Good morning to everyone. It is Wednesday the 6th of November. I hope you're doing well. I'm going to talk over the ongoing US-China trade war is the main focus for the briefing this morning. We're also going to have a look at the expectations around what the Fed are going to do in December and how that's been evolving. Also some commentary out of Fed's cap plan overnight and looking at the inversion of the yield curve. We've also had some German data and then we'll look at the calendar ahead and wrap up my kind of half of the brief and then I'll hand you over to Sam. So back to back to normal now and then he'll look at the technical levels and some of the setups across the different charts. Kicking off those we always do with the overall sentiment for this morning and it's a pretty quiet open overall. In the currency markets I mean the dollar index is trading pretty flat overall. Currently down one tenth so major currency pairs in the top left euro dollar and cable. Pretty sideways action thus far in terms of since the close of Wall Street last night in the Asia-Pacific session. Pivot level just providing a bit of near-term resistance in the cable futures at the moment. Equity-wise again equally so pretty tame open for European indices for the moment. The US similarly quite flat as to is the US 10-year oil. Slight loss at the moment trading sub its pivot and the $57 handle. $56.93 not too much in the way of any new fundamental headlines there driving price. So we're going to delve straight into the main kind of topic and get things underway. So talking about this US-China trade negotiation that's been ongoing and trying to finalize this idea of phase one at least of looking to do various different things and to start with I thought I would go over a few key points that summarize I think the current state of play and of which when I go through I think you'll see that there's plenty of support of to follow the notion that I think that it's going to be incredibly difficult I still see for this deal to get done to the point of which I think the market given how it's now positioned will be satisfied what I mean is I think there's room for a significant disappointment if history repeats itself and the whole dialogue becomes undone at the last minute which I think given the points are going to run through there's a fairly high prospect of that happening. So starting off and want to talk about what the Chinese want kind of know what the US want what did the Chinese want and a comment came out yesterday in a report from their state media talking about people familiar with the deliberations saying that Beijing has asked the Trump administration to pledge not only to withdraw threats of new tariffs so withdrawing new tariffs remember their duty commence in December they've also asked to eliminate duties on about 110 billion dollars of goods imposed in September and negotiators are also asking to lowering the 25 percent duty on the 250 billion dollars that Trump imposed last year so they want no implementation of December they want the rollback of what happened in September and they want the rollback or lowering at least of the duties on the on the quarter trillion dollars worth that was imposed in 2018 they are saying that the reason why they're asking for so much is because if you want China to continue buying large quantities of agricultural goods and in most importantly the issue that hasn't been tackled but you want us to play ball on cracking down on intellectual property theft well then that's the cost as far as the US consent now big problem if you're America because obviously tariffs or as he's become known the tariff man now which is Donald Trump this has been his primary weapon in this ongoing negotiation so if he now is to start not implementing or in fact rolling back and lowering of previous tariffs I think ultimately this is too big a reversal for the tactics that's being deployed thus far for a start now obviously there are stresses on both sides of each economy at the moment we saw that evident in the US trade data yesterday on Tuesday for September it showed tariffs have hit both US imports from China fell 4.9 percent from the prior month to the lowest in more than three years US exports to China dropped 10 percent to a five-month low as well so you could argue there's that there's some will to get a deal done given the consequence on the economy but remember for me there's a much bigger narrative at play and this is about Trump and the election this is all part this deal not so much I think him so full focus thinking about its implications on the economy it's more about popularity and getting the job done in order to secure a second term now this does come with some political risks then because by agreeing to lift the duties that could backfire because obviously this has been the staple of his policy ever since the pre-campan campaigning period that we had in 2016 when it was all focused on the US getting a bad deal China manipulating their currency and all these different types of things and so I think by agreeing to lift the tariffs again I think it's too bigger a step down from that narrative into what is and will remain a real staple linchpin of his campaigning going forward over the next kind of 12 18 months or 12 months I should say November obviously actually is the US elections on the 6th or the 8th next year it's pretty much a year to date as far as where we are at the moment the other thing was there's been a bit of talk about where could this be this actual talk of given the APEC meeting was cancelled because of the civil unrest in Chile and the last few weeks the South China Morning Post reported this morning that Xi Jinping's Brazil trip may be too soon for China to sign a partial US trade deal this is quite key then because Beijing has not agreed to a stopover in the US on way to the summit according to a source with details of agreement not yet finalized the removal of US tariffs remains the sticking point on the Chinese side so again it might be that we don't even get to the definitive nature of finding out where they're going to go to sign this deal because it might all come undone even before then but for me where the deal is going to be signed is quite symbolic particularly on the political front now Beijing saying that Brazil trip is too soon one of the areas that has been tabled although it's been pushed back by the Chinese is signing the deal somewhere in the US now interestingly one of the areas which has been put on the table is Iowa now Iowa for me is particularly interesting as a short list or being a candidate on the short list of potential locations so this is a look at the office of the United States Trade Representative if you were to go on their website you can see basically definitive detail about fact sheets on different areas and locations within the US now Iowa is particularly interesting because it's the second largest exporter of agricultural products in the entire United States of America and so this is particularly important why because well if you go back to 2016 when we had the previous US election Donald Trump won this area with 51.1 percent of the vote Hillary Clinton received 41.7 percent of the vote Trump carried Iowa by the largest margin of victory of any Republican candidate since Ronald Reagan in 1980 Trump enjoyed the support of working class whites in the agricultural industry of course and and what's the main product that they produce in Iowa soybeans and of course we know the back history of that and what the the ongoing tensions have been and tariffs and purchasing of goods between the US and China so I just find it incredibly difficult then for Trump to meet the conditions of what China want in order to get this deal done from the political point of pandering too much to the Americans but also making that concession if it were to be in an area like Iowa which is that whole understanding of protecting American farmers and so on it completely goes against that so that in addition as well to the fact that you know tariffs are seen as such an important enforcement tool think about this as well one of the main things that the US has wanted and particularly Robert Lighthizer the trade secretary has said before is that we cannot remove the tariffs because we really don't trust the Chinese are going to follow through with their commitments on various different things by keeping you know keeping the weapon on the table if you like that helps keep China honest that they'll follow through and see and implement then parts of their side of the deal all of this being said then whether it's the rolling back and meeting China on its tariffs whether it's the political risks that I think are too severe for the US to meet China on their request the loss of enforcement and the leverage the fact there's well the equities are all-time highs which we know Trump then tends to become a little bit more assertive with the rhetoric because he almost feels like he he feels like there's a bit of room for maneuver in order that the equity market can come back a little bit and that would be okay given how elevated they are at the moment so he might feel a bit more relaxed on that front you've also had US economic data whether it's non-farm payrolls on the consumer side in the non non-manufacturing ISM yesterday which was improving new orders are up employment constituent improving from the prior month does this give Trump them a little bit of support or put his mind at ease that really well he can't meet them in the middle not at least at this point and therefore do these talks all come undone I think it would be probably in my mind if I was an advisor to Trump I'd want to push this out a little more towards the middle of December when those December tariffs are going to kick in I wouldn't want to be signing any deal right now because I'd want to keep the the pressure on China China talking up a bit of a big game at the moment I'd want to test that take it down to the 11th hour as normal part of the negotiation process so that being said I think the markets are overtly confident about the outcome of this and if the trade deals do start to break down then I would anticipate some sudden pressure to come in to the equity market and that to initiate a degree of some risk off trade now with that being said then a couple of other things to have a look at obviously a lot of people looking at this equity market now having touched record all-time highs thinking well we're at these levels are we entering overbought territory and just having a look here on this chart from Bloomberg you've got the MSCI All Country World Index which isn't quite at the peak that we saw at the beginning and around Jan Feb of 2018 before we saw that correction global market but the 14-day RSI Bloomberg are looking at is more overbought than it has been at any point since we had that big sell-off at the beginning of 2018 you'll remember this elevation here when equities are really rallying one in a one-dimensional kind of move was when the corporate tax cuts got underway and that was in end of 2017 into 2018 when we hit that original all-time high so I've also read in this morning a lot of people talking about the put-call ratio is incredibly high at the moment all these things indicative that people are at some point positioning themselves for potential downturn in the equity market and I do think that this trade war will come undone and that could be then the catalyst then that creates then quite a severe correction as per what we've seen of course on many times gone by remember if we put the S&P back onto a daily continuation chart if you give me one moment I'll bring it up so we can have a look let me just quickly remove some of these studies just to make the chart a bit clearer our transition so this of course tells a you know a very big story of of many different fundamental catalysts this was when the corporate tax cuts came in the market was kind of overheating almost with its forecast and Fed tightening policy risks started to come in and we saw a big pullback we then had the biggest pullback of all which was this time last year we're in the midst of that selloff because the Fed was still tightening or signaling to do so the escalation in the trade war the risk of a no-deal Brexit political populism across Europe multitude of different things then we had the recovery and then the Fed started to cut rates and we started to see the episodes then fed cut market fall fed cut September market fall and now we've got to where we are at the moment of course at these all-time highs so I do think there's some downside that will definitely come I guess the the the clearest near-term target here would be a reversal back down I'm talking more medium-term if this trade deal does come undone that 30 23 24 error in the futures market would put us back to that previous double top that we had to define the price action at 2019 that would be the first place any further more meaningful pullback then I guess you've got to be looking at then those previous areas from May of 2019 and the top of around the late 2018 price action but I'll leave Sam to go into that I mean that's definitely much more longer term rather than the intraday environment so yeah that's my latest kind of take on the the US trade situation with China I just think it's a just a matter of time really but I think you've got to be a bit more responsive now to headlines as they come out because now things like the general election for the UK an interesting conversation I had one the guys on Monday was that you know we went through a period of many months looking at cable and it was really whipsaw price action a lot of hearsay and rumors driving the price whereas really now I think cable is pretty boring and what that does mean is I think we do get a bit of a reversion back to perhaps a little bit of a look at the economic data if anything of substance comes out the Bank of England I think it's largely a bit of a non-event tomorrow but overall I think really it's unless there's a major campaigning error on behalf of predominantly Boris Johnson then I think it's all pretty quiet till we get up to that election date on December 12th so I do think then that means that the macro hierarchy of key influencing themes has changed a little bit the composition Brexit's dropped off and I think trade war is right back up there at the top where do we stand at the moment in terms of the market's expectations on the Fed side of things well this kind of reflects that notion that I think if the trade deal did blow up that there could be a decent move in reaction because at the moment going off the language adopted by the FOMC in the most recent meeting where Powell was kind of signaling that this is the end of the recent rate cutting cycle expectations are very much that rates will remain where they are when the Fed next meet on the 11th of December I remember 11th the day before that election takes place the expectation here is for 94.8% so only 5% of the market's pricing in one more cut now what does that mean well Fed's cat plan he did speak last night yesterday evening late in the US session he said a steeper yield curve a sign that Fed rates are now appropriate and two things one who is cat plan so for those who need the refresher here's the hawk dove kind of crib sheet of the FOMC cat plan is a non-voting member but is a leaning dove so it does make that commentary quite interesting on two twofold one not only is he more neutral to dovish stance and typically that's slightly more of a hawkish comment but cat plan is going to be a voter as of the calendar commencement of 2020 so what typically happens is we get closer towards the end of that year and we get the rotation of the regional presidents cat plan will be a voter and so what he says now can be important for factoring in even though he doesn't have a voting right in the December meeting the other thing is you remember a couple of months ago everyone was getting almost obsessed with the inversion of the yield curve particularly with the calendar timing of the fact that equities were almost peaking as we were going into the beginning of October and on a year to year basis the beginning of October with this inversion of the yield curve kind of obsession that the market had meant that people were thinking well maybe we get a repeat of what we had in Q4 of 2018 and they couldn't be more wrong anyone who was calling for that and I know Bloomberg are really pumping that at the time but me and Sam remember in the briefings we were we were very much of the opinion that the the inversion of the yield curve was massively overhyped and thankfully for our call it's proven to be true because that 210 spread now the yield curve is back at levels lasting in July as yields continue to reflect the notion that you know that was very much overdone in terms of its initial pricing as the economy as per some of the data points that we've had and as per now the Fed pricing on the right cycle side has kind of eased back into more of a neutral stance and the yields are reflecting that environment. Final bit of news I just wanted to touch upon and I hand you over to Sam was we had German factory orders this morning possibly as helps a little bit of the risk appetite this morning as I've been delivering the briefing just a little edge higher in some of the global stock futures the DAX managing to get its head back above the pivot in the futures market and Germany's industrial orders rose by 1.3 percent month-on-month in September above expectations which were the 0.1 percent now this particularly important because the German economy of course has been under quite substantial stress of late and this was a substantial beat within that reading albeit it does tend to be a fairly volatile data set it was the first month of the increase in factory orders for Germany though importantly since June and boosted by a rise in both domestic and foreign orders so it's likely to alleviate at least near term any of that concern that was amounting for that particular country. Okay quick look at the calendar what else is there to come then for the session well this morning you do get the final readings of the various PMI numbers on the service side but remember these are final readings so are unlikely to have much in a way of substantial market reaction. Elsewhere eurozone retail sales non-event I'm not quite sure why this is bolded I've never in my life seen eurozone retail sales move markets and so I would not really factor that in if you're looking at any euro related trade strategies for this morning so then that takes us into the US session pretty light actually labor costs productivity numbers coming out for Q3 in the US and then you've got the oil infantry numbers so what I'll do is I'll post the API's from last night into the chat and then while Sam's going over the oil chart he can bring up to speed with what was the numbers from last night. Speakers quite a few to look out for a variety of ECB and Fed ECB in the morning Fed in the afternoon one of the most interesting on the Fed side would be Feds Williams who is a current voter speaking at the Wall Street Open 230 London and then Feds Harker who will be a voter next year is speaking towards the closing Wall Street and for any fixed income traders you've got a 27 billion 10-year note auction coming out of the US Treasury later. Other than that I would say I'd definitely keep an ear out for any developments and rumors about what the latest is in regards to the ongoing US-China trade talks. As I said I do feel that that is one of the major risks now that could well turn around this market having traded at all time record highs particularly in the equity space right with that I'll leave it to Sam now to finish things off and I'll see you in the chat room. Thanks very much. Hi guys good morning we're all good just having a quick look over stocks actually to begin with and bringing the DAX along with the FTSE actually starting in the morning quite nicely just pushing half hour into the open here and we have gone above yesterday's highs and we're keeping a watch on pretty much where we're trading now if we can confirm a break above there or not you can see was the high from both Monday and Tuesday yesterday so having an attempt to get in above there and of course if we put this on that longer chart you can see this is the highest the DAX has been for a very long time so keep a watch on that we're almost into with the last few sessions a bit of a mini range as well you can see again early hours in the Asian session we came down to test yesterday's low which was the overnight low from Monday as well so key point to keep a watch on there to see whether we can hold or not above if we were to drift lower of course just be aware that you are coming back then into the middle of the range again so a couple of potentially key levels with the way we've been trading this morning quite well respected trend line here in the DAX and you can see one two three four tests of that so it might be worth later on to see if we do drift lower how that reacts as well the pivot has already acted as a good level of support once broken through but if we were to come back down again it could well get choppy but the moment the DAX enjoying life and so is euro stocks and that's helping to drag the S&P up towards its pivot you can see similar move obviously not as not as big however as as european stocks as well key level to the upside 30 78 a bit of a breakdown area from yesterday post cash open and also the evening high as well so just the three points above where we're trading currently looking again longer term at this market and we haven't quite had a test back of what was the post let's you know it's not the 30th of october that would be 30 54 which is of course not too far away from where that trend channel was so still something to keep a watch on that if we were to have a couple of down days and see how that holds out as a good line in the sand for for the buyers and sellers to to fight over so let's just put that on and and see where that would come in for now just remove the pivots bring in the trend we one sec you can see here it's going out a bit lower town you can see that yeah coming in around sort of 30 60 or so as well which is also not far away from the high that we had on the first so still one to keep an eye on there's a couple of nice levels as well in the euro which i would have as lines in the sand as well and it's going to bring in here on a 60 minute chart just looking at the the previous high that we had on the 21st you can see we created this nice trend that from the 24th we then broke through in the 30th came back to find support in the 5th which was of course yesterday before that break down and that's certainly somewhere i would have marked up on any push back towards there you can see yes it looks now mid-range but certainly being well respected over previous times the low that we had yesterday could argue a decent enough area from the 16th or that was quite choppy we're just trying to push above those previous lows that we had on the 25th and 29th we are above that on the stock on the the spot market so just be slightly careful of getting in too aggressively yes we've got the hurdle now we've just attacked here on the 30th so that somewhere i would still be looking at for a point of resistance but the key line in the sand i would have as that trend line there moving on to the pound it is boring i have to say at the moment waiting for opportunities to to get in and it's not really delivering as of yet from a technical point of view let's have a look see if this trend line hasn't come into play just yet which would be just above 129 the highest from yesterday evening this is looking on 60 minutes so quite a bit of resistance around there that i would be focusing on to be fair and then maybe from the lows to see if we can get anything squeezed in as well but yeah i think opportunity wise there's better ones out there the pound at the moment probably not where my focus is going to be until we get some further developments and that course could happen from today onwards but for now i'd be looking elsewhere main move up yesterday gold decent push to the downside a couple of trend lines broke loads of the week and last back end of last week broke as well 1504 key level nice breakdown and we came down to test some of the lows that we had from the back end of last month which you can see just looking at this whole area is such a key zone and gold is a market where it still doesn't know where it wants to do what it wants to do shall we say but at the moment if we can have a strong dollar and positive trade talks it does have to reprice this is whether that can last or not i think will be the term in fact but there's a line in the sand where it could potentially get a slightly bit more ugly 1480 the lows that we had back on the 11th are going to be key because a break below there while you've got some possible levels 1476 that low that we had from the first of last month would be an area to keep a watch on with gold the way it can move after in a strong day to the downside don't necessarily getting get too aggressive looking for those shorts we've come a long way so there could well be a slight retracement in the mix as well pivot looks to me like it could be a quite a nice area 1492 that famous 1492 level decent little breakdown area from there also the pivot and not a bad place perhaps to get the continuation down from there might as well get a fib on that level as well oil just to wrap it you can see we had a bit of a build last night from the chat 4.6 mil cushing and also build gasoline however decent draw and distillates to there's that 930 candle hardly anything hardly anything off that and a quiet day has to be said for oil yes say however just where we're trading now quite a nice level previous high of yesterday before we pushed higher similar time to where we are now so expect a reaction point around here if that was to break through then those lows from yesterday would be the next key level to target and also we've come with a couple of these markets where we have made recent highs worth us having on these potential trend lines and you can see actually we're in the 15 minute now we've got quite a good test of that one two and almost that third there so definitely something to have on that for oil as well any questions as usual please do let us know quick run through that calendar again as I'm saying those retail sales numbers unlikely to move things some data coming out shortly which we know is always one to keep an eye on for the euro remember that line in the sand a couple of us numbers later before oil but this week in general looking to be relatively quiet so I wouldn't be looking to to trade too much let the market come to you would be my advice today hope you have a good trading day and I'll catch you all in the chat


Thank you very much for the invitation so you know as as you were just told we've been here all week you might have seen us occupying this space and sort of frantically hacking our way through the week so so yeah this week we've been here since Monday for an event called Astro hack week this is the third time this event happens it was really it's really one of the events that are organized by the Morse and Sloan Data Science Initiative that is a cooperation between BIDS and NYU and University of Washington and it's been one of these things that really all three institutions have organized together so our organization team has come from all three and this is now the third time it happens the first time was in Seattle second time was in New York this time it was Berkeley's turn but it was really more and slow enough have made that possible both in terms of the funding but also in bringing the right people together so what is Astro hack week I'm gonna talk about this in a little more detail basically for the last five years five days around 50 of us have been in the space the people that were here represent sort of all of astronomy all of the different subfields in astronomy we've given tutorials we've given lectures and we've been hacking there are 28 projects currently in progress and the people who are now not here are somewhere in the paleontology museum to hack some of them are held in the cafe and they're frantically working because at 3.30 we're gonna have our wrap-up session where everyone presents what they've been working on so you might now sit there and ask why why why would you do this and that's a very good question the answer is sort of two falls and the first answer is that in all of science data is has been exploding for the last few years and you here at bids probably know this as well as I do here's just an example this is astronomy data archive one of many and this is just basically a plot of how the data increased between 2008 and 2014 and that's here in terabyte and so they've gone for maybe like 30 terabyte to more than that 650 terabyte in these few years and so somehow all that data out of all that data we would like to get science out of it so we need to figure out how and that's not only a problem for astronomy you know social scientists have started doing analyses with Twitter data and Twitter data seems to be growing exponentially the LHC and CERN are producing massive data sets and then one thing to point out is that it's not just data that we collect from real sources but these are climate simulations and these big simulations are basically you can consider data too because they produce massive amounts of information that you somehow need to mine and so in terms of another example from astronomy this is how astronomy used to work it used to be someone who goes to a telescope who looks through the telescope who makes a photographic plate of wanted they want to observe and then they by hand looked at that photographic plate and did their research this is not what we can do today this is an example of what's going to happen in the next few years this is the square kilometer array which is currently being built in South Africa and Australia and that square kilometer array will produce 960,000 terabytes of raw data per day so that's a lot of data that we're starting that we start to have to take into account yeah oh I'll take questions whenever these two pardon so maybe it's kind of a prosaic observation but looking at that ramp up which maybe looks a little sigmoidal now there were increased costs we're it raises the question of where's the data being stored and what are the cost burdens of that aside from the functionality that having that data gives and I mean with magnet you know like a an 8x over four years sort of thing that must mean that expenses are perhaps shifting yes so I am not an expert in the sort of structure of data management in astronomy but I think I mean this is this is an infrared science archive that's run by NASA this is all from telescope space telescopes that's to run by NASA and NASA has an growing awareness that they need to do data management and they are actually starting to do some data management for products produced by the community but yes I mean there's another project called LSS T which is a big US project the large synoptic survey telescope and their software budget alone is around 50 million dollars and a series of considerable fraction goes into a data management group that six that sits at six different institutions and over five to ten years produces the archive and produces the necessary infrastructure there's work that's being done with NCSA with the national computing facility so I think there's growing awareness also within the funding agencies that this is a problem but it is true that sort of long-term storage is something that's not entirely solved especially for the SK for example right yes yeah so that's actually not something we've talked about much this week except for a tutorial on databases but it is it is true that is one of the problems any other questions okay so what I'm trying to say is basically that the modern data that we gather is heterogeneous it comes from different sources that need to be incorporated it is usually complex the data that I work with in my daily life is actually not that big it's big enough it's small enough that you can store it on a hard disk but it's usually very complex it usually has strange strange sources of uncertainty it's usually my time series are not continuous they have gaps in them and there's all sorts of clever ways you need to try and figure out how to deal with them a lot of data is dirty especially data like Twitter data that comes from the you know from the real world but and then there's this big problem of high volume and high velocity which is current mostly called big data and so this is not only something that happens in academia that's been happening in the sort of data science industry along for a while now and the response to that was to have a sort of data science new thing called data science that incorporates different things including statistics advanced computing visualization but also domain expertise and scientists traditionally I certainly when I think back to my undergrad and to my PhD we're sort of actually only trained in a subset of that and that subset is not enough anymore to really deal with with the data that we're getting and so the point is that I think academia is really lagging behind industry and methods development and data driven research and Jake Fander plus who's at our partner institution has had two very good blog posts on that so the question that we were asking ourselves is how do we encourage researchers to learn and adopt new methodology how do we get them to even talk to people who work on the methods the other question that we were asking ourselves is within our field if you think about traditional conferences they're very seg where people normally exchange knowledge they're very segregated by subdiscipline so here's just like I just picked up the banners from the next few conferences in the next year and you know there's like an international work of spectral stellar libraries or physics of the intra cluster medium and these are extremely specialized subfields within astronomy but if all you ever do is hang out with people who work on the physics of intra cluster medium you'll never figure out what the exoplanet people are doing and maybe they're actually developing methods that help you but you would never know about it and if that problem is even worse when you think about data problems between disciplines we don't know what people in climate science are doing but they might be developing the next best tool that will help us deal with the SKA data so the second question we had was how do we get researchers to talk to each other about methods and so our general objectives for running this workshop is to teach new data science methods to researchers and to increase connectedness between them and that includes sort of fostering exchange between between disciplines it also includes built networks and lasting collaborations that people can then expand on once they go home and the sort of final final goal is to promote open science so that people that other people who cannot make it to the workshop can take advantage of this too and so our answer to this is astrohack week and I'm going to try to convince you that that's a good idea and that we can actually deliver on these goals so in order to explain to you what astrohack week is I'm actually going to start explaining by what astrohack week is not astrohack week is not a conference if I think about conferences that's sort of the picture I think of and that's a bit unfair because I actually searched for like board scientists at conference when I look for a picture but what I would like to convey here is that traditional conferences are often knowledge transfer from someone standing in the front to an audience that might listen or might not listen and there's very limited time for actual exchange during breaks or in the evening and that happens sort of accidentally it's not the main reason for the conference usually the second comment is that there is a difference what conferences do is what one of our participants called science driven communications you have someone standing in front talking about their results they don't usually talk how they got there when I prepare conference talks they just say you know here's a problem and here's this awesome result I got and here's all the exciting physics that I got out what we wanted to do is data driven communication and we wanted people to talk about here are the data sets that we have and the problems we have with those data sets and then someone else could stand up and said I actually have that same problem and I can help you solve that so if you think about how extra hack week works it's really I have hundreds of pictures of like a bunch of people standing around a laptop discussing together about a certain data problem the other thing it's not it's not a summer school even though we have tutorials summer schools are traditionally top-down so you have senior scientists on the front who will teach junior scientists something about their expertise this is exactly not what we want the hack week principle is that everyone learns from everyone and especially in a lot of ways junior people are better at adopting new methods and adopting new technologies so we try to admit both junior people who are experts at things and senior people who are open to learning from them so there is a much more level playing field in terms of teaching okay let me tell you a bit more about how it works the ingredients are kind of three things we have tutorials we have breakout sessions and we have hacking and they all sort of make up a very very mix of what people actually do at astro hack week the first time we ran tutorials we kind of decided on a bunch of topics that we thought would be interesting for the field and then run them and that worked really well so the next time we just gave people a really vague message and say we're just going to do data-driven stuff they applied anywhere which where we should really got about but we asked them what would you like to learn about and this I don't know how well you can see this but this was our astro hack week 2015 board of topics that people were interested in and we just wrote them all down and it turns out there are sort of fields that emerge and one of them is kind of inference and sort of Bayesian statistics and MCMC they're sort of data driven stuff like classification and machine learning and they're sort of exploratory data analysis and sort of big data methods and so from there we then came up with some kind of kind of program and that work that sort of bootstrapping has worked really well one thing that we learned is that actually tutorials at astro hack week are scarier for the lecturers than for the participants and this is because on almost any topic there will be experts in the room and we got a lot of feedback from our lecture saying like oh but what if I say something that's maybe not entirely correct and oh that person in the room is much more an expert at it than I am so the solution that we had to this is that we encourage a lot of peer learning because it's such a diverse group what we did was basically put people in groups and there would someone who is lecture who would give a bit of a lecture and then do exercises and during the exercises we asked the other experts in the room to help so we encourage participant to help with the teaching which worked out really well and that is a sort of formal that is the formal institute the formal learning that we instituted but that is all the sort of the tutorials are all the formal teaching that we implemented but then what we encouraged are so good but breakout sessions and they were usually short tutorials sort of 30 to 45 minutes that we encouraged participants to lead and they were usually on topics that came up during the week oftentimes they were sort of further topics on something that was considered discussed in the lectures but oftentimes they were just sort of practical things that came up so we actually have a lot of things like profiling python or licensing code and all the sort of practical things that come up and researchers daily life but that they don't necessarily learn about and one of the nice things about these tutorials is that they're usually quite on the spot and very informal so usually someone stands up like here and said I can talk about Gaussian processes for a while but I haven't prepared anything but in that sense it becomes very informal and more of a discussion than a sort of lecture and that we learned helps our helps people learn as well and then the sort of heart of astro hack week is the hacking of course this is last year we drew a sort of hack Atlas where people could write down where they were hacking so you would know where to find them but one thing I'd like to define is actually what a hack is and I'm not going to define it we asked our participants to define it last year and so I found a few which I think are very good sort of incorporate what we think hacks are so one way to define a hack is as a small project with a clear goal that has to be completable in the time that you are there you should not start you know your new research project for the next half year but instead say here's a small thing that I can try and either it works then I can build on that or it doesn't then I'm just going to abandon it but that's one of the ways that astro hack week works you need to have something that you can finish you can also it's perfectly reasonable to cobble together quick and dirty solution what you will often hear is things like oh this is a toy problem that has nothing to do with reality but if I can make this work then and that works then I can go to the more complex problem a lot of the times that involves really quick and dirty programming hacks you know code that you wouldn't want to release to other people but just making it work in the time that you can and then the third sort of definition involves simplifying a complex problem and that kind of goes back to this idea that it needs to be completable and often to make it completable you need to sort of compress it and simplify it down until you can really approach it in practice here's how hacking works every day people stand up after lunch and pitch projects that can actually also the first time we also made people stand up and say what they were good at if they weren't pitching a project they had to say what they were good at and everyone had to be good at something because everyone is but then people pitch projects there would be some time for people to discuss what they were working on and this year we had lots of small hacks there was lots of movement between groups and then people hack and that these hacks can take an hour they can take four days depending on the project at the end of each day people put present their results their results are it has to be something tangible it can be a plot it can be a blog post it can be you know a notebook anything but there has to be something tangible that comes out of a hack that tangible thing could also be a plot that says what I tried really didn't work I failed on this but that's okay and then they learned something from it and that's good too so just to show you maybe that's a work okay so here's the example we've been doing everything on hackpad and that is our sort of central open source of everything that happens and anyone will add their hacks and you can see there's people working on this right now and they are sort of bunch of active projects with links to like github with links to to ipython notebook with links to explanations and so anyone of us can sort of go there and read and that's where we also go at the end of the day to help people to look at hacks that happened it's at this link I think my slides will be online afterwards right maybe yeah I can put them online and then all the links will be there okay so here's just this is one of the few pictures where I have people smiling it turns out no it turns out people are not actually unhappy they're just extremely concentrated there's usually like a bunch of people sitting around a computer going like trying to figure out something really hard but this is just really how we work it's a bunch of people sitting around a laptop really really getting things done in practice one way we encourage open science is by just asking people to do everything openly that happens at astro hack week all the descriptions at the hackpad are officially you know can be seen we encourage people to put stiff in git and on github just to be ever really open and so that's that's one of the basically the structure is the thing that we can steer the other thing that we can steer that is really crucial to astro hack week and then we can only steer to a certain extent are our participants because they're a huge part that makes up makes up astro hack week and the decision that we made for astro hack week early on that we decided that diversity promotes excellence and that we wanted to be a really that we wanted astro hack to be a really diverse group and when I mean diversity I mean diversity along of along many different axes and one of them is that we want a broad diverse diverse group between binnett Guinness and experts that can be beginners and experts in statistics and machine learning in stellar evolution in anything really but it's again different from a summer school where you have sort of experts who teach and then everyone else is a novice at astro hack week we're trying to get people from all levels to be involved this also includes sort of diversity in academic seniority we got under everything from undergrads from senior professors and again in a lot of ways senior people can learn from junior people as well especially you know junior people person stands up and say hey I'm going to do a git tutorial and most of the senior people I know actually don't use git so sometimes they go like hey I can finally learn this git thing my phd student keeps talking about one of our reasons for doing this was foster exchange between topics and so we also try to admit people from different disciplines within astronomy and usually we actually have people from outside astronomy as well there's usually a couple of computer scientists there there's sometimes people from industry there's sometimes last year we had I think a chemical engineer the year before we had someone who works on sort of geospatial map data and somehow these people always also bring extremely valuable experiences in the group and then we also care a lot about diversity and background and with that I mean sort of you know diversity in terms of gender in ethnicity and racial background because again we think it's important for the group to have that so astro hack week is really as only as successful as its participants so one question that we ask is how do we get a good mix of participants where mix means you know we want to diverse group with different backgrounds and academic sub-disciplines and everything from beginners to experts and all of our interesting interesting things that people could be beginners to experts to and also everything from undergrads to to academic seniority and when I organized astro hack week in New York last year I sat down and said these are my goals when I find my participants I wanted to optimize the mix for all of these diverse things I wanted to reduce my own bias sort of unconscious biases I also kind of wanted it to be transparent and I wanted to be accountable for how we do that and so that was basically me last year when we got 168 applications for 50 spots and I was like what what do I do with this and I was very fortunate because my office mate is a computer scientist and he said you're a data scientist you should be data science with this and so the first thing that we realized that once you have a group of people that you're happy with coming and that group is still larger than the spots that you have what you have is a complex optimization problem and it turns out computers are really a lot better at complex optimizations than humans are so we decided to let a computer do it and so it's got sort of two and a half steps one is find all in your in your input set find all your your participants that you think are appropriate to have at the workshop and we asked a few questions like why do you want to come to extra hack week and if someone had given a completely crazy answer we had said well maybe maybe that person isn't actually serious about coming then we also pre-selected a few experienced hackers because you do need some seeds for people who can spark you know hacks and who know what they're doing and then we used an algorithm to break ties between the rest of the people conditioned and our what we thought was our ideal mix of participants and so here's for an example of one of our cat several categories one was statistics experience and that goes sort of from little to none to expert and we asked people to rate themselves and here you can see the blue is the sort of fractions of our input data set and then the black lines are the targets that we set the algorithms and then in blue and green is what actually comes out and so we decided we wanted a bunch of experts we wanted a bunch of people who knew nothing and then we wanted most of them in between but there was a conscious choice that we kind of made and as you can see the algorithm can't doesn't always completely manage to hit the goals and that it's because it's optimizing over a large range of different categories so in the grand scheme of things depending on imbalanced your input data set is it can't always do that here's here's the other one of the ones I care about we asked people very much care very much about we asked people whether they considered themselves a minority in the field in terms of gender identity and we also asked them whether they consider themselves a minority in terms of racial or ethnic identity and one thing that we learned previously is that representation matters a lot we heard a lot of from our especially female participants that they said they felt much more comfortable at the workshop because there was a significant fraction of women there so that was also conscious choice that we made to optimize beyond the ratios of the input data set because we thought it would be good for our workshop this code is actually also an astro hack week project it's online if you're interested for your own own workshop I really I really care a lot about the sort of selection problem for participants and I would love if people wanted to talk about that more because we're still sort of optimizing our approach and approving every time I do this okay so I talked a lot about how we make astro hack week work but you might still say well but does it actually work does it what was supposed to do so we run a survey every every year and I'm not I'm not a social scientist so I'm not really qualified to evaluate this in the sort of quantitative way but I can give you a few things I can show you a few things that we learned from the survey so one thing we asked them is what did you learn at astro hack week one thing that people said they learn is not to be too afraid of Bayesian methods which I thought was a great success they learned sort of some machine learning and where the resources are and there were sort of corrected misunderstandings about things they already knew and that was the thing I would really wanted really to to see that they thought they learned some data science method they learned machine learning and especially they learned where to find resources that they can can go to ourselves and that was really one of our goals they also said they learned about new tools they learned about practices they improved their programming skills they learned about profiling and commenting code they learned about team coding one year we enforced pair coding for a day and so these are all the practical things that we wanted people to take away all the things that would make their make would make their day-to-day life better and make their computational results better and improve open science and then there was sort of more social things one of the one one person answered that the main thing they took away from astro hack week was that they're not alone and they went on to explain that they're the only person in their department who does sort of data intensive research and that person said they felt really alone and they didn't think anyone else was doing this and coming to astro hack week they like there's this whole group of people and they came away with many connections that helped them and then there were sort of specific specific interactions where they said Brendan Brewer is a statistician we invited and they sort of drove a new project forward by quite a lot just by meeting that person astro hack week these are all sort of anecdotal we also asked them questions that relate to to the more slow and goals and one of them is sort of one of the big goals is to learn about data science career so we just asked them do you believe that astro aga was useful for your future career and of the 28 people who responded basically all of them said to some extent yes except for two who said they don't know so similar we have similar results for the questions that they think that the skill are applicable outside academia which is also important in terms of making sure researchers might transition to industry from academia we asked them do you feel that the things they learned improve their day-to-day research and most of them did which I was really pleased by because we really wanted everything to be applicable and then we also asked them whether they felt that it made them more comfortable to do open science and a lot of people there were a lot of discussions about how to do open science how to write papers out in the open and most of them came away with a feeling that they're happier about that do I do I have time okay if you if you don't mind I'm gonna show you just a couple of projects to give you a range of ideas of what the kind of stuff about come up okay so here's one they're all really sort of astronomy so I'm not going to go into much of the detail but here's a project that I picked for one main reason and the main reason is that the paper is written by a group from Chile and a statistician from New Zealand and so these are two groups that are very geographic and very different geographical locations they come from different backgrounds and work in different institutions one is an astronomy department the other one is a statistic statistics department but they managed to get together at astro hack week and start a project that ended up in a paper a few months after astro hack week so and this was a month this was a project that used some very complex sampling algorithms and very complicated statistical methods to find planets in in what's called radial velocity data from stars and that's generally a hard problem and they managed to to get a good result out of that as a and you know that that collaboration started astro hack week and persisted beyond that this is a project from this year not everything is in astronomy so that was a project where they looked at the fraction microscopy of molecules and one thing that we discussed a lot at astro hack week this year was optimization methods including stochastic gradient descent and there was this group that said we want to learn more about stochastic gradient descent and we want to use it and I think I think this is gonna yeah this is a video and then the video shows you how how the model sort of slowly converges on the molecular structure which is quite similar to the one they put in so that's a project that has nothing to do with astronomy but uses expertise that people built up from astronomy to solve that kind of problem there's sort of software projects SN Cosmo is a big software code that deals with exploding stars and they were sort of models that for a long time should have been implemented but weren't and at astro hack week a couple of people sat down and said that's gonna be my hack we're just gonna implement this new feature in the software and that happened there were a couple of people who decided they wanted to learn more about machine learning and they picked a project they are interested in in this case these are sort of large-scale structures in the universe so every point in here is a galaxy and then they decided they really wanted to find filaments in there and sort of clusters in there and they use machine learning and here is an image of the filaments that they found the filament confidence and in black you can see these are all what the machine learning algorithms are things as filaments and these is what the machine learning algorithm things as sort of clusters over densities of galaxies and so they started out not really knowing anything about machine learning and by the end of the week they had this sort of working example and people write tutorials today we had a tutorial about auto differentiation and that's something where someone said I would like to learn auto differentiation and that was in the experiment this year where we said you learn about us you group learn about auto differentiation and by the end of the week you're going to give a breakout on this and that's a kind of scary thing for people to do but it was useful because there was expertise in the room that people could immediately use once they had a point even in their breakout session where someone asked a question they couldn't directly answer so that worked really well there's also some projects that have kind of nothing to do with astronomy somehow we ended up talking a lot about color maps this week and so one of the hacks was a few people who did this project called urban goggles where they make custom color maps based on queries about cities on flicker so for example they gave it a query string Manhattan at night and then it produced a color map for your plots based on that or they gave it San Francisco and you know San Francisco has a different color map so there's these kind of projects usually that people also do to teach themselves stuff and what else do I have this was then one that was born out of a needs because we have a lot of people I straight went to learn something and we have a lot of expertise at Esther hack week and it's not always clear how to pair up people so these people asked asked the participants what do you want to learn about and what you're expert on and then they just build a graph model for matching up people and so every person you know this person wants to learn about grab probabilistic graphical models this person knows about graph probabilistic graphical models so there is an error between them and they did now the same I don't have a profit but they did the same for breakout session where they say these people want to learn these people have the expertise here's how you should do your breakout session and then the final one I have is they built a Twitter bot based on David Hogs David Hogs a faculty member at NYU based on his blog and his his Twitter account and they just wanted to learn how natural language processing and deep learning worked so they built a Twitter bot that would just respond to you if you tweeted it it's unfortunately no longer active but you know they said they learned a lot from it and it was a kind of fun thing that they wouldn't do in their daily research life even though they're going to take carry this knowledge back with them and they might be useful so as the last thing I have a few lessons that we've learned so far and we keep learning one thing that we learned is that participants come to Astro hack week with vastly different expectations and goals and that's a function of the diversity of people who come and that's both a good thing because anyone can get in something interesting and useful out of Astro hack week we have people who come with the expressive goal to learn about how hacking works and how hack how to organize hack weeks we have for example undergrads often come to learn about methods or even PhD students learn about sort of machine learning and Bayesian statistic all the thing they think will be useful in their research it's also a problem because it makes it hard to evaluate whether the workshop was successful because if everyone comes with different goals then it's hard to find common metrics to evaluate whether it worked the other thing that we learned is that student travel support is absolutely crucial we had I had people come to me and say I am glad you could fund me because my supervisor would not pay for this so there are you know cases where supervisor said oh just go to a traditional conference this isn't really useful also if you do want to have undergraduate students there's almost never any money for undergrads to travel and our the undergrads that came here have always been incredibly involved and have been incredibly made incredibly valuable contributions so we really encourage to have them there this relates directly to the more slow working group the space where you are matters in various ways it needs to facilitate hacking and in in that sense we're extremely grateful to bids because this space was perfect because it had these tables they can easily reconfigure all the chairs move and so you can quickly move around and say my group needs a table and they go just go pull it to the side and do it there's a bit of opportunity for people to sort of have breakout sessions while the rest of them works and that's been really important too but so space is really important to us one thing that we continue to address is the imposter syndrome is everyone here know what the imposter syndrome is the imposter syndrome is basically the feeling that you don't belong into the group where you currently are that everyone around you is much better at everything and you is and you're an imposter you don't belong here and at some point someone will find out that you don't belong here and will throw you out and it turns out that's that happens we learned that already during our first astro hack week we that happens to basically everyone we told people this year it happens to the undergrads it also happens to the senior professors I said earlier that our lectures you know every lecture I talked to last year before they talk they were like oh my goodness there's like this person in the room who's much better at this than I do and what if I say something wrong and oh my goodness and that is also kind of an expression of the imposter syndrome so one of the things what we've done is just explicitly address it at the beginning and tell people you will feel that way and you will feel that way because everyone around you is an expert at something they'll be an expert at different things and that means that it can feel like everyone around you is an expert at more expert at everything than you are and I think having people having making people have that in their minds help helps address it but if anyone has ideas about how to do that better I would love to discuss that with you too and as I said earlier especially for minority participants just having representation helps a lot as well in practical terms food and coffee are absolutely crucial especially the coffee and one important thing that we learned is that you want to have it in house you don't want to have people go and fracture up because they will fracture up along the groups that already know each other so you really want people to stay in there which means which means getting in catering the evenings are important to us too it turns out it is really useful to know a bar nearby that has big tables wi-fi and beer and I can guarantee you that people will just go and keep hacking that happened every time this this so far and it's here I just followed people I don't remember so this year's organizer yeah yeah so Kyle our organizer was the one who sort of scoped out places it is it is a good thing to know yeah Kyle Kyle would be the person to ask that is true yeah we did we try to get we try to get sort of usually have one afternoon or one evening somewhere else and we were quite lucky that github offered us their space for the afternoon on wednesday and so on wednesday we sort of hacked at github and it was it was incredibly exciting for everyone because we we do all our work on github and so just to be there was very cool it's also just a very good space for hacking so a lot of it got done there's also a lot of things we actually don't know and so we still don't have a good idea of how we can measure success we ask people whether they think they were successful and learn something and sort of hit the metrics but measuring this quantitatively is actually quite difficult and that's sort of an unsolved problem sort of one question that I keep grappling with is what's the sort of ideal mix of participants in terms of their different attributes that we're interested in we made a choice we made certain choices for this it is not clear to us that those are the best or ideal choices so we continue to think about that too another question is what's the right balance between learning and project work the way we did it was we had lectures or tutorials in the mornings and then the afternoon's free for hacking and for breakout sessions and this year for the first time we did it that we had only four days of lectures instead of five and the final day was just hacking we'll see how that works out and then one question that I keep also thinking about is how we can mitigate imposter syndrome how can we make shy people stand up and talk as well because we don't want astro hack week to be dominated by a by a few people who are very confident in their abilities because that again increases intimidation for other people so let me finish with asking you should you run a hack week and I think here are the reasons why you might want to run a hack week you might want to run a hack week if you have more data then you won't know what to do with or and or your data is very complex um you should also run it if you think you might need better or new methods that you need to get and maybe from other fields um if you think this kind of collaboration and networking is important beyond what you get done and sort of um the breaks at traditional conferences astro hack week is a really good way or a hack week is a really good way to make that happen because there is this space and there's this time for people to start these collaborations and sort of get projects off the ground um we think it's also been really good at promoting open science and we keep hoping that people will carry that back into their home institutions again how to measure that is kind of difficult so conclusion yes you should run a hack week um there is no it's not not just astro hack week anymore actually next week there will be neuro hack week in Seattle um and that was sort of originally based off astro hack week for neuroscientists and then there will be a geo hack week in november as well and both of these are at the east science center where astro hack week got started as well so they've been very active in this um i've put some resources on the slide if you're interested our website is on there we have a github organization for astro hack week where there's all our materials and actually most of our organization happens in github issues so if you want to you know follow along with some of our discussions that we've had about how to make this successful and how to invite speakers um that's all on there um there's our hack pad if you want to look at our hacks i've written a bunch of blog posts about how to organize astro hack week with all my experiences from last year and i continue to keep writing blog posts as i learn more um yeah the the tool that we wrote is on there as well and then there was an incredibly insightful blog post earlier that was called the horror of hack days by someone who really hates hack days for many of the reasons that we think about the imposter syndrome and we tried very much this this um this time to address that type of person and make it easier for them no no no that was someone just from tech but this got linked somewhere on twitter and i saw it and i thought oh my goodness i bet there's people like that among us as well and i bet there's people who might not want to come to astro hack week because they might feel like that and so we've been thinking about how to address this and how to make astro hack week welcoming to people who are more maybe introverted or something like that um and that's basically all i have to say thank you for giving me up the opportunity this has been a big group effort kyle barberry has been the main organizer here and i'm sure if you have specific questions he'd be happy to answer too the organization team in general has been kyle barberry, phil marshal, david hogg, jake funderplass and me we've been sort of doing this for the last three years um i think fernando was on the organizing team the first time too um and then brian mcphee has been helping me in um a lot with with the participant selection part of it um so thank you again and i'll take any questions you might have


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Hello, welcome back to the cookie conundrum and recipe for success, an industry conference and summit from Quantcast on the demise of third-party cookies. We've 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. Zhao Lin, Managing Director Solutions at Zaxis and Somar Simpson, Vice President and Product at Quantcast. Stellar panel, looking forward to this conversation. 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. Tell us about the impact to you guys and the reliance you guys had on them and what are you going to do to prepare for this next level change? Sure, I mean, I think like everyone in this industry, there's a sniffing reliance and I think it's something that a lot of talk about audience targeting, but obviously there reliance that third-party cookies pervasive across the whole at-tech ecosystem, martech stack. And so we have to think about how that impact our vendor, the vendors we work with, what it means in terms of our use cases across marketing, across advertising, across site experience. So without a doubt, it's significant, but we look at it as, listen, it's disruptive and disruption and change is always a little scary, but overall it's a long overdue reset. I mean, I think that our perspective is that the cookies as we all know was, it was a crutch, right? Sort of a technology being used in a way it shouldn't. And so as we look at what's going to happen, presumably after Jan 2022, then it's a good way to kind of fix some bad practices, practices that led to data leakage, practice sort of devalue for our perspective. So we offer it as publishers. And I think that this is, and a key thing is that we're not just looking to, as we look through post-Jan world, not just kind of recreating the prior world, because the prior world was flawed, or I guess I could say the current world, since it hasn't changed yet, but the current world is flawed. Let's not just replicate that. Let's make sure that third-party cookie goes away, other work around like fingerprinting, things like that also go away. So philosophically, that's where our heads at. And so as we look at how we are preparing, you look at sort of 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? Obviously, are we aligned with the regulatory environments? In some ways, we're not looking just to Jan 2022, but Jan 2023 where there's going to be the majority of our audiences we cover 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 sort of 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 it, old world or next-gen kind of the current situation kind of flawed. And as you think about programmatic, the concept is mind-blowing in what needs to be done. So we'll come back to that. Cause I think that original content view is certainly relevant. It's a huge investment and you got great content and audience consuming it. Sal, 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 in the right context, right? So yeah, 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. You know, everyone, everyone has a megaphone now. Everyone's, you know, everyone's here. Everyone's connected. So how are you impacted by this new notion? You know, if the cookie list future was a TikTok dance, we'll be dancing right now, and at least into the next year. 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. The end of the third party cookie does not mean the end of programmatic. 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 along this 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. We're on the channel, so clients want to drive outcomes through multiple touch points of a consumer's journey, whether that's programmatic, whether that's a cookie-free environment like connected TV, digital at home, audio, gaming and so forth. So we're gonna 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. Creative in itself is cookie-less, so there will be more focus on how to drive 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 AI will become increasingly important. 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 tech stack to deliver these outcomes for them. And AI in its core is just taking input data 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, sale, site activity. And using that to optimize in real-time, regardless of what vendor or what channel we're on. So as we're learning more about this cookie-less 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 in real-time, great stuff. Somewhere with your background in journalism and the tech VP of product at Quantcast, get the keys to the kingdom over there. And it's interesting, journalism's about truth and good content, original content. But now you have a data challenge, problem, opportunity on both sides, brands and publishers coming together. This is a data problem in a way, it's a tech stack. Not so much just getting the right ads to show up at the right place at the right time. It's really bigger than that now. What's your take on this? So first, I think that consumers already accept that there is a reasonable value exchange for their data in order to access free content. And that's a critical piece for us to all understand. Over the past, oh my God, probably two years, since even before the GDPR, we've been doing a ton of discovery with customers, both publishers and marketers. And so we've kind of known this cookie going away thing has been coming. And Google's announcement just kind of confirmed it. And it's been really, really interesting since Google's announcement, how the conversations have changed 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. From the marketer perspective, there's real fear there. There's like, oh my God, it's not just about delivering ads. It's about how do I control frequency? How do I measure success? Because the technology has grown so much over the years to really give marketers the ability to deliver personalized, advertising good content to consumers and be able to monitor it and control it so that it's not too intrusive. On the publisher perspective side, we see a slightly different response. It's more of a yes, right? We're taking back control. We're going to stop the data leakage. We're going to get the value back for our inventory. And both things are a good thing, but if it's not managed, it's going to be like ships passing in the night, in terms of them coming together, right? And that's the critical piece is that they have to come together. They have to get closer. You've 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? Yeah, it's a fascinating, I love your insight there. I think it's so relevant and it's got broader implications because if you look at how data has impacted these big structural changes and refactoring of industries, look at cybersecurity, 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. They want to wall garden. They want to hoard all the data 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 Chris and Homer, you're in the wheelhouse. You've got original content and there's other providers out there. So is there a sharing model coming as with privacy and these kinds of services, is the open come back again? How do you guys see this, the confluence of open versus walled gardens because you need the data to make machine learning good? So I'll start off. I mean, listen, I think you have to give credit to the walled gardens have created. And 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, I'd say as journalists, I think that there is a case of the importance of funding journalism, but ultimately we need to make sure we're meeting the KPIs and the business needs of the buy side. And I think around that, it is the sort of three core pillar set. It's ease of access. It's the scope of activation and targeting and finally measurable results. So as I think as us as an individual publisher, so we have multiple publications, so we do have scale, but then in partnership with other publishers, perhaps to organizations like pre-bid, I think we can, we're trying to address that. And I think we can offer something that's compelling and transparent in terms of what these results are. But obviously, I want to make sure it's clear that transparent terms of results, but obviously where there's privacy in terms of the data. And I think the form of, I think we've all heard a lot of data clean rooms, a lot of them out there flogging those wares. And I think there's something valuable, but I think it's the right, who is sort of the right partner or partners. 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 virality and across multiple omnichannels, as I pointed out, it's happening. That's the distribution now. So that's almost an open garden model. So it's like... Yeah.  And got a scale for advertisers. Now we're in a place where, you know, I kind of think that each of those big networks are gonna have to come together and work together to compare in size to the to the walled gardens. And yeah, this is something that we've talked about before of an open garden. I think that's definitely the right route to take. And I agree with Chris. It's about publishers getting as close to the market as possible, working with the tech companies that enable them to do that and doing so in a very privacy-centric way. Zau, 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 going to be okay, the parachute will open. The future is not going to be as grim. It's a real opportunity, but if managed properly, what's your take on this? Is it just more first-party data strategy? And what's your assessment of this? So we're collaborating right now with all brands on how to distill very complex cookie-less future, what's gonna happen in the future, to six steps that we can take right now and market should take. The first step is gather intel on what's working on your current campaign, analyzing the data sets across cookie-free environments so you can translate those tactics eventually when the cookies do go away. So we have to look at things like temporal or time analysis. We could look at log-level data. We could look at site analytics data. We could look at brand measurement tools and how creative really impacts the campaign's success. The second thing we can look at is geo-targeting strategies. The geo-targeting strategy has been underrated because the granularity in geo-data could go down all the way to the local level, even beyond zip code. So for example, the census block 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. We wanna optimize investments around markets that are working, so strong markets, and then test in underperforming markets. The third thing we can look at is contextual. So contextual by itself is cookie-free. 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. The fourth one 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 RFIs 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 fifth thing, and I think this is very important, is first-party data. That's gonna matter more than ever in the cookie list future. Brands will need to think about how to access and develop their first-party data, starting with the consumer seeing a value in exchange for the information. It's a goldmine in understanding your consumer, their intent, their journey. 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 insights. And lastly, the sixth thing that we can do is begin informed prospecting by dedicating test budget to start gaining learnings about cookie lists. One place that we can start and it is under-invested right now is Safari and Firefox. They have been cookie lists for quite some time. So you can start here and begin testing here. Work with your data scientist team to understand the right mixes to target and start exploring other channels outside of just programmatic cookies, like CTV, dig it out of home, radio, gaming, and so forth. So those are the six steps that we're taking right now with our clients to 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 list future? I think the one certain answer is there definitely is not just one solution. As we all know right now, there seems to be endless solutions. A lot of ideas out there, proposals with the W3C, work happening within other industry bodies, private company solutions being offered. And it's a little bit, it's enough to make everyone's head spin and to try to track it, to understand it, understand the impact. And as a publisher, we're obviously, a lot of people are knocking on our door. They're saying, hey, our solution's one that it's going to bring in lots of money. All the buy sides are going to use it. This is the one, like unlock all the spend. And so our experience so far is that none of these solutions are, because I think everyone's still testing and learning. No one on the buy side from our knowledge is really committed to one or a few. It's all about a testing stage. I think that putting aside all that noise, I think what matters the most to us as publishers is actually something Summer mentioned before. It's about control. If we're going to work with a, again, outside of our sort of internal identifier work that we're doing, if we're going to work with an outside party or an outside approach, does it 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 privacy compliant. What information gets shared out there? What is it, what's released within the bid stream? If it is something that's attached to someone, a declared user, a registered user, that if that then is not somehow amplified or leveraged off on another site in a way that is leveraging bid 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. And so those controls are critical. And I think to have those controls as publisher, we have to be collectively be disciplined in what solutions that we sort of, we test out and what we eventually adopt. But even when that adoption point arrives, it definitely will not be one. There will be multiple, because there's just too many use cases to address. Great insight there from you guys at 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. We're on a new one now. We talked about the open internet and 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 data is at the heart of it. What do you see as the new future for journalists? Reward journalism, is there a way, is there a path forward? So there's what I hope is going to happen. And then I'm just going to ignore what could, right? There's a trend in market right now and a number of fronts, right? So there are marketers who are leaning in to wanting to spend their marketing dollars with quality journalists, focusing on BIPOC owned and operated, really leaning into supporting those businesses that have been, and those publishers that have been ignored for years. I really hope that this trend continues. We are leaning into helping marketers curate that supply and really speak with their dollars about the things that they support and value in market. So I'm hoping that that trend continues and it's not just sort of like a marketing blip, but we will do everything possible to kind of like encourage that behavior and give people the information that they need to find, you know, truly high quality journalism. That's awesome. Chris, 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 soundbite 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. It's going to be quite a ride. I think that's an understatement. I think that there, I wouldn't be surprised if Google delays the change to the Chrome by a couple of months and may give the industry so much needed time, but no one knows. I guess, I guess, except for someone somewhere we are 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 going to cover across all facets of the industry, all facets of, you know, from advertising marketing. So just be prepared. Okay. Is that all? Yeah, along those same lines, be prepared. Nobody knows what's going to happen in the future. You know, we're all dancing in this together. I think for us, it's planning and preparing and also building on what we've already been working on. So Omnichannel, AI, Creative. And I think clients will lean more into those different channels. Awesome. So take 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 and trying everything out, learning and iterating as fast as we possibly can. Awesome. And I love the cat in the background over your shoulder. I can't stop staring at your wonderful cat. Selma, thanks for coming on Zao. Chris, thanks for coming on this awesome panel. Industry breakdown of the cookie conundrum, the recipe for success, data, AI, open the futures here. It's coming. It's coming fast. I'm John Furrier with theCUBE. Thanks for watching.