Hello and welcome once again to the rugged rockhound. Today is actually a continuation of the day where we've been digging out the blue opal geodes. We're here once again with Tim and Doug. We are going to be in some old tailings from way back in the day. We're looking for some Bertrandite, which is basically a purple stone that's got all kinds of stuff in it, beryllium, fluorite, all kinds of stuff. But it's usually very pretty, so we're going to go see if we can find any. It might be a little few and far between because these are just old tailings. We'll see. So Tim found a piece that is apparently part of the Bertrandite. So it comes in multiple colors, so you'll get these tan colors and blackish and whiteish, but purple is what you're looking for. Okay this is apparently a nodule that you can they can come in. I knocked the edge of it off and in this case I don't see any purple, but that's what it kind of looks like. So that's kind of what we're looking for. Here's this one cut open. Oh, white, black, and brown. Okay, up on the hill now. Starting to see pieces all over, mostly what you're going to get is white, orange, tan, brown, black, but what you're really trying to find is purple. You can also find red, which is really nice, but it's the purple that you really want, which I haven't found yet. We'll keep looking. Here's this one cut open. Oh, very grayish. Almost a green. I think I see one right here. Yeah, that's a bigger piece. Might be better. Hmm, keep looking. Here's this one cut open. Oh, that's kind of a red down there. Yellow, brown, black. Okay, made it up here and Tim has already found some purple. There you can see some purple and he found a nice one apparently. Oh, look at that. Put that in the light. Oh, maybe in the shade. Might be even better actually. Oh, look at that. Look at the purple in that. Tim's got a good one. This one we already saw, we had broken it off here, so we decided to cut this part. Oh, not as much purple. It's still nice. Now that we're here, we're seeing little bits of purple everywhere, so there's a little bit of purple there. I was just seeing a bunch of purples. They're kind of all over now, but they're mostly tiny. So what you're trying to do is find bigger pieces, not little ones. Okay, let's see if we can find some big ones. Okay, just found this one. It's got purple in it. Nice. That's my first good one. Here's this one cut open. It broke a bit, but we got that in it. That's really nice, that purple in there. Cool. I see something hiding in the bushes.  Looks great. Here's this little one cut open. It broke up a bit, but look at that. That's really pretty. Nice stuff. This is a larger piece. Got some red in it, but I haven't seen a purple, so I may not have purple in it. Here's a big piece, and I think I see a little bit of purple in the middle of it. It's mostly black, but that might be really nice. Cool. Here's this one cut open. It fractured a bit. Very dark black, but it also has some dark purple in it. This looks like a dark piece right here. Yeah, it's very dark. It's kind of cool though. So here we have one of the pits people have been digging into. Found this on the side. No purple, but it's still a very pretty piece. Nice. It's getting purple over there. So I dug into the side a bit, and you start to see that it's when it's white like this, and it comes out. It's kind of this white rock, because you can see it. I knocked the edge off this. I don't see any purple in this one yet, but there may be a further in. And then like this one has a white look to it. So that's what we're trying to look at. So if we see things that have like a white look like that, then that's probably what we're looking for. Yeah, except this one's also not a very good looking one. But interesting. We're figuring this out. This is actually my first time ever being here. So I've been getting a few more pieces here. Found some larger ones that look like they'll be good purple. This one I'm excited for. Some of that I don't know. But yeah, some good purple ones at least. Here's this one cut open. Whoa. That's really cool. That. Here's this one cut open. Oh, that's a very dark purple. That. Here's this one cut open. More of a brownish, but it's got a little bit of purple in it. Here's this one cut open. Oh, that's nice. It's very dark, but maybe you can see it's got all those little lines in it. So cool. Here's this purple one cut open. It's very dark. It's definitely got purple. Here's this one cut open. Nice. Looks like a moss jasper. Here's this little one cut open. Whoa. More purple. Very nice. Here's this one cut open. It's like gray. A tiny bit of purple here and there. There's this little one cut open. Oh, nice purple in that. That looks like an owl. Staying back at you. Here's this one cut open. More purple and white. Cool. Here's this one cut open. Oh. Cool. Once again, dark purple. Found this on the surface right there. Saw that sticking out. Flipped it over and nicely got a little purple in it. Nice. Here's this one cut open. Not too bad. It's got purple. Some other things going on. Some moss dendrites. Cool. I believe I found some purple. Small but pretty. So Tim found a piece that looks like it says fall. F-A-L-L. Maybe an E at the end. Interesting. It's got purple on it. That's some purple in it. Looks weird though. I know. Just found that. There's some interesting colors in it. Looks more like a quartzite though. Not quite what we're looking for. And we're back to the vehicle. We just got back from finding all that Bertrandite. Looks like we got some good stuff. You guys have already seen it all cut open. I haven't yet. But it probably looks really nice. So need area. And I forgot to give you GPS coordinates. So here you go. There's you some GPS coordinates to where we parked. Come out here and get some. Until next time, there's treasure everywhere.


What's up everybody Jason for jazzymscasebreaks.com. We just sold out twenty twenty one bomb and sapphire five box break picker teams number five So yes guys, we just did number one But you guys skipped over and sold out number five before two three and four So we're skipping to number five guys. So again Also, this break includes a sapphire box that we're giving away if you buy two or more teams I remember you buy two teams to get one entry by four teams to get two entries six teams three entries Etc. If you only buy one team would not be entered to win top name wins the box ship sealed and all cart ship So what we're gonna do here guys is we're gonna Alphatize and we'll do the randomizer at the end of the break So good luck These look very nice sapphire is just something else And again everybody here is picker team number five again So if you looked at our break schedule, just to quickly show you guys We just finished number one. We're going to number five now And then we'll do four after some sweet way for soccer and then we'll be doing two and three later on this afternoon So there you go, everybody. So again TJ Tom Jones got Dodger's last spot mojo There you go, and we should have some of these available for personal. So if Nick hasn't put them up already Unless like I said, we were using all of our our inventory for Breaks today, but I assume that at some point we might put some up on the personal side Same thing with you a for Euro software. I'm sure we'll have personal to that too And again guys, we are gonna paint even top-load all the firsts and of course any autographs or color cards We get for the rest We will be penny-sleaving the rest of the cards at least keep the cards protected from any surface scratches that they may not have already Just in case So everything will be at least penny-sleave, but all the autographs Bowman's first and Numbered cards would be top-loaded open all five boxes first So again after this guys, we have you wait for Euro soccer two-box hobby break right on countries number one And we'll skip to some sapphire again And then we'll go to some cup and then the rest of it should be sapphire up until Joe gets here Again these packs just rip so easily. It's amazing. I love it The sounds of the packs like I can honestly Somebody should put this like on the calm app I don't know I Can go to sleep to this Very peaceful to me Like the other day I seen an ad for the calm app. It was so funny like Don't get it wrong. I mean maybe I'm weird that I want to listen to this as I go to sleep, but There was a video that they posted up that it was a guy cutting like Some sourdough bread or some kind of fancy bread with a knife and you can hear like how rough it sounds onto the knife I was like, I don't know if I want to hear that but I guess I guess that's what it's part of Austin Wells, Mick Abel, Simon Woods Richardson, Ronnie Mauricio, Bobby Wood Jr Colton Wilkner, Kirby, Eustinial Diaz, Dylan Dingler, Rosario, Heston Kerstak, Kevin Alcantara And our first autograph is Brainer to 99 92 out of 99 for the Boston Red Sox going to Logan Gorman Bailey, Kowar, Jordan Adams, and Kevin Alcantara to 99 And a blaze Jordan right behind it as well. That Yankee one is a really nice one PJ. Look at that man You have the Yankees in this one How about that? Alcantara is a big prospect in this 2021 Bowman Sweethead brother base All right, I gotta say guys, we'll Penny Steven topple this a little bit later And it's going to box two Blasovic Thompson a lot of blaze Jordan in these first couple boxes. Very nice Jake Willowville first Freeman Montero Robinson Liberator Stott Robert Hassel Marsh Addison Reyes, and we got Trent 24 to 25 for the Angels it's a little orange Signed as a free agent from the Bahamas Born in 2000s a little youngster there 21 Nice break a nice hit for the Angels Charles with that one the over Sanchez, Romo, Kovacco, Abrams, Jared Kelly, Junior Severino, Meyer, Jarvis Casey Martin, and for the Dodgers Jake Vogel It's a nice one green Xavier Howard, Alec Thomas, Wander Franco, Feliciano, and Corbin Carroll So far there's that along to the third box Allen Thomas, Wander Franco, Ivan Johnson, Vernon Davis, Garcia, Casas, Amaya, Nailer, and Jonathan India to 75 Cincinnati Reds Jason Dominguez, and Mariel Bautista Another one for the Reds It's going to Raven Denna, Manoa, Josiah Gray, Riley Green, Morrell, Rodriguez, Antonio Gomez, we got Trent and Wander Franco to 25 for the Tampa Bay Rays Raymond with that one 44 to 125 Ed Howard, Ramos, Gonzalez, Carson Tucker, and Dax All right, the next one Luis Garcia, Casas, Allen, Cabrera, Jordan Walker, Bo Nailer, Vernon Davis, Rose Lewis and a Jose Tenna to 99 first for Cleveland What an eye Blade, Ramirez first and Jared Kelly for the White Sox Going to DePorter I go DePorter Nick Maitan, Antonio Gomez, Hancock, Cavalli, Morrell, Esmalmena, Gonzalez, Carson Tucker, and Marco Luciano to 75 Zyphene, last box guys Jordan Walker, Bo Nailer, Brennan Davis, Starr, Gray, Alvarez, CJ Abrams, Maximo Costa, Manoa, Callahan, and Dylan McLean Bowman's first for Texas, Texas Rangers Going to EA Jarvis, Christopher Morrell, Antonio Gomez Xavier Howard, Cavalli, Carroll, Nick Gonzalez, and Ryan Vallejo to 125 for the Colorado Rockies Colorado Rockies at I, and Mario Feliciano All right guys, and there you go Oh sorry, did I forget this? Bobby Whit, and Alexander Ramirez, sorry Now there you go Again all the first will be top loader and everything else will be at least penny sleeved I gotta go grab some top loaders really quick but there's these three hits right there that I'll top load right now And then of course quick little recap before we do the giveaway So Tanna, Kelly autograph, Luciano Orange, Veen, India Orange, Latista, Water Franco, Green to 125, Dominguez, Blaise Jordan first, Trent Orange to 25 first, Autograph Bobby Whit Jr, Make a Bell, Heston Kirstad, Kevin Alcantara, Blaise Jordan, Brainer Kevin Alcantara to 99, Jake Vogel, and Water Franco again right there So I appreciate it guys, this was Bowman's Sapphire number five Again guys, we'll come back soon and break open number four And then I believe three and two also sold out So let's switch scenes So again coming up next guys is you UEFA Soccer But again, let's quickly switch scenes here And I actually messed up this time frame, I was supposed to be supposed to say 245 I might have to push this back a little bit really quick to 310 Just to be safe But again if you buy two or more teens you have a chance to win a free box So again we offer the test guys our names Anthony, Doe, Anthony, Lou Both of you guys have got an entry Charles, Daniel, both have two D-Mac has two D-Porter, EA with two, I with two John Paul, Josh X-Line with two Logan has one as well PJ you got four Raymond with four Richard Miller with two Those two entries and one entry And TJ just with one right there So out of the possible 15 we got 13 So switch scenes Roll the dice Got ourselves a four to four eight the hard way Customer name and number one gets the free box One Two Three Four Five Six Seven Going to Anthony After eight times There you go Anthony congratulations man So again appreciate it guys this is break number five Coming up next is Little UEFA Soccer Which actually just released today And then we'll hop back on to Bowman Sapphire number four Then to that we'll do some cup And then we'll go to two and three And then by then we'll figure out what else is coming up next But again Joe will be back here guys at about 5.30 And then you have late night wish on tonight Appreciate you guys Jaspiescasebricks.com


Hello friends, today we are going to see merge join. So, learning outcome for this session is students will be able to analyze the cost of join operation using merge join algorithm. So, previously we have all seen nested loop join and block nested loop join, today we are going to see merge join. So, this is used to compute the natural joints and equi joints. So, consider that we are having R and S 2 relations and the of course R intersection S will give you the common attributes. It is assumption that both the relations are sorted on this common attributes while performing the join operation. So, as you know equi joint means it is of the form R dot A is equal to S dot B. So, where A and B are the set of R are the attributes or set of attributes from the relation R and S. So, if you will consider these two relations student and text now here ID is going to be the common attribute on which we can perform the join operation. So, let us look at the algorithm how merge join works. So, it is assumption that again both the relations are sorted on the common attributes on which we are performing join operation. So, PR and PS are the pointers to the first tuple of relation R and S. Now, this will be executing the join operation till we are not reaching the end of file or end of relation. So, in this what we have to do is that we have to first read all the tuples which are pointing by the PS or for all the tuples from S having the same value for the common attribute. So, assume that ID is a common attribute then we have to read all the records of ID of relation S having the same value for ID. So, that is what it is that take the first record to the set S then go on reading that records of relation S till the value for this joining attribute is same. And once we are get we have found all the tuples having the same value for that join attributes then we will do this next comparison with relation R. So, once we have gathered that particular records in set S then the tuple which is pointed by the point of PR will be compared with all the tuples which we have gathered in set S. And those we are satisfying the conditions that will be added to the result. So, first record will be compared if it is then second record till you the value of the join attribute is same as the value of join attribute in set S. Once you find the mismatch then we have to go for reading the next tuples from relation R again sorry S again and again start the comparison. So, this is what algorithm is you can go through this algorithm and we will see with this algorithm with example. So, consider again two relations one is employee and the second one is department. So, common attribute here it is department ID. So, as per the algorithm this PR and PSR pointing to the first tuple and this is a TS which is pointing to again the first the TS is nothing but the first tuple which we are taking to the set. So, first value that is 0 1 has been taken. So, the next pointer is TS 1 which is pointing to the next record of S. So, as we have seen we have to bring all the records of relation S having the same value. Since department ID is a primary key for this department relation we will be getting only one record in set S since 0 1 and 0 2. So, 1 or 2 are not matching. So, S will have only a single record and then we have to start comparison of this record of R with this S. So, first record is compared with this and this department ID is 1 this is also 1 and that is why this record will be added to the result. Then go for the second record again the department ID of the second record is 1 here is also 1. So, second record will also be added to the result then then it will be moved to the third record. Third record is having again the department ID same as what we are having in relation sorry set S and that is why the third record is added. Now, the fourth record is having this department ID 2 whereas, we are having the records of department ID 1 that is why we will fetch the next record from relation S. So, this S this is the first record which was pointed by TS that is going to be 2. Again there will be only a single record since it is in primary key. So, this is now compared with the again records of relation R. So, this is 0 2 and this is also 0 2. So, this is going to be matched that is why this record of this 1004 nilish 2 and cells will be added to the result. Next record is having again the department ID as 2 which is same as what we are having it in S. So, it will be added and this next record will be the values having 3 which is which is not same as S that is why we will move the next pointer of this S or we will fetch the next record of S and here it will be now 3. So, again comparison will start. So, this is 3 and this is 3. So, record of this will be matched then again the next record of 1007 having department ID 3 is going to be matched then the next record is also having the department ID as 3. So, this is going to be matched now this is 4 and this is 3 which is mismatched that is why we have to read the next record of this S. So, once we have read this again it will be matched and the record will be added. But now the next record or the pointer there is no record for the record in R and that is why this algorithm will stop though there is a record in this department and thus we are getting the result of this join operation using merge join. So, here we have considered employee as an outer relation and this department as inner relation. So, let us switch this relation sequence. So, now department consider it as an outer relation whereas, this employee as an inner relation and then we will see how this algorithm will work. So, what we have seen is that first fetch the records having the same ID same value for the join attribute join attribute is department ID. So, first record is 1, second record is also 1 department ID. So, that will be again taken into the set S. Third record is also having the same value that is why it will be also taken into S. In previous scenario department ID was a primary key and that is why we were getting only one record. Now here employee ID is an primary key whereas department ID is a foreign key and that is why we are getting three records. Now this is two which is not same as one. So, we will stop fetching the records of S and then we will start comparing the records of R. So, first is compared with this one and the record will be added as an result. So, again the first will be compared with second. So, since there are three records now pointer is not moved. So, first record will be compared against all the records and the third one will be this. And then now once we have compared all the pointer will be moved and this value is 2 whereas this value is 1 and that is why the next records of relation employee will be fetched. So, at this moment stop the video and try to write down the next records or the sequence of records that we will fetch in relation and we will get the result. Yeah of course, this is the value 2. So, these two records will be brought to will be written to set S and these two will be compared with this record pointed by PR. And these two will be compared and the result will be against both the records will be taken into result. Then this is 3 and this is 2. So, this is mismatched. So, this 3 will be brought to main memory. Then again this 3 and all these 3 records will be compared and will be added. Then the fourth record will be read and this will be matched and then it will stop because there is no further record in employee. I hope you have understood that how more join is working. So, what is the requirement here is that each set SS of all the tuples with the same value for join attributes. So, it means that in this scenario this is the set SS should fit in main memory. And if it is not fitting it in main memory, then we have to perform block nested loop join for comparison. Okay? If either of the relation is not sorted, first we have to sort it on the joining attribute and then only we can perform more join. What about cost analysis? So, since all the records will be read only once, so this is going to be the result as the same as number of blocks. So, number of block transfers will be sum of block transfer in both the files. So, it will be BR plus BS. So, if BBB buffer blocks are allocated to each relation, then the number of disk will be BR divided by BB plus BS divided by BB. So, if either of the relation is not sorted, of course for sorting that relation, the cost or the cost required for sorting that relation should be taken into consideration. But since we are having a single pass through both the files in this merge join, merge join request is more efficient as compared to other join provided both the relations are sorted on the joining attribute. So, consider our same example which we have seen for the other join operations that is the records or the student and text relation here. So, block transfers, so students since containing the 400 blocks and text is containing 100 blocks, I think it is a reverse one. So, student is containing 100 blocks and text is containing 400 blocks, total required is 400 block transfers. So, SICK operations currently we are assuming that one buffer block is allocated to each input relation that is why total SICKs will be 500. So, SICKs are more and but the block transfers are very less in this merge join. So, you can compare the result of all these three joins looking at you can see the result in various join operations over there. Thank you.


Christians and demon possession part 6. Are there any verses in the bible that show Christians having demons cast out of them? What about the crowds to which Philip preached? Acts chapter 8. Crowds listened intently to Philip because they were eager to hear his message and see the miraculous signs he did. Many evil spirits were cast out, screaming as they left their victims. Some might misunderstand this to mean that the crowds were saved before undergoing exorcisms. But, as with any scripture used in an attempt to teach that Christians can be demon possessed, a little observation causes that idea to completely collapse. The bible doesn't say that everyone in the crowd was saved. It tells us that they listened intently to what he was preaching. But this doesn't mean they were all born again. So, Acts chapter 8 is not an example of Christians having demons cast out of them. Follow for part 7.


If the price is much lower than what the rental market is asking right now, it's probably a scam. Rental scams, what they're basically doing is they're going on to the rental websites. Cut and pasting the ad and making up their own ad, reducing the price to where it really looks like a great deal. Making a property that usually rents for $1,500 or renting it for $800. And you're getting people that, well, a better jump on this is a great deal. Without even going to see the property, they may drive by it, but they don't ask to see the inside, which is the number one mistake. So they get in contact with the suspect, the suspect sends them a rental agreement. The victim's looking at it, looks real, they're signing it, emailing it back. The suspect then asks for a deposit, then asks for first month's rent. Well, right there you're already into it, you know, a couple grand. You can lose money quickly on this. Make sure they have a key to the residence. Make sure you get inside. If someone won't meet you or if it's too far away, you need to pick a different option.


Let's move on to the keynote. We have a keynote by Professor Patrick Glauner from Degendorf Institute of Technology. Here is some data about him. He is a professor, an executive advisor, an expert witness for the parliaments of France, Luxembourg and Germany. He is a book author and a programmer in AI. He became a full professor at the age of 30, which, well, I'd have about a few months until that. I think I'm running out of time. Anyway, he is ranked among the worldwide academic data leaders by CDO Magazine and Global AI Hub.  So we will hear about, well, actually, that topic, how it is transforming the global economy, but also discuss the advances made in AI technology, the opportunities that AI presents for businesses and economies, and of course, the challenges that AI poses. The stage is yours. Well, thank you very much for the introduction. Hello, everyone. It's very nice to meet you, and it's really impressive to see what has developed from your two EU projects, also that you have a huge infrastructure now and all those users around the world. That's really impressive. Today, I will talk about how AI is changing the worldwide economy, which is happening right now, and it's happening very, very rapidly. There are many opportunities, but there are also challenges in terms of competition, but there are also regulatory challenges. And I'll talk about this, and in fact, also a lot of AI projects in industry, they fail, they don't succeed, so I'll also talk about some of the reasons for that and share some of my best practices. When I deal with lawmakers or board members of companies, they often struggle to invest in AI because they don't know what AI is, and if you talk to AI experts, everyone will just give you a different definition of what AI is, and I think that makes it so confusing to externals. From a very abstract point of view, I like to say AI allows us to automate manual or human decision making, and we as humans, we are really good in making decisions, and if we can speed up that decision making, we can do business processes faster, cheaper, better, and so forth. So AI is a huge opportunity for every industry. I will talk now a little bit about transformers and artistic AI. Probably you have recently seen these really interesting figures that are created by AIs. For example, one AI is called Dali, which generates such images or something like this, and you can just send a query to the AI, and the AI will start generating really interesting images. Who has seen this before? So maybe just raise your hand. Well plenty of hands, that's great. So this is now all over the internet with really interesting images, and I think this will now put a lot of pressure on graphics designers, for example, because you don't need to pay someone a lot of money, who spends a lot of time on designing images. Now an AI can do this, and those AIs have made a lot of progress in the last few years, and they are getting better and better, and I think we just look five years back that was not possible at all, so I'm just curious where we will be in 10 to 20 years, and I think in terms of content generation, that's a huge opportunity so we can generate content much better and much faster. What is behind that so-called AI revolution that we have now been witnessing for the last decade? Most of that is driven by deep neural networks. So neural networks are actually something that goes back to the 1950s, and there were different peaks and different tropes in history, and it was already noticed in the 80s and 90s if you build very large neural networks, so you have a lot of hidden layers between the input and the output, these neural networks tend to become extremely powerful because one big challenge in AI is, or in general in machine learning, that you need to describe features of your data, that most models cannot be trained on raw data, but deep neural networks, they can not only do a classification or generation, whatever, but they can also self-learn features, and that makes them so interesting. And if you put a lot of hidden layers between the input and the output, they learn increasingly more complex features, so maybe in a computer vision task, the first layers start to learn, to recognize edges, later layers, more complex things like faces and so forth, and this figure was from a Google experiment some 10 years ago in which an AI learned to distinguish between cats and humans, but that was maybe a toy project, but that has become extremely powerful, I said that was already noticed in the 80s and 90s, but in that time we lacked cheap computing power, we lacked a lot of data, and we lacked the theoretical framework for making this happen, and all of this has made a lot of progress now in the last ten years because we have more data, cheaper computing power, plus some advances in the underlying methodology. Now when we wonder again, how can we generate such very fancy images? Most of that is now based on so-called transformers, we just heard in the talk before briefly about transformers, what are actually transformers? Transformers are one kind of neural network, concretely it's a so-called sequence-to-sequence model, initially comes from text, so you can put in a text and you get a text out of it, could be used for, for example, text generation, question answering, translation, and such sequence-to-sequence models are quite tricky to build, and if you use something that is called self-attention, then you build transformers, and the transformers are something that were proposed a few years ago and have made a lot of progress, so behind all these very fancy models like GPT-3, maybe you heard about GPT-3, there are transformers behind it, but transformers are now also used for vision tasks, and you can suddenly now turn a text into a very fancy image. If you're curious to build your own transformers, I can recommend a book to you. That book here appeared very recently, it's called NLP with Transformers, it's published by Huckingface, so Huckingface is now one of the leaders in text mining, and you don't need to start from scratch, they also provide a lot of models and you can train them online, you can download them, you can push your own models, and you can just reuse a lot of functionality and then fine tune your models, and because if you start building transformers from scratch, that's a little bit tricky, you need to spend more time on it, but if you use Huckingface, you can actually make a lot of progress very quickly, and that book just appeared a few months ago, so feel free to read it. There's not so much theory in the book, it's very hands-on, so I'm not affiliated with Huckingface, but I just like their software and that book, and it's very hands-on. Now a lot of decision makers in industry all over Europe, they actually still wonder why they should actually invest in AI, it's the same with politicians, and why should you actually invest in AI and now, right now, well maybe your business goes well right now, but there are a lot of changes coming, especially if you look at China, so until a few years ago, Western companies went to China for cheap manufacturing with low requirements on sustainability and the environment, but the game has changed a lot in the last few years because China has become a massive innovator in high-tech, in particular in AI. I don't know, who of you has been to China? Just raise your hand, just a few, so my wife is from China and she's from Shanghai, obviously Shanghai is very well-developed, but the average Chinese is still relatively poor compared to Europe or the Western world, and China sees AI as a huge opportunity to increase prosperity and also to become a leader in many sectors, and if you're good in automating decision-making, you can eventually become a leader in every sector, and here in Europe we have an aging society, many experts will retire in the coming years, we have a lack of doctors and all of this, and AI is actually a huge opportunity to address that issue because we can automate decision-making and then maybe we can still proceed with less doctors and with less experts in industry. If we don't invest, China will do this and China will beat us finally in industries in which we lead right now, like Germany leads in automotive, in manufacturing and so forth, and I think that's true also for other European countries, and at some point China will then be the leader in automotive for manufacturing or chemistry, whatever. So the world is changing and I have a book that I can definitely recommend to you, on the left it's called AI Superpowers, published by Mr. Kei Fu Li, who is an investor who works in Beijing, he previously worked in the Silicon Valley. You should read that book, it's entirely written for decision-makers, but he provides a lot of examples how the worldwide economy is changing, how China is innovating. When I worked in a very large German corporation in mechanical engineering, I was the AI lead and I tried to convince the board that they should invest more in AI. When I bought that book for them and said, hey, read it, unfortunately they didn't read it, they said, oh, it's in English, we're sorry we don't understand that book. So that was quite scary and these people earn millions and millions every year and they weren't able to read that book. I think there's now a translation in other languages as well. The second book here is by two Harvard professors, which does not focus on China more on the worldwide economy and they also describe how business models are changing, how innovation is there thanks to AI. Feel free to take a look at the book as well, I can highly recommend it. Now there are these shocking figures that a lot of AI projects actually fail. We always read about really cool AI projects in the news or in academia, the sad truth is something like 80% of AI projects fail or they don't add any value. The companies start building prototypes and finally it doesn't work out. And that's obviously a very shocking number because the return is obviously not there if 80% of the projects fail. And I'm a professor but I also worked in industry before and I run my own consulting business on the side and I have collected a few reasons why so many projects fail and I want to discuss that now and also provide some advice. So when you do an AI project you can usually take one of two directions, either you add it into your products and services and you sell AI to your customers or you use AI in-house in order to optimize your value chain, so your internal processes. You can take both directions at the same time or take one before but what I often notice is a lot of companies don't know how good or how bad they are in running a specific process. So you need to measure how good or how bad you are right now with your task. So you need KPIs or metrics and a lot of companies turns out they don't have this or they don't measure correctly and then they build an AI and finally they have an AI if the project succeeds but maybe the AI performs worse than before. So you always need KPIs in the beginning and you need to measure how good or bad you are before you optimize something and that is really in a lot of companies surprisingly not the case and without the KPIs you won't succeed. Then my second advice is never start thinking about an AI project. I see a lot of companies that keep telling me hey I want to do an AI project and you find all sorts of advice on the internet how to look for AI projects in my view that doesn't work because AI is the solution and if you start with the solution often you start creating problems that actually don't exist. So start with problems, look at problems and solve them as easy as possible. Sometimes it's just a few rules in order to solve it. Sometimes you may need a neural network but always start solving the problem and maybe you just have to rethink your business process from scratch so bring in the human intelligence first and then the AI on top in order to further optimize. But AI obviously looks promising I would say if a number of criteria are there for example if a process is very expensive and AI is about optimizing so maybe you can optimize the cost. If a process takes very long so humans make decisions they need a long time maybe you can speed this up using an AI. Furthermore in a lot of companies if you ask two experts to do the same thing like you have two engineers and you ask them to calculate something they may come to different conclusions. So AI can also allow us to reduce the uncertainty and make more uniform decisions and there are obviously more criteria but if some of these criteria are fulfilled maybe AI is promising but always look at the simplest solution possible first and maybe just rethink your business process reduce dependencies and so forth and then maybe your problems are resolved and you don't need an AI. There are other reasons why so many AI projects fail. I think one reason is education. A lot of universities in their courses and programs they mainly focus teaching machine learning algorithms which are very very important for AI projects. But finally you will just spend a minor part in many projects on building the actual model and training it because you need to collect data you need to define KPIs you need to explore the data you need to build a computing infrastructure and finally you also need to integrate your AI into a bigger IT landscape and all of these tasks are often ignored in academia and then I know a lot of students they graduate and I think now as a data scientist they would be building AI models full-time and actually they will be doing this but they will be doing many many other things too so universities should also address all these other tasks and provide a more uniform education because those are the real challenges out there. Then I told you about deep learning and the deep learning now allows us to do a lot of things and deep learning is really cool but it's not a silver bullet so when you have a machine learning task always start with a simple model maybe just a decision tree or something and maybe that already gives you a 99% accuracy and you're done maybe from a business perspective that is totally fine. If it's not enough yet then build increasingly more complex models but building them comes with a lot of costs so always start simple and then become increasingly more complex as needed and deep neural networks they are not generally better than other models for certain tasks they're certainly extremely helpful like for usually for text problems or in general language problems, vision problems but they're not in general better than other models so that's very important to remember and I often see companies they start building a super big neural network and finally they're not happy with its performance yeah maybe they should have just built a decision tree initially and maybe that one would perform even better so build increasingly more complex models also use auto ML whenever you can don't start from scratch building new architectures only do this if you really have to do it but for many machine learning problems you can just use auto ML which means automated machine learning so you have a lot of different models at hand and you as a human you don't need to try them out all manually a computer program can do for can do this for you so you just say I have a supervised learning problem I feed in my data in my labels and actually I don't care what model in the background should be used just find me the model that works the best and find me the best pre-processing technique and their libraries like auto SK learn which built on top of scikit learn in Python and they do that work for you but they don't do naive trial and error in fact internally such libraries also use machine learning in order to optimize your machine learning problem so they do a few shots using machine learning in order to find you the right machine learning model so has any one of you ever tried out something like this auto ML if so maybe just raise your hand I see auto ML as a huge opportunity because it also reduces training training time for experts so maybe if you go into engineering they are very smart engineers and you just don't want to spend five years on training them to become AI experts because they're domain experts so enable them to use auto ML auto ML may not necessarily give you the best model but it will certainly give you a very good model in a very short amount of time and you don't need a five-year education in AI in order to start building AI so I think that's something very very positive now for the last few minutes I also want to talk about legal challenges around AI it is obvious that there are new legal problems because of AI and lawmakers are assessing this right now extremely critical or from a very critical point of view take a look at this one here the moral machine it's a website has anyone ever seen this if so maybe also raise your hand okay some know it so you're given a scenario there's a car and you have pedestrians and now someone will die either on the left the pedestrians on the right the car will bump into the wall and the people in the car will die you need to pick and you're given 13 questions like this each time you get new scenarios generated and then you get a statistic for example if you prefer to save all older people younger people there can also be rich people around homeless people around so you need to choose who should survive from an ethical perspective that's extremely tricky and there's not that one ethical framework all around the world here in central Europe we may prefer to save younger people whereas if you go to China there's a particular respect towards the elderly so they would maybe rather save older people is it better to kill two or three people if you have to pick that is all very tricky and lawmakers are thinking about that right now because they want to come up with new laws that finally will be implemented in autonomous cars so this is a very tricky question where I talked about GPT-3 you can use GPT-3 to build your own chatbot and someone came up with that scenario and wrote to the chatbot hey I feel very bad I want to kill myself the AI says I'm sorry to hear that I can help you with that should I kill myself I think you should so new questions about liability will arise eventually now the big question is how do we address such legal questions the European Union has proposed an AI act last year so an AI act would be a AI law for all over Europe or all over the European Union was proposed last year has anyone ever heard about the AI act so just a few and it's more than a hundred pages of very broad very nonspecific language and the people who wrote it frankly they don't really know about AI so they require that in high risk applications you need a perfect data set a bias free a complete data set absolutely bias free otherwise your AI would be illegal and what is a high risk AI well everything from which you could get some theoretical damage so even a Google search would be high risk eventually because you could get a mental collapse just from the search result so the AI act I think has a very good intention but it just doesn't work and if it becomes a reality we will finally stifle innovation in Europe something we also see with the GDPR the data protection law here in Europe had a very good intention but puts an extreme pressure on SMEs and finally makes it very difficult for small and mid-sized companies to innovate and finally the GDPR in Europe made Google and Facebook and Amazon even stronger even for it was meant to limit their power but made them even stronger because they are only able to put thousands of engineers and lawyers on these challenges and small and mid-sized companies are not I have advised a number of polyaments on this topic so polyaments invite expert witnesses for such hearings last year I advised the German Bundestag and the French National Assembly when the European Affairs Committees had a joint session I advised the parliament of Luxembourg this year and just on Monday this week I was again in the German Bundestag and there was a big public hearing about the AI act in the Digital Affairs Committee and I also gave a speech there and spoke about the pros and cons of the AI act but the fact is if the AI act is ratified in its current form it will kill AI innovation all over Europe and that's certainly something we should not let happen if you're curious about that whole topic you can check my website clowner.info and up there I uploaded the written evidence I wrote for the German parliament last year and it summarizes in four pages what seemed to me to be the key issues of the AI act it's written in German but there's also an English translation on my website I probably have one minute let me check the time yeah still one minute I want to talk about quantum computing so we see and you also mentioned we're at the end of an innovation cycle and I think this also applies to many parts of AI because we build ever-large models we build ever-large GPUs but finally I think we could do so much better and quantum computers are now a topic that have become more important in the last few years I think they're still quite a bit away from large-scale commercial application but they are coming and they're making progress and they're getting bigger and they're becoming cheaper and at some point they will be commercialized more and they're also machine learning algorithms that have been proposed for quantum computers so quantum computers were completely different to our traditional computers our traditional computers think in terms of bits so on or off exclusively you're in the one state or the other with a quantum computer you have so-called qubits and you can be in both states at the same time and that can give you in certain applications an exponential speed up in terms of the computing time it's a very interesting topic and there are machine learning algorithms that have been described that take advantage of these quantum effects and they could allow us to train models substantially faster and with much less data so that's certainly something we should keep in mind in the next few years because quantum computers will be commercialized more and this could really help us to speed up or make major progress in AI in the coming decades thank you very much if you're curious to learn more about all of these topics I've published three books that cover those topics among others all around AI for free to take a look at the books maybe to give you some new insights thank you very much


Today on the breakfast, new guidelines of the all-progressive Congress, Giva Meci and Gigi Adler, 72 hours to resign or forget 2023 ambition. Just where does the pendulum swing? Also on the breakfast, it's time to show integrity. The National Drug Law Enforcement Agency and DLEA urges political parties to make drug integrity tests part of the screening requirement for aspirants. And we'll be reviewing the biggest stories making headlines across the national days. Welcome to the breakfast this first day morning. I am Justin Agadone. I am Messier Boppo. It's good to have you join us this beautiful Thursday morning. Yes, it is indeed a pleasure to have you join us. How is the weather at your end? Here in Lagos, it's been a bit chilly. It's been raining since last night. Although it has stopped better, some places are experiencing some drizzling. Messier, how was the weather around you this morning? I really don't know why people start conversations saying how's the weather. What's with the weather? Because it is a welcome change. Lagos has been crazily hot for some time. So with the rain last night, I'm sure some people slept like babies or logs of wood. While some people slept like logs of wood, others probably would have their roof taken off or maybe in the floor of their car drowned or something. Messier, are you talking out of experience? No, I'm just saying that everywhere is flooded. You know what it is. A lot of people, when it gets to this point in time, maybe we'll just stick with the dry season and just stay with sun and all of that. Because whenever it rains, it's usually not a fantastic experience. I mean, just the last rain before this one, it was really a disaster. Where you had a lot of people saying houses were destroyed, you know, floored and what have you. But we need to be on top of the game.  We cannot wait for the government all the time. So some people would say, oh, you have the markets floored, you have everywhere blocked, you can't. But every day, it just shows you that human activity is responsible for all of this action. So the gutters are clogged. Thank God you said thank goodness. Yes, that's it. We are responsible. Mercy, we need the rain for so many things. Mercy, you don't want the greens. You don't want to eat. You don't want vegetation and all of that. And all that is because of the rain. If you say it doesn't, you don't want it to rain for one year just because of floods and every other thing.  Why do you know how many people are going through a lot? Yes, but that's the more reason why we should not throw things into the canal and free up our drainages. I know a whole lot be hopes on the citizens to do the right thing. But then the rain should rain. Let's move away from the rain. And the flood should take people's houses away and people should just perish. Okay, let's slide on to top trending. The rains are not trending right now. Burnton, the education minister module is trending. There are reports that he is speak top. He's a presidential firm for 100 million. And he has gotten a whole lot of Nigerians talking. It's all in the wake of the ASUS strike. Remember, ASUS has been on strike since February the 14th. And a lot of people are saying, what does this really tell? He is the education minister. And right now his main thought, his main business right now is to contest for presidency in 2023 when our students are at home and lecturers are just there. Seating with the federal government, not meeting up to the demand since 2000. And how do you raise all of that? First of all, we go back to the point where we say everyone has a right to contest. Because the constitution allows everyone the opportunity to vote and be voted for. So yes, within their rights, they are acting not contrary to the law. And that's it. Otherwise, the other restriction would be whether or not you're a criminal or you have any criminal records. Which would be the responsibility of the police and also the judiciary. So it's a long conversation. But however, it's interesting. But I don't understand what's going on. If you notice, there's a pattern. And a lot of people would just say it's a conspiracy theory.  He's also declared yesterday. I'm not partisan now. Of course, I don't belong to any political parties. But you declared independent candidates yesterday. No. The constitution hasn't given all of that. No one listened to me. That's number one. So let's forget about it. The ambition is dead on arrival. So the point is... I'll vote for you. Would you listen to me? Sometimes. No. Both alone cannot make me go anywhere. I'll gather my constituency, my interview. Okay, let's move away from that. The major thing here is... I don't know what's going on. But if you look at the pattern, especially with the APC. You found out that every minute now there's going to be a primaries for the presidential ticket. I mean, for the presidential primaries on the 30th and the 31st. So it's going to be 30th through the 31st of May, right? Am I correct? Yes, next month, yeah. And then you still have a lot of people, persons coming out. So yesterday we talked about the governor of cross river states. Now we have, you know... Education minister. So what's really going on? But normally if you look at how this goes is if you have a lot of people come out. For instance, at the end of the day, you have the elections. Then you have Justin, Messi, every other person out in the race. You know what happens? We begin to split the votes. Okay. I don't know if you get it. I do understand you. So I don't know if this is a strategy at the party level. Because there's going to be splitting of votes. So maybe just one vote will get someone there. It's... You have to split it. At the end of the day, the party will decide on just one person. No, no, no. But the APC is not saying that they're going to... It's not a consensus issue. No, what I'm saying is that even if they all declare interests and they have bought forms and paid 100 million, eventually it's just one of them that would actually in a flight... And that's because it's still possible that within this, okay? Within this particular space, you have the fact that we want you to become the flag bearer. You want me to? But because... No, I'm just citing, for instance, now. We want you to become the flag bearer. Yes. But because we cannot come out to say we want consensus, and that's because we're going to offend a lot of persons in the party and also other regions. And so we just say, let's leave it open. So we leave it open. So we leave it open. And then we have a lot of people come out to say, hey, we're also... We want to become president. Not necessarily that they want to become president. Because I don't understand. They know they cannot win the... They cannot become the flag bearer of the party. But they come out. So why? What's the intention behind all of this? So it's going to split the votes within the delegates. It will actually do that. So you probably would have a group of persons who would say, okay, we want a certain person to become the flag bearer. And all of the attention will be pushed in. And these conversations are usually not... I get all of that, but mostly the concern for me here is that those who are responsible to govern the space of the country are no longer doing that because of the interest group thing. We're even going to discuss that more later on the show. The APC New Guidelines and the ministers and those who have political appointment being asked to resign. They have better like three days to do so. So they can actually decide if they want to contest or if they want to stay in government and face the business of governance. For instance, we have asked to be on strike for over two months. And discussions have been stored over time. I know that he has the constitutional right, the minister that leads to contest for the presidency. But the fact is that I feel it's morally wrong, although morality doesn't really count in most cases in Nigeria. Morality is not law. But the students are at home. Letters are not teaching. And the content right now is never about how to fix this promise about your personal ambition for 2023. Maybe he thinks that when he gets there, he's going to solve the problem. Why doesn't he? He should do that right now. We probably might not just be within his entirely not in his space. But he is the head of education in Nigeria. So everything be hopes on him. If lecture is on strike, first of all, everything points to his direction. So everything points to his direction. You also cannot forget the fact that if you look at it, you ask yourself, what's the budget for education? That's also another issue. So as much as it might feel like he's a king in his space, he's not really a king. But not holding brief for him, it is what it is. Politics, I mean, interests, no permanent friends, no permanent enemies, no permanent interests. And that's what it is. But if you look at the APC, and if you look at the fact that every minute, I mean, just before we leave this conversation, we might just have another person who's going to be declaring. I won't be surprised. They are interested in coming. They have barely three days to do that, because from their constitution, they have to do that 30 days before the primaries. But let us move on away from the APC, away from those who are declaring their intention to run for the presidency. You'll docheies in the days. In as much as I don't like discussing people's private business. When it comes to private business, we talk about it. Justin, you don't need to become very organized. She's organized. Yeah. I have my dogs in the room. But the thing is that phynaetograms are coming at him because of what he said about his wife, or his first wife. Right now, what we hear is that you'll docheies announcing on his second wife. It was everywhere. And he talked about his son, that he got through his second wife. But what many people were talking was that there was a time he was just pouring a whole lot of praises on his first wife, how the woman stood for him for like 15 years and all of that. And why? Mercy. The story has changed. You're smiling. Because there's a lot to talk about. I mean, a young man is this Yola Doche. A lot of people have respect for him and not also looking at the fact that he has come out to political ambition. Right? But you know, in Nigeria, we have not really incorporated the issue of, you know, your personal life. I mean, that into your political chase and race. In all the climes, it would be a very big deal. It would be a big issue.  But let's just continue to say that we're a developing nation and also our democracy is very nascent and so we have a long way to go. But this isn't public space. You know, it definitely means that it's going to be a public conversation. At the time where he had constantly talked about his wife, 16 years. I mean, he mentioned on that particular tweet the best thing that has happened, and she's never stressed him out. But trust Nigerians to do that all of this. No, no, no. I mean, how do you even explain? I mean, look at it now. How do you juxtapose all of that?  The best thing that's happened to you, 16 years. She's never stressed you. And then two seconds after, this 2022. So we have you saying you have a second wife. He also has a tweet. The good thing is the internet will never forget. Tweeter will never forget you. All of your tweets, including my tweets. So sometimes I'm very careful about the tweets that I actually put up. Because, you know, this space for them. I put out tweets. I tweet, boy. You know how you can do tweets. You know how you can put out tweets and 20 years after from now, the tweets will come back and haunt you. True. So people have actually gone back again to that tweet where he tweeted about.   It's not that you were a man. It's the fact that you are breeding confusion for the next generation. Where did we hold you now? There's no correlation. Any people say there's no correlation. Where did we... Oh, no. You know, but the wife... I don't know what to say. I mean, it looks to me very confused and tired. You know how it is in an African society tradition. A man can decide to marry as many wives as he so wishes, so long as he can. Sometimes, he doesn't even have the money to take off the wives that you have, for a wife like that.  A wife who wants to go on vacation. It's a lot. So you have to get... No. But the wife has actually commented, right? Yes, she has.  No, no, no. I'll just listen. So he tweeted that. He said, my son, everybody meets my son. I'm paraphrasing now, and I can't remember what he said. Everybody meets my son. You know, it's a fantastic grade. My second wife, okay. Where is this coming from? And the wife went ahead to say, may God judge you both. That's a lot of fire. So what do you actually infer from the wife's comment? She's not happy? She's bitter? Does that sound like she's happy? So she's bitter. Doesn't she have the right to be? Does she have the right? When you say bitter, there's something like bitter. She's not bitter. I mean, there's a lot of betrayal there. Don't tell me bitter. There's something bitter about that. Who betrayed who? Oh, Justin. Who made the spot? Why are you doing this? Well, it's quite unfortunate. But I think it's just a human thing. We're humans, and so we're bound to be, you know, on this side and this other side. And you can never tell what would happen.  Whatever comes, it comes. Uwisha Yoledoche and his family are all of the best. But we'll slide away from all of that and move on next to the vice president, Professor Yemi Shiba Joe, who is saying that he owes no allegiance to anybody. But of course, I'm the owes of office that he has taken. You know, there's been so much talk since the vice president declared his ambition to contest for the presidency in 2023. A lot of people, you know, have come out to say that he has betrayed his principle. That's the former governor of Lagos State, Bola Ahmed Tinibu. And all of this came to the fore just yesterday. And once again, the vice president said, you know, categorically that he owes no one any allegiance, anything outside his office. I mean, he couldn't really be bothered. So the question would now be, is that a statement of fact? Mercy, who does he owe allegiance to? Do you think that's a statement of fact? Does he owe allegiance, you know, except to the people that he's wanting to protect? I'm asking you again. I also chew it back again. Well, you need to know that politics, like you have rightly mentioned, we also understand that when people come together, there's interest, personal interest is number one top on the notch. And so people would always have permanent interest. The interest would never change. And so they can have affiliations. You know, they could have friends, but these friends would actually always change. And so that's why you say, you have no permanent friends, no permanent interests. Oh, okay. No permanent. Permanent interests. Permanent interests, but no permanent friends. So it means that friends could always change. So the fact that I'm friends with you today might not necessarily mean that I will be friends with you tomorrow, but my interest would always be constant. And so if my interest is constant, then anything can happen. I can decide to have another affiliation with another. But it's a lot. So with this, the reason why sometimes I think that we need to take a break, not so much, but because this is actually going to be within the political arena, the conversation would be within the party level right now. Because with all that everyone will do, I mean, if you look at the list of persons who are contesting now, who wants to become the flag bearer of the APC, the list is almost endless. So you have the former governors, you have governors, you just have a lot of people on that particular list. But the party will decide, and that's what's going to happen. Right? So it is when the party moves there, at the end of the day, the party has a decision, then we know we have a flag bearer for the party. Nigerians will definitely decide whether or not the one, this particular candidate. So it's okay to have all of this concerns and all of that. But let's also understand that at the end of the day, even when nations come together, you have the United States, you have Nigeria, you have Ghana, you have Germany, you have Japan, they would always act in their interest. National interest and personal interest would always supersede, you know, every other interest. And that's why you constantly have the fact that policies don't really reflect the interest of the people, but reflect the interest of a certain. So maybe the elites who were supporting him in his clan and all of that. Yeah, like, speaking of supporting, all of this came out from what happened, his visit to Oregon State and how the governor, Dr. Babi Odu, said that he has their support. I don't know if he was speaking for himself or the entire people of Oregon State, but then the vice president actually was quoted as saying that he has served Buhari, that he should be the next president. It doesn't really work like that. It really doesn't. Is that a continuum?  Let's take a, for instance, I mean, let's take a look at Artiku. You remember that Artiku and the former president, Ulushiguna Basundura, you know, he was the vice president. Like you have said, it does not really follow a complete logic, you know, just because you have served with your principle or the president, that's Mohammed Buhari, that you deserve next. It still comes back to some people saying entitlement and all of that. Does it really follow? So is it like a pattern? Just because I'm not trying to, I'm just trying to understand what he meant by that he has served the president, so he deserves, you know, to be the next. Because a whole lot of things come to play. So I think that that's a comment and sentiment. I mean, it's a very sentimental comment. Okay? So it's like saying, because I am a graduate, it therefore means that I have a job. Are the best of jobs really? No, not necessarily a best of job.  That's not even, you know, that's not even a criteria for it. So because you have graduated does not mean that you should have a job. You have to work for it. I mean, we have a lot of graduates who are out there who don't have a job. So there's a lot, I mean, a lot of work.  Now, and if you look at politics, like we see all the time, politics goes beyond saying you have a beautiful face or you're very popular. And then you say, hey, I'm here. I want to become president. And that's fantastic because I see a lot of people who say they want to become president. And sometimes I feel a little bit, you know, what's going on? Do you think that the office of the president is a joke? But because the Constitution says we all have a right, then everybody has a right. But it goes beyond that. There are a lot of issues that are involved. And if you look at the peculiarities surrounding our own climate, we talk about money politics. It's very big. We don't need to be very pretentious about this. Let's not begin to say, oh, it doesn't really happen. It happens. Money changes hands in our politics. And let nobody begin to say, we'll come up with evidence. We know all of that. That's what happens. So you begin to ask yourself, do you have the financial muscle? Yesterday we had a presidential aspirant of the People's Democratic. But then he mentioned the fact that in 2019 or there about, he could not stand a chance because he didn't have the money. The question is, do you have the money now? So money plays a major role. So you ask yourself, do you have the money? Let's even forget it also. Let's not talk about that. But that's the reality. On the other hand, you also need to talk about structures. Elections are not just one by saying, oh, you want to be President. Fantastic. You have worked everywhere. That's not, that's not it. So if you talk about a certain political party, you have the fact that you have 774 local governments across the entire federation. What structures do you have in this local government? So if you have the old, because you see the hype on Twitter, it's okay to have young people go on Twitter. But how many young persons have the PVC to vote? And mostly you go because I have monitored elections at the grassroot level. And I tell you the people who come out to vote, these are older people. These are very old people. Now do they recognize because it is what it is. So then look at the sign. What sign is this, the logo? So how do we identify this party? And they are being told when you see a certain party, this is a certain party.  Do people recognize it? Let's not even forget that poverty is very strong for us. It's a major thing. And some people, I've also taught that politicians have weaponized poverty. So poverty has become a tool where they constantly ensure that people stay poor because when you stay poor, you don't understand the dynamics.  All right. It is actually something. But let's move on to our last and top trending for today. You remember the terrorism Act of 2013. All right now, the Senate is actually amending its mercy. And right now they are saying that there should no longer be payment for ransom to kidnappers. There are several angles to this because a lot of people would say, remember all the kidnappings that we have had in the country, you know, in Keduna, in Niger State, when specifically the governor of Keduna State had said that they would not negotiate with terrorists. But all behind closed doors, what we don't get to hear is that what actually went down, how did these people secure the release of those kidnaps? At the end of the day, monies have changed hands. But so we don't get to hear this. But what is here is that they would not negotiate with terrorists. And sometimes the parents of these kidnapped victims because they are very worried about their children, you know, they do negotiations for themselves because they feel the federal government or the state government are not really doing the needful. So they take the bull by the horns and they negotiate and they contribute and they pay ransom to these kidnappers. I wonder what would happen with development? Well, it's a very dicey conversation and some people will say that the senate having to ban payment to ransom to kidnappers, it's very apt for us. But let's even look at it now. This is actually coming at a time where you have, you remember the fact that the trains were attacked. You remember that train kid attacked that happened. Yeah, but you don't have your train. Yes. And so you have passengers who were kidnapped and so payment for ransom. The government over time have said we don't pay ransom. People, the arguments, we've tried to say, hey, we're not, we don't negotiate with terrorists. We don't pay ransom to kidnappers. But is that really the true state of things? But this is coming at a time where this families, victims, you know that recently you had all of that report and so you still have some persons who were still in the hands of this kidnappers. The kidnap that happened. And so the families are already saying that we're ready to embrace negotiation, whatever it is, we're willing to pay, you know, to ransom to get our loved ones being freed. So what's the way out? What's the solution? Will these people be able to, I mean, what's the solution the government's providing? The government enough to actually secure the release of their family members? Exactly. I mean, it's also to the point where you have families saying, okay, we're ready to have this negotiation with kidnappers and to ensure the release of those who have been kidnapped. It's a lot. But let's see how it is. We've not been very, very, very, very great with laws and implementation of some of the loss that we do have. But fingers across, we'll see how all of this develops. That's the much we can with more interesting conversation, generating reactions in different spaces. In the meantime, we will step away and when we come back it'll be time for us to look at the front pages of a national daily splicing with us.


Citizen science is a growing field of research which allows members of the public to participate in scientific research alongside professional scientists. In the case of hydrology, this can involve collecting data related to water levels or flow rates, or even helping to monitor water quality. Citizen science can be used to supplement existing data sets, providing additional information about specific areas or phenomena. Additionally, it can help to increase public awareness of environmental issues and provide a platform for engaging citizens in the scientific process. This article was authored by Watter E. Bytart, Watter E. Bytart, Watter E. Bytart, and others.


Uganda has begun the distribution of the third batch of the COVID-19 AstraZeneca Vaccines it received from the Global Covax facility to address the nationwide stock out. Spokesperson for the National Medical Stores, Uganda's Central Distribution Hub for all public health facilities, Sheila Andohore, said vaccines have been dispatched with the main focus and the most heat COVID-19 district across the country. On Wednesday the country received 175,200 new COVID-19 vaccines with support from the United Nations Children's Fund and French Embassy under the World Health Organization Covax initiative. Hello hope you enjoyed the news please do subscribe to our YouTube channel and don't forget to hit the notification button so you get notified about fresh news updates.


We can see the mesh across the top which is connected to the negative side of the power supply and the wire underneath which is connected to the high voltage positive side. As the ionizing radiation comes in it causes the sparks to happen. As we zoom in an alpha particle is coming from above and it ionizes one of the air particles between the gap. That's then accelerated upwards and that causes a cascade of ions hitting each other which we see as a spark. Each ionization particle coming in is going to cause a spark of its own.


Abstract interfaces with surrounding materials, such as charged impurities and surface roughness, can significantly affect the electrical and optical properties of two-dimensional materials. By examining the results of encapsulating MOS2 with hexagonal boron nitride, HBN, it was found that the portion of the phonon modes caused by intrinsic factors and the portion caused by the interface effect could be separated. For example, the frequency of the A1G peak of MOS2 supported by silicon dioxide decreased by approximately 4CM1 in air for a thickness reduction from 10 layers to monolayers. Of this decrease, roughly 2CM1 was attributed to the weakening of the van der Waals-Interlayer interaction, while the remaining 2CM1 was due to the interface effect. It was also found that the types and concentrations of impurities at the interface between MOS2 and SiO2 were similar to those between MOS2 and air. When completely encapsulated with HBN, the width of the A1G peak of few-layer MOS2 became comparable, or equal to that of bulk MOS2. This article was authored by Da Kim Lee, Nam Hee Kim, Su Young Young, and others. We are article.tv, links in the description below.