So, welcome back everyone. So, this here this equation that we wrote out was the Bellman equation for when you are considering the when you are considering history dependent randomized policies. Now, what is this? Let us look at this equation a little more closely. Optically this looks very similar to the equation that we had for Markov policies. Now, what is the notice that the notice that here once again the equation asks us to find these functions for every value of the argument. So, in other in particular you we want we need to compute this function j t of h t for every h every possible history h t by doing this optimization here on the right hand side. We again have the terminal condition where j n of h n is this defined to be R n of s n for all h n where R n was the terminal reward. So, this is this is essentially very similar to what we had for Markov policies. However, the complexity of solving something like this is far more simply because you have you now have to do this for every possible history you have to do this for all h t here which which then entails an extremely large number large number of optimization problems to be solved. So, you need to do this for each and every possible history that could be realized in the problem. So, what we will do now is actually use this particular equation and conclude from here that without loss of generality one can actually work with only Markov policies. And the way to do that would be what way we would do that is by showing actually that this this function here j t of h t is in fact not even a function of h t it is a function only of only of s t the only of the current state s t. So, in other words although this equation asks us to write this for all possible histories we will actually show that it one does not need to do this for every possible history it suffices to do it only for the current state and that that tells us everything that we need to know about the about the value function. And there as and from there we basically conclude that therefore that the optimal policy can also be chosen as just to be a function of just the current state one does not really need to know the entire history. So, this is the agenda for the for now for us. So, the are before I proceed I want let me let us note down a few let us make a few observations. You note down we have a few several j's now that we have written out. So, first was this quantity j star j star and we wrote it to be a function of s 0. So, this is the optimal policy or sorry the optimal value value function for the entire problem for the MDP as a whole. Then we had a j pi this is the value under a policy pi under a policy the value that you would get under a policy pi. Then we had also j pi t and we wrote this to be a function of h t since we were considering history dependent policies. So, this here is now the value of the truncated problem value or reward by value I just mean essentially expected reward remember is just means expected reward truncated problem. So, what is this truncated problem this is from time t onwards this is that is this particular quantity then there is j star t sorry this is from time t onwards under policy pi under policy pi and j star t is the optimal value or optimal reward optimal expected reward of the truncated problem and this was under history dependent randomized policies under policies from pi h r and now we just wrote out a third j which sorry a fifth j which is j t of h t this is the value function computed through the Bellman equation. So, this is what we get when we write out the Bellman equation. So, that is j t of h t. So, what we get from the Bellman equation is has no star and no pi on it j star t is the optimal reward that we would get of the truncated problem overall history dependent randomized policies j pi t is that reward that we would get when we have a policy pi starting and we are looking at the truncated problem and j pi without the index t is the expected reward under a policy pi but for the entire problem and j star is the expected reward for the optimal reward for the entire mdp. So, now our main result is going to be that there is actually there is not much difference between some of these quantities in particular what we will show is that j t of h t is actually nothing but j star t of h t in other words we will show that these two quantities here are equal. So, and the way to observe this is actually by noting that when we are by is again coming from the principle of optimality. So, the principle of optimality tells you that if you are optimal for the entire problem then you also are optimal for every sub problem that emerges then now I can apply this principle of optimality from any time onwards. So, if I am if I have a policy which is optimal from a certain time onwards and then look at say a time t onwards and then I look at the policy look at a problem from a time t dash where t dash is greater than t then that then I would be then that policy truncated from t dash onwards would also be optimal. In other words the what this would mean is these functions which are being computed recursively here are actually what they are computing is the optimal reward is they are in fact computing the optimal reward because after all at the terminal stage we have when we are at the terminal stage we have this is the this is the optimal reward and the optimal reward at the the penultimate stage would be given through this equation and from and then the one before that would be given by a similar equation and so on. So, consequently it is actually follows rather in a rather straightforward way that we have the following theorem. Suppose J t for t equals 0 to n solve the Bellman equation Bellman equations then we have that J star t of h t is equal to J star is equal to J t of h t for all histories h t in capital H t and for all times t. So, if you are able to get if you have a bunch of functions J t that collectively solve these Bellman equations that means they are obtained from these Bellman equations then you can then you can conclude that they actually give you that at any time t you have that and for every history h t you have that J t is equal to J star t. Now the this is actually very interesting and powerful because J star t remember was refined for just some one instant in time and J t in order to even compute J t we had to do you know you had to apply Bellman equation up until you know starting from n going backwards until time t. So, in the process of solving Bellman equation we actually end up finding all the J t's that we need to solve that we need. You cannot sort of isolate one of them and find only one of them at that time at any at a time because they are all interlinked and they are all the computation works backwards in time and you need to compute the the the later ones in order to compute the one the prior ones. Now, but once all of those computations are done what this theorem is basically assuring us is that you have actually found the optimal reward not just for a one particular time but for every time and for every history right. So, every history for every history and for every time J star t is equal to J t J star t of h t is equal to J t of h t. So, now let us let us take this one step further and let us see what this has to say about the dependence of J star J t or equivalently J star t on on s t right. So, here is our next theorem for each t from 0 to n J star t of h t depends on h t only through s t. Now, the we will we will actually prove this particular result but notice that this claim here is talking of J star t which is the optimal reward for a truncated problem starting with a history h t it is actually if one less simply looks at the problem in its the truncated problem by itself it is not evident that the history is irrelevant but once one notices that this is in fact equal this J star h J star t of h t is equal to J t of h t. In other words you see the once we have this above theorem it is possible for us to use Bellman equation because that is what defines these J t s and then conclude something about the nature of J star t of h t. So, that is what we will be doing now. So, we will prove this we will prove this by induction the proof will be by induction. So, to begin with let us notice this that we have J n of h n which is equal to J star n of h n and that is equal to r n of s n right. So, this is equal to that the terminal reward. So, in other words is J n is a function of only s n or equivalently J n and then that is and hence J n star as well but I will not keep writing this again and again. So, J n is a function of only s n. Now, what we will do is assume that the claim is valid t equal to say k plus 1 till n. So, form a time k onwards k plus 1 onwards the claim is we will assume that the claim is valid this is our remember this is our induction hypothesis. Now, based on that let us ask the next question let us now do a calculation. So, now let us look at J k star of h k that is equal to J k of h k and that is equal to the maximum over a in a sk. So, this is I am now using Bellman equation. So, r k of sk, a plus J in s probability of J given sk, a and J star k plus 1 k plus 1 now has to be a function of h is in this. So, in place we can write I can write it actually J itself instead of writing J star. So, let us write J and this is a function of h k plus 1. Now, by noticed that so firstly notice that this here.  So, remember that, but also remember by induction hypothesis by the induction hypothesis J k plus 1 is which is of h k a J is equal to J k plus 1 of J and why is that well that is because J here is the state at time k plus 1 the state at time k plus 1. So, therefore, this is actually J k plus 1 of J of small j. So, once I put so this can now be substituted. So, I get max of a in a sk of r k of sk, a plus the sum from in J in s p of J given s comma a J k plus 1 of J. Now, let us observe a few things about this. If you look at this, I have made a small mistake here this should be sk yeah. All right. So, now let us observe a few things about this. So, the if you look at these terms here, let us see what where what what we have here. See this here is depends on sk this here depends on sk this depends on sk all right. So, you have a dependence here on sk, but you do not have any dependence on any of the any part of the history prior to sk. So, in other words this here this entire expression here is actually dependent on only is a function only of sk. So, this here is a function only sk. So, as a result of this although they have an h k on the right hand side what he left hand side what you have on the right hand side is a function that depends only on sk consequently consequently we have that J k J k of h k is equal to is just a function of sk J k of sk and which means then by induction the result holds for all t. So, we have concluded that the value function at any time at any time t and hence also the optimal reward from for any truncated problem from t onwards all of these are a function of only the state at that time. Now as a result of this we can we we also conclude we can also conclude another important thing we see that when we are maximizing here when we are maximizing here now the optimal the right hand side here is a function of just the current the state at time at time k. And remember that from the Bellman equation the policy that the policy is simply obtained by maximizing this quantity maximizing this right hand side the maximizing action here defines for you the policy and because now the see when we first wrote out the Bellman equation here there was a dependence on the history there was a dependence here on the history there was the h k was present here in this problem. So, in general the of the policy would have been a function of h k but however we have now been able to reduce it to a function of just sk. So, from here therefore we also conclude the that the there exists that okay let me write it this way we can also conclude that the action the optimal action at can be taken to be a function of s t all right in other words. So, the reason that is because this quantity this particular expression now does not have any trace of h k. So, it only depends on sk therefore the maximizing action also depends only depends only on sk. So, the optimal action can be chosen to be a function of s t. So, as a result of this in fact now we we also observe another another thing here that there is no that this this entire the entire if you write out another observation from here is that there is nothing to be gained also from randomizing simply that is because the this particular expression that we get is the same as what we would have the Bellman equation that we are getting here is the same as what that we would have got if you are optimizing over deterministic Markov policies. So, as a consequence of this so we conclude two things no benefit in randomization and no benefit in the history in using the history in other words there is this is our theorem that there exists a deterministic Markov policy that is optimal. In other words if you if you look at this if you look at the max of this over all policies that are Markov and deterministic this is equal to the max of of the same quantity over policies that are history dependent and randomized. So, this is therefore our justification for why one can work with only Markov policies. So, the once the history is known to us the the state is also known to us but the cost depends only on the state and the and the probability the transition probability also depends only on the state and as a result knowing all this history is immaterial. So, what you only need to know is the current state. So, the optimal reward or the optimal cost that we incur is a function only of the state we are in. Now, this has become this has become possible primarily because of the structure of the Bellman equation that the Bellman equation allowed us from the Bellman equation we were able to reduce everything to this sort of a structure in which only the state of the system appeared. So, this is the key structure that is that is being exploited in order in all of Markov decision theory and therefore all of Markov decision theory basically works with primarily works with Markov deterministic Markov policies. So, what we will see in the next class is that this kind of an extension this kind of a result is not true once we are once we allow once we assume that we do not know the state remember so far knowing the history was also giving us knowledge of the state. But there can be problems in which knowing the history is not completely telling us what the state of the system is that gives us this gives rise to a different and interesting class of problems called problems with imperfect information. So, those will we will start discussing those problems from the next class.


Well, the internment of Japanese Americans is so well-documented, it's wonderful. You can go back and look at primary sources, they're just text that are the laws themselves, that are the cases. The Supreme Court did consider not one, two, three, but four major cases that remain important precedent to this day. Those are a part of the legal background to this story. But in addition, there's been a wonderful effort to collect oral histories through the Densho Project and others, much of which is available online. And you can hear from folks who are in their 60s, 70s, 80s, even older, who will recall their memories from the time when they were, in some instances, just children. But in other instances, teenagers and already grown up as they recount the stories of life before Pearl Harbor, during the camps, and of how after the war was over and the normalcy of the 1950s was upon us, how they attempted to put together the pieces. And this gives you a sense firsthand from people who were there, what it was like. You can also take a look at the many artifacts that have been preserved. There are art exhibits now of what people made, arts and crafts projects where they took cast-off bits of wood, where they took debris and carefully painstakingly carved small toys and jewelry and other objects that they gave to family and friends within the camp. Because these camps involve so many people, they involved children, the elderly, entire families. This was the relocation and confinement of an entire community. It was very different, and it gave people an opportunity, even as they were behind the barbed wire and under the gun towers and the watchful eye of soldiers, to try to put together life, a different life to be sure, but to put together a life that had a sense of community and belonging. And there are many objects now that are a testament to what that life was like. There also are objects that have been saved that were part of the internment itself. Washboards, for example, that women used when they were doing laundry. This is when they didn't have washing machines and dryers. They had a single day of the week dedicated to laundry where all the women would go out and in a communal manner do the laundry, and that's painstaking, back-breaking work that most of us don't know today. But they had washboards, old-fashioned washboards and tub, steel tubs that they had to use. And there are museum exhibits where you can see these objects, where they've been preserved, where they actually have tried to recreate the barracks or one of the rooms that they had. And so all of this helps to give people a sense of what it was really like. And these primary sources are so rich in their detail and in what they convey, the meaning that is packed in there, that even the best secondary source, the best nonfiction books that have been written or fiction even about the internment camps, those can't come close to capturing for us what a primary source can show us, what it can teach us. And so, though, we need to have the background and context where we're just looking at an object at a washboard or a basin. Well, you might not even know what that is. And so it has to be explained. It has to be situated within the sense of life as it was lived then. And that's why it's important for historians to do the work that they do. So the secondary sources complement the primary sources. They help us by giving us the interpretive framework, because if all we're doing is looking at objects and we don't understand what those objects were for, how they fit into the day-to-day lives of the people who owned and used them, well, then we've really missed out on an important part of the story.


In this reaction alkyne reacts with alkyne reacts with alkyne reacts with mercury acetate in presence of water in presence of water to form hydroxymercureal compound is that hydroxymercureal compound in bracket right on oxymercureation this first step is oxymercureation which on reduction which on reduction with NaBH4 which on reduction with NaBH4 gives NaBH4 is sodium borohydride good reducing NaBH4 is sodium borohydride which on reduction with NaBH4 gives alkyne in bracket right on demercureation ok it is demercureation ok alcohol the reaction is suppose we have CH3 CH double bond CH2 on hinted with HG OAC fold wise this is the first step reagent HG OAC fold wise in H2O in the second step we use NaBH4 alkyne radio ok the product of this reaction is alcohol right so the product here is CH3 CH2 OH acetic acid cct ac acidic acid it C is this Cns3 C double bond ok this slide S scenario is this ув The entire thing if I write down here it is HgO, OcCs3, C double bond, O with HgO. So what is the problem directly after the first step? I will write down, mechanism will discuss 2-3 points to try to understand the mechanism and discuss, okay. We will get a cyclic ring into this, intermediate cyclic ring, okay, no rearrangement here. You see overall what is happening in this reaction, across this double bond H and O H get attached, right. So the first step you will get an epoxy mercurinium ion, the intermediate stage, right. And that on reduction gives this alcohol, okay. I will discuss the mechanism also. One note you write down here first. In this reaction, in this reaction, carbocation does not form, hence no rearrangement. Carbocation does not form, hence no rearrangement, okay. Next point. This reaction follows, this reaction follows anti-addition of, anti-addition of H plus and O H minus. H plus and O H minus. Next point. The first step is oxymerculation, oxymerculation, oxymerculation in which mercurinium ion forms, mercurinium, mercurinium ion forms, which is a three member HG plus, I will write down the reaction, mercurinium ion forms, which is a three membered cyclic ring, three membered cyclic ring, okay. So the first step of the reaction, if I take this example, CS3, C double bond, CH2, CH3. This on reaction with HG, OAC, O2, and H2. Or first step of this. So what happens, this pi electron is taken up by this metal and we get a mercurinium ion here, CS3, C, CH2, HG, delta positive OAC. Here we have CS3, intermediate. This we call it as mercurinium, cyclic mercurinium ion, which on one reaction with water, which is present here on reaction with water. This lone pair attacks onto this carbon, this is also delta positive because this loses pi electron, okay. This lone pair attacks onto this carbon, always remember here this attack is on more substituted carbon and not this one here, right. So it forms CS3, C, CS3, OH2 is positive, CH2, XG, OAC. This attacks over here, this lone pair is taken up by this silica. Now from this what happens? The removal of proton H plus forms OH here. So the product is CS3, C, CS3, OH, CH2, HG, OAC. This loses electron, right. And because of this bond also mercurinium ion, mercurinium also one has slightly positive parts. Sir, if it loses bond shouldn't it be a proper plus rather than just a delta plus? No, no, no. It's not like, it's a, tendency to form a three-membered, it is not completely developed. Okay. Because it is very less stable, three-membered ring. Angle strain is very high. Yes. So it won't form there. Okay. Sir, where does that OAC hold, where does one more? It forms a complex, we'll have a mercury compound there. It forms a complex that we are not writing it up. Here also it forms. We'll get a silver metal and in the final reaction you'll get this also. Acid. Acid. Okay. This is here. HG is here only, right. In the next step it will come out. De-mercuration. Since mercury is getting attached with this carbon atom, so this step is oxy-mercuration. Right. Oxy-mercuring acid is attached here. It is oxy-mercuration. Now the next step when you add NAVH4 in basic media, that NAVH4 provides what? Hydride ion that will replace this, forms metal of this and acetic acid. So after this, the second step you can directly write down. CS3, TCS3, OH, CS2, HG, OAC with NAVH4, OH- it gives CS3, CCS3, OH, CS2, HG plus acetic acid. So what are we trying to achieve now by doing this? So what's the point? So how do you drop that acid? How do you drop that acid? Or does it come out itself? This one. This one? Yeah. This is positive charge, right? Yeah. It is, it is not stable. So she said being electrocuted. Leave and we get a carbocation. No carbocation does not form in this reaction. So to stabilize. So in the dehydration of alcohol, we remove the H2O plus. Yes, yes, yes. For that we have, we heat that. Elimination always requires some temperature for that. You have to heat this and it will come out. Sir, the second one, we said we put it in, like in the prime of OH-, whether that means that we put it in water. We are getting emotional. Water, if you put it in water, then it will attack the lone pair. See, for elderly ones, for any reaction, the path mechanism is different. You cannot say sir, why can't we put water here? If you put water, then has its lone pair present water molecule? That it will attack onto this. You will probably get tired or exhausted. We do not know what will be the product in that case. Okay. That's why we are using this. Actually, this Na, why we are using this base here? Because this will take this OH-, forms NaOH, right? And then this boron pH board releases hydride ion, which attacks onto this and the hydrogen attacks with this. Silver, sorry, mercury material form and AC OH also form. So for all the reactions, you cannot change solvent, right? You do not know what is the behavior of that solvent under a given set of conditions. Okay. See, all these things, mechanism are not important. What is important that we have to keep in mind is the attack of this, see this H2O is behaving as a nucleophile here, right? H2O is a nucleophile. OH is not a nucleophile. Eventually, OH gets attached with this carbon atom when H2O attack onto this. But nucleophile is not OH, nucleophile is H2O. Nucleophiles are the molecules or species which attacks onto the positive charge, the carbon atom. So this is behaving as a nucleophile, right? It is a nucleophile here. Okay, it is a nucleophile. So you always remember the nucleophile attacks on the more substituted carbon, okay? This is one very important point you have to remember, right? Okay, overall what is happening? The addition of OH and H plus. According to what rule? You see, according to Markovnikov rule, right? So this reaction follows Markovnikov rule. Addition of OH and H takes place here. You're right on this. Follows Markovnikov rule. Addition of OH and H. So how are OH and H easy on different sides? Yes. How are they on different sides? There's an attack over here. This is all over here. No, no. This is what this will be. This is what I have written. This is why I am not doing this on this side. Because this attack is anti-antibiotic. So why is that? Because if this attacks on this side, right? Then this bond is difficult to break. And there will have the hindrance of this HG bond. There's an entire group with a bulky group. This will produce hindrance to this attack of this water, which is a nucleophysium. That's why it's an attack from the backside. Anti-antibiotic. Okay. Sir, if you find an attack with the most functional carbons, it doesn't mean that an electrophile attack will lead to an over-touch. No, it's not like that this year. Actually, this part of the time is stable because of this tool. So, since this is stable, so it is stable. So the positive charge here forms very easily in comparison to this. That's why this will attack on the side where it's a more positive attack. Understood? Yes. So it is Markovnikov rule. Addition of OH- and F- plus on the double bonded carbon atom. First step till here is oxymarculation. Next step is removal of the compound of mercury. That is demarculation reaction. Okay? Markovnikov reaction. Demarculation reaction. Okay? Now, one thing very important here, sometimes in the question what they do, they don't write here or give here H2O. Listen to the gap for this one, right? Because you see from where this OH is coming from which molecule? From H2O. So if they replace this H2O with CS3OH, then the product will not be an alcohol, but it is an ether in that case. Okay? Why? Because the nucleophile here is what? CS3OH. Here CS3OH will attack on to this and then H-plus goes out, so we'll have 4 CS3 will attack on to this carbon atom. Got it? Okay? So if you have CS3OH here, the name of the reaction is oxymarculation. No, not oxymarculation. It is alkoxymarculation demarculation reaction. Got it? Write down the next reaction. Same thing. Instead of H2O write down this, hitting you write down alkoxymarculation demarculation. What? It's not antidex. Antidex. Antidex. Back from the opposite side. It is alkoxymarculation and demarculation reaction. Sir, I'm still confused about how this is going to work out. See, this pi electron, you can combine this with Hg. But it's forming a bond. Right? This will be like a bond. This thing, this was the 2 electron. Distributed between the 2 atoms. Now it is distributed in 3 atoms. Okay. Yeah. So what? It forms a complex. Actually it is like bromine halogenation. Yeah, halogenation. Similar to that. Bromonium ion forms over there. Halonium ion like this one. Similarly here also. Sir, but here the C isn't getting formed. Here also we'll have carbon dioxide. Same thing here also. Okay. Sir, why doesn't that carbon halogen? This carbon also halogen. But this one is more stable because of these 2 with halogen. That's why I'm showing you. So that it will attack over here. Okay. Okay. So we'll write down this reaction with HgOac, CsGOH. The name of the reaction is alkoxymarculation, demarculation. And the product in this reaction is an ether, not alcohol. Okay. Done.


No, no, no, no, no. I was reading my whispers, frick. I was reading my whispers, frick man. I'm right behind me.


Okay, we're back. We're live with likable science here on a given Friday and my co-host who is the host actually really is Ethan Allen. Hi, Ethan. Hey, Jay. How are you doing? Good. I really enjoy these conversations with you. It's like Mr. Science, you know, except you're the scientist, not me. Anyway, so the thing we decided to talk about today is about artificial intelligence and what we call this, new miracles with artificial intelligence and what what provoked this whole discussion is a piece in this morning's MIT newsletter which talks about artificial intelligence with the capability of taking large volumes of text of any kind and summarizing them in short paragraphs to tell you what they're really saying. I remember in years past in my law firm they would take the junior associate and tell here's a book of text, you know, you read the whole thing, make little notes and then tell us in a paragraph what it says, you know, or the West publishing company, all the legal publishing company, give you one sentence about what this case says and now it can be automatic. It's quite remarkable. How do they do that, Ethan? Yeah, I know. It's a sort of this quantum leap from computers simply understanding words to actually understanding the meaning of whole sentences and whole paragraphs, right, which is really a very different thing to grasp the point of and it's not just go do this, you know, it's much much more sophisticated to be able to extract meaning and and put something back out. How do you do that?  It's like magic. Well, it's again, it's what they call this deep learning business where you don't try to just program every possible scenario into it. You set the machine up and let it start extracting meaning out of the world as it finds it. Imagine, you know, how powerful it is. You have a loan document, could be anything, and the machine, the machine is looking through it, looking for words, looking for phrases, trying to, you know, get a pattern, pattern count, what is that and the other thing, sort of like what NSA does with our email and trying to make sense. I'm sure NSA is using artificial intelligence in the same way. I'm sure they must be right. But if you think about it too, then this gets in the whole thing, you know, who, who has written the software to extract what from it and to give it what kind of slant, right? I mean, if it's, if these machines start reading the bills that are going through the House or the Senate and extracting them, are they going to give it a conservative or a liberal slant in the summary? Yeah. Well, you know, it's really mind-boggling. So the thing has to go through. It's going to find a word. We're going to put the word over here. Let's hold that word. That's going to find that word again over here in a different context. And then we see what the context is and we compare the two and then we learn something from that. And then it's doing this with every word in the whole, you know, whatever, thousands of pages, whatever it is, and then it's sort of making sense of which words are more important, which words are repeated in what context. And it's, it's like learning. It's making rules about what this is all about. And then ultimately it's coming up with a conclusion. It's brilliant to be able to do that. But they talk about reinforcement learning now in these machines, where these machines are basically set up to reward them, I guess, reward themselves on. I don't know what the machine gives itself as a reward for sort of finding a pattern and sort of being able to make sense out of that and seeing how that repeats and can be used again.   But it basically learned very subtle, very sophisticated things. They point out in one of these articles that Alpha Go 37 moves into the game made a move that when people saw it, the observers or expert Go players looked at and thought the machine was crazy. There's some fluky, weird mistake that it made because it was just placed a new dot somewhere that made no sense to anyone. And later on in deep analysis, they found out that this machine calculated one that no person would virtually ever make this move. There's like a one in 10,000 chance that anyone would ever make that move. So no one would think about it. And two, that it was a very powerful move to make. And therefore, of course, it completely sort of blindsided its human opponent with it and blew the guy out of the game. So it would be very, very creative. And it's not like that move was programmed in its inventory of moves. It invented the... Exactly. It saw an interesting opportunity. And yeah, so this was apparently a good thing to do on a lot of levels for it to win the game. Think about that as a method, a pathway to deal with problems of all kinds in all endeavors, human endeavors. Then you realize the applicability. I want to talk to you about that. My thought about this is that it's going to change humanity. It can write, for example. So when it goes into this document and writes a summary for you, it writes it. It's writing the English language or whatever language. It's actually composing sentences. That's another element. It's talking to you in English. It's not talking to you in the language, in the words and the terms that it has just analyzed. It has created it's a third-party observation and it's giving you an explanation of what it has learned. The learning and the explanation are two separate things, so that's another incredible function. Right. And presumably it could give you a summary in Chinese or Arabic or Hindi or whatever. Language is no barrier. I wonder what the code looks like. It must be incredible. So let's talk about the implications and the other ways that this kind of thing... One thing, for example, we just had a show about congressional process in Washington. Oh, that's so hard. Now is really hard. But we have legislation that is special interest legislation. We have legislation that is based on animosity, based on all the wrong reasons, emotional, low reasons, bad reasons. Why can't we do legislation with artificial intelligence, Ethan? Can you think of a good reason why we can't do that? Couldn't do much worse than I can remember. That's true. We can only be better. At least it would look at evidence, presumably, and say, yes, there are reasons to believe that this is a good policy and that we should do this and no, there aren't any reasons to do the other. Or there are reasons for it and this is going to benefit these groups, but it's going to hurt these groups. I'm not saying that we can get input. People could come and testify. They can testify as long as they want. They can write tones about whether to do it or not do it, and this artificial intelligence machine would be able to summarize in its own learning process what's the good, what's the bad, make some judgment calls, and incorporate a huge amount of data. Go make research. Inquire, go extend inquiry. But again, then you're placing a fair amount of trust in whoever sort of built that machine. That's another issue. That their political agenda is neutral or positive in some sense. That's the scary part. I mean, I'm sure that within the next decade or two, because there's a lot of smart guys and you know how computer programming is sort of geometric. It keeps on getting smarter and smarter and faster and faster. Let's assume we can do this, but as you said, you raised this question earlier. It pervades the whole discussion. What's the agenda here? It's the agenda built in the code. And hopefully now that should become clearer as we move ahead because they're now asking the machines be able to explain why they make certain decisions. So you can see this and we've talked before about self-driving cars. And if your car suddenly chooses to stop, presumably, it's got some reason that it's chosen to stop in the middle of a highway. It's either seen something or it's sent some approaching danger, right? And it should be able to tell you that, right? I mean, it's not doing it so randomly. I'm like, oh gee, I feel like stopping right now. It should be able to tell you. You're in English.  And I mean, we saw, I just saw recently the earliest nuances of this. My wife and I were driving and she was driving and thought the cruise control on this rental car was broken because the car would slow down, sometimes below the speed. And then what she realized was that only when her vehicle was coming closer to a vehicle in front of it, the car would slow down. And as soon as she would move to an open lane, her car would resume its cruise control speed immediately. But again, it knew and it presumed we could have told us this. Yes, you're approaching this car ahead. The screen comes up. It's no effort at all. The screen comes up and says, you know, you're approaching another car, silly. Why are you doing that? We're stopping you. And tell you why. So you were involved in the learning process. You learned too. It beeps at you as you cross white lines now. These cars just say, you know, like, oh, you've moved into a different lane.  Yeah. And it reports you. Yes, and it begins to alert you. As soon the police will be involved, they'll want to have a report of this. And your insurance company too, which will raise your rates. But this all, that's sort of primitive in the sense you're talking about a car that's driven by a human person. Soon enough in not many years, AI, all through AI, the very AI you're talking about will enable us to have automated cars, which is near, near term. And there was a wonderful study recently done on that they actually put, like, 20 human drivers and one automated car into the track. And the 20 drivers, human drivers, they would tend to, cars would tend to bunch up and they'd get these sort of traffic jam things, simply putting one AI car in there actually smooth the whole traffic flow out. And the drivers wouldn't bunch up in the same way because this car understood and sort of, you know, it's the same kind of thing if you watch a flock of birds, right? And they move as if they're one organism, right? The flock will swirl and stretch out. And all these birds are, they're not all plotting elaborate course, right? They're actually running off of fairly simple rules about how they behave to their relatives. These would be the next bird. Right, yeah.  The swarm, the flock, they already got this kind of thing for drones, right? The drones fly in a swarm. I mean, all kinds of drones are doing this now, you've seen them. And that means it's the same sort of rules. They all connect up in some way to watch the guy next to them before you know it. The swarm is moving in exactly the right place and coordinated. Right. But you know, this suggests, this sort of coordination suggests, you know, more about my legislative initiative. You know, it means that the legislature would compare notes with other legislatures. In fact, so the national, I mean, let's assume the country stays in the same general legal configuration. So Congress is doing its thing, taking testimony, making decisions, giving reasons, detailed reasons why it made the decisions. It's comparing notes with the state legislatures who will likewise have a little box about that big, right, doing AI, what's going on in the state. I guess it's the old federal state system, but query, do you need multiple boxes? Or you can have one big box. And query, what is the agenda of that one big box? What if some wise guy programmer decided he was going to put something in there to favor this or that or the other thing? And now you have the whole country run on some kind of corrupt agenda. Right. And I mean, once you're going to do it on that level, like why bother with respecting national boundaries? Let's just go up and put AI in charge of the whole world. Tell us how we should be behaving. By the way, we didn't talk about the president yet. But I don't know why you need a human being to be a president. Why can't you have an AI president? I think it'd be a lot more rational than what we got now and a lot safer and a lot more in tune with the people. The machine would listen to the people. It would summarize all the testimony. It would come to some reasonable conclusion if programmed. Presumably would not send out nasty tweets. Right. It would send out nice tweets. Explanation tweets. See, this is a great idea. I like it. I like it. I hope it happens soon. But wait, we haven't talked about the courts yet. I've always thought, you know, I mean, the law is mechanized in a certain sense. You go to court. Everybody's got the precedent out. You can find it very easily now on the web. You can not only find the case that supports you, you can find all the cases that oppose you. And you can argue them out in front of a judge. Well, why can't that judge be a little box this big, artificial intelligence? And he listens. He listens dutifully to everybody. He summarizes in his own mind. He's really smart, just a little box. And, you know, he makes a decision. And then he explains his decision. And you can see that he's in the right place. You can see he's made good judgments. Why can't we do that, Ethan? No. And there's actually virtually no reason we couldn't do that. I presume we'd want to put a system like that in place with some sort of human oversight where the judge box would make a recommendation. And a human panel might, you know, at least for the first few thousand of them, would decide whether that was reasonable or not reasonable, right? But, you know, I really think this is coming. And the question is whether it looks like the government we have. Do you have a president? Do you have a congress box? Do you have a state box in every state? Or is it just one box that all the, you know, the checks and balances are already in the one box? You know, it's hard to imagine in the human condition a box that would be smart enough to say, now, now, boys, just relax. We're not going to do any wars. Everybody has to act properly. And we're going to make all those value judgments for you. Just listen. And if you don't listen, we'll make you listen. There we go. Scary. So much so, let's take a break. Okay. We're an exciting discussion about AI. We're calling this here Unlikable Science with Ethan Allen, my co-host. We're talking about new miracles with AI. And it's really, it's dazzling. It's miraculous, but it's also a little scary. Talked about the, you know, the swarm effect and how an object which is run by AI would know what the other objects are doing, even if they weren't the same objects. Everybody would have its place. You could order society this way, not only the cars on the highway. Everybody knows its place. And so, you know, the power of this to, you know, govern, to make political decisions, legal decisions, regulatory decisions, engineering decisions, it would be nearly perfect. Well, I don't know. Did you ever read a book many years ago with a small child, I read it, A Wrinkle in Time, Madeline Dlingel's rather dystopian view of when there's a, I want to tell us about it. All the kids are bouncing their balls all in sync with one another. I mean, just, yeah, very, Jim Carrey movie. What was it with the white picket fences? The Truman story. Everybody behaves just a certain way. Don't diverge, don't. So, you know, that's the problem. But between now and then, before you even get to that problem is, will people accept a replacement of human judgment, however flawed it may be for the machine? This is exactly why I brought up with the thing earlier with the cruise control in the car. People are accepting it.  Yes, people don't want to get driving their cars, maybe, but they're perfectly willing to have the car tell them, basically, and indeed act in their best interest and make their life safer and or easier. Now, there are maybe some drivers who don't want a driver who wants a tailgate, may find it's very hard to tailgate. I don't know. I'm smiling because I'm thinking of two millennials who are coding this thing. And one says to the other, we need a very authoritative voice on this box. Let's see if we can get an Edward R. Murrow voice here, so that when we tell people what they need to do, they'll believe us and accept right away. I think that conversation would probably take place because you have to be authoritative about it if you want people to follow it. And not only the voice, but everything it does, it has to be credible, you know? What was it, Hal, in 2001? Space Odyssey, right? Yes. I forget the fellow's name who was in the spaceship with him, but it says something like, Steve, you can't do that here. And, of course, Hal went crazy. And that's really the old fear about all of this, is that however you program it, you know, whatever the program puts in, however it teaches itself, you know, because the problem is, yes, you could put an agenda in. Remember, though, this is AI, and AI learns, and AI will make its own agenda, and you don't know what that will be. And some decision that it makes, it looks crazy to you, may actually be a very smart decision, as, again, we just saw with AlphaGo, where it made a crazy move according to all the observers, and it turned out to be, it was the move that sort of shifted the whole game into winning mode.  Okay, we don't, it's a special hard-coded thing. As much as three laws of robotics or whatever, right? Right.  Point is that the guy who might write an agenda is not really the end of the program, because the program learns and could find its own way to do whatever it wants, like that special move and go. So, I don't know how we fix that, and I don't know how people not worry about that. Would you worry about that? Yeah, I mean, I'd be concerned about it. I'd want to see, I'd want to see sort of the system and operation in what we call sort of low-risk, you know, situations and have it working really well for a good long while before I'd be willing to sort of turn over my whole life and accept it unquestionably, right? But it's fun. I don't feel that way about cars. I'd happily have a self-driving car and sit back. Yeah. But you know, what surprised me actually is that I figured who got, I think Apple got a permit or something recently from California to test their cars. And the same with Google and not one of the major car companies, too. And what surprised me is they actually gave them a permit, because I think a lot of people in government, you know, with the bureaucratic kind of look at things, will want to retain control. They will not want to have a machine arguably controlled by the programmer doing things on their turf. So they will resist. And I suggest to you that this question I asked you about, would you agree with this, is a question a lot of people would ask. And when government, who holds the keys, is asked that question, they say, I don't want to give up my authority here, because I know in the end I'll be out of a job. Yeah. And I mean, there's an interesting thing. We are actually, we're in the midst of sort of a struggle right now. Are you going to let government control those things, or are you going to let sort of private industry control those things? The marketplace as it were. And I would certainly argue there are functions that are not well controlled by a marketplace type situation. Healthcare and education being two obvious things where I don't really want a competitive market in those, I want somebody with oversight saying what's best for the patients or what's best for the kids. And yes, you consider some economic issues. Well, as you say that, I think to myself, industry can be very influential and very crafty. And they can get the government to be an extension of their self interest. They can get the government to be self interested in their view of things as they are. And so government, at least in our recent lifetimes, I think government has gotten maybe too sensitive to that and has adopted self interest as a motivating feature rather than doing the common good. I wish we could go back to a time when Mr. Smith went to Washington and we did the common good. But you can program a machine to do the common good. The machine would have no self interest. The machine would be programmed to do it in the interest of the community in general. That would be infinitely better than the problem of humanity. Yeah, absolutely. I mean, we are sitting here, we're sort of replaying the tragedy of the commons every day in terms of keep throwing plastic and crud into the oceans because sort of, hey, big oceans, it's not disappear as soon as you throw it in there and it's gone, right? But of course it really isn't gone. It just adds to more crud and more crud. And yeah, a sort of sensible, reasonable overseer as it were would say, hey, you can't keep doing this indefinitely. This is bad for the ocean. It's bad for all the life forms in there. It's ultimately going to hurt your fisheries. It's going to deprive 3 billion people a day of the protein they need.  So let's not do it. Right, right. So we don't do it. It's a priority. It reminds me of the comparison of the American democratic system and for that matter the European democratic system in China, which is more totalitarian. And Xi Jinping finds, and his Politburo and his Central Committee find that a given thing, like, you know, damaging the environment is not a good thing, he can come down and say, no, we're not doing that. And my word counts. Don't have a big discussion. My word counts. That's the end of the conversation. All done. Same thing with, like, sea level rise. We're going to protect this harbor, this island, this shore from sea level rise. That's a high priority because we look ahead. We, Xi Jinping and the Politburo and the Central Committee, and for that matter the Black Box, have the capability of looking ahead and making a decision based on what they see in the future, not within the election cycle. Relative to that, our system is not nearly, our system here is not necessary, not nearly as efficient, don't you think? Right, right. Now our system, yeah, takes longer to change. It's very sort of sluggish to respond to changes. People do get sort of their vested self-interest dig into place.  End of discussion. Boom. Yeah. And it's done. Yeah. And, you know, that's, I mean, both things have that place, both things have that place. I mean, this is going to happen. You know. And the only question is when it's going to happen. And what resistance it meets before it does happen. Right. But we start seeing things like sea level rise and other environmental issues that we, we can't, you know, we, this country, we can't see into the future. We just have blinders on about that. Some day we're going to have to have a machine tell us, no, you got to do this. Yeah. Well, I mean, the recent slew of evidence that's accumulating that points out that a lot of common air pollutants, particularly the tiny particulates that are ubiquitous now, basically are associated with the onset of dementia. And clearly it's not anyone's interest to have a whole population of people getting dementia earlier than they need to, or earlier than they should otherwise. And yet basically people are still, you know, building new coal plants and spewing the stuff out in the air, even though, you know, five years, 10 years down the road is going to be huge human health costs. Public health is huge. And you could, you could save a population, give them much better health care. And you can also, what was it in 1984, where they all marched down a road at the end, that that was the end of them, reaching a certain age or life condition. That was the end of you.  At the end, that was the end. And in the meantime, the population in general, it's like the herd, has to get cold, right? Sure. The population in general, you can say that's immoral, but I'm not sure it is immoral. No. I mean, you look at now, this was from some years ago, I heard this figure, I don't know what it is today. But at that point, they pointed out that something like 60 cents of every dollar in our health care system went to care in the last 30 days of people's lives. Whereas less than two cents at that point is going to prenatal care or something, which is like insane. And what I see recently, the last six months of a person's life in this country, that person will, his health care costs will be six times the average health care cost. It's just, you know, and it's not efficient. And it's doesn't help them very much either. And all of the long-term studies in public health show that indeed giving young children, particularly neonatal, and in the early years, giving them good care, a good healthy food, good environment pays off big time.  And it continues to pay off for days down the road. Yeah.  There's all kinds of junk food in there that is damaging the health of the population. And we haven't gotten to the point where we actually make rational choices, I mean, as a government, about what should be on those shelves and what shouldn't be on those shelves. Because a lot of it is damaging our children and ourselves. So, you know, I do hearken back to the notion that in some cases, a good machine with lots of authority would actually be, you know, a better deal. And furthermore that a good machine could make complex decisions without the baggage of, you know, emotional, hostile, you know, aggravated, imperfect decisions. You know, for example, and I leave you with this rhetorical question, you can respond or not, would a machine do better at the health care bill? You bet it would. Yeah, could it be worse. And so many things are pending in Congress right now. And how long would it take? Not long. Exactly, exactly. Thank you, Ethan. Thank you, James. Always great. Let's do it again. That's a need.


Hi everybody Sean with jaspyscasebrakes.com and jaspys.com here doing a quick personal break of soccer and basketball for Louis He's picked out his box and his packs already. So now it's just time to do it personal breaks available on jaspys.com So we've got a box of spectra soccer from 2016 that we're gonna start with So we're supposed to be looking for five autographs or memory card per box Try to figure out where the autos and stuff are so I can try to set this up We'll just go for it and figure it out together. Oh nothing else in there These cards are awesome. Look it Andres eniesta from Spain. It's a great design Very nice Gareth Bale Not numbered just the base cards, but even those look incredible Mark Homsik from Sulakia Joe Hart Relic Goalkeeper from England That one's numbered 184 out of 199 international fabrics Benedict Haudes From Germany numbered 123 out of 149 Matteo Darmian From Italy numbered 33 out of 199 Phil Agilka from England Jersey and Otto 57 out of 149 Jamie Vardy Jersey and Otto 127 of 199 you've got a die cut of our own Gunnarsson From Iceland numbered 31 of 35 Ermir Lenjani from Albania Numbered 53 of 75 There's the spectra for you now Select cool. Good luck Select 1617 Marco Ruse Tony Cruz Nasir Chawdli is your auto from Belgium Westley Snyder Robin Van Persie Christian Fuchs Yeah, bogey Miguel Leun Angolo Conte Steven Lichtensteiner Not sure if this is yellow or gold Yellow to 48 out of 125 Diego Ruland Miralem Yannick Wesley Snyder Robert Almer Fernando Muzlera These these parallels the green and yellow these aren't numbered, but they are obviously a shorter print Gonzalo Higuang Shane Long Leona Messi Steven Lichtensteiner Gary Cahill Mehmet Topal Sergio Aguero Arturo Vidal Harry Kane Robbie Keen Blue Number 251 of 299 Kevin Strutman Alvaro Morata Luis Suarez Luis Suarez Get one stuck to the back of that Derejo Osirna Anatoly Tomosciuk Silver No numbering Dries Mertens Marcus Rashford Andres Gomes I thought Relic But Cristiano Ronaldo And Yao Motino Dual Relic Dual Team Memorabilia I was hoping I was doing a lot of it, that would have been awesome Gregorz Krizałek Danny Rose Michael Bradley Paul Pogba Neymar National Pride That's an awesome looking card there These are not numbered, but Very nice card Miralem Pianek David Silva Rache Eden Hazard Rui Patricio David Alava Andre Schurl Tiago Silva Yao Motino Fabian Char Germaine Lenz Anwar Elgasi Mario Manzukic Nicholas Otamendi Relic Juan Mata Numbered out of $1.99 Robert Almere Javier Mascherano Wayne Rooney Aaron Ramsey Martin Harnick Eduardo Vargas Dai Cut Numbered to $2.49 Diego Godin And that's the select box Cool Now, Chronicles fat packs We got 10 of them Trey Young Terence Davis Jared Culver Kobe White Matisse Dible Rudy Gobert Jordan Pool Carmelo Kobe White Bradley Beal Nikhil Alexander Walker Carson Edwards-Bronze Brandon Clark Phoenix Jared Culver Matisse Dible Marquis Bronze Cam Reddish Kobe White Playbook Yannis Luminance Kawaii Cam Reddish John Morant Luminance Probably one of the coolest photos he's got of all of his cards Rui Hachimura Threads Paul Gasol DeAndre Hunter Russell Westbrook Kawaii Darius Garland Bronze LeBron James Hometown Heroes Goga Patadze Essentials Darius Garland XR Jordan McLaughlin DeAndre Hunter Nikhil Alexander Walker Cameron Johnson Playbook Cameron Johnson Luminance Kristaps Porzingis Brandon Clark Threads DeAngelo Russell Rui Hachimura Prestige Nikola Jokic Cam Reddish Score Nas Reid Carson Edwards Crusade Ryan Recon PJ Washington Jr Essentials Bronze Matisse Dible Anthony Davis Luka Donchich Playbook Rui Hachimura Luminance Keldon Johnson Zion Williamson Playbook Cam Reddish Steph Curry Eric Pascal Anthony Davis DeAndre Hunter DeMontes Sabones Eric Pascal Flux RJ Barrett Essentials Jackson Hayes Marquis RJ Barrett Playbook Steph Curry Seku Demboya Nicola Melly Jackson Hayes Kevin Love James Harden Chris Paul Yannes Steph Curry Goga Pataze Bronze DeAngelo Russell Cam Reddish Anthony Davis Marquis Bronze Jackson Hayes Yannes Hansa Tecumpo Kendrick Nunn Eric Pascal Kevin Porter Cambo Walker Darius Garland PJ Washington Jr Jason Tatum Taco Fall Eric Pascal Playbook Bronze Rui Hachimura Darius Garland Kendrick Nunn Elements Rui Hachimura PJ Washington Jr Romeo Langford Kendrick Nunn Zion Williamson Anthony Davis Seku Demboya Jamal Murray Cam Reddish Julius Randall Tyler Harrow Chris Paul Very nice. Prism Update Kevin Durant PJ Washington Jr Kevin Porter Jr Seku Demboya Playbook Nikila Alexander Walker Jared Culver Terrence Davis Jordan Poole Yannes Jared Culver Donna Mitchell James Harden Dearon Fox Seku Demboya Score Terrence Davis Bronze Matisse Dible Darius Garland Romeo Langford Bronze Tyler Harrow Romeo Langford Deandre Hunter Darius Garland John Morant Kai Bowman Yannes Kevin Durant Carson Edwards Kairi Irving Romeo Langford James Harden Bronze Kawhi Leonard Hometown Heroes Trey Montwaters Marquis Trey Young Koby White James Harden Jordan Poole Luka Donchich Tyler Harrow Threads LeBron James Anthony Davis Jimmy Butler Carson Edwards Cam Reddish Bronze John Morant Flux Very nice John Morant Recon There you go, Lewis. Thank you very much. And guys, that was a personal break for Lewis. A couple soccer boxes and some Chronicles basketball fat packs available on jaspys.com for all personal breaks. jaspyscasebreaks.com for all group and case breaks. I'm Sean, and we'll see you next time. Thank you, guys.


Hey y'all, what's up? My name is Sonali. Welcome to my channel if you guys are new and if you are, definitely hit that subscribe button down below. And if you are already subscribed, make sure your post notifications are turned on so you never miss a video of mine. Today, I'm filming a Princess Polly Hall and she's a color girl now. I'm not saying I'll never wear neutrals again because that will always have my heart, but I'm trying to, you know, slide in some colors here and there for the spring and the summer because I'm just really vibing with especially greens but just like colors that make my skin tone pop and I don't know, I'm just really loving it. So I have a lot of colorful pieces to share with you guys in this haul. Thank you so much to Princess Polly for sponsoring this video. I do have a discount code so if you see anything in this haul, I will link everything down below as well as my discount code in the description box. Princess Polly ships from within the United States and there's free express shipping to the U.S. on orders over $50. Princess Polly also offers shop pay and after pay to all U.S. customers. Let's get into the haul so I can show you guys everything I'm going to be wearing over the spring and summer. First off, we have to start with this dress because I think it is honestly one of the most flattering dresses I've ever put on my body, something about like how it cinches in at the waist right here but that flows out right here. Then it has these puffy sleeves which I said in my grad dress video that puffy sleeves are definitely going to be a trend throughout like a spring and summer and I'm literally seeing them everywhere which I'm not mad about because I think they're beautiful. If you've been following along for a while, I've been sticking to neutrals and trying not to buy a lot of color just because I feel like color is harder to re-wear and I wanted more of like a capsule collection, more of like a basics but I feel like this one was too pretty to pass up on and I did get a lot of color in this haul just because I feel like that is the vibe for spring and summer and I don't think I mentioned this in the intro but I'm going to Italy and I think I'll be in Italy when you guys are watching this video so if you guys are not already following me on Instagram, do it because the content is about to be insanely incredible over there. It is so underscored gnarly and I'm also going to be posting on my TikTok which is also so underscored gnarly. I just feel like I imagined myself walking through an Italian garden in this beautiful dress. Oh my god, I've always wanted a blazer dress just like this but this is kind of an issue right here because I think there's only two buttons. Like I'm pretty sure you're supposed to wear spanks under it which I can totally do. Don't have any on right now so I'm just like trying to like hide it but basically there's like two buttons right here and then I wish there was like a third one just you know to close this up a little bit but like how freaking flattering is this and it's not like super like bulky. I feel like this is going to be perfect for like a dinner at night because it is going to be a little bit chillier on the trip. So it's long sleeve, I wanted some long sleeves but I didn't want to get too many because you know we're going into spring and summer over here so that wouldn't have made sense but I feel like this can definitely be worn still in like the spring and summer because obviously it's a dress it's a little crazy down there. Oh my gosh this with heels I love it and also I plan to do like a branding shoot hopefully in the spring and summer and I'm 100% going to wear this. Imagine me computer in hand, phone in hand, I'm obsessed. Here's the next dress and it's very up to date on the trends because it has this little corset right here and then it also has kind of those like puffy sleeves. Well I guess it's not really puffy sleeves because it would be more puffy up here but a linen sleeves I guess and I think this dress is so flattering obviously it cinches you in at the waist and then what I love about this one is that it has like this little cammy right here since it has the cowl neck. I've had a lot of body suits and a couple of dresses and tops that have this like satin cowl neck and I feel like I'm always trying to pull it up because I don't want it to show too much but this dress actually has like this little cammy underneath which is so nice so you don't have to like really worry about that. Up next we have this white mini dress and this one actually has a different texture than most I feel like it definitely looks a little bit more unique and it has these little lettuce ruffles. Why did I just call it that? Is that what they're called? I don't know what they're called but it definitely adds a unique touch to it. It's kind of like that baby doll style almost because it has this little bow and we love the adjustable straps. Thank God for that. This dress is so comfortable and I feel like it's going to be perfect for those nights where I obviously want to look good because we're in a foreign country but I don't really want to dress up because I'm pooped from the day. This is just going to be perfect because you literally just have to like throw it on add some cute shoes maybe some accessories but that's definitely the easy part so this dress right here has again a really unique texture to it which I love it's in this beautiful kind of brown golden champagne color I don't even know all of the above but it's stunning. Those are all my dresses but now we'll move into the tops that I got so starting off with this beautiful off the shoulder moment I love that it looks so relaxed and just so cool girl vibe I don't know what either way to describe this but I feel like this is going to be perfect for those like chilly summer nights not that that happens very often here in awesome but if I go back to Georgia I could totally bring it and I don't know just like lounging around the house with sweatpants on like that would be such a cute outfit I think I found my new go-to going out top look how freaking stunning this is it's definitely more like revealing than I usually choose but I think it's so pretty the colors everything the cut on this top is so good and just flattering in general and if I wanted to dress it up for Italy or just like dinner in general I think it would look so cute with some linen pants almost like the slacks that I wear all the time but those in a linen kind of material I think that would be so cute and very beachy even though Italy's weather is not going to be very hot it's going to be like very mild I think Positano is going to be like up to 60 which is still cold to me like I need to bring sweaters so that's an issue that I need to deal with because I feel like I packed very summery outfits and I purchased a lot of summery outfits so now I need to look back and see what sweaters go with what just to make sure I'm prepared and not cold because when I get cold I get like so just like irritated because I'm just like I don't want to do anything I just want to like go home and wrap myself in a blanket and obviously I can't just go home if I'm in Positano because I need to explore so I need to definitely be more prepared with that but I think this would look so cute in Italy I'm so excited here is the next top and I don't know why it makes me look like a brat's doll like I feel like everything about the cut it's very flattering cinches and at the waist well it's a crop top so I guess it ends at the waist but you get what I mean oh my god I love it so much and I feel like the quality on especially the long sleeves because I've actually had a long sleeve from Princess Polly before the quality on these things are so good they're so comfortable I just know this is gonna be a go-to on my trim especially during the colder days really quick I just threw this over the top that I was wearing but here is the blazer that I got which I was so excited about it because it definitely reminds me of something from the 90s like straight up from the 90s not even trying to be with the times today but it's kind of iconic for that so I did think it was gonna be a little bit that year on me because I just wanted to like throw it on over and not kind of like make it a whole fit like almost like use it as a cardigan or a sweater which I still could do and I think it still looks very like chic but now I just need to think about how to style it so let me know if you guys have any suggestions last but not least we have this neutral top which is kind of similar to the other one I mean just like a neutral long sleeve but also not similar at all because it has like a little collar here and it's a lot more revealing I would say there are three hooks right here so you can kind of decide how much you want to cover or how little you want to cover I just love how it flares out like this because it kind of gives you that illusion of a really nice like waist super snatched and I actually had a similar top saved for this Italy Turb I was trying to like just save inspo and see if I could find it online and I think it's pretty close so I'm pretty hyped about it since we're done with tops we can get on to the bottom half of my body I got one pair of jeans and one pair of shoes and these are the jeans I saw them online and I was instantly intrigued because it's kind of like a flared leg at the bottom and a raw hem I am a sucker for a raw hem my favorite thing about these jeans are that they are so high-waisted but sometimes with Princess Polly Jeans I have a lot of extra fabric right here in the crotch it doesn't look horrible most of the times but I guess it's when my legs are like really together it kind of like does something weird down there but other than that they are really comfortable too pretty thin so if I'm packing them they're not going to take up a whole bunch of weight which is a big plus I have one more thing to show you guys and that's shoes so I'm going to put you guys down there but these shoes make me want to literally book a trip to Nashville like yesterday I just am obsessed with the way they look let's just get into it here they are I've been wanting white cowgirl boots for so freaking long and I feel like cowboy boots are not going anywhere anytime soon I also live in Austin, Texas where I feel like it's definitely acceptable to wear them like at all times and I think these are just so perfect they're like the perfect height they're also like very lightweight so it's not like you're wearing anything too heavy on your feet because sometimes cowboy boots can just take a lot of effort to walk in because they're so heavy but these are not and I just love where they hit on my knees I think they're just adorable that wraps up my Princess Polly haul I hope you guys enjoyed this video let me know if you guys have your eye on anything specific and a reminder that everything is linked down below as well as my discount code which will also be on the screen now so you can save some money the Instagram feed is about to be poppin in Italy as well as the TikTok so again go follow me over there it is so underscore gnarly without the G and I will see y'all in my next video bye


Hello, you're watching International Daily Roundup by People's Dispatch, where we bring you some of the top stories from around the world. Let's take a look at today's headlines. UN Security Council holds a meeting on Ukraine, pipeline burst causes oil spill in Ecuador and Amazon, US political prisoner Leonhard Peltier contracts COVID-19, and finally NGO Warns of Coerced transfers of Rohingya refugees. Russian diplomat Vasily Nbenzia has accused the US and NATO of creating war hysteria in Eastern Europe. He was responding to US representative Linda Thomas-Greenfield during a UN Security Council meeting on January 31. Greenfield reiterated US claims of an imminent Russian invasion of Ukraine. She called on the UN to take urgent steps to prevent war, stating that it would lead to horrible consequences. The US has said that Russia has amassed over 100,000 troops at the Ukrainian border. Nbenzia disputed these numbers on Monday and added that the deployment was defensive against NATO's military threats. He added that the talk of war was provocative in and of itself. He also accused the US and NATO of engaging in megaphone diplomacy and pumping weapons into the Ukraine. Russia has repeatedly denied allegations of an invasion. Ukraine's own President Zelensky has rebuked the US and its allies for overhyping the situation. These countries have repeatedly also issued threats of sweeping sanctions on Russia. Meanwhile, Moscow has demanded that NATO halts its military expansion in Eastern Europe. It is also seeking guarantees that Ukraine will be barred from membership to the organization. NATO and the US have rejected these demands. Russia has said that it is looking for military technical solutions without a physical invasion to address these threats. It has also asserted that Ukraine should respect the 2014 Minx Protocol. US and Russia diplomats have planned to discuss the situation in a phone call on Tuesday. Meanwhile, Poland has become the latest NATO ally to pledge weapons support to Ukraine. Next we go to Ecuador, where an oil spill has now spread to a protected area of the Amazon rainforest. The disaster took place on January 28th after a pipeline owned by the private OCP Ecuador ruptured. The company has claimed that the damage was caused when a rock struck the line in a mudslide. While the extent of the spill is not known, the government is calling it a major pollution event. Oil has also contaminated the Koka River, which supplies water to indigenous communities. OCP had initially claimed that the pipeline had burst in a zone which was not directly exposed to rivers. It also said that the oil had been contained. However, videos shared by the Quechua people in Socumbios province showed the extent of the devastation. OCP announced on Saturday that it stopped pumping and initiated cleanup and environmental remediation. The pipeline in the Naipo region has the capacity of 450,000 barrels of crude oil per day. This pipeline along with a state-owned project had also burst in April 2020, contaminating the Koka River. The rupture was caused by soil erosion along the river banks. According to Amazon Frontlines, the disaster impacted over 27,000 Quechua people living downstream. A lawsuit seeking justice and urgent preventative measures was dismissed by a lower court. The case is still pending before the constitutional court. Activists in the US have renewed their demands for the release of Leonhard Peltier. The 77-year-old indigenous elder and activist is the longest-serving political prisoner in the country. It was announced on January 28 that Peltier had tested positive for COVID-19. He has been imprisoned for 46 years without any evidence that he committed a crime. Peltier suffers from serious comorbidities, including diabetes and an aortic abdominal aneurysm. Since he has contracted COVID, his life has been put at greater risk. He is being held at the Coleman Federal Correctional Complex in Florida. The facility has not provided booster shots to detainees despite the threat of the Omicron variant. Supporters are demanding that Peltier be immediately transferred to a hospital. Peltier was a member of the American Indian Movement, which organized an occupation in the Pine Ridge Indian Reservation in 1973.  The action was followed by years of violent attacks by FBI-sponsored vigilantes. In 1975, two agents in unmarked cast stormed the ranch where Peltier and other activists were staying. A shootout ensued and the agents and one Indigenous man, Joseph Stuntz, were killed. In 1977, Leonard Peltier was sentenced to two consecutive life sentences. This was after a trial rife with misconduct, FBI coercion of witnesses and a biased chewerer. Peltier remains imprisoned despite even his trial prosecutor admitting that it is unjust. And for our final story, Fortify Rights has warned of involuntary transfers of Rohingya refugees by Bangladesh. The Burmese NGO has said that over 1,000 people were shifted to Bhasanchar between December 18th and January 6th. The remote island is flood prone and largely inaccessible. Fortify Rights spoke to 10 refugees in Bangladesh, a campaign charge government official and representatives from the UN and the Nila Union. It also reviewed mobile videos filmed by refugees showing coercion leading up to the transfers. A man was beaten in December 2021 before being forcibly sent to Bhasanchar. An internal UN document raised concerns over the separation of families, restrictions on movement and risk of arrest. The UN has also noted the presence of unaccompanied children on the island. Marges or community representatives reported being pressured to get people to relocate. Officials reportedly told them that they would deny shelters, bamboo and other supplies. Around 1 million Rohingyas fled to Bangladesh to escape the genocide perpetrated by Myanmar's army in 2016 and 2017. Officials reportedly also suggested to the Marges that aid agencies would only be allowed to operate on Bhasanchar. There was also an attempt to clear out two blocks of camp 25 on December 16th. This is a violation of a Bangladesh UN agreement which assured that relocations would be voluntary. And that's all there is for today's episode. For more such stories please visit our website peoplesdispatch.org and follow us on Facebook, Twitter and Instagram. Thank you.


This is kind of another story about the cost of regulation. So one of the dreams Amazon has, and has at for a long time, Jeff Bezos articulated this 10 years ago, was that ultimately what they would like is to be able to deliver your packages to your doorstep using drones. So the drone technology exists. Drones are powerful enough. Certainly today, they weren't 10 years ago, but Bezos could project into the future and see that they were going to be. But drones are powerful enough to be able to take a lot of the packages that we all get, and basically airlift them from some kind of regional warehouse to your doorstep.  And of course, drones don't have to deal with traffic. They're pretty cheap to run. They are challenging because they could probably only do one packet at a time, so they're not incredibly efficient, so you would have to pay for it. But they would provide this amazing additional service and that, again, could improve our standard of living quality of life pretty substantially if we could just get the stuff that we need whenever we need it at basically very little cost. The drones shouldn't really interfere with commercial flights. They fly pretty low, so it's not clear that there is, but of course, the FAA, Federal Aviation Agency, you know, is responsible for regulating drone technologies and Amazon has been trying to get the FAA to approve their ability to do something called Prime Air, Prime Air, which would deliver packages within hours or within 30 minutes, actually, from the time you ordered them immediately to people, right? But in order to get approval from the FAA, Amazon has to run several hundreds of hours of flying without any incident and they have to provide all their data to the FAA. The FAA then has to analyze it, review it, decide if they approve it or not. You know, so Amazon has the technological challenge that they have, you know, designing the drones, building the drones, making sure the drones are big enough, safe enough, they can carry these packages, have the distance, can carry them for far enough and get back and all of that. Then it has, so it has the technological challenge. It also has a logistic challenge. The logistic challenge is a real challenge. How do I actually get these drones to be commercially viable and which warehouses does it make sense for? Which customers that it makes sense for? Which products does it make sense for? Can do things that are too heavy and so on. So, you know, Amazon has to focus on the logistics which are not easy. And then, of course, it has to deal with the regulatory issues of getting regulatory permission in order to do that. They have to solve the first two problems and what they're doing right now is they're running two test locations, one in Lockford, California and one in, I don't know where the other one is, it doesn't say here. It just says two test locations in Lockford, California. So maybe they're both in California. Maybe they're both in this area in California. I thought the other one was like in Texas or something. I think it is. I think that's a typo in the article. Anyway, so there, yes, the other one is College Station. This is the home of Texas A&M. The other one is College Station in Texas. And both of them are pretty small locations. Lockford, California is home to about 3,500 people. So they're testing out on small. They're doing this, of course, again, not easy to do, but they're accumulating the data. They're being in a lot of delays, a lot of delays in terms of what they exactly want and what they can do and regulation, regulation. So while, so far, Prime Air signed up about 1,400 customers for services between the two sites.  It's not clear that these are the ideal people for this. So Amazon has subsidized the service. They're giving our customers gift cards and all kinds of other stuff to get them excited about this.  I mean, just imagine these kind of issues. I don't know, flying cars. I've seen a number of companies working in flying cars. But just a number of different products that are out there, a number of services, a number of things that could be improving our lives that are stuck, stuck. Not for any development reason, not because of lack of technology, not because of lack of capital, not because of lack of ability or customers, but just because they're waiting for the regulators, sitting and waiting for the regulators, waiting for permission, waiting for permission. One of the ways to think about capitalism is capitalism is a permissionless society. You don't wait for permission. You don't ask for permission. You do. And one of the signs we don't live in a capitalist society is you need permission for everything. If you're an entrepreneur in so many fields, certainly in the banking space, you need permission for everything. Thank you for listening or watching the Iran Book Show. If you'd like to support the show, we make it as easy as possible for you to trade with me. You get value from listening. You get value from watching. Show your appreciation. You can do that by going to iranbrookshow.com slash support by going to Patreon, subscribe star locals and just making a appropriate contribution on any one of those channels. Also, if you'd like to see the Iran Book Show grow, please consider sharing our content and of course, subscribe. Press that little bell button right down there on YouTube so that you get an announcement when we go live. And for those of you who are already subscribers and those of you who are already supporters of the show, thank you. I very much appreciate it.