Hello everyone, welcome to our first day seminar of 2022. We have Marcus Huddy here from DeepMind, but before I pass over to Marcus, I'll do a quick acknowledgement of country. So I'd like to acknowledge the traditional custodians of this land, the Nanowall and Nambri people on whose land we host this seminar, with our respects to elders past, present and imagined. So take away Marcus. Hello everyone, thanks for showing up, so plentiful. Yeah, so this is work. So normally I work on AGI, but I got involved in this HMI project very early on. Seth really wanted me to be in this project, and I thought maybe I should do something in this area. And I was probably the first one in HMI to do some research. But beyond this work, I didn't work in this area anymore. But there's plenty of more to do with respect to this idea, and you will see it's just an idea. And I thought this idea can't be new, and I still sort of think maybe 50 years ago, somebody presented I had this idea, because it's really simple. Also on the other hand, I talked about this to various people, and somehow even difficulties understand it, although I think it's really, really super simple. I mean, making it work in practice, there's more things to do. Okay, let me start. So this is about fairness. And without regret means various ideas out there to achieve fair solutions, but often they come at a cost. I mean, Bob Williamson has a paper about the cost of fairness. I could have called it cost, but sort of regret is the difference between an optimal solution and what we can achieve. And so this difference in this solution is actually really zero, which I think is quite nice. So first give motivation a main idea. It's a bit abstract, but don't worry, I go through a running example throughout the talk. Then I give the classical approach to fairness by constraining the solution space. Then the main idea of fairness without regret. And then some details, which I don't have time to talk here about, which are also important. Okay, so first on terminology, the goal is to optimize some primary objective while also caring about a secondary objective. And when I talk about optimal or best or solution quality or regret or something is relevant or comparable or so, I always refer to the primary objective. And when I talk about fairness or just or equitable or so that refers to this secondary criterion we also care about. And this is quite abstract. For this talk, it doesn't really matter when we talk about just or equitable or fair. It is just not some mathematical function. So I usually use the word fair without implying that they are all the same. So these details don't matter for this talk. Okay, so here is the one classical solution of achieving fair solution. So let S be a solution space. Say potential students, you can admit to a university. And then you have some objective, which you want to maximize. Maybe you want to hire admit the students, which have best high school grades or high IQ or whatever. There's a scholar admission criterion and you just select maybe a couple of students, which maximize this criterion, whatever it is. Okay, sometimes these solutions appear to be unfair. And we want to do something about it. And a popular bit crude and brute force approach is to just constrain the solution space. Rather than considering all potential solutions, we just limit the solution space to solutions which we deem to be fair. Okay, the problem is that in general that reduces the solution quality. So fair optimal solutions are inferior to if you just ignore the fairness. So that's also sometimes called the accuracy fairness trade off points of paper by Bob Williamson in 2018. Okay, there's another problem that often we should not be or cannot be very certain about objective itself. So, you know, think about student admission, and we have all kinds of criteria. And how do we aggregate this into one number, right?  And then we have some feeling that all these things are somehow relevant. But what's the relative weighting, for instance, or even are they additive or multiplicative and so on.  And to putting this into a similar single number, we can do that. But I don't think we will ever agree on, you know, a unique way of doing that. Okay. But I will turn this problem actually into a feature. Okay. So, I use the problem that the primary objective is often itself uncertain in order to get fairer solutions. Okay. So, instead of looking at a single objective, you can see that here, let's be more honest and look at, you know, a set of objectives. Let's assume you have a committee and, you know, we can't agree what is the right criterion. And maybe we just take all the suggestions from the committee or the convex hull or whatever. So then we have a set of objectives. And well, for each objective, you tether, you know, this parameter space can be discrete continuous or whatever I give examples later. We consider or look at the optimal solution. So for each utility, you tether, we get an optimal solution as star tether. And by design, this optimal solutions, they are incomparable, right? I mean, the optimal solution with criterion A and B, they're optimal in their own right. And you cannot really say, you know, which one is better, unless you would have a meta criterion or something like this. Okay. And for now, we have this set of optimal solutions. And now we consider a secondary criterion. They have fairness criterion. We say a solution has a certain fairness, you know, real number, and we want to maximize fairness. And we can easily do that by just picking out of this set of optimal solutions, the one which is deemed to be most fair. And because all these optimal solutions are incomparable, we're not really compromising on the first optimality criterion. And, yeah, so that's here, then we take sort of all the optimal solutions with respect to the different tethers and take the arc marks over tether. And then we have the optimal fair solution. And in a sense, that's all that is. And now I'm just going through the more details. An example and how that might work. So, but before I do that, let me say what I'm not talking about. So first, you know, there's no probabilities, no machine learning, no fancy optimization algorithm, although we need a fancy optimization, which is a bi level optimization problem, which is very tricky. And so for a large scale problems, we need to solve this problem. I mean, there are packages, but probably a custom made solutions would be useful.  You just have a primary objective, you have a fairness notion. And in this way, we can get fairer solution. I also don't consider, you know, bias in the data, which is a big issue. So you have to debias the data somehow, which is difficult, but maybe not impossible. I'm also not presenting a ready algorithm, unless you just take an off the shelf bi level optimization algorithm, which may be good enough. So there's plenty of more left for you if you want to work on that. Okay, so I think I can just skip through this. And there's some related work on bias and fairness, fairness enhancing interventions. And you know, this notion of fairness is, as you probably all know, is very contradictory or contentious and controversial. And Brian, what is his head in or so he gave the first HMI talk here. He had a very interesting take on this. How these different notions, you cannot satisfy. So essentially, you can only have calibration. If you want calibration, then all the other traditional fairness notions go out of the window. Yeah, the bias in the data I will not address. And while here's some work, and while here's some work, which achieves or tries to achieve fairness by constraints, which I, you know, argues inferior to this, to this new idea. Okay, so how does fairness by constraint work? So you have an overall score you. So I just, just give you an example, rather than sort of the general theory, there's anyway, there's not much to the general theory, either. So assume you just consider IQ and grade of the students. And IQ is between 50 and 200, or I mean, realistically, maybe a more narrow range and grade between zero and 10. And both you think are relevant, maybe equally, but we cannot just add them because, you know, the scale is totally the scale is totally different. It's from 50 to 200. It's from zero to 10. So maybe we rescaled the IQ by 10, then it's five to 20 and zero to 10. That seems to be now a comparable scale. And then you can maybe add them up. Or let's take a weighted average of IQ divided by 10 and grade and choose 10 to be one half if you deem there equally important. So here's some examples. So I just, you know, invented six students with IQ and grade. And here's just the initial of the students. And here you see them on this 2D plot, you know, horizontally is the IQ, vertically is the is the grade. And now the fairness criterion will be on the next slide will be, you know, with respect to gender. And you see here's a blue, so male and the red or female. Okay. So, so solution basis S, we maximize some mobility function, which you'll find on the previous slide, we say 10 to one half, which seems to be reasonable. We get the optimal solution. And the example, we have six students. Let's say we can only admit two students. I mean, of course, it's a very small example. And so the total score is of course just the score of the two students, which we want to admit. So we take the arc marks over all subsets of students in this population, restricted to that, we can only admit two students. And this gives us the optimal solution. And in this case, it's a simple threshold strategy. We look for the best student with respect to you one half, and then the second best student. And this will give us Bob and Zach. So if you quickly look at this diagram, since these criteria are equally weighted, so the diagonal lines here are the iso lines. So when you look at Bob and Zach, they lie on the most left top right part. So these are the best students with respect to this criteria. Okay. So here, because we admit two students, so it would be actually more appropriate to plot pairs of students. So we take all pairs of students, they're 15 pairs of students, some of the IQ and some they're great. And then we have this plot here. These are just the initials. But then also if you look at the diagonal here, you see B set, which is Bob and Zach, they lie on the diagonal and they're optimal. It's just in a different way of presenting that or reducing it to selecting a single point in a solution space. Okay. So that was an optimal solution by definition. And so maybe some people think this is not fair because we admitted two males, despite if you look at the average IQ of the females and the average grade of the females and then the males, they're exactly the same. So the average is 120 for the IQ and eight for males. So they seem to be sort of, you know, have equal qualities. But for some reason, this system admitted two men. Okay. So you know, some approaches to have quotas, let's say we have a quota of at least 30% women.  Okay. So what we do now, or what the constraint approach does is you decrease the size of the solution space to fair solutions. You have this fairness function, which is either fair or unfair, and you only say solutions which are fair. Okay. And then you just maximize with respect to the fair solutions. And if you do that in the example, in the example, you get, you can just look at the diagram and shift the diagonal and only consider the fair solutions. You get Bob or Sack together with, you know, Amy E for ISA. So in this case, there are six fair, so equally good fair solutions. And what you also see, if you just, you know, add the numbers, you see that the fair solution gets your score of 21, while the optimal solutions without fairness constraint gives you 22. So there's a cost to it. So you lose, you know, either one grade or 10 IQ points by insisting on a fair solution. Okay. So, and that's the reason why I call it regret. So this is the optimal solution here. This is the optimal fair solution. And generically, you know, this is larger than zero, and that's the regret or the cost. You suffer from fairness. Okay. So how can we avoid this? Okay. It seems impossible. But as I mentioned, I mean, we just wait IQ and grade 50-50. And, you know, it is plausible, but we could, for instance, you know, if you just take a grade and differentiate between stem and hash grade, and then say, okay, we have Q, stem grade, hash grade, and maybe they're equally important. So make one third, one third, one third, and if you split it in this way, you would weigh IQ of one third and the grade two-third, which seems to be also quite plausible. It's really hard to argue that one is better than the other. So we already have a range here of the parameter teta, right, between one half and one-third, which seems really completely incomparable. So what we do, we turn this problem into a feature. So we consider now a set of optimality criteria. So for each teta, which we deem to be reasonable, we consider this utility teta. For each teta, we get an optimal solution with respect to, I should call it teta optimal solution. And as I mentioned, by assumption, they're really incomparable. And then we design a second fairness criterion. In this case, it should not be sort of a binary fair or not fair, but sort of graded somehow, more fair or less fair. And then in this set of solutions, we try to find the fairest solution. So this one on the left-hand side is sort of the among the optimal solutions. It is the most fair one. And this one here is sort of which teta in my set of reasonable utility functions led to the optimal fair solution. Okay. So in the example, so let's assume now, so I argued sort of one-third seems to be good. So maybe two-third is also fine. So let's just take the interval between one-third and two-third, which I think is totally fine. And let's say the fairness criterion is the difference between male and female admissions, whether the minus or is not going to minimize that or the minus we want to maximize. So zero would be the best. And so what you can see in the simple example for teta star to be 0.35, it's just a random number in the interval. I mean, I think between one-quarter and which is outside of the interval and I can't remember anymore, you know, there's a range where it works. You achieve the solution and will be Amy and Zach. So the fairness criterion will be zeros or maximized. So this is a fair solution. Okay. So now, if you look at the solution and we calculate the utility with respect to teta star of this fair solution, which is 20.1 and we calculate the unconstrained solution, which is 22, it also looks like that this fair solution is inferior. But that is an illusion, because we are comparing an apple with pairs. So these two quantities, so different u-tetas should never be compared. And I could easily add a constant to the u-teta. So if I add just say, I add, I mean, the optimization is here, let me see. So here you maximize over S. So if I add here any function of teta, the maximum will be exactly the same. Okay. And here, in this optimization, it doesn't really matter. So I could just add to u-teta some function of teta. And the optimization criterion and the fairness criterion would be unaffected. And then if I add this, a proper number, I could easily even achieve here on the left hand side some higher number. I could, you know, achieve whatever I want, 100 or whatever, if I'm just adding a constant. So the fair solution looks even better than the general solution.  That's the whole point. Okay. So some comments. So this is very generic. It doesn't rely on any specific utility function or criterion F. And also, it doesn't continue. So discrete can be linear or nonlinear. I mean, this example was linear, for instance. Yeah. It doesn't solve the problem right, you know, which criterion in which attributes you use. I mean, it's the same with this or with the constraint-based solution. But in a sense, it's easier because you don't have to, you know, if you don't mind a mistake, you just put it also in the pool of utility functions. And it's not as severe if you just fix a very specific utility function, which turns out to be sort of maybe not the right one or a suboptimal one. Okay. So here, let me go through the protocol, how you should apply that. So the first is you choose your space S or sort of the attributes you want to consider in your objective. Then you look at a class of utility functions. In the simplest case, it would be a linear combination. And then you should choose a reasonable class. And I'm thinking here about a committee who sits there. And, you know, we have these ATL scores and whatever the scores at universities and then university central comes up with all kinds of formulas. And I guess that's a committee. And they probably don't agree. And what they should do is everyone puts sort of their own, hopefully, reasonable opinion in, and then maybe you just take the convex hull of all these suggestions, what is a reasonable primary objective that gives you a class TETA and then the class of utility functions. And then again, you choose some fairness criterion and what you deem to be fair. And then the rest is in some calculation. And you should do that in this order, right?  Oh, okay, that's too, too, too, too male students. That's unfair. Let's just rig the system somehow right to make it fair. So it's better to follow this protocol.  And then, you know, ideally accept the outcome. Because once you start iterating, you start to rig the system. Okay, so, so now the details which are skipped over. This is a very nasty optimization problem. So it's a bi-level optimization problem. It's actually a special case. So it's not a generic one. So maybe they are, that would be an interesting sort of research project for, for, you know, from the computer science side, whether this special structure can be exploited to get more efficient algorithms. Because in general, it's a really hard problem. One could try, and, you know, you can read a little bit in the, in the, in the tech report which I have out there. One could try just, you know, gradient descent, you know, it's always the first choice. And it's a double optimization. But you can differentiate through the art max. And you get some sort of dual alternating, you know, I like in games, like a settle point optimization. Unfortunately, for the linear case, it's easy to see that it completely breaks down. But on the other hand, if it's linear parameterized, this problem is very close to multi-objective optimization and parity optimality. So, so if you have a multi-objective optimization problem, and you look at the parity front, and then you clip the parity front to the allowed solutions, so which are in the set tether. And then you optimize, we find the most fair solution in this parity front. Then this is sort of an algorithm for, for this bi-level optimization in this special case of a linear combination. And there are algorithms out there for, for finding the parity front. Okay. So, from the machine learning perspective, I mean, usually, you know, we're on some machine learning here, right? So there's no machine learning so far. So, rather than hand designing utility functions or people, you know, have data, right?  And maybe that can inform us to improve the utility function or the objective we are designing, rather than just, you know, guessing by hand. And indeed, it should, but it will never shrink it down to a point, right? So first, I mean, we only have finite data, so we can only estimate this parameter just to find that position. And even if you have infinite data, then we probably disagree on what does it mean for a student to be successful later.  So then we just have sort of this disagreement on a higher level.  So, while machine learning can help to make this space more narrow, it will still be a set. Okay. Oh, I'm pretty good in time. It really happens. So that's sort of a summary and a discussion. Perfect fairness cannot always be a, oh, no, sorry, I didn't mention that. So while this approach, which are called far work, can improve fairness and this example sort of achieve perfect fairness, it not necessarily achieves perfect fairness.  I mean, it's fairer usually than, you know, taking a fixed criterion, but it is not maybe sort of perfectly fair. But on the other hand, if you constrain your solution space, you know, then you get suboptimal solutions, which is not good. And also this fairness business is pretty controversial. I mean, there are so many different notions and people cannot agree on it. So maybe it's actually better not to strive for perfect fairness with respect to a questionable criterion, but just to improve the fairness with respect to criterion we can more easily agree on. Yeah. So yeah, I said, you know, if you have a set of objectives, you know, that solves a lot of conventions. But also, I mean, maybe we cannot agree on this set. But I would argue it's easier to be in a set and in the easiest case, you have five experts and you just take, you know, this convex hull and, you know, that's it. So it should be easier to agree on a set rather than on a single point. You know, some ethicists don't like machines to make ethical decisions. But if you look at the protocol, there's actually four out of five steps under human control. And on the last step when you do the optimization, the machine does it. So there's a lot of human control there. But as I recommended, do that in the order not to rig the system. And, you know, I made a lot of simplifying assumptions. I mean, especially that the data is not biased. And well, that's how science works. I mean, sort of these last four points are, you know, the critique I got during this talk previously, that this is really, you know, there's even a paper about the abstraction fallacy or five abstract fallacies in fairness research. And of course, I'm aware of that.  So maybe those who don't know that's not my area of research, I usually work on, you know, theoretical general AI. And that was just, you know, a brief stint into fairness in machine learning. I'm not working on that anymore. Okay. And, you know, I don't claim that this is perfect, but what I claim that it's fairness without regret, this approach is at least superior to fairness with constraint. Because, you know, doesn't solve all problems. And what has to be done. So we can have stochastic uncertainty in the data. And we can also have stochastic uncertainty in the objective, you know, maybe even the objective can only evaluate it probabilistically. And I have treated that, that should not be too hard. If you have missing data, I don't have it on the slide, if you have missing data, then you have to do data imputation. And it looks like that you could just use the similar approach here, you just impute the data in such a way that you get the fair solution. But this was, would get you actually extremely unfair solution. So you can read in the tech report, and why the data imputation, it doesn't work in the same way as sort of objective function imputation. Okay. As I mentioned, bias data, I have not treated, which is a big issue mentioned machine learning could help. So it would be nice to combine it. And maybe the most interesting next step would be to quantify how much fairness you gain by this approach. So you look at the optimal solution and the optimal fair solution and compare their fairness level. And I think you cannot say much in general. So you have to look at a concrete instantiation of the problem, you know, on the utility function at the fairness criterion. And of course, you know, the larger the space or the size of the utility function, and the more fairer because it includes more and more optimal solutions, the more fairer, and then the optimal fair solution will be. We are in finally this very hard optimization problem, which yeah, not to be solved. Okay, here's some references. And wow, that's the end of the talk. And I think the first time in my life that I ended early. Thank you very much. I don't know if people on Zoom can hear me because we have some mic problems. So maybe you can speak into this mic and share. So I guess if people have questions on Zoom, they should write the questions in the chat. It's going to be easier. But so maybe I can start actually with a question. So this approach is presented in case you have like a single question, one short question, what's your decision? Can you apply it if it's continuous? You mean sort of sequential decision making? I mean, if we're talking about making decisions about loan applications where you get new or online instances every five minutes or the next 10 years, sort of reinforcement learning. But you mean sort of I get new instances and then I want to update my previous solution because I have more students tripling. You have more knowledge and I mean knowledge in which I mean, okay. So you've got more instances with the results and Yeah, most students, right? No, that's different. Most students would be sort of you have more students and kick some previous ones out and more knowledge would be that maybe at the beginning you don't have all their grades or something and then the grades. I haven't thought about in this context, but it seems like I mean, at least abstractly, I mean, if you have new knowledge on your students, right?  And practically, of course, you would hope, you know, that there's an incremental way of, you know, if you have graded descent, you would start with a previous solution and then adopt the solution or something like this. Not sure whether there's anything specific in this idea and in relation to new information.  And then we'll ask Kathy and then Saripa. So I just wanted to say, yeah, I think this is a really a great intervention into this debate. And it's, it really brings true, I think, with the way that people rationalize sort of affirmative action in many contexts. So for instance, in university hiring, you know, we're all aware that we want to say have more women in faculty, right? And legally, you're only supposed to use fairness as a tiebreaker. But I think in practice, what people have realized is that, you know, within the past being working with a very narrow notion of merit and what makes a good faculty member. And that, you know, if we sort of allow for different criteria and allow some incomparability between, you know, being good at certain utility functions, as you say, then there's sort of a broader space of maximal people that you can apply a tiebreaker to.  But if you have a broader idea of what makes a good candidate, then you get more candidates coming in. But it sort of attracts exactly the kind of thing that you're trying to model here precisely, having a multi-objective goal. It's not clear how to weigh up the objectives. So yeah, I guess this is just a comment to say, I actually think it looks sort of fancy and technical, but this is exactly what people want to do when they're wanting to implement affirmative action measures, I think. I was curious that you talked, that you called my talk an intervention into the discussion. I wouldn't call it tiebreaker because, I mean, tiebreaking, it doesn't really break ties. It allows for non-total ordering, right, and for incomparable candidates. And tiebreaking sounds like, okay, this candidate has 0.7, this is 0.7, and then the tiebreak. And here, but maybe you're meaning that, right? So here it's really, these candidates are really incomparable in the sense I have defined. But yeah, it's very similar. Yeah. Yeah, I just want to cheer on them this way of doing things. I think it's an intervention compared to the standard way that the algorithmic fairness discussion has gone, where there's no questioning of how we should perceive the goal, the objective function, and it's all about how we measure, how we balance fairness versus best achieving the goal.  Thanks, which is very interesting, right? I mean, since university is trying to do something which is similar, you would expect that then, you know, the fairness community tries to sort of formalize this approach, which apparently has not been done. I mean, who knows, but I couldn't find anything in the literature, and so far nobody pointed it out. We want to maximize the renewable energy sources. But because of the network's structure, some of the customers are, and the network constants are more sensitive to some of the customers. So it is not really beneficial for the network to allow those customers to install the renewable energy sources. But it is not fair. So how this applying, because the objective is very concrete, you want to insert there as much as resources in the system, how this approach is going to be in that sense. I don't know exactly the setting what can imagine that. So if, well, the power plants or whatever, you know, have a very precise criterion, what is good, or what is an environmental friendly or so on, and everyone agrees on it, that's the criterion. Then this approach doesn't help you, right? Because it says, okay, I mean, if that's the optimal liquid, then you have to use it. But usually, right? I mean, for instance, how do we measure the environmental impact, right? I mean, there's large disagreement how the cost of the future cost, and also how many generations CO2 or so will lead to, but you should sort of be honest and estimate, okay, so maybe that's the smallest estimate and the largest estimate, and then you have a range of what does it cost to have not the renewable energy or not allowing these people to put power in the grid or whatever. And you just, you know, look at the, I mean, it should not be sort of, you know, from minus infinity to infinity, but you have a range where you feel, you know, this captures the truth somehow. And then you crank the handle here. And then if there is a point in this utility space, right, so that these users can feed in without causing too much harm, then you will get a fair or more fair solution. But even if then, they cannot feed in the electricity or I didn't understand it completely, then they're out of luck. I mean, this framework either, right? Then you have to go to hard measures like fairness constraints, and then really, you reduce the overall solution quality for society. Hi, thanks. This is really awesome. I want to push on Alba's point, because I think there's a lot of interesting stuff there. In particular, I think that it comes up when you're thinking about like a decision problem where it's both a component of distribution, of like distributing goods where you think fairness would play a role, and something about burning, where you think it might, you might want to support it off. And in particular, I think that like when you're doing a one shot decision, you're absolutely right that like there's so much uncertainty in how you justify your objective, that it doesn't like completely just choose the one that also happens to be your fairness considerations. But once you're in a sort of like, yeah, I guess online learning situation, once you start doing that, you're basically you're biasing the dynamics of that learning towards a particular path in the safe space. And so here's the example. I'm going to think about like IP. That's one you talked about here. You might think that there's this one thing that you're trying, it's trying to do both those things, essentially. It's trying to measure like the particular kind of robust like aptitudes in a population. And on the other, it's like then it's been inevitably into how we distribute goods in society. And so there's so much like uncertainty in that objective, like there's it's actually great breaks down into a bunch of different components and different aptitudes, which have different weights and there are different choices of how to measure each component. And so you might say, okay, well, given that it's going to feed into this distribution problem, let's choose the one that makes it so that IQ is like the most kind of fairly distributed, like more evenly distributed across like the global population. And that would make but the problem is that that this concern this is how I think is reflected in the debate about this sort of scientific practice. Which is that when you do that every single time, but then you're kind of cutting off a section of like science and scientific exploration that would like that would look at in different ways that you could have measured it, right? And so it's, it's like you're not just making a decision for this one time distribution, you're making a decision kind of in perpetuity for science, that this is how every time we're going to do this. And somehow that that feels for a lot of people, I think that it unduly biases the decorative science. I most likely have not understood the question of the comments. So I try to, so that the two things, but maybe they're sort of somehow related. One is if you just take sort of IQ and, but you know, there are multiple facets, right? And it's just one aggregate number does not, it's not enough to really capture, say the diversity in what society needs.   You just break it up and then you have three scores, right? And then you apply the same technique, right? Rather than just having one score, you use these three scores. And but the other question, I think that was more like that over time, somehow you say that this will bias the system or the society more and more because it's because it's sort of used over a longer time period. So I didn't understand if you could just choose the one that like, I think it roughs people the wrong way. Maybe that, yeah, it biases the learning, right? Like, if the learning actually does something to do with you use this measure and you take a measurement and then you beat it back. But can you give an example of how the learning biases the system or the society in the long run? I mean, in general, what you're saying is that the one measure so that they can kind of compare the results across each other because they decide they find and they sort of standardize what I view is, right? And if they and if it is a concern, maybe that if we use kind of fairness considerations in that where it's supposed to be a peer-based like learning-based thing, then that means that the sort of every time you go out and measure things with an eye towards this measure, opposed to a different one, you're you're learning something a little different. And that might not be a problem because you don't know exactly what you're trying to learn. But if everybody simultaneously is is is trying to optimize for one thing or another, you're you're moving what you can, but you're you're shrinking, I guess, the state case of what could be learned. Okay, maybe understood it, maybe not, but it's just answer a different question. And maybe someone else can sort of maybe clarify what your real question is. So there's one thing I write about in the report.  So we need this diversity for for having a great team to achieve something. Okay, and maybe that's what you're hinting at. If you have one criterion, you're going to optimize for that, then the only higher engineers maybe or something like this, which is not good. Yeah. So this is a this is different from what I talked about. So if your objective is to say, design a rocket, right, to Mars, right, and you know, there's humans in a mass, you need, you know, engineer, you need a computer scientist, you need a social scientist or whatever, right, if this is the objective to fly to Mars, then in order to achieve that, we need this diverse group of people. And so you have to put this into the objective itself, right, because if you design the objective, like, you know, maximizing the number of, you know, highest IQ engineers, right, and then something on a social scale goes wrong, right, you know, you know, then you have designed the wrong objective. And this is not addressed by this work, right.  But if the diversity itself is relevant for achieving the objective, you have to design your objective in such a way that it achieves this, at least within this framework. So there are many questions, so we're going to move. So Patrick fast room had a question. So he's saying that you're using a lexical ordering on the on the objectives. And so what he argues is that you should you should treat fairness as a criterion and just add it up with the utility function and then compute the particle from there. Well, we could do it, but why is it a good idea? And what is the relative weighting? Do you do sort of, you know, you choose the way in such a way that you get, you know, a solution which is as fair as you want to have. And also, you know, there could be on different scales, right. I mean, I don't say that I don't have these problems, but I think it would just be, I mean, if you would add up fairness with utility with a parameter lambda, and you vary lambda from zero to infinity, right, you would get a parade of front, right, which then sort of you trade off fairness with optimality. But how far are you allowed to go? But I mean, when you have multiple objectives, that's what you're doing anyway. Right. So you have to choose a lambda compared to objective. Yeah, but I, okay, you can argue I choose this tethers, right, but I'm not sure how would I choose. So I have sort of the discrepancy between male and female students, and then I have an ATAR score. How would I weigh them a prior without looking at the result and choosing the parameter in such a way that the outcome is what I want? I would have quite difficulties. Your answer is that this is an elegant method to not have to choose this one. Yes, yeah. Yes, yes, and I don't get zero regret for this method. Yeah, but that's important. You choose that before the fact and based on your honest belief on how important are these different criteria in order to say achieve, you know, highest income later or best grades or positive impact in society and not looking at the fairness part. I think that's, that's, that's crucial. That's a crucial difference. Okay, maybe I have no understanding. So I thought this is, I thought this is what you're doing. You've got a whole range for data, right? And then basically you're going to find the set of optimal solution, one for each data, right? Which gives you a parietal front restricted to this, to this data thing. And then what I thought you did is among those picture, among those optimal, those points on the parietal front pick up those for which the fairness is optimal. Yeah, but the point is I don't take the whole parietal front. I only take, and by the way, this parietal front picture only works if you linearly combine criteria, right? So this approach, I mean, if I multiplicatively combine them or so. Okay, let's first stick, you know, with a linear combination, right? So I only look at the part of the parietal front, where I believe, so I assume I have this IQ and grade, right? It would be quite disingenuous to say, you know, IQ is not relevant at all, or grade is not relevant at all. So I definitely exclude or it's just relevant for 5%. So I would say, you know, maybe between 20% and 80%, I would be fine. And that's the important thing that you, that you restrict it to a reasonable range. And I then looked for the optimal fair solution in this restricted range. And second, this parietal front picture and multi-objective optimization way of dealing with this only works if you have linear combined sub-goals, right? And it could be an arbitrary complex function, right? Nonlinear. And then you cannot drizzle that out. So I have sub-goals, right? And it could be an arbitrary complex function, right? Nonlinear. And then you cannot drizzle that out. So I have a, so normally parietal front is here, one criterion here, one criterion here, and you have the diagram, right? And the parietal front is optimal, or every point on the parietal one is optimal with respect to some specific linear combination of this criterion. But linear is just a special case, which is, you know, happens often. But, you know, there can be much more complicated objective. Well, it's a, it's a bi-level optimization problem, and you just crank the handle. I think you raised your hand for quite some while. It seems to me, and there's a, there's a, what I'm going to call meta fairness kind of problem, because we use fairness in context here, but it seems like if you were to run a competition and you were going to tell the competitors that you were going to grade them this way, there was a range of possible objective functions. And then we were going to find whoever we were going to score all the entrance and pick the winner according to each of those possible objectives. And then we were going to hit it with the fairness criterion to choose after all the scores are submitted, which objective function we use. That doesn't sound like a competition like I want to take part in. It seems, that seems like it's unfair. Maybe it's unfair, it seems like it's subject to manipulation, maybe, right? Where somebody who could come after the fact could, could poison the objective functions that they don't do well with unfair solutions, so that those objective functions don't be evaluated, forcing you on the back end to use the objective function that's favorable to them. So you're sort of picking how grades are assigned after the scores are submitted, so you don't know up front what you need to do to win. That doesn't seem, in some sense, fair, but in a different sense than to use the word in the talk. Is that am I missing something or? Okay, would you say that this system or would you say that constraining the solution space is more unfair or equally unfair or less unfair? You just say, okay, we admit 30% women, that's it. That doesn't feel like it's subject to sort of adversarial things. It doesn't feel like it's manipulable in the same way as that this is. Okay, so I don't think that it's in any way manipulable.  The scores in IQ, so nobody can mess with this. And you should actually design, maybe I should put that in the list when you should look at it at the students. So that should become just between step four and five. And then you look what you believe is important, right? And get your relative weighting, right? And again, you don't look at the students. And I see where you're coming from. And then you look at this bi-level optimization and say, okay, we have this pool of optimal solutions. And now we're peeking around just to make it fair, right? And somebody loses out because of this. But what's the alternative? So we have this set of optimal solutions. How would you pick? I mean, they're incomparable by design. You could pick randomly, right? I don't know, maybe that's fairer, right? But I mean, then we should, you know, push fairness completely out of the window. Would you say they're incomparable by design? This occurred to me a couple of times during the talks, so I'll take advantage of the fact that I had before to ask. Aren't you using the fairness criterion to compare things that you repeatedly say are incomparable by design? Isn't that what the fairness does? Well, this is the secondary criterion, right? So the view here is we have a primary objective for whatever, right? But some people think fairness is important, right? We also want to integrate it. But we don't want to really, if it's possible, to degrade the primary objective. So the best thing would be if we can achieve both, right, rather than having a trade-off. And you can do this with this method. And maybe you could argue, I mean, sort of expanding this primary objective to a set, right, is sort of a trick to sneak in fairness. But I try to argue actually to having a point utility is very hard to justify. So it's much more honest to say, well, we have this potential criteria, and we cannot really tell which is better. From the primary objective perspective, and then because we cannot really tell, you know, why not choose a fair solution? It doesn't cost anything. There's no regret. Of course, you know, the student who loses out, yeah, you know, would not like it. He would like to have to randomize or whatever. But I mean, someone will lose out. Sorry, it's one. So we're going to end this now. But Marcus is going to be around for another week or so. Yeah. Yeah. So you can impact things directly. We're going to have a five-minute break. And then we're going to have a short interview about your experience at DeepMind. Yeah. And also afterwards, I have the rest of the day free, maybe after lunch. I'm on holiday, as you can see. So just come to me, and then, you know, we can, I can schedule you for the day, or then some other day if you send me an email.


It's a presentation of TFNN. The Tom O'Brien Show is produced every business day. Tom takes your phone calls toll-free at 1-877-927-6648 internationally at<PHONE_NUMBER>. Let's go to Ben and San Jose. Ben, what's going on, brother? Hey, Tom, how are you doing, man? I'm doing great, man, yourself? I just wanted to thank you and your team and everything. I've been using your technique with the 10-minute charts, watching the VIX, and just making a fortune here on the futures. Isn't it interesting? That's awesome, man. It's wonderful. Thanks, Tom. I appreciate it. OK, man. Have a great one. Have a safe one. Now, Tom O'Brien. Welcome, everyone. This is Jacob Schup filled in for Tom O'Brien. Give us a call at<PHONE_NUMBER>. You can send me an email at jacobatTFNN.com. Can hook you out a little there. Some great news. Our boy, Steel Dynamics, up 5.76% today. New cores up as well. Some phenomenal news. Remember, we're looking at that around the $90 area. I mean, that really came back. I foresaw it going over 100 and staying there for a little bit, but I can't really anticipate these kind of major buys off the top here. New cores up as well. Let's check it out here. Same kind of pretty stellar move for the Steel sector today. Folks, June 15, that's in two days for all of you. Tim Ord is having the final part of his trading webinar series. This is going to be on gold. And no better segue to get into it as gold is down today. So having Tim's insight, maybe looking at what he sees regarding bottoms, regarding how we're going to progress from this point on, that's going to be invaluable. Take a look at gold. So we had CPI, obviously, come out today. And there was a positive outlook on it. We had the GDX down 1.3%. I had a buddy before I went on. He sent me Ignico Eagle. That is now 1.85% today. So the XAU is also down. Some bearish looks, at least, on the XAU for that. If we take a look at CPI, I can pull it through somewhere here. This is on the BLS.gov's website here. So this isn't going to be the core. But if you look here on energy, this is really what affected the CPI today. We're down 11.7 on energy commodities, 20.4 on gasoline, all types, 19.7, fuel oil, 37. I mean, this is pretty nuts. We did have an increase in food away from home and food at home. So looking at companies, maybe like Chipotle and everything, going forward, as consumers are kind of staying away from maybe higher-end restaurants, they're still going to be eating out. That's now just become essentially like a part of American culture. So that will stick around in looking for those companies that provide cheaper food away from home. Eggs have gone down, though, which is stellar. So we have a little bit of a depression regarding that. The major fear I have, and of course, also shelter, was up as well. That, it takes a while, I suppose, for the rates to kind of affect that. Hopefully, we will see an actual genuine decrease in this going forward. The core CPI still, at a significant point, but the total CPI was down basically because of energy. I do get concerned if we run into something where, for whatever reason, some kind of unforeseen circumstance comes around and knocks energy up. We could be back in kind of a realm of trouble a little bit, at least on this sentiment of people in the economy. So that's what we're looking at. We are seeing a depression in gold because of that. And also always defer to Tom regarding gold, but that's what I'm seeing today. So kind of an interesting little development as well. I still think a lot of analysts and banks are looking at, there is disinflation, I suppose, with this, but the deflation isn't particularly seen yet. So we still, I would say, are not really out of the woods. But the market, as it is now, is lovin' it anyways. Of course, we're rallying pretty heavily. So some news here, I wanna take a look. This has a little bit of impact. The US EPA is gonna release a biofuel blending mandate, a rule by June 21st. Folks, that's my birthday. After delay, court filing shows. The US Environmental Protection Agency, the EPA, is expected to release a final rule on biofuel blending, volume mandates for the years 2023, 2025 by June 21st, after seeking a one-week extension on a deadline for the rule, according to the court document on Tuesday. The EPA was sent to issue a final rule by Wednesday under a court-ordered deadline, but has agreed to an extension with Industry Trade Group Growth Energy, according to the filing. Reuters reported the delay early on Tuesday, setting an anonymous source. The final rule is set to mark a new chapter of the Renewable Fuel Standard Program, which is more than a decade old while Congress set out specific goals for the program through 2022. The law expands the EPA's authority for 2023 and beyond to change the way the RFS is administered. At the December proposal, the EPA would require oil refiners, and this is the important part, to add 20.82 billion gallons of biofuels to their fuel by 2023, 21.87 billion gallons in 2024 and 22.68 billion gallons in 2025. It's been interesting. I've been watching a lot of, I think it's on Business Insider and a few other sources, but just looking into some of the biofuels they have, you know, they're using a little bit of biofuel generated by algae. I think it's interesting and kind of pushing this through to a point where we can actually see some kind of impact. However, and I think I might have touched on this a little bit yesterday, but this report came out here. And it was fossil fuel company, net zero plans are, quote, largely meaningless. The number of fossil fuel companies setting net zero mission targets have risen sharply over the past year, but most fail to address key concerns, making them largely meaningless. Some 75 of the world's largest 112 fossil fuel companies have now committed to reaching net zero. That's the point at which greenhouse gas emissions are negated by deep cuts in output elsewhere and methods to absorb atmosphere carbon dioxide. But that's up from just 51 a year ago. Most targets, this is the key point, guys. Most targets do not fully cover or rather they lack transparency on what's called scope three emissions. And these include the use of company products, the biggest source of emissions for fossil fuel companies. And they don't include short-term reduction plans, the report added. That made them largely meaningless. Pretty intense, huh? The report also found that none of the fossil fuel companies were making the needed commitments to move away from fossil fuel extraction or production. As it stands, some 4,000 countries, state, region, cities and companies, globally, have now committed to net zero. So that was last November, the UN issued on what a good net zero strategy was. And you know, this, so many times, I think they call it greenwashing, but companies use this in a lot of ways to get people to basically buy their products and see them as a progressive company. That was something also with, how do you call it, like, fair trade. And I, you know, it's these kind of progressive terms that they put on the products to make people feel better about buying them, and in reality, they're not hitting the point. I learned about fair trade is it's not really a practice more than it is like an organization.  Folks, stay tuned. We have Basil Chapman with us live next. We have exciting news, Tigers. This June, Tim Ord of the Ord Oracle will be hosting two webinars, providing insight into his renowned market timing methodologies. On June 8th, Tim will delve into the S&P 500, teaching sentiment indicators, identifying market bottoms and divergence, and so much more. On June 15th, Tim pivots to the gold market, taking a look at cycle analysis, ratio studies, advanced decline indicators, and other important tools for analyzing this sector. Sign up today on TFNN.com. TFNN, educating investors. Currencies, commodities, and bond markets are as important as ever right now with how they're driving the volatility in equity markets across the globe, which is why it's a great time to try out Teddy Kegstad's Tiger Forex Report. Teddy Kegstad breaks down the Forex markets every Monday using his 30-plus years of experience as a trading veteran of futures, forex, stocks, and options. Teddy releases his weekly Tiger Forex Report every Monday morning with coverage of all the major currency pairs, including the Dollar Index, the Euro Dollar, Pound Dollar, Dollar Swiss, Dollar Yen, as well as many more. And he also has weekly coverage of the crude oil market and the 30-year T-bonds as they both influence forex markets tremendously. 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If you're not satisfied, let us know and you'll get a full refund within 30 days of signing up. Subscribe to the Fibonacci 24-7 newsletter today, TFNN.com, educating investors. Toll-free at 1-877-927-6648 internationally at<PHONE_NUMBER>. Welcome back, folks. Take a look over here. We have the opening call newsletter by Basil Chapman. So every morning I look at the newsletters, just seeing what's going on. I proofread some of them. And Basil's is always... I love it, right? He's informative. He writes enough to get it across, but it's concise as well. You can try this for 30 days. You don't like it for whatever reason. I can't imagine why, but you can get your money back. Mr. Basil, are you there? Hi, how are you? Doing all right. How are you doing? Jacob, it's good to hear you at this hour. You're doing a great job. Thank you, Basil. It's good to have you on. Thank you. So what are we looking at today? You were talking about a newsletter, and what's really interesting is that in the Chapwave, the concept that I originally founded way, way back when I was hand charting with pencil and ruler on the engineer paper, was that if I identified the lowest low bar, if there were four peaks, that is, each higher peak was alphabetized, peak A, then the next peak, which starts, the leg starts a penny above the left side, peak A starts leg B, then it makes a peak B, etc. Until it gets to do the fourth highest peak, that essentially over the years developed so that I take it from a bi-signal, upgrade it to a bi-mode, and it applies that it should go to at least a peak D. That's the core. At D, other things can happen. So what's interesting here, especially since I'm on with you at this particular time, at what is it, 3.20 on the Tuesday, the day before Fed meeting, this is the 13th of June, we've got a leg C. And for subscribers, we are long, we're actually long from this low right here in the October loan. This is the weekly chart of the Dow, we're long the Dow diamonds, that's the one to one long, and we're long the three times long. We've held it even though that's really just a trading position, but we've managed the stops of health, it's been very good. And we've added to it on the way up, we've even shortened it, about a week ago, we added to it. And here we are in leg C. So this is the difficulty. So leg C, with the Fed meeting, you say, is this it? The irony of the whole thing is that if all the technicals are good, and this daily chart shows you that the MACD, the moving average convergence divergence, it's really strong. The stochastic is really strong at 94%. On balance volume, this blue line is lagging a little bit. I'm going to show you something in a moment or two. I'm period moving averages over the 14. I mean, everything that you want is here for a buy mode to continue. So we are waiting, regardless of what happens with the Fed, we're waiting for maybe a pullback and then perhaps a minor high towards the 34,000, today's high 34,000, 310, maybe above to get your leg D. And then we've got to be careful. Now, what's interesting is in the weekly chart, we've already got a leg D, even though it's technically under the last April, I was at, I'm sorry, is that April? It was the December, the week of the 16th of December. That peak in the 34,700, this is still in the waveform, it's in D. So there are a couple of things that are going on. So we've got the down in legs C. You were mentioning just a moment ago, oh, what was the stock? Did I write it down? You were talking about it and I thought, oh, that's a great example. Let me see if I can see it in my list of stocks that we... We were looking at AEM, STLD, AMD. Yes, STLD, that's right. It's the steel dynamics, if I'm correct, right? Steel dynamics. So steel, STLD, where is it? Let's see here. It's only in a leg B in the daily chart. And the MACD is good. The stochastic is actually at 82% on balance volumes lading, but the red little gray line is improving. The weekly chart doesn't look great but the daily chart has had a huge pullback from its peak E, but it's still acting very well. So here I am and I'm saying, okay, well, I'm getting a little cautious because the tech sector, and I'll show you this here, we have in the tech sector, we have a stock I've spoken about very often when I've been interviewed by Tom for weeks now. I'll be saying, we have a stock called Symbartic Inc. It's in the AI, the artificial intelligence, robotic warehouse automation systems. And we're long from the 21s and it's actually now more than double, it hit 47 today. But I wanted to say, so in the one air, now look at the little blue line, the on balance volume is the exact opposite of something like a caterpillar or your STD, which is steel dynamics. Look, caterpillars in leg E, but the stochastic strong, but the on balance volume is lagging. So in my show this morning, and I'll do that again tomorrow in my show at 10 o'clock. What I wanted to emphasize was that this is a rotational market and that somehow or other that whole artificial intelligence area could, in fact, we also have bots, which is BOTZ is the symbol. This is Global X Robotics and AI ETF. The week is a little extended. It's in leg E. If you look at the technicals, look at that on balance volume. What else is fantastic with the on balance volume? Just be ready for some kind of a pullback, just a pullback, not a smash with a pullback. The daily says the same thing. So the way I'm looking at the market right now, and I should also mention we are long from the 24s at 20. You there, Basil? Looks like we might have lost Basil for the time being. We'll try to get him back. There he is. Oh, you lost me. Am I back? Yeah, you're back, Basil. We can hear you. I think that we've got a rotational correction that is possible coming up, and that means that some of the sectors that have done really well take a bit of a breather, and the sectors that are like in the cyclical, as I just showed you, catapult, like you just mentioned, STLD, which is Steel Dynamics, that area starts to see some strength. That's the way this market works so often that when one sector takes a breather, the sectors that will lag and start to play catch up. So I'm kind of intrigued, and as I say, we've got to be a little careful. We are at sea, and that's the way we expected to be, but this is where I start to get a little bit cautious, just on the shorter term, the daily charts, the weekly charts actually are all, and the broadening of the market rally has extended enough for me to say I think that the market would cope with some negative news from the Fed, but at the same time it's really important to be a little careful in the sectors that have really run to the upside in an extreme way. Fascinating. Fascinating. And so tomorrow you're going to go over that a little bit more, right? I'm curious what you're looking at with the blue line. I think about 45 seconds left, but are you looking for that convergence? Where it converges with the other stochastics, or how are you looking at that? No, the blue line is a completely independent thing. Joe Granville, years ago, we used to add up the price. It's a running total of a bar that closes up. You add it to the running total of the volume, and if it's down, you subtract it. It's a very simple technique. Once they've made it a line, and you have to do that calculation, it's fantastic. So I'll talk about that tomorrow. It's almost independent of everything else. This is the only thing I use as an overbought and oversold signal. The others can keep going a lot longer than your patients. Fascinating. Basil, thank you so much. We'll definitely tune in tomorrow, 10 Eastern time. Basil, thank you so much. Thank you very much, Jacob. Bye now. See you tomorrow. Sharpening your skills as an investor is like getting better at playing a musical instrument. You have to practice, sure, but you also need excellent instruction from experts. At TFNN, you'll get advice and guidance from the authority in technical market analysis, and it's not just dry, tedious text either. 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There's no catch or added costs when you join our community of traders. In the Tiger's Den, you can look over the shoulders of Tom O'Brien and the other TFNN hosts while they analyze charts during their live Tiger TV programs and join an interactive trading community with hundreds of members exchanging ideas, interact with other tigers and tigers as they share trading ideas, news analysis, and discuss the market action all trading day, even at night and on the weekends. The Tiger's Den at Discord is accessible on mobile or tablets as well, so it's always at your reach. To sign up today and become a part of this educational community of traders, just visit the front page of TFNN.com. This segment is brought to you by Think or Swim. For more information, just click the Think or Swim banner on the front page of TFNN.com. Folks, alright, so to run over this quickly, we have Disney. They have some issues regarding their movie release and some of their streaming. They're delaying a lot of their movies, all of the Star Wars, Dead Pools, Avatar sequels, and this is getting in the marvels as well, and this is getting pushed out to like 2031, Deep 2029, 2026. So they're having some issues and this is essentially just caused by production delays with the ongoing WGA strike, partly, and they recently paused some filming on some of their other stuff, but regardless, this stock has obviously been tanked pretty hard this year. We reached as far down as 87.01. It did creep back up to 95, but really, I think the beautiful spot for this stock is at the 100 level, and it might take us a while to actually get back up there. Hopefully with the return of their old CEO we can see something positive. I have shares in Disney, still above my entry price, but who doesn't want it higher from that? So, we'll see. Next level we're trying to get at is 95, but again, we have this 92 area that will test and see if it can. It had big volume and then tried to get back up and then just went even deeper, you know, quite a bit of a sell-off for that stock in general. So Oracle was great. People loved it. It took a little bit away from AMD. We had William yesterday asking about AMD. Still think it's a great stock to get. It was down a little bit today. Looks like we had some profits taken out of Nvidia as well, even though we're up about 4%. It was trading higher, however. Still, if you look at on a year to date, AMD is looking solid. They're going to unveil their new chip. Okay, and this is to compete with Nvidia. So this is the Ryzen chip. It's going to be added. Let's see here. Meta is using it as its cloud chip. This is pretty big for it and this is the Ryzen Pro 740. Obviously, the government as well is using their Mi300 chip in their supercomputer. So it looks good for it. I get a little nervous here, not with AMD or you know, not with the chip sectors at all, but with this AI hype, right? So I saw this article. This was in the financial times and it was a week old AI startup. It raised 105 million and this was Euros. It's in Europe, but it's kind of giving you an insight how the global market is looking at this. We've obviously dumped a ton of money. You think about it too, like most of this upward pressure in the market is from this AI rally, you know, and other companies basically adopting AI or saying they are. And of course the generative AI is massive, but it begs the question, you know, a lot of money being dumped into AI companies that you know, might be taking back a little bit. This was Mistral AI. It's going to be a generative AI such as chat GPT and all that. They have some old Google Microsoft people, but just to show like this company again is a month old all right. It has no product whatsoever, has barely any developments and it raised 105 million in Europe's largest ever seed round. Mistral AI's first round of financing values, the Paris based company essentially at 240 million. So are we seeing just like a hyper, you know, inflation of these of these stocks? Again, I don't think this is the same with chips. Chips aren't going anywhere. These are really the future of this digitalized world certainly as we become more and more digitalized, but it's something to keep in mind you know, obviously this will generate a lot of income, but that's when things like that start happening, I start getting a little bit, you know, suspect I suppose but we'll see. GameStop regarding some of our memes the CEO picked up 10 million in stock, which was just insane. We're looking at a little bit, that's Ryan Cohen stock worth 10 million securities filing show Tuesday shares. The company jumped 7.7% early trading. They are down nearly 80% from their peak of 120.75. There are still so many people who are stuck in this stock and it's just kind of absurd. I mean, here we are at its highest peak right here. 125 and just a slow burn. This company, I don't like it. It doesn't really do anything. Nobody buys games physically anymore. It's all done on stores. If you're going to want to go buy action figures or whatever, you get those on Amazon far better. It seems to me, it surprises me we have like a local mall here and they're still in there. One of the things that they, I guess, pull people in on is kind of like test experiences which I guess is positive, you know, especially when we're having AI, excuse me virtual reality come out it'd be cool if they adopted something like the Apple goggles or anything else but other than that what's keeping this company afloat a lot of the time is kind of the meme investors on Reddit. Cohen promised investors a digital pivot with a focus on e-commerce and again, right, like that's the smart thing to do and I mean that in a general sense, right but you're still competing Microsoft and Sony sell all their games on their platforms from their stores, right there's no need right now for, this is my analysis of it but there's no need right now for, you know, a middleman to get me video games or get products like that when I can just purchase them either directly from the seller or on something like Amazon so they ousted even Matt Furlong which is a former Amazon executive and he was handpicked to lead the largely brick-and-mortar operations and companies expansion and that's obviously concern for a lot of investors so some interesting development on that, people still love that stock but I think it's just because they're getting into it. With speed a little bit about copper yesterday, you know, less spots are being found with copper they're spending a longer time getting it out but it seems that the, as more and more copper is being used and integrated into EVs and technology we have companies like Citigroup, they're looking at it and seeing a big top out of copper over the next few years this is a little graph that Citigroup came out with and they're expecting it 15,000 by 2025 so it'll be interesting to see I definitely think copper is certainly getting it's harder to find these kind of deposits of it right and so I was speaking on this with a friend and I was saying there's basically being used in kind of this EV blow up and this tech blow up and they ask what about lithium, that's a good question too because lithium is massive but lithium I was doing some research on it third most abundant element in the universe, honestly we could see this kind of chart of lithium prices and kind of topping out here I was even listening to Elon Musk about it and it's just easy to get lithium now and more and more different techniques in order to extract lithium are being developed ways that you weren't able to prior on the copper topic as well Musk was talking about it he's looking in actually to Mongolia to develop a copper mine so that'll be interesting to see it develop that way but keep an eye on these copper stocks obviously we're just taking a look at southern copper if I can get this back up here we had a nice move today as well and I'll just look on year to date for them get the general idea of it the high at 8198 we're creeping back up to that 75 level we had some decent volume moving up on it so we'll see I'm honestly considering it I need to do a little bit more research before I get into anything copper but it certainly has peaked my interest as of recently folks when we get back we'll talk a little bit about these API kind of wars that are going on essentially and really what that means for future AI integration folks stay tuned we'll be right back before you decide it's impossible get some advice from the experts you might find that it's not so impossible after all for daily market overviews that give you direction on the key indices selective stocks and commodities subscribe to the opening call newsletter at tfnn.com the opening call newsletter is written by Basil Chapman creator of the trading methodology known as the Chapman wave the Chapman wave up down sequence and identifying price turns finding the peaks and valleys and stock prices get the opening call newsletter by Basil Chapman and your inbox every day first time subscribers also get a 30 day money back guarantee if you're not satisfied let us know and you'll get a full refund within 30 days of signing up tfnn.com educating investors biotech is booming but for how long whether you think the biotech bull has room to run or has run its course trade LABU or LABD directions daily S&P biotech three times bull and bear ETFs visit directioninvestments.com slash biotech today an investor should consider the investment objectives risks charges and expenses of the direction shares carefully before investing the prospectus and summary prospectus contain this and other information about direction shares to obtain a prospectus or summary prospectus please contact direction shares at<PHONE_NUMBER> the prospectus or summary prospectus should be read carefully before investing an investment in the funds is subject to risk including the possible loss of principle the funds are designed to be utilized only by sophisticated investors such as traders and active investors distributor foresight fund services LLC we have exciting news this June Tim Ord of the Ord Oracle will be hosting two webinars providing insight into his renowned market timing on June 8th Tim will delve into the S&P 500 teaching sentiment indicators identifying market bottoms and divergence and so much more on June 15th Tim pivots to the gold market taking a look at cycle analysis ratio studies advanced decline indicators and other important tools for analyzing this sector sign up today on tfnn.com tfnn educating investors this program is brought to you by Vista Gold traded on NYSE American and TSX under the symbol VGZ all right welcome back folks today I was speaking a little bit about how Reddit was having a blackout due to the CEO's new API rule right and so the API is is application program interface and it essentially allows two different applications to communicate with each other okay what was going on with Reddit is there were some companies that were using Reddit data in order to kind of make knockoff Reddit apps right and this is for like better user experience or whatever what Twitter had done a while ago was start charging an immense amount of money in order to access API data and the goal behind that right was to basically squeeze out competition so you couldn't afford actually to use that data on the API Reddit is doing this as well squeezing them out and this is kind of how you know AIs are being run right like they're taking data from other programs and are able to utilize that data to do whatever right just this is what's so nice about the AIs kind of the sky's the limit with what you want to do and in the future we can I could see honestly some companies really kind of strangling each other regarding this right like you don't want this person's generative API to access your information and vice versa to where we get into quite a you know almost a gridlock at least in development with it you know furthermore this is kind of a new way that companies are monetizing their kind of traffic right in the data that they're collecting from us this is brought up specifically in the case of Reddit right a lot of the people aren't liking it so they're protesting Reddit excuse me Reddit got tossed yesterday because of it but you know in the past this kind of raw data had been given to companies for them to process and essentially market but now this is going to be used in order to really create entirely new programs via generative AI and so you know I think one of the big questions that's coming out of this is you know personal data ownership over that and how if companies are going to you know obviously we're on their platforms and we agree to this kind of stuff when we access their platforms but there's that discussion around you know does the individual have any rights to their data you know constitutionally or legally will they be able to profit off of it I mean it got to a point there's a a big knock off of the Reddit application right I forget the name of it but everyone uses it and with these new rules they would have to spend 24 million dollars a year accessing the API and that's impossible obviously for a free application right so you know if that's the what could happen you know in a world imagine if that's a more consumable amount of money right as opposed to just trying to be used to strangle competitors will people on those applications that data is being harvested from will they get to see any of that money should they get to see any of that money it's pretty interesting on top of that it's not just these companies taking your data but there is a new classified excuse me a declassified report that came out that the government is doing so as well and they are purchasing large amounts of commercial data essentially we'll pop into this a little bit and I'll link that in the den right now because I always forget to do these things but it's interesting to look into it and so according to this newly declassified government report confirms for the first time that the US intelligence and spy agencies purchased vast amounts of commercially available information on Americans including data from connected vehicles web browsing data and smart phones now by their own admission in this which is good and this opens up discussion kind of around these concepts of privacy the US government admits that the data it purchases this is in this report clearly provides intelligence value but it also raises significant issues related to privacy and civil liberties and what they're saying in the report is that it cannot you know it can cause embarrassment for American individuals but can also in some cases pose safety concerns so you know as technology becomes greater reaching and as pervasive as it is and really the main goal of a lot of these you know products really being data collection and the utilization of that data for a better product I think this discussion is going to become far more prominent in society the office of director of national intelligence to classify the released excuse me and release the January 2022 data reports on Friday following request by senators Ron Wyden to disclose how the intelligence community uses commercially available data the classified report is the US government's first public disclosure revealing the risks associated with commercially available data of Americans that can be readily purchased by anyone including adversaries and hostile nations it's another interesting point and we talk about that quite a bit right in the United States does not have a privacy or data protection law governing the sharing or selling of Americans private information and this is a quote now in a way that far fewer Americans seem to understand and even fewer of them can avoid commercially available information includes information on nearly everyone that is of a type and level of sensitivity that historically could have been obtained by other intelligence gathering capabilities such as warrants wiretaps surveillance and other methods essentially according to the report the ODNI does not know which federal intelligence agencies are buying Americans personal data so I definitely you know if this declassification gets the amount of attention it really should I think in the coming year well we'll see a meaningful congressional hearing kind of on this right and we'll actually get a little bit of you know some kind of regulation or at least better transparency on this is going in and really again as a culture we need to enforce this idea you know of all your information kind of being online and being intelligent with what you give your information to right so the way you behave online all of your information that has been monetized for a while but now we're seeing that state actors are purchasing it as well you know this is our government so you know people have differing opinions on that but the concept of foreign government as well having that is something to keep in mind so another interesting article pulled up here is we have the US homeowners see equity drop for the first time in a decade somehow Ohio sees more gains so that's good for Ohioans they need a break every now and then for the first time in more than a decade US homeowners have lost equity in their home although Ohio homeowners continued to gain ground according to the real estate and mortgage services core logic American homeowners with mortgages saw their home equity drop 0.7% or an average of $5400 from the first quarter of 2022 to the first quarter of this year in 2023 the loss which totals 108 billion nationwide is the first loss since early 2012 and reflects the decline in home prices seen in much of the country although not in Cincinnati again that's great shout out to the Ohio and Tigers I'm glad you guys are seeing some gain still homeowners on the west coast and throughout the west saw the biggest declines in home value and also you know we've been speaking about the west I want to pull something out as well we were talking about Hilton just basically saying they're not going to pay that $758 million debt they owned trying to find it here but the largest shopping mall in San Francisco also decided to say hey you're not paying anymore going to find this yeah ok the west field that abandons the larger San Fran mall so you know west coast is already in kind of some dire straits in general so this is the average home owner in California excuse me in Washington at least lost $74,000 in equity in California down $59,600 Utah down $37,700 is pretty intense moves we'll talk a little bit more about the west field abandonments when we get back folks stay tuned are you looking for a way to consistently add winning trades to your portfolio Tom O'Brien is here to help Tom O'Brien has been successfully trading markets for over 30 years a frequent contributor to TD Ameritrade Network and CNBC Tom O'Brien founded TFNN over 20 years ago to help educate investors just like you Tom's daily market newsletter market insights is published every morning when the markets open to give you the competitive informational edge you need to succeed these newsletters are packed full of Tom's advanced technical analysis and are geared to deliver comprehensive strategies for a successful portfolio get Tom O'Brien's newsletter market insights today and try all of our products and newsletters 30 days risk free with our money back guarantee at TFNN.com you might think that if you want to be successful at trading in the stock market you're going to need a crystal ball after all it's impossible to predict the future right like any endeavor in life before you decide it's impossible get some advice from the experts you might find that it's not so impossible after all for daily market overviews that give you direction on the key indices selective stocks and commodities subscribe to the opening call newsletter at TFNN.com the opening call newsletter is written by Basil Chapman creator of the trading methodology known as the Chapman Wave the Chapman Wave up down sequence gives you an edge in identifying price turns finding the peaks and valleys in stock prices get the opening call newsletter by Basil Chapman in your inbox every day first time subscribers also get a 30 day money back guarantee if you're not satisfied let us know and you'll get a full hand within 30 days of signing up TFNN.com educating investors everything in the universe is governed by the Fibonacci sequence this mathematical principle is responsible for everything from the most aesthetically pleasing artwork to patterns in the stock market to stay on top of stock patterns you can take advantage of sign up for the Fibonacci 24-7 newsletter at TFNN.com when you subscribe to a better in day trader Larry Pesavento on stocks you need to pay attention to and you can trust Larry's analysis after all he's got 45 years experience as a day trader Larry will also provide daily charts videos and data on the key markets that he's tracking expect notifications from Larry on market movement you need to act on at any time first time subscribers also get a 30 day money back guarantee if you're not satisfied let us know and you'll get a full refund 30 days of signing up subscribe to the Fibonacci 24-7 newsletter today TFNN.com educating investors don't forget you can listen to TFNN live on your mobile device 24 hours per day go to TFNN.com then hit watch Tiger TV that's TFNN.com then hit watch Tiger TV welcome back folks before we went on break we were talking about how the Westfield is a larger San Fran mall and stops paying its 558 million dollar loan John said in the den he was saying Nordstrom is the anchor it's a huge mall and that's true and Nordstrom is not renewing its least in August that store occupied 312,000 square feet when Nordstrom closes the mall will only be 55% least and that's below the 93% of other Westfield malls across the US it's nuts basically they blamed unsafe conditions and lack of enforcement against rampant criminal activity out here pretty pretty significant you know this is going to we were always talking about the smaller banks holding a lot of these CREs but this is where we start seeing it become a major problem I think and you know I don't know what the path forward is as the mayor even said their path of recovery remains clouded certainly it does alright some interesting stuff just to close this out artificial intelligence what can it what can't it do well it's going to give us a new Beatles song if we need that it's taking I actually have a close friend of mine he's my MMA coach and he's just been clicking away on AI and he's making you know all of us he's taking our audio and making and this is what they're doing with John Lennon so I don't know Paul McCartney went on the BBC Radio 4 they used a quote-unquote extricate John Lennon's voice from an old demo and they were using it to complete the song so that you know that's just bizarre I know in the I can't remember which Star Wars movie it was but it was one of the smaller ones they basically AI the grand moff back into it even though he passed probably like in the 80s or something like that pretty crazy world and we're already going to I'm already seeing it like on advertisements on YouTube using the voices of famous people in order to sell like clearly scam programs and products so yeah he be wise out here I don't know folks thank you so much for joining me I'll be back again tomorrow Tom will be back I believe Tuesday of next week we're out Monday thank you so much folks have a great rest of your day


Hey everyone, welcome back to Dumbest of All Galaxies. Today we're talking Star Wars Episode 9 Predictions. Prediction 1, Kylo Ren is actually Anakin's father. Had you wondered about the mask, right? Prediction 2, Snoke is Jar Jar and Yoda's dad. But that one was obvious.  Prediction 3, Rey is actually Padme. She finds her parents and they tell her she's Padme. Rinse and repeat.