Welcome to Building Tomorrow, a show about the ways tech, innovation, and entrepreneurship are making the world healthier, more peaceful, and more prosperous. I'm your host, Paul Matzko, and I'm joined by show stalwart, Aaron Ross Powell. We have a special guest in the studio today, John Aristotle Phillips. He's been called many things during his career, from the A-bomb kid to a wolf of K Street, which I thought was a particularly clever epithet, but he is one of the founders of the political prediction market, Predictit, and the CEO of Aristotle, which is a big data campaign consultancy. John, thanks for coming. Thanks for having me. Now, we have a lot of ground to cover, and I do want to get to the A-bomb kid. That's the tease at the outset, John designed an atomic bomb at an age when the rest of us were, you know, doing nothing, playing Fortnite or World of Warcraft. But let's start with Aristotle. Most of our listeners come from outside the DC Beltway, sort of not going to be quite as familiar with what campaign consultancies, big data firms do. So what is Aristotle? What does it do for political campaigns that are its customers? Sure. Well, first, again, thanks for having me on the show. So Aristotle is a 35-year-old company that my younger brother and I started when we graduated from college, and it was that created and continues to this day to be the largest provider of political technology, and I'll explain what that is, on a nonpartisan basis in the United States. So if you're running for president or dog catcher, if you are trying to get an initiative on the ballot and you're not getting any love from the political parties, you come to Aristotle. And Aristotle provides you with the top of the line software, the voter data, petition management tools, get out the vote tools, online targeting, fundraising online, that anything you need to run a modern campaign we provide. And often we will have three or four candidates seeking the same seat or office using our products. How much has, so you said it started 35 years ago? Yeah. So the technology has changed a bit in that time. Well, there was no technology. So there were no, that literally was no technology. There were no personal computers, okay? So what's the difference then between kind of what you're doing now and what you were doing 35 years ago? Oh, boy. You know, I'll tell you what's the same. What's the, we'll do that first. What's the same is we like to think of ourselves as helping voters in the, at the end of the day, voters hear points of view or hear from potential elected officials points of view they wouldn't otherwise hear if it were just up to the Democratic or the Republican Party. We see the parties as, you know, I have many friends who work at the DNC and the RNC. I ran, I myself ran as a candidate right before we started Aristotle as a candidate for U.S. Congress. I won the Democratic primary and lost the general election. So, so we are an alternative to the two-party structure and we, there are a vast number of campaigns that are not partisan in the United States. Um, and so we, we help citizens take matters into their own hands if they want to, uh, run a campaign independent of the parties, uh, but we also, we also provide technology to a lot of the party elected officials on anybody. Look, if you're running a primary challenge against an incumbent, you can, you are guaranteed of walking into a buzz saw, uh, courtesy of the incumbent protection rackets that, that, that some of the party officials are. So, so this is what we do. We, we help you run a modern political campaign and we work, we do a lot of campaigns outside the United States as well. And I'd be happy to talk about those as well. I just got back from, uh, I did the election in Kenya for the opposition, um, uh, and had sort of interesting things happen there. Yeah. We definitely want to talk about Kenya and, uh, in a second. Um, but right before we get to Kenya, uh, and some of the international elections, uh, to your point about the bipartisan nature of Aristotle, I think I saw that in 1992, you were, you know, helping the data side of things for simultaneously the Clinton campaign, the, uh, Bush campaign and the Ross prose campaign all at the same time. Right. We were doing more than just data for Ross Perot. Okay. Yes. That's correct. So Ross prose actually wasn't just the raw data. You also were, that's right. We were advising on the campaign. Okay. Now, when you went, when you said, I have great stories from the Perot campaign, you guys, I can imagine. Some would say that make the Trump campaign look normal. No, and I, and I have enormous respect for Mr. Perot. So now was, was this, uh, as you get, got involved with Aristotle in 83, I think I read that it was, was this desire to kind of disrupt the two-party system? I mean, part of the DNA of the organization from the get go. I mean, did you go into it thinking of, of, of, of breaking that monopoly? It was, it wasn't so much breaking the monopoly, but when I ran, I mentioned I, I won the Democratic primary and the party, the, the, the party in Connecticut put up a, fortunately for me, a really bad candidate, but they wanted to make sure that that really bad candidate got the nomination because, you know, my theory was they wanted to elect the Republican. So they, so the fact of the matter was we couldn't, it was as simple as this. We could not get the registered voter list for the congressional district in which I was a declared registered candidate contesting the Democratic nomination and this is Fairfield County, Connecticut. This is not some place where it's hard, the voter list should be hard to come by. We couldn't get it. And, and so we, we had to fight to just to get the basic, the seed corn of a political campaign. You're being sabotaged by your own party apparatus. Yeah. I mean, it was, it was part of the process and, and, and, and ironically, I mean, candidates will tell you irrespective of their party affiliation. There's nothing better than winning a primary if you want to test your team and, and your message and the rest of it. But, but often that just, you know, a lot of people don't win their primaries. I was, we, we got into this discussion here because we talked about, we were talking about, you know, the, the parties are, are loathed to provide assistance to primary challengers to their own incumbents. They're, they're in the incumbent protection business. And so when you have, whether it can be Bernie Sanders or it can be, you know, somebody running for, for state legislature, if you're challenging somebody who is a Democratic incumbent, it's, it's, it's expected that the party is going to try to make it very, very difficult for you. And you have to think about, do you really want to be using the software and the lists and the other information that the party can turn off a week before the election?  And you also don't want to be, you know, data is, is, is extremely important, your names, your, the people that have said they're going to support you, the people who as a result of your door-to-door efforts have entrusted you as a candidate or as a volunteer for a candidate that they're supporting, you know, grandma has given her personal cell phone number to the, to your campaign. They, they don't, they're not doing this with the intention that it's going to get hoovered up by some institution in Washington, inside the Beltway, and then sold to a publisher's clearinghouse or provided to an advocacy group, which grandma doesn't even agree with. So that's, so having control of your own data is very important. So if I'm, say, I'm a candidate, I'm going to primary my Congress person.  What kind of data can you give me? Where, where are you, or Aristotle, getting that data? And then how is the data you provide me in the way that you get it different from the hoovering up lots of data about people and selling it? Because you got it from somewhere. Right, right. So the analogy, the, the example I was using is where somebody provides non-public data to a campaign and trusts that data to somebody who's gone door to door for the campaign. And the presumption there to use grandma's example is that that information, that non-public information is being entrusted to this entity and not to a national political party or an advocacy group to which the national political party is selling or giving or providing that data. Okay. There's a difference between that non-public data and the registered voter list, that list that my campaign was unable to get when I ran for U.S. Congress. The, the, it is essential. I'll jump ahead to where this line of question may be going. It is essential in my opinion that if you are contesting an election or have a legitimate use for the registered voter list, that under the Equal Protection Clause, you, you must be able to get the same data as the Democratic or the Republican nominee for the party. My company, one of the things I'm proud of is that we have where necessary challenged refusals to provide data to Aristotle. This is again, the basic registered voter list to provide that information for, to Aristotle for us in turn to provide it to those who are authorized to get it, candidates. So going back to 2000, you know, the McCain campaign was being denied and the presidential campaign was being denied access to the registered voter data in a nearby state. They came to us. We had to challenge the, the denial and we prevailed and we prevailed because the secretary of state went to the elections commission, the attorney general went to the elections commission and said, are you crazy? You can't pick and choose who's going to get access to the registered voter list. It's available to anybody. If you got it, sure, you got to be a declared candidate. You got it. You can, you cannot use it for commercial purposes. Yada, yada, yada. All that being said, access to, and it's not just the, the data anymore. It's also the, the, the tools, the software, the online fundraising, the ability to take credit cards, all those things, a modern campaign requires those sorts of things. And if, you know, if, if you're denied access to Facebook because you've got a point of view that's out of sync with the mainstream or because you're a libertarian or for whatever reason, you know, I think, I think that's a real problem for the democratic process and you need to have access to those tools to communicate. So there, a lot of this data is coming from, you know, public databases that are sensibly supposed to be available to, you know, to those who request them, like, you know, voter registration lists and the like. I spent a little time tooling around on the Aristotle website and it looked like there's information about potential voters that goes beyond anything that would be on the list. There is. So where does that data come from? So, so we collect the, it starts as the base file is the registered voter list. And on top of the registered voter list, if there are listed telephone numbers or if there are people on a do not call registry, we match that data. If there are other types of information that have, so for instance, there are public record databases that we purchase access to and the public record databases will include things like if they are, well, an example would be if they're a donor to a political cause or to an advocacy group or they have their veterans, for instance, we do append that information to the registered voter list. Which I guess when you donate to a political campaign, there's an expectation that that information is now public information in those states. Well, it's a require, it's a, it's a legal requirement that it be made public information. So you have to, you have to, if you're a campaign, part of what our software does is it discloses who gave money, who gave money to you as a candidate and complies with the laws regarding that. Now, there's certain limits on how you can use the information and their limits in terms of the threshold amount, but by and large, the, if you make a contribution to a political candidate or a cause or sometimes to a, to certain types of nonprofits, those lists are available and you should, people should not be under the assumption that they're, they're not. So I think I'm trying to suss out the distinction between, I mean, we, we, Aaron proposed kind of a scenario where, or actually you proposed the scenario where, where there are unsavory organizations hoovering up data that grandma, you know, the grandma wouldn't want her phone number to be used for a publisher's clearinghouse list. She would find that an annoying breach for privacy versus what is publicly accessible, what people are essentially voluntarily disclosing, you know, trying to maintain that distinction. I mean, is that something, how does Airstyle avoid dipping a toe into the. Sure. That's a great question. So, so, and the line is moving. Right. In terms of what people, what the assumption is with respect to use of personal information. I use the Facebook example. I would not want to be in their shoes in terms of trying to figure out where that line is that you're going to cross at some point if you're licensing to a, you know, certain, for certain applications, what might have been acceptable or not really considered five years ago, that's changed since then. And it's going to continue to change. If you look at GDPR in the, in the EU, for instance, which is a privacy legislation that, that any company is not just restricted to companies in the EU, it's companies all over the world that may have information on you, on EU citizens, you have to comply with it. There, we've touched on two different areas, but they're connected as you point out. One has to do with legally mandated and accessible information like a registered voter list, or I would go further, I would say it's also includes contribute, contribution lists. Okay. It's not optional to report whether or not somebody gave you a contribution of $200 aggregate year to date. If you're a federal candidate, it's not an option, you have to report that. And there's, there are reasons to do with campaign transparency, et cetera, et cetera. Certain, certain signatory, if you sign a petition in certain jurisdictions, that prediction has got to be public. And if the petition is public, then it, you know, in, in most jurisdictions, it, it is subject to disclosure and to use for various purposes. So that's one type of information. Another type of information is personal information that you may entrust. I may give you right my personal cell phone number on the back of a card and slip it to you after this interview today. I don't expect you to go out and sell that to somebody else. You use the word. Docs you on Twitter or something. Well, you use the word unsavory. I didn't, I don't think I said unsavory organizations. I said there, there are, there's a whole ecosystem within 30 miles of where we sit today, which is built upon sort of the, the attempts to get power and then to retain power. And that's what politics is, right? It's how we decide to adjudicate a lot of that stuff. My, my sense is that the balance has shifted. The parties for some time, the parties are always trying to figure out what their, if they're still relevant. They were trying to figure out if they were still relevant 35 years ago when we started Aristotle. Our intention at the time was to make it easier for people to make the very best case they can, no matter how weird or bizarre or mainstream their point of view is, and we include a lot of incumbent politicians use our products and we're delighted to have them as customers.  If you don't want to live your political career taking orders from other people, then you need to control it and controlling it means controlling your message means controlling your campaign, and controlling your campaign means you got to control your data. You have to be able to be able to access it without somebody looking over your shoulder and you'll be able to use it or withhold it. If you want to, if you're a candidate and you want to cut a deal with it, with a trade association or an advocacy group with whom you feel it's either a matter of principle that you align yourself with them or you feel that it's politically expedient, I don't really care about that. I mean, that's fine. Then you share your data with others, but if you want to keep it to yourself and you don't want your own data sometimes being used to undermine your own campaign, then you should look at a company like Aristotle. Has being in this industry changed how you personally give out information online, how you personally, are you more conscious of your own online privacy because of what you do? Not that Aristotle itself is hoovering up the information, but I mean has it made you more conscious as an individual consumer? You know, as an individual consumer, yeah, I think it has, I think it has, but I think, you know, again, how you choose to present yourself, your online self is up to you. I have a daughter who's now in medical school and I used to cringe when I would see all the stuff she would do online because, you know, she's a teenager and she had lots of friends. You know, it's stuff that we wouldn't even dream about. I said earlier that technology wasn't even used in campaigns when we started Aristotle. The first software program for a political campaign ever was written by my younger brother, my partner to this day. And it was written on an Apple II computer, right? And we scrounged and saved and got an Apple II computer, which was not technically the first personal computer ever, but it was certainly, this was one of the first of that batch. And, you know, that was the first time, up until that point, you kept the names of your supporters in a shoebox. And this was the idea was, you know, hey, this new fangled thing called a computer, can you be used to organize your lists and then you can print mailing labels or, you know, door walking sheets. So anyway, point being, none of that occurred before, you know, when we started the company. And there were milestones in terms of in unexpected ways. I mean, we were the first to offer online fundraising. So online fundraising, which is ubiquitous today, was not possible, first of all, before there was online, but also the Federal Elections Commission had a rule against making a contribution with a credit card. And the theory at the time was, well, credit cards can be very dangerous because you might make it, it might be a corporate credit card and then you make a contribution with a corporate credit card and then you have a corporation corrupting the election. Well, you know, you can get cash, right, which is a lot easier if your objective is to corrupt an election. So, but it effectively prevented online contributions that you could not put a contribution on a credit card. We challenged that rule along with another company, you know, in this space of another pioneer. And, you know, we said, look, there's no reason that corporate, that credit cards can't be used. You got the same disclosure issues, you got disclosed and made the contribution, and if it was a company that did it, you got to disclose that. So anyway, long story short, we've, we've, there have been a lot of changes and many of them things you wouldn't conceive of. We, as we move further and further into an era where campaigns are relying on data and, you know, and the bigger the campaign, the more data and the more they want access to, do, do we run into situations where almost the campaigns kind of are drowning in data, where the data, there's like diminishing returns. I'm, I'm mentioning, so years ago at Kato, we had, when there were the fusion centers that were set up after September 11th, so that law enforcement intelligence groups could talk to each other. We had a panel discussion here and I remember much the discussion was, and you saw this with like then the NSA surveillance stuff, was if you're, if all you're doing, if you're just trying to get your hands on data, at some point, like the data becomes almost counterproductive because you just, you have like so much of it that you can't figure out what it means and so you have to go back to, you know, like just ordinary detective work to find the terrorists or whatever. Do we, do we run that risk in campaigning? Like do sometimes campaigns just like, well I just, if I just get more data, I'll win, but there's like, what do you do with it? Yeah, yeah, definitely. I mean, look, if you don't have a, I, I, it's one of the reasons I, I do, do foreign campaigns and, and find them so enlightening and, and rewarding in a way is because you get back to the message, right? So you get back to how important what, you know, what the ballot question is. What is a voter in Uganda? What's on her mind when she walks up and checks the box and, and stands in the sun all this time and then finally gets to the front of the line, checks the box, casts her ballot. Sometimes at great risk to their personal safety or the family's security. The, the data is, the data and the software and the online funding, everything we've talked about. It is, yes, if you run a, often in a political campaign, you have no control over three of the four things that make, that decide what it's going to be, among them being, you know, the state of the economy. You have no control over the state of the economy.  You have no control over that. Did somebody else pick the, your opponent? But, but you do have control over, oh, then surprises, right? External factors. War, peace, all this other stuff goes on. But you do have control over the quality of the campaign you run. And that's where the data does come in. And yes, there's too much data. People are, you know, I've seen it happen before where, where campaigns have very sophisticated systems and they pull the wrong voters to the polls on election day. I think that's what happened in large part in, in 2016. Oh, interesting. All right. So, I mean, you don't need to pull the wrong voters, too many of the wrong voters when elections are decided by a couple thousand votes in certain, in certain areas. And so if you get, it's not just the wrong data, but it's misinterpreting how to use the data. And then on top of that, and more important than all that is, what's your message?  And are you a good, are you a good carrier of that message? And do people, do people like you? You know, it's a, it's a big part of it, too. So, that's tantalizing, where I mean, I think our listeners are going to perk up when they hear 2016, wrong voters pulled out of the polls. Flesh that out, out a little bit for us. Well, I think, I think in, you know, in, certain turnout models, if you are, you know, I'll, I'll oversimplify it.  Five meaning I got her, she's going to vote for me. And one means, you know, nah, I hope she stays home on election day. All right.  And then you have to think really hard about whether you want to pull your threes, right? It depends on where you are and, and, and how confident you are. And you think the wins, a lot of factors that are scientific and a lot of factors that are not scientific. That assumes you're able to pull your voters at all.  Or the ground has shifted underneath you in a particular area and those three, people that were threes or actually should be two and a halfs or twos, you know, you can be, find yourself in a situation where you're pulling the wrong kinds of voters. I think this happens often in political campaigns, where they have the execution down. They've got tons of volunteers and they're, they're, they're loaded for bear and they snatch defeat from the jaws of victory. Sometimes, yeah, the turnout numbers in this last election in 2018 are phenomenal. I mean, it's, it is historically high turnout by almost, you know, a third more than in an off-year election. So everybody was turning out. I mean, everybody was turning out their voters this last election. Did somebody, did some of the wrong voters get turned out by the, by the, you know, the opposition? Probably. Yeah. When your margins of few tens of thousands of votes across the couple of states on the national election or it's a few hundred or a few thousand votes in some of these close, well, now going to recount elections, that makes a big difference.  Okay. And the faulty information doesn't just, isn't just how we're doing in Arizona. Right. But it's also, you're so confident that you're doing really well in places where you're not doing so well that you end up losing Pennsylvania. So turning from domestic elections to overseas, you mentioned Kenya sooner. And so you were in Kenya to help, if I understand it correctly, the opposition party was running a candidate against the kind of ruling government in Nairobi. And there had been some really disturbing questions about fraud in the previous election cycle. There had been violence. So set up for us, what were you exactly trying to do for the opposition party there? So in Kenya, Aristotle was hired by Rila Odinga. And Odinga is a Kenyan political leader. He was well known for, in terms of what he stood for. He is a gifted politician, in my view. He was running against Kenyatta, who is the incumbent. And Kenyatta also had some things to be said for him. But obviously, I thought that Odinga was really, had a great message, great background and make a great president of Kenya. As you point out, the election against the backdrop, Kenya is supposed to be the cradle of democracy in Africa. It's supposed to be the shining star. It's how the rest of the continent, and you don't need to look very far to see examples of places where it's just a shambles. And the elections are a sham. But Kenya is supposed to be above that. And there are many, many millions of Kenyans who desperately want that to be the case. We were hired to help with the strategy and the transparency, the election day operations and transparency. And had been operating in the country for some time, working with the with Odinga's campaign and the party. About two weeks before the election, the technical head of the elections commission was abducted and murdered, dismembered and his body was dumped in the jungle. Despite the offers from Scotland Yard, from the FBI to help investigate the murder, the Kenyan government declined to take advantage of that at the time. So that was two weeks before the election. And the election was marred with regularities. Before election day, so this is the weekend before the election, on the election, what happened was my colleague, one of the people that works for Aristotle, was abducted. And I was abducted. Guys came in with the hoodies, broke through the door, handcuffed me, and tossed me in the trunk of a car. And off we went. So you know in your mind that this was after the abduction murder? After the murder of Masada. Okay, wow. That's running through your mind, okay. Yeah, I mean, I've been involved in political campaigns in a lot of, you know, rough places. I was involved in the campaign Afghanistan for the first, the man who's currently the president of Afghanistan. And, you know, Tunisia was the first real election after Arab Spring. So I've, you know, I'm accustomed to a certain amount of risk, but so we were abducted. And for about 24 hours we were held, you know, unable to communicate. At one point, it's sort of, I don't know if we have time to talk about this, but it's sort of, you know, in terms of sort of what happened during the 24 hours, it was, I'll put it this way, it was undemocratic. And, you know, the bigger tragedy was that the elections really were quite flawed. We had turned the corner, I say we, I really mean Odinga had turned the corner with the help of his supporters and the many millions of Kenyans that wanted to change in Kenya. The polling indicated that we were doing much better. And we, Odinga really had changed the entire tone and likely outcome of the election in a debate performance that Kenyana did not bother to show up for. So there was a certain contempt for the democratic process. But also, you know, it's hardball and there's a lot at stake.  Odinga is, as I say, is a gifted leader. There are other very gifted Kenyan politicians that strive for democracy who will, you know, that will hear about in the future. It's by far and away not the worst place I've run an election campaign. And I love Kenya and I love the Kenyans. I'd love to go back, but I can't go back right now. Especially not at your risk of being abducted again. So I think some set of our listeners are going to hear that story and think about American political campaign consultant going overseas to help run a, you know, campaign in a, you know, in countries with sometimes questionable adherence to democratic norms. Though Kenya is not, I mean, has a more stable democratic foundation than, then say, Afghanistan or Tunisia did when you were there. And they're going to think about maybe someone who has more headlines in a similar situation. They're going to think maybe Paul Manafort, right? Someone else an American political campaign consultant known for running campaigns in unstable countries, going through political turmoil. So, you know, how is what you're doing different than, say, what Paul Manafort was doing? And I think I remember reading you ran a campaign in Ukraine. I don't know if it was at the same time Paul Manafort was also in Ukraine. I mean, what's that, not whether you have a personal relationship with Paul Manafort, but like that, that idea of Americans exporting political campaign consultancy to countries around the world. How do you, you know, how does that play when something like Paul Manafort happens? Is that, is that hard for Aristotle? Because people aren't thinking, oh, he backed this sympathetic dissident in Kenya. They're thinking, oh, another American getting involved in intervening in foreign politics. Right. So, it's a great question. And it's not a question that we don't ask ourselves in terms of what's our role going to be. I mean, we get, I'd like to think because of our reputation, track record, we, you know, we get asked to consider working in, in many countries and occasionally we, we turn it down. What we do is, and I'm making this, I'm not connecting this to Manafort at all, what, what we do is bring modern western style campaign techniques and technology and even strategy to campaigns where, and to places where that can make a difference. We are, often find ourselves on the opposite side, sometimes on the same side as other western, and by that I mean British, French, Italian, Spanish, Greek, German campaign teams that we've either been on the opposite side of in places where we've run elections or on the same side. So, these, the, the demand for campaign, for competent campaign management, especially, especially when it comes to election day tabulation and transparency. You know, a lot of these elections are disputed and they're disputed. I mean, we have recounts going on here right now. But a lot of these campaigns are, a lot of these election results are disputed because there is, and there's often widespread evidence of cheating, right? Being able to spotting the signs and being able to do the kind of forensics to say, look, had it not been for the destruction of all these ballot boxes over here, or these, you know, 125% of the registered voters here in this part of Ukraine could not have voted for all for the, you know, the Moscow back candidate. Those kinds of, it is, the West has a stake in the outcome of these elections. I was always struck by just how many American government employees are employed in Kenya. It's, it's a very large number. They're doing security, they're doing, you know, wildlife, all kinds of things that, that, so the US has a stake there as well. So I, you know, we're quite proud of the work that we do. The, the, the, the campaigns and the environments in which we find ourselves are, are, it's not Democracy 101, but we think we can make a difference and we're, we're quite open about it.   And, you know, to the level, to the extent, as we said before, to the extent that the quality of the campaign, the communication, especially where there's going to be election cheating, that you can communicate that quickly, you can spot it, you can deter it, or worst case, you can, you can quantify it after the case. That's important. It's important whether the US government recognizes, you know, regimes that are elected with flawed democratic process. If we want to say that we're supporting a democratically backed government in a particular country, then the democratic process should be allowed to work its way through the, through the election. We ran long in our interview with John tonight, so we decided to cut the episode into two parts. Tune in next week as we change topics to discuss the political prediction market that calls elections more accurately than any pollster. We'll also talk about John's well-earned reputation back in the day as the A-bomb kid who designed a nuke in his college dorm room. You heard that right. Until then, be well. Building Tomorrow is produced by Test Terrible. If you enjoy our show, please rate, review, and subscribe to us on iTunes or wherever you get your podcasts. To learn about Building Tomorrow or to discover other great podcasts, visit us on the web at Libertarianism.org.


As Sonya said, I was trained in statistics. But I'm also part of, at Carnegie Mellon, we have a very large collection of people, generally speaking, in machine learning. And we started mostly through statistics in the School of Computer Science. I actually joined the School of Computer Science to help create a department, a machine learning department, some years ago. And when we did that, we had a retreat to discuss different perspectives. And one of my computer science colleagues during this retreat got up and he said, you know, I finally figured out the difference between statisticians and computer scientists. Statisticians want to solve problems with 10 parameters and get it right. Computer scientists want to solve problems with 10 million parameters and get an answer. So these two disciplines have kind of merged together. And now what I think we're all after is, you know, really, how can we get it right while also scaling up? I'm going to talk about this a little. But I have to warn you, I don't have deep or insightful things to say. It's really just kind of the emerging frontier now. But maybe my perspective is a little different than some others you might hear because I'm a statistician. So we are interested in, and by the way, at the beginning, I'm going to repeat a lot of what Nico said with different words. So we're interested in the problem of what's often coding, which is to elucidate the representation and transmission of information in the nervous system. And the first answer to how neurons represent and transmit information came from a series of results. Here I've listed three different, it's the middle button, right? The other one. OK. Three different sets of investigators, all of whom ended up getting Nobel Prizes for work on different systems, partly articulating this general fact that neurons transmit information by modulating firing rate. And I'm just going to show you some data from Heartline in the 1930s. He recorded from Horseshoe Crab or Limulus, which has a large optic nerve coming from one of the eye, well, from the pair of eyes. And he recorded from a single neuron after when shining a light on the retina. And here's a bright light. You get rapid discharges. These are the spikes that Nico talked about. If you let the light get dimmer, you get less rapid discharge. And dimmer is still even less rapid. And the point is that the firing rate seems to encode the strength of the stimulus. Now these data were interesting, but they're a little deceptive. Oh, and by the way, he called the place where you have to shine the light in order to get that particular neuron to respond. He called it the receptive field, which is a term I'll come back to in a second. These data are deceptive because if you look at most data in cortex, the layer of the part of the brain that most people, I think, are interested in, you see a lot of variability. So here's one experimental trial. And here these crosses are the spikes as they occur across time. This is when a particular cue emerges. And this is time in milliseconds after the cue. Then the second line is the second trial, which is as nearly identical as the experimental can make it to the first trial and so forth. There are 15 repetitions. There's a lot of variability. This is one of the things that I wanted to impress on all of you. There's a way you can summarize this that's very popular. It's a nice histogram where you just average across the trials and normalize so that you get in spikes per second here. And it shows the general tendency over time for the neuron to fire. But it's still, as you can see, very noisy. This is why there are interesting statistical problems.  But then as time went on and Nico was saying, as we get into, say, the 1980s or so, people started asking more complicated questions. How is information represented in ways that go beyond the gross changes in firing rate? And in particular, how does the network of neural responses vary with experimental conditions? So throughout, the statistical aspect is we have to identify the signal together with some extraneous noise. And that's why there are statistics. So here's again the Utah array, which was used in the data I'm going to show you by a former computer science student, Ryan Kelly, very talented guy who did a postdoc with me for a short period before going on to Google. And here are these raw tracings coming off the electrodes that Nico talked about, filtered either for a local field potential or spikes.  And when they do, then they're considered to be spikes. And then the next step is to spikes from the same neuron. And then the next step is to show where they occur in time. The blue is another set of spikes of waveforms from a different neuron. So you get a different second spike train off that electrode. And here's a nice movie that Brian put together to show the nature of the data that you can get. This is primary visual cortex from an anesthetized animal who's being shown a movie.  And each circle is the receptive field for a different neuron. When it's red, that neuron is firing. When it's black, it's not firing. And down below, you'll see the spike trains as the movie evolves. So the point really, the reason I'm showing you this is to give you the sense that there's a very rich set of data that can be collected on these arrays. It's complicated, large numbers of neurons. And it allows people to ask more complicated questions about the structure of the network. I also wanted to mention, and there's a nice graph that was made by these guys Ian Stevenson and Conrad Curtin a couple of years ago, where they looked at the number of neurons being recorded versus the data publication. This is a log scale. And there's exponential growth. So now we have a couple hundred neurons. This number is going to grow. We can expect it to grow substantially over the coming years. So there's more and more interest in methods for analyzing this kind of data. So how does the network of neural responses vary with experimental conditions? The complications here, we have dozens to hundreds of neurons. There are unknown interactions. There's multiple timescales involved. And many possible measured or often unmeasured drivers of activity. Now, the data itself, these spike trains, actually, the data set is not very big by today's standards. Here around 300, 3 times 10 to the 8th data values. The big challenge is the complexity of the relationships. So we don't yet have comprehensive analytical methods. We do have, I think, a solid statistical framework that can produce some simple network descriptions. Nico showed some. I'll show you some more. Sonia's worked on this quite a bit. Jonathan, there's a lot of people doing a variety of things. I think they're all fairly closely related. And I think we are at a stage where we can articulate the problems that need to be solved in order to scale up. So in the remainder of the talk, I'll give you a quick example and just recapitulate. So this is just an example. Not something we've done already, but we're trying to do with one of the neurophysiologists in Pittsburgh. And it's similar to something, again, that Nico showed. There's recording in two brain areas here, the frontal eye field and V4. And the general question is what happens in the brain during a visual attention task when the monkey has to concentrate on one particular location in space. And one of the appealing notions about which there's, I would say, fairly strong circumstantial evidence is that the two areas will communicate through these oscillations of the type that Nico showed. And the notion is that one area will oscillate, that the other area will tend to oscillate, and that this will produce synchronous firing in one of the areas, say, here. And that will effectively increase the firing rate of neurons in that area. So the statistical step that I wanted to take was to establish the relationship of the synchrony to the oscillation. Or, for that matter, other possible variables, the question being how much of the synchronous spiking is due to the oscillation. So now let me show you a little bit more about the way we go about this. You can get a feeling for the nature of the computation. Spike trains occur either in theoretical contexts, meaning the math, in continuous time, shown here. And here the natural framework and probability theory for stochastic events in time is what we call point processes. But the data occur in discrete time, typically at the one millisecond level as a binary time series, a series of zeros and ones, one when there's a spike, zero when there isn't. If we're looking at synchronous firing, then we're talking about the case where, let's say, two different neurons fire in the same time bin. This time bin, in our work, was five milliseconds. And here's two different pairs. One pair over here is a roster plot, again, like I showed you earlier. Another pair, one, is two different neurons. Here's a different set of two neurons. And the circles indicate the times at which both neurons fired in the same time bin. And it turns out that these two pairs were picked out to show very different behavior with regard to synchrony, though you cannot see it with a naked eye. One of the sources of this difference is illustrated in this picture. So here's one neuron on many, many repeated trials, 125 repeated trials. And you'll see these bands of activity which indicate that, and I should say, the stimulus was changing across time. So these bands of activity indicate stimulus-related firing. So here's a place where the stimulus was driving this neuron to fire. Down here, you'll see maybe not a vivid representation, but nonetheless, there are bands of activity where this neuron tended to fire for certain stimuli. Over here is a different representation. On every line, we have a different neuron's activity. So this is the first trial for 128 neurons. This is the second trial, again, as nearly as possible, exactly the same experimental conditions. Second trial for the same 128 neurons. And now you see bands of activity that seem to run across all of the neurons. But interestingly, that activity occurs at different times on different trials. So this appears to be not stimulus-related. And in fact, it presumably has to do with the fact that an anesthetic was used. So there's a well-known phenomenon under anesthesia that you get these slow waves of activity going across large numbers of neurons, and that seems to be what's happening. But if you're gonna look for synchrony and you wanna see whether the synchrony is related to the stimulus, the problem is you're gonna have a lot of neurons firing synchronously during these slow waves. So one of the things that Ryan said about doing was to really untangle that, remove that effect from the assessment of synchrony. So the models that we use are based on firing rate. As I indicated, this is the place you start. And you might, one way to think about what firing rate is, is you just take some window of time, which could be broad, a big window, and you just count the number of spikes and divide by the length of time. That answer is useful for some purposes, but it suffers a number of statistical defects and what has evolved really began in the 1960s and has gotten more sophisticated more recently, is what I would call the statistical signal processing answer. Firing rate is really represented by an instantaneous intensity of firing. It's a smooth function, and this is now within a point process framework. And what we say is the probability of spiking in a small window of time based on some driving variables, which could just characterize the experiment, is what's called this intensity, or conditional intensity. There's probabilities of the intensity times the length of time, delta. And this is now what we call the firing rate function. So we now model that in various ways, yes. Please try not to point at me with... Oh, sorry.  I don't know, it's not going on. Is there some way that this goes off? It stays on, I don't know why. Okay, sorry. I'll try not to blind you. So here's the variable that would determine the firing rate, and it involves the spiking history, that is how, when this neuron has spiked in the past, this allows for non-quasant spiking. And then there are other more interesting variables involving the stimulus, or this field potential that Nico talked about, or possibly the activity of other neurons. So statistical models of spike trains are, they involve two things. There's a universal formula, it's rather simple, that characterizes the noise in the spike train as a function of the intensity function, and then there's also a model for the intensity. Oops, sorry. So here's the formula, the formula itself doesn't matter, but what matters is we've got the probability of spiking in terms of this intensity function, and then we've got the model for the intensity, which have some effects for stimulus, history, and other variables. And this will now have anywhere from dozens to hundreds, occasionally thousands of parameters. It's what we, and statistics generates what we call a likelihood function, and that is maximized in order to get the estimates that we need in order to fit the model. So there's an optimization problem over anywhere from dozens to hundreds or thousands of parameters. Then this fits in the framework of modern regression, which is just like least squares, except it's more general. And we have a variable, a response variable that's observed, this would be the spikes, the spikes in our case. There's some function of variables that characterize the experiment, and then there's some notion of noise that gets combined with the deterministic part of the representation. So here we would call this point process regression, or it sometimes comes under the GLM, generalized linear model technology. Again, in our case, the noise comes from this part of the model, and the deterministic part is the way the stimulus or spiking history or other variables affect the firing rate function. So model for the intensity, and then we have a data fitting method, which is typically maximum likelihood, it's another involving optimization, so it's again optimization over a fairly large number of parameters, and then the statistical inference. And I wanted to mention that this is often based on resampling methods, most commonly we use resampling methods, which means that having done this fitting, which could take some time computationally, you then have to repeat the process hundreds or thousands of times in order to do the statistical inferences. Significance tests or confidence intervals. And this often, when students do this, they will often have to let the computer run overnight in order, if they're using their own laptop, it'll often run overnight, and one of the efforts that people have started to make is to speed this process up. So for synchrony, we want to look at covariate effects, what is it that drives synchrony, and I'm going to skip the methods here that we used, it actually took a fair amount of mathematical and conceptual work in order to come up with a way of kind of fitting synchrony into this framework. But I'm just going to go back to these two pairs of neurons. It turns out that when you analyze these two pairs, in both cases, you have strong, very strong evidence statistically for excess synchrony above what would be predicted by chance. When we then introduce a covariate for the slow wave activity, this is where the two pairs are different. In the first left pair, it turns out from this regression analysis, you can see that the synchrony is due entirely to the slow wave activity, but in the right pair, it's not due to the slow wave activity, at least not solely to the slow wave activity. So this is a case where a pair of neurons show stimulus-related synchronous activity. And then we can then get the proportion of spikes that are explained by a given covariate. So we have the firing rate or the time-bearing firing rate. For the first pair, we have half the synchronous spikes are explained by the time-bearing firing rate, and when you include the network history, you get all of them, at least up to statistical error. Whereas in that second pair, you never get more than about 50% explained, and the rest presumably is from the stimulus, that is, the stimulus is actually driving the synchronous spikes. And now what we're after really is not so much the slow wave activity, but the beta oscillations or gamma oscillations, beta is what Nico talked about, gamma is a little faster, and currently we're looking at data to try to see how much is explained by those kinds of rhythms. Yeah, okay. So the challenges in getting it right while scaling up, I mentioned not so much about the size of the data. However, I did want to mention that if we look at the spike trains, we have small data sets, but if we look at the original recordings, which Nico showed, the actual voltage tracings, that's actually big data. That's like 50 gigabytes roughly for an hour of recording. And now you have the strategic question, which I think runs through all of big data. I've seen it, you can certainly see it in genomics where there's, you know, you start with the alignment files, and then actually to do most, almost all statistical analyses, you go for like, heplotype analysis, you go to variant call format, which is a drastically reduced format of the data. Here again, typical, you start off with the raw data and a drastically reduced format, and I think it's a strategic question, how often do we want to reduce the data exactly when, and I think it's an interdisciplinary question. I don't think it's something that should be decided upon merely for computational convenience. I trust that you all agree, but this is where you need interdisciplinary teams. Complicated relationships involve complicated statistical models and difficult computations. The hard part is getting it right. Now in our case of synchrony, there were more than 8,000 pairs of neurons. I just told you about two pairs, and the hard parts, one of the hard things in the statistics is to say, well, you're gonna get a lot of these synchronous, you're gonna see synchronous activity purely by chance. You have to account for the 8,000 pairs, and this is what's sometimes called multiple comparisons, and we had to develop a new method for dealing with that in this particular context. Still comparatively easy, but took about a year of work, at least part-time between two professors, my point being that even the relatively easy problems when you're trying to get it right can take some real effort. The harder part is that the data can be sparse relative to the model complexity. So for example, if you look at triplets, rather than two neurons, you look at three. Here, from our data, you see very few triplets, and if you wanted to study something about the way triplets are related to the stimulus, you would have a hard time because the data are sparse. So currently, there's lots of interesting analysis being done with parallel spike train data. Please keep in mind that these data are highly noisy, and so we need statistics. I would say that in the next five years, there will be substantial progress, but it won't be through, I would guess. It's rare to have big breakthroughs. I think more will build building on existing ideas and statistical machine learning, which are things like parallel optimization, online learning, and so forth. And especially this will involve using models with unmeasured drivers where we can characterize some of the activity using variables that we introduce into the model to characterize, again, to try to identify things that we haven't already measured. So again, we need to be clever with the experimentation. Nico showed an interesting graphic that talks about the iteration of analysis and experimentation. This is also something I think is very important and will involve new ways of thinking. We also need to be clever with the computation and with the statistical analyses, but I think importantly, as I've tried to emphasize already, these three areas kind of have to merge and we all have to work together in order to be sufficiently clever to tackle the big data problems. Thank you. Questions, go to. So, thanks for a very nice talk. One thing you didn't talk about was this problem of spike sorting. Yes. So is that something which is less of an issue, you think, for the Utah arrays or how would... No. So this is, yeah. So why... So it's the problem that we all like to sweep under the rug. Ryan, as it happens in that data set, was very, very careful. He did it by hand. That is, he went back very carefully over all of the units. And actually only about 50% of them were high quality and he was confident that they were well sorted. So is it a big problem? Absolutely. So for those who are not aware, identifying those distinct waveforms, I showed you a picture that Ryan made where there were the blue waveforms and the red waveforms. That was the nice case. There's much more... Well, equally commonly, there are very difficult problems in trying to distinguish them. And yes, Jonathan has worked on this recently. Many people work on it. It is a very important problem and one that definitely needs to be solved, especially for things like synchrony, as we're interested in. Yeah. Okay. Yes. Srijoy. Do you think your analysis would scale up to other data sets? Like could you apply your analysis to Niko's data sets or the data sets that Jonathan's working on or are they very specific to the data set that Ryan had in hand? No, no, I'm anxious to apply it to Niko's data. Absolutely. That is, I don't see a scale issue in what we have done so far because we've tackled what I say is relatively easy. You can work in pairs of neurons. That's not hard in terms of scale. What's harder is when you want to combine larger numbers and you have to figure out how you're going to do it because as I said, the first issue is the data or sparse. So you have to come up with a different paradigm. In fact, Jonathan's going to talk about one kind of paradigm to deal with, I think, with larger numbers of neurons but it requires different ways of thinking. See a chance to team up, so to say, with computer scientists who do this mining of large set of data which then can be transformed into neuroscientific questions. Are you talking about if we had all the data kind of? No, I think, for example, we heard this morning this talk about this ex-mine. Yes. I think that these kinds of methods could be in some cases be transferred to our problems as well as we, for example, did with the frequent items at mining. Do you, did you get in such kind of... Well, I'm not, are you still, you're talking about text, you're going into journal articles and so forth still? No, no, no, no, no. No, it's about data. I'm talking about your scientific question, problems. Yes. Bike trains and so on. And I think people here have a lot of tools available which, so to say, need to be transformed into our kind of problems. Right, so yes, I'm certainly very anxious to see what the possibilities are. And I think to me, the important thing is to build into any of those efforts, the analytical tools, actually I would maybe build into is not the right word, to take account of the analytical tools that people will want to apply. So if you're going to, let's say, put a lot of data sets up in a repository and you want to have relevant ontologies and so forth, you need to take account, I think, of the analytical methods that are likely to be used. So in that sense, I think it's absolutely essential to collaborate. Certainly there will be great opportunities for analysts to use those kinds of repositories. But only if they're useful to the analysts. Okay, thank you very much.


Hello and welcome to the session in which we will discuss reporting with different opinions from year to year and with different auditors. What is the big idea? Well, here's the big idea. Let's assume we are looking at this audit report. This audit report is for the PCAOB, which is Foreign Assure, and we are auditing the balance sheet of X1 and X2, which is competitive balance sheet and the three year for the related statement of income changes of equity, cash flow, so on and so forth. Now notice we are presenting comparative financial statements and let's take a look at the opinion. In our opinion, the financial statement present fairly for Adam Company for the year X2 and X1 and the result of operation for the each of the three years ending period, December 31, X2. So far, so good. And notice the date. It's February 12, 2020 X3. Now what happens if we audited if we're auditing only year X2 and someone else audited X1 or notice here, both X1 and X2 got unqualified opinion. What if we had different opinion for year X2 or year X1? Obviously, the report would look different. Here we are assuming the following, the same auditor audited X1 and X2 and the opinion is the same, which is unqualified opinion for both years. But what happened if we have a different opinion from last audited period? What if another auditor audited the financial statement last year? We did not do it. What if we changed the opinion from the prior year? So the prior year was qualified. Now it's unqualified. What if last year was reviewed or compiled and not audited? What if last year was not even reviewed, compiled or audited? What do we have to do under those circumstances? So this is what we need to discuss in this session. Before we proceed any further, I have a public announcement about my company, farhatlectures.com. Farhat Accounting Lectures is a supplemental educational tool that's going to help you with your CPA exam preparation as well as your accounting courses. My CPA material is aligned with your CPA review course such as Becker, Roger, Wiley, Myles. My accounting courses are aligned with your accounting courses broken down by chapter and topics. My resources consist of lectures, multiple choice questions, true-false questions, as well as exercises. Go ahead. Start your free trial today. No obligation, no credit card required. So let's review real quick what we just did. So when comparative financial statements are presented, the auditor's report should refer to each statement presented and on which audit opinion is expressed. Because if you're looking at year two, what is your opinion of year two versus your opinion in year one? If they're both the same unqualified, it's easy. We saw the report. Why? Because the report date for the audit of the most recent financial statement is used, the auditor should update the audit report on the financial statement previously issued. If any, now when we say update should update, the update can mean reaffirm, basically no change, or it could mean a change of the original opinion as the result of the change in condition or information coming to the auditor's attention. So you could also change your opinion. If comparative financial information is presented, but not covered by the auditor's opinion, so we are showing the prior year, but we did not do the work, the auditor should indicate clearly in the audit report the character of the auditor's work, if any and the degree responsibilities of the auditor is taking. Simply put, if the auditor is engaged to express an opinion on all period presented, which is that's usually the case, the auditor should consider whether the information included in prior period contains sufficient detail to constitute fair presentation with the applicable reporting framework. So if you are reporting for year two and you did not do year one, you want to make sure year one's still good, we have sufficient appropriate evidence. Starting with different opinion within the same report, so we could have a different opinion within the same report, one opinion, a different opinion from year one to year two. The auditor's current year report will generally cover all financial statements for all year presented. So you're going to give your opinion for the current year, but you might have prior year data. For example, the auditor might express a qualified, adverse, disclaim or included emphasis of a matter or other matter paragraph or explanatory paragraph with respect to one or more financial statement for one or more period while expressing a different opinion on one or more financial statement of another period. Again, X1 and X2, we could have an opinion for X1 and a different opinion for X2, two different opinion. This could be qualified, this could be unqualified, this could be unqualified, this could be modified. Whatever, we could have two different opinion. That is what we're trying to say. Now the best way to illustrate this is to look at various samples about how we do this. For example, this is an AICPA report, which is for non-issuer private companies, independent auditors report on the audited financial statement. And here we have a qualified opinion for this period and unmodified the prior period. Notice two different opinions. So the current opinion is qualified. Notice we have a qualified opinion. Now we just, the standard verbiage at the beginning, we have audited December 31st, X1 and X0, except for the effect of the accompanying X1 financial statement. So there is a qualification here for not capitalizing certain lease obligation. Everything else from the prior year is good, but this year we have an exception. And this exception made the current year qualified, but the prior year is still, the prior year is unqualified. Then we have to explain our basis for the qualified opinion. And we explain basically, Adam company has executed from property and that in the accompanying X1 balance sheet, certain lease obligation, blah, blah, blah, they will explain the issue. The point here, we have two different opinion and you have to explain what happened. Let's take a look at this example. We have unmodified this period and disclaimer on the prior period. Unmodified means we are using the AICPA. We are dealing with the non-issuer or a private company. We have audited the financial statement of Adam company, which compromised the balance sheet X1 and X2 and the related financial statement. In our opinion, the balance sheet of Adam company for year X2 and year X1 and the statement of income changes in stockholders equity and cash flow for only X2 present fairly in all material respect. Notice we only for the other financial statement, we only have X2. So notice what we're doing is we cannot, we will not express an opinion on the result of operation, which is the income statement and cash flow for year X1. So we are modifying the opinion. Then we discuss the basis. Why did we modify the opinion? Notice two different opinion. One is unmodified, it's unmodified because it's for the current year, but the prior year was a disclaimer. We have a different opinion. If we are dealing with a publicly traded company or an issuer, which is a PCAOB, basically the same concept. Now we're going to have a qualified for the current year and unqualified for the prior year. We have audited the financial statement. Again, in our opinion, except for the effect on the X2 financial statement, not capitalizing certain leases. So we are qualifying. We are qualifying for X2. However, X1 is good. So also we have to look at the opinion on the financial statement. We have to kind of explain what is going on. Now, also we are dealing with a public public company. We have a clean opinion for the current year and disclaimer of the prior year. Basically the same concept. For the current year, we have a clean opinion. So for the current year, we have audited everything. The financial statement of the balance sheet, cash flow statements of operation. For the prior year, we disclaim. We don't discuss everything. We only cover the balance sheet for the prior year. So basically we disclaim. Why? Because we did not observe the taken of physical inventory for year X0, which was the prior year. Therefore, we disclaim on the prior year and we have qualified. So notice what we did, how we did the disclaimer. Now, what happened if we have a change of opinion from the prior year? So the prior year could be qualified. Now it's going to be unqualified. When would that happen? Well, the auditor might discover new evidence during the current audit that affect the accuracy of the previously issued financial statement and the opinion expressed. Then the auditor would revise their opinion in the current year's report. For instance, if the report was qualified for the financial statement due to a deviation from the financial reporting framework from gap and the prior year statements are now restated. The qualification would not be appropriate anymore. That's what we have to do. That's, that's, that's the idea. What we do is we add an emphasis of a matter. If it's a private, if it's for a non-assure, we add this information there, or we add this information in an explanatory paragraph. It's a public company. And we would say something like this. For example, we have to put the date of the report. And it did not present fairly last year. The reasons why, you know, carried property, planned and equipment at a price value that's not acceptable. And Adam company did not provide the deferred income taxes with respect to the differences between income and financial reporting. That's the reason it was qualified. Now, as described in note X, Adam company changed its method. Now, we have to clearly say that they changed their method. And the financial statements are restated. And as a result, we changed our opinion. So you have to show all of this, all of this. What happened if we have a predecessor auditor and we present the report? So we did not audit the prior year. The prior year was audited by some other company. And now we're auditing the current year. What do we have to do? Well, the first thing we have to do is figure out because we did not do the audit is the audit still valid? Is that opinion still valid? In other words, was there any changes to the current presentation of the prior period? Maybe or maybe a subsequent event happened that rendered the opinion and accurate? We don't know. So what do we have to do? We have to ask the predecessor auditor to do the following or they'll produce the predecessor auditor will have to do the following read the statement for the current period. So so the old auditor will have to read the statement for this period. And does it make sense to them? Why would it should make sense to them? Because they audited the prior period. Also compare the current period to the prior period. See if this makes sense. Basically, look at analytical procedures, get a letter of representation from the auditor, the base simply put the old auditor should ask the new auditor for a letter of representation, the one what asking them if they find any information that would make their opinion on valid. So basically, the old auditor is asking you, well, you did the audit this year. Do you think is there any information I need to be aware of where it's going to change my opinion from the opinion that I created last year that I issued last year. Also get the same letter from management. Ask in management. Is there any information that you provided to me last year that's no longer now valid, that I need to change my opinion simply put because you issued the opinion, you want to make sure management did not make any changes to the information they they presented to you last year. And also ask the current auditor. In your opinion, is there anything I should be aware of where I need to change my opinion? And how to date the report because now the report is different. If it's unrevised, you would you would use the original date. If it was revised, you would use a dual date. If the revise means the there's a new opinion or there's some changes in the report. What happened if the report was issued by the another auditor, but you are not issuing the report. So it was issued, but you're not showing a comparative. You're not showing the information from the prior year. When the successor auditor does not include the predecessor report, successor means the current predecessor means past the prior one. They should only express an opinion on the current financial statement. Your opinion is only on the current financial statement. Now you will add an emphasis of a matter if you're a private company or an explanatory paragraph if you're a public company, stating that another auditor without naming them, unless you merge with them, audited the financial statement. And you have to tell us in that emphasis of a matter or explanatory paragraph, depending what type of client you are dealing with, if it's a sure or non assure what type of opinion was given. If it's a modified opinion, state the reasons was there any emphasis of a matter or other matter or explanatory paragraph? If they had this, then you have to mention this in the report. So you're adding this information in an explanatory paragraph and an emphasis paragraph if they had any and date of the date of the predecessor's audit report. Now what happened if the prior financial statements were not audited? Well, if they were not audited simply put you mark them as unaudited. That's as simple as that. So everyone can see that the prior year that you are presenting were unaudited. What happened if the prior year statements were reviewed or compiled rather than audited, which is a lower level of assurance as far as review? When the current period financial statements are presented in comparative form with the prior period that were either reviewed or compiled. And the report on the prior period is not reissued. You're not reissuing the report. The auditor should include a disclosure and the other matter paragraph for non-issure or explanatory paragraph for issuer in the report. What should they say? Well, what they should they say in this explanatory paragraph or in the emphasis of a matter, depending what type of a company you are dealing with. Was it reviewed or compiled because reviewed is a limited assurance compiled, you're not providing any opinion. Basically, explain the scope of the services that reviewed is lower than an audit and compilation is lower than a reviewed. If there's any modification, describe the modification, also the date of the prior report. What happened if you, if the prior financial statements are not audited, reviewed or compiled? Well, if that's the case, if that's the case, the financial statements should be clearly marked as such the prior financial statement. Okay. And we would include a disclosure. What would that disclosure go? Either emphasis of a matter, if it's a private company or explanatory paragraph, if it's a publicly traded company. And you will state there that it, the auditor did not audit, review or compile the prior year financial statement. And the auditor takes no responsibility because you did not do any work. What should you do now? Well, go to farhand lectures and look at MCQs through false additional resources. That's going to help you in understanding these topics. This topic is testable on the CPA exam. Good luck, everyone. Study hard. And of course, stay safe.


Hi everyone, Sean with jaspyscasebreaks.com here doing 2020 flawless 2 box case picker team number 12 random number lock edition. Dolphins, Packers, Eagles, Steelers, Washington football team, Raiders, Rams, Bills, and Cardinals. So 1, 2, 3, 4, 5, 6, 7, 8, 9 different teams in this random number block. Every spot gets a random number for the break 0 through 9. For example, if you get the number 3, you'll get all cards with 3 being the first number next to the slash on the left side of the slash. If the card is 243 out of 299, 13 out of 999, 3 out of 25, 43 out of 49, 93 out of 99, and so on, number 3 will get the card. 0 will get any and all redemptions for the teams, including one-of-one redemptions. The number 1 will get any in person, live, one-of-ones. Here is the list of customers from PJ McNeely down to PJ McNeely, and the numbers from 0 through 9. Dice roll to randomize both lists and pair them up 3-3 6 times. 6 in final time. David Garcia down to PJ McNeely. And 6 times on the numbers. 6 in final time. 9 down to 7. 6 times on the numbers. 6 times on the names. And 6 times on the dice. So, David Garcia with the 9 spots. Robert Flores with the 3. Matthew Shure with the 4 and the 2. Hans Hartley with the 6. And PJ McNeely with 0, 5, 8, 1, and 7. Somehow PJ all ended up right next to each other. So, PJ with the 0 spot gets any and all redemptions for those 9 teams listed right here. Even known one-of-one redemptions, but that doesn't matter because he also has the 1 spot, so he'll get any live one-of-ones as well. So there you go, PJ. And there's the random number block randomizer for the Dolphins, Packers, Eagles, Steelers, Washington football team, Raiders, Rams, Bills, and Cardinals for the 2020 Flawless 2-Box Case. Pick your team number 12 coming up next. I'm Sean, jaspyscasebreaks.com. Thanks for hanging out. See you next time.