We're glad to know you're still there, it's breakfast on Plus TV Africa and we just talked about the fact that Human Rights Council of the UN, all the other African countries were voted for and almost every country was better than us by like 50 times or more because we had just 3 votes while others had like 150 votes, 170 something, 180 something, Nigeria had 3 votes when it came to the UN Human Rights Council and it's very worrisome how our image has come to be that we'll be having just 3 votes. Now talking about ranking we also now have Nigeria ranked 4th on the World Bank borrower's list. Even now IMF is still talking about the fact that we might need to borrow again before we can sterilize in some quarters and all that and this government came and said that they will not be borrowing money but we keep seeing borrowings upon borrowings and even some people are saying that the money for the palliatives is being borrowed. It is a worrisome thing. So now this ranking of Nigeria as 4th in the World Bank that is giving some people the heebie-jeebies as they call it. We have a guest that will help us talk about this. He is the Technology and Media News Editor at Business Day, he's Mr. Frank Eleanya. Good morning and welcome to the program Mr. Eleanya. Thank you so much and always a pleasure to be on the show this morning. Yeah, when it came to human rights we ranked the last. When it came to borrowing we ranked 4th. 4th best, world best. Your comment is... What does this mean to our economy and to us as a nation? Being ranked the 4th on the World Bank borrower's list. Okay, so it means, it's like you said from the beginning, it's a very worrisome situation and it's not also surprising because anyone who has been watching the way or the results which we have been borrowing money in recent times would know that this is ultimately going to be the case and if we continue in this part we will probably maybe make number one in the very, very near future. It says something about the efficacy of the government, whether it's the previous government, whether it's the current government, you know, I know the current administration will say, oh, we inherited some of this debt, you know, and they also made promises that they were going to reduce the volume of debt that Nigeria has, but so far it's been over 4 months now that they have been in power and some of us have not seen what, for any indication, that's a plan in place to reduce Nigeria's debt profile in state. What we have seen is a one-ton borrowing here and there. You mentioned the palliatives where the states were giving 5 billion Naira each for palliatives. You and I know how that palliative has done. I don't know if you have to go on palliatives, you know, every Nigerian was supposed to get it. You know, of course, many states are taking that money on loan and they haven't paid back. And of course, we have also seen that the government has also been trying to increase the dollar supply in the market and the first attempt they made wasn't successful, which was the 3 billion dollars that they wanted to borrow from African bank and, of course, they were breaking their own commitment, but that became an audio money. I didn't know if we didn't get that money, but I think the monies that they have brought into the system have also been borrowed, you know, some of them coming from the World Bank themselves that are also releasing the statistics, you know, we also still have an approval for 1.5 billion dollars that the government also intends to borrow, which it claims that it wants to put into small businesses to help them grow, you know, so there are other plans, you know, we also borrowed the thing is that we are sitting on a keg of gunpowder considering the fact that our projects isn't even enough for our capital projects. The deficit in the project right now has risen to over 100%. So what that means is that we are, every money that we are making will naturally go into something in the debt profile that we have, you know, but then because we have issues that need to be addressed, we need to continue to borrow and to fund those issues. And I've said before that the government hasn't made it easy with the government or the kind of team that has recruited. This is a government that has the largest number of cabinets at presently 46 with a new Minister of Finance that was brought in, you know, so they have all the policies and all of them also have gone on a recruitment spree, recruiting A's here and there. Even the government, even the president himself, I would say that about 6 to 24, 8 to the Expedia team, to other teams that he has, you know. So all of that is part of the cost that we are incurring, which hasn't helped us. If we're to go to the Nigerian Korea Satisfaction Reset Release, it's said that debt profile has risen by 75.7% as a second quarter of this year. And you would expect that the third quarter is going to be a lot higher than it is. We're not just borrowing us now, we're also borrowing from the banks. We are deeply indebted to Nigerian banks, you know, and that's our domestic debt profile. So if you look at it from every side, we are just not... The indexes are not just favoring us. But then you listen to the IMF yesterday say that Nigeria's debt profile is not too much or it's not a sassy sort of that it can still be managed, but it will require some form of a prudent. Well, then there's no prudence yet in the system. That's a very big problem. And I also need to say that taking loans or borrowing isn't the same, really. It's not.  But the problem we've had over the years has been what we invest our loans on. We borrow money for consumption mostly. And that is where we are because the returns from consumption you know is zero because the people that you borrowed from need you to pay them back, you know. So it's not just about borrowing. It is what are you investing the money that you're borrowing on. And so far we have invested prudently. We haven't invested in infrastructure. Yeah, the government is making some noise here and there and all that, but we've heard it noise before that they translate to anything.  You know, so a lot of things are just going right, so far. Yeah, well, the Senate president said that because he mentioned the Lagos-Ibadon Expressway, he said it's time now to work on the Lagos at Biokuta. Maybe that's the next campaign cow that we will be seeing because Lagos-Ibadon Expressway has been there. Every government comes and talks about it and then leaves it the way it is or just does some palliatives. Now, Senate president is saying something about the Lagos-Ibadon Expressway. Let's wait and see. But now, you mentioned something that they wrote in the system may not even be from this government. Don't you think it's high time we left other governments and concentrate on the government that is now? Any government that comes, let's talk to them about what they are doing wrong. For instance, for six months, the previous government, which this one took over from, which also is the same party, the budgeted about 3.6 trillion Naira for fuel subsidy to take us from January to June. Now, the present government is borrowing money and is going to spend 3.27 trillion. Okay, well, let's say May. Federal government may spend 3.27 trillion Naira on palliatives and loans. Fuel subsidy for six months, 3.6 trillion. Palliatives for three months, 3.27 trillion. Who does that? And then tomorrow you would still say that previous governments are the ones that brought the road upon us. Let's start to blame whoever is at the hem of affairs at that moment. Don't you think so? Yes, you're absolutely correct. So, you're absolutely correct. You look at it, it's been blame banter here and there, you know. If you bring in a new government tomorrow, those two blame the previous one.  They don't take responsibility. They don't say, okay, I'm here now. So, it has become my responsibility to make things a lot better than I meant it.  You know, and it was the other person. And the question is, what are you going to do? And it starts with what the vision that you're bringing on board. And I keep insisting I've not seen any form of vision that this government wants to project to Nigerians, you know. It's a lot of haphazard statements here and there. We are going to do that, we're going to do that, and we're going to do that. We're going to do that, we're going to do that. You're not sure exactly where they want to put their hands on. So, it's a problem. It's a very, very big problem. And then you mentioned about the borrowings that they have made, you know, 3-point, 3-point. The thing is that, and I've said it before, it is about, they borrow to consume. When you are borrowing money to consume, it's like borrowing money to burn it. Because there's no expectation of return. There's no expectation of return because if you're putting money in palliatives, now you and I know that we don't have any, as any clear plan or any clear strategy that this government has for the palliations, for them, it's about I give $8,000 to this person or maybe $50,000 to this person or that person and all that. Are you giving them on the basis of the loan to return back? If you're giving them on the basis of a loan to return back, what are the modalities that they're going to return that money back? We have seen the Angkor-Bruwas plan that went kaput, all the money that the previous CBI governor collected to fund that program, all of them has disappeared, so we're no longer talking about it now and nobody's been held accountable for it. Now, here we are again, somebody's going again for another palliatives. Okay, we saw the trade-off money. I know for sure that we are yet to account for the monies that were borrowed from those women or that we saw on TV that got maybe $10,000, $15,000 or $20,000 or $50,000. But we are yet to account for how much exactly was dispensed during that program. All of those things are palliatives. This is another plan for another palliatives. Again, we are going in some trajectory. There is no plan as to when are we going to return the money that we're about to borrow. It is a disaster waiting to happen. Okay, and also, I think maybe there is some kind of incentive for these politicians that when they borrow this money, because nobody holds them account for it, some of that money of course get cornered into their pockets. So for them it's like something that they always look forward to. Let's give them palliatives. Of course, we're going to see maybe about 50% of it. It is what has happened to the Lagos in Banana Expressway. I'm not a Bertian man, but if I were to put my money on anything, I would tell you that Lagos in Banana Expressway would not be completed in the next four years. Because the same thing will happen to it. If it is already happening to the palliatives, if we don't really have a plan for palliatives, what plan do we have to complete some of these projects that have been hanging there for the past 20 years? That's where the issues are. And we just need a seismic change as in the way governance is viewed. And you cannot do that if you have the same kind of people who have been there and done the same thing all over and over again and all that. They are going to repeat the same thing. There is no way a leopard cannot change the sport. That's what we're finding ourselves now. So whatever it is that they're telling us about palliatives, it is absolutely not going to bring in any return. Somebody will say, oh, but it's going to give some relief to the citizens. How many citizens? Do they even have the data of the citizens? We don't have a census. We don't know how many we are. People keep saying you're 200 million, 200 and 20, 200 and 10, 200 and 15, 200. Who exactly has the exact number of how many Nigerians we have? And do we have the number of Nigerians in the rural communities? Because those are the people that have been targeted with these palliatives. Do we have accurate number of these rural Nigerians, where they are, the communities where they are, can we pinpoint the houses? How many houses that are there? So that's how you begin to know that somebody is serious when they come to you to say, OK, we're going to give palliatives in order. Because they've already mapped out, OK, this is where we're targeted. This is how many people we're going to look at. We already have their details on our system. And we know how to return back this money. You know, and lastly, is the fact that after they do all of this, Nigerians don't hold them accountable. So they will repeat this all of it again and again and again. Because they know that nobody's going to cry. Nobody's going to protest. Nobody's going to say anything. You know, because that's why it keeps happening. If we have a judicial system that has the metal to exact or to pronounce punishment and then an executive arm that enforces those punishments, you know, people will begin to fear to do certain things in this country. But there's no more fear anymore in the system. There's no more fear. Anybody can just do anything.  They know nobody's going to pursue them. Amurabas can relay you on the road and just collect your phone. There's no camera to hold them accountable. Even in cases where there are cameras, the police will sit on the case for like ages and nothing happens out of it. So when you have a system like that, there is no point, you know, trying to expect so much from anything happening from the government side and all of that and all that. There needs to be a proper structure. We don't have a proper structure. We don't know, but I've not seen any. I start to be corrected. But I've not seen any proper structure from the prison administration. And that is on their table. It's something that they should address urgently, quickly. And before you start talking about palliative, start talking about data. How do we bring our, as in, know how many Nigerians that we have? How many are we? So all of that is how you know how serious these governments are. But so far, I don't think they are. Okay, well, as we wrap up, just if you have any suggestion on how to remove ourselves from the food position on the World Borrower's List, you've said a lot of things in the course of your talking, but for purpose of emphasis, just some bullet points, few of them, how you think Nigeria can exit that position and be in a better place. Prudence is number one. And the prudence must ask from the government. It must ask from them for saying, we are cutting down the cost of governance. I can never say it enough. It is high time we see a government that prioritizes saving. That knows that there's something called investment in the future. We want to invest in the future. So because of that, we're going to deny ourselves some things. We're going to deny ourselves some of these luxuries. We're going to deny ourselves some of these high salaries. We're going to deny ourselves some of these aids that are not doing anything around us. And we're going to deny ourselves these luxurious traveling methods. I saw the other day, the president's son was traveling in the presidential fleet, one of the presidential fleets. If that's what we have reduced our presidential fleets to, why do we still have it? Why do we still have it? About nine of them, once you pack one of them, you're spending millions on each of them. So for every day they pack, millions are going out. So why do you need all of that? I've told someone before, look, if the president can't, if the president is serious, that I recognize the fact that we are in a very dire situation, what he would have done is sell off all of those fleets and start using the commercial flight. Take for instance, if you take, just for the purpose of our discussion, an airpiece for instance, the president makes a case of taking airpies wherever foreign trips that he's going to. Airpies doesn't have licenses in different countries. And that's because they don't trust the Niger market and all that, but if a Nigerian president can maybe start taking commercial flights like airpies or maybe any other green airways, whatever the name is, and it takes them abroad all the time, it was fast forward. They do fast forward the rate at which our, our aviation operators expand into other markets.  So that's where you're creating value for the aviation industry. That's where you're creating a value chain even, because these guys don't have to increase the fleets they have because the president uses them, the president's minister uses them. So there's just more market for them to do something. They have more money to pay salaries. They can now recruit, you know, and it also means that the president will not pay more attention to the aviation industry. So once it has to be, it has to be deliberate. It has to be deliberate. The president has to be deliberate in showing that he's a prudent person. And then of course, look at all of these ministries that are not doing anything. He even went and created more ministries. I'm wondering what exactly do you need them for? The Ministry of Science and Technology, the Ministry of Communication, okay, the Ministry of Science and Technology is now a Ministry of Science, Innovation, and Technology. Now, there's another one called Ministry of Communication, Innovation, and Digital Economy. You have all of them like that replicated, replicating different things. So you got many of them as several of them together and I just create departments inside those ministries. Half somebody heard them. Half people heard them. As it stands now, we don't even know if the permanent secretaries are doing the job that is supposed to be doing because there's a lot of aides everywhere running around.  So there's a lot of things that needs to be done. Prudence in governance. That's how you save money. That's a wrap up, okay. Well, if we begin to talk about problems of Nigeria, it just sparks something in our minds. Thank you so much, Frank, for coming on the show this morning to hear your thoughts. We've been talking with Frank Eliana, a public affairs analyst. That's how I'd like to describe him today, even though he works with business day newspaper. He is a technology and media news editor at business day. We'll take a short break and when we return, we'll go to our next hot topic, which is the advice that was given to the president to go after barons responsible for bunkering. Stay with us.


Next we'll move on to item 1.6 a presentation on dark store loophole property tax shift by Kurt Watensky The deputy executive director of the League of Municipalities Kurt please come forward Thank you, mayor. Good evening. Good evening everyone. My name is Kurt Watensky. I'm deputy director of the League of Wisconsin municipalities. I understand the city of Sheboygan has Plans to have a referendum on the ballot in April asking voters whether they approve Asking the legislature to close the dark store and Walgreens loopholes. I'm here to give a little explanation A little background about dark store and Walgreens property tax loopholes and answer any questions the council may have or the mayor may have and So to start out with though what I'd like to do is we have an our association and just by the way So I I'm here from the League of Wisconsin municipalities We're an association of all the cities in the state including the city of Sheboygan and all the villages also so they're 190 cities and about 413 villages and Also by way of background last fall last November 17 counties and about six municipalities Conducted referendum on the same issue and they all passed overwhelmingly most Most close to 90 percent some between 70 and 90 percent Asking the legislature to close the dark store and the Walgreens loopholes now Let's talk a little bit about what those loopholes are and I'd like to start out with this a video that our association has prepared Basically to educate the public about this tax issue If there are big box or other chain retailers in your community your property taxes may be going up That's because certain commercial properties have been able to obtain special tax reductions Perhaps you've heard of these tax breaks. They're known as the dark store and Walgreens loopholes Unless the state legislature closes these loopholes other property owners including homeowners will pay an average of 8% more in property taxes Even more in some communities Communities like the town of Grand Chute, Ashwabanan, Sheboygan, West Bend, Franklin, Pleasant Prairie, Sun Prairie, La Crosse, and Rice Lake You see the total amount of property taxes a local government may collect is strictly limited by state law The size of the property tax pie stays pretty much the same from one year to the next What can and does change is how the pie gets divided When one kind of property like big box stores pays less Other kinds like residential and mom-and-pop businesses pay more Let's take a closer look at the dark store loophole Attorneys for big box chains claim that a brand new store in a busy area Has the same value for tax purposes as a vacant boarded-up dark property located in an unpopular location Here's an actual example A low store in Wauwatosa is assessed for taxes at 13.6 million Lowes claims it's only worth 7.1 million Yet lowes spent in excess of 16 million to acquire the land and build the structure Lowes argues that even the land was devalued from 9 million to 3 million dollars once the big box store was constructed And lowes insists its store can only be compared to vacant dark stores for property tax purposes Ironically, as big box stores fight for a smaller share of property taxes, they use more municipal services Big box stores demand more police, fire and ambulance service than other commercial properties and way more than residential properties Every day municipal police, fire and ambulance respond to calls from big box stores Meanwhile, the cost of paying for these services gets shifted to homeowners, the class of property using these services the least The other loophole is called Walgreens after a 2008 Wisconsin Supreme Court decision That ruling allows national pharmacies and other businesses who lease their store to claim the value of their properties for tax purposes is less than half of the actual sale price Here's another real example Walgreens challenged the city of Oshkosh's assessment of its store The court of appeals, relying on the 2008 decision, decided that the value of the Walgreens property was 2.2 million Much less than the 4.3 million actual 2009 sale price of the property Other taxpayers, mainly homeowners, now have to cover Walgreens former share of the taxes Plus, Oshkosh taxpayers had to pay the corporation a tax refund of $69,500 Nearly 300 pharmacies in Wisconsin can take advantage of this loophole, including stores in Ashland, on Alaska, Dodgeville, Beaver Dam, Weston, Appleton, and Sturgeon Bay, and in your neighborhood Other commercial and manufacturing businesses that lease their space are also beginning to use this same loophole Only the Wisconsin State Legislature can stop this unfair tax shift Tell your state legislators to restore fairness and common sense to the property tax system by closing the dark store and Walgreens loopholes Call your state legislator through the legislative hotline at 1-800-362-9472 or visit darkstoreloopholes.org All right, that's a fairly good 30,000-foot explanation of a fairly complex topic What we're talking about is how do assessors for cities, villages, and towns, because they're the ones who are responsible, determine the fair market value of certain commercial properties And we have some fundamental differences of opinion between property owners like big box stores and their tax attorneys and municipal assessors around the state and municipalities And that fundamental difference of opinion is over the tax strategy that are being employed by these property owners, commercial property owners for the most part Is resulting in tax shift over to other property tax payers So just let me give you a perfect example everyone can understand If you and your neighbor are sharing the cost, I'm going to bring this down really fundamental level We're not even going to talk about property taxes, we're going to talk about other things If you and your neighbor are sharing the cost of a pizza, and you say I will pick up two-thirds of that cost I'll pay for this tonight, say it's a $20 pizza, I'll pay $15 And your neighbor says, initially says okay, but then looks at his wallet and says you know what, I only have $3 So I can't contribute the full of $5 So what happens, it's still $20 pizza and it still has to be paid for so you have to pick up it So the cost, more the cost shifts over to you That's what's happening with some, as a result of these tax strategies that big box stores and stores, and medium box stores Like Walgreens and CVS that use two different tax strategies that I'll explain in a little more detail The person who's picking up more of the cost of the pizza in Wisconsin for property tax purposes are homeowners Because already homeowners in Wisconsin pay 68 to 70% of the total property tax levy So the rest of the property taxes, 30% are paid by agriculture, manufacturing, and commercial So whenever anyone on the agriculture, commercial, and manufacturing side of property tax payers figures out a way to pay less Than other tax payers have to pick it up And really the only group that's left for the most part are the 70% of homeowners who are paying property taxes So that's what we're talking about here is trying to avoid more of the property tax burden from shifting from one class of property Commercial over to another class of property homeowners The other thing we're talking about here, especially on the Walgreens side And I just want to kind of give a 30,000 foot level look at this tax evasion effort Is right now as a result of this 2008 Wisconsin Supreme Court decision that Walgreens won against the city of Madison in 2008 It was referenced in that video We have a situation where property like CVS stores and Walgreens are selling So the real estate is selling for between $4 million and $8 million in Wisconsin That's the going fair market value right now Mainly investors are buying those properties Pools of investors will buy them But they have to be assessed as a result of that Wisconsin Supreme Court decision in 2008 At roughly half of that amount So they're assessed at between $2.1 and $3 million And they're selling on the market for between $4 million and $8 million So if you're home, maybe your home is assessed at $250 to $300 million And you think you could sell it I'm sorry, I'm used to Madison prices, sorry Your home, not million, $250 to $300,000 And your neighbor sells for $300,000 And you just bought your home for $305,000 But you argue to your assessor, well it really should be half of that My assessed value for property tax purposes should be more like $150,000 You'd be left out the door But that's exactly what's happened as a result of that 2008 decision for commercial leased properties That's how Walgreens and CVS own 80% of those properties are leased, they're not owned And they're relying on an argument that they're leased The amount that they actually pay to the owner is above market And shouldn't be calculated or considered as part of the determination that an assessor takes in The information an assessor takes in to determine the value of the property So would you mind showing that? So here's some examples from around the state Walgreens and CVS assessed versus sale values And you can see the sale values range from 4.2 million to 8.7 million But the assessed values are 2.4, well really just hover around 2.4 to 3.4 million So we have two pieces of legislation Our association worked with legislators last session to introduce in the legislature And the first piece of legislation would close the dark store loophole Which is a loophole that mainly big box stores are using And their tax attorneys argue before the assessor that their assessed values are too high Because you shouldn't look at us as an open thriving store in a good location You should value us as if we were a closed vacant store that's no longer in a popular location And that argument is winning in other states around the country It is not the Court of Appeals and the Supreme Court have not totally bought in Wisconsin We want to make sure that that doesn't happen So we've introduced legislation to just make it crystal clear to courts And to the assessor and to property owners that it's not appropriate to use dark or vacant stores And the sale prices of those stores to determine the fair market value of a thriving open store And so our bill basically just says exactly that It says that the appropriate comparable for a store or any property Has to be of similar age, similar size, maybe a similar retail But also if it's dark and vacant that's not an appropriate comparable for something that's open Okay so that's one piece of legislation, got introduced last session Our other piece of legislation would reverse this 2008 Walgreens decision By basically telling assessors that it's appropriate, it's okay to look at the actual rent That a commercial lease property is paying And also it's okay to use recent sale prices of that property When trying to determine the value of that property for property tax purposes We don't throw out that information and pretend it doesn't exist So those are our two bills, last session they were introduced as I said We had overwhelming support among legislators including legislators from the Sheboygan area Like Senator Lemehue and Representative Kotsma So we had 84 co-sponsors on our dark store bill And there's only 132 legislators in the state legislature in both houses And then we had 62 legislators sign on to our Walgreens reversal bill So you're probably wondering well why didn't those bills pass if they had overwhelming support That's a good question because they also had very strong opposition And the opposition as you might imagine are businesses who are currently taking advantage of these The kind of current state of the law and naturally do not want to see those loopholes or those tax strategies closed So we were not able to get over that opposition And leadership in both houses were not convinced that it was necessary to schedule these bills for a vote So we did not get a vote in either the senate or the assembly It came out of a senate committee unanimously and I also want to emphasize that these were bills that were supported We had huge bipartisan support so we had strong Republican authors and Republican supporters as well as many Democrats on the bill It's probably one of the only policy issues in the state capitol in the last year and probably this year That cross over partisan lines and we have support from both Republican and Democrats on a significant substantive policy issue So this session flash forward to this session What's the status? So the same authors that we had last session are with us and are introducing a bill And they're actually going to circulate their bill for co-sponsors Asking other legislators to sign on to that bill in the next week or so And instead of two bills this session we decided to go with one bill and address both issues in one bill It's less confusing and we just think it makes more sense And so we anticipate we'll have similar strong bipartisan support But you never know and so referendum questions on the ballot where citizens can communicate to their legislators By voting yes on whether they want the legislature to address these issues by passing legislation Are critical and lend support and political pressure on addressing this issue Also another thing that's changed from last session is that Governor Evers, new governor Has publicly told us and also said it in a number of other forums That he will include in his budget both these bills, the language out of both of these bills In his budget which he's going to introduce next week on February 28 So we anticipate we'll have two ways for the legislature to pass Darkstore and Walgreens legislation Either by separate piece of legislation that will be introduced probably the next three weeks Or a budget bill that will be, language will be in the budget that will be introduced in a week and a half So we're very hopeful that this session will be able to pass legislation that creates a fair tax system And addresses kind of the inequities between which classes of property are paying property taxes Which are avoiding paying some and shifting some of their bills on to other taxpayers


Hello everyone, I'm Gabrielle over here in Oxma Toyota you guys we have a couple of brand new Highlanders, but I picked this one out to do a video on because it's in the beautiful exterior blueprint Which you don't see a whole lot right now, but you guys this is a new 23 Toyota Highlander. It's the XLE and this is the hybrid so hybrids right now are so important And right now with this you will get a combined 35 miles per gallon so really great for a SUV Now for the exterior you're going to have the led headlights with the led fog lights For Toyotas with the Toyota symbol you are going to have that blue which signifies the hybrid You are going to have 18 inch wheels All right guys, let's go in the interior you're going to have the graphite. There is all-weather floor liners There's going to be a sunroof There is a 12.3 inch touchscreen You're going to have Apple CarPlay Android Auto.  Thanks for watching and I'll see you soon. Bye


Hi, I'm Whitney Merrill with the Crypto and Privacy Village. Here we have folks from all over working on puzzles, talking about cryptography, talking about privacy, and really sharing knowledge, both introductory topics on privacy, as well as like deep crypto topics like quantum-resistant cryptography. I think we try to create a puzzle for all different types of people with different types of knowledge. And so I see kids working on our puzzles. I see adults working on our puzzles. The hope is that you have to work with other people who have different types of knowledge in order to solve it. So there's not like a specific persona who is the one that we're trying to target to try to solve the puzzle. The Crypto and Privacy Village is a 501C3. So we take donations at DEF CON and then give it like a thank you gift with that donation. We've been running this village now for our ninth year and this helps us do just kind of the basic infrastructure, our storage unit, our annual operating costs, et cetera, but we are 100% volunteer run, but all of the money that's donated to us goes to continuing to operate this village year after year. We have a puzzle going on right now called the Gold Bug. We've been running it for many years and so folks are working on that puzzle. I'm Kevin Cryptok on Discord and Twitter. I'm here with the Gold Bug Challenge. The Gold Bug Challenge is a crypto puzzle. This year we have a series of 11 puzzles. Our theme is back to school. So each of the puzzles is based off of a different school subject. If you go to our website, goldbug.cryptovillage.org, you can download all the puzzles. We also have a BBS system up there where you can submit your answers. I encourage you to go on our Discord and find a team to work on some of the problems. Your first challenge is gonna be figuring out what you're supposed to do. It's largely a bunch of classic crypto style puzzles, so not a lot of deep RSA or electric curve crypto or anything like that, but definitely not something that is gonna be obvious. The teams that have won in the past have spent their entire con working on the contest. It takes some resiliency and some ingenuity and creativity to get to the end. I will say that as the contest goes on, we will start dropping hints to help teams make progress. Everyone enjoys a contest that enables you to have those eureka moments.   We want you to have fun. So we'll help you along. Please ask questions, come visit us in the crypto privacy village. We have a table set up and we're eager to help you make progress. Even if you know nothing about cryptography or very little about privacy, come on down, come check out our content. We have all of our previous talks that we've had over the years available on YouTube, either through Defton or through our YouTube channel, which if you look up crypto village, you'll find it. And we really wanna be a place where anyone can feel welcome. And so come on down.


All right, so we've done a few useful things with equilibrium constant so far. If we know the value of an equilibrium constant, we can use that to determine which direction the reaction is going to proceed. We can use that to go a little further and determine exactly how far forward or backwards a reaction will proceed and where it will reach equilibrium. But doing either one of those things requires that we know the value of the equilibrium constant, the value of this constant, k. So far, in the problems we've considered, we've considered the value of that constant to be given to us. And sometimes that's true. Sometimes you're given a value of k or you can look up a value of k. But the value in having an expression like this one and some other equations is we can calculate the value of k for ourselves. We can obtain the value of k, in fact, a bunch of different ways. We can certainly do an experiment. If we have a reaction we'd like to know the equilibrium constant for, we can throw some reactants and or products into a container, let the reaction proceed until it reaches equilibrium. If we measure how much of each reactant and product we have, use that to calculate the reaction quotient, which must be equal to the value of k, then we've determined the value of k experimentally. There's another way that we've seen, I'll just say, from the free energies. We can use the free energy change of a reaction to calculate the value of k. Remember we've seen an equation that says the value of an equilibrium constant is e to the minus Gibbs free energy change for that reaction divided by RT or divided by kT. So if we know the Gibbs energy change for that reaction, we can calculate k directly from thermodynamics in that way. We can also get the value of k from partition functions, as this expression says. Our understanding of equilibrium constants most recently is that it's this product of partition functions raised to their stoichiometric coefficients. So if the reactants and products in our reaction are simple enough that we can understand what their partition functions are based on what we already know about quantum mechanics or statistical mechanics, then we can use this expression to write them down directly. So for example, we know for gas phase molecules, in particular, monatomic and diatomic molecules, we know how to write down a partition function. Let's consider the case of a diatomic molecule that obeys the particle in a box in the harmonic oscillator in the rigid rotor assumptions, as we've talked about previously. That partition function, we can think of that being a product of the translational partition function, the rotational partition function, and the vibrational partition function. And we've talked about this one less, but also we might need to include an electronic partition function. So again, if we know how to write each of these down for a diatomic molecule, then we can just take their product. In fact, we do know how to write these down. If the gas phase molecule behaves like a particle in a box, so behaves like an ideal gas, then the partition function is going to look like 2 pi mass of the molecule kT over h squared raised to the 3 halves multiplied by volume. That's what we've considered previously as the translational partition function for an ideal gas. Vibrational partition function, we've also talked about, that's in the classical limit if the molecule is behaving classically. We write that as a temperature divided by a rotational constant, and then there's also a symmetry number in the denominator. One for a asymmetric heteronuclear diatomic molecule, two for a homonuclear diatomic molecule. The vibrational partition function, the most convenient way when we wrote that down was that's e to the minus vibrational temperature over 2 times the temperature minus e to the minus vibrational temperature over 1 times the temperature. That's the vibrational partition function. Electronic partition function, if we're at a temperature high enough to be exciting electronic states, that's going to depend a lot on the chemistry of this particular molecule. Most often those electronic excited states are very much higher than kT, way out of range. The only thing we need to know about the electronic partition function, the molecules are going to be stuck in the electronic ground state of the molecule, and all we need to know is the degeneracy, the electronic degeneracy of that ground state. That might be a singular or a doublet, there might be some integer number of states in that ground state. That's a fairly complicated expression, but there's nothing in there that we haven't talked about previously. That's how we write down the partition function for a diatomic molecule. There's one slight caveat or complication we need to talk about, which is that when we've written down partition functions for diatomic molecules previously, we haven't had to worry too much about the zero of energy, but now we are going to have to worry about that. Just to remind you what I'm talking about, if I have some potential energy as a function of R for a diatomic molecule, there's some vibrational states and stacked on top of those, there's some rotational states. So these are the energy levels in a diatomic molecule. When we've talked about those previously and said, for example, energy of the ground state is 1 half h nu, that's relative to this being the zero of energy. That the zero of energy is at the bottom of this well. But if we're talking about a reaction like, let's say, our reaction of h2 plus Br2 plus, not plus, becoming two molecules of HBr, then for an H2 molecule, if we draw the diagram for an H2 molecule, we've assumed that the zero of energy is at the bottom of the H2 covalent bonding well. However, if we're also in the same reaction talking about Br2, the zero of energy can't also be at the bottom of the Br2's bonding well. Br2 has a much more shallow bonding well. The bond energy of a Br2 molecule is considerably smaller. So if this is the curve for H2, this might be the curve for Br2. So the zero of energy can't simultaneously be here and at the bottom of this well. And there's nothing more special about the bottom of the H2 well than the bottom of the Br2 well. And we have to be consistent and use the same zero of energy for both systems. So we don't end up using this as our zero of energy. In fact, if I draw an HBr bonding well as a well, that's got yet a different depth for the bottom of its well. What these do all have in common is, at least certainly the way I've drawn them, is they all approach zero at the same level. Remember, these are potential energy curves. If I break the covalent bond and move these two H atoms far apart from each other so they don't have any interaction with each other anymore, their interaction energy has reached no potential energy. So instead of calling this the zero of potential energy, it makes sense to call the dissociated molecules, this dissociation limit, that's the zero of energy. So in that case, the energy at the bottom of the well is a substantially negative number. It's a different negative number for H2 than it is for HBr than it is for Br2. That has an effect on the vibrational energy levels in particular. So instead of saying, so previously we've said the zero point energy, the energy of the ground state, is 1 half h nu, or 1 half k times the vibrational temperature. That's 1 half h nu above this point. Now that we've changed our zero of energy, we'll write the zero point energy is going to be negative d sub b, the depth of this bonding well. Negative d sub b takes us all the way down to here. And then on top of that, I add 1 half h nu to get back up to the ground state. So 1 half k theta. When we use these energies, 1 half h nu or 1 half k theta, in things that become partition functions, when I have terms that look like e to the minus energy over kt, if in my previous case, if the energy was just 1 half k theta, that would look like e to the minus 1 half k theta over kt. If I simplify that a little bit, the k's cancel, and I've got e to the minus theta over 2t, where I've pulled that 2 into the denominator. That's the origin of terms that look like this, e to the minus theta over 2t in the numerator here. Now that I've changed the zero of energy, I have to write this term e to the minus ground state over kt. That's going to look like e to the minus negative d. So minus minus becomes plus over kt. And also, e to the minus 1 half k theta over kt, which is the same as it was before. So the presence of this shift in the zero of energy, this minus d sub e, introduces an extra term in this Boltzmann factor. I get an e to the positive dissociation energy over kt in addition to the previous term I had, e to the minus theta over 2t. So what that means, if I go back to this expression for the partition function, when I'm writing down the partition function, not just of a single molecule in isolation, but a bunch of different molecules where I have to keep the same zero of energy, what I have to be careful to do is, in addition to these terms, also throw in a term that looks like e to the positive dissociation energy from the bottom of the well over kt to account for those differences in the zero of energy. So now that we've made that correction, we can see that all we need to do to calculate an equilibrium constant is to combine a bunch of these partition functions. If I have a bunch of diatomic molecules reacting as reactants and products, I just need to combine partition functions in this way to calculate an equilibrium constant, and that's what we're gonna do next.


You know you're referencing August and I have to like look behind me and think August is way over there Looking at the clock, and I'm looking at that too, and I'm like two is so sassy. It's like my name is Bailey My name is purple Well, I have what you call spatial sequences. My type of synesthesia is object personalities You know I form a synesthesia is sound to color synesthesia Synesthesia is a neurological phenomenon where one sensory stimulation or cognitive pathway leads to an automatic involuntary experience in another sense or cognitive pathway Synesthesia comes in a few different forms and even within a form each person's experience differs Well, I have what you call spatial sequences And that's where your brain kind of takes a timeline and puts it into a pattern so basically For my year July is like way over there January's right here December's over there August is Way over there, and it kind of just goes in a little circle My type of synesthesia is object personalities So I give numbers and letters and those kind of things personalities and moods and genders I guess and Yeah, you know I form a synesthesia is sound to color synesthesia So I hear sounds and I see colors in response to them and also I have graphene synesthesia So I see like shapes with colors too. So on a day-to-day basis. I see color and everything I can't control it. So everybody's voices music Sounds everything has color to me and so since I see it so frequently it just kind of goes to the back of my head Like I don't really you know pay much attention to it It's not like everything is so distracting because I'm seeing all these colors But um it makes life more enjoyable. I think it adds a little spark to life Well on a day-to-day basis anytime somebody's planning something. I have to think about that location I pretty much just plan In the space around me. So so if somebody brings up something My memory will just like feel like it's way over there, and I'm like, okay Yeah, it was probably in June July area Basically all my memories are just stored into that space too So I just kind of mentally walk myself over there and just kind of look around and I just all these memories will pop up But brown that location and that pretty much helps me wear Where things are and stuff. I don't know okay, it's like and and Look at it.  It's happy It's really happy, and it's just like out there like sticking its arms out like Okay, okay Three three has a distinct face So ugly. Okay, but three is so annoying. Oh my god. He's really needy and he's like Why are you not doing this for me? Why? When I looked at the alphabet s and t had like this conflict and s s would bully t and I don't know. I guess it's cuz they're facing each other, but s has a really like mean Cruel attitude and she uses her curves to get what she wants.  So I never really thought that I was different. I thought everybody had it It wasn't until I was in eighth grade that me and my friend were sitting in gym class And I was just we were just kind of bored and I turned to her and I was just like so Tammy What color is a to you and she was like what then like what what colors a and she's like I don't know what you're talking about and I was like come on don't play stupid with me What colors a and she's like no Bailey really I don't know what you're talking about and I got really kind of freaked out Like what's what's wrong with her or what's wrong with me? I didn't know and I started feeling really like Just really weird like something's wrong with me Like I don't know what it is and I don't know if it's bad or and I was just really confused for a long time until finally I was in class one day in my My English teachers on class and she had this whole bookshelf and on the side of one of the books It had it was called a mango shaped space and it had The the letters on the side of the book were all different colors And they had different like fuzzinesses and textures and stuff the way I see them So I was kind of like hey I kind of see the alphabet like that But not exactly those colors and I just was interested and I pulled out the book and I read the back And I figured out like this girl has the same thing I have and I read the whole book and I was so excited to know that Yeah, I was different, but I wasn't crazy. Something wasn't wrong with me I was just I had something different and so I found out that I had synesthesia Through that then once I started talking to more people I started realizing that other people do have it and so that was kind of cool to help my friends I have three friends now who have synesthesia who never knew they had synesthesia before I Get this letter from my cousin and she's like. Oh, yeah, I Realized something I read this book and it had this girl and there's colors and letters and blah blah blah blah And it's called synesthesia and I'm like oh I've heard about that. Let me do some research So look it up and I'm reading and there's all these different experiences that I can relate to I come to school And we're talking about synesthesia and I'm like oh, that's kind of interesting and I just kind of put two and two together And I was like wait, I kind of have that that kind of is what's going on up here. You know People's voices have colors to me music has color to me names have colored me the whole alphabet as each letter has different colors and then The way they're pronounced might change the color a little bit like my name's Bailey So my name's purple, but then the rest of the colors the last of the letters I mean throughout my name kind of changed the color of the the strong B Because like the L is orange and then the Y is kind of yellow for me A and four are the same color and that just makes so much sense My cousin has synesthesia and sometimes we'll talk about it with each other and he'll say like the the letter L is blue And it and it just like it doesn't make sense to me. It's like how can that even be possible? It's orange Me and my other friend. I met who has the same thing. We always talk about like Stupid phrases where people say oh, let's move the date back and we're like move the date back that means you're moving it like Like closer to me not like farther away, which is something it's the language and You how your brain works isn't meshing together Synesthesia for me it can affect my moods because I have anxiety pretty bad. So like if I'm looking at Let's say I'm looking at a poster and it's in like one words in cursive one words in a bold like Helvetica or something like that and it just irritates me because they're like two different things And I'll be looking at that and I'm like great now my mind's like thinking all right Well, that word is a whole different feeling than this word when a letter is a capital It has a whole different like adult feeling to it sometimes and if it's lowercase It's a different personality so like capital e that's like Royal Lowercase e happy go lucky little e Sometimes people say like oh well, then you must be really great at remembering these things and like you know Names and stuff and I'm like not really because it can actually hinder my remembrance of people's names like For me Samuel and David have like the same colors So I might meet someone to be like oh hi my name is David and then like a week later. I meet that person.  You're just really Imaginative you're just insane just cuz we have synesthesia doesn't make us Like freaks it makes us intricate and significant people who have this Sometimes don't want to talk about it just because they don't want to be judged I guess but it's something you can't control like for anyone with synesthesia their experience is automatic if I just think about the Letter a it's automatically in my head pink. I don't have to even think about it when I hear a sound It's automatically that color. I don't have to sit and think like oh what color was that sound? I might have to think of how to describe it, but it just comes into my head so immediately and I think anybody with synesthesia can agree with that in my head. It's completely normal. I'm like Doesn't everyone see that? Well finding out that I had synesthesia If anything it really changed my perspective About how different people can see the world in drastically different ways Well synesthesia is like a huge influence on me because it really changed how I look at things and like Creativity is like the basic rule of life pretty much and when you think about it all these different letters and Numbers they all have their own stories and personalities And it's just like we're surrounded by these billions of people that we don't even know But they've all got a story to tell and they all have something intricate about them And it's just like hey these little numbers and letters have the same exact thing. So it's like look at that I think synesthesia has made me more creative because You're so used to having these strange abstract connections all the time, you know shapes light music light Space Time just all these strange connections and that's really what creativity is is finding the connections between two Ideas that you've never really thought would connect before so beautifully.  I can know This person thinks that way. It's like oh well I think this way and I see something dramatically different that I know nobody else in this room Probably can relate to so I like giving that experience to people to help show them That they're not alone and how they're so dramatically different Everybody has their own unique Flavor to the world. I kind of like it. I like to be different. So it's it's nice I mean some people don't understand it, which is okay for them But I like it some people have art. Some people are musically talented. I am synesthesia Synesthesia has changed my life definitely It's not just a way of thinking but really it's a way of seeing the world and I wouldn't have it any other way


Hey everybody, Forrest here, ripping 2021 Chronicles football hobby, six bucks number nine random team style. You can always find the results at latentsportscards.com. There's also a link for these results in chat. Good luck everybody and thank you very much for joining. How far back? There's a Sutton of 399 Denver, Pitts, prison black rookie Falcons, clear vision Eli Mitchell, White Sox biggest disappointment. I'd say probably Tigers, White Sox. That's Silver, Kenneth Gainwell, Eagles rookie Phillies maybe. Jalen Waddle, prison black, orange. That is sick to 75 with a color match. That's awesome. Kyle Pitts score for the Falcons. Yeah, probably the White Sox. Aaron Rodgers, Jersey card, Packers to 199. Maybe the Giants. Justin Fields Luminants, but then again, they were like a Cinderella team last year. Model for the Dolphins. I'd put the Phillies up there too. That's fair. Bucks, rookie Jersey, auto Kyle, Trask to 75. Gainwell, Chase, Panini rookie. Najee to 99 Steelers rookie. Najee, Flux, Fields, magnitude. Silver, Chubba Hubbard, Panthers, prison black. JC Horn to 99 Panthers from playoff. Trevor Lawrence, Dynagon, Javante, Cowboys, Bill Bates, auto, not numbered. Ascension, Smith Hubbard, Sir Tannen, Holland. Victor, you don't hit anything in your case break. I haven't touched any of the boxes yet. So what are you going to say then? It's true. Sam actually hasn't touched any of the boxes. I have not. He was like my buddy sent Victor's in some breaks tonight. Yeah. I'm like, I'm not going to mess with it. Eagles, Kenneth Gainwell, Orange prison black, rookie, auto. There you go. Eagles to 75. Gridiron Kings, Mack Jones to 1.99, Giants, Cadarius, Tony, Chubba Hubbard, Kellenmont. I've been joining some Victor's masterworks break. Gavante, Smith, Scorer, Silver, Eagles, G-Hop, 3.99, Cardinals, Deco and Pitts. I haven't heard anything illegal. I was thinking about asking Rich today, but it was, it was not the right time. Orange rookie, Scorer, Hutto, Michael Carter, Jets, out to, wow, to 10, numbered to 10 for the Jets. He said first down, I should have known. I forgot, Scorer always has weird colors and numbers. Tray Lance, Niners, Prison Black, Fields, Clear Vision. I do not know a legal though, honestly, I don't know. Because last year, we did like a bunch of select because it released at the national, but I don't know. 10.99, Brandon Cooks, wait, wait, wait, wait. Mr. Brandon Cooks, number to 99. 11.99, that's when I block your field of view. Do the old switcheroo. Biggest hit of the break right there. Silver Ravens, Rashad Bateman, rookie, Flux. That's the next one right there. Good Iron Kings, Chase, Ramandre, Mack on the playoff. Not going to tolerate Rashad Bateman's lander. That's what I said was like the best card. Kyle Pitts, Falcons, Jericky Jersey to 3.99 from Plates and Patches. Wilson, Brady to 3.99 for the Bucks. Jamar Chase, Prison Black, Bengals and Jamar Chase, Clear Vision. I'll bring it up again, I'll leave it as what we're like, we're like two weeks away, right? Something like that? Yeah, we're like two weeks away, dang. Trevor Lawrence, Jalen Waddle to 75, Prison Black, Orange Rikki Otto. It was just a base, I think. There you go, Dolphins. That's awesome, probably a Magna one. That's pretty nice. Lamar Jackson to 3.99 and Ravens. Kellen Mann, Prison Black, Vikings and Jamar Chase, playoff. I'm being harassed by Sunvicta, I'm being harassed. Silver Tralance, score, Niners. Got Kaderius Tony out of 99, it's so hard to read on this. Giants, Trevor Lawrence score update, Trevor Lawrence, Clear Vision. It's funny, Sunvicta's always like, yeah, I know what it's like to be a streamer. I don't really harass people. Oh yeah, and then he comes in. This is where Sunvicta does all his harassing. That's where he's like, that's where he makes the exception. That's where he blows off steam, right here. It's a 50, Patriots Irving Friar Otto. Irving Friar. Pits, got Flux, Mack Jones. Pits on the magnitude. Najee Harris. Oh, Bankus, we're here and that's all. Gregor So to 1.99 Bills. It's Tony twice. Silver, ETN Rookie, Jags, Mack Jones. Luminance, Green, Penny Sewell to 49, Lyon's Legacy. Michelle, Sunvicta, I finally ripped some, some Pokemon Go. It did not go so well. Sound like that one. Jersey at 3.99, Falcon's Kyle Pits. I thought it was pretty awful and funny at the same time. Got Justin, Field, Zack, Wilson, Mond. Got a Radiant Greninja and a Radiant Heatran, I think is what it was. So yeah, but what are your, where's the EB collection box and an ETB? And yeah, it was, it was not great. Wasn't good. Shvante, Waddle, JC Horne to 99 Panthers. Devante Smith, Prism Black, Kyle Pits. Oh yeah, yeah, sorry, sorry. We also ripped Astral Radiance, a booster right after. Sorry, ripped Astral Radiance booster right after. I got everything mixed up. Silver, Jets, Rookie, Michael Carter on the score. Astral Radiance was better though. We did better than Astral Radiance. Nico, Mack, Legacy Rookie. Devante Smith, what was our best hit and go? I don't know man, I don't want to talk about it. Shrey Lance, Jersey Card Niners at 399. Yeah, we, my wife and I. Bateman at 399, Ravens, Kyle Pits, Dynagon. Diami Brown, Washington to 60. I don't know, 35, sorry, 9 to 35, I thought it was a 60. To 35 on that auto. Trevor Lawrence, Clear Vision to 49. Ellinger, Fields, Prism Black and Devante Smith. Yeah, we opened cards together a lot. We came to the shop and just, we're just distraught. Silver, Prism, Rookie, Trey, Lance. Prism Black, excuse me. Gratz Niners, Mills Mafia. Got Pits, Gold Zone, Falcons to 50. That's pretty sweet. Kyle Arm Pits. Whoa, dude. I'm going to use that. I'm going to use that joke. I'm going to use that joke. Shuba Hubbard on the silhouette, Rookie, Jersey Auto Panthers. Sir Tan Lance. No, I have not Blake. Najee Heresy in book. If you got a link, send it to me. Got Justin Fields, playoff there. I'm sure some news will come out about it soon. I'm trying to think, what was the best Pokemon Go hit that we had? It was some Altar, it was some V Altar. Nothing crazy. That was out of an ETB and a EV collection. The nicest car was the Mewtwo Promo. Buck for the Trask Auto to 75. Like a Parsons, Lawrence there. Got one of one Panthers, Christian McCaffrey, Chronicles Base. Sick one Panthers. The Platinum was like a Panthers color match too. That's awesome. Ego and Micah. Of course, Powell. Nice break for a man. Sewell, Silver. Gainwell, Chase. Palmer to 50. I'm sorry, 99 Chargers. Tray Lance, Prison Black. Niners. Davis Mills, Clear Vision. Chuck Foreman. Auto to 50 for the Vikings. Ascension Auto. Good old Chuck. Tyler Murray. It's a hard to flip there, man. Angles to March, Chase. Jersey Card to 399. Fields Luminance. Mitchell. Mond and Jefferson. Why should he do that illegal? Am I missing something here? I'm not picking up on the joke that he's going at now. Devontae Smith. Kateria Stoney to 399. Giants. Rondale Moore. And Azizo Jallari. Silver or Dynagon. Jalen Waddle Dolphins. Owe. Purple Terrace Marshall Jr. Panthers. 2199. Jamar Chase. And Eskridge. Yeah, I mean, another order just came in, but it's not a big one. It's a basketball on the cups of the baseball. Yeah, if you want to know, Sam, we got it done by 2 a.m. Yeah, I'd say so. He's a little bit. Bills. Gabriel Davis. Mato Nott numbered. Trevor Lawrence. Najee Harris. Dynagon. Jalen Waddle. Mond. Big comeback against the Reds. Davis Smith. Reven Jordan. 399. Saxons. Where's the Reds? Kateria Stoney. Rookie Patch Auto. Giants to 99. From Plates and Packs. Go Giants. Good amount of Tony in this, too. I feel like there's been him a lot. ETN to 99. Jags. Mills. Update from score there. Silver. Dynagon. Najee Harris. Steelers. Lance Luminance. Ian Book to 199. Saints. Trey Lance. And Trey Lance. Yeah, so there's a little bit of space of versionals. Not a lot, though. Sam Ellinger. Lance. Harris. Trevor Lawrence. Red. Prism Black. Boom. 28 of 99. Great card. Jags. Norberto. Congrats. Zach Wilson. Prism Black. Terrace Marshall. Junior. Ricky Jersey. Card Panthers to 399. Bonte. Waddle. Surtain. There's a max score update in Book. Legal. I'm a bot. I don't know what that is. I'm an NPC. Narellx. Terrace Marshall. Junior. Jamar Chase. Trey Lance. Kyle Pitts. Kyle Pitts. Aaron Rodgers. Autos. Kaderious Tony. Chuck Foreman. Chuba Hubbard. Kyle Trask. Diami Brown. Irving Friar. Michael Carter. Kenneth Gainwell. Bill Bates. Kyle Trask. It's Silver Prism Black. Trey Lance. Niners. Red Prism Black. Trevor Lawrence. Jags. One-of-one Christian McCaffrey. Panthers. Chronicles there. And Orange. Shailen Waddle. Dolphins. Prism Black. Rookie Auto. That's an awesome one. To 75. I'll do it. One more.


So, yeah, first thing I have to admit, well, I mean, my excuse is like Dan has done it as well. The actual presentation has been built on React. Sorry. And what's your name? Jew. Yes. So, I wanted to just like talk a little bit about machine learning because I find it extremely interesting. And Jamie was really kind and he said yes, so here I am. So, my name is Jew. You can find me online on Twitter as Rcam with a four. And I work for a company here in London which is called AlphaSites. You should check it out. And we have a lot of my colleagues here. And also, Ed is leaving today, so please give it out for Ed. So, we're just going to play a game at first, which is I'm going to show you some steals from a couple of movies and you have to say the name of the movie. Are you guys ready? Close. Eventually. Eventually right. Come on. Come on. Good one. Good one. Yeah. Okay. Good. Good. Much better. So, if you think about, you know, like artificial intelligence in computer science, we're always talking about how computer science should, like artificial intelligence, should be how computers do stuff that humans do, right?  Actually, computer intelligence is usually quite alien, usually has a certain disregard for the human life or lives. And it's just like something that humans don't really understand really well or at all. So, for me, it was always kind of weird that when we're talking about artificial intelligence, we're actually trying to make computers do exactly what human beings are doing. So, when I learned about machine learning, I thought, well, this just looks something more like the movies has taught me, right? So, it's something where the machine sort of learns about itself. You don't really understand what the machine is doing. At some point, the machine will tell you that the answer is something. And, yeah, you can't really discuss with the machine at that point. So, I thought it was cool just to show a little bit of slideshow initially of what actually you are using every day which has been built in machine learning. So, I think the most boring part of this thing is image processing because we have the technology which is called OCR, which I'm sure most of you are familiar with, which I'm sure most of your grandparents are familiar with. Mostly because this was invented at this point almost 30 years ago, right? And the idea is quite simple. There's like some numbers and some characters which are written by a human and the computer is able to recognize that. And we always take this completely for granted, but if I asked you as like part of your weekly iteration to build this in the next iteration, would you think you would be able to do that? Any takers? No? What's the challenge? You have two weeks to re-implement OCR. Two weeks to implement OCR, I will use R&D solutions. Very good, very good. So, I think there's a very nice comic from XKCD which is really about like this issue, right? You want to build an app which checks, which like a user can take a picture and you have to check if it's taking this picture in a natural park. It's super easy. You just like get the GPS information, you check if that coordinates are inside the park and you get it. But then as soon as you have to understand if the picture is representing a bird, you're like, yeah, I don't know, seems pretty hard. Usually it takes five years in a research team, right? Apparently someone in the world has these sort of research teams because when I upload pictures on the internet, there's like some weird algorithms which are able to recognize where are the faces of the people in the picture which I think is strikingly similar to how Terminator operates, right? Because if you want to resolve the human issue, you understand where the hat is and try to address that. I can do this in two weeks. That's great. There's a website with free movies and to download the movie you have to tell if there's a bird on the photo or not. Some SEO and it's very working. Yeah, sure. But let's say you can't use any existing library. You have to build it from scratch. Yeah, I know it's better. I would use human pages. I'm sure that this is how Facebook is doing it. They actually have like really a lot of human beings like selecting the faces of the people in the pictures. So this is all fun and all. But then there's this company which is called Google which has been developing these self-driving cars and this car has been already tested on the streets for almost like a billion miles, right? This car has been driving already for a billion miles and probably in 10 years if this technology actually works, it means like all the people right now are driving for a living. They won't have a job, which is, yeah. Okay, so let's switch to something more interesting, language processing. So as modern like citizens of the modern world, you're familiar with something which we call spam and luckily there's like some programs which are aptly named to destroy this spam and all these programs are actually like application of machine learning techniques. So like to detect if an email is spam is a typical machine learning task. You get the email, you have to understand if it's actually your grandma's engineering, the recipe of her cookies or it's like this Nigerian prince who wants to give you a fortune. I'm not talking about you're rich. Other than that, there's also stuff which is called sentiment analysis which is quite important if you're a politician, maybe a politician. Well, it's really important for politicians and basically the idea is that you get a lot of raw data like tweets or Facebook posts and you try to understand how people feel about Trump or Hillary just like from the text of the tweet. Something really interesting I've just read about this matter is that apparently Trump has two devices. He has an iPhone and has an Android phone and someone ran a text analysis on the tweets that Trump writes and has figured out that whenever he writes from the Android his tweets are more aggressive, they're more like inflammatory, they try to insult his opponent and for example whenever he's posting from this Android phone he never uses tags, right? Probably he doesn't understand and that's my theory. But whenever he's using his iPhone he's always using tags, he's always like complimenting like the US Olympic team and he's like being like very politically correct. So the theory in that article is that actually Trump actually has a Samsung Galaxy and we know that for a fact and probably his PR team has an iPhone or something like that. And actually if you read the article you can actually see like the split of the usage of terms so whenever he's like on his iPhone he's like bad, evil, problem and stuff like that. It's pretty interesting. So you should vote for him. Or like when you're using like Siri or like whichever program you try to tell the computer something and the computer has to understand it. To keep like the talk interesting we're going to talk about games, right? And usually games are fun and all but then there's this guy who is apparently one of the best players of Go in the whole world and he played against them a certain machine built actually here in London by DeepMind and he lost quite badly. I think 5-1? 4-1, yeah. And in the past 20 years Go has always been the go-to game. To basically showcase the fact that computers aren't as smart as human beings because in Go there are so many combinations as soon as two players start to put like four pebbles the number of combinations explodes so rapidly that we always use the game as a way to prove that computers are dumb, like we humans are smarter than that like we can do better in machines and apparently it's not true anymore and like all the machine learning experts were thinking well probably in the next 15-20 years we'll have like a natural program which will beat like a real champion and apparently the future is here because yeah it's quite scary. I think like a lot of like professional like Go players professional Go you know just like enthusiasts were really shocked by this and they expected a 9-dan champion to be beaten by a machine which is fair. So this is the same company who developed AlphaGo and it's called DeepMind Space here in London you should apply, hopefully it'll get you. I just want to show they built this little program which learns to play Atari games and basically at the beginning of the game he doesn't really understand what the breakout is I'm sure you guys are familiar with like what the breakout is there's a ball, there's like bricks like you don't have to let the ball, oh no my internet isn't working is it right? Oh okay awesome. So this is like 10 minutes in the training you see like the computer is pretty dumb like he doesn't really understand like what the goal is it's just like let's the ball fall down like come on whatever but and this is like 10 minutes inside the training he's just like trying to press some buttons and basically what the machine learning algorithm is doing is like trying to optimize the score and after 120 minutes of playing I think it plays pretty well and this is basically how I think like anyone starts like playing breakout like when you understand like what the goal of the game is and then at some point you lose because the ball is too fast or I don't know like you learn stand stuff right after another two hours of training the machine learning algorithm understands something is that if it's able to dig a tunnel in one side of the wall he doesn't have to do anything because the ball will like like clear stuff like by itself it's pretty like lazy algorithm right typical like computer scientist and they built this like general purpose program to play Atari games so when the game starts the algorithm doesn't even know what the game is like doesn't know like what the goal is you just know that he has to optimize a certain score and that's it which I find is really fascinating I can't see my... I should find again my cursor okay it's here we're back oops maybe let me try it yeah react there was oh okay cool we have another video here awesome so um there's this guy who built this neural network to play Mario actually some mario is like welcome back Zeppelin here you're watching a skilled player play Super Mario World but this player is not human it's a computer program I wrote called mario this program started out knowing absolutely nothing about Super Mario World or Super Nintendo's in fact it didn't even know that pressing right on the controller would make the player go towards the end of the level it learned all of these things through a process called neuro evolution in this video I want to teach you about how mario learned to beat this level donut planes won what his brain looks like and how it's all based on actual biological evolution let's start out by actually looking at mario's brain let's play it again but this time we'll look at mario's brain as a smith it's a simplified view of the level so basically like this is how the algorithm views the world there's mario somewhere there's like some obstacles and on the right you're able to see at some point that there are the possible keys which he can play while playing the game and what you see inside here this like mesh is actually the brain the neural network and basically depending on where mario is and where like how the environment looks like it decides which keys to press and initially it's really really like fun because you see like the the program like when it starts and it doesn't really understand stuff it's complicated but it's enough to make at least some progress at the level let's take a look at a sample of mario's how that works this is one of the randomly generated but eventually he'll get like smarter he'll build a huge neural network and he'll be able to play that game like we saw before which is pretty sweet the neural networks that appear where's my cursor why can't I get it back okay good one more video and yeah we can like show something more different actually this is an application of machine learning techniques to robotics so they wanted to they had this skeleton of different types of animals and the skeleton has muscular like a certain structure which like the muscle like were connected with right and they wanted to understand what sort of tension like you need to provide the muscles to keep the skeleton like walking right and they use machine a machine learning algorithm to train models in order to walk and they also gave them a certain targets like to like go to a certain speed and stuff like that and these are all the generations of the of the machine learning set up right you see like generation 1921 is just like he's got it and here like you can see like sort of like a giraffe is like oh my god look at this this is my favorite one look at generation 80 he's like he's almost got it or he's like really really lucky look at that he's like really there and rock and oh no no no no and then it's like yeah and then what is great is that as I said like you they optimize you for a certain target speed so for example for a human being they said like this certain speed and when you increase the speed see the guys like sort of running right which is you know expected but what happens if you try to apply this to other type of animals with like other like bone structure basically like you can see like this guy like sort of like is still able like to run but for an animal like this what we guys think like is the best way to run right like the model itself like understands and well if we can say understands in a certain way that this is the best way for this model to run you can watch the whole video it's like great at some point it goes on into like simulating like oh no it's not here like simulating an environment so it tries like to throw stuff at the thing and the thing has to like counterbalance and or like altering gravity so have you ever guys like seen like the pictures on the moon right and it's like frequently similar okay back let me just find okay cool now I got it so when the first time I saw this I was like wow this is like really impressive I want to do something like this and I really wonder to understand like how that's the whole thing work and so what I did is just like follow the Coursera course on machine learning which is really nice I really recommend like if you if you find this sort of stuff interesting I think you should definitely start with that one and basically the big like the core idea is that like there's like a definition which is like formal and then like the actual definition is that a computer program is going to perform a task when its performance can be measured by a metric which improves the big experience so basically you teach the machine how to do it how to do something you teach him how to understand like how well it's doing this certain thing and then you keep repeating this process so for example like when you're playing chess it could be like look at all the previous matches that were ever played by human beings and then you try to play against this program and see like how well it fares or this is like another classic machine learning sort of like recognized task which is to recognize Obama's face in pictures you just like provide this ton of pictures and the algorithm has to understand if there is Obama in it this is also good application for terminator I guess and for something more serious it could be like something as serious as tumor prediction so you take a historical data so pictures of tumors x-rays scans and you associate these scans with the fact if like the tumors malignant or benign and then you measure like how accurate you are and it's pretty insane to think about it so usually all these things work in a very similar way which is there is always a sort of training set so some certain way that we teach the algorithm like what is right then there is this feature like there is this process called feature selection where in all the data which is there in the training set you just select the features that you think are important and the last part is the test set so basically you just like take a little bit of your training set and you just like keep it on the side and you just use it to check if your model is accurate because the problem with the training set is that if you just use the whole training set you train an algorithm you could train possibly an algorithm which just matches 100% of the training set but that's not what we're trying to do we're trying to build a model which then we can apply to the real world so whether we use the test set for is just like to have a sort of reality check like are we doing something which is like real? will it actually work in the real world or not? so there is two like big types of machine learning algorithms supervised and unsupervised and usually on the ones on the left we are telling the machine ok this is the right answer and you have to learn how to tell if this is like what the answer is so for example spam detection is a supervised task because we have a lot of emails and we tell the machine ok this is spam this is not spam on the other hand we have something which is called unsupervised which means that honestly as humans we don't really know every time what the right answer is and to imagine it would be let's say you have a program which is called Google news and the goal of Google news is to scour the internet for every sort of news and article and then you want to have a front page of this product we hypothetically call Google news and we want to show all these articles which sort of belong to the same topic we want to group them together when you load Google news you'll see that it actually does this so for every big news there's links from newspapers all around the world and they have an algorithm which is doing this clustering operation is trying to understand what is this article talking about and grouping them all together which is pretty impressive so like the well-known techniques in supervised learning called linear regression we also see a couple of examples of this and logistical regression and neural networks for the unsupervised ones they're really useful for doing this sort of clustering for doing anomaly detection so you want to understand if there is something wrong at some point and for another operation which is called dimensionality reduction which means basically that in a lot of these machine learning tasks there's so many features you're considering to predict the price of a house and there's 25 different factors that could impact the price of this house but then you want to draw a table or a graph to represent it and our human brain is not able to process a graph built in 25 dimensions so usually they use these sort of techniques to try to reduce the dimensions to two or three and just to show these are the main factors of this model so linear regression is something which is pretty the idea is very simple so let's say I showed you a graph like this this is profit in ten thousands of dollars and populations of cities in ten thousands of people and I show you all these axes and I ask you then to predict what happens if someone is around 17 I would say probably someone is in this area and if you run the algorithm on it you'll do yeah that's like pretty much true but as I said before imagine if this problem was in 15 dimensions would you be able to point with your hand where the optimum is or where the prediction is like our human brains are incredibly skewed towards two three dimensions sometimes four if you're good but other than that you're just like you can't even imagine where it is and instead if you use a numerical approach it just works just as well with ten thousand dimensions so actually logistic regression is just about linear regression is about predicting a number and logistic regression is about predicting a Boolean so for example here we have these two sets it's like test about like microchip sizes and you see we have y equals zero y equals one and like here we can still figure out that probably the best way to describe that is that circle and the same the same reasoning applies here we could do this because it's in two dimensions if it was in ten thousand dimensions which is not something out of this world every sort of modern machine learning techniques deals with hundred thousands of features so you couldn't do that possibly um the last thing I want to talk about are called neural networks and if there's only one thing I hope you can remember after tonight it's just like this usually you always say neural networks are structured after our neurons and because our neurons have a nucleus they have these little dendrites where they use as inputs and then they emit one output through the accent so if you think about a neuron is this entity which has a lot of inputs and emits one single signal which usually is like either high or low so one or zero and don't be scared please so this is like really like the same thing just expressing like a more mathy situation right like you have a transfer function inputs and then you have some sort of like activating function I don't know what it is and then we have a single output the activation function actually is really important you don't really need to care about the maths like behind it but it's just the shape of the thing is interesting it's just like this sort of ass right and what we care about like this special function is that if we pass something which is greater usually than four it goes to one if we pass something minus four it goes to zero you just need to care about that right it's a function where you pass something which is like four more it goes to one something less than minus four it goes to zero so what if I told you that this is actually the implementation of OR gate using this neural network and we have this two inputs this is called the bias function so it's like something we always add is minus 10 and instead here are just like boolean values right so we have x1 and x2 and what we do there is just we multiply this value by this this value by this with some minus 10 and we plug it into the activation function so right so we can try it together right it's like zero zero it means zero zero minus 10 and as we remember minus 10 is less than minus four so it goes to zero if we try this it will be 20 minus 10 which is greater than four it goes to one same thing for this and here it goes like 40 minus 10 30 which is greater than four one so this is actually the neural network which implements an OR gate and as you can implement OR gate you can implement AND gates you can implement XOR gates and like basically when you can implement like the logic gates you can do everything in programming and in production basically you see neural networks which look more like this right I think the neural network one of the two neural networks which was used in alpha go has 16 layers and one thing to note notice that is the first layers called input layer and the layers which are in the middle are called hidden layers and in the hidden layers actually they store the actual brain of the algorithm like the actual these parameters which are stored in the hidden layers are actually what are controlling the intelligence of the neural network and in the end they just emit an output so ok cool I just wanted to show you this thing which comes back from 1991 is this guy which is called Yan Likun which is the grandfather of on-machine learning and now works at Facebook as their machine learning center oh really? oh no it's starting in 1991 or maybe I'm just mistaken is it working? and then he's like really happy about it so maybe we can cut there I mean I can't blame him I lost my mouse again how is it possible ok cool so how much time do we have? like 5 minutes? yeah so I just oh wow maybe well I won't show this video but you should watch it it's basically a clip from 2001 Space Odyssey and they trained a neural network on Picasso's paintings and they applied it on top of the movie as a filter so basically there's a whole clip of the movie which goes through like this movie through Picasso's eyes which I think is pretty sweet so I just wanted to do a quick demo because I thought it wasn't really fair to show all these like bright things without nothing like serious so just disable display and mirror displays cool awesome so this is my React server running can you guys see? right? ok a bit bigger good so for example like the thing I was showing you guys before about the supervised learning so like this is the the same example I showed you before and on the right there's like Octave which is this open source version of Matlab which is running and it just like prints out the result of the linear regression and it's just like this straight line which is not particularly interesting what I think is quite interesting instead is to see how actually this thing is calculated so the algorithm basically builds this sort of 3D representation of where the optimal points are and basically it starts at any point here and slowly goes down to the bottom of the valley and there is this mathematical formula and this mathematical process which sort of ensure you well it's not like you're not like 100% sure but they give you enough confidence that you've reached a certain minimum of that graph maybe it's not like the absolute like best minimum but it is a minimum and usually you can structure a problem so that you're pretty confident you've reached the end of it and as I said this thing which is run on these two dimensions it could be run on 100 dimensions and the mathematical formula works just as well it's just differential equations so yeah it is so and then this is like another way of representing this sort of problem with our eyes we can see the various slices of the cake it's sort of how we represent the rain and you can see here is the highest or well in this case the lowest part and how the algorithm operates it starts in any random point and tries to move around and see if it's going in the right direction and then as soon as you realize it's not moving anymore it tells us okay I probably have found like a local minimum okay which is good um octave and here like oh whoa so and this is like the other thing I showed you before so it's really similar so it just like runs the logistic regression on top of that yeah it tried okay good and it just like calculates this circle and all this is like MATLAB code which runs it's really fun so you should try it what I did on my own is to try to use an open source python framework which is called Theano but probably like you've heard about TensorFlow and Theano is something really similar there is another library which is called Keras which acts as an level of abstraction so you can both use Theano and TensorFlow and you don't have to change your code just change basically the adapter and what this does is it um does the digital recognition that we saw in the video so if I just do python mist I'll print out some stuff and this is like the test set data so there's 60,000 images they're all 28 by 28 pixels each one of the pixels is treated as an input in the neural network so basically this neural network has 700 I think it's written somewhere let's say 700 inputs and then there's this thing which is running which is trying to optimize the accuracy rate of the recognition and you see like after like 4 epochs it goes to 90 where is it 97.6 which I think like from the papers I've read it's really close to the human accuracy and I just like trained it for like 10 seconds and this is like how the best humans can do this task and like most like modern algorithms when like are trying to solve this problem they can achieve like 99.4 like accuracy on these tasks and the reason why the algorithms like make mistakes is because like the data isn't perfect right so for example like sometimes the algorithm makes a mistake because he thinks that this is a 2 and probably I could guess it's a 4 but it doesn't really look that much like a 4 to be honest and what is this like here like he predicts a 0 is actually a 6 so you can't really like blame the algorithm that much and instead when the algorithm works it looks like quite good so looks something like this this is in the prediction it's there somewhere so this like when the algorithm gets it's right and he recognizes this is a 5 which is pretty creative and if I had to show the code of this it's quite simple I think I have to close the Python graphs and it's just well I just keep like how the data is important and all that but basically this is like the heart of the whole thing it's creating a neural network where there's like 704 input inputs and then there's a hidden layer of 512 nodes there's another one like that and then like this is like the output layer since this is called a classification problem because we're trying to understand which one of the 10 digits it is so it needs like 10 outputs just like to try to understand that but as you see is pretty simple like the library makes like everything like really simple and then basically the way that you like run it you just say model fit you pass the training data you pass the output in the training you say like a couple of like options and like in this case I passed the validation data so that the algorithm is able to understand if it's like proceeding in the right direction or not okay cool so let me just go back I think I lost so yeah I just wanted to show this very last video which I think is it's really nice so like this guy programmed having some trouble with those menus so this game does not work well at all and that's not surprising playing Tetris well requires some thinking ahead and this algorithm does not think very far ahead there it was pausing a game for no reason and I think the reason it stacks up the blocks like that which is the worst possible tetris strategy is that it gets three points or so when it puts a block on top of another block so this is a really bad greedy planning and let's force fast forward a bit to see how this all ends it's not good so now it's almost done and pauses the game because as soon as he unpauses he will lose and really the only winning move is not to play thank you and so I just like ended with oh this is like something that will send me before so just like put it here oh no the robbers are killing us but why it was never programmed to do this and you'll notice that there's a little bug in the code so just be careful there never do this like you can do like the Yoda assignments so just put true on the left and like this will fail and you won't kill all the humans in the world so and yeah that's it sorry I was like a bit long