Thank you very much. Thank you for inviting me. How's the volume if I talk from here? Can you hear me back there? No, yes. Thank you. I'm in a difficult spot because lunch is at 12.10. And it's 10 minutes to 12, so I have to be done in 20 minutes. So I'll use the pillow approach, which is to go at 5 billion words a second. No, I'll try to make it. Let's see how it goes. So first of all, let me start way too broad and argue that the fundamental mandate of neuroscience, in my opinion, is to relate what happens in neural circuits to what happens as we act and move our body and behave. And there's lots of institutes with this kind of name that are being formed around the world. And so it used to be that actually people wanted to go from genes to behavior, but now we're getting a little more rational. We want to go from circus to behavior. And so that's just reason for a few minutes, at most a couple minutes, about what it takes. So a very tired analogy is about machines that we can build to machines that someone else has built and the evolution has. And so imagine that this is the most complicated machine that we can build currently, I think. Imagine that we were trying to reverse engineer it. We have video games, all sorts of things. We have a bunch of laptops arrive on Earth. And we can play with them and open them and figure out that there's microprocessors. And people get Nobel prizes for discovering the transistor instead of inventing it. And so what would be the level of understanding that you would want for this kind of system? Well, most arguably, the level you'd want is the level of algorithms, languages, operating systems. Once you realize that there's that level, you could actually decouple the computations from the circuits, which could be very useful if some of these laptops that have landed are from different companies and different microprocessors, et cetera. And so you break the question into three. What is being computed? And that is a question that if you understand it, it helps you then guide research into how it is computed and also why it is computed. OK, so if we now take it to the animals and brains and people, the idea is that there's this intermediate stage of neural computations. And that's what we're trying to understand. If you understand what is being computed, then that really guides very well our research into how it is computed. And this idea of breaking the question into three is basically David Maher's idea from the 1980s. And when enough time goes by, you can recycle ideas pretending that they are yours. And that's what I did recently.  And it's crazy. You should actually go from understanding the connectome into everything forward. And I find that a bizarre statement. OK, so now what I think is the common interest in this session and maybe this meeting is the understanding that the computations of interest are performed by neuronal populations. And what I'd like to do now is, so this would be a bunch of neurons responding to one stimulus. So what I'd like to do now is first give you what I think is a very well developed example of a situation in which we can describe the activity of a whole population of neurons with very simple equations, which then can in turn guide research into the underlying circuits and guide research into why you'd want to compute those equations. And so that's going to be an example from four years ago published. And I'll try to go fairly quickly over that. And then I'll tell you all the reasons why this approach instead runs into difficulties and where are the frontiers of this approach. And that will be unpublished to work. All right, so take this population. This is the population that we actually recorded from in my lab in the cat, visual cortex. This is the same technique that you heard about in at least two talks. You insert a Utah array, which is a 10 by 10 array into the, in this case, the visual cortex of an anesthetized cat. And as you might know, in carnivores and primates, perhaps including humans, there are these maps of orientation preference, such that this is primary visual cortex and this is four millimeters by four millimeters to give you an idea, such that neurons that are sitting next to each other tend to prefer similar orientations of visual stimuli. And so when you shoot an array like this 10 by 10 into this map, by chance, some of the electrodes land in places that like one orientation, some electrodes land in places that like another orientation. And so the first thing you can do is let's pretend that all of these electrodes gave you nice, well-isolated neurons. It's actually not true. Some of them do. Some of them just give you more unit activity. But it doesn't matter here because all the neurons that are sitting next to each other have similar preferences for a stimulus orientation. So I'll keep on saying neuron when, in fact, I'm not sure that each electrode gave me an actual single neuron. And so what we can do is we can say, look, I'm going to take all of the neurons recorded here and I'm going to label them by their preferred orientation. And then when I show a stimulus, for example, a stimulus that has this orientation, I get this pattern of population activity, which I can then make cuter by, say, averaging over bins of neurons and fitting with a Gaussian. And up to now, I'm making a statement that is absolutely circular because I'm saying, well, let me actually say it. Then you'll realize how circular it is. When I show a stimulus that has 45 degrees of orientation, the neurons that prefer 45 degrees of orientation fire a lot. The neurons that prefer minus 45 fire very little. Well, duh, that's how I defined them. But still, at least this gives you an idea that things are behaving the way they should. And now we can go on to ask a slightly more interesting question, which is what happens when you put two stimuli on top of each other. And when you do that, then it guides you through these six stimuli that were shown to the cat. When you show nothing, oh, by the way, all of these are populations of neurons. So for example, this is the activity of neurons preferring different orientations. And so when I show nothing, I get nothing. When I show something horizontal, the neurons that prefer horizontal fire a lot. The neurons that prefer vertical don't fire. When I show something vertical, the neurons that prefer vertical fire, the neurons that prefer horizontal don't fire. When you increase the contrast of, you quadruple the contrast of horizontal, you increase the responses. You don't quadruple them. There is a bit of saturation, but that things happen. But you see that this Gaussian stays in the same shape, just scales. All right. There's a mildly interesting case shown here, which is when you put two orientations on top of each other and they have the same contrast or the same strength. As you might imagine, there's a symmetry there, and the symmetry is preserved in the brain. So you get activity in the neurons that like horizontal and the neurons that like vertical. And these individual activities are a little bit smaller than the activities you measured when the stimuli were alone, but it's OK. They're still there. The really striking case is this one, where you've taken a stimulus that gave you a very reasonable response when presented alone, and you sum it to a stimulus that also gave you a nice response, but it's stronger in contrast. And the one that's stronger in contrast completely wins as if completely killing the response to the lower contrast stimulus. There's no trace in here of a response to this orientation. And so basically, you go from a regime of summation to a regime of computation. Now, if you want, you can use an equation to summarize these results. And this equation is the computation that I was telling you is the final result of this analysis. It's called the normalization equation. It says that the primary visual cortex makes the following computation. It says, aha, I'm responding to two stimuli. I'm going to give responses that are Gaussian. The population would be Gaussian. Gaussian responds to the first stimulus, and Gaussian responds to the second stimulus. And I'm going to scale them by contrast, but I'm also dividing by two numbers. One is a constant, and one is the root-mean-square contrast, which is simply the square root of the sum of the square contrast. Now, I'm actually hiding the fact that there are some exponents, which are important. I'm simplifying this equation for you. But all I want to say is that this really, really simple equation gives rise to these curves. Once you've fitted the single orientation cases, you have no more free parameters, and you can very well predict both this regime of summation and this regime of computation. And just to show you the behavior of the model, I'll go now to a case in which we have way more stimuli, way more population responses. Remember, each of these graphs is the activity of about 100 neurons in response to one of these stimuli. And the curves here come from the model. And you can see that the model does a very good job. And in particular, it captures a regime of simple summation, which is a regime that you have a low contrast, a regime of sublinear summation where you still have summation but the responses are smaller than what you would have predicted by just summing, and regimes of vicious winner-take-all competition that you see when there is different contrast involved. And we think that this kind of computation has wider roles in the brain. It's been observed in various places. But even in visual cortex, you could imagine that you are looking at two orientations at the same time, but you want to pay attention more to one or the other if you had a mechanism that effectively increased the contrast of one of the two. And by the way, that's how attention is thought to work, at least by many in the field. That would push you off the diagonal and the orientation you're interested in would win over the one that you're not interested in. All right, so reasons to be happy, up to here, are that we have a very simple equation, normalization, which describes the collective responses of a ton of cortical neurons. It accounts for striking flexibility in the way the cortex operates, depending on context, high contrast, low contrast, different contrast, et cetera. And it seems to operate in many other neural systems. I have no time to show you. From olfaction in flies to value estimation in humans. By value, I actually mean economic value. And this is in written and reviewed that we recently published. Okay, now, the idea is that maybe normalization is a canonical neural computation, which you could define as a computation that is replicated across brain regions to apply similar operations to different problems. Much like the operating system of a computer or pieces of instructions in software would be used by different programs to achieve different results. And it's possible that I overemphasize this normalization equation, just because that's the one that I happened to study for the last 20 years and I study sensory systems. And if you studied other systems, chances are you would bring up different computations as thinking that they're probably canonical and maybe you're right. And so I only talked about this one, and but there's lots of others that are very interesting and we could talk about. But what I'd like to do now is to go to where this approach, I wouldn't say fails, but where the frontiers of this approach are. And mostly I'm gonna go to the business of noisiness of responses, which is the fact that responses, so for example, here are six population responses to the same stimulus. And as you can see, they're not the same. There's variability. And this variability, I'd like to tell you a little bit about it. And so the first thing I'd like to do is tell you that the origin of this variability is largely cortical. So if you record from two parts of the brain, this one is the lateral genicular nucleus, which I will call the output of the eye, which is a bit of a simplification. And if you record in visual cortex and you record now from one neuron, one neuron here and one neuron here, what you see is that if you repeat the same stimulus three times, you get three times essentially the same response from this neuron here. And if you repeat the same stimulus three times, you get three times very different responses from a neuron in visual cortex. In other words, the lateral genicular nucleus, or sorry, the eye basically works as a camera. You take three pictures of the same image, you get three identical pictures. The visual cortex works the way you would like actually a brain to work.   You'd like to think, oh yeah, I've already seen that face, right?  And so this is what happens in the cortex, it doesn't happen before. And by the way, this has nothing to do with the fact that neurons in visual cortex are noisy, sorry, this is just a quantification of this variability which I'll skip. It turns out the neurons in cortex, as neurons in most other places are extremely precise machines. The only reason why we use a Poisson model to describe them is that we have no idea of the noise that goes into them. But the neurons themselves are not noisy at all. They're incredibly perfect machines. Okay, so it helps when you give a talk to know what your next slide is, but I have no idea, so I will advance. Ah, and this is supposed to play by itself, and it's not, so I don't know how to do it. So what I'd like to do now is show you the activity of a bunch of neurons recorded with calcium imaging in two-fold of microscopy. And yeah, is it showing? If it's not showing, let's not worry. Yeah, look over, slide over, cause, yeah, no. It's not working. It's loading right now. Okay. It's loading. Play it outside. Yeah, it's just sad when you give your slides, always bring your laptop, I think. This is what I'm gonna remember. Okay, so there was a beautiful movie here of neurons firing together, and I think actually the analysis of this kind of data is one of the frontiers of contemporary neuroscience because you get the activity of hundreds of neurons at a time, and you see interesting patterns of activity and you won't see them. Okay, so what I'd like to do now is bring you to actually super current research in my lab where we're characterizing the fact that some neurons act as soloists and other neurons act as choristers. And that will help us predict pairwise correlations between neurons, which is one way to describe the activity of a population. So here now we move to yet another technique, which is a technique that has been alluded to of these neuron access probes, these multi-site electrodes. The advantage of these electrodes is that each spike gets collected by multiple sites on the electrode, which means that you can actually, it's a principle of tetrodes basically, you can much better discriminate the spikes from different neurons, and I share the lab with Kenneth Harris, as Sonia mentioned, and Kenneth is the master of spike sorting. And so pretty much what happens next is that you can believe that what I'll show you next is from actually very well characterized single neurons. And here's an example of traces recorded from the end of this electrode. And we will be talking about neurons with these different spike shapes. So what I'd like to do now is show you the spike trains of those neurons, which are hard to see. This is blue, it was one of them, this is black, this is red. I think there's green here, yes. And if you want, you can also compute an average across the population of the activity during these various seconds. And what I'd like to point out now is that some of these neurons, for example the red one, are extremely well correlated with the population, and we'll call that neuron a chorister, meaning it's member of the choir or of the orchestra you're playing. And other neurons like the black one are very independent or uncorrelated with the population, and we'll call that a soloist. So what I'd like to do in the next three minutes is tell you a few things about choristers and soloists. And the first thing I'd like to do is tell you how useful this concept is to describe pairwise correlations between neurons. Now clearly if you want to describe a population activity, you need to look at more than pairwise correlations. But let's just take that as an example of a possible goal here. Clearly if you want to measure pairwise correlations, that's a problem of order n square. So if you have 100 neurons you need to measure in the order of 5,000 pairwise correlations. And I'd like to show you that knowing whether neurons are soloists or choristers actually brings down this problem to order n. And to do that I'll use a modification of a model that we published which works as follows. Take that spike train and convert it into a sequence of zeros and ones like at least Jonathan's show, maybe other speakers as well. And now allow me to measure the following things. First of all, we will measure the average over time of each neuron, the average activity of the time. That's just how much that neuron likes to fire. That's just one number per neuron. Then allow me to measure the instantaneous population rate which means how much the population is firing at each moment. But I don't need to keep track of time. I can just accumulate that into a probability. And that will be a distribution of population rates. Whoa. And that's shown here, it's a probability distribution. And then allow me to measure this one more thing which is how much of a chorister or soloist each neuron is. That is how much I expect the population, well, what is the correlation between population rate and activity of the neuron. That's again an order N kind of thing. So now I have order N, order N, and the distribution. And I'd like to show you that if I know that, I can actually do a pretty good job at predicting the pairwise correlations between neurons. So here's an example on the abscissa. We have the observed pairwise correlation between neurons. This is spontaneous activity. And so for example, this is a pair where there's a 10% correlation between the two neurons in the pair. This is a pair where there's a little bit of a negative correlation. And up here instead, we have the predicted pairwise correlations predicted by this really simple model. And it does a really nice job. And here's how it does in terms of percent of variance that it explains. In some cases it predicts a lot of the correlations. In some cases less. And it turns out that that depends on how variable the population rate is. But I'll have to move on, I can't get into the details. So this is one thing I can tell you about soloists and choristers. If you know who's a soloist and who's a chorister, you can really use that to predict pairwise correlations. I wanna tell you two more things about soloists and choristers. The first is that it turns out that choristers are way more responsive in the visual sensory than soloists. And that's another thing I'm showing here. And third, you can actually probe causal connectivity by using optogenetics, which works by shining light on mice that have been genetically engineered so that the neurons in certain places respond to light directly. And when you do that, you discover that the choristers are way more causally connected than the soloists. I'm gonna come to a summary now and tell you that I told you three things about choristers and soloists. If you know who's a soloist and who's a chorister, you can predict n-square pairwise correlations, you can predict central responsiveness, and you can predict causal connectivity. I'm in your lunchtime, so I'm going to skip the philosophy, skip the movie that is not gonna run anyway, skip the behavior, and tell you that basically I think we're doing quite good progress in understanding the computations performed by populations, but what I wanted to tell you in the philosophy, maybe I'll tell you just one thing, is that when we work in visual cortex, we're so lucky that we can tag each neuron by so many attributes, where it is in cortex, what it likes, what orientation it likes, what spatial position, retinal position it occupies, et cetera. But when we work in all sorts of other places in the brain, we don't have this luck of being able to tag neurons. And I think that's why we need to look for intrinsic structure in population activity. And that's it, thank you so much. I was just wondering, in the situation, in the competition situation, where one orientation is inhibited, why is it we can still perceive both orientations? Yeah, I get that question often. So the first two quick answers are, number one, we're not cats, number two, the screen is not properly calibrated. But more generally, you can demonstrate these effects in humans. You just need a much more controlled situation. There are lots of psychophysical experiments where this kind of competition has been observed, and actually the same equation works very well. So thanks for a very interesting talk. One, I think some people, at least what I've heard from the discussion, say that, oh, this is for simplified stimuli. So, but this is very different in the natural imaging. What is natural images and natural image processing? What is the response to that? Oh, okay, so simple stimuli are helpful. Okay, I'll give you, simple stimuli are very helpful to characterize the properties of the early visual system, such as the retina, the LGN, primary visual cortex. The moment you start going beyond that, you start running into serious difficulty. And especially when you get to areas interested in, for example, recognition of faces and things like this, you'd be crazy if you didn't use faces to study them. So there's that aspect. Personally, I had a research program that started a few years ago, which was, aha, I'm going to take, for example, the LGN, and I'm going to first characterize how it works in response to simple stimuli like ratings, and then I'm going to test whether this extends to movies, okay? And this worked. It was a lot of work, but it was okay. The moment I got to primary visual cortex, I saw that the primary determinant of responses seems to be this ongoing activity. And it seemed to me more interesting than the details of the mathematics of image processing, and that's why my lab now focuses mostly on that. But I think if one has the guts to do it, sure, characterize the system with ratings, dots, bars, and then ask how far, and then obtain models that are non-linear, otherwise you're wasting time, and then characterize whether that predicts the responses to movies. It's a tough job, though. Now we have, let's start here. Thanks, Matteo, that was a really nice talk. I'm curious about the soloists and choristers, and I'm wondering if you could tell us anything more about the tuning properties First I was going to say maybe it's a sampling issue, like the soloists are choristers with some other population you didn't record, but then you showed us that they have different responsiveness, and so did you characterize their tuning properties or what are they good for? First of all, it's entirely possible that the soloists are choristers of some other population, right? There's, absolutely. And what are the properties? Okay, so we looked at visual responsiveness, then we looked at a litany of other properties where we could find no difference. So we looked at orientation tuning width, we looked at the size of the receptive field, we looked at contrast sensitivity. That doesn't mean that there isn't an effect there. Our main intent here is to reduce the dimensionality of the description of the population.  But it's just you immediately go into this more abstract world, which many of us find us in. So same question to you, right? You've got these traveling waves, but wait, don't you have some autotopic representation in there? How do they two relate? And in a way, you don't need the first to talk about the other and vice versa. So I think there's still a disconnect there. Yeah, so someone who's had a foot in both the experimental side and some of this statistical and theoretical analysis, one of the, there are two things that worry me a little bit, and I'd be interested in the comments of anyone, which is why I save this to the end. One is we end up for population activity having to make some sort of random connectivity theory or sparse connectivity theory. And yet, when you look through a microscope at the brain, it's not random, and we don't know if it's actually sparse. We think it is, but we don't know. And I'm worried, number one, what if it's really just a few inputs that dominate the responses to any neuron you're looking at, but you just don't happen to be able to record them? And the second is everyone talking about behavior, we've started trying to collect data and correlate behavior in a more complicated way than just training an animal to do one simple thing. And I would make an appeal to people in this room. We need methods to deal with those kinds of data. So, yeah. Because it's tremendously bothersome that we can't quantify. It's a lot to discuss between now and lunch, but what I can tell you is what I'm trying to do in the lab. And so what we think is true, but this is so new that it's possibly quite wrong, is that these kind of top-down signals, which are an expression of connectivity. And by the way, by top-down, I mean these signals that are not driven by visual input, but rather by other things in the brain. For example, what the animal is doing, what it's thinking, what its reward is, what its task is, et cetera. We think that most of these signals land in the apical dendrites. So what we're doing now with the photomicroscopy is looking at responses of the apical dendrites of neurons and comparing them to responses of the soma. And if this movie were working, you would see how this happens as you zoom down. So the question you bring is about connectivity is a huge one and I don't have an answer to it, maybe others do. But what I can tell you from the standpoint of single neurons is that we can look at places that we save different types of connectivity. So the dendrites, the basal dendrites or the apical dendrites, and try to compare different properties of responses and of activity in these different levels. And maybe that gets a little bit of connectivity, but that's just my personal take. Thank you very much. So this, I hope you've got now some idea about the problems we face. And I want to thank the speakers again and the portrait is over now. Okay, if we could have all of the speakers first, all of the speakers, if you follow that woman up there, Rosa, she can take you to lunch. So.


Clown fove of people are now hiding on tables shaking with Rob surprised it Now I can do this. I mean whatever Bah, all right Comments VGX Rob. He was a founder and suddenly he's gone when bankruptcy hits. Yeah, it's pretty amazing. Isn't that how it works out? I don't know Didn't avoid you come back from a similar loss hack and now it went around. I mean you I mean the document save was put out in 2018 Thoughts on Cody the currency of the internet built on Cordano like Cardano don't only know much about Cody. Sorry That's pretty That's pretty good. Why not clown the CEO put clown music when CEO saying take risk management I will do that next time Jion Chao very nice Are you cash gonna ask that story in your ledger? I already took my profits. I'm just just now accumulating if There's some huge. Hey, there we go If there's some huge Big huge bump, I'll probably take some profits on the way, but I have to take a look at my spreadsheet and see which ones I had Question let's see stop it stop it There there you go. All right, so How do you think the Voyager case will linger I think it'll linger for quite some time and Hmm there was there there was two stories that we went over About these two different financial institutions that went through bankruptcy One it took three years and then one took three months and people were saying well, no company has ever come back That was in use financial instruments. That's not true. I mean we see Apple went through it that worked out. Okay, of course because they had hard assets GMC and Ford maybe GMC on about for GMC definitely did and they came back because they had hard assets But the other ones that we talked about in the video. They were just financial instruments and they came back So let's see No, this is real Rob Rob never looked at good. Let's see Do you have a spreadsheet example? I can borrow I should put one out actually I should I should So do I keep the do I get to keep the crypto that I bought a Voyager or no really comes down to how they restructure again There was two different ways There was them for the for the restructuring and they do everything in-house and they get lenders and and of course You'll probably have to take a haircut.  What would be the point of doing that because you can't really acquire the customers because the customers like f you I'm out of here. You're not gonna give me anything back after you acquired them What's the point even being here and then of course if some people say well, it's to eliminate competition Who cares just let them die anyhow so I would think that if a third party comes around and they buy them up they would have to give some Equity back to the people that are lost and then the but the question that would be well How much I don't think it's a complete loss unless they go to chapter 7 Which is straight-up liquidations, but again, we took a look at the finances. There is still some there Just depends Okay, so this is a good question Rob with ledger is a safe to stake assets or will they go the other hand so look There was a story we talked about with ledger and them partnering up with some group Where you're able to I'm sorry to stake your assets. Yeah, you can There is a way to stake your assets through ledger and they but the thing is they have to you have to understand that every single Project every single blockchain is different like there may be some lock-up here. It's like for a theorem I don't think you can just put it in and take it out on your ledger I know for a fact if you're doing outside of ledger and you are staking your theorem That's locked up until the merge happens and who knows when that's gonna happen now with Cardano. It's different Cardano is you can just put it in and put it out anytime you want to essentially. I think it was like a like a couple of minutes weights or something like that's about it Through avalanche, I want to say it's 24 hours 40 hours something like that and then Other ones have different different time frame lock-up. So for that situation As far as staking assets, I don't stake my assets via ledger. I just put them into a wallet like dead list and go that way Researcher saying My question is if the apps on active, why is the value going up? So Lucy is it the Voyager token are you talking about Voyager? I'm guessing you are are you talking about the Public stock. I don't know Thanks, man. Use Chrono for a to staking. You can do that You know man, we both live in Puerto Rico at some point we should do a show We're not too busy Doing all the other things that you're doing Apple Good guy. I know if you guys don't know crypto crow you should watch this show He's the og One of the og's that I used to watch all the time And I still I still do it from time to time. I just like it's a damn busy with stuff Justin that's his name. I've got your name crow Justin Voyager I just had tokens so I'll get stuck to Depending on the plan that that goes through it could be so if they go in that Situation where they say we we're gonna do it ourselves. Yes for the third party Probably not but it's all up in the air Welcome people losing their life savings is nothing. Yeah, you know what? This is probably the worst bear market I've ever gone through and this is my second one the first one was bad enough I mean drawdowns of 85 95% is pretty crappy. I must admit but what's crappier even more so is just losing everything because Even if even when Bitcoin went 85% down didn't really matter You're just like well, I'll just stick it there and just wait. It'll go back up And it did and that was good But now with sales use and Voyager and some other ones Shutting off with draws you're like well, that's a hundred percent. Am I ever gonna see that again? That's what makes this probably the worst one. So if you can get through this if you can get through this bear market crypto winner where exchanges are shutting down without being hacked on my dad and The upcoming recession which I believe we're in right now With a war in Ukraine and different problems with supply chain issues, especially in China as they do also lockdowns And of course some new random various virus that will come about tomorrow. Who knows Then you get all the rewards you deserve and then also. Yeah defy rug pulls good one. All right, soft Yes, exactly Andrew you don't your keys don't any crypto. Yeah, so Jackie thoughts on Simon James interview I thought it was it was like a novella, I mean it was Like when me and Simon were talking behind the scenes He's like I just can't say much of stuff NDA's and such like that. He goes, but I'm gonna tell you right now He goes I just do not trust And I'm like I get it and then when I when I heard him on James James's channel And he's talking about the things behind the scenes and the things that Celsius did and how they kind of threw him under the bus and Pretty much just lied about their organization. I was like God. I was like damn and that's a If they did that to accompany that invested heavily into and what do they do to just to retail people, you know like us Shoot oh When I checked the app, I see the value going up for all coins. I don't know how that's possible I mean, maybe it is let's see This is the value that I have in coin get go unless The market is somehow I can refresh this just to make sure Don't know man on how that is Yeah, it was painful to watch that's for sure BR McKay the FDIC rejected my voyage reclaim FDIC insurances only applies in the like of it. I'm an insurance Metro Bank did not fail interesting Which Let's take a peek here real quick. Shall we? Bob's Twitter account You look at and now I'm looking for July 6th No, this it in July 5th my first season, right? This is it the two things Two things customers with a crypto on their accounts will receive an exchange a combination of the crypto Proceeds from 3ac recovery common shares in the newly come new company and void your tokens just like we talked about With the reorganization that will the documentation that was worth in the chapter of bankruptcy And this one right here. It's important to note that as part of this announcement We are confirming that customers with USD deposits in their accounts will receive access to those funds after reconciliation and fraud prevention process is completed with Metro commercial Bank and remember and I'm not You know saying like this is gospel and it's definitely gonna happen. I'm just saying this is what was said I Can only report the news. I can't make the news Remember ready to go the amount now. It's December 31st active users Ah, here we go 350 million of cash is held at Metro commercial Bank. So 350 million dollars It's quite a bit of change. That's quite a chunk of change. So I would have hoped that they can say well, okay Hopefully they don't use that too move it and Pay for their debts All right, so long as a scam There is a word we we don't hear too often in crypto. Let's see Thank so long is not a word those projects Fraud is part of Russians everyday life. Let's see Good question. Why is Voyager still minting VGX and dumping huge amounts on buy-outs? I'd take a look at the on-chain analysis of that that would be interesting if someone could send that to me Rob if you've got that tag me in Twitter. We'll talk about that later Yeah, so crypto starter says is I trust capital safe. This is a big question One's going along a lot and I've been in talks with those guys to come on the show It looks like the next two weeks or so. Hopefully they said that look You have to understand I trust is a very again. Don't trust anybody verify even even right now Even me I could be lying straight to your face just like people have been lying to my face So what I trust It's a Roth IRA they can't in their terms of service and Eula didn't say anything about rehypothecation or Loaning thing out or doing anything like that, which is what Celsius and Voyager was their downfall I trust the the credit that you put in there is for your retirement account And there are certain laws and regulations that have to go along with that for them even be called a Roth IRA So they say like look, we don't we're doing they said we're doing just fine because people are still gonna trade within their account because it's It's a it's a tax It's a tax savings when you trade within your Roth IRA account You can like when Bitcoin went to 60,000 you could have sold Bitcoin in your Roth IRA account stuck it in cash Waited to drop down to 20,000 then bought three more Bitcoin You could have done that and there would have been no tax implications because it's a retirement account So they said we make 1% for all transactions and we've done so well, we don't even charge a monthly fee and You know when people get that magical age of the nine and a half. Hopefully I make it who knows Then they take it out tax-free this is the exact same thing that Was it kind of confused with you're like Peter Teal the PayPal founder This is exactly he did he put his PayPal shares into his Roth IRA when they weren't weren't they were pennies and pennies and pennies And now it's worth five billion dollars. Guess how much he's paying in taxes 0.0% when he turns 59 and a half years old The only a of x I don't a little it's not that much of the of course also we have The avalanche stake pool so got on some but that that doesn't really move around With a name like I trust exactly you can't trust nobody man I don't care if your mom text you for your coinbase password. It's probably a scan Steve Austin. That's the bottom line Is Ada good DCA by? No, tell you this You know that company that's being sued Solana It went down a couple of times. I don't think it has ever been down and With all these D5 products getting rug pulled. I don't think anything's been rug pulling on Ada and Cardano Goes a little bit slower, but seems to work out. Okay And the ecosystem, you know what that's a great segue. Why don't I go to this? So There's a link in the description And it looks something I can't show you the link, but it just says Cardano's a ghost chain And there's a ghost emoji And it'll take you to here to google docs and you can share it with anybody you want to Here's the car ecosystem the Cardano ecosystem in 2020 here it is in 2021 in august here It is in november 2021 and here it is on april 2022. I couldn't fit it in there So yeah, and then of course here's the projects being built on Cardano over a thousand. This was on 6th of june 2022 Here's the amount that's actually being staked. Here's the value of that staked value Here it is as far as a reference and just staking ratio still around 71 Percent it may have gone down a little bit or up, but I think it's pretty amazing that with Cardano You can stake and unsteak anytime you want to slide There's a waiting period in ethereum is god knows how long that is With that merge and it's still up there 71 percent Of course, this is all based on not ratio percentage because there's by and saying 81 percent But it's based on the actual market cap. So third highest not too bad and of course with defi Yeah, not doing so on defi or the whole value locked in defi, which is 72 billion 45 is on the theorem chain, which makes sense if they're in everything ever built on that Everybody built a lot of stuff in ethereum, but things are changing now They're building an avalanche on the building on salon and are building on cardano and just remember Bitcoin for its defi only has 118.55 and of course cardano is 118.56 So it barely beats bitcoin in that yay cardano So if you want to take a look at that link in the description and that's called cardano is a ghost chain Yeah Vicki says get your funds off itress. Please look You can do that. You can take your iphone. You know what if you guys have any questions about itrust This is what I do and did You know, you can contact a real person over at itrust. Let me show you something I trust Name like I trust that was funny. Let's see Yeah So Wish I would have showed it to you probably would have helped So Here's itrust capital if you go to it's this itrust capital dot com that thing that's above my head all the time that thing right there That's itrust Anyhow, there's a link in the description like everything else and when you go there if you click on support You can just call them And just say hey man, what's up? I don't want to get rug pulled. What's happening? How can you alleviate my fears and go from there if at some point again? I can't tell you what to do.  I think it will look at the usdc and we'll see Another good reason who knows what could happen take it all out of usdc put it in cash that's Not investment advice just what I will probably do because I don't want to deal with it anymore Ah, good question Can you do a cannonball in the pool when you sign off today would be sweet? Sure would be sweet, but I can't do that until bitcoin hits 100,000 be right there with you Rob, do you think in voyaging? I don't have that much in there. I think like 128 bucks or something like that So Will I get that back? Yeah, probably. I mean from what I just read out to you pretty sure The crypto though, which I left vgx tokens on there Should have sold it That was me Look at this one Wargasm. Hoskinson and his cult of crypto cryptards. I've never heard that. It's pretty good. It's pretty funny Look, everybody's gotta I will say this though You know, I like people say that's a cult. That's a cult. This is a cult. Bitcoin's a cult This is a cult Cults do well I didn't that didn't come out right. Let me say that again Cults around businesses do well. So take a look at apple I kind of consider that a cult people wait like all the time for these stupid phones And they wait around the block for no good reason yet one of the biggest companies in the entire world also crossfit Uh, that's a cult man. I mean I don't have nothing against Crossfit I just know a lot of injuries and getting great shape and all that stuff But it's crazy. That's a cult and uh, those people are diehards on there Some people call it a cult and it's called, you know diehards and whatever else, right? But then in crypto and digital assets, there's a lot of cults out there. Let's be honest And uh, that unfortunately or fortunately if you want to look at it Is what drives a lot of this of the progress because the people like are not going to give up That's just how it is So when I look at like projects, I'm just looking like what kind of community do they have here? How strong are they and how much how long are they willing to go? XRP had a pretty huge cult still do. Cardano is pretty pretty massive Voyager had voyager heroes and there's still people who you know are into that that thing It's just to me. I look at those types of passionate communities to say Might be something to look into Yeah vgx rob I was never going to give up on voyager, but they gave it on us. It's a good quote Hello val Oh, yeah, man investor brand invest herbal life for sure What you think of nexo? a lobo Seems to be sticking around seems to be one of those people who wants to uh, who wants to buy up these distressed assets Distressed companies. So I'll do that Jack's I'm on the aida and xrp cult stick around. You probably do pretty well Oh ETA and cannot deep dive give me some more time. My grandson's here. So I got really that that's most of my time right there Proof of work or proof of stake. I gotta tell you If it wasn't for aetherium and cardano honestly I'd just be like proof of work. Let's just do that just go from there, you know because It's amazing me and ben from in the cryptiverse. We're talking before the dca show on friday And I was like, you know ben the longer I've been here the more I just become a big bitcoin maximalist He's like any he chuckles and he looks away. He's like, no Bear markets do do that. There's a more bitcoin maximalist born every day. I was like, that's probably true Uh, so yeah, if it wasn't for those two probably that but Nah, you know what I can't say like that because I always for There are and this is the big question about utility that we talk about what Just think to yourself and I had a another whole series lined up, which I was going to go over um, Sri Lanka And how the canadian banks just not collapsed But they just shut down this weekend because of uh, rogers telecommunications broke and then we're going to talk about texas The bills are electricity costs going up super high and of course why You know, that's actually good for bitcoin miners because they can just shut off and they get paid to shut off Which is crazy. I was going to go over all this stuff and because When I was thinking about this, I'm like, well, where's the real like like utility and things like that And and we could see because of like Sri Lanka and because of the canadian effects and because of the different countries Like I see this real utility for bitcoin if you want to use that as like to get out of the system I'm pretty sure people in Sri Lanka everything is collapsing. They can't even afford electricity and food They're bankrupt the whole country's bankrupt So if you hold on to that fiat now they're in negotiations with the imf that never goes good um So I think a lot of them would like to be out of the system and before bitcoin there was no options for that I mean you could you could buy gold But you better hope that your Broke poor neighbor doesn't find out that you have bitcoin so he can sorry from gold and silver so he can steal it right from your face At least the bitcoins is a little bit of an anonymity and you actually pay for things So I just see stuff like that and then there's other projects that I think are going to be good like this play to earn That is an actual real world utility and I'm not talking about axi infinity that that game's dumb It just is I don't understand that game. I know people will say well, I play it But is it fun? I don't think it really has in a lot of replayability Other than the fact that people just pay it play it to actually get paid. There's good games coming out people will want to actually play them gamers worldwide and Enter into the billions of people So I think that's going to do it and then move to earn like I'm always talking about sweat coin I think that could be a great thing and I talked about why Did a deep dive on my second channel van de gen you can check it out why I think it's gonna be good, but The proof of work in bitcoin is probably is obviously my number one hole these days But uh bitcoin ethereum cardano do just fine Learned self-defense from a wrench attack jerky. Thanks my man for putting that out again When you have millions of worthless dollars you should send on gold and silver and probably bitcoin I gotta tell you I don't understand why why the gold and silver and bitcoin crypto people clash. We should really be in the same camp You know I I own a little gold a little silver I hope my brother's own a lot And they rub it in my face all the time.  Do you want to talk about it? I'm like, yeah, I'll talk about it at some point. That's it I was Show us your hot tub. It's right there There it is All right Thanks The old medic you do a whole little medic sold some so I thought I was gonna go So it's like Probably probably all these all coins are gonna be great next bull run. Just not this bull run What are these mythical things called profits? Yeah, I know trust me give it two years or so and Everything will be a little bit different and then lastly we gotta get out of here Do you think the CEO of Celsius will ever show up and explain to everybody what he did and all the s that happened with this company? I think there's better chance that steve or lick will come out before alex machinsky at least steven voyager there at least in the lead Making all these documents public because of the chapter 11 bankruptcy Celsius still has to get there and I don't know so That's it That's all we got so look. Thanks for everybody for showing up. I appreciate it If you like today's video thumbs up subscribe all that great stuff down there and that's all we got for today I'll see you guys tomorrow. I appreciate you guys showing up on a sunday. That's amazing and uh That's all for today anyhow I will see you guys mañana adios


This video is called using ratios to find the side lengths of triangles. It is very similar to the video you just watched where you were using ratios to find angle measures of a triangle. It's just very important to read the directions carefully and make sure you're answering what is being asked. This particular problem says the ratio of the measures of the sides of a triangle is 3 to 4 to 5. Its perimeter is 72 inches and it wants you to find the measure of the sides of the triangle. Now this time it's not appropriate to set your ratio up equal to 180 degrees because it's the angles that add up to 180. Side lengths do not add up to 180 degrees. That's why we were given the perimeter because the side lengths of a triangle will add up to a perimeter. So this time my ratio is 3 to 4 to 5. So I'll have 3x plus 4x plus 5x. Well that will all add up to not 180, but this time will be 72. So when you have a side length problem, you'll add your ratios up to the perimeter. When you have finding an angle measures problem, you'll add your angle measures up to 180. So now let's go ahead and solve this. 3x plus 4x is 7x. 7x plus 5x is 12x. So 12x equals 72. And then when I divide both sides by 12, I get x equals 6. Now be careful. Many of you will be tempted to say, oh, I found x. My problem's done. I can move on. This problem did not ask you to find x and wants the measures of the side lengths. So we have to keep going. Shouldn't be too hard though. My ratios were 3x, 4x, and 5x. So I'll have 3 times 6, 4 times 6, and 5 times 6. All I did was replace the x with 6 because we found it to be 6 earlier. So 3 times 6 is 18, 4 times 6 is 24, 5 times 6 is 30. Does 18 plus 24 plus 30 add up to 72? 3, 4, 5, 6, 7, it sure does. So the answer to this problem would be 18, 24, and 30. That would be the side lengths of my triangle. They told us the perimeter was inches, so technically we should write inches to label our answers. You'll have to ask your teacher how strict they're going to be about labels during this chapter.