Let's talk with Pedro Domingos. Hi Pedro, how are you doing? Great, how are you? Fine, fine, everything okay? Is there any movement in the White House? Can you see it from there? Well, I'm actually in Washington State, not Washington, D.C.  Yeah, I'm even not so soft on Amazon, so tech not policy. Yeah, well, Pedro is a professor in the University of Washington and his keynote today will be about Microsoft Logic. So Pedro, thank you for being here with us. Let me encourage people to make questions. You have the platform to make questions, of course, in any language and we will translate it for Pedro. So Pedro, thank you again and let's hear your keynote about this Microsoft Logic. Alright, let's see here. Can you see my slides? Yes, we have them. Okay, very good. So I'm going to talk about unifying logical and statistical AI with Markov Logic. The intelligence systems need to deal with the complexity and the uncertainty of the real world. And the language of choice in AI for deal with complexity is first order logic and the language for dealing with uncertainties probability. So we need to combine the two to make progress in AI and that's what this talk is about. How do we combine first order logic with probability and equally important once we have that unified representation? How can we learn a reason efficiently using it? And the basic idea is very simple. And it's the following. A logical knowledge base is a set of hard constraints on the set of possible worlds. If we violate even one instance of one formula, the world becomes impossible. And that's what makes it so brittle and that's what we want to avoid. So let's do the following instead. Let's make the formulas soft constraints such that when a world violates a formula, it becomes less probable instead of impossible. And as you violate more and more formulas, the problem just degrades gracefully. And we're going to give each formula a weight that corresponds to how strongly we believe in it. Something that we really believe in has a high weight and something that we're not very sure about has a lower weight. And then the probability of a state of a world is just the sum of the weights of the formulas that the world satisfies, exponentiated and normalized. So that's the general intuition. Let's make this a little bit more precise. So a Markov logic network, or MLN for short, is a set of pairs FW, where F is a formula in ordinary first-order logic with usual syntax and W is a real number. Together with a set of constants representing objects in the world, a Markov logic network defines a Markov network, an undirected graphical model, with one note for each grounding of each predicate in the MLN. A grounding of a predicate is replacing its variables by constants, so then it talks about specific objects. And one feature for each grounding of each formula in the MLN with a corresponding weight. Now that's a bit of a mouthful, so let's see a simple example to illustrate these notions. Smoking causes cancer and we'd like to get people to stop smoking, but it's hard to do that because people are influenced by their friends and if their friends keep smoking, then people are likely to keep smoking as well. So let's start with a couple of statements in natural language. Smoking causes cancer and friends have similar smoking habits. We can translate this easily into first-order logic as follows. For every x, smokes of x implies cancer of x and for every xy, friends xy implies that smokes x is equal to smokes y, i.e. they're either both true or both false. Now this was easy, but the thing that's a little odd here is that the statements in natural language were true and these statements in logic are actually false because not everyone who smokes gets cancer and certainly not all pairs of friends have the same smoking habits. Now we can make this be true and useful again by making these statistical statements by adding weights to these formulas and now making them formulas in Markov logic. And the formulas that we believe in more will have higher weights. So in this case, smokes implies cancer will have a higher weight. So this is a very simple MLN with just two formulas, but what does it really mean? What does this represent in terms of the real world? Well, let's suppose we have a world with only two people in it, Alice and Bob. So two constants to represent them. And now let's follow our recipe for building a Markov logic network. So we're going to have one node for every grounding of every predicate. So for example, we're going to have smokes of Anna, which is just the Boolean variable that's truly if Anna smokes and falls if she doesn't. Same thing for smokes Bob and same things for cancer and Anna and cancer Bob. Now what about friends xy? Well, we're going to have friends Anna Bob, another Boolean variable. We're also going to, that's true for friends. We're also going to have friends Bob Anna because friendship is not symmetric. Bob could be a much better friend of Anna than she's of Bob and that actually happens a lot in practice. And we're also going to have these degenerate cases of friends Anna Anna and friends Bob Bob that maybe have to do with their self-esteem. So now what we have is a set of Boolean variables and then the MLN defines a probability distribution over that. So what is the probability distribution that it defines? Well, the definition of MLN says that there's going to be a feature for every grounding of each formula. And when you have a feature in a graphical model, what that means is that you have a direct connection between the nodes corresponding to the variables involved in the feature. So for example, we're going to have an edge between smokes Anna and cancer Anna because there's a formula connecting the two and same for Bob. What about the second formula, slightly more complicated? Now what we have is three predicates, right? So what we're going to have is connections is a triangular clique connecting all three predicates in that formula for every possible combination of instances. So we're going to have a clique between friends Anna Bob smokes Anna smokes Bob and so on. And now what we have is just, you know, a graphical model a Markov network over these, over these variables. And what is the distribution that this represents? Well, the probability of a world is just the sum over all formulas of the weight of the formula times the number of times in the data that that formula in that world that the formula is true. And, you know, not exponentially the normalize. So now this becomes an ordinary log linear or exponential family model or Markov network. Notice that the Markov logic network by itself does not represent the distribution. It's more like a program for creating distributions and that's actually what makes it powerful. And Nemolyn is a template that represents many different distributions, some over a very large world some of very small ones, depending on what constants you apply it to. And at this point, you might be thinking, well, this is nice but this is going to be very inefficient, right? Because there's going to be an explosion. There's going to be an exponential number of variables and this isn't even going to fit in memory. And of course, what we're going to see is how we're going to make this all efficiently enough to be usable in practice. But the first thing you can do that is very easy but goes a very long way, is to just have typed variables and constants. If you have a predicate like works4xy you only need to replace x by people and y by organizations and that already gets you a long way. Also, in this talk, I'm just going to talk about the very basics of Markov logic. But bear in mind that the full range of contracts of first-order logic like functions and existential quantifiers and infinite and continuous domains are all possible even though I won't cover them yet. So how does Markov logic relate to first-order logic? Well, actually very nicely what happens is that first-order logic is the special case of Markov logic that you get when you let the width scale to infinity. When the widths go to infinity the constraints become hard and you go back to the case where just violating one of them makes the world impossible, which is what you want. Of course the more interesting case is what happens when the widths are finite, that's what we're really here for. And here there's also something interesting that you can say which is if the knowledge base is satisfiable meaning if you can make the formulas all true at the same time and all the widths are positive then the satisfying assignments meaning the assignment of truth values to the predicates that makes all the formulas true that is a mode of the distribution. So the worlds that first-order logic likes are still there in Markov logic they're just the modes of the probability distribution. And as you move away from the modes the probability creates gradually which the behavior you'd like to see. Even more importantly however the knowledge base does not have to be satisfiable. In logic if there's a contradiction in the knowledge base then anything follows and basically things fall apart which makes it very hard to build large knowledge bases or knowledge bases that are combinations of knowledge from multiple sources. In Markov logic there is no problem at all with contradictions. If there's a contradiction you just add up the widths on either side and you get the resulting probability. What about the statistical side? The other property of Markov logic is that essentially all the types of statistical models that we know and love are special cases of Markov logic. Things like graphical models including both Markov networks and Bezier networks, logistic regression, hidden Markov models conditional random fields many of the deep architectures in use today are simple special cases with Markov logic and we can just write these down with often just and the key restriction that you have to impose to go from general Markov logic to the statistical models we know is that we have to assume that all the predicates are zero area meaning they have no arguments or they actually have one argument which is equivalent. What this means is that you're assuming that every object exists in its own separate world so there are no interactions between objects technically that there is IID and then if you think about is a very strong limitation it doesn't like you model things like social networks or metabolic networks in biology or the web with Markov logic it's very easy to have interactions as we saw in the example of friends and smokers you just have relations with more than one argument and then waits on the corresponding formulas. So that was the representation it's nice because it's general and it's simple because this is the things that we wanted to encompass but of course this is not going to be very useful unless we can do inference with it efficiently so let's look at inference now for a moment what is inference in Markov logic? Well in some ways it's no different from inference in a probabilistic model you want to compute the probability of some query given some evidence and the MLN and the constants that it's being applied to and this is equivalent to remember when you have constants that just combines with the MLN to produce a Markov network and then when you have evidence what that does is it replaces the evidence variables just by their fixed values so they disappear so at the end of the day what you have is you want to compute the probability of a query given a Markov network and now for this you can apply any probabilistic inference algorithm that you like but for example Markov chain Monte Carlo belief propagation and so on there is a very big problem with this however which is that the ground Markov network that you create when you replace the variables by constants in all possible ways is going to be too large in fact it's going to be exponential in class error so if you have a clause with say three arguments and even just a thousand objects in your domain you already have a billion variables so this is not going to scale at the time it's not even going to fit in memory so we need to do something else and what is that something else well this is where the idea of lifted inference comes in in first order logic you can prove theorems about infinite domains like the real numbers in a finite number of steps because you do inference at the level of whole sets of objects as opposed to one object at a time so what we would like to do is bring that all the probabilistic reasoning so let's think about how this might happen the probability of a query is just the sum over all the worlds where the query holds of the probability of the world so the probability that in a smokes is the sum of the probabilities of all the worlds where in a smokes now this is very easy but of course the problem is that there is an exponential number of worlds so you can't compute it this way now if a world can be divided into independent sub worlds so for example every family is independent of every other family or every country is independent of every other country then the probability of the world is just the product over the sub worlds of the probability of the sub worlds so this is already a big simplification this is kind of like what is exploited in graphical models but now further if we can group the sub worlds into kinds of sub worlds that all act the same way so there's families of one type there's families of another type so the probability of a world is just the product over the kinds instead of the probability over the worlds of the probability of any sub worlds in the kind raised to the number of sub worlds in the kind and this is another exponential improvement so with the combination of these two exponential improvements now inference can actually be tractable now of course usually in an MLN you don't have independent sub worlds because what would be the point but the key thing is that once you start conditioning on evidence your graph starts to break up into independent sub graphs and that's when we can apply this so to go back to our early example of friends and smokers with our two constants Alice and Bob you know here's a big complicated graph now if you condition on smoking right so first of all the smokes on Anna smoking so first of those nodes disappear right as smokes and smokes Bob but also because they disappear the edges that go into them disappear as well so now all we have is a bunch of independent sub worlds in this case each containing just one predicate and now further if Alice and Bob either both smoke or neither does then there's only two kinds of worlds there's cancer cancer and cancer Bob are one type and there's all the friends predicates that are another type so instead of computing the probability of this rule by multiplying you know all of the individual ones you can just if you look at the lower right corner here take the probability of cancer and square it and take the probability of friends and raise it to the fourth power and so notice now how quick this has become of course this is what happens on a good day on a bad day for example Anna smokes and Bob doesn't or vice versa so we actually have four kinds of worlds and in the worst case this kind of little difference will actually not buy you anything just as in first order logic what will typically happen is that you're somewhere in between the two in the best case you're the the complexity of inferences order of the size of the MLN which is really good because it's small in the worst case it's the order of the size of the ground mark of an hour which is bad but typically in between the two and there's actually there's typically a lot of lifting that you can do and there has been a lot of work in this in this whole direction we have managed to synthesize it all into a procedure that we call probabilistic theorem proving which is a generalization of both theorem proving as that implies and inferencing in probabilistic graphical models and the nice thing about PTP as we call it is that it generalizes it encompasses essentially all the major types of inference that people do in AI so for example here on the lower left hand corner of this you have a propositional theorem proving which of course is a special case of this which is equivalent to satisfiability testing and that's you know where people started out back in the 60s and if you want to count the number of satisfying solutions to formula instead of just saying if it's satisfiable or not then you get the model counting problem if you add weights to the formulas then you get weighted satisfiability which is the same as inferring the most probable explanation so this is what is called MPE inference in a graphical model and if you combine those two you actually get weighted model counting which is what probabilistic inferences now all of these can be lifted to first order and for example when you lift propositional theorem proving you get ordinary first order then proving when you lift weighted model counting you get lifted weighted model counting and if you combine all of these you get the full probabilistic theorem proving procedure which is really just lifted weighted model counting what about learning? this is all not going to be very useful if we don't have some way to learn the MLNs you might be able to write down formulas but they're probably being complete and partly incorrect and also coming up with the weights is something that people are not very good at so we definitely want to be able to learn these things from data well the data in this case is not going to be a single table as in traditional machine learning it's going to be a full relational database and we're going to learn from that we're going to in this talk I'm going to make the closed world assumption which is that a predicate that is not in the database is assumed to be false sometimes this is not the right assumption in that case you can use EM versions of the algorithms that I'm going to talk about but I won't cover that yet so there's two main tasks as usually in machine learning there's learning parameters i.e. the weights of the formulas that can be done either generatively or discriminatively and we're going to look at each of those in turn and there's learning the structure of the model which is learning the formulas themselves and then there's other things like transfer learning and discovering latent variables and whatnot which have also been done in Markovogic but I won't go into here so let's start with generative learning and the good news is that this is actually surprisingly simple we actually thought going in that because we're removing the idea assumption this is going to be very, very hard as it turns out the math is exactly the same we want to maximize likelihood we can maximize it by gradient descent it's a convex problem so there aren't even any local optimites and if you look at the expression of the gradient it's actually very intuitive the partial derivative of the log likelihood with respect to a weight is just the difference between the number of times that that formula is true in the data and the expected number of times that it's true according to the model so if the formula is true more often than the model says then its weight needs to go up if it's true less often than the model says then its weight needs to go down and once they all match we've reached the maximum likelihood solution so this is all very straightforward there's one very big snack here which is to compute the expected number of true groundings of a formula according to the model we have to do inference and of course inference in general is intractable and we have to do this at each step so most of the time this is not going to be feasible so what can we do instead? well we can use a strategy that was first proposed for Markov Networks by Julian Bisak back in the 70s and that is to use something that is similar to likelihood but yet tractable to compute and what he proposed and is used for MLNs as well today is what is called pseudo-likelihood so the pseudo-likelihood is just the product over all variables of the probability of the variable given its neighbors in the evidence in the data and this of course is tractable because computing the probability of a variable according to its so-called Markov Lancet is a simple operation and this is also a consistent estimate meaning that as the amount of data that you're laying from grows the estimates of these probabilities of these conditional probabilities go to their true values it's widely used in areas like vision and special statistics but of course it has some limitations the main one is that this tends to work well for short chains of inference not surprisingly because when you're doing pseudo-likelihood inference is one step away from a variable to its neighbor what this means is that when you have longer chains of inference often pseudo-likelihood will give you very poor results so what can you do in that case well you can do discriminative learning which is actually usually the best thing to do because it tends to give better results throughout machine learning and here as well so the idea in discriminative learning is that we know a priori which of variables why we're going to be querying on and which one's which variables X are going to be evidence and then all we do is optimize the likelihood the conditional likelihood of Y given X as opposed to the joint likelihood of all the variables which is a much harder problem and now everything works pretty much the same way the nice thing is that as you condition on evidence a lot of the modes of the distribution tend to disappear this is what makes probabilistic inference hard is that there's usually a large exponentially large number of modes and they're widely separated but once you start conditioning on evidence they start to disappear and often it winds up being the case that the answer can be well approximated by just the probability at the peak, at the single biggest mode so instead of having to do this probabilistic inference we can just find what the most likely state is which instead of being sharpy complete is only NP complete and use those counts to give our answer so more concretely how can you do this well there's a number of ways but the simplest and the oldest is to use an algorithm that was first proposed by Mike Collins for training hidden markup models and that's the structured perceptron so the structured perceptron works as follows it assumes that the network is a linear chain because that's of course what an HMM is and what it does is it starts out with all the weights being zero and then it does the following sum number of times it finds the most likely state of the hidden variables for example what words you're speaking given the observations for example the sounds that you're hearing this is of course a typical application and of course there's a famous algorithm for doing this called the Viterbi algorithm so use the Viterbi algorithm to infer the most likely state of Y and then you just do a gradient descent step where your gradient is the difference between the count of each state in the data and the count of each state in this MAP Y that we infer we multiply that by learning it we add that to the weights and that's the step of gradient descent and at the end we return the average of this over all steps why the average and not the last result because you tend to generalize better that way this is both seen empirically and there's theorems to that effect now of course what we want to do is to start with perceptron for instance in Markov logic what we actually need to do to make that happen is very simple, is to just allow the network to be an arbitrary graph and now instead of Viterbi we use probabilistic theory improving so we infer at each step we infer the most likely state of Y using PTP and the rest of the algorithm works as before and this is quite simple it's very effective and it's probably the most widely used algorithm for learning MLM weights so there's some more sophisticated ones that tend to be faster what about learning structure we wouldn't be able to learn the formulas from data, not just learn weights for predefined formulas and if you think about this this is the problem that generalizes both the problem of feature induction in Markov networks and inductive logic programming that deals with inducing logic programs from data so there's a lot that we can draw on here there's also some significant differences one is that in inductive logic programming you only learn horn clauses because that's what a prolog program is made of and the goal here is to use any clauses so you need something a bit more general also in inductive logic program you typically use some kind of accuracy of information gain as your evaluation measure but here we're learning a probabilistic model so it should be a likelihood and then if you think about how this is going to work every time we modify a formula so we have a new candidate formula we need to relearn the weights and this is potentially a big problem because relearning the weights is itself not very fast I know we're going to have to do this for potentially millions of candidates so you'd think this would be a big bottleneck surprisingly it actually turns out not to be if you do a couple of simple things one of which is you start the new weight optimization at the old weights because when you change a formula most of the weights don't change and if you use a fast optimization algorithm or other quasi-Newton method type like LBFGFs or such you typically are converging just an iteration or two surprising the bottleneck turns out to be something else it's counting clause groundings just counting the statistics that you need to go into this actually turns out to be the bottleneck because actually doing that surprisingly is itself a sharpie complete problem and when we were learning weights we only had to do that once at the beginning but now we have to do that for every candidate formula fortunately here there is also a simple solution which is to subsample you don't actually need to count every single grounding of the formula friends xy blah blah blah you can subsample maybe a thousand groundings and from that you can infer within some bounds what the actual answer would be which is enough to do the learning so with these using these various things learning Markov logic is about as fast as traditional in that logic programming or learning features in Markov which is not super fast but it's as fast as the things that we unify and later we will see how to do things that are even more efficient so of course to be more concrete when you do structure learning there's a number of choices that you have to make the first one is the choice of initial state if you want to learn purely from there you can start with unit clauses so isolated predicates and then you start adding things to them or you can start with the hand coded knowledge base that somebody wrote down and then you can revise it by learning and often this is actually the idea of use of Markov logic is you put in your knowledge and then you revise that knowledge using structure learning now you need search operators the obvious ones are of course having and removing a little from the formula also flipping the sign of a little because people often write down good formulas but the implications are in the wrong direction we also need an evaluation function the natural one to use of course is to the likelihood because it's the most efficient but we need to add to that some kind of prior to combat overfitting and the obvious thing to do here is the same thing that people do in graphical models which is to have us a prior that penalizes divergence from the initial network so every time you either remove a little you pay a penalty and you only do that if the gain in likelihood exceeds the cost of doing that so very simple but very effective and then finally of course you need to choose what kind of search to use people have you know tried all kinds of things beam search which comes more from the ILT side shortest first search that comes more from the Markov network side and a whole bunch of others and you know it's in some ways a matter of taste which one you want to use okay so we have Markov logic as the unified representation we have some fairly efficient inference and learning algorithms for it are we done or actually no we're not done by any means because this is still not industrial strength to have something that can be deployed in industry it has to be very scalable and it has to be very reliable anything that requires approximate inference you know is a little bit dubious to using production so what we need to do is actually what people have done in classic AI which is come up with tractable subsets of Markov logic such that inference in them will always be efficient and exact and then you know we can really deploy this and be confident that it will work as expected and that's exactly what we've done we've developed something called tractable Markov logic which as the name implies is a tractable subset of Markov logic summarizing this language is very similar to the classic ones you know basically it has objects and sub parts and class hierarchies and it's by exploiting them that the inference is made efficient so in tractable Markov logic there are three types of weighted rules and facts sub class rules as the name implies are rules of the form a family is a social unit a sub class fact is something of the form the Smith-Sara family then there are sub part rules things like every family has two adults notice that not every family has two adults but that's okay for us because at the end of the day this guy is going to have weights and just going to be statistical regularities and the sub part fact is something like the first adult in the Smith's is Alice and then of course finally a relation rules like you know in every family every adult is a parent of every child again not always true but true a lot of the time and we can learn to wait for it and then finally of course just simple facts like Alice and Bob are married and the remarkable thing is that inference in this language is always linear in the number of rules and objects it's not just tractable it's linear and in fact in practice when you actually do the evaluation for a query it's sub linear linear would be looking at all the rules but it's typically only to look at a small subset of them so what happens at the end of the day is that in tractable Markov logic you can actually do interactive querying answering a query typically takes less than it takes a fraction of a second and it gives you probabilistic answers that are exact that are not approximate why does that happen well the reason is that by design the structure of the knowledge base mimics the structure of the computation that has to happen when you're computing probabilities so in particular the probability of an object given its class is the product over the subparts of the object of the probability of the subpart given the class so this is one thing that makes things tractable and the other one is that the probability of anything given a class is a mixture model it's the sum of the subclasses of the probability of that thing given the subclass times the probability of the subclass so what's going to happen is that for every object node there's going to be for every object in the knowledge base there's going to be a product node in the resulting sum product network of the computation and for every class there's going to be a sum node summing over the subclasses and as an application of this we have developed what is to our knowledge by far the largest probabilistic knowledge base ever built we did it by extracting information from classic web sources like dvp, yago, and mel and merging them all together which of course is something that Markov Logic is very good for and at the end of it we have a knowledge base with millions of objects and billions of parameters but as I mentioned you can return exact answers to queries in fractions of a second this of course is just one of many applications that people have done Markov Logic has been applied widely in natural language processing and in information extraction which are obvious applications but also in things like link prediction, social network analysis in robotics and vision in computational biology psych the famous largest knowledge base ever built parts of it have been made probabilistic using Markov Logic also personal assistance the famous DARPA-KELO project that Siri grew out of used Markov Logic as the core representation in infant language and many others Markov Logic has been widely researched in academia in fact this body of work is one of the most widely cited bodies of work in AI of recent times it's also been used widely in industry not just by the large tech companies like Google and Facebook but by many others as well and research and progress continue so to summarize I would say that at this point we have largely succeeded in unifying logic and probability using Markov Logic there's of course many languages to do this but Markov Logic is the most general and simplest and also by far the most widely used and the most developed and what Markov Logic does is just assign weights to first order formulas and then treat that as the features and weights of a probabilistic model we and many others have developed powerful and inference and learning algorithms for Markov Logic and I believe that we now have a good foundation for modern AI where you don't have to start with logic and then you know deal with and certainly using hacks or start with graphical models and deal with relations using hacks which is what people did before there's of course still much more to do if you're interested in finding out more about Markov Logic I recommend you take a look at this article that came out last year in the communications of the ACM there's also a book that's a little bit older there's things that we talked about here that aren't covered in the book but it still covers a lot of things that the article doesn't there's also a nice website Alchemy the URL is right here that contains both the Alchemy open source implementation but also pointers to other open source implementations and also papers, pointers to the literature, MLNs, data that you can learn from and so on thank you and I will take questions now so thanks so much Pedro let's give some minutes well, minutes seconds to get some questions people is a little bit shy today so maybe there's no questions but let's see no questions since there's no questions so thank you Pedro for being with us today take care and keep in touch maybe people are shy now but maybe later they drop your line or something like this so that's the idea to keep in touch and begin the conversation so thank you Pedro thank you


Hello everybody and welcome back to Matt Smith Plays. This time I'm joined by a special guest. Introduce yourself. Hello. I'm Rory.  Yeah. It is a scary time. I'm assaying me. Oh, she's over there. Look, she's right there. Oh, there she is with the shortest skirt. Sorry, I shan't look. Yeah, yeah. Oh, yeah, no, I'm allowed to look. Thank you very much. We've played, well, I've played City of the Daleks, Blood of the Sidemen, so this time we've played TARDIS. Maybe we should have played the gunpowder plot, because you're actually in that one. And I think you're wearing the same clothes as you do it. Obviously, I'm thinking ahead.  Let's go. I like the t- I've been in there a couple of times. Oh, I've- Oh, no, what's happened to me? Got upside down. What? Wrong way up, wrong way up! Name one of your holidays that didn't end up in a big, mad disaster. A bit sassy. She is a bit sassy. It's a good point though. Ah, parish! Getting to reference. To what? Well, so both of those were fourth Doctor Storks. I think the writer has a bias. Amy has a bias, between us. No, I don't think that's true. She does. I think she- obviously she chose you. When? In her Amy's- what is it? Oh, oh god, Matt, me and me! Oh, is that the end of the episode then? Yes, rest in peace, me. Oh, thank you for watching, I know. I enjoy playing in the game. It was a lot of fun, but no, no, she doesn't have a bias. Remember in Amy's choice where Amy made her choice? She chose you. But then I saw- she had a camera in her room. Did you? Yeah, yeah, see all sorts of things there.  She tried to kiss you and you were a bit like- Oh, you saw that? She wasn't trying to kiss- I'm in my pants.   Oh dear, sperm. Oh my god. Don't try too hard, Doc.  Oh, you're right.   We're both terrible at charades. Those are terrible. It's charades! Get it right, Amy! I'm going to mine a Doctor Who episode. You've got to tell me which one of these. Are you ready? Ten words. No, no, no, no, no. Three words. Two, the second word. Eleven. Yes, yes, yes, good. And then, uh... Third word. Hour. Live hour! Yes! That's really fantastic as you are. Oh my god. Forget this game, then.  We're going to play My Wife. Yes, look, I'm your wife. Oh my god. She, uh... You'll be happy to hear I can't upskirt. Not that I was trying to, but... Yeah, you shouldn't be doing that. Look at that. You know what it's time for? What do you think it is? You know what? I think it's me. I think it's me. Actually, that's a very good point. Not quite. What do you think of William Hart now? Do you think he's a good doctor? I think he's a bit old. I think they're better young. I bet you do. Rory always likes them. Okay, so let's see what we have to do. Let's examine. Right, turn back around for a second. This one is the only one glowing, so I think it's this one. All right, take it from here. Roryranicus Ponticus. What's me, what's me, what's me, Nene? I don't know which one to press. That one. Why are they all the same? Oh, it's apparently the TARDIS just wasn't Tom. What do you say? That's why you should get me to fire TARDIS, Doctor. Let's think, so she's wearing that outfit and I'm wearing that jacket, so I assume this is after victory, yes? Yeah.  Yeah, she did that, didn't she? Yeah.   Tractor beaming attached. Tractor. Go to the fire. Boom, boom. Go past the wits early thing. Left, then your other left through the sub room. Careful not to trip over the sun, lounger. Then you'll see a green door. Don't go in there. Are you getting this? I've got to remember. Yeah. It's going to be, it's going to be... It's going to be what? A jelly baby. What should I do? Darn it. You've done that a lot. Yeah, true. The viewers at home want to see you die. God, you're correct. Give me something. You get to live. Give me something. Is that it? I get to live. Dear, you can be the sheriff. Oh, my God. It's like time called mercy. It is like a time called mercy. That's supposed to be like oil. Like where's the bowl? Oh, yeah.  There's always... It's the same... Oh, this is rubbish. Whoever made this game was a hack. All right. Here we go. Down here. Let's get the ponchos from Amy's choice. Oh. That's strange. Why am I back there? Here. Is that your Amy Pond impression? No, I'll do a better one. Oh, that's strange. I'm waiting right back here.  Right. Oh, yeah. Right. Oh, hello. Hello, me. Jump out. Examine. I need to rescue the dog. I need to rescue the dog. Don't be funny. It's hard. It slips into another lip tape. Well, we can't have that. That was very good, by the way. I've practised it. Do you want to hear it? Do you know it? Do you want to hear my clarion impression? Do it. Oh, my God. Oh, that was another collectible. It was in the roundel. Oh, I get it. You take it. You take it. Go on. All right. I think this is going to be... The master. Oh, my God. Okay. What did you get for that? Um, ever, ever... What do I get? Have this. Oh, thank you. Go on. To fix more.  I genuinely didn't know that was the master. I'm quite proud of myself. Let's see how I just... How it's described. A time lord liked the doctor, correct. But not like the doctor. So he's like the doctor, but he's not like the doctor. He's not like the doctor. He's ready to go for someone. It really is. I fully don't know where we go. I've tried every route. Let's try going up again. It's the only... I'll go up here in a minute. Good idea. Oh, my God. I can talk on the monitor. I already spoke to him. He didn't have any answers. Do it again. On the mechanical console. Oh. On the mechanical console. Why are you calling it a mechanical console? Okay. We've got to point things up. It's just a console. Let's see which one. That one. That one. All right. I was good the last time. You take the control. Watch this, doctor. Because it's too easy for me. Because I know how to control the TARDIS.  No. I'm just trying to... You're better. You just...  So, all of them have to be pointing up. Oh. As opposed to what you've just done, which is down. Well, there's no up. There's no up. This is that one, too. Oh, you did it. Well done, Roranicus. I am the sheriff, doctor. You are the sheriff. I am the sheriff. So, now I assume we can go up the stairs. What are these supposed to be, these white things?  Oh, that's a lot of fun. It's a lottery. Yeah, we've made it to the... We're the ticking room more than the drawing room. Can you do any drawing? Do a funny drawing. Do a drawing of your wife. Of my wife? Yeah. There's some pens there. Do some drawings of your wife as we're in the drawing room. Oh, right. Oh, I've just drawn your jacket. Oh, you know what's fun about this room? Oh, don't do that.  I was going to clear it off, mate. Oh, it's very kind of you, but that's not how it works. Who's that fucker over there? That's a good one. He looks like Colin Firth. Who's that? He's holding a sock. Is that supposed to be a timeless doctor? Who is that supposed to be? Right, tell you what. You have another guess. Well, I got it right last time. You got it right last time? I did get it right, and this time I want to get it green for correct. It's next to an ood ball, so I think it's going to be an ood. Not quite. Oh, there's another one. Look, there it is. Oh, can I guess one? Okay, you're of course wrong. Right, so we've had a companion. We've had a master. We've had a jelly baby. A jelly baby. And a doctor. And a doctor. It literally covers all the bases. Pollocks. I'll go for another doctor then. Oh, good idea. I'm going to go for McCoy. McCoy was found in blood of the cyborg. Oh, no, no, no. Would you like another guy? Yes, please. What does that one be found then? I think we found David. Yeah. We found McCoy. Yeah. And we found Hartnell. Yeah. And we found Colin Baker as well. Right. So not those four. McGann. McGann is a good choice.  McGann was found in blood as well. Oh, Jesus. We found a lot of collectibles because I have a collectible key. Right. A jelly baby. Then just get on with it. Who would have guessed that crap? What? Okay. Friends. He does that one. This was out in series five. That was really helpful. Okay. Let's see. So we've got a scarf. The fourth doctor specifically. Let's see what else we've got. We've got a fact about a gramophone. Apparently it's the eighth doctor's gramophone. I hear him. A gun. A gun. Brrap. What are we doing here? I can't remember. I don't know. I have a doctor. That's the cigarette stuff. I know that actually. Oh, yeah. Time. Is that it? It's just a clock. It's a clock, yes. Oh, what? What's that noise? Oh, dear. Nothing good, I bet. There's no noise. Let's just... It was just... It's the latest screwdriver. Because we found the master. John Sims. Is she gonna pick it up and actually use it? Is she gonna kill someone with it? Best thing is she's not dead for long. Do it again! Oh, dear. She broke my vase of yellow light. Where's the fire going? It's a weird reference, isn't it? Yeah. Why couldn't it be another Sonic? Because they're trying to be really clever. There's more wires. Wow.  I am. That is... You can find me later, doctor. You've written a speech bubble, but she's not saying anything. Is that metatectoral commentary? Yeah. Oh, you can do this. I've never done this before, doctor. I'm a nurse. I'm not an engineer. Oh, well, you're about to learn. I don't know why I've got this to this. It's not mine. Oh, look. Hand cuffs. This is when his wife chained me to a radiator whilst dressed as a sexy police woman. And I'm not talking about the 11th hour. This mouse is really starting to do my head in, doctor. Yeah, it's not great. The doctor is a cop, guys. What was that? Sorry, do you want to come on our adventures? Well, I want to come on adventures, yeah. Oh, I bet you do. You know what I mean? All right, let me shuffle back in. Oh, did it. It was easy. Doctor o'clock. You've done your bit. Well, thank you, guys, for having a watch. Don't forget to like and subscribe. And comment.  Yes, do that. So, let's see Rory. Push button. Really? Go on. That's wonderful. You're an actor. I am. And I owe you a new thingy of aftershave. Aftershave. Aftershave. Who breaks the vase thinks it's aftershave?  Don't talk... That's my wife. Oh, your wife isn't important. We're playing a video game. She is to me!  I made a plastic, and it's fantastic. That have a gun on me packing there. I went to the gym once. Who's gym? Dim the fish. Yeah, that's it. We're playing as me now. So the second eyes stop.  I'll play you. Your objective is to find out what is happening. What a very vague, vague thing. It's because it knows I'm vague in the head. You are quite vague. I need to find out what's going on. You can't just go milling about the TARDIS. What do you think it is? A mediocre episode? Something could be going on somewhere else in the TARDIS. It's bigger on the inside. Yeah, you did know that, didn't you? When you came in, you thought you were all big and hard.  I'm certainly hard. What's this face? What's that face? That's not me. Is that me? No. Oh, no. When you punched me, Amy disappeared. Oh, dear. I've killed Amy. Amy's stuck in the future. But time doesn't really exist in the TARDIS. Right? I don't like this. I don't like it. I don't like you.  I might as well just always have that like brow. You can feed back them from both directions at once. My boots. They're very light. What boots are you going right now? I'm wearing socks. I'm wearing this Mr. Men socks. Well, that's what you want to see. That's content you deserve. Only fat. Field attackion feedback, Luke. Oh, easy peasy. Go on, Rory. Build the attackion. That's a leisure high reference. Oh, is it? Yeah.   That's all I know. Sorry, excuse me. That's what I thought. In fact, I wasn't in the 50th anniversary. It does my head in. He had a triple deadlocked force field. It didn't. It just... Oh, I made that last episode. No, no. The first episode. A little sonic. It's a hit-the-space bath. There's your sonic. Oh. No, it's a bit long, that sonic, isn't it? Look, it's going to do a Jodie Whitaker. Oh, dear. And you don't even name it in the right place, is it?  You know what fun thing you get to do now? Oh, no. Ha-ha! Yes. Oh, no. Oh, this pissing puzzle is truly heinous. Careful. Careful. You broke it. I want to die. Gotta start it again. Doctor, watch this. I'm watching. That was incredible. Thank you, Doctor. Well done. I was doing that on purpose because it's the third time lucky. Of course, I believe you.  It sounds like a load of bollocks. According to this game, it's an emerald from Minecraft. Have you played Minecraft? Have you played any other games, Rory? What do you like to play? Tetris. I like my Atari 5000s. Atari 5000? Yes! Good choice. I was there when it was invented. Oh, yeah. What did you play on it? Um, I played Your Wife and I won. I'm going to stop mentioning Your Wife now. I don't want to get punched because it still hurts. It's because I'm an Auton, mate. You're still an Auton? I thought we fixed that. Oh, no, I decided to get some plastic surgery done. Oh, that's good, and? Yes! Well done, Rory! A book of weeping angels. Did you know, Doctor? Don't tell me. The image of an angel? I can't remember the rest. I can jog your memory. I don't like playing games. Oh, but you're so good at them, he said sarcasticly. Oh, remember when we went to Venice, Doctor? Oh, good times, yes. Where we had that fight. What was I going to mention there? Yeah. Now, do you remember the thing you said about the image of an angel? Yes, that's right, Doctor. I can tell you, the full quote is an image of an angel becomes itself an angel. So... I doomed the show! Ah, perfect advice for keeping track of time. It's a bit big, isn't it? It's very big. Not to brag. Ah, very easy. Oh, it's my scarf. Not my scarf, your scarf. Don't try and steal my stuff, unless you've got one. Apparently Oz could have one, so maybe you can buy it in all good retailers. Yeah, they talk to experience. Do you think we should do this? I think we should. To save the world. To save your wife. Oh, I love my wife. Oh. Ah, it's another one of these. Have fun. If you can't do it, then this is where you're going. The short way. Oh, no. I spent too much time looking after that big box of junk. It is. You made a big finished box set about it. But yes, I went in it. Your wife went in it. It's your turn if you don't get this right. Oh, you have to start again. Ah! It was the wrong way up. Wrong way up! The laser beam's all in this game, and I'm in my pants. I don't mind it really, I don't. But the fact that you're fully erect is really off-putting. Well, you know me, I get turned on by electronic fields and spectrographs and tachyon emissors. Good to know. I like that Amy's tachyon emissors are quite good. And your sterile no more rivers feed. Imagine if I cocked up right now. So I can say anything about him, and he can't retort. Milisk. It isn't to me! Right, noted.   And understood. So we need to send it. We need to find a good place to store the tachyon feedback loop. I reckon we've got to send it back to the drawing. You've always been me over, Doctor. I like how they mentioned the 10th planet and episode two of the adventure games. The two main Cybermans. Oh yes, they're very similar in style to each other. Let's see if this holds up. The sudden number counted every Doctor on screen except for eight and nine. Oh, on screen, they did specify. Yeah, yeah, yeah. Is this still correct? Well done, the adventure games, they future-proofed it. Where's Amy? Where's Amy? No, not yet, unfortunately. Working on it though. I've got a question for you actually. What? You know, River, your daughter. Yeah.  Not anymore. No? You know when I started not caring? I knew we were putting it just there. Oh, it's a lovely hat you're wearing.  It's one of yours, I think. It is. I believe it's Sylvester McCoy's hat. Oh, I like Scottish people. Actually, you know what you are. That's why the teftoks are better than you. You know what you are. I'm going to breeze past that. You know what you are? You're Vincent van Gogh in that hat. It's series five, Vincent and me. Align the two signals on this bloody... Good luck, Doctor. Well done. Good-o. This made me dream. Good-o. I had to dream. Good-o. Stop good-oing me. This is what screwed me over on the last game. I died. Thank God. Already? Oh, you're not very good at this, are you, Doctor? I'm really not. You're going in... You're going in this. Again. No, not again. Not again. I'm going twice. Once in Pandorica Opens and once in the Doctoral Experience. The Doctoral Experience. Oh, so you have been there then, so you'd like to... Amy! I can do that. Amy! Is she back? Is my wharf back? No, I'm recording a voice message. What are you going to do? What are you going to do? What are you going to do? Oh, you always just sit and watch whenever a pond's in trouble. This is why she likes me better. I didn't say... She does it to me! Stop punching me. It hurts. That's the last time I've found a doctor. There's a lesion in time. There's a lesion in... What's a lesion in time? Give it up for me, Doctor. There's a lesion of the Cybermen. Oh, when I blew them up? Oh, the Cybermen. Yeah, that was very impressive. I did work. Yeah, nice try, Doctor. We did not get enough. All right, you get to keep it. Oh, I've got my watch back. I've got to pull myself together and work out and meet back with the Doctor. You don't say. We need to repair the time, lesion. Do you think you're... That's awful quality. What? Which Doctor of the First War scar? Oh, this is quite cool, isn't it? Well, the first Doctor, technically. Oh, you were there for this. You'll know this. In vampires in Venice... I like the don't-know option. Oh, I don't. I don't know. I like fish. Fish. What was your favourite fish, Doctor? Fish...toes. That's a subversion of expectations. I'm very funny. Who tried to conquer Earth in the Christmas invasion? That was a tenant one. It was The I Don't Know. It was The I Don't Know. Oh, they are terrified. Yeah, I don't know. I ate those ones. We met them once, didn't we? No. Yeah, you go, me. Which story did Cybermen first appear in? Oh. Earth shock. No, I don't think it's the shock of the Earth. Oh, very good. It's The 11th Planet.  Doctor, get rid of Amy and take me on board. I am the best companion. You know, honestly, I might. It's quite... If you can get them all right, I will consider it for like a minute. Jim Jones. What did the Doctor use? Jack Harkness. I don't think it's another... The don't-know... I don't know. I don't know. I don't... I don't know. Nice thing. That's very good. Magic. What's the name of John Smith?  There's quite a lot. What weapon does Whithersong have in silence in the library? Recorder. I almost pressed that, then. Did you actually?  Correct. I'm good at this. Stop saying that. I'm good at this. You are. Sorry. Yes, you are very good at this. What story did the... Oh, grow up, you...  No, no, no, no, no, no. Rory Williams. Rory Williams for the win. Yes, go me. Why is she covered in peace? Stop. What? Stop. Stop. In the name of love. Speak. Speak. Who is this? I've got no clue. Who prisoner? There'd be nothing left. It's the Daleks, Doc. Just the Daleks. They're Daleks. They're called Daleks. What colour do you think they are this time? I have a good mind to just leave you in there. Oh, I've always wanted to hate me. Then we'll be at the Tyson gun. Oh, he's died again. Oh, no, he's asleep. Okay. Thank Christ. Oh, he's fine. Sorry, Doctor.   But this is the best part. We're about to fly the TARDIS. And it's all you. You've got this. And if you win, I will give you the key to the TARDIS. You will become an official companion. What do I do with the key? You unlock the TARDIS. You get to access the TARDIS. The TARDIS? What's the TARDIS? You get unlimited access to the TARDIS.  Are they back? For fuck's sake, Rory. Just fly the pissing TARDIS. Appreciate our players. Amy or the Doctor? Well, that's absolutely...  Have a look at that. Well, I saw the Doctor make a new TARDIS.  That's funny, that is.  I think she is. I might divorce her, actually. Do you think I should divorce the Doctor? It's for the best. I'm just staring at you from across. This is me looking at you. Godot! It's like when I saw Donna try and fly the TARDIS. She nearly made a hole in South Wales. South Wales? Well, no one needs one. Oh, you got... I didn't know the TARDIS isn't a Bunsen burner. You can make some hot chocolate with that. I think we just have to pull the Go Lever. What is that? Oh, is it that one there?  The Lever for Go. I'm not going to divorce Amy. I'm going to stay at home, drive me car, be a nurse, sod your TARDIS. Oh, you know what? Sod my TARDIS, or sod your wife. I do sod my wife, actually. How do you? Well, someone has to... She likes it like that. Say something that I would get angry with so I can punch you. Your wife? She isn't to me! Oh, there we go. Are you joking? He's just down to launch. To Amy genuinely can't find the TARDIS. Women drivers is what Moffat is saying. He's a sexist. Moffat is a sexist. Oh, like, we didn't know that. We've got 60 seconds to get the TARDIS. 60 seconds? Quick, knock the doors. Uh, okay. I think that's this one. I don't think there's a natural time there, is there? There must be. 60 seconds. Pay attention, Pon. You might need to remember this one date and have to pair up the Atom Accelerator. I know what that one is. That's the sir. Oh, Moffat's stressing out a doctor. You're stressing out? I'm the one who's flying. Why does he need to do this so quickly? I'm spamming it. Uh, I think it... Which one was this? Oh, come on. Oh, no, it's not that one. Okay, Moffat, let me stop. Stop with the bunsening! Ah, I... Ah, I failed. Doctor, you're used to driving your own machine. Apparently, sir. On marks.  Do you want to know? 10 seconds. Oh, it is that one. I knew that one. Well done, Doctor. So, Doctor, we'll touch the speed.  That's all right. I got it this time. One more time. Oh, don't fart, please. Oh, no. All right. So, are you ready? And we're off! 60 seconds. Pay attention, Pon. Yes, yes, yes. You said that. Can you imagine me messing up this many times and saying, pay attention, Pon? It's weird to see that screwdriver in the air console. Yeah, it doesn't really make sense to me, Doctor. Didn't he blast you with that? Did he go... Never made any sense to me. Oh, specials always. One last control before launch the space-time throttle power. Yes! I did it! And we're off! Figures on the buttons. Are we ready to play the game? Who's going to be the lucky one? It's not quite, Doctor. You're not yet. I like this. Perfectly safe. That's perfect, Doctor. Play. Wait, where are we going? Is this like a next-time trade? Here we are. London after the great flood of the 20th century. It's actually a next-time trade. Oh, my God. It's the shadows of the Vastun Arad. I never met them, did I? Ah! Oh! Easy. High five! I say most of it's because of me, but that's alright. Well, that's okay. You're wrong. Oh. But yes, thank you for joining us. I hope you enjoyed. This has been Rory Pond and Matthew Smith. Let's play. Yeah, we played quite a lot. With each other. Wait, no. Hang on. That's a bit hemorrhotic. Congratulations. You were completely a venture. Now why not try one of the other episodes? Why is she looking at me like that? Because she doesn't like you. She likes me. That's how we know. Ah, it's not that. She looks like that. Right in the comments, hashtag Rory is the winner. Don't do that. Don't control the comments. I'm the master. As you know, you know what? Comment who's the best? Matt Smith or Rory Williams? Why is it either the actor or the character? Doctor Who wrote rules. I like that. See you next time. Goodbye.


This week has been a fantastic experience, I think, for all involved. The first sport security senior management course that we have run for Interpol member countries has seen 27 member countries participating in an interactive and engaging learning environment with the National Center for Spectator Sport Safety and Security. We've heard a lot this week about some of the key challenges to protecting major events globally and it's provided a really wonderful forum for all the participants from all regions of Interpol's member countries to come together, share their experiences, learn together and develop networks that will serve them in good stead in the future. We've got a really exciting time coming up approaching the five year anniversary mark of the cooperation agreement between Interpol and Qatar in its preparations for the 2022 FIFA World Cup to mark those milestones which is the halfway point of the agreement between Interpol and Qatar. We have a number of key events coming up over the next several months. So building on the work that we've done here in relation to the sport security senior management training we have a legislation experts group next month, October 2017 which will bring together experts in the legal field to talk about issues related to sports integrity particularly anti-doping and match fixing issues. In November we have our first major event safety and security conference. It will be hosted in Doha, Qatar from the 7th to 8th of November and bring together really thought leaders in the field of major event safety and security to share their experiences, share their good practices and again provide a very effective learning and networking environment for those tasks with protecting these major events around the world. Following on from that in December we're back to Doha for our third safety and security experts group and the focus of those discussions is going to be around how the police and the public safety authorities work with the private sector particularly the private security industry to protect our public spaces. So it's a very busy time, a very exciting time for us as we approach that halfway mark of the project in the agreement with Qatar.


So a new adventure begins maybe ladies and gentlemen welcome back to the channel My name is Jack the Buster I should know be today We have this a 37 of our hunter hunter watcher called King and going King being of course going father who we know Let know little to nothing about aside from the fact that he restores like ancient ruins I'm pretty sure that's what so told said but nonetheless like I said We were off on a new adventure killer one going departing from the heavens arena and I guess back off to whale island to check in with I totally blanking on her name We're gonna hop right into it. You need to channel chop us up hit that big web boss I miss you feature episodes don't without like on this one Let's just hop right into it if he gets here this it gets there this episode. That's gonna be so wholesome He's okay all safe and sound I bet she couldn't even believe her ears at first Almost like she's not even surprised that he's okay and that he was able to take care of himself He made a friend. Oh, that's right. He has sent her letters.  Yeah, bro. He's taking a bubble back the home life so relax He's without tradition His little speed back to the seat, bro, if only she knew like the benefits that came with it Imagine it snapped. There's no replacing that bad boy Dude, this is so freakin wholesome. I can't they're all fun and adventure Something to eat the forest. He's like ah same old gone. Ah, this is the tree that we first saw him on He's teaching him a fish Well, they weren't lying they found something to eat There's a lot to show killer what to First friend both of their first friends.  Now the title of the episode is ging and gong, so I'm guessing we're gonna get I mean maybe more info on his dad this episode Hopefully The bad feeling that his dad's not even alive man like like this entire time He's gonna search for him and search for me. He's not gonna be there. He's gonna be gone Just like a feeling in my gut Yeah, okay There's no driver anything no goal hard to care about What you like about looking forward when you don't have anything to look forward to There's so freaking adorable, bro. Yeah, you said it was such a serious face Oh, no wonder he never really had any friends here. I was never friends with her Except for that one girl they both had this one girl that like I mean unless you know what was closer to her than gone was with her He's like Do you like being my friend?   Oh, wow, that's longer than I thought She knows it's inevitable she really does Huge it's a little box Oh shoot she been holding out on us like this, okay spill Is going technically her nephew then I'm trying to find the tree No, nephew would be brother Got a cool motorcycle Yeah, he looks like going something I want his face looks so much like going I'm very curious as to what's in this box then I feel like that line is gonna hold a lot of value later on Wait I see He wasn't even born here Wow Okay, so I think the mother might be alive too then One of them has to be dead, but So the mother is actually even more a mystery than the father But he's inserted his father because father had that drive to come a hunter and he wanted to figure that out Yeah, what's the box? That's all she knows Can I insane amount but still it's important like I said, we hadn't really known anything about the mom or shenian been mentioned Is it jing or ging? He just wants to know more Has he never used his name like that does he as he always said my father I don't think I even realized it This is so cute Jing I think it's jing I can't tell I feel about him because he kind of abandoned going but at least he abandoned them in like the right hands, you know But also I mean, where's the mom too. I mean he went off and had gone So she probably never even met the mom Looking for So is there really nothing in the box is it just a box I'm so confused It's literally looks like going with short hair. It's bizarre. I feel like the two Silver bracelets are very defining too.  What are you gonna do? It's a trick box. So it's probably like interconnected until you can find the one way to open it True yeah, can't smash it or anything maybe like if he can get it open It'll give him like a hint to where his dad might be because he said yeah He said don't give it to him till he's a hunter. So he would have the skills at that point Does it have something to do with like his hunter card? Yeah He's like magnetic or something Just start like tapping it on the outside Oh Bro me and gone in the same thought this makes way more sense Oh my gosh, what's open what is that? Yeah, but they have like inscriptions Nothing was connecting them Does it I don't know I mean it was just for an end to keep it together Yeah, okay, what about the box inside? Oh true. Oh Dude, this is crazy What is that? Oh, this has to be stupidly important put the ring on oh it has the inscription Inside the ring No, I don't think so. Maybe it's to protect him Copy the cassette tape. Oh dude. This is gonna be crazy. What if it's a message from him? Like it's a fancy looking cassette That is so He is a very intimidating commanding sounding voice I mean the thing is to all the stuff in this box looks very very I guess like well made is what I'm trying to say like it's not some cheap stuff Like I feel like King is gonna be a very big deal. It's building him up to be but this kind of solidifies it What are these these instrument? What is this ring the ring itself looks like it would give you like superpowers I'm glad we've wrapped all the way back around to Whale Island and Mito has gotten to see how far gone has come But that he's the same kid and she treats him the exact same and it also gets that that really cute little moment where he talks About how he sees her as his mom But I feel like goings month-long stay here is going to be cut very short if this tape gives us any idea of where Ging actually is right now because that is goings entire goal right now And he's gonna book it if he knows exactly where to go with that being said has been in for episode 37 I'm Hunter Hunter watch your drop a comment down below the way through the episode We thought my reaction read that bombshell on a scale of one to ten and as always I hope you guys have a Wonderful rest your day as we usually want to catch you in the next one this base