>>what? Hello, I'm john free with the cube. I want to welcome Conrad Feldman, the founder and Ceo of Kwan cast here to kick off the quan cast industry summit on the demise of third party cookies. The events called the cookie conundrum, a recipe for success. The changing advertising landscape, super relevant conversation just now. More than ever. Conrad welcome to your own program kicking this off. Thanks for holding this event. It's a pleasure. Great to chat with you today. So a big fan been following your company since the founding of it. Been analytics is always the prize of any data driven company. Media. Anything's all data driven now. Um, talk about the open internet because now more than ever it's under siege. As I, as I mentioned in my open, um, we've been seeing the democratization, a new trend of decentralization. We're starting to see um, you know, everyone's present online now, Clay Shirky wrote a book called, here comes everyone in 2005. Well everyone's here. Right? So you know, we're here, it's gonna be more open. But yet people are looking at as close right now. You're seeing the big players, um, or in the data. What's your vision of this open internet? >>Well, an open internet exists for everyone. And if you think about the evolution of the internet, when the internet was created for the first time really in history, anyone that had access to the internet could publish the content, whatever they were interested in and could find an audience. And of course that's grown to where we are today, where five billion people around the world are able to engage in all sorts of content, whether that's entertainment or education, news, movies. What's perhaps not so widely understood is that most of that content is paid for by advertising and there's a lot of systems that support advertising on the open Internet and some of those are under siege today certainly. >>And what's the big pressure point? Is it just more control the data? Is it just that these walled gardens are wanting to, you know, suck the audience in there? Is that monetization driving it? What's where's the friction? >>Well, the challenges is sort of the accumulation of power into a really small number of now giant corporations who have actually reduced a lot of the friction that marketers have in spending their money effectively. And it means that those companies are capturing a disproportionate spend of the ad budgets that fund digital content. So the problem is if more of the money goes to them, less of its going to independent content creators. It's actually getting harder for independent voices to emerge and be heard. And so that's the real challenges. That has more power consolidates into just a limited number of tech giants. The funding path for the open Internet becomes constrained and there'll be less choice for consumers without having to pay for subscriptions. >>Everyone knows the more data you have the better and certainly, but the centralized power when the trend is going the other way, the consensus is everyone wants to be decentralized more truth, more trust all this is being talked about on the heels of the google's news around, you know, getting rid of third party cookies and others have followed suit. Um, what does this mean? I mean, this cookies have been the major vehicle for tracking and getting that kind of data. What is gonna be replaced with what is this all about? And can you share with us what the future will look like? >>Sure, Well, just as advertising funds the open Internet is advertising technology that supports that advertising spend. It supports sort of the business of advertising that funds the open Internet. And within all of that technology is the need for different systems to be able to align around um the identification of for example, a consumer, Have they been to this site before? Have they seen an ad before? So there's all of these different systems that might be used for advertising for measurement, for attribution, for creating personalization. And historically they've relied upon the third party cookie as the mechanism for synchronization. Well, the third party cookie has been in decline for some time. It's already mostly gone from actually apple safari browser, but google's chrome has so much control over how people access the internet. And so it was when Google announced that chrome was going to deprecate the third party cookie, that it really sort of focus the minds of the industry in terms of finding alternative ways to tailor content and ultimately to just simply measure the effectiveness of advertising. And so there's an enormous amount of um innovation taking place right now to find alternative solutions. >>You know, some are saying that the free open internet was pretty much killed when, you know, the big comes like facebook and google started bringing all this data and kind of pulls all sucks all the auction in the room, so to speak. What's this mean with cookies now getting, getting rid of um, by google has an impact publishers because is it helpful? I mean hurtful. I mean, where's the where is that, what the publisher impact? >>Well, I don't think anyone really knows right now. So first of all, cookies weren't necessarily a very good solution to the sort of the challenge of maintaining state and understanding those sorts of the delivery of advertising and so on. It's just the one that's commonly used, I think for different publishers it may mean different things. But many publishers need to be able to demonstrate the value and the effectiveness of the advertising solutions that they deliver. So they'll be innovating in terms of how they use their first party data. They'll be continuing to use contextual solutions that have long been used to create advertising relevant, relevant. I think the big question of course is how we're going to measure it that any of this is effective at all because everyone relies upon measuring advertising effectiveness to justify capturing those budgets in the first place. >>You know, you mentioned contextual come up a lot also in the other interviews we've done with the folks in the around the internet around this topic of machine learning is a big 12 What is the impact of this with the modernization of the solution? You mentioned cookies? Okay cookies, old technology. But the mechanisms in this ecosystem around it or not, it funds the open internet. What is that modern solution that goes that next level? Is it contextual metadata? Is that shared systems? What's the it's the modernization of that. >>It's all of those and and more. There's no there's no single solution to replace the third party cookie. There'll be a combination of solutions. Part of that will be alternative identity mechanisms. So you know, you will start to see more registration wars to access content so that you have what's called a deterministic identify there will be statistical models so called probabilistic models, contextual has always been important. It will become more important and it will be combined with we use contextual combining natural language processing with machine learning models to really understand the detailed context of different pages across the internet. You'll also see the use of first party data and there are discussions about shared data services as well. I think there's gonna be a whole set of different innovations that will need to inter operate and it's going to be an evolutionary process as people get used to using these different systems to satisfy the different stages of the media fulfillment cycle from research and planning to activation to measurement. >>You know, you put up walled gardens. I want to just touch on the on on this kind of concept of walled gardens and and and and compare and contrast that with the demand for community, open internet has always fostered a community vibe. You see network effects mostly in distinct user communities or subnets of sub networks. If you will kind of walled gardens became that kind of group get together but then became more of a media solution to make the user is the product, as they say, facebook's a great example, right? People talk about facebook and from that misinformation abuse walled garden is not the best thing happening right now in the world, but yet is there any other other choice? That's how they're going to make money? But yet everyone wants trust, truth community. Are they usually exclusive? How do you see this evolving, what's your take? >>Well, I think the open internet is a, is a forum where anyone can have their voice, uh, put their voice out there and have it discovered and it's in that regard, it's a it's a force for good look. I think there are there are challenges, obviously in terms of some of the some of the optimization that takes place with inside the walled gardens, which is, is sort of optimized to drive engagement can have some unintended consequences. Um obviously that's something that's, that's broadly being discussed today and the impact on society, but sort of more at a more pointed level, it's just the absorption of advertising dollars. There's a finite amount of money from advertisers. It's estimated to be $400 billion this year in digital advertising. So it's a huge amount of money in terms of funding the open Internet, which sounds great except for its increasingly concentrated in a tiny number of companies. And so, you know, our job at Quan cast as champions of the free and open Internet is to help direct money effectively to publishers across the open internet and give advertisers a reliable, repeatable way of accessing the audiences that they care about in the environment they care about and delivering advertising results. >>It's a publisher, we care a lot about what our audience wants and try to serve them and listen to them. If we could get the data, we want that data and then also broker in the monetization with advertisers, who might want to reach that audience in whatever way. So this brings up the question of, you know, automation and role of data. You know, this is a huge thing to having that data closed loop, if you will for for publishers. But yet most publishers are small, some niche. And even as they can become super large, they don't have all the data and more, the more data, the better the machine learning. So what's the answer to this as it goes forward? How do we get there? What's the dots that that we need to connect to get that future state? >>So I think it takes it takes companies working together effectively. I think a really important part of it is, is a more direct conversation with consumers. We've seen that change beginning to happen over the past few years with the introduction of regulations that require clear communication to consumers about the data that's captured. And y and I think that creates an opportunity to explain to your audience is the way in which content is funded. So I think that consumer that consumer conversation will be part of the collective solution. >>You know, I want to as we wind down this kickoff segment, get your thoughts and vision around um, the evolution of the internet and you guys have done some great work at quan Cast is well documented, but everyone used to talk about traffic by traffic, then it became cost of acquisitions. PPC search. This is either mechanisms that people have been using for a long, long time, then you know, your connections but audience is about traffic, audience traffic. If this if my family is online, doesn't it become about networks and the people. So I want to get your thoughts and your vision because if community is going to be more important than people agree that it is and things are gonna be decentralized, more openness, more voices to be heard. You need to dress ability. The formation of networks and groups become super important. What's your vision on that? >>So my vision is to create relevance and utility for consumers. I think that's one of the things that's often forgotten is that when we make advertising more relevant and useful for consumers, it automatically fulfils the objectives that publishers and marketers have, everyone wins when advertising is more relevant. And our vision is to make advertising relevant across the entire open internet so that that ad supported model can continue to flourish and that five billion and hopefully many more billions in the future, people around the world have access to high quality, diverse content. >>If someone asked you Conrad, what is quant cast doing to make the open internet viable now that cookies are going away? What's the answer? >>So well, the cookie pieces is a central piece of it in terms of finding solutions that will enable sort of planning activation and measurement post cookies and we have a lot of innovation going on. There were also working with a range of industry bodies and our and our partners to build solutions for this. What we're really trying to do is to make buying the open internet as straightforward for marketers as it is today and buying the walled gardens. The reason the walled gardens capture so much money is they made it really easy for marketers to get results, marketers would like to be able to spend their money across all of the diverse publishes the open internet. You know, our job at Comcast is to make it just as easy to effectively spend money in funding the content that they really care about in reaching the audiences that they want. >>Great stuff. Great Mission. Conrad, thanks for coming on. Conrad Feldmann founder and Ceo here at the cookie conundrum recipe for success event, Quant Cast Industry summit on the demise of third party cookies. Thank you. Conrad appreciate it. Thank you. Yeah, I'm john ferrier, stay with us for more on the industry event around the middle cookies. Mhm Yeah, yeah, thank you. Mhm. Welcome back to the Qantas industry summit on the demise of third party cookies, the cookie conundrum, a recipe for success. I'm john furrier host of the cube, the changing landscape of advertising is here and shit Gupta, founder of you of digital is joining us chief. Thanks for coming on this segment. Really appreciate, I know you're busy, you've got two young kids as well as providing education to the digital industry, you got some kids to take care of and train them to. So welcome to the cube conversation here as part of the program. >>Yeah, thanks for having me excited to be here. >>So the office of the changing landscape of advertising really centers around the open to walled garden mindset of the web and the big power players. We know the big 34 tech players dominate the marketplace so clearly in a major inflection point and we've seen this movie before Web mobile revolution which was basically a reply platform NG of capabilities. But now we're in an error of re factoring the industry, not re platt forming a complete changing over of the value proposition. So a lot at stake here as this open web, open internet, global internet evolves. What are your, what's your take on this, this industry proposals out there that are talking to this specific cookie issue? What does it mean? And what proposals are out there? >>Yeah, so, you know, I I really view the identity proposals and kind of to to kind of groups, two separate groups. So on one side you have what the walled gardens are doing and really that's being led by google. Right, so google um you know, introduce something called the privacy sandbox when they announced that they would be deprecating third party cookies uh as part of the privacy sandbox, they've had a number of proposals unfortunately, or you know, however you want to say they're all bird themed for some reason, I don't know why. Um but the one, the bird theme proposal that they've chosen to move forward with is called flock, which stands for Federated learning of cohorts. And essentially what it all boils down to is google is moving forward with cohort level learning and understanding of users in the future after third party cookies, unlike what we've been accustomed to in this space, which is a user level understanding of people and what they're doing online for targeting tracking purposes. And so that's on one side of the equation, it's what google is doing with flock and privacy sandbox now on the other side is, you know, things like unified I. D. Two point or the work that I. D five is doing around building new identity frameworks for the entire space that actually can still get down to the user level. Right? And so again, unified I. D. Two point oh comes to mind because it's the one that's probably got the most adoption in the space. It's an open source framework. So the idea is that it's free and pretty much publicly available to anybody that wants to use it and unified, I need to point out again is user level. So it's it's basically taking data that's authenticated data from users across various websites you know that are logging in and taking those authenticated users to create some kind of identity map. And so if you think about those two work streams right, you've got the walled gardens and or you know, google with flock on one side and then you've got unified I. D. Two point oh and other I. D. Frameworks for the open internet. On the other side, you've got these two very differing type of approaches to identity in the future. Again on the google side it's cohort level, it's going to be built into chrome. Um The idea is that you can pretty much do a lot of the things that we do with advertising today, but now you're just doing it at a group level so that you're protecting privacy, whereas on the other side of the open internet you're still getting down to the user level. Um And that's pretty powerful. But the the issue there is scale, right? We know that a lot of people are not logged in on lots of websites. I think the stat that I saw is under five of all website traffic is authenticated. So really if you if you simplify things you boil it all down, you have kind of these two very differing approaches. >>I guess the question it really comes down to what alternatives are out there for cookies and which ones do you think will be more successful? Because I think, you know, the consensus is at least from my reporting, in my view, is that the world agrees. Let's make it open, Which one is going to be better. >>Yeah, that's a great question, john So as I mentioned, right, we have we have to kind of work streams here, we've got the walled garden work streams, work stream being led by google and their work around flock, and then we've got the open internet, right? Let's say unified I. D to kind of represents that. I personally don't believe that there is a right answer or an endgame here. I don't think that one of them wins over the other, frankly, I think that, you know, first of all, you have those two frameworks, neither of them are perfect, they're both flawed in their own ways. There are pros and cons to both of them. And so what we're starting to see now is you have other companies kind of coming in and building on top of both of them as kind of a hybrid solution. Right? So they're saying, hey, we use, you know, an open I. D. Framework in this way to get down to the user level and use that authenticated data and that's important. But we don't have all the scale. So now we go to google and we go to flock to kind of fill the scale. Oh and hey, by the way, we have some of our own special sauce, right? We have some of our own data, we have some of our own partnerships, we're gonna bring that in and layer it on top. Right? And so really where I think things are headed is the right answer, frankly, is not one or the other. It's a little mishmash of both. With a little extra something on top. I think that's that's what we're starting to see out of a lot of companies in the space. And I think that's frankly where we're headed. >>What do you think the industry will evolve to, in your opinion? Because I think this is gonna, you can't ignore the big guys on this because these programmatic you mentioned also the data is there. But what do you think the market will evolve to with this, with this conundrum? >>So, so I think john where we're headed? You know, I think we're right now we're having this existential existential crisis, right? About identity in this industry, because our world is being turned upside down, all the mechanisms that we've used for years and years are being thrown out the window and we're being told they were gonna have new mechanisms, Right? So cookies are going away device ids are going away and now we got to come up with new things and so the world is being turned upside down and everything that you read about in the trades and you know, we're here talking about it, right? Like everyone's always talking about identity right now, where do I think this is going if I was to look into my crystal ball, you know, this is how I would kind of play this out. If you think about identity today. Right? Forget about all the changes. Just think about it now and maybe a few years before today, Identity for marketers in my opinion has been a little bit of a checkbox activity. Right? It's been hey, um, okay, uh, you know ad tech company or a media company, do you have an identity solution? Okay. Tell me a little bit more about it. Okay, Sounds good. That sounds good. Now can we move on and talk about my business and how are you going to drive meaningful outcomes or whatever for my business? And I believe the reason that is, is because identity is a little abstract, right? It's not something that you can actually get meaningful validation against. It's just something that, you know. Yes, You have it. Okay, great. Let's move on, type of thing. Right. And so that, that's, that's kind of where we've been now, all of a sudden The cookies are going away, the device ids are going away. And so the world is turning upside down in this crisis of how are we going to keep doing what we were doing for the last 10 years in the future. So everyone's talking about it and we're trying to re engineer right? The mechanisms now if I was to look into the crystal ball right 2 3 years from now where I think we're headed is not much is going to change. And what I mean by that john is um uh I think that marketers will still go to companies and say do you have an ID solution? Okay tell me more about it. Okay uh Let me understand a little bit better. Okay you do it this way. Sounds good. Now the ways in which companies are going to do it will be different right now. It's flock and unified I. D. And this and that right. The ways the mechanisms will be a little bit different but the end state right? Like the actual way in which we operate as an industry and kind of like the view of the landscape in my opinion will be very simple or very similar, right? Because marketers will still view it as a tell me you have an ID solution. Make me feel good about it. Help me check the box and let's move on and talk about my business and how you're going to solve for my needs. So I think that's where we're going. That is not by any means to discount this existential moment that we're in. This is a really important moment where we do have to talk about and figure out what we're going to do in the future. My just my viewpoint is that the future will actually not look all that different than the present. >>And I'll say the user base is the audience. Their their data behind it helps create new experiences, machine learning and Ai are going to create those and we have the data you have the sharing it or using it as we're finding shit Gupta great insight dropping some nice gems here. Founder of you of Digital and also the Adjunct professor of Programmatic advertising at Levi School of Business and santa Clara University professor. Thank you for coming dropping the gems here and insight. Thank you. >>Thanks a lot for having me john really appreciate >>it. Thanks for watching. The cooking 100 is the cube host Jon ferrier me. Thanks for watching. Mhm. Yeah. Mhm. Hello welcome back to the cookie conundrum recipe for success and industry conference and summit from Guanacaste on the demise of third party cookies. Got a great industry panel here to break it down chris Gunther Senior Vice president Global Head of programmatic at news corp chris thanks for coming on Zal in Managing Director Solutions at Z axis and Summer Simpson. Vice president Product at quan cast stellar panel. Looking forward to this conversation. Uh thanks for coming on and chatting about the cookie conundrum. Thank you for having us. So chris we'll start with you at news corp obviously a major publisher deprecation of third party cookies affects everyone. You guys have a ton of traffic, ton of audience across multiple formats. Um, tell us about the impact to you guys and the reliance he has had on them. And what are you gonna do to prepare for this next level change? >>Sure. I mean, I think like everyone in this industry there's uh a significant reliance and I think it's something that a lot of talk about audience targeting but obviously that reliance on third party cookies pervasive across the whole at tech ecosystem Martek stack. And so you know, we have to think about how that impact vendor vendors, we work with what it means in terms of use cases across marketing, across advertising, across site experience. So, you know, without a doubt, it it's it's significant, but you know, we look at it as listen, it's disruptive, uh, disruption and change is always a little scary. Um, but overall it's a, it's a long overdue reset. I mean, I think that, you know, our perspective is that the cookies, as we all know was it was a crutch, right sort of a technology being used in way it shouldn't. Um, and so as we look at what's going to happen presumably after Jan 2022 then it's, it's a good way to kind of fix on some bad practices practices that lead to data leakage, um, practice or devalue for our perspective, some of the, you know, we offered as as publishers and I think that this is a key thing is that we're not just looking to as we look at the post gender world, not just kind of recreating the prior world because the prior world was flawed or I guess you could say the current world since it hasn't changed yet. But the current world is flawed. Let's not just not, you know, let's not just replicate that. Let's make sure that, you know, third party cookie goes away. Other work around like fingerprinting and things like that. You know, also go away so philosophically, that's where our heads at. And so as we look at how we are preparing, you know, you look at what are the core building blocks of preparing for this world. Obviously one of the key ones is privacy compliance. Like how do we treat our users with consent? Yeah, obviously. Are we um aligned with the regulatory environments? Yeah. In some ways we're not looking just a Jan 2022, but Jan 23 where there's gonna be the majority of our audiences we covered by regulation. And so I think from regulation up to data gathering to data activation, all built around an internal identifier that we've developed that allows us to have a consistent look at our users whether they're logged in or obviously anonymous. So it's really looking across all those components across all our sites and in all in a privacy compliant way. So a lot of work to be done, a lot of work in progress. But we're >>excited about what's going on. I like how you framed at Old world or next gen kind of the current situation kind of flawed. And as you think about programmatic, the concept is mind blowing and what needs to be done. So we'll come back to that because I think that original content view is certainly relevant, a huge investment and you've got great content and audience consuming it from a major media standpoint. Get your perspective on the impact because you've got clients who want to get their their message out in front of the audience at the right time, at the right place and the right context. Right, So your privacy, you got consent, all these things kind of boiling up. How do you help clients prepare? Because now they can go direct to the consumer. Everyone, everyone has a megaphone, now, everyone's, everyone's here, everyone's connected. So how are you impacted by this new notion? >>You know, if if the cookie list future was a tic tac, dance will be dancing right now, and at least into the next year, um this has been top of mind for us and our clients for quite some time, but I think as each day passes, the picture becomes clearer and more in focus. Uh the end of the third party cookie does not mean the end of programmatic. Um so clients work with us in transforming their investments into real business outcomes based on our expertise and based on our tech. So we continue to be in a great position to lead to educate, to partner and to grow with them. Um, along this uh cookie list future, the impact will be all encompassing in changing the ways we do things now and also accelerating the things that we've already been building on. So we take it from the top planning will have a huge impact because it's gonna start becoming more strategic around real business outcomes. Uh where Omni channel, So clients want to drive outcomes, drew multiple touch points of a consumer's journey, whether it has programmatic, whether it has uh cookie free environment, like connected tv, digital home audio, gaming and so forth. So we're going to see more of these strategic holistic plans. Creative will have a lot of impact. It will start becoming more important with creative testing. Creative insights. You know, creative in itself is cookie list. So there will be more focused on how to drive uh brand dialogue to connect to consumers with less targeting. With less cookies, with the cohesiveness of holistic planning. Creative can align through multiple channels and lastly, the role of a. I will become increasingly important. You know, we've always looked to build our tech our products to complement new and existing technology as well as the client's own data and text back to deliver these outcomes for them. And ai in its core it's just taking input data uh and having an output of your desired outcome. So input data could be dSP data beyond cookies such as browser such as location, such as contextual or publisher taking clients first party data, first party crm data like store visitation, sales, site activity. Um and using that to optimize in real time regardless of what vendor or what channel we're on. Um So as we're learning more about this cookie list dance, we're helping our clients on the steps of it and also introducing our own moves. >>That's awesome. Data is going to be a key value proposition, connecting in with content real time. Great stuff. Somewhere with your background in journalism and you're the tech VP of product at quan cast. You have the keys to the kingdom over there. It's interesting Journalism is about truth and good content original content. But now you have a data challenge problem opportunity on both sides, brands and publishers coming together. It's a data problem in a way it's a it's a tech stack, not so much just getting the right as to show up at the right place the right time. It's really bigger than that now. What's your take on this? >>Um you know, >>so first >>I think that consumers already sort of like except that there is a reasonable value exchange for their data in order to access free content. Right? And that's that's a critical piece for us to all kind of like understand over the past. Hi guys, probably two years since even even before the G. D. P. R. We've been doing a ton of discovery with customers, both publishers and marketers. Um and so you know, we've kind of known this, this cookie going away thing has been coming. Um And you know, Google's announcement just kind of confirmed it and it's been, it's been really, really interesting since Google's announcement, how the conversations have changed with with our customers and other folks that we talked to. And I've almost gone from being like a product manager to a therapist because there's such an emotional response. Um you know, from the marketing perspective, there's real fear there. There's like, oh my God, how you know, it's not just about, you know, delivering ads, it's about how do I control frequency? How do I, how do I measure, you know, success? Because the technology has has grown so much over the years to really give marketers the ability to deliver personalized advertising, good content, right. The consumers um and be able to monitor it and control it so that it's not too too intrusive on the publisher perspective side, we see slightly different response. It's more of a yes, right. You know, we're taking back control and we're going to stop the data leakage, we're going to get the value back for our inventory. Um and that both things are a good thing, but if it's, if it's not managed, it's going to be like ships passing in the night, right? In terms of um of, you know, they're there, them coming together, right, and that's the critical pieces that they have to come together. They have to get closer, you got to cut out a lot of that loom escape in the middle so that they can talk to each other and understand what's the value exchange happening between marketers and publishers and how do we do that without cookies? >>It's a fascinating, I love love your insight there. I think it's so relevant and it's got broader implications because, you know, if you look at how data's impact, some of these big structural changes and re factoring of industries, look at cyber security, you know, no one wants to share their data, but now if they share they get more insight, more machine learning, benefit more ai benefit. So now we have the sharing notion, but that goes against counter the big guys that want to wall garden, they want to hoard all the data and and control that to provide their own personalization. So you have this confluence of, hey, I want to hoard the data and then now I want to share the data. So so christmas summer you're in the, in the wheelhouse, you got original content and there's other providers out there. So is there the sharing model coming with privacy and these kinds of services? Is the open, come back again? How do you guys see this uh confluence of open versus walled gardens, because you need the data to make machine learning good. >>So I'll start uh start off, I mean, listen, I think you have to give credit to the walled gardens have created, I think as we look as publishers, what are we offering to our clients, what are we offering to the buy side? We need to be compelling. We shouldn't just be uh yeah, actually as journalists, I think that there is a case of the importance of funding journalism. Um but ultimately we need to make sure we're meeting the KPI is and the business needs of the buy side. And I think around that it is the sort of three core pillars that its ease of access, its scope of of activation and targeting and finally measurable results. So as I think is us as an individual publishers, so we have, we have multiple publications. So we do have scale. But then in partnership with other publishers perhaps to organizations like pre bid, you know, I think we can, you know, we're trying to address that and I think we can offer something that's compelling um, and transparent in terms of what these results are. But obviously, you know, I want to make sure it's clear transparent terms of results, but obviously where there's privacy in terms of the data and I think the form, you know, I think we've all heard a lot like data clean rooms, a lot of them out there flogging those wears. I think there's something valuable but you know, I think it's the right who is sort of the right partner or partners um and ultimately who allows us to get as close as possible to the buy side. And so that we can share that data for targeting, share it for perhaps for measurement, but obviously all in a privacy compliant >>way summer, what's your take on this? Because you talk about the future of the open internet democratization, the network effect that we're seeing in Vire al Itty and across multiple on the on the channels. Is that pointed out what's happening? That's the distribution now. So um that's almost an open garden model. So it's like um yeah, >>yeah, it's it's um you know, back in the day, you know, um knight ridder who was who was the first group that I that I worked for, um you know, each of those individual properties, um we're not hugely valuable on their own from a digital perspective, but together as a unit, they became valuable, right, and got scale for advertisers. Now we're in a place where, you know, I kind of think that each of those big networks are going to have to come together and work together to compare in size to the, to the world gardens. Um, and yeah, this is something that we've talked about before and an open garden. Um, I think that's the, that's the definitely the right route to take. And I and I agree with chris it's, it's about publishers getting as close to the market. Is it possible working with the tech companies that enable them to do that and doing so in a very privacy centric >>way. So how do we bring the brands and agencies together to get ready for third party cookies? Because there is a therapist moment here of it's gonna be okay. The parachute will open. The future is not gonna be as as grim. Um, it's a real opportunity. But if managed properly, what's your take on this is just more first party data strategy and what's your assessment of this? >>So we collaborated right now with ball grants on how did this still very complex cookie list future. Um, you know what's going to happen in the future? 2, 6 steps that we can take right now and market should take. Um, The first step is to gather intel on what's working on your current campaign, analyzing the data sets across cookie free environment. So you can translate those tactics eventually when the cookies do go away. So we have to look at things like temperature or time analysis. We could look at log level data. We could look at site analytics data. We can look at brand measurement tools and how creative really impacts the campaign success. The second thing we can look at is geo targeting strategies. The geo target strategy has been uh underrated because the granularity and geo data could go down all the way to the local level, even beyond zip code. So for example the census black data and this is especially important for CPG brands. So we're working closely with the client teams to understand not only the online data but the offline data and how we can utilize that in the future. Uh We want to optimize investments around uh markets that are working so strong markets and then test and underperforming markets. The third thing we can look at is contextual. So contextual by itself is cookie free. Uh We could build on small scale usage to test and learn various keywords and content categories based sets. Working closely with partners to find ways to leverage their data to mimic audiences that you are trying to target right now with cookies. Um the 4th 1 is publisher data or publisher targeting. So working with your publishers that you have strong relationships with who can curate similar audiences using their own first party data and conducting RFs to understand the scale and reach against your audience and their future role maps. So work with your top publishers based on historical data to try to recreate your best strategies. The 15 and I think this is very important is first party data, you know, that's going to matter more than ever. In the calculus future brands will need to think about how to access and developed the first party data starting with the consumers seeing a value in exchange for the information. It's a gold mine and understanding of consumer, their intent, the journey um and you need a really great data science team to extract insights out of that data, which will be crucial. So partner with strategic onboarding vendors and vet their ability to accept first party data into a cleaner environment for targeting for modeling for insight. And lastly, the six thing that we can do is begin to inform prospect prospecting by dedicating test budget to start gaining learnings about cookie list 11 place that we can start and it is under invested right now is Safari and Firefox. They have been calculus for quite some time so you can start here and begin testing here. Uh work with your data scientist team to understand the right mix is to to target and start exploring other channels outside of um just programmatic cookies like CTV digital, out of home radio gaming and so forth. So those are the six steps that we're taking right now with our clients to uh prepare and plan for the cookie list future. >>So chris let's go back to you. What's the solution here? Is there one, is there multiple solutions? What's the future look like for a cookie was future? >>Uh I think the one certain answers, they're definitely not just one solution. Um as we all know right now there there seems to be endless solutions, a lot of ideas out there, proposals with the W three C uh work happening within other industry bodies uh you know private companies solutions being offered and you know, it's a little bit of it's enough to make everyone's head spin and to try to track it to understand and understand the impact. And as a publisher were obviously a lot of people are knocking on our door. Uh they're saying, hey our solution is one that is going to bring in lots of money, you know, the all the buy side is going to use it. This is the one like I ma call to spend um, and so expect here and so far is that none of these solutions are I think everyone is still testing and learning no one on the buy side from our, from our knowledge is really committed to one or a few. It's all about a testing stage. I think that, you know, putting aside all that noise, I think what matters the most to us as publisher is actually something summer mentioned before. It's about control. You know, if we're going to work with a again, outside of our sort of, you know, internal identifier work that we're doing is we're going to work with an outside party or outside approach doesn't give us control as a publisher to ensure that it is, we control the data from our users. There isn't that data leakage, it's probably compliant. What information gets shared out there. What is it, what's released within within the bid stream? Uh If it is something that's attached to a somewhat declared user registered user that if that then is not somehow amplified or leverage off on another site in a way that is leveraging bit stream data or fingerprinting and going against. I think that the spirit of what we're trying to do in a post third party cookie world so that those controls are critical and I think they have those controls, his publisher, we have collectively be disciplined in what solutions that we we test out and what we eventually adopt. But even when the adoption point arrives, uh definitely it will not be one. There will be multiple because it's just too many use cases to address >>great, great insight there from, from you guys, news corp summer. Let's get back to you. I want to get your thoughts. You've been in many waves of innovation ups and downs were on a new one. Now we talked about the open internet democratization. Journalism is under a lot of pressure now, but there's now a wave of quality people really leaning in towards fighting misinformation, understanding truth and community and date is at the heart of it. What do you see as the new future for journalists, reward journalism is our ways their path forward. >>So there's uh, there's what I hope is going to happen. Um, and then I'm just gonna ignore what could write. Um, you know, there's there's a trend in market right now, a number of fronts, right? So there are marketers who are leaning into wanting to spend their marketing dollars with quality journalists, focusing on bipac owned and operated, really leaning into into supporting those businesses that have been uh, those publishers that have been ignored for years. I really hope that this trend continues. Um We are leaning into into helping um, marketers curate that supply right? And really, uh, you know, speak with their dollars about the things that that they support. Um, and uh, and and value right in market. So I'm hoping that that trend continues and it's not just sort of like a marketing blip. Um, but we will do everything possible to kind of like encourage that behavior and and give people the information they need to find, you know, truly high quality journalism. >>That's awesome chris Summer. Thanks for coming on and sharing your insight on this panel on the cookie list future. Before we go, just quick summary each of you. If you don't mind just giving a quick sound bite or bumper sticker of what we can expect. If you had to throw a prediction For what's going to happen in the next 24 months Chris We'll start with you. >>Uh it's gonna be quite a ride. I think that's an understatement. Um I think that there, I wouldn't be surprised if if google delays the change to the chrome by a couple of months and and may give the industry some much needed time, but no one knows. I guess. I guess I'm not except for someone somewhere deep within chrome. So I think we all have to operate in a way that changes to happen, changes to happen quickly and it's gonna cover across all facets of the industry, all facets of from advertising, marketing. So just be >>prepared. >>Yeah, along the same lines, be prepared, nobody knows what's going to happen in the future. Uh You know, while dancing in this together. Uh I think um for us it's um planning and preparing and also building on what we've already been working on. Um So omni channel ai um creative and I think clients will uh lean more into those different channels, >>awesome. So we'll pick us home, last word. >>I think we're in the throwing spaghetti against the wall stage. Right, so this is a time of discovery of leaning in trying everything out, Learning and iterating as fast as we possibly >>can. Awesome. And I love the cat in the background over your shoulder. Can't stop staring at your wonderful cat. Thanks for coming on chris, Thanks for coming on. This awesome panel industry breakdown of the cookie conundrum. The recipe for success data ai open. Uh The future is here, it's coming, it's coming fast. I'm john fryer with the cube. Thanks for watching. Mhm. Yeah. Mhm. Mhm. Welcome back to the Quant Cast industry summit on the demise of third party cookies. The cookie conundrum, a recipe for success. We're here peter day. The cto of quad cast and crew T cop car, head of product marketing quad cast. Thanks for coming on talking about the changing advertising landscape. >>Thanks for having us. Thank you for having >>us. So we've been hearing this story out to the big players. Want to keep the data, make that centralized control, all the leverage and then you've got the other end. You got the open internet that still wants to be free and valuable for everyone. Uh what's what are you guys doing to solve this problem? Because cookies go away? What's going to happen there? How do people track things you guys are in this business first question? What is quan cast strategies to adapt to third party cookies going away? What's gonna be, what's gonna be the answer? >>Yeah. So uh very rightly said, john the mission, the Qantas mission is the champion of free and open internet. Uh And with that in mind, our approach to this world without third party cookies is really grounded in three fundamental things. Uh First as industry standards, we think it's really important to participate and to work with organizations who are defining the standards that will guide the future of advertising. So with that in mind, we've been participating >>with I. A. B. >>Tech lab, we've been part of their project Triarc. Uh same thing with pre bid, who's kind of trying to figure out the pipes of identity. Di di di di di pipes of uh of the future. Um And then also is W three C, which is the World Wide Web Consortium. Um And our engineers and our engineering team are participating in their weekly meetings trying to figure out what's happening with the browsers and keeping up with the progress they're on things such as google's block. Um The second uh sort of thing is interoperability, as you've mentioned, there are lots of different uh I. D. Solutions that are emerging. You have you I. D. Two point oh, you have live RAM, you have google's flock. Uh And there will be more, there are more and they will continue to be more. Uh We really think it is important to build a platform that can ingest all of these signals. And so that's what we've done. Uh The reason really is to meet our customers where they are at today. Our customers use multiple different data management platforms, the mps. Um and that's why we support multiple of those. Um This is not going to be much different than that. We have to meet our customers where we are, where they are at. And then finally, of course, which is at the very heart of who contrast is innovation. Uh As you can imagine being able to take all of these multiple signals in including the I. D. S. And the cohorts, but also others like contextual first party um consent is becoming more and more important. Um And then there are many other signals, like time, language geo location. So all of these signals can help us understand user behavior intent and interests um in absence of 3rd party cookies. However, uh there's there's something to note about this. They're very raw, their complex, they're messy all of these different signals. Um They are changing all the time, they're real time. Um And there's incomplete information isolation. Just one of these signals cannot help you build a true and complete picture. So what you really need is a technology like AI and machine learning to really bring all of these signals together, combine them statistically and get an understanding of user behavior intent and interests and then act on it, be it in terms of providing audience insights um or responding to bid requests and and so on and so forth. So those are sort of the three um fundamentals that our approach is grounded in which is industry standards, interoperability and and innovation. Uh and you know, you have peter here, who is who is the expert So you can dive much deeper into >>it. Is T. T. O. You've got to tell us how is this going to actually work? What are you guys doing from a technology standpoint to help with data driven advertising in a third party cookie list world? >>Well, we've been um This is not a shock, you know, I think anyone who's been close to his space has known that the 3rd Party Cookie has been um uh reducing inequality in terms of its pervasiveness and its longevity for many years now. And the kind of death knell is really google chrome making a, making the changes that they're gonna be making. So we've been investing in the space for many years. Um and we've had to make a number of hugely diverse investment. So one of them is in how as a marketer, how do I tell if my marketing still working in the world without >>computers? The >>majority of marketers completely reliant on third party cookies today to tell them if they're if they're marketing is working or not. And so we've had to invest heavily and statistical techniques which are closer to kind of economic trick models that markets are used to things like out of home advertising, It's going to establishing whether they're advertising is working or not in a digital environment actually, >>just as >>often, you know, as is often the case in these kind of times of massive disruption, there's always opportunity to make things better. And we really think that's true. And you know, digital measurement has often mistaken precision for accuracy. And there's a real opportunity to kind of see the wood for the trees if you like. And start to come with better methods of measuring the affections of advertising without third party cookies. And in fact to make countless other investments in areas like contextual modeling and and targeting that third party cookies and and uh, connecting directly to publishers rather than going through this kind of bloom escape that's gonna tied together third party cookies. So if I was to enumerate all the investments we've made, I think we'll be here till midnight but we have to make a number of vestments over a number of years and that level investments only increasing at the moment. >>Peter on that contextual. Can you just double click on that and tell us more? >>Yeah, I mean contextual is unfortunately these things, this is really poorly defined. It can mean everything from a publisher saying, hey, trust us, this dissipated about CVS to what's possible now and has only really been possible the last couple of years, which is to build >>statistical >>models of the entire internet based on the content that people are actually consumed. And this type of technology requires massive data processing capabilities. It's able to take advantage of the latest innovations in there is like natural language processing and really gives um computers are kind of much deeper and richer understanding of the internet, which ultimately makes it possible to kind of organize, organized the Internet in terms of the types of content of pages. So this type of technology has only been possible the last two years and we've been using contextual signals since our inception, it's always been massively predictive in terms of audience behaviours, in terms of where advertising is likely to work. And so we've been very fortunate to keep the investment going um and take advantage of many of these innovations that have happened in academia and in kind of uh in adjacent areas >>on the ai machine learning aspect, that seems to be a great differentiator in this day and age for getting the most out of the data. How is machine learning and ai factoring into your platform? >>I think it's, it's how we've always operated right from our interception when we started as a measurement company, the way that we were giving our customers at the time, we were just publishers, just the publisher side of our business insights into who their audience was, were, was using machine learning techniques. And that's never really changed. The foundation of our platform has always been, has always been machine learning from from before. It was cool. A lot of our kind of, a lot of our core teams have backgrounds in machine learning phds in statistics and machine learning and and that really drives our our decision making. I mean, data is only useful if you can make sense of it and if you can organize it and if you can take action on it and to do that at this kind of scout scale, it's absolutely necessary to use machine learning technology. >>So you mentioned contextual also, you know, in advertising, everyone knows in that world that you've got the contextual behavioural dynamics, the behavior that's kind of generally everyone's believing is happening. The consensus is undeniable is that people are wanting to expect an environment where there's trust, there's truth, but also they want to be locked in. They don't wanna get walled into a walled garden, nobody wants to be in the world, are they want to be free to pop around and visit sites is more horizontal scalability than ever before. Yet, the bigger players are becoming walled garden, vertical platforms. So with future of ai the experience is going to come from this data. So the behavior is out there. How do you get that contextual relevance and provide the horizontal scale that users expect? >>Yeah, I think it's I think it's a really good point and we're definitely this kind of tipping point. We think, in the broader industry, I think, you know, every published right, we're really blessed to work with the biggest publishers in the world, all the way through to my mom's vlog, right? So we get to hear the perspectives of publishers at every scale. I think they consistently tell us the same thing, which is they want to more directly connected consumers, they don't wanna be tied into these walled gardens, which dictate how they must present their content and in some cases what content they're allowed to >>present. >>Um and so our job as a company is to really provide level >>the playing field a little bit, >>provide them the same capabilities they're only used to in the walled gardens, but let's give them more choice in terms of how they structure their content, how they organize their content, how they organize their audiences, but make sure that they can fund that effectively by making their audiences in their environments discoverable by marketers measurable by marketers and connect them as directly as possible to make that kind of ad funded economic model as effective in the open Internet as it is in social. And so a lot of the investments we've made over recent years have been really to kind of realize that vision, which is, it should be as easy for a marketer to be able to understand people on the open internet as it is in social media. It should be as effective for them to reach people in the environment is really high quality content as it is on facebook. And so we invest a lot of a lot of our R and D dollars in making that true. We're now live with the Comcast platform, which does exactly that. And as third party cookies go away, it only um only kind of exaggerated or kind of further emphasizes the need for direct connections between brands and publishers. And so we just wanna build the technology that helps make that true and gives the kind of technology to these marketers and publishers to connect and to deliver great experiences without relying on these kind of walled >>gardens. Yeah, the Director Director, Consumer Director audience is a new trend. You're seeing it everywhere. How do you guys support this new kind of signaling from for for that's happening in this new world? How do you ingest the content and just this consent uh signaling? >>Uh we were really fortunate to have an amazing, amazing R and D. Team and, you know, we've had to do all sorts to make this, you need to realize our vision. This has meant things like, you know, we have crawlers which scan the entire internet at this point, extract the content of the pages and kind of make sense of it and organize it uh, and organize it for publishers so they can understand how their audiences overlap with potential competitors or collaborators. But more importantly, organize it for marketers. So you can understand what kind of high impact opportunities are there for them there. So, you know, we've had to we've had to build a lot of technology. We've had to build analytics engines, which can get answers back in seconds so that marketers and publishers can kind of interact with their own data and make sense of it and present it in a way that's compelling and help them drive their strategy as well as their execution. We've had to invest in areas like consent management because we believe that a free and open internet is absolutely reliant on trust and therefore we spend a lot of our time thinking about how do we make it easy for end users to understand who has access to their data and easy for end users to be able to opt out. And uh and as a result of that, we've now got the world's most widely adopted adopted consent management platform. So it's hard to tackle one of these problems without tackling all of them. Were fortunate enough to have had a large enough R and D budget over the last four or five years, make a number investments, everything from consent and identity through context, your signals through the measurement technologies, which really bring advertisers >>and Publishers places together great insight. Last word for you is what's the what's the customer view here as you bring these new capabilities of the platform, uh what's what are you guys seeing as the highlight uh from a platform perspective? >>So the initial response that we've seen from our customers has been very encouraging, both on the publisher side as well as the marketer side. Um I think, you know, one of the things we hear quite a lot is uh you guys are at least putting forth a solution, an actual solution for us to test Peter mentioned measurement, that really is where we started because you cannot optimize what you cannot measure. Um so that that is where his team has started and we have some measurement very, very uh initial capabilities still in alpha, but they are available in the platform for marketers to test out today. Um so the initial response has been very encouraging. People want to engage with us um of course our, you know, our fundamental value proposition, which is that the Qantas platform was never built to be reliant on on third party data. These stale segments like we operate, we've always operated on real time live data. Um The second thing is, is our premium publisher relationships. We have had the privilege of working like Peter said with some of the um biggest publishers, but we also have a very wide footprint. We have first party tags across um over 100 million plus web and mobile destinations. Um and you know, as you must have heard like that sort of first party footprint is going to come in really handy in a world without third party cookies, we are encouraging all of our customers, publishers and marketers to grow their first party data. Um and so that that's something that's a strong point that customers love about us and and lean into it quite a bit. Um So yeah, the initial response has been great. Of course it doesn't hurt that we've made all these are in the investments. We can talk about consent. Um, and you know, I often say that consent, it sounds simple, but it isn't, there's a lot of technology involved, but there's lots of uh legal work involved as it as well. We have a very strong legal team who has expertise built in. So yeah, very good response. Initially >>democratization. Everyone's a publisher. Everyone's a media company. They have to think about being a platform. You guys provide that. So I congratulate Peter. Thanks for dropping the gems there. Shruti, thanks for sharing the product highlights. Thanks for, for your time. Thank you. Okay, this is the quan cast industry summit on the demise of third party cookies. And what's next? The cookie conundrum. The recipe for success with Kwan Cast. I'm john free with the cube. Thanks for watching. Mm

>>mhm Yes. Welcome back to the Qantas industry summit on the demise of third party cookies, the cookie conundrum, a recipe for success. I'm john furrier host of the cube. The changing landscape of advertising is here and Chip Gupta, founder of you of digital is joining us Chip, thanks for coming on this segment. Really appreciate, I know you're busy, you've got two young kids as well as providing education to the digital industry. You got some kids to take care of and train them to. So welcome to the cube conversation here as part of the program. >>Yeah, thanks for having me excited to be here. >>So the house of the changing landscape of advertising really centers around the open to walled garden mindset of the web and the big power players. We know the big 34 tech players dominate the marketplace. So clearly in a major inflection point and we've seen this movie before Web mobile revolution, which was basically a reply platform NG of capabilities. But now we're in an error of re factoring the industry, not re platt forming a complete changing over of the value proposition. So a lot at stake here as this open web, open internet global internet evolved. What are your, what's your take on this, this industry proposals out there that are talking to this specific cookie issue? What does it mean? And what proposals are out there? >>Yeah, so, you know, I I really view the identity proposals and kind of to to kind of groups, two separate groups. So on one side you have what the walled gardens are doing and really that's being led by google. Right, so google um you know, introduce something called the privacy sandbox when they announced that they would be deprecating third party cookies uh as part of the privacy sandbox, they've had a number of proposals unfortunately, or you know, however you want to say they're all bird themed for some reason, I don't know why. Um but the one the bird theme proposal that they've chosen to move forward with is called flock, which stands for Federated learning of cohorts. And essentially what it all boils down to is google is moving forward with cohort level Learning and understanding of users in the future after 3rd party cookies, unlike what we've been accustomed to in this space, which is a user level understanding of people and what they're doing online for targeting tracking purposes. And so that's on one side of the equation, it's what google is doing with flock and privacy sandbox. Now On the other side is, you know, things like unified, I need to point or the work that 85 is doing around building new identity frameworks for the entire space, that actually can still get down to the user level. Right? And so again, unified I. d 2.0 comes to mind because it's the one that's probably got the most adoption in the space. It's an open source framework. So the idea is that it's free and pretty much publicly available to anybody that wants to use it and unified, I need to point out again is user level. So it's it's basically taking data that's authenticated data from users across various websites you know that are logging in and taking those authenticated users to create some kind of identity map. And so if you think about those two work streams right, you've got the walled gardens and or you know, google with flock on one side and then you've got unified I. D. Two point oh and other I. D. Frameworks for the open internet on the other side, you've got these two very differing type of approaches to identity in the future. Again on the google side it's cohort level, it's gonna be built into chrome. Um The idea is that you can pretty much do a lot of the things that we do with advertising today, but now you're just doing it at a group level so that you're protecting privacy whereas on the other side of the open internet you're still getting down to the user level. Um And that's pretty powerful. But the the issue there is scale, right? We know that a lot of people are not logged in on lots of websites. I think the stat that I saw is under five of all website traffic is authenticated. So really if you if you simplify things, you boil it all down, you have kind of these two very differing approaches. >>I guess the question it really comes down to what alternatives are out there for cookies, and which ones do you think will be more successful? Because I think, you know, the consensus is at least from my reporting in my view, is that the world agrees, Let's make it open, Which one is going to be better. >>Yeah, that's a great question, john So as I mentioned, right, we have we have to kind of work streams here, we've got the walled garden work work stream being led by google and their work around flock, and then we've got the open internet, right? Let's say unified I. D to kind of represents that. I personally don't believe that there is a right answer or an endgame here. I don't think that one of them wins over the other, frankly, I think that, you know, first of all, you have those two frameworks, neither of them are perfect, they're both flawed in their own ways. There are pros and cons to both of them. And so what we're starting to see now is you have other companies kind of coming in and building on top of both of them as kind of a hybrid solution. Right? So they're saying, hey, we use, you know, an open I. D. Framework in this way to get down to the user level and use that authenticated data and that's important. But we don't have all the scale. So now we go to google and we go to flock to kind of fill the scale. Oh and hey, by the way, we have some of our own special sauce, right? We have some of our own data, we have some of our own partnerships, we're gonna bring that in and layer it on top. Right? And so really where I think things are headed is the right answer, frankly, is not one or the other. It's a little mishmash of both. With a little extra something on top. I think that's, that's what we're starting to see out of a lot of companies in the space and I think that's frankly where we're headed. >>What do you think the industry will evolve to, in your opinion? Because I think this is gonna, you can't ignore the big guys on this, has these programmatic, you mentioned also the data is there. But what do you think the market will evolve to with this, with this conundrum? >>So, so I think john where we're headed? Um, you know, I think we're right now we're having this existential existential crisis, right? About identity in this industry, because our world is being turned upside down, all the mechanisms that we've used for years and years are being thrown out the window and we're being told that we're gonna have new mechanisms, right? So cookies are going away device IEDs are going away and now we got to come up with new things and so the world is being turned upside down and everything that you read about in the trades and you know, we're here talking about it, right? Like everyone's always talking about identity right now, where do I think this is going if I was to look into my crystal ball, you know, this is how I would kind of play this out. If you think about identity today, Right? Forget about all the changes. Just think about it now and maybe a few years before today, Identity for marketers in my opinion, has been a little bit of a checkbox activity. Right? It's been, hey, um, okay, uh, you know, ad tech company or media company, do you have an identity solution? Okay. Tell me a little bit more about it. Okay. Sounds good. That sounds good. Now can we move on and talk about my business and how are you going to drive meaningful outcomes or whatever for my business? And I believe the reason that is, is because identity is a little abstract, right? It's not something that you can actually get meaningful validation against. It's just something that, you know, Yes, you have it. Okay, great. Let's move on, type of thing. Right. And so that, that's, that's kind of where we've been now, all of a sudden the cookies are going away, the device IDs are going away. And so the world is turning upside down. We're in this crisis of how are we going to keep doing what we were doing for the last 10 years in the future. So everyone's talking about it and we're trying to re engineer right? The mechanisms now if I was to look into the crystal ball right two or three years from now where I think we're headed is not much is going to change. And what I mean by that john is um I think that marketers will still go to companies and say do you have an ID solution? Okay tell me more about it. Okay uh let me understand a little bit better. Okay you do it this way. Sounds good. Now the ways in which companies are going to do it will be different right now. It's flock and unified I. D. And this and that right. The ways the mechanisms will be a little bit different but the end state right? Like the actual way in which we operate as an industry and kind of like the view of the landscape, in my opinion will be very simple or very similar, right? Because marketers will still view it as a tell me you have an ID solution, Make me feel good about it. Help me check the box and let's move on and talk about my business and how you're going to solve for my needs. So I think that's where we're going. That is not by any means to discount this existential moment that we're in. This is a really important moment where we do have to talk about and figure out what we're gonna do in the future. My just my viewpoint is that the future will actually not look all that different than the present. >>And I'll say the user base is the audience. Their their data behind it helps create new experiences, machine learning and Ai are going to create those and we have the data. You have the sharing it or using it as we're finding shit. Gupta great insight dropping some nice gems here, Founder of You of Digital and also the Adjunct professor of Programmatic advertising at Levi School of Business and santa Clara University Professor. Thank you for coming, dropping the gems here and insight. Thank you. >>Thanks a lot for having me john really appreciate it. >>Thanks for watching the cooking 100 is the cube host Jon ferrier. Me. Thanks for watching. Yeah. Yeah.

>>don't talk mhm, >>Okay, thing is my presentation on coherent nonlinear dynamics and combinatorial optimization. This is going to be a talk to introduce an approach we're taking to the analysis of the performance of coherent using machines. So let me start with a brief introduction to easing optimization. The easing model represents a set of interacting magnetic moments or spins the total energy given by the expression shown at the bottom left of this slide. Here, the signal variables are meditate binary values. The Matrix element J. I. J. Represents the interaction, strength and signed between any pair of spins. I. J and A Chive represents a possible local magnetic field acting on each thing. The easing ground state problem is to find an assignment of binary spin values that achieves the lowest possible value of total energy. And an instance of the easing problem is specified by giving numerical values for the Matrix J in Vector H. Although the easy model originates in physics, we understand the ground state problem to correspond to what would be called quadratic binary optimization in the field of operations research and in fact, in terms of computational complexity theory, it could be established that the easing ground state problem is np complete. Qualitatively speaking, this makes the easing problem a representative sort of hard optimization problem, for which it is expected that the runtime required by any computational algorithm to find exact solutions should, as anatomically scale exponentially with the number of spends and for worst case instances at each end. Of course, there's no reason to believe that the problem instances that actually arrives in practical optimization scenarios are going to be worst case instances. And it's also not generally the case in practical optimization scenarios that we demand absolute optimum solutions. Usually we're more interested in just getting the best solution we can within an affordable cost, where costs may be measured in terms of time, service fees and or energy required for a computation. This focuses great interest on so called heuristic algorithms for the easing problem in other NP complete problems which generally get very good but not guaranteed optimum solutions and run much faster than algorithms that are designed to find absolute Optima. To get some feeling for present day numbers, we can consider the famous traveling salesman problem for which extensive compilations of benchmarking data may be found online. A recent study found that the best known TSP solver required median run times across the Library of Problem instances That scaled is a very steep route exponential for end up to approximately 4500. This gives some indication of the change in runtime scaling for generic as opposed the worst case problem instances. Some of the instances considered in this study were taken from a public library of T SPS derived from real world Veil aside design data. This feels I TSP Library includes instances within ranging from 131 to 744,710 instances from this library with end between 6880 13,584 were first solved just a few years ago in 2017 requiring days of run time and a 48 core to King hurts cluster, while instances with and greater than or equal to 14,233 remain unsolved exactly by any means. Approximate solutions, however, have been found by heuristic methods for all instances in the VLS i TSP library with, for example, a solution within 0.14% of a no lower bound, having been discovered, for instance, with an equal 19,289 requiring approximately two days of run time on a single core of 2.4 gigahertz. Now, if we simple mindedly extrapolate the root exponential scaling from the study up to an equal 4500, we might expect that an exact solver would require something more like a year of run time on the 48 core cluster used for the N equals 13,580 for instance, which shows how much a very small concession on the quality of the solution makes it possible to tackle much larger instances with much lower cost. At the extreme end, the largest TSP ever solved exactly has an equal 85,900. This is an instance derived from 19 eighties VLSI design, and it's required 136 CPU. Years of computation normalized to a single cord, 2.4 gigahertz. But the 24 larger so called world TSP benchmark instance within equals 1,904,711 has been solved approximately within ophthalmology. Gap bounded below 0.474%. Coming back to the general. Practical concerns have applied optimization. We may note that a recent meta study analyzed the performance of no fewer than 37 heuristic algorithms for Max cut and quadratic pioneer optimization problems and found the performance sort and found that different heuristics work best for different problem instances selected from a large scale heterogeneous test bed with some evidence but cryptic structure in terms of what types of problem instances were best solved by any given heuristic. Indeed, their their reasons to believe that these results from Mexico and quadratic binary optimization reflected general principle of performance complementarity among heuristic optimization algorithms in the practice of solving heart optimization problems there. The cerise is a critical pre processing issue of trying to guess which of a number of available good heuristic algorithms should be chosen to tackle a given problem. Instance, assuming that any one of them would incur high costs to run on a large problem, instances incidence, making an astute choice of heuristic is a crucial part of maximizing overall performance. Unfortunately, we still have very little conceptual insight about what makes a specific problem instance, good or bad for any given heuristic optimization algorithm. This has certainly been pinpointed by researchers in the field is a circumstance that must be addressed. So adding this all up, we see that a critical frontier for cutting edge academic research involves both the development of novel heuristic algorithms that deliver better performance, with lower cost on classes of problem instances that are underserved by existing approaches, as well as fundamental research to provide deep conceptual insight into what makes a given problem in, since easy or hard for such algorithms. In fact, these days, as we talk about the end of Moore's law and speculate about a so called second quantum revolution, it's natural to talk not only about novel algorithms for conventional CPUs but also about highly customized special purpose hardware architectures on which we may run entirely unconventional algorithms for combinatorial optimization such as easing problem. So against that backdrop, I'd like to use my remaining time to introduce our work on analysis of coherent using machine architectures and associate ID optimization algorithms. These machines, in general, are a novel class of information processing architectures for solving combinatorial optimization problems by embedding them in the dynamics of analog, physical or cyber physical systems, in contrast to both MAWR traditional engineering approaches that build using machines using conventional electron ICS and more radical proposals that would require large scale quantum entanglement. The emerging paradigm of coherent easing machines leverages coherent nonlinear dynamics in photonic or Opto electronic platforms to enable near term construction of large scale prototypes that leverage post Simoes information dynamics, the general structure of of current CM systems has shown in the figure on the right. The role of the easing spins is played by a train of optical pulses circulating around a fiber optical storage ring. A beam splitter inserted in the ring is used to periodically sample the amplitude of every optical pulse, and the measurement results are continually read into a refugee A, which uses them to compute perturbations to be applied to each pulse by a synchronized optical injections. These perturbations, air engineered to implement the spin, spin coupling and local magnetic field terms of the easing Hamiltonian, corresponding to a linear part of the CME Dynamics, a synchronously pumped parametric amplifier denoted here as PPL and Wave Guide adds a crucial nonlinear component to the CIA and Dynamics as well. In the basic CM algorithm, the pump power starts very low and has gradually increased at low pump powers. The amplitude of the easing spin pulses behaviors continuous, complex variables. Who Israel parts which can be positive or negative, play the role of play the role of soft or perhaps mean field spins once the pump, our crosses the threshold for parametric self oscillation. In the optical fiber ring, however, the attitudes of the easing spin pulses become effectively Qantas ized into binary values while the pump power is being ramped up. The F P J subsystem continuously applies its measurement based feedback. Implementation of the using Hamiltonian terms, the interplay of the linear rised using dynamics implemented by the F P G A and the threshold conversation dynamics provided by the sink pumped Parametric amplifier result in the final state of the optical optical pulse amplitude at the end of the pump ramp that could be read as a binary strain, giving a proposed solution of the easing ground state problem. This method of solving easing problem seems quite different from a conventional algorithm that runs entirely on a digital computer as a crucial aspect of the computation is performed physically by the analog, continuous, coherent, nonlinear dynamics of the optical degrees of freedom. In our efforts to analyze CIA and performance, we have therefore turned to the tools of dynamical systems theory, namely, a study of modifications, the evolution of critical points and apologies of hetero clinic orbits and basins of attraction. We conjecture that such analysis can provide fundamental insight into what makes certain optimization instances hard or easy for coherent using machines and hope that our approach can lead to both improvements of the course, the AM algorithm and a pre processing rubric for rapidly assessing the CME suitability of new instances. Okay, to provide a bit of intuition about how this all works, it may help to consider the threshold dynamics of just one or two optical parametric oscillators in the CME architecture just described. We can think of each of the pulse time slots circulating around the fiber ring, as are presenting an independent Opio. We can think of a single Opio degree of freedom as a single, resonant optical node that experiences linear dissipation, do toe out coupling loss and gain in a pump. Nonlinear crystal has shown in the diagram on the upper left of this slide as the pump power is increased from zero. As in the CME algorithm, the non linear game is initially to low toe overcome linear dissipation, and the Opio field remains in a near vacuum state at a critical threshold. Value gain. Equal participation in the Popeo undergoes a sort of lazing transition, and the study states of the OPIO above this threshold are essentially coherent states. There are actually two possible values of the Opio career in amplitude and any given above threshold pump power which are equal in magnitude but opposite in phase when the OPI across the special diet basically chooses one of the two possible phases randomly, resulting in the generation of a single bit of information. If we consider to uncoupled, Opio has shown in the upper right diagram pumped it exactly the same power at all times. Then, as the pump power has increased through threshold, each Opio will independently choose the phase and thus to random bits are generated for any number of uncoupled. Oppose the threshold power per opio is unchanged from the single Opio case. Now, however, consider a scenario in which the two appeals air, coupled to each other by a mutual injection of their out coupled fields has shown in the diagram on the lower right. One can imagine that depending on the sign of the coupling parameter Alfa, when one Opio is lazing, it will inject a perturbation into the other that may interfere either constructively or destructively, with the feel that it is trying to generate by its own lazing process. As a result, when came easily showed that for Alfa positive, there's an effective ferro magnetic coupling between the two Opio fields and their collective oscillation threshold is lowered from that of the independent Opio case. But on Lee for the two collective oscillation modes in which the two Opio phases are the same for Alfa Negative, the collective oscillation threshold is lowered on Lee for the configurations in which the Opio phases air opposite. So then, looking at how Alfa is related to the J. I. J matrix of the easing spin coupling Hamiltonian, it follows that we could use this simplistic to a p o. C. I am to solve the ground state problem of a fair magnetic or anti ferro magnetic ankles to easing model simply by increasing the pump power from zero and observing what phase relation occurs as the two appeals first start delays. Clearly, we can imagine generalizing this story toe larger, and however the story doesn't stay is clean and simple for all larger problem instances. And to find a more complicated example, we only need to go to n equals four for some choices of J J for n equals, for the story remains simple. Like the n equals two case. The figure on the upper left of this slide shows the energy of various critical points for a non frustrated and equals, for instance, in which the first bifurcated critical point that is the one that I forget to the lowest pump value a. Uh, this first bifurcated critical point flows as symptomatically into the lowest energy easing solution and the figure on the upper right. However, the first bifurcated critical point flows to a very good but sub optimal minimum at large pump power. The global minimum is actually given by a distinct critical critical point that first appears at a higher pump power and is not automatically connected to the origin. The basic C am algorithm is thus not able to find this global minimum. Such non ideal behaviors needs to become more confident. Larger end for the n equals 20 instance, showing the lower plots where the lower right plot is just a zoom into a region of the lower left lot. It can be seen that the global minimum corresponds to a critical point that first appears out of pump parameter, a around 0.16 at some distance from the idiomatic trajectory of the origin. That's curious to note that in both of these small and examples, however, the critical point corresponding to the global minimum appears relatively close to the idiomatic projector of the origin as compared to the most of the other local minima that appear. We're currently working to characterize the face portrait topology between the global minimum in the antibiotic trajectory of the origin, taking clues as to how the basic C am algorithm could be generalized to search for non idiomatic trajectories that jump to the global minimum during the pump ramp. Of course, n equals 20 is still too small to be of interest for practical optimization applications. But the advantage of beginning with the study of small instances is that we're able reliably to determine their global minima and to see how they relate to the 80 about trajectory of the origin in the basic C am algorithm. In the smaller and limit, we can also analyze fully quantum mechanical models of Syrian dynamics. But that's a topic for future talks. Um, existing large scale prototypes are pushing into the range of in equals 10 to the 4 10 to 5 to six. So our ultimate objective in theoretical analysis really has to be to try to say something about CIA and dynamics and regime of much larger in our initial approach to characterizing CIA and behavior in the large in regime relies on the use of random matrix theory, and this connects to prior research on spin classes, SK models and the tap equations etcetera. At present, we're focusing on statistical characterization of the CIA ingredient descent landscape, including the evolution of critical points in their Eigen value spectra. As the pump power is gradually increased. We're investigating, for example, whether there could be some way to exploit differences in the relative stability of the global minimum versus other local minima. We're also working to understand the deleterious or potentially beneficial effects of non ideologies, such as a symmetry in the implemented these and couplings. Looking one step ahead, we plan to move next in the direction of considering more realistic classes of problem instances such as quadratic, binary optimization with constraints. Eso In closing, I should acknowledge people who did the hard work on these things that I've shown eso. My group, including graduate students Ed winning, Daniel Wennberg, Tatsuya Nagamoto and Atsushi Yamamura, have been working in close collaboration with Syria Ganguly, Marty Fair and Amir Safarini Nini, all of us within the Department of Applied Physics at Stanford University. On also in collaboration with the Oshima Moto over at NTT 55 research labs, Onda should acknowledge funding support from the NSF by the Coherent Easing Machines Expedition in computing, also from NTT five research labs, Army Research Office and Exxon Mobil. Uh, that's it. Thanks very much. >>Mhm e >>t research and the Oshie for putting together this program and also the opportunity to speak here. My name is Al Gore ism or Andy and I'm from Caltech, and today I'm going to tell you about the work that we have been doing on networks off optical parametric oscillators and how we have been using them for icing machines and how we're pushing them toward Cornum photonics to acknowledge my team at Caltech, which is now eight graduate students and five researcher and postdocs as well as collaborators from all over the world, including entity research and also the funding from different places, including entity. So this talk is primarily about networks of resonate er's, and these networks are everywhere from nature. For instance, the brain, which is a network of oscillators all the way to optics and photonics and some of the biggest examples or metal materials, which is an array of small resonate er's. And we're recently the field of technological photonics, which is trying thio implement a lot of the technological behaviors of models in the condensed matter, physics in photonics and if you want to extend it even further, some of the implementations off quantum computing are technically networks of quantum oscillators. So we started thinking about these things in the context of icing machines, which is based on the icing problem, which is based on the icing model, which is the simple summation over the spins and spins can be their upward down and the couplings is given by the JJ. And the icing problem is, if you know J I J. What is the spin configuration that gives you the ground state? And this problem is shown to be an MP high problem. So it's computational e important because it's a representative of the MP problems on NPR. Problems are important because first, their heart and standard computers if you use a brute force algorithm and they're everywhere on the application side. That's why there is this demand for making a machine that can target these problems, and hopefully it can provide some meaningful computational benefit compared to the standard digital computers. So I've been building these icing machines based on this building block, which is a degenerate optical parametric. Oscillator on what it is is resonator with non linearity in it, and we pump these resonate er's and we generate the signal at half the frequency of the pump. One vote on a pump splits into two identical photons of signal, and they have some very interesting phase of frequency locking behaviors. And if you look at the phase locking behavior, you realize that you can actually have two possible phase states as the escalation result of these Opio which are off by pie, and that's one of the important characteristics of them. So I want to emphasize a little more on that and I have this mechanical analogy which are basically two simple pendulum. But there are parametric oscillators because I'm going to modulate the parameter of them in this video, which is the length of the string on by that modulation, which is that will make a pump. I'm gonna make a muscular. That'll make a signal which is half the frequency of the pump. And I have two of them to show you that they can acquire these face states so they're still facing frequency lock to the pump. But it can also lead in either the zero pie face states on. The idea is to use this binary phase to represent the binary icing spin. So each opio is going to represent spin, which can be either is your pie or up or down. And to implement the network of these resonate er's, we use the time off blood scheme, and the idea is that we put impulses in the cavity. These pulses air separated by the repetition period that you put in or t r. And you can think about these pulses in one resonator, xaz and temporarily separated synthetic resonate Er's if you want a couple of these resonator is to each other, and now you can introduce these delays, each of which is a multiple of TR. If you look at the shortest delay it couples resonator wanted to 2 to 3 and so on. If you look at the second delay, which is two times a rotation period, the couple's 123 and so on. And if you have and minus one delay lines, then you can have any potential couplings among these synthetic resonate er's. And if I can introduce these modulators in those delay lines so that I can strength, I can control the strength and the phase of these couplings at the right time. Then I can have a program will all toe all connected network in this time off like scheme, and the whole physical size of the system scales linearly with the number of pulses. So the idea of opium based icing machine is didn't having these o pos, each of them can be either zero pie and I can arbitrarily connect them to each other. And then I start with programming this machine to a given icing problem by just setting the couplings and setting the controllers in each of those delight lines. So now I have a network which represents an icing problem. Then the icing problem maps to finding the face state that satisfy maximum number of coupling constraints. And the way it happens is that the icing Hamiltonian maps to the linear loss of the network. And if I start adding gain by just putting pump into the network, then the OPI ohs are expected to oscillate in the lowest, lowest lost state. And, uh and we have been doing these in the past, uh, six or seven years and I'm just going to quickly show you the transition, especially what happened in the first implementation, which was using a free space optical system and then the guided wave implementation in 2016 and the measurement feedback idea which led to increasing the size and doing actual computation with these machines. So I just want to make this distinction here that, um, the first implementation was an all optical interaction. We also had an unequal 16 implementation. And then we transition to this measurement feedback idea, which I'll tell you quickly what it iss on. There's still a lot of ongoing work, especially on the entity side, to make larger machines using the measurement feedback. But I'm gonna mostly focused on the all optical networks and how we're using all optical networks to go beyond simulation of icing Hamiltonian both in the linear and non linear side and also how we're working on miniaturization of these Opio networks. So the first experiment, which was the four opium machine, it was a free space implementation and this is the actual picture off the machine and we implemented a small and it calls for Mexico problem on the machine. So one problem for one experiment and we ran the machine 1000 times, we looked at the state and we always saw it oscillate in one of these, um, ground states of the icing laboratoria. So then the measurement feedback idea was to replace those couplings and the controller with the simulator. So we basically simulated all those coherent interactions on on FB g. A. And we replicated the coherent pulse with respect to all those measurements. And then we injected it back into the cavity and on the near to you still remain. So it still is a non. They're dynamical system, but the linear side is all simulated. So there are lots of questions about if this system is preserving important information or not, or if it's gonna behave better. Computational wars. And that's still ah, lot of ongoing studies. But nevertheless, the reason that this implementation was very interesting is that you don't need the end minus one delight lines so you can just use one. Then you can implement a large machine, and then you can run several thousands of problems in the machine, and then you can compare the performance from the computational perspective Looks so I'm gonna split this idea of opium based icing machine into two parts. One is the linear part, which is if you take out the non linearity out of the resonator and just think about the connections. You can think about this as a simple matrix multiplication scheme. And that's basically what gives you the icing Hambletonian modeling. So the optical laws of this network corresponds to the icing Hamiltonian. And if I just want to show you the example of the n equals for experiment on all those face states and the history Graham that we saw, you can actually calculate the laws of each of those states because all those interferences in the beam splitters and the delay lines are going to give you a different losses. And then you will see that the ground states corresponds to the lowest laws of the actual optical network. If you add the non linearity, the simple way of thinking about what the non linearity does is that it provides to gain, and then you start bringing up the gain so that it hits the loss. Then you go through the game saturation or the threshold which is going to give you this phase bifurcation. So you go either to zero the pie face state. And the expectation is that Theis, the network oscillates in the lowest possible state, the lowest possible loss state. There are some challenges associated with this intensity Durban face transition, which I'm going to briefly talk about. I'm also going to tell you about other types of non aerodynamics that we're looking at on the non air side of these networks. So if you just think about the linear network, we're actually interested in looking at some technological behaviors in these networks. And the difference between looking at the technological behaviors and the icing uh, machine is that now, First of all, we're looking at the type of Hamilton Ian's that are a little different than the icing Hamilton. And one of the biggest difference is is that most of these technological Hamilton Ian's that require breaking the time reversal symmetry, meaning that you go from one spin to in the one side to another side and you get one phase. And if you go back where you get a different phase, and the other thing is that we're not just interested in finding the ground state, we're actually now interesting and looking at all sorts of states and looking at the dynamics and the behaviors of all these states in the network. So we started with the simplest implementation, of course, which is a one d chain of thes resonate, er's, which corresponds to a so called ssh model. In the technological work, we get the similar energy to los mapping and now we can actually look at the band structure on. This is an actual measurement that we get with this associate model and you see how it reasonably how How? Well, it actually follows the prediction and the theory. One of the interesting things about the time multiplexing implementation is that now you have the flexibility of changing the network as you are running the machine. And that's something unique about this time multiplex implementation so that we can actually look at the dynamics. And one example that we have looked at is we can actually go through the transition off going from top A logical to the to the standard nontrivial. I'm sorry to the trivial behavior of the network. You can then look at the edge states and you can also see the trivial and states and the technological at states actually showing up in this network. We have just recently implement on a two D, uh, network with Harper Hofstadter model and when you don't have the results here. But we're one of the other important characteristic of time multiplexing is that you can go to higher and higher dimensions and keeping that flexibility and dynamics, and we can also think about adding non linearity both in a classical and quantum regimes, which is going to give us a lot of exotic, no classical and quantum, non innate behaviors in these networks. Yeah, So I told you about the linear side. Mostly let me just switch gears and talk about the nonlinear side of the network. And the biggest thing that I talked about so far in the icing machine is this face transition that threshold. So the low threshold we have squeezed state in these. Oh, pios, if you increase the pump, we go through this intensity driven phase transition and then we got the face stays above threshold. And this is basically the mechanism off the computation in these O pos, which is through this phase transition below to above threshold. So one of the characteristics of this phase transition is that below threshold, you expect to see quantum states above threshold. You expect to see more classical states or coherent states, and that's basically corresponding to the intensity off the driving pump. So it's really hard to imagine that it can go above threshold. Or you can have this friends transition happen in the all in the quantum regime. And there are also some challenges associated with the intensity homogeneity off the network, which, for example, is if one opioid starts oscillating and then its intensity goes really high. Then it's going to ruin this collective decision making off the network because of the intensity driven face transition nature. So So the question is, can we look at other phase transitions? Can we utilize them for both computing? And also can we bring them to the quantum regime on? I'm going to specifically talk about the face transition in the spectral domain, which is the transition from the so called degenerate regime, which is what I mostly talked about to the non degenerate regime, which happens by just tuning the phase of the cavity. And what is interesting is that this phase transition corresponds to a distinct phase noise behavior. So in the degenerate regime, which we call it the order state, you're gonna have the phase being locked to the phase of the pump. As I talked about non degenerate regime. However, the phase is the phase is mostly dominated by the quantum diffusion. Off the off the phase, which is limited by the so called shallow towns limit, and you can see that transition from the general to non degenerate, which also has distinct symmetry differences. And this transition corresponds to a symmetry breaking in the non degenerate case. The signal can acquire any of those phases on the circle, so it has a you one symmetry. Okay, and if you go to the degenerate case, then that symmetry is broken and you only have zero pie face days I will look at. So now the question is can utilize this phase transition, which is a face driven phase transition, and can we use it for similar computational scheme? So that's one of the questions that were also thinking about. And it's not just this face transition is not just important for computing. It's also interesting from the sensing potentials and this face transition, you can easily bring it below threshold and just operated in the quantum regime. Either Gaussian or non Gaussian. If you make a network of Opio is now, we can see all sorts off more complicated and more interesting phase transitions in the spectral domain. One of them is the first order phase transition, which you get by just coupling to Opio, and that's a very abrupt face transition and compared to the to the single Opio phase transition. And if you do the couplings right, you can actually get a lot of non her mission dynamics and exceptional points, which are actually very interesting to explore both in the classical and quantum regime. And I should also mention that you can think about the cup links to be also nonlinear couplings. And that's another behavior that you can see, especially in the nonlinear in the non degenerate regime. So with that, I basically told you about these Opio networks, how we can think about the linear scheme and the linear behaviors and how we can think about the rich, nonlinear dynamics and non linear behaviors both in the classical and quantum regime. I want to switch gear and tell you a little bit about the miniaturization of these Opio networks. And of course, the motivation is if you look at the electron ICS and what we had 60 or 70 years ago with vacuum tube and how we transition from relatively small scale computers in the order of thousands of nonlinear elements to billions of non elements where we are now with the optics is probably very similar to 70 years ago, which is a table talk implementation. And the question is, how can we utilize nano photonics? I'm gonna just briefly show you the two directions on that which we're working on. One is based on lithium Diabate, and the other is based on even a smaller resonate er's could you? So the work on Nana Photonic lithium naive. It was started in collaboration with Harvard Marko Loncar, and also might affair at Stanford. And, uh, we could show that you can do the periodic polling in the phenomenon of it and get all sorts of very highly nonlinear processes happening in this net. Photonic periodically polls if, um Diabate. And now we're working on building. Opio was based on that kind of photonic the film Diabate. And these air some some examples of the devices that we have been building in the past few months, which I'm not gonna tell you more about. But the O. P. O. S. And the Opio Networks are in the works. And that's not the only way of making large networks. Um, but also I want to point out that The reason that these Nana photonic goblins are actually exciting is not just because you can make a large networks and it can make him compact in a in a small footprint. They also provide some opportunities in terms of the operation regime. On one of them is about making cat states and Opio, which is, can we have the quantum superposition of the zero pie states that I talked about and the Net a photonic within? I've It provides some opportunities to actually get closer to that regime because of the spatial temporal confinement that you can get in these wave guides. So we're doing some theory on that. We're confident that the type of non linearity two losses that it can get with these platforms are actually much higher than what you can get with other platform their existing platforms and to go even smaller. We have been asking the question off. What is the smallest possible Opio that you can make? Then you can think about really wavelength scale type, resonate er's and adding the chi to non linearity and see how and when you can get the Opio to operate. And recently, in collaboration with us see, we have been actually USC and Creole. We have demonstrated that you can use nano lasers and get some spin Hamilton and implementations on those networks. So if you can build the a P. O s, we know that there is a path for implementing Opio Networks on on such a nano scale. So we have looked at these calculations and we try to estimate the threshold of a pos. Let's say for me resonator and it turns out that it can actually be even lower than the type of bulk Pip Llano Pos that we have been building in the past 50 years or so. So we're working on the experiments and we're hoping that we can actually make even larger and larger scale Opio networks. So let me summarize the talk I told you about the opium networks and our work that has been going on on icing machines and the measurement feedback. And I told you about the ongoing work on the all optical implementations both on the linear side and also on the nonlinear behaviors. And I also told you a little bit about the efforts on miniaturization and going to the to the Nano scale. So with that, I would like Thio >>three from the University of Tokyo. Before I thought that would like to thank you showing all the stuff of entity for the invitation and the organization of this online meeting and also would like to say that it has been very exciting to see the growth of this new film lab. And I'm happy to share with you today of some of the recent works that have been done either by me or by character of Hong Kong. Honest Group indicates the title of my talk is a neuro more fic in silica simulator for the communities in machine. And here is the outline I would like to make the case that the simulation in digital Tektronix of the CME can be useful for the better understanding or improving its function principles by new job introducing some ideas from neural networks. This is what I will discuss in the first part and then it will show some proof of concept of the game and performance that can be obtained using dissimulation in the second part and the protection of the performance that can be achieved using a very large chaos simulator in the third part and finally talk about future plans. So first, let me start by comparing recently proposed izing machines using this table there is elected from recent natural tronics paper from the village Park hard people, and this comparison shows that there's always a trade off between energy efficiency, speed and scalability that depends on the physical implementation. So in red, here are the limitation of each of the servers hardware on, interestingly, the F p G, a based systems such as a producer, digital, another uh Toshiba beautification machine or a recently proposed restricted Bozeman machine, FPD A by a group in Berkeley. They offer a good compromise between speed and scalability. And this is why, despite the unique advantage that some of these older hardware have trust as the currency proposition in Fox, CBS or the energy efficiency off memory Sisters uh P. J. O are still an attractive platform for building large organizing machines in the near future. The reason for the good performance of Refugee A is not so much that they operate at the high frequency. No, there are particular in use, efficient, but rather that the physical wiring off its elements can be reconfigured in a way that limits the funding human bottleneck, larger, funny and phenols and the long propagation video information within the system. In this respect, the LPGA is They are interesting from the perspective off the physics off complex systems, but then the physics of the actions on the photos. So to put the performance of these various hardware and perspective, we can look at the competition of bringing the brain the brain complete, using billions of neurons using only 20 watts of power and operates. It's a very theoretically slow, if we can see and so this impressive characteristic, they motivate us to try to investigate. What kind of new inspired principles be useful for designing better izing machines? The idea of this research project in the future collaboration it's to temporary alleviates the limitations that are intrinsic to the realization of an optical cortex in machine shown in the top panel here. By designing a large care simulator in silicone in the bottom here that can be used for digesting the better organization principles of the CIA and this talk, I will talk about three neuro inspired principles that are the symmetry of connections, neural dynamics orphan chaotic because of symmetry, is interconnectivity the infrastructure? No. Next talks are not composed of the reputation of always the same types of non environments of the neurons, but there is a local structure that is repeated. So here's the schematic of the micro column in the cortex. And lastly, the Iraqi co organization of connectivity connectivity is organizing a tree structure in the brain. So here you see a representation of the Iraqi and organization of the monkey cerebral cortex. So how can these principles we used to improve the performance of the icing machines? And it's in sequence stimulation. So, first about the two of principles of the estimate Trian Rico structure. We know that the classical approximation of the car testing machine, which is the ground toe, the rate based on your networks. So in the case of the icing machines, uh, the okay, Scott approximation can be obtained using the trump active in your position, for example, so the times of both of the system they are, they can be described by the following ordinary differential equations on in which, in case of see, I am the X, I represent the in phase component of one GOP Oh, Theo f represents the monitor optical parts, the district optical Parametric amplification and some of the good I JoJo extra represent the coupling, which is done in the case of the measure of feedback coupling cm using oh, more than detection and refugee A and then injection off the cooking time and eso this dynamics in both cases of CNN in your networks, they can be written as the grand set of a potential function V, and this written here, and this potential functionally includes the rising Maccagnan. So this is why it's natural to use this type of, uh, dynamics to solve the icing problem in which the Omega I J or the eyes in coping and the H is the extension of the icing and attorney in India and expect so. Not that this potential function can only be defined if the Omega I j. R. A. Symmetric. So the well known problem of this approach is that this potential function V that we obtain is very non convicts at low temperature, and also one strategy is to gradually deformed this landscape, using so many in process. But there is no theorem. Unfortunately, that granted conventions to the global minimum of There's even Tony and using this approach. And so this is why we propose, uh, to introduce a macro structures of the system where one analog spin or one D O. P. O is replaced by a pair off one another spin and one error, according viable. And the addition of this chemical structure introduces a symmetry in the system, which in terms induces chaotic dynamics, a chaotic search rather than a learning process for searching for the ground state of the icing. Every 20 within this massacre structure the role of the er variable eyes to control the amplitude off the analog spins toe force. The amplitude of the expense toe become equal to certain target amplitude a uh and, uh, and this is done by modulating the strength off the icing complaints or see the the error variable E I multiply the icing complaint here in the dynamics off air d o p. O. On then the dynamics. The whole dynamics described by this coupled equations because the e I do not necessarily take away the same value for the different. I thesis introduces a symmetry in the system, which in turn creates security dynamics, which I'm sure here for solving certain current size off, um, escape problem, Uh, in which the X I are shown here and the i r from here and the value of the icing energy showing the bottom plots. You see this Celtics search that visit various local minima of the as Newtonian and eventually finds the global minimum? Um, it can be shown that this modulation off the target opportunity can be used to destabilize all the local minima off the icing evertonians so that we're gonna do not get stuck in any of them. On more over the other types of attractors I can eventually appear, such as limits I contractors, Okot contractors. They can also be destabilized using the motivation of the target and Batuta. And so we have proposed in the past two different moderation of the target amateur. The first one is a modulation that ensure the uh 100 reproduction rate of the system to become positive on this forbids the creation off any nontrivial tractors. And but in this work, I will talk about another moderation or arrested moderation which is given here. That works, uh, as well as this first uh, moderation, but is easy to be implemented on refugee. So this couple of the question that represent becoming the stimulation of the cortex in machine with some error correction they can be implemented especially efficiently on an F B. G. And here I show the time that it takes to simulate three system and also in red. You see, at the time that it takes to simulate the X I term the EI term, the dot product and the rising Hamiltonian for a system with 500 spins and Iraq Spain's equivalent to 500 g. O. P. S. So >>in >>f b d a. The nonlinear dynamics which, according to the digital optical Parametric amplification that the Opa off the CME can be computed in only 13 clock cycles at 300 yards. So which corresponds to about 0.1 microseconds. And this is Toby, uh, compared to what can be achieved in the measurements back O C. M. In which, if we want to get 500 timer chip Xia Pios with the one she got repetition rate through the obstacle nine narrative. Uh, then way would require 0.5 microseconds toe do this so the submission in F B J can be at least as fast as ah one g repression. Uh, replicate pulsed laser CIA Um, then the DOT product that appears in this differential equation can be completed in 43 clock cycles. That's to say, one microseconds at 15 years. So I pieced for pouring sizes that are larger than 500 speeds. The dot product becomes clearly the bottleneck, and this can be seen by looking at the the skating off the time the numbers of clock cycles a text to compute either the non in your optical parts or the dog products, respect to the problem size. And And if we had infinite amount of resources and PGA to simulate the dynamics, then the non illogical post can could be done in the old one. On the mattress Vector product could be done in the low carrot off, located off scales as a look at it off and and while the guide off end. Because computing the dot product involves assuming all the terms in the product, which is done by a nephew, GE by another tree, which heights scarce logarithmic any with the size of the system. But This is in the case if we had an infinite amount of resources on the LPGA food, but for dealing for larger problems off more than 100 spins. Usually we need to decompose the metrics into ah, smaller blocks with the block side that are not you here. And then the scaling becomes funny, non inner parts linear in the end, over you and for the products in the end of EU square eso typically for low NF pdf cheap PGA you the block size off this matrix is typically about 100. So clearly way want to make you as large as possible in order to maintain this scanning in a log event for the numbers of clock cycles needed to compute the product rather than this and square that occurs if we decompose the metrics into smaller blocks. But the difficulty in, uh, having this larger blocks eyes that having another tree very large Haider tree introduces a large finding and finance and long distance start a path within the refugee. So the solution to get higher performance for a simulator of the contest in machine eyes to get rid of this bottleneck for the dot product by increasing the size of this at the tree. And this can be done by organizing your critique the electrical components within the LPGA in order which is shown here in this, uh, right panel here in order to minimize the finding finance of the system and to minimize the long distance that a path in the in the fpt So I'm not going to the details of how this is implemented LPGA. But just to give you a idea off why the Iraqi Yahiko organization off the system becomes the extremely important toe get good performance for similar organizing machine. So instead of instead of getting into the details of the mpg implementation, I would like to give some few benchmark results off this simulator, uh, off the that that was used as a proof of concept for this idea which is can be found in this archive paper here and here. I should results for solving escape problems. Free connected person, randomly person minus one spring last problems and we sure, as we use as a metric the numbers of the mattress Victor products since it's the bottleneck of the computation, uh, to get the optimal solution of this escape problem with the Nina successful BT against the problem size here and and in red here, this propose FDJ implementation and in ah blue is the numbers of retrospective product that are necessary for the C. I am without error correction to solve this escape programs and in green here for noisy means in an evening which is, uh, behavior with similar to the Cartesian mission. Uh, and so clearly you see that the scaring off the numbers of matrix vector product necessary to solve this problem scales with a better exponents than this other approaches. So So So that's interesting feature of the system and next we can see what is the real time to solution to solve this SK instances eso in the last six years, the time institution in seconds to find a grand state of risk. Instances remain answers probability for different state of the art hardware. So in red is the F B g. A presentation proposing this paper and then the other curve represent Ah, brick a local search in in orange and silver lining in purple, for example. And so you see that the scaring off this purpose simulator is is rather good, and that for larger plant sizes we can get orders of magnitude faster than the state of the art approaches. Moreover, the relatively good scanning off the time to search in respect to problem size uh, they indicate that the FPD implementation would be faster than risk. Other recently proposed izing machine, such as the hope you know, natural complimented on memories distance that is very fast for small problem size in blue here, which is very fast for small problem size. But which scanning is not good on the same thing for the restricted Bosman machine. Implementing a PGA proposed by some group in Broken Recently Again, which is very fast for small parliament sizes but which canning is bad so that a dis worse than the proposed approach so that we can expect that for programs size is larger than 1000 spins. The proposed, of course, would be the faster one. Let me jump toe this other slide and another confirmation that the scheme scales well that you can find the maximum cut values off benchmark sets. The G sets better candidates that have been previously found by any other algorithms, so they are the best known could values to best of our knowledge. And, um or so which is shown in this paper table here in particular, the instances, uh, 14 and 15 of this G set can be We can find better converse than previously known, and we can find this can vary is 100 times faster than the state of the art algorithm and CP to do this which is a very common Kasich. It s not that getting this a good result on the G sets, they do not require ah, particular hard tuning of the parameters. So the tuning issuing here is very simple. It it just depends on the degree off connectivity within each graph. And so this good results on the set indicate that the proposed approach would be a good not only at solving escape problems in this problems, but all the types off graph sizing problems on Mexican province in communities. So given that the performance off the design depends on the height of this other tree, we can try to maximize the height of this other tree on a large F p g a onda and carefully routing the components within the P G A and and we can draw some projections of what type of performance we can achieve in the near future based on the, uh, implementation that we are currently working. So here you see projection for the time to solution way, then next property for solving this escape programs respect to the prime assize. And here, compared to different with such publicizing machines, particularly the digital. And, you know, 42 is shown in the green here, the green line without that's and, uh and we should two different, uh, hypothesis for this productions either that the time to solution scales as exponential off n or that the time of social skills as expression of square root off. So it seems, according to the data, that time solution scares more as an expression of square root of and also we can be sure on this and this production show that we probably can solve prime escape problem of science 2000 spins, uh, to find the rial ground state of this problem with 99 success ability in about 10 seconds, which is much faster than all the other proposed approaches. So one of the future plans for this current is in machine simulator. So the first thing is that we would like to make dissimulation closer to the rial, uh, GOP oh, optical system in particular for a first step to get closer to the system of a measurement back. See, I am. And to do this what is, uh, simulate Herbal on the p a is this quantum, uh, condoms Goshen model that is proposed described in this paper and proposed by people in the in the Entity group. And so the idea of this model is that instead of having the very simple or these and have shown previously, it includes paired all these that take into account on me the mean off the awesome leverage off the, uh, European face component, but also their violence s so that we can take into account more quantum effects off the g o p. O, such as the squeezing. And then we plan toe, make the simulator open access for the members to run their instances on the system. There will be a first version in September that will be just based on the simple common line access for the simulator and in which will have just a classic or approximation of the system. We don't know Sturm, binary weights and museum in term, but then will propose a second version that would extend the current arising machine to Iraq off F p g. A, in which we will add the more refined models truncated, ignoring the bottom Goshen model they just talked about on the support in which he valued waits for the rising problems and support the cement. So we will announce later when this is available and and far right is working >>hard comes from Universal down today in physics department, and I'd like to thank the organizers for their kind invitation to participate in this very interesting and promising workshop. Also like to say that I look forward to collaborations with with a file lab and Yoshi and collaborators on the topics of this world. So today I'll briefly talk about our attempt to understand the fundamental limits off another continues time computing, at least from the point off you off bullion satisfy ability, problem solving, using ordinary differential equations. But I think the issues that we raise, um, during this occasion actually apply to other other approaches on a log approaches as well and into other problems as well. I think everyone here knows what Dorien satisfy ability. Problems are, um, you have boolean variables. You have em clauses. Each of disjunction of collaterals literally is a variable, or it's, uh, negation. And the goal is to find an assignment to the variable, such that order clauses are true. This is a decision type problem from the MP class, which means you can checking polynomial time for satisfy ability off any assignment. And the three set is empty, complete with K three a larger, which means an efficient trees. That's over, uh, implies an efficient source for all the problems in the empty class, because all the problems in the empty class can be reduced in Polian on real time to reset. As a matter of fact, you can reduce the NP complete problems into each other. You can go from three set to set backing or two maximum dependent set, which is a set packing in graph theoretic notions or terms toe the icing graphs. A problem decision version. This is useful, and you're comparing different approaches, working on different kinds of problems when not all the closest can be satisfied. You're looking at the accusation version offset, uh called Max Set. And the goal here is to find assignment that satisfies the maximum number of clauses. And this is from the NPR class. In terms of applications. If we had inefficient sets over or np complete problems over, it was literally, positively influenced. Thousands off problems and applications in industry and and science. I'm not going to read this, but this this, of course, gives a strong motivation toe work on this kind of problems. Now our approach to set solving involves embedding the problem in a continuous space, and you use all the east to do that. So instead of working zeros and ones, we work with minus one across once, and we allow the corresponding variables toe change continuously between the two bounds. We formulate the problem with the help of a close metrics. If if a if a close, uh, does not contain a variable or its negation. The corresponding matrix element is zero. If it contains the variable in positive, for which one contains the variable in a gated for Mitt's negative one, and then we use this to formulate this products caused quote, close violation functions one for every clause, Uh, which really, continuously between zero and one. And they're zero if and only if the clause itself is true. Uh, then we form the define in order to define a dynamic such dynamics in this and dimensional hyper cube where the search happens and if they exist, solutions. They're sitting in some of the corners of this hyper cube. So we define this, uh, energy potential or landscape function shown here in a way that this is zero if and only if all the clauses all the kmc zero or the clauses off satisfied keeping these auxiliary variables a EMS always positive. And therefore, what you do here is a dynamics that is a essentially ingredient descend on this potential energy landscape. If you were to keep all the M's constant that it would get stuck in some local minimum. However, what we do here is we couple it with the dynamics we cooperated the clothes violation functions as shown here. And if he didn't have this am here just just the chaos. For example, you have essentially what case you have positive feedback. You have increasing variable. Uh, but in that case, you still get stuck would still behave will still find. So she is better than the constant version but still would get stuck only when you put here this a m which makes the dynamics in in this variable exponential like uh, only then it keeps searching until he finds a solution on deer is a reason for that. I'm not going toe talk about here, but essentially boils down toe performing a Grady and descend on a globally time barren landscape. And this is what works. Now I'm gonna talk about good or bad and maybe the ugly. Uh, this is, uh, this is What's good is that it's a hyperbolic dynamical system, which means that if you take any domain in the search space that doesn't have a solution in it or any socially than the number of trajectories in it decays exponentially quickly. And the decay rate is a characteristic in variant characteristic off the dynamics itself. Dynamical systems called the escape right the inverse off that is the time scale in which you find solutions by this by this dynamical system, and you can see here some song trajectories that are Kelty because it's it's no linear, but it's transient, chaotic. Give their sources, of course, because eventually knowledge to the solution. Now, in terms of performance here, what you show for a bunch off, um, constraint densities defined by M overran the ratio between closes toe variables for random, said Problems is random. Chris had problems, and they as its function off n And we look at money toward the wartime, the wall clock time and it behaves quite value behaves Azat party nominally until you actually he to reach the set on set transition where the hardest problems are found. But what's more interesting is if you monitor the continuous time t the performance in terms off the A narrow, continuous Time t because that seems to be a polynomial. And the way we show that is, we consider, uh, random case that random three set for a fixed constraint density Onda. We hear what you show here. Is that the right of the trash hold that it's really hard and, uh, the money through the fraction of problems that we have not been able to solve it. We select thousands of problems at that constraint ratio and resolve them without algorithm, and we monitor the fractional problems that have not yet been solved by continuous 90. And this, as you see these decays exponentially different. Educate rates for different system sizes, and in this spot shows that is dedicated behaves polynomial, or actually as a power law. So if you combine these two, you find that the time needed to solve all problems except maybe appear traction off them scales foreign or merely with the problem size. So you have paranormal, continuous time complexity. And this is also true for other types of very hard constraints and sexual problems such as exact cover, because you can always transform them into three set as we discussed before, Ramsey coloring and and on these problems, even algorithms like survey propagation will will fail. But this doesn't mean that P equals NP because what you have first of all, if you were toe implement these equations in a device whose behavior is described by these, uh, the keys. Then, of course, T the continue style variable becomes a physical work off. Time on that will be polynomial is scaling, but you have another other variables. Oxidative variables, which structured in an exponential manner. So if they represent currents or voltages in your realization and it would be an exponential cost Al Qaeda. But this is some kind of trade between time and energy, while I know how toe generate energy or I don't know how to generate time. But I know how to generate energy so it could use for it. But there's other issues as well, especially if you're trying toe do this son and digital machine but also happens. Problems happen appear. Other problems appear on in physical devices as well as we discuss later. So if you implement this in GPU, you can. Then you can get in order off to magnitude. Speed up. And you can also modify this to solve Max sad problems. Uh, quite efficiently. You are competitive with the best heuristic solvers. This is a weather problems. In 2016 Max set competition eso so this this is this is definitely this seems like a good approach, but there's off course interesting limitations, I would say interesting, because it kind of makes you think about what it means and how you can exploit this thes observations in understanding better on a low continues time complexity. If you monitored the discrete number the number of discrete steps. Don't buy the room, Dakota integrator. When you solve this on a digital machine, you're using some kind of integrator. Um and you're using the same approach. But now you measure the number off problems you haven't sold by given number of this kid, uh, steps taken by the integrator. You find out you have exponential, discrete time, complexity and, of course, thistles. A problem. And if you look closely, what happens even though the analog mathematical trajectory, that's the record here. If you monitor what happens in discrete time, uh, the integrator frustrates very little. So this is like, you know, third or for the disposition, but fluctuates like crazy. So it really is like the intervention frees us out. And this is because of the phenomenon of stiffness that are I'll talk a little bit a more about little bit layer eso. >>You know, it might look >>like an integration issue on digital machines that you could improve and could definitely improve. But actually issues bigger than that. It's It's deeper than that, because on a digital machine there is no time energy conversion. So the outside variables are efficiently representing a digital machine. So there's no exponential fluctuating current of wattage in your computer when you do this. Eso If it is not equal NP then the exponential time, complexity or exponential costs complexity has to hit you somewhere. And this is how um, but, you know, one would be tempted to think maybe this wouldn't be an issue in a analog device, and to some extent is true on our devices can be ordered to maintain faster, but they also suffer from their own problems because he not gonna be affect. That classes soldiers as well. So, indeed, if you look at other systems like Mirandizing machine measurement feedback, probably talk on the grass or selected networks. They're all hinge on some kind off our ability to control your variables in arbitrary, high precision and a certain networks you want toe read out across frequencies in case off CM's. You required identical and program because which is hard to keep, and they kind of fluctuate away from one another, shift away from one another. And if you control that, of course that you can control the performance. So actually one can ask if whether or not this is a universal bottleneck and it seems so aside, I will argue next. Um, we can recall a fundamental result by by showing harder in reaction Target from 1978. Who says that it's a purely computer science proof that if you are able toe, compute the addition multiplication division off riel variables with infinite precision, then you could solve any complete problems in polynomial time. It doesn't actually proposals all where he just chose mathematically that this would be the case. Now, of course, in Real warned, you have also precision. So the next question is, how does that affect the competition about problems? This is what you're after. Lots of precision means information also, or entropy production. Eso what you're really looking at the relationship between hardness and cost of computing off a problem. Uh, and according to Sean Hagar, there's this left branch which in principle could be polynomial time. But the question whether or not this is achievable that is not achievable, but something more cheerful. That's on the right hand side. There's always going to be some information loss, so mental degeneration that could keep you away from possibly from point normal time. So this is what we like to understand, and this information laws the source off. This is not just always I will argue, uh, in any physical system, but it's also off algorithm nature, so that is a questionable area or approach. But China gets results. Security theoretical. No, actual solar is proposed. So we can ask, you know, just theoretically get out off. Curiosity would in principle be such soldiers because it is not proposing a soldier with such properties. In principle, if if you want to look mathematically precisely what the solar does would have the right properties on, I argue. Yes, I don't have a mathematical proof, but I have some arguments that that would be the case. And this is the case for actually our city there solver that if you could calculate its trajectory in a loss this way, then it would be, uh, would solve epic complete problems in polynomial continuous time. Now, as a matter of fact, this a bit more difficult question, because time in all these can be re scared however you want. So what? Burns says that you actually have to measure the length of the trajectory, which is a new variant off the dynamical system or property dynamical system, not off its parameters ization. And we did that. So Suba Corral, my student did that first, improving on the stiffness off the problem off the integrations, using implicit solvers and some smart tricks such that you actually are closer to the actual trajectory and using the same approach. You know what fraction off problems you can solve? We did not give the length of the trajectory. You find that it is putting on nearly scaling the problem sites we have putting on your skin complexity. That means that our solar is both Polly length and, as it is, defined it also poorly time analog solver. But if you look at as a discreet algorithm, if you measure the discrete steps on a digital machine, it is an exponential solver. And the reason is because off all these stiffness, every integrator has tow truck it digitizing truncate the equations, and what it has to do is to keep the integration between the so called stability region for for that scheme, and you have to keep this product within a grimace of Jacoby in and the step size read in this region. If you use explicit methods. You want to stay within this region? Uh, but what happens that some off the Eigen values grow fast for Steve problems, and then you're you're forced to reduce that t so the product stays in this bonded domain, which means that now you have to you're forced to take smaller and smaller times, So you're you're freezing out the integration and what I will show you. That's the case. Now you can move to increase its soldiers, which is which is a tree. In this case, you have to make domain is actually on the outside. But what happens in this case is some of the Eigen values of the Jacobean, also, for six systems, start to move to zero. As they're moving to zero, they're going to enter this instability region, so your soul is going to try to keep it out, so it's going to increase the data T. But if you increase that to increase the truncation hours, so you get randomized, uh, in the large search space, so it's it's really not, uh, not going to work out. Now, one can sort off introduce a theory or language to discuss computational and are computational complexity, using the language from dynamical systems theory. But basically I I don't have time to go into this, but you have for heart problems. Security object the chaotic satellite Ouch! In the middle of the search space somewhere, and that dictates how the dynamics happens and variant properties off the dynamics. Of course, off that saddle is what the targets performance and many things, so a new, important measure that we find that it's also helpful in describing thesis. Another complexity is the so called called Makarov, or metric entropy and basically what this does in an intuitive A eyes, uh, to describe the rate at which the uncertainty containing the insignificant digits off a trajectory in the back, the flow towards the significant ones as you lose information because off arrows being, uh grown or are developed in tow. Larger errors in an exponential at an exponential rate because you have positively up north spawning. But this is an in variant property. It's the property of the set of all. This is not how you compute them, and it's really the interesting create off accuracy philosopher dynamical system. A zay said that you have in such a high dimensional that I'm consistent were positive and negatively upon of exponents. Aziz Many The total is the dimension of space and user dimension, the number off unstable manifold dimensions and as Saddam was stable, manifold direction. And there's an interesting and I think, important passion, equality, equality called the passion, equality that connect the information theoretic aspect the rate off information loss with the geometric rate of which trajectory separate minus kappa, which is the escape rate that I already talked about. Now one can actually prove a simple theorems like back off the envelope calculation. The idea here is that you know the rate at which the largest rated, which closely started trajectory separate from one another. So now you can say that, uh, that is fine, as long as my trajectory finds the solution before the projective separate too quickly. In that case, I can have the hope that if I start from some region off the face base, several close early started trajectories, they kind of go into the same solution orphaned and and that's that's That's this upper bound of this limit, and it is really showing that it has to be. It's an exponentially small number. What? It depends on the end dependence off the exponents right here, which combines information loss rate and the social time performance. So these, if this exponents here or that has a large independence or river linear independence, then you then you really have to start, uh, trajectories exponentially closer to one another in orderto end up in the same order. So this is sort off like the direction that you're going in tow, and this formulation is applicable toe all dynamical systems, uh, deterministic dynamical systems. And I think we can We can expand this further because, uh, there is, ah, way off getting the expression for the escaped rate in terms off n the number of variables from cycle expansions that I don't have time to talk about. What? It's kind of like a program that you can try toe pursuit, and this is it. So the conclusions I think of self explanatory I think there is a lot of future in in, uh, in an allo. Continue start computing. Um, they can be efficient by orders of magnitude and digital ones in solving empty heart problems because, first of all, many of the systems you like the phone line and bottleneck. There's parallelism involved, and and you can also have a large spectrum or continues time, time dynamical algorithms than discrete ones. And you know. But we also have to be mindful off. What are the possibility of what are the limits? And 11 open question is very important. Open question is, you know, what are these limits? Is there some kind off no go theory? And that tells you that you can never perform better than this limit or that limit? And I think that's that's the exciting part toe to derive thes thes this levian 10.