Coming up on DTNSA Swiss Court rules that a like or even a share can be defamation an Indian politician deep fakes himself And can you really replace your laptop with the tablet? This is the Daily Tech news for Friday February 21st 2020 in Los Angeles. I'm Tom Merritt and from studio Redwood I'm Sarah Lane drawing the top tech stories from lovely Cleveland, Ohio I'm Lynn Peralta and I'm the show's producer Roger Chang We also have with us today host and producer of the in touch with iOS podcast Dave Ginsberg Dave. Welcome. Good to have you in the show, man Thanks. Thanks for having me and I'm from Chicago Excellent excellent another Illinois person. Thanks for having me on show. Yeah, I'm looking forward to this We're gonna talk with Dave in a little bit about that idea of replacing your laptop Or even maybe your pc with a tablet We were just talking about Possibly sponsoring a pig at a state fair and all kinds of other stuff on good day internet become a member at patreon.com DTNS let's start with a few tech things you should know Samsung named former finance minister buck j1 as its board chairman the first time the position has been Filled by a non-executive director box served as outside director at samsung since march of 2016 and replaces outgoing Lee sang hoon who was jailed in december for interfering with union activities Sony and facebook both announced they will not attend the game developers conference next month in san francisco Siding concerns over the coronavirus facebook still plans to make the announcements It had planned for gdc through videos online question and answers and more It's odd because facebook's just a ride down the 101 from san francisco. So it wouldn't have been far But they just don't want to gather After being banned in columbia on february 1st uber is legally operating again in the country After a court cited with a taxi company that sued uber and ruled that uber had violated competition rules The company changed its model to allow riders to rent a vehicle along with a driver And will technically act as a point of contact between the two parties in columbia Let's not mince words Libra has looked like it's falling apart as a possible cryptocurrency but It broke the string of bad luck shopify announced it will join The libre association and invest at least 10 million dollars in libra's reserve operate a note on its network All the things you have to do to become a member of the libre association That puts them Different in a different position than voto phone visa mastercard stripe paypal mercado pago bookings holdings in ebay All of whom have withdrawn from the libre association in the last six months or so Libra association now stands at 21 members if you're counting Let's talk a little more about uh facebook wanting to give you some money Yeah, not a lot of money turns out to improve voice recognition Facebook announced a new program in its viewpoints market research app called pronunciations that will pay users to make voice recordings Users will record the phrase hey portal followed by a first name from their friends list Recording a list of 10 friends with each phrase spoken twice will earn the user 200 points in the viewpoints app Which could be cashed out when you get to 1 000 points for five dollars And that's through paypal five us dollars. Yep recordings will not be associated with a facebook profile So says the company and won't be shared without permission Pronunciation is available to us users older than 18 with more than 75 facebook friends It's funny. I floated this almost in jest by a couple of my friends who are not Huge fans of facebook and they were like great you get five dollars and facebook will be deep faking you for the rest of your life And I was like, okay. Well, that's a little silly, but but this does seem like okay it is good to uh kind of pull the crowd and see how The speech recognition could be better and what better than your own community But at the same time it's a little insulting the the money situation Considering what you have to do because whether or not it's anonymized. It is a personal experiment I don't know if it's insulting facebook already knows all this stuff about you already like the risk is almost zero here It's not a lot of money, but I guess it's better than them just stealing it. Dave. What do you think? Yeah, it's just I just can't see them stealing That easily. I mean the the funny thing is is the five bucks. I mean just just to record the voice the But I think it's gonna be good because you know people have trouble pronunciations It's just you're still worried about the privacy part of it. I think I mean the principle here is right You you need this kind of data to train this stuff to be better Uh, and people have said you shouldn't be taking my voice recordings You should be paying and so when facebook pays people still find things to criticize about Yeah, yeah, it's they're always going to criticize something about facebook. I mean saying, uh, hey Portal followed by a first name from a friends list is not very revealing You know and and you only have to do it 10 times. That's that's actually a pretty good hourly wage relatively speaking yeah, I think I think maybe the First name from the friends list is the only thing I kind of was like I mean If you're just trying to figure out speech recognition Why would it have to be someone's first name because you need to vary you need to vary it up And you don't want to use last name because that is more identifying Well, not a last name, but it could be lots of different words, right? Right, but first name is easy because like just look at your friend list and pick these words I suppose and that helps cover a bunch of different phonemes Uh, new mexico attorney general hector balderas filed a lawsuit alleging chrome books provided to schools in the new mexico area for free To schools as I said collect personal information from children younger than 13 years old without parental consent Uh, the complaint claims that google collected geolocation information Website visits internet history search engine records student contact lists voice recordings and use student emails for advertising purposes in violation of the children's online Privacy protection act and potentially a violation of new mexico's unfair practices act google denies the claims saying they are factually wrong And told cnet quote g suite for education allows schools to control account access and requires that schools obtain parental consent when necessary My guess is some of this is innocent. Some of this is google's a cloud service So you share your information with the cloud which means with google in order to allow the cloud service to work Teachers need to be able to access student information to make this all work. So some of this might just be overreacting They are also claiming that advertising was sold and advertising google says is not sold on these g suite accounts for education So either new mexico's wrong or google was doing that, uh, which is absolutely a violation of not only the law, but what they've said they do Yeah, it sounds like if google was actually doing that and knew about it. They would have said Uh, you're right oversight. We we have now reversed this action The company saying we don't do this. This is wrong Does lead me to believe that maybe someone in new mexico's is not understanding what's going on But again, it wasn't just accusing them in which the case you're right. They could say oops. Sorry oversight They found a lawsuit. So that takes away the option to say oops. Sorry oversight because that could undermine your case Right, uh, but it it seems like this would be something that someone would have noticed earlier I don't know. Uh, i'm curious what the facts are behind this. Dave. Does this bug you? Yeah, it's just I mean chromebooks generally kind of question what What they are going to do because they have it's wide open and even when these kids put the information in there There's no time what google's going to do to share with it. So I I yeah, it does bug me a little bit on that Well, this might bug you might not the federal supreme court of switzerland upheld a lower court ruling that likes and shares of content on facebook Can be considered illegal defamation In the case the defendant had liked and shared posts of fellow animal rights activist erwin kesler Defaming him as anti-semitic and a neo nazi the court cited the potential for such content to spread rapidly on social media Could make likes and share defamatory in nature and swiss law only requires that an act be communicated to a third party To meet the threshold of defamation The decision set a major factor in a defamation case would be how visible a shared post was outside of an immediate friend network Of the defamed individual. Yeah, so this is it's important to note what sarah said about in swiss law You only need to communicate the act Uh, you know only need to communicate to a third party to meet defamation If it's defamatory, obviously it has to be defamatory But once it's determined to be defamatory in the united states We have different laws a lot of times you have to show things like actual malice or you have to show knowledge that you knew It wasn't true. There's different standards So we're talking about swiss law and what the court's saying. I think is less controversial than the headline Which is they're saying the act of liking and sharing isn't The thing that gets you in trouble. It's when you spread it widely Uh, when you are are actually making this, you know go viral If you're just sharing it with a few friends and family Uh, and maybe you don't know then I don't think the court is saying that's what happens But once you've spread it widely then you are now communicating it in there if I'm reading this right You are you are now intentionally communicating it and under swiss law. That's all that you need to prove You don't mean to prove the person knew it was bad knew it was wrong or whatever if it's defamatory And you're spreading it then you're communicating it to third parties It's interesting that Again, we're talking about swiss law here, but that you know, the law is basically saying If you weren't paying enough attention, I don't know you liked something because it was from a friend and you know You it wasn't that you thought someone was a neo-nazi or anything like that swiss law was like Well, don't share things like that then you're gonna get in trouble too. It's not it's not a matter of Uh, sort of blindly liking and sharing and passing along information, which is how Lots of things on the internet work right now and people could say, I don't know. I'm sorry I didn't mean to do that or I don't believe with the you know, what the person the original original poster was saying So it's like you need to or you should not Engage at least if you are likely to share this outside of your immediate network Right, so if you if you keep things private or you just don't have a lot of people following you The average swiss citizen might not have to worry about falling afoul of this accidentally But with great power comes great responsibility. And so if you are someone that Has the potential to make something go viral then the swiss court here is saying then you need to consider that before you start Spreading something.  I I feel like this is somewhat reasonable to say you are also responsible For the content you spread if you if you can spread it far and wide Yeah, I felt that I felt that right exactly. I mean, I'm not a swiss citizen So obviously like I don't have to follow this law, but I mean there's none these types of topics I would never like or follow in you know for in my lifetime. It's just not stuff I I'm into and I know there's people out there who like to have that stuff, but uh Again, I'm glad don't listen. I don't live in the swiss swiss Well, I I'd say it's a lovely country. It really is. It's lovely. I mean, I don't mind visiting I I I don't know that if I lived in Switzerland, I'd worry about this Too much and I am somebody Who has the ability, uh, you know, certainly on twitter to spread something far and wide But I I also know that even in the united states I could be subject to defamatory, you know Considerations given that I I do a podcast that is this is widely operated So I'm comfortable with that and I think we need more of these kinds of decisions that say look You you can't always blame the platform There are things the platform should be doing well and be doing better and there are things that individuals should take responsibility for I think that's an important point Earlier this week vice reported that india's bjp party. That's the party of norendra modi The the leader of the government right now partnered with political communication firm the ideas with a z factory to create a translated version Of a plea from the bjp party president manoj towari Asking for people in deli to vote for his party The original video was recorded by towari sitting in a chair The ideas factory then trained a lip sync algorithm on speeches by towari to learn his mouth shape That's what deep fakes do and a dubbing artist came in and recorded his speech in the harjan v Language something that towari doesn't speak That recording of the dub actor was put into the original towari video And the algorithm then modified towari's mouth to match the words of the person speaking harjan v So it wasn't faking towari's voice But it was faking his appearance to make it appear as if he were speaking in the harjan v language The modified video was then posted across 5800 whatsapp groups to reach people In the harjan v speaking migrant worker population in deli and attempt to persuade them to vote for his party in the upcoming election Now this sets off a lot of people because it's using a deep fake in an election But it's not the usual thing you assume which is someone using it to attack someone and make them look bad It's using them to make The leader of the party Look like they speak the language of a group. They want to vote for their party Even though he doesn't so it is faking it It's faking it for accessibility So that people can understand the message, but it's also Implying that hey look he speaks our language when in fact he doesn't Yeah, I think that I was trying to I was thinking to myself Okay, there are a lot more languages spoken in india than the us where we live But if this were a politician In the us that didn't speak spanish, let's say and it was a really good deep fake And it was attempting to persuade a lot of spanish speaking voters to vote for this politician You know, it gets the area gets a little gray, right? Yeah, there is an accessibility factor to this And and and a person who doesn't speak english Let's say if that was the first language that the politician spoke If they if they're now receiving a message that they're really on board with that's not necessarily a bad thing but it also is it's it's You're not lying But it's almost as if hey i'm one of you But but you're not and you didn't have to learn a language and you don't necessarily have a lot in common With the people that you're trying to get to vote for you So I'd be interested to see you know if anybody takes a lot of issue with this because it's clever But it's messy If it weren't political, I don't think people would have much problem with it, right? If it's if it's a cdc, you know the equivalent of a of a disease control agency Putting stuff out there and they're like look we we we didn't have time to film 15 different people We wanted it to all come from the mouth of the leader of our of our agency My people might have less problem with it. Dave. What do you think? Yeah, you'd have to run on head there and this political. I mean just anything that's political It's it you would everybody makes a big huge deal out of it if it was if it was nothing else then We wouldn't be talking about this right now. So it yeah, that's that's really why I see things. It's just right I I would add the one difference is that there are alternative methods For accessibility besides doing a deep fake to get your message across you could use Captioning you could use an overdub of someone else's voice If the whole point is to convince people that this person is authentically A native havarti harvon. Yep our biannese speaker. I think it's it's a little kind of a it's it's kind of like Yeah, just ingenious But also is is the whole point to convince people that he is a native harian vi speaker I imagine they would say no, that's not the whole point. The point is to have the words of our party leader delivered in their language Right, yes, you could have someone else read them, but then they're not coming from the party leader And yes, you could put captions, but that's not quite as accessible if someone can't read as having it spoken Or you could have it spoken with someone else's voice over his voice I mean they do it all the time with translations Right, and so if you're going to do that why not make the mouth Match and just make it make it smoother Does it matter at that point when really it's the message and not the connection of the lips? Right and and and the point is being that is that the message is not at issue here We're not talking about there being any lies in the message. This is just a hey, please vote for us message right So I I could I could see where the line gets real fuzzy there of like well, wait If I could just have somebody else speak it Why can't I have somebody else speak it and match it up to the video of the politician like what what's so wrong with that We're not saying he speaks harian vi. We're not trying, you know, it's it's interesting There's there's a lot of it kind of goes back to swiss slide if you're not paying enough attention You might think he is Right and a and that's a fair point. It could imply a lot of things Google announced that starting on august 3rd new google play apps that request background location will need to pass a review Existing apps will need to pass the review starting november 3rd And the policy will apply to google's own apps as well Google says the review will look at if the apps core functionality Justifies the background location data gathering and developers can request feedback from google starting in may About the background location review This is the opposite of our other stories today and that I don't think anyone could disagree that this is a good thing Wow amazing Yeah, so you want background location. Well, why And then developers like well, here's here's here's my really good reason google's like, okay So we're not only asking people in the app if they want it to be on we're asking the app developer To justify why they even want to ask for it to be on and we're applying it to our own apps Not a lot of room for criticism outside of worrying like oh, well, maybe google won't really Be hard on their own apps or something like that But you know, who knows right at that point you can just you just don't trust google to begin with So I think this is doing all the right things All right one response to the growing trend against accepting cash is To pass laws requiring you to accept cash. So that's happening in a few cities in the united states We've talked about it before on the show as cashless becomes more popular They're saying look we're not going to let retailers not accept cash because that cuts some people out of society Who don't have access to cashless forms of payment? However in sweden where the society is already mostly cashless like 90 plus percent cashless The reichsbank central bank announced it is launching a year-long pilot of a digital currency called the e-krona The reichsbank says it wants to make a digital payment system that is accessible To everyone so they're coming at it from the other end saying well if we're gonna have cashless Let's make sure that everybody can access it somehow Now this test is just a test rick's bank rick's bank economist gabriel soderberg told technology review quote There is currently no decision on issuing an e-krona how an e-krona might be designed or what technology might be used This is the beginning right there. This is how they figure out all that stuff is like let's run a pilot Let's try some things. Let's collect feedback But I thought it was interesting as as a different approach to solving the accessibility problem Versus just requiring the existing system to stay to say well, let's see if we can actually make the new system work for everybody Yeah, and if it and if it does work if the e-krona can can can work in sweden and it is sort of a tested model This can apply to lots of other economies where the cashless society is a real issue for people who who need to deal in cash And it also keeps you know private payments from taking over the entire cashless system if the if the government is issuing its own digital money Then there is you know the trust of the central bank here in this case Behind that rather than having a Libra association or something like that Move in and take over what would otherwise be the normal central banks province. So there's that aspect of it as well Dave you have any thoughts on this? Yeah, that's that's welcome to see this I mean i'm i'm hoping this continues across the world cashless payments is just here to stay I use Apple pay all the time and it's it's cashless So, you know, it's uh, it's it's definitely something um that's looking forward to as long as you could make it where everybody can use it And that's that's no easy thing. That's that's a tall order But but this is how you investigate if that's possible by doing these kinds of tests Hey, folks, if you want to get all the tech headlines each day in about five minutes Be sure to subscribe to daily tech headlines dot com We talked earlier this week about microsoft office bringing together all of their apps into a bundled app for ios and android And and there's a minimal version of it for android tablet. There's one coming for ios We're seeing more and more tools pitched toward not only mobile but tablet and Dave you you You do use a tablet as your daily driver. Am I right? I use I I can't I can't admit that I mix with I try to use. Okay. Okay. You're on your way Oh, yeah, I've been on my way for years to the presentation about it I mean, I've been I've always been an advocate of wanting to use it when we say tablet I think we're talking about an ipad because it it That's why I wrote this in here. I said, how do you identify between a tablet and a tablet? That's a laptop because you know, I work with with people that have Tablet laptops and a lot of convertibles out there. Yeah, they're then pull away keyboards It's a full-out tablet just like an ipad. So that's why I said are we using that instead of Instead of using ipad as opposed to a A lab the laptop tablet. Let's take the ipad as our example tablet for the for this case If somebody says look, I just want to use the ipad. Well, they're gonna have to get a keyboard, right? Have to get a keyboard have to get don't I can get a mouse now and they've made that accessible And there's a touchpad availability for it as well. So Yeah, you do need to get that if you want to be the most productive, you know typing on the screen keyboard can be cumbersome hard hard to maneuver after time So you're definitely going to want a keyboard But there's so many of them out there that even if you know, we saw bridge at at CES They they have they're coming out with their new one with the pro plus with the entire case and everything and plus a touchpad built into it. So So there's really where I thought we're really going to kick it in the high gear with the start to making an ipad You know a full-time daily driver I think a lot of people assume well if all you need to do is email and web surfing then sure I can use a tablet But yeah, what about power users? What about enterprise users? Well, yeah, the power users we get I mean There's a ton of great software out there You know podcasters are using ipads as their daily driver for doing their podcasts Video editing and forwarding huge huge things and as well as productivity you talked about Microsoft office Earlier this week. I've talked about it on my on my podcast just yesterday So it's a Microsoft office is a really big driver of of ios I really like seeing it and and having as a standalone app with all having the standalone apps but now you have the All-in-one app, which is is going to be great and and the the cool thing too is it does work an enterprise I did test it so um And with when we if you drive over to enterprise a little bit, you know You talk about systems that are designed more for mobility in my they have the access systems And that's where the challenges enterprise systems meaning there are specific applications that are that are not outside the On the other world the internet I think the one thing that that trips me up in the idea of relying on the tablet Uh, well two things one is just just the processing power Sometimes I just need a bigger one and and I don't think there's any way around it That's that's just maybe over time going to go away But that's that's the same as choosing between an ultra light and a more powerful laptop So it's almost a false comparison But the other thing is The ability to do quick window switching And I know that's getting better all the time But even still even with mouse support in ipad os It just doesn't feel as fast and fluid as the time-honored window interface that that you get on a good old-fashioned laptop No, I agree with you on that. Um, the mouse right now is I don't know if I can just say it's usable at this point I mean you got to go deep into accessibility just to get to it To set it up so but the nice thing is like I said with these bridge keyboard touch pads I think it's going to be more fluid I'm really interested if they haven't got I've been really set out into the market yet But uh, be interesting to see how how that touchpad will interface with it And you know hardware itself there's so many choices Adding to the ipad but as far as going back to your point about the processing I think the processing has actually has gotten a lot faster And I find it almost equivalent to some laptops that I've I've tested As far as that goes Yeah, where is the line because I know you can definitely do video editing Uh, but you can't really do 3d rendering you can't you can't do some of the higher end stuff you can't process There's a limit to what you can process. Yeah, I mean those are going to be the extreme pro pro users And yeah, there's no way an ipad's going to do that for them No good at video editing and just using iMovie or any type of video editing. It's out there Is going to be be good. So I'll touch a little bit more on enterprise why Why ipads would be ideal is uh, you might get into situations where High-risk locations have to be traveled to like like china russia Then you know the ipads are the way to go because they you know, those two governments want to Encrypt your device and you can't keep it with a password. So iPads, they don't have choice Yeah, and honestly, I that's another advantage to the chromebook is because you have to log in with your account Uh, you you can have separate accounts You can log in with a with a not a dummy account per se but sort of a travel account if you will And that's that's a nice thing about the ipad os as well and and even android Android tablets can do the same thing. So let's say very good point. Well, thank you, uh, Dave for For walking us through that i'm i'm curious what what people out there think Especially if you are someone who uses a tablet more often, uh, send us an email feedback at daily tech news show dot com Also, thanks to everybody who participates in our sub reddit You can submit stories and vote on other stories at daily tech news show dot reddit dot com You can also join in the conversation in our discord. It's the best community around Which you can join by linking to a patreon account at patreon.com slash dtns Let's check out the mailbag Well, duane wanted to weigh in on our discussion yesterday about apple's app store policies and what is working and what needs to change Duane says apple's policy is an attempt to strengthen and maintain the ios economy If large developers were able to place their free apps in the app store and permitted to be processed externally Then the large developers would be able to take advantage of the entire ecosystem without contributing anything to the ios economy Large developers would get for free app store editorials the developer tools wwdc entry all the overhead of making and growing the platform more egregiously It would mean that small developers would be subsidizing the ecosystem for everyone apple solution may not be the best But I do think that putting up a roadblock for developers to prevent the willful bypass of contributing to the app store ecosystem While providing the opportunity for small developers to put up free apps for distribution without cost Put apples policy in a more egalitarian term as opposed to a pure money grab duane and I had a a great email conversation about this because my my response to him was Yes, and all of the big companies Still take advantage of all of those attributes that you mentioned Without paying amazon prime video audible comics ology. They're all in there without paying Fandango in there without paying netflix now in there without paying So they're doing all the things that he says And he had some good points about the fact that yes, but It does hold the line and it sets the precedent and and it makes him space For for those small developers But it was an interesting point to say like look there is a there is a reason to hold that line I think where duane and I ended up in our email conversation was wouldn't it be nice if there was some compromise if there was a way To say look we need you to contribute To the upkeep of the app store But also in a way that didn't hurt us as users and make us like You know and just don't make it illegal to link to something is because kind of where I sit on this Anyway, Dave. I'm curious where you sit on this idea of not allowing anyone to even link out to another way to pay for something Yeah, apple gets really weird with the payment stuff. I I think It's it's gonna be interesting to see where this goes Again, like you said putting something and putting What people want as far as their apps and what they do they give apple taking control of it is Definitely a big thing. But uh, yeah, this is gonna be interesting. This is definitely gonna be interesting to see where this goes Shout out to patrons at our master and grand master levels including de gracia a daniels ken haze and brad schick Len Peralta has been with us as he is most fridays drawing during the show What have you been inspired by this week? You know in a couple weeks I'm going to be putting this tablet versus laptop To the test as I go out to seattle to draw live in front of an audience And I have to agree with most of what you're saying, you know the computing power things like that But really what it comes down to is just creativity. What do you want? What do you feel comfortable with and that's what this is sort of it's sort of like can't we all just get along here Tablet versus laptop. Why do we have to put them? You know against each other and because I don't want to have to carry both I know I know but but look at these guys. These are look at their little tablet and computer faces We can coexist. Yes, they can all just just get along and that's what this is all about. It's called tablet versus laptop It's available right now at my patreon. If you're a patreon subscriber Uh, and it's also at my online store at lennperalta store dot com And also I want to mention I will be at convey ux in seattle in a couple weeks putting this to the test And we will see how I do so uh, stay tuned Sounds good. Len. Thank you so much. Also. Thanks to Dave ginsberg for being on our show today Happy Friday to you Dave. It was so nice to have you let folks know What you're up to and how they can keep up with your work Yeah, happy Friday to you as well. Thanks for having me You can find me on my website in touches ios.com. That's my podcast I've been doing that for about two about thumbs three years now having a lot of fun doing that talking about ios and then If you want to reach out to me on twitter, I have twitter handles dave g 65 Folks, you can get a whole lot more out of daily tech news show For just two bucks a month If you are listening to this on the public feed and you've heard some ads You can get an rss feed out of patreon that doesn't have the ads It includes extra shows like our bonus show where we look back at old lineups or or live with it Where sarah and roger talk about spending a lot of time with gear and giving in-depth Reviews of that time that they've spent and the only way to get that stuff is to become a patron So stop down right now. And uh, if you've got two bucks that you can afford to spend this month We'd love to have it patreon.com slash dtns We'd also love your emails. You have some feedback for us Our email address is feedback at daily tech news show dot com We're live monday through friday 4 30 p.m eastern That's 21 30 utc and you can find out more at daily tech news show dot com slash live back on monday talk to you then This show is part of the broad pants network Get more at frogpants.com Club hopes you have enjoyed this brover


So our next speaker is Matt Newman, and are you here, Matt? I want to share your screen. You're on mute. Yeah, I know. I have a t-shirt, by the way, that says you're muted, but I shouldn't wear that today. OK. Sorry, give me a moment. I want to introduce you. So our next speaker is Matt Newman. Matt studies climate prediction and predictability on timescales ranging from weekly to decadal with an emphasis on the use and diagnosis of empirical models constructed from both observations and the output of climate models. He's also known to like to have a cup of coffee before going to work. And we have had many interesting conversations over a coffee in the morning. And his favorite food is blackberries. Remember that. That's pretty good. Thanks. Thank you. I'm looking forward to your talk. Yeah, so I'm actually back in my office for the first time in over a year just for this talk. So got all the cobwebs out and we're ready to go here. All right. So I'm going to be talking about the characterizing predictable dynamics. You'll notice a little asterisk there. And that's because what I'm going to do is I'm going to use what are called empirical dynamical models rather than what people are more familiar with in terms of physical dynamical models. So in Richard Seeger's immortal phrase, these are physics-free models. But first, I kind of wanted to go back to this. This is always drives me a little nuts. You've seen this picture a number of times already, I think, in the last week. And in particular, it's this idea of saying, well, here we are in this S2S time scale. And this is where the predictability is coming from. The MGO, land, surface data, and other sources. And I can't help looking at this thinking that something is missing on that time scale. And perhaps that something is ENSO. Because even though we think of ENSO as seasonal, it turns out that we can actually expect higher S2S scale when ENSO occurs. And so here's an illustration of that point. This is a week four scale of the IFS, which is the European operational model. This is the one that was operational in 2017. 20 years to on-cast for the winter. Week four scale for geopotential height on the top and for surface temperature over North America on the bottom. And there's two things that are pretty clear here. Number one, most of the scale is during ENSO years. So all the other things are nice, but it's also good to remember ENSO is the big dog and when the big dog barks, that's when there's predictability. But there is some scale at non-ENSO years and it can occur in different places, obviously, than ENSO. And so what we're interested in more generally here is looking at a situation where the average scale is very low. I mean, these values are in general on the average for a weekly average, they're typically point two to point four. And what we'd like to know is can we do better? Can we identify when the forecast will have higher scale? And that's important because for the most part, for most users, this level of scale, especially in non-ENSO years, is really probably not enough to really pay a lot of attention to. It's got academic interest, but maybe not so much practical interest. So what I'm interested in, what I'm gonna be talking about then is predictability on climate time scales and kind of going back to what Judith talked about. One way to think of predictability is it's the limit at which your forecast probability distribution function for day-to-day weather looks identical to the climatological distribution. At that point, you're just predicting climatology and that's about two weeks for daily weather. This is why we're interested in taking longer averages. And so again, it's worth thinking that climate predictability is not really the same problem necessarily as weather predictability. It's sort of the statistical mechanics problem. We're really interested in predicting aggregates of daily weather. We don't expect to predict individual events. So you can already think that a lot of what's gonna be relevant for predictability on these timescales is going to involve some averaging. Now there's two ways to do this. You could do predictability as a model construct. Use perfect model studies, which some people may have talked about. But then there's this more fundamental question about whether there are real limits even to S2S prediction, to seasonal predictability. And so there's been a body of work going back particularly to Val Madden and Leith and others trying to estimate from the observations essentially some slow predictable signal on this weekly seasonal timescale relative to fast weather noise. And weather noise, remember that's unpredictable. There's always gonna be a lot of it. So these signal and noise ratios are going to typically be low. And that of course is related to the fact that we have such low scale on average. So how could we estimate the signal and noise ratio? This is just by the way an illustration of how you can derive skill. You can actually derive an expected skill given a signal-to-noise ratio. Basically as the signal is getting larger relative to some spread, you would expect to have higher scale. So again, you could do this in a model. You can get the signal from the ensemble, mean of the model, and you can get noise from its spread. That's pretty straightforward, but there's a question about whether this is realistic. Obviously if the model for example doesn't typically have enough spread, it could actually be overconfident. If it has too much spread, it could be underconfident. And in that case, its actual skills could actually be better than its predictability. So again, we wanna do this in observations. So the way we're taking the approach we're following is basically estimating natural variability as weather noise. So you're trying to get an estimate of what the weather noise is. And then anything beyond that is signal. This goes way back to the early 60s, Gilman et al, a couple other papers. And as is always the case, lurking in the background is Ed Lorenz, who basically kind of gave him the idea apparently. And the idea is that they were just looking at daily weather and they found even with daily weather that the memory of the weather was only on the order of a few days. And you could do a pretty good job just by modeling it as something called red noise, very simple auto-regressive one, AR1 processed, where you have some time scale of memory. And then otherwise, the variability is just being forced by white noise. So white noise means equal power on all time scales, no memory effectively. Madden went and tried to do something similar, a little more sophisticated looking at seasonal averages, trying to see if the seasonal average variability might be higher than what you might expect just by averaging the daily red noise, found a small difference, suggested that might be predictable. There have been more sophisticated techniques since, but again, it's always important to remember that you have to make some assumption about the noise. You have to figure out empirically how to separate the signal and noise. So what we're gonna do is we're gonna try to come up with the framework that we can do this empirically. And it's fairly simple. We're starting with the idea that we have a highly non-linear system. And if you had a GCM obviously, you'd have a resolved part of the non-linearity, and then you'd have some non-linear portion which is parameterized, sub-grid scale diffusion, so on and so forth. And then there's always actually a residual, basically that's not even parameterized. So you're throwing away in all contexts some little bits of noise, which relates to what Judith talked about last week. So what we're gonna do is we're gonna coarse-grain the system because again, remember, what we're thinking about here is we're not trying to predict instantaneous values of whether we're trying to predict some sort of aggregate. So we're gonna be taking some averaging and as we take some averaging, what we wanna do is we wanna separate the slow part that we're hoping is predictable from the fast part, which may be chaotically non-linear, but it may be unpredictable on the time scales that we're interested in. The daily weather is not predictable on two-week time scales, but maybe we can get something on the three and four and five-week time scale. So we do what amounts to a Taylor's expansion and you'll get a linear term, obviously, linearized term, but you'll also get some maybe noise terms as well. This is the part that's kind of unpredictable. Now you get other terms, higher order terms than those higher order terms could be the deterministic predictable non-linearity, but we're gonna ignore them and we're gonna see how far we get. We can collect terms like this. You'll get a noise term here. You'll also get a noise term that has a linear state dependence, which we're going to ignore mainly because we're interested in doing this empirically. It doesn't impact anything I'm gonna talk about. I may return to it at the end. The only real difference is it allows for non-Gaussian statistics with linear predictability. And so we end up with a rather simple equation. Essentially, we're just gonna try to predict all the dynamics as a multivariate linear system. So the key thing here is this X is a state vector. It represents all the variables in the system at all grid points as a function of time. And so what we wanna do is we wanna see how well we can do. How much of the predictability of X can we capture in this fairly simple linear way? And then given that, it also gives us some idea of what the predictable non-linearity that we've ignored is contributing to the system. So what does this linear operator represent? First, it's worth stressing. It's not a linearization. So we're not linearizing the system. We're not assuming that the non-linear term is small. Allowing for a potentially large non-linear term. What we're saying is that the time scale of the non-linear term is small. And that's what allows us to coarse-grain the system, that we have relatively slow linear dynamical time scales and the chaotic non-linearities are pretty fast. So on the time scale that we're trying to predict on the weekly, monthly time scale, the non-linearities, while they provide a tremendous amount of variability, maybe they don't provide so much predictability. So we can parameterize our effects. You can think, for example, of synoptic eddies feeding into a block, and to maintain the block, the block is sort of your slow time scale that synoptic eddies feeding in are fast. And so it's that flux that the eddies are providing, which is allowing the block to maintain. So again, there's that kind of time scale separation. Barotopic versus baroclinic dynamics, that's also kind of a natural time scale separation. Another way to think of this, that Cecile Penland likes to use is, think of it as the dynamical version of the central women's theorem. Again, we're doing aggregates. This is the key thing here. We're averaging over a lot of individual fast events. We're looking for some slower envelope of those events. And as you average events, even in a highly nonlinear system, you tend to make it more Gaussian. And when you make it more Gaussian, you make it more linear. It's also important to remember that the linear dynamics, again, this is multivariate system. That operator is asymmetric. And it's asymmetric because the system is asymmetric. If you have a shear in the system, then obviously you're gonna have different advection time scale at one location than another. So the location matters. And also different variables interact differently. So wind blowing on the ocean, for example, will drive changes in the sea surface height, but there's no sea surface height, a variable that's in the wind momentum equation. And so again, that kind of asymmetry allows for the dynamics to be asymmetry. And that's gonna be important because that means that the, when you look at the dynamical modes of the system, when you do an eigenanalysis of L, that those eigenmodes are not orthogonal. And as a result, they can evolve in such a way that they can cover up each other and then uncover each other. And that can give you transient anomaly growth even in a stable system. Okay, so we could, in principle, we could imagine kind of deriving this system from first principles, that would be kind of hard. But in this assumption, if we're in a system where the nonlinear areas are mostly fast so that on a slower climate time scale, they're essentially unpredictable and their effects can be linearly parameterizable, these are assumptions which we wanna test, then we can empirically model the system in this linear way where X is some state vector representing a series of maps, remember, and then it's being forced by some white noise. S indicates that it could also have some spatial structure, no temporal structure, though. And again, we can do this because we can, in principle, this is what I just said before, sorry, we can actually infer this in an inverse sense. If we have a system of this type, it implies a relationship between the lag co-variability and the zero lag co-variability of the data. And so we can derive this linear operator from the data, just from the covariance statistics, just like we can do this for a univariate AR1 system. It's exactly analogous. And then the noise statistics, which I'm not gonna talk about, although they're very important, but I won't talk about them today. You get that from a balanced relationship. Now, from a practical standpoint, this linear inverse model is computed in a low order space. It involves essentially, effectively, an inversion of a covariance matrix. So we have to truncate into a low enough EOF space, a low enough dimension that inversion is tractable that we don't get errors, very large errors. And one of the things that means is that the limb is a low order model. It's on the order of tens of degrees of freedom, maybe a hundred, as opposed to millions of degrees of freedom that you get in the model. That makes it a lot easier to run in a forecast sense. So you can run forecasts with the limb in basically less than a minute. Now we're assuming that this is a good fit for the data. We're assuming that's a good observation is we can test that with something called the tau test. There are various ways to do this, but typically what we're looking for is that it doesn't really matter what lag we choose to derive this L. Essentially, we could look at one month lag covariance or we could look at two month lag covariance or three month lag covariance for monthly data. And in principle, the linear operator should always be the same. There are other tests one can do, basically one can look at the spectra, one can compute for example, a one month lag covariance for the tropical Pacific and then test to see that the resulting limb gives the spectra out to decadal timescales and matches observations to the extent that it does. And typically it does, then the system is acting in a fairly linear way over month to month, year to year. I'm not gonna show that here. So how do we do this? How do we do forecast? Well, this is pretty simple equation. You can just integrate this forward. Obviously, if you ignore the noise, then you have an ensemble mean forecast that can be computed very simply because this is an empirical technique. We have the usual problem of having enough training data in principle. In practice, the easiest way to do that is something called cross-validation. We typically take out 10% of the data, compute the linear operator, the limb, and then use that limb to make forecasts, make on-cast rather for the missing 10% and cycle through. So obviously that's different than in a GCM where you just run the initial conditions forward for the model for, say, the last 20 or 30 years. Now, again, remember that the noise is, in this case, is independent of the system. So that means that when we do probabilistic forecasts with the limb, all we're really looking at is a shift of the ensemble mean. We're predicting some sort of signal and that signal is going to give a shift and so you're going to see a change in the tail probabilities simply because of the mean shift. That actually allows you to look and see, well, so how good a forecast system is this? If I compare it to a system which could potentially have changes in spread, are the probabilities better or worse or the same in the limb as compared to a system where I could potentially have changes in the width of the PDF as well as in its position? So again, here's the complete forecast system. Now we do want to consider that spread. So we're going to have an integrated noise term. We'll get into the details of that. This is essentially the forecast system including the ensemble. So each noise realization will give you a different member here and again, you'll end up with a Gaussian distribution. Again, for any given forecast, there is this forecast error but that forecast error, again, it's only a function of the lag of the lead time. It's not a function of the state of the system. That means there is no spread-scale relationship. So if to the extent that the limb predictability is useful, it suggests that there may not be a spread-scale relationship on these time scales. So what is predictability in this context? Well, the nice thing about this is that I know what my forecast signal is, that's this term right here and I know what my forecast noise term is, that's the statistics I have here. So I can basically determine a signal-to-noise ratio at every forecast time. And given that signal-to-noise ratio, this is just kind of a complicated, this is the actual math, but without getting into the details, I can use that predicted signal-to-noise ratio, expected signal-to-noise ratio to give me an expected forecast skill. And so I'm going to use this row infinity that we like to call it, but it's basically in this case, the predicted anomaly correlation and one can derive other metrics from signal-to-noise. This predicted anomaly correlation to stratify forecast scale. So I'm looking for a forecast where I predict that my scale is going to be higher. And I look at other forecasts where I predict that the scale is going to be lower. And I want to compare those predictions to what actually happened, both for the LIM and for operational numerical GCMs. So this is a calculation like that for ENSO. This is a LIM using tropical anomalies of SST and sea surface height and wind, comparing it to the NMME. I'm looking at an ensemble mean of eight operational models. And we're looking at that for 82 to 2010. So here on the left, I'm showing the month six forecast. Along the equator, I'm showing the, it's a skill score. It's sort of an error measure, but basically a score of one would be perfect. And the blue line here is the LIM and the red is the ensemble mean of the eight models, the little lines here are the individual models. The green is the predicted spatial variation. And so the first thing you can see is that in general, the predicted spatial variation is being mimicked by the LIM. The LIM is basically picking up that same structure. Now we can also look in time and see how the evolved pattern correlation is. And what we find again, is that there's a very high correlation between the LIM variation in skill in blue from year to year and this ensemble mean is NMME actual forecast scale. The correlation at six monthly is 0.8. And again, both of the variations are basically being predicted by the LIM. So the LIM is able to predict spatial and temporal variations in its own skill. And that actually seems to also match the spatial and temporal variations that we see in GCNs. All right, so that was a seasonal. Now we'll switch to a sub-seasonal time scale. So this LIM is a little different. We're constructing this out of a lot of atmospheric variables here in particular, there's a vertically integrated diabetic heating is a component of the LIM state vector. And we're gonna compare that to two operational models, the EC model IFS, which was operational in 2017 and the CFS, which is actually operational now. And again, it's key to remember both of these are bias corrections. So they're actually, they're already somewhat empirical. If you look at the skill of these models without bias correcting, the error is gonna be quite a bit bigger. All right, so here's the weeks three, four scale and the weeks five, six scale for mean sea level pressure for the 99 to 2010 period, because that's all we have for the CFS. And again, you can see that in all three models, there's a similar pattern of scale, very clear maxima in the North Pacific, a secondary maxima or another maxima on the Atlantic. And these maxima very match very well, regions of lower scale are also lower in both the models and the LIM. The LIM typically has a poorer scale when it's bad. So there's a tendency for the LIM when it's scale is low, it's scale is really low. But when it's scale is high, it's typically comparable with the couple of models. And you can kind of, you can sort of see that here. But again, the LIM is basically capturing the spatial distribution of regions of high and low scale. Now we can also again look at that temporally and this is kind of in a way, it's the central figure of this talk. The LIM is identifying what we would call forecast of opportunity. In other words, when we look in the LIM and find the 10% of the time when the LIM is predicting that its scale is gonna be highest. So this is at the time of forecast, the LIM is saying this is gonna be one of my higher forecasts. We can then look at what the LIM actual skill is in those scenarios. And that's these orange, the dark orange bars here. And we can compare that to the LIM scale when it's predicting low scale. And you can see it's definitely getting that stratification even gets in the first week. But you can see that stratification as you go on through, obviously, there's a lot of sampling issues on 12 years of high and cast. What's nice is that the LIM is also capturing the IFS and to a lesser extent, the CFS variation in scale. So again, the LIM is picking out the higher IFS scale versus the lower IFS scale. And again, here's our higher IFS scale and lower IFS scale. So the LIM is not predicting just its own high scale cases. It's predicting the model high scale cases. And this is a better metric as it turns out. You can look in the paper if you're curious. Again, to looking at a spread scale relationship. If you go into the IFS ensemble and try to compute high and low scale cases based on the spread scale relationship, you won't get nearly this sort of a separation. So again, it's just basically suggesting that to the extent that the LIM is capturing the variability, sorry, capturing the predictability of the real system, that most of the predictability is just coming about because of this mean shift in the signal, not so much because of changes in spread. That's in the Pacific. This is the NAO, the black line is the IFS scale. Now actually we've captured about the top 15% here. And so that black line is representing the IFS scale for the NAO in the 15% of the cases when the LIM is predicting higher scale. And the orange is the LIM's own scale in those circumstances. And that scale is considerably higher than in all the remaining 85% when the LIM is predicting lower scale. Here's the IFS and here's the LIM. If you remember, like I said, when the LIM is bad, it's awful. Part of this has to do with this low order truncation of the LIM because we're not representing the entire system, but we're representing the part that's largely predictable, which is all we really care about on these timescales. So where is this coming from? How does the LIM do this? It's magic. Now it's pretty simple. If you go back to what I was talking about earlier, this linear dynamics, like I said, is non-normal. What that means is that initial conditions can, depending on how they're oriented, extract energy essentially or from the system, from the basic stages of the system, but they can only do that for finite time periods. They're not exponentially unstable. These initial conditions, they can initially be in a position where they can extract energy, but they evolve. And as they evolve, they change their shape and they change their pattern and they evolve in such a way that they are no longer able to grow and then they begin to decay. So these are all transient events, but these transient events can last for weeks and that allows a lot of predictability. So for example, without, again, getting into the details of how you compute this, if this is the initial 250 millibar stream function pattern and this is the initial heating pattern in the limb, 14 days later, this is the pattern that will evolve. This is essentially kind of a combination of an ENSO and MJO heating. Basically the MJO heating starts kind of out of phase with the ENSO and it ends up being in phase with ENSO. And as that evolution occurs, there's a large amplification of the stream function anomaly, but if you were to look at a, I don't know, 40 or 50 I figured exactly when this will have decayed so that is now less than it was initially. But that transient growth is important because when that transient growth occurs, that means that we're getting a larger signal. And so our signal to noise ratio can be particularly large if initial conditions have a particularly large projection on this pattern or potentially this pattern or this pattern. And that's what's shown here. These little stoplight plots are showing the various cases when there was a large projection on one and or two and or three. So all three, just one and two, one and three and so on. And again, these are stratified by three things. First, the black bar shows the limb predicted skill in those cases. So you can see this is basically working as the initial condition is projected more on these initial patterns, the initial optimal structure, you get more signal growth. And so the expected skill is higher. The red shows the actual skill. So again, we're predicting the variation in skill. And this is kind of an old model as MRF 98. It's somewhat worse than the CFS is today. But even in that case, we were still predicting the stratification of the actual model as well. Hey, Matt, could you wrap up in the next three minutes? Yeah, I am. I've got a clock and I'm almost on schedule. So this is just kind of an illustration of what this non-normal amplification looks like. Essentially, again, like I said, the Eigen modes of the system are non-orthogonal. They're not like EOFs. You can't partition the variability in the Eigen modes. One can't really talk about so much variability in this Eigen mode and so much variability in this Eigen mode. And if I add it up, it doesn't work that way. They cover each other up and then they reveal. So for example, for ENSO, a lot of ENSO can be understood as this so-called four-year Eigen mode, which is similar to theoretical Eigen modes that have been determined in this two-year fast SST mode, which initially cover each other up. So the SST anomaly is pretty small, but here's this heat content anomaly. Nine months later, this mode has evolved but somewhat slowly. This one has evolved quickly. It's changed sign and decayed and you end up with the SST amplification. Similarly in the atmospheres for the PNA, for example, if this is the initial 200 millibar stream function anomaly, that seems to be a consequence of a structure that looks like this, which I'll just call an internal. It's unrelated to SST basically. And then here is a component which is related to SST basically related to ENSO. So these two are covering each other up. You have a small initial stream function, normally the SST and heating and the tropical Pacific are large though and they're driving this growth so that after 15 days, there's a large amplification of the PNA. You'll notice this is entirely flip sign while this has stayed almost the same. So this is evolving more slowly. This is evolving quickly and goes from destructive interference to constructive interference giving us transient growth and predictability. Okay, I'm off by a minute, not too bad. All right, so to conclude then, the predictable S2S variations, what we see is that they are largely driven by these linear but non-normal dynamics. And we can make that claim because we've tested this. So a low order linear model, in fact, reproduces the, to a very good approximation, the GCM ensemble mean scale and largely predicts both the limb and operational model spatial and case to case variations in the scale, which means that predictability, although there can be certain preferred states, those preferred states are being arrived through linear dynamics. They're largely being driven because certain initial conditions, if they're forced basically randomly will evolve into particular states, those particular growing structures that give rise to more predictable signals, which also means because we're doing this in a limb that the initialization only needs to be correct in a fairly small subspace. So it's interesting that we want to try to get a perfect initial condition, but a lot of what we're getting in the initial condition on these longer time scales just generates noise. And it does not necessarily yield predictability. So again, while these empirical models are physics free, they do constrain physical dynamical models. So we do want to be thinking about how do you get, how do you go from these highly nonlinear physical systems and arrive into a system where most of the predictability is largely linear? And I just want to have one, this is sort of a postscript, but this also kind of says something important about S2S forecast in general, because it's again, useful to remember that on average S2S forecasts have low scale. We're fighting against the weather noise, which is not predictable. And even in the best locations, the average scale is generally below what is considered to be useful. And that suggests the importance of this predictability problem because while there are some users who could use small shifts of probability, so an anomaly correlation of point two gives you a bit of a shift, maybe that's still useful. There are a lot of users, I would say most users, that most decisions are binary. And that's particularly true if you're not particularly rich. You don't usually get to come back from a bad forecast if you're not very rich. If you're a ski area and you have a low snow year, you can survive it. So these issues are important. And so to me, it's really suggests the importance and not just from a scientific perspective but from a societal perspective of trying to identify these relatively few forecasts of opportunity and being able to confidently identify them ahead of time so that we can go to people who need forecasts and say, this is a forecast that you can trust. And I'll stop there. Thank you very much. Thank you, Matt.


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I can't tell what it is Oh, yeah Dave winfield jj bladay brett madey rookie refractor Ezekiel Doran brice harangue rookie refractor obar and walker rookie tatis rather green logano hoppy rookie max mire and wilson contraris blue refractor to 150 cards Down to five on obsidian first obsidian five and 12 adley rookie von grissom rookie refractor And royals rookie Otto max castillo josh young Bieber headliners soto scantino von grissom, uh, that's rookie design variation osataka yoshida Degram refractor and michael harris Cal michel rookie and nice clayton kershaw gold refractor to 50 for the dodger garret michael rookie mookie mariano headliners holland gorman rookie vinyan sosa matthew libertore rookie refractor Awesome, that's mezz finest moments auto keith hernandez That's a cool one That is crofford christopher morrell rookie biren buxton purple refractor to 250 peraza Jazz chism michael confordo aqua refractor to 199. Hey, thanks. Scott. Appreciate that gunner hinderson seager biren buxton rookie nullan ryan headliners bias kig cavalli rookie sky blue refractor to 300 moustaka yoshida rookie max scherzer refractor michael harris rookie headliners shoe otani And padres rookie auto matthew batten Um, I'm not sure. I don't know if anybody else's pulled one. I haven't this is only my uh third case and then I did maybe almost a case of uh single boxes yesterday during personal garret coal purple michael mookie ezekiel doran rookie refractor nullan gorman vinyan sosa gibriel mariano rookie design variation peraza christopher morrell rookie kunya headliners gunner hinderson rookie rubies and chism cal rally down to four and 12 on obsidian basketball 14 and 17 on chronicles football and 28 on each of our select football reindeer ozerana refractor and nice This is marlin's rookie gold refractor auto to 50 jj bladay Go solid hit for miami Garo miami machado aqua refractor to 199 lanzo onio cruz refractor Obsidian basketball number 10 has nets mavericks rockets. Whoa Hang on. Sorry about that. My mouse hit the keyboard. All right. Anyways Nets mavericks rockets and grizzlies four to go On that first obsidian basketball All right, we need some big hits here. We've seen a few nice ones, but Time for a huge hit Spencer steer rookie Headliners randy johnson chato Hunter brown and gunner hinderson rookie blue refractor to 150 orials Go a nice one. Oh, it's 27 of 150 jj bladay Spencer steer rookie refractor to bar Bryce harper headliners And dodgers auto steve garvey all the chuck and wit I saw garvey in the last game Cruisin rally, uh, null and r and auto blue aqua vapor to 250 grit baby rookie and hunter brown rookie refractor bulky rookie Vinnie pasquantino rookie aqua shimmer to 175 francisco alvarez rookie one grissom adrian bel tray headliners And corbin carol rookie liberator miguel vargas rookie refractor carrot coal Vinnie pasquantino rookie design variation royals and rockies This is rookie design variation gold refractor auto 47 of 50 ezequiel tovar Nice hit colorado Riley green rookie Jordan walker Shea langa leers rookie lava to 100 Logan ohapi rookie Meyer jj bladay rookie Fractor just young rookie meneses. All right two boxes left Yoshida rookie Albert pooholes headliners Nice that is rookie Black gold or kinsy croix black gold design variation gabarro moreno Thanks Dylan Prado calmichael jazz chism jr rosegold 75 bello and harris There's mookie oscar colas rookie refractor Lenin sosa itchero headliners Nolan gorman rookie Christopher morel rookie Jt real muto purple refractors 250 Aizado jose conceco rosa and crawford miggy and lubeb gunner henderson rookie Mike trout world's finest Moreno black and gold kinsy Yeah, could you put that in recap please? Headliners aronado Mara Figuero marté Turner uh cori seager green lava to 99 Christopher morel rookie design variation And pirates rookie Greenwave auto to 99 leover piguero Moreno DL hall rookie refractor Kodai sanga rookie All right one box to go Give my hands voice warmed up here Boom All right Ready Cheche pla day and mark maguire rivalries Bless you Griffey headliners Scherzer Cori seager sky blue 300 genami And one more case of finest up after this leover piguero rookie refractor 80 rookie Headliners mookie We have netts mavericks rockets and grizzlies left on obsidian There's a link Anthony vaultbay rookie Aaron judge Aqua refractor to 199 Corbyn carol rookie and francisco alvarez rookie Vaughn grissom petalanzo refractor and Dodger's auto shawngrain Riley Wow, let's see a big hit here to close this out Liebertore jordan walker that is blue aqua vapor rookie to 250 cardinals These all those in green Muto Derek jeter headliners Riley green rookie Jordan walker rookie trade turner Adley rookie Ohapi Corbyn carol rookie design variation Goldie and young What's up, William? Oh, great man. How are you? Got an awesome weekend Can forward to a fun week here Uh, that is rockies rookie green refractor audits 99 michael toglia Last pack I'll to vey refractor correa and harper All right. Well Not the best case we've seen today. Um, let's do a recap And gabriel morano kinsu kroy Um black gold rookie The d-backs That's the rookie design variation. Thanks. Well, you appreciate that Jordan walker rookie blue aqua vapor to 250 cardinals Gunner Henderson rookie blue refractor That's the 150 orials Dodgers gold refractor to 50 clayton kershaw Mets finest moments auto keith hernandez Base autos we had Dodgers shawngrain A's josecan seiko Dodgers steve garvey Padres rookie matthew batten Royals rookie max castillo Uh nationals right and nationals rookie alex call Rockies rookie brian servin White socks rookie carlos pares Rangers rookie bubba thompson Padres rookie blue refractor auto to 150 agai risario Rockies rookie green refractor auto to 99 michael tuglia Pirates rookie green wave auto to 99 leo verpeguero. Thank you Marlins rookie gold refractor auto to 50 jj bladay Rockies rookie design variation gold refractor auto to 50 is zee keel tow bar And cubs rookie green wave auto to 99 christopher morel That'll do it for the break. Thank you everybody. We have case 20 up next