In short
Podcast Summary: The James Altucher Show - The Age of Prediction
Episode Overview In this episode of *The James Altucher Show*, James interviews authors Christopher Mason and Igor Tulchinsky about their book *The Age of Prediction: Algorithms, AI, and the Shifting Shadows of Risk*. The discussion focuses on the transformative role of predictive technology across various industries, including finance, medicine, and data analysis.
Key Themes and Discussions
The Importance of Predictive Skills
- Data Analysts as Future Careers:
- James emphasizes that future job roles will heavily revolve around data analysis and prediction, more so than traditional careers related to AI coding or engineering.
- The capacity to comprehend data and its implications is deemed vital across all industries.
Types of Prediction
- Three Categories of Predictions:
- Statistical/AI Predictions: Involves using data to model predictable outcomes (e.g., insurance risk, stock market behavior).
- Subjective Opinions: Predictions based on personal insights and beliefs (e.g., economic forecasts).
- Definitive Predictions: Certain future events based on established scientific facts (e.g., astronomical predictions).
The Data Explosion
- Genomic Data Growth:
- Christopher Mason highlights that genomic data is growing at an unprecedented rate, surpassing astronomical data.
- This "genomical" data can lead to insights about diseases and individual health risks.
Predictive Technologies in Medicine
- Potential of Genetic Analysis:
- Technologies can now analyze an individual's DNA to predict health risks, ancestry, and potential responses to medications.
- The conversation touches on the ability to modify genes in embryos and adults to eliminate genetic diseases.
Predictive Algorithms in Finance
- Igor Tulchinsky's Hedge Fund:
- Igor manages a hedge fund that uses vast data sets to predict stock market behaviors.
- The discussion reflects on how prediction differs in finance versus genetics, emphasizing that discovered patterns might vanish once widely known.
Ethical Considerations of Prediction
- Implications of Predictive Models:
- The potential misuse of predictive algorithms raises ethical questions, particularly in areas like insurance and personal data usage.
- Predictive algorithms must balance between enhancing decision-making and privacy concerns.
Future Predictions and Innovations
- Looking Forward:
- The hosts speculate on future advancements in predictive technology, such as:
- Health Monitoring: Everyday tools that can analyze personal health data.
- Space Exploration: Predictive algorithms aiding in interplanetary missions.
- Cultural Predictions: Using data to forecast trends in music or other cultural phenomena.
Conclusion The episode concludes with a consensus that we are entering a transformative era of prediction, where understanding and utilizing data effectively will be crucial to future success across multiple fields. James encourages listeners to embrace the age of prediction as a vital skill for career development.
Key Takeaways
- Data Analyst roles are projected to become increasingly important.
- The growth of genomic data is reshaping healthcare and predictive medicine.
- Ethical concerns regarding predictive algorithms must be addressed.
- Future innovations may lead to unprecedented predictive capabilities in various domains, including personal health and space exploration.
For further insights from the episode and to explore the authors' viewpoints on predictive technology, check the book *The Age of Prediction*.
Written by AI. May contain mistakes. Listen to the episode to check what was said.
Transcript
Automatic transcript. May contain errors.0:00Look, as a manager of people, as an employer, as an entrepreneur, and as an investor in start-ups, I can tell you the most important thing for your business is the quality of the quality of people of quality of quality of business. The best part is that great candidates are already on LinkedIn. Employees hired through LinkedIn are 30 % more likely to stick around for at least a year compared to those hired through the leading competitor. And I will tell you that the great thing about LinkedIn is that you're not just looking at random people. You're able to see the people who your friends and trusted peers and colleagues, who they trust and who they've hired in the past and who they recommend.
0:42And hiring doesn't have to be complicated. Realistically, when you have a business to run, you don't want to spend hours on hiring. You want to hire the right person as quickly as possible. That's why LinkedIn Jobs AI Assistant suggests immediately 25 great fit candidates daily so you can invite them to apply and keep things moving. Hire right the first time. Post your job for free at linkedin.com slash Altature, then promote it to use LinkedIn Jobs' new AI assistant, making it easier and faster to find the top candidates. That's LinkedIn.com slash Altature. Post your job for free. Terms and conditions apply.
1:24I think the biggest job of the next generation, like this is what I would do if I was figuring out what to study and train in is predictor. We've seen examples of this. We saw the movie Moneyball, where they used data about baseball players to predict which baseball teams would do the best. And I used to write algorithms for the stock market to use data to try to predict how stocks would act. And we're going to talk about that more today. And people use data also now to analyze the genome to predict what diseases someone might have and on and on. But the technology has gotten so good so fast. I'm happy to talk with the two authors of the book, The Age of Prediction, Christopher Mason and Igor Telchinsky.
2:15Now, Igor is interesting from the financial perspective. He has a$7 billion hedge fund, which analyzes millions of pieces of data around the world to predict stocks, to predict simply what's going to happen with stocks tomorrow or an hour from now or 10 seconds from now. And Christopher Mason uses data from the human genome to study what diseases we can start curing, what sicknesses someone might have or what traits someone might grow up with. So I wanted to know, what is the state of this industry? Like how much can we really predict How can we get better at it? What are the limitations? And then we just had a fun time while I pitched different ideas.
2:59So here's Igor and Chris, authors of The Age of Prediction.
3:09This isn't your average business podcast and he's not your average host. This is The James Altucher Show.
3:26You know, Chris, I'm amazed at all the things you were able to discover about people by analyzing their genetics. Yeah, basically, it's a predictive algorithm that's in every cell, right? So you have bits of DNA, RNA, proteins, and you leave these everywhere you go. So thinking of the forensics chapter, you're probably thinking about maybe, or even just the cancer diagnostics we can do with DNA. It's extraordinary. So the book is called The Age of Prediction, Algorithms, AI, and the Shifting Shadows of Risk is the subtitle. If I can try to summarize at a 20 ,000-foot level, it seems like there's three types of prediction.
4:04One is where you use statistics slash AI to model things that can either be very predictable or somewhat predictable, like things ranging from insurance risk to cancer risk using genes to stock market predictions. Then there's the kind of prediction where it's just someone's opinion, like Rifkin analyzing what the economy is going to be like five years from now. And then there's the kind of prediction where it's pretty definite. So the solar system is going to eventually collapse. I can predict that and I know 100 % chance I'm correct. Would you say that's roughly three categories of prediction?
4:45Yeah, based on facts, based on optimism, and based on reality. Yeah, I like that. What about based on pessimism? It's the same as optimism, right? It's just a flip the side. It just goes negative direction. But you can still use pessimism and look at the same facts and view it as it could almost be depressing to you if you think, oh, we're doomed because the The sun will engulf the earth in a few billion years. But you could think, well, no, that means we know when we got to get moving by. It could be exciting and get you moving. Exactly. So you point out the exponential growth in data. Maybe you could describe that a little bit, like how much more data we are generating now than even 10 years ago and what that means for prediction.
5:31I'll jump in. First is I think the amount of data, certainly in genomics and just the ability in biomedicine to generate data, is what's often been called, at least in genetics, is genomical amounts of data. Most people think of astronomical amounts of data as being really big and involving exabytes or yodabytes of data, which is trillions of terabytes. But it's actually genetic data and genomic data are now eclipsing the amount of data made by telescopes and astronomy. So there's a paper that just described it's called, is it genomical data or astronomical data? Which one's bigger? And concluded that there's actually more genomic and biomedical imaging data than there is astronomical data.
6:10So when you think of really large, you have to think of things in trillions of terabytes, not quite today, but in the near future. And that really basically means every day that you wake up, there's more data than any other day in human history. Let's do a little thought experiment. So let's say I wake up and now I can sample my DNA in seconds and do some diagnostics like right away or some AI or some statistics can do some diagnostic right away. What theoretically could I learn about myself this morning from my DNA? If you grab your DNA, so there's a lot of DNA in your body, but half of it is actually microbial DNA that moves and changes and evolves.
6:51Sometimes every 20 minutes, the bacteria are dividing. So you'll learn about any changes in your microbiome, which are the small creatures in and on and around you that have moved. So maybe you could pick some up from, you could pick up obviously like a pathogen like flu or COVID, SRCV2, which causes COVID. You could, of course, you get sick, but a lot of them are actually things that are the anchor for a full ecosystem that itself is a little pharmacy. So you can see if there's any changes in your gut microbiome. If you have problems with your gut, for example, you can look at your epigenetic changes, or it's not your DNA, but also how it's packaged and regulated.
7:27And of course, you can get new mutations. Every day you get mutations, most of them are harmless, but some of them could be the beginnings of a cancer that you could see if you saw it that very first day. What about from the DNA itself? So like you, and this is not something you would find in the morning, you would, when you sequence my genome or whatever, you would get all the kind of hard-coded things about my DNA. What can you see from that? Well, from there, you can tell a lot about you. like your ancestry. So are you, for example, do you have any Jewish ancestry? We had a fun part of the book where we talked about Igor as a, it was Jewish, but then at the end of the chapter, he got even more Jewish because the databases got updated.
8:04So we can see, you know, the databases, this is a good lesson of prediction is that your predictions are only as good as your training data and your databases. And, you know, when you change the databases, you'll get slightly, usually improved predictions, but sometimes they can go the other way. But so, you know, we can look at ancestry, your risk-taking likelihood, how fast you process caffeine or other drugs. So you can really be predictive about how and what way drugs and molecules will be processed in the body of any person. So some of this seems predictive. Some of this you know for a fact.
8:35So for instance, with the DNA, there are some genes where, and I'm going to simplify it with my language, but if they're on, you have like Tay-Sachs disease, for instance, and if they're off, you don't. And so it seems like some things, they had enough data that they were able to figure out which single mutation genes cause which diseases. And some data though is more predictive, like, oh, is this person more likely to be happy or sad or Jewish or not Jewish? And you do that by matching tens of thousands of humans who have sequenced their genome. You know what they, this person was Jewish and happy and this person was something else and also not happy maybe.
9:18And then you can start to build together probabilities based on a new genome. Yeah. In a nutshell, that's in the ballpark, right. And basically you look for differences in the phenotype or what people express as a trait and you compare that to the genome. And it could be everything from height, for example, which is there's no one gene for height. There's not even two. There's probably several hundred genes that really mediate how tall you are, but it is very heritable. If you look at tall parents, they'll have tall kids and short the inverse. So it is very heritable, but very complex. So it's what's called polygenic, meaning just more than one gene that influences that trait in a highly heritable way.
9:57And so as we found more of these genes, they'd be built into the models where we can predict to within about an inch or so how tall you'll be. So if you take a baby at birth, sequence the DNA, we can get down to probably about within an inch of how tall they'll likely be. And how far are we from the technology to manipulate a gene at birth or a sequence of genes at birth to change someone's height? We're actually doing it, not for height, but we're doing it for diseases and modifying DNA as we speak. So you can actually modify it. Embryos have had disease genes removed, for example, for hypertrophic cardiomyopathy or a heart disease gene, or even in an adult, there's been treatments to get rid of beta thalassemia by doing gene editing in the person's body as an adult for sickle cell and beta thalassemia, these blood disorders.
10:43Now, are these single gene diseases? Correct. So with multiple gene diseases, this is where the data is immense. The possible permutations of which genes could be... Let's say height is caused by 100 different genes out of what, like 32 ,000 genes or some outrageous number? About 60 ,000 total, yeah, yes. So the permutations are in the... I don't know, quintillions, quadrillions. So it's impossible to use statistics or a computer for that. Is this something like AI could start to figure out when there's multiple genes involved, like feeding it through neural networks the way they did with ChatGPT?
11:22You could basically feed the data from basically millions and millions of patients and their clinical metadata and their traits and essentially learn what are the new signatures that are driving some of these changes. But it wouldn't just have to be genetic data. It could also be what was in your diet or what was other factors in the environment And then what else mediates that last few percent? You can build into some of these models as well. But how far are we from really the technology to really understand things like height or intelligence or cancer? You know, all of these factors that involve hundreds, maybe thousands of genes.
11:57We're in some cases, we're very close. I'd say the height, for example, is pretty well teased out. Even autism risk, even though autism is complicated and there's several hundred genes, many of them are now consistently being identified. So we can explain a lot more of autism than we could certainly 10 years ago and almost couldn't barely do it at all 30 years ago. So I think even complex diseases or complex traits, we can now explain pretty well. And you can edit them. You can edit dozens of genes at one time. It's called multiplex editing. In pigs, for example, they've done up to 60 genes at a time.
12:29There's new trials. They can give you up to 100 edits all at the same time all over the genome. The worry, though, is it's not perfect. So you can, it's like, you know, sending someone with a bunch of erasers through your book. And if it's correct, you would very precisely do changes in the text of life. But if it's messy, then you'd, of course, be hard, it'd be hard to read the book because you've made too many mutations. So that's what we're working on now. Because the more genes involved, the more you could have side effects in editing them. Like what if some set of the genes for height are also related to genes for, I don't know, some disease or whatever, cancer?
13:00That is the risk. There are new methods for CRISPR. One's called prime editing, where instead of breaking both strands of DNA and swapping out a chunk and then having that occur sometimes off-target effects, meaning not where you want them to be, prime editing breaks only one strand and actually is much more precise. So you can use some of those methods, or there's actually a quest by many companies right now and a lot of money being invested to try and find newer and more precise methods for editing. But the technologies are here today, and they're only going to get better. So Igor, this strikes me as, so you've been involved in like, for instance, predictive algorithms for stock market predictions.
13:34This strikes me as a little different than that because the difference between the genome and its relationship to diseases and traits in the body, that's probably accurate. Like once they figure it out, they know. Once we know these genes affect height, we know forever that those genes affect height. But with the stock market, the more people know something, the less likely it is to work next year. So for instance, if you're going to predict new additions to next year's Russell 2000, so you start buying the stocks now, well, once everybody starts predicting that, it's too late to use this algorithm.
14:10That's right. That's right. In genomics, what you figure out does not affect the subject. But in finance, the fact that you figured it out is going to change the way the system behaves eventually. I mean, I take some blame on that of some algorithms that I used to use stopped working after I wrote about them. So because I have the misfortune of loving trading, but also writing. And for a while, you know, so I had an algorithm that it might have been, you know, it seems statistically significant to me. The past 80 times, roughly, the queues gapped up between 0.4 and 0.6%. you could short at the open and they would be flat at some point within the next hour or so.
14:58And it was like an ATM machine for me. Every time it happened, I made money until I wrote about it. And then it was actually random after that. Well, you made the market more efficient on the other hand. I'm a hero. Very hero.
15:26Look, as a manager of people, as an employer, as an entrepreneur, and as even an investor in startups, I can tell you the most important thing for your business is the quality of the people you hire. The best part is that great candidates are already on LinkedIn. Employees hired through LinkedIn are 30 % more likely to stick around for at least a year compared to those hired through the leading competitor. And I will tell you that the great thing about LinkedIn is that you're not just looking at random people. You're able to see the people who your friends and trusted peers and colleagues, who they trust and who they've hired in the past and who they recommend.
16:09And hiring doesn't have to be complicated. Realistically, when you have a business to run, you don't want to spend hours on hiring. You want to hire the right person as quickly as possible. That's why LinkedIn Jobs AI Assistant suggests immediately 25 great fit candidates daily so you can invite them to apply and keep things moving. Hire right the first time. Post your job for free at linkedin.com slash altature, then promote it to use LinkedIn Jobs new AI Assistant, making it easier and faster to find the top candidates. That's linkedin.com slash altature. post your job for free. Terms and conditions apply.
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17:30Strategies rooted in strength and backed by experience. Ask a financial professional how Pacific Life can help you today. Pacific Life Insurance Company, Omaha, Nebraska, and in New York. Pacific Life and Annuity, Phoenix, Arizona. So there's this spectrum between, okay, given data, we can know some facts, like for instance, on the genomic side, or given data, we can predict, but we don't want to tell people our predictions if we're making use of it. So there's like a spectrum there. So like Moneyball, the book by Michael Lewis, predicting baseball outcomes, that's more like a stock market style prediction.
18:05Because if you know that everybody, if you could draft people who are good at walking, then everyone's going to draft it and the arbitrage goes away for teams. That sounds right. That sounds right. What are other categories like that? Maybe even traffic, right? If a Google algorithm is routing everyone in one direction, soon it will not be empty. And where there is no traffic, there will be traffic. And possibly most algorithms that make predictions in systems in which the user is participating will be like that. It's only if you're predicting something away from your ability to influence it that it should not change too much.
18:49Did you ever get nervous in your hedge fund career that you just were going to run out of algorithms that eventually they are all the algorithms? Because now there's like 30 ,000 PhDs in every hedge fund trying to find these discrepancies in the data or in the outputs. But did you ever worry all the arbitrages would be gone, the market would be smoothed out, and that's it? In the beginning, I used to worry about it. I would have these interviews, and everybody was worried about it, that the market would become efficient. But it never has. And actually, simply logically thinking, somebody has to make it efficient, right?
19:32So somebody is going to be there making it efficient no matter what. It may just not be you. That's the problem. But can't my predictions also be mean reverting? So if a prediction worked regularly for a while because presumably it modeled some mob psychology, and then if it stops working for a while, won't it mean revert and eventually work again? Yeah, and when it does, it'll come back to life. It'll get turned back on. Okay. Yeah, that makes sense. And also, obviously, you talk in the book a lot about insurance and insurance risk. And the entire insurance industry is built on predictive modeling.
20:13But that's been the case for, let's say, hundreds of years. What's new in insurance, in predicting human behavior now that has helped the insurance industry? It's the immediacy in real-time data. You can use real-time information coming, let's say from people's driving to adjust their rates. You can use body sensors to change outlook on the health of an individual and so on. I imagine it used to be that you fill out a form once a year, and that's kind of where it's stood now. Now there's more and more data coming in. So the insurers are getting a clearer picture, which theoretically is good for everyone.
20:59Our car insurance, for example, they put a little device that goes in your glove box, basically, it says, okay, it tracks your speeds and GPS coordinates and looks to see if you're speeding, basically. But when we got our recent insurance, my wife didn't want it in the car. She's like, I don't want that thing in our car. So we purposely took the higher rate of insurance just because she didn't want that in the car. So the insurance companies now are saying, well, you get a discount if we put this device in your car so they can build better models of you. But you could always just not take it, I guess.
21:28but then they make you pay more for it. Yeah. Have you ever read the book 2041 by Kai-Fu Lee? So Kai-Fu Lee is a big AI technologist from way back, essentially the father of speech recognition. And he wrote this book, basically a bunch of scenarios about how predictive AI might work in 2041. He uses, in the first story, he uses insurance as an example where this family gives over all of their emails in exchange for a discount, but then the insurance company can model them better, their insurance rates spiked because I guess one member of the family from her emails, it could be determined that she was in a, let's say, not pleasant relationship.
22:08And so her risks of accidents they knew would go up according to their data. So there was kind of pros and cons to the enormous amount of data we can now capture to use for prediction. But my question here is, this actually is even is closer to the genomics kind of model, where just because you know something, it still might not prevent it from happening as opposed to the stock market predictions. Yeah, you mean like, you're not necessarily fated in the sense that, you know, most genetic risks are probabilities, but some things are really hard to avoid, like Taysak disease or cystic fibrosis, where you almost certainly get some, or Huntington's disease, there are some diseases where it's going to be really hard to avoid.
22:49But even that, as you write a bit in the book, because of the CRISPR and different genome modification systems, you're no longer subjugated to the shuffle of genetic lottery you got as an embryo. You can, in theory, modify it or tweak it or think about what you hand down to the next generation. For the first time really ever, we have the ability to kind of tweak what is that risk. I feel like it's still like, yes, for this, again, for the single gene mutations that are causing diseases, you can turn an on and off switch and get rid of disease using technologies like CRISPR. But most things are more complicated.
23:24And I'm just wondering, what are the first complicated things that we're going to be able to solve? I think... You know, like heart disease. Yeah, or even like heart disease would be one. And there's mutations that can drive this. And there's even known genes for hypercholaristhemia that we could target. Some of those have already been targeted, actually, in terms of, again, it's usually one or two genes that have been targeted, and there's more than one. So in those cases, you know, or for For example, if you look at certain kinds of cancer, they're driven by like a BRCA1 and 2, for example. A lot of breast and ovarian cancers are driven by a handful of genes.
23:56Probably the top 30 genes alone would explain 90 % of the cases or so, so 90, 95 % of the cases even for ovarian cancer. So if we know what those genes are, you can constantly be scanning in the blood for anyone to see, do we see a spike of any mutation and say, aha, I see it. And we're going to kind of like whack-a-mole, take and get rid of that mutation. and if another mutation comes in a different gene, see it and then go after that target. So I think it would end up being, you rarely would need to go after, say, 15 genes at once. You'd probably do it over time for more complex diseases like cancer.
24:28But for some things like height, if you really wanted, for example, if you think you're going to be, for example, really, let's say, four foot two and you wanted your kid to be taller. So again, this sounds hypothetical, but something like this will probably happen where someone says, okay, I have a safe way to do to make sure your kid is tall and do it in an IVF clinic. And it doesn't happen yet, but the closest thing is a company called Orchid, which is doing this for embryos. They sequence the genome of each embryo, and then you pick the one that you want based on that selection. Because you could predict, okay, this embryo is going to be a female, tall, athletic, high IQ, and this embryo, and let's say with odds on each one, but pretty good odds.
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25:13And this embryo, there's odds that, oh, low IQ, not athletic, males. And so I'm just going to abort that embryo and give birth to this one. Yeah, it was just sort of happening today with Orchid at least. There's one company, there's other ones that are also coming on into the market that are trying to guide IVF basically, but it's embryo selection. And let's just... So I see. So embryo selection before embryo modification. It's a little easier to do that because modification, we don't know yet. We're just making guesses on the odds. Like we don't know a hundred percent chance this person is going to be six foot three, but oh, it's like a 60 % chance, which is better than these other embryos.
25:55So then we don't have to take our risks with modification. What do you think in China they're doing? What they're doing there is a lot more of the somatic methods I've seen where they're doing things, I mean, in your body as an adult, modifying your cells. They've been looking a bit more at embryos and also actually being much more aggressive with which new modified cell therapies, basically genetically modify cells, re-infuse them into the patient, and then, you know, have a new targeted therapy that happens. But it's targeting something that from our own publications, we wouldn't recommend because if you're targeting what you think is only on a cancer cell, but it turns out it's also on 20 % of all the other cells in your body, that'll probably be very painful because you suddenly unleash all these angry immune cells that are attacking what it thinks is on a cancer, but it's also on regular cells.
26:44It seems like this could be really powerful for anti-aging. Depending on which model of anti-aging you believe, if you attack telomeres and change the genes in them so that they don't shrink over time, or you are able to inject new ones in and they attach to you. I don't know how it all works. So they attach to your cells or whatever. It seems like this could be really great. Like these things called Yamanaka factors that they're researching in Asia, I guess, but there's overlap with cancer. Apparently, the more you get this kind of treatment, the more likely you are for cancer. So there's all these risks.
27:20Yeah, absolutely. And so I think you have to balance what is going to be the likely benefit from it from any possible side effects or do no harm is the basis for most medicine. And in some cases, we might not know, though. We think we know or we looked at a mouse, but we don't know yet of a human. So that's why clinical trials always start small. You start with 10 people, maybe eight people, start small for people that really need it, and then you slowly expand. What about the kind of stuff that Palantir does? So given a set of bank transactions and a bank customer, they could make a guess or a prediction as to whether this customer, this bank customer, is a terrorist or not, for instance, or is involved in some kind of financial fraud.
28:01Like, and again, this is more related to kind of the stock market prediction stuff, because as you know more how they're predicting you, you could modify your behavior. But how close are we to really modeling human behavior like that? I mean, credit card companies do that today, I think. Like they'll, they'll look at any spending patterns, look at any changes in behavior, try and guess whether, well, one, just to guess whether it's you, did someone take your credit card and if someone else, or did someone grab your phone? And then the other thing that they'll do is just look to see, you know, are you a risk and some other capacity?
28:37And so I think, I mean, the other thing it does is, for example, is iTunes or Spotify, try and make a playlist based on what you've listened to before, which is great. But then when my daughter got my phone and started listening to all of her songs, it totally screwed up my algorithm. So now it's no longer my ideal playlist, I'll just tell you. So given that that kind of prediction, like the Netflix or Amazon style, like since you bought this, you might like this. That's been around for a while. Yes, I'm sure it's improved. But what's really cutting edge with that? Like I get it that with genomics, this kind of mapping all the permutations of possible data to real diseases and human traits, that's important.
29:15Obviously, the stock market is an immense problem that can never be fully solved. What things are blowing you away on the kind of social modeling, you know, given the sheer amount of data we now have as compared to 10 years ago? So the movies that get recommended to me, I actually like a fair percent of the time. So life is getting easier. Less thinking to do. Yeah. And also, I guess I used to get called all the time by credit card companies saying, oh, this is suspicious behavior, but it was just me being me. Now I don't get as many calls. It seems like they are better at modeling if a credit card is you or not.
29:57You might still be a suspicious person just by your weird habits, yes, but at least it knows that now. Yeah. But, okay, what do you do in situations like March 2020 when the advent of COVID happens, and you talk about this in your book, the market falls. It's the equivalent of what Nassim Taleb would call a black swan event. Like the market falls eight standard deviations more than normal in a short amount of time, something that should never happen one in a trillion times, and yet it happened. And what do you do when there's really no model? And again, the stock market's considered like a fat-tailed kind of curve as opposed to a bell curve.
30:36How do you take into account situations like that, where you can suffer significant loss, treating it like a bell curve? You kind of divide things into ripples and the waves. So the waves are things like you described, these gigantic events that are specific, and you can see them, identify them. And the ripples are, you know, the stock of toothpaste company moves 0.1 % when something else happens. So we create the ripples, but we stay neutral to the waves. So when an event like 2020 happens, it shakes us somewhat, but we're more or less neutral. So we stand through it. But where there are waves, there are ripples too.
31:21And the ripples start going in all kinds of different directions. I see that that totally makes sense. So like, for instance, if something big is happening, it's affecting the entire market over a long period of time, whether it's a day or weeks or months, you kind of that's that's not your business. But let's say for 20 seconds, the Canada markets deviate from the US markets by a wider spread than usual. You could say, OK, within the next 20 seconds, they usually snap back and you could play things like that, whether regardless of the larger wave that's happening in the markets. Yes, it's like that, but it's not only the time.
31:57The ripple can have a long duration, but it's just very weak so that nobody else is really trading it but you.
32:19Shopify's point-of-sale system helps you sell at every stage of your business. Need a fast and secure way to take payments in person? We've got you covered. How about card readers you can rely on anywhere you sell? Thanks. Have a good one. Yep, that too. Want one place to manage all your online and in-person sales? That's kind of our thing. Wherever you sell, businesses that grow, grow with Shopify. Sign up for your$1 a month trial at shopify.com slash listen. Shopify.com slash listen. I see. So in AI, of course, you know there's this difference between unsupervised learning and supervised learning.
32:58Much like how ChatGPT was built initially, you could use unsupervised learning to find where the AI finds that the context of some patterns of language is related to some movements of stock. We don't know what the connection is, but there seems to be a connection. And then from there, you could just start figuring it out. You just need the statistical relationship. You don't need to understand why it works. It may not be possible to understand why it works. And by the time you understand why it works, it won't work. Right. Yeah, I mean, other things you can do with the data, I'm trying to think of other layers of that data.
33:35So, for example, where do we see higher rates of different diseases or cancer or infections could be related to what is your other toxins nearby? Do we see, you know, other direct, like a geospatially informed view of healthcare could help you stratify risk and even find causes or, you know, essentially why some people would be getting sick. It's something you can maybe use all the Google imaging data, for example, from Google Earth and look for trends there. A simple thing is any number of trees or nightlights in a neighborhood can influence health in some ways. And so you could use that. I don't know if you could make a lot of money on that, but you could at least stratify risk and help people towards better outcomes.
34:10I wonder if there are things that we could look at that we're not looking at that could help us decrease, let's say, a spike in accidents in some geography or things that we just haven't even thought of because we don't know the connection. We don't really comprehend why there would be a connection, but there is one. Yeah, that's a really good thing about a lot of the tools for stratification, all these AI tools. We'll find the patterns and then can build that into a model. You can essentially leverage that to make a better prediction. We actually do this, for example, when you sequence a potential pathogen from a sample, from a urine sample, for example.
34:45We look at all the facets of the data, not just what species is there, but statistics on the fragments of DNA that came out or the pH of the urine or other factors that could better diagnose the UTI. And everything goes into the models. And essentially, we don't even need to know why the model gets better. But if it can predict better what pathogen is present, then we can use it. And actually, some of these are under review now by the FDA. to really embrace some of the AI algorithms because they work really well and they'll lead you to a better way to do diagnostics and care. What about just like pundit predictions?
35:21So somebody goes on CNBC and says, well, I think gold is going to go up because of geopolitical stress, blah, blah, blah. Do you think humans have gotten better given more understanding of history, more data about recent events? more opportunities to predict and see how those predictions turn out? Do you think humans have gotten better at being essentially pundits? The answer is paradoxically no, because the more the ability to predict things improves, the more people lean on those predictions, the more they're used. And what remains is a more and more unpredictable world that gets harder and harder to predict.
36:08So by the time somebody is saying something about gold on CNBC, everything he knows has already been figured out and other more notorious pundits, and there's probably nothing left to pundit about. What about a macro trader like someone like George Soros, who famously predicted the collapse of the pound in the early 90s? I think it was 1991 or 1992. And was it just luck that some macro traders succeeded and others didn't? Or was there something else? Did they have some special insight that maybe has kind of disappeared from the markets now? I think they had insight and understanding, and there was not too much competition for a high level of insight, but these days there is.
37:02Yeah, it seems like that's the most, and the trading arms of all the banks seem to be very quant-focused because they do all the high-frequency trading and so on. So again, like where's, definitely in medical, there's opportunity. You have a lot of data, so now we can start figuring out which multiple genes relate to what characteristics and traits. Although there, it seems like a math problem. You have to deal with these exponential size math problems that computing can't do. Is quantum computing a solution for figuring some of these things out if and whenever there is quantum computing? I'm not sure there ever will be.
37:39I'm not sure I understand it. Is that a solution to the exponential problem? If it does what it's supposed to do, it could help and give us just that much more compute capacity on the planet. That would certainly help, but I think a lot of it would also be some of the testing would be done on the ground, or you could do some of it with model systems. But I mean, I would be the first one to jump in line if we had solid quantum computing up and running. It'd be great. There are classes of problems that quantum computing can crack, and classes of problems that still remain unattainable. Really? What's the type of problem that quantum computers can't crack?
38:18You know, these days they're selling encryption algorithms, which are quantum proof. So that's one type of algorithm. Which is a good example, because classic encryption right now, let's say the way Bitcoin's encrypted, that can be solved by a quantum. It cannot be solved. Factoring a hundred digit prime number, it cannot be solved by a thousand supercomputers linked together. But a quantum computer can do it in a second. So that's a classic example. And now you're saying there's algorithms that could make cryptography quantum-proof. There are. There's no computer that can solve all problems. So because what I worry about is, are we hitting a point of, let's call it peak data, where we have the maximum amount of data for certain categories that we can basically handle because the computers are not fast enough?
39:10and they're not fast enough, not because the chips are slow, but because mathematically it's too exponential a problem. Yeah, that can happen. The data, the rate of the growth in the data may simply exceed the computing power's ability to digest the extra data. I don't think we're there yet, but we certainly could because it's generating so much data. So yeah, but peak data indicates that the data will have less utility over it, or that will have peaked past the ability to use the data. So I don't know if I'd call it peak data. It's just unwieldy. We've reached the past to being able to wield and manipulate data efficiently to only having various degrees of efficiency.
39:56Because I think the data, assuming it's clean data, should still get more useful as you get more of it and over time. Right. So you'd have to sort of come up with more categories that you're studying in the data. But again, genomic data seems like it's there. For 15 years, we could predict Tay-Sachs and other single gene mutations, like what single genes cause which diseases. But I feel like for something like possibility later in life of stroke or predicting IQ, something like that, which requires hundreds of genes, we're never really going to be able to... The data's there and it's possible.
40:32The algorithms are there, but our computers are never going to be fast enough to solve them. I mean, maybe today, but I wouldn't say never. I'd never say never because I think there still could be, in 50 years, there could be something that's even beyond quantum or that's some other variation of a computing or even just efficiencies of the algorithms could be improved in ways we can't imagine now. So maybe in the short term, yeah, but I think long term, I would say never. I mean, if you go back 200 years ago, it would have been inconceivable that people would routinely fly through the air in airplanes.
41:08It was, you know, no one would have believed you, right? And no one believed even the Wright brothers for a while. So I think, you know, a century is a long time with current humanity. Well, it's really interesting you say that about, you know, the algorithms might improve. That's an area I haven't thought. So obviously, over the years, over the centuries, statistics has improved. So instead of just trying to match something against a normal curve, now there's all these very sophisticated algorithms for speech recognition, vision recognition, and so on. How much more do you think the math can improve?
41:43Because that also seems to be very, we might be at peak math in terms of how much math is actually useful that's coming out of the academia. Yeah, like math departments. There's no new math. I mean, there's a lot of work on, you know, since you're reconciling quantum mechanics with Newtonian physics, and that is still something that should be solved at some point, but there's no new kinds of numbers being discovered or entirely new kinds of calculus, right? Most of that's been done since the 16th century, and so I think there are some... I mean, Newton probably would have argued he was peak math.
42:21I don't know. I'm a long time ago with calculus, but... But even statistics has, with, again, the rise of the need for pattern recognition, I feel statistics has evolved in the past 30 or 40 years, like these hidden Markov processes and other techniques to really do sophisticated pattern matching. And I just wonder, can that be improved? Depends what you call an algorithm, right? Maybe the basic algorithms can't be improved and don't need to be. But if you look at something like AlphaZero or ChatGPT as an algorithm, then yes, from time to time, a groundbreaking algorithm does appear. Yeah, but I wonder how much of that was, okay, more advances in adverse neural networks versus the speed of computers finally.
43:11Look, the speed of computers were there to solve more or less computer vision 20 years ago, but I feel only in the past five, 10 years, it was fast enough to handle large language models like ChatGPT. And that was purely a speed thing. How much was related in an algorithm thing? I think there were elements of both. Yeah, I agree. Yeah, that makes sense. And also just the amount of data. You couldn't build large language models until you had lots of data to look at. I mean, all those things together, the data, the algorithms, the compute, then you could do it. But you really couldn't do it 15 years ago.
43:46Yeah, and actually, to your point, we didn't even have the data. We didn't have all written text up until last year stored anywhere in one easy-to-use place. But even that, with the speed, it took a bunch of supercomputers a year and a half to crunch the large language model that's now ChatGPT, and then another year and a half of supervised learning. So it'll be interesting to see how that speeds up. So, Chris, I know you're interested mostly in predicting breakthroughs in medical technology and Igor in financial and stock market predictions. What other things would you like to predict? I think the methods, you can use them anywhere.
44:25What we're doing now is we're actually predicting data itself. Data has this property that you mentioned before that when you predict it, the data doesn't change from the fact that you predict it. So we're predicting data and you can model most things as data. So then you just start going into different industries and how much can each industry be improved through prediction and through algorithms and through just getting rid of that 90 % of the work that's kind of mechanical in nature, if you consider that ChatGPT is a mechanical thing in the end. Do you think I can predict what a hit song will be?
45:09So let's say I take every hit song of the past 20 years and feed it into my statistics slash AI machine. And then I use AI to create a video with a beautiful woman or guy and boom. Do I have a hit song? Do you think that's possible? Good beat, good voice. I mean, there are some things that are catching us to it. There's definitely signatures of songs that are poppy, if you will. And they're popular. So I think you could build a model and maybe you could get 99 % true, but I don't know if you could ever say 100 % like any model. You may have to model influencers and feed the song to the right influencers to get it popularized.
45:52Yeah, that's a good point. But maybe I can create an influencer thing modeling. So, okay, here's everything I said on Instagram over the past year that got a million likes or more and break down those. And then here's what every influencer looked like. So now I'm going to come up with the average super influencer. And then boom, now I'm going to feed that influencer a song and make a record label and use that. True vertical integration. Yeah. People wonder if this rise in predictability is going to bring down creativity. But I sort of think it's going to bring up creativity because now it's going to free up your resources in some ways to come up with even more creative ideas.
46:36What do you guys think? it certainly frees you up to ask questions and get very quick answers, which frees up creativity because it saves time. I think it will. I think it's like any new technology. It can be a tool or a weapon, right? In this case, it could be a great tool for creativity. AI, of course, people are afraid of AI because it could be, in theory, weaponized. But on the creativity side, I think it's going to time saver. It's going to be inspiring. You can craft, as you've probably done with the stable diffusion or other tools. You can just describe the landscape you're imagining and it creates it for you and you can build from there very quickly these amazing portraits.
47:13I think it's phenomenal. It lets you do what I always wish I could do as a kid, describe something, a scene, an idea of say, a rabbit with 12 different tentacles that was playing the harpsichord and also juggling pool cues. I could never really draw that, but I can get an AI algorithm to make that in five seconds. So it's phenomenal. But maybe there's a problem there where One of the reasons why people always say, oh, the book was so much better than the movie is because when you're reading the book, you're kind of constructing the movie in your head instead of it being... And then suddenly you see the movie and you're so disappointed because it wasn't as good as that movie you built in your head.
47:52But now we're going to be able to see basically the movies that we build in our head much faster. Yeah, which is still... I mean, it could be good and bad, but I think mostly good because you could get in the movies. out, like you'll see what's present faster, but then also you could have 55 variations of it. You can change the seed kernel for most of the AI art, for example. So you make 50 versions of the thing you were just thinking, and you actually might then imagine things that you weren't quite originally thinking. So you don't just get one imagination, you get 50 of them. Or you can do some pruning, maybe prune it down to 25 that you like.
48:25But I think that's you know, just imagine if you could have 50 brains instead of one, you kind of can have that today, which is pretty amazing. So what do you think? So we're in the age of prediction. Obviously, I should tell all my kids to be data analysts because that's going to be just this huge profession for the next 20. I'm making that prediction. That's going to be a huge profession for the next 20 or 30 years. 20, 30 years from now, what do you predict we'll be seeing in our predictive abilities and how we use it in society that will just blow our minds? Curing cancer, by the way, won't blow my mind because I expect that.
49:00Yeah, that's supposed to happen. Yeah, it's got to be. But it could be, you know, every toilet I'll be monitoring you. Like every morning you get a little update report on every molecule in your body. You would get information about the environment around you, around your home or your apartment. I think you would, I think we'll see prediction coming even from other planets. Like, for example, the Perseverance rover landed on Mars. It took too long for a signal to go from Mars to Earth. So it had to use image recognition software during a landing to get there. So we'll start to see prediction algorithms and tools even send data back from other planets like Mars.
49:35You'll be able to get news from the future because it will be mostly predicted. On a micro scale, I can say, oh, this person crossing the street moves like someone who's going to rob a car in the next day. So, you know, kind of minority report style predictions, you know, the movie with Tom Cruise. So what types of news events do you think will be predictable? Maybe elections. Elections, I mean, we saw this in Cambridge Analytica. They might be tweaked before they even happen. So you might know the future because you've made the future. In a similar way, it's possible. I think if you examine news headlines and just examine the news, you will find patterns in it already that so much news follows other kinds of news and it is predictable and so on and so forth.
50:26but nobody is really putting that into a news service That's fascinating So what you're doing there is you're disconnecting news a little bit from reality, which is what newspapers probably do anyway and you're saying tomorrow's headline is more based on today's headline than in the actual events that happen today Yes, so you may be able to print tomorrow's newspaper better than the actual paper that's going to be printed tomorrow because tomorrow's paper will have some noise in it from new sources, et cetera, et cetera. But your prediction is based on mathematics. Well, The Age of Prediction, such a fascinating book.
51:14I really do think the job people should be preparing for is data analysts because that's going to be used in every single industry. more than prompt engineers or AI coders because AI is going to write its own code. Being able to understand what data to look at and why and how to make use of it, whether it's the medical industry or sports or stocks or insurance or art, this is going to be such a valuable skill to have. And it's a just beginning field. The creativity there is going to be amazing. But the age of prediction is like a guidebook to what's happening and what's going to be happening and all the ways people use prediction technology.
51:54And such a great book. How did you guys team up to write it? Like, why did you write it together? How do you know each other? Originally met actually at a lunch at Cornell's campus and then just started brainstorming about data and then started walking through the lab, chatting about the use of data for medicine and overlapping with finance. And it just became exciting to think about more ways to do partnerships, brainstorming. We have a fellows program that goes between WorldQuant and Cornell as well. so people can go back and forth. It's a nice exchange of ideas and expertise between the institutions.
52:27Interesting. You know, Cornell in Manhattan, like again, I'm talking about 25 years ago, used to have a computational finance. Their computational finance department was in Manhattan. I don't think it exists anymore there, but I'm not sure. But anyway, thanks so much for coming on the show. Really, this is like my favorite topic, the age of prediction. Thank you so much. I hope you guys come on again. I really appreciate it.
53:03Oh, that was for free.
From the publisher
?The Age of Prediction is such a fascinating book! After reading it, I really do think the job people should be preparing for is "data analyst" or "predictor", because that's going to be used in every single industry, more than prompt engineers or AI coders - because AI is going to write its own code. Being able to understand what data to look at and why and how to make use of it, whether it's the medical industry or sports or stocks or insurance or art, this is going to be such a valuable skill to have, and it's a just beginning field. The creativity there is going to be amazing.The Age of Prediction: Algorithms, AI, and the Shifting Shadows of Risk by Christopher Mason and Igor Tulchinsky is like a guidebook to what's happened, what's going to be happening, and all the different ways people use will prediction technology.Igor has a $7 billion hedge fund, which analyzes millions of pieces of data around the world to predict stocks, whether something will happen tomorrow, or an hour from now, or 10 seconds from now.Christopher Mason is geneticist and computational biologist who has been a Principal Investigator and Co-investigator of many NASA missions and projects. I wanted to know: What is the state of this industry? How much can we really predict? How can we get better at it? What are the limitations? How close are we to manipulating DNA for disease gene removal? Can single-gene editing be done within a living human? We talk about all of that, and then just have a fun time while I pitched different ideas. Enjoy our interview with Igor and Chris, authors of The Age of Prediction.-----------What do YOU think of the show? Head to JamesAltucherShow.com/listeners and fill out a short survey that will help us better tailor the podcast to our audience!Are you interested in getting direct answers from James about your question on a podcast? Go to JamesAltucherShow.com/AskAltucher and send in your questions to be answered on the air!------------Visit Notepd.com to read our idea lists & sign up to create your own!My new book, Skip the Line, is out! Make sure you get a copy wherever books are sold!Join the You Should Run for President 2.0 Facebook Group, where we discuss why you should run for President.I write about all my podcasts! Check out the full post and learn what I learned at jamesaltucher.com/podcast.------------Thank you so much for listening! If you like this episode, please rate, review, and subscribe to "The James Altucher Show" wherever you get your podcasts: Apple PodcastsStitcheriHeart RadioSpotifyFollow me on Social Media:YouTubeTwitterFacebook
------------What do YOU think of the show? Head to JamesAltucherShow.com/listeners and fill out a short survey that will help us better tailor the podcast to our audience!Are you interested in getting direct answers from James about your question on a podcast? Go to JamesAltucherShow.com/AskAltucher and send in your questions to be answered on the air!------------Visit Notepd.com to read our idea lists & sign up to create your own!My new book, Skip the Line, is out! Make sure you get a copy wherever books are sold!Join the You Should Run for President 2.0 Facebook Group, where we discuss why you should run for President.I write about all my podcasts! Check out the full post and learn what I learned at jamesaltuchershow.com------------Thank you so much for listening! If you like this episode, please rate, review, and subscribe to "The James Altucher Show" wherever you get your podcasts: Apple PodcastsiHeart RadioSpotifyFollow me on social media:YouTubeTwitterFacebookLinkedIn
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