982: In Case You Missed It in March 2026

0:00Jon Krohn:This is episode number 982, our In Case You Missed It in March episode.

0:08Jon Krohn:Welcome back to the Super Data Science Podcast. I'm your host, Jon Krohn. This is an In Case You Missed It episode that highlights the best parts of conversations we had on the show over the past month. In episode number 975, I ask author of The Next Renaissance, a bestselling book, highly recommended. I've been reading it. I love it. And the author is named Zach Kass. In this clip, he talks about how AI might help schools tailor their education to each student. Do you think that AI tools can play a positive role as well with things like personalized learning in the classroom, helping people? So it means that this situation that we've had historically, if I think back to my grandmother, she was in a school where all grades were in one room and a teacher is having to figure out how to teach all grades at once.

1:04Jon Krohn:Grades one through eight, or I guess in the US they would say first grade through eighth grade. I'm Canadian, Zach.

1:10Zack Kass:Where did she grow up?

1:12Jon Krohn:uh, in Ontario, uh, in, in rural Ontario, uh, no electricity, no running water. Uh, yeah, in the 1930s, 1940s, uh, you know, kind of like a two hour drive out of Toronto. And, um, yeah, so, you know, so her experience of education was where a teacher has 20 kids and those kids are all different ages, you know, five to 13. Um, and, you know, we figured out over time, how to get children, for the most part, in most parts of the developed world, you'll now have kids that are around the same age, around this, you know, kind of a one-year band progressing together. But of course, even in that scenario, all these kids are learning at slightly different rates.

1:58And so the kids that are really good at math, in the math portion,

2:04Jon Krohn:They're bored. And so it seems to me like there's this opportunity with AI to be having personalized education for everyone. And maybe the teacher kind of becomes more of offering oversight and encouragement and that kind of human element, the empathy. I'll turn the floor over to you.

2:22Zack Kass:I mean, we could, and actually I do. The book, if it has a lasting legacy, it will likely be its ideas for parents. And I actually, I wrote about it this morning. I wrote in a LinkedIn post that I think that I didn't realize when I was writing the book who I was writing it for. And And in fact, I wrote the book originally as simply a place to think about my own arguments. Like it wasn't, it really wasn't for anyone else. And I never really considered that people would read it. Like I didn't really consider that anyone would read it. And this is also an aside, but it's fun. In the process of writing it, I started getting really into a bunch of other nonfiction.

3:14Zack Kass:And I was just devouring my favorite nonfiction because writing nonfiction, is uh entertaining non-fiction is quite challenging i mean especially if you're talking about a technical subject and so i went back and read a bunch of my favorite non-fiction writers one of which is hunter s thompson who's just you know all time that's

3:35Jon Krohn:fear and loathing in las vegas uh is his most famous title i think it's certainly one of them

3:40Zack Kass:yeah yeah fear and loathing in las in las vegas fear and loathing on the campaign trails is there's there's a long list but um but he uh he wrote once the worst part about writing a book of meaning is that someone smart will have to read it and he for all of his sort of uh nihilism he was quite sensitive and like didn't like didn't like the idea like didn't like critics and didn't like the idea that people would, I mean, most great artists don't. And I became paralyzed when I realized as we were wrapping up the book that people outside of my circle would have to start reading it. And was, and, and like still to this day, I'm quite nervous for people to like it.

4:25Zack Kass:But I also in the process didn't consider who would read it. Like I, I, you write with most great writers, right. With someone in mind, right. With like a reader in mind. And I wrote with like ideas in mind. I was like, I just need to, you know, I need to put these on paper and sort of stare at them. And it became very clear to me in the last probably two weeks, since, you know, the book's been out a month, we sold 37 ,000 copies in the first month. And in the last two weeks, it became very clear. I wrote the book for parents And I wrote the book for anyone responsible for another human right now who is trying to make sense of a world that is increasingly unfamiliar against a relentlessly dystopian backdrop of news and media.

5:15Zack Kass:who wants to feel like they are an active participant, who wants to have evidence in defense of their hope, who wants to be able to wake up over breakfast and say to their kids, no, you're going to be great. To say to their kids what our parents said to us, or at least what our parents would have wanted to say to us. The world is going to be a special place and you're going to make it better. And here's what I'm going to do to make it better. And here's, here's the role we all need to play. And that's the evidence we tried to give people and, and the, and the, and the conversation we tried to start.

5:55Zack Kass:The chapter about education is the best chapter in the book. I don't really, I think it's like, I'm an objective observer as much as anyone. And like, it's just, it was my favorite to write in part because my wife is a Waldorf teacher. Waldorf is the, is an alternative education school, much like Montessori. That's a little more alternative, quite frankly, in the ways of its thinking. And I feature in the education section, three luminaries, three education luminaries, two of whom are living and are very important. One is Eva Moskowitz, who has rebuilt a charter, rebuilt public education, actually.

6:30Zack Kass:Eva Moskowitz ran for education board in New York City on reform. And they shunned her, they kicked her out. She went and started this now exceptionally famous charter school in New York that everyone should look up and learn about called Success Academy. And Success Academy has, I think it's basically the largest, you know it, John.

6:50Jon Krohn:I know it, not because I know anything about it, but I work at, I have this fellowship at this really cool AI startup in New York called Lightning AI. And, and so, and what this fellowship means is that I work out of their office every day, which is pretty sweet because they're doing amazing things and next door to the lightning a office on 22nd street next to madison square is a success again and so i see i see the sign and i see the kids coming out with their uniforms on uh but i actually don't know anything about it other than there's that yeah that's kind of weird

7:24Zack Kass:connection that i see it all the time it is a remarkable so it's the largest public school system in the in the country i think now she runs now it's not technically public because it's it's charter or it's not defined as public. And the more you learn about Eva and the more you learn about Success Academy, you learn that she's sort of become an enemy to the traditional education state. But she has built a program that is guided on accountability principles. And she's doing a much better job. So I simply argue in the book, if we only hold parents, teachers, and students more accountable and treat this as a matter of life and death, right?

8:04Zack Kass:Which you can make the argument education is in many, you know, it's like the outcome of an individual who graduates college, those who don't, et cetera, or graduates high school, rather, those who don't. Like it's a critical moment that we should treat with a whole lot more importance. Then I feature Mackenzie Price and Mackenzie Price is building Alpha School, which believes that it can reinvent traditional education and take it a step further than Eva Moskowitz by building schools where the student spends two hours a day at a terminal, at a screen, doing self-guided learning, as you described, where 100 students in the same grade at the same moment can be learning different things at different rates.

8:52Zack Kass:And that terminal then feeds back to the student what they're doing well, what they're not, and their guide, what they call guides, not teachers, who support their education and their parents so that everyone can be on the same page about their development. And it works empirically for some students. It has a higher attrition rate than others. I call this out. I don't want to be sycophantic. Some students opt out immediately, but they have some really high performers academically. The rest of the five hours or six hours of the student's day spent outside in physical activity, in game theory, in life skills, and learning this idea of, you know, sort of grit, the characters and qualities of a great person.

9:34Zack Kass:It's working. I mean, parents are voting with their feet right now. And Mackenzie is a friend and I'm a big supporter of what she does. And I think even if you don't believe in alpha school, you can believe that it is on the right track. And then I spotlight Rudolf Steiner. And Rudolf Steiner founded Waldorf School 150 years ago and made the argument at the time very simply, which was that we were destroying, as he put it, it was the death of the soul and spirit of the child, the industrialization of education as we hauled kids off to the modern classroom and sat them at desks and made them learn about math so that they could eventually work in a factory, that was the destruction of the soul and spirit of the child.

10:19Zack Kass:Now, what he didn't appreciate, or maybe he did, but didn't really calculate, was how well we would do economically such that we would justify the industrialization of education, which we really do, we can, that we needed to industrialize education so that we can arrive at this moment. And my argument in the book is that if we borrow from luminaries like Eva and Mackenzie and Rudolph's original writings, what we can actually inform is a future of education that harkens somehow to a past prior to the economic optimization of a child. Where children and the purpose of childhood, I argue in the book, is to understand yourself without economic incentives, which no adult gets to do.

11:06Zack Kass:You cannot, as a rational adult, explore yourself in an open, honest way without economic incentives, but you can as a child. You can know what it means to be a human without wondering if you need to make money, ideally for most children. And the sanctity of that experience is so important. And we've lost it in industrial education. So much from a young age is about getting a good job. You need these skills in order to get a good job, but maybe that's not what we need to do anymore. And it's not because I think work goes away. We can talk about this. It's not because I think we stop working. It's because I critically think that the opportunity now to give people the tools to actually explore a world that is going to be way more explorative are abundant.

11:53Zack Kass:and to your point the role of the teacher is no longer to be the smartest person in the room it was never really to be the smartest person in the room they just had to be it's to inspire it's to create a safe space where kids can actually explore what it means to to be human in a way that that sort of only childhood can offer us at that young age and it and it and it could restore i think it could restore and in many ways lead to a spiritual awakening i mean one of the critical arguments of the book that I don't, I have to make carefully because I don't want to sound too woo-woo outright is I think that the next Renaissance is in many ways a spiritual awakening where people actually start to reconsider the rat race we have been on for a long time and what it truly means to be human and why we are here.

12:41Zack Kass:And that will start with education. That will start with redesigning the classroom to actually let the child understand why they are learning, how they learn, what it means to sort of explore versus, you know, knowing something.

12:59Jon Krohn:My interview with Zach really highlights why a rigorous and tailored education is so critical for students all the way up to those who teach them. I expect we will see plenty of AI agents emerging in this next renaissance. For Kyungyung Cho, such agents will soon help us to direct our attention to the developments that matter. In episode number 977, I speak to the renowned NYU New York University professor about how he thinks AI will come to explore the world in a similar way to humans. When you talk about actively mining data, I suppose this is like an agent searching the web for relevant information or in the not too distant future, perhaps a physical embodiment, like a robotic embodiment, being able to explore the physical space and maybe even interact with objects in its space.

13:43Absolutely. In fact, all of those included, in some sense, they are not that different from each other. Say you want to run some kind of actual experiments. You want to test whether a particular protein is going to bind to yet another protein that we want to design a drug for. Now, one way you can imagine is that, well, I'm going to try all possible proteins and then see if any one of them bind to this particular target protein. Of course, all possible proteins is, that doesn't make any sense. There are too many of them, right? We don't even know what kind of proteins exist that are stable and can be synthesized within a cell or whatnot.

14:18So we cannot simply say that we will try all possible proteins. So then what we do, we're going to use a very smart algorithm to pick only small number of the proteins at a time to test. And then based on the feedback, we're going to continue to choose the next batch of the proteins so that we can actually find a good binder way earlier than trying out all those that put all possible proteins. And then this is a sample efficiency we gain, right? And it's actually the same with the search as well. In other United, of course, we think of the search as a completely software-based, let's say, implementation or the system.

14:54At the end of the day, we use Google search. We are embodied, right? So we are a physical robot ourselves. And then the same thing. Internet has so many documents. One way to figure out which documents are relevant for my question is to read each and every one of them and then try to decide which subset matter. But again, there are too many of them. So what we want to do is that we want to interact with the search engine to find or figure out how to find the small subset of time. And then after a couple of the rounds, I'll be sure that I have found the relevant documents. So these things are all the same thing.

15:28Now, of course, when it comes to physical robots as well as physical experimentations, there are a lot of issues, not necessarily because of the AI algorithms, but because physical words are much more fragile than software words.

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16:43Jon Krohn:Claude.ai slash superdata. Mm-hmm, mm-hmm. You have to be careful. You can be tricky, for example, to pick grapes without shattering them for a robot to be able to do. But it's something that robots are starting to figure out more and more. It's an area that I'm personally excited about a lot. Now co-supervising this AI robotics research at the University of Auckland, and we'll see hopefully some exciting things come out of that. Definitely out of the research in general, exciting things will come out. I know Jan Lekun is bullish on world models. He's got his advanced machine intelligence startup that he has going.

17:21Jon Krohn:Even though this is a separate effort, you two co-authored a 2015 paper last year that won the best paper award at ICML in the workshop on physically plausible world models. This paper was called Planning with Latent Dynamics Models. And we'll have that in the show notes, of course, for listeners to check out. But does that tie into the conversation that we were just having, that research? Yeah. And then it actually ties into to all the things that we have talked about a bit, like the information processing and whatnot. So what does it mean for us to know about the world? Because we talked about the world model.

17:56And then it turned out that that alone is a very big question. Some people believe that if I can imagine what's going to happen in the future at a very high, let's say, quality or high fidelity, then I may be able to say that I understand about the world. Some people say that that's not true. We actually don't have a good picture or the imagination that is high fidelity. We have a very high level concepts that are extracted. We know how those concepts interact with each other. And then that's probably enough. But of course, who's to say which one is better? But nice thing about this large scale machine learning or the AI era now is that in many cases we can test them.

18:38So, you know, we all have heard about and maybe tested those OpenAI Sora models or the Google's Genie models and all those video generation models like the Runway has one. These are amazing models. And then some people who are building these models in their mind have this thought that by building this kind of video generation model or the action video generation model, maybe we can build a machine learning model or the AI system that understands the world. And then using that kind of imagination to plan out what's going to happen. But then on the other hand, some might think and then it's very natural for us to question whether that is necessary.

19:18Say I want to travel to Paris. I'm not going to plan out every single step and then trying to imagine every step I actually take from here all the way to JFK and then walking toward the gate, walking to my seat, having a seat. I don't really care about that. What I'm going to imagine is that say I already made it to Paris. What am I going to do? What is going to the first restaurant that I'm going to go into and then trying to order some wine and whatnot. But I don't really need to imagine all those steps in between. So then one might say that actually what we need is a very high level picture of how the world works and then be able to jump back and forth.

20:00And then that's what we meant by the latent dynamics. And then that's the kind of foundation on top of which what Jan has been calling as a JAPA, that is a joint embedding predictive architecture. And then also this idea, of course, goes back like decades, right? Many people in neuroscience have been thinking about it and control theory has been all about, can we find the abstraction or the small number of the knobs that actually matter in controlling any of the systems? And then we all work at that level. Then we're going to project it down eventually. So which one is the right way to go about?

20:33It's unclear in my view, because the nice thing about predicting every step of the way makes computation extremely regular. What that means is that it's very good with the current digital computers to implement and scale up. On the other hand, just intuitively saying, we don't do this kind of a step-by-step, let's say, imagination. So there is a kind of hope that maybe we really don't need to do that because we have a proof of concept here. It's that, yes, we can skip all those steps. Which one will be right? It's unclear. But something will have to be done in this latent space rather than the pixel space.

21:10Jon Krohn:As Kyung-young says, it isn't enough to replicate our reality and expect our intelligence systems to operate to their full potential. Intelligence systems will require abstract states as well as raw observations for us to see meaningful development. With this in mind, let's move on to the people who are developing the AI systems. In episode 973, I asked the AI performance engineer and famed O 'Reilly author Chris Fregley how he uses automated processes and code scripting in his work. Speaking of tips and ways that people can be getting ahead more quickly, you mentioned to me prior to us recording that you've been loving using AI coding assistance.

21:51Jon Krohn:And so, you know, tools like Cursor, Cloud Code, Codex, how has your workflow changed as a result of these kinds of tools? how does that impact in particular AI systems performance, optimization? And yeah, you know, what do our listeners need to know about where this is going? For sure. I tweeted about this a couple of weeks ago. If you're manually writing code in this year, 2026, you are way behind. And I would not have said that if I didn't spend all of last year watching this sort of evolution, because I started off in my sort of legacy ways, right, where I was writing every line of code. And I vowed for 2026 to only use these coding assistants and to see how far I could get.