Algorithm, M.D.

a professor of neurobiology at the University of Pittsburgh. He helped build the technology that allowed a patient called Jan Sherman to control a robotic arm with her mind. In order to do that, Andy's team needed to give the arm away to know where Jan wanted it to move. They had to read signals directly from her brain and teach a computer to understand them. So they built a brain computer interface a b c I. We implanted electrodes

in her head. You have to put on these bulky connectors with thick cables going to a bank of amplifiers and computers. It sounds like something out of a science fiction film. But once Andy could see Jan's neurons firing, reading her intentions was simpler than you might think. What we found is that the rate that these neurons fire is related to the direction that the arm moves. When you add their signals together, you can get a very

precise representation of that movement. It's a very very simple algorithm, and it's like listening to a Geiger counter um and each click of the Geiger counters the same, but as you get closer to a radioactive source, those clicks come closer together. Listening for those clicks was the breakthrough necessary to translate Jan's thoughts into signals that the robotic arm could understand, and this meant that Jan could move again.

She could reach out and touch her husband, and Andy's team filmed Jan using the arm to complete a more playful goal that she'd set to feed herself chocolate for the first time in ten years for a woman, one giant fight for DHDR. Jan was able to do that with the robot, and not only that, but she made graceful and beautiful movements. That's what really blew me away. It looks much like a real arm in hand, and for someone who studies movement, it was really quite beautiful.

Andy is a scientist and a researcher not given to being sentimental, but the way he talks about Jan moving her robotic arm, it's like describing a dance a ballet. It's one of the most inspiring examples of humans and machines working together in concert that we've come across in all of our reporting for Sleepwalkers. But it also raises profound questions about the future of our health, our bodies, and our society. What are the implications positive and negative?

As AI makes us ever more able to decode complex systems like the brain or the human genome, how is AI poised to change the world of medicine. I'm as Veloshin and this is Sleepwalkers, So Kara I found Andy's story completely mind blowing, no pun intended. You know it shakes one of the last remaining mysteries. For the most part, neuroscience is still very much a black box problem. We know what's happening in the brain, but neuroscientists can't always know why and how, which is a lot like the

problem of black box AI. We are moving quickly into a world where sensors can read us better and better know they can read pupil dilation, carbon dioxide in the breath to understand people's emotions. Yeah, this is the stuff we talked about an episode four around using AI to better read biometrics with Poppy Chrum. The difference being in this case is that Andy isn't monitoring the outside of

our bodies, so the privacy concern is less. To read Jan's brain, he had to drill into her skull and place electros into the surface of her brain and then connect them to a computer, so that's unlikely to creep up on you. Google is not going to be doing that to get geolocation, not yet. Because that said, and he told me one of his big priorities is trying to figure out how to achieve the same effects without needing invasive surgery, so that people can use this technology

at home. Talking about reading the brain, one cutting edge application for AI is to restore language. A few months ago, I was actually reading this paper in Nature, and I'm not sure all podcast listeners read Nature. Well, thanks, Carol, you do the hard work so that we don't have to. I guess why might they pay me the big bitcoin um? But you know, basically this article was about decoding the human brain to produce language based on how the brain

tells the mouth to move well. Funny enough, this is something I spoke to Andy about a few months before your paper and Nature was published, Kara, and he was talking about exactly this. So if you record from motor areas associated with producing language, you can start to recognize certain words and phrases. Even I think that's realizable in the your term. What Andy is saying is that to create spoken words directly from the brain, we don't actually

need to read thoughts. We can just look at the last step, the moment when thought becomes action as neurons fired to move our tongue, our lips are jaw to create sound. And just like with jan, an algorithm can listen to those neurons and allow thoughts to become actions. And this could transform lives. So if you could start to recognize words and language from brain activity, that would be very helpful for people who are locked in with

a l S and can't communicate. We talked in the last episode about how many technological breakthroughs have come out of DARPER. That's the branch of the Defense Department charged with inventing technological surprises, well neural prosthetics or robotic limbs controlled by the mind have been an area of heavy investment for the agency. You may remember Gil Pratt from earlier in the series. He's now the CEO of the Toyota Research Institute, but previously he was at DARPA where

he worked with none other than Andy Schwartz. So he was involved in a project at DARPA which was called Revolutionizing Prosthetics. And the project that I started was to see if we could actually help some of the experimental

patients that he had to perform even better. So Kara I was interviewing Gil Pratt about his work at Toyota and on self driving cars, and we got talking about his interest in these human machine owner ships, and I said, let me tell you a story as a scientist called Andy Schwartz and his patient Jan and Gil was like, um, yeah, I worked on that. Yeah, no, no no, no, literally, it's funny. And it also shows the long arm of DARPA intended.

Again that's far away from military applications. You know, darp AS interest in prosthetics is actually because of veterans, many of whom lose limbs on the battlefield, which highlights again the dual use nature of so much innovation. Right, um and The work Gil was doing at DARPA with Jan was all about doing about a job of interpreting her brain waves using the existing models. My group made a system called arm assist that watched what Jan was trying

to do. It inferred Okay, now she's trying to pick up a block. Now she's trying to move it over to the left. Now she's trying to drop the block. You know, in a way very similar to if you use power Point and you have like snapped to grit or snapped the object turned on, it will help you move the mouse to where it thinks you want to go. This system helped her move the arm to where it inferred she wanted to go based on a very noisy

signal that was coming out of her brain. That noise in the signal was partly because the connection between Jan's brain and the decoding computer was weak. So Gil's team Adoppa designed a program to boost Jan's intentions. We tested her by randomly turning the assists on and off. And you can think of the assist as a guardian, like what we're developing for the car to stop you from having a crash, and we had the guardian in this case help as little as possible, but still be effective

at helping her to reach the goal. The amount of help that we gave her was so low that she couldn't tell whether it was on or off. But when Guardian was turned off, Jan's success rate fell. And in a human machine partnership is not just the computer that adapts to the human brain also adapts to the algorithm. And the's algorithms are simple and they rely on human programmed rules. So when they see cause in this case, enough neurons firing at the same time, they create an effect,

a movement. But what's tantalizing is that effect that output hints at something far deeper and infinitely complex personality. I like Moby Dick where he talks about Captain Ahab walking on the deck of the ship, and by observing his movements, you can really understand what he's thinking about. If you think about movement, it's really a communication between your innermost thoughts in the outside world. Andy and his team saw this come to life before their very eyes when they

connected another subject, Nathan, to a neural prosthesis. The robotic arm moved in accordance with the personality of the user Jan when she moved. She was very careful and gentle, and Nathan is more are of a video gamer. He's a younger guy. He's more of a competitor, so he would move much faster, and so he would pick up an object and then instead of placing it carefully in a receptacle, he would basically toss it into the receptacle

to be faster. Today we can see the difference in Nathan and Gen's personalities from how the brain controls a prosthetic arm. We can infer personality from output, but we don't even have sophisticated enough tools to ask why. As we get better and better at these computational approaches, will gain a much better understanding of the way the brain works. Rather than having some major event causing some simple consequence. Instead, it's more like a perfect storm where many factors come

together to generate a consequence. And if we understand which of those events are important and how they're combined, then

we can start to understand brain function better. If you see a cup and you're thirsty, if we could realize that what you want to do is to drink from it, then we could understand that you want to grasp the cup from the side, and if we could distinguish that from you grasping a cup with the intention of passing it to me, you might hold it now from the top, then we could do a better job generating the correct

movement for Andy. The next frontier is more deeply understanding the human brain carrot by developing new models and better algorithms. And that's really a computer science problem as much as a medical problem. And then really the next frontier in medicine is all about using AI to decode complex interactions. And in fact, you reported a piece on exactly this phenomenon. Yeah,

I spoke to a woman named Regina bars Ali. She's a computer scientist m I T and her own experience of how she was diagnosed with breast cancer actually inspired her to work on bringing AI into the realm of medicine. When we come back, we'll here from Regina and take a look at other ways AI is changing diagnostics and the future of our health. I remember still, I went to mamogram and they told me a high density but you shouldn't worry. Half of the population have high density breasts,

so don't worry about it. That's Regina bars Lie. Regina is a professor of Electrical Engineering and Computer Science at m I T. I couldn't believe in it. I didn't feel anything. There was nothing wrong, you know. I was continuing my morning runs and being fine. It was later on after her mammogram that Regina found out she had breast cancer, and as it often does, the news came as a surprise. And if I'm looking at myself, I really cannot explain what was wrong that I got this

disease that clearly didn't have any family history. I'm exercising, I'm meeting healthy and for many many patients that I'm me during my own journey, UH, their diagnosis came to them as the biggest surprise. The thing is, according to current medical standards, breast cancer risk is based on a few factors. Are you a woman, and are you old? Do you have the Brocko gene? And do you have

breast cancer in your family? But those are relatively simple inputs and they don't account for what complex systems we are above eighty percent of women who diagnosed with breast cancer there first in their families, so it's not clear you know what causes it. According to the Susan Becoman Breast Cancer Foundation, breast cancer is the most common cancer

amongst women around the world. Every two minutes a case of breast cancer is diagnosed and a woman in the United States, and every minute somewhere on earth a woman dies of breast cancer. That's more than four hundred women per day. So if you're listening to this podcast, you probably know someone who has been or will be diagnosed with breast cancer in their lifetime. And with that many people affected that they are bound to be some oversights,

like in Regina's case. I discovered that my own diagnosis was delayed because the malignancy was missed in the previous mammograms. And I also discovered that this is not a unique experience. So the question I asked is is it possible for us to know ahead of time what's to come? In other words, can we look into the future prior to

our diagnosis. Regina had already been a long time computer scientists at M I T. She thought often about machine learning in her work, but this new personal hardship redirected her thinking. When you go to the hospital, you see like real pain of other people. You see people who go through key most radiation, even though the hospital just want stop away from M I T. I just was

not aware there is so much suffering. And at that point when I came back, I was thinking, we create so much you know, exciting new technology it and I see why I we are not trying to solve it. It's it's just a travesty. And so Regine and her colleagues that M I T began training a deep learning model on over ninety thod mamograms from mass General Hospital, and with such a large data set, they were able to predict a patient's risk of breast cancer by comparing

one mammogram to tens of thousands of others instantly. My firm belief was that despite the standard risk factors, there is a lot of information in women's breast. Human eye which even seen you know, thousands or tens of thousands images over their lifetime, may not be really able to

detect it with a great clarity. However, if machine which is uh you know, trained on sixty images where it knows the outcomes, it can identify the difference in pixel distribution that I likely correlate to a future things that may come. The amount of data which you can train a computer on versus a doctor is massive and the AI was able to detect smaller details than the human I could pick up, so this does feel like a

perfect application for the strengths of machine learning. Fifteen months ago we put first our density model, which is every mammograms that goes through months general it shows a prediction to the radiologists and around of the times radiologists degree with the machine, and when they disagree, the more experienced

radiologists typically sides up with the machine. So Regina's early efforts are doing really well, and the hope is that more hospitals around the country will begin using these models for early detection and risk assessment. And that's not only because understanding risk is important, but also because misdiagnoses have led to unnecessary surgeries. I read this book The Emperor Whole Melodies. It's a book about comes, so I really like let me just say one particular moment it really

choices me out. It's about how men, male surgeons who are treating women with the surgeries this firmly believe that the more you cut out of you know, women's body, the beta is w likelihood. In the early twentieth century, there was an insurgence of doctors performing radical mastectomies, removing the entire breast, thus permanently disfiguring a patient, and radical mastectomies became the norm for much of the twentieth century. The reasoning was that removing a lump leaves the risk

of tumors growing elsewhere, so why take the risk. By the nineteen eighties, it was clear that radical mastectomies weren't actually in effective treatment for many patients, but according to Regina, surgeons still remove too much tissue out of an abundance of caution. The reason it happens, it's not because there is some evil doctors that wants to look another surgery. It's just because people are uncertain and it's high risk, and many times conceptician would say, I am ready to

go for the harshest treatment to minimize that chances. So what we demonstrated that with machine learning you can actually identify a city percent of the populations it doesn't need this type of surgery. Regina told me that had these deep learning models been in place at the time of her early mamograms, she might have detected her risk two years sooner. And in many cases, early detection makes a big difference in how a patient chooses to treat their cancer.

Since developing the breast cancer detection model, Regina is now co leading m I T S J Clinic, which is a new initiative focusing on machine learning and health. What I hope that we as a society advance since then and we are ready to bring you know, the recent science and help women, and even if it means that we need to change our beliefs about how risk assessment works. And Regina hopes that as a society we can move toward greater acceptance of using machine learning to enhance medicine.

Whichever one does a bit, Joel, that one should prevail. So big question, how do you feel, Kara about putting your health into the hands of an algorithm? You know, after speaking to Regina, who told me that she probably would have been diagnosed two years sooner using her models, it seems as though machine learning provides this unparalleled form of detection right because the most seasoned doctors simply can't compare thousands of data points at once. I don't think

the issue is algorithms replacing doctors. It's more a matter of equipping doctors the sharper tools that they can do their jobs. They've got to provide a patient with information and allow that patient to make informed decisions. Algorithms don't have a bedside manner. You know. What you say about the shop of tools reminds me of our conversation about creativity and episode two, using AI to give artists, musicians,

screenwriters new tools to do better work. At the same time, just like the art world, the medical profession sits on this enormous pedestal where we have to trust what they say because most of us don't have the tools to question them, right unless you're on WebMD. Yeah, the bane of every doctor's life. Yeah, you know, doctors have a hard enough time explaining medicine to patients. Imagine them having to explain artificial intelligence. Well, from experience, we can say

good luck to him. What's crazy to me actually is that Regina and her co author, this woman, doctor Connie Lehman, who's a radiologist at Mass General, were rejected from every single federal grant they submitted at first. Why would they

be rejected from all those federal grants? Because as much as there is a ton of buzz surrounding AI, I think people have to appreciate how new this frontier is right, and using machine learning to make predictions about people's cancer is very, very new, and it's going to take doctors a really long time to learn how to convey this information to patients. So that's where Regina is right now, figuring out how doctors can explain to patients, Hey, AI

helped us determine your cancer risk. Well, part of problem is that we don't yet have explainable AI. So it's not just that it's hard to explain it to patients, it's actually a black box. You may remember Sebastian Thrun, the founder of Google X from earlier in the series. As well as self driving cars and flying cars. He

works on medical diagnostics and he recognizes a problem. One of the conundrums of machine learning is that when you open up in doing that brook you look at like hundreds of millions of numbers, but he can't quite understand what's happening. So people are are concerned. People look at that Brooks say, add the thing of diagnals and cancer,

what does it do. We've talked about the black box problem in AI and how hard it is to trust decisions that can't be explained, but Sebastian is quick to point out that human beings can also be difficult to decipher. Let's remind ourselves our doctors are also black boxes. You can't open up the brain of your doctor and ask what was this here she using for diagonals and cancer. It's a fair point, and it's one has also been

noted by Siddathan Kache. He is one of the world's foremost cancer doctors and the Pulitzerprise winning author of The Emperor of All Maladies. That's the book that Regina referred to earlier, and Siddartha has also written extensively about how AI is changing medicine. I've been a huge fan of his work for a long time and actually ambushed him after a talk he gave in order to persuade him

to an interview for this podcast. So thinking about AI helping diagnose patients, it's worth asking is the human doctor so very different? One problem that I think is fascinating

is when a patient comes into the hospital. If you ask a particularly astute physician, that physician can actually describe to you what the most likely journey of that patient will be in the hospital, whether they're likely to stay for twenty five days suffered through bacterial stepsis, you know all from peeking in through the door of in emriency room, Siddartha started to wonder how doctors make those lightning fast calls, and it got him interested in understanding what the brain

is doing when a doctor makes a die ignosis. We actually understand very little about how human beings make diagnoses. I mean, the studies that have been done so far suggest that most people make diagnosis in a kind of recognition sense rather than an algorithmic sense. The classical description of how we make diagnosis was extraordinarily algorithmic, sort of goes down a series of elimination. It's not this, it's not that. Now. Whence the alto says algorithmic, he doesn't

mean using a computer algorithm. He means using rule based logic. If this, then that, et cetera. But what he learned was that despite what the textbooks say, that's not actually how doctors make a diagnosis. When you put doctors inside MRI machines and ask the question how do they make diagnosis? In fact what lights up is parts of the brain that are much much more to do with pattern recognition. Here's a rhinoceros, Here's not a rhinoceros. Here's an elephant,

here's not an elephant. Especially mature doctors make diagnoses based on patent recognition, and they'll flip around like moths around the flame and ultimately slowly arrive at the target. It's much more geographical way of thinking rather than linear. They're using a combination of Bayesian or prior probability understandings. They're using pattern recognition. They're understanding things about the patient and

figuring out what to do. Hearing Saddatha speak about doctor's cara in terms like prior probability understandings and Bayesian statistics really does make it sound like he's describing AI rather than people. Well, it kind of is. You know, neural networks are purposely modeled on the human brain. It's not as easy as causing effect. It's about drawing on a lifetime of experience to make best guesses based on competing information that we have to weigh appropriately in micro second.

It's no easy task. It's funny because we've worn several times on this series that we shouldn't be surprised when our creations reflect us, and yet it's almost impossible not to be. I feel a sense of uncanny chills when Saddatha is gribes a human doctor working like an algorithm. And he wrote about this in The New Yorker with the headline AI versus m D And he made this point, which is that human and machine processes of making diagnosis are converging. And it makes me wonder who's going to

have the final word. Well, I asked Sebastian thrun exactly that question. He will die of cancer a lot. I believe many of those deaths is actually preventable using artificial intelligence. It's amazing how diverse diagnostics you get. When you show a set of dermatologists the same set of images, some will say cancers as I would say five. And Sebastian has a personal interest in the topic. My family, unfortunately has a long, long, long history of cancer. My my

sister passed away last year. My mother passed away a young age. So one of my questions I had in my life with me is since my mother died, um, maybe we should not work on on treatment. We should really focus on detect not diagnostics. Diagnosis of skin cancer doesn't require looking inside your organs. You can just look at the person from outside and it turns out we're not heavenal symptoms. Before it becomes dangerous, it sits therefore

quite a vie. It grows below your skin, it spreads, and then it destroys your liver, and then your first symptom might be that back pain or a yellow face. Maybe we should just look every single day. In fact, Sebastian his work to make it possible for people to check themselves every day. He published a paper in Nature called Dermatologist level classification of skin cancer with Deep neural Networks.

What he demonstrated is that a program that runs on an iPhone performs just as well as dermatologists at diagnosing skin cancer. It sounds transformative, but Siddhartha has a very specific concern about this kind of technology entering the mainstream.

Well Over diagnosis is an important risk. A classic example of that is a lesion in the breast, a spot that is actually not reast cancer, but it's picked up and described as breast cancer, that leads to a biopsy, the barbsy leads to complications and so forth, and at the end of it you discover that that you know you've achieved not very much, except for subjecting a woman to an unpleasant procedure with unpleasant costs. We don't want

to catch just early cancer. We want to catch the early cancers that are likely to kill you, the other ones that are unlikely to become anything. We actually want to be able to reassure patients that they don't need a biopsy. Regina told Kara how screening enabled by machine learning could reduce unnecessary mystectomies, But according to Siddartha, we also need to be cautious about overscreening pushing us into

unnecessary procedures. And as AI and sensors become more ubiquitous, enabling us to constantly search for illness, there may be psychological implications that were not fully prepared for. This is the very or valiant notion of previvors. It's the word that I first encountering clinic in it was a woman who had brack of one mutation, but in fact did not have any breast cancer. She called herself a pre vivor of breast cancer. She was a survivor of a

disease that she yet did not have. Our culture hasn't reached the place that you know, we're routinely thinking of ourselves as previvors. But it has reached a place where surveillance is is constant. You know, you're moving from colonoscopy to mammogram to p s A test, to medical exam to retinal exam. And you can imagine stringing together with future devices, a culture in which the body is always being hunted scoured for being a potential locus of future disease,

and that will I think distrought culture fundamentally. It's a very Orwell and very scary idea, said Arthur Ludes. Of course, Sir George Orwell, whose novel four was prescient about the culture of surveillance that's now blooming around us. But I never thought about surveillance in medical terms before. And who might be surveilling our bodies. One might be a health insurance company or the government interested in, you know, who's

healthy and who's not healthy. There was a chance meeting between Siddartha and Sebastian that God Siddartha thinking about AI and medicine, but the two have fundamental disagreements on the risks and rewards of surveiling the body. I love Sid as a person, but I can tell you any doctor who tells you less data is better for you is irresponsible. If I could give you information with your skin cancer every day, you will live longer than if it's just

consulted avntologies every year or two. But also the unpredictability of death is part of the human experience. Our culture would be very different if we walked around with signs on our foreheads which told us the number of days that we had left to live. What Siddartha is describing is not some thought experiment. Using AI to predict time of death is fast becoming a reality. But what inputs does it use? And how am I knowing when we will die? Change our culture? Join us after the break.

Doctors are actually very poor at predicting death. If you look at the pattern of how people die, most people don't decline along a predictable path towards their death, so it's often a series of strings that snaps. If you think about the human being being held together like a puppet on on many strings, it's not that the puppet

slowly crumbles at a predictable pace. It's that all of a sudden, three strings collapse and the hand comes dangling down and the body and medicine tries to prop that piece up, and in doing so now two more strings get cut, and the and the foot collapses, and when

a certain number collapses that nothing can be done. So it's a fundamental failure of homeostasis that makes death very hard to imagine, conceive, And of course there is an emotional component to this, but unlike human doctors, AI doesn't get distracted by emotion. It looks at evidence and historical data to establish patterns. The algorithms actually do quite well in predicting death. What is it attaching weight to? Is

it a combination of things? Is it the fact that someone has a brain metastasis and has a slight rise in some blood value of some solid that predicts that this person is likely to do very badly in the next few days. You know, as you refine it further and further, many subtle things might start coming up that we don't know about, and those will be the most interesting ones. It's not just additives. That phrase. It's not

just additives. It's important to me, Kara because it connects the dots between what sadd Arthur is saying about predicting time of death and what Andy Schwartz was saying about getting better decoding the human brain. They're both about understanding systems where one plus one doesn't necessarily equal to where unexpected results emerge from complex systems. Thinking about this makes me physically ill. Um He's literally talking about predicting when

we will die and mind blowing. You know what, if you could know when you will die could change how we choose to live our daily lives. It's one of the things I find most disturbing this whole series. So much of how we live and how we aspire and what we hope for is connected to our uncertainty about when we're going to die. That and I can change

all of that. It's part of this more global line of thinking, which is that AI kind of takes the fun out of things well, I mean, and it also a big part of Western culture is the Bible and the fruit of that forbidden tree, and in Paradise Loss, there's this warning to know, to know no more, and there's always been this idea in history that there's something magic about not knowing. Yeah, you know, Milton aside, Okay, okay, okay, Nat you go. We can start to think about what

this might mean more practically. Lifespan data is directly linked to life insurance policies. You know, this would significantly change how much people pay. You also think about personal injury law, which takes into account how long somebody is going to live, so you can determine how much money they should get for loss of quality of life. These are things that would be greatly impacted by people knowing when they're going

to die. Right. So, Datha calls death the ultimate black box, which I think is actually the perfect description of black box. We know we will die, we just don't know how, and we don't know when. And with AI it's similar. We know we'll get a result in endpoint, but we don't know exactly how our input factors are combined to get there. Ironically, although AI itself is a black box, it's helping us unpack other black boxes, death being the

ultimate one, the brain being another. And then there's the human genome, the unique pattern of our DNA that makes each of us us. And we've met the genome, but there are a lot of concerns about decoding it, that it might be a sort of Pandora's box. Well, there's that story about the scientists in China who edited the genes of two babies using crisper and all the ethical concerns of creating genetic babies exactly. So I spoke with Andy Schwartz about actual progress being made in decoding the

human genome. If you go back to the late ninety nineties, and the race was on to discover the human genome, and the sound bikes were as soon as we understand all the genes, we can cure disease. So, for instance, there was a breast cancer gene, and there was an Alzheimer's gene, and if we just knew what those genes were,

we'd be able to eradicate these diseases. Well, it's been twenty years now and we're just beginning perhaps to get some sort of genome based therapies that might address some of these And what we found is that there's no simple cause and effect. Very rarely are there simple gene defects correlated disease. Rather, these diseases have hundreds of genetic basis and each of those is relatively weak, but combined together,

they generate these diseases. And so it's becomes a computational problem and we start looking again at this as a complex system where causality is no longer clear. And these are the complex computational problems. We're getting better and better at solving Sadartha himself is interested in exactly this area,

the confluence of genetics and computation. In fact, in twenty eighteen he gave a talk at Vanderbilt University called from Artificial Intelligence to Genomic Intelligence, and it's an area where we're making rapid progress. The first papers are just starting to appear there in preprint. One of them is extraordinarily interesting.

It appears to be able to predict height based on an algorithm and genomic information to all parents tend to produce to all children short parents and to produce short children. But we did not have ways to predict based on genetic information what your actual height was going to be. The question becomes, well, how do you take the genome and out pops height from it? If you can do that, that means you can take a field genome in utero

and predict this person's future height. You know, based on these first few papers that I read about this arena, you require deep learning to do this. I wanted to understand why this prediction of height requires deep learning algorithms, not simple ones like Andy used to interpret Jan's brain waves. It's not just additive you can just add up across multiple variations in the genome and arrive at a risk score. It's that there are interactions between genes that have to

be captured. Again, these are early days for artificial intelligence being unleashed on genomes, but it seems to me that complex problems of genetic architecture will soon be predictable using these kinds of algorithms, and that ability to predict raises

huge questions for all of us. If you want to know the height of your unborn child, but you want to know the risk of dyslexia, those questions are almost certainly likely to lead to extraordinarily acrimonious public conversations about what should be done and what shouldn't be done in terms of accessing the data, who's to store the data, how much privacy we should have about it, and how much it will distort human culture to have these pieces

of knowledge. Um So, if you think that clinically going when you're going to die is is going to distort culture, in knowing how tall your child is going to be in the future will also distort human culture. We haven't ever lived in a place or a space or a time when that knowledge has been predictable from a fetus. Artificial intelligence is giving us incredible power to see into the future, to ask and answer questions about the generations

to come. But it is up to us, our generation, to decide how we want to use this awesome power. But one thing that artificial neural networks can't do is defined principles. They can only work on classifying things that we tell them to classify. There is still a human telling the artificial neural network what it should be doing. There is something very fundamental about the human brain, a scientist's brain, a doctor's brain, and artist's brain that asks

questions in a fundamentally different manner, the why question. Why did this happen in this person in this time? Why does the melanoma appear in the first case? What is the molecular basis of that appearance? The most interesting mysteries of medicine remain mysteries that have to do with the y, and despite being at the absolute cutting edge of medical research, Saddatha's most important guiding principle was written in ancient Greek

over two thousand years ago. Remember the Hipocratic oath begins first, do no harm it's maybe the single profession where the oath of the profession is in the negative. And this is for a reason. It's for a profound reason in medicine, because we're intervening on bodies, because we're intervening on homeostasis, because we're intervening on cultures. Effectively, the capacity to do harm arises very quickly, and so the first do no harm injunction in the Hippocratic oath is is an important

thing to keep in my own mind. Um, you know, what are the harms that arise if I were to start knowing my risks of future disease, not just what advantages would I get in society. And this battle is happening in my mind, I assume in the minds of virtually every doctor. As we move forward into this uh beautiful and perilous future, in a world where our choices can create new beauty but also a new peril. It's important that we move into that future with real care.

And it's something sad Arthur has thought a lot about personally, because, like Sebastian Throne, he has a family history of heritable conditions. The risk is of schizophrenic disease and bipolar disorder, and right now the algorithms to predict this still don't exist. As the project of sequencing lots of genomes and asking what diseases people have matures, this data set will become available maybe five ten years from now. I will be past the period I suppose where that will make a difference.

But to my children and my grandchildren, it might make a difference, and they'll have to make that decision. I will advise them individually, and it will depend on humanistic understanding of what an individual's desire to understand their own risk is. There's no algorithm that predicts that understanding. As AI advances, we're being faced with more and more urgent

ethical choices. This, in turn, may put a new emphasis on the humanities, or, as Kai Fuey suggested, place a new premium on personal attention, human interaction, and emotional care. Once we give up some of the diagnostic pattern recognition material to machines, it will be time to play. It will be the time to play in the arena of human therapeutics, human biology, the complexity of the human interaction,

the art of medicine. My hope is that medicine, being more playful, will become more compassionate, more able to take into account individuals and their individual destinies rather than bucketing people in big categories. It means having more time to spend with humans. You know, we are so constrained by time that even compassion gets three minutes, we won't become more robotic, will become less robotic as the robots and our own What's the data describes is the holy grail

of the AI revolution. Could it allow us to be more human, to be better doctors, more fulfilled workers, and greater artists. Could it take routine work out of our hands and allow us to take better care of each other. It's a compelling vision, but as always, it has a dark side. While most doctors are guided by their hippocratic oath do no harm, there's no guarantee that new technologies

will stay in the right hands. The line between healing and upgrading our bodies is thin and contested, and as AI improves, we can begin to translate desires directly from brain activity, modify the physical traits of our children through gene editing, and accurately predict when we will die. In the next episode, we ask what does all of this mean for our future? As a species. We speak to the world's leading thinker on these questions. You've all know

her are author of Sapiens and Homodaeus. I'm Ozveloshin. See you next time. Sleepwalkers is a production of I Heart Radio and Unusual Productions. For the latest AI news, live interviews, and behind the scenes footage, find us on Instagram, at Sleepwalker's podcast or at Sleepwalker's podcast dot com. Sleepwalkers is hosted by me Ozveloshin and co hosted by me Kara Price, with produced by Julian Weller, with help from Jacobo Penzo and Taylor Chakoin mixing by Tristan McNeil and Julian Weller.

Our story editor is Matthew Riddle. Recording assistance this episode from Joe and Luna to Brina Boden and Joseph Friedman. Sleepwalkers is executive produced by me Ozveloshin and Mangesh Hattikiller. For more podcasts from My Heart Radio, visit the I Heart Radio app, Apple Podcasts, or wherever you listen to your favorite shows.