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7 months
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| 2s | foreign |
|---|---|
| 9s | so as you see we very much share this |
| 13s | positive vision of Hilmar |
| 15s | hello FanFest thanks a lot for having us |
| 18s | here it's great to be back in Reykjavik |
| 20s | after this rather lengthy covet break |
| 24s | it has been a long time |
| 26s | but we're not idling through these years |
| 29s | so much so that in 2020 we introduced |
| 33s | the third iteration of project Discovery |
| 35s | inside Eve and as you will see with |
| 38s | quite amazing results |
| 41s | today I'm here with two of my fellow |
| 43s | partners in crime with Jerome valdespool |
| 46s | from Michael University |
| 47s | and Ryan Brinkman from dotmetics slash |
| 51s | University of British Columbia |
| 53s | and these great brains will bring the |
| 56s | real scientific beef to our talk today |
| 58s | so I try to be rather short but I have |
| 62s | some really interesting news that I must |
| 64s | share with you |
| 67s | so |
| 68s | some of you might know me |
| 70s | um I'm the CEO and co-founder of a small |
| 73s | Swiss company that we named massively |
| 75s | multiplayer online science or MMOs we're |
| 79s | the ones who kind of seduced CCP on this |
| 82s | crazy adventure which became a very |
| 85s | successful and long-term collaboration |
| 88s | in the field of Citizen science |
| 91s | and the idea was to use this amazing |
| 94s | force of collective human cognitive |
| 97s | capabilities that you guys the eve |
| 100s | Community provides |
| 102s | to advance science |
| 105s | now I'm also an adjunct at McGill |
| 107s | University |
| 108s | and also I'm maybe an honorary Eve Dev I |
| 113s | mean I have an eve death shirt which I |
| 116s | are really appreciate but I think the |
| 120s | real utmost test will be whether I get a |
| 123s | Viking sword from Hilmar for the Thames |
| 126s | here so we'll that's to be seen |
| 130s | so project Discovery it was really a |
| 132s | hell of a nine years actually this story |
| 133s | started in 2014 with a call to and in |
| 137s | our grand ccpc gal and that call started |
| 141s | a series of events that led up to what |
| 143s | we know today as project discovery |
| 146s | it was not obvious it was not with a |
| 149s | risk or challenges to pull off such an |
| 152s | unprecedented collaboration |
| 155s | but really with the amazing help of the |
| 159s | teams at CCP and with your continuous |
| 162s | support which was |
| 165s | started right from the beginning right |
| 166s | in 2015 when we introduced our first wig |
| 171s | ideas about this com concept at Fan Fest |
| 174s | all these poor fruit and in 2016 we |
| 177s | finally released project Discovery the |
| 180s | first iteration was a collaboration with |
| 181s | the human protein Atlas a team led by |
| 184s | Emma Lundberg |
| 185s | and we asked players to classify to |
| 189s | improve the labeling on a massive data |
| 192s | set that they have of this beautiful |
| 194s | immunofluorescent microscopy images of |
| 197s | human cells |
| 198s | this is a data set that is used by tens |
| 200s | of thousands of researchers worldwide |
| 204s | now this project already broke records |
| 207s | so I |
| 208s | think you might remember that the |
| 210s | ultimate reward for project discovery |
| 213s | was the sisters of Eve combat suit which |
| 216s | was supposed to be handed out some in |
| 219s | some three or four weeks after launch |
| 221s | the first one was unlocked 17 hours in |
| 224s | 17 hours and the second one six minutes |
| 225s | later so yeah the eve Community proved |
| 228s | to be quite something |
| 231s | now in 2017 we switched to hunting for |
| 234s | exoplanets and we had the pleasure and |
| 238s | honor to have uh Nobel Laureate |
| 241s | Professor Michelle Meyer on the project |
| 244s | from the University of Geneva |
| 246s | he is the one who discovered the first |
| 248s | exoplanet |
| 249s | and I vividly remember that after his |
| 253s | talk |
| 254s | Professor Myers talk at Fan Fest |
| 257s | Burger CCP Burger came to me and said |
| 260s | that he out near the Deep players which |
| 263s | sounded like the ultimate Eve compliment |
| 267s | and in 2020 we jumped on a new project |
| 270s | in a time of urgency and need |
| 273s | this was to help improve our scientific |
| 276s | tool set in Immunology that can help |
| 278s | covid research and so much more in the |
| 281s | future |
| 282s | now this was a milestone project which |
| 284s | because we managed to show that |
| 286s | with everything in place |
| 289s | we can act as a rapid action force |
| 292s | and just to give you an idea from the |
| 295s | fir from the time that Berger sent me an |
| 298s | |
| 299s | about you know is there anything that we |
| 301s | can help to the time when we launched a |
| 304s | project it took only six weeks that's |
| 307s | quite remarkable |
| 310s | now I mentioned to you that you will |
| 311s | hear about what uh you your work |
| 314s | collectively collectively has amounted |
| 317s | to |
| 318s | soon |
| 320s | and believe me it's nothing short of |
| 323s | ground baking |
| 326s | so this is where we are now everybody is |
| 328s | happily clustering blood data in Eve |
| 331s | but before I pass on the mic I want to |
| 333s | talk a bit about the future |
| 338s | sorry I couldn't resist so this might be |
| 340s | the only time |
| 342s | one more thing so there is a thing that |
| 345s | came up consistently during all these |
| 348s | years |
| 349s | from you guys and from other |
| 351s | gamer communities as well |
| 354s | and it seems that in 2024 finally we'll |
| 357s | be able to respond to this request |
| 363s | in 2024 we're launching a new mobile |
| 366s | platform a new mobile citizen science |
| 368s | platform |
| 369s | uh the place science IOS and Android app |
| 372s | and it's supporting API that will |
| 375s | substantially increase our capabilities |
| 377s | to serve the scientific Community with |
| 380s | this tool set that we're building for |
| 382s | almost 10 years now |
| 384s | now for you primarily what it means is |
| 387s | that |
| 388s | when you're not |
| 390s | in front of Eve at your PCS you're on |
| 393s | the go you can still play with your |
| 395s | favorite Eve minigame project discovery |
| 398s | and you can help analyze scientific data |
| 401s | you can collect points and with all |
| 403s | these points you can get get back to Eve |
| 405s | and reclaim your Rewards |
| 407s | sort of like a project Discovery |
| 410s | companion app if you will |
| 413s | but it's more than that |
| 415s | we have not will have not only project |
| 417s | discovery on this platform but it will |
| 419s | act as a host of for different citizen |
| 422s | science minigames some of these will be |
| 425s | co-created with our partners in the game |
| 427s | industry some of them will be just plain |
| 429s | old citizen science app |
| 432s | these games will |
| 435s | vastly improve our capabilities to to |
| 438s | open up this platform for other research |
| 441s | initiatives |
| 444s | and what's important for you is that |
| 447s | regardless of which project you |
| 450s | contribute to |
| 451s | you get the same achievements same |
| 453s | points and the same sweet rewards inside |
| 456s | Eve |
| 459s | now with all this we really hope that |
| 462s | this will become sort of a platform for |
| 465s | like-minded players players for whom |
| 469s | doing good by playing is not an oxymoron |
| 473s | and the reason why I believe in this is |
| 475s | that well partly because |
| 479s | these last years I think I talked to |
| 481s | over 100 100 of game developers |
| 485s | none of them discarded this idea on the |
| 488s | contrary everybody was super inspired |
| 491s | the real reality is that |
| 495s | the fact that we pull this off project |
| 497s | Discovery CCP or Borderlands science |
| 499s | with gearbox |
| 501s | is almost a miracle |
| 503s | game developers are super swamped |
| 506s | and I guess you know better than me |
| 510s | this endless |
| 512s | things you want to see in the game to be |
| 516s | fixed to be introduced to be changed an |
| 520s | endless wish list that you could curate |
| 522s | for Eve dabs so |
| 525s | with all the dedication that CCP have |
| 528s | for this project there is only 24 hours |
| 531s | a day and seven days a week so |
| 534s | with this step we believe that we can |
| 536s | relieve a lot of pressure on the game |
| 539s | devs in-house |
| 541s | and the game we can make it much more |
| 544s | accessible for other games and other |
| 547s | gamer communities to join forces with us |
| 549s | and be part of this story |
| 553s | and finally |
| 555s | we at MMOs will do our best to steer |
| 559s | this app in a good direction and to do |
| 561s | all the development and to serve as many |
| 563s | research projects as possible but one |
| 566s | thing we want to avoid is to |
| 569s | for us becoming a bottleneck in this |
| 572s | process |
| 573s | so what we'll do is we'll take this API |
| 576s | that we're developing and we'll open |
| 579s | this up for partnering research |
| 581s | institutions |
| 582s | so and this is the Grand Vision that |
| 585s | in the future you might be playing with |
| 588s | some of the well-known citizen science |
| 591s | projects out there and you're still |
| 593s | contributing to the same achievements |
| 595s | and points and Eve rewards and this is |
| 598s | not just wishful thinking I've been |
| 599s | talking to many of the major citizen |
| 602s | science projects out there and everybody |
| 603s | is super excited about this possibility |
| 609s | so this is what we're planning and I |
| 612s | believe if we're doing a good job |
| 614s | we'll probably write citizen Science |
| 616s | History again together so I hope you're |
| 619s | excited as excited as we are |
| 624s | there is one caveat that that I have to |
| 628s | say is that um |
| 630s | of course at this point nothing is |
| 632s | carved in stone there's a lot of things |
| 633s | to sort out so things might and can |
| 637s | change in the following months as we |
| 639s | progress with this project also look out |
| 641s | for the eve newsletter because we'll |
| 644s | send out a link to sign up for beta |
| 646s | testing in the coming month |
| 651s | and finally I cannot stress enough that |
| 655s | without your support continuous support |
| 658s | enthusiasm and encouragement project |
| 661s | Discovery would not exist |
| 664s | so a huge thank you for all of you who |
| 666s | became a citizen scientist through you |
| 669s | online |
| 670s | thank you and please welcome Jerome |
| 672s | valdispil |
| 683s | foreign |
| 686s | I'm a professor of computer science at |
| 689s | McGill University |
| 691s | I'm working in bi-informatics and human |
| 693s | computer interactions |
| 695s | part of my research is to blend together |
| 697s | computer and human brain to solve the |
| 701s | most fundamental impressing scientific |
| 703s | challenges |
| 705s | so you may want to ask why do we need a |
| 708s | human brain at the age of computers and |
| 710s | Ai and I try to partially answer your |
| 712s | question here |
| 715s | so in science we try to solve |
| 717s | fundamental problems like protein |
| 719s | folding finding patterns into DNA |
| 722s | you never access to the ground truth |
| 725s | you can only measure some different |
| 727s | parameters that potentially can estimate |
| 730s | the quality of the answer you're |
| 731s | providing |
| 733s | the problem here is like these |
| 735s | parameters are not comparable directly |
| 738s | so the best you can do is to compute a |
| 741s | set of solution that are all optimizing |
| 744s | these parameters and that's what we call |
| 746s | the Pareto front |
| 750s | so if you look at this a graph here you |
| 754s | can measure one parameters you do size |
| 756s | or the density and what you try to |
| 757s | compute is this green line here the |
| 759s | problem is like the parameters we're |
| 760s | estimating are not perfect |
| 763s | so in truth what you want to compute is |
| 765s | the region near this part of the front |
| 767s | the solution are near optimal |
| 770s | and this arguably computer can do it |
| 772s | very well but this is where things start |
| 774s | to be complicated |
| 775s | because eventually in this set of |
| 777s | solution we want to find |
| 780s | the true Solutions and how do we do that |
| 784s | we do that by calling out to scientists |
| 788s | so scientists seat look at different |
| 791s | solutions in different parameters and |
| 794s | all agreeing together about where the |
| 797s | ground truth should be |
| 800s | and what it generates is something that |
| 803s | computer cannot do it generates Trust |
| 807s | the problem we have is science we have |
| 809s | massive amount of data ridiculous amount |
| 812s | of data |
| 813s | so it's not possible for a small |
| 815s | research team to go for this data even |
| 817s | if we know it's fundamental it's |
| 819s | essential for good research |
| 822s | and this is where cities and science aim |
| 824s | to solve the problem |
| 826s | the principle of teaching science is to |
| 828s | gather a lot of people |
| 831s | that will look at the data and then we |
| 833s | rely on the human patent Recreation |
| 835s | skills to identify the most promising |
| 838s | Solutions and the agreement generated by |
| 841s | by the citizen scientists we generate |
| 843s | the ground we identify the grounds with |
| 845s | solutions that experts usually find |
| 848s | and then suddenly we can generate trust |
| 851s | again for massive amount of data |
| 855s | one problem that is fascinating me that |
| 858s | is ubiquitous in science is clustering |
| 861s | in a small abstract form it's about |
| 864s | finding patterns within distribution of |
| 868s | data |
| 870s | and identifying these patterns is not |
| 873s | simple it cannot be embedded into a |
| 875s | single mathematical function often |
| 878s | sometimes we want to identify something |
| 880s | that is done sometimes that uncertain |
| 881s | rules of continuity there's many |
| 883s | different parameters and the only way to |
| 885s | really robustly identify the solution |
| 888s | is to do it by having many people humans |
| 893s | agreeing together where the solution is |
| 895s | so we had work doing on that in my lab |
| 897s | for for a large time |
| 900s | uh uh for solving the clustering problem |
| 903s | and in creating robust solution |
| 904s | involving crowdsourcing but we had one |
| 907s | major issue engagement |
| 909s | we never managed to have enough people |
| 911s | sitting here on the table looking at the |
| 912s | same data and agreeing together |
| 915s | so how do we solve that |
| 917s | this is where project Discovery enters |
| 920s | in 2016 when I heard for the first time |
| 922s | about project Discovery I was amazed for |
| 925s | the first time in my life I understood |
| 927s | that something can change science for |
| 930s | the first time in my life I understood |
| 931s | there was |
| 933s | thousand tens of thousands hundreds of |
| 936s | thousands of people that could be here |
| 938s | looking at the same data and solve this |
| 940s | this problem and most importantly what |
| 943s | convinced me it was the key for that is |
| 946s | the enthusiasm generated by the |
| 948s | community in general |
| 949s | so I gave a call to Attila and say I'm a |
| 951s | fan your project can change science I |
| 955s | just want to hear more about it and |
| 957s | eventually we discussed and we became |
| 959s | friends and then |
| 962s | pandemic Hunters |
| 964s | 2020 Attila calls me |
| 967s | and tells me well we have an issue yes I |
| 971s | know uh and I'm in touch with CCP and |
| 975s | TCP wants to help they want to do |
| 977s | something we have a fantastic tool |
| 979s | project discovery that that already |
| 981s | achieved uh scientific Milestones but |
| 985s | they want to help and they want to make |
| 987s | something fantastic do you have an ID |
| 990s | yeah I do have an IDE I know a problem |
| 993s | that can make a difference in biomedical |
| 994s | research we know that it works and most |
| 997s | importantly |
| 998s | I know an expert of the field that can |
| 1001s | help us to make it real and this is |
| 1004s | where Ryan brings man's Ender |
| 1016s | hi I'm Ryan I'm a scientist I'm also a |
| 1019s | gamer a pretty hardcore gamer I beat Eve |
| 1022s | about three years ago by not playing |
| 1025s | it scares the crap out of me |
| 1028s | um because I'm a data scientist and I |
| 1031s | like my life |
| 1033s | um so I do Photoshop bioinformatics |
| 1035s | informatics and as part of that I go |
| 1038s | around the world I've given hundred |
| 1039s | talks in front of scientists and I go up |
| 1041s | here and tell them the cool I've |
| 1043s | done and at the end of it they applaud |
| 1046s | um I feel good |
| 1048s | um and you and this is for you if you're |
| 1049s | listening in Soviet Russia |
| 1053s | I'm here as a presenter telling you |
| 1056s | about the cool stuff that you've done |
| 1057s | and I just want to applaud you |
| 1062s | for the last Thousand Days thousands of |
| 1065s | you have been playing Project Discovery |
| 1067s | you've analyzed millions of plots |
| 1071s | and the data that you're generating is |
| 1073s | going to change the world it's going to |
| 1075s | help us find cures for cancer it's going |
| 1077s | to help us find cures for all kinds of |
| 1079s | diseases and it couldn't have happened |
| 1081s | without the people in this audience who |
| 1083s | played project discovery |
| 1087s | thank thank you |
| 1090s | thank you so much and not just for me |
| 1092s | all the data that has been submitted is |
| 1094s | going to be released for any use |
| 1096s | whatsoever by other scientists around |
| 1098s | the world and I know this is coming and |
| 1100s | they are so very excited |
| 1102s | it's incredible |
| 1105s | um |
| 1106s | the reason why this is so important is |
| 1109s | because the data you're analyzing is |
| 1111s | going to help us understand the immune |
| 1113s | system so right now as you're sitting |
| 1115s | there |
| 1116s | your bone marrow is pumping out stem |
| 1118s | cells these are these white blood cells |
| 1120s | differentiate into many many different |
| 1122s | types of cells |
| 1124s | we can understand the functions of these |
| 1126s | cells by looking at the proteins on the |
| 1128s | cell surface |
| 1130s | and these these proteins on the cell |
| 1132s | surface tell us what these cells are |
| 1134s | doing right now in your blood there's |
| 1136s | some cells in your blood called t cells |
| 1139s | and these are the one these are the |
| 1140s | cells in your blood that help us or when |
| 1142s | if you've gotten Cova before |
| 1145s | um we or help mount your immune response |
| 1148s | to the disease there's other cells in |
| 1150s | your body that can be re-engineered to |
| 1152s | cure cancer |
| 1154s | there's a and then twenty thousand |
| 1156s | eighteen there's a Nobel Prize given out |
| 1158s | for the discovery of car T therapies |
| 1160s | there's now six FDA drugs approved that |
| 1163s | can cure cancer we're not going to find |
| 1166s | a single cure for cancer but car T |
| 1168s | therapy is probably the brightest thing |
| 1170s | that's ever happened in in the history |
| 1171s | of understanding this disease there's |
| 1173s | other cells in your body that can detect |
| 1176s | bacteria that have come into your into |
| 1178s | your system and Target them so other |
| 1180s | cells called natural killer cells can |
| 1182s | find them and attack them and get rid of |
| 1184s | them if we didn't have a functioning |
| 1186s | immune system we'd all either be living |
| 1187s | in bubbles or be dead |
| 1189s | and flow cytometry is a technology that |
| 1192s | helps us understand this |
| 1194s | so how this technology works as we come |
| 1196s | up to somebody sneak up on them stab |
| 1197s | them |
| 1198s | take their blood and then what we do is |
| 1201s | we label proteins on the cell surface |
| 1203s | with antibodies that are conjugated to |
| 1206s | fluorochromes such that when these cells |
| 1209s | pass one by one pass a laser in a flow |
| 1211s | cytometer they glow |
| 1212s | and the amount of light that these cells |
| 1214s | give off is proportional to the amount |
| 1217s | of protein they have on that cell |
| 1218s | surface that we've labeled them with and |
| 1220s | we're label them with all kinds of |
| 1221s | different uh fluorescently conjugated |
| 1223s | antibodies so we can see what's there |
| 1226s | so if you played project Discovery |
| 1227s | you've seen plots that are shown here |
| 1229s | like on the right hand side of the |
| 1231s | screen so the cells on the very right |
| 1233s | have 10 100 a thousand times more of |
| 1235s | that approaching on the cell's surface |
| 1237s | and because of that we can infer the |
| 1239s | function of those cells while the cells |
| 1241s | on the left have ten hundred thousand |
| 1243s | times less and then by drawing the boxes |
| 1246s | around these Gates that the scientists |
| 1248s | are doing right now all over the world |
| 1249s | and enumerating how many sit in that box |
| 1252s | we can infer you know if how that much |
| 1255s | that percentage changes from sick versus |
| 1256s | healthy treated versus untreated and |
| 1259s | this is how we understand how the immune |
| 1260s | system works flow cytometry is the only |
| 1262s | technology that allows us to do this in |
| 1264s | the proper way |
| 1266s | it sucks |
| 1267s | it sucks for lots of reasons the |
| 1269s | analysis part sucks |
| 1271s | um it's very complex we're looking at 40 |
| 1273s | dimensional data on a two-dimensional |
| 1275s | screen and the scientists have to try to |
| 1277s | figure out the right Pathways to go |
| 1279s | through that data and find all those |
| 1280s | cell populations it's time consuming to |
| 1283s | analyze one sample from a patient now |
| 1286s | there's many plots they have to look at |
| 1288s | that uh to make that analysis not just a |
| 1290s | single one it could take 30 minutes to |
| 1292s | two and a half hours that a scientist is |
| 1294s | looking at that plot to understand for |
| 1296s | example if that patient has minimal |
| 1298s | residual disease and going to die of |
| 1300s | cancer we're making diagnosis life and |
| 1302s | death decisions based on Flow cytometry |
| 1305s | data analysis and it's so important |
| 1308s | it's not cheap to pay these people |
| 1310s | doctors and scientists not me but get a |
| 1313s | lot of money to do their work or or you |
| 1315s | can Outsource it to another country and |
| 1316s | wait three months to get the answer back |
| 1318s | and that sucks and between scientists |
| 1321s | it's it's a challenging thing if you |
| 1323s | play the game you know where exactly do |
| 1325s | these do we want to put those Gates and |
| 1326s | even professional scientists people who |
| 1329s | do this for a living have trouble and |
| 1331s | they can get about to 32 percent |
| 1332s | difference in between two scientists |
| 1335s | and there's just so much data out there |
| 1337s | that there's no way they can possibly |
| 1338s | look through it all |
| 1340s | so obviously if you've been awake for |
| 1342s | the last three years AI machine learning |
| 1345s | is the way to go to solve this problem |
| 1346s | so all we have to do to do that is go |
| 1348s | out and get the data that the scientists |
| 1350s | have analyzed and then use that to train |
| 1352s | an algorithm because you need data to |
| 1354s | train machine learning it's in the name |
| 1356s | right machine learning well the problem |
| 1358s | is scientists don't share their data of |
| 1362s | how they've analyzed that and there's |
| 1363s | lots of reasons for that and we won't go |
| 1365s | into it in the today's talk but there's |
| 1367s | no way we can get the amount of data you |
| 1369s | need to train machine learning |
| 1370s | algorithms to make this possible |
| 1373s | that's where project Discovery is CH oh |
| 1376s | wait I didn't have this but it's a good |
| 1377s | one this is where project discovery |
| 1378s | changed the game |
| 1380s | yeah |
| 1381s | yeah |
| 1384s | um |
| 1385s | but you as smart as all of you are and |
| 1387s | there's some really really smart people |
| 1388s | that play Eve online I get that but |
| 1391s | there's no way that we could teach all |
| 1392s | of you the biology that the scientists |
| 1395s | have in their head for how to trace |
| 1397s | through these patterns of data and the |
| 1399s | reasons why they're getting certain |
| 1401s | populations |
| 1402s | it just there's just too much |
| 1405s | information in there to do but um it's |
| 1408s | kind of like Shakespeare and not that |
| 1410s | you guys are monkeys but with enough of |
| 1412s | you we can write Shakespeare |
| 1416s | and that the hypothesis that we had at |
| 1419s | the at the beginning of this whole story |
| 1421s | when covid came out is that you if I |
| 1424s | give you guys plots like this you can |
| 1427s | probably figure out what the things are |
| 1430s | without actually understanding anything |
| 1432s | about the biology |
| 1433s | and you guys did it it's incredible and |
| 1437s | not only is that I just have to thank |
| 1438s | the CCP people who are here who are at |
| 1441s | the very very start of covet in the |
| 1442s | space a few weeks re-engineered the same |
| 1445s | kind of technology and the same kinds of |
| 1447s | plots that professionals who do this for |
| 1449s | a living |
| 1451s | do within Eve online and it looks just |
| 1454s | like this and the Hope was that if I |
| 1456s | give you guys some pictures that look |
| 1457s | like this and say can you draw circles |
| 1460s | around the important bits you would and |
| 1462s | you do again and again and again 24 |
| 1465s | hours a day tens of thousands of you are |
| 1467s | doing this and you're doing a really |
| 1469s | fantastic freaking job and please don't |
| 1471s | stop |
| 1472s | at the lowest level in this plot is 6.5 |
| 1476s | million plots per month |
| 1479s | it's incredible it's amazing |
| 1482s | it was so much data that we had to write |
| 1485s | new algorithms you guys are just going |
| 1487s | through it so fast we're going to figure |
| 1489s | out better ways to give you data so we |
| 1490s | had to invent new algorithms and I'm |
| 1493s | sorry for those of you are playing this |
| 1494s | right at the beginning of covid you |
| 1495s | probably saw a lot of the plots that |
| 1496s | looked the same because we didn't have a |
| 1499s | way to find heterogeneous plots so you |
| 1502s | would see things that were different so |
| 1503s | because of this we had to invent whole |
| 1505s | new ways to figure out how to show you |
| 1507s | data so you could give us data and so we |
| 1509s | just published a paper on this and |
| 1510s | hopefully we've been playing the game |
| 1511s | recently you've started to see more |
| 1514s | patterns in the data and that's really |
| 1516s | important for us to be able to change |
| 1517s | train machine learning algorithms |
| 1519s | because it's that variety that's going |
| 1521s | to allow us to do more and more with |
| 1523s | that |
| 1525s | so for those of you who played a game |
| 1527s | that now my chance to tell you something |
| 1529s | that some things that we've learned that |
| 1530s | maybe can up up your game |
| 1533s | is what I'm showing here is the same |
| 1536s | plot that's been analyzed by five |
| 1538s | different people and yeah you have to |
| 1539s | apologize where you see those lines |
| 1541s | overlapping that's not really the way it |
| 1543s | is it's just when you have 400 million |
| 1544s | plots you can't do things perfectly it's |
| 1547s | just we don't have enough compute time |
| 1548s | so they make some we had to make some |
| 1549s | shortcuts in how we drill those |
| 1550s | boundaries just assume those aren't |
| 1552s | overlapping so you can see on the left |
| 1554s | hand plot somebody drew two then |
| 1556s | somebody else do three and four and five |
| 1559s | and six these aren't the droids you're |
| 1561s | looking for |
| 1563s | um here's another plot same kind of |
| 1565s | thing somebody's drawn three and then |
| 1567s | somebody else Drew four five six seven |
| 1568s | Gates around the data this is also not |
| 1571s | really what you're looking for some of |
| 1573s | you people are amazing |
| 1577s | this and I've shown I've given talk like |
| 1578s | I said give talks all over the world on |
| 1580s | this and I show scientists what you guys |
| 1582s | have done this I'm not making this up is |
| 1585s | better than expert scientists do there's |
| 1588s | lots of reasons for that the main one is |
| 1590s | they're so focused on the biology and |
| 1591s | the parts that are interesting they |
| 1593s | ignore everything else |
| 1595s | and that's sad because there's there's |
| 1598s | many things we can do with |
| 1599s | photosynometry data we can make |
| 1600s | diagnosis on patients and in order to |
| 1603s | make that diagnosis we have to find |
| 1604s | specific cell populations and see how |
| 1606s | much those have changed and the reason |
| 1608s | why we look at those specific |
| 1610s | populations is because some professor |
| 1611s | and their grad student and their grad |
| 1613s | student and their grad student for tens |
| 1615s | of tens and tens of years have studied |
| 1617s | specific cell populations I've |
| 1618s | understood those are important |
| 1621s | there's lots of information in this data |
| 1623s | there's so much information that they |
| 1626s | can't look through at all but because of |
| 1627s | the way you are analyzing this you're |
| 1629s | putting boxes around all the things and |
| 1632s | from that not only can we do diagnosis |
| 1634s | of patients we can do discovery of new |
| 1637s | things and that's the way we're going to |
| 1639s | not only find new drugs but also to make |
| 1641s | sure that drugs that you that people are |
| 1643s | going to get are safer so they're not |
| 1645s | just targeting the things that we know |
| 1647s | about but there's nothing that that we |
| 1648s | call off-target effects or the new drug |
| 1651s | that we give you hits something in the |
| 1652s | immune system that we never knew to look |
| 1654s | at but then you're going to end up |
| 1655s | growing a new toe or something and |
| 1657s | that's going to make you sad |
| 1659s | so what you see here for example this |
| 1661s | this person this is where I shine the |
| 1662s | laser in somebody else's eye you can |
| 1665s | I don't know where it's it's the laser |
| 1668s | doesn't really work you can see the |
| 1670s | person on the very bottom right what |
| 1672s | they've done is they've drawn a a gate |
| 1674s | around every feature so you have to |
| 1677s | remember the the order on these axes is |
| 1679s | a log order so the cells on the left are |
| 1682s | have tens of thousands of amounts more |
| 1685s | of that protein and the cells on the |
| 1687s | other side that I said left versus right |
| 1689s | so because it's so much more protein on |
| 1692s | that we're inferring those cells have a |
| 1694s | different function there's no truth in |
| 1696s | this there is no right answer there is |
| 1698s | no way that these cells can say hey this |
| 1700s | is the function I have until you sort |
| 1701s | them out in a tube and do some kinds of |
| 1703s | experiments on them but you can't do |
| 1705s | that until you put the Box around them |
| 1706s | so you can put them into a well and do |
| 1708s | experiments and so this is amazing |
| 1712s | and lots of you are doing this and we I |
| 1714s | don't know how you knew to do this |
| 1715s | because we didn't really tell you and |
| 1716s | one things that we're that we're hoping |
| 1718s | to do is start giving better |
| 1719s | instructions to allow you to do this but |
| 1721s | enough of you are doing this it's |
| 1722s | amazing |
| 1724s | so not only just one or two or three |
| 1727s | people are giving us the right answers |
| 1729s | what we're doing is we're enough of you |
| 1731s | are doing this so we might get 15 or |
| 1733s | more people that are doing the really |
| 1735s | kind of perfect Gates that we want and |
| 1738s | then what we can do is we can make a |
| 1739s | consensus out of all those people to get |
| 1742s | closer to the truth than any one of you |
| 1745s | guys can do by your by yourself and |
| 1747s | that's this the consensus of multiple |
| 1749s | Gamers that were using in part to train |
| 1752s | machine learning algorithms |
| 1756s | so not only are there fan fests for |
| 1758s | gamers who play Eve there's Fan Fest for |
| 1761s | scientists we call them conferences |
| 1764s | and |
| 1765s | um we go there and I we go there and |
| 1767s | give talks about science and I get on |
| 1769s | stage and say we're doing some cool |
| 1771s | stuff and look what I've invented |
| 1773s | two years ago we played Eve online in |
| 1777s | Philadelphia and we set it up in a booth |
| 1779s | and we had professional flow cytometry |
| 1781s | people there is about as many of them |
| 1783s | there as there are people here |
| 1786s | and we had them play so you can see them |
| 1787s | they're playing the same game you guys |
| 1789s | played |
| 1791s | and so we did something we we compared |
| 1794s | the professional scientists |
| 1796s | Against The Gamers now we kind of gamify |
| 1799s | the game that they were playing they |
| 1801s | only had a minute and 30 seconds to do |
| 1803s | as many plots as they could so you guys |
| 1805s | can stare at it as long as you want if |
| 1807s | we allowed the scientists to do that |
| 1808s | they would still be there today because |
| 1810s | they want to get things exactly right |
| 1813s | it's not an exact comparison but the |
| 1815s | punch line is |
| 1817s | the statistics that p-value that we got |
| 1819s | on that results of you versus the |
| 1821s | scientists you see where this is going |
| 1823s | you did better |
| 1825s | statistically better |
| 1831s | and I got Shivers Shivers I have I have |
| 1834s | Shivers right now it's amazing because |
| 1836s | that gave us the confidence that the |
| 1838s | data you're doing is going to allow us |
| 1840s | allow us scientists me and others who |
| 1843s | are going to use this data to do amazing |
| 1845s | things |
| 1847s | so that's the data that we're using to |
| 1848s | train the machine learning algorithms |
| 1850s | and this is this is great this is really |
| 1851s | simplified break it down data that we |
| 1854s | can just feed thousands and thousands |
| 1856s | and millions of plots and train Michelin |
| 1858s | learning algorithms and we've done it |
| 1861s | we have two proof of concept machine |
| 1863s | learning algorithms that we developed |
| 1864s | using completely different machine |
| 1866s | learning approaches |
| 1868s | that can identify every feature into |
| 1871s | edible polygons the same way that they |
| 1873s | were done it by hand down all possible |
| 1875s | hierarchies and like like I said this is |
| 1878s | going to enable two things it's going to |
| 1880s | enable diagnosis of patients we hope if |
| 1882s | it gets good enough at least in a very |
| 1885s | broad sense more importantly it's going |
| 1888s | to enable Discovery at scale in a way |
| 1891s | that is simply not possible with the |
| 1892s | Technologies and the software that we |
| 1894s | have available today |
| 1896s | and we can put this into software |
| 1899s | so that somebody can click a button and |
| 1901s | Gates appear that fast and that's |
| 1904s | trained exactly on data that came out of |
| 1906s | you guys they can get the gates that |
| 1908s | fast remember how fast it so they might |
| 1910s | be 20 or so plots that they have to go |
| 1912s | through we can generate ones that quick |
| 1914s | it's a game changer I've showed this to |
| 1917s | exec senior level management at |
| 1920s | pharmaceutical companies and they cannot |
| 1922s | wait to get their hands on this software |
| 1924s | because again this is going to allow |
| 1925s | them to look at off-target effects and |
| 1927s | drugs and get drugs faster and cheaper |
| 1929s | that cure so many diseases of the immune |
| 1931s | system |
| 1933s | so this is the result that we can get |
| 1935s | out of machine learning algorithms |
| 1937s | trained on Project Discovery data I have |
| 1939s | shown this to scientists again they |
| 1941s | can't believe how good this is It's |
| 1943s | amazing |
| 1945s | now is it always that good so this is |
| 1948s | one other example of how the machine |
| 1950s | learning algorithms can pump out of data |
| 1952s | and it looks pretty good I'm really |
| 1955s | happy with this result but it's actually |
| 1958s | one of the worst performing results |
| 1960s | scores that we had it's important that |
| 1962s | we show the bad data so the F1 score |
| 1964s | that I'm showing here you guys love math |
| 1966s | maybe it's a harmonized mean of |
| 1968s | sensitivity and specificity you can kind |
| 1970s | of figure it out as a percentage it's |
| 1972s | not a very good Mark 57 you kind of want |
| 1975s | it to be like one or 0.57 you want it to |
| 1979s | be one so why is it so bad |
| 1981s | because scientists can't make up their |
| 1983s | minds of what they want |
| 1985s | so the challenge here in what you show |
| 1987s | what I'm showing on the left hand side |
| 1989s | of the screen is the gold standard and |
| 1991s | the right hand side is the result of |
| 1992s | machine learning algorithm that was |
| 1994s | based on Project Discovery and what's |
| 1996s | shown in red is where the difference is |
| 1998s | between the truth and |
| 2002s | what the algorithm came up with |
| 2005s | and so the challenge here is humans are |
| 2008s | doing two different kinds of things at |
| 2010s | the same time |
| 2011s | the humans |
| 2014s | um in at the bottom of the plot whereas |
| 2017s | that that smear smears are really hard |
| 2019s | where there's no definable |
| 2021s | information about where something ends |
| 2024s | and something else begins and so on the |
| 2026s | bottom of that plot |
| 2028s | the scientists or what what we what they |
| 2031s | believe they want is different than |
| 2033s | what's shown on the left hand column |
| 2035s | because in the on the bottom they've |
| 2037s | kind of drawn the line between the |
| 2039s | populations on the left and the right |
| 2040s | and on the left hand column they said |
| 2042s | that smear in the middle goes into its |
| 2044s | own population and so in the same kind |
| 2046s | of context based on biology |
| 2049s | based on their understanding of the |
| 2051s | disease which is something you guys |
| 2052s | don't have a handle on but because this |
| 2054s | is cd14 versus cd16 we have to make |
| 2057s | different choices about what to do with |
| 2058s | those different bits they're making |
| 2060s | different choices about how to separate |
| 2061s | that stuff out you guys can never do |
| 2063s | that |
| 2063s | but |
| 2065s | um what we're what we're learning from |
| 2066s | this process is what we can do is have |
| 2069s | the scientists give us a template just |
| 2071s | give us one sample that you've done the |
| 2073s | analysis on and we'll use that to drive |
| 2077s | the machine learning approach to their |
| 2079s | version of Truth |
| 2081s | and and that seems that that's going to |
| 2084s | work for us in cases where we make |
| 2086s | um they want to make more refined |
| 2088s | decisions and that that's going to still |
| 2089s | help us a lot because then they don't |
| 2091s | have to get 300 samples they might only |
| 2093s | have to get two or three and they'll |
| 2095s | have to do a few because not every |
| 2097s | person who's sick is looks the same in |
| 2100s | the same way in their immune system and |
| 2102s | so they might have to get one healthy |
| 2104s | person one sick person and maybe one |
| 2106s | person with three heads and then we can |
| 2108s | use those templates and match them with |
| 2110s | the sample that we're trying to analyze |
| 2112s | and say which of these templates that |
| 2114s | they've analyzed do we want to use on |
| 2116s | top of the machine learning driven |
| 2118s | approach that has been coming that's |
| 2120s | come out of project Discovery and that |
| 2122s | looks like it's going to work really |
| 2124s | really really well |
| 2126s | not only are we going to help do the |
| 2128s | analysis of the of the data to find |
| 2131s | those cell populations |
| 2133s | because you guys are so awesome that's |
| 2135s | given us the idea that we can do more |
| 2137s | so the other problem that scientists |
| 2139s | have is once they identify those cell |
| 2141s | populations we have to talk about them |
| 2144s | we have to have names that we can |
| 2145s | converse in which is why we have things |
| 2147s | like T cells and B cells and natural |
| 2150s | killer cells that we can say oh that |
| 2152s | natural killer cell population is 22 so |
| 2156s | we have to have these labels on these |
| 2157s | cells well it turns out that's also not |
| 2159s | standardized the when the when the when |
| 2162s | the scientists talk about them they |
| 2164s | don't have some they have some words but |
| 2167s | people aren't using them in a |
| 2168s | reproducible or even computational way |
| 2171s | so we could do massive analysis |
| 2174s | so we can find everything but what did |
| 2178s | we actually find |
| 2179s | so the next stage that we're doing is |
| 2182s | not only are we doing this breadth of |
| 2183s | data that you've been doing so far is |
| 2186s | we're going to go down specific Pathways |
| 2188s | of data that for example in the |
| 2189s | diagnosis of cancer or a diagnosis of |
| 2192s | some other disease of the immune system |
| 2193s | we're going to go deep down these paths |
| 2196s | and you guys are going to analyze the |
| 2197s | data and because you have a full view of |
| 2199s | the data and you can put boxes around |
| 2201s | all these dots that's going to allow us |
| 2204s | to make maps computational maps and |
| 2207s | those computational Maps again we're |
| 2208s | going to give out to everybody and I |
| 2210s | talked to a whole basically every |
| 2212s | software company that does this |
| 2215s | we're all going to work off the same map |
| 2217s | that's going to come out of the data |
| 2218s | that you guys are providing and we're |
| 2219s | going to put the labels on there through |
| 2222s | as a second step that we can get from |
| 2225s | literature and we're going to get all |
| 2226s | the scientists in the room to agree on |
| 2228s | those labels and that way everybody in |
| 2230s | the world is going to be coming off the |
| 2231s | same map and using the same words and |
| 2234s | that way we can compute on data in a way |
| 2236s | that we just can't do today in any way |
| 2239s | very large big data analysis it's going |
| 2243s | to be amazing |
| 2245s | and you have to understand this is going |
| 2247s | to completely change the workflows that |
| 2251s | in one company alone at BMS there's 800 |
| 2253s | people who do nothing but analyze flow |
| 2255s | cytometry data that's just one big |
| 2257s | pharmaceutical company that not taking |
| 2259s | part all the other foreign all the big |
| 2261s | Pharma companies That's not including |
| 2263s | all you know thousands of people in all |
| 2265s | the academic Labs not just in one site |
| 2268s | and so what has to happen is many many |
| 2270s | different kinds of things and through |
| 2272s | the work that has been done through |
| 2273s | project Discovery we're going to change |
| 2275s | that workflow and automate that such |
| 2277s | that |
| 2279s | um once the data comes off the machine |
| 2280s | we can suck that up into the cloud we |
| 2283s | can use the ml gating that's come off |
| 2285s | project Discovery to gate all everything |
| 2287s | that's in there we can put labels on |
| 2290s | those populations and then there's other |
| 2291s | stuff we can do that's not really tied |
| 2293s | to project Discovery but it is enabled |
| 2295s | because of this technology coming online |
| 2297s | such as we can do all the statistics |
| 2299s | that people want to do and then we can |
| 2300s | pull out the things that are different |
| 2301s | for example this green this green line |
| 2304s | is a patient who's had a very different |
| 2307s | reaction to a drug than everybody else |
| 2309s | this is how science work this is how |
| 2312s | discoveries are made this is how we |
| 2314s | prevent people from dying from drugs |
| 2316s | that we give them when we didn't know |
| 2317s | that there's some kind of effect because |
| 2318s | we just haven't looked at enough data |
| 2320s | this is kind of fundamentally change how |
| 2323s | science is being done because it allows |
| 2325s | scientists to do science rather than all |
| 2328s | that you guys are doing right now |
| 2330s | and that not only can we do that against |
| 2333s | the data that people are generating |
| 2334s | today once we have those tools in place |
| 2336s | we can go back and apply them to all the |
| 2339s | data that these pharmaceutical companies |
| 2341s | have in their databases it's just |
| 2343s | sitting there they collect it they don't |
| 2345s | throw all this out but they can't do |
| 2347s | anything with it because it's not been |
| 2348s | analyzed in a harmonized way so we can |
| 2350s | scale this technology across all their |
| 2352s | past and future data and we can put this |
| 2355s | in a standardized platform and this |
| 2357s | digitization of data is going to |
| 2360s | fundamentally change how discoveries get |
| 2363s | done it's going to fundamentally change |
| 2364s | and then the investment that anybody has |
| 2369s | done on data so we can return that and |
| 2372s | this is the rri the return on investment |
| 2374s | of that Discovery dollars |
| 2376s | worth nothing |
| 2377s | extra effort because it's all automated |
| 2379s | and the beauty of this |
| 2381s | is that if somebody somewhere discovers |
| 2383s | this new cell population that I don't |
| 2386s | know |
| 2387s | toe fungus or something right and we |
| 2389s | found something that nobody's ever |
| 2390s | looked at before great it cures toe |
| 2393s | fungus |
| 2394s | um what else does it do |
| 2397s | and because of this Automation and |
| 2399s | standardization of the pipelines and the |
| 2401s | analysis and the way we can now push all |
| 2403s | this data through and have it all |
| 2404s | sitting there waiting as soon as |
| 2406s | somebody finds something |
| 2408s | we can automatically see well what else |
| 2410s | has this been seen in that we haven't |
| 2412s | looked at it before because we didn't |
| 2413s | know to look there |
| 2416s | that's crazy cool |
| 2419s | so |
| 2420s | it's because of you guys this happened |
| 2423s | there's so many people have contributed |
| 2425s | 20 000 people did the really really |
| 2427s | really good job |
| 2429s | is basil here |
| 2431s | Maybe |
| 2432s | no what we did is we ranked everybody |
| 2436s | from one to twenty thousand who's given |
| 2438s | us data for project discovery that ended |
| 2441s | up in the really good consensus |
| 2443s | go to tinyurl.com project discovery |
| 2446s | see if your name is there email me and |
| 2449s | if you happen to be the top ranked |
| 2451s | person who's here at Fan Fest |
| 2453s | swag |
| 2457s | that would be awesome and really all of |
| 2460s | you even if you're not basil or Zarina |
| 2462s | or aberrend Oren or the vipes |
| 2466s | from me from a behalf of the community |
| 2468s | thank you again so very much |
| 2471s | I also want to thank CCP for making this |
| 2473s | happen |
| 2474s | um especially David and Julia if you |
| 2476s | guys are here |
| 2478s | um really the love that they have for |
| 2480s | this project discovery that allowed this |
| 2481s | to happen |
| 2482s | couldn't have happened without them |
| 2484s | couldn't have happened Without You 710 |
| 2487s | 000 different players accounts and |
| 2488s | Counting lots of people who've been |
| 2490s | involved many many scientists are |
| 2492s | interested in this data many scientists |
| 2494s | have been |
| 2495s | um working to get this done have to |
| 2497s | thank our funders and now again thank |
| 2499s | you all so very much |
| 2505s | foreign |