Let me just distribute so please take one and pass it to your neighbor. Okay. So our next lecture is a is another worldwide specialist of random matrices, I would say coming from the statistical mechanics side. I'm very happy to. I'm sorry. Go ahead. That is part of the school. And so I highly recommend. So two references it was asked on the on the chat. Of course the book by Mark potters and jumpy Bush showed that he mentioned, but also they are very very clear and introductory notes by Paul that can be found on archive. They are that are great. So, so what are you going to talk about. Okay. Can you hear me. So everything will run. Thanks. Thanks very much. First of all, for, for the great introductions, it's real thrilled to be back at ICTP for for a school. My last one was in 2017. I spent three years here at ICTP as a postdoc 10 years ago, unfortunately, so this is great comeback. So coming to the topics of the school. I will of course talk about random matrices. I will not talk much about random graphs. I will talk about statistical mechanics. I will not talk much about machine learning but I promise I will mention the word inference at least once during this, this lecture, actually technically twice because I just said it. So, I have prepared some handouts with some material that will be helpful during my my lectures, and I encourage you to go over the this material and ask me questions if these are not clear. Maybe it's just a collection of material equations formally that will be helpful during during this lectures. I selected a specific class of problems, which I find very, very instructive that have been most of the most of these things have been developed at King's where I am by young Fyodorov and his PhD student, Rachel to blame. I wasn't very much involved in in this during the development of this problem but we are now coming back to it. Because the problem is very rich is very instructive there are a lot of things that we don't know or we don't understand so I thought it would be a perfect, a perfect set of problems to start with. And the, the, the problem or the the class of problems goes under the name of procrastis problems from the name of the character in in Greek mythology, who was son of Poseidon. So, Rob, and the bandit who essentially invited people like strangers over to his to his mansion, he would force them on on his bed, and then he would proceed by stretching their limbs and chopping their legs, until they would fit his bed. And unfortunately, nobody would fit his bed is bad exactly and the class of problems are. They take the name from, from, from this particular operation of forcing people with a constraint on their height into a setting where they would not like to stay. So, in the, in the case of the orthogonal procrastis problem which is depicted in the in the first figure in the in the handout, you essentially have a set of points, a, for example, in the plane, and you rotate each of these points, individually, and to get a new set of points to which you add some some noise so in the end, you displace the final positions of these points, and you get a new set of points that we call that's a triangle, set B. So in the orthogonal procrastis problem, you are given the set a the original set a, and then the set B, which is the rotated set after you've added some noise. And the question that you are asked is how you can, can you reconstruct the best rotation matrix omega that would map a into something as close as possible to the actual set B. So the constraint is the fact that the matrix that operates that that performs this operation must be a rotation rotation matrix. In the oblique you procrastis problem which is the one that I'm that I'm dealing with here, we are instead given a set of equation so a linear set of equations with a nonlinear constraint. And this is as follows, you are given a matrix, a, with M rows and N columns. And the A is applied to a vector X to give a vector B. So this is the linear system that we want to solve the vector B is a vector with M entries, real vector with M entries, and we have a constraint, though, on the solution of X on the solution of this linear system, that is that the modulus square of X must be equal to a constant. Let's call it, and so X leaves on on a sphere. This is a quadratic constraint, but it must be a solution of this linear system. So in the procrastis setting X is the poor passer by the, the person that is invited by procrastis in that satisfies this constraints on on his normal height. He doesn't want to be stretched or to have his legs chopped. So B is procrastis hammer, and B is procrastis bad. Okay, so we want to act on something that that has a constraint in such a way that it fits another, you know, another location which is is is bad. Now to analyze this, this problem so M is number of rows, and is the number of columns so in this linear system setting M is the number of equations, and N is the number of variables. So we will start with considering the case and larger than N, but we will also relax this this assumption so we will get results, also for M smaller than, than N. So how do you measure the in general, of course, for a given instance so you want to find the solution X of this of this problem. In general, this system of equation will not have a solution. Okay, so how do we measure whether this system, whether we are approximating the solution in a good way or in a bad way. Well we introduce utility function or a loss function, let's call it H of X, and defined for example in terms of the square norm of the difference. So when when this square norm of the difference is exactly equal to zero, then the system as a solution. So this square norm is larger than zero then in general we are not able to solve this system but this is a measure of how good we are performing in approximating the solution to this to the system. Okay. So this is a loss function that can also be written explicitly in terms of the components. So, okay, from one to M, and then we have summation over J, one to N. Okay, J, X, J minus BK square. And we will consider randomized version of this of this problem to understand questions of typicality like what is the typical behavior of the system. So we will take a the interest of the matrix A and B, the interest of the bed vector as random random variables. And AKJ will be Gaussian random variables, which makes clearly the cost function, random function for which we can, we can compute the statistics of interesting observables and BK is Gaussian as well. And all the entries are Gaussian with the following covariance structure. So they are ID with variance with variance sigma square. Okay, so this is the, the setting. This is M and sigma square. We are interested in two objects. The first one is the statistics of the critical points. I will tell you in a minute what critical points are. And then the second point is the statistics of the minimal loss. The minimal loss is zero if the system can be solved. It is larger than zero if the system cannot be solved, but still we would like to understand how close we are to solving the system exactly. Okay. This problem can be tackled by using the Lagrange multipliers method. So we define a Lagrangian that depends on X, which is equal to our loss function to which we add a constraint on the norm of the vector of the vector X. So we want the norm to be equal to N. So this is object, the solution must leave on on a sphere. And then what we need to do is we need to find the critical points of this Lagrangians Lagrangian so the solution in components, we need to take the derivatives with respect to X. So this is the R of the Lagrangian and in particular and set this equal to zero. Okay. Yeah, sorry, this this by by this symbol I mean the. So this is equivalent to the square norm of the vector of the vector X. So this this stands for the dot dot product of the vectors X. So, if you take this, this object here, and you differentiate this object with respect to XR, then you get a factor of two that comes that comes down and then you get a factor that multiplies X, X are coming from from here, right. So what you what you get by differentiating it is one half times a factor of two, then you get the summation over K of this object square, which is summation over J, aka J XJ minus BK. So you get an extra factor differentiating inside here, which is AKR minus lambda over two times to XR equal to zero. Okay. So this goes away. So we recast this equation for the critical for the critical points of this loss function in matrix matrix form. So you get precisely a X minus B here. And then you get. Sorry, there's no two here. And then this object here is multiplied to the left by a transpose. This term here that is summed over K. So you get a transpose times a X minus B, which must be equal to lambda X. So that's the equation for the critical points. And we can proceed by multiplying this a transpose in.  So a transpose a X minus a transpose B, which must be equal to lambda X. So I move X to the left. So I get a transpose a minus lambda identity that multiplies X must be equal to a transpose B. So a transpose a has been, we discussed this with with mark, and I thank him for helping me out a lot here. And remember, a is a rectangular matrix and times and so this is precisely the V shirt matrix corresponding to the matrix a. So let's call it w. The solution to my problem to finding the critical points of this loss constrained loss function is that X must be equal to w minus lambda identity to the minus one applied to a transpose B. Now, which is, if I'm not mistaken the solution of the standard rich regression also right. I guess, that's the case. Yes. But don't take my words. Yeah, this is a solution of regularized rich regression. I think, I think, I think it must be but I was hesitant to make a statement of this type but thanks for jumping in. So now we have this solution. We are still missing something right because we have a constraint that X must must leave on the sphere. So we need now to impose the constraint that X leaves on the sphere, which fixes the Lagrange multiplier. Okay. So what we need to do is we need to compute X transpose X. We need to transpose X transpose X we need to transpose all this, this term starting from the right. So we get B transpose a, and then we get w minus lambda identity to the minus one. But then there is an X. So this becomes to the minus two applied to a transpose B. And this object here must be equal to N, our constraint. This is just a number. If you if you analyze all the sizes of the matrices and vectors involved is just a number and this, this equation fixes the value for the Lagrange multiplier lambda. Okay. Now we need to work on this on this bit here. Hey. Okay. So first of all, here we have one over w minus lambda I to the power two. And I claim that this object can be expressed in a series, which is K plus one w to the power k divided by lambda to the K plus two. I cannot prove this, but what you prove it by taking minus the derivative with respect to lambda of one over lambda identity minus w, and you expand this object in a standard geometric series. So this is our first ingredient. And then we have a second ingredient, which is. We plug this object in in here. What we will have to deal with is something like a w to the K a transpose. That's the kind of elementary object that we have to deal with. So a w to the K a transpose. And it as a, what is w, it is a transpose a transpose a K times multiplied by a transpose. Okay. I'm just expanding the power K of the of the wichord. And now I can regroup these terms starting from a, and I get a transpose a transpose a transpose. And what Mark discussed, we have a transpose a transpose a number of times k plus one times. So this is w a race to the power k plus one.  So if if the wichord matrix is n by n, the anti wichord matrix is m by m where m is larger than n in general. Okay. Good. So now we are able to massage a bit this this term here this constraint. So for this constraint we have that a w minus lambda identity to the minus to a transpose can be written as a sum over K of K plus one over lambda to the K plus two. So this is w to the K a transpose this one using the property number two is K plus one divided by lambda to the K plus two anti wichord matrix race to the power K plus one. Okay. So we can refold this, this sum into this form anti wichord divided by lambda identity of size M minus anti wichord to the power to. I'm just rereading this, the first property from right to to left. And now we have an expression that depends on the anti wichord matrix that has the same eigenvalue, the same non zero eigenvalues as the original matrix and plus a number of zero eigenvalues and minus. And so it is natural now to perform a spectral decomposition and try to rewrite this object in terms of eigenvalues and eigenvectors of the anti wichord matrix, which is to say, of the wichord matrix as well. Okay. So if we do that. And, and this, I think the spectral decomposition here is on page one of the of the handout or so. So I spell it. I'm not doing the full derivation but essentially everything can be written in terms of the end known non zero eigenvalues of the anti wichord matrix here. So this is the composition in terms of the projector projectors over the, I, I can space. So if you're not familiar with the Brian cat notation this is just, you know, column vector and row vectors, nothing, nothing more fancy than that. So now what we have is this object here that needs to be plugged in in here in the constraint. So what we get is that X transpose X is summation. I want to end s I divided by lambda minus s I square. So s eyes are the eigenvalues of the wichord matrix or the non zero eigenvalues of the anti wichord matrix. And then we have be, be I be I be. So here we have the, we are projecting the eigenvectors of the wichord matrix over the vector of the procrastus bed. Okay, the known term of the, of the linear system. Of course, this term here is nothing but be transpose be I square. It is just, it is just a number here it is just a coefficient here in this expansion. It is a random number of course that depends on the randomness in the bees. So for non-mathematicians this bracket notation is that. Yeah, it is, it, it is just the column vector and the and the row vector, which is, I mean, this one, you can interpret as a dot product here and the dot product there. So, and these two terms are identical. So this is essentially the square of this, of this number. So if we know that be is a Gaussian Gaussian vector with variance sigma square. So we can write that be is sigma times psi, where psi is zero one so each components of psi are are zero one. And so if we, if we replace this notation in here, we get the important point. So let me highlight it in purple. So we have that. Over I from one to n s I divided by lambda minus s I squared. Psi transpose vi squared must be equal to n over sigma squared. So I pulled out a sigma square from from here and I put it there. So this is an equation for the Lagrange multiplier lambda. And it depends on the eigenvalues of the we should matrix corresponding to the matrix of coefficients of our linear system, and it depends on the noise of the procrastin bed. Okay, the terms that the known terms of the way. Good. So let's now try to analyze a bit this equation for the constraint. So let me just stress that constraints in this problem this non linear quadratic constraint is extremely important it changed, it changes the entire physics of the of the problem. Okay, so the presence of a non linear constraint in a linear system changes the changes the physics of the problem entirely. Is everything clear if there are questions just stop me now or problems might start snowballing. Okay, so let's, let's try to draw a sketch of this function, let's call it phi of lambda as a function of the Lagrange multiplier that the left hand side here for a fixed realization of our disorder. So for a fixed realization of our eigenvalues, the eigenvalues of we should are positive, remember or non negative. So the situation is as follows. We have a set of locations for the eigenvalues of our we should matrix, the we should matrix obtained from the known coefficients of our. matrix. Here we have lambda. Here we have phi of lambda. So you see that this function on the left has poles at every location of the eigenvalues so it will diverge every time we hit an eigenvalue of we shared. So that at the beginning here, and then we will have like some sort of our Bollock quadratic behavior here, whose depth depends on the. So the depth of these minima depends on the coefficients. Here, which are, which are random. Okay. What happens is that this object here, this function geophile of lambda must be equal to a constant and over Sigma square, but this constant changes the level changes depending on the noise of the bed. So we need to draw a line here. And the number of intersections of this line with with this curve corresponds to the number of real Lagrange multipliers that are satisfying the equation for the critical points of my loss of my constrained loss function. This line is at position and over Sigma square. The noise becomes large. This line goes down. And at some point, the number of hits, the number of matches that you are that you're finding decreases as the noise increases up to a point where you will have only two solutions here. Okay. There are typically zero or two solutions between every pairs of eigenvalues. So zero or two solutions apart from the critical limiting case here, and the number of solutions of the critical equation changes with the noise. Sigma going to zero, we typically have to end solutions corresponding to these these points here on either side of an eigenvalue for very large noise, only two solutions will survive. So if we plot the number of solutions as a function of Sigma. So what we will get is a staircase staircase type of behavior that starts from two N, and then BK up to a point where for for very large Sigma, only two critical solutions survive. This is what in in in the literature in the somehow somewhat fancy way is called gradual topology, trivialization. It means that when you increase the noise, the topology of your of your space trivialize trivializes from a situation where you have two N critical points. Remember, we are counting all critical points, we are not just counting minima, we are counting minima saddle and maxima of your of your loss loss function, but for small noise, we have a rough landscape with a lot of critical points. When when the noise increases, then you gradually lose complexity in your in your landscape until only two of them, a minimum and a maximum survives.  And of course when you, when you, this is just for one realization of your disorder, if you average over many such matrices, a and many vectors. On the other side, what you expect is that this staircase will with smoothing, and you will get something like you're expecting to get something like this. A smooth, a smooth curve. That's why it's the topology trivialization. So this, this problem is one of the few examples where this smooth curves curve can be computed. Exactly. Not only that, but also for finite M and N. Now, the few cases where this, the full landscape of critical points can be characterized analytically for finite M and then so without any large and approximation and between now and now will try to essentially describe how to sketch this, this calculation. The question is, of course, is this something that we are very much interested in. This is not the best thing that we would like to compute right. What we would like to compute is the statistics of the minimal loss or the statistics of the critical points grouped by type. So at the moment we are totally unable to compute the statistics of critical points by type. So dividing the minimum from the saddles and from the maximum that that is that is something we are unable to do. And, and, and, and, and averaging over the disorder. I mean, the staircase is, of course, for a given instance for a given realization, the average you can compute it for finite N and M, but this includes all critical points, not, not just not just minimum. Okay. So turning the minimum. I just mentioned one thing, and then we, we start. And there is a theorem, which helps us. It's a theorem by Brown in 1967, which, which states that for this loss function for the loss function defined by a H of X that I gave at the beginning. A chain of so inequalities are preserved between the Lagrange multipliers and the value of the loss function. So, so if you pick values of the Lagrange multipliers that are solution of this to this equation, and they are sorted, then the corresponding loss function evaluated that those solutions are exactly in the same in the same relationship. So smaller Lagrange multipliers correspond to smaller loss functions. So if this is the case, then of course, the implication is that the minimum value of the loss function is obtained in correspondence of the minimal value of the Lagrange multipliers, which is this one. So this solution is the most important one because it gives the minimal loss loss function whether this is zero. Of course it means that the system is compatible. If it is larger than zero, it means that the system is not compatible, but how far we are from from full compatibility is determined by this by this number. So the minimal loss is obtained here where X mean is, I hope I, yeah, of course I erased it, but X mean is w minus lambda mean identity to the minus one applied to a transpose be that that was the formula for the solution of the critical point that I gave you a few minutes ago and then erased. So we will not use this now but we will use this, this later so we have a precise characterization of what is the minimal vector. I mean, the argument of the loss loss function in terms of the minimal Lagrange multipliers, which sits here. The minimum. No, it changes sign actually and you can see you can see from here you can you can characterize the point at which it changes side using mean field type of argument like a self self averaging property essentially. So, it does change sign and but once you plug it in here everything, you know, everything is taken care of by the by the final form. Okay, any other question. Yeah. I absolutely have no idea. But maybe, yes. I, I don't know. I don't know enough about that to answer this but probably, maybe yes, I don't know. Sorry. Anything else. Yeah, okay. The Lagrange multipliers negative or positive. The, the, what, what changes. I mean, we, we need to have, I mean, we are going to see a precise solution to this to this problem in like tomorrow. Maybe we can wait until then to just discuss, because we will have a precise expression for for the minimal loss and for the corresponding. I can, I can values. So maybe, maybe it's, it's just a bit premature.  Okay, if you don't mind. So can you say one word about this monotonic property because it seems very strong and that's like my multiplier imply that. Yeah, I mean, the, the, the, the proof is very technical and it is specific for this for this type of, of loss loss function so I don't think it extends. It is a general, it is a general feature. I don't have, I don't have a very strong intuition because the, the, the proof is based precisely on this quadratic quadratic loss function and, and, and it's constrained. It's associated constraints so it requires some some convexity argument that only applies to this type of loss function. It is true that it is a very strong, strong property. I don't know how generalities in in in this paper it is specific so it heavily hinges on on this specific form of the loss function. So, and there is no sort of heuristic that I couldn't I couldn't find one. And, and, and, but we are lucky that that that we've got it because we have an analytical handle on on the minimum, but it is, it doesn't seem to be very general. It's, it's, it's an accident that makes this problem. So rich and lucky. In the machine learning term this this cost function would represent the training at all in this rich regression problem. And so this brown theorem tells you that you should look for, not to minimize this training or you should look actually for small regularizations, which I didn't know it's. Yeah, but, but exactly, but it's not a general, it's not a very general problem property. So, what happens to the red box when N is really large because I expect that scar product to have some self averaging properties and as I, as I've heard, five minutes ago maybe that's our product converges to one or something like that. Yeah, I mean, the question is very, is very interesting, and even more interesting is the fact that the right hand side depends on this combination and over Sigma square right. And what we are going to do is everything at finite and essentially, or in in a larger limits, so that this term is not non problematic. But of course, this, this term might have different behavior and the solution to this to this equation might have a very different behavior depending on how Sigma square actually scales with N. So, not only we have a we have a problem of how N goes to infinity but we have a problem of how Sigma square the noise scales with scales with them. Unfortunately, we will not have time to discuss all the different regimes, but, but young in his paper discusses discusses very different regimes depending on whether Sigma is of order one or order one over N. And, and, and this creates, I mean, the larger limit of this equation is very different in the two cases. In general, what what what you have is that this trivialization does not survive necessarily the large and limit. So in, in a wide range of of regimes. You will get the three realization that is almost immediate. So you will essentially to the situation where you only have a minimum and a maximum always in the larger limit. So trivialization is is a property of the of a finite and or finite and problem problem. So, why do you actually call the Sigma square noise because they see be as the solution that you're looking for. No, access to access the story as the constraint that you're the constraint that you are enforcing but what would you call Sigma square noise. I mean, maybe, maybe, maybe it's not, it's not the best choice of words but in the context of the orthogonal process, I think the term is appropriate because if you remember, you had a set in the orthogonal which is analog of this one, you had a set of points a that you would rigidly rotate into another set and then you would add some perturbation. And at the end of this operation you're given the results so clearly calling this noise is very, very appropriate because if the noise is very large, the final points are very far from the from the from just the final point of the rotation. Right. So, so that that is why I'm referring to it as as noise. How much time do I have a few minutes maybe or it's over. Well, let me just, let me just give you the technical bit that we're going to use just a formula and then we go for lunch. Yeah. So, I just wanted to show you how we would compute this average number of critical points for finite M and N because there is a lot of technical machinery that might be useful to you. Who knows when, when one of these tricks might be might might come handy. And so to compute this. This average curve, we will make use of the so called cat's rice formalism, which is also summarized in the handouts formula three. So the cat's rice formalism allows to compute. So that the setting is, we have a set of equations in K variables. And a system of K equations in K unknowns may have many solutions and the number of isolated solutions in the domain is given by the cat's rice formula, which is N of the is what is an integral over X one. It's K over the domain D of what, well, you have delta F one delta of F K, and then you have the absolute value of the determinant of the F I be X K. And the formula looks complicated but essentially if you look at it, it's quite should be quite obvious, these direct deltas take care of the fact that F one and F K must be a solution of this of the system so they must be equal to zero. And this is the absolute value of the determinant so this is a Jacobian of of a transformation and the absolute value is is crucial. And the proof of it is, is difficult but in one dimension, it's, it's a bit more heuristically it's a bit more obvious. So imagine that you have a function. So let me throw it. You have a single single variable, and you have a certain level, you, and you want to find the number of solutions of this function F of T equal to you in a certain domain. What you would, you would do to compute this object. Well, you would, you would set certain variable V equal to you, you would integrate over the variable V, and you would enforce that V is equal is inside your domain. V equals to F of T. You would get essentially the integral over DT F prime of T delta of F T minus you in the domain D. So essentially the cuts rice formalism in K dimension is the extension of this the solution of this simple problem in one in one dimension, and you should not forget to include the, this Jacobian Jacobian term here. So what we are going to do is, we are going to try to write this cuts rice integral for our problem, and then try to average this, this integral over the disorder, the disorder is given by a the matrix of coefficients, and the matrix of known, two terms so A and B. And, and, and so there is, there is quite a lot of work to do but these averages can be performed exactly in this problem for finite and, and, and, and. Okay, and in the course of the, of the next lecture we will learn a few techniques on how to compute this type of, of averages. Okay, I'm done any, any questions. We start at four and we can have an unjust above their at four sorry at two, 2pm.


I'm Mark Uptigrove, the director of the LBJ Presidential Library, and it's my pleasure to welcome you to a brand new year of Friends of the LBJ Library programming. Last year in March, we hosted a sold-out Friends program featuring Joe Califano, LBJ's top domestic aid upon the re-release of his classic book, The Triumph and Tragedy of Lyndon Johnson 25 years after its initial publication. Joe was interviewed that night by his longtime friend, the journalism great Bob Schieffer, who was just three months away from relinquishing his seat as host of the number one rated Face the Nation and retiring from CBS after 46 fruitful years. Tonight, Joe Califano and Bob Schieffer returned to the LBJ Library stage for another sold-out Friends of the LBJ Library event. But this time, the tables have turned. Joe will interview Bob on his remarkable run in journalism that has yielded Bob every award imaginable and designation by the Library of Congress as a bonafide living legend. It's my pleasure to welcome to the stage Joe Califano, who will introduce his friend, Bob Schieffer. So ladies and gentlemen, please welcome a living legend of his own, Joe Califano. The one thing Bob has gotten every award known to man. But Bob did retire a few months ago. It's turning out to be the shortest retirement since Frank Sinatra's first retirement. He's coming back as a special correspondent for CBS to cover the presidential campaign we are all been watching. So I've known Bob a long time. I just have a little video, a little film to show you that CBS News prepared to start the evening off. But I just want to say that in the world of sort of gotcha journalism and to watch face the nation is one of the most extraordinary experiences. And Bob made that show what it is. It went through a lot of iterations before then that's all written about incidentally in his book, This Just In, which is written years ago. I give it a plug because I read it in getting prepared for him. And it is the most extraordinary and revealing and interesting book in the world of journalists and I've read in maybe ever. I recommend it highly. I don't want to say a lot now. I'm going to let Bob talk in response to my questions. I must say I said one other thing. Lyndon Johnson would be proud of me because he always used to say those SOBs, they never have to answer any questions. They just ask them. So it's my turn tonight. But first, to introduce Bob and a few things about him you may not know, can we roll the tape? Is that what they say, Bob? Roll the tape. Ladies and gentlemen, as the screen slowly rises, let's welcome Bob Schieffer. This is, we are good friends, so remember that. We'll put it to the test. I want you to remember it when you're making commentary, if I do any op-eds on the election. But we are at the LBJ Library. Do you remember the first time you saw our LBJ? Yeah. What about it? And that's when I really fell in love with politics. I was, it was 1948. I was 11 years old. And Dewey was running against Truman. But the big story down in Texas, of course, was that Lyndon Johnson was running for the United States Senate. And it was the big event in our neighborhood. Because we heard he was coming to campaign on the vacant lot where we played baseball out on the northwest side of Fort Worth. And everybody, I mean, everybody was talking about it. And my dad took me down there and other, my friends' dads took them down there. And the reason we all went down there was, we heard he was coming in a helicopter. And we had never seen a helicopter. And so it was a big event. We're all standing down there and all of a sudden, up in the sky, we hear this noise and then over the noise of this airplane that had no wings, we'd never seen anything like this on this bullhorn. This is your candidate for the United States Senate, London B. Johnson, I'll be there. We knew at that moment how Moses felt when he realized that burning bush was talking directly to him. We didn't know if it was a politician. We didn't know if it was God. We didn't know what it was.   And so he made a wonderful speech and at the end of the speech, he took his hat off, threw it out into the crowd, wave goodbye, got on that helicopter and flew away.  I can remember every minute of that. I can't remember the campaign commercials from the last election. Not only that, I don't want to remember them. I don't want to get them on me, you know? And, but some years later, I was telling Jake Pickle, who I know many of you in the audience know, he took Lyndon Johnson's place, that congressional seat when he did get elected to the Senate.  And he said, oh, yeah, yeah. He said, that was my job in the campaign. And I said, what? He said, I was the hand catcher. And I said, what? He said, let me tell you something. He said, Lyndon Johnson was the tightest man on the face of the earth. He said, he wasn't going to waste a hat on a campaign rally. So he said, my job was to drive as fast as I could to wherever that rally was going to be, get on that front row, and said, when he saw me, then he would throw that hat to me. I would take the hat, run around behind the helicopter, give it to him, and then they'd fly on. And I'd go to the next. So, you know, after that, I decided I love politics. And who wouldn't? And I still love it just as much today as I did then. Although I must say, it's not quite as good right now. It was in those days.  You know, you wrote in the book and you've often said, you start as a police reporter, how important that was. Yeah. Why was that so important? Well, you know, I was the night police reporter to Fort Worth Star Telegram. And I had worked at a little radio station in Fort Worth when I was going to TCU. I got my first job in journalism at age 20. And so I'd been hanging around with cops for a long time before I even went to work for the Star Telegram. And I got to know them really well.  If you're a sports writer, they have a nice seat for you at the football game. They have a buffet. They have someone to help you with the stats. They want you there. But nobody wants a police reporter to show up, because he only shows up when things are really bad. And I've always thought, if you can learn to get your business done in a business-like way, keep your head, then you're not going to get the politicians on the make are not going to phase you very much. That's not going to be very hard to deal with. And you just kind of learn to do that. I think being a police reporter and seeing a side of life that most people don't get the opportunity to see, I think it's just the best training for anything. I mean, if you want to go work in the library, I think it'd be good training. I highly recommend it to people going into journalism. It's also a lot of fun. The video we saw, the film we saw, mentions a little bit about Oswald's mother. How did that all happen? Where were you then? Well, I mean, people win the Pulitzer and do things and they show great courage most of the time and they work deadline under pressure. But a half of it is just being in the right place at the right time. I mean, there's just no question about that. And I had, since I was a police reporter, I wasn't assigned to cover Kennedy when he came to Dallas on that fateful night. And I was really pretty upset about it. And he got a wonderful, wonderful reception in Dallas. I mean, in Fort Worth, 10,000 people showed up at Carswell Air Force Base that night just to see him get off Air Force One. And Vice President Johnson came. And the next morning, when Kennedy made the last speech of his life, it was at the Hotel Texas up in Fort Worth. And my brother, Tom, who's out here now, was in high school and my mother got him up and they went down and got to see Kennedy out in the parking lot where he made another speech. My wife, Pat, went with her father to that breakfast at the Hotel Texas and she got to see him. But since I didn't get off work till three o'clock in the morning, I was sound asleep. I mean, which I normally was. And at that time of day, and Tom came in and walked me up and they heard it on the radio when it happened and said, you better get up and get to work. The president's been shot. And I was just in a complete fog. And Joe, one of the things that people who are not as old as some of us here don't understand, nothing like this had ever happened before. We've had a lot of these tragic events since then. But we didn't know, we were terrified. We didn't know what had happened. We didn't know if this was beginning of World War III. They closed off the borders with Mexico. So that was kind of hanging over us. And we had a big B-52 base out at Carswell and Fort Worth and we knew if there was going to be a nuclear war, we'd be one of the first targets. So anyway, I had rushed down to the office and when I got there, I was just trying to help out on the desk and this phone call comes in. And I almost hung up the phone. And then I heard her say, yes, I think my son, I heard on the radio is the one they've arrested. And so I kind of put aside that part about we don't run a taxi service here. And she said, tell me where you live. And so she lived out on the west side of Fort Worth out near Arlington Heights High School. And so I had a TR-4 sports car in those days. And I thought, well, I said, I'll be right out there to get you. And in fact, I told her, I said, when I hang up, I said, in the interest of national security, if you would not answer the phone if it rings again.  I mean, I'm not sure I would do that today, but I did. And so I went back to the auto editor of the Star Telegram and his name was Bill Foster. And the ethics all around were a little bit different in those days than they are now. The way it worked is a local car dealer would give him a car and he would drive it all week and then he would write up a little report on it in the Sunday paper. And these were generally good reviews.   So anyway, I said, Bill, what kind of car do you have this week? And he said, I've got a Cadillac sedan. And I said, that's perfect. So we went up to the city editor so we took that car and Bill drove and I sat in the backseat and when we got out there to the west side when we got there, I got in the backseat with her. And I mean, she was a lunatic.  She was this itinerant who had traveled around had been married multiple times and at one point had put Lee Harvey and his brother who was a fine person, apparently. He put them in an orphan's home and told them they were a burden to her and things like that. He was just a sorry pass. But what was interesting to me in the car, she never once expressed any remorse or any sympathy for the president about his family. She never once asked about his wife. All she kept saying was they're gonna feel sorry for his wife, meaning Oswald's wife and they'll give her money. And she said they'll do like they've always done with me. They'll just turn their back on me. And it was so harsh that I didn't put some of it in the story.  And I just kind of censored myself. I think if I'd have put all of it in there, we'd have had a fuller picture earlier of what he was really like, which we didn't. But anyway, we get to Dallas and I got to the police station and I just said to the first uniform officer I saw and I always wore a hat in those days. So I'd look like a cop, which we all did on the police beat. And I said, I'm the one that brought Oswald's mother over here, where can we put her? So these reporters won't be bothering her. And sure enough, they found us a little office back there in the burglary squad and we got back there and then I could go from there out into the hallway where all the other reporters were and kids today in this age of the cell phone, they don't understand. In those days, if you didn't have a phone, you didn't have a story. So this was a great advantage to us and we were churning out these extras. And finally, about dark, she said, do you think they'd let me talk to my son? And I said, well, I don't know, I'll go ask. So I went to Captain Will Fritz and I said, she'd really like to see her son. And he said, well, we probably ought to do that. And so they took me, Mrs. Oswald, by this time his wife, who spoke Russian, spoke no English, and the babies showed up. And we all went down to this holding room in the jail.  And finally, there was a guy standing over in the corner and he said, who are you? And I said, what? And he said, are you a reporter? And I said, well, yes. And he said, son, and he used a stronger language than this, but basically he said, you get yourself out of here. And he said, if I ever see you again, I'm gonna kill you. And I think he meant it at the moment. I mean, it would, whatever he did, I apologized, you know, said I didn't realize I wasn't supposed to be here and excuse myself and got out of there, which was the end of the story for me. But again, you know, it's this whole business of why would someone like to be a reporter? It's, you know, it's adventures like that. What other profession could you have? I mean, even in the midst of that great tragedy and have an adventure like that. And I'll just never forget it. I've always loved being able to go behind the police lines, I've always loved finding out something for somebody else to know about it. And talking to the people directly who make the news to me, that's what journalism is all about.  I have. Just quickly, what did you think of Nixon? I believe he was the most unusual person that I, I mean, I'd literally mean that, that I've ever run across. He was so nervous around people he didn't know. And I never understood why someone who went into the most public of all professions, politics, would be as private as he was. I'll just tell you one little, the first time I met him, and we've forgotten about this, but Nixon in his first term actually held church services in the East Room of the White House. And why nobody made a commotion about that, I guess they would these days, but nobody said anything about it. And every Sunday they would invite people there and they'd have a visiting evangelist or a minister and they'd sing hymns and then everybody'd get in a little receiving line and you'd go up there and shake hands with the president. And these became social paybacks for people on the White House staff. If you invited one of them to dinner at your house, they would invite you to this, you know, to the White House. So this is a big deal. So the only two reporters, I always got assigned, I'd just come to the bureau and I always got assigned to go over there just to make sure there wasn't any news. And if there was, I'd have to give it to Dan Rather, who was the White House correspondent, but I mean, you know, that's what the rookie does. So anyway, I mean, the only other reporter that came was Helen Thomas and she always showed up for UPI. And one morning we were over there and she said, get out of here, let's go get in line. We'll shake hands with the president. And I said, well, we're not supposed to do that. And that's for the, no, no, she said, go ahead. So we'll ask him some questions. I said, well, I don't have any questions. And she said, well, we'll think of something. So I get up there, well, there was this, there was this story going around that Nixon was gonna bring in some new advisors. And we didn't know if they were coming from outside the government or inside the government. And so when I got up, I couldn't think of anything else to say and I shook hands with him. And I said, Mr. President, these new advisors, will they be in-house advisors? He said, no, no, they'll be out-house advisors. And then he said, no, no, that's not what I meant. And he turned around and walked up and left the room, left all his guests there.  But he was really odd. What about Jerry Ford? He was my favorite. He was. Quite frankly, from a personal- Did you ever get to interview him? Oh yeah, many times. And in fact, the first time I interviewed him, the first time he called on me by name at a televised news conference. And he looked down and he said, Mr. Schieffer, and my mind went absolutely blank. I don't know why, I could not think of a single thing. And I thought, oh my God, you know, all the bosses are watching in New York and all that. And finally, I just, in desperation, I said, Mr. President, what about the Russians? Well, it turned out he had something he wanted to say about some Russian, they were building a new bomber or something and he didn't like it. But anyway, the answer he gave made the front page of the New York Times the next day. And I've never been prouder of a question, you know? But again, you know, it's better to be lucky than smart, I guess. Barbara Walters thought you stole some scoop of hers was that this, when people ask me, what is the biggest scoop, the one I'm most proud of? And I'll tell you, this is what it was. He was in the 76th campaign and I was covering President Ford. I was the White House correspondent. And we went up to New York for, they gave some excuse, the real reason we went up there was he was gonna be interviewed by Barbara Walters, who was the host of the Today Show in those days. And so he did the interview late in the morning and it was gonna be played the next morning on the Today Show and Barbara put out the word that if anybody interviewed him before her interview aired, they would be killed. And Barbara really had a way of making people believe that she could make that happen. She's the best reporter I think I ever went up against. But I mean, I'm telling you she was really tough competition. So immediately after she did this interview, we of course all said about in the White House press corps to bust her scoop. So I went to the press secretary and asked if I could interview he said, no, we've given Barbara our word that we're not gonna do that. So then I went to the President's White House Chief of Staff, he was 32 years old. I called him Dick in those days. I later would call him Mr. Vice President, Dick Cheney, who I must say was one of the straightest and best staff people I ever worked with and certainly the most accessible White House Chief of Staff.  I'd speak to him more than I would talk to the press secretary. Well, you know, that just doesn't happen anymore. But anyway, I went to him and I said, Dick, I said, what do you think? He said, no, no. He said, we've given our word to Barbara. He said, we just can't do it. Well, then I get this call from Walter Cronkite and he said, Bob, I heard the President's going up to Yonkers this afternoon. Do you think it's possible I could come up and see him and maybe get an interview? And I said, oh, Walter, I hadn't even told Walter yet about him being interviewed by Barbara Walter. I said, let alone. And I said, well, Walter, I don't know. And let me go see what I can do. So I go back to Cheney. He said, no, we're not gonna do it. He said, we've given Barbara our word. And I said, Dick, let me ask you this. I said, do you think it would be possible that we could just get Walter in to say hello to the President? I said, I know he likes Walter and Walter likes him. And he said, well, we might do that. And I said, do you think it would be all right if we just had a camera go in there with him? And he said, we're not gonna do any interviews. I said, no, no, no, I understand that. He said, well, all right. But he said, no interviews. I said, okay, fine. So at the appointed time, they took us around to the back of this hall where Ford was getting ready to do something or other. And there was this big story going around about whether flu shots were a good thing. This is just when they started. And they didn't know if he was going to, if the President was gonna take a flu shot and set an example for the country. It was a big story. So anyway, we go around there and at the appointed time, Walter takes the microphone in his left hand and the cameraman's over his shoulder and he walks in and he walks in and sticks out his hand and the President said, hello, Walter. And Walter said, hello, Mr. President, or you're going to take your flu shot. And Ford almost burst out laughing because he knew what was going on. And he said, yes, Walter, I'm decided that as President, I should set the example and take my flu shot. And I thought that the press secretary was gonna literally wrestle Walter to the ground. He was absolutely furious. But to make a story that got too long short, the CBS Evening News that night began this way. Good evening, President Ford told me on the campaign trail tonight in an exclusive interview that he would take his flu shot. And then we proceeded to play the entire interview all seven seconds of it. And I gotta tell you, nobody, only one person knew how that came about and that was Walter Cronkite. And Walter took care of me from that day forward. That was my biggest scoop. President Carter, Jimmy Carter. I like President Carter. The reason I like Gerald Ford so much personally is he was so at ease. And he had been the Republican leader up on the hill. He knew reporters, he didn't run around with them, but he was used to dealing with reporters. And we just had a wonderful relationship with him. He, when he'd go out to veil, he'd always, for Christmas, he'd always arrange a schedule so the reporters, they'd let us take our families out for that. So we could be with our families, so the Secret Service could be with their families. And he was just very, very caring. President Carter was much different. He was distant in a way. I liked him. He had some rough times, but as you go through and I evaluate all these presidents, President Carter did two very important things. He passed the Panama Canal Treaty and he also did the Camp David Accords, which I think is the single most significant thing that's happened regarding the Middle East, certainly in my lifetime. What about Ronald Reagan? He was an interesting, very genial guy. We always had the feeling that while he was friendly and so forth that maybe he really didn't know your name. He knew you were one of the reporters, but he was never discourteous. He was always kind, I mean, mannerly to the reporters. And he was very interesting in that Ronald Reagan understood that communication is not just what you say, but sometimes how you say it, why you say it. People used to make fun of him about being a movie actor, but he always said that was one of the most important tools in his communications tool chest in the sense of that one time I was sent over and we always would go over to the White House and watch when the helicopter took off from the South Lawn just in case something went wrong. Well, I mean, you know, that's what being a real, I don't mean to be gruesome about it, but that's what reporters do. You always have to be there when the president goes outside. You know, it's the world we live in. But he walked out and it just happened, I was the only person out there that day and there was a camera. And he walked out and he got to the door of that helicopter and he turned around and he waved goodbye to all the people over here. Then he turned and he waved goodbye to all the people over here. And I looked around and I was the only person there. There was nobody out there. But Ronald Reagan always looked like the president. And I think that was a big part of who he was and a big part of his success. And he said in the movies, he said, most people only see themselves in the bathroom mirror when they're shaving in the morning. He said, in the movies, you realize there's a close-up, there's a medium shot, there's a reverse and there's a wide shot. And so you never saw a bad picture of Ronald Reagan. I wrote a book about him called The Acting President and which I still think is the best title of any book written about Reagan. And I look back and it's the first book I wrote and I wrote it with another guy and that's where I really kind of learned how to write a book. And I was determined to try to be the first one out with the book on him. And so he came out early after he left in 1988. And I look back on that book today and I learned a great lesson and that was the book is still accurate but it's not true in the sense. In the sense that we didn't know then that the Soviet Union was gonna collapse. We didn't know then what the effects of some of his policies were gonna be. I mean, I don't think he's totally responsible for the collapse of Soviet Union but I think he had something to do with that, a great deal to do with that. And I kind of made a rule after that that I was not gonna pass a judgment on any presidency until about five or six years. I just think you cannot make a judgment immediately on a president's success or failures. What about H.W. Bush father? I thought he was one of the nicest people that I've ever met. I always thought he was a better public servant than a politician. I thought his handling of the collapse of the Soviet Union, which was kind of the work of he and Brent Scowcroft. I thought it was really something, really a great success the way he did that and that would have been a good one to, you could have screwed that up pretty good and he didn't. I wanna, this is both a current question and a past question relating to President Clinton. In your book, you wrote and I'm quoting, my rule about reporting on the private lives of public officials has always been that what they do in private is off limits unless it interferes with their public duties. But Clinton crossed the line when he took advantage of a young intern in the Oval Office. He compounded the offense by making his loyal secretary, Betty Curry, drive from her suburban home on her days off to clear Miss Lewinsky into the White House grounds and required her to sit there and guard the door while they held trists. I always wondered why she must have thought as she sat there waiting for them to finish. The question is really, Donald Trump and others now have thrown Bill Clinton's conduct into the presidential campaign of Hillary Clinton. Is that news? Is it relevant to Hillary Clinton's campaign? Is his conduct relevant to the... Is it fair game in other words? Yeah, I think it is. And I think an issue is what the candidates decide are issues and sometimes they're good and sometimes bad. I'd just say a couple of things about that. I noticed one of the candidates, I think Carly Fiorina said something to the effect that Hillary should have left him or something along that line. I think a marriage between two people, I think those two people work out the rules of what it is. And if what he did is okay with her, then that part of it is okay with me. I don't see that as an issue. But a grown man and an intern, a 20 year old, that's somebody's child. And I don't think there's anything to be proud of there. Is it fair politics to go after him on that? I think it probably is. But I would wonder, is it good politics? And I say that because I've kind of watched the Clintons and observed them for a long, long time. And the one thing you can say about them is, whether it was the Paula Jones thing or the Whitewater or after they came to Washington, all of these other questions, they always managed to hang around until their opponent takes it one step beyond where it ought to be. And look at Newt Gingrich, look at Tom DeLay, more recently in the whole impeachment thing and more recently there's the Benghazi committee. You know, I mean, I think there are serious questions that can still be asked about Benghazi and what was her role, why she didn't come out and defend that instead of the administration sending out someone who had no connection to it whatsoever and sent her out to be the spokesman. And then you come down to this Benghazi committee where you put, after I say, I think there are serious questions, but to put her through a Spanish inquisition for 11 hours, I mean, that's a travesty. I think they basically took that off the table with that. And you know, they have said they're not gonna have any more open hearings now. But I mean, to sit there and watch that thing, and I mean, I've gotta say, my favorite moment. And that was when one of the members of the committee asked her, and it was a woman, I don't recall her name, said, were you alone that night? And she said, yes, I was. And the woman said, well, were you alone all night? And Hillary just broke out in just this uproarious laughter and soon everybody else was. And this woman said, well, what's so funny? I don't see what's funny about that. Well, everybody in America thought it was funny and everybody got the joke, but her. So I think that committee here wound up helping her. And I think, you know, if they go after him, I'm not here to defend him, let me tell you that, but I'm not sure it's gonna help them and they could wind up hurting them. The attacker is what I'm saying. We got a little into the presidential campaign, and you moderated three presidential debates, of Bush and Kerry in 2004, Obama and McCain in 2008, and Obama and Romney in 2012. And now we've seen debates, debates, debates. You think these debates help? Do you think? I do. Why? I see, and I'm talking about the general election debates. Because you can actually, this is the first time that you really had the opportunity, both candidates have, to have an extended discussion. And what happens in these primary debates, like we're seeing now, where you have 13, 14 people on the stage, it's impossible to have a serious discussion. What you, it's more like the third grade. It's about attention getting, me, me.  And it's impossible for anybody to look serious, I think, during those debates. And I think that's part of what we're seeing right now. But the debates do move people. I mean, the needle moves after the things that, the two things that will move the needle is pouring a lot of advertising into a campaign. And that used to move the needle, and that's what's unusual about what we're seeing here. There seems to be no correlation now among the people who are leading in the polls and the ones who are spending the most money. And I don't know if that might be a good thing if people have no longer pay attention to these nasty campaign ads. I don't know if that's good for TV, but I think it might be good for the whole thing in general. But what's happened here during this campaign, Joe, is it's entirely different than any of the campaigns we've seen up until now, two different ways. I think the chickens have finally come home to roost. You know, we've been coming to this for a long, long time and I think we're there now. I think the way our electoral process is totally and completely broken. I think it has been so overwhelmed by money that a lot of serious people have decided they just want nothing to do with it. It is, you know, running for office and what you have to do now is just so odious that serious people say, I just don't want to do that. And the serious people who do, they run and then they're unable to get anything done. And so the result of that is we're getting the people who are willing to do what you have to do. And that is to spend 60 or 70% of your time raising money. And it's not a very interesting way to spend your life. And so, you know, the people who are part of the talent pool now is a different talent pool. I sincerely and honestly believe these are not bad people, but they're different. The people who are willing to do this now. And I just think the, you know, I mean, when I went to see Lyndon Johnson, you know, what was interesting when I was 11 years old about that rally in Fort Worth was that every single person who had anything to do with getting Lyndon Johnson to that place that day did it for free. You know, it used to be, politics used to be an amateur operation and we have managed now to outsource what we used to get for free to these consultants and gurus and strategists and people who have all these services to sell. And it's costing millions upon millions of dollars. Our last campaign, our last election cost $7 billion, that's B with a billion. The presidential race, the last one, cost two and a half billion dollars. Both candidates spent over a billion dollars. Both of them and both of them, whoever they happen to be will probably wind up spending more than that this time around. And it is just totally overwhelmed the system. You know, when we came to Washington we used to invite Republicans and Democrats to come to our house. They all knew each other. Their wives knew each other. The kids went to school together. Now nobody knows anybody. They're there for two and a half days, three days and they have to go back home to raise this money or at least they think they do. And in the meantime, nothing happens and nothing gets done. And that's why people like Trump, people are just outraged. They're frustrated and I thought from the beginning that he would be a serious candidate and I still think he is. Has he proposed any solutions? Not that I know of so far. But this has become a campaign more about attitude than issues. Now the second thing that makes this campaign different, this campaign also reflects the changes in our culture that have been brought about by social media. I mean, when you look at the language of this campaign and you know, where candidates stand up and use obscenities in front of a lot of crowd this large, it just didn't used to be that way. But the campaigns have become like a blog post and these threads that come after it.   And then the next guy said, no, you're both jerks. And then somebody calls somebody a name that I wouldn't say on this stage. And we just go from the inane to the profane. And I think we're seeing the impact of that on the whole process. The other part is we're now overwhelmed by this totally false information that shows up on the internet and it takes life. There was a very serious conversation going on on a talk show out in Iowa last week that I just happened to pick up on where they were talking about that this guy claimed that he headed on pretty good authority that if Hillary Clinton lost the nomination that Barack Obama had decided not to leave the White House. He would just stay. And I mean, they were discussing this seriously. One of my granddaughters sent me an email last night. She'd run across something on the internet where Donald Trump had told People Magazine in 1996 that if he ever ran for office, he would run as a Republican because the Republicans were the dumbest people he'd ever run across. And they believed anything on Fox News. And I emailed her back and I said, this is really something. I said, have you tried to find out if this is true? Well, she Googled and I immediately Googled. It turned out there were about six stories. This apparently has just kind of gone viral on the internet. It has no basis in fact. But over and over, we're seeing these kinds of things showing up, you know, Barack Obama's gonna settle 250,000 Syrian refugees in this country.  There's no basis in truth whatsoever. But this is part of the conversation we're seeing now. Just on that, what's the role with all this going on? Has the role of television changed? Are newspapers capable of countering this or dealing with it or what? You know, in the midst of this communications revolution, I mean, we basically are having a crisis in journalism. And basically it is among print media newspapers. Print newspapers are having a harder and harder time of finding a business model that they can remain profitable. And I mean, these are businesses. And in a country that's founded on a free press, you can't be the government. I mean, that's what separates us from totalitarian society is there are two sources of news in a democracy, the government and independently gathered information in newspapers. And with more and more, there are just fewer and fewer newspapers now. And they've had to cut back. They're doing the best they can. But you know, there are some good-sized newspapers that don't even have city hall reporters anymore. They still send somebody to the city council meeting. How many Texas newspapers now have an Austin Bureau? When all of them used to have. When Pat and I left Texas in 1969, every newspaper in the state, major newspaper, was locally owned. Now I believe there are no locally owned newspapers in the entire state. And that has made it harder to get the information out and getting true information. What's happened is, we're now basing our opinions on different data. You know, it used to be, if you got your news from maybe there were three networks and maybe an independent station in your town and most towns had at least one pretty decent newspaper. And while you may not agree with the editorial page, you took it as true that what was in the front of the paper, the news stories were true. I mean, that they had been checked out and you could depend on that to be true. Well, now, if you listen to this television channel, you get one set of facts. If you listen to this television channel or radio or what, you get another set of facts. And then you're basing your opinion on those quote facts. But we're not all using the same facts anymore. And that is another thing that is impacting on this campaign. I mean, these things are just kind of all coming together at one time. I'll just go back a little bit in history and tough times and nasty hearings and what have you. I think you were covering the Congress during the Clarence Thomas Anita Hill hearings and during Senator Power's hearings. What about, what was that like? Who did you believe or could you tell? Clarence Thomas? What was the other one? Well, let's start with them. Clarence Thomas and Anita Hill. That hearing. Oh, those. And how that, you know. You know, I'll never forget one Saturday. And it was during all of that. And they were doing a special hearing up on the Senate. And I believe it was on Saturday. And Dan rather was co-anchoring and I was there as kind of the analyst. And he said something or other. And he said, Bob, why is this different? And I said, well, I can never recall another time, Dan, when you gave a warning to parents that what you're about to hear may not be suitable for your, I mean, these were the first X-rated hearings, you know. And it was very, very difficult. Was the toughness of that and then the Bork hearings that came to, was that a change in the Senate?  I mean, it was, the whole thing was rather uncomfortable to me. And I really never came to any hard and fast conclusions about it. But it was different. You wrote a little about Senator Tau's hearing to be Secretary of Defense. Yes. And what happened among the senators or? Well, you know, the Tau hearings and Sam Nunn, who was chairman of the Armed Services Committee, there was. Maybe we should refer, he was nominated to be the Secretary of Defense. Secretary of Defense. By President Reagan. No, it was Senator Bush, wasn't it? By President Bush. Yeah. And he was, he was a Republican senator. Yeah. And he had been on a ranking member of the Armed Services Committee. And Sam Nunn, who was the chairman of the Armed Services Committee, there was a real alcohol problem in the military. And Senator Nunn decided that there was rumor that Senator Tower had problems along that line. And whatever the case, Nunn blocked him from doing that. And as one of the interesting things I brought out in the book, Dick Cheney, who was then a Congressman, Republican Congressman from Wyoming, came up to Sam after a speech that Sam had made over at one of the hotels in Washington. And he said, you know, Sam, I just want you to know there are a good number of us on our side of the aisle that think you're doing the right thing here. And of course, when Tower's nomination was blocked, then George W. Bush picked Dick Cheney to be Secretary of Defense. And the rest is history. I know it. We never got to George W. Bush and what you thought of him. I thought he was a great leader in a time of crisis. I think he, what he did on 9-11, I think he'd showed real leadership. And I thought he was a very good example of what the influence a leader can have and what the role of leadership is. You know, and I think now, I mean, and one of the things that stunned me when his brother first started running for president and I think it really hurt him is he asked me, he said, knowing what you know now, you think your brother did the right thing by going into Iraq. And he kind of hemmed and awed and never came up with an answer. Well, I think anybody, including George W. Bush, would be my guest. I haven't asked him about this, but I think anybody who knew what we know now would not have done that because, you know, we thought they had weapons of mass destruction and it turned out the intelligence was wrong. And so I think that, you know, I think that it was in retrospect a mistake, but I don't know what I would have done knowing what they thought they knew at that point. But, you know, we, as it turned out, I mean, I don't give all the blame to George W. Bush on that we, as it turned out, we now know we went in for the wrong reasons, but I think we also left in the wrong way. And I think when you, you know, just create a power vacuum, which is what we did when we began that huge withdrawal, I think that was as big a mistake as going in in the first place. You mentioned briefly Donald Trump when you thought he was a serious possibility at the beginning, very few people did. I didn't, I don't want to say I thought he was a serious person. I said I thought he had to be taken seriously. That's what I said, or that's what I meant to say.  And you say, this is different. What's so different about it? We've all had these ups and downs and bizarre things, but. Well, what is different about it is just the tone of it for one thing.  And they will, it will come down between whoever wins that and sort of the establishment side of the party. I think we're past that now. I think it's gonna be between Trump and Cruz. And I think Trump may wind up being, by default, the establishment candidate. Now, I mean, we're a long way from that so far, but I think that could be the way this ends up. And who wins? I don't know. If you were, I'll close on this one last question. If you were advising young people today that were interested in journalism, and what would you tell them they were to do? I mean, it's not all luck, it is luck. I understand that. Well, I just think it's a sense of humor or a sense of... I mean, the best advice I could give them would be answer the phone. That worked for me. I think we should end that.  In a serious kind of way, nobody answers the phone anymore.   And every time, I mean, I just sort of write it down.  I think you ought to answer the phone. I think one of the things that's gone wrong with our culture is this kind of lack of communication we have. I had a reporter call me up the other day. He's a kid, and sent me an email and said, I want to interview you about something. I said, sure, I said, here's my phone number. I'll be in the office all afternoon. He sent a thing back and said, I'd rather deal by email. Well, how do you ask follow-up questions by email? I mean, the key to interviewing is not the first question you ask, but the question you ask after he answers the first question. And I've always thought, you know, the best follow-up question is, what do you mean by that? Or, I'm not sure I understand. Because then you know if they know anything beyond the talking points. And I don't see how you get that done on email, but the way younger people now are communicating, they're uncomfortable having conversations. They want a text or they want to send an email back and forth. And I think it's making it harder for us to communicate all around. But the idea of being a reporter, I think no matter what you do, you just sort of pick out something you really like to do and try to get good at it.  And it's, I can't think of anything. And journalism is not for everybody. But for me, I mean, it's what I wanted to do when I was a little boy. And I got to do it when I grew up. It's really all I ever wanted to do. But it's not for everybody.  And it's not, you know, you don't get off work at 4.30 every day. I always laugh and say, you know, I started on the weekend shift at CBS and never got off it. My pay was a little better at the end than it was at the beginning, but it just isn't a life for everybody. But if you turn that way, I can't think of anything that I could have done that was just more fun than I had. I can't think of anybody that's ever done it better and made it more interesting for all of us. Thank you very much. Thank you. I think we have in the audience the distinguished legal scholar, lawyer, advisor to President Johnson and chairman of the LBJ Foundation, who, Larry Temple, who are gonna come up here for a minute. Well, we've just witnessed the stories of the preeminent, the preeminent journalist of the last 50 years. And I don't think there's any dispute about that. And Bob Schieffer represents the best of his profession. His talent, his integrity, his credibility are what we would all aspire for with everybody in your profession, Bob. Unfortunately, not everybody has that. But I want to say that more than that, Bob has been a wonderful friend to all things LBJ over the last 25 or 30 years. I one time told him that he made a mistake because we asked him to do something and I said, you never say no. And because you never say no, we come back. And we ask again and again. And I don't have enough fingers to tell you how many times he's been on this stage, either as a panelist, emceeing a program, speaking about someone or interviewing as he did Joe last year. And we've had some programs in Washington and he's generously given his time to emcee those programs. So tonight, what we wanna do is to pay tribute to him for what he's meant to, not only this country, but specifically meant to the LBJ Library, the LBJ School of Public Affairs, and the LBJ Foundation. We wanna recognize his career, a career that's not over, still going on. And also recognize and let him know how much we appreciate what he has done for all things LBJ. And I'm gonna ask Linda Robb and Lucy Johnson to come up and they have a presentation. And I'm gonna read the inscription of the presentation that will be made to Bob. And it says, Bob Schieffer, a champion of the First Amendment and a lifetime of journalist integrity and excellence with the respect and gratitude of the LBJ Foundation. And we do respect you. We admire you and we're grateful to you, Bob, and so I know that Linda and Lucy will make this presentation to you. Thank you, guys. Thank you, that is so great. Wonderful, I have a First Amendment lawyer daughter. I know you did. Thank you all very much. But it was a good thing for Lucy, my daughter. I said, you always have to check them. Thank you. Okay, thank you. That's my best part. Bob, in our part of the world, years ago, when you went out to the well to get your water, a real friend was somebody who'd go with you and look out on the horizon and seeing if there were bad folks on their way towards you. Well, my father loved that tradition of recognizing that a real friend would be somebody who would go to the well with you. And Bob, that's the kind of real friend you have been to generations of Johnson's, to this foundation, to this library, to this school, and for being so loyal, so noble, so good, so funny, and so available, we are eternally grateful. Thank you from the bottom of our hearts. Thank you, sir. You are somebody that this country, this foundation, and this family can believe in. Thank you from the bottom of our hearts. Shadda, are you gonna say anything? Let me just. He's unaccustomed as I am to. Let me, I just have, this is a total surprise to me. And I got to Washington just as President Johnson was leaving. I never got to interview him as president, but I did get to talk to him here at the library after he was president. And I must say, as Joe wrote in one of his books, we live in Lyndon Johnson's America today. No person has had a greater influence on our lives and our society than President Johnson did. And he was, to me, the most amazing politician that I ever knew about. He knew how to make Washington work. And I think the further we get away from his presidency, we more appreciate, the more we appreciate his ability as a politician. He was a politician in the very best sense of the word. And we could use a lot more than that, of that, on both sides of the aisle today. He loved politics, he'd love to get things done, and he never forgot who he was. I think that Lyndon Johnson was going to be remembered as one of the great presidents of the United States of America, as far as I'm concerned, he was. Thank you all so much for coming out tonight. I really appreciate it. Thank you.


Georgie, you parody-making son-of-a-bitch! Piece of trash. Looks like he's made a parody. Well, what it's like to be Tyrone Magnus? What if Tyrone Magnus was your roommate? I know this is gonna be funny. Let's check it out. Hey, Tyrone, I was wondering... There's, like, these girls that, uh... I walk past them every day. It's... It's cool. It's cool. Um, can I, like... If you're staring at me, and, like, smiling at Google, and I don't know if they, like, like, beat or something, I just wonder if you could help me out with that. The Sandwich Dammit! Bup Bup! Every single one! Uh, you know what? I just... Forget that. Forget that. Got that from Ryan Wright. That's fine. That's fine. Just forget it. You told me that's how you supposed to The Sandwich Dammit. That's the Bup Bup people. I don't think that's the only way. I think that's a little, uh, abrasive. I mean, this is one of the ones I've been... I didn't know it was gonna be that funny. I'm serious. That was funny, man.    Nah, I appreciate it. It's actually, uh, humbling. It's, uh... Thank, thank you. I appreciate you taking the time to make a little skit. I was gonna say, with me, I wasn't there, but I make a skit out of me. Okay, there we go. So, uh, if y'all enjoyed, uh, Georgie's video, make sure you go subscribe to him. If you enjoyed my reaction, hit the like button, subscribe and share. If you didn't, okay, hit the like button, subscribe and share. One million subscribers. Woo!