Jenny and Christine, thank you very much for joining us. Jenny Vingle is a machine learning engineer at Mixpanel and Christine is a future tense fellow and the senior editor at the New Atlantic. Sorry the New Atlantis, my apologies. So serendipity. This is kind of the fun part of the panel I think because as Ed and Jen and Ian talked about at the beginning there's this algorithms tend to generally not surprise you very much. They will offer predictable things. Occasionally they will offer you something really wrong and then sometimes they will offer you something surprising and delightful. My personal experience is I don't find algorithms surprising enough. I don't know which one of you wants to kick off but I feel as if when I for instance all right I get a couple of different email newsletters. A few different email newsletters that suggest to me links. One of them is from Twitter and it is based on the people I'm following on Twitter and it you know it offers me things that it thinks I'll be interested in. Another is curated by a guy who happens to be a friend and a colleague of mine but even if he weren't I would always find it much more interesting the things that Twitter sends me just because he is an interesting mind and he has more capacity to surprise me than anything that Twitter will send me. So is that just because I'm difficult or is there something wrong with algorithms or is it that by their nature their program to satisfy the average and therefore not be very surprising? I think it's just that it's a really hard problem. Yeah I mean so like I kind of I do so I like my background just for people as I'm a machine learning engineer like I have a I did a PhD in this and now I've done kind of two startups that are very ML focused so I'm purely a practitioner at this point and so I'm very in the data all the time and you know I really agreed with a lot of stuff that was said in the first panel about how when the algorithm does it right it's really not surprising like for the company that I work at now I built a model to figure out who was gonna start paying us and it's like oh look the most relevant future is people who look at the pricing page like that's not surprising you know and that means I did it right right if it shows you something truly surprising your model is probably wrong it's not that your model is so incredibly clever that I like figured out some right all of these algorithms are written by people right they're not it's not like the algorithm is writing the algorithm is writing the algorithm and something is just gonna magically get conjured up that can do can do magic right that that's not how it is like we're still really bad at machine learning and so like to me it's it's like so my I was used to work at a place that was a personalized news reader and so we really you know we tried to scour the whole web for links and then show people what they would like to read so exactly what you're talking about with the newsletter except it was just like a feed and you know I would never advocate to somebody that you should get all of your news from an algorithm like that doesn't make any sense to me like you're definitely gonna have situations where there's stories that are of interest to you that are not related to any of your interests are not related to anybody that you're connected to they're not related to any obvious thing about you maybe they're just like a big news story that for some reason you know it just caught your attention and you're like super fascinated by this story and and it will never get that right the the the the system that we wrote which did a very good job of recommending stories to me since I was one of the authors you know still didn't get me everything there would still be like a big story would come up like the FIFA scandal for instance I even said I'm not a sports person I don't care about sports but like I thought that was really interesting it's like a bunch of higher ups and this thing are all actually getting busted like I totally cared about that story I got zilch for my my prismatic feet on it you know but the the feed still got me a lot of stuff that I would never have gotten otherwise that I was very very interested in like it helped me say super informed about all of my interests all of my disparate interests I could just know you know what's the latest ML news what's the latest like in the like the world of knitting what's the latest in the world of interior design and and you know the serendipity was never we never tried to explicitly model serendipity like I don't know how you would do that and so the irony to me was like the times that the model kind of did what felt like serendipity like when would give me an article about like a knitting pattern for some like mathematical structure or something that was actually combining interests in a way that I didn't realize people did before to me that was like I got lucky like that was kind of what it is and so I think it can be very good at finding you the stuff you care about and it's really good at you know we can be very good at like just like the large amount of data like that's what it's gonna do it's gonna go through the massive amount of data that you would never go through yourself and find you stuff that may be of interest to you and that's it's really just like one part of the whole puzzle to me I guess I don't know right well so I think one thing I'd like to say is that there is no such you cannot manufacture serendipity and thank you for saying that I assumed I was gonna have to sit here and tell you you couldn't say that but I mean serendipity is the op the opposite of serendipity is manufacture engineering it if you think about when we talk about a serendipitous experience it's something that happens to us for God knows what reason when you think about though what I think is curious about the discussion going on coming out of tech companies now is that they are saying we can manufacture serendipity why there's a huge amount of hubris there's an underlying almost a moral argument being made there about this is what these things can do and I think most of the people who are on the panels today are not making that kind of argument practitioners tend not to be tend to not to overreach in that way but I do think we need to have a conversation as a culture about why we even have the phrase manufactured serendipity it's ridiculous and I think it does go back to somebody mentioned the uncanny valley on an earlier panel and what struck me is that you know the uncanny valley when you see a robot that's so human like that it creeps you out because you know it's not human we don't have an uncanny valley when it comes to algorithms do we what happens is that after the effect after after we find out that there's something that that an algorithm or a tech company has learned about us that disturbs us when we find out they know it then we kind of go it's too much like the Facebook you know constantly experimenting on its users story which as you pointed out quickly died off so I think the kind of cultural conversations we're having about these technologies are shaping our understanding of privacy our understanding of what we can and should expect from our machines and the software that drives them and ultimately we're having the wrong kind of conversations because we're accepting wholesale a kind of tech Silicon Valley fueled happy picture image of you know let's manufacture serendipity you can't do it I mean one of the thing because as you say human beings design these things they are flawed we don't have appropriate number of auditors even looking at these these things and telling us through transparent procedures how to fix fix them when they go wrong can I what I'm curious what company actually can I be doubles advocate for a second I'm gonna suggest that you have a you've been an essentialist about serendipity you're being the Antonin Scalia serendipity here because because let's say that some social network service or dating out whatever says to me hey you should really meet Laszlo from Hungary and based on you know some digital data trail that I've left matches me up with Laszlo from Hungary how is that essentially different from if all of various circumstances in my life led me to be walking down the street and to bump into Laszlo from Hungary the middleman the fact that there is a middleman that is placed between this human relationship and I think this the one question I wanted to keep asking every member of every panel and I'll probably pester you afterwards with the question is what should we not be asking algorithms to do much of what we're discussing is here all the things we want them to do we're getting better at doing this there are going to be things that we should want them not to do in there there's this great ill Wilson quote where he said you know technology and science is what we can do morality is the things we decide we shouldn't do and that I think is a conversation we need to start having about algorithms in the same way it's been going on an AI for a while I think and in science fiction there's a rich discussion of a lot of these issues so yes in some ways I am essentialist because I think when if you'd bumped into Laszlo on the street that would have been a deeply human and a deeply private interaction and no one else would have known that you know Laszlo and I have a friend who's worked with all the people that cameras pointing well exactly no but I mean there's something to have a friend who never went on Facebook because she helped organize political dissidents and if you ask her why she's not on Facebook she goes first of all I don't want people to know who my friends are and they don't want me to know you know so there is I think something yes it sounds very Luddite almost but that I think is the difference right sorry I interrupt it you were gonna come back on something that I was actually just curious which companies you think are claiming to manufacturers that are in deputy Eric Schmidt was interviewed and said we meant we can manufacture serendipity we can do that now I mean you hear the rhetoric out of Silicon Valley hot leaders all the time about manufacturing there's actually so is it wayfarer or there's a company that has developed a basically a muse an algorithm to give you good guidance through museums and the person was interviewed I think was in business week a couple years ago said the reason that he decided to create this this app was that he'd been in the British Museum on a tour and he had a really awful tour guide and the group next to him had this fantastic tour guide and he thought you know human beings are so inconsistent I want to be on that tour so let's completely standardize this experience using out and using all the wonderful tools we have and when I read that story I thought that's terrible I mean you had the bad tour guy but then you have a great story about your bad tour guide or you have he had a human experience that he found unpleasant and his solution was to engineer it out of existence by creating something that would make that experience standardized and so I think that if you I think I spent a lot of time looking at what tech companies say about their product not the people who engineer the people actually making this up don't say this it's the people who talk about it I think and and there's serendipity pops up in a lot of the marketing for apps any sort of jeep especially the new indoor GPS apps the discovery apps and by the way you must be born because I share a Spotify account with one of my nine and a half year olds and so he has his playlist and I have mine and so I'm constantly being given recommendations for weird Al Yankovic songs like not again I mean it's so I do think that you can when you share information you can have these machine-aided discovery tools but that I think is still different than serendipity but is that then just so is serendipity just another word for for more options or I don't know I mean I'm sort of wondering almost whether we we're just arguing about words rather than about reality what do you think Jenny I definitely came in thinking we're gonna argue the serendipity I mean the semantics of what that was definitely something that I would that I kind of assumed it's gonna end up I mean I'm kind of on the side of like you know if it walks like a duck and it cracks like a duck it's a duck like if it feels serendipitous and it happened to have been manufactured like to me it still kind of feels like serendipity and it could just be that you know part of that whole process of the serendipity was you know the computer in the middle at some point but like I mean I don't I guess I don't even really know what it means to me serendipity is just like when you kind of have a random thing that seems like a coincidence but is like nice you know I mean like like I don't even know what the formal definition of it is and it reminds me like my dad's my dad always used to say when we were growing up the biggest coincidence I'm sure he didn't make this but he was like the biggest coincidence would be if there were like no coincidences and it kind of feels it feels related to me where it's like you know that like something like it just doesn't like things are gonna happen things are gonna come together and like maybe in this case it was that way in this case it was this other way but it's just it it doesn't feel different to me it's not like the computer is some other or whatever that like doesn't get to count and I guess like so I I don't read a lot of the Silicon Valley marketing speak I try very hard to avoid it but so like so when I was actually reading kind of the questions leading into this and it was talking about the quest to manufacture serendipity like which to me kind of seemed foreign because I'm like I never tried like I just try to like get as close to the right answers I can and if I kind of mess up in a funny way it feels like serendipity but like to me it just seems like if someone was actively trying to like manipulate me with certain with like manufacturing serendipity is a really explicit goal like you're just gonna pick up on it right it's kind of like the viral clickbait and that's how like the first few times you saw an article that was like you know what this like where they like it's the new style like the kind of developed where you're like you know what this woman did to stand up against whatever will blow your mind and then you read it and you're like oh yeah you kind of like said something to someone once right like I don't like you and you kind of click on those like two or three times and then you realize okay this is just like an emotional manipulation that I'm just like gonna tune out now right every time I see you know an article that has a like you'll never believe what number X is I'm just like I'm obviously not gonna click on that and so to me it's either like it's crappy and it's obvious and then you just ignore it right you learn it and you ignore it or it like actually gets good right if if prismatic had actually gotten good enough that like people were just super pumped to read everything that they read and it actually found you awesome stuff like who cares that an algorithm made it right it's like either it feels valid to you or it doesn't and that the source kind of just just feels irrelevant to me I think it's because to me there's no black box like I know what the algorithms are doing and so it's just a person trying their best to like figure out how to like show you the content you might like right like I have no malicious intent when I do that it's like literally like I'm just trying my best right but there is algorithm or other the people who make algorithms I'm gonna suspect or argue have an incentive to make those algorithms work for as many people as possible and that necessarily implies a certain reversion to the mean in other words as I said before I don't think you're gonna try as hard or prismatic would have tried as hard to surprise me because it's trying to get a certain number of clicks from a certain number as many people as possible basically right so it's reversing to the mean whereas so in other words it's it's optimizing for a large group whereas my friend who curates the newsletter is really basically saying this is what interests me and if you happen to be one of the people who share my interests great that's like the thing that makes it technically hard and therefore fun is that like you want it to work well for everyone but I don't I don't see any reason why that automatically means a reversion to the mean like like if it I mean it may end up doing that but that's certainly not how do I put this it's like we're still gonna be like if our if our goal is to maximize clicks like say our goal really is to just like straight up maximize clicks like we're gonna do that by getting more people to click more stuff which means like we have to be showing those people things that they want to click on right and so like it we you could be some little and say okay well we're just gonna try to show you like the clickbait right and it was definitely the case that like naive early implementations just promote the clickbait right because people fucking click on it and it's just like whatever you can't stop them I don't know and then so we actually sorry sorry and so like this is what happens in your an engineer and not a scholar but like we I mean like so tell us that that was bad like we actually mean we definitely saw like okay this thing attracts clickbait and then you sit there and you brainstorm like how do I get rid of the clickbait like what's the solution it's not it's not like that the goal is to send you clickbait it's it's similar to the stuff where people are talking about where it's like the algorithm develops as the people use it right and so it's like if everybody clicks the clickbait the model is gonna learn to show you clickbait but like you know you've kind of got two options there like either you let your algorithm degrade and you let it sit there and just learn to show people more and more clickbait and then more and more people stop using your app because nobody cares about the clickbait and then you got a business or you take time to actually evaluate your data you look at what it learns you look at a bunch of people's feeds you look at what they click on and you begin to see the pattern okay it seems like we have a tendency to show people clickbait so why don't we add some features that try to identify that this is clickbait and you know you know actually hopefully you will then learn that like these particular features that are a bit more nuanced are actually things that people don't like so for instance we even added a feature that was like is this article likely to be a listicle you know because it's like nobody cares about listicles and we weren't explicitly modeling listicles but like once you can then actually model this is a listicle then you can learn that people ignore listicles and then you can down weight them and so it's like you clearly need a human but it's not once again it just gets back to this sense of like like these problems are hard right like the the person who was up here earlier who was talking about like systems versus ML and they like to have you know zero to 100 and you know I'm kind of happy with 70% or something it's not that you want 70% it's that you want to solve problems that are not solvable with traditional techniques right like there is no deterministic algorithm that is going to produce articles that you're going to like like I just don't know how to write that there's nobody out there that knows how to write that and so you say okay well you know I want to find cool stuff to read like my current sources they're not that great like I go on Facebook it's an echo chamber I go on Twitter it's an echo chamber like I'm just going to try my best and it's going to evolve and either I'm going to do a good job or I'm not but there's not I don't know there's not the underlying intent that I think a lot of people kind of attribute and it's kind of it's unfortunate because I think the marketing speak then just adds it all in I mean it's like you sit there and you just know the marketer's going to oversell whatever you build and you kind of have nothing that you can do about it right but the echo chamber that you mentioned is obviously part of this question right all the all the filter bubble is Eli Paris are called it this notion that we're just you know we get the stuff that our that our friends are interested in and so we come circumscribed and then that ends up limiting our political discourse because we only see the things from from people whose opinions we make we may share do you think either of you that that is reversible first of all do you think it is actually a real problem the filter bubble second can algorithms themselves solve it I mean I know there's some debate about Eli's particular model of the filter bubble but it does seem to be the case and certainly anecdotally you talk to anyone they will say if you look at I'm reading all the same things that my friends are reading and I think it's why some the online curators who do the best people like brainpickings.org there are a couple of really quirky sites where a human being with weird interests or you know a broad range of interests like your friend just for the love of wanting to share that creates something that then you know appeals to a broad number of people although and I love her site but maybe half the stuff on there is not stuff I'm gonna click on but the stuff I do find there is fascinating I never would have found anywhere else so it is a problem the filter bubble and it certainly has led to polarization of political and cultural discussion but whether we should turn to an algorithm to fix it is I think a question we shouldn't there are so many questions we should be asking before we even ask that and I think that's true of a lot of these algorithmic discussions which is I totally agree with you I think a lot of what people are creating with algorithms are trying to solve a problem but for a lot of these things like who should be paroled and for how long what sort of surveillance should we have of certain communities and for what reason there's a starting point about justice and about equality that an algorithm should have nothing to do with now maybe you bring the algorithm in after you solve these moral and ethical and political problems and you might not totally solve them along the way but I do feel like the our way of thinking and approaching some of these problems now because the tools are so nifty and so incredibly powerful is to start with the algorithm to look at what we can figure out like if a gunshot goes off here that means this person's car is likely to be stolen and we get so excited because it is exciting it's powerful but that's exactly why we need to first get through those tougher human questions which are not going to be solved by any algorithm I kind of have mixed feelings I guess so I think that like there's there's clearly like a human element right if a human decides to implement a system that's going to decide how to sentence people like that human should be very confident that is going to be like a very good system before they make the decision I don't think you can blame if you somebody whose job is to just kind of build the best system they can and that system is not very good and that system has a lot of bias I think it's it's mistaken to kind of blame the system that you should blame the the person who chose to put that system into practice but and I do think sentencing is like a particular hairy one that I don't know anything about and I don't want to make a lot of opinions on but I think there are a lot of situations where people like in the previous panel about fairness right and it's like is the algorithm fair like is the algorithm gonna particularly harm me as opposed to other people and and I think they're like you know my opinion really is sort of like if it's if it's if it's better than live ship it it's like if it's better than what humans are already doing even if some people you know someone's gonna kind of get get the short end of the stick no matter what so the example that I'm actually thinking of here is like self-driving cars right I think for a long time there was like the beginning of the idea of self-driving cars and people were super uncomfortable with it and it was like what if a car gets into an accident was somebody dies but if I die as a result of this algorithm what if my kid dies a result of this algorithm and I think it's actually kind of it's been slow enough the progression that people have gotten sufficiently comfortable with the notion of like you know self-driving cars will have fewer accidents few people will draw which few people will die from car accidents if everybody has a self-driving car you however may be the unlucky person who ends up dead who would not have ended up dead if everybody was driving right like themselves because it just it happens to be that the algorithm in that moment had to make a decision between this car and that car and it went for that car and like I'm sorry you know and so you know you could argue that's unfair but to me an aggregate that's still like a way better outcome right I'd rather have an outcome where where fewer people are dying unnecessarily or where you know potentially where few people being sentenced you know incorrectly even if it does mean that some people will have a bad outcome from the algorithm because in my mind previously other people were just having the bad outcome from a human you know before a human died from a drunk driver and now that human gets to live and this other human who was just driving and happened to be in the wrong place at the wrong time you know dies and that was the result of an algorithm whereas the first one was the result of a human and I'm like I don't really but as we said earlier the difference is that when a human makes a bad decision you can challenge the human find out why they did it maybe hold them responsible or accountable figure out kind of what went wrong and with an algorithm that's a lot harder you can find out usually I if you actually willing to date you can find out why an algorithm did something I mean it's not a mystery but there's another part that we're missing here it's not just fewer people will die if we have self-driving cars versus now you know it's not an either or because there's another thing that goes extinct when you have an all self-driving culture and that is the human skill of learning how to drive a car now you can make an argument for why we shouldn't we don't need the skill anymore it's not necessary but you know if for some reason something went wrong and the self-driving cars could no longer drive themselves then where does that leave us in terms of our skills and you know this is a debate that goes on with automation in almost any industry particularly flight recently but I so I think that but again the discussion becomes X number of people die here why number of people die here so obviously this is X is better than Y but in there is this whole human component that isn't easily quantifiable that is still nevertheless crucial for a functioning society and in a lot of these debates that's what never gets discussed I mean the toll booth operators right it's cheaper to have easy pass it's more efficient we can track everybody goes through things but you know then you lose toll booth operators as a human beings who had these jobs who spoke to people who you know they were wonderful interviews with the ones who were the last toll booth operators over the Golden Gate Bridge I mean some of them prevented others from you know committing suicide I mean they were they're human beings so it's fine you can you can embrace and more efficient algorithmically driven system in a number of areas of life but we should have the discussion about what we're giving up and it should include these non-quantifiable things well yeah I mean I don't want to get too much into this you know this other question of automation and jobs and people and stuff there is something though slight tangent on self-driving cars maybe but there's something that I'm noticing now is starting to happen which is up to now algorithms have been defining what we're going to see in the digital space right they define what puts will see on Facebook what gets highlighted for us on Twitter all of those sorts of things so they are shaping our digital world and what we're starting to see now is that they're beginning to impinge on our physical world so I mean the most obvious example is the one that Ed gave of Google Maps giving him directions and he's sort of not even thinking possibly not even looking where he's going he's just kind of following the map blindly and that start influences what he sees similarly if the map starts when Google Maps and Apple Maps start giving transit instructions they you may start avoiding certain subway lines or something or even certain neighborhoods because just the transport directions tell you not to go there with self-driving cars you also start to get a more mediated experience of you know maybe which roads you go on what you see on those you know what you experience on those roads so very gradually they start to shrink the boundaries of your physical world to the point where maybe we get to the point where algorithms start making it less likely that you will go to certain places certain neighborhoods see certain streets do certain things so that sort of reduction is that a reduction of is that a loss of serendipity I suppose well if you combine it with things like ubiquitous computing and some of the wearable sensor technology that's being developed now I mean the massive amount of data that we can give off just by being living human beings walking down the street you combine all those things and yes I think it does start you start to live in kind of an electronic pen in the way that they do for veal they're fattening for slaughter not to make a dramatic I'm just kidding but but no but I do think that that again those are the questions of how much autonomy do we want to give human beings because we're selfish we're violent we're a mess we're messy messy creatures and the engineering solution doesn't like a mess I mean several people on different panels today talked about how algorithms are so great you know what a mess but we get in there and we solve these problems and they do and many problems should be solved that way but some of the problems that we wrestle with and will continue to wrestle with as a species cannot be solved that way and when we try to solve them that way we just end up creating a lot of unintended consequences as several people mentioned today or really kind of undermining real human serendipity okay but that's you know we're going to have a mix of both right so what is what exactly would you like to see that preserves the right balance more cultural political social legal questioning of these algorithms before we take them for great before they're actually out there and running and I mean self-driving cars is a really good example because already the laws are being passed in states across this country that say yeah we can allow self-driving vehicles I don't think we've even started to have the ethical moral and legal discussions around they're starting but we should have been having those long before and I think anytime an algorithm algorithmic fueled way of looking at the world or an engineering solution is imposed I mean there's we have we have decades of you know sort of engineering ethics and theory and I think one good field to look to for guidance is bioethics so if you look at bioethics there has often there are often these moments where people come together and say you know what we have the power to do this and now we need to ask a harder question which is should we do this and if we do want to do it what is the path that we're going you know a Silamar there was recently one about this with genetic manipulation we need to be having those discussions in the tech space more than we are I think we have a lot of hype then we have practitioners who are solving problems and we don't have enough and we have wonderful tech critics as well but I think we do need to be bringing these more into the policy space and into the legal space I mean a lot of these questions are going to get answered in the courts I think I think people are going to get sued and the decision will come and that is not the best way to answer a lot of these questions. As I said earlier today the tech moves faster than the law does and then policy does so like what's what's your feeling about that question of how to have the discussion in a way that doesn't slow it down I mean I if I'm being totally honest I think a lot of the concerns that I hope this isn't too inflammatory I think a lot of the concerns that people have and a lot of the fear that people have is generational like I think it's I think it's like the older generation now like like basically my age and above is really worried and my age and below doesn't really care very much about you know your data getting out people using your data they grew up with the technology and they see the benefits and it's not that you know I'm not naive about the the downsides of the technology and giving up data I just believe the upside is so much greater like I believe the fact that I no longer have to navigate I can just I can just let the thing do it it just it frees up brain cycles it frees me up to do other things that I actually care about it's like we got cell phones 20 years ago we stopped memorizing phone numbers does anybody miss memorizing phone numbers like no I don't think so right and so like I just kind of think a lot of this stuff is gonna you know everyone's gonna fight for a while all the people that are fighting are gonna die and then it's gonna be young people who are just like yes my data it's out it's fine and they'll fight about something else like the next version of it or whatever okay that's great all right time to some questions oh sorry we have okay well one of the back and then this gentleman sorry hi Daria Stigman and this has been a fascinating discussion all the way around and I wanted to throw out one more thing and I agree with you about generational because I just don't I mean I think there's some issues of around health that are different but the bigger question I have that no one's really brought up is the issue in terms of search and how we use this of personalized search versus incognito because I think it makes a really big difference in whether we're aggregating all the data which really incognito is doing the best job from from a broader thing than personalized which puts you even more and more into a bubble so I just want to throw that out I don't know if you don't say something I don't have anything super insightful I mean I kind of think that the you know there was just general there was just incognito for a really long time basically and then they added personalized and to me personalized is just like you know it it knows which like like facet of the word like which you know if it's a word that has more than one definition like jaguar or something right it just knows which one I mean it knows that I care about the sports team or I care about the car and it makes like the car ones come to the top and honestly the the biggest thing I like about is it just means like if I search this before and I clicked on something it's gonna like move that one up so like it doesn't feel like that bad of a of a of an echo chamber the personalized search to me because it's like when you're searching you're so specific with what you want but I don't it's not that's not like a great answer so I don't know if you think more insightful no just don't share your you know make if you don't share a computer it's a different set of questions than if you do in terms of personal right but from one generation to the next thank you I am from France and between Washington and Geneva now and both the both of you the following question in US you have between 5.5 million to 6.7 million youths from 16 to 24 years old out of school out of job proportionally the same situation in Japan is getting much worse in Europe so this generation of divide or I wonder would you recommend because the life will continue you're going to work hard silicon valley nothing will stop I hope but I wish nothing to stop it but how can we work on this divide between other cultures other generations in this case and to address urgently this issue we have with young generation as I said from 16 to 24 years old as far as I know White House declared it as a national security problem now how do you feel in this gap if somebody would ask you how do you address this issue so you're asking about unemployment this huge social economic cultural gap between underserved and those better served economically social and culturally most of those 6.7 million they they don't understand very much about applications about algorithm they have their own implicit algorithm how do we address this it's kind of outside of us are in the video if you want to say anything on that I mean I wish I had a good answer for you but I don't well it does I mean one one thing it's not going to answer your question and I think an economist and political scientists would do much better but it did make me think of the fact that you're now seeing some of the tech companies Facebook already and some of the others are starting to be sued by the victims of terrorism for example by the victims of anything that happened that might have been organized online which I think is kind of a fascinating pushback if you think about this in the algorithmic serendipitous context so one of the great things about our these online spaces is that you do meet people in who you would never meet in physical space and you often find connection with them but this question of responsibility when when in sight inciting violence or inciting terrorism happens who's responsible the people who you know created the platform or the people who perform the violence and I do think that the younger generation setting aside all the economic challenges they will face they are starting to wrestle with this issue of how they want to live their lives online and I do think that I mean they're not going to Facebook as much anymore they're they're finding ways to mask their identity online in a much more sophisticated way than any of us did there's a reason Snapchat was so huge is that you know these messages disappear so I think in some ways they're a lot savvier in terms of having access to the online space that's that's actually there are tons of people here in New America who look at that issue and brilliantly so I would say ask them because but I do think this question of responsibility you do see a lot of this now with Facebook and some of the bigger companies are having to tackle that so I like to reframe the original question can algorithms facilitate serendipity for example it seems that with it one of the one the activity trackers there's a quote I heard that you know self-deception covers its own tracks so if we can have data that shows our blind spots and allows us to see things if algorithms can take away something and maybe for example Netflix says this is the movie with subtitles that people who never read used want movies of subtitles would like or documentary or whatever that is does that open up some possibilities that does that facilitate serendipity I'm a defender of self deception so you'll hear no argument I mean I think it's it's still I mean I think it comes down to the semantic sense of what you think serendipity means but there's still you know a human had to sit there and decide that they wanted to model that specific phenomenon like I think it kind of comes down to like there's the so for movie recommendations for instance right like there's a lot of and if you were just trying to write a recommender system for movies like there's a lot of aspects that you could consider and you could try to be like oh what someone's like their first time in a genre what should I show them or if it's there should I show them a series of that like you can kind of think about all of these these like one-off questions that you could try to answer and that you could try to explicitly model and so you could try to be like okay I want to get people to broaden their horizons so I'll show them you know whatever they've never seen a subtitle movie so I want to figure out the best subtitle one to show them but anything that you do like that it's like there's a thousand of those possible things and you thought of three of them and you kind of implemented them and so it's to me that's just like part of your recommender system and like the hard part is trying to you know do something at a somewhat meta level to kind of cover all the cases you like haven't thought of like that's kind of the the hard part of machine learning is like how do I handle the situations that I didn't think about previously and and like to me that's kind of where the act the actual like algorithmic serendipity occurs because it's the the part of the system that like you know you weren't explicitly like I am trying to do X and said you're like I'm trying to do A, B and C and this other thing you know F appeared as a result of this combination of B and C and so like it's it's hard for me to actually picture anything that's like an explicit goal like that really leading to serendipity in a in a manufactured sense I guess because I it's it's the bigger picture. Well pretty much out of time but we'll take one more question really briefly. All right is this on? All right I don't want to be that guy but I haven't I guess this is more of an observation than a question but a couple times during this afternoon Gideon you've mentioned things about people not having control their data or data being cheap because they're not really paying attention to it there's been a lot of there's been talking earlier panels about the role of transparency and knowing when you're in an algorithmic sort of monitored field and sort of pulling all this I think one of the things that hasn't been really discussed here much is the question of design behind some of these systems in that there's been such a push in the last 20 years to make our electronics and our electronic mediated interactions seamless and this push for seamlessness sort of erases it erases boundaries it removes friction and those points of knowing where boundaries are or knowing where friction is are so the points that give people a chance to grab on to stuff and if there was more more borders more seams and less seamlessness I think there would be a lot more potential times for people to be able to realize oh my data is going out here and I need to do something to stop that it's kind of like that art installation that Jacqueline mentioned of the the vibrations that go in and out of your phone I don't know is there a way to reconcile those two because obviously design and designers want things to be a seamless as well and so do users for that matter yeah I mean I think if you make things less seamless your app will fail and then the one that's actually seamless will succeed and it won't it won't you know it I think that's a kind of a non-starter but I really do philosophically I guess believe this hasn't come up yet but like your data privacy is your responsibility right like you choose to use the internet you choose to use apps you choose to go online and like you know you can you can opt out of that but like it's it really is your responsibility like this is the world we live in now where like when you use websites they collect how you use it I mean I hate to break it to you but every website you use does experiments on you like look up a B testing it's all of the time and all it does is make your experience better and like that's on you to op you've opted in and like I just don't I don't have a lot of sympathy for people who were like oh my data got away I'm like you know encrypted know about I am not I'm the only person on all of these panels who isn't on Twitter I'm not saying that probably I'm just I'm not on Twitter I will be on Twitter I have not opted into Twitter I don't mind them on Twitter it might appear on Twitter through I did not give any any permission for that and you can't people have jobs where they are monitored with badges that see whether they wash their hands after they go to the bathroom I mean that you are you cannot opt out of so many of these things some of them yes you absolutely can you don't have a very high cost I don't think you own all of the data that is peripherally related to you like if I'm in a panel with you and I choose to tweet about that and include your name like I'm I just feel like I should be allowed to do that like I don't think I have ownership I obviously that's a free speech issue but I'm thinking in terms of opting out I think we increasingly live in a society where you can't opt out a lot out of a lot of this tracking and certainly of the surveillance I'm I just and if you look at workplace surveillance right there you can see how many times I mean you you have a choice of what having a job or being tracked constantly on a job and people are trying to fight that but you know they need a paycheck so that part of it gets gets my libertarian hackles all you know raised because I think that we don't we have fewer and fewer fewer less and less autonomy than we should have in some of those spaces just as the debate is getting interesting I'm told I have to stop it so thank you both thank you everybody else thank you all very much for sitting through this


So, my name is Ile, I recently graduated from Boston University and moved to Visa Research. So, today I'm going to talk about the work I've done back in Boston with Vinod and Hottak about GJ-15 multi-linear map with permutation branching program. August the 21st, 2018, Palo Alto, heavy snow. Alice finds a public encryption scheme which is post quantum secure based on Schrodinger's equation. Alice finds out that she missed a post quantum cryptography competition round one, but she still finds it interesting to share the idea and let everybody join the attack. So, she does it by posting it on the blockchain and offers 100 bitcoins to whoever breaks it. Not only does Alice post the scheme on the blockchain, she does it cool by encrypting the 100 bitcoins using Wynn's encryption. So, what is a Wynn's encryption? So, Wynn's encryption is, it takes an MP instance X and a message M. It has the functionality that if it is a satisfiable instance, then anyone who has the potential Wynn's can use the Wynn's to decrypt the message. And the secret is that if X turns out to be unsatisfiable, which means nobody can find any Wynn's, then the message is hidden under the encryption. So, in this case, you can think about Alice encrypting the statement. There is an attack to Alice's PKE scheme based on Schrodinger's encryption. And if someone finds a polynomial time attack, it can be the Wynn's. And if nobody finds the attack, then the 100 bitcoins as the message will be hidden forever in the Wynn's encryption. So, what do we know about Wynn's encryption at today? So, just as a quick summary of the current status, we have several Wynn's encryption candidates more or less based on multi-linear maps. None of them are based on established cryptographic assumptions. So, these are the citations. You can look at them afterwards. So, what's the relation of Wynn's encryption into the title GJS-15 Beyond Promotation Branching Programs? So, GJ-15 is one of the leading, not the leading, one of the candidate multi-linear maps. And from multi-linear maps, and especially GJ-15, we know how to build a lot of cryptographic applications, such as constrained PIF, various forms of auscultions, and multi-linear, multi-party key agreement. But it turns out that multi-linear maps, and also including GJ-15, it's a design framework. So, it does not guarantee that whatever you build from GJ-15 or multi-linear map, it satisfies the security you want. So, it gives you construction, and in fact, each construction we need to analyze in a different way to understand whether the actual construction is secure or not, depending on the application. So, this is on the board. It summarizes something we have known about what multi-linear maps or GJ-15 can imply. And it turns out they live in different spectrums. So, some of them can be proven as secure as LWE. Some of them we have explicit attack, and some of them we don't know how to prove, we don't know how to attack. So, we call them candidates in this talk. So, the main motivation of this work is we want to systematically study GJ-15 in a systematic way. And the goal is to try to discover new attacks and, more like, say, more insights, say, how to prove how to attack this GJ-15 kind of framework to show what we can build more or what we can break from the previous constructions. And maybe I would just go through this kind of, like, give some intuition via the lens of witness encryption. So, maybe we can build it. Maybe we can break all of the candidates. Something would happen. With this motivation, let me come to the summarize of this, our result. So, we have a lot of results. So, let me try to pick the highlights. It turns out we have new insights as the results in all the three spectrums. But more, I think, more kind of meaningful or more results we can abstract, it turns out to be on the proof side. So, this will be the focus of my talk, that we introduce new lattice tools and the new analysis techniques for GJ-15 multi-linear map, such that they lead to better, they lead to different private constraint, PIF, and lockable obfuscation for general branching program. So, the immediate consequence is improving the efficiency, and I think there's more interesting property we can dig into that. So, also, we propose new attack to the existing IO candidates on GJ-15. So, we end up seeing, like, the encryption still stands as candidate, and sort of like another way to say is, this result should be written as, we try to build a lot of technique to imply witness encryption. We didn't make it, but we provide a lot of insights, and hopefully I can convey some of them during this talk. So, before I start the technical part, I want to just relieve myself because GJ-15 multi-linear map, it's a very beautiful object, but it's a little bit hard to understand for people who first see it for the first time. So, in this talk, I will just sketch the surface of, give you a flavor of what's going on, and hopefully it can convey as much idea as we hope for. So, multi-linear map, just a brief recall, it's motivated by Bonnet and Silverberg. So, you can think about if you have a lot of group elements like the S1, G2, S2, G2, S3, if there's some magical way to compute the product of the secret in the exponent without revealing them in the middle, then this is kind of the easiest way to think about multi-linear map. Or you can think about multi-linear map as homomorphic encryption plus public zero test. So, this makes a clear distinction because homomorphic encryption, I suppose, secret decryption functionality. So, we have a few candidates for multi-linear map. We know how to do a bilinear map which takes one product in the exponent 30 years ago by the way-pairing algorithm of Miller. For M multi-linear map with M bigger or equal to 3, we only have the first candidate since the first breakthrough of Gaugatour at 2013. And now we have three candidates they are all based on non-standard use of lattices, which means we don't know how to base the security on sort of like the shortest vector problem in general. Among the three candidates, the one of JJJ15 is mostly, this is the one which is close to LWB, which I'm going to focus in this talk. So, think about, so let me give you my understanding of JJJ15. So, think about the ring LWB analogy of this idea of multi-linear map. So, you put all the secrets as the LWB secret. So, here we think about the secret. It lives in the space where it's commuted. So, for example, the secret is a matrix, it's not a vector, or it's just a ring element in the ring LWB setting. And somehow we want to have a way to magically map the exponent together without rebuilding them in the middle. So, this is the difficult part. So, JJJ15, this concept is also appeared as cascaded LWB in actually different works. Actually, after you draw it clear, it's kind of like a chain in all these LWB samples in some special way. So, you can also think about it as the blockchain in the multi-linear maps. In the end, you will see a chain, and each chain has like two slots. It's not like a blockchain which only have one line. You can, if you focus, it sucks, this is only like a, this is a general blockchain. So, how to do it? So, let me start the example by just doing the chaining for two chains. So, to connect to LWB sample, we will use the public matrix of the left sample to also trap the sample some preimage which connects to the next sample. So, this is the brief idea. How to do it? We need to use the trap the sampling algorithm. It has the functionality that, given any image Y, given the trapdoor of, given A and this trapdoor, you can sample a small preimage such that A times D equals to Y. And if you sample the D matrices as the preimage of LWB sample SA plus E, and you publish the D matrices essentially as the encoding of the LWB secrets, then to evaluate, you just take the product of A0 and D1, D2, evaluate, it will give you something like, the functional part has the product of S times A and the rest of the part are all small, small noise, relatively small compared with the modulus Q such that it realized the functionality in some sense. So, before talking about the witnessing question of the multi-linear map, I want to mention briefly what's the harness of arguing security of a multi-linear map. So, because we are using trapdoor sampling, so therefore the LWBness of the left sample is not guaranteed in the beginning. So, just to keep it in mind, I will mention it later in the discussion of how to prove anything meaningful. So, as I mentioned in the beginning, we want to talk about witness encryption together with multi-linear map. So, the brief idea of building witness encryption for multi-linear map is you encrypt the instance, you encrypt the message, and homomorphically evaluate it to make a public test whether the potential witness satisfied the instance. But this is kind of saying nothing, it's like for most of the application you can think in this way. So, it in turn shows multi-linear map is very powerful. What I want to highlight for a particular witness encryption constructed by Gentry at all is just to forget about all the details, it can magically turn the witness encryption into a multi-linear map candidate, into a witnessing candidate such that you can think about the resulting plain text can be modeled by a read once branching program. And this branching program is also low rank. Think about the matrix branching program. If you represent it by matrices, it's the low rank matrices. So, in some sense, low rank matrices are bad news for multi-linear maps because something I didn't put on the slides that the previous provable case I showed for logable obfuscation and the private construction PIF they're all based on permutation branching program. So, low rank branching program is something that we haven't studied like whether it's secure or not. The good news is the resulting plain text you can think about is a read once branching program. So, it's potentially much easier to think about. And it has other nice structure which I didn't kind of abstract here. All right. So, so far I've concluded that there is a witness encryption candidate for GJJ15 it can be resulting to a low rank matrix branching program as the secret. So, in some sense, if we can prove GJJ15 works for all the low rank branching program, then in some sense we are done like this, like prove the theorem. So, we ask can we show anything special can we show anything secure for low rank branching program used together with GJJ15 and in this work we say yes in some limited case. What we will show is there is a special case of low rank branching program when it is used together with GJJ15 it can be proved as secure as ALWE. So, this special instance is when there's one slot that is always random, for example where the zoom is at the bottom right diagonal, if this slot is always occupied on all the matrices then our theorem shows this kind of branching program can be proven as secure as ALWE. So, the top right part can be anything even zero or whatever you want. So, before telling you how to prove anything let me tell you a little bit about where can this special type of branching program be useful. In fact, we don't know how to build a witness encryption or IO from this type of branching program. Our candidates are only a stone's throw from this type of branching program. What we do know is how to simplify the private constraint PRF and the locable obfuscation using this framework. Let me just give you one example. So, if you take the private punctual PRF constructed by Bonnet, Louis and Wu which is described under the multi-linear subgroup decision assumption. If you instantiate in GJJ15 you can instantiate in the following way so that there's a right bottom slot is the always random slot which will guarantee that you get a PRF and the top right part will be a punctual slot where you embed all the functionality you want. Just to give you a brief flavor in fact you can prove it from LWE. So, now let's come into the proof and first let's formalize what we are trying to prove. What we are trying to prove is what we call semantic security all the way back from like the semantic security encryption. We also want to broad idea here. In some things we want to say the top part is the real instantiation like all the AS you see these are using capital sampling and all the secrets they contain the plain text they contain the dangerous thing you want to hide. We want to show that under some situation the real construction is indistinguishable from sort of a simulated construction where everything is like A matrix is random you make sure it's random D is like a random Gaussian it doesn't contain anything about the secrets on the top. So this is the goal of the proof. So, in fact before telling you the proof of GG15 with low rank branching program it's worth to take a quick overview of GG15 used together with permutation branching program. It will be helpful. So, on a high level GG15 with permutation branching program is so we need to start a proof strategy from the right-hand side of the LW sample to the left-hand side. Because the right-hand side is the only A matrix which we didn't use it to do a capital sampling. So, first we need to use a lemma of a permutation branching program LWE which says if the S secret looks like a permutation branching program tensoring some random Gaussian matrix S, then you can prove LW sample with this structure kind of matrix is indistinguishable from random. So, this is the special part where we use where the previous works use permutations. So, with this strategy you can go one step left by turning LW samples to random and then you use the GPV chapter sampling lemma to close the chapter because the purpose is like all the way back we want to use the fact that the chapter of A is not used to go all the way left and using GPV. So, GPV says if the image is random then there is an oblivious way to sample it without using the chapter of A. Alright, so using LW plus GPV LW plus GPV again and again again we go all the way from right to left and eventually we can close all the chapter and it turns out to be a simulated distribution. So, this is the end of the replay of the previous proof technique. So, you may ask, so now what's the difference for LW matrix branching program? So, now we will see the difference. So, to see the difference let's zoom in the even the first chunk of the chapter sampling and eventually we will get LW matrix let's just think about like two block LW matrix and it's now useful to actually separate the public A matrix into a top part and the bottom part. So, the bottom part is still nice as we guarantee that we put a random matrix on all the lower right diagonal of all the secret matrix. So, we can also still guarantee that the wide bottom part looks like random. But to record to trigger GPV we need to show that the entire matrix is uniform which is not the case here. So, the real problem is like how do we go left to try to close the chapter of something on the left. So, to handle this kind of like a half scenario we need to introduce a new lattice chapter lemma which says if you have an image where the top part is anything the bottom part is uniform then you can use half of the chapter sorry, you can close the bottom part the chapter of the bottom part and use the chapter of the top part to sample a prime image of Z and you can pretend still this D lives in the right distribution. In fact, these two distributions are statistically close. So, using our first lattice lemma we can at least do one step left and since we guarantee that all the matrices has the right bottom part being a random so all the way to the left closing the chapter of the bottom part. So, this is kind of halfway done and to show the entire semantic security we still need to handle the top part and in fact the most interesting information is usually on the top part because we are going to encode whatever we want on the top or left matrix. So, how do we handle the upper matrix?  So, we don't separate them you can say that if you separate them the secret can be actually explicitly reviewed. So, if you only publish the sum of all the left matrix which in other terminology you can think about is as the left bookend in the traditional obfuscation from JJJ15 then we are going to use the second property that if you assemble the preimage of anything plus small error and if you are just a sample it directly and you watch this D matrix this is clearly a preimage of Y under the function A. But we have a funny observation that if we can hide the A matrix and the Z plus E matrix then you can actually show this D matrix the preimage is indistinguishable from a random Gaussian matrix. So, this is like a new lattice lemma that if you assemble a preimage and also hide the functionality in some sense then it gives you a preimage which is indistinguishable from some canonical distribution. So, with the second lattice lemma we can actually go all the way right because first we had the left matrix, the A top it's using trapdoor as fine so it can be hidden by the bottom part you turn this D matrix into Gaussian and you can use this strategy all the way to the right. So, let's review the proof again. So, it's kind of like two steps so the first step is more complicated the permutation branching program in the sense that the permutation branching program you only need to do one way. So now we first close the trapdoor of the bottom and then handle the top part using the second lattice lemma. So using these two tools we can show that this type of branching program used together with GJS-15 is as secure as the SWE. Right? So, this summarized the proof part and all the interesting technique in the proofs. So, let me just say a little bit about attacks. I say we can only prove certain type of low rank branching program but what about the rest of them? So, for the rest of them we show that some of them can be broken. In fact instead of telling you like what's the theory behind the attack let me just tell you that attack algorithm is very simple. So, you first compute a matrix by collecting a lot of evaluations on the potential IO candidate then you test the rank of this matrix. So, this is the analysis by picture for a read once branching program. The analysis is going to be very, very complicated if you work with a read many times branching program. In fact we also published the stage code of just demonstrated how to attack and I also run the attack on the code of written by Shai at all and you can also try it by yourself. So, okay, so in the end what about witness encryption? So in some sense we don't know how to we don't know how to make a witness encryption which falls into the categories that we can prove anything and in fact which I didn't say is like the attack can be also used to break all the assumption we make towards the witness encryption but still we get some witness encryption we don't know how to break and we believe it's kind of a stone throw from provable one so it's leaving as a candidate I hope people are interested to look at it and also we also write an IO candidate which demonstrate what we don't know how to prove. So this honestly I have a little bit less confidence but still even after this summer I don't know how to break it. And these are the related works so the two lattice lemmas are also mentioned in the concurrent work in fact they appeared earlier they just didn't publish an e-print of Goya Toa to build Trader Tracing from ALWE and also there's another work by they illustrate the limitation of our attack on digit 15 based IO candidates what I think about one of the interesting future direction is you can still try to build applications using the simple multi-linear map framework I think about you are using subgroup decision assumption or slots or something like that and try to instantiate using digit 15 because I believe there's something to explore in this direction and try to see if it can be proven from ALWE so that's it thanks for your time


What's up everybody? Jason here for jaspyscasebrakes.com. We just sold out 2023 hip rate football autograph limited edition series 48. Dual case break 20 total boxes, random teams number two. And again guys, you get a random team through two cases of stuff. There's the potential chasers you can get. So should be, should be a fun one there. I'm gonna zoom in so you guys can just see from right there. There you go, you guys can see that. All right, so let's do the break guys. Here's the dice roller. And here's the customaries that I bought in and one from the filler guys. So thank you guys for making this happen. So here we go, dice roll it. Just two times, thank you guys. One and two. Joe at the top, Del and Christopher there. And two times, one, two. Carolina Panthers out of the Kansas City Chiefs. All right, so Joe, you have the Panthers. Niggles with the Raiders, Joe with the Giants, Bangles, Eagles, AA run with the Patriots, Joe with the Seahawks, Bears, Commanders, as well as the Bills, Dolphins, Jaguars, Justin with the Falcons, Joe with the Lions, Christopher with the Colts, Joe with the Packers, Joe with the Chargers, Luke with the Cardinals, Joe with the Browns and 49ers, Christopher, with the Texans, Michael with the Bucks, Joe with the Saints, Ravens, Steelers, Vikings, Christopher with the Jets, Joe with the Cowboys, Titans, Rams, and then Michael with the Broncos and then Christopher with the Chiefs. So there's your guys' list of names and teams. If you guys wanna trade, I'll give you guys a quick minute. If not, we'll start the break momentarily. All right, I'm assuming no trades then, guys. Let's close them. All right, here we go. All right, so this is 10 boxes here, guys. St. Brown to 49 for the Detroit Lions. You know I'm not lying. Joe, how you going, man? On the board. Just like that, just like that. I'm gonna adjust it. That's number to 49. That's cool. Now for the Jets' Sauce Gardener, 12 out of 25. Jets, Jets, Jets, Christopher. On the board. DJ Moore for the Panthers. Going to Joe. That's a rookie autograph from 2018. NT the Future, on-card autograph of Damian Pierce. He's been banged up the last few weeks, but Texan's still rolling. Texan's Christopher. Having a good break, man. Already with a nice couple hits. That a rookie in our Donald? It is. Turkey Red, AD for the Rams. Joe, there you go. That's right, check those out number two right now. So what this means, right guys? Congrats, you just got a big hit from Hipporate. 20, 20 contenders, rookie ticket, relic and autograph PSA nine. So that must be a chaser there, I assume. And yes it was. So Joe, you get $20 to Hipporate. So nice, we'll tag down here with the card so that way it stays with it. No problem, man, hey. All you, buddy. You got this rolling, man, so definitely deserve it. All right, let's keep it going, guys. Arson's absolute autograph for the Cowboys. Joe, another one for you, buddy. All right, two more in the first case, guys, and then we have another case. Little Danny Dines, unfortunately, Giants and him, which does not look great and obviously injured now, but still hit there for the Giants going to Joe. Skymore from Impeccable to 35. Kansas City Chiefs, going to Christopher. What's up, Oliver? How you doing, buddy? All right, guys, well that was the first case and now it's pretty nice stuff, right? Got that case at Tua, AD rookie, Damian Pierce, DJ Moran, a little sauce, little Michael Vick, nice ride, baby. Steph came back today, too. One-two punch, Chad and SGA, SGA with another 40-point game, man. They're definitely one of the most young, exciting teams. Nice Christian Watson, green scope for the Green Bay Packers, 53 out of 75. Nice one there for the Green Bay Packers, going to Joe. Now, guys, if anybody on the Instagram said wants to do any personals, I'll be free and open to do some after this. Pretty much nearing the end, though, but if anybody wants to squeeze in a little Percy, definitely get it so that way I can rip it off to this, guys. So I'm just showing you guys a group break that I'm doing on YouTube right now. But once we're done with it, we'll do some Percy's. Ramondri Stevenson, gold out of 25. Nice two-color patch in autograph. That's a nice patch for a certified. Patriots, going to AA-ROM. Jameson Williams, three-color patch in autograph and certified to $199. Lions, Joe, pick it. Two-color patch in autograph to $75, going to Joe. I don't know if this is a case hit, although there is only one, two, three, four, five, six cards chosen, this could be one of them. But we'll see at the end, I can double-check. That could be the case hit, though. Nice Kenny Pickett RPA. Black Prime selections, three-color patch in autograph. John Dotson to $25. Commander's also going to Joe. Halfway through the case, guys. Five more to go. We got a Tony Pollard, dual-relegant autograph from Phoenix to $125 for the Cowboys. Going to Joe. Thank you, brother. Man, look at that. You put in the work, and I'm probably not surprised at anybody if you're getting these hits, man. It's ended off strong. Hop's Mini 2013 DeAndre Hopkins rookie for the Houston Texans. PSA-9, that's awesome. Christopher, you're having a break, man. I mean, I know you got a handful of spots as well, man, that's cheap. You're getting some nice hits. Bonte Smith. Schmitty. Four out of 25.  That's another one there for Joe. We got a Sauce Gardener for the Jets. Another one there for Christopher. I can do it right after this, right, man? Graph to 10 for the Minnesota Vikings. Joe, getting the last hit of the break. There you go, guys. This was a pretty solid case. I really want to say that Kenny Pickett might have been the case hit. Let me double check, though, because there are times where we don't find all the hits and make just collages up to some of them, but I don't know if this is the same year, though. Let's see. Oh, no. Oh, damn it, it's not available. Yeah, I'm not sure, but I have a feeling it has to be the Kenny Pickett, though. It looks like they don't have it up no more. But yeah, I'm pretty sure it was that Kenny Pickett, because I mean, it's a pretty big card. So I appreciate it, man. Thank you guys so much. And like I said, unfortunately, if you didn't hit, sorry, guys, I mean, only 20 hits, 32 teams, but thanks for giving it a shot. Jaspy's casebreak.com. But before I leave, actually, you guys do get the promo spots. So that way you can come back and try to potentially win some extra cash back just for buying with this. It's gonna be a lot of entries, but you never know, all it takes is one. So I'll put you guys right here and bold it. Boom, hip rate. Sweet. Appreciate it, guys. Thank you.


This video is brought to you in part by TrueTech Tools quality tools essential support What's going on guys? So today we're winterizing the chiller and the boiler system here at this old church It's an aqua snap. It's a plate heat exchanger on it. It's not got a lot of surface area So we're getting drained out got another guy here to help me Actually watches my videos, which is kind of funny. So we got 255 or 50 gallon 55 gallon barrel drums there and What we're doing is getting We got to get the water out of the system now this goes To a big old air handler up there above the ceiling What they've been doing is running just plain water and then every year They got to drain it or refill it and then they got air in the system and it's just a hassle These are some of the one McLean boilers. I just did the service on these That's gonna be our hose to get the Like all into the system we're gonna pump it in through the boiler there So we're gonna push it out with gravity because you figure the air handler Even though we're coming up out of the basement The weight of the water is going to shove it all down and then we're gonna fill it back in that way, too Which what we're gonna do is we're gonna measure out the glycol into The big container then we'll know how much we've taken out and then we'll be able to pump it back in Yeah, it's better than what it was. Let's start in close. We just put a block under there When I open this that'll give you some relief now Take the pressure off of it, and then I can open that up I'm gonna close this back up so we can get your stuff. Yep. Go. This is their ladder So I got to go up here climb the chicken Chicken ramp here get up here to the air handler but All of our fancy controls on here, which is ridiculously crazy said we're Going to Let the water pressure like a water tower shove it down so I'm gonna go ahead if you right now hit this See if water comes out, which I know we're gonna put a valve on there, which It's gonna make it a lot easier So then it will replace that auto-believe All right, Joe.    We ain't sucking air into the boiler about 75 gallons Maybe we don't need as much as we thought we would I mean that's about 65 gallons something like that. Yeah, we'll stick it in there run it for a while And we'll see where our percentages are at we don't want to go too high because then we won't have the heat transfer that we want There it goes All right, so I stopped the pump I'm gonna run upstairs then if you want to open that back up you should be okay, and I got it valved off. I Know but don't you want me back upstairs first? I don't like that. It looks crooked or Dogs hind leg. I don't Don't have to do about that. It's threading in there though. That's what sucks All right, as you can see the new one is a lot better shape than the old one, so Let's get this threaded up and get it in there. This should do a little better than what it was You know Looks a lot strer and what did before that's for sure. This is a dedicated refrigerant hose. I used to blown out Things with nitrogen or run a water through it doesn't have an end on the other side. So it works out good that way And we got our bucket here All right, Joe. I'm ready Open that up Let the juice loose. Well, it's been a while at least 15 20 minutes But I just heard some gurgling of water down there, which it's got to come across and then Come up and fall down which I doubt we got everything out of this coil because it's below grade but we're pumping the remaining remaining in there we got the 55 gallons in and I'm starting to add some more which we should have full coil water here We got the chiller that was completely empty, which like I said, it's just a plate heat exchanger. So it's not very big We got a few other areas of the church here. That's Probably full of water yet too. So the thought in theory of this was the water level comes down And then it goes back up with glycol versus I'll go with his idea over over mine So he just stopped it while I was adding oil had some up here. I'll see yanked out my One for my circulator. You can see the seals down there. They're not, you know encompassed but It definitely Better than what it was then we're gonna use our fracknometer and we'll find out where our levels are at but We've got to let it all mix together first for we can get accurate accurate readings There we go There we go. We're getting there start to throttle it down. So we don't get a bunch of Finally got my first splash, but there's still air coming Yeah, it's starting to hit now finally. It's coming and going whoop got it Yeah, I started getting solid flow Then now I kind of cracked it a little bit and slowed it down. So it's coming and going and since we get this filled up We'll kick on that circulator and get her all mixed together All right, it appears we got solid water. I think I'm gonna go ahead and kick my pump on So you get the fan something something all right, so we have a Damper here that just kicked on That's got another thing of air Quite the conglomeration here Got one coming in going back. You got a bypass basically so I can go through here. It's kind of Interesting Open less seat close more heat There's our control guys of stuff there Not seeing the circulator pump call yet may have to kill these I Killed power to it and it started reset Got some old pneumatic controls here. I got into the computer system and Got it running. I don't know if this one here is on the same Deal as the other one You can see you got another boiler over here a little burger so Yeah, it's not calling those are gonna stop these variable speed Circulators, okay, it must work because their handlers running now now this one here said open for less heat close for more I want this thing to circulate Theoretically it don't look like the circulator pump is calling because the VFD is Nothing's marked one way or the other so you don't really know exactly what they're trying to do Yeah, pumps not calling All right, so we got our circulator pump running and we have our valve cranked a hundred percent So we should be flowing so we should be flowing some heat now if the boilers running like it should so let's go look and see All right, just calling so it says yep, it's spinning Kind of let it do it's a little thing or just a little drizzle Starting to get a little green. I think here it comes. I got it cranked just a little bitty bit There a couple there they're tiny So this one here has got boilers that have had issues with air I'm hoping because I thought that we had some issues with the control boards possibly acting stupid All right, so we've got two on the plane of Slim fit we've got two of them here. We've got two circulators They come together in parallel Come up here to our top Got water movement coming over here to our air separator Got our pump down here, which she would think is kind of a corny design Old-fashioned tank expansion tank no bladder type Comes up to here. This is the most confusing mess. We've got valves here that Modulate it's really confusing Got the chiller out here. We like I said, we just finally got it I labeled things with arrows and what they do and how to bleed it because we got a big old air handler up top Finally took care of all that nobody labeled none of that crap So everything is so much easier now to find So over here on our control system Just got in talk to the control guy Figured out what my numbers were Made sure it was on and off. He ticked it on and off. It is the boiler right there It's calling made sure we had voltage going to it We got it over to it. So come over to here And I had a look at my book. So 16 here and here is your thermostat call for heat Come here and run ac volts We come to it. I've gently got in here because I didn't want to move nothing And we show no voltage. We go to ground Got voltage here. Check the next terminal going up. We got voltage here So we are looping voltage back and forth It should be calling So when we come down to here Basically, he doesn't say that it's even calling for it since standby Nothing's circulating this boiler is 68 degrees. There's boiler temp 68 degrees System temp 118 Same thing. This is a shadow boiler. That's the politically correct term Pressure switch is not accurate. It's not shut down on that. It would be flashing if there's an issue Outdoor temp is 43. So that temperature sensor seems to be working right She's not calling for nothing. She's not locked out. That's what's kind of crazy here We can unplug this here I thought this controller is maybe going bad. It still says network flc standby We can come over here to our meter just Not that the voltage didn't tell us what we needed but We can go between these two terminals to see if we got resistance between them 0.7 ohms 0.1 ohms Added some dielectric grease to my connections Uh, not so much this one But inside the meter.  It made a huge difference I mean 0.1 ohms that's unheard of These are american made leads are made for uh, these are called Probe master they're american made unbelievable. Joe sheer gave me these at the uh Florida show down there and man these things are awesome You got to modify them a little bit. They got this weird finger protector thing, but These things are awesome So far they've I've had them what since march So nine months of being wound around it which aren't supposed to really do So that's plugged in there Yeah, the blue p14 that's your supply and return sensors So that's those Do we have double ups? I don't know if they're stealing because see we've got our own right there And I don't know if they've tried to stick the ones from the boiler in there. I don't think they did Okay. Yeah, there is sensors underneath there There it is The other one So it is reading its own sensors for that and then the control system has its own sensors Okay, well, we are technically only like 43 degrees outside and it's thinking system temps 118 Even though the boiler is 68 Thing is i'm not sure why we're picking up all that extra temperature My thing i'm doing right now is narrowing down. Is this an issue with it thinking that I am warm enough because it thinks the system temperature is 106 degrees and you feel the boiler. She's not been warm for a while Feel the circulator. It's cold this one here Uh, this one here might be running Yep, she's running now it worked on sunday. They said come down here. You can see See that it's running So what we got going on here is it thinks that the uh temperature doesn't need to be that high So let's go ahead and play with it a bit. Let's check some says some settings here And then I'll let you know what I changed or found whatever Now I thought we were calling to the attic air handler, which is for the main sanctuary area So when I come in here and I stick my finger in the little tear that i've used in the pasta field, they're both cold Uh, I don't know it should be recirculating Around let's see what we got over here for heat. It's not even hot.  This is an issue I've had in the past Let's go over here. See if we got warm cold there Thumb check there ouch. Now that's that's hot right there I don't like that. That's definitely warmer than I want to put my finger on which makes no sense because This right here should be hot But yet it's not hot which makes no sense because the water's got to go this way So water comes through goes around over to here because it can't go that way because it hits the ball valves And yet it's hot on top. This is screwy as flipping setup. I've seen it's it's such a piping nightmare It's been a nightmare all the way around just a pure nightmare Yeah, see it's not even hot here. So we've got air issues We got air issues again The whole reason why we switched just to glycol because every year They were draining this boiler down or the Chiller down because it's outside And it run the straight water And then when they turn it back on they get air in it and it was because one particular person was afraid that If there was a leak that it would damage more things with the glycol than what just water would do But every year we fought this battle of air So the way I ended up figuring out how to bleed it because I have an auto bleed upstairs I have a bleeder over here. You have an air separator Because everything's in parallel-ish areas. Okay, so you come in here through the Circulator up T's can come this way it comes across To there comes down and go through the air handler back up You got a modulating valve here. So right now if it was to call through it would come through Going down And back so right now the water is going to come through T over that way. There's a check valve there Get pulled in Goes across In this position here with it going flat and straight down It's occupied. So we're really just occupying the downstairs You're basically just running the water on through it's going in a circle So in reality you're just going on through and going around it's it's confusing when you don't think about it Or when you actually think about it But the circulator there partakes of some of that water takes it down and jams it back to here So it's just kind of going around doing a loop thing And then it steals a little bit off that T over there because down through the coil and comes back up to here All right, so we're going to go up here in the attic Look at the air handler. This is where I had to Valve it off the last time and force it to be bled downstairs. I It sure acts like it's got air in it Circulators are running, but it's not warm up here. I got a auto bleed here What we ended up doing or I ended up doing Was stopping it on the outgoing side here and see an Occupy It actually stops the flow a hundred percent the one downstairs just partakes of it kind of screwballed An Occupy just lets it recirculate This had worked for two weeks. No problem. Now all of a sudden something got Changed. I ain't sure exactly how this happened, but so this basement area is all done by this air handler that we've been working around Basically, you just got two air handlers This one here and that one over there They're in parallel. It's just a big loop. It goes around But for whatever reason Just turns into a A major pain. I mean, I literally had to hook that hose on there because it had so much air and it just was going all over a little spitter spudders And so What we got now is the upstairs cannot come back. I stopped it So it's got to go through here It can go all the way here and I've closed this So it's got to go that way go through that check valve goes down through That into the circulator through the coil Back up through the coil Up and through here through the valve this valve is basically like this pattern and go through or this way So with it being Straight up and down it stops the flow from going this way But it does allow it to come up from the bottom and go left When it comes up from the bottom and out because it came down back up again It went around to the left and goes around to this loop the loop It goes back over to here Go over there It goes backwards, which would have been better if this would have been put over here So it's got to come over here head dead heads into that closed valve. So it has no choice but to go this way Comes through and on out and circulates around and around If we put our finger on this It's not hot the boilers were was calling So what I ended up doing the last time Is I closed this valve here which ended up Fortion it through that loop that I just showed you So I closed this And I'm starting to feel it get a little bit warm And then maybe do to close again And this is how I got the air out of it the last time Because there's no good way to get this air out of here. It's it's kind of a I don't know. This is an existing System that we tied into And it worked and it has been working just every year. It's a battle to get it blood And then usually you're good to go after that Whatever reason Things just took a dive. We're getting some air there I am warm definitely warm And it's looping its way around so downstairs air is taken care of Hopefully upstairs is good because like I said, I stopped it coming out of the coil So any air should have been trapped in the coil then into the bleeder. So we're going to go up there Do that real quick and open it back up see if everything runs So I just opened that valve coming out of the coil and I'm waiting for this one to get warm This is my incoming side. So it was starting to Okay, it's getting to the point where I do not want to leave my thumb on it We're good there It's come out at 84 and rising. So it's cranking some air That's why that water don't feel very hot coming out of the coil because it's going in the duct work where it should be going I found out It's the first time I've had a glide call So I was looking at burst factor versus freeze slush And so I probably needed to add some water anyway Because currently my burst is like negative 30 and we're never that cold But my slush was around five, but if we should never slush you think about it if these things are running So and it's not a chiller. So we're running a boiler So we're never going to be at five and worried about slush because they're going to be hot water My controls guy used to be an HVAC tech and He's he's always got a good answer for something. He's the one that kind of helped me figure out how to get this thing bled He's always been helpful And he's doing everything that I'm trying to do with the new guys here And online is to just try to help and not discourage and run everybody down I know he don't watch my videos or nothing like that because You know to somebody like him, they're boring. We got that I just want to double check a few other things in the book here just to make sure Right now. I mean, we're we're holding pretty good. We got the outdoors Sensor on there. It's trying to hold about 134. So everything's looking pretty good Well guys, that's going to wrap that one up.  It was a little high I think at the 34 mark which we calculated it in and it kind of can't end up being a little on the the higher side there It was recommended to me by one of the other guys to go with like 29 percent. So I think what happened is when it got shut off it basically started to liquefy and the air separated and then it either pocketed in the system, which is what I think ended up happening And then the sensors the temperature sensors got hotter quicker because They had no water removing the heat from them and then it Didn't call for heat from the boiler And the boiler didn't run so the water wasn't warm at all So even if it had a little bit of air in there, it wasn't completely locked But I think it just shut down because of that. So once I did that Uh, let it all out and everything it just seems like that took care of it. Um, it's working good Uh, I wrote on the paperwork that we may need to come back and add a little more water to it And it flushed a little little glide call out, but It's kind of a trial and error thing right now I was siding on the air of caution All right, like I said, I haven't done a bunch of it we can get down to Zero to negative 10 15 just depends um When it's like stupid unusually cold but anyhow But there's like I said in the past it it's a difference between burst and slush Slush we're not worried about since we're using it in a boiler Burst is what we're worried about so I got the Sheet downloaded from the factory on what exactly those percentages are at because you can't just go off your Anometer Uh, because it usually will show you the Freeze protection not the burst And so that's about it If you guys enjoyed the video and you want to see more like it, please hit the thumbs up button And we will go from there until next time guys. We'll catch you on the next one later


Welcome to Toffy TV on this historic day as Everton finally got the planning application for Bramley-Mawd Dock, the new Everton Stadium, approved by the British Government. It obviously had gone through local city council approval that was given about a month ago. It went down then to the government who had three weeks to respond. They asked for some extra time and just a week and a half later from that we finally got the news that Robert Jemwick has approved it and it's huge news. The Secretary of State, House and Communities and Local Government sent a letter to the club this morning to confirm that it can go ahead. The 52,888 capacity stadium on the banks of the Royal Blue Mersey can happen and will happen and it's back to the club now and the club now will press forward with all the financial side of things getting all that sorted and hopefully we will get a spade in the ground in the coming months. Obviously the dock needs to be filled in and then the stadium will go out of that and it's such an exciting, exciting moment as an Evertonian. Someone who's been sitting around for the last 30 odd years, if you go back to the Peter Johnson era when he wanted to move Everton to a KB golf course and Gilmoss and then obviously there was the King's Dock which looked like it was going to be absolutely amazing. It never happened and then KB which Keogh obviously fought so hard and bravely fought to keep Everton in the city against that. That never happened and then there was the Mutants of Walton-O-Park which seemed like a perfect one. There was also the Wallacey Tunnel, there was that one as well but now finally with this one we've got it and even with this one there was talk it was going to be up in Crockstaff. Luckily enough Everton four years ago went with Bramleymore Dock. We did a video, me and Baz did a video four years ago this week basically, a guide to Bramleymore. You can watch that on the channel where we drove down and we had a look round and a couple of years ago then we got the plans, got to see the plans. It's just felt such a long process and here we are today finally given the approval by the government after so much hard work by people at the club who've done an amazing job to put this together to make sure every eye was dotted and every T was crossed and make sure to, because obviously there was the whole heritage people who were against this haven't changed the plans. Now the stadium looks absolutely amazing with those steps sloping down to the riverfront and it just looks amazing and to have that news today that it's finally going to go ahead is just such a big moment and obviously we know what will be leaving behind in Goulderson Park but obviously there's plans for Goulderson Park and the legacy of that as well so that's fantastic that's going to happen. It's just really really good news isn't it? It's just really good news now that we can go ahead with it and hopefully see that spading the ground in the coming months. The development and the plans for Goulderson's legacy will deliver 1.3 billion boost to the economy, will create 15,000 jobs and will attract 1.4 new visitors to the city. And it's just the idea of having somewhere which is not only for Everton on a match day, generate money, generate money on a non match day house somewhere we can go which will now have Everton Museum which we've been craving for years somewhere that will, you can go and you know a big club shop that you can go down to and go and have a coffee somewhere you can go in the summer and walk down by the river and which connect the city centre to the dock area and hopefully the things that's other things in the area that spring out of it. It's just going to be such an amazing development for the city and for Evertonians and as I said it's something that for the first time in a very very long time it's like Everton have gone for it and have done it and it's just brilliant brilliant news, it's just brilliant brilliant news and looking forward to seeing you know it go up in this thing grow out of the ground. And we've all seen the pictures of what it looks like towards the city centre and seeing those dots joined up towards the city centre as well so it's not wasteland, it's going to be tangible things there, brilliant boost for the city and yeah and it's obviously going to be very very sad at Goulderson Park leaving Goulderson Park and obviously now going back we haven't a lot of people haven't been in there for the last year so the idea of being able to go there from next season we've all just got to enjoy it and make it happen. Every time we go there in events and not take it for granted because we haven't got it for much longer and we haven't had it for the last year so it's huge, it's absolutely huge for the club and we'll be doing a lot more on this in the coming weeks and months and one of the things we want to do is redo that guide and give you a little look at what it looks like now not much has changed but just give everyone an update. Make sure you check out that video as well it shows you the drive from Goulderson to Branwymore so anyone's not familiar with the area can get an insight of what it actually looks like so make sure you check that out but it's fantastic news and obviously in the weeks and months Evan will reveal more about what's happening, they've already got people down there on site doing their tests on the brick work so things are already happening already, everything's in place and it's just a case now of connecting all the dots and going forward and getting the work started and then seeing the stadium grow. Out the ground and seeing a lot of heads fall off the people who thought this was never ever going to happen so really really looking forward to that so huge news today as I said we'll have more news on this in the days, weeks and months coming forward going forward and that's really it isn't it? The only other real news is Dominic Havillian scored twice last night for England as everyone I'm sure is aware so we're good news from him so fantastic news for the club today and we go forward now don't we with the hope of that new stadium on the horizon and big things for the club, there you go. Make sure you give this video a like make sure you subscribe if you haven't already and if you want more great videos daily live videos the exclusive interview with Jamie Carragher is over there as well. On page and make sure you go and check that out the link is in the description and on the screen now. Thanks for watching. See you later.