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Speaker 3: H'm Malcolm Gladwell and you're listening to Smart Talk with IBM.
Speaker 3: When doctor Laura Jahi began treating epilepsy patients in the
Speaker 3: early two thousands, she noticed something unsettling. Different surgeons could
Speaker 3: look at the exact same case and recommend completely different treatments.
Speaker 3: One surgeon might remove one part of the brain, another
Speaker 3: a different part, and a third might not operate at all.
Speaker 3: Doctor j Hi believed there had to be a better way,
Speaker 3: one grounded in data. That conviction set her on a
Speaker 3: path that would lead her to become Chief Research Information
Speaker 3: Officer at Cleveland Clinic and the executive program lead for
Speaker 3: the Discovery Accelerator. The Discovery Accelerator is a ten year
Speaker 3: partnership between Cleveland Clinic and IBM where researchers are using
Speaker 3: AI and quantum computing to make incredible discoveries in healthcare
Speaker 3: and life sciences. I sat down with doctor j Hi
Speaker 3: to explore what's happening now, what's possible with quantum computing,
Speaker 3: and where this next era of biomedical discovery is headed.
Speaker 3: Epilepsy was your kind of specialty within neurology, correct? Yes,
Speaker 3: what led you to that?
Speaker 4: I was always fascinated by the brain. You know, it's
Speaker 4: the part of our body that lives still to this
Speaker 4: day the most to be discovered. So I was always
Speaker 4: intrigued by areas that leave more for discovery. And epilepsy
Speaker 4: was my pragmatic side, wanting to choose a subspecialty where
Speaker 4: the problem can be fixed. In epilepsy, there are many
Speaker 4: medications that are very effective, and there's a brain surgery
Speaker 4: that we can do to stop seizures when medicines don't work.
Speaker 5: That attracted me.
Speaker 4: You know, compared to other areas in neurology, like stroke,
Speaker 4: for example, or dementia, where usually the damage is more definite.
Speaker 4: I wanted to be able to tell my patients that
Speaker 4: you have a big problem, but here's what I can.
Speaker 5: Do to fix it, and epilepsy offered me that.
Speaker 3: But what would There must have been interesting and intriguing
Speaker 3: unsolved problems in neurology.
Speaker 4: Oh my gosh, it's the whole brain, isn't it. Before
Speaker 4: starting to deal with artificial intelligence and research. Right in
Speaker 4: the neurology, I'm dealing with real intelligence, the human brain
Speaker 4: and how it works and how we think and how
Speaker 4: we make decisions, and how it.
Speaker 5: Can grow and evolve.
Speaker 4: And so there is many untaped questions in neurology.
Speaker 5: And that's part of the fascination in it.
Speaker 4: In epilepsy in particular, it's an electrical disease in the brain.
Speaker 4: It's actually one of the conditions in a neurology that's
Speaker 4: a perfect alignment of all of the scientific disciplines. It's
Speaker 4: biology and physics and chemistry all working together to make
Speaker 4: us who we truly are. Right, So every other discipline,
Speaker 4: if you think of computing, for example, it's purely electricity.
Speaker 4: Or if we do drug development or drug discovery, that's
Speaker 4: mostly chemistry experiments in the lab, that's mostly biology. But
Speaker 4: the human brain is all of those put together. The
Speaker 4: cells in our brain secret these chemical substances that diffuse
Speaker 4: everywhere and hook up where they need to to trigger
Speaker 4: certain circuits and then trigger some effects afterwards. So it
Speaker 4: was just an elegant science that has big impacts.
Speaker 3: We're shortly going to get there and talk about you've
Speaker 3: taken on this kind of very techn aology focused roller
Speaker 3: Cleveland Clinic. I'm curious about if we go back to
Speaker 3: when you were just starting out at Cleveland Clinic. Yeah,
Speaker 3: how much were you thinking about this sort of technology piece,
Speaker 3: about what technology could do for your specialty? About was
Speaker 3: that on your mind or is this something you've come
Speaker 3: to more recently.
Speaker 4: Well, it's been a journey, right, and when I started
Speaker 4: training as a clinician, my priority was just to learn
Speaker 4: how to better care for my patients and how to
Speaker 4: be a better physician.
Speaker 5: Right, And then I.
Speaker 4: Realized that clinical practice provides an immediate reward. I'm interacting
Speaker 4: with a human being and helping them in the moment,
Speaker 4: so there is that immediate reward that comes with that.
Speaker 5: But that wasn't enough. I wanted something more.
Speaker 4: So then I learned biomedical research practices, and research offered
Speaker 4: me this path towards a future. You know, the reward
Speaker 4: there is more long term. I'm studying discovering things that
Speaker 4: could help many people in the future, even though I
Speaker 4: will never get to see them or meet them, or
Speaker 4: you know, have that immediate satisfaction. So that shifted me
Speaker 4: from being a pure clinician to being a clinician scientist.
Speaker 4: And as that journey progressed, it became very clear, as
Speaker 4: medicine evolved over the past twenty years, that we cannot
Speaker 4: do any good biomedical research without understanding data and technology.
Speaker 4: You know, the balance is shifting from most of the
Speaker 4: research is happening we call it, you know, on the
Speaker 4: wet bench, with actual experiments, physical experiments, to a place
Speaker 4: where most of the work is happening through compute and
Speaker 4: simulations and data. So I became much more involved for
Speaker 4: my personal research in building big data models and learning
Speaker 4: about AI and you know, learning about technology in general.
Speaker 4: And then as that journey progressed, I was fortunate enough
Speaker 4: to be a role for Cleveland Clinic where my job
Speaker 4: is to bring that technology and bridge it to research
Speaker 4: for all researchers across our healthcare system.
Speaker 3: When you were talking about how in your own research
Speaker 3: you were moving in that direction, what was your own
Speaker 3: research focused on. What were you looking at?
Speaker 4: I was looking at brain surgery for epilepsy. It's an
Speaker 4: intervention that's been around for decades actually, but when I
Speaker 4: started practice, I was shocked by, you know, the practice
Speaker 4: that we had were making decisions around surgery, like, you know,
Speaker 4: what patients should get it versus not, how likely is
Speaker 4: it to work? What part of the brain should we remove.
Speaker 4: All of those visions were at the time and the
Speaker 4: early two thousands driven by clinical opinions. Right, you know,
Speaker 4: you have an experienced surgeon, they decide to do this.
Speaker 4: Somebody else might decide to do something completely different. And
Speaker 4: I didn't feel that that was the right way to
Speaker 4: practice medicine.
Speaker 5: You know that we.
Speaker 4: Needed to be more evidence based and data driven. So
Speaker 4: I went in the business in research of building models,
Speaker 4: predictive models that can ingest data from all of the
Speaker 4: tests that we would do about on these patients before to.
Speaker 5: Figure out surgery.
Speaker 4: So I learned how to analyze all types of data
Speaker 4: from genetic data and individuals to pictures to electrical recordings
Speaker 4: and then combine those into these prediction models.
Speaker 3: So basically you're looking at you're taking large numbers of
Speaker 3: surgeries for epilepsy, and you're seeing what kind of connection
Speaker 3: there is between the success of the surgical intervention and
Speaker 3: the underlying presentation of.
Speaker 5: The patient exactly.
Speaker 4: So I would be telling the patient what is your
Speaker 4: specific chance of becoming seizure free with surgery instead of
Speaker 4: giving them statistics about you know, like you know, in general,
Speaker 4: how well is that going to be effective? So that
Speaker 4: piece of individualizing medicine.
Speaker 3: So Cleveland Clinic decides to create a post called Chief
Speaker 3: Information Officers.
Speaker 4: If research information we have research, yes, always, chief information
Speaker 4: officer runs it.
Speaker 3: Oh yes, chief research information.
Speaker 5: Yeah, so it for research.
Speaker 3: This is parenthetically a huge job. Yes, So you apply
Speaker 3: for this, do you know that you're going to be
Speaker 3: thinking and talking and dealing with quantum.
Speaker 4: I started in January twenty twenty, and like every leader
Speaker 4: who's put in a position, remember everybody tells you should
Speaker 4: read that the first ninety days, Oh great, how to plan.
Speaker 4: So I was reading that and doing my listening tours
Speaker 4: to understand, and then COVID hits and I had a
Speaker 4: call from our executive suite about all, there is this
Speaker 4: thing called COVID that's coming. We will start testing people
Speaker 4: in four days. People will want to do research with COVID.
Speaker 5: Make it happen.
Speaker 4: So it was there's no chapter in the book about that,
Speaker 4: you know, So so I then it really it was
Speaker 4: like a pressure cooker, you know.
Speaker 5: Test with Yeah, I have to create.
Speaker 4: This access to data, structured you know, resources so that
Speaker 4: we can learn from it as quickly as possible. And
Speaker 4: so that was a catalyst. So then comes twenty twenty one.
Speaker 4: That was the year of our centennial one hundred years
Speaker 4: for Cleveland Clinic. So everybody, not just me, we were
Speaker 4: in a mindset where we were thinking long, long term,
Speaker 4: you know, like what made us specialized till now? How
Speaker 4: do we stay relevant? Where is the world going to
Speaker 4: be ten years from now? And what should I get
Speaker 4: going right this moment to shape that and be ready
Speaker 4: for it. And that's when quantum came in my mind,
Speaker 4: where unless we invest in it now twenty twenty one,
Speaker 4: we will not be ready for this next computer revolution
Speaker 4: that's coming after AI.
Speaker 3: Everybody in the world right now is doing nothing but
Speaker 3: talking about AI, and you are already thinking one step
Speaker 3: beyond the quantum? What's different about the opportunity that quantum
Speaker 3: creates for American research than AI.
Speaker 5: Biology?
Speaker 4: The human body, by definition, is much closer to fundamentals
Speaker 4: of quantum, you know, to quantum mechanics and quantum physics
Speaker 4: than it is to AI. AI is a classical computing
Speaker 4: approach that in essence reduces every piece of data to
Speaker 4: a black or white binary categorization of a.
Speaker 5: One or a zero.
Speaker 4: At its core nature around us, the human body, there
Speaker 4: is nothing categorical about it. It's that whole, you know,
Speaker 4: continuum of colors of life. Quantum its principles are that,
Speaker 4: you know, so there's all the scientific principles about quantum
Speaker 4: physics and superposition and in tanker, all of these complex
Speaker 4: things that people have a hard time with. But for
Speaker 4: in essence, it really is much more aligned, Like if
Speaker 4: I want to draw a colored picture, I will not
Speaker 4: go and pick up charcoal. You know, It's much easier
Speaker 4: for me to draw it if I had a colored
Speaker 4: palette with me.
Speaker 5: And quantum offers that.
Speaker 4: There is plenty of situations in medicine that are just intractable,
Speaker 4: you know, meaning it's not an issue just of it
Speaker 4: being AI being slow, or it doesn't have enough data,
Speaker 4: or if only we got more GPUs, you know, we
Speaker 4: can answer those questions. There are some problems in medicine
Speaker 4: that are fundamentally such that even if you give me
Speaker 4: all the GPUs in the world, there is no way
Speaker 4: that AI can model accurately how these molecules in the
Speaker 4: body are interacting among each other, or how comp electrons
Speaker 4: are moving within the mitochondria. These are the engines within
Speaker 4: our cells. There's these fundamental things in biology that AI
Speaker 4: and classical computers are just not built to be able
Speaker 4: to simulate.
Speaker 3: So you must go to dinner parties, doctor j. High.
Speaker 3: People must ask you what is quantum computing? What do
Speaker 3: you tell them?
Speaker 4: Yes, although I often, you know, we talk about other
Speaker 4: things at dinner parties.
Speaker 3: Eventually it comes down to you. If I was at
Speaker 3: that dinner party with you, I would ask you what
Speaker 3: is quantic computer?
Speaker 6: I would say, depends on how much time we have
Speaker 6: at the party to explain it. The short answer would be,
Speaker 6: it's a completely different way of working with computers than
Speaker 6: what we're used to right now. You can imagine that
Speaker 6: AI as being like a car that you take to
Speaker 6: go from one place to another. No matter how fast
Speaker 6: that Ferrari can get and how much fewre you put
Speaker 6: in it, it's never going to be a fighter jet.
Speaker 5: It's never going to be a plane.
Speaker 4: AI is the car, the plane is quant They are
Speaker 4: ways to get from point A to point B, but
Speaker 4: they work very differently, and we always use them in together.
Speaker 4: If I'm flying from Shaker Heights to Yorktown Heights, I
Speaker 4: drive to the airport, get on the plane, then take
Speaker 4: an uber to get to Yorktown Heights. In research, we
Speaker 4: will do the same. We do some piece of it
Speaker 4: in AI, some piece of it in Quantum, and then
Speaker 4: go back and forth.
Speaker 3: Back in twenty twenty one, Cleveland Clinic and IBM announced
Speaker 3: it they were starting something called the Discovery Accelerator. What
Speaker 3: is that?
Speaker 4: It's an initiative, a program, a partnership really that is
Speaker 4: designed to a bridge at advanced computational tools and technology
Speaker 4: through the leader in that space, IBM with biomedical science
Speaker 4: and research and life sciences problems, and that is a
Speaker 4: Cleveland clinic. We called it a discovery accelerator because that
Speaker 4: was our goal. You know, we were both on both
Speaker 4: sides challenged to the fact that discovery in medicine was
Speaker 4: just taking too long. The classical example that really brought
Speaker 4: it to life to us then was drug discovery that
Speaker 4: it took over a decade and it still does actually
Speaker 4: over a decade. From the moment that there is a
Speaker 4: compound that someone in a lab biomedical lab thinks it
Speaker 4: would be effective to treat a certain disease, it takes
Speaker 4: about ten to thirteen years from that moment to when
Speaker 4: that drug is on a shelf for a patient to
Speaker 4: get tool from a pharmacy. And that was just too
Speaker 4: much of a gap to allow when we have so
Speaker 4: many health conditions that we needed to address, and a
Speaker 4: big part of that gap could be computationally solved. Better
Speaker 4: simulation of compounds, designing drug trails that are more efficient,
Speaker 4: you know, that would finish faster. So that was the
Speaker 4: motivation to bring computational tools and technology closer to biomedical researchers.
Speaker 3: Cleveland Click and IBM team up, and I'm assuming the
Speaker 3: IBM Quantum guys and other people descend on Cleveland and
Speaker 3: you have your first meeting with them. Are they telling
Speaker 3: you things you would never thought of? Or I mean,
Speaker 3: I'm just curious about what's the difference between what you
Speaker 3: thought was the potential was and what you discovered the
Speaker 3: potential was.
Speaker 4: That is an excellent question if I had to prioritize.
Speaker 4: Is one lesson that I learned over the past few
Speaker 4: years of doing this. It is that you can never
Speaker 4: know what your you know where your brain is going
Speaker 4: to go and discovery until you talk. You know, you
Speaker 4: have to open it up and really listen to try
Speaker 4: to learn what the other people are saying.
Speaker 5: It's it went both ways.
Speaker 3: Can you give you an example.
Speaker 4: So, okay, we built the quantum right, so it took
Speaker 4: it took like eight months to get this machine put together, and.
Speaker 5: We put it in our cafeteria. That's the whole other story.
Speaker 3: Where you put the quantum machine computer in your cafeteria? Yes, yes, yes,
Speaker 3: Can you see it when you're eating lunch?
Speaker 1: Yeah?
Speaker 4: Yeah, we have people eating lunch around it all the time.
Speaker 4: We wanted to have a machine that people can see.
Speaker 4: Otherwise it the program wouldn't launch properly.
Speaker 5: So I needed to have it physics.
Speaker 4: So we had to look at retrofitted in existing space,
Speaker 4: and the cafeteria space worked out.
Speaker 5: It was far enough from the street, you know, there was.
Speaker 4: No vibration that it was a double floor, you know,
Speaker 4: so the ceiling was high enough. It just like technically
Speaker 4: fit all of those requirements. And it was either there
Speaker 4: or we put it in our data center, which is
Speaker 4: a building and like another city close to Cleveland, and
Speaker 4: we picked the cafeteria.
Speaker 3: Yeah, now why I know this is sort of this
Speaker 3: is kind of hilarious, but there's a serious point I
Speaker 3: I want to touch on, which is why do you
Speaker 3: need it on premises?
Speaker 4: On the premises Cleveland Clinic research, We needed to change
Speaker 4: how we think about research and shift the mindset of
Speaker 4: all of our researchers and all.
Speaker 5: Of our researchers.
Speaker 4: I'm talking about three thousand individuals who are one hundred
Speaker 4: percent doing biomedical research and Cleveland Clinic two one hundred
Speaker 4: and thirty labs individual pis. So that's the scale that
Speaker 4: I'm talking about that we had to create an impact
Speaker 4: on so.
Speaker 5: Having it having it be there was.
Speaker 4: As much for inspiration and to trigger our motivation to
Speaker 4: change as it was a you know, a practical solution, say,
Speaker 4: because we had the space and the connections and all of.
Speaker 3: That I saw in it. IBM headquarters. They're beautiful.
Speaker 4: Their works of art they are They're gorgeous, and the
Speaker 4: one that we have is the most gorgeous one of all.
Speaker 5: This is not me, you know, the mom bias talking
Speaker 5: about it.
Speaker 4: It is you know, they got an award, the Red
Speaker 4: Dot Award for Design went to IBM and Cleveland Clinic
Speaker 4: for our quantum and the quantum that IBM built for
Speaker 4: RPI a couple of years after hours. They modeled it
Speaker 4: after hours, not after the you know the ones from before.
Speaker 3: Just just so people know, we're talking about basically a
Speaker 3: small garage.
Speaker 4: Size eleven foot by eleven eleven feet eleven feet.
Speaker 3: You know, the cube small.
Speaker 4: So it's a it's a glass cube, and the glass
Speaker 4: comes all the way from Italy. It's the same glass
Speaker 4: that protects the Crown jewels and you know the Mona
Speaker 4: Lisa and all that. So so there's a glass all
Speaker 4: around and then there is the tube that you see,
Speaker 4: the stainless tube that's shiny, you know, and clean, but
Speaker 4: the technologies all inside of it, you know, the the
Speaker 4: leer and then the processor and the bottom.
Speaker 5: And it's just a fascinating thing to watch. And it hums.
Speaker 5: It makes the sound so even it sounds alive.
Speaker 3: You have a great deal of affection for your computer,
Speaker 3: and we.
Speaker 5: Love our machine.
Speaker 3: I want to go back to something you said before,
Speaker 3: which is when you had your initial conversations with IBM,
Speaker 3: both sides learned things that they hadn't previously thought of.
Speaker 3: Give me an example of something that either side hadn't
Speaker 3: realized could be done with this new technology.
Speaker 4: Sure, I mean on the equivalent clinic side, we thought
Speaker 4: that what quantum should be good for is that it
Speaker 4: would be a faster computer, right, so that we have
Speaker 4: these big data sets that are requiring much more compute
Speaker 4: power and you know GPUs than what we have and
Speaker 4: we should.
Speaker 5: Just run them on the quantum.
Speaker 4: And what we came to realize is that quantum actually
Speaker 4: does not do well with these large data sets. What
Speaker 4: it does well with our smaller, better defined data sets,
Speaker 4: but ones where simulation is more important, you know, we
Speaker 4: have to run them through multiple models, you multiple simulations
Speaker 4: of how they interact. As an example, one of the
Speaker 4: very first projects we threw at Quantum was wanting to
Speaker 4: predict complications cardiac complications from general surgery using electronic health
Speaker 4: record data. So data from our electronic health records are
Speaker 4: by definition these really big data sets. You have in
Speaker 4: them every single thing that you know about the patient
Speaker 4: that you've collected. And we thought that Quantum would help
Speaker 4: us build models, better models with this data. And it
Speaker 4: failed miserably. It wasn't good, you know, at doing that thing.
Speaker 3: And why didn't it? Like that problem because it's too
Speaker 3: it's too bigger than wieldy.
Speaker 4: Well, because the hardware isn't tready right, So the hardware
Speaker 4: with Quantum, that issue was back then. You know, now
Speaker 4: we've upgraded our processor. But still Quantum now is limited
Speaker 4: by noise and by its ability to correct for errors
Speaker 4: when it's doing computation. And the more data that you
Speaker 4: throw at it that you require it, you know, to
Speaker 4: put in its system, you know, to model interactions, the
Speaker 4: more errors it's likely to make. So then the harder
Speaker 4: it is for it to get to an answer.
Speaker 5: That you can trust.
Speaker 4: Now, we made a lot of progress sense, which I'm
Speaker 4: sure we'll get to.
Speaker 5: I hope we'll get.
Speaker 4: To with some recent breakthroughs that we made in that
Speaker 4: space with modeling large compounds and interactions. But the way
Speaker 4: that got us to where we are now where we
Speaker 4: could model large interactions, it took us figuring out that
Speaker 4: we shouldn't be doing everything on quantum.
Speaker 3: So give me an example of a problem that quantum
Speaker 3: is ideally suited for that the quantum really.
Speaker 4: Likes in drug discovery, for examine chemistry, it likes chemistry
Speaker 4: a lot because in chemistry what it has to What
Speaker 4: we wanted to model is how a drug that we
Speaker 4: put in our body is going to interact with We
Speaker 4: say the protein you know, the target, the ligand that
Speaker 4: it needs to bind too, like you know, the drug
Speaker 4: is the key and it needs to fit in a
Speaker 4: lock in certain parts of the body to open it,
Speaker 4: get in, do it stay. And there are many keys,
Speaker 4: many potential compounds that we could test for many parts
Speaker 4: of our body. We don't really know how they're going
Speaker 4: to interact. Tradition, what we do is we have to
Speaker 4: build all the keys. We have to manufacture all these
Speaker 4: compounds and then do actual trial clinical trials, put them
Speaker 4: in people, put them in animals and see what happens.
Speaker 3: See which one is best?
Speaker 4: See yeah, which like two fit best together. So we
Speaker 4: use AI to try to help us with that. But
Speaker 4: AI can only model what it had learned, right, So
Speaker 4: for rare diseases conditions that there isn't enough information out
Speaker 4: there on what the you know, the locks look like,
Speaker 4: it's really hard to then model it with AI and
Speaker 4: get an accurate prediction of whether there's a fit or not.
Speaker 4: Quantum does not rely on the previous data for its modeling.
Speaker 5: Quantum does.
Speaker 4: It's modeling purely based on the physical characteristics of the
Speaker 4: compound of the key you know that you're designing. So
Speaker 4: that makes it ideal because it's then untethered with It's
Speaker 4: not limited by do we have enough samples or don't
Speaker 4: we have enough samples, or you know, what the previous
Speaker 4: studies find or not. It's purely based on those physical properties,
Speaker 4: those quantum properties. So we found ourselves in situations where
Speaker 4: we were able to predict the fits between certain targets,
Speaker 4: and where in conditions like Alzheimer's disease, for example, we
Speaker 4: published where the quantum based modeling of the compound was
Speaker 4: much better than what we would have gotten with what
Speaker 4: we got right, Like, we did it both ways, and
Speaker 4: the quantum one was the better fit than the AI
Speaker 4: generated one.
Speaker 3: This is fair. Quantum is a little more of an
Speaker 3: artist and a little less of a of a kind
Speaker 3: of nerd AI sounds nerdy to me. What seems like
Speaker 3: creative and artistic, creative, creative.
Speaker 4: It's like people, Yeah, it's it opens up path that
Speaker 4: you never knew existed. That's why when I get asked
Speaker 4: about what do I see is the best, you know,
Speaker 4: the the most important breakthrough that quantum is going to
Speaker 4: allow us to do, my answer is I really don't know,
Speaker 4: because we I You know, when other people invented these
Speaker 4: new technologies, I don't think they really knew that they're
Speaker 4: you know, like think of laser. I don't think the
Speaker 4: person who invented laser thought that they will be used
Speaker 4: to scan groceries at the grocery store.
Speaker 5: You know.
Speaker 4: But so technology developing technology, the way I think of
Speaker 4: it is like having a baby. You know, you raise
Speaker 4: it as best you can, but then they're going to
Speaker 4: go off and do their thing, and you will be
Speaker 4: tying them down if you restrict them to just what
Speaker 4: you thought they should do, you know, So it.
Speaker 5: Opens up that.
Speaker 4: Space, that creative space for us to ask questions.
Speaker 5: Differently than we used to.
Speaker 4: We should train our mind to stop starting from classical
Speaker 4: and then trying to squeeze it into quantum. We have
Speaker 4: to learn how to think quantum up front, right from
Speaker 4: the beginning, which we haven't really been doing as a
Speaker 4: scientific community since our inception. We were trained, and how
Speaker 4: do you convert what you're thinking into a formula that
Speaker 4: you can ask from a computer which is a classical
Speaker 4: right computer with quantum because of how it works, it
Speaker 4: can answer questions, It can look at problems in a
Speaker 4: very different way. So we have to think differently about
Speaker 4: the questions and how we ask them.
Speaker 3: With IBM, you recently modeled protein with over twelve thousand atoms.
Speaker 3: Talk to me about that and why it's so meaningful
Speaker 3: for drug discovery.
Speaker 4: So in October of twenty twenty four, so just eighteen
Speaker 4: months ago, the largest compound biological compound that could be
Speaker 4: simulated with quantum was ten atoms big, and it was
Speaker 4: unfathomable back then that we will get past the thousand
Speaker 4: or few thousand atom simulation in the foreseeable future. And
Speaker 4: what our team with IBM and with Rieken in Japan,
Speaker 4: published last month April twenty twenty six, is a simulation
Speaker 4: of the electrical properties of an enzyme trips in in
Speaker 4: the body That is twelve thousand, six hundred atoms big
Speaker 4: for you know, orders of magnitude beyond what anybody thought
Speaker 4: was possible in that span of time. And the reason
Speaker 4: why that happened was because of a you know, innovations
Speaker 4: in the technology itself where the teams stopped thinking of
Speaker 4: quantum and AI as competitors and instead thought of them
Speaker 4: as different members of the same team. Right you're you're
Speaker 4: We're now in basketball season in the US, the n
Speaker 4: b A, and you need the you need defense, but
Speaker 4: you also need your center, somebody to shoot.
Speaker 5: You need all of the pieces to work together.
Speaker 4: So with this, it was figuring out where do I
Speaker 4: put you know, when do I put AI on the field,
Speaker 4: When do I put Quantum on the field, and how
Speaker 4: do I tell them.
Speaker 5: To work together.
Speaker 4: It's the scientific terms the quantum centric super computing. So
Speaker 4: quantum is at the center, but we're using our supercomputing
Speaker 4: tools AI classical to interact with it and split that
Speaker 4: big problem of the twelve thousand, six hundred atoms into pieces,
Speaker 4: where some pieces are best served with quantum and others
Speaker 4: are best served with classically.
Speaker 3: This distinction that we now cling to AI and quantum
Speaker 3: are these very different things develop by different people for
Speaker 3: different purposes. What you're suggesting is that's probably going to
Speaker 3: go away. Yeah, in the future, these things will work together.
Speaker 3: It's going to become teamwork and not one on one
Speaker 3: competition exactly.
Speaker 5: And it is that now in Keeveland, Clank.
Speaker 4: I mean, the way we've evolved our priorities with IBM,
Speaker 4: it's a realization that both organizations have come to where
Speaker 4: really to for progress to happen, we should stop seeing
Speaker 4: them as separate. We should put them together and work
Speaker 4: to the best of what each piece of technology can provide.
Speaker 3: One theme running through a lot of your your what
Speaker 3: you've been talking about is that the arrival of this
Speaker 3: new technology requires the researcher to behave differently and that's
Speaker 3: one of the reasons why you want the quantum machine
Speaker 3: and the on full display. And then you were talking
Speaker 3: about how you have to ask different kinds of questions.
Speaker 3: I'm curious, can you can you can you elaborate on
Speaker 3: that a little bit. So take me back, for example,
Speaker 3: to your earliest days. If I had given you all
Speaker 3: these tools that people have now, how would your research
Speaker 3: have proceeded differently? What would you have done differently?
Speaker 5: You know, I would have.
Speaker 4: Looked at the molecular components of the human brain as
Speaker 4: they relate to outcomes of brain surgery way earlier than
Speaker 4: I did. The first ten years of my career doing
Speaker 4: research was all spent building models that were purely based
Speaker 4: on brain waves.
Speaker 5: And pictures of the brain.
Speaker 4: It wasn't until after I hit a wall with my
Speaker 4: models aren't getting past that eighty percent accuracy threshold that
Speaker 4: I started thinking, oh, you know, there must be something
Speaker 4: genetic or you know, more biological that is influencing this.
Speaker 4: Had I been exposed to quantum at least as a
Speaker 4: onsept right, to quantum computing and what quantum science is
Speaker 4: back then, I think it would have opened up my
Speaker 4: mind to realize that it's not just about what I see.
Speaker 4: You know, there is hidden relationships that exist within the
Speaker 4: human body, and that's our genetic makeup and our chemical
Speaker 4: makeup that influence what comes to the surface that urge
Speaker 4: to dig deeper.
Speaker 5: The other thing, it would have changed.
Speaker 4: Is it would have made me reach out to engineers
Speaker 4: and physicists and mathematicians much earlier in my career.
Speaker 3: Yeah, yeah, yeah. And how would it have changed is
Speaker 3: a very kind of prosaic question, but just kind of
Speaker 3: the day to day life of someone doing medical research.
Speaker 3: I mean, the oh wow, you walk into the office
Speaker 3: in the morning. How does your day proceed differently when
Speaker 3: you're when you have these kinds of tools at your fingertips.
Speaker 4: That's the fundamental question in biomedical research right now, and
Speaker 4: it's it's less to do with quantum, more to do
Speaker 4: with agentic AI, right, these agents that we can work
Speaker 4: with now to help us how to think more creatively
Speaker 4: and how to do work that up until now was
Speaker 4: more like Scott work that the researchers had to do,
Speaker 4: whether it was you know, so the hypothesis generation has
Speaker 4: always been the most creative part of aspect of scientific research.
Speaker 4: But what comes after it with the data collection, for example,
Speaker 4: that was always such a repetitive, you know, exercise, and
Speaker 4: then the analysis after that was something that was very
Speaker 4: resource intensive and you had to try so many different
Speaker 4: approaches before you get to an answer, and that was
Speaker 4: fairly complex. Now with access to urgantic AI and you know,
Speaker 4: some quantum. Of course, we can spend more of our
Speaker 4: energy on the creative thinking part of the aspect of
Speaker 4: the work and less on the you know, just that repetitive.
Speaker 3: When you look around, I'm assuming you walk around Cleveland
Speaker 3: Clinic and you have lots of conversations with some of
Speaker 3: the most brilliant medical researchers in the world. Are you
Speaker 3: satisfied with how quickly and aggressively they are adopting these
Speaker 3: new technologies or do they still need encouragement from you
Speaker 3: to do you have to say that? People? Wait, I've
Speaker 3: got this machine in the cafeteria. You should be using
Speaker 3: it for this problem.
Speaker 5: How much are you doing.
Speaker 3: That kind of encouraging in cheerleading or is it unnecessary?
Speaker 4: No, there's plenty of cheerleading that's necessary people. You know,
Speaker 4: humans don't like to change. It's hardwired in us. So
Speaker 4: there is plenty of cheerleading. But what happens is, i
Speaker 4: mean the way we built our program to where we
Speaker 4: are now, So Cleveland Clinic now is pretty much winning
Speaker 4: every global competition. And Quantum for life sciences, whether that's
Speaker 4: the welcome trust, you know, Quantum for biological applications. We
Speaker 4: our partner, there was a startup in Finland, Algorithmic.
Speaker 5: They're brilliant.
Speaker 4: We worked with them to develop a photodynamic drug therapy
Speaker 4: for cancer or whether it is the NIH with they
Speaker 4: had an X price challenge for quantum or university is
Speaker 4: that we're collaborating with. We have a program that's bringing
Speaker 4: startups to our ecosystem. We give them access to the
Speaker 4: machine if they have a question that is significant enough
Speaker 4: for life sciences universities that we're building a bachelor's, master's,
Speaker 4: PhD programs with on quantum computing. We developed that whole
Speaker 4: ecosystem around it. If that's not cheerleading, I don't know
Speaker 4: what else would qualify for cheerleading. But then what happens
Speaker 4: is these early adopters, the risk takers, become the cheerleaders themselves, right,
Speaker 4: and then they work with it, they achieve success, and
Speaker 4: we're nothing but competitive in medicine and science. Right, So
Speaker 4: then it becomes okay, so and so I did this
Speaker 4: with this machine, Let me learn it so I can
Speaker 4: do the same. And it becomes this virtuous cycle of
Speaker 4: then innovation and growth and people wanting to work together.
Speaker 5: It's just been fascinating to watch.
Speaker 3: You said that people don't like change, but you clearly do.
Speaker 5: I do. That's the mindset of researchers, right.
Speaker 4: A researcher is someone who is not afraid to fail,
Speaker 4: actually sees failure as a chance to learn something right,
Speaker 4: to do better the next time. So we get rejected
Speaker 4: all the time and we don't care, right, we keep
Speaker 4: moving on with papers.
Speaker 5: Grant applications. So that mindset is what all of.
Speaker 4: Research is built around. So it's a very forward looking mindset.
Speaker 4: And Cleveland Clinic as they health system, we wouldn't have
Speaker 4: survived one hundred years. We wouldn't have done all of
Speaker 4: the firsts that we had. Serotonin the chemical that drives
Speaker 4: the whole science of psychiatry and neuroscience that was discovered
Speaker 4: in Cleveland Clinic. So as an organization, we have enough
Speaker 4: of people who think that way that they will be
Speaker 4: the early adopters who will pull the others with them.
Speaker 3: Yeah, one last question, look ahead ten years, pat me
Speaker 3: a picture of what quantum and related technologies look like
Speaker 3: for a place like Cleveland Clinic and tenures.
Speaker 4: Well, what I hope is that ten years from now,
Speaker 4: if I if I'm seeing a patient in clinic who
Speaker 4: has bad epilepsy, and for the love of you know,
Speaker 4: I can't figure out what medicine do I need to
Speaker 4: prescribe to them to make them seizure free. I will
Speaker 4: be able to send them to get a blood test
Speaker 4: that we can then run through some analytical platform that
Speaker 4: leverages both quantum and AI that can model exactly for
Speaker 4: that patient what compound, either existing.
Speaker 5: Or not to be developed.
Speaker 4: It's some new chemical that we haven't tested for that
Speaker 4: indication yet is going to treat them, you know, make
Speaker 4: them seizure free. It's and I think quantum is ideal
Speaker 4: for a condition like mine epilepsy, because we are a
Speaker 4: rare disease, and I think that benefit that it's going
Speaker 4: to have will start with rare diseases, you know, as
Speaker 4: I explained earlier, So take any other rare disease. We
Speaker 4: should start there and then expand from that to more
Speaker 4: complex things like cancer for example, and others. But those
Speaker 4: rare conditions where we are left now completely scratching our
Speaker 4: heads and going intuition. It will make our care truly precise,
Speaker 4: and it will make drug development a more tailored exercise
Speaker 4: than the way it is now, where it's like a
Speaker 4: hammer that's looking for nails?
Speaker 3: Am I right in thinking that of all of the
Speaker 3: over the one hundred year history or more now of
Speaker 3: Cleveland clinic, this sounds like the absolute best time to
Speaker 3: be a Cleveland clinic.
Speaker 5: Yeah. I love it. No complaints, no complaints, And I
Speaker 5: am hiring.
Speaker 7: I need I need those quantum researchers, those people who
Speaker 7: are wanting, you know, to apply quant to genetic research,
Speaker 7: imaging research, every single aspect of biomedical research.
Speaker 4: We can't afford to just wait on the sideline and
Speaker 4: until the technology is ready and then you know, then
Speaker 4: it will teach us. It is ready now that twelve
Speaker 4: thousand Atham Sam relation wouldn't have happened just a year
Speaker 4: and a half ago.
Speaker 5: It's quidinn.
Speaker 3: So the headline of this conversation is we're hiring.
Speaker 5: Yes, Yeah, that's a good headline. We're growing, how about that.
Speaker 3: Smart Talks with IBM is produced by Matt Ramano, Amy Gains, McQuaid,
Speaker 3: Trina Menino and Jake Harper. Engineering by Nina Bird Lawrence,
Speaker 3: Mastering by Sarah Buguerer, Music by a Gramoscope, Strategy by
Speaker 3: Cassidy Meyer, Sophia Derlon and Tatiana Lieberman. Special thanks to
Speaker 3: doctor Laura Jahi, Alicia Real Cooney, and the Cleveland Clinic team.
Speaker 3: Smart Talks with IBM is a production of Pushkin Industries
Speaker 3: and Ruby Studio at iHeartMedia. To find more Pushkin podcasts,
Speaker 3: listen on the iHeartRadio app, Apple Podcasts, or wherever you
Speaker 3: listen to podcasts. I'm Malcolm Gladwell. This is a paid
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Speaker 3: represent IBM's positions, strategies, or opinions.