφυσιά, ήταν περίπου 40 άνθρωποι. Τώρα, 350. Καλά πάμε. Τα άνθρωποι often have different motivations, they ask me different questions about this technology. Τώρα, στην Ελλάδα, a lot of the interest is about how to find ways to invest. Okay, so how many people are interested in investment? Great. And how many people are interested in technology? You're lying. So, and people ask me often, what should I invest in? And I don't do investment advice, I'm a technologist, but tonight what we're going to do is we're going to talk about that. So, tonight's talk is about the next Bitcoin, which is a question I get all the time, which is what is going to be the next Bitcoin, right? Sometimes I get asked what is going to be the next Ethereum. So, that's what I want to talk about tonight. And in order to help you, I'm going to start by telling you the answer at the beginning as a spoiler. So, the next Bitcoin, which you might consider investing in, is going to be Bitcoin. But here's why, and this is important to understand. A lot of people are looking for an opportunity to invest in something that did what Bitcoin did since 2009. They're looking for an opportunity to find the next thing that's going to be super successful, that's going to maybe do 10X or 100X in its price. Not going to happen. And the reason it's not going to happen is because Bitcoin was created and born in very, very unique circumstances. So, what are those circumstances? Some people call this Bitcoin's virgin birth, the immaculate conception, right? Which I'm sure is blasphemy, especially in Greece, but that's okay. So, what do we mean by the virgin birth of Bitcoin? You got to realize that back in 2009, when Bitcoin was launched, and if you went to anyone and you said, hey, how about instead of governments doing money, the internet does money, and everybody was like, yeah, that's never going to work. That can't work, no. And then if you explain nobody, it's a really interesting algorithm. First of all, most people, when they hear the word algorithm, they automatically switch off. Unless they're computer scientists, you say algorithm, they're like, there must be someone else at this party I could talk to. Because this person just said algorithm, so we're not there. If you say it, it's very interesting. It's a decentralized system that allows you to create a monetary supply of deflationary assets that's distributed in a fair method through approval for a consensus. And then at that point, they're really like, there must be someone else at this party I can talk to.  Because if you believe it works, what are you going to do? If you actually believe this thing is going to do 10x or 100x, it's going to increase in value 100 times, what are you going to do?  You go to better parties. And if you know that, then everybody does it. And if everybody does it, then it doesn't happen.  No, not really. Remember me? Is this a good time to buy Bitcoin? I heard that the price... Yeah? Is this a good time? No. If you're calling me for the first time in 28 years to ask me, this is a clear indication to sell, probably. Like at this point, I'm beginning to think I should sell because if it's beginning to attract your kind of people who had no interest, they'd be busy 2 years ago. Now is the time to get out. This is what happens with markets. If too many people think that it's a sure bet you're dealing with suckers.  So, if you're like, no, this is a sure thing, I've told all my friends about it. θα το δούμε. Ευρών śmετινά με τον ναγό και η διακόληση εσύ, αυτό είναι το time να γινήσει. Κομμάτω για να αφού τον Πόλη και και να δουλεύω και να το διαπραγματικώ. Δημιουργείς, σχέση και οδηγίας, σχέση και γνωπερού. Γιατί γνωλεύω δεν πιρ Kliss, 40% της αρχιής σε ένα ανασχέσης. Δεν υπέφερης, δεν υπέφερης από τον κλείο που σκοτήθε. Η γνωρίζωση δεν παίρνει από το κυβέρνητο ή τίποτα συμφωνία. Η γνωρίζωση δεν είναι έκανη από τον Πρόεδρο Τρομπ σήμερα. Η πίκονα έκανη από τον Πρόεδρο Τρομπ σήμερα. Αυτό είπε ότι δεν είχε καλύτερη στιγμή και πιστεύει από πρόεδρος. Είμαι πίσω πίσω πίσω από τους φίλους. Η απόσταση είναι ότι η γνωρίζωση και η εξωγεία είναι ένας εξωγένωσης που μπορεί να φτάσει. Και αν πίσω πίσω πίσω πιστεύει, η γνωρίζωση και η εξωγεία που βλέπεις από πίκονα δεν πάρει. Επίσης που μου πω πίσω πιστεύουν ότι ναι, πιστεύω να σε πιστεύω. Yeah, yeah, but what should I buy? And that's the problem, right? Because if you have an indication of something you should buy, you shouldn't really go buying that, okay? And if you need to ask someone else what you should buy, that means you don't understand the technology well enough to make that decision on your own. Are you confident enough to say, I think this technology has some unique differentiation, good market fit, the timing is right, the momentum of the market, the liquidity is there? Like, if you understand all of that, then okay, you're qualified to make that decision on your own, and if you're not qualified to make that decision on your own, you really shouldn't be investing in this thing, right? So, is it an investment or not? And what is the next Bitcoin? And that's the fundamental problem, because there is no next Bitcoin. Bitcoin was able to achieve what it achieved because nobody for three or four years believed that this thing could work. That's why it did what it did. When you see someone investing in a technology that every single person around them think is insane, and then they make a hundred times a return on their investment, the reason they made a hundred times a return on investment is because everyone around them thought they were insane. They were taking an enormous risk, right? I'll tell you about the first time I bought Bitcoin, okay? So, I had known about Bitcoin for almost a year, and I was reading all of these scientific papers and I was trying to understand how it worked. And it was very interesting to me as a computer scientist. I was very interested in the technology, but I also wanted to try to use it. So, in order to try to use it, I needed to buy a tiny amount of Bitcoin. So, my comfort at that point was I needed to buy like $100 worth of Bitcoin. So, in 2012, I decided I'm going to buy $100 worth of Bitcoin. Now, in order to do that, I had to go to a grocery store to use a service called Moneygram, where you stand in front of a red telephone in America and you call someone so they can charge your credit card to transfer money. And this is used mostly by poor people who don't have a bank account in order to pay their bills. So, you put cash in the machine, or you give cash to the person at the grocery store, and once they verify that, they send that cash so you can pay your electricity bill or your telephone bill. It's used by people who don't have bank accounts. I used that service to send $100 worth of Bitcoin to an exchange in Japan, which was the only exchange that existed for Bitcoin at the time, called MT Gox. And if you know anything about MT Gox, you know all of the horrible things that happened after. But they didn't happen to me at the time, because I actually managed to buy some Bitcoin that way. Every single person in my life said that I was crazy. Not only that I was crazy, but that I had lost all of my money, which I was okay with. Alright, it's $100. Now, people today want the same answer. They want to find out what can I invest in today, where I can put in $100, and it's the next Bitcoin, and it does one of these things. And the simple answer is that that doesn't exist. And the reason it doesn't exist, because the whole world is looking for that. And when they're looking for that, what happens is, people with a lot more experience in markets and a lot more money than you are now interested. So if they see something, they think could work. What do I mean by work? I don't mean they think this is the technology that could replace Bitcoin. What I mean is they think that you will think that this is the technology that could replace Bitcoin. They're like, oh, lots of people who know nothing about investments will think this is the one. It's got slick marketing, it's got some people on the web page, it's got some fancy words like quantum, AI, drones, autonomous. And people who know nothing about this market, the people who are looking for the next Bitcoin, are going to believe this is the next Bitcoin. So what they do is they get into the market with a lot of money when the price is really early on, then they wait for all of you to go, I'll just put in a hundred euro. What's wrong? Worst case, I lose my hundred euro. Yeah, that's exactly what's going to happen. And that system of investment is called pump and dump. So what they do is they put the money into this investment, then they go out on social media and they buy people to go and say, this is the next thing, this is the next thing. You go in and say, I'll just put in a hundred euros just in case.  You're better off playing the lotto, right? You're better off playing propo. Do they still have that here? It's like the football sweepstakes. And so as soon as lots of people who are not informed about these investments, go and buy it with their little hundred euro investments, they then do the second part of the plan, which is called dump, right? So they did the pump, now they do the dump.  And all of the people who put in a hundred euros just in case go, ah, that wasn't the one. But this next one that's IoT, AI, drones and quantum is going to be the next bitcoin. So then they repeat and so on and so forth. The bottom line is that bitcoin happened at a moment in history when nobody was expecting this thing to work. And none of the people that were really involved in the early days believed that this was a good investment. They believed it was an interesting technology. And in order to try and use that technology like I did, you had to get some of it to try out. So it kind of accidentally became an investment. And by the way, if you think that story starts with, and then I went to the grocery store and I picked up the red phone and I bought bitcoin at $2 and then I became rich, right? That's not how it works. That's exactly what happened. I bought $100 worth of bitcoin. Each bitcoin was $2, so I bought like $50. Next week I bought another $50, right? And then you're like, oh, yeah, so wow. This is how it actually works. Three months later it goes from $2 to $8. And I go, yeah, score.  I can buy an Xbox. So I sold it all and bought an Xbox. And this keeps happening again. This has happened to everybody who's gotten involved because it's very easy to think, oh, I would understand immediately that this investment would go to like thousands of dollars. But if you told me in 2012, oh, it's $2 now, but in 2019 it's going to be $13,000. Yeah, right. That's never going to happen. And if I told you today, let's do the same experiment.  How many of you think yes? OK. That's unrealistic. That's totally unrealistic. It might happen, right? But it might not. And do you take that kind of risk? Do you take the risk in January to say, oh, it's $3,000. It's been crashing for two years straight. Now is a good time to buy. Everybody thinks they do that. The strategy seems simple. On its face, buy low, sell high. In practice what you end up doing is a somewhat similar strategy called buy high, sell low. Where what you do is when everybody gets excited, you buy some. Then it crashes, then you panic, then you sell it to the lowest price and then you keep repeating that. How do I know? Done it. Right? Who hasn't? OK, really. Who hasn't? Oh, OK. Five people. Very good. So the bottom line is that we're all looking for this next opportunity and the reason it's not going to happen is because of the unique circumstances. I like to think of this a bit by comparing it to biology, right? And if you think about biology and you understand how evolution works and how an environmental niche or area works, if there's a very successful species, right? And that species finds an environment in which it is very successful. It has the right amount of food. It has no predators, et cetera, et cetera. It will dominate that environment, like dominate it.  Rabbits everywhere, right?  Because if you do that in an environment where there's no anything fitting in that particular niche, right? And it has no predators and it has plenty of food, what is it going to do?  That's what it's going to do. And then you have a lot of rabbits. I mean, you know, they pop out eight in every generation which is approximately every six days. So before you know it, talk about exponential growth. Like Bitcoin has nothing compared to rabbits. The bottom line is that if you have an environment in which a species becomes successful, it dominates that environment. But interestingly enough, if you then introduce another species that is similar but slightly more efficient or more successful, it fails completely. Why? Because that environment has already been dominated and simply a sheer numbers mean that the new species, even if it's more efficient, cannot propagate in that environment because there's five of it and there's six million rabbits. So even if it manages to be very successful, it doesn't meet very many of its own kind but it keeps meeting rabbits, right? And every time there's an opportunity for food, a whole bunch of rabbits show up. This is the idea of biological evolution. If you have an environment in which there is one organism that is very successful, it dominates that environment and it then closes that environment to everything else that can fit in that environment. So what happened with Bitcoin? Bitcoin found a very unique application and that was non-government money that can spread globally that is completely uncensurable, decentralized, unstoppable and borderless in a pure digital form. Okay, that application in that environment is now taken. So when someone comes and says, we can do a slightly better Bitcoin. Great, you can do a slightly better Bitcoin. Nobody will care because all of the software developers have not been trained in your slightly better Bitcoin, right? So if Bitcoin is B, let's call the other one, let's start it with a C, so it's the next iteration. Sitcoin. So let's say you have Sitcoin and it comes along as like, we're the next Bitcoin and maybe that's true. Maybe it's a bit more scalable or has better security characteristics or is easier to use and it's got enough differentiation that if they both started out, it would win. It would absolutely win. If they both started out at the same time, it would win. But now it starts out in an environment where all of the developers already know how to do Bitcoin, where the book says mastering Bitcoin, where the software libraries are for Bitcoin, where the exchanges have Bitcoin and it has a lot of liquidity, where there's 26 different wallets for it, where you can easily find people at the local Starbucks or some better coffee shop to trade and get some Bitcoin. But if you start calling, well, do you have some Sitcoin? And you're like, well, no, but we do have some Bitcoin, but like it's better. Yeah, but I don't have any and everybody I know is already interested in this other thing. So how much does the new thing have to differentiate? Well, this is the real challenge because if it differentiates enough, if it's different enough from Bitcoin, well, then it's no longer occupying the same environmental niche. It's no longer competing against Bitcoin. Now it's competing for something else and maybe it becomes dominant in that space. And in order to do that, you have to do a lot of hard work because if you want to start a new cryptocurrency with its own security, you have to bootstrap that security in an environment where people are going to attack you on day one. On day one, nobody attacked Bitcoin. On day two, nobody attacked Bitcoin. On day 1074, nobody attacked Bitcoin. Nobody had really heard of it. Nobody thought it would work. On day 2500, nobody attacked Bitcoin, right? It took years before anybody thought this thing would actually work to start attacking it and by that time, it was strong enough to resist the attack. Now you go out and you're like, I have an idea and it's better than Bitcoin. Everybody goes, run! They attack your system and the next morning it's gone, right? Because you get hacked, you get crashed. You go out and do proof of work. Oh, I've got a new proof of work algorithm and everybody comes in with their GPUs. Why do they have GPUs? Because they've spent the last five years mining Ethereum or whatever, right? So they have plenty of GPUs. They're like, oh, could I take a few thousand GPUs and throw it to this new coin and see if I can dominate the network? They sure can, right? So then they dominate the network and there goes your security. They're like, oh, okay, we can't do proof of work. Let's do proof of stake. Proof of stake is going to be much easier because then what it really needs in order for someone to attack it is rich people.  There's lots of them and they're all trying to break crypto now. So you start proof of stake and who buys all of the stake? Rich people. And the only reason rich people didn't buy all of the Bitcoin in the beginning is because they thought it wasn't gonna work. But if you try to do it today, they're all looking for the next Bitcoin that they can sell to you. To the people who don't know any better. I remember when Ethereum launched and I remember receiving the email, the Vitalik sent out, to ask for comments. And this was in December of 2013. And he sent it out to a bunch of people in the space and he said, listen, I've got this new idea. We're looking for developers. We're looking for investors. And I'd like to invite you all to read the white paper. So I went and I read the white paper. And I had one question. And this question was, why are you not doing this on Bitcoin? Why would you go to all of the effort of launching a separate chain? So I called Vitalik on Skype and I talked to Vitalik in his kitchen and he patiently explained to me that he had already tried to do it on Bitcoin. He had tried to do it with what's called the master coin protocol on top of Bitcoin. And he had very quickly realized that Bitcoin was too specific to work for the purpose that Ethereum was. And that you couldn't really do the same security model. And he said, I think this is general enough, different enough and important enough that it's worth the effort to bootstrap a new security chain. It's really, really hard to do. And Ethereum was different enough that it did it. Maybe, we'll see, right? Maybe it will work. It's not yet certain. It seems to be doing okay so far sometimes, sometimes not so well, right? But they managed to bootstrap a new security chain because it was different enough from Bitcoin. But then the interesting thing that happened is now, if you talk to people who are really into Ethereum we can do everything, including what Bitcoin does. No, you can't. No, you can't because all of the things you did to differentiate enough from Bitcoin mean that you're playing in a completely different environment. You're playing for the generalized flexible smart contract space and every choice you make for that design means you can't play in the highly specialized, highly secure, robust, we can resist attacks from governments space for the open free money of the world application that Bitcoin already fully occupies. Ethereum can't be the next Bitcoin. But the funny thing that happens next is while Ethereum people are like, we could be the next Bitcoin. Some other people are going, yeah, but we could be the next Ethereum. And before you know it, everyone in Ethereum is looking over their shoulder and going, who are you? Where did you come from? Why are you coming from my application? It's okay because they can't be the next Ethereum either. For exactly the same reason, the application space for a virtual machine-based, general smart contracts application platform is solidly occupied by Ethereum. Not because it's the best, but because it got there first, survived long enough and did okay-ish for long enough to dominate the space. And now, all of the libraries are for Ethereum, all of the application developers are for Ethereum. And as a result, if you're trying to make your own thing to compete against that, you can't find enough attention. So, there isn't an next Ethereum because that space is already taken. What does it take for one of these technologies to be replaced? What does it take for something to replace Bitcoin or to replace Ethereum? It doesn't happen because someone invented a slightly better solution or even a much better solution. That will work. Instead, it can only happen if the system that already occupies this space fully fails on its own. So, in order for something to be the next Bitcoin first, Bitcoin has to fail. Like really spectacularly fail. Not a bit, not like, oh, we lost $200 million because someone hacked an exchange again. Obviously, that's not enough. Not we've been banned by China for the 37th time. And this time, they really mean it. That's not enough, right? Not Trump just called us drug dealers. Not enough. Like, we've been called drug dealers before by more serious people. It's not enough for even Facebook or JP Morgan to say, hey, we can do cryptocurrency too. No, you can't. Not enough. The only way Bitcoin fails is from the inside. It has to fail because of a critical vulnerability that cannot be fixed or that people don't want to fix. It has to fail because of a failure of community. It has to fail because of failure of trust or culture. And that's really difficult to do in a technology that has so many participants who have so many different motivations, different cultures, different languages, different operating systems, different software platforms, all collaborating around one core basic set of ideas. They have to all be disappointed for different reasons simultaneously. Very difficult to happen. And for the same reason that if you want to find the next Ethereum, first, Ethereum has to fail at being the current Ethereum in order for something else to pop up. Now, this is both good news and bad news. If you're trying to push the next chip coin, it's bad news because I'm sorry to tell you you don't have the next Bitcoin or the next Ethereum. In fact, many of the ideas out there are either too early to have any success or not differentiated enough from the current marketplace. You can have the best idea and the best team in the world. And if it's not your time yet, you fail completely. Some people say, but look at Facebook or MySpace or Google, Google was the 21st search engine. They waited until the first 20 failed. Right? How many here used Alta Vista? Yeah? How many here still use Alta Vista? Now remember, Microsoft came along and said, we're the next Google. We even have a fun name. It's like, bing! How many of you use bing unless you're on a new computer where you can't install an alternative? Right? It's very difficult to unseat a technology that has already fully occupied a space. Now, the good news is, at the same time, that if one of these technologies fails and vacates that environment, we now know that it's a rich environment that can support a very, very successful system. Which means there will be a whole line of candidates waiting to be the next X. Which should be really bad news to all of the governments and regulators and large corporations that are hoping so hard after 10 years of evidence that it's just not happening that this year, Bitcoin will surely fail. This year, Bitcoin will shut down. They've been hoping. They've been telling themselves, don't worry, there's no way governments would allow this. There's no way this will be successful.  Well, here's the bad news for them. The moment it fails, every single competitor that has a slightly better solution and specifically the ones that fix the thing that caused it to fail in the first place will flood that environment and bootstrap something new. And if they're really smart, if they're really smart, they're going to call it Bitcoin. Because that's the best branding you can do. And so it never dies because who occupies the space and has the name keeps changing but you still occupy the same space and maybe even you still occupy the same name. And that's why the next Bitcoin is Bitcoin. Thank you.


LDL numbers, if they're high, is that really bad? Watch this. Our next caller is Sean from Minnesota. Sean, what's happening? How can we help you? Hey, how's it going, guys? Thank you, Sal. Thanks for having me on. Yeah, so I have a question about LDL cholesterol.  I work out a lot to the point where I almost overtrain. I'm about six foot two, 200 pounds. I've worked out basically every day since high school, so about 15 years now. And because of that, I eat a lot of calories.  I've also determined that eggs are probably the main source of protein that works best for my digestive system. And in about May of 2021, I started tracking my macros to try to put on some more muscle. And it's worked. I've put on probably about five pounds of muscle. I have not increased my waist circumference. The problem is my LDL cholesterol. So I had labs drawn in July of 2021. And my LDL cholesterol was very good. It was 78 milligrams per deciliter, which is well under the 100, but they recommend you stay under. Then after that, I increased where I was eating a lot of eggs, probably about eight eggs per day with yolks on average and a little bit more red meat throughout the rest of 2021. I had labs drawn again in December. And my LDL was 146, but it shot up. It was pretty high. And then of course, after that, I said, OK, you know, I need to go more either plant-based, whatever. And I guess I heard something. I believe Sal, you said in the episode recently about the eight worst people to take diet advice from. You mentioned something, I think, I'm paraphrasing here about how LDL cholesterol, high LDL cholesterol alone is not necessarily, you know, that much of a big deal if you don't have other risk factors. And so I'm wondering if you can elaborate on that a little bit and kind of what your take is on my situation. Yeah, sure, Sean. So first off, I want to say, obviously, I'm not a doctor. And this is not my field of expertise. So my expertise is in fitness. So what I'm going to comment on now is coming from somebody who's a fitness expert, not a doctor. OK, so I'm not an expert in this. But there's a context that is typically looked at when you're assessing risk. LDL is part of that picture, but you have LDL HDL ratio. You have other lipids that they're looking at in your blood, your blood sugar, body weight. And then LDL can also be broken up into different types of LDL. Some are more damaging. Others now they're talking about being more protective. The total LDL number can also matter. So at some point, if it just gets too high, then that's something that is caused also for concern.  Like I'll use a couple of personal examples. I eat up to 12 egg yolks a day. Most of the meat that I eat is saturated fat. My LDL is always under 100. My total cholesterol is borderline too low. Now, Doug is somebody that, obviously, Doug, our producer, he reacts very differently to saturated fat. And he has to actually control it a little bit because it can have some, what would look like adverse effects on his blood lipids. So it depends on the person as well. Now, my comment on that show was simply this, was when there's one blood marker, unless it's really bad, it doesn't tell you nearly as much as what the whole picture is, right? You got to look at everything to kind of make more of an accurate assessment. And I think really good doctors who are experts in this field will tell you that. So that's what I would look at and I would talk to your doctor about those things. Do you think that Dr. Ran and Dr. Todd would cover a subject like this inside our hormone? Now, they're hormone specialists. Now, they are somewhat knowledgeable about cholesterol because it's something that can get affected with hormones. So you could try asking them. But I would speak specifically to somebody who's an expert in this field because there's a lot we're learning about this. Like what I said earlier about different types of LDL, this was barely discussed just like seven years ago. It was just LDL and then we've discovered, wow, there's smaller denser particle LDL molecules and then there's those that are larger and fluffier and one is more damaging. The other one tends to be more protective. LDL in general is associated with protecting you from infection and illness. So it's also essential. So like having it too low isn't good from what I've read. But there's a lot of complexity to this and I would look at all of these things. So, and I would talk to somebody who's an expert. Well, so that's what I was gonna ask you then. If you were not to just defer to a general practitioner, doctor, who would you defer to that? Maybe who we know that you think could be able to dive into his labs a lot better than, because that's the only, this is the problem with the GP right here, right? They see that and then their first thing right away is, oh, let's fix that, let's get that lower. Dr. Stephen Cabral has got some pretty good takes on cholesterol numbers, blood lipids, the context of the whole thing. Triglycerides, right? That's something else you wanna pay attention to. He's somebody that's really good. But I would bring these exact questions up to your doctor and say, okay, what's the right ratio of HDL to LDL? I would say, can I do a test to determine what kind of LDL particles that I have to see what the difference is? And is my LDL so high that none of that matters? Or am I within the range where I can look at the whole context of things and pay attention? It's a lot less clear, or I should say obvious than it used to be. Used to be, oh, over 200 cholesterol was really bad. And then we saw these long-term studies that showed that people with higher cholesterol in some cases live longer. People with higher LDL tend to get less infections. So my point is with what I said was, we used to look at a single number and be like, oh, good or bad, but it's way more complex than that. Right, it wasn't the fear of potentially that leading to heart attacks and sort of epigenetically unlocking some of these problems down the road. Is that still something that's a very real fear that doctors are afraid of? Yeah, and I know LDL, if it gets too high, everything else, the context of everything else can look whatever it wants and it still might not necessarily be a good thing. But like I said, it's kind of... I've heard conflicting information about it now, so it's tough to answer. It's really weird how some people react to saturated fat in terms of their lipid numbers. And I mean, like I said, I'm a perfect example.   Like my cholesterol is almost too low. So it's very interesting. So that's... So I would, like I said, I would ask an expert those questions specifically. I know the ratio makes a big difference of HDL, LDL. Then there's a ratio of those two, your total, your triglycerides play a role and then the type of LDL that you have. I know all those things now are starting to show that they are important. I would just caution taking the, if it's the general practitioner that he's talking to and referring back to him, I would just caution that I don't know his extent. I think if you ask those questions, you'll know. Like, hey, what are my LDL? Can I get a test that shows me what kind of LDL? Yeah, well, I don't know. I don't think there is one. Like then you know, okay, I might need to find someone that, really looks a little deeper. Yeah, if I was, back when I was trained, I always had like a doctor that I knew because I had trained one and then I'd keep them on tabs because this is something I would defer, right? I would, this is not my level of expertise, but I know enough and experienced enough where I've had healthy, really healthy clients still test high like this. So finding somebody that is more, or that's well versed in this besides just their, because a lot of times they would come with their, from their recommendation from their doctor saying, hey, my doctor says my cholesterol's too high and they would just give me some number and say that's all you were told, nothing else. What about all these other markers? Well, okay, let me refer you over to my buddy over here. Go get it ran and ask these questions and see. Yeah, well, part of the problem, Sean, it was that, and this was from doctors that I trained. They actually told me this, that they said, you know, one of the issues is we invented a pharmaceutical drug that was so effective at lowering cholesterol. Like you take a statin and it will lower your cholesterol. Yeah. And so what happened was because we had something that was really effective, that was easy, you just take a pill, that they focused really hard on that because it was something so easy to treat. Yeah, everything became a nail. Yes, and so that was, I guess, part of the issue, but we know now that it's a little bit more complex than that, so, and again, the genetic factor's huge, man.  There's a small percentage of people that still have to do that, so it depends. So again, I would go to your doctor and ask all these specific questions and see if you get the answers you're looking for and if not, then find somebody who has a say. And if you're not following already Dr. Steven Cabral, I think that's his Instagram too, right? Yeah, S-T-E-P-H, I think, Steven with a P-H. Yeah, Cabral. Okay, you said, I'm sorry, Steven, is it Cabral? Yeah, Steven Cabral. C-A-B-R-A-L. You could actually go back. Okay, Cabral. We did an interview with him on here, so it was a really good interview. We might have actually touched on cholesterol with him. I think we did. I think we did touch on cholesterol with him, so that'd be a good episode to listen to anyways. He's actually got a podcast, too, that it does really, really well, and I think he does like we do where we field questions. He would be a great follow in general, I think, for these types of questions. Okay, perfect. And what were the names of the other? There was a couple other doctors you guys had mentioned. Yeah, I don't know. Did we mention more right now? For right now. Dr. Ruscio, maybe? No, no, no, no, no, we didn't write anything. Yeah, that's okay. Maybe it was just Cabral. Yeah, I mean, we have Dr. Friends for specific things, like Jolene Brighton and also Dr. Ruscio and... But Stephen, Dr. Cabral would probably be... Yeah, we have like that, we recommend for like gut health, functional medicine, so it kind of depends, but Cabral is up your alley for what you're asking. Oh, Dr. Allo, he's also, he's a... Yeah, he's a cardiovascular specialist, so he was on a podcast as well. Dr. A-L-O. I was trying to think of him. Yeah, he's also something that might run. Okay, perfect, yeah. Yeah, when I listen to this, I'll remember what we said and then look those doctors up. So yeah, if we've got just a couple more minutes here, I was just gonna say, yeah, I kind of actually heard the same type of thing when I was reading in Ben Greenfield's book, Boundless. He actually had a section about that where he talked about some of the same thing, how it really comes down to the different types of LDL particles. And so there's a lot more to it than just HDL, LDL. And then I guess just one last question. So with the testing, you've talked about Sal a lot about how different body types, different genetic types, we react differently to saturated fats. So can they test for that at a typical clinical setting? I don't think so. I think the way you know is you... By teasing it out. Yeah, you tease it out. Like I use Doug as an example. Him and I, if we ate the same diet, our blood lipids would look very different. So I think that's really the only way at this point. Okay. All right, thanks for calling, Sean. Yeah, I appreciate it very much, guys. Thank you. No problem. Thank you. Yeah, gone are the days of just, ah, cholesterol over 200 is bad for you. And LDL is always bad. Well, I mean... It's way more lower. Here's the medicine. Yeah, it's way more... The unfortunate part though is gone, the days are not gone. They're still getting general practitioners that still talk like this. But that's all they say. Oh, it's high. Let's get it down. Here, take the statin. Or we need to change your diet up right away versus looking at all the factors. So I find that I still get stuff like this, but I know damn well that I'm not an expert in this. Like, this is not my field of expertise and I know there's such a... Bio-individuality. Yeah, that's hard to say. I have a lot of fun with this because when I get my blood work, when I used to, I don't do this anymore, but I used to have fun with it. And before I would get the results, doctor would test my blood and I'd say, yeah, I'm interested in seeing my numbers look like. I eat about 12 eggs a day and I eat probably one to two pounds of red meat every day.   Yeah, they're always like, oh my God. Like, oh, this is gonna be really bad.  Do you sweat mayonnaise?  Like, oh, this doesn't make any sense, you know. Hey, if you enjoyed that clip, you can find the full episode here or you can find other clips over here. And be sure to subscribe.


All right, let's start the day reversing down a hill Uh oh, uh oh Uh oh Uh oh It's the bit where your feet get really far away from the ground I think that's good enough Ready to rock and roll? Good morning world Welcome back to our circumnavigation around the globe and motorcycle We're here in the city of Donghae in South Korea And today is not much of a riding day We've got only about 10 kilometers to go But it's a very exciting day Because we are heading to the ferry terminal To take the ferry over to Russia Yoo! I have been waiting for this day for such a long time Yeah, I can't believe I just said like we're about to take the ferry to Russia How crazy does that sound? Yes, it's crazy! Can you pinch me? I don't know if this is true or not Oh my god I'm just going to do a U-E here Okay Navigation says to go the other way Now I know we're not traveling very far But let me show you guys where we are and the route for today So this was our route all the way from one side of Korea to the other And we are now here on the coast And today we're going to be heading 10 kilometers further down the coast To this little area here The port of Donghae It was really a short but sweet visit here in South Korea It was really cool because our original route didn't include South Korea actually So it was like a surprising visit The original plan was just to go straight in from Canada to Vladivostok But then we found out that there weren't any air freight options into Vladivostok So the closest place you could actually air freight a motorcycle was Seoul, South Korea And then luckily there's a 24 hour ferry that takes you from here in Donghae All the way over there Yes I like ferry rides Yeah and this will be the longest ferry ride we've ever taken 24 hours solid on a ferry And yesterday I asked them if there was any entertainment or anything You know, nice performance, cinema, whatever No, no, there's nothing But they have a bed for every person and they have like pillows and blankets and stuff So I think it would be nice and comfortable Yeah, it's going to be really really interesting Yeah That's for sure Look at this guy's planting some flowers over there South Korea is really really really like on the top with decoration for towns and cities Like they have flowers everywhere Yeah, like the gardeners, they're just absolutely amazing Like it's all like a botanical garden everywhere It's amazing There must be like a contest going on or something, you know Yeah Between every village I think the gardeners really do take pride in their work Yeah So yeah, I mean we didn't get too much time here in South Korea But we did get to see one of the most spectacular palaces in the whole country We got to come to an amazing national park right through the mountains We got to eat on the floor some beautiful tofu Yes So you know in the end I'm pretty happy with our time here It was short but sweet But now the real journey begins Because once we get to Siberia, we have a long way to go Oh yes, we have a long way to go But we are on our sort of like homeland Exactly, the piece of land that's connected right? It's like once we land in Vladivostok We basically just have to ride back There is no more major shipping routes Nothing like that, it's just ride back a couple of ferries and we'll be there Exactly Unbelievable, I can't believe it I can't believe it It truly is the final leg of the journey coming up But we are going to be on a really tight time schedule in Siberia Because we applied for our Russian visa a year ago or nearly a year ago But the visa only lasts for one year But that was like the only time we could apply for it It was in between the African and South American legs But that visa is going to expire in about four to five weeks from now So we have that amount of time to cross the whole of Siberia, Mongolia and get to Kazakhstan And when we are in Kazakhstan we take a holiday Exactly So it's going to be a pretty intense month coming up With a lot of riding, I can't wait for the challenge It's going to be super awesome Yeah It's pretty crazy though because yesterday we went to the port Just to check in with customs and just prepare ourselves And know kind of the process of what we have to do today And yesterday we found out that when we got Bumblebee out of customs in Seoul at the airport Yes, Bumblebee's here Bumblebee is officially happy and alive They forgot to give us a really important thing It's like this sticker that you put on the bike And that is supposed to be the permit to actually ride Bumblebee on the roads of Korea Yesterday when we went to the customs and they said to us Okay, you just need two things to give us You need to give us the temporary import permit And you need to give us back this sticker What sticker? We didn't get any sticker So then they actually contacted the customs department at the airport And were like, guys, where's the sticker? And they were like, oh my god, we have actually still got the sticker here So in the end they actually emailed over the sticker to the customs department here at the port And then they printed it out for us and gave it to us Because they were like, guys, if you're on the road and you don't have this sticker You can't be on the road, like you're illegal And we were like, oh, we just rode across the whole of Korea We didn't even realize But anyway, they were really nice and they sorted it out for us So now we have everything we need to clear customs on the export process Yes, our contact here from the port His name is Simon Hi, Simon He's really, really super friendly And he told us yesterday everything we have to do and how we prepare ourselves And he took really his time with us and went with us for everything we have to do Which is pretty amazing They said to be there at 10 o'clock and it's 9.30 now So we're all good with time We're on German time today Yes, I like it I like to be on time The process starts around 10 o'clock I've got to drop off the bike next to the ship What they call the ship side, ready to be loaded Actually, I don't do the loading of the bike myself Unfortunately, they're not letting me ride the bike on They have their own people to bring all the cars and bikes onto the ferry And I was like, are you sure they'll be all right with this bike? I mean, it's pretty big and heavy But they said the person would probably be fine So No, they have even bigger bikes here in South Korea Where we see all the Harleys Look at these bikes That's true That's true, the Harleys It's a pretty cool job, isn't it? Riding all the bikes onto the ferry For sure It's so cool And then immigration doesn't even open until half past one And we don't even start boarding the ferry Until like the early afternoon So apparently the ferry is supposed to leave around 4pm We're going to be spending the whole day Just hanging around the port I like it Yeah, it's pretty chilled Okay, so it looks like the gate Into the passenger terminal is not actually open yet I think actually they might not even open it until 10pm So we are a little bit early So what I'll do is just pull up here And just, I guess we'll just wait in front Yeah I don't know if this guy takes the ferry as well Hey, yes, how's it going? Good I'm overlander too Cool Yeah, I came from Cape Town Oh, awesome But my bike has been 3 years stuck in London No way So now you finally pick up your bike Awesome I came from Cape Town all the way to Norway Yeah Nice And then into Sampetsburg Down to Iran Iran was a mizer You can talk about it by that And then into Turkmenistan And then Uzbekistan Tajikistan Yeah Kyrgyzstan Kazakhstan Russia I'm thinking how to try to get some rubles Talk more Yeah, yeah, all right Thanks, guys Thank you Okay, I'm going to park up in front Perfect, all right So the guys here said that we're not allowed to like film the scenery around the port But he said it's okay for us to kind of film ourselves So we just unpacked Bumblebee completely because they said we couldn't leave anything on the bike And now Bumblebee is completely naked So now I'm waiting until they tell me to take Bumblebee over to the ship side to be loaded And then we'll make our way over to the passenger terminal and just hang around, I guess Yeah, enjoy life Just enjoy life That's Mikhail from Russia He's from Havarovsk Which is actually a city that we're going to be heading through after Vladivostok It's about 700 kilometers from Vladivostok So that's cool So we're just waiting to weigh all of the bags 50 kilograms together allowed to be checked in and then 25 kilograms each allowed to come with us on board This is everything from Bumblebee Both panniers, rear box and the two duffel bags So this is basically all the personal belongings we have in the world Yeah, exactly It doesn't look like very much, is it? We don't have a lot of stuff Minimalists Minimalists, that's the way So there's a big, big map of the east sea on the wall And we're here on the edge of Korea in Donghae And you can see the ferry goes up past China there and up to Russia The eastern dream New home to see Yes! It's happening We are going towards the ferry and we'll take us to Siberia for a day Okay Yep, let's go Do you want to be top bank or bottom bank? Because I have to go to the toilet probably bottom, hey? Okay, alright, I'll be top Everybody here has like their own complete bed We've got light and everything It looks so cosy I feel like on the square trip We've got to get out of these motorcycle clothes because I am hot I want to get some nice comfy socks on Here we go Our mini apartment on board the eastern dream Look at that So much privacy That's not bad, is it? That is not bad at all It's so cosy Wow So we're just going to have some sandwiches with some egg that Lavi brought up this morning and some crisps It's great Absolutely great So 25 hours and let's go And you look really great in your new hat as well Cool There we go Look, I think we're moving We are officially on the move They're just leaving the port of Donghe Donghe Next stop Ladi Vostok Bye bye South Korea Bye bye Disappearing into the distance behind us My Korean really early night I'm seeing some rain clouds over there So it's the perfect time to just snuggle in Entering a new country which is really exciting Yeah, and then we have to go through the whole customs process and we have to basically try to get Bumblebee as fast as we can onto the streets of Russia Maybe it takes two to four days the customs process So no idea what's going on there I don't know But we have a contact called Yuri and we've been talking to him via email for like the last year telling him that we are coming to Russia at one point We're coming to Russia We're coming to Russia and now we really are coming to Russia Yes, and that's it from us today We hope you enjoyed the video If so, please give us a thumbs up Subscribe to the channel Share the video with your friends and family below and we will see you next time First views of Siberia What a crazy place I can't believe we're here It's gonna be crazy