the proceedings are still on their way. The talk will be by Karin Ulrich and Hendrik Heuer. Karin works at the University of Amsterdam and Hendrik at the University of Bremen. Karin researches on machine learning and Hendrik is doing academic work on human-machine interaction in Bremen. Please give them a warm welcome. Thank you very much. Good morning.  We would like to start with a quote from Kate Crawford, the founder of the AI Now Institute. She is principal researcher at Microsoft and a professor at NYU in New York. And she once said this year at a symposium that humans fear that computers are too intelligent and will conquer the world.  So we would like now to draw on a couple of examples. Some of you might know them, but we are basically aggregating them to paint a bigger picture of the phenomena. And why are we talking about banality of the approach? Because some of the systems that have been designed are actually pretty simple. And we'll see how they impact our lives during our talk. So a couple of words about ourselves. Karn is researching at the University of Amsterdam, Bayesian methods and deep learning at the University of Amsterdam. And I'm a research assistant at the University of Bremen and I'm focusing on human machine interaction and especially trust issues. We would like to start with an example that most of you will know. The detection of spam. Sometimes it works, sometimes it doesn't. We would like to look at the technical setup behind it, which is actually a quite astonishing one. And the difference that we would like to talk about is imperative programming and machine learning. The first imperative programming is basically what everyone would think of first. It's imperative programming in a way that it's very absolute. So if Viagra is written as here in an email, then it's a spam email. Otherwise it's a good, valid message. And machine learning is a different system of predicting. It's an iterative system and we're looking to predict whether or not an email is a spam email. And this is done by looking at a lot of different examples and classifying them if they're spam or not. And this is being calculated with a resulting error. So if you actually change the parameters that led to this error calculation, you can also, of course, change the error and minimize it, which is an own field of research in itself. And after this, you go back to step one and can basically, in iterative circles, change the outcome of the process. So this is not as absolute as the algorithm doesn't have to, in this example, first need to really detect whether or not something is spam or not, like in the imperative programming case. We're continuing now to medicine as a field and the detection of breast cancer. So we have different features that distinguish benign and malignant cases of breast cancer, separated by a couple of features like curves, points. Some examples are shown above. So if you take two characteristics, for example, the number of points as the first one and the pixels as the second case, you can see that there's a separation, a line of separation, indicated here in blue and red. So mathematically, you can basically draw a line through this data set, and that's called a decision boundary. It doesn't need to be linear, it could be more complex, actually it is more complex actually. So this approach is kind of an analog approach to what actually a doctor would do. They're learning and drawing on their experience and then decide whether or not something is benign or malignant. And the big advantage of machine learning is that we can draw on a huge set of examples and have a better line of distinction based on a huge number of examples. And you can also see that there are a lot of red cases to the right of the picture. And this means that very often we're actually making errors when predicting breast cancer. And very often that actually... So if I have people that I tell that they have a bad breast cancer and that they actually have a benign breast cancer, they might be very stressed. Before we continue, I want to talk about the power of big data. And what we want to do here is to look at personality markers. We use data that is very easily accessible, but it can be used to predict very complex personality markers. For this study they had a lot of volunteers that gave their Facebook profile with all of the likes and all of the information, like their sexual interests or their religion. What the researchers did was to use a very simple model, a simple regression model, to predict based on the likes what personality markers could be detected. With a accuracy of 95%, you could detect whether somebody was white or African-American. This actual interest could also be detected with very high accuracy. The political interest, the religion, and also the experience with drugs are the form of the family and the parents in the youth. Predicting the experience with drugs is not as easy as predicting your gender or sexual interest. We show this because it gives a lot of possibilities of discriminating people. What we want to show here is that we have to think about privacy because you can detect a lot from just very simple data. Using this model you can find likes that are very important for a specific personality marker. You can think about this for yourself which of these pictures is related to the intelligence, which of them is more about the sexual interest. The interesting thing is that this approach of looking at pictures and basically drawing conclusions about the personality features associated with them is comparable to approaches that companies like Google are approaching. Basically we are using big data to detect humans and single persons. This kind of research can also be used to identify people in a pool of voters that are more likely to be convinced to vote for your own party. This has also been covered by the media in the general public running up to the German election but also in the context of the Trump election in the US. We think this is a very likely use case. We have also seen that this has enabled kind of a discrimination 2.0 for example if an employer can detect or predict whether or not an employee is homosexual or not and then draws his own conclusions. This discrimination actually can be more concrete and we can see this in the legal system in the US where actually it is being predicted whether or not somebody will fall back and recommit a crime after being sentenced to jail. Here there is an example. To the left there is a man, VP. He has done two crimes robbing a store and to the right there is a woman. She has committed four different cases as a minor being less than 18 of age. So both of them a category is being allotted. To the left there is three, low risk to the right. There is eight, high risk. And what actually happened in reality, what we are looking at is a prediction, remember that, is that the guy to the left immediately committed a new crime after being released and the woman to the right didn't. So in this case the prediction was wrong and also discriminatory in a way because the woman right is a woman of color. And we also know this from statistics. We are distinguishing this there between type one and type two error. The type one error means that the person that is being predicted with a high risk is likely to commit a crime. And type two error is that a person is predicted with a low risk but actually does commit a new crime. Here the probability is the other way around. White people have a higher chance of falling into this error than people of color. So how does this happen? We assume that those are good programmers who have learned their stuff and know what they are doing. What you want to learn is a function that goes from X to Y. The problem is that X is not a sample from the population but instead just the people that have seen by the police. So it's not representative. This could be a self-fulfilling prophecy because maybe black people might just be checked more often and maybe that leads to a bias in the dataset. On the other side the prediction is also biased because it is based only on people who were processed by the legal system. So you also have a Y prime. If you have a jury in your legal system then this discrimination might be more or less extreme depending on the people. What you can do with machine learning is build a system that kind of hides this discrimination. So let's go back to taking direct influence. Those systems are sold as tools that can be used to help you in predicting crimes. But the problem is that the people that use these tools they trust those algorithms. There is another example from the Deutschlandfunk. Veronica Heller who was working for the legal system and was suggesting a punishment for the... The problem is it's more about social things and not just about the machine learning because even if the system would be fair it always means that the results must not be interpreted in a fair way. People might still make errors. And there are probably a lot of people here who built these systems. The bias based on these data is everywhere. This is an example from Google. We were searching for three white teenagers on the left and on the right we were looking for three black teenagers. Technology shows societal problems. We have social technological systems here that show a problem view but can also enforce them. There is another example here from statistical translation. You have to know that the Turkish language does not differentiate between different grammatical genders as the German language would do. So if you then use Google Translate in this example and translate... He is looking after children and she is a doctor then the gender doesn't show up in the Turkish language. But when you translate it back the gender was mixed around the other way. So the doctor was suddenly male and the babysitter was female. So what we do when we translate is we calculate probabilities and then just use the most probable result. So even very minor errors can be reinforced and then you get these problems that we have seen here. This can be explained technologically but what does this mean for the world view that you get from using a translation service like this? Our next example using Facebook shows how transparent or intransparent this influence can be. Facebook is a very social system where users are both... users that are consuming but also producing content. Facebook always wants to show users relevant stuff and uses a lot of data. In this example from this paper people have 200 friends and like 80 pages. So if every third person does something there are a potential of 90 posts. A chronological view is not very useful. So Facebook is sorting the posts using an algorithm. The problem is that this process is very intransparent so the users don't know and understand the algorithms and they think that if you talk to them that the algorithms are objective and independent. And I'm talking about the study which is from the computer and the action community and 62.5% of the volunteers in the study did not know about this news curation. There were 40 volunteers and they are representative for the US population. 25% did not even know that it was sorted at all. The users are also angry if they don't see posts from close friends and family members. The interesting part is that the volunteers are looking for the problem with themselves or other people but not with the algorithm that Facebook uses. They think that their friends don't share stuff with them because they are not very close friends, they don't know each other very well. That's why we have this quote. I always assume that I'm not very close with this person so what the hell? I don't see their baby photos because I don't know them very well but actually the person shared their photos with everybody but the algorithm took the decision to show the photos to the other user. So these systems that operate in a hidden they have a very huge impact on inter-human relationships. So what do these news systems actually achieve? Other examples are Netflix or YouTube.  So if I look at ten videos and in four of them refugees are criminals then the system will show me more videos of that type so it will change my world view. So those filter bubbles and echo chambers they get created online but also in real life with your friend group where certain world views are shared but on the internet they're more transparent so they're all under the same YouTube logo. So those are problems that we have to solve and this concludes my part on the human-machine interaction a field that's affected concerned with those questions and I would like to hand over now to Kahn who will focus more on the technical side of things. Yes, hello. So we've now already shown that algorithms are impacting us already today in our daily lives and we've seen to what extent this happens once they have been set in place. And we've also seen that they don't necessarily actually deliver what is being put into them in terms of trust and now we would like to actually show you in what cases this is especially not recommended to trust an algorithm. And what's important here is something that we call bias. There are two biases. One is the bias of data, our own values, value judgments, whether or not we know them or not. That's the first category of a bias and the second one is the bias of the model. So when a population is misrepresented in the data that is the basis of the algorithm so what engineers build into the algorithm to make the predictions. In this process the bias of the model can actually reduce the bias of the data. At the same time it's also possible that the bias of the data is impacting the model bias and we'll show you now four different examples showcasing a couple of these biases. And machine learning has made a lot of progress in the last years and also respective research has been triggered by the success of artificial intelligence. One of the studies of this latest research is elaborating it's done in Shanghai and this research is claiming that you can detect criminals by looking at their passport photo. And we will now focus on this because it's contradicting other research findings basically using social economic context to predict your potential crime rate. And what those researchers have done is they've taken 1,800 photos of Chinese citizens 700 of these photos were depicting criminals so people who have been sentenced for a crime and 1,100 photos were randomly selected from the internet from such like LinkedIn and other sources. And let's have a look at a couple of data points to actually see what this study dealt with. So here we have six examples, six images and one row consists of pieces of criminals and one of legally people who have not committed a crime. People who have not committed a crime. I would like to now do a vote. Please left or right hand, whether you think the top or the bottom row is non-criminal. So most of you probably think that they can identify the ones who have committed a crime. And somehow we think we can predict this by looking at a couple of data points. For example, his shirt or a slight smile and in contrast to this there might be a strong light put into your face like when a picture is being taken from you in prison and on the other side there is a makeup or Photoshop even being used to tweak your picture used for a resume or an application. So the algorithm is for example detecting whether or not Photoshop has been used on a picture. And this is maybe an extreme case of how data is actually impacting the outcome of an algorithm. So that's data bias. So the sample can be screwed and also the data can be biased but the amount of data that is used to base the algorithm on and there's also a reporting bias in this case basically when data is being, when and how data is being identified for the algorithm and also the modeling assumptions are important in this context. So before the hype there's been this no free lunch theory. No possible model would work in that scenario and so we would have to change our assumptions looking at a specific problem and use limited assumptions but then without the model showing that it's not adequate it fails nonetheless and so it's our job as scientists to point out these cases and to really check whether algorithms are valid in a certain case or not. But so the success so far of machine learning let's forget these rules quite easily. I think that models are so flexible that there are no limits anymore but I now want to point out an example that still hasn't changed even though we've had this deep learning hype and that's goal setting. So what is success? Hendrik already talked about mistakes and correction of mistakes that we need to do to train algorithms It's often the case that we don't really know what exactly is a mistake and that's up to the engineer to decide. So for example on YouTube they know how long people spend on their site but maybe there's not even a person in the room with the computer but the computer is still as YouTube open and also with clicks it might not necessarily mean that a person likes a certain video. So just imagine we have a data set of translations and we want to measure whether a text has been translated correctly. How can we test that? So word by word or paragraph by paragraph if we use a word for word measure then that doesn't work through too many languages and then paragraph by paragraph that might not work as well because maybe the algorithm doesn't have the right context and then what about synonyms? Is that depending on the data set that might be interpreted differently by the algorithm and so that means we need to make decisions at Google and other leading translation software companies they translate sentence by sentence they tend to think of sentences as independent and only the most predictable word gets computed. So back to our example from the beginning she's a woman, she's a doctor and then we translate it into Turkish and then back again then we get she's a woman, he's a doctor and now we see how this happens the algorithm doesn't know about the context so the algorithm only translates the most frequent occurrence so it's not necessarily bias within the data but bias within the assumptions that simplify the model we use way too much and something else if we change the period to a comma then the algorithm is suddenly able to correctly translate because it knows about the context so another example of this bias phenomenon how can bias be get bigger over time we have active learning that means there's data on this databases we train an algorithm but we're not quite sure about something so we ask a human expert and then there's always the question of have I correctly treated this data point and then the algorithm gets more data in the future to learn better and to not have to ask the human being so often and this works well but what about a data point that is described not correctly described and then of course we have human beings that are not as perfect as we like to think so we have the assumption of perfect data and perfect human being in this model but that leads to this wrong data point being ignored and so the cause of distortion isn't visible anymore and that leads to more distortion and then of course if we have more distorted data then they keep on piling up because there's no human being assigned to the task doesn't even know about it and so of course Google and Facebook the big players aren't transparent most of the time but this might be an example where something like that might have happened so we have an African-American woman bottom row in the middle and she's assigned the term guerrilla and of course this was quite huge in the media but Google didn't quite explain exactly how that happened so first of all it might be that the description is always provided by human beings they might just like to troll but still that might not be an adequate explanation groups of people in our society might be marginalized and for those marginalized groups they very often are not included in testing procedures and we can also think of Microsoft's Tay Chatbot that was brought very quickly to write racist messages and so transmitting learning there's an exercise for the algorithm to make it learn but there's too little data to actually get it to learn and so we have another kind of exercise to help our original problem with more data but this again leads to distortion, leads to bias so for example if we have robotic arms a whole full of robotic arms, a thousand of them they're not that good at learning so what happens in modern robotics is that you have your systems being trained in models so that's not reality but for example surrounding lights and of course the idealized version of a robotic arm that's not true to reality at all and so of course the future with self-driving cars more space in cities, more fluid traffic and all the promises that come with that can be quite skeptical about that because of course self-driving cars have been trained mostly in models and not in reality and so data points they can be close together or can be very much distant from each other and to find new data points that is called interpolation but find data points outside of that frame is called extrapolation and that's not very easy to do interpolation going from one or more data points to another can answer important questions for example how would the child of Kim Jong-un and Donald J. Trump look like, what would that look like also the changing video for example this is an edited version of a porno with a face of the Wonder Woman actress and so of course you know there's lots of possibilities you don't want to think too much about that and there's cases where we also don't want to make a wrong assumption but this is really hard when we talk about extrapolation and modern science still hasn't found the right answers for that and so what we need to remember is that algorithms make these assumptions and that can even lead to hostile attacks on algorithms for example you take a data point from a data set and then you have their properties for example traffic signs and then find a very similar data point that the algorithm treats as something completely different and the spooky thing is that this even works with printed versions of the same attack even if you don't use the same model the same data set so if you're interested in that please stay for the next presentation in conclusion I really want to point out that in all systems of modern machine learning there's an evaluation of correlation going on it's not about causation mathematical causational reaches of cause and effect in systems is very much a new field and the description of correlation is just one of the steps necessary for that so before we go over to the Q&A session Hendrik and I still want to point something out we really hope we pointed out the limits of machine learning but of course that won't keep us as a society from reaping the benefits of this new technology so in medicine, biology and of course that raises political questions that concern us all so these aren't decisions that scientists can shoot or must make on their own so who's responsible for making decisions human beings or machines is it about engineers or corporations when who's at fault and how can we actually enforce existing laws when confronted with this new reality of algorithms and how and why should they be regulated so thank you very much for your attention and yeah, let's hear some questions thank you, thank you, Hendrik thank you very much we're now opening up the round for questions please line up at the microphones microphone thank you very much for the talk really interesting I think there's a really important subject and how we deal with it especially because it has real impact on politics and mobilization and all those things Hendrik, you mentioned artificial intelligence and its application to let's say political propaganda Cambridge Analytica and so forth and also as a second topic the filter bubbles that you mentioned and I'm interested in especially one question in the context of Brexit and the Trump election what are the possibilities, the options that are there to actually oppose and counter that opinions are being echoed and even strengthened through the phenomena that you mentioned AI and big data so do you have any concrete suggestions to counter those developments? thanks very much for the question so that's about how do we tackle these problems and the political sphere and this is a huge question and I feel like it's going to stay with us for quite a while so with our friends and in corporations when we get together for me it's about how can we support people how can we visualize algorithms how can we make people understand what happens there and our perspective is how can we open up this black box and how can the system show how secure it really is and that's a lot of work for example there's the fairness accountability transparency machine learning there's a huge group of people from all sorts of backgrounds in psychology, sociology and of course computer sciences that deal with these kinds of questions please leave the room as quietly as possible and try to keep using the right door microphone 5 please I'd rather have a short comment in machine learning what we've seen with regard to the gorilla example isn't this an example of the cost function? since we've said the algorithm it's as expensive to take the class of gorilla and the class of human it's as costly as mixing up also other words this needs to be differentiated a bit more good point of course you can defend against that sort of thing and say this is worse than that for example with Google photos where we want to have all these categories it's also a task for human beings it's not just about machine learning and the problem with this bias and that was what it was about is more complex and it's not just about this cost function and the point was also about there must have been a wrong data point and then how this bias was calculated in some sort of negative spiral when the algorithm looks at that and maybe that's too much simplification going on of reality and of course these are really easy examples to illustrate this here and of course there's always blind spots and this might be one as well because Google might not be diverse enough and they didn't test their photo algorithm on enough data points before they made it public microphone one thank you very much I think this is a great approach of putting this topic on the agenda making it more popular you talked a lot about bias and very often when talking about bias it's about neutral and judgment and you also mentioned machine learning in that context and in this context with regard to political debates bias and screwing is that actually the right term to describe political phenomena or should this be maybe described differently with a different term? so you know we're not all about communications that's not our expertise there's people that can do that a lot better than us but you know people like us we were doing a PhD within this field as computer scientists how we can deal with these problems but it's going to be a long-term task to really get this out onto the street there's a question from the IRC is human learning not comparable to machine learning aren't the challenges the same or are they different? do you want to do this? well that's a question it's not just about machine learning it's also about evolutionary theory and psychology and I don't know too much about that but the idea behind machine learning is to find mistakes communicate them and then adjust and correct the model I'm not sure whether that's the case for us humans as well so in the sense that our brain is optimized over time I feel like I've heard psychologists deny that so the problem with this question isn't necessarily how does machine learning work it's how do we ourselves work and I think we're still trying to figure that out and the models of machine learning are very very rough estimations of how we ourselves work and so back then there was the thought of the body as a machine and now we have these neural networks and I feel like that's the model we apply to think of ourselves so I feel like there's always some adjustment going on and I feel like for us as human beings all the assumptions we make all the stereotypes that exist that might be useful from time to time but in totality they don't apply all the time so that might be the case for machines and for humans microphone 4 maybe a bit naive but are there metrics with regard to the bias that we see every day in the net are there techniques to visualize those kind of biases to make it known so people realize that this is a highly complex topic and I'm looking for ways of this complex topic being visualized so people know and get aware of those statistical problems there's research for this there's the MIT Media Lab that deal with this sort of thing they have examples of individual people what sort of gender bias they have for the people they follow on Twitter for example they only follow mails so that's part of the puzzle but this sort of media education that has to be taken seriously if we want to use all these algorithms if we want to drive around in self-driving cars we really have to take this seriously there's always blind spots and up front it's not possible to take all of those into account and for some more context regarding this question from algorithmic science point of view for us we don't have a we don't distinguish between positive and negative distortion for us there's just properties that let us draw conclusions and when we say that we have certain values that are just proxies for different data sets for example where I live is often a proxy for where I am from or other data points and so if I know what sort of data points are sensitive and shouldn't be taking into account to draw further conclusions then I can feed this into an algorithm and have the result computed from that so to actually see how the algorithm makes predictions what I'm really interested in is not the distinction between good and bad but rather a visualization of data sets and those results for example that one is based on a very small number of data points and others are based on a high number of data points and this I think I'm looking for visualizations of such cases yeah there are cases like that and we can measure that but it's always a simplification it's always the question of how easy can we make it how much can we simplify it there's this core approach and that's not about how do I have to think about my hypotheses when I look at the data but it's more about how good is my data for my hypotheses and this approach allows us to make different statements so for example if I have an outlier data point then that changes the statement I can make and then I have to be upfront about it and say that this is a very vague guess but the research for that is still sort of doing baby steps microphone one please thank you very much for this talk and also thank you very much for mutually translating this ongoingly on a constant basis I'm interested in your take on how useful those approaches are to take milestones and evaluate the opportunities that those approaches machine learning and so forth actually yield for society that's what we wanted to get at it's a political question and we don't want to answer that question for the law so it's about society dealing with this getting together and finding answers yeah I know but once you have to find a milestone separation between good and bad and you look at the data points on one and the other side and if you look at those data points and some are actually relevant and others are not let's say we have a point x a milestone with a certain quality criteria attached like a car doesn't kill more humans than a human driver and are there is there something like values grouped around this boundary that you can draw conclusions from or are all those points scattered around the decision horizon? I think it's problematic to sort of focus on this point because it's about a lot more environmental factors and then just take this one singular point and draw conclusions from that that's probably too simplistic and once again want to point out the political question and self-driving cars they can never test crashes at least not with humans so these crash scenarios are only ever tested in simulations and that exactly is the problem that we have today at least as far as I know we have very few scenarios where we have training in certain model scenarios what assumptions can we actually transfer into the real world and so even really pointing out these assumptions is very hard for us today and so it's still quite a long way to go Thank you again for the talk the discussion connected to all of this is actually how can we find truth and how can we make a computer find truth we have the same in science like how do I design my sample is there a bison in the sample have you ever dealt with this parallelism so we're scientists and of course we have to know for ourselves what we found out yesterday there was this talk science is broken so it's always very hard to have the right sample size and to think of the effect size and this is an epistemological question aren't there kind of values you can orientate yourself to with regard to neural networks are there any values you can rely on with regard to layers or parameters well this question is maybe it goes into early 90s early neural networks, Baltimore machines, Hoffman networks, stuff like that we can say that when they're saturated how many bits of data were put into that system but for the non-linear systems that we use today it's not possible to say that anymore it's just an estimation it's not an exact size but can't you say you need a certain amount of data points to saturate your system and therefore we need a sort of pre-validation approach but that's what happens you have this huge data set another data set and a training or evaluation where you really have to check did we actually learn something or did we just learn to repeat our input data set but this is still a PhD dissertation waiting to be written these biases are not new we've known them for decades in statistics what has now actually changed with artificial intelligence and in this context do you know any studies that filter bubbles really have an impact that you can also measure I've heard a lot about it in the media but I've never come across any scientific study that has shown that you are actually strengthening and making something stronger that hasn't been there before I forgot the first question please repeat it sure what has changed with the biases in machine learning no of course that's our new assumptions are new as well so we have great data sciences hyped right now at universities but now people go ahead and throw stuff into their systems maybe to make money but don't really think about these problems at all regarding the second question I'm pretty sure there's a lot on echo chambers out there I'm not sure what you asked exactly whether it was possible to test scientifically how an approach to testing this would work but I'm pretty sure there's lots of literature on echo chambers but I can't think of an offer right now thank you very much for the talk again I see that there are more questions please approach the speakers right after with your questions thank you very much everyone


Holy cow It's a good crowd. Honestly, I can say that when I do this talk, I do a similar talk a clock I get six people in the usual suspects. I Want to say thank you all for coming to this talk Can you not hear me? I apologize. Can you hear me now? Okay, we will now peace to this tone for the rest of the meeting The front office are now deaf. I am sorry They will need water All right, well, this is a this is about a 24-slide thing However, the first couple slides are just The sorry All right, well, and I'll go over the quick part fast and then people who are coming in setting up This is a talk on the future of Apple We have a new function of Apple coming in Because rel 8 is coming to be coming out in the next month Years soon my name is Steven Smudgen This is Kevin Fenzie. Hello Today's topics we'll be going over Sort of a history of Apple in five slides Where we are going and then I would like to have the good portion of the meeting towards talk with us So if I I know it's 50 but at 20 minutes before that Okay Apple in five slides Okay, this is the presenters Kevin Fenzie is a rock star He also wrote East for Rush I've been stuck here on this planet for quite a while and try and get off but We have a higher heckler Just to keep me honest if there's any time I am not honest and he doesn't catch me anyone in the audience can catch me Okay, Apple is a set of packages that are branched off of Fedora It has a really bad name Called Apple because the fact is being all its system administrators We had a very hard time coming up with a naming scheme So first naming scheme of what it was After about six months of arguing over the other name schemes. We just stuck to it. Everyone else knew about Apple versus The next gen or the future We normally do packages That people want on rail But well for various reasons red hat would not want to Support because well It's gonna be very expensive to deal with NGIX and six other different HTTP servers We started a while ago and Well, our normal path is we take from Fedora of the release that rail was based off of We well these days it would be a Branch, but originally it was sort of like a CVS fork of those packages. We branch them into Apple Branches and then we rebuild them in the Fedora Koji servers Currently we we started off with rail five well, well for rail five was our main starting point and then we did rail six and Seven or our current current name once Who uses Apple? I Hope many of you use that Raise your hand if you use Apple daily or if you use it with some system that you're dealing with We branch everything Fedora would not be built without Apple that is actually one of the reasons Apple was started inside of Fedora We had a ton of packages we needed to build the entire our own rep infrastructure with Rolling it out just as we were about to turn it off And it's we mostly are most of the community members who are focused on Apple are more towards the slower paced Wanting to get it into a long-scale production system, which leads us to our next Graph The lower levels here are your fedora users over time Each of these as you can see they branch down And they slowly die off Branch down slow down. This is the Apple curtain Down here is Apple five It's still going slowly off Apple six Was kind of slowing down for a bit and then it's all sudden started picking off again Because people are finally realizing that red hat is not turning on Apple five a rail five releases again So they're going those fist boxes are getting to six seven is picking up here We're looking at sort of site data For the future that's rawhide So it's a little bit here. We'll hide actually was used quite a lot back here then it kind of went away and then kind of It's just at the very top here It doesn't really show up enough to in my picture to be because the red why So yeah, each of these are different sort of eras of fedora usage Anyway, it's used quite a lot and it's continuing to grow and I'm expecting That when eight comes out there will be a yet another And six will start doing this sort of 20-year draw One thing to note from the last slide Just to mention very recently Apple seven past Apple six, right? Yeah, okay, so that might be Stacked stacked graph in if these were separate ones Apple seven has finally after five years reached the number of users that are it and it was almost a race where it didn't Because this jump up If this is still going down it would have happened in I was thinking it was going to happen about here in January, but also in a whole bunch of rail six systems came online using Apple and Apple seven More to get there But it's finally more users Systems out there there now this doesn't mean that we know that this is not every system and there's a Low weight on this if there's a whole bunch of systems behind a firewall. There's still counting one So this is sort of the minimum Size of these communities not the actual size or a guess of the maximum size So who makes Apple happen Apple's run by volunteers both from centos and fedora communities The build system is fedora Koji and this is really just Sort of a historical accident because that's that what's what was there. That's what what we had and At this point, you know, if you were designing things logically, I don't know how many of you were here for the last talk the Penrose discussion but Right now everything kind of flows very strangely if you thought of it logically you might think that add-on packages would be something on top of Centos rather than on top of Rel in fedora, so it's all Sort of what we had at the time and historical Basis There's a steering committee That has five to seven people from the various communities on it make decisions about overriding guidelines coming up with How things will be done, etc. etc. and The release engineering and things like that are kind of done as a ad hoc thing When as people have time so there's no Employed people who work on Apple Apple is just a side gig for people who do fedora release engineering and Centos Community work and Centos build system and things like that. So It's really kind of amazing how Well, we've done with the resources that we have All right, so where are we going? There's obviously lots of things to discuss especially with regards to new rel but Let's start out with complaints and issues that we've had Kind of for the existence of Apple One of the problems we run into is that rel supports things for a very very very very very very very very long time and When you're a package maintainer and you say oh Sure, I'll maintain my package for Apple.  It's it's something that people have to go in and Hunt down the package figure out what what's what download it the selves manually downgrade it, etc So we've run into that We've run into the divergence problem from fedora So we're taking fedora packaging components specs and sources and so forth Building them against rel and at the start of the cycle. That's great They're sort of in sync. That's it's the version that that works with those libraries and that Package set but as time goes on things diverge So the fedora version will bump, you know 20 versions But you don't want to do that to the Apple version you want to keep there an older stable one and then there's new macros in the fedora side That cause it not to build on the rel and you run into a version skew problem there We also run into the problem for our centos users When a new rel version comes out There's a lag between when that version is released by red hat and released by sent the centos version if we move along and Pull in that new rel version and build stuff against it Then the centos users might not have compatibility against that So we run into that sort of version skew problem there as well for for short periods of time And then there's there's other issues that we've run into over time as well Anybody have one that isn't up here? Problems you've run into issues Or does that cover most of them? Yeah Yep, yep, that's another one that we didn't have up here is architecture support So there's a certain Certain architectures supported in rel certain ones that are only in centos We build against rel so we run into problems trying to build against those secondary architectures With fedora koji so yep So here's a little reminder for our Apple 6 users Even though Apple 7 just barely passed Apple 6 Yel 6 is going to end of life or at least high maintenance or whatever the term is In 2020 so we're just not going to do much to Yel 6 There's it's going to be basically the way it is now and just keep puttering along until until the end of life so for 7 We've been talking a long time about Doing changing a little bit of the procedure we use for when new releases come out right now Each Apple repository is just that collection of Apple packages that gets created each time we push out new updates But we're talking about maybe moving to a system where we have a base repository Which is created at the time of that release and then have an updates repository on top of that What this would give us is the ability to actually downgrade packages again Because there will be a package that's in the base repository when the release first came out It'll also give us the ability to Have people to Centos users could point to the previous release until the new centos release comes out So they could not run into this incompatibility with the new releases It's a little bit more work, but we're looking at probably doing that to Save some of these problems. I made a mistake on the slide. It's everything I Forgot to change that everything stays static was an everything path. It should be say stuff stays static Yes, right. Yep Yep So this also would give us an ability to archive old releases so if right now Apple doesn't support any of the older point releases, so if you have a Rel 6.8 box and for whatever reason you never want to update to 6.11 Too bad. Apple only supports 611, but if we move to this scheme, although it won't help Apple 6 but For 7 if we move to the scheme you can actually continue to point at the archive version You won't get updates, but you'll at least have those still have those packages Let's see Okay, now comes the meat of this argument Apple 8 beta came out this November October and there's no Apple or sorry EL 8 route red hat Enterprise Linux 8 came out in November There is no Apple 8 yet, and there's been a lot of people wondering when is Apple 8 coming out Apple 8 is been delayed because we've got to work out a lot of different things Apple is not built the same way as Fedora is the Koji system takes Has to know has to sort of built a package to know about the package to use it and We didn't build any of the rail packages and so we cheat by having it Koji look at an outside repository That gives you enough information for how we've done things with rel 6 5 6 and 7 but it doesn't work well with modules because There are times where modules may need things that we don't know exactly through an outside repository without extra work And it without extra logic that we needed to do There are that means we have to we're gonna have to break a lot of things and as somebody said recently I was in a meeting. It's better to break everything and have everybody scream at you at once Then break it in small bits over time, and they go away because they're tired of dealing with all your death of south thousand cuts so this is a good time to break everything and We're trying to come up with all the things we could break in one time to get it done Our current pathway looking forward to try and get an Apple 8 out is we will not be building modules for Apple 8 beta The beta Apple will be in a different path so that people aren't you know, it'll have a different path so that we can Work through all the different changes we want and we're not affecting the main Apple It'll be pub alt Apple versus pub Apple once we get to 8 general release or whatever. It's called GA GR GQ We will go with putting it in The regular pathway It's a beta for on our part as much as it's a beta for anything else, but it will be there. I'm hoping by March It will have packages that are non modular and We may Be able at we will work our butts off to try and get modular in there is when we can EL8 again comes with modules, so we were going to have to put in later rules to deal with all this If you want to replace the rail package currently in Apple We don't allow that if it Red hat ships a broken aws cli If Apple take if rail takes Ansible inside and then sit down and then takes it in Just for example not not not that was picked Purely as a random one because it's an a No You'll be able to replace it with a module and a module stream that'll have a naming scheme that will make sure that we don't replace the rail at Ansible, but we will be able to Have You'll have your module of Ansible and it will put the packages in there and it will know not to you won't get broken ish Again if you want to support multiple versions of something you will have to use a module Modules do not give you Parallel Installability they give you parallel availability if you want parallel install ability There's a lovely project called SEL, and I would love you to support them If you want and to deal with the other programmer problem package your problem of God I Don't want I want to upgrade this to something that's supportably You can put a lifetime now as a policy in on your module There's a thing called the product PDC FPDC Fedora project definition center or some other an acronym that fits those letters You'll be able to put a poll I want this to be released supported for this one release and Then at the next release you can pull it out. I don't care. I'm not good. I may say I want it back in But if I don't want to support it anymore, I'm taking it out and Because we'll be using a path system Sorry, you will be able to Got my slides mixed up paths were supposed to be here. Okay, I Had to rewrite most several of these slides this morning because we came to several agreements of what we were going to do with epa late this morning Packages in EL8 are shipped through two channels and A third channel that is not a two-channel system The base OS are packages That will not majorly change or this is how it was explained to me because I'm a two-year-old So I may not have the exact right definitions and somebody who actually knows them like Josh or Mike back there who's not there can tell me Appstream are things that are more likely to add and remove over a half-time. There's going to be some sort of cadence That they will upgrade things and change things and fix things and make things and go faster And then there's a bunch of stuff that you might need to build stuff, but most users of a package don't need and Having that in there always ends up with well, I installed this and it's I'm pretty sure this broke my system So you have a grub problem. No, I'm pretty sure that GCC develop broken my system Those sort of issues that support has to deal with The putting it in a channel that's not by default where it's going to be a unless you really needed it That's what the code ready Linux builder is. I missed one again. I was supposed to put this as an acronym We will build all Apple packages against these channels they will all be in a some similar way To be decided as we learn this but that's what we'll be building against I'm saying this because there are some other channels that are in rail. There are what do we call those like HA high availability and Add-ons we will not be building against us. We're only going to be building against these three. It's in an add-on We can replace it, you know in the old-fashioned way that we're doing stuff One of the big problems other big problems is well, which add-ons are real but is Apple building against? Well, we're building against this in this. Okay. We didn't know that please take it out. Well, we've been building against it for six years We're gonna start off with just three things if somebody finally says oh, we really want you to build against it from now on We'll add it in but only after they have signed in blood Is that? All right The past we showed in the earlier with seven you might have noticed that there was this slash stuff a Suck in that it didn't make any sense The stuff is going to be here because in fedora. We call it everything, but people said it's not everything anymore because we have modules so I came up with the Replacement which is stuff? We have stuff We have modules if it's not a module. It's stuff Those packages will be called stuff things anyway The pathways will allow things to be and it'll be again as Kevin went on it will allow us to go through and do both archiving regular archiving Regular building outs of things and have a two-channel system for backups and pullouts and pull-ins We will rotate these at regular intervals to be decided because we don't know what rel eights Rotation cycle is if rotate it rel decided it was going to rotate every month We couldn't keep up with that because we're all volunteers There's nobody here who's going to have a full-time job of okay I'm updating things if it's a six-month cycle we may do that But it'll be some cycle that we can keep up with as volunteers Any other proposals from the audience that we the Brian can catch down he's got something to do All right We have a lot of questions We know we have a lot of questions about how late it's going to happen I'm not sure that's a big enough number for the number of questions we have We think we know how that some of these things are going but we haven't run it yet, so it's and it's not that we're a special case and It's not a statement about modules and thing the way we build things has been always of a special case and so we Are making things harder for ourselves because we can't do certain things Oh Module we're not filled service It's sort of a thing that sits next to koji or on top of koji and whips koji into doing what it wants to do Pretty much yeah What modules in it can be so again some of these are policy and some of these are we're going to do it in this Fair in this all area we're going to find out it breaks We'll figure out why it breaks and then we'll go okay. How can we make it not break? But we would we won't break regular Apple while we're doing that's the main thing I want to do because if you're already on Apple 6 and you have or Apple 7 and you have stuff that you need doing You don't want us Futs in with your stuff while we're trying to fix something else again Directory layout changes we think we have been a proposal Somebody might say oh, that's gonna cause this problem, and I need to really fix that okay Composed changes again Currently what happens is every night? Apple is not so when you look at for door you see an 18 you see pub Linux releases 829 slash and all that is composed Once and stuck on the mirrors Because that's how and then updates gets composed every night and move stuff gets pulled in Apple It's like raw hide every night It gets composed and a new thing put out So that means that it's rolling and stuff comes in comes out and sometimes it doesn't happen at all because something else broke but it there's no It that's sort of the way we've done it because it was a quick hack and we've done it now for 10 years So we they're going to be composed changes to make it so that we have this once a semester I'll call semester semesters can be any length of time My son has now had a semester a fall semester. It'll be longer than a spring semester. So yes Composed changes can for door modules get auto-composed when a commit happens. How will we deal with this? How will we deal with new modules and dead modules and modules that have come in in the middle of a stream and The combinatorical number of module Interactions that can happen because you can have module a stream be compiled module B stream a Compile module C stream Z and they all have to know each other and But some of that we don't know because it's done from somewhere else There were a lot more questions, but I knew I had a very short time to get this thing done So I wanted to make sure that you guys had a lot of time to talk to us There are about six slides. I deleted here and a trash fire and a really cool image that Matt Mike had given of a tornado fire tornado All right Jumping to the last of the set Which you can't really do but we'll do anyway this is computer and we just not a number What do you think what are your plan? What are your wishes for? rel Apple 8 to meet your needs because We need your input and your health because we are done as like I said, we're volunteers most of the people who work on this are Fedora well it started off with mostly Fedora people and now it's mostly sent us people who come and help us do stuff My primary job is anything but Apple Although I so We need your input to know when not I'm not Going down a rabbit hole thinking that I'm helping you and I'm really not helping you that makes sense you sir So Apple packages are Going to be in the same spot as all those Apple specs are still in that same spot They're just so what'll happen is because they didn't show this on here. I will try and mind this out Kevin stand here No, no, we're here. Sure Kevin There is a there is a pack in the get there is Kevin Under Kevin there are several branches Floor your hand up This is Fedora rawhide This is for door 30. This is for door 29. Sure. This is 27 blah blah blah blah blah blah Apple 7 EL 6 and then there will be on this hand same Kevin C6 C7 C8 and potentially 5 or Stable or whatever you want to call the stream branch for the thing is for the module which could be built for fedora sent us Apple everything so what you would do is you would go into a Source That fedora project org or source that sent us that org or whatever the name it will be You will do a get clone after you or Done a what is it to do a fork and a fork and then PR PR it back And then the PR will go in the person who is maintaining it can then go Yes Where it needs work, you know the usual BS And then that's how those things will done. So if you want to fix an apple package, you will fork the apple You'll fork Kevin to Steve Steven will then have all those branches you will Make the thing and then you'll do a PR and then Kevin will accept it or not onto the right branch That sound other than just yeah We don't care So the question was a very long statement about wanting stream branches to do everything Yeah, okay, the answer was Yes, sorry the question was what do you want the answer is I want stream branches to be able to have lots of branches So that I can pull stuff into rel or out of rel and it a person can build against the stream branch and have Solution that they need Whether it is supported by red hat or not certain things will be supported by red hat and Will have been signed by red hat so they'll know it's not supported and then certain things won't And the other big thing that stream branches give is is the timeline Support timeline issue because we have lots of people who are like They get a request. Hey support for Create a branch of your package for Apple and they say no I don't support Apple because I don't want to be here for ten years running this thing Whereas if we have a stream branch we tell them okay Just do a stream branch and put whatever you're willing to support You know if your upstream is only going to have this thing for two years You could say two years and then I'm out so one of the other proposals that I didn't put up here and I took off the slide Came up because I didn't want to be thrown out of the room or and Burnt at a stake afterwards was Apple 8 will all be modules Nothing but modules in Apple 8 Because it solves all the problems by creating new ones Everything is a problem and you just have to find the next solution The problem was that we don't have a lot of things as modules open VPN is not currently a module Clam AV is not currently a module and that's a lot of extra work to put on to Packagers Who may not be interested in going down modular path at this time? Also had put a lot of work on us trying to convince them that we needed to do it that way and that would be a very easy no And I for them There's also a lot of stuff that's in Apple 6 and 7 now that are like really small add-on packages So they're like you know PHP blah blah blah and it's one library That does one niche thing and it seems to me like we'll have a lot of difficulty getting maintainers to say Oh, I'll build a module for this one little tiny teeny tiny thing Whereas building it as a traditional package is makes more sense But I think it gets back to Also, how difficult is it to make a module how difficult is it to maintain a module and maybe at some point down the road We make that easier to the point where those people are Satisfied to do a module for that kind of thing. So I guess the goal could be Apple 8.9 Is all modular picking a number that I'm not going to stick to Because Apple is not supported Like Very strong preference Those three years Yeah, there's a lot of upstreams that behave that way that are like Here's our our current thing and we're planning to you know, totally redo it so if you are able to get those plans or know those plans you can set the life's appropriately and Provide some kind of upgrade path to where they you know get the next stream or at least they know that there's another stream of The next thing. Yeah Another proposal that was on the table which comes up is that but it was I It is a lot of work on our part is That at every those dot releases when rail comes out We tell the maintainers at that point they can pull stuff in or out We're gonna unofficially do that if you're a maintainer and you've got something really old and you're like When six dot whatever the next one is Comes out There's stuff you want or not. We're not doing anything six when seven dot Seven Starts coming out. We're gonna let the maintainers know that a if you need to update or you need to do stuff We're gonna make a fast-track process Do this send this email out say that you're gonna do it and then when we branch to seven seven You can go do that We'll pull that in and then you'll have the seven six tree there Which has all the old stuff you need that old stuff you got it seven seven will have the new stuff So I'm hoping as I'm hoping most of you guys are ops people Whatever that cool word is these days Sis admins Do are you guys question or? Okay, oh Yeah, sorry, I asked for raised hands and then I didn't so I'm hoping you're mostly this because I'm trying to make sure so that your jobs are not broken the last thing you need is October 31st, you've gone to the beer Fest that because it's the last day of October Fest or whatever the last day of October Fest is and Your pager is melting in your pocket because Apple just upgraded a bunch of stuff and you weren't ready for it But on the other hand for the ops people in the room. I also want to make it so that you're not spending the next 8 to 2024 worrying about your rel 7 package Without being able to move forward in a way that you have a way to do so Yes, sir I That's my plan it's my plan for this the problem and the reason why I'm trying to set expectations in this room Is that in the past? Whatever came out on EL 6 first day. We did this We in infrastructure have only had enough cycles and such to do that Till now till 2020 1120 and we're going to move to an incremental improvement structure Right Yes, sorry 10 minutes We will So yes, that's like I said module solve a lot of problems and us moving to modules in a incremental as We fix things and get things going way which should help you solve your user problems Better without having to use a hammer But it will we won't be able to have everything on the first day and it may be slow that's Yes, sir So, yes, I know I that There are ways. Yes, you could have that happen and That I'm going to expect to be the as you said the new RPM hell It'll be some other thing. We will have to figure that out We'll have to figure that out because the the issue is is currently the solution everyone is going to is Not using packaging at all and just dumping a tar ball on a bunch of disc drives People have gone back to that. I have run into so many systems where I couldn't get it in Apple because the Apple version was too Old so I just I kept the package on Apple because I didn't want to do this But I just did a tar ball over the other stuff and it's broken Because now I have two different versions running on the same system We're going to run through that It's going to happen. We'll have to solve it as we go. It is not And it may be unsolvable. There are problems that are unsolvable. We all live with that Resist admins. We have users We'll work with it as best we can So I want to mention one other quick thing about modules that might be a big win For the Yale side of things in that say say Python 5 comes out or something like that and Some Apple maintainer makes a package or a module of it builds it up and it's really popular and people start using it and then say maybe It's something that is Desired for support. So it maybe gets pulled in and there's a module that comes out in a later Yale release. That's a Python 5 or whatever Having the ability to see what things are succeeding and popular in Apple may be a means for determining what things are worth Supporting commercially or putting more resources behind, you know down the road. So it may be a win in that direction Thank you, sir. We got two minutes. Sorry. Can I track what packages are most popular? No, I No, no, what? by popularity What how support would know is support would get a lot of things saying hey, I'm getting this from Apple You're a red hat a customer. You got your you have a way with our hend to get that we do not have a way of knowing this all I know is This IP address asked for this repository Everything else is just best guesses Right any other questions? Yes, sir So, okay Our pressure on the builders mirrors I'm archiving off stuff and Nobody mirrors archive So very few people mirror archives. So only those mirrors that are archiving the old so 7.6 will be here 7.7 will be out 7 that's when 7.8 comes out 7.6 will move off the archives So the disk space will just be a little bit more, but it should not be greatly more than what it is and if it turns out that it is turning into greatly more then we will work out some other solution because the mirrors are the hidden lifeline on here that I didn't put up here, which is There are Packagers in Apple. There are sent us ops Who do the other stuff and then there's these whole big room of people who aren't in here? Who run mirrors that you get your packages from? They do it off mostly off their own time. They they are not paid by it usually their university and they just have free bandwidth and they they haven't told their boss that they're using it for this So if you run into a mirror of a manager somewhere or somebody's running a mirror thank them buy them the lunch someday and Anyway, I think that's about it for us up here if you don't have any of our talks I Will let you out early so you can get to the coffee place before anyone else


Israel says it's anti-Semitic to invoke the genocide convention against it. South Africa has invoked the genocide convention, formally launching a case at the UN's International Court of Justice, accusing Israel of genocide for its mass atrocities in the Gaza Strip. Israel immediately responded by... accusing South Africa of blood libel. Blood libel, for those who don't know, refers to the way medieval Europeans would falsely accuse Jews of murdering Christians in blood sacrifices in order to justify persecuting them. Which is to say, Israel has responded to South Africa's accusations by accusing South Africa of anti-Semitism. False accusations of anti-Semitism are all Israel and its defenders have left. It's the only tool left in their toolbox. Once you've exhausted the But Hamas and But October 7th excuses they make for Israel's deliberate butchery of civilians via airstrikes and siege warfare, false accusations of hating Jews is all that remains.  It causes people who care deeply about human rights to take a step back and say, hold on, am I guilty of embodying the same hateful prejudices which led to the Holocaust? And shuts us down and shuts us up, even as Israel rolls out its own Holocaust against Palestinians. It exploits a noble, healthy inclination we cultivate in ourselves and good faith in order to support the horrific genocidal nightmare in Gaza in entirely bad faith. It exploits our good nature to advance a profoundly evil cause. It's despicable. It's depraved. Israel apologists always speak as though all critics of Israel are constantly obsessing over Jews. When nothing remotely like that is happening, it's a fantasy. The only reason people like me ever make any mention of Jewishness is because 90% of the arguments made by Israel's defenders rely on babbling about Jews and anti-Semitism, and those arguments need to be addressed. If Israel's defenders weren't constantly babbling about Jews and anti-Semitism, it would never occur to me to think about those things in relation to what's happening in Gaza. And I'm quite sure the vast majority of people on my side of the issue are the same. When you see mass atrocities of unfathomable horror unfolding in real time in a nonstop deluge of video and photo evidence, the very last thing on your mind is what religious faith the perpetrators espouse. It's not something normal people think about. Throughout my life I've had a positive view of Jews and Jewish culture because so many of the people I've admired and been influenced by have been Jewish. But other than that, it's not something I've really thought about much.  People in our society simply do not feel that way about Jews. Real anti-Semitism does exist, but it's a small fringe view. Normal people just want the mass slaughter of children and the ethnic cleansing to stop. If I saw someone murdering a child, there are many things I might say and do, but the very last thing that would ever occur to me would be to wonder what religion he is. It's the silliest, most nonsensical narrative in mainstream politics and media today. And that's why fewer and fewer people are buying it. There's only so many times the boy can falsely cry wolf before the villagers stop running to his defense. Hopefully this desensitization that Israel and its apologists have created doesn't have dark consequences in the future.


Going on everyone Sam here with late in sports cards ripping 23 Bowman Chrome Bay Baseball HTA choice 12 box case number one random team style There's a look again at your random results as always you can find these on the website under the results tab as well as the link in chat Yeah, Garrett you're right there with the Mets So good luck everybody, let's do it here. We go still two in there empty case up top Here we go. Let's get started. Come on, baby. Let's see a super Super Fractal. That'd be awesome All right box one Got a base auto for the Reds Alfredo Duna Alfredo Duna base first auto cut choice exclusive K to Hunter to one fifth. Uh, yes to 150 Yes, there were some Molloy's on the 10k sir. Yes, sir.  It's Chris Newell to 150 Third base is wide open next year. Yeah, they'd be pretty big honestly That'd be pretty big That's a Jacob Melton base auto The Astros a Luis Lara Wow That's huge this next one Wow, wow, wow, wow, wow Who's got them Holy Going to James D true gold Filmin Celestine 25 or no. Yeah, 25 of 50 Holy cow, dude That's a massive massive massive card Good Lord massive card Congrats to that one Charles a with some spots tomorrow We've got um Nathan Q the spot in to Jeffrey H with some personals Michael D with a couple of personals as well my D. Yeah, that's a huge hit that Celeste and Kate Hunter first base for the Reds again Reds killing it man killing it Got a first of yours bar Zuleta for the blue jays base auto and Refractor Jacob Belton to 499 for the Stroes Nice one there. He's done On to the next one. Omari Daniel first base auto for the twins. It's nice choice exclusive to 150 Dario Laverde Freddie Michelle to 499 refractor first auto for the twins. Nice one Minnesota Plays some patches isn't for a bit that actually a couple maybe an hour or so give or take around there we have three more of these after and they're all 20 minutes long so around that that uh, I would say got um Victor Asturiz ice one there for the Guardians Got Devis and De Los Santos Ice one there for the Diamondbacks. He's a big prospect in this one And then Refractor Yosavar Zuleta ice one there for the blue jays to 499 on to the next that's a uh Joey Ortiz base auto for the Orioles a base auto of Ruben Santana for the Diamondbacks Purple is Joel Diaz to 250 nice one congrats Garrett There you go Garrett. Nice one there, buddy. It played Tidwell first base auto for the Metz Got a choice exclusive to 150 Carlos De La Cruz The Phillies nice one there uh, Philadelphia choice exclusive And true green is Adon Sanchez to 299 for the Cubs. Grats Chicago That is going to be Daniel Mateo for the Rangers Nice one there congrats Texas got a Paul McIntosh Let's go into the Marlins. Congrats Marlins up John Johnny, how you doing buddy John K in the house and a Ben Metzinger to 299 Ben Metzinger on the White speckle Did you follow their NASCAR uh, Luis Reyes? I saw him there for the White Sox I'm pumped for plates, dude. Yeah, I'm pumped That is going to be Luis Torres first base auto for the Angels Grats LA and Blue Rewave for the White Sox to 150 Luis Reyes Congrats Chicago Um, we are down to just There it is uh six to go on plates and patches everybody. No four to go. Wow. Jeez Four to go on plates is as well. Do you really need me to save every single one? No, okay Done all right four to go. That's the most uh, the closest one to being sold out Two to go everybody Two to go do a quick recap after as well got a uh, junior Franco for the Diamondbacks base auto Base auto of Trey Faltin Trey Faltin and Blue Rewave nice Johnsock shim to 150 for the Pirates. Congrats Pittsburgh Nice one there John with two spots. That's what got us to four. Thank you John That is what got us to four Appreciate you John No, that got us to two We're at two to go on that lat the second plates and patches case guys. Thank you all appreciate that six to go on Divis As well six to go on Divis Adrian Pinto nice one there for the Lujays That is a green lava is Spence Kaufman Go into the Padres Ariel Castro 31 of 250 for the twins Pinto beans. Yes, sir Garrett.  Sorry regime Got Will Warren That is going to be base first auto for the inks going to the Phillies brian rencone to chew at 99 On the way to speckle. All right quick recap here. Everybody's massive massive card in this break. Holy cow. We hit A true gold of like two of the three biggest prospects so far Sean Kind of insane, dude Kind of insane We've got base autos will Warren Javier Rivas Adrian Pinto trey falteen jr. Franco Daniel Mateo blade tiddwell freto duno jacob melton louise laura Yosvara Zulita Kate hunter devison de los santos victor is torrez o marie daniel joey orteez ruben santana louise torrez louise reyes paul macintosh The 499 jacob melton and yosvara zulita as well as freddie michel To 299 ben metzinger brian rencone to 250 ariel castro joel Diaz to 150 carlos de la cruz Dario laverde kate hunter chris newell john sock shim louise reyes to 99 we've got spence kaufman and eight on sanchez And gold felman celestine Going to the mariners That'll do it for the break guys. Thanks again for the fill up next is number two


But these are what we're seeing for the smuggling routes with Tripoli, Libya being the main point of departure for immigrants or migrants trying to get over to Europe. It's a $10 billion a year industry, illegal immigrants. Making it very simple. Smuggling is voluntary. The person wanted to go. It is a crime against your sovereign nation for them to be trying to sneak into it.