1 00:00:02,000 --> 00:00:07,400 Speaker 1: I'm Malcolm Globwell and you're listening to Smart Talks with IBM. 2 00:00:07,720 --> 00:00:11,079 Speaker 1: When doctor Laura Jahi began treating epilepsy patients in the 3 00:00:11,119 --> 00:00:16,319 Speaker 1: early two thousands, she noticed something unsettling. Different surgeons could 4 00:00:16,320 --> 00:00:21,080 Speaker 1: look at the exact same case and recommend completely different treatments. 5 00:00:21,440 --> 00:00:24,239 Speaker 1: One surgeon might remove one part of the brain, another 6 00:00:24,440 --> 00:00:27,800 Speaker 1: a different part, and a third might not operate at all. 7 00:00:28,560 --> 00:00:31,120 Speaker 1: Doctor j Hi believed there had to be a better way, 8 00:00:31,520 --> 00:00:35,600 Speaker 1: one grounded in data. That conviction set her on a 9 00:00:35,640 --> 00:00:38,840 Speaker 1: path that would lead her to become Chief Research Information 10 00:00:38,920 --> 00:00:42,800 Speaker 1: Officer at Cleveland Clinic and the executive program lead for 11 00:00:42,880 --> 00:00:47,160 Speaker 1: the Discovery Accelerator. The Discovery Accelerator is a ten year 12 00:00:47,200 --> 00:00:51,720 Speaker 1: partnership between Cleveland Clinic and IBM where researchers are using 13 00:00:51,760 --> 00:00:56,400 Speaker 1: AI and quantum computing to make incredible discoveries in healthcare 14 00:00:56,760 --> 00:01:00,000 Speaker 1: and life sciences. I sat down with doctor j High 15 00:01:00,200 --> 00:01:04,240 Speaker 1: to explore what's happening now, what's possible with quantum computing, 16 00:01:04,800 --> 00:01:08,600 Speaker 1: and where this next era of biomedical discovery is headed. 17 00:01:10,280 --> 00:01:12,959 Speaker 1: Epilepsy was your kind of specialty within neurology? 18 00:01:13,120 --> 00:01:13,919 Speaker 2: Correct? Yes? 19 00:01:13,959 --> 00:01:14,840 Speaker 1: What led you to that? 20 00:01:15,600 --> 00:01:19,480 Speaker 3: I was always fascinated by the brain. You know, it's 21 00:01:19,560 --> 00:01:22,840 Speaker 3: the part of our body that leaves still to this day, 22 00:01:22,880 --> 00:01:27,199 Speaker 3: the most to be discovered. So I was always intrigued 23 00:01:27,280 --> 00:01:33,920 Speaker 3: by areas that leave more for discovery, and epilepsy was 24 00:01:33,959 --> 00:01:38,480 Speaker 3: my pragmatic side, wanting to choose a subspecialty where the 25 00:01:38,560 --> 00:01:43,480 Speaker 3: problem can be fixed. In epilepsy, there are many medications 26 00:01:43,520 --> 00:01:46,440 Speaker 3: that are very effective, and there's a brain surgery that 27 00:01:46,480 --> 00:01:49,400 Speaker 3: we can do to stop seizures when medicines don't work. 28 00:01:50,560 --> 00:01:51,760 Speaker 2: That attracted me. 29 00:01:51,920 --> 00:01:55,760 Speaker 3: You know, compared to other areas in neurology, like stroke, 30 00:01:55,840 --> 00:01:59,440 Speaker 3: for example, or dementia, where usually the damage is more. 31 00:02:01,600 --> 00:02:03,360 Speaker 2: I wanted to be able to tell my. 32 00:02:03,400 --> 00:02:06,560 Speaker 3: Patients that you have a big problem, but here's what 33 00:02:06,600 --> 00:02:10,079 Speaker 3: I can do to fix it, and epilepsy offered me that. 34 00:02:10,960 --> 00:02:14,720 Speaker 1: But what would there must have been interesting and intriguing 35 00:02:15,200 --> 00:02:18,919 Speaker 1: unsolved problems in neurology. 36 00:02:19,680 --> 00:02:22,240 Speaker 3: Oh my gosh, it's the whole brain, isn't it. Before 37 00:02:22,440 --> 00:02:26,600 Speaker 3: starting to deal with artificial intelligence and research. Right in 38 00:02:26,639 --> 00:02:30,720 Speaker 3: the neurology, I'm dealing with real intelligence, the human brain 39 00:02:30,880 --> 00:02:34,200 Speaker 3: and how it works and how we think and how 40 00:02:34,200 --> 00:02:40,600 Speaker 3: we make decisions, and how it can grow and evolve, 41 00:02:40,800 --> 00:02:46,720 Speaker 3: and so there is many untapped questions in neurology, and 42 00:02:47,360 --> 00:02:48,240 Speaker 3: that's part. 43 00:02:48,000 --> 00:02:50,480 Speaker 2: Of the fascination in it. 44 00:02:50,720 --> 00:02:56,160 Speaker 3: In epilepsy in particular, it's an electrical disease in the brain, 45 00:02:56,440 --> 00:03:01,639 Speaker 3: it's actually one of the conditions in neurology. That's a 46 00:03:01,800 --> 00:03:08,680 Speaker 3: perfect alignment of all of the scientific disciplines. It's biology 47 00:03:08,800 --> 00:03:14,920 Speaker 3: and physics and chemistry all working together to make us 48 00:03:15,440 --> 00:03:17,040 Speaker 3: who we truly are. 49 00:03:17,440 --> 00:03:19,440 Speaker 2: Right, So every. 50 00:03:19,200 --> 00:03:22,799 Speaker 3: Other discipline, if you think of computing, for example, it's 51 00:03:22,840 --> 00:03:28,080 Speaker 3: purely electricity. Or if we do drug development or drug discovery, 52 00:03:28,120 --> 00:03:33,760 Speaker 3: that's mostly chemistry. Experiments in the lab, that's mostly biology. 53 00:03:33,880 --> 00:03:38,320 Speaker 3: But the human brain is all of those put together. 54 00:03:39,960 --> 00:03:43,480 Speaker 3: The cells in our brain secret these chemical substances that 55 00:03:43,640 --> 00:03:47,360 Speaker 3: diffuse everywhere and hook up where they need to to 56 00:03:47,520 --> 00:03:52,680 Speaker 3: trigger certain circuits and then trigger some effects afterwards. So 57 00:03:53,400 --> 00:04:00,240 Speaker 3: it was just an elegant science that has big impacts. 58 00:04:00,600 --> 00:04:03,440 Speaker 1: We're shortly going to get there and talk about you've 59 00:04:03,480 --> 00:04:07,400 Speaker 1: taken on this kind of very technology focused roller Cleveland Clinic. 60 00:04:07,640 --> 00:04:11,600 Speaker 1: I'm curious about if we go back to when you 61 00:04:11,640 --> 00:04:14,600 Speaker 1: were just starting out at Cleveland Clinic. Yeah, how much 62 00:04:14,640 --> 00:04:18,279 Speaker 1: were you thinking about this sort of technology piece about 63 00:04:18,279 --> 00:04:21,839 Speaker 1: what technology could do for your specialty. About was that 64 00:04:21,920 --> 00:04:23,919 Speaker 1: on your mind or is this something you've come to 65 00:04:24,000 --> 00:04:24,640 Speaker 1: more recently. 66 00:04:26,320 --> 00:04:29,440 Speaker 3: Well, it's been a journey, right, And when I started 67 00:04:29,480 --> 00:04:33,600 Speaker 3: training as a clinician, my priority was just to learn 68 00:04:33,600 --> 00:04:37,080 Speaker 3: how to better care for my patients and how to 69 00:04:37,160 --> 00:04:42,200 Speaker 3: be a better physician. And then I realized that clinical 70 00:04:42,279 --> 00:04:47,960 Speaker 3: practice provides an immediate reward. I'm interacting with a human 71 00:04:48,000 --> 00:04:51,039 Speaker 3: being and helping them in the moment, so there is 72 00:04:51,120 --> 00:04:54,839 Speaker 3: that immediate reward that comes with that. But that wasn't enough. 73 00:04:55,520 --> 00:04:59,719 Speaker 3: I wanted something more. So then I learned biomedical research. 74 00:04:59,760 --> 00:05:04,479 Speaker 3: Pract this is and research offered me this path towards 75 00:05:04,480 --> 00:05:08,360 Speaker 3: a future. You know, the reward there is more long term. 76 00:05:09,040 --> 00:05:13,640 Speaker 3: I'm studying discovering things that could help many people in 77 00:05:13,680 --> 00:05:16,680 Speaker 3: the future, even though I will never get to see 78 00:05:16,720 --> 00:05:20,080 Speaker 3: them or meet them, or you know, have that immediate satisfaction. 79 00:05:20,240 --> 00:05:23,760 Speaker 3: So that shifted me from being a pure clinician to 80 00:05:23,839 --> 00:05:28,719 Speaker 3: being a clinician scientist. And as that journey progressed, it 81 00:05:28,839 --> 00:05:32,400 Speaker 3: became very clear, as medicine evolved over the past twenty years, 82 00:05:32,400 --> 00:05:36,920 Speaker 3: that we cannot do any good biomedical research without understanding 83 00:05:37,520 --> 00:05:42,040 Speaker 3: data and technology. You know, the balance is shifting from 84 00:05:42,320 --> 00:05:45,719 Speaker 3: most of the research is happening we call it, you know, 85 00:05:45,720 --> 00:05:50,159 Speaker 3: on the wet bench with actual experiments, physical experiments, to 86 00:05:50,800 --> 00:05:55,320 Speaker 3: a place where most of the work is happening through 87 00:05:55,640 --> 00:06:00,400 Speaker 3: compute and simulations and data. So I became much more 88 00:06:00,400 --> 00:06:05,320 Speaker 3: involved for my personal research in building big data models 89 00:06:05,400 --> 00:06:09,600 Speaker 3: and learning about AI and you know, learning about technology 90 00:06:09,680 --> 00:06:15,240 Speaker 3: in general. And then as that journey progressed, I was 91 00:06:15,360 --> 00:06:18,440 Speaker 3: fortunate enough to be an enroll for Cleveland Clinic, where 92 00:06:18,520 --> 00:06:22,080 Speaker 3: my job is to bring that technology and bridge it 93 00:06:22,200 --> 00:06:27,120 Speaker 3: to research for all researchers across our health care system. 94 00:06:28,080 --> 00:06:30,679 Speaker 1: When you were talking about how in your own research 95 00:06:30,720 --> 00:06:33,159 Speaker 1: you were moving in that direction, what was your own 96 00:06:33,160 --> 00:06:35,000 Speaker 1: research focused on. What were you looking at? 97 00:06:36,680 --> 00:06:43,440 Speaker 3: I was looking at brain surgery for epilepsy. It's an 98 00:06:43,480 --> 00:06:48,000 Speaker 3: intervention that's been around for decades actually, but when I 99 00:06:48,120 --> 00:06:51,960 Speaker 3: started practice, I was shocked by, you know, the practice 100 00:06:51,960 --> 00:06:56,280 Speaker 3: that we had were making decisions around surgery, like, you know, 101 00:06:56,360 --> 00:07:00,719 Speaker 3: what patients should get it versus not, how likely is 102 00:07:00,760 --> 00:07:03,359 Speaker 3: it to work, what part of the brain should we remove. 103 00:07:03,920 --> 00:07:07,640 Speaker 3: All of those decisions were at the time and the 104 00:07:07,760 --> 00:07:12,560 Speaker 3: early two thousands driven by clinical opinions, Right, you know, 105 00:07:12,640 --> 00:07:14,920 Speaker 3: you have an experienced surgeon, they decide to do this. 106 00:07:15,080 --> 00:07:18,600 Speaker 3: Somebody else might decide to do something completely different, and 107 00:07:19,440 --> 00:07:21,520 Speaker 3: I didn't feel that that was the right. 108 00:07:21,400 --> 00:07:22,680 Speaker 2: Way to practice medicine. 109 00:07:22,680 --> 00:07:25,280 Speaker 3: You know, that we needed to be more evidence based 110 00:07:25,320 --> 00:07:28,960 Speaker 3: and data driven. So I went in the business in 111 00:07:29,040 --> 00:07:35,520 Speaker 3: research of building models, predictive models that can ingest data 112 00:07:35,760 --> 00:07:38,600 Speaker 3: from all of the tests that we would do about 113 00:07:38,880 --> 00:07:40,520 Speaker 3: on these patients before to. 114 00:07:40,480 --> 00:07:41,600 Speaker 2: Figure out surgery. 115 00:07:42,080 --> 00:07:48,200 Speaker 3: So I learned how to analyze all types of data, 116 00:07:48,240 --> 00:07:53,600 Speaker 3: from genetic data and individuals to pictures to electrical recordings, 117 00:07:54,040 --> 00:07:58,680 Speaker 3: and then combine those into these prediction. 118 00:07:58,440 --> 00:08:03,720 Speaker 1: Models you're looking at. You're taking large numbers of surgeries 119 00:08:03,760 --> 00:08:06,560 Speaker 1: for epilepsy, and you're seeing what kind of connection there 120 00:08:06,640 --> 00:08:10,360 Speaker 1: is between the success of the surgical intervention and the 121 00:08:10,440 --> 00:08:11,960 Speaker 1: underlying presentation of. 122 00:08:11,960 --> 00:08:13,440 Speaker 2: The patient exactly. 123 00:08:13,560 --> 00:08:17,280 Speaker 3: So I would be telling the patient, what is your 124 00:08:17,440 --> 00:08:22,200 Speaker 3: specific chance of becoming seizure free with surgery instead of 125 00:08:22,240 --> 00:08:25,960 Speaker 3: giving them statistics about you know, like you know, in general, 126 00:08:26,240 --> 00:08:29,000 Speaker 3: how well is that going to be effective? So that 127 00:08:29,160 --> 00:08:32,160 Speaker 3: piece of individualizing medicine. 128 00:08:32,280 --> 00:08:35,559 Speaker 1: So Cleveland Clinic decides to create a post called Chief 129 00:08:35,600 --> 00:08:36,560 Speaker 1: Information Officers. 130 00:08:36,640 --> 00:08:40,520 Speaker 3: If research information we have research, yes, always chief information 131 00:08:40,640 --> 00:08:41,800 Speaker 3: officer runs it. 132 00:08:42,600 --> 00:08:44,440 Speaker 1: Oh, yes, chief research information. 133 00:08:44,679 --> 00:08:46,200 Speaker 2: Yeah, so it for research. 134 00:08:46,440 --> 00:08:51,839 Speaker 1: This is parenthetically a huge job. Yes, so you apply 135 00:08:51,960 --> 00:08:54,320 Speaker 1: for this, do you know that you're going to be 136 00:08:54,400 --> 00:08:57,319 Speaker 1: thinking and talking and dealing with quantum. 137 00:08:57,640 --> 00:09:03,520 Speaker 3: I started in January twenty twenty, and like every leader 138 00:09:03,559 --> 00:09:06,439 Speaker 3: who's put in a position, remember, everybody tells you should 139 00:09:06,480 --> 00:09:10,680 Speaker 3: read that the first ninety days great how to plan. 140 00:09:11,280 --> 00:09:14,800 Speaker 3: So I was reading that and doing my listening tours 141 00:09:15,120 --> 00:09:19,960 Speaker 3: to understand, and then COVID hits and I got a 142 00:09:20,000 --> 00:09:25,360 Speaker 3: call from our executive suite about all, there is this 143 00:09:25,400 --> 00:09:28,760 Speaker 3: thing called COVID that's coming. We will start testing people 144 00:09:28,840 --> 00:09:32,920 Speaker 3: in four days. People will want to do research with COVID. 145 00:09:33,360 --> 00:09:37,679 Speaker 2: Make it happen. So it was there's no chapter in 146 00:09:37,720 --> 00:09:38,960 Speaker 2: the book about that, you know. 147 00:09:39,040 --> 00:09:45,960 Speaker 3: So so I then it really it was like a 148 00:09:46,040 --> 00:09:50,000 Speaker 3: pressure cooker, you know, test with. Yeah, I have to 149 00:09:50,080 --> 00:09:56,000 Speaker 3: create this access to data, structured you know, resources, so 150 00:09:56,040 --> 00:09:58,080 Speaker 3: that we can learn from it as. 151 00:09:58,080 --> 00:09:59,160 Speaker 2: Quickly as possible. 152 00:10:00,040 --> 00:10:03,920 Speaker 3: And so that was a catalyst. So then comes twenty 153 00:10:04,000 --> 00:10:07,400 Speaker 3: twenty one. That was the year of our centennial one 154 00:10:07,480 --> 00:10:10,760 Speaker 3: hundred years for Cleveland Clinic. So everybody, not just me, 155 00:10:11,000 --> 00:10:14,280 Speaker 3: we were in a mindset where we were thinking long, 156 00:10:14,520 --> 00:10:18,319 Speaker 3: long term, you know, like what made us specialized? Now 157 00:10:18,720 --> 00:10:21,880 Speaker 3: how do we stay relevant? Where is the world going 158 00:10:21,960 --> 00:10:25,360 Speaker 3: to be ten years from now? And what should I 159 00:10:25,679 --> 00:10:29,680 Speaker 3: get going right this moment to shape that and be 160 00:10:29,840 --> 00:10:32,760 Speaker 3: ready for it. And that's when quantum came in my mind, 161 00:10:33,559 --> 00:10:38,760 Speaker 3: where unless we invest in it now twenty twenty one, 162 00:10:39,400 --> 00:10:42,960 Speaker 3: we will not be ready for this next computer revolution 163 00:10:43,120 --> 00:10:44,600 Speaker 3: that's coming after AI. 164 00:10:45,200 --> 00:10:47,720 Speaker 1: Everybody in the world right now is doing nothing but 165 00:10:47,800 --> 00:10:52,360 Speaker 1: talking about AI, and you are already thinking one step 166 00:10:52,400 --> 00:10:56,840 Speaker 1: beyond a quantum what's different about the opportunity that quantum 167 00:10:57,440 --> 00:11:00,880 Speaker 1: creates for a metic research than AI? 168 00:11:02,480 --> 00:11:03,160 Speaker 2: Biology? 169 00:11:03,679 --> 00:11:11,760 Speaker 3: The human body, by definition, is much closer to fundamentals 170 00:11:11,760 --> 00:11:15,800 Speaker 3: of quantum, you know, to quantum mechanics and quantum physics 171 00:11:15,840 --> 00:11:20,839 Speaker 3: than it is to AI. AI is a classical computing 172 00:11:20,920 --> 00:11:26,200 Speaker 3: approach that in essence, reduces every piece of data to 173 00:11:26,360 --> 00:11:31,440 Speaker 3: a black or white binary categorization of a one or 174 00:11:31,480 --> 00:11:37,960 Speaker 3: a zero. At its core nature around us, the human body, 175 00:11:38,040 --> 00:11:43,840 Speaker 3: there is nothing categorical about it. It's it's that whole 176 00:11:44,160 --> 00:11:52,120 Speaker 3: you know, continuum of colors of life. Quantum its principles 177 00:11:52,160 --> 00:11:56,480 Speaker 3: are that, you know, so there's all the scientific principles 178 00:11:56,520 --> 00:12:01,800 Speaker 3: about quantum physics and superb position and in tankerment, you know, 179 00:12:01,840 --> 00:12:04,200 Speaker 3: all of these complex things that people have a hard 180 00:12:04,200 --> 00:12:07,360 Speaker 3: time with, but for in essence, it. 181 00:12:08,000 --> 00:12:11,480 Speaker 2: Really is much more aligned. 182 00:12:11,520 --> 00:12:15,000 Speaker 3: Like if I want to draw a colored picture, I 183 00:12:15,080 --> 00:12:18,240 Speaker 3: will not go and pick up charcoal. You know, it's 184 00:12:18,320 --> 00:12:21,000 Speaker 3: much easier for me to draw it if I had 185 00:12:21,720 --> 00:12:25,800 Speaker 3: a colored palette with me, and quantum offers that. There 186 00:12:25,840 --> 00:12:31,000 Speaker 3: is plenty of situations in medicine that are just intractable, 187 00:12:31,480 --> 00:12:34,680 Speaker 3: you know, meaning it's not an issue just of it 188 00:12:34,800 --> 00:12:38,760 Speaker 3: being AI being slow, or it doesn't have enough data, 189 00:12:39,240 --> 00:12:42,160 Speaker 3: or if only we got more GPUs, you know, we 190 00:12:42,240 --> 00:12:47,160 Speaker 3: can answer those questions. There are some problems in medicine 191 00:12:47,520 --> 00:12:51,360 Speaker 3: that are fundamentally such that even if you give me 192 00:12:51,440 --> 00:12:54,480 Speaker 3: all the GPUs in the world, there is no way 193 00:12:55,160 --> 00:13:00,600 Speaker 3: that AI can model accurately how these mollyules in the 194 00:13:00,679 --> 00:13:05,880 Speaker 3: body are interacting among each other, or how compounds electrons 195 00:13:05,920 --> 00:13:10,640 Speaker 3: are moving within the mitochondria. These are the engines within 196 00:13:10,800 --> 00:13:16,680 Speaker 3: our cells. There's these fundamental things in biology that AI 197 00:13:16,880 --> 00:13:21,079 Speaker 3: and classical computers are just not built to be able 198 00:13:21,160 --> 00:13:21,760 Speaker 3: to stimulate. 199 00:13:22,480 --> 00:13:26,040 Speaker 1: So you must go to dinner parties, Doctor j. High. 200 00:13:26,400 --> 00:13:29,760 Speaker 1: People must ask you what is quantum computing? What do 201 00:13:29,800 --> 00:13:30,560 Speaker 1: you tell them? 202 00:13:31,040 --> 00:13:34,280 Speaker 3: Yes, Although I often, you know, we talk about other 203 00:13:34,360 --> 00:13:35,360 Speaker 3: things at dinner. 204 00:13:35,080 --> 00:13:40,080 Speaker 1: Parties, eventually it comes down to you. If I was 205 00:13:40,120 --> 00:13:42,800 Speaker 1: at that dinner party with you, I would ask you 206 00:13:42,840 --> 00:13:43,800 Speaker 1: what is quantum computer? 207 00:13:44,200 --> 00:13:46,640 Speaker 3: I would say, depends on how much time we have 208 00:13:46,760 --> 00:13:50,760 Speaker 3: at the party to explain it. The short answer would be, 209 00:13:51,040 --> 00:13:56,400 Speaker 3: it's a completely different way of working with computers than 210 00:13:56,440 --> 00:14:00,160 Speaker 3: what we're used to Right now, you can imagine that 211 00:14:00,080 --> 00:14:03,120 Speaker 3: AI as being like a car that you take to 212 00:14:03,160 --> 00:14:07,000 Speaker 3: go from one place to another. No matter how fast 213 00:14:07,240 --> 00:14:10,880 Speaker 3: that Ferrari can get and how much fewer you put 214 00:14:10,920 --> 00:14:13,880 Speaker 3: in it, it's never going to be a fighter jet. 215 00:14:14,200 --> 00:14:15,760 Speaker 2: It's never going to be a plane. 216 00:14:16,640 --> 00:14:19,800 Speaker 3: AI is the car, the plane is quant They are 217 00:14:20,440 --> 00:14:23,400 Speaker 3: ways to get from point A to point B, but 218 00:14:23,640 --> 00:14:29,720 Speaker 3: they work very differently, and we always use them in together. 219 00:14:30,160 --> 00:14:34,960 Speaker 3: If I'm flying from Shaker Heights to Yorktown Heights, I 220 00:14:35,240 --> 00:14:38,000 Speaker 3: drive to the airport, get on the plane, then take 221 00:14:38,040 --> 00:14:41,880 Speaker 3: an uber to get to Yorktown Heights. In research, we 222 00:14:41,960 --> 00:14:44,440 Speaker 3: will do the same. We do some piece of it 223 00:14:44,480 --> 00:14:46,960 Speaker 3: in AI, some piece of it in quantum, and then 224 00:14:47,040 --> 00:14:47,560 Speaker 3: go back and. 225 00:14:47,520 --> 00:14:51,440 Speaker 1: Forth back in twenty twenty one, Cleveland Clinic and IBM 226 00:14:51,520 --> 00:14:55,000 Speaker 1: announced that they were starting something called the Discovery Accelerator. 227 00:14:55,440 --> 00:14:56,080 Speaker 1: What is that. 228 00:14:56,920 --> 00:15:01,400 Speaker 3: It's an initiative, a program, a partnership really that is 229 00:15:02,120 --> 00:15:10,000 Speaker 3: designed to bridge advanced computational tools and technology through the 230 00:15:10,120 --> 00:15:16,800 Speaker 3: leader in that space, IBM, with biomedical science and research 231 00:15:17,040 --> 00:15:22,800 Speaker 3: and life sciences problems, and that is a Cleveland Clinic. 232 00:15:23,280 --> 00:15:26,840 Speaker 3: We called it a Discovery Accelerator because that was our goal. 233 00:15:27,240 --> 00:15:31,320 Speaker 3: We were both on both sides challenged with the fact 234 00:15:31,320 --> 00:15:34,160 Speaker 3: that discovery in medicine was just taking too long. The 235 00:15:34,320 --> 00:15:39,200 Speaker 3: classical example that really brought it to life to us 236 00:15:39,280 --> 00:15:43,360 Speaker 3: then was drug discovery that it took over a decade 237 00:15:43,560 --> 00:15:47,640 Speaker 3: and it still does actually over a decade. From the 238 00:15:47,800 --> 00:15:51,480 Speaker 3: moment that there is a compound that someone in a 239 00:15:51,800 --> 00:15:56,720 Speaker 3: lab biomedical lab thinks it would be effective to treat 240 00:15:56,760 --> 00:16:00,520 Speaker 3: a certain disease, it takes about ten to thirteen from 241 00:16:00,520 --> 00:16:03,400 Speaker 3: that moment to when that drug is on a shelf 242 00:16:03,560 --> 00:16:06,160 Speaker 3: for a patient to get to from a pharmacy. And 243 00:16:06,200 --> 00:16:09,840 Speaker 3: that was just too much of a gap to allow 244 00:16:10,520 --> 00:16:13,920 Speaker 3: when we have so many health conditions that we needed 245 00:16:13,960 --> 00:16:16,880 Speaker 3: to address, and a big part of that gap could 246 00:16:16,880 --> 00:16:22,680 Speaker 3: be computationally solved better simulation of compounds, designing drug trails 247 00:16:22,760 --> 00:16:23,280 Speaker 3: that are. 248 00:16:23,200 --> 00:16:25,720 Speaker 2: More efficient, you know, that would finish faster. 249 00:16:26,320 --> 00:16:33,520 Speaker 3: So that was the motivation to bring computational tools and 250 00:16:33,600 --> 00:16:36,560 Speaker 3: technology closer to biomedical researchers. 251 00:16:36,760 --> 00:16:40,400 Speaker 1: Cleveland Click and IBM team up. And I'm assuming the 252 00:16:40,480 --> 00:16:44,280 Speaker 1: IBM quantum guys and other people descend on Cleveland and 253 00:16:44,320 --> 00:16:47,480 Speaker 1: you have your first meeting with them. Are they telling 254 00:16:47,480 --> 00:16:50,680 Speaker 1: you things you would never thought of? Or I mean, 255 00:16:50,840 --> 00:16:53,160 Speaker 1: I'm just curious about what's the difference between what you 256 00:16:54,920 --> 00:16:58,920 Speaker 1: thought was the potential was and what you discovered the 257 00:16:58,960 --> 00:16:59,680 Speaker 1: potential was. 258 00:17:01,040 --> 00:17:05,040 Speaker 3: That is an excellent question. If I had to prioritize. 259 00:17:05,119 --> 00:17:09,000 Speaker 3: One lesson that I learned over the past few years 260 00:17:09,000 --> 00:17:13,119 Speaker 3: of doing this, it is that you can never know 261 00:17:14,359 --> 00:17:17,480 Speaker 3: what your you know where your brain is going to 262 00:17:17,560 --> 00:17:23,879 Speaker 3: go and discovery until you talk. You know, you have 263 00:17:23,920 --> 00:17:27,040 Speaker 3: to open it up and really listen to try to 264 00:17:27,119 --> 00:17:28,679 Speaker 3: learn what the other people are saying. 265 00:17:29,119 --> 00:17:31,280 Speaker 2: It's it went both ways. 266 00:17:31,440 --> 00:17:32,480 Speaker 1: Can you give me an example. 267 00:17:32,640 --> 00:17:35,160 Speaker 3: So, okay, we built a quantum right, so it took 268 00:17:35,480 --> 00:17:38,240 Speaker 3: it took like eight months to get this machine put together, 269 00:17:38,720 --> 00:17:40,440 Speaker 3: and we put it in our cafeteria. 270 00:17:41,080 --> 00:17:42,280 Speaker 2: That's the whole other. 271 00:17:42,280 --> 00:17:48,240 Speaker 1: Story where you put the quantum machine computer in your cafeteria. Yes, yes, yes, 272 00:17:50,080 --> 00:17:51,679 Speaker 1: can you see it when you're eating lunch? 273 00:17:52,119 --> 00:17:54,920 Speaker 3: Yeah? Yeah, we have people eating lunch around it all 274 00:17:54,920 --> 00:17:57,879 Speaker 3: the time. We wanted to have a machine that people 275 00:17:57,960 --> 00:18:02,280 Speaker 3: can see. Otherwise it it the program wouldn't launch properly, 276 00:18:02,400 --> 00:18:06,200 Speaker 3: so I needed to have it physically. So we had 277 00:18:06,240 --> 00:18:09,280 Speaker 3: to look at retro fitted in existing space, and the 278 00:18:09,320 --> 00:18:13,320 Speaker 3: cafeteria space worked out. It was far enough from the street, 279 00:18:13,400 --> 00:18:17,080 Speaker 3: you know, there was no vibration that it was a 280 00:18:17,200 --> 00:18:20,400 Speaker 3: double floor, you know, so the ceiling was high enough. 281 00:18:20,720 --> 00:18:24,199 Speaker 3: It just like technically fit all of those requirements. And 282 00:18:24,280 --> 00:18:29,320 Speaker 3: it was either there or we put it in our 283 00:18:29,440 --> 00:18:32,959 Speaker 3: data center, which is a building and like another city 284 00:18:33,240 --> 00:18:36,920 Speaker 3: close to Cleveland, and we picked the cafeteria. 285 00:18:37,200 --> 00:18:39,720 Speaker 1: Yeah. Now, I know this is sort of this is 286 00:18:39,800 --> 00:18:41,920 Speaker 1: kind of hilarious, but there's a serious point I I 287 00:18:42,000 --> 00:18:45,720 Speaker 1: want to touch on, which is why do you need 288 00:18:45,760 --> 00:18:46,600 Speaker 1: it on premises? 289 00:18:46,800 --> 00:18:50,440 Speaker 3: On the premises Cleveland kinnect research. We needed to change 290 00:18:51,000 --> 00:18:56,600 Speaker 3: how we think about research and shift the mindset of 291 00:18:57,240 --> 00:19:02,080 Speaker 3: all of our researchers. And all of our researchers, I'm 292 00:19:02,119 --> 00:19:06,600 Speaker 3: talking about three thousand individuals who are one hundred percent 293 00:19:06,880 --> 00:19:11,400 Speaker 3: doing biomedical research in Cleveland Clinic, two hundred and thirty 294 00:19:12,160 --> 00:19:17,800 Speaker 3: labs individual pis. So that's the scale that I'm talking 295 00:19:17,840 --> 00:19:21,640 Speaker 3: about that we had to create an impact on. So 296 00:19:21,960 --> 00:19:28,240 Speaker 3: having it, having it be there was as much for 297 00:19:28,760 --> 00:19:34,000 Speaker 3: inspiration and to trigger our motivation to change as it 298 00:19:34,160 --> 00:19:36,960 Speaker 3: was a you know, a practical solution, say, because we 299 00:19:37,040 --> 00:19:41,120 Speaker 3: had the space and the connections and all of. 300 00:19:41,040 --> 00:19:44,920 Speaker 1: That I saw in it IBM headquarters. They're beautiful, they're 301 00:19:44,920 --> 00:19:45,520 Speaker 1: works of art. 302 00:19:45,720 --> 00:19:49,040 Speaker 3: They are They're gorgeous, and the one that we have 303 00:19:49,840 --> 00:19:53,600 Speaker 3: is the most gorgeous. 304 00:19:53,200 --> 00:19:56,120 Speaker 2: One of all. This is not me, you know, the mom. 305 00:19:57,400 --> 00:19:59,440 Speaker 4: Bias talking about it. 306 00:20:00,600 --> 00:20:03,520 Speaker 3: You know, they got an award, the Red Dot Award 307 00:20:03,680 --> 00:20:08,560 Speaker 3: for Design went to IBM and Cleveland Clinic for our 308 00:20:08,640 --> 00:20:16,960 Speaker 3: quantum and the quantum that IBM built for RPI a 309 00:20:17,000 --> 00:20:21,120 Speaker 3: couple of years after hours. They modeled it after hours, 310 00:20:21,160 --> 00:20:23,800 Speaker 3: not after the you know the ones from before. 311 00:20:24,040 --> 00:20:27,560 Speaker 1: Just just so people know, we're talking about basically a 312 00:20:27,600 --> 00:20:30,760 Speaker 1: small garage eleven. 313 00:20:30,680 --> 00:20:34,399 Speaker 3: But size eleven foot by eleven eleven feet eleven feet, 314 00:20:34,480 --> 00:20:38,240 Speaker 3: you know the cube, So it's a it's a glass 315 00:20:38,400 --> 00:20:42,240 Speaker 3: cube and the glass comes all the way from Italy. 316 00:20:42,920 --> 00:20:48,440 Speaker 3: It's the same glass that protects the crown jewels and 317 00:20:48,800 --> 00:20:52,639 Speaker 3: you know the Mona Lisa and all that. So so 318 00:20:52,760 --> 00:20:56,359 Speaker 3: there's a glass all around, and then there is the 319 00:20:56,440 --> 00:21:01,040 Speaker 3: tube that you see, the stainless tube that's shiny, you know, 320 00:21:01,080 --> 00:21:04,399 Speaker 3: and clean. But the technologies are inside of it, you know, 321 00:21:04,440 --> 00:21:08,119 Speaker 3: the chandelier and then the processor and the bottom and 322 00:21:08,320 --> 00:21:14,080 Speaker 3: it's just a fascinating thing to watch and it hums. 323 00:21:14,160 --> 00:21:17,439 Speaker 3: It makes the sound so even it sounds alive. 324 00:21:19,760 --> 00:21:22,800 Speaker 1: You have a great deal of affection for your computer. 325 00:21:22,560 --> 00:21:24,359 Speaker 2: And we love our machine. 326 00:21:24,960 --> 00:21:27,200 Speaker 1: I want to go back to something you said before, 327 00:21:27,280 --> 00:21:30,840 Speaker 1: which is when you had your initial conversations with IBM, 328 00:21:31,359 --> 00:21:35,280 Speaker 1: both sides learned things that they hadn't previously thought of. 329 00:21:35,560 --> 00:21:39,560 Speaker 1: Give me an example of something that either side hadn't 330 00:21:39,600 --> 00:21:41,960 Speaker 1: realized could be done with this new technology. 331 00:21:42,040 --> 00:21:46,240 Speaker 3: Sure, I mean on the equivalent clinic side, we thought 332 00:21:46,600 --> 00:21:50,720 Speaker 3: that what quantum should be good for is that it 333 00:21:50,760 --> 00:21:53,840 Speaker 3: would be a faster computer, right, so that we have 334 00:21:54,000 --> 00:21:58,920 Speaker 3: these big data sets that are requiring much more compute 335 00:21:58,960 --> 00:22:01,000 Speaker 3: power and you know, GIP used than what we have 336 00:22:01,080 --> 00:22:04,520 Speaker 3: and we should just run them on the quantum. And 337 00:22:04,920 --> 00:22:09,240 Speaker 3: what we came to realize is that Quantum actually does 338 00:22:09,280 --> 00:22:12,320 Speaker 3: not do well with these large data sets. What it 339 00:22:12,359 --> 00:22:16,879 Speaker 3: does well with our smaller, better defined data sets, but 340 00:22:17,040 --> 00:22:21,879 Speaker 3: ones where simulation is more important. You know, we have 341 00:22:22,040 --> 00:22:27,400 Speaker 3: to run them through multiple models, multiple simulations of how 342 00:22:27,440 --> 00:22:30,960 Speaker 3: they interact. As an example, one of the very first 343 00:22:30,960 --> 00:22:35,800 Speaker 3: projects we threw at Quantum was wanting to predict complications 344 00:22:35,800 --> 00:22:40,399 Speaker 3: cardiac complications from general surgery using electronic. 345 00:22:39,840 --> 00:22:40,960 Speaker 2: Health record data. 346 00:22:41,040 --> 00:22:45,720 Speaker 3: So data from our electronic health records are by definition 347 00:22:45,880 --> 00:22:49,080 Speaker 3: these really big data sets. You have in them every 348 00:22:49,119 --> 00:22:53,400 Speaker 3: single thing that you know about the patient that you've collected, 349 00:22:54,160 --> 00:22:58,119 Speaker 3: and we thought that Quantum would help us build models, 350 00:22:58,160 --> 00:23:00,600 Speaker 3: better models with this data. 351 00:23:00,720 --> 00:23:04,320 Speaker 2: And it failed miserably. It wasn't good, you know, at 352 00:23:04,359 --> 00:23:05,480 Speaker 2: doing that thing. 353 00:23:05,560 --> 00:23:07,919 Speaker 1: And why didn't it like that problem because it's too 354 00:23:08,240 --> 00:23:09,399 Speaker 1: it's too bigger than wieldy. 355 00:23:10,000 --> 00:23:13,480 Speaker 3: Well, because the hardware isn't tready right, So the hardware 356 00:23:13,640 --> 00:23:17,800 Speaker 3: with Quantum, that issue was back then. You know, now 357 00:23:17,800 --> 00:23:22,160 Speaker 3: we've upgraded our processor, but still Quantum now is limited 358 00:23:22,200 --> 00:23:28,160 Speaker 3: by noise and by its ability to correct for errors 359 00:23:28,240 --> 00:23:33,960 Speaker 3: when it's doing computation. And the more data that you 360 00:23:34,119 --> 00:23:36,600 Speaker 3: throw at it that you require it, you know, to 361 00:23:36,800 --> 00:23:41,240 Speaker 3: put in its system you know, to model interactions, the 362 00:23:41,240 --> 00:23:44,120 Speaker 3: more errors it's likely to make, so then the harder 363 00:23:45,080 --> 00:23:47,800 Speaker 3: it is for it to get to an answer. 364 00:23:47,440 --> 00:23:48,800 Speaker 2: That you can trust. 365 00:23:49,359 --> 00:23:53,280 Speaker 3: Now, we made a lot of progress sense, which I'm 366 00:23:53,320 --> 00:23:56,359 Speaker 3: sure we'll get to I hope we'll get to with 367 00:23:56,960 --> 00:24:00,119 Speaker 3: some recent breakthroughs that we made in that space with 368 00:24:00,520 --> 00:24:03,720 Speaker 3: modeling large compounds and interactions. 369 00:24:04,200 --> 00:24:06,399 Speaker 2: But the way that got us. 370 00:24:06,240 --> 00:24:09,399 Speaker 3: To where we are now, where we could model large interactions, 371 00:24:09,760 --> 00:24:13,439 Speaker 3: it took us figuring out that we shouldn't be doing 372 00:24:13,520 --> 00:24:15,200 Speaker 3: everything on quantum. 373 00:24:15,520 --> 00:24:18,880 Speaker 1: So give me an example of a problem that quantum 374 00:24:19,080 --> 00:24:23,600 Speaker 1: is ideally suited for that the quantum really likes. 375 00:24:23,440 --> 00:24:27,520 Speaker 3: In drug discovery, for examine chemistry. It likes chemistry a 376 00:24:27,560 --> 00:24:31,600 Speaker 3: lot because in chemistry what it has to what we 377 00:24:31,720 --> 00:24:36,119 Speaker 3: wanted to model is how a drug that we put 378 00:24:36,200 --> 00:24:39,360 Speaker 3: in our body is going to interact with We say 379 00:24:39,359 --> 00:24:43,520 Speaker 3: the protein you know, the target, the ligand that it 380 00:24:43,560 --> 00:24:46,000 Speaker 3: needs to buy in too. Like you know, the drug 381 00:24:46,080 --> 00:24:47,840 Speaker 3: is a key and it needs to fit in a 382 00:24:47,920 --> 00:24:50,880 Speaker 3: lock in certain parts of the body to open it, 383 00:24:51,320 --> 00:24:55,840 Speaker 3: get in, do it stay. And there are many keys, 384 00:24:55,960 --> 00:25:01,320 Speaker 3: many potential compounds that we could test for many parts 385 00:25:01,359 --> 00:25:03,640 Speaker 3: of our body. We don't really know how they're going 386 00:25:03,680 --> 00:25:05,840 Speaker 3: to interact. Traditionally, what we do is we have to 387 00:25:05,960 --> 00:25:08,280 Speaker 3: build all the keys. We have to manufacture all these 388 00:25:08,320 --> 00:25:12,200 Speaker 3: compounds and then do actual trial clinical trials. Put them 389 00:25:12,200 --> 00:25:14,560 Speaker 3: in people, put them in animals, and see what happens. 390 00:25:14,800 --> 00:25:15,840 Speaker 1: See which one is best? 391 00:25:15,920 --> 00:25:19,639 Speaker 3: See yeah, which like two fit best together. So we 392 00:25:19,760 --> 00:25:22,560 Speaker 3: use AI to try to help us with that. But 393 00:25:22,800 --> 00:25:25,800 Speaker 3: AI can only model what it had learned. 394 00:25:25,680 --> 00:25:26,520 Speaker 2: Right, So for. 395 00:25:28,000 --> 00:25:33,400 Speaker 3: Rare diseases conditions that there isn't enough information out there 396 00:25:33,600 --> 00:25:37,240 Speaker 3: on what the you know, the locks look like, it's 397 00:25:37,320 --> 00:25:40,359 Speaker 3: really hard to then model it with AI and get 398 00:25:40,400 --> 00:25:43,199 Speaker 3: an accurate prediction of whether there's a fit or not. 399 00:25:44,760 --> 00:25:49,920 Speaker 3: Quantum does not rely on the previous data for its modeling. 400 00:25:50,040 --> 00:25:55,600 Speaker 3: Quantum does its modeling purely based on the physical characteristics 401 00:25:55,600 --> 00:25:59,760 Speaker 3: of the compound of the key you know that you're designing. 402 00:26:00,320 --> 00:26:04,840 Speaker 3: So that makes it ideal because it's then untethered with 403 00:26:05,440 --> 00:26:08,080 Speaker 3: It's not limited by do we have enough samples or 404 00:26:08,119 --> 00:26:10,439 Speaker 3: don't we have enough samples, or you know, what the 405 00:26:10,480 --> 00:26:14,919 Speaker 3: previous studies find or not. It's purely based on those 406 00:26:14,960 --> 00:26:19,439 Speaker 3: physical properties, those quantum properties. So we found ourselves in 407 00:26:19,520 --> 00:26:25,160 Speaker 3: situations where we were able to predict a fit between 408 00:26:26,119 --> 00:26:31,080 Speaker 3: certain targets and where in conditions like Alzheimer's disease. For example, 409 00:26:31,920 --> 00:26:38,320 Speaker 3: we published where the quantum based modeling of the compound 410 00:26:39,080 --> 00:26:43,879 Speaker 3: was much better than what we would have gotten with 411 00:26:44,359 --> 00:26:46,439 Speaker 3: what we got right, Like, we did it both ways, 412 00:26:46,480 --> 00:26:49,320 Speaker 3: and the quantum one was the better fit than the 413 00:26:49,440 --> 00:26:50,880 Speaker 3: AI generated one. 414 00:26:51,240 --> 00:26:54,320 Speaker 1: This is fair. Quantum is a little more of an 415 00:26:54,400 --> 00:26:57,520 Speaker 1: artist and a little less of a of a kind 416 00:26:57,520 --> 00:27:03,080 Speaker 1: of nerd us nerdy to me, what seems like creative 417 00:27:03,080 --> 00:27:03,720 Speaker 1: and artistic? 418 00:27:03,920 --> 00:27:04,520 Speaker 2: Creative? 419 00:27:04,760 --> 00:27:07,520 Speaker 1: Yes, creative, it's like people. 420 00:27:07,600 --> 00:27:14,840 Speaker 3: Yeah, it's it opens up path that you never knew existed. 421 00:27:14,920 --> 00:27:17,320 Speaker 3: That's why when I get asked about what do I 422 00:27:17,440 --> 00:27:20,879 Speaker 3: see is the best, you know, the the most important 423 00:27:20,880 --> 00:27:23,760 Speaker 3: breakthrough that quantum is going to allow us to do, 424 00:27:24,280 --> 00:27:28,399 Speaker 3: my answer is I really don't know, because we you know, 425 00:27:28,440 --> 00:27:34,720 Speaker 3: when other people invented these new technologies, I don't think 426 00:27:34,800 --> 00:27:36,920 Speaker 3: they really knew that they're you know. 427 00:27:36,840 --> 00:27:37,800 Speaker 2: Like think of laser. 428 00:27:38,240 --> 00:27:41,200 Speaker 3: I don't think the person who invented laser thought that 429 00:27:41,640 --> 00:27:44,560 Speaker 3: they will be used to scan groceries at the grocery store. 430 00:27:44,640 --> 00:27:44,840 Speaker 4: You know. 431 00:27:45,280 --> 00:27:50,280 Speaker 3: But so technology developing technology, the way I think of 432 00:27:50,359 --> 00:27:52,360 Speaker 3: it is like having a baby you know, you raise 433 00:27:52,440 --> 00:27:54,879 Speaker 3: it as best you can, but then they're going to 434 00:27:55,000 --> 00:27:58,439 Speaker 3: go off and do their thing, and you will be 435 00:27:59,200 --> 00:28:02,160 Speaker 3: tying them down if you restrict them to just what 436 00:28:02,200 --> 00:28:03,520 Speaker 3: you thought they should do. 437 00:28:03,560 --> 00:28:08,120 Speaker 2: You know. So it opens up that. 438 00:28:09,680 --> 00:28:15,000 Speaker 3: Space, that creative space for us to ask questions differently 439 00:28:15,320 --> 00:28:18,600 Speaker 3: than we used to. We should train our mind to 440 00:28:18,760 --> 00:28:23,919 Speaker 3: stop starting from classical and then trying to squeeze it 441 00:28:23,960 --> 00:28:27,359 Speaker 3: into quantum. We have to learn how to think quantum 442 00:28:27,440 --> 00:28:32,320 Speaker 3: up front, right from the beginning, which we haven't really 443 00:28:33,000 --> 00:28:37,120 Speaker 3: been doing as a scientific community since our inception. We 444 00:28:37,119 --> 00:28:40,400 Speaker 3: were trained, and how do you convert what you're thinking 445 00:28:40,720 --> 00:28:46,480 Speaker 3: into a formula that you can ask from a computer 446 00:28:46,600 --> 00:28:51,120 Speaker 3: which is a classical right computer with quantum because of 447 00:28:51,320 --> 00:28:54,920 Speaker 3: how it works, it can answer questions, It can look 448 00:28:54,960 --> 00:28:58,320 Speaker 3: at problems in a very different way. So we have 449 00:28:58,440 --> 00:29:02,000 Speaker 3: to think differently about the questions and how we ask them. 450 00:29:02,520 --> 00:29:06,920 Speaker 1: With IBM, you recently modeled protein with over twelve thousand atoms. 451 00:29:07,400 --> 00:29:10,400 Speaker 1: Talk to me about that and why it's so meaningful 452 00:29:10,440 --> 00:29:11,640 Speaker 1: for job discovery. 453 00:29:12,560 --> 00:29:17,520 Speaker 3: So in October of twenty twenty four, so just eighteen 454 00:29:17,600 --> 00:29:23,080 Speaker 3: months ago, the largest compound biological compound that could be 455 00:29:23,120 --> 00:29:29,840 Speaker 3: simulated with quantum was ten atoms big, and it was 456 00:29:30,120 --> 00:29:34,479 Speaker 3: unfathomable back then that we will get past the one 457 00:29:34,520 --> 00:29:40,240 Speaker 3: thousand or few thousand atom simulation in the foreseeable future. 458 00:29:41,440 --> 00:29:45,560 Speaker 3: And what our team with IBM and with Rieken and 459 00:29:45,680 --> 00:29:52,480 Speaker 3: Japan published last month April twenty twenty six is a 460 00:29:52,600 --> 00:29:59,680 Speaker 3: simulation of the electrical properties of an enzyme trips in 461 00:30:00,080 --> 00:30:04,360 Speaker 3: the body that is twelve thousand, six hundred atoms bit 462 00:30:05,240 --> 00:30:09,160 Speaker 3: for you know, orders of magnitude beyond what anybody thought 463 00:30:09,360 --> 00:30:12,960 Speaker 3: was possible in that span of time. 464 00:30:13,440 --> 00:30:15,400 Speaker 2: And the reason why. 465 00:30:15,240 --> 00:30:21,000 Speaker 3: That happened was because of a you know, innovations in 466 00:30:21,160 --> 00:30:27,840 Speaker 3: the technology itself where the teams stopped thinking of quantum 467 00:30:28,040 --> 00:30:34,000 Speaker 3: and AI as competitors and instead thought of them as 468 00:30:34,320 --> 00:30:35,560 Speaker 3: different members of. 469 00:30:35,560 --> 00:30:36,760 Speaker 2: The same team. 470 00:30:37,120 --> 00:30:41,440 Speaker 3: Right, you're we're now in basketball season in the US 471 00:30:41,760 --> 00:30:44,720 Speaker 3: the NPA, and you need the you need defense, but 472 00:30:44,840 --> 00:30:49,640 Speaker 3: you also need your center, somebody to shoot. You need 473 00:30:50,400 --> 00:30:54,520 Speaker 3: all of the pieces to work together. So with this, 474 00:30:55,240 --> 00:30:58,240 Speaker 3: it was figuring out where do I put you know, 475 00:30:58,320 --> 00:31:00,920 Speaker 3: when do I put AI on the field, When do 476 00:31:01,000 --> 00:31:03,720 Speaker 3: I put quantum on the field, and how do I 477 00:31:04,000 --> 00:31:04,800 Speaker 3: tell them. 478 00:31:04,960 --> 00:31:05,920 Speaker 2: To work together? 479 00:31:06,400 --> 00:31:11,600 Speaker 3: It's the scientific terms the quantum centric super computing. So 480 00:31:11,720 --> 00:31:15,480 Speaker 3: quantum is at the center, but we're using our supercomputing 481 00:31:15,840 --> 00:31:22,080 Speaker 3: tools AI classical to interact with it and split that 482 00:31:22,200 --> 00:31:26,200 Speaker 3: big problem of the twelve thousand, six hundred atoms into 483 00:31:26,280 --> 00:31:30,440 Speaker 3: pieces where some pieces are best served with quantum and 484 00:31:30,560 --> 00:31:32,720 Speaker 3: others are best served with classical. 485 00:31:33,440 --> 00:31:38,360 Speaker 1: This distinction that we now cling to AI and quantum 486 00:31:38,440 --> 00:31:41,280 Speaker 1: are these very different things develop by different people for 487 00:31:41,360 --> 00:31:45,400 Speaker 1: different purposes. What you're suggesting is that's probably going to 488 00:31:45,480 --> 00:31:48,720 Speaker 1: go away. Yeah, in the future, these things will work together. 489 00:31:49,440 --> 00:31:52,440 Speaker 1: It's going to become teamwork and not one on one 490 00:31:52,440 --> 00:31:54,000 Speaker 1: competition exactly. 491 00:31:54,240 --> 00:31:56,480 Speaker 2: And it is that now in Cleveland Clinic. 492 00:31:56,560 --> 00:32:01,720 Speaker 3: I mean the way we've evolved our priority is with IBM. 493 00:32:02,440 --> 00:32:06,400 Speaker 3: It's a realization that both organizations have come to where 494 00:32:06,880 --> 00:32:10,840 Speaker 3: really to for progress to happen, we should stop seeing 495 00:32:10,880 --> 00:32:16,480 Speaker 3: them as separate. We should put them together and work 496 00:32:17,200 --> 00:32:20,760 Speaker 3: to the best of what each piece of technology can provide. 497 00:32:21,080 --> 00:32:25,800 Speaker 1: One theme running through a lot of your your what 498 00:32:25,880 --> 00:32:29,600 Speaker 1: you've been talking about is that the arrival of this 499 00:32:29,840 --> 00:32:39,120 Speaker 1: new technology requires the researcher to behave differently and that's 500 00:32:39,160 --> 00:32:42,120 Speaker 1: one of the reasons why you want the quantum machine 501 00:32:42,120 --> 00:32:45,000 Speaker 1: and the on full display, and then you were talking 502 00:32:45,000 --> 00:32:47,040 Speaker 1: about how you have to ask different kinds of questions. 503 00:32:47,240 --> 00:32:50,959 Speaker 1: I'm curious, can you can you? Can you elaborate on 504 00:32:51,000 --> 00:32:54,440 Speaker 1: that a little bit? So take me back, for example, 505 00:32:54,480 --> 00:32:57,120 Speaker 1: to your earliest days. If I had given you all 506 00:32:57,120 --> 00:33:02,280 Speaker 1: these tools that people now, how would your research have 507 00:33:02,440 --> 00:33:05,200 Speaker 1: proceeded differently? What would you have done differently? 508 00:33:07,040 --> 00:33:15,959 Speaker 2: You know, I would have looked at the molecular. 509 00:33:17,720 --> 00:33:22,480 Speaker 3: Components of the human brain as they relate to outcomes 510 00:33:22,520 --> 00:33:25,400 Speaker 3: of brain surgery way earlier than I did. The first 511 00:33:25,480 --> 00:33:29,680 Speaker 3: ten years of my career doing research was all spent 512 00:33:30,040 --> 00:33:34,680 Speaker 3: building models that were purely based on brain. 513 00:33:34,480 --> 00:33:39,120 Speaker 2: Waves and pictures of the brain. 514 00:33:40,360 --> 00:33:44,920 Speaker 3: It wasn't until after I hit a wall with my 515 00:33:45,120 --> 00:33:50,240 Speaker 3: models aren't getting past that eighty percent accuracy threshold that 516 00:33:50,720 --> 00:33:54,680 Speaker 3: I started thinking, oh, you know, there must be something 517 00:33:54,760 --> 00:33:58,800 Speaker 3: genetic or you know, more biological that is influencing this. 518 00:33:59,480 --> 00:34:05,280 Speaker 3: Had I been exposed to quantum at least as a concept, 519 00:34:05,400 --> 00:34:10,279 Speaker 3: right to quantum computing and what quantum science is back then, 520 00:34:11,360 --> 00:34:14,560 Speaker 3: I think it would have opened up my mind to 521 00:34:15,000 --> 00:34:19,200 Speaker 3: realize that it's not just about what I see. You know, 522 00:34:19,480 --> 00:34:26,280 Speaker 3: there is hidden relationships that exist within the human body 523 00:34:26,440 --> 00:34:31,600 Speaker 3: and that's our genetic makeup and our chemical makeup that 524 00:34:31,880 --> 00:34:37,440 Speaker 3: influence what comes to the surface that urge to dig deeper. 525 00:34:38,120 --> 00:34:40,319 Speaker 3: The other thing it would have changed is it would 526 00:34:40,320 --> 00:34:43,719 Speaker 3: have made me reach out to engineers and physicists and 527 00:34:43,840 --> 00:34:48,520 Speaker 3: mathematicians much earlier in my career. 528 00:34:48,800 --> 00:34:53,279 Speaker 1: Yeah, yeah, yeah. And how would it have changed This 529 00:34:53,360 --> 00:34:56,640 Speaker 1: is a very kind of prosaic question, but just kind 530 00:34:56,680 --> 00:35:00,560 Speaker 1: of the day to day life of someone doing better research. 531 00:35:00,680 --> 00:35:03,880 Speaker 1: I mean, the oh wow, you walk into the office 532 00:35:03,880 --> 00:35:06,920 Speaker 1: in the morning. How does your day proceed differently when 533 00:35:06,960 --> 00:35:10,960 Speaker 1: you're when you have these kinds of tools at your fingertips. 534 00:35:12,719 --> 00:35:18,120 Speaker 3: That's the fundamental question in biomedical research right now, and 535 00:35:18,160 --> 00:35:21,040 Speaker 3: it's it's less to do with quantum, more to do 536 00:35:21,200 --> 00:35:26,480 Speaker 3: with agentic AI, right, these agents that we can work 537 00:35:26,560 --> 00:35:30,919 Speaker 3: with now to help us how to think more creatively 538 00:35:31,400 --> 00:35:36,840 Speaker 3: and how to do work that up until now was 539 00:35:36,960 --> 00:35:41,120 Speaker 3: more like Scott work that the researchers had to do, 540 00:35:41,280 --> 00:35:45,560 Speaker 3: whether it was you know, so the hypothesis generation has 541 00:35:45,640 --> 00:35:50,480 Speaker 3: always been the most creative part of aspect of scientific research. 542 00:35:51,239 --> 00:35:55,120 Speaker 3: But what comes after it with the data collection for example, 543 00:35:55,320 --> 00:36:01,880 Speaker 3: that was always such a repetitive, you know, exercise, and 544 00:36:01,920 --> 00:36:06,759 Speaker 3: then the analysis after that was something that was very 545 00:36:06,920 --> 00:36:10,800 Speaker 3: resource intensive and you had to try so many different 546 00:36:10,800 --> 00:36:14,799 Speaker 3: approaches before you get to an answer, and that was 547 00:36:15,040 --> 00:36:16,040 Speaker 3: fairly complex. 548 00:36:16,520 --> 00:36:18,560 Speaker 2: Now, with access to. 549 00:36:20,080 --> 00:36:26,480 Speaker 3: Urgantic AI and you know, some quantum, of course, we 550 00:36:26,600 --> 00:36:31,160 Speaker 3: can spend more of our energy on the creative thinking 551 00:36:31,520 --> 00:36:36,000 Speaker 3: part of the aspects of the work and less on 552 00:36:36,160 --> 00:36:39,520 Speaker 3: the you know, just that repetitive. 553 00:36:39,120 --> 00:36:42,920 Speaker 1: When you look around, I'm assuming you walk around Cleveland 554 00:36:42,920 --> 00:36:45,920 Speaker 1: Clinic and you have lots of conversations with some of 555 00:36:45,920 --> 00:36:49,880 Speaker 1: the most brilliant medical researchers in the world. Are you 556 00:36:50,040 --> 00:36:55,239 Speaker 1: satisfied with how quickly and aggressively they are adopting these 557 00:36:55,239 --> 00:36:58,840 Speaker 1: new technologies or do they still need encouragement from you 558 00:36:58,920 --> 00:37:02,480 Speaker 1: to do? You have to say, people, wait, I've got 559 00:37:02,480 --> 00:37:05,160 Speaker 1: this machine in the cafeteria. You should be using it 560 00:37:05,200 --> 00:37:08,000 Speaker 1: for this problem. How much are you doing that kind 561 00:37:08,040 --> 00:37:10,760 Speaker 1: of encouraging in cheerleading or is it unnecessary? 562 00:37:11,880 --> 00:37:16,920 Speaker 3: No, there's plenty of cheerleading that's necessary people. You know, 563 00:37:17,960 --> 00:37:22,920 Speaker 3: humans don't like to change. It's hardwired in us. So 564 00:37:23,320 --> 00:37:28,040 Speaker 3: there is plenty of cheerleading. But what happens is, I mean, 565 00:37:28,120 --> 00:37:32,400 Speaker 3: the way we built our program to where we are now, 566 00:37:32,480 --> 00:37:37,160 Speaker 3: so Cleveland clinic now is pretty much winning every global competition. 567 00:37:37,360 --> 00:37:42,120 Speaker 3: And Quantum for life sciences, whether that's the welcome trust, 568 00:37:42,160 --> 00:37:47,680 Speaker 3: you know, Quantum for biological applications, we our partner. There 569 00:37:47,960 --> 00:37:51,160 Speaker 3: was a startup in Finland Algorithm Make there brilliant. We 570 00:37:51,239 --> 00:37:55,480 Speaker 3: worked with them to develop a photodynamic drug therapy for cancer, 571 00:37:56,360 --> 00:37:59,640 Speaker 3: or whether it is the NIH with they had an 572 00:37:59,640 --> 00:38:06,000 Speaker 3: ex price challenge for Quantum or universities that we're collaborating with, 573 00:38:06,640 --> 00:38:10,239 Speaker 3: we have a program that's bringing startups to our ecosystem. 574 00:38:10,680 --> 00:38:13,560 Speaker 3: We give them access to the machine if they have 575 00:38:13,719 --> 00:38:18,600 Speaker 3: a question that is significant enough for life sciences, universities 576 00:38:18,680 --> 00:38:24,839 Speaker 3: that we're building a bachelor's, master's, PhD programs with on 577 00:38:24,960 --> 00:38:31,000 Speaker 3: quantum computing, we developed that whole ecosystem around it. If 578 00:38:31,040 --> 00:38:34,680 Speaker 3: that's not cheerleading, I don't know what else would qualify 579 00:38:34,840 --> 00:38:39,560 Speaker 3: for cheerleading. But then what happens is these early adopters, 580 00:38:39,600 --> 00:38:43,680 Speaker 3: the risk takers, become the cheerleaders themselves, right, and then 581 00:38:43,880 --> 00:38:47,720 Speaker 3: they work with it, they achieve success, and we're nothing 582 00:38:48,960 --> 00:38:52,960 Speaker 3: but competitive in medicine and science. Right, So then it 583 00:38:53,000 --> 00:38:55,640 Speaker 3: becomes okay, so and so I did this with this machine, 584 00:38:55,880 --> 00:38:57,919 Speaker 3: Let me learn it so I can do the same 585 00:38:58,440 --> 00:39:03,960 Speaker 3: and it becomes this verse virtuous cycle of then innovation 586 00:39:04,120 --> 00:39:08,080 Speaker 3: and growth and people wanting to work together. 587 00:39:08,239 --> 00:39:10,320 Speaker 2: It's just been fascinating to watch. 588 00:39:11,200 --> 00:39:14,520 Speaker 1: You said that people don't like change, but you clearly do. 589 00:39:15,239 --> 00:39:19,040 Speaker 2: I do. That's the mindset of researchers, right. 590 00:39:19,120 --> 00:39:22,360 Speaker 3: A researcher is someone who is not afraid to fail, 591 00:39:22,880 --> 00:39:26,120 Speaker 3: actually sees failure as a chance to learn something right, 592 00:39:26,440 --> 00:39:30,920 Speaker 3: to do better the next time. So we get rejected 593 00:39:30,960 --> 00:39:33,279 Speaker 3: all the time and we don't care, right, we keep 594 00:39:33,760 --> 00:39:36,080 Speaker 3: moving on with papers. 595 00:39:35,600 --> 00:39:36,720 Speaker 2: Grant applications. 596 00:39:37,080 --> 00:39:43,960 Speaker 3: So that mindset is what all of research is built around. 597 00:39:44,120 --> 00:39:49,440 Speaker 3: So it's a very forward looking mindset. And Cleveland Clinic 598 00:39:49,480 --> 00:39:52,640 Speaker 3: as the health system, we wouldn't have survived one hundred years. 599 00:39:52,680 --> 00:39:56,560 Speaker 3: We wouldn't have done all of the firsts that we had. 600 00:39:56,880 --> 00:40:01,040 Speaker 3: Serotonin the chemical that drives the whole all science of 601 00:40:01,520 --> 00:40:07,560 Speaker 3: psychiatry and neuroscience that was discovered in Cleveland Clinic. So 602 00:40:07,880 --> 00:40:13,360 Speaker 3: as an organization, we have enough of people who think 603 00:40:13,440 --> 00:40:17,080 Speaker 3: that way that they will be the early adopters who 604 00:40:17,080 --> 00:40:20,920 Speaker 3: will pull the others with them. 605 00:40:21,200 --> 00:40:28,480 Speaker 1: Yeah. One last question, look look ahead tenures. Pat me 606 00:40:28,520 --> 00:40:32,799 Speaker 1: a picture of what quantum and related technologies look like 607 00:40:32,880 --> 00:40:35,120 Speaker 1: for a place like Cleveland Clinic and tenures. 608 00:40:36,480 --> 00:40:40,360 Speaker 3: Well, what I hope is that ten years from now, 609 00:40:41,040 --> 00:40:44,000 Speaker 3: if I if I'm seeing a patient in clinic who 610 00:40:44,000 --> 00:40:47,759 Speaker 3: has bad epilepsy and for the love of you know, 611 00:40:47,880 --> 00:40:51,200 Speaker 3: I can't figure out what medicine do I need to 612 00:40:51,239 --> 00:40:55,120 Speaker 3: prescribe to them to make them seizure free, I will 613 00:40:55,160 --> 00:40:57,600 Speaker 3: be able to send them to get a blood test 614 00:40:58,840 --> 00:41:05,160 Speaker 3: that we can then run through some analytical platform that 615 00:41:05,280 --> 00:41:11,319 Speaker 3: leverages both quantum and AI that can model exactly for 616 00:41:11,400 --> 00:41:17,200 Speaker 3: that patient, what compound, either existing or not to be developed. 617 00:41:17,440 --> 00:41:21,279 Speaker 3: It's some new chemical that we haven't tested for that 618 00:41:21,440 --> 00:41:27,560 Speaker 3: indication yet is going to treat them, you know, make 619 00:41:27,680 --> 00:41:33,960 Speaker 3: them seizure free. It's and I think quantum is ideal 620 00:41:34,560 --> 00:41:38,279 Speaker 3: for a condition like mine epilepsy, because we are a 621 00:41:38,360 --> 00:41:42,080 Speaker 3: rare disease, and I think that benefit that it's going 622 00:41:42,160 --> 00:41:44,759 Speaker 3: to have will start with rare diseases, you know, as 623 00:41:44,800 --> 00:41:48,759 Speaker 3: I explained earlier, So take any other rare disease, we 624 00:41:48,760 --> 00:41:52,880 Speaker 3: should start there and then expand from that to more 625 00:41:52,960 --> 00:41:57,399 Speaker 3: complex things like cancer for example, and others. But those 626 00:41:57,520 --> 00:42:02,239 Speaker 3: rare conditions where we are left now completely scratching our 627 00:42:02,320 --> 00:42:08,000 Speaker 3: heads and going by intuition. It will make our care 628 00:42:08,200 --> 00:42:13,200 Speaker 3: truly precise, and it will make drug development a more 629 00:42:13,960 --> 00:42:19,600 Speaker 3: tailored exercise than the way it is now, where it's 630 00:42:20,080 --> 00:42:22,600 Speaker 3: like a hammer that's looking for nails? 631 00:42:22,960 --> 00:42:24,680 Speaker 1: Am I right in thinking that of all of the 632 00:42:24,800 --> 00:42:26,640 Speaker 1: over the one hundred year history or more now of 633 00:42:27,200 --> 00:42:30,080 Speaker 1: Cleveland clinic, this sounds like the absolute best time to 634 00:42:30,080 --> 00:42:30,920 Speaker 1: be a Cleveland clinic. 635 00:42:31,160 --> 00:42:38,120 Speaker 2: Yeah. I love it, No complaints, no complaints, And I 636 00:42:38,200 --> 00:42:38,920 Speaker 2: am hiring. 637 00:42:39,160 --> 00:42:44,560 Speaker 4: I need I need those quantum researchers, those people who 638 00:42:44,600 --> 00:42:50,040 Speaker 4: are wanting, you know, to apply quant genetic research, imaging research, 639 00:42:50,600 --> 00:42:53,719 Speaker 4: every single aspect of biomedical research. 640 00:42:54,160 --> 00:42:57,640 Speaker 3: We can't afford to just wait on the sideline and 641 00:42:59,160 --> 00:43:02,000 Speaker 3: until the technolog she's ready, and then you know, then 642 00:43:02,120 --> 00:43:04,240 Speaker 3: it was teach us it is ready now. That twelve 643 00:43:04,280 --> 00:43:09,440 Speaker 3: thousand atom simulation wouldn't have happened just a year and 644 00:43:09,480 --> 00:43:11,320 Speaker 3: a half ago at Quidin Yahn. 645 00:43:12,160 --> 00:43:15,399 Speaker 1: So the headline of this conversation is we're hiring, we hired. 646 00:43:15,480 --> 00:43:19,960 Speaker 2: Yes, Yeah, that's a good headline. We're growing, how about that. 647 00:43:24,239 --> 00:43:27,200 Speaker 1: Smart Talks with IBM is produced by Matt Ramano, Amy 648 00:43:27,280 --> 00:43:31,959 Speaker 1: Gains McQuaid, Trina Menino, and Jake Harper. Engineering by Nina 649 00:43:31,960 --> 00:43:36,560 Speaker 1: Bird Lawrence Mastering by Sarah Bruguerer, Music by Gramoscope, Strategy 650 00:43:36,600 --> 00:43:41,920 Speaker 1: by Cassidy Meyer, Sophia Derlon and Tatiana Lieberman. Special thanks 651 00:43:41,920 --> 00:43:45,880 Speaker 1: to doctor Laura Jahi, Alicia Real Cooney, and the Cleveland 652 00:43:45,920 --> 00:43:49,560 Speaker 1: Clinic team. Smart Talks with IBM is a production of 653 00:43:49,640 --> 00:43:53,879 Speaker 1: Pushkin Industries and Ruby Studio at iHeartMedia. To find more 654 00:43:53,920 --> 00:43:58,680 Speaker 1: Pushkin podcasts, listen on the iHeartRadio app, Apple Podcasts, or 655 00:43:58,800 --> 00:44:02,759 Speaker 1: wherever you listen to. I'm Malcolm Gladwell. This is a 656 00:44:02,800 --> 00:44:07,319 Speaker 1: paid advertisement from IBM. The conversations on this podcast don't 657 00:44:07,360 --> 00:44:12,000 Speaker 1: necessarily represent IBM's positions, strategies, or opinions.