Business, Innovation, and Managing Life (April 19, 2023)

Hello, everyone. Welcome to another episode of Q and A about business, innovation and managing life. Let's see. We have a question here from Jeff. If you have a goal with many clearly defined milestones which depend on each other in order to get to the finish line, how do you generally approach that? Interesting question. So, you know, some things that have definite orders are things like writing something. I tend to like to write sequentially. If I write sort of a, you know, section eight before I write section five, I almost always regret it because I figure people will probably read what I write sequentially, or at least more or less sequentially. And so the things that I write later can depend on the things that I write earlier, but not vice versa. You know, it's interesting because, for example, right now I am doing a project, yes, it's related to LLMs and things like that, where I am waiting for some other people to finish various things before I can take the next step on it. And it's always a bit frustrating for me when I'm in this situation. But the thing that I've noticed is it's better to just not try to do the project when one's waiting for other people to finish something than to try to do the project and get very frustrated because the pieces are not ready. It's better to wait until sort of all the pieces are assembled and then go to the next step and do that. And so what I tend to do is because I don't really know how long it's going to take for these other things to get finished, I tend to be doing things which are. Which have a little piece project, so to speak. So, for example, I just ground down, I was up at 5,000 unprocessed email messages, and over the last few days I've ground that down to a little bit less than a thousand. That's always a very frustrating thing because with email, for example, because on a given day I'll get 5, 600 messages, in a sense, sadly, almost no spam, so to speak. So they're all real messages from either within our organization or outside and so on. And sort of my big goal is to get as many of those messages processed within the day, so to speak, as possible. And sometimes that very often it's like, okay, here's a message. I know where it goes. It's not really something I need to deal with. Somebody else will deal with it. It goes in a particular stream to be dealt with. But then there's always a certain residue of messages where it's like, oh, I should Think about that. And it needs some thought before I can respond. And sometimes it's like an informational thing where it's like, yes, I should really read the thing that somebody has provided. And, oh, gosh, it's going to, you know, I'm not, I don't have a chance to do it right now. So, for example, for things where I'm, I'm going to read it in the future, I usually have a bunch of folders for projects that I intend to do, and I will just copy the thing into the folder for that project. I won't look at it at that time. Then whenever it is, you know, six months from now, five years from now, whenever I'm actually going to get serious about that project, I'll actually look at the thing that I, that I saved away. But then there's always, when I go through and process email, there's always a residue, as I say, of things where I, you know, I'm not sure it's going to take me some effort to deal with that, but person deserves a real reply and, or, and then those get leftover sort of residue. And I had a residue of about 5,000 messages that were difficult for one reason or another. And so I just ground those down recently, last few days. So lots of people who might have been expecting a response from me sometime in the last six months or so finally got one because I was grinding down these email messages as I was waiting for some things to get finished that will allow me to do the next step in a project that I'm doing right now. And I find that's much better to do that than to be kind of, you know, trying to do the project when I don't really have the materials I need to do it. And now there's also the question of when it's things that I'm doing myself step by step, so to speak. And I guess what I do is I just go step by step, do the next piece, do the next piece, the next piece. I'm often wildly optimistic about how little time it's going to take to do all these pieces. And I think, I just don't think that hard about, you know, how long is it really going to take do all those parts. I have to say, quite often there are projects where I realize I really shouldn't start them until it's pretty close to when they're really needed. So, for example, a typical thing is part of the reason for that is because you kind of don't know all the things that you're going to know by the time it's really getting time to do that project. So a good example of that is, I don't know, giving talks, for instance. I think the worst talks I've ever given in my life are the ones that I prepared fairly long in advance. Somehow it's like when you actually know sort of when you've seen the audience, when you've got a sense of some event that you're going to or something like that, that's the time when you can, for me at least, when you can sort of think through what am I actually going to say, rather than long in advance, where it's all very abstract, so to speak. And similarly, when you have a product or something or some announcement you're going to make, it's like, well, you could prepare the announcement a long time in advance, but then the thing that actually ends up being the thing you're going to announce often is not what you thought it was a month in advance, let's say. And it's much better to wait sort of as long as possible and then really hustle at the end, so to speak, to be able to get the message out. I find it works much better, it's much crisper than to try and do something long in advance, where it's all kind of abstract. You don't really know what you're going to be talking about. But I guess that's the best I can say about how to sort of go through a project with. With milestones. I mean, I think for me, I tend to prefer I do a project, I'm doing it sequentially, I do the piece I'm going to do and I do that as solidly as I can. I don't sort of say, well, let me make a kind of shoddy version of that and I'll come back to it later. For me, the. The effort of coming back to something later is always much greater than the. The total of the effort of over the sort of the shoddy piece and then coming back to it and fixing it later. That in aggregate is much more effort than just doing it right the first time. And I also find that when I'm really sort of in the groove or something of doing it, I have a much better chance to do a good job than if I do it in pieces where, oh, I more or less understood it and then I come back later and I kind of forgotten how it works. And then I sort of patch something in because I don't want to really spend all that effort to really get in the groove again. And the only real way to get in the groove again is to sort of redo the first part of the project. So that's a few comments on that. Let's see. Gosh. Patrick is asking, do I have a sort of bucket list, some things I hope to see or experience in life that I haven't yet? Well, you know, I maintain a kind of a long list of projects that I hope to one day do. And some of these projects, well, I, the one that was on my list for a long time was figure out things about the fundamental theory of physics. And in 2019 I really started. I don't know what you do in a. Well, started really, really working on that and made huge progress and that's been very exciting. There are all sorts of projects. For example, another one is kind of this idea of developing sort of a symbolic discourse language, a kind of computational representation, symbolic representation of kind of all the elements of everyday language. That's something I've long intended to do. I've now kind of had a kick to really do that by the success of LLMs and reproducing kind of linguistic fluency, so to speak, that really shows that that really can be done. And I always thought it could be done, but this is really good evidence that it can be done and that's something I want to do. There are lots of projects that I have that involve writing things, taking knowledge that I've accumulated and trying to provide an exposition of that knowledge. Those are always. There are a whole bunch of those that I really want to do. I think what I find generally is there are projects that if I look at what I'm actually doing at any given time, about half the things that I'm doing are things that sort of opportunistically arrived fairly soon beforehand and half are things that I've sort of long planned for many decades. In terms of other kinds of things, you know, I've traveled a certain amount around the world and you know, there's a trade off there of oh, how difficult is it to do it versus how exciting is it to see this or that thing. I would say that I'm not, I don't have a hugely sophisticated list in terms of those kinds of things. I think that there's certainly are. Oh, I've often said that there are things that I'm kind of reserving for later in life and I, I don't know if I'll ever get around to them. Like for example, I, I, I, I'm not, you know, I've I've chosen to do things where I like what I'm doing and so I'm kind of a, a work all the time kind of person. And I don't like read fiction books, things like that. I just haven't, haven't done that. I've written fiction either, except a few. Well, I've made a few efforts but I think maybe an interesting way to communicate some of the things I'm interested in. But we'll see what happens with that. But that's an example of something I haven't sort of taken the time to do. I suppose there are projects I'm long been curious about that I sort of hope to do, but. Well, there are projects where the ambient technology or ambient situation in the world hasn't gotten to the point where one can really do it. And when it does, I'm going to be excited about doing it. One that's been a long time interest of mine is kind of molecular scale computing. I think we're finally through ideas from the physics project, practical things in terms of automated labs, things to do with actually modern AI advances. I think we're finally to the point where all the kind of pieces are lining up that it will be possible to make a serious effort at that. You know, the trick is not to start a project like that at a point when there's kind of a decade of kind of infrastructure development that has to be done before you really get to the unique things associated with that project. Reminds me of my first company I started in 1981, which had, where I didn't do all the management myself and a number of incredibly, well, dumb decisions were made. I thought they were dumb at the time and they were dumb in the light of history. But one of them was sort of an interesting one in this regard was we built a software product. The software product had a hard time running on the computers on the sort of workstation class single user type computers of the time. And so a bright idea of the management I had brought in was let's build a computer, the hardware of a computer to be able to run the software. And it's like I was like, let's not do that. We have a software company. It's a whole different problem to build hardware. And by the way, in the world at large that hardware will get built before long. It is the wrong thing for us to try to build that. And it's better to concentrate on things where you know, you can just wait and that will eventually happen. And so there are, there are projects like that Where I. I kind of. Where it's kind of like, wait for it to happen, you know, There are a few other projects where I'm. I'm very curious about kind of talent development. And one of the projects that I've long been sort of poking at is dealing with kids and trying to find sort of high talent, low resource kids around the world and kind of help them do interesting things. And we've had the opportunity to do that with summer camp and a bunch of mentoring kinds of things that I've done. We've. We've done a certain amount of that. But it's been awfully difficult to kind of reach beyond the kind of stratum of society that we kind of normally deal with. And that's. That's a thing that I've long kind of tried to figure out, how can one do this? And it's been. It's been surprisingly challenging. It's one of these things where, from my point of view, part of the. Part of the sort of point is that I think there's lots of. It will be nice to kind of tap lots of talent in the world that I think exists, but which is not getting channeled into kinds of things where, at least from my point of view, it seems like it's as productive as possible. And I'd like to figure out how to make that work. But as I say, it's awfully difficult just logistically and structurally and so on to reach. As I say, we have a summer camp. I think we're renaming it this year, our High School Summer Research Program. I think that's what it's named. I better learn the name quickly. But with that program, we've had some terrific students over the years and many terrific students. And we did manage to get good geographic distribution of students by. Basically, we run an ad for the summer research program program on wolframalther. And that is a thing that's seen by lots of relevant kids. But I have to say that even though we've sort of been very insistent that we provide scholarships and things like this, we've had a disappointingly small number of kind of low resource applicants to that program. And so that's an example of one of those things that I'd like to solve. Maybe it's absolutely unsolvable, but. And maybe. Maybe I'm sort of wrong to try to solve it because the sort of. The kids we might reach there might just never care about the kinds of things that we have on offer in terms of technology and science and so on. I'M a little disappointed if that's true because I like technology and science. I think they've been important for the world, so to speak, and it seems like having a larger pool of, of talent able to contribute to them would be a good thing. And I think they're fun and so I'd like to see other people have fun with them. But so far that's been one of the things that I haven't. I've been. Been poking out for a long time, I suppose, related to that. You guys are asking me implicitly all, all sorts of all the projects that I haven't gotten to, so to speak. Another project that I, about a decade ago I did start doing some somewhat serious work on, but never really pursued it properly was a thing, I guess I was calling it the trajectory project at that time, which was to solve the following problem. So you have kids, and maybe they are kids with reasonable access to resources, but they are middle school age, early high school age, whatever high school age. And you ask them what do you want to do when you're grown up? And they say, I don't have any idea. Well, you know, at some point they kind of have to decide what they're going to do when they're grown up. And you know, too often I've seen cases where people, you know, they go to great schools, they go to great colleges, and then at the end they're like, well, what am I going to do? And there's a recruiting fair and some large company comes and provides good cookies at the recruiting fair. And pretty soon they're working for that large company and they wake up 20 years later and they're like, well, you know, what I really do with myself here? Was that really the right thing? And the issue was that nobody really, they never had a reason to think about, hey, what are the things that they might like to do? What is it that they're good at doing? They might have done things in school where there are a limited set of subjects that one studies in, for example, K12 school, and it's like, okay, I'm good at math, let's say, or I'm good at some, some other activity that's part of the sort of K12 story. But in fact, the thing that the person really would be, you know, fantastically find fantastically fulfilling is this thing that just didn't happen to be something that they were ever exposed to in the kind of K12 sequence, so to speak. And it's particularly true when you look at emerging fields of the kind of the new job category that only just started to exist, you know, six months ago or something. The majority of kids don't know about those kinds of things or don't really understand what they are. And the people who are kind of like, oh well, you should be this profession, that profession, the other profession, many of those things are kind of trailing stories of, well, that's what seemed like a good idea 25 years ago. And so I think the thing I was interested in was to try to figure out how do you kind of connect sort of kids to things they might be interested in and how do you kind of expose the things that are emerging, things that the 15 year old or something who gets really interested in this thing and that thing is going to have a great story over the next 40 years, let's say, as opposed to the 15 year old who gets in, who is, you know, I'm going to do this profession that's been around for a long time and these are the last 40 years of when that profession is really a vibrant thing. You know, this is the time when yes, you can have a, you can, you can be a such and such, but it's going to be a trailing story. You know, I think, for example, low level computer programming is probably one of those things that, these are the late years of that story. Just like when I was younger, you know, assembly language programming was a big thing, but that was, you know, these are kind of the, there was a moment when that became sort of not really a thing. And there are lots of things where the sort of the progress of automation, for example, makes those job categories not as exciting as they had been in the past. But there are lots of new things that open up and there's often a trade off. You know, it's rather personality dependent. Do you get into something which is kind of new and emerging where, where when people say what do you do? And you say I do this. And then the next question is what on earth is that? And you have to explain it. And for some people, like myself, for example, that's always, that's a nice place to be because the, the thing you have to explain is often the thing that's kind of in high growth and where the things you do are likely to have sort of a high impact on the future of that area. Whereas for other people it's much better to be, well, you know, I do this and everybody says, oh, okay, I know what that is. And it's kind of like there's a well defined track and there's, it's you know, I think for me it's very sort of figuring out what to do is not a stressful thing. I like figuring out what to do. Whereas I think for, for other people sometimes, you know, when there isn't a clear path, it's like, oh my gosh, should I do this or this or this or this? And it's very difficult to make that decision and very stressful to make that decision. For me, I suppose I, I, maybe it's just a sign of arrogance or something, but I just like, I'm going to decide I'm gonna do this, I'm gonna do that, and you know, I will get it wrong some fraction of the time, but it isn't, you know, I don't find it difficult to make those, that succession of decisions. Anyway, that's a few thoughts on things I've been interested in. This whole sort of trajectory project of identifying what are the things that are sort of the emerging areas that are really sort of interesting things for people to get into as sort of an early person in that area. And then what is it? The other thing that's interesting is what is it about the things that you've done or that you really find exciting and what is it that, you know, like, for example, there's plenty of somebody who says, okay, I'm great at math. They say in, let's say, I don't know, early high school or something. And then it's like, well, okay, you can do algebra really well. And that might or might not be relevant to doing, for example, research mathematics. It's the skills needed to do. Take math as an example. There are different things people sort of get into in math at a school level. One is, I'm very precise. I can solve these problems. I'm quick and organized and so on. That's one kind of thing that could be something where you're quick and organized about almost any field. It doesn't have to be math and algebra and things like that. Another is, I really like the aesthetics of these kind of abstract things fitting together in this nice way. Again, that's something that math is not the only field that has that characteristic. But if you really like that stuff and you're good at math, and then you say, oh, I'm going to work on some thing that uses math and it's very applied and it's very sort of, there's lots of approximations and so on, you probably aren't going to like that because if what you really like is that elegant, you know, perfect fitting together of ideas that happens in kind of in areas of mathematics, that's a different thing. Or another thing is people who like things like math competitions where it's like just how much you know, you know, probably not going to get every question right, but you're going to do it really fast and you're going to win relative to the other people. That's again a different type of interest in math or the generalized idea of math than sort of the person who likes the abstract pieces fitting together those kinds of things. So identifying which of those is really the part that you like and then matching that with what occupations really make use of those kinds of interests, that's sort of an interesting challenge. And I think that's something. Well, I think, you know, on many, in many individual cases I've kind of sort of tried to make that match or suggest that match to people. And it's always, I don't know, it's very, it seems very satisfying on all sides when that works out. And it seems like if one can do that more generally, it's an interesting thing. You know, I did do, as part of that project, I did do something I found interesting at least I did a bunch of surveys of kids of sort of what, what are things you would like to have achieved or would you, would you have like it if you achieved at some point in life and you know, from you know, having your own reality TV show to taking a one way trip to Mars to I don't know, being elected to some government position to all these kinds of things. And it is interesting that people have just tremendously different things that they're interested in, not interested in. And I have this suspicion that those things are surprisingly constant through life and that it's rather rare that people, you know, who were not interested in such and such a thing then, you know, in such and such a kind of achievement then become really interested in it later on and so on. And I found I was, I was going to do and I didn't get around to it. A survey of people that I know and trying to say when you were 12 years old, let's say, what would you have filled in to such a survey if you. And now sort of what happened next, so to speak. Anyway, it's a hypothesis and it's something I'd like to be able to test. All right, let's see. Lot of more technical things here. Prutz asks what are common business skills that science people are commonly lacking? You know, the one of these, I may have mentioned this before. The one of These that always drives me crazy is not keeping the thinking apparatus engaged when confronted with non sort of science related problems. You know, it just turns out I was just talking to somebody recently who's a kind of science oriented techie person, rather successful person who's off, who's about to start a new company and they were talking to me about some very abstract science thing and then something comes up about their company and I offer some sort of very practical advice about how to deal with certain issues. And they're like, they seem very confused. It's like, how can somebody like me who thinks about sort of deep science issues also know about and think about kind of these pragmatic, you know, how do you get from here to there with companies type questions? Well, the answer is I don't really see these as that different. I mean it's all about can you think your way through this particular question. And you know, you can apply the same kinds of sort of practical, you know, same kinds of thinking thematic and otherwise that you might apply in something like science to the, to, to these questions about sort of how to set up a company and things like this. I, I, I'm always just amazed at people where I know they have kind of the, the, the sort of intellectual orientation and, and, and capabilities and then they, they say things about sort of businesses they're setting up and it's like how can that possibly be right? And you know, have you not thought about this? And it's like well no, I didn't think about that. And it's like, but you can. Another thing that tends to happen is people will say well, I'm a science person and oh, there are these business people and they're experts in that. And then they sort of just switch off and don't kind of apply their own analytical thinking capabilities to things on the sort of more business and strategy side of companies. I mean, I have to admit I did that a little bit in the first company that I started when I was 21 years old, 20 years old. And I had the self image that I was a kind of a science person and didn't really know about companies. I really didn't know about companies. I mean I really had, had no experience with that whatsoever. But I discovered, you know, as that went forward, I kind of discovered with that, well, you know, there are these decisions and it's like, I think the obvious thing to do is this. And it's like well the folks who I brought in to run the company did the opposite. And it's like no I don't think that's going to work. And sure enough, it didn't work. And you know, I didn't consider that. It wasn't like I was saying, oh, wow, I'm so, so clever about this business stuff. It was just like, this is really ob. And you know, and it really, I think in some sense, relative to the kinds of things one has to figure out in doing at least good science, these kinds of questions, you know, look, good business is difficult as good science is difficult. I would say that they are somewhat difficult in the same kinds of ways. One is the strategy. What are you going to study in science? How are you going to study it? What are you going to try and do in business? How are you going to do it? That's one thing. The other thing is all the details that matter and figuring out which details matter and which do not matter. You know, when you're doing science, how do you actually get the right answer? How do you. What what matters? You know, there are things where you just don't need to worry about it because it doesn't matter to getting the right answer or whatever. Similarly, in business, there are things where there are lots of details you could worry about and it doesn't matter. And there are other details that you might not worry about and it really does matter. And so, you know, getting that balance right and, you know, I can't claim that I'm ever perfect at that, but, you know, one, one gradually learns to get a reasonable balance of that. Those are, those are important. And to me, there is a remarkable similarity between, I would say strategic level science and strategic level business. They're really, it's all about sort of what are you trying to achieve, what's the real point? By the time something that can happen in both science and business is, oh, you're doing this very complicated thing and it's all a bit fuzzy what the point is. And there are steps you're taking and you know what those steps are. And the steps individually might seem sensible, but the whole picture is a little bit fuzzy. And that usually doesn't end well. I mean that to me, at least, at least my style of doing these things, it's really important to try to try and, you know, clarify what is the real point? What is the what, what are you really trying to talk about? And that's relevant for both science and business. And what's needed to do that is, is very similar. I would say that if you are kind of a, a science person who can do the mechanics of the science but not the strategy of the science. That's not a great sort of background for doing the strategy of a business. If you're, you know, the mechanics of a business, I don't know how similar, how I would compare them really to the mechanics of doing science. They're both kind of crafts of some kind, but the, the, I don't know how those really compare and the detailed skills are probably a bit rather different. But, but at the strategy level, I would say there's considerable commonality which people often do not recognize that, that there is a commonality in that sort of keeping the, the thinking apparatus that has been perhaps so carefully honed in science engaged while doing these kinds of business things. That's, that's the thing. Now, you know, one thing that can happen is, you know, can you actually enunciate what the main point is or do you always like hold back from that? Sometimes both in science and in business, people do that and it's somehow, it's almost like that's just too important and too, and too kind of, I don't know, sort of too sacred to really discuss. You know, what the real point is, is just that thing, which is never mentioned kind of thing. And at least in my way of doing things, that's bad. You know, the more you can expose what is the main point? Can you really explain the main point, the better off you are. Let's see. Gosh, So many questions here. Lots of interesting ones. Oh boy. Okay, here's another one about. Well, okay, I'll answer one here from Rudy. Did you ever imagine you would end up as a CEO or did you plan to stay in the world of academia? You know, if you'd asked me at age 12, 13, something like that, what do you want to do when you've grown up, I would have said, be a physicist. And the good news was that by the time I was, I guess I was already, by the time I was like 16, 17, I was sort of in the flow, doing kind of parts of the sort of international physics community and going to physics conferences, writing physics papers and so on. And by the time I was, I guess 20 or so, I was about as card carrying as physicists usually end up being. And it was great that that happened so quickly because I hadn't lost energy by that time. And you know, then I had the sort of the strange experience of realizing, gosh, you know, the things I'm doing, I could just keep doing them for the rest of my life. And I kind of, I think that I don't think I really specifically. Well, I do remember one time I was like 25 when I specifically thought to myself, you know, I'd given some after dinner speech at some conference and I thought to myself, I could end up giving the same speech for the next 50 years. And I said, I'm not going to do that. Now I have to say somewhat amusingly that at least one science discovery about this Rule 30 cellular automaton and the way that you can get complex behavior from simple rules, I have to say I must have shown that same discovery so many times in my life and sort of almost every time I remember back to, you know, showing that at this after dinner speech, so to speak, at age 25 and like, oh my gosh, it's, you know, it's 35 years later or whatever and I'm still showing this. And at some point like everybody I might show it to will have already seen it and absorbed it and I won't have to show it anymore. But that still hasn't happened even although lots of people have seen this, it still hasn't fully happened by this point. And I think some of these things, it's kind of like it takes more than a lifetime for some of these ideas to get absorbed. But in terms of, I kind of backed into starting my first company as a result of having developed a bunch of technology that clearly needed a corporate home and there wasn't really any way to do it other than to start it myself. And as I mentioned earlier, you know, when I probably my, my big mistake in my first company was to not realize that I was perfectly capable of CEOing companies. And I suppose looking back at my earlier life, so to speak, I had organized many things. It wasn't like I was, I was not, you know, I suppose when people are in school and things like this, they often have, oh, you can be the, you know, the head of the such and such club. That is a club that's been going for, well, you know, I went to school in England, so a club that's been going for 200 years or something in the US it would be a shorter time probably. But you know, you can be the head of the existing thing and you can get elected to be the head of that existing thing. I didn't do that at all. Or you can just like start your own and thing of whatever it is. And I did that many times. And I suppose looking back I realized that other people weren't really doing that. And that was probably a sign if I had been able to pay attention to it that, oh yeah, you know, you could probably do sort of entrepreneurial type things. I would say that didn't help that because I was kind of a sort of advanced academic kid that I had a lot of feedback from people about, oh, you're doing so well in sort of all this advanced academic stuff. And feedback along the lines of, you know, oh, because I was pretty young compared to other people who were my contemporaries, so to speak, in, you know, whether it's graduate school or being a young professor or being in sort of high school type type stuff or college type stuff. So, you know, the, the view from the outside was the implication of, okay, there's this kid who's five years younger than everybody else around them. They, you know, it doesn't make sense that they would be kind of the organizing kid. But. And that was kind of the, the self image that I sort of absorbed, which turns out not to be correct. And so it took me a little while to kind of throw off that kind of incorrectly self and externally imposed sort of self image, so to speak. And so I think now, you know, I have to say, you know, I started my first company when I was working at Caltech and in a sense, my first company and the dynamics of what happened with it ended up having me eventually leave Caltech. And had those kind of machinations not occurred, I probably would still be a professor there, you know, whatever it is, 40 something years later. So in a sense it was probably a. And I have many friends who kind of stayed in the sort of, I'm going to be a, you know, I'm in the physics business and I'm staying in the physics business. And they spent their whole careers there. And I'm glad I didn't do that. For me personally, I think it's a, I mean, for many people it's a terrific thing to do. For me personally, it's been great to kind of do things that sort of alternate between doing technology and practical things in the world and basic science and intellectual kinds of things. I found that very stimulating. I think that many of the things I've achieved on both sides of that picture I would never have been able to do had I not been alternating between them. So I'm happy that it worked out that way. But yeah, I would say that it wasn't until probably age 23 or something that I would have said, yeah, this CEO thing is a, is a likely thing. And I'm not just going to be. I'm not going to be an academic. I think I also realized Only quite slowly that the world of academia and I don't know how it's evolved in the last 30 something years, but it was a world where sort of doing, sort of, sort of doing the kinds of things that one feels one should strategically and entrepreneurially do was, was neither easy to do nor in some cases appreciated, so to speak, in the sense that, you know, well, I'm just going to go out and organize this thing and it's like, no, you can't do that. We've got this whole process of, of, you know, and it's been, you know, and you've got all these machinery at universities and in academia and so on and you can't just go out and, you know, make this up for yourself. And it's like, well, yes, you can. And that's, you know, I'm, I'm going to just do that. And so, you know, that's the way I have tended to like to do things. And it wasn't a particularly good fit. And I think that would have become sort of increasingly evident. Did become increasingly evident. Let's see. Oh my. There's lots of questions here. Let's see, Yumi is asking, do you work more than you should? What's a good work time for yourself ratio during the day? Look, I may be a bad example partly because I work on things I like to do and I'm, you know, it's comparatively rare that I'm working on things where I'm like, oh, I really hate doing this. So, you know, I work sort of pretty much all the time, probably for, I don't know, maybe couple of hours during the day I won't be working. But that's the most. And that's only when I'm with, you know, family members or whatever else. And it's, it's. I'm, I'm mostly doing what I consider to be things I like to do. And other people would say, oh my gosh, you're working. They might say that, I don't know, I mean, like, like doing these live streams is something which I like to do, I find fun. And some people might say that's working. And some other people might just say, well, it's a, it's an interesting hobby type thing. So I would say that for me, if you. It's also been the case in my life that many things that I've done, they kind of have started as hobbies and then they become much more serious. And I suppose one definition of seriousness about many things one does is do they involve Lots of other people, by the time they do, they're kind of not really as convincingly hobbies. And so I think for me I, you know, I just, I don't really need, and in fact don't find it easy to take sort of the break where I'm quotes not doing anything. I don't know what I would do if I wasn't doing anything. I, I don't even know what the sort of the definition of that would be. I mean I, I, I tend to, I've always had, at least before the pandemic I had the, the thing that, well typically my wife and I would, would go and see you know, one movie in a movie theater every week. I don't watch television or anything like that, but that was my one kind of outlet of exposure to the, the culture of the world, so to speak. That is the culture of the world. I'm not sure but you know, I've not found, yeah, as I say, I may be a bad example but for me, you know, I managed to do things as work, so to speak that I like to do and so that's what I spend my time doing. I think it's also the case, I mean another thing, I, I like people, I interact quite a lot with people, but somehow an awful lot of the, the interaction that I do with people is sort of very high content interaction. Whether it's people I know who do science or technology or whatever and I'm talking to them about the content of what they do or you know, it's those kinds of things. I'm not, and maybe it's to my detriment. I'm a, I'm a terrible small talk person. I have to say. I always find that really difficult and don't manage it past a few sentences usually. I'm usually on to talking about something that's, that feels like it has real content for me. I, you know, I'm not, not arguing that that's necessarily the best way it could be, but that's something that I find for myself. Let's see, David is asking, suppose scientist X has published an important empirically valid idea that all other scientists completely ignore. How should scientist X proceed? Well, let's see, that's happened to me many times. Sometimes you just wait and few decades go by and people say oh wow, that was a really cool thing. And you know, that's like okay, great. Sometimes, you know, there are times when I've just decided it's just not worth, you know, you can go and you can advertise things and you can tell everybody about it and people kind of give you blank looks. And why does that happen? It happens because there's some paradigm that's developed where everybody thinks within that paradigm and you've done something which is a bit outside that paradigm and people just don't get it. And as things evolve, if you're lucky and you know, and it's kind of a like if you live long enough, so to speak, the ambient paradigm will evolve to the point where people do begin to get it and you don't get the blank stares, people say, oh, yeah, okay, I understand how this works now. I have to say that's one attitude of just wait. And there are things, certainly things I think I've discovered and so on, where the expected waiting time is quite long. And some of them it's a decade or two. Some of them it's more like 50 or 100 years, which we don't get to typically wait for that time. And so that's one point of view. Another question is, does it matter if you're sort of, I've got this great idea and it should be. It's like, why does it matter? Does it matter to you personally because you can't make a living unless people believe in your idea? Does it matter because you can see the world is careening off in some terrible direction, and if only people could see this idea, they would, you know, the world would go. Be going in a better direction. You know, I would not claim, if you look at things I've done about, I don't know, fundamental theory of physics or, or the foundations of mathematics and so on, these are not things where I could say, if nobody paid attention to this for another hundred years, I, I don't think it would be, you know, it's not going to be a dramatic change in terms of sort of the practical things that happen in the world. Let me correct that a little bit. I think that some of the things that we now understand about the fundamental theory of physics, for example, and how deeply computational it is, basically give one a sense of how much more important computation is than one might have thought. And there are many, many practical things in the world for which that realization is very important. About things about computational irreducibility, what's predictable in the world, what's not, things like this. Well, you might say, well, how can that really be relevant? Because somehow underneath, in fundamental physics it works differently. Well, actually, no, we pretty much know now that in fundamental physics it works that way. So it's sort of A grounding for those kinds of ideas. Kind of like if you're in the Copernican moment, so to speak. Probably the most significant thing about Copernicus was not the detailed theory of the motion of planets that he had, which even at the time wasn't that great a way to compute things, but the fact that what he showed or argued was something where there were things you could conclude from science that you could not conclude by just sort of everyday kind of intuitive thinking, so to speak. You know, the Earth seems like it's just staying still. How can it be moving, so to speak? Well, you can get the science argument that it is moving and you know, you can the importance there, that sort of really fundamental importance in a sense, is the realization that there are things about the way the world is that are not obvious to our senses, so to speak. And I suppose in that sense, you know, understanding the fundamental theory of physics has the same kind of role in the sense that it's talking to one about there are fundamental things about the world being computational that one might not have realized. One might have said, no, no, it isn't going to work that way. There's going to be a formula for how, a mathematical formula for how everything works out. And no, that's, you know, once you know things are really computational all the way down, you know, that's not how it's going to work. Now, I would say when you talk about some, you have this hypothetical of a scientist who's published a valid idea that other people ignore. The next question is, how well did you explain your idea? There are plenty of ideas that I've seen where it was a real challenge to dig that idea out of the explanation that was given for it. And, you know, the value of a sort of. I don't think that you sort of get full points, so to speak, unless you can actually explain the idea with enough clarity and enough kind of expositional wrapping that it is actually communicatable to people. Now, you know, as I say, when. When the paradigm gets far enough away from the paradigms that have existed before, that gets comparatively difficult. I mean, my own experience, for example, I developed a bunch of ideas in the 1980s which I spent the 1990s trying to package in a way that would be absorbable. And that ended up with this big book I wrote called New Kind of Science, that was a 1200 page book that came out in 2002. And I think that thing, that book was a sort of a complete enough packaging that those who did want to understand the sort of fairly large paradigm that I was trying to introduce were capable of doing so. Not to say that everybody was interested in doing that, but those who were, were able to do so. Had I written, as I did in the 1980s, the kind of the four page paper about some particular part of that, it was not absorbable. The overall paradigm was not absorbable from four page papers. The overall paradigm was a sort of a thousand page story that required sort of some effort of exposition. And so I think that's another thing to realize that there's, you know, you haven't finished it. Just because you understand it and can explain it to yourself or think you can explain it to yourself, that's not the end of the story. You have to be able to, to get an exposition of it that's understood by other people. And sometimes that involves, can you make, can you turn the thing into sort of the elevator pitch for the science? Can you crispen it down and not have, and have a good name for the thing you're talking about and not have to say, oh, it's this long explanation and then everybody's kind of tuning out before you get to the end of the explanation. So I think that's. And then, you know, there's another piece, you know, science has a very social character. It's, you know, quite a lot of science is spread person to person. It's remarkable when you look at the sort of genealogy of who is the student of who and the grand student of who and so on, how much fields of science have grown genealogically, so to speak like that. I think that. And sometimes when you're dealing with a particular scientific community, it's a big investment to kind of make the kind of essentially social connections to that field. And sometimes it's a challenging thing because like for example, a bunch of work I've done in recent times about metamathematics and the foundations of mathematics. I would say that the bad thing about that field is it was a very energetic field in the 1920s, 1930s, 1940s. Most of the people who did it at that time aren't around anymore. And if, if what I had been doing I had done at that time, it would have had an immediate large audience. The. I think in today's time, I frankly, as I say, well, who should I talk to about this? It's like, it's actually hard to find people. And I've been pleased. You know, I put out a book about metamathematics which I'm. I was very surprised to find out it Just. I think it just sold through its second print run. So I'm happy to see there's more interest in mathematics and the foundations of mathematics out there than I had expected. But it's one of these things where there isn't an existing community, so to speak, to kind of go after. And that's a thing where you will find, if you have some great new result, idea, whatever, you know, is there an existing community? Is it something that plugs into something that's already there, or is it something that goes off in a completely different direction and that, you know, if it plugs into something that's already there. One of the things that can be difficult is you have to sort of convince the, the people, the incumbents, so to speak, that it's worth looking at something different. And I know from my own experience, and I know from talking to other people, it is shocking the extent to which you can have sort of obvious, let's say empirical kinds of discoveries where it just plainly, you know, right there you can see such and such is what's going on, not going one, whatever. But people who have been sort of pursuing that field will kind of somehow manage to block that out and will often end their careers, you know, decades later, never having accepted some particular thing that happened differently. I think it's a challenge and it's something, for example, somebody like me tries to aspire to is when new information comes in actually absorb can be difficult. There are things where I've long believed X, Y or Z and turns out it isn't the case. And you get evidence and sometimes you've long believed it and somebody says, look, I've got this experiment and it proves it isn't like that. And you look at it and you say, I don't think that experiment can possibly be right. And I'm sufficiently convinced of what I thought I knew that I'm going to say I don't think that experiment is right. And some fraction of the time when gets more experienced, it's a larger fraction of the time one's correct in that assumption. Experiments are hard and they're sometimes wrong. And sometimes ideas are so clean and strong that you kind of know those are right. And if the experiments say otherwise, then the experiments are probably wrong. But there are also cases where things one sort of long believed, one realizes, well, that wasn't quite right. And it's unlike now. There's a question of how quickly can one absorb the. So the new information and start going in that direction. And I would say that one doesn't want to immediately accept that the people one runs into where as soon as something's written and some they see it posted somewhere on the web or something, it's like, oh, the world must be this way or that way. And it's like, well, no, just think about it a bit. That doesn't seem convincing. So you don't want to be too quick to just say it's going to be this. I'm often reminded by people in my company of cases where I jumped onto new technologies and things like that too quickly and reminded, for example, virtual reality in the 1990s. I was like, we should support this, we should do things with it. And it's like, well, it was a bust at least at that time. And that was a case where I kind of jumped on something too quickly. I thought I could see the value in it, but it was the wrong time for all sorts of somewhat detailed reasons. And then there are cases where, oh, like I'll give you an example where I probably jumped too slowly. When the web first came out, I was like, oh yeah, another Internet sort of connection mechanism like wais, like gopher, like this, like that and the other. And it took me probably six months to realize, no, the web is different, it's actually going to take off. And that was a case where I was a little bit too slow on the uptake of some new direction. And I think. But the thing that, you know, I can see it right now with LLMs, for example, people who've been sort of long time linguistics people and long time, sometimes even AI people, and it's like, well, this worked differently than we all expected. None of us thought it would work quite this way. I don't think I know a single person who could have given me the roadmap of what's actually happened with LLMs and the way that they achieved linguistic and common sense fluency at the point they did, I don't think anybody knew that that was going to happen in that way. And there are people who sort of had a long investment in kind of the way of thinking about human language in terms of ideas about linguistics and so on. And frankly, what the LLM successes ChatGPT and so on show us is it just doesn't work that way. It's different. And in a sense, I suppose I didn't have any particular investment other than knowing a certain amount about these other ways of thinking about this. So for me it's sort of an easy pivot in a sense to make to realize we just learned something new and respond to it. But that's a case where you can kind of see that there will be for sure people who will insist that ChatGPT doesn't really understand language and so on and so on and so on. Whatever understand means. That's a separate topic. But, you know, isn't really doing language. It's really something different. It's just a, a thin layer and so on and whatever else. But no, it is showing us clearly that there is something about how language works that is very well captured by that system. And to deny that is, is just to, you know, is, is to ignore something which is effectively an empirical scientific discovery. And so, you know, being able to absorb that and go with it I think is a significant thing. Oh, here's a question. Okay, Patrick here is asking what was the last movie I watched? It was a, you know, I'm afraid the pickings have been a bit slim of light post pandemic. They've been quite similar. This one was actually a nice movie about that I saw. Oh gosh, what was it about? It was about, in a sense, the Discovery of Michael Jordan by Nike was a nice movie. Interesting, nice movie. Not perhaps I'm not being a very sports oriented person, not necessarily the kind of thing I would naturally have seen, but was sort of the only thing on offer that wasn't a horror movie or something like that. And I don't go to see those. But I think Patrick is asking. It's interesting to see how old movies imagine the future compared to what it's actually like. Yeah, no, I mean, I think that sometimes things have been sort of shockingly on track and sometimes there, I think, I mean, I did a big analysis of for example 2001 A Space Odyssey back in 2018, which was its 50th anniversary. And it's charming the things, you know, the whole sort of exploration, exploitation of space obviously didn't happen the way that movie hit imagined. I think that's an accident of history. I think it could have happened. It's just there wasn't a compelling enough reason to make it happen. There was sort of a 50 year hiatus there and you know, we'll see what kinds of compelling reasons are or are not found for really putting lots of efforts into space. But clearly there are at least enough to justify a certain amount of significant effort now. But I think it was the sort of portrayal of the AI and things like that in 2001 was not so bad. I mean it was off by 20 years or something like that. But that's nothing I Think the, you know, there were cute things like the video phone that, you know, yes, it had a key, you know, it had a keypad, key keys, you pressed everything with buttons. And likewise in their spacecraft they had sort of a separate screen for every different function of the computer. The idea of a multi window interface hadn't been imagined at that time. And there are lots of other detailed things that were kind of a little bit off. But overall it's remarkable. You know, the video phone finally did come. I mean, video phones had existed in some form or another since the 1950s and it took a really long time for them to arrive. I think sometimes with movies and predictions of the future, it's easier to know what will happen than it is to know when it will happen. And I know I've been wrong by decades in predicting when things will happen. And well, like for example, recently with the whole LLM thing, that's a. Ahead of schedule as far as I'm concerned kind of thing and happening in a different way than I expected. Whereas there are other things about, oh, I don't know, you know, I don't know things. Some biomedical kinds of things where I'm kind of like surprised that hasn't been figured out yet. But. Aaron asks what do I order from the concession stand at movies? Okay, okay, this is, I'm, I'm such a creature of habit. I can answer that question. It's. For what it's worth, I, I usually get, I, I used to just get a thing of Raisinets because it has sort of a. I like fruit kinds of things, I like chocolate. And that's my combination moment. And then I, I started not having dinner when I'm going to go to a movie. And so then I get some popcorn without that. I can't stand having butter on it and things like that. But that's. And I usually eat part of my way through the popcorn. Anyway, the yummy is asking what's more fun being a professor at a university or teaching People live about things that interest you. I haven't been a professor for 35 years and I wasn't really a professor for terribly long. I wasn't that excited about the professor mechanism for teaching. I mean, I've done a fair amount of teaching at our summer school, summer camp. I do kind of a thing with kids every week and those things are fun. And the, I think that I have to admit that I don't think I've ever taught sort of to an official curriculum. And that's kind of, you know, I've never been in the kind of. The students have to get through this, you know, have to hit, you know, get over this particular hurdle. I haven't done that. That seems unappealing to me personally relative to the kind of more free form kinds of things. And for me, the thing that's always, always fun about teaching stuff is when people like, ah, I understand that that's always, that's. That feels very satisfying to have managed to, you know, communicate an idea to somebody and have them have fun with it, so to speak. That's, that's always good. I, I think I'm, I'm sure the. Yeah, I can't really comment on the, on the traditional being a professor thing because I really, really haven't done it in a serious way. The. Let's see. All right, a few more questions perhaps. Ah, another sort of personal one from Rowan here. What are the foundations that make you a decent scientist? Thank you for the endorsement. First of all, let's see. I guess the things that make one a decent scientist are different from the things that make one a potentially great scientist. I would say the decent scientist thing, it's be organized, be careful, sort of do things systematically. That's, that will make, you know, and, and sort of. Yeah, I think those are the important things and be able to learn sort of, you know, know enough about the target area, so to speak, be organized, do things carefully enough that's relevant. Whether you're doing computer experiments, physical experiments, whether you're doing mathematics and you actually get the theorem right or not. Those kinds of things. I think that's a lot of what it takes to be a decent scientist. The other thing is to kind of keep trying to sort of push through things. Don't kind of give up too quickly or figure out the other path that gets you to the, to where you want to get to. To be a potentially great scientist is a bit of a different story because it mostly is about strategy. It's mostly about what should you study? What do you do when you get stuck? Do you just keep bashing? Do you know when to just keep bashing? And do you know when you should kind of go around that obstruction or, you know, change the project and so on? Those are the things that are needed, I think, to, to be able to do sort of great science. And I would say that the, the really being able to drill down and understand thematically what's going on. That's another characteristic that you don't need to be a decent scientist. You need that if you're going to Be a great scientist, so to speak. I think that, you know, it is a. It's a very humbling thing, I think, when you are trying to really drill down and understand the essence of things. Because in the end, something that may have taken you a really long time to understand, once you really get through to that, it's going to be quite simple. It's going to be something that it's like, how can I have spent however many years. And in the end the answer is blah, where it's really a simple thing. And I think people, you know, that say to really push, push, push until you get to that point is something. And I think it's also another thing that's important. I would say somewhere between being a decent scientist and being great scientist is to accept being wrong from time to time and to kind of, you know, you do an experiment and it works out differently than you thought. For me, it's always computer experiments, so I'm physical experiments. It'll work out differently than I thought because something will have gone horribly wrong. But you do some experiment or you analyze something and you thought it was going to go one way and it goes a different way. And the question is, do you handle that and just say, okay, I'm going to understand that direction, or do you obstinately believe it has to go the way you thought it would go? And one place where that gets into trouble, and some areas of science is particularly prevalent is when there's sort of a personal ideology that sort of intersects with the things you're doing in the science. You know, I've just got to prove X, Y, Z, because that's what I ideologically believe that's kind of deadly because that just leads one to, you know, that's. That's not a science story anymore, so to speak. I would say that. I was going to mention other characteristics. I mean, I would say that another thing to be somewhere between a decent and great scientist is expositional capability. Can you actually explain to anybody else what you're doing? You can be a decent scientist and just sort of fill in more lines in the giant chart of things that have been figured out in some area of science. And that's a useful and incrementally sort of valuable thing to do. And you don't need huge expositional skill to be able to do that. If you want to be talking about things that are more, as I was mentioning earlier, a little bit more sort of fresh and original, that's a different thing. I was going to mention another point which is about intuition. And most of the time, at least when I'm doing science, I'll have, in order to set a goal for some particular project I'm doing, I will have some intuition about how that project is likely to work out, may turn out to be wrong. And it may turn out that when I actually do it, that intuition was off track, and I have to kind of adapt based on that. But having sort of good intuition about what might be true is an important piece of science. And there's some areas of science that are more and less kind of theoretical. You can have intuition about theory just as you can have intuition about experiments. To say, I'm going to have no intuition. I'm just going to do the experiment and see what happens. The problem with that is without intuition, it's hard to know what experiment to try to do. And you see that a lot in biology, biomedicine, things like this. It's kind of like there's a certain amount of. There's an obvious experiment. You might as well just try this, try that, try this, you know, this thing in this way. But the times when great science gets done, somebody had an intuition about how, oh, it might work this way, that we've never even imagined it could work before. Let's try that experiment. And I think that's, you know, that's kind of the. That side of things. You know, I think for myself, at least, in trying to do science, one of the things that I value a lot and I think is pretty important is this thing about can you really grind things down to their essence? You know, it is not necessary for doing decent science to be able to do that. It is perfectly adequate to just do the things which are kind of, you know, let's take the next step. And that is something contributing to a long chain of important things. But if you really want to kind of do a breakout thing, I think you typically have to be able to grind things down so that you really know the sort of essential primitives of what's going on. Not the sort of higher level, oh, I just take the next step in this direction that was already defined. Let's see, Memes is asking about thoughts about the future of education. You know, it's interesting that I have to say this is an area that for me is evolving slightly week by week, and it'll evolve a bit more. As I understand really what it takes to use LLMs in education. Because the thing that is. I would say that the first thing to say, which I think I've said before here is the extreme specialization of many kinds of education, I kind of think is increasingly self defeating. And the thing which is increasingly important in education is kind of to be able to think broadly. And by the way, an important piece of modern thinking is kind of computational thinking. Computation is sort of this great paradigm of our century. And that's the thing that is kind of the formal foundations that you can use to kind of build a tower of thinking that goes beyond just the I thought this, then I thought this, then I thought this. It's the thing that allows you to sort of build a solid tower. Mathematics has the same characteristic. Computation is a much more general story for that. Plus, when you've built something computationally, like in Wolfram language or something, you have the capability of take this thing I built computationally and feed it to a computer and have the computer help me sort of make progress with that. And I think one thing that better be part of the future of education is people really learning computational language, really learning the ideas, the concepts of computation as they have been convinced to learn the concepts of mathematics over the past thousand years or so. And I think that that better be part of the future of education because it is the great paradigm of our times and the thing that really allows kind of to go beyond just the human common sense discussion to build something formal that by the way, we now have computers that can help us make progress with it. But I would say the thing that to me is sort of interesting right now is, you know, there's different modes of education. I mean, there's. The lecture is being given, people hear the lecture. There is, and that has value, particularly if the, if the lecture is good and you know, all those kinds of things. There's also the sort of the personalized education thing. You know, somebody was asking me what very recently actually what my learning style is. And I think the choices were kind of reading or listening of, you know, just go read the book or listen to the lecture type thing. And I realized that I'm terribly spoiled because an important mode of learning for me is neither of those. It's what I might call asking. And you know, when I try to learn about some field, well, the, the, you know, I'll, I'll read a certain amount of stuff. I typically, I'm not, I, I prefer reading to, to watching videos and things. That's just a personal preference and probably also a feature of the sort of a form factor of my life because, you know, I'm not a person. I don't commute to work and things like that I don't have a time when I'm. I'm, you know, I'm readily able to listen to stuff and so on. But the, you know, for me, the number one way, the zero earth way to learn things is to have a project related to those things. And that's kind of usually the driver for me of learning anything. And that's an important piece of kind of what motivates and defines what I want to learn. And I would say that the other part of it, which is the spoiled part, is the asking part of learning things, which is. Yeah, I mean, I, I've, you know, often I'll seek out some expert in some particular area and I'll just ask them a bunch of things. And the thing that, you know, that's, it's often, I hope, fun on both sides because often the things I'm asking are things that come from an attempt to kind of foundationally understand some field. And they're a little different than anybody's going to ask if they're not really trying to drill down to the foundations. But my mode has been you ask a person, you get responses in real time to the questions you're asking and so on. And so now the question is, how can that be generalized? Do LLMs? Let us generalize that idea so that a much broader range of people can do that sort of personalized asking style of education. And I think the answer will be yes. I mean, I think what one has to do. And, you know, this is what we've done with. Well, the first step is the OpenAI Wolfram plugin for ChatGPT is kind of. You have to back the linguistic interface that's provided by the LLMs with sort of hard content, so to speak, and hard computation. It's the linguistic interface. And the kind of common sense of LLMs doesn't take you the full distance, but it does, it does give you this kind of rich linguistic interface which allows things to happen that I think will make it much more feasible to do sort of personalized tutoring, education and so on. I mean, it's a. Even the very basic aspect of, you know, you're teaching a kid math or something and you want them to understand, I don't know, fractions or something like this. And you formulate some scenario that involves their favorite kind of animal doing something amusing and et cetera, et cetera, et cetera. It's got a lot of words, but it's also, it's somehow engaging. And if you can, I don't know how well, this Will work. But it's certainly something interested in is, can you couch the question in terms that for whatever reason the kid will resonate with and will care about and will be, oh, not thinking, oh, that's some abstract math thing. I'll never relate that to anything I do. But as soon as you relate it to, I don't know, collecting Pokemons or something or whatever it is that somebody does, and you can do that in a. In a decently engaged way, they'll. They'll think, oh, you know, let me not get into Pokemons because I know nothing about them. But the. Oh, I'll give you an example. A kid, one of mine, actually, who really was not into sort of learning arithmetic, except that was interested in business. And so turns out anytime there was a sort of arithmetic computation that involved dollars. Yes, that was, you know, that was immediately interesting and related and, you know, 15% of this. Yep, we could work that out. But the abstract, you know, you know, 15% of some abstract number just never really, you know, never really engaged. Was never really was that interested in that. So that's a case where, you know, if the LLM can couch the sort of the raw math in terms that are relatable to some kid, for example, then that's already something. The next issue is can one. This is sort of the first time in history where it's possible for a machine to understand the working that somebody goes through. I mean, yes, usually it's been like, we'll get an answer, okay, we can tell you if the answer is right or not. But if you show all your work in mostly machine is just like, I don't know what's going on here. Now we have a mechanism where you can actually understand that you can have a machine understand that, just as you can have a machine understand something about how you're building up a piece of code you're writing. And so I think the analysis of working, the analysis of a thought process, or even the description of a thought process, you know, kid gives answer. Machine says, why do you think it's that the kid types something or probably later says something, and the machine can really give an analysis of that. And that's interesting. And I think the machine will gradually build up an effective model of what's happening for the kid, for the learner. And I would strongly expect that it will be possible for machine to preemptively explain things in a way that is appropriate for that particular person. I mean, I know for myself, for example, this is again one of these sort of spoiled Admissions is, you know, lots of stuff comes in for me and I don't necessarily have a chance to read it, but there's people who work with me who will have a chance to read it. And those people know how to explain stuff to me. You know, it's like yes, we can have some whole elaborate story about some new, I don't know, AI development or something. And there's a way that I have of understanding things and you know, based on stuff I've worked on and know about and so on. And there are, you know, I have people working with me who are pretty good at explaining to me, oh, that's, you know, it's like a multi way system. It's doing this, that and the other. That explanation will be useless to many people, but for me, because it plugs into things I already know. It's like, okay, I get it, you know, then then, or this business model is like this other company that I know, okay, I get it. So I think the same will be true for, for everybody which with kind of LLM based education that it will be much easier to present new ideas to somebody in a way that relates to the things they already know and is sort of a good presentation for them that may be different from a good presentation for somebody else. So I think, you know, I see that as being quite successful and I think the, you know, what I would imagine is that there's, you know, do they need to be humans in the loop? Almost certainly, yes. I mean particularly, I would say maybe less so at the very early stages of education, but later on I kind of think for most people that having a human sort of care about, interact with, et cetera, you know, having a human who is sort of expressing the value of the thing that's being, being learned is pretty important to people. I mean, there are people sometimes like myself who are sufficiently self motivated to learn things. It doesn't matter whether there's a human who, who loves the subject and is going to explain it to me in a very enthusiastic way. I don't care, you know, if I'm enthusiastic about enough about it. I'm just going to drill down and learn that stuff. And you know, certainly people get into that kind of situation. But I would say that a much more common situation is there's a teacher who's very passionate about some subject and that passion kind of rubs off on students who then get motivated to learn that subject. And I think that that particular loop is probably a worthwhile one that will sort of keep humans in there. But when that isn't available, and even in the mechanics of learning things, I'm fairly confident that this kind of personalized approach to education is finally now, very surprisingly, something that will be possible. I mean, people have been talking about adaptive learning systems for 40 years, maybe even 50 years, and they've usually been a bust. They've usually really not worked. I mean, they've been very sort of. They've got a big map of different things that one might learn about, and they've got different paths that one can follow, but it really isn't. And they do something, but it's not the magic that one might hope it would be. And perhaps there will be more magic. Now, I have to say, in sort of criticism of what I'm saying, whenever there's a new technology, from movies to television to computers to all these things, one of the things people say about it at first is, oh, it'll be so great for education. It's sort of a repeated statement about every new technology. And sometimes it pans out, usually it doesn't. I kind of think this one is different, but we'll see. And I kind of think this. This ability to sort of really relate the educational content to the student is going to be important. The right. I probably should. Oh, I need to wrap up here. I got to go do my day job here. All right. A lot of interesting. Questions for another time. I look forward to. Oh, boy. Lots of interesting things here. It's. This is. It's like a candy store of interesting questions. Thank you all. Well, we'll get to more another time, and thanks for joining me this time and see you another time. Bye for now.