WEBVTT 1 00:00:01.577 --> 00:00:03.550 (soft music) 2 00:00:03.550 --> 00:00:05.940 Are algorithms getting less important? 3 00:00:05.940 --> 00:00:08.090 As algorithms become commoditized, 4 00:00:08.090 --> 00:00:09.800 it may be less about the algorithm 5 00:00:09.800 --> 00:00:11.350 and more about the application. 6 00:00:12.490 --> 00:00:16.320 In our first episode of season two of "Me, Myself, and AI", 7 00:00:16.320 --> 00:00:17.990 we'll talk with Craig Martel, 8 00:00:17.990 --> 00:00:19.580 Head of Machine Learning at Lyft 9 00:00:19.580 --> 00:00:21.930 about how Lyft uses artificial intelligence 10 00:00:21.930 --> 00:00:23.980 to improve its business. 11 00:00:23.980 --> 00:00:25.950 Welcome to "Me, Myself, and AI", 12 00:00:25.950 --> 00:00:29.180 a podcast on artificial intelligence in business. 13 00:00:29.180 --> 00:00:30.690 Each episode, we introduce you 14 00:00:30.690 --> 00:00:33.020 to someone innovating with AI. 15 00:00:33.020 --> 00:00:35.930 I'm Sam Ramsbotham, Professor of Information Systems 16 00:00:35.930 --> 00:00:37.570 at Boston College. 17 00:00:37.570 --> 00:00:39.597 I'm also the guest editor for the AI 18 00:00:39.597 --> 00:00:41.870 and business strategy big idea program, 19 00:00:41.870 --> 00:00:44.660 at MIT Sloan Management Review. 20 00:00:44.660 --> 00:00:46.810 And I'm Shervin Khodabandeh, 21 00:00:46.810 --> 00:00:48.510 Senior Partner with BCG 22 00:00:48.510 --> 00:00:52.360 and I co-lead BCG's AI practice in North America. 23 00:00:52.360 --> 00:00:55.250 And together MIT SMR and BCG 24 00:00:55.250 --> 00:00:57.860 have been researching AI for five years, 25 00:00:57.860 --> 00:01:00.040 interviewing hundreds of practitioners 26 00:01:00.040 --> 00:01:02.060 and surveying thousands of companies 27 00:01:02.060 --> 00:01:03.990 on what it takes to build 28 00:01:03.990 --> 00:01:07.180 and to deploy and scale AI capabilities across 29 00:01:07.180 --> 00:01:08.960 the organization and really transform 30 00:01:08.960 --> 00:01:10.593 the way organizations operate. 31 00:01:13.410 --> 00:01:15.320 Today we're talking with Craig Martel. 32 00:01:15.320 --> 00:01:17.720 Craig is the Head of Machine Learning for Lyft. 33 00:01:17.720 --> 00:01:19.480 Thanks for joining us today, Craig. 34 00:01:19.480 --> 00:01:20.980 Thanks Sam, I'm really happy to be here, 35 00:01:20.980 --> 00:01:22.760 these are pretty exciting topics. 36 00:01:22.760 --> 00:01:24.960 So Craig, Head of Machine Learning at Lyft, 37 00:01:24.960 --> 00:01:28.400 what exactly does that mean and how did you get there? 38 00:01:28.400 --> 00:01:30.470 So let me start by saying 39 00:01:30.470 --> 00:01:32.320 I'm pretty sure I won the lottery in life 40 00:01:32.320 --> 00:01:33.740 and here's why. 41 00:01:33.740 --> 00:01:36.790 I started off doing political theory academically 42 00:01:36.790 --> 00:01:39.280 and I have this misspent youth 43 00:01:39.280 --> 00:01:40.370 where I gathered a collection 44 00:01:40.370 --> 00:01:42.240 of master's degrees along the way 45 00:01:42.240 --> 00:01:43.780 to figuring out what I want to do. 46 00:01:43.780 --> 00:01:45.910 So I did philosophy, political science, 47 00:01:45.910 --> 00:01:47.440 political theory, logic, 48 00:01:47.440 --> 00:01:49.060 and I ended up doing a PhD 49 00:01:49.060 --> 00:01:50.620 in computer science at Penn. 50 00:01:50.620 --> 00:01:53.380 And you know, I sort of thought 51 00:01:53.380 --> 00:01:54.950 I was going to do testable philosophy, 52 00:01:54.950 --> 00:01:57.210 and so the closest to that was doing AI. 53 00:01:57.210 --> 00:01:58.540 So I just did this out of love. 54 00:01:58.540 --> 00:02:00.820 Like I just find the entire process 55 00:02:00.820 --> 00:02:04.550 and the goals, and the techniques, steps really fascinating. 56 00:02:04.550 --> 00:02:07.180 All part of your master plan, it all came together. 57 00:02:07.180 --> 00:02:09.880 Not at all! I just fell into it, I fell into it. 58 00:02:09.880 --> 00:02:11.820 So how did you end up then at Lyft? 59 00:02:11.820 --> 00:02:13.740 So I was at LinkedIn for about six years 60 00:02:13.740 --> 00:02:15.410 and then my wife got this phenomenal job 61 00:02:15.410 --> 00:02:17.080 at Amazon and I wanted to stay married 62 00:02:17.080 --> 00:02:19.330 so I followed her to Seattle. 63 00:02:19.330 --> 00:02:21.230 I worked for a year here at Dropbox 64 00:02:21.230 --> 00:02:22.840 and then Lyft contacted me 65 00:02:22.840 --> 00:02:25.980 and I essentially jumped at the chance because 66 00:02:25.980 --> 00:02:27.530 the space is so fascinating. 67 00:02:27.530 --> 00:02:29.990 I love cars in general 68 00:02:29.990 --> 00:02:32.210 which means I love transportation in general 69 00:02:32.210 --> 00:02:34.860 and the idea of transforming 70 00:02:34.860 --> 00:02:37.840 how we do transportation is just a fascinating space. 71 00:02:37.840 --> 00:02:39.460 And then in my prior life 72 00:02:39.460 --> 00:02:41.980 I was a tenured computer science professor, 73 00:02:41.980 --> 00:02:43.870 which is still a big love of mine, 74 00:02:43.870 --> 00:02:46.950 and so I'm an adjunct professor at Northeastern 75 00:02:46.950 --> 00:02:50.100 just to make sure I keep my teaching skills up. 76 00:02:50.100 --> 00:02:52.900 Craig, your strong humanities background 77 00:02:52.900 --> 00:02:55.200 in philosophy, political science, 78 00:02:55.200 --> 00:02:57.540 you mentioned logic, all of that. 79 00:02:57.540 --> 00:03:00.293 How did that play for you in your overall journey? 80 00:03:01.220 --> 00:03:02.580 So that's really interesting. 81 00:03:02.580 --> 00:03:04.840 When I think about what AI is, 82 00:03:04.840 --> 00:03:07.180 I find the algorithms mathematically fascinating 83 00:03:07.180 --> 00:03:08.910 but I find the use of the algorithms 84 00:03:08.910 --> 00:03:10.760 far more fascinating because 85 00:03:10.760 --> 00:03:12.450 from a technical perspective 86 00:03:12.450 --> 00:03:14.870 we're finding correlations 87 00:03:14.870 --> 00:03:17.470 in extremely high dimensional non-linear spaces. 88 00:03:17.470 --> 00:03:19.670 It's statistics at scale in some sense, right? 89 00:03:19.670 --> 00:03:22.300 We're finding these correlations between A and B 90 00:03:22.300 --> 00:03:24.030 and those algorithms are really interesting 91 00:03:24.030 --> 00:03:26.180 and I'm still teaching those now and they're fun. 92 00:03:26.180 --> 00:03:28.100 But what's more interesting to me 93 00:03:28.100 --> 00:03:30.760 is what do those correlations mean for the people? 94 00:03:30.760 --> 00:03:33.620 I think every AI model launched 95 00:03:33.620 --> 00:03:35.560 is a cognitive science test. 96 00:03:35.560 --> 00:03:38.390 Like we're trying to model the way humans behave. 97 00:03:38.390 --> 00:03:40.700 Now, for automated driving 98 00:03:40.700 --> 00:03:42.730 we're modeling the way cars behave in some sense, 99 00:03:42.730 --> 00:03:44.610 but it's really we're modeling 100 00:03:44.610 --> 00:03:47.270 the right human behavior 101 00:03:47.270 --> 00:03:49.590 given these other cars driven by humans. 102 00:03:49.590 --> 00:03:52.860 So for me, I think the goals of AI, 103 00:03:52.860 --> 00:03:55.890 I look at them much more from humanity's perspective, 104 00:03:55.890 --> 00:03:58.840 although I can nerd out on the technical side as well. 105 00:03:58.840 --> 00:03:59.780 Can you say a bit more about 106 00:03:59.780 --> 00:04:02.750 how Lyft organizes AI and ML teams? 107 00:04:02.750 --> 00:04:03.800 We have model builders 108 00:04:03.800 --> 00:04:04.780 throughout the whole company. 109 00:04:04.780 --> 00:04:06.470 We have a very large science org. 110 00:04:06.470 --> 00:04:08.330 We also have what we call ML-SWES, 111 00:04:08.330 --> 00:04:09.930 so ML software engineers. 112 00:04:09.930 --> 00:04:11.900 I run a team called Lyft ML 113 00:04:11.900 --> 00:04:13.740 and it consists of two major teams. 114 00:04:13.740 --> 00:04:15.170 One is called applied ML 115 00:04:15.170 --> 00:04:17.520 where we leverage expertise in machine learning 116 00:04:17.520 --> 00:04:19.070 to tackle some really tough problems. 117 00:04:19.070 --> 00:04:20.730 And also the ML platform, 118 00:04:20.730 --> 00:04:22.620 which drives my big interest 119 00:04:22.620 --> 00:04:24.860 in operational excellence on getting ML 120 00:04:24.860 --> 00:04:28.410 to make sure it's effectively hitting business metrics. 121 00:04:28.410 --> 00:04:30.330 What do you think, because I think Craig, 122 00:04:30.330 --> 00:04:32.010 you're still teaching, right? 123 00:04:32.010 --> 00:04:34.950 Yeah, I adjunct teach at Northeastern University 124 00:04:34.950 --> 00:04:36.160 here in Seattle. 125 00:04:36.160 --> 00:04:39.870 So, what do you think your students, 126 00:04:39.870 --> 00:04:42.280 sort of, should be asking that they're not, 127 00:04:42.280 --> 00:04:44.630 or maybe to state it in another way, 128 00:04:44.630 --> 00:04:47.510 what would they be most surprised by 129 00:04:47.510 --> 00:04:49.610 when they enter the workforce 130 00:04:49.610 --> 00:04:52.840 and actually do AI in the real world? 131 00:04:52.840 --> 00:04:55.160 The algorithms themselves 132 00:04:55.160 --> 00:04:57.780 are becoming less important. 133 00:04:57.780 --> 00:04:59.700 I'm hesitant to use the word commoditized, 134 00:04:59.700 --> 00:05:02.410 but to some degree they're being commoditized, right? 135 00:05:02.410 --> 00:05:05.630 You could pick one of five, one of seven, 136 00:05:05.630 --> 00:05:08.320 you could try them all model families 137 00:05:08.320 --> 00:05:10.090 for a particular problem. 138 00:05:10.090 --> 00:05:11.560 But what's really happening, 139 00:05:11.560 --> 00:05:14.440 or what I think is the exciting thing happening, 140 00:05:14.440 --> 00:05:16.620 is how those models fit 141 00:05:16.620 --> 00:05:19.700 into a much larger engineering pipeline 142 00:05:19.700 --> 00:05:22.620 that allow you to measure and guarantee 143 00:05:22.620 --> 00:05:25.350 that you're being effective against a business goal. 144 00:05:25.350 --> 00:05:28.250 And that has to do with the cleanliness of the data, 145 00:05:28.250 --> 00:05:30.450 making sure the data is there in a timely way, 146 00:05:30.450 --> 00:05:31.620 classic engineering things. 147 00:05:31.620 --> 00:05:35.960 Like are you returning your features at the right latency? 148 00:05:35.960 --> 00:05:38.300 So the actual model itself 149 00:05:38.300 --> 00:05:40.940 has shrunk from say, 85% of the problem 150 00:05:40.940 --> 00:05:42.540 to 15% of the problem. 151 00:05:42.540 --> 00:05:44.960 And now 85% of the problem is the engineering 152 00:05:44.960 --> 00:05:46.900 and the operational excellence surrounding it. 153 00:05:46.900 --> 00:05:49.840 And I think we're at a point of inflection there. 154 00:05:49.840 --> 00:05:52.680 So do you believe with the advent 155 00:05:52.680 --> 00:05:56.410 of auto ML and these packaged tools 156 00:05:56.410 --> 00:05:58.930 and your point about over time, 157 00:05:58.930 --> 00:06:00.740 it's less about the algo, 158 00:06:00.740 --> 00:06:03.430 more about the data and how you use it? 159 00:06:03.430 --> 00:06:06.540 Do you think the curricula, and the training, 160 00:06:06.540 --> 00:06:09.590 and just the overall orientation 161 00:06:09.590 --> 00:06:12.990 of data scientists ten years from now, 162 00:06:12.990 --> 00:06:15.110 would be dramatically different? 163 00:06:15.110 --> 00:06:16.850 Like, should we teach them different things, 164 00:06:16.850 --> 00:06:18.890 different scales? Because it used to be 165 00:06:18.890 --> 00:06:21.310 a lot is focused on creating the algorithms, 166 00:06:21.310 --> 00:06:22.920 trying different things, 167 00:06:22.920 --> 00:06:24.140 and I think you're making the point that 168 00:06:24.140 --> 00:06:27.790 that sort of plateauing, what does that mean 169 00:06:27.790 --> 00:06:31.023 in terms of the workforce of the future? 170 00:06:31.940 --> 00:06:33.660 Yeah, I think that's great. 171 00:06:33.660 --> 00:06:35.120 I'm going to say some controversial things here 172 00:06:35.120 --> 00:06:37.590 and I hope not to offend anybody. 173 00:06:37.590 --> 00:06:41.010 That's why I asked so I hope that you will! 174 00:06:41.010 --> 00:06:44.370 So if you look just five or ten years ago 175 00:06:44.370 --> 00:06:46.340 in order to deliver the kind of value 176 00:06:46.340 --> 00:06:48.530 that tech companies wanted to deliver 177 00:06:48.530 --> 00:06:52.180 you needed a fleet of PhDs, right? 178 00:06:52.180 --> 00:06:54.010 The technical ability to build those algorithms 179 00:06:54.010 --> 00:06:55.443 was extremely important. 180 00:06:56.300 --> 00:06:57.550 I think the point of inflection there 181 00:06:57.550 --> 00:07:01.200 was probably TensorFlow, 2013 ish, 182 00:07:01.200 --> 00:07:02.930 where it wasn't commoditized. 183 00:07:02.930 --> 00:07:04.220 You still needed to think very hard 184 00:07:04.220 --> 00:07:06.760 about the algorithm but the actual getting the algorithm 185 00:07:06.760 --> 00:07:08.010 out the door became a lot easier. 186 00:07:08.010 --> 00:07:10.720 Now there's plenty of frameworks for that. 187 00:07:10.720 --> 00:07:12.490 I wonder -- this is a real wonder -- 188 00:07:12.490 --> 00:07:13.860 I wonder the degree to which 189 00:07:13.860 --> 00:07:15.860 we're going to need specialized machine learning, 190 00:07:15.860 --> 00:07:19.000 AI data science training going forward. 191 00:07:19.000 --> 00:07:23.220 I think CS undergrads or engineering undergrads in general 192 00:07:23.220 --> 00:07:26.810 are all going to graduate with two or three AI classes 193 00:07:26.810 --> 00:07:29.140 and those two or three AI classes 194 00:07:29.140 --> 00:07:31.330 with the right infrastructure in the company, 195 00:07:31.330 --> 00:07:32.790 the right way to gather features, 196 00:07:32.790 --> 00:07:34.950 the right way to specify your label data. 197 00:07:34.950 --> 00:07:38.460 If we have that ML platform in place, 198 00:07:38.460 --> 00:07:40.980 people with two or three strong classes 199 00:07:40.980 --> 00:07:44.030 are going to be able to deliver 70% 200 00:07:44.030 --> 00:07:45.750 of the models a company might need. 201 00:07:45.750 --> 00:07:46.810 Now that 30%, 202 00:07:46.810 --> 00:07:49.380 I think you're still going to need experts for awhile. 203 00:07:49.380 --> 00:07:50.890 I do, I just don't think you can need it 204 00:07:50.890 --> 00:07:51.740 like you used to need it, 205 00:07:51.740 --> 00:07:54.723 where almost every expert had to have a PhD. 206 00:07:55.660 --> 00:07:57.730 Yeah, I actually resonate with that Sam 207 00:07:57.730 --> 00:08:01.090 in an interesting way, it sort of corroborates 208 00:08:01.090 --> 00:08:04.240 what we've been saying about what it takes 209 00:08:04.240 --> 00:08:06.170 to actually get impact at scale 210 00:08:06.170 --> 00:08:09.830 which is like, the technical stuff gets you only so far 211 00:08:09.830 --> 00:08:12.830 but ultimately you have to change the way it's consumed, 212 00:08:12.830 --> 00:08:15.040 and you have to change the way people work, 213 00:08:15.040 --> 00:08:18.703 and the different modes of interaction between humans and 214 00:08:19.710 --> 00:08:21.440 AI, I guess that's a lot of the humanities, 215 00:08:21.440 --> 00:08:23.410 and the philosophy, and the political science, 216 00:08:23.410 --> 00:08:25.710 and the how, sort of, the human works 217 00:08:25.710 --> 00:08:28.600 more so than what the algo does. 218 00:08:28.600 --> 00:08:29.960 Well that's a good redirection too, 219 00:08:29.960 --> 00:08:31.040 because if we're not careful, 220 00:08:31.040 --> 00:08:32.530 then that conversation slips us 221 00:08:32.530 --> 00:08:36.430 into the curriculum being DevOps more. 222 00:08:36.430 --> 00:08:38.720 And so what Shervin is pointing out is that 223 00:08:38.720 --> 00:08:40.600 maybe that's a component, of course too, 224 00:08:40.600 --> 00:08:42.210 but there's process change 225 00:08:42.210 --> 00:08:45.693 and more, let's say business-oriented initiatives. 226 00:08:46.710 --> 00:08:47.820 So what other kinds of things 227 00:08:47.820 --> 00:08:48.920 are you trying to teach people? 228 00:08:48.920 --> 00:08:49.940 Or what other kinds of things 229 00:08:49.940 --> 00:08:51.640 do you think executives should know? 230 00:08:51.640 --> 00:08:53.330 I mean, we can't have the ... 231 00:08:53.330 --> 00:08:55.120 everybody can't have to know everything. 232 00:08:55.120 --> 00:08:56.890 Then it would be a bit overwhelming. 233 00:08:56.890 --> 00:08:58.250 I mean that perhaps that's ideal 234 00:08:58.250 --> 00:08:59.730 if everyone knows everything, 235 00:08:59.730 --> 00:09:02.490 but what exactly do different levels 236 00:09:02.490 --> 00:09:04.003 of managers need to know? 237 00:09:05.170 --> 00:09:06.930 I think the top decision maker 238 00:09:06.930 --> 00:09:11.100 needs to understand dangers of a model going awry 239 00:09:11.100 --> 00:09:14.450 and they need to understand the overall process 240 00:09:14.450 --> 00:09:16.690 that you really need label data. 241 00:09:16.690 --> 00:09:17.830 Like there's no magic here. 242 00:09:17.830 --> 00:09:20.475 That they have to understand there's not magic, right? 243 00:09:20.475 --> 00:09:22.050 So they have to understand 244 00:09:22.050 --> 00:09:24.320 that label data is expensive, 245 00:09:24.320 --> 00:09:26.300 that getting the labels right 246 00:09:26.300 --> 00:09:28.030 and sampling the distribution 247 00:09:28.030 --> 00:09:29.460 of the world that you want correctly 248 00:09:29.460 --> 00:09:31.390 is extremely important. 249 00:09:31.390 --> 00:09:32.960 I believe they also have to understand 250 00:09:32.960 --> 00:09:33.990 the life cycle in general, 251 00:09:33.990 --> 00:09:36.250 which is different than, you know, two week sprints. 252 00:09:36.250 --> 00:09:38.980 We're going to close these JIRA tickets, right? 253 00:09:38.980 --> 00:09:40.817 That data gathering is extremely important 254 00:09:40.817 --> 00:09:42.600 and that could take a quarter or two. 255 00:09:42.600 --> 00:09:44.920 And that the first model you ship 256 00:09:44.920 --> 00:09:47.200 probably isn't going to be very good, 257 00:09:47.200 --> 00:09:49.910 you know, because it was from a small label dataset 258 00:09:49.910 --> 00:09:51.770 and now you're gathering data in the wild. 259 00:09:51.770 --> 00:09:53.590 So there's a life cycle piece 260 00:09:53.590 --> 00:09:55.090 that they need to understand. 261 00:09:55.090 --> 00:09:57.370 And they need to understand that, 262 00:09:57.370 --> 00:09:59.410 unfortunately in a lot of ways, 263 00:09:59.410 --> 00:10:02.380 maybe not for car driving but for recommendations, 264 00:10:02.380 --> 00:10:04.840 the first couple that you ship get iteratively better. 265 00:10:04.840 --> 00:10:06.770 So I think that's extremely important for the top. 266 00:10:06.770 --> 00:10:08.610 I think for a couple of levels down, 267 00:10:08.610 --> 00:10:09.500 they need to understand 268 00:10:09.500 --> 00:10:11.160 like the precision-recall trade-off, 269 00:10:11.160 --> 00:10:13.920 the kinds of errors your model can make. 270 00:10:13.920 --> 00:10:16.070 Your model can either be making false negative errors 271 00:10:16.070 --> 00:10:17.330 or false positive errors 272 00:10:17.330 --> 00:10:20.160 and I think it's extremely important as a product person 273 00:10:20.160 --> 00:10:22.260 that you own that choice. 274 00:10:22.260 --> 00:10:24.110 So if we're doing document search, 275 00:10:24.110 --> 00:10:26.230 I think you care a lot more about false positives. 276 00:10:26.230 --> 00:10:27.500 You care a lot more about precision. 277 00:10:27.500 --> 00:10:31.010 You want the things that come to the top to be relevant. 278 00:10:31.010 --> 00:10:33.180 And for most search problems, 279 00:10:33.180 --> 00:10:34.880 you don't have to get all the relevant things. 280 00:10:34.880 --> 00:10:36.570 You just have to get enough of the relevant things. 281 00:10:36.570 --> 00:10:38.700 So if some relevant things are called non-relevant 282 00:10:38.700 --> 00:10:40.570 you're okay with that, right? 283 00:10:40.570 --> 00:10:42.030 But for other problems, 284 00:10:42.030 --> 00:10:43.930 you need to get everything. 285 00:10:43.930 --> 00:10:45.130 Document search, that's fine. 286 00:10:45.130 --> 00:10:47.320 But yeah, Lyft as well, like put it in the context 287 00:10:47.320 --> 00:10:50.080 of one of these companies where you've had a precision 288 00:10:50.080 --> 00:10:53.220 and recall trade-off, false positive, false negative. 289 00:10:53.220 --> 00:10:54.930 I think luckily at Lyft 290 00:10:54.930 --> 00:10:56.890 we have nice human escape hatches 291 00:10:56.890 --> 00:10:58.140 which I think is extremely important. 292 00:10:58.140 --> 00:10:59.780 Like all these recommendations 293 00:10:59.780 --> 00:11:01.580 ideally should have a human escape hatch. 294 00:11:01.580 --> 00:11:03.910 So if I recommend a destination for you 295 00:11:03.910 --> 00:11:05.410 and that destination is wrong, 296 00:11:06.630 --> 00:11:09.073 no harm, no foul, you just type the destination in. 297 00:11:10.060 --> 00:11:12.020 So I think for Lyft as a product 298 00:11:12.020 --> 00:11:13.260 I think we're pretty lucky 299 00:11:13.260 --> 00:11:15.330 because most of our recommendations, 300 00:11:15.330 --> 00:11:16.580 which are trying to lower friction 301 00:11:16.580 --> 00:11:17.940 to get you to take a ride, 302 00:11:17.940 --> 00:11:20.690 it's okay if we don't get them exactly right. 303 00:11:20.690 --> 00:11:22.000 There's no real danger there. 304 00:11:22.000 --> 00:11:24.070 Self-driving cars, that one's tough 305 00:11:24.070 --> 00:11:25.530 because you want to get them both. 306 00:11:25.530 --> 00:11:27.440 You want to know that's a pedestrian and you 307 00:11:27.440 --> 00:11:30.040 also want to make sure you don't miss any pedestrians. 308 00:11:30.040 --> 00:11:31.960 And the idea of putting a human in the loop 309 00:11:31.960 --> 00:11:33.603 there is much more problematic than just saying, 310 00:11:33.603 --> 00:11:35.420 all right, here's some destinations, 311 00:11:35.420 --> 00:11:36.520 which one do you like? 312 00:11:37.960 --> 00:11:40.840 Craig, earlier you talked about, 313 00:11:40.840 --> 00:11:43.160 you know, how AI in real life 314 00:11:43.160 --> 00:11:45.850 is like a bunch of cognitive science experiments 315 00:11:45.850 --> 00:11:47.300 because it's ultimately about- 316 00:11:47.300 --> 00:11:48.380 For me at least. 317 00:11:48.380 --> 00:11:52.008 Yeah, and it brought up the idea of 318 00:11:52.008 --> 00:11:56.150 unconscious bias. And so like we as humans have become 319 00:11:56.150 --> 00:11:59.766 a lot more aware about our unconscious biases 320 00:11:59.766 --> 00:12:01.550 across everything, right? 321 00:12:01.550 --> 00:12:03.380 Because they've been ingrained 322 00:12:03.380 --> 00:12:06.910 through generations and stereotypes, et cetera. 323 00:12:06.910 --> 00:12:08.710 And just our past experience, right? 324 00:12:08.710 --> 00:12:11.670 Like a biased world creates a biased experience 325 00:12:11.670 --> 00:12:14.290 even if you have the best possible intentions. 326 00:12:14.290 --> 00:12:15.610 Exactly, right? 327 00:12:15.610 --> 00:12:19.450 And so I guess my question is, 328 00:12:20.340 --> 00:12:24.763 clearly there is unintended bias in AI, has to be. 329 00:12:26.650 --> 00:12:29.100 What do you think we need to think about now 330 00:12:29.100 --> 00:12:31.920 so that 10, 20 years from now 331 00:12:32.870 --> 00:12:36.100 that bias hasn't become so ingrained 332 00:12:36.100 --> 00:12:39.130 in how AI works that it would be so hard 333 00:12:39.130 --> 00:12:40.643 to then course correct? 334 00:12:42.230 --> 00:12:43.063 (laughs) It already has. 335 00:12:43.063 --> 00:12:45.200 So the question is how do we course correct? 336 00:12:45.200 --> 00:12:48.530 So let me start by saying I was on a panel 337 00:12:48.530 --> 00:12:51.880 for Northeastern about this movie "Coded Bias." 338 00:12:51.880 --> 00:12:53.600 So if you haven't seen the movie "Coded Bias" 339 00:12:53.600 --> 00:12:55.100 you should absolutely see it. 340 00:12:55.100 --> 00:12:58.770 It's about this MIT media lab undergraduate black woman 341 00:12:58.770 --> 00:13:01.240 who tried to do a project 342 00:13:01.240 --> 00:13:03.720 that didn't work because facial recognition 343 00:13:03.720 --> 00:13:06.540 just simply didn't work for black females. 344 00:13:06.540 --> 00:13:10.070 It's just an absolutely fascinating social study. 345 00:13:10.070 --> 00:13:12.860 The dataset that was used 346 00:13:12.860 --> 00:13:16.130 to train the machine learning, 347 00:13:16.130 --> 00:13:18.830 so the facial recognition algorithm, 348 00:13:18.830 --> 00:13:22.440 was gathered by the researchers at the time, 349 00:13:22.440 --> 00:13:23.740 and the researchers at the time 350 00:13:23.740 --> 00:13:25.410 were a bunch of white males. 351 00:13:25.410 --> 00:13:26.950 And this is a known issue, right? 352 00:13:26.950 --> 00:13:28.870 There's a skew in the way the dataset is gathered. 353 00:13:28.870 --> 00:13:31.730 Look, there's a similar skew in all psychological studies. 354 00:13:31.730 --> 00:13:33.500 Psychological studies don't apply to me. 355 00:13:33.500 --> 00:13:36.410 I'm 56, psychological studies apply to college students 356 00:13:36.410 --> 00:13:39.450 because that's the readily available subjects, right? 357 00:13:39.450 --> 00:13:41.580 So these were the readily available people 358 00:13:41.580 --> 00:13:43.240 because of the biased world 359 00:13:43.240 --> 00:13:45.090 and so that's how the dataset came about. 360 00:13:45.090 --> 00:13:46.750 So even if no ill intention 361 00:13:46.750 --> 00:13:48.170 the world was skewed, 362 00:13:48.170 --> 00:13:49.060 the world was biased, 363 00:13:49.060 --> 00:13:50.070 data was biased, 364 00:13:50.070 --> 00:13:53.000 it didn't work for a great number of people. 365 00:13:53.000 --> 00:13:55.530 And not a lot of females were part of the training set 366 00:13:55.530 --> 00:13:58.760 and then the darker your skin, the worse it got. 367 00:13:58.760 --> 00:14:00.220 And there's all kinds of technical reasons 368 00:14:00.220 --> 00:14:02.890 why darker skin has less contrast, blah, blah, blah, 369 00:14:02.890 --> 00:14:06.410 but that's not the issue. 370 00:14:06.410 --> 00:14:10.660 The issue is, should we have gathered the data that way? 371 00:14:10.660 --> 00:14:12.860 What is the goal of the dataset? 372 00:14:12.860 --> 00:14:14.150 Who are our customers? 373 00:14:14.150 --> 00:14:15.530 Who do we want to serve? 374 00:14:15.530 --> 00:14:17.200 And let's sample the data 375 00:14:17.200 --> 00:14:19.870 in such a way that it's serving our customers. 376 00:14:19.870 --> 00:14:21.480 We talked about this earlier about the undergrads, 377 00:14:21.480 --> 00:14:22.860 I think that's really important. 378 00:14:22.860 --> 00:14:24.570 One way to get out of that 379 00:14:24.570 --> 00:14:26.330 is diversity in the workplace. 380 00:14:26.330 --> 00:14:27.740 I believe this so strongly. 381 00:14:27.740 --> 00:14:31.830 And you ask everybody, all of these diverse groups, 382 00:14:31.830 --> 00:14:33.440 to test the system 383 00:14:33.440 --> 00:14:36.120 and to see if the system works for them. 384 00:14:36.120 --> 00:14:38.240 When we did image search at Dropbox 385 00:14:38.240 --> 00:14:40.350 we asked all of the employee research groups, 386 00:14:40.350 --> 00:14:41.340 please search for things 387 00:14:41.340 --> 00:14:43.890 that in the past have been problematic for you 388 00:14:43.890 --> 00:14:45.380 and see if we got them right. 389 00:14:45.380 --> 00:14:47.440 And if we found some that were wrong, 390 00:14:47.440 --> 00:14:50.020 we would go back and regather data 391 00:14:50.020 --> 00:14:51.970 to mitigate against those issues. 392 00:14:51.970 --> 00:14:54.160 So look, your system is going to be biased 393 00:14:54.160 --> 00:14:56.190 by the data that's gathered, fact, just a fact. 394 00:14:56.190 --> 00:14:57.740 It's going to be biased by the data that's gathered. 395 00:14:57.740 --> 00:14:59.770 You want to do your best to gather it correctly. 396 00:14:59.770 --> 00:15:01.420 You're probably not going to gather it correctly 397 00:15:01.420 --> 00:15:03.330 because you have your own unconscious bias 398 00:15:03.330 --> 00:15:04.163 as you point out. 399 00:15:04.163 --> 00:15:05.440 So you have to ask all the people 400 00:15:05.440 --> 00:15:07.090 who are going to be your customers 401 00:15:07.090 --> 00:15:09.060 to try it, to bang on it, 402 00:15:09.060 --> 00:15:11.090 to make sure it's doing the right thing. 403 00:15:11.090 --> 00:15:12.940 And when it's not, go back and gather 404 00:15:12.940 --> 00:15:14.320 the data necessary to fix it. 405 00:15:14.320 --> 00:15:18.100 So I think the short answer is diversity in the workplace. 406 00:15:18.100 --> 00:15:19.310 Craig, thanks for taking the time 407 00:15:19.310 --> 00:15:20.220 to talk with us today. 408 00:15:20.220 --> 00:15:22.120 Lots of interesting things. 409 00:15:22.120 --> 00:15:24.360 My pleasure, these are really fun conversations. 410 00:15:24.360 --> 00:15:27.810 I'm pretty nerdy about this so I enjoyed it very much. 411 00:15:27.810 --> 00:15:29.390 Your enthusiasm shows. 412 00:15:29.390 --> 00:15:30.530 Really insightful stuff, 413 00:15:30.530 --> 00:15:31.893 thank you. Thank you guys. 414 00:15:35.670 --> 00:15:36.930 Well Shervin, Craig says 415 00:15:36.930 --> 00:15:38.610 he won the lottery in his career 416 00:15:38.610 --> 00:15:39.670 but I think we won the lottery 417 00:15:39.670 --> 00:15:40.680 in getting him as a guest 418 00:15:40.680 --> 00:15:42.860 for our first episode of season two. 419 00:15:42.860 --> 00:15:43.703 Let's recap. 420 00:15:44.590 --> 00:15:47.050 I mean, he made a lot of good points. 421 00:15:47.050 --> 00:15:51.160 Clearly the commoditization of algorithms over time, 422 00:15:51.160 --> 00:15:54.750 and how it's more and more going to be about 423 00:15:54.750 --> 00:15:58.040 tying it with strategy going back to key business metrics, 424 00:15:58.040 --> 00:16:01.330 making change happen, the usage. 425 00:16:01.330 --> 00:16:03.040 I really liked his point on 426 00:16:03.040 --> 00:16:07.630 what it takes to get the bias out of the system 427 00:16:07.630 --> 00:16:10.890 and how bias is already in the system. 428 00:16:10.890 --> 00:16:12.450 The commoditization is particularly important. 429 00:16:12.450 --> 00:16:13.500 I think it resonates with us 430 00:16:13.500 --> 00:16:14.720 because we were talking about this 431 00:16:14.720 --> 00:16:15.910 from a business perspective. 432 00:16:15.910 --> 00:16:17.430 And so what he's saying is that, 433 00:16:17.430 --> 00:16:18.780 a lot of this is going to become 434 00:16:18.780 --> 00:16:21.080 increasingly a business problem. 435 00:16:21.080 --> 00:16:23.570 When it's a business problem it's not a technical problem. 436 00:16:23.570 --> 00:16:25.590 I don't want to discount the technical aspects of it 437 00:16:25.590 --> 00:16:26.580 and certainly, you know, 438 00:16:26.580 --> 00:16:29.290 he brings plenty of technical chops to the table, 439 00:16:29.290 --> 00:16:31.530 but he really reinforced the 440 00:16:31.530 --> 00:16:33.900 this is a business problem now aspect. 441 00:16:33.900 --> 00:16:35.590 Yeah, I mean in five minutes 442 00:16:35.590 --> 00:16:38.620 he basically provided such a cogent argument 443 00:16:38.620 --> 00:16:41.700 for our last two reports, right? 444 00:16:41.700 --> 00:16:44.500 The 2019, 2020, it's about strategy 445 00:16:44.500 --> 00:16:47.760 and process change and process redesign and reengineering 446 00:16:47.760 --> 00:16:52.200 and it's about human and AI interaction and adoption. 447 00:16:52.200 --> 00:16:53.910 And what's also a business problem too 448 00:16:53.910 --> 00:16:55.430 is the managerial choice. 449 00:16:55.430 --> 00:16:57.120 I mean, he came back to that as well. 450 00:16:57.120 --> 00:16:59.230 He was talking about some of these things 451 00:16:59.230 --> 00:17:01.290 are not clear-cut decisions. 452 00:17:01.290 --> 00:17:04.350 There's a choice between which way you make a mistake. 453 00:17:04.350 --> 00:17:07.290 That's a management problem not a technical problem. 454 00:17:07.290 --> 00:17:10.520 And it also requires managers 455 00:17:10.520 --> 00:17:11.730 to know what they're talking about, 456 00:17:11.730 --> 00:17:14.080 which means they need to really, really understand 457 00:17:14.080 --> 00:17:17.530 what AI is saying and what it could be saying 458 00:17:17.530 --> 00:17:19.840 and what's its limitations, 459 00:17:19.840 --> 00:17:21.750 and what's the art of the possible. 460 00:17:21.750 --> 00:17:23.690 And I also really liked the point that 461 00:17:23.690 --> 00:17:26.690 as you get closer to the developers 462 00:17:26.690 --> 00:17:28.280 and the builders of AI, 463 00:17:28.280 --> 00:17:30.170 you have to really, really understand the math 464 00:17:30.170 --> 00:17:33.120 and the code because otherwise you can't guide them. 465 00:17:33.120 --> 00:17:33.980 Although don't you worry 466 00:17:33.980 --> 00:17:35.280 that we're just running into this thing 467 00:17:35.280 --> 00:17:37.040 where everyone has to understand everything? 468 00:17:37.040 --> 00:17:38.790 I feel like that's a tough sell. 469 00:17:38.790 --> 00:17:40.200 Like if the managers have to understand 470 00:17:40.200 --> 00:17:41.460 the business and how to make money 471 00:17:41.460 --> 00:17:43.020 and they have to understand the code. 472 00:17:43.020 --> 00:17:46.060 I mean, having everyone understand everything is obviously- 473 00:17:46.060 --> 00:17:47.210 Well I guess the question is 474 00:17:47.210 --> 00:17:49.810 how much do you have to understand everything? 475 00:17:49.810 --> 00:17:53.050 I mean, a good business executive 476 00:17:53.050 --> 00:17:54.540 already understands everything 477 00:17:54.540 --> 00:17:57.000 to the level that he or she should, 478 00:17:57.000 --> 00:17:59.760 to the point of asking the right questions. 479 00:17:59.760 --> 00:18:01.400 I think you're right. 480 00:18:01.400 --> 00:18:04.410 But I think, isn't this like what Einstein said that 481 00:18:05.310 --> 00:18:07.190 you don't really understand something 482 00:18:07.190 --> 00:18:10.330 unless you can describe it to a five year old. 483 00:18:10.330 --> 00:18:13.340 You know, you can describe gravity to a five year old 484 00:18:13.340 --> 00:18:16.380 and to a 20 year old and to a grad student 485 00:18:16.380 --> 00:18:19.290 in different ways and they will all understand it. 486 00:18:19.290 --> 00:18:21.270 The question is, at least you understand it 487 00:18:21.270 --> 00:18:22.830 rather than just say, I have no idea 488 00:18:22.830 --> 00:18:24.390 there is such a thing as gravity. 489 00:18:24.390 --> 00:18:26.440 So basically teaching and academics 490 00:18:26.440 --> 00:18:27.273 are really important. 491 00:18:27.273 --> 00:18:30.020 Is that what Shervin has just gone on the record as saying? 492 00:18:30.020 --> 00:18:33.070 I think the idea that managers 493 00:18:33.070 --> 00:18:36.193 and senior executives need to understand AI, 494 00:18:37.470 --> 00:18:39.320 itself is not a slam dunk 495 00:18:39.320 --> 00:18:40.900 because you're raising the right question. 496 00:18:40.900 --> 00:18:42.640 What is the right level of understanding? 497 00:18:42.640 --> 00:18:44.810 And so what is the right level of synthesis 498 00:18:44.810 --> 00:18:47.650 and articulation that allows you 499 00:18:47.650 --> 00:18:48.870 to make the right decisions 500 00:18:48.870 --> 00:18:51.100 without having to know everything, 501 00:18:51.100 --> 00:18:54.550 but isn't that what a successful business executive 502 00:18:54.550 --> 00:18:57.840 does with every business problem? 503 00:18:57.840 --> 00:18:59.590 And I think that's what we're saying, 504 00:18:59.590 --> 00:19:03.610 that with AI, you need to know enough to be able to probe 505 00:19:03.610 --> 00:19:06.400 but suffice it to say, it's not a black box. 506 00:19:06.400 --> 00:19:09.050 Like a lot of the technology implementations 507 00:19:09.050 --> 00:19:11.170 have been a black box in the past. 508 00:19:11.170 --> 00:19:12.110 And that helps get back 509 00:19:12.110 --> 00:19:13.830 to the whole learning more, 510 00:19:13.830 --> 00:19:15.860 and where to draw the line, 511 00:19:15.860 --> 00:19:18.140 and help to understand that balance. 512 00:19:18.140 --> 00:19:20.150 I guess after the discussion of gravity 513 00:19:20.150 --> 00:19:21.600 each one of those people would understand 514 00:19:21.600 --> 00:19:23.610 more about gravity than they did before. 515 00:19:23.610 --> 00:19:26.320 And so it's a matter of moving from current state 516 00:19:26.320 --> 00:19:27.709 to next state. 517 00:19:27.709 --> 00:19:28.820 Yeah. 518 00:19:28.820 --> 00:19:30.140 Craig made some important points 519 00:19:30.140 --> 00:19:31.820 about diversity in the workplace. 520 00:19:31.820 --> 00:19:33.280 If the team gathering data 521 00:19:33.280 --> 00:19:35.300 isn't hyper aware of the inherent biases 522 00:19:35.300 --> 00:19:36.720 in their data sets, 523 00:19:36.720 --> 00:19:39.313 algorithms are destined to produce a biased result. 524 00:19:40.150 --> 00:19:42.260 He refers to the movie "Coded Bias" 525 00:19:42.260 --> 00:19:45.113 and the MIT media lab researcher, Joy Buolamwini. 526 00:19:45.970 --> 00:19:49.200 Joy is the founder of the Algorithmic Justice League. 527 00:19:49.200 --> 00:19:50.850 We'll provide some links in the show notes 528 00:19:50.850 --> 00:19:53.720 where you can read more about Joy and her research. 529 00:19:53.720 --> 00:19:55.070 Thanks for joining us today. 530 00:19:55.070 --> 00:19:56.650 We're looking forward to the next episode 531 00:19:56.650 --> 00:19:58.200 when we'll talk with Will Grannis 532 00:19:58.200 --> 00:19:59.640 who has a unique challenge of building 533 00:19:59.640 --> 00:20:01.763 the CTO function at Google Cloud. 534 00:20:02.720 --> 00:20:03.673 Until next time. 535 00:20:05.990 --> 00:20:08.730 Thanks for listening to "Me, Myself, and AI". 536 00:20:08.730 --> 00:20:10.340 If you're enjoying this show 537 00:20:10.340 --> 00:20:12.450 take a minute to write us a review. 538 00:20:12.450 --> 00:20:14.080 If you send us a screenshot 539 00:20:14.080 --> 00:20:17.370 we'll send you a collection of MIT SMR's best articles 540 00:20:17.370 --> 00:20:20.910 on artificial intelligence free for a limited time. 541 00:20:20.910 --> 00:20:22.380 Send your review screenshot 542 00:20:22.380 --> 00:20:25.776 to smrfeedback@mit.edu. 543 00:20:25.776 --> 00:20:29.609 (soft upbeat music continues)