1 00:00:08,080 --> 00:00:11,039 Hello, and welcome to the Physics World weekly 2 00:00:11,039 --> 00:00:11,539 podcast. 3 00:00:11,919 --> 00:00:13,219 I'm Hamish Johnston. 4 00:00:13,855 --> 00:00:14,674 In this episode, 5 00:00:14,974 --> 00:00:16,675 I'm joined by two physicists 6 00:00:17,135 --> 00:00:18,114 who are exploring 7 00:00:18,494 --> 00:00:20,195 how artificial intelligence 8 00:00:21,054 --> 00:00:23,634 can be used to predict future directions 9 00:00:24,015 --> 00:00:26,035 in quantum science and technology. 10 00:00:27,029 --> 00:00:27,769 They are 11 00:00:28,070 --> 00:00:30,649 Mario Kran, who heads the artificial 12 00:00:31,189 --> 00:00:33,210 scientist lab at Germany's 13 00:00:33,670 --> 00:00:37,049 Max Planck Institute for the Science of Light, 14 00:00:37,429 --> 00:00:38,329 and Felix 15 00:00:38,804 --> 00:00:39,304 Frohnert, 16 00:00:39,765 --> 00:00:41,384 who is doing a PhD 17 00:00:41,844 --> 00:00:42,824 on the intersection 18 00:00:43,204 --> 00:00:46,024 of quantum physics and machine learning 19 00:00:46,405 --> 00:00:47,945 at Leiden University 20 00:00:48,405 --> 00:00:49,304 in The Netherlands. 21 00:00:50,164 --> 00:00:52,104 Earlier this year, Felix, 22 00:00:52,460 --> 00:00:54,000 Mario, and colleagues 23 00:00:54,460 --> 00:00:56,719 published a paper called discovering 24 00:00:57,659 --> 00:01:01,039 emergent connections in quantum physics research 25 00:01:01,500 --> 00:01:04,079 via dynamic word embeddings. 26 00:01:04,984 --> 00:01:07,385 And they join me today to chat about 27 00:01:07,385 --> 00:01:08,125 that work 28 00:01:08,424 --> 00:01:12,125 in which they analyzed more than 66,000 29 00:01:12,505 --> 00:01:13,005 abstracts 30 00:01:13,385 --> 00:01:14,924 from the quantum literature 31 00:01:15,305 --> 00:01:16,924 to see if they could predict 32 00:01:17,305 --> 00:01:19,325 future trends in the field. 33 00:01:27,450 --> 00:01:29,870 Hi, Mario and Felix. Welcome to the podcast. 34 00:01:30,729 --> 00:01:33,530 Hi, Mitch. Very nice, to see you. Glad 35 00:01:33,530 --> 00:01:34,349 to be here. 36 00:01:34,834 --> 00:01:35,655 So, Mario, 37 00:01:36,274 --> 00:01:38,274 can we start off by sort of delving 38 00:01:38,274 --> 00:01:40,055 into the history of this? 39 00:01:40,914 --> 00:01:43,155 Apparently, in the past, people have tried to 40 00:01:43,155 --> 00:01:43,655 use, 41 00:01:44,515 --> 00:01:46,935 data from the scientific literature 42 00:01:47,369 --> 00:01:49,149 to predict the evolution 43 00:01:49,930 --> 00:01:53,049 of a research field. Can you talk a 44 00:01:53,049 --> 00:01:55,930 bit about some of that early work and, 45 00:01:56,329 --> 00:01:58,030 and how successful it was? 46 00:01:58,969 --> 00:01:59,469 Yes. 47 00:02:00,444 --> 00:02:02,284 There have been a lot of, work in 48 00:02:02,284 --> 00:02:04,865 the past where people try to use this 49 00:02:04,924 --> 00:02:07,905 enormous amount of data and see how 50 00:02:08,525 --> 00:02:11,985 researchers work and how researchers could work. So 51 00:02:12,189 --> 00:02:14,770 looking at the strategy of how, 52 00:02:15,150 --> 00:02:17,250 research is done. This has interestingly, 53 00:02:17,709 --> 00:02:19,409 mainly been done by computational 54 00:02:19,789 --> 00:02:20,289 sociologists 55 00:02:21,069 --> 00:02:21,729 who look 56 00:02:22,189 --> 00:02:24,530 at science as a whole and analyze 57 00:02:25,069 --> 00:02:25,569 what 58 00:02:25,995 --> 00:02:28,235 strategies that people use and how we could 59 00:02:28,235 --> 00:02:29,775 accelerate those strategies. 60 00:02:30,555 --> 00:02:32,955 And maybe one of the most exciting work 61 00:02:32,955 --> 00:02:34,655 here in this field that at least, 62 00:02:35,514 --> 00:02:37,835 motivated me to look into this was a 63 00:02:37,835 --> 00:02:38,655 paper from 64 00:02:39,010 --> 00:02:40,550 02/2015, 65 00:02:41,330 --> 00:02:42,230 led by, 66 00:02:43,090 --> 00:02:43,590 Chicago 67 00:02:44,050 --> 00:02:45,189 computational sociologist, 68 00:02:45,490 --> 00:02:46,389 James Evans. 69 00:02:46,770 --> 00:02:49,430 And that paper, it was published in PNAS. 70 00:02:49,569 --> 00:02:51,590 It was called choosing experiments 71 00:02:52,215 --> 00:02:52,955 to accelerate 72 00:02:53,254 --> 00:02:54,474 collective discovery. 73 00:02:55,094 --> 00:02:58,075 And, therefore, the first time, I've seen how 74 00:02:58,135 --> 00:03:01,275 people use millions of scientific papers and patents 75 00:03:02,855 --> 00:03:06,474 to analyze what other concrete strategies that humans 76 00:03:06,694 --> 00:03:07,169 did, 77 00:03:08,689 --> 00:03:11,669 showing that those strategies are at a collective 78 00:03:11,810 --> 00:03:13,349 scale actually quite, 79 00:03:15,009 --> 00:03:15,509 improveable, 80 00:03:15,810 --> 00:03:17,969 let's say, like that. And then they did 81 00:03:17,969 --> 00:03:19,810 a lot of computer simulation to come up 82 00:03:19,810 --> 00:03:21,750 with potential different strategies. 83 00:03:22,375 --> 00:03:24,375 And they have pointed out that if you 84 00:03:24,375 --> 00:03:27,575 would follow if the collective would follow different 85 00:03:27,575 --> 00:03:29,594 strategies, you could theoretically, 86 00:03:30,455 --> 00:03:31,754 come to much faster, 87 00:03:33,094 --> 00:03:34,395 progress in science. 88 00:03:34,879 --> 00:03:36,800 But this was in 02/2015, 89 00:03:36,800 --> 00:03:37,280 and, 90 00:03:37,760 --> 00:03:38,740 this motivated 91 00:03:39,040 --> 00:03:40,479 me at the time that was still during 92 00:03:40,479 --> 00:03:43,199 my PhD to also try the same thing 93 00:03:43,199 --> 00:03:45,520 in my own field in quantum physics. And 94 00:03:45,520 --> 00:03:46,560 then we also, 95 00:03:47,944 --> 00:03:51,004 tried to use hundreds of thousands of papers 96 00:03:51,465 --> 00:03:52,284 to predict, 97 00:03:54,264 --> 00:03:56,504 what scientists will do in the field of 98 00:03:56,504 --> 00:03:58,584 quantum physics. And then we use the same 99 00:03:58,584 --> 00:04:02,310 techniques that James Evans, teams have used. Knowledge 100 00:04:02,310 --> 00:04:04,389 graphs where you have concepts that are the 101 00:04:04,389 --> 00:04:05,849 notes and edges 102 00:04:06,150 --> 00:04:07,930 that are formed when two concepts 103 00:04:08,549 --> 00:04:09,209 are connected, 104 00:04:11,030 --> 00:04:13,269 by a single paper. So you see somehow 105 00:04:13,269 --> 00:04:13,930 the evolution 106 00:04:14,229 --> 00:04:14,729 of, 107 00:04:15,764 --> 00:04:17,064 of what scientists 108 00:04:17,845 --> 00:04:18,824 did in the past. 109 00:04:20,164 --> 00:04:22,004 And that a number of, 110 00:04:22,564 --> 00:04:24,644 there are a number of follow-up papers came. 111 00:04:24,644 --> 00:04:26,725 So this one was published in 2020 in 112 00:04:26,725 --> 00:04:29,285 PNAS, and then we did the AI competition, 113 00:04:29,285 --> 00:04:32,009 but all was based on the same idea 114 00:04:32,009 --> 00:04:33,709 of using this knowledge graphs. 115 00:04:34,329 --> 00:04:34,649 And then, 116 00:04:36,569 --> 00:04:37,149 I met, 117 00:04:37,529 --> 00:04:38,029 Felix, 118 00:04:39,370 --> 00:04:40,110 and Evert, 119 00:04:41,209 --> 00:04:43,529 and they had a very different idea of 120 00:04:43,529 --> 00:04:44,589 how one could 121 00:04:44,970 --> 00:04:45,470 integrate 122 00:04:45,914 --> 00:04:48,414 scientific knowledge and especially the dynamics, 123 00:04:48,794 --> 00:04:51,354 and that is what this specific paper is 124 00:04:51,354 --> 00:04:51,854 about. 125 00:04:52,714 --> 00:04:55,354 I see. Okay. And and I I suppose 126 00:04:55,354 --> 00:04:57,034 it's well, is it obvious? I mean, it 127 00:04:57,034 --> 00:04:58,634 makes sense to me that you've got a 128 00:04:58,634 --> 00:05:01,589 huge amount of data, and that's where machine 129 00:05:01,589 --> 00:05:03,129 learning, artificial intelligence 130 00:05:03,830 --> 00:05:05,830 can really help. And and I think in 131 00:05:05,830 --> 00:05:07,689 the paper that you've published recently, 132 00:05:08,149 --> 00:05:10,310 you've looked at how machine learning can be 133 00:05:10,310 --> 00:05:11,449 used to look for 134 00:05:11,830 --> 00:05:14,250 emergent connections between subfields 135 00:05:14,985 --> 00:05:18,204 in quantum physics with the ultimate goal of 136 00:05:18,584 --> 00:05:19,084 forecasting 137 00:05:19,384 --> 00:05:21,245 the future direction of research. 138 00:05:22,264 --> 00:05:25,785 Can you explain what a subfield is and 139 00:05:25,785 --> 00:05:27,324 and perhaps give an example 140 00:05:27,785 --> 00:05:28,764 of an emergent 141 00:05:29,199 --> 00:05:30,879 connection. I think may maybe a lot of 142 00:05:30,879 --> 00:05:32,879 our listeners would know what a subfield is, 143 00:05:32,879 --> 00:05:33,379 but 144 00:05:33,680 --> 00:05:35,300 the emergent connection bit 145 00:05:35,680 --> 00:05:37,779 might be something new to them. 146 00:05:38,720 --> 00:05:41,379 Felix, could you, could you address that? 147 00:05:42,560 --> 00:05:45,014 Yeah. Sure. Of course. So for us, 148 00:05:45,655 --> 00:05:46,875 subfields in 149 00:05:47,254 --> 00:05:50,235 quantum physics within kind of this broader category 150 00:05:50,295 --> 00:05:51,495 would be something like, 151 00:05:51,895 --> 00:05:55,254 single photon quantum optics. Different sublet would be 152 00:05:55,254 --> 00:05:56,634 gravitational wave physics 153 00:05:56,935 --> 00:05:59,495 or topics related to, I don't know, fault 154 00:05:59,495 --> 00:06:00,955 tolerant quantum algorithms. 155 00:06:01,470 --> 00:06:03,729 All of these would fall under the umbrella 156 00:06:03,789 --> 00:06:06,930 term of quantum physics, but in of itself 157 00:06:06,990 --> 00:06:09,629 deal with very kinds of different questions and 158 00:06:09,629 --> 00:06:10,129 techniques. 159 00:06:10,509 --> 00:06:13,310 While quantum optics might focus on questions like 160 00:06:13,310 --> 00:06:14,449 how to generate, 161 00:06:14,975 --> 00:06:16,834 interfere, or detect single photons, 162 00:06:17,214 --> 00:06:19,394 topics like fault tolerant quantum algorithms 163 00:06:19,694 --> 00:06:22,274 might deal with questions about error correction 164 00:06:22,574 --> 00:06:23,794 or resource estimation, 165 00:06:24,095 --> 00:06:26,735 which, of course, fall under this umbrella term 166 00:06:26,735 --> 00:06:29,714 of, quantum physics but are conceptually quite 167 00:06:30,689 --> 00:06:32,529 distinct. But what we have seen in the 168 00:06:32,529 --> 00:06:33,029 past 169 00:06:33,330 --> 00:06:36,370 is that despite these differences, there can be 170 00:06:36,370 --> 00:06:37,590 fruitful overlaps 171 00:06:38,050 --> 00:06:39,029 between different 172 00:06:39,410 --> 00:06:39,910 subfields 173 00:06:40,529 --> 00:06:41,910 in general in science. 174 00:06:42,449 --> 00:06:45,430 One example that is quite prominent 175 00:06:45,764 --> 00:06:46,504 in the, 176 00:06:47,125 --> 00:06:49,845 kind of physics community has been, I think, 177 00:06:49,845 --> 00:06:52,805 the application of, machine learning to the study 178 00:06:52,805 --> 00:06:53,544 of quantum 179 00:06:53,845 --> 00:06:54,664 phase transitions, 180 00:06:55,044 --> 00:06:56,564 which is a connection that has only been 181 00:06:56,564 --> 00:06:57,944 made in around, 182 00:06:58,725 --> 00:06:59,625 2017, 183 00:07:00,004 --> 00:07:00,360 I think. 184 00:07:00,919 --> 00:07:02,699 Before that, these two 185 00:07:03,319 --> 00:07:03,819 fields 186 00:07:04,199 --> 00:07:07,959 were kind of completely distant. Machine learning at 187 00:07:07,959 --> 00:07:09,719 that time was mostly used for things like 188 00:07:09,719 --> 00:07:11,500 image classification or regression, 189 00:07:11,879 --> 00:07:14,535 and quantum phase transitions were studied with kind 190 00:07:14,535 --> 00:07:15,595 of very 191 00:07:16,055 --> 00:07:17,115 physics specific 192 00:07:17,574 --> 00:07:18,074 tools. 193 00:07:18,375 --> 00:07:20,634 But then in around, 2017, 194 00:07:20,935 --> 00:07:23,814 researchers realized that you could use measurements of, 195 00:07:24,294 --> 00:07:26,694 quantum systems to kind of treat them as 196 00:07:26,694 --> 00:07:29,750 structured data similar to images, making it possible 197 00:07:29,810 --> 00:07:32,129 to kind of combine these two techniques or 198 00:07:32,129 --> 00:07:35,649 apply machine learning methods from computer vision to 199 00:07:35,649 --> 00:07:36,550 physics problems. 200 00:07:36,930 --> 00:07:40,229 And this kind of quite simple connection but 201 00:07:40,370 --> 00:07:41,349 powerful idea, 202 00:07:41,685 --> 00:07:43,865 Combining these two distinct ideas 203 00:07:44,725 --> 00:07:47,585 has led to many fruitful results, basically. And 204 00:07:47,605 --> 00:07:48,264 the goal 205 00:07:48,845 --> 00:07:49,345 for 206 00:07:49,925 --> 00:07:51,384 this research project, 207 00:07:51,764 --> 00:07:53,285 Mario and I did, is to try to 208 00:07:53,285 --> 00:07:55,285 build or kind of improve a data driven 209 00:07:55,285 --> 00:07:58,470 method methods that helps to assess or even 210 00:07:58,470 --> 00:07:58,970 forecast 211 00:07:59,350 --> 00:08:01,850 ideas of kind of that type, basically. 212 00:08:02,870 --> 00:08:04,629 I see. And so, I mean, one thing 213 00:08:04,629 --> 00:08:05,129 that 214 00:08:05,589 --> 00:08:07,509 I can think of in, you know, in 215 00:08:07,509 --> 00:08:10,245 in experimental physics, for example, And I'd like 216 00:08:10,245 --> 00:08:12,004 to see, I mean, have I got the 217 00:08:12,004 --> 00:08:14,405 right idea here when I'm thinking about emergent 218 00:08:14,405 --> 00:08:14,905 connections? 219 00:08:15,525 --> 00:08:17,465 You know, for for example, you've got superconducting 220 00:08:17,764 --> 00:08:18,264 circuits. 221 00:08:18,725 --> 00:08:20,504 And, you know, in the past, superconducting 222 00:08:20,884 --> 00:08:22,985 circuits were were used to make 223 00:08:24,069 --> 00:08:24,810 very sensitive 224 00:08:25,189 --> 00:08:28,389 detectors of magnetic fields, weren't they, squids, which 225 00:08:28,389 --> 00:08:30,790 were used in medical physics and and and 226 00:08:30,790 --> 00:08:32,330 other research. But now 227 00:08:32,710 --> 00:08:35,610 superconducting circuits are used as qubits 228 00:08:35,924 --> 00:08:36,664 in quantum 229 00:08:37,044 --> 00:08:38,024 computing. So 230 00:08:38,404 --> 00:08:39,845 is that the sort of thing that you're 231 00:08:39,845 --> 00:08:43,245 looking at? You know, the the use of 232 00:08:43,245 --> 00:08:44,585 of of one quantum 233 00:08:45,284 --> 00:08:48,404 technology suddenly or well, probably not suddenly, but 234 00:08:48,404 --> 00:08:48,904 slowly 235 00:08:49,919 --> 00:08:50,419 becomes, 236 00:08:51,279 --> 00:08:54,399 useful in another area of physics? So moving 237 00:08:54,399 --> 00:08:54,899 from 238 00:08:55,759 --> 00:08:56,259 sensing 239 00:08:56,639 --> 00:08:59,440 to quantum computing. Is that, I mean, are 240 00:08:59,440 --> 00:09:01,360 those the sort of things that you're trying 241 00:09:01,360 --> 00:09:03,840 to predict in the future, or am I 242 00:09:03,840 --> 00:09:04,820 getting that wrong? 243 00:09:06,014 --> 00:09:08,835 Yes. This would be one of the examples. 244 00:09:09,134 --> 00:09:10,355 There are a number of other 245 00:09:10,735 --> 00:09:11,875 examples that are, 246 00:09:13,134 --> 00:09:16,014 where you would, as a human, potentially not 247 00:09:16,014 --> 00:09:17,554 see the connection, 248 00:09:18,120 --> 00:09:19,340 But just by having 249 00:09:20,360 --> 00:09:22,759 access to hundreds of thousands or millions of 250 00:09:22,759 --> 00:09:25,879 papers, the machine sees that there is some 251 00:09:25,879 --> 00:09:27,019 underlying connection. 252 00:09:27,399 --> 00:09:28,539 And then it can, 253 00:09:29,240 --> 00:09:32,059 say, okay. Very likely, this will be connected 254 00:09:32,120 --> 00:09:35,274 because I've seen this underlying connection before, and 255 00:09:35,274 --> 00:09:36,735 that always leads to connections. 256 00:09:38,075 --> 00:09:38,575 Yeah. 257 00:09:40,154 --> 00:09:42,795 I see. And, I mean, obviously, you know, 258 00:09:42,795 --> 00:09:44,955 being able to predict the future is very 259 00:09:44,955 --> 00:09:45,455 important. 260 00:09:46,990 --> 00:09:49,309 So who, I mean, who would benefit from 261 00:09:49,309 --> 00:09:51,389 this? I mean, it are you thinking of 262 00:09:51,389 --> 00:09:54,289 scientists who are trying to plan their career, 263 00:09:54,509 --> 00:09:55,950 or are you thinking of, 264 00:09:56,830 --> 00:09:57,889 research funders, 265 00:09:58,830 --> 00:10:00,924 looking at, you know, new areas to put 266 00:10:00,924 --> 00:10:02,225 money into or companies 267 00:10:02,924 --> 00:10:05,245 trying to work out what, you know, new 268 00:10:05,245 --> 00:10:08,125 technologies will emerge in the next little while. 269 00:10:08,125 --> 00:10:09,904 I mean, it sounds like it could benefit 270 00:10:10,524 --> 00:10:11,825 a lot of people. 271 00:10:13,840 --> 00:10:15,279 So I think for both, 272 00:10:15,840 --> 00:10:18,799 Mario and me, the motivation in the beginning 273 00:10:18,799 --> 00:10:21,220 was actually just a bit of, self interest 274 00:10:22,320 --> 00:10:25,120 at first. Because as researchers, I think one 275 00:10:25,120 --> 00:10:27,414 of the hardest tasks is to consistently come 276 00:10:27,414 --> 00:10:29,514 up with good and fruitful 277 00:10:29,975 --> 00:10:32,634 research ideas, either for ourselves 278 00:10:33,095 --> 00:10:34,475 or for our students. 279 00:10:34,934 --> 00:10:37,095 Because in order to kind of come up 280 00:10:37,095 --> 00:10:39,700 with many good research ideas, you need to 281 00:10:39,700 --> 00:10:42,019 have a mix of, like, good intuition about 282 00:10:42,019 --> 00:10:44,659 the topic, some experience, and sometimes even just 283 00:10:44,659 --> 00:10:46,019 a bit of luck to come up with, 284 00:10:46,339 --> 00:10:47,080 good ideas. 285 00:10:47,459 --> 00:10:49,379 And the hope for both of us was 286 00:10:49,379 --> 00:10:51,139 to kind of use such a model as 287 00:10:51,139 --> 00:10:52,360 sort of like an artificial 288 00:10:52,820 --> 00:10:54,695 muse in the sense that we can have 289 00:10:54,695 --> 00:10:57,414 a data driven method that helps us to 290 00:10:57,414 --> 00:11:00,695 uncover or assess combination of research topics that 291 00:11:00,695 --> 00:11:03,095 no one has considered before, where there's, like, 292 00:11:03,095 --> 00:11:06,074 no benchmark of, combining these two things 293 00:11:07,254 --> 00:11:07,754 together. 294 00:11:08,240 --> 00:11:10,579 So who would benefit from that? Basically, 295 00:11:10,879 --> 00:11:13,539 anyone working in science who regularly needs to 296 00:11:13,600 --> 00:11:16,259 generate new research ideas or kind of project, 297 00:11:17,440 --> 00:11:18,259 project topics. 298 00:11:18,959 --> 00:11:21,139 Yeah. Good benefit from this tool, I think. 299 00:11:22,125 --> 00:11:25,184 I think the biggest benefit could come from 300 00:11:25,485 --> 00:11:25,985 collaborations, 301 00:11:27,804 --> 00:11:28,785 suggesting collaborations 302 00:11:29,325 --> 00:11:29,825 for 303 00:11:30,205 --> 00:11:32,465 researchers that are not in the same field, 304 00:11:32,764 --> 00:11:35,730 potentially that are in very far away fields. 305 00:11:35,970 --> 00:11:37,429 For instance, let's say I have 306 00:11:37,889 --> 00:11:38,870 a friend who works 307 00:11:39,570 --> 00:11:42,070 in astrophysics. I know nothing about astrophysics. 308 00:11:42,850 --> 00:11:44,389 Now I want to work together. 309 00:11:44,850 --> 00:11:45,509 I would 310 00:11:45,889 --> 00:11:48,709 potentially not even know where to start. 311 00:11:50,154 --> 00:11:52,735 I have my techniques. They have their techniques. 312 00:11:53,914 --> 00:11:54,735 I have no idea, 313 00:11:55,434 --> 00:11:57,514 what is their connection. But the machine has 314 00:11:57,514 --> 00:12:00,075 seen hundreds of thousands or millions of papers, 315 00:12:00,075 --> 00:12:02,335 and they could see some underlying 316 00:12:03,070 --> 00:12:04,450 connection that might, 317 00:12:05,389 --> 00:12:07,070 be possible in the future that has not 318 00:12:07,070 --> 00:12:07,809 been possible, 319 00:12:08,829 --> 00:12:11,149 or that has not been done so far. 320 00:12:11,149 --> 00:12:12,990 So I see a lot of potential for 321 00:12:12,990 --> 00:12:13,490 those 322 00:12:13,950 --> 00:12:15,089 very surprising, 323 00:12:15,470 --> 00:12:16,450 very distant 324 00:12:18,225 --> 00:12:18,725 collaborations. 325 00:12:19,105 --> 00:12:21,044 That would be really great. 326 00:12:21,985 --> 00:12:24,464 I see. And I wanted to ask you 327 00:12:24,464 --> 00:12:24,865 about, 328 00:12:25,345 --> 00:12:27,444 the data that you use to train 329 00:12:27,745 --> 00:12:28,644 your system. 330 00:12:29,024 --> 00:12:31,024 I mean, I'm guessing you that you're using 331 00:12:31,024 --> 00:12:31,764 the titles 332 00:12:32,360 --> 00:12:35,740 of, peer reviewed papers. You're using the abstracts. 333 00:12:35,879 --> 00:12:37,820 Are you also looking at, 334 00:12:39,079 --> 00:12:41,100 references that are made in papers 335 00:12:41,559 --> 00:12:45,000 in order to to make connections? What what 336 00:12:45,000 --> 00:12:46,539 sort of input do you have? 337 00:12:48,004 --> 00:12:50,084 It's actually quite quite simple. So we use 338 00:12:50,084 --> 00:12:54,084 publicly available preprints of quantum physics papers from 339 00:12:54,084 --> 00:12:54,985 the archive. 340 00:12:56,004 --> 00:12:57,225 In general, researchers 341 00:12:57,605 --> 00:12:59,705 submitted their manuscripts to, 342 00:13:00,164 --> 00:13:03,705 websites like the archive before formal, formal publication, 343 00:13:04,139 --> 00:13:06,879 which makes the consent quite easily accessible, 344 00:13:07,580 --> 00:13:10,300 for us. And in particular, we focus on 345 00:13:10,300 --> 00:13:14,000 the abstracts of each preprint in this quantum 346 00:13:14,139 --> 00:13:14,639 physics, 347 00:13:15,580 --> 00:13:16,080 subsection. 348 00:13:16,725 --> 00:13:19,384 The idea behind that is that the abstract 349 00:13:19,764 --> 00:13:20,264 provides, 350 00:13:21,044 --> 00:13:23,684 provides a condensed summary of the kind of 351 00:13:23,684 --> 00:13:26,644 core ideas of the paper, which typically also 352 00:13:26,644 --> 00:13:29,125 includes kind of one or two sentences about 353 00:13:29,125 --> 00:13:29,865 the background, 354 00:13:30,325 --> 00:13:32,579 some information about the main results, and then 355 00:13:32,579 --> 00:13:35,139 also some information about the the impact, 356 00:13:35,779 --> 00:13:36,600 maybe even, 357 00:13:37,059 --> 00:13:38,039 about that paper. 358 00:13:38,579 --> 00:13:41,079 And only focusing on the abstract 359 00:13:41,379 --> 00:13:43,220 and not, for instance, on the kind of 360 00:13:43,220 --> 00:13:46,339 main figures in the paper simplifies the data 361 00:13:46,339 --> 00:13:49,404 quite a bit because, well, typically, in abstracts, 362 00:13:49,404 --> 00:13:50,625 you don't need to deal with, 363 00:13:51,165 --> 00:13:53,504 formulas or citations or references. 364 00:13:54,205 --> 00:13:56,524 And it also kind of, takes away all 365 00:13:56,524 --> 00:13:59,404 the guesswork with things like, figures, for for 366 00:13:59,404 --> 00:14:02,610 instance. Because what abstracts are typically text based, 367 00:14:03,470 --> 00:14:04,449 which means it's, 368 00:14:05,070 --> 00:14:07,730 much easier to kind of extract the information 369 00:14:07,949 --> 00:14:09,889 from that for the machine learning model. 370 00:14:11,070 --> 00:14:13,709 I see. And and is it possible to 371 00:14:13,709 --> 00:14:15,649 explain in simple terms 372 00:14:16,774 --> 00:14:19,254 how the the machine learning is used to 373 00:14:19,254 --> 00:14:21,174 process these data? I mean, you know, sort 374 00:14:21,174 --> 00:14:23,414 of keep in mind that you're you're speaking 375 00:14:23,414 --> 00:14:24,154 to physicists 376 00:14:25,014 --> 00:14:25,834 rather than 377 00:14:26,134 --> 00:14:28,154 computer scientists. Is there 378 00:14:28,559 --> 00:14:30,899 is there an easy way to describe 379 00:14:31,440 --> 00:14:33,779 how it's done? Or may maybe there's not 380 00:14:34,240 --> 00:14:36,320 an easy way. But, I mean, you know, 381 00:14:36,320 --> 00:14:38,000 how would you explain it to a colleague 382 00:14:38,000 --> 00:14:40,980 who's a physicist rather than a computer scientist? 383 00:14:42,504 --> 00:14:46,044 For sure. So in the machine learning pipeline, 384 00:14:46,184 --> 00:14:49,004 there are three main steps. There is a 385 00:14:49,144 --> 00:14:52,584 data collection phase, there is the words embedding 386 00:14:52,584 --> 00:14:55,485 phase, and a training and prediction phase. 387 00:14:55,820 --> 00:14:58,539 The first step, data collection, kind of connects 388 00:14:58,539 --> 00:15:00,639 to the previous question you asked, 389 00:15:01,740 --> 00:15:04,299 where we kind of take a bunch of, 390 00:15:04,779 --> 00:15:08,080 quantum physics publications, look at the abstract, 391 00:15:08,634 --> 00:15:10,894 and then from each abstract, identify 392 00:15:11,274 --> 00:15:14,235 key scientific concepts we are interested in tracking 393 00:15:14,235 --> 00:15:17,054 over time, which in our case are, well, 394 00:15:17,355 --> 00:15:19,615 quantum physics concepts, things like, 395 00:15:19,995 --> 00:15:20,495 entanglement, 396 00:15:21,115 --> 00:15:23,534 quantum circuits, or phase transition. 397 00:15:24,610 --> 00:15:25,590 The second step, 398 00:15:26,049 --> 00:15:26,549 the 399 00:15:27,330 --> 00:15:30,129 word embedding is then the kind of key 400 00:15:30,129 --> 00:15:34,070 idea of this paper. Because in order to 401 00:15:34,210 --> 00:15:35,830 use machine learning to 402 00:15:36,769 --> 00:15:37,269 connect 403 00:15:37,730 --> 00:15:38,549 these concepts, 404 00:15:39,004 --> 00:15:40,784 You need to be able to translate 405 00:15:41,164 --> 00:15:42,384 these words 406 00:15:42,764 --> 00:15:45,804 into numbers that a machine learning model can 407 00:15:45,804 --> 00:15:49,084 understand and process that somehow encodes their meaning 408 00:15:49,084 --> 00:15:49,584 and 409 00:15:49,964 --> 00:15:50,464 usage. 410 00:15:51,004 --> 00:15:54,460 And, yeah, kind of simple ideas like just 411 00:15:54,460 --> 00:15:57,019 giving each word you're interested in a unique 412 00:15:57,019 --> 00:15:59,740 number, of course, wouldn't really work because this 413 00:15:59,740 --> 00:16:02,160 would throw away all the contextual information 414 00:16:02,540 --> 00:16:05,565 about how similar or dissimilar certain words are 415 00:16:05,725 --> 00:16:07,105 or how they are used, 416 00:16:07,804 --> 00:16:10,524 in the context of these abstracts. So what 417 00:16:10,524 --> 00:16:13,404 we do instead is a technique called word 418 00:16:13,404 --> 00:16:16,845 embeddings, which basically turns each word into the 419 00:16:16,845 --> 00:16:17,345 abstract, 420 00:16:17,820 --> 00:16:18,320 into 421 00:16:18,779 --> 00:16:19,679 a high dimensional 422 00:16:20,460 --> 00:16:23,820 vector, which somehow encodes its meaning or how 423 00:16:23,820 --> 00:16:25,200 it is used. 424 00:16:25,740 --> 00:16:28,940 And then you kind of use these, 425 00:16:29,740 --> 00:16:32,460 vectors for the machine learning model. I tried 426 00:16:32,460 --> 00:16:34,355 to come up with a simple example to 427 00:16:34,355 --> 00:16:35,654 kind of highlight 428 00:16:36,115 --> 00:16:38,154 how this could look like. And for this, 429 00:16:38,154 --> 00:16:39,815 I have, like, two sentences. 430 00:16:40,674 --> 00:16:42,294 So the sun is hot 431 00:16:42,674 --> 00:16:45,794 and the moon is cold, which you could 432 00:16:45,794 --> 00:16:48,455 try to encode with such a word embedding. 433 00:16:48,620 --> 00:16:50,620 And in the end, what you would look 434 00:16:50,620 --> 00:16:52,000 for in the embeddings 435 00:16:52,379 --> 00:16:54,459 are then kind of a representation of how 436 00:16:54,459 --> 00:16:56,220 these individual words are used in these two 437 00:16:56,220 --> 00:16:58,779 sentences. So for instance, on some kind of 438 00:16:58,779 --> 00:17:01,179 axis in the embedding, the words sun and 439 00:17:01,179 --> 00:17:02,325 moon might 440 00:17:02,804 --> 00:17:05,285 share one dimension because they're used as the 441 00:17:05,285 --> 00:17:06,904 subjects in the sentences. 442 00:17:07,285 --> 00:17:09,865 The words hot and cold might then lie 443 00:17:09,924 --> 00:17:12,805 might then lie on different axis because they 444 00:17:12,805 --> 00:17:14,505 kind of represent the temperature 445 00:17:15,285 --> 00:17:15,990 of an object. 446 00:17:17,029 --> 00:17:19,269 And the idea is then that somehow the 447 00:17:19,269 --> 00:17:20,650 embedding also encodes 448 00:17:21,990 --> 00:17:24,150 how these different words relate to each other. 449 00:17:24,150 --> 00:17:26,150 So for instance, if you would compute the 450 00:17:26,150 --> 00:17:27,769 overlap between the vector 451 00:17:28,085 --> 00:17:30,984 of the word sun and hot, it should, 452 00:17:31,444 --> 00:17:33,865 if the embedding is well trained, be larger 453 00:17:34,085 --> 00:17:36,964 as then the embedding between sun and cold 454 00:17:36,964 --> 00:17:38,884 because sun and hot are kind of more 455 00:17:38,884 --> 00:17:39,865 closely aligned. 456 00:17:40,325 --> 00:17:42,005 Yeah. This is kind of the the general 457 00:17:42,005 --> 00:17:44,390 idea of how we want to encode the 458 00:17:44,529 --> 00:17:46,309 underlying information about the physics 459 00:17:46,690 --> 00:17:47,589 into these 460 00:17:48,130 --> 00:17:48,630 vectors. 461 00:17:49,250 --> 00:17:50,950 And then the only thing that this, 462 00:17:52,769 --> 00:17:54,769 machine learning model that we then kind of 463 00:17:54,769 --> 00:17:56,210 train to do the predictions in the end 464 00:17:56,210 --> 00:17:58,115 does is look at, 465 00:17:58,974 --> 00:18:01,455 two of these vectors and then try to 466 00:18:01,455 --> 00:18:03,474 predict whether these two vectors 467 00:18:03,775 --> 00:18:05,634 will appear in the same abstract 468 00:18:05,934 --> 00:18:06,674 in a given, 469 00:18:07,855 --> 00:18:08,595 time frame. 470 00:18:09,150 --> 00:18:10,910 What we do in the paper is we 471 00:18:10,910 --> 00:18:11,410 define 472 00:18:11,710 --> 00:18:14,049 a training window, so for instance, from 473 00:18:14,430 --> 00:18:16,670 2000 to 02/2010, 474 00:18:16,670 --> 00:18:19,170 and look at which concept pairs were not 475 00:18:19,390 --> 00:18:22,515 linked yet in this tracking window. And what 476 00:18:22,515 --> 00:18:24,595 the model, tries to do is then to 477 00:18:24,595 --> 00:18:28,195 predict which of these unconnected pairs are likely 478 00:18:28,195 --> 00:18:30,695 to appear together in future abstracts 479 00:18:31,075 --> 00:18:33,795 within a time window of three years, for 480 00:18:33,795 --> 00:18:34,295 instance. 481 00:18:34,789 --> 00:18:36,789 And then the idea is that if you 482 00:18:36,789 --> 00:18:38,710 want some kind of use this machine learning 483 00:18:38,710 --> 00:18:41,029 model for inference to make kind of further 484 00:18:41,029 --> 00:18:43,769 predictions, you basically just shift the time window 485 00:18:44,069 --> 00:18:47,190 from kind of 2,000 to 2,010. You could 486 00:18:47,190 --> 00:18:49,829 also use it to predict from 2,010 to 487 00:18:49,829 --> 00:18:50,684 2,020 488 00:18:51,164 --> 00:18:53,404 or even, further. That's kind of the the 489 00:18:53,404 --> 00:18:54,305 general idea. 490 00:18:54,684 --> 00:18:55,985 I would like to mention 491 00:18:56,445 --> 00:18:56,945 that 492 00:18:57,245 --> 00:18:59,505 what Felix just explained is the 493 00:18:59,884 --> 00:19:00,785 main difference 494 00:19:01,164 --> 00:19:02,924 from our work to what has been done 495 00:19:02,924 --> 00:19:03,424 before. 496 00:19:03,839 --> 00:19:05,519 So before I close what I mentioned in 497 00:19:05,519 --> 00:19:07,299 the beginning, the computational sociologists 498 00:19:08,000 --> 00:19:10,339 and also in my own previous 499 00:19:10,720 --> 00:19:13,519 work, we have built up knowledge graphs. And 500 00:19:13,519 --> 00:19:14,339 knowledge graphs, 501 00:19:15,039 --> 00:19:18,559 have concepts that are the vertices of the 502 00:19:18,559 --> 00:19:20,875 graph, and edges are drawn when, 503 00:19:21,974 --> 00:19:24,234 two concepts are mentioned in the paper. 504 00:19:25,335 --> 00:19:25,835 Now 505 00:19:26,294 --> 00:19:28,154 when I just use the concepts, 506 00:19:28,774 --> 00:19:31,595 I throw away everything about the 507 00:19:32,054 --> 00:19:33,269 context of that word. 508 00:19:34,150 --> 00:19:35,930 That's exactly what Felix mentioned. 509 00:19:36,309 --> 00:19:38,009 Basically, I just define 510 00:19:38,309 --> 00:19:40,390 the word as a number and remove all 511 00:19:40,390 --> 00:19:41,529 of the other information. 512 00:19:42,070 --> 00:19:44,710 But now with the technique that, Felix just 513 00:19:44,710 --> 00:19:47,244 explained that we published in this paper, we 514 00:19:47,244 --> 00:19:49,585 get much more context of the words 515 00:19:50,044 --> 00:19:52,065 by using this more modern, 516 00:19:52,605 --> 00:19:55,565 word embedding instead of knowledge graphs. That comes 517 00:19:55,565 --> 00:19:57,265 with extra technical, 518 00:19:58,044 --> 00:20:00,144 challenges that we had to, 519 00:20:00,525 --> 00:20:01,025 solve, 520 00:20:01,380 --> 00:20:03,720 But then you can get much more context 521 00:20:04,099 --> 00:20:06,680 of the word, of the phrases itself, 522 00:20:07,059 --> 00:20:07,880 and, hopefully, 523 00:20:08,180 --> 00:20:09,799 then get better predictions. 524 00:20:11,059 --> 00:20:14,339 I see. And you use data that went 525 00:20:14,339 --> 00:20:16,259 back to 1994. 526 00:20:16,259 --> 00:20:18,055 So that's, you know, we're looking more 527 00:20:18,595 --> 00:20:20,454 than thirty years ago. 528 00:20:21,954 --> 00:20:23,634 I mean, if you sort of run your 529 00:20:23,634 --> 00:20:25,335 system using older data, 530 00:20:25,795 --> 00:20:28,375 do do you find that it predicts things 531 00:20:28,835 --> 00:20:30,214 that actually happened 532 00:20:30,789 --> 00:20:32,410 a decade or two later? 533 00:20:33,990 --> 00:20:36,970 The short answer to that would be yes. 534 00:20:37,509 --> 00:20:38,410 Well, congratulations. 535 00:20:40,630 --> 00:20:42,250 And there are basically 536 00:20:42,549 --> 00:20:45,190 two different types of predictions our model can 537 00:20:45,190 --> 00:20:48,044 make. Our model can make validation predictions 538 00:20:48,345 --> 00:20:49,404 where we can still 539 00:20:49,704 --> 00:20:52,044 check the outcome and truly 540 00:20:52,345 --> 00:20:53,085 new predictions 541 00:20:53,384 --> 00:20:56,444 where we don't entirely know the outcome yet. 542 00:20:56,585 --> 00:20:58,424 As you mentioned, our model was trained on 543 00:20:58,424 --> 00:21:01,259 data from nineteen nineteen four till, 544 00:21:01,660 --> 00:21:02,559 2017, 545 00:21:02,859 --> 00:21:04,859 in our case, where the model was kind 546 00:21:04,859 --> 00:21:06,640 of used to predict, 547 00:21:07,900 --> 00:21:09,980 contact connections within the next three years, so 548 00:21:09,980 --> 00:21:11,279 until 2019. 549 00:21:11,820 --> 00:21:14,320 And the validation predictions we did 550 00:21:14,904 --> 00:21:15,644 were then 551 00:21:16,025 --> 00:21:18,184 by, kind of shift the time window, as 552 00:21:18,184 --> 00:21:20,744 I explained for the, previous question, where we 553 00:21:20,744 --> 00:21:21,644 tried to predict 554 00:21:22,904 --> 00:21:24,684 or could yeah. Validation predictions, 555 00:21:25,464 --> 00:21:26,904 from 2020 556 00:21:26,904 --> 00:21:28,664 till 2023 557 00:21:28,664 --> 00:21:31,220 where there still is real world data that 558 00:21:31,220 --> 00:21:32,599 we can test against. 559 00:21:33,139 --> 00:21:35,480 And our model managed to kind of discover 560 00:21:36,019 --> 00:21:39,139 a couple of emergent research directions there, which 561 00:21:39,139 --> 00:21:42,765 includes ideas like using tensor network methods to 562 00:21:42,765 --> 00:21:45,724 simulate local quantum circuits, so quantum circuits with 563 00:21:45,724 --> 00:21:47,105 only minimal entanglement, 564 00:21:47,724 --> 00:21:50,384 or ideas of using machine learning to optimize 565 00:21:50,445 --> 00:21:51,984 the structure of, 566 00:21:53,325 --> 00:21:54,144 quantum circuits. 567 00:21:54,460 --> 00:21:56,539 And these kind of, two examples and a 568 00:21:56,539 --> 00:21:58,779 few more are things we discuss in the 569 00:21:58,779 --> 00:22:00,320 paper in detail, basically. 570 00:22:01,820 --> 00:22:03,680 The question about truly 571 00:22:04,059 --> 00:22:04,720 new predictions 572 00:22:05,340 --> 00:22:07,440 is slightly more complicated, 573 00:22:08,615 --> 00:22:09,434 I would say. 574 00:22:10,774 --> 00:22:11,274 One 575 00:22:11,654 --> 00:22:12,154 idea 576 00:22:12,615 --> 00:22:14,534 that I need to discuss for further first 577 00:22:14,534 --> 00:22:15,914 is the idea of, 578 00:22:16,454 --> 00:22:16,954 calibration 579 00:22:17,335 --> 00:22:18,294 of the, 580 00:22:18,855 --> 00:22:20,554 prediction model. Because 581 00:22:21,095 --> 00:22:23,390 in the end, what you are generally interested 582 00:22:23,390 --> 00:22:25,569 in is to have kind of a 583 00:22:25,869 --> 00:22:27,809 small subsets of predictions 584 00:22:28,109 --> 00:22:30,930 where the model is super subbed that, 585 00:22:31,630 --> 00:22:32,369 these predictions 586 00:22:32,990 --> 00:22:33,650 are actually, 587 00:22:34,509 --> 00:22:36,190 correct. So one thing that you need to 588 00:22:36,190 --> 00:22:37,569 check first is 589 00:22:37,964 --> 00:22:39,184 is the confidence 590 00:22:39,884 --> 00:22:42,525 of your machine learning model aligns to the 591 00:22:42,525 --> 00:22:43,025 probability 592 00:22:43,404 --> 00:22:46,625 of being right or false in this prediction? 593 00:22:46,684 --> 00:22:48,044 This is kind of one section in the 594 00:22:48,044 --> 00:22:49,105 paper where we analyze, 595 00:22:49,724 --> 00:22:51,265 like, how our model performs, 596 00:22:52,044 --> 00:22:54,279 in this aspect. And it turns out that 597 00:22:54,279 --> 00:22:56,519 this actually works kind of well. So if 598 00:22:56,519 --> 00:22:58,200 we look at the output probability of the 599 00:22:58,200 --> 00:23:00,519 model, this is aligned quite well with the 600 00:23:00,519 --> 00:23:03,259 probability of the model being false or correct. 601 00:23:03,559 --> 00:23:05,420 So what you can do then is basically 602 00:23:06,359 --> 00:23:06,859 predict 603 00:23:07,615 --> 00:23:09,934 many, many concepts, like, tens of thousands of 604 00:23:09,934 --> 00:23:10,434 concepts, 605 00:23:10,734 --> 00:23:14,255 and then rank them by their probability of 606 00:23:14,255 --> 00:23:15,554 actually occurring 607 00:23:15,934 --> 00:23:16,674 in the future. 608 00:23:17,375 --> 00:23:20,170 And we we did that and found a 609 00:23:20,170 --> 00:23:21,710 subset of predictions that have 610 00:23:22,009 --> 00:23:23,630 over 99.9% 611 00:23:24,009 --> 00:23:24,509 probability 612 00:23:25,289 --> 00:23:27,930 of occurring. And this was very interesting because 613 00:23:27,930 --> 00:23:29,230 most of these concepts 614 00:23:29,529 --> 00:23:32,730 seem to be related to single photon quantum 615 00:23:32,730 --> 00:23:33,230 optics. 616 00:23:33,609 --> 00:23:36,474 So things that are related to concepts like 617 00:23:36,474 --> 00:23:38,734 photon detector and spatial modes 618 00:23:39,035 --> 00:23:41,295 or concepts like polarization entanglements 619 00:23:41,674 --> 00:23:42,174 and, 620 00:23:42,554 --> 00:23:43,375 single photon, 621 00:23:44,075 --> 00:23:45,214 single photon source. 622 00:23:46,234 --> 00:23:48,234 And this was actually something I had a 623 00:23:48,234 --> 00:23:51,130 chat about with with a quantum opt optics 624 00:23:51,130 --> 00:23:53,930 professor from Leiden University, where I'm doing my 625 00:23:53,930 --> 00:23:54,430 PhD. 626 00:23:54,890 --> 00:23:57,690 And all of these predictions actually somehow seem 627 00:23:57,690 --> 00:23:58,269 to make 628 00:23:58,730 --> 00:24:00,430 sense, but are also already 629 00:24:01,130 --> 00:24:03,230 known. So the catch with that is basically 630 00:24:03,724 --> 00:24:05,184 that some of the publications 631 00:24:06,445 --> 00:24:08,924 that were in the kind of subset of 632 00:24:08,924 --> 00:24:09,904 quantum physics, 633 00:24:10,525 --> 00:24:11,744 abstracts that we used 634 00:24:12,285 --> 00:24:14,765 mentioned these concepts. But, of course, we did 635 00:24:14,765 --> 00:24:15,424 not specifically 636 00:24:15,805 --> 00:24:18,865 train our model on optics papers, 637 00:24:19,259 --> 00:24:22,400 which means it's kind of solve these connections, 638 00:24:22,539 --> 00:24:23,440 make these connections, 639 00:24:23,820 --> 00:24:26,220 but it never saw the actual kind of 640 00:24:26,220 --> 00:24:26,720 papers 641 00:24:27,180 --> 00:24:29,580 where these concepts are explicitly mentioned. So there 642 00:24:29,580 --> 00:24:30,240 are kind of 643 00:24:30,779 --> 00:24:33,644 two different interpretations I can make on that. 644 00:24:33,724 --> 00:24:36,205 Kind of one optimistic interpretation would be that 645 00:24:36,205 --> 00:24:38,464 our machine learning model kind of has discovered 646 00:24:38,605 --> 00:24:41,325 the concept of single photon quantum optics on 647 00:24:41,325 --> 00:24:41,984 its own, 648 00:24:42,285 --> 00:24:44,384 and the kind of slightly more conservative, 649 00:24:45,164 --> 00:24:47,460 interpretation would be that in order to get 650 00:24:47,940 --> 00:24:49,880 very meaningful predictions in the future, 651 00:24:50,659 --> 00:24:51,960 one would need to train 652 00:24:52,579 --> 00:24:54,759 our model in kind of all physics applications 653 00:24:54,980 --> 00:24:56,679 to rule out these 654 00:24:57,059 --> 00:25:00,039 kind of trivial or simple predictions 655 00:25:00,445 --> 00:25:02,205 that might have not occurred together in the 656 00:25:02,205 --> 00:25:03,825 training set, but 657 00:25:04,445 --> 00:25:06,205 might be, like, somewhere out there on the 658 00:25:06,205 --> 00:25:07,105 Internet, basically. 659 00:25:08,285 --> 00:25:10,765 I see. And is that is that something 660 00:25:10,765 --> 00:25:11,585 that's possible, 661 00:25:12,605 --> 00:25:14,465 you know, to expand the training 662 00:25:14,920 --> 00:25:16,380 to include all 663 00:25:16,920 --> 00:25:19,640 physics papers? I mean, I mean, first of 664 00:25:19,640 --> 00:25:21,420 all, you know, would you have the computing 665 00:25:21,960 --> 00:25:24,440 resources to do that, and and would that 666 00:25:24,440 --> 00:25:25,180 be useful, 667 00:25:26,359 --> 00:25:27,980 in terms of making predictions? 668 00:25:28,954 --> 00:25:30,894 Maybe you wanna speak about that, Mario? 669 00:25:31,434 --> 00:25:31,934 Yes. 670 00:25:32,714 --> 00:25:33,615 So this is, 671 00:25:34,075 --> 00:25:37,755 definitely possible. Actually, we had, different status where 672 00:25:37,755 --> 00:25:38,894 we not only used, 673 00:25:39,275 --> 00:25:40,950 quantum physics as 674 00:25:41,509 --> 00:25:42,970 input data, but also, 675 00:25:45,109 --> 00:25:48,230 entire physics. Or in one study, we even 676 00:25:48,230 --> 00:25:49,690 used all published 677 00:25:50,149 --> 00:25:50,649 papers, 678 00:25:51,509 --> 00:25:52,329 that exist. 679 00:25:53,525 --> 00:25:55,944 I just looked it up 58,000,000 680 00:25:56,244 --> 00:25:59,224 research papers. So that is possible to take 681 00:25:59,365 --> 00:26:00,505 much, much larger, 682 00:26:02,964 --> 00:26:03,464 subsets 683 00:26:03,845 --> 00:26:04,325 of, 684 00:26:04,884 --> 00:26:05,339 science. 685 00:26:06,220 --> 00:26:07,200 Of course, then 686 00:26:07,579 --> 00:26:09,119 using this data will become 687 00:26:09,500 --> 00:26:10,000 significantly 688 00:26:10,460 --> 00:26:12,000 more computationally expensive 689 00:26:12,460 --> 00:26:15,339 so that the process will be more expensive. 690 00:26:15,339 --> 00:26:15,839 But, 691 00:26:16,380 --> 00:26:18,799 if we really want to go into predicting 692 00:26:18,940 --> 00:26:19,440 novel 693 00:26:20,194 --> 00:26:22,375 research directions, then we have to do this. 694 00:26:22,515 --> 00:26:24,115 So I think in our paper, we showed 695 00:26:24,115 --> 00:26:25,654 it as this huge potential. 696 00:26:26,275 --> 00:26:27,494 And now 697 00:26:27,795 --> 00:26:28,855 we could go into, 698 00:26:29,634 --> 00:26:32,355 thinking about using it for whole physics or 699 00:26:32,355 --> 00:26:35,320 for whole natural science or just whatever 700 00:26:35,779 --> 00:26:36,759 for all science. 701 00:26:37,859 --> 00:26:39,700 Right. Okay. And is that, I mean, is 702 00:26:39,700 --> 00:26:41,400 that something that you and your 703 00:26:41,700 --> 00:26:44,420 collaborators would be interested in? Or do or 704 00:26:44,420 --> 00:26:45,539 do you think that, 705 00:26:46,580 --> 00:26:49,434 that, you know, may maybe maybe people working 706 00:26:49,654 --> 00:26:52,235 or studying other fields, people who study 707 00:26:53,095 --> 00:26:54,154 how biology 708 00:26:54,455 --> 00:26:57,815 evolves, for example, would, would join with you? 709 00:26:57,815 --> 00:26:59,460 I mean, do do do you see this? 710 00:26:59,940 --> 00:27:01,399 I mean, is this a new field 711 00:27:01,700 --> 00:27:03,700 or a growing field that, 712 00:27:04,339 --> 00:27:06,200 that you think you've made a significant 713 00:27:06,500 --> 00:27:07,720 contribution to, 714 00:27:08,500 --> 00:27:10,740 and, you know, other people will follow on 715 00:27:10,740 --> 00:27:11,480 from you? 716 00:27:12,464 --> 00:27:12,964 There 717 00:27:13,664 --> 00:27:15,265 there are, there are a lot of groups 718 00:27:15,265 --> 00:27:16,244 that look into 719 00:27:17,825 --> 00:27:20,805 this direction or related direction. It's a huge, 720 00:27:21,744 --> 00:27:22,644 field to, 721 00:27:23,744 --> 00:27:25,049 to try to come up with 722 00:27:25,690 --> 00:27:29,610 computer inspired new ideas in a, data driven 723 00:27:29,610 --> 00:27:30,110 way. 724 00:27:30,809 --> 00:27:32,970 We show one method how that is done. 725 00:27:32,970 --> 00:27:34,350 There are many other methods. 726 00:27:35,450 --> 00:27:36,830 Now the question is, 727 00:27:37,369 --> 00:27:37,869 is 728 00:27:38,455 --> 00:27:40,555 predicting what scientists will do, 729 00:27:41,654 --> 00:27:43,815 is this the one thing you need to 730 00:27:43,815 --> 00:27:46,075 discover new ideas or are there other things? 731 00:27:46,535 --> 00:27:48,855 For instance, you can think about, can I 732 00:27:48,855 --> 00:27:49,355 predict 733 00:27:49,735 --> 00:27:50,235 impactful 734 00:27:50,775 --> 00:27:51,914 research directions? 735 00:27:52,940 --> 00:27:54,779 And just a few months ago, we have 736 00:27:54,779 --> 00:27:57,519 also published in machine learning science and technology 737 00:27:57,579 --> 00:27:57,980 and, 738 00:27:58,380 --> 00:27:58,880 IOP, 739 00:27:59,980 --> 00:28:00,480 journal, 740 00:28:00,859 --> 00:28:02,240 a paper where we showed, 741 00:28:02,779 --> 00:28:04,539 that was, led by, 742 00:28:05,065 --> 00:28:07,785 my former post doc, Shumei Gu, who is 743 00:28:07,785 --> 00:28:08,924 also coauthor 744 00:28:09,225 --> 00:28:10,684 of the paper with Felix, 745 00:28:11,705 --> 00:28:14,445 that we showed that you can actually predict 746 00:28:14,505 --> 00:28:15,005 also, 747 00:28:15,865 --> 00:28:17,644 impactful research directions. 748 00:28:18,220 --> 00:28:20,240 So let's say you have two concepts 749 00:28:20,940 --> 00:28:23,600 that have never been started before together. 750 00:28:24,700 --> 00:28:26,000 Now the question is, 751 00:28:27,019 --> 00:28:28,160 will there be papers 752 00:28:28,460 --> 00:28:31,740 that started it? And the next question is, 753 00:28:31,740 --> 00:28:33,759 will those paper that studied this 754 00:28:34,195 --> 00:28:36,595 topics that have never been studied before, will 755 00:28:36,595 --> 00:28:37,734 they be impactful? 756 00:28:38,115 --> 00:28:38,775 Will they, 757 00:28:39,474 --> 00:28:40,775 create a lot of citations? 758 00:28:41,234 --> 00:28:42,615 And very surprisingly 759 00:28:42,914 --> 00:28:45,015 to us, this is also predictable. 760 00:28:46,410 --> 00:28:48,970 So now we can also predict in some 761 00:28:48,970 --> 00:28:52,430 way what future research directions could be impactful. 762 00:28:54,009 --> 00:28:56,170 Of course, what Felix has done, what is 763 00:28:56,170 --> 00:28:58,730 new, and what could, could be done and 764 00:28:58,730 --> 00:29:00,730 what might be done. And then I think 765 00:29:00,730 --> 00:29:03,644 the final question is what research directions 766 00:29:03,945 --> 00:29:05,005 are interesting 767 00:29:05,384 --> 00:29:06,205 for humans? 768 00:29:09,305 --> 00:29:10,045 For that, 769 00:29:11,224 --> 00:29:12,285 one could do, 770 00:29:12,904 --> 00:29:14,205 large scale evaluations, 771 00:29:15,384 --> 00:29:16,285 using humans. 772 00:29:16,599 --> 00:29:17,419 We have started, 773 00:29:18,039 --> 00:29:20,359 doing this, and I think there are thousand 774 00:29:20,359 --> 00:29:21,899 different things one can do. 775 00:29:23,159 --> 00:29:24,779 One thing I've seen 776 00:29:25,079 --> 00:29:25,899 in this whole, 777 00:29:27,240 --> 00:29:30,105 this whole range of work is the humans 778 00:29:30,404 --> 00:29:33,144 are exceptionally good in coming up with ideas, 779 00:29:33,284 --> 00:29:35,444 and it's really not clear where those ideas 780 00:29:35,444 --> 00:29:36,184 come from. 781 00:29:36,484 --> 00:29:38,404 I think there's a lot of a lot 782 00:29:38,404 --> 00:29:39,944 of things that we need to understand. 783 00:29:40,325 --> 00:29:42,644 From the human perspective, why are human create 784 00:29:42,644 --> 00:29:45,150 scientists? Where are those ideas coming from? That 785 00:29:45,150 --> 00:29:47,089 we can artificially recreate this. 786 00:29:48,029 --> 00:29:48,690 I see. 787 00:29:48,990 --> 00:29:51,409 And and what about, you know, this specific 788 00:29:51,470 --> 00:29:51,970 project, 789 00:29:52,669 --> 00:29:56,190 that that you, Felix, and, Mario are working 790 00:29:56,190 --> 00:29:56,595 on? 791 00:29:57,394 --> 00:29:59,474 Are you are are you following up this 792 00:29:59,474 --> 00:30:01,894 research? Are are you planning on improving 793 00:30:02,515 --> 00:30:03,734 your machine learning 794 00:30:04,275 --> 00:30:04,775 technique, 795 00:30:06,194 --> 00:30:08,515 or maybe applying it to more data? I 796 00:30:08,515 --> 00:30:10,515 suppose well, you have applied it to more 797 00:30:10,515 --> 00:30:12,779 data. What what what's next for you? Or 798 00:30:12,779 --> 00:30:14,640 are you moving on to something else? 799 00:30:15,580 --> 00:30:17,580 So there are a couple of kind of, 800 00:30:18,380 --> 00:30:20,940 obvious follow-up research directions that, 801 00:30:21,420 --> 00:30:22,559 one could pursue. 802 00:30:23,100 --> 00:30:23,660 There are, 803 00:30:24,955 --> 00:30:27,035 a couple of ideas about how to improve 804 00:30:27,035 --> 00:30:29,515 the embedding that we came up with, 805 00:30:30,394 --> 00:30:31,134 even further. 806 00:30:31,595 --> 00:30:33,695 One of these ideas would be that 807 00:30:34,075 --> 00:30:36,414 the kind of main application of our 808 00:30:37,035 --> 00:30:39,215 embedding was to kind of replace the information 809 00:30:39,275 --> 00:30:41,210 in the knowledge graph as Mavi 810 00:30:41,509 --> 00:30:42,809 mentioned earlier. But 811 00:30:43,109 --> 00:30:44,490 you don't necessarily 812 00:30:45,269 --> 00:30:46,869 need to view it in this kind of 813 00:30:46,869 --> 00:30:49,669 binary setting of either only using the knowledge 814 00:30:49,669 --> 00:30:51,990 graph or or embedding. You could also come 815 00:30:51,990 --> 00:30:54,315 up with some kind of combined representation of 816 00:30:54,535 --> 00:30:55,355 both that, 817 00:30:56,134 --> 00:30:58,535 takes kind of as much information as possible 818 00:30:58,535 --> 00:30:59,734 and then just kind of gives it to 819 00:30:59,734 --> 00:31:01,734 the machine learning model and lets it do 820 00:31:01,734 --> 00:31:02,394 its thing, 821 00:31:02,695 --> 00:31:03,195 basically. 822 00:31:04,055 --> 00:31:05,035 The other, 823 00:31:05,734 --> 00:31:07,595 obvious direction would be to, 824 00:31:08,134 --> 00:31:09,119 apply our methods 825 00:31:10,319 --> 00:31:11,619 to other fields. 826 00:31:12,319 --> 00:31:14,819 Like, on the archive alone, there are 827 00:31:15,119 --> 00:31:18,880 more than 15 other primary categories beyond quantum 828 00:31:18,880 --> 00:31:20,720 physics that we could try to use this 829 00:31:20,720 --> 00:31:22,579 method on where the data is basically 830 00:31:23,039 --> 00:31:25,285 publicly available and on the Internet ready to 831 00:31:25,285 --> 00:31:29,224 use. This includes topics like, mathematics, computer science, 832 00:31:29,525 --> 00:31:32,505 or economics or biology where we could also 833 00:31:32,724 --> 00:31:34,884 try our method on and see if we 834 00:31:34,884 --> 00:31:38,910 can find emergent research connections in these fields. 835 00:31:40,029 --> 00:31:41,630 And Mario, what about you? What are you 836 00:31:41,630 --> 00:31:43,250 looking forward to in the future? 837 00:31:44,269 --> 00:31:47,009 Yeah. So beyond things like predicting, 838 00:31:48,349 --> 00:31:50,529 more general properties like cetaceans 839 00:31:50,990 --> 00:31:51,490 and, 840 00:31:52,029 --> 00:31:53,730 maybe surprise and so on, 841 00:31:55,204 --> 00:31:55,704 one 842 00:31:56,164 --> 00:31:59,045 could one could probably make big steps into 843 00:31:59,045 --> 00:32:01,144 improving their AI models themselves 844 00:32:01,765 --> 00:32:03,464 by performing AI competitions. 845 00:32:04,244 --> 00:32:06,265 So I think the data that Felix, 846 00:32:07,285 --> 00:32:10,005 has is very well equipped for that, where 847 00:32:10,005 --> 00:32:11,990 you would have a pool of prize money 848 00:32:11,990 --> 00:32:15,669 and then a hidden dataset, and participants need 849 00:32:15,669 --> 00:32:16,809 to provide 850 00:32:17,509 --> 00:32:18,089 the best 851 00:32:18,470 --> 00:32:21,029 models that they can come up with to 852 00:32:21,029 --> 00:32:21,529 predict 853 00:32:22,875 --> 00:32:25,994 for predict, for instance, the future of, quantum 854 00:32:25,994 --> 00:32:26,494 physics. 855 00:32:27,755 --> 00:32:30,234 And then test in that case, we could 856 00:32:30,234 --> 00:32:30,734 compete, 857 00:32:31,595 --> 00:32:34,154 techniques like knowledge craft, the techniques that we 858 00:32:34,154 --> 00:32:36,095 have published in these papers, 859 00:32:37,670 --> 00:32:39,990 with many, many other techniques that other people 860 00:32:39,990 --> 00:32:42,549 come up with. And that is, I've seen 861 00:32:42,549 --> 00:32:44,490 this, now one time, 862 00:32:45,109 --> 00:32:47,829 very clearly that this is very useful. There 863 00:32:47,829 --> 00:32:50,410 are many, many diverse techniques that usually 864 00:32:50,789 --> 00:32:51,930 be in such competition, 865 00:32:52,375 --> 00:32:54,794 techniques that you alone would never think about. 866 00:32:55,894 --> 00:32:56,394 It's 867 00:32:57,494 --> 00:32:59,034 significantly more technical, 868 00:33:00,134 --> 00:33:01,194 ideas, significantly 869 00:33:01,654 --> 00:33:03,894 simpler ideas that you would not even believe 870 00:33:03,894 --> 00:33:05,034 that they might work. 871 00:33:05,740 --> 00:33:07,579 So that would be really great. And I 872 00:33:07,579 --> 00:33:08,240 think this 873 00:33:08,700 --> 00:33:10,460 type of data that we, 874 00:33:10,940 --> 00:33:13,579 prepared here would suit very well for such 875 00:33:13,579 --> 00:33:14,400 AI competitions. 876 00:33:15,419 --> 00:33:17,119 I see. And and Mario, 877 00:33:17,579 --> 00:33:19,839 you you mentioned the journal, Machine Learning 878 00:33:20,515 --> 00:33:21,575 Science and Technology. 879 00:33:23,315 --> 00:33:25,714 Can you and you're on the editorial board 880 00:33:25,714 --> 00:33:27,255 of that journal. Can 881 00:33:27,714 --> 00:33:29,154 you just give us a little bit of 882 00:33:29,154 --> 00:33:32,134 a flavor of what sort of physics related 883 00:33:32,674 --> 00:33:34,375 research the journal publishes? 884 00:33:35,690 --> 00:33:37,630 Yes. So this is 885 00:33:38,009 --> 00:33:40,329 probably one of the most important, if not 886 00:33:40,329 --> 00:33:41,150 the most important, 887 00:33:41,769 --> 00:33:42,990 journal in that 888 00:33:43,369 --> 00:33:44,509 field. Because 889 00:33:45,049 --> 00:33:46,430 machine learning research, 890 00:33:46,890 --> 00:33:49,789 getting it published in physics journals, 891 00:33:50,204 --> 00:33:50,944 can be 892 00:33:51,325 --> 00:33:52,944 quite challenging because, 893 00:33:53,644 --> 00:33:56,684 physicists are looking at different things that we 894 00:33:56,684 --> 00:33:58,704 might look at. Also publishing 895 00:33:59,484 --> 00:33:59,984 those, 896 00:34:01,005 --> 00:34:01,505 techniques 897 00:34:01,884 --> 00:34:02,865 in AI, 898 00:34:05,799 --> 00:34:08,140 conferences might also be very challenging because 899 00:34:08,920 --> 00:34:11,239 those people also look at very different things. 900 00:34:11,239 --> 00:34:13,719 So there was before this channel, there was 901 00:34:13,719 --> 00:34:15,719 really a gap where it's not clear how 902 00:34:15,719 --> 00:34:17,954 would you even publish such works. And now 903 00:34:17,954 --> 00:34:18,595 I think, 904 00:34:19,315 --> 00:34:20,454 MLST became 905 00:34:20,914 --> 00:34:21,414 the 906 00:34:21,875 --> 00:34:22,775 the two go 907 00:34:23,155 --> 00:34:23,655 address 908 00:34:23,954 --> 00:34:24,855 for exactly, 909 00:34:25,315 --> 00:34:26,135 such works. 910 00:34:26,675 --> 00:34:29,555 And then a lot of works on how 911 00:34:29,555 --> 00:34:30,179 you would, 912 00:34:31,059 --> 00:34:31,960 use new 913 00:34:32,260 --> 00:34:34,280 ideas for machine learning in 914 00:34:34,739 --> 00:34:36,839 physics and chemistry and biology. 915 00:34:37,300 --> 00:34:39,619 Our work is a little bit, outlier because 916 00:34:39,619 --> 00:34:41,699 it even goes a step further to kind 917 00:34:41,699 --> 00:34:43,844 of meet the science, but you see that 918 00:34:44,005 --> 00:34:46,344 MLC is, quite open in, 919 00:34:46,804 --> 00:34:47,304 disrespect. 920 00:34:48,085 --> 00:34:48,585 So, 921 00:34:49,045 --> 00:34:51,364 that's why I'm super happy that the channel 922 00:34:51,364 --> 00:34:53,844 exists and also super happy that they invited 923 00:34:53,844 --> 00:34:56,984 me to join the, editorial board. 924 00:34:57,969 --> 00:35:00,849 Well, that's great. Thanks. Thanks, Felix, and Mario 925 00:35:00,849 --> 00:35:02,710 as well. Thanks for coming on the podcast 926 00:35:02,769 --> 00:35:04,769 and talking about your research. It's, 927 00:35:05,250 --> 00:35:06,070 it's a fascinating 928 00:35:06,369 --> 00:35:08,469 application of, machine learning, 929 00:35:09,170 --> 00:35:10,630 and artificial intelligence. 930 00:35:10,930 --> 00:35:14,054 And, yeah, we hope to, to learn more 931 00:35:14,054 --> 00:35:16,054 about it from you and your colleagues. Thank 932 00:35:16,054 --> 00:35:16,554 you. 933 00:35:17,094 --> 00:35:19,355 Thank you very much. Thank you. Bye. 934 00:35:27,730 --> 00:35:30,930 That was Felix Frohnert of the University of 935 00:35:30,930 --> 00:35:34,469 Leiden and Mario Krenn of the Max Planck 936 00:35:34,530 --> 00:35:36,710 Institute for the Science of Light. 937 00:35:37,170 --> 00:35:39,510 Thanks to both of them for a fascinating 938 00:35:39,730 --> 00:35:40,230 discussion. 939 00:35:41,195 --> 00:35:44,155 You can find their open access paper in 940 00:35:44,155 --> 00:35:44,815 the journal, 941 00:35:45,114 --> 00:35:46,175 Machine Learning, 942 00:35:46,554 --> 00:35:47,775 Science and Technology, 943 00:35:48,394 --> 00:35:51,135 which can be found on the IOP Science 944 00:35:51,195 --> 00:35:51,695 website. 945 00:35:52,394 --> 00:35:53,775 Just look for the title, 946 00:35:54,279 --> 00:35:54,779 Discovering 947 00:35:55,239 --> 00:35:56,299 Emergent Connections 948 00:35:56,759 --> 00:35:58,859 in Quantum Physics Research 949 00:35:59,239 --> 00:36:01,739 via Dynamic Word Embeddings. 950 00:36:02,679 --> 00:36:04,519 I'm afraid that's all the time we have 951 00:36:04,519 --> 00:36:05,819 for this week's podcast. 952 00:36:06,454 --> 00:36:08,375 I'll sign off with a thanks to our 953 00:36:08,375 --> 00:36:12,235 producer, Fred Ailes, and an invitation for you 954 00:36:12,375 --> 00:36:13,994 to join us next week.