So I've been writing about time management for a long time now. When I was 22 years old, I wrote a book called How to Become a Straight-A Student. Here it is for people who are watching instead of just listening. It was supposed to be a book of no-nonsense advice about how to get good grades in college.

Had a quiet start. This book's actually quietly done well in the background. I think it sold like over 300,000 copies quietly since it came out in 2006. Anyways, this book includes a chapter. I'd forgotten this, but I saw the other day, this book includes a chapter where I describe a time management system for students.

And the title of that chapter is How to Manage Your Time in Five Minutes a Day. This was written back 20 years ago. So I thought what would be interesting would be for me to go back and reread that chapter. Let's go back and revisit my ideas from 20 years ago about how to manage your time.

Because what I'm really curious about is to see where was I on the something that remains true today? And where are there, where is there advice from 20 years ago that no longer holds to today? So what works from before and what doesn't? I decided not to really revisit this until we were live here on air.

So this should be, should be interesting. God knows what's in here, Jesse. I'm looking forward to it. Yeah. It's going to be a lot of, let me tell you this by 2025, our biggest problem is going to be finding air parking for our flying cars. And let me tell you the, I guess I was going to do a whole like Donald Trump thing, but I only know, I guess he was on TV then.

I don't know. All right, let's get into it. I'm going to open my book here. Page 19. Boom. All right. Manage your times in five minutes a day. I'm nervous about this. I haven't read this in a while. Here's the intro to this, this chapter. Real straight A students, like most reasonable students, hate time management.

After all, college is supposed to be about intellectual curiosity, making new friends, becoming obsessed with needlessly complicated drinking games. See, that was social proof, Jesse. I was proving to the reader. See, I'm young too. I'm on your page drinking. An overwhelming interest in time management is best left to harried business executives.

Oh man, that's me now. Or perhaps pre-meds at the same time. However, you can't abandon all attempts to keep tabs on your schedule. Uh, and so I'm going to give you some time management, blah, blah, blah. All right. So the setup is like, we're reluctantly going to do time management.

We don't want to spend too much time thinking about this. So I guess I was really worried about students saying like, look, man, this is square. Cause that's the way we talked back then, Jesse. This is square, man. I'm looking for tubular advice. I don't want to hear about time management.

So I was being careful about it. All right. I lay out now in the chapter, here are my criteria for a time management system. Four things. One requires no more than five to 10 minutes of effort in a single 24 hour period. Two doesn't force an unchangeable minute by minute schedule on your day.

That's interesting. That changed. Three helps you remember, plan and complete important tasks before the very last moment. And four can quickly be restarted after periods of neglect. Interesting thing I'm noticing already, Jesse, is like this tone reminds me of early Tim Ferris. But this is before, for our work week is before I knew Tim or I'd read his book.

So there's, it must've been in the air back then. That's sort of really declarative, you know, like ambitious, like we're going to do this. We're going to do that. It was a tone that was in the water back then. All right. What you need. So my system requires a calendar and a list.

And I say, the list is some piece of writing material that you can update throughout the day. You do have to carry this with you. So make it something simple, like a sheet of paper ripped out of a notebook each morning. So one sheet of paper each day and a more permanent calendar.

I said the permanent calendar could be physical or it could be digital. All right, let's get into the details of this system from 20 years ago. Record all of your to-dos and deadlines on your calendar. This becomes your master schedule. The one place that stores everything you need to do.

The key to our system, however, is that you need to deal with your calendar once every 24 hours. Each morning, you look at it to figure out what you should try to finish that day. Then throughout the day, whenever you encounter a new to-do or deadline, simply jot it down on your list.

The next morning, you can transfer this new stuff from your list onto your calendar where it's safe. And we're back where we started. The whole system can be summarized in three easy steps. Jot down new tasks and assignments on your list during the day. Next morning, transfer these new items from your list onto your calendar.

And three, take a couple of minutes to plan your day. All right. I then give an example schedule for a day. So I guess you look at your calendar each morning. You write down on your sheet of paper your schedule for the day. And so the example I had in here was on one side, a list of appointments.

So it was like 10 to 12 econ. 12 to 1 lunch with Rob. 1 to 145 government reading. 2 to 4 government class. 4 to 530 finish government reading. 530 to 630 start French essay. And then I had a list called things to remember. And I guess these are the things that you would jot down during the day.

Econ study group moved. French quiz laundry. Start searching summer internship opportunities. So this is interesting, Jesse. It looks like the plan you write down includes basically rudimentary time blocking. Oh, it was. Okay, here we go. So let me look at this a little bit closer. So when you're making your plan for the day, I'm jumping ahead here.

Look at your calendar entry for the current day. It will probably contain a handful of appointments and to-dos. Your goal is to figure out how much of this work you can realistically accomplish. You might be tempted to simply copy all of these tasks into your today's schedule column and then treat it as a simple to-do list for the day.

Don't do this. If you want to avoid getting overwhelmed by your work, you need to be smarter about your time. Here's what you should do instead. I bolded this. Try to label each of your to-dos for the day with the specific time during which you're going to complete it.

Be honest. Don't record that you're going to study for three hours starting at three if you know you have a meeting at five and be reasonable about how long things really take. I also say you can batch together little errands and you should try to end your day at an appropriate time.

The only other thing that's important here is when you update your calendar year each day, you move the stuff that you didn't get done on to other days. So let me look at this example here. This was the kind of tricky part back then. Blah, blah, blah. Blah, blah, blah.

Okay. So not everything gets done. The things that doesn't get done have to exist somewhere. And so you move them on to another day when you think you're going to get it done. Interesting. I'm interested by myself. You would appreciate this, Jesse. On one of my sample list here, I have one of the entries from five to seven, quote, get huge.

I was being a little bit facetious there. I think that means go to the gym. All right. Interesting. So what were the features of this? And then what do I think works? And what do I think doesn't work? All right. So back then I was saying, I'm just going to summarize all of this, right?

All of your tasks need to have a place to live. And back then I said, why don't you have them live on your calendar? So obviously like appointments and stuff exist on the specific days and specific times when it happens, but to-dos, you put them on the day you think you're going to do them.

And then when you get to a current day, you have some stuff on your calendar that's timed, like classes and meetings, but you also are going to have this list of things, to-dos that happen to be assigned to that day. Then you make roughly a time block plan. That's what I was talking about way back then, 20 years ago.

Make a plan for your hours of your day, like when you're going to do what, when your appointments are, see if you can fit the things, the to-dos you had on your list. Can you fit them in there? You might not fit everything, or you might try to fit everything and not everything gets done.

Well, that's fine because what you do is the next day, when you create your new plan, you're going to take everything from your list from the day before that was added new, this capture stuff and put it places on your calendar. This is also when you'll take the stuff you didn't get done and make sure those show up for a new day.

And maybe you move them to the current day, or you look into the future to find a day that's less crowded. And so in this plan, you make a plan, you do your best, you capture stuff on your sheet of paper throughout the day. The next day, the new stuff and the stuff you didn't get done goes back to your calendar, and then you make a new plan for the day ahead.

So what I like about this plan, and then I'll get into what I think is missing. I like the full capture methodology. Now, I can tell you, I had just read Getting Things Done, David Allen's book, not long before this, that book came out, I think in 2004, maybe 2003.

I wrote this book largely in 2005. So I had just read that. And so clearly that full capture idea of everything you need to do needs to be written down somewhere. There's nothing that you only are keeping track of in your head. That suffuses this system. And so I like that.

I still think that's a very good idea. Things you're only keeping track of in your head either cause stress or will be forgotten. The other thing I like about this, and this was obviously a big emphasis in my student book, low friction. I was really careful that when you go through your day, you just have like a sheet of paper with you that you're glancing at to be like, what should I do next?

And you can just jot things down on. It's a paper in your pocket, right? No systems, no logging into anything, no complicated planners back. It would just jot things down on this paper. And the only time you really have to grapple with tools beyond that sheet of paper is once every morning where you say, okay, let me move things back into my calendar.

Let me update my calendar. Let me make a plan for the day ahead. And that was supposed to take only five minutes. So I like that simplicity. I like the planning, the rudimentary planning in this system too, of tasks go on a day. So you're kind of making a plan like this day looks light.

Let me try to do things there. As opposed to just having a bunch of to-dos and saying, let me try to get some of these done today. So it was a much more of an intentional, that's a time block planning philosophy, being intentional about your time as opposed to just being reactive or saying, hey, what should I, what should I work on next?

Hey, it's Cal. I wanted to interrupt briefly to say that if you're enjoying this video, then you need to check out my new book, Slow Productivity, The Lost Art of Accomplishment Without Burnout. This is like the Bible for most of the ideas we talk about here in these videos.

You can get a free excerpt at calnewport.com slash slow. I know you're going to like it. Check it out. Now let's get back to the video. This plan works really well with something I talked about later in this book and I still talk about today, which was put your regular work on your calendar as well.

So this type of planning for a student works really well when regular work like reading assignments, problem sets, labs, stuff you know is due on a regular basis. When you put the time when you're going to work on those things on your calendar in advance for the whole semester.

And so then that just becomes part of your day. You're not even thinking about studying is just the same as like going to class. I have class at 11. I have studying from 1 to 2.30 on this type of reading. So making that more automatic worked really well with this system.

Another thing I added later, I think after this book came out, I talked about this on my blog and it's a little known fact that, well, maybe somewhat known, Jesse, but my blog, Study Hacks, which now became my newsletter as well, originally started in the aftermath of this book as a way to discuss ideas that didn't make it into this book that were about this book.

It was the missing chapters for the straight A student book was its original purpose. And I, there I introduced the idea of, you know what, at the beginning of the semester, do an extra planning. So this is the origin of multi-scale planning where you go and find all your major deadlines and start working backwards from those deadlines and putting markers on your calendar.

Like three weeks before midterm, study plan for the midterm, you know, a month before the term paper, make your plan for when you're going to write this midterm and then get that onto your calendar. So it was all about like when you encounter your day, the stuff was there that you need.

Okay. So what's missing here? Well, obviously the task volume is very low, which would make sense for a college student in the early two thousands. If you were, let's say a standard 2026, 2025 knowledge worker, your task volume is going to be much, much higher because of tools like email and Slack.

That's not even dealt with in here. The task are things like do your laundry or pay your cell phone bill. You had a sort of very reasonable amount of tasks. So of course they could live on your calendar in the modern professional context. You might have many hundreds of different things to do.

So to try to like assign everything to what day you think you're going to do it, that does not make sense with modern, uh, modern task volumes that you would see with sort of modern jobs. Um, I'm time blocking, but I'm doing it roughly. Whereas by the time I got 10 years after this book was deep work, I was like, no, no, just time block specifically, draw the blocks, block out all your working hours.

Here, I was kind of roughly doing it. I was like, write down what to do as you want to do. And maybe like mark with the time you think you're going to do them that evolved into, you might as well just block your hours of your working day. Um, this is also missing shutdown rituals, which is very important, have a way of making a transition from I'm working to not working.

That's very clear. I think that's particularly important in college context. You'll just work forever, but it's important in other work contexts as well. But all in all, I think this is actually, I am seeing seeds of lots of my ideas in this system and I like it simplicity. So where would this make sense?

I guess if you don't have a large task volume, you're not plugged in the email, having to send them, receive 150 messages a day on 17 committees and working on 19 projects. If you have a pretty controlled task volume, a pretty autonomous schedule, something like this could work. Just make your calendar, your home.

That's where your tasks live. You make a plan each day based on the calendar for the day, maybe throw in some higher scale planning, you know, once a month or once a semester, get some major deadlines on there. It works well. It really would take only five minutes a day.

So there is a lesson here. It's like not every system needs to be the type of system that a super busy modern business person would need to tame their work. You know, some situations, your workflow is simpler, simpler systems work. As long as you keep the key ideas that show up here that have remained in my thinking about time management organization to this day, which is full capture, some intention over your time, as opposed to just working off of a list.

And, um, basically that actually, those are the two things, full capture and intention about your time. And that's there. I guess maybe I'll throw a low friction in there and keeping the friction as low as possible. So I don't know. I think it holds up. Desi, what do you think?

20 years later? Yeah, I've read that book and I've gifted it to a bunch of kids that I coach that go off to school. Yeah. School is easier. School is easier. The main thing missing from this book is focus, training your mind. That's the missing chapter that today I would add if I was rewriting it.

Um, you didn't start that till after your freshman year though, right? Yeah. Yeah. This was my, uh, God, what's the timeline? No, I graduated in 2004. This book, it came out in 2006. No, I'm talking about the process of you doing the, you doing the work. Oh. Like you studying that way.

Yes, that's right. So that's right. So the, the, the, the backstory of this book, okay, how to become a straight A student, the, the original draft, and this is true of this, the final draft, like most of the ideas are mine, right? Because, uh, I did very well in school after my freshman year.

So you're absolutely right about that. So my, my backstory was I went to Dartmouth college coming out of just like a public school. I wasn't coming out one of these elite private schools where they learn how to study. And so my first year was fine, but not great. I think I had to pass fail a course or two here to prevent like a bad, like a really bad grade.

And okay. And I was rowing crew and I was kind of busy. And then I had this change where I said, I want to get serious about being a student. I got very systematic about my study habits. Why am I studying this way versus that? How am I managing my time and my schedule?

And I experimented to figure out a set of tools that worked well. And that's when my grades got fantastic and got four O's every quarter, sophomore, junior, and senior, except for my senior spring in which I got one A minus. And it was in political philosophy, which is why I hate John Locke to this day.

Damn you, Locke. I actually, it's true, Jesse, when we were doing the promotion for this book, I went back to Dartmouth and got my transcript. And so we would send it. We would send like copies of my transcript to prove that like I actually got all these A's or whatever.

Yeah. So yeah. So then I figured out, so basically what I figured out is if you're systematic about being a student, by which like treat it like a business executive would treat their job, like really caring about how you do it. school wasn't that hard. Like you could do, you could do well without as much time as you thought.

So basically what I did to pitch this book is I had gone to, I was an early inductee in the Phi Beta Kappa because I had such a high GPA. So like the top 1% of students, they would induct in the Phi Beta Kappa early. So I was, I was very, you know, I was, I think like top five in my class or something, right out of a thousand.

I had a very high GPA. And that was back when it was harder to do that, especially in computer science. And so I went to this thing with like the top 30 students GPA wise in the class. And I was like, oh, wow, like I recognize some of these students.

They're not grinds or whatever. And, and I interviewed them a little bit about like, how do you study? I think I interviewed everyone. I sent them an email interview and it was like based off my techniques and their techniques that I came up with most of the ideas in the book.

But then after I handed in the first draft of the manuscript, my editor was like, uh, Ann Campbell is her name that she was like, I think, you know, we need to hear from other students, not just from Dartmouth and from your class at Dartmouth. So then I went out and surveyed, uh, straight A students from a bunch of schools.

And then I integrated their quotes kind of into the book and I changed some of the, I learned some things from them and I used their ideas, but a lot of the ideas were in place, like before I interviewed them. So there's like, you know, there's a lot of quotes from students all thrown through.

Those kind of were added later. So that's a little bit peek behind the curtain. How did you find the straight A students at other schools? I would look at things like, uh, back then my technique was typically look for press releases or articles from schools that would say things like Phi Beta Kappa class announced.

And then what I would do, this was my technique for my first book as well, is all you have back then, all you had to do was find any student's email address and you could learn the naming convention. So you would find anyone's email address at Harvard and you'd be like, Oh, it's first name dot last name at FAS dot Harvard dot edu, right?

Faculty of arts and sciences. And they're like, great. Then I'll see this other student's name on a press release about Phi Beta Kappa university or whatever at Harvard. So I'll just do their first name dot last name at FAS. I would learn to format and then I would cold email.

And that used to work back then. Um, and that's how I would do it. And I got the interviews and I threw it in. Uh, so those are those, those are those ideas. But basically it was like, look, you treat, man, look at these old references, reality check bands, you treat school systematically.

But you know what the thing is I've learned? I think this is true about a lot of jobs. Forget school. I think a lot of jobs, like you have some sort of reasonable system for organizing yourself, especially like in the first 10 years of a job, I think you're at our age jobs become more about like leadership and your ability to work with other people and other types of things.

But like in the first 10 years of your job, 15 years of your job, you have some sort of system for organizing yourself, keeping track of what has to do some sort of way you make plans for what you're going to do. It really seems like a superpower. People are just like, you're amazing.

Like it, people are so haphazard that having a plan can get you like the equivalent of straight A's. So I think that that's cool to see that, that system, I guess, more or less holds up. But that's the thing that's missing is back then, I never would have thought about focus.

It didn't, we didn't have phones. When I started, most people didn't have laptops. There was no way to avoid, this goes back to our recent episodes, Jesse, there was no way to avoid getting lots of cognitive exercise. Like studying was boring. You had no distractions. You were at a desk at a carrel somewhere.

There was nothing there to distract you. I mean, you could walk across the room to wait in line for a public computer terminal to check your email. Like that was it. So like people just got lots of, you just read and thought and studied and you never, you never thought about focus as mattering.

10 years after this book came out, especially 20 years after this book came out, this is like the number one problem faced by students is that they're so distracted that it's their brain is in a perpetual state of fragmented cognitive context, which makes it impossible to focus. So like that would have to be chapter.

But I talk about focus in here as being important, but I don't see it as something like practice and protect. I just say if you, if you focus really hard, you get your work done faster, but I would have a much bigger chapter about now if I was ever to, was ever going to rewrite that.

I don't really have time for that though. All right. So there we go. That's our deep dive. We got a lot of good questions. So let's do those. But first take a brief moment to hear from one of our sponsors. We'll talk about our friends at Cozy Earth. I love my Cozy Earth bamboo sheet set.

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First questions from Alex. I'm starting a multi-scale plan. How specific should my quarterly plan be? For example, take long Saturday rides starting at 50 miles, increasing five miles each week, or bike regularly to prep for the 100 miler? That's a good question, Alex. I don't think we've had that one before, but it's a critical one.

All right. So when you have a complicated long-term plan, the way I like to think about its interaction with multi-scale planning is that your quarterly plan gives you a pointer towards what you think is important on that time scale, but then you might have completely separate systems and rules and processes and tools that you use for that goal.

Like for example, in this quarter, post my like surgery and other injuries, I've prioritized my quarterly plan. I call them semester plans because I'm an academic. It's pretty clear about getting back in shape in certain ways, right? That has with it. I mean, what I'm doing, I have a pretty complicated training plan and I have a trainer who's helping me and this and that.

That's not all of my quarterly plan. My quarterly plan says we're focusing on getting back in shape, like generally aiming towards these goals. And then, you know, I have like my true coach app and working with my trainer and I have a particular way of a specific scheduling system.

I'm using this, like these are the days I'm exercising and I kind of have a schedule worked out and this and that. So that's not all my quarterly plan. My quarterly plan is reminding me what's important. And then I have complicated other tools I'm using to track this. Like in your case, Alex, no, your quarterly plan would say prep for the hundred miler.

And then however you track and train, you probably have a training notebook where you're keeping track of like what you're doing on each stage. You might have some training plan that you're following and you can keep track of that however you want to keep track of that. So yeah, you don't, your quarterly plan doesn't need to have the details of exactly how you're pursuing big goals.

It's just a reminder that when you look at it every week, when you're making your weekly plan, oh yeah, I need to make sure that I'm making progress on these big things. And then whatever system you want to use to work on those other things, you can use those systems.

All right, what do we got next? Next is Andy from Vienna. My son, 10 years old, reads mangas for hours. Does he had to reading mangas count as reading? I think it's manga. Mangas. Or is it manga? I think it's manga. Mangas. This is like Japanese novels. Yeah. I asked somebody about them, so they're like, yeah, I'm a little concerned on, but I love seeing him reading books, having books in his hands.

This is actually like a longstanding debate. I was looking this up before. Does manga, I hope it is manga now that I'm saying it that way. You're going to get a lot of hate mail. Very popular. Does manga count as reading books in some sort of way? Here's the thing.

It's good. Reading analog things is good, right? You're holding something in your hand that's not electronic. You're engaging in a story. There's a sort of visual literacy that you build up reading manga, trying to understand what's happening graphically. And there's a linguistic proficiency growing as well because the dialogue and the words, and you're seeing different people communicate.

Some of the other things you get in novel reading is there to some degree. You're simulating the other minds of the characters in your own minds. There's some world building happening in your head. You have the visuals in front of you, but there's still a sort of like 3D version of that world that you're building in your head.

There's complicated emotions that you're following. There's logic. Like it's good. I think reading analog things, is good. It brings you into a world that takes time. There's no algorithm flipping to random manga panels. You actually have to read it sequentially. So no, it's not, it's not a bad thing.

Would you be like, should you be content with that being the only thing your son reads? I would say, no, I would also want to try to introduce more traditional text-only reading as well. They're both good. Text-only reading is going to do certain things though, that you're not going to get just in manga, right?

I mean, first of all, the processing requires more intense. So it's more of a cognitive workout. You have to do a lot more in your head with text reading. So if you're reading a novel, you have to actually like construct a much more detailed model of what you're reading in your head.

There's more, it tends to be a little bit more nuanced too, because you have descriptions of what's going on that are third person. So you get kind of like a more emotionally real world and you can do more sort of mental simulations and get more empathy and pure linguistic processing just requires more focus.

So you're, you're practicing, focusing your brain more. So I don't know if we're going to draw a physical analogy. Manga is like, yeah, there's something that, that like my kid likes to do outside and it gets them moving and they're outside and it's better than just being inside. And reading might be like, they're actually, they're, you know, jogging or running or it's like, they're not just outside getting movement, but are like actually like training in a systematic way.

It's, it's going to get you an even better shape than just being outside, moving around. I saw a suggestion somewhere. A librarian was saying there's certain like light Japanese novels that are a good stepping stone for manga. It's sort of similar themes, but in purely textual form. And that might be a good way.

I mean, for kids, I'm all about matching books to interest. Like you, you, what you want is books, find the books that they're really excited to read and don't worry too much about what that genre is. Find what they're really excited to read because you want that exposure of not just reading, but being very excited about reading and getting pleasure out of it.

You don't want it to be like math homework. You have to read your chapters. This is a good book and you're good because you got through these chapters. I've been a big believer in like, you really want to find, and it takes a lot of work. I mean, this is sort of my job in our house is because we have boys and I spend a lot of time because it really matters trying to find the book that's going to like spark each of the kids and they're going to be excited about reading it.

And it's not always obvious. You'll try, this isn't working. This isn't working. And you have to find a thing that really works. And it's not always obvious. Like my, my 12 year old really likes not just Tom Clancy, but the, the really like Red Storm Rising. So the, the Tom Clancy books where it's not Jack Ryan going on adventures, but it's, we're going to just work through the scenario of like a world war, like all the military technology or this or that.

And so like those sorts of books, he gets very excited about, right? It's a very specific genre, but fine. We'll like find those books. And, and, you know, that's, that's what you're going to read. My 10 year old likes a sci-fi more, but only specific. And so I'm constantly trying to search and try different books to try to see like what's actually going to work.

Cause I just want them to be excited about reading and then just get used to it. And then you can read good stuff. I don't know. You can do that later. I read a lot of Crichton when I was growing up. Crichton, Grisham, all the sort of big nineties Clancy, all the big, there's like big nineties writers, genre writers.

I was sort of raised on those and I came out. Okay. All right. Who do we got next? Next is Yolanda. How can I efficiently separate notes and reference information from actionable tasks in my productivity system without duplicating work? I use Trello for task management, but struggle with vague projects and scattered information across multiple tools.

What strategies or rules can help streamline this process and keep everything organized? Well, Yolanda, my general approach is if what we're talking about is there's some information you need specifically to complete a task, like you're summarizing a report. And so like what mattered, the task is to summarize a report.

So like the relevant information is that report, or you're trying to, uh, schedule something and the relevant information is like what people told you about their availability. I just moved that in the Trello onto the card corresponding to those tasks. If I could attach the relevant files to the card for the task that needs those files, I'll do it.

If I can copy information from an email to a back of a card, and now I have the information there that I need, uh, I'll just do that. Sometimes if there's like a really detailed email thread, right? And so, and I have a task based on that, what I'll do is I'll copy, cause I use Gmail, the subject line of that thread into the task card.

And I'll say, just search for this. So I'll know what to search for in the archive, the pull up exactly that thread. So I won't, you know, it's not being copied in, but I say, here's what to do. Um, I do that with book. Sometimes when I have a, like a book blurb, I need to get started on and they've sent me an electronic copy.

Sometimes I'll attach the file to the card, or sometimes I'll just put the title of the email in which that file is attached. And I'll just search and find it and print it or load it onto a reader. You know, when the time comes for information, it's more complicated, like you're working on an article or something, and there's going to be a lot of different sources, right?

Where you don't want three dozen sources attached to a Trello card. My general rule is when possible, store research information in the tool you're going to use when you need it. Right? So I write books and articles using Scrivener. I move relevant notes and links and resources and files that are relevant to projects, like to a Scrivener project for that article or for that book chapter.

So I just move the information straight in the Scrivener. When I'm writing an academic article, I use a LaTeX markup language that mathematicians use, right? I'll just move stuff I need right into the LaTeX file in which I'm working on. I have an idea. I'll just start building a LaTeX file, basically like I'm writing a technical article and I'll just add citations and notes right into that document and it grows and grows.

And then eventually what happens is the actual paper starts getting written at the front of this and the notes get pushed to the back. And then eventually they get moved out and you have the final paper. But I just put the information like proofs and links and citations and summaries, like right into the same tool I'm going to use to write the final paper.

So for complicated information, if you can keep it close to the tool you're going to use, I tend to think that's, that's probably best as well. So with the email search, do you just keep the email on your inbox? Well, so in Gmail, it archives. So you archive it, it's out of your inbox.

And then you, uh, it's searchable at that point, but Gmail search is not very good. So you really need the, uh, exact subject line. So I'll just copy, like I'll actually copy and paste the subject line and just put it right on the card. Do you ever go into your archive folder and take stuff out or is it just...

It's not a folder. So the Gmail paradigm, it's based on search. So the Gmail paradigm is the archive. It's like the internet or something. You never navigate it. It's just something you search into. So you just have this, like that was their big idea. Like we're good at search.

So when you're done with a thing, don't organize it to a file, a folder, don't have like a hierarchy of different folders, just archive it. And you can just use our smart search, define what you need later. The unstructured storage. The problem is their search is not that smart.

Like the whole reason why Google searching works well on the web is that it's leveraging all this extra information in particular link structure. So it knows like this page is probably what you're looking for because it's being linked to by a lot of other important pages. You don't have any of that information when it's just your own emails.

So it's really just doing like a much more standard search. It's not very good to be honest. So you really need like a subject line. Like I know exactly what I'm looking for. And if you want to pull up again, you always need to have the subject line. Yeah.

Yeah. Yeah. That's the way that works. All right. Who do we got? Next question is from Diana. How can I improve my ability to communicate effectively in spontaneous workplace conversations, especially when I struggle with clarity, organization, and understanding others' needs? I speak well for planned presentations, but not as well in off the cuff scenarios.

Well, Diana, I think a good rule of thumb, and Jesse will agree, is you want to break the ice. The thing is you want to find some sort of characteristic of the person you're talking to, preferably like an immutable characteristic. They have no control over it. make fun of them and then say, you're burned.

That is like, it kind of breaks the ice. And then you're not going to be, you're not going to be, uh, you're not going to be so nervous. Right. Like you so bald. He's got to talk like that. You so bald. I'm blinded by light off your head. You're burned.

Right. And then the boss is like, all right, well, I mean, you're clearly fired. Um, no, it's a good question. Speaking is a practiced art. I think people don't recognize this. Younger people supposedly are worse at this because there's actually less in-person spontaneous interaction than before, because more things are done on phones and more things are done digitally, like through text messaging.

So there's this, this idea out there that I think Sherry Turkle wrote about this. Like there's this idea out here that younger people who are coming up with smartphones are particularly bad at in-person conversation. They don't want to sit down and talk to their bosses. They certainly don't want to call someone that's really fraught.

When Jesse and I were growing up, that was a big part of our childhood, right? Was calling someone and you would say the person's name, Mr. and Mrs. You'd be like, you know, uh, hi, Mr. Miller. This is Cal Newport. It's like Jesse there. And you had to learn how to do that and like how to, how to interact.

Interestingly, and maybe just because I'm in like elite institutions, I haven't seen this issue. Like my Georgetown students, they're super articulate, but that might be self-selecting. When I was up at Dartmouth teaching a couple of years ago, these kids are super articulate, but that might not be a representative sample.

Um, that being said, speaking is hard. Speaking is hard. Like I make a living speaking. I've been working at it for a really long time. It's the degree to which right now, for example, speaking on a podcast that I'm having to rely on essentially cognitive muscle memory training, not to say, um, not to say like, to get the right pauses, to keep things moving.

Like that's very practiced to have an idea that you're pursuing to pursue it clearly, not to get caught in a cul-de-sac, you know, that takes practice. So what I'm trying to tell you, Diana, is it's not a problem that you're struggling right now. It's, you haven't practiced it yet.

It's no different than you say, look, I picked up a banjo the other day. It's not going well. It doesn't sound good. It's like, yeah, of course you got to practice. But if you practice, you'd get better. And that's the way you want to think about speaking off the cuff, writing and reading actually helps people who read a lot and write a lot.

They're just being exposed so much to structured formal language that you build those structures, those predictive structures in your head. It's like you're a language model and you get better at reproducing it. So reading will help trying to write very clearly will also help. Practicing is ultimately a thing that's going to help best.

You can practice in situations where it's lower stakes. So like with your friends or family, you're having conversations over dinner. Say, hey, I'm going to try a little harder in this interaction here. I'm going to be a little bit more formal. Just straight up practice is going to make you better.

There are heuristics that can help as well. So for example, if you're talking to someone at a business meeting or at a conference, or you're standing up to make a point, sometimes it's useful to think about the following structure. Set up point contrast implication. Let me clearly set up what I'm going to say.

So here's the context or the stakes. Hey, I know that we were, we've been talking, you know, recently about making our meetings more efficient. I have an idea there to share. That's set up. So make it very clear what you're going to talk about. Point. Deliver clearly. Here's my point.

So here's the original information I'm offering. So in this sample, this is where you might say something like, we have too many meetings that could probably be consolidated into fewer. We should think about ways of combining meetings. You've set up what you're going to talk about. You've made your point.

Then you contrast, right? This is the dialectical approach. As opposed to what we have been doing, which is if anyone has something they want to discuss in a group, they send out a separate invite for it. So you've made your point, what you want to deliver. You contrast it to existing points, and then you give details and implications.

So the way we might do this, for example, would be instead to have a standing meeting block every afternoon that can be used for multiple purposes. The implications of that details would be some stuff might have to wait or that meeting might get rushed, but I think it's probably worth it.

So if you hear someone follow that pattern, you don't recognize that they're following that pattern, but it seems very persuasive. Let me cue you in setup. Let me cue you into what I want to tell you about. Let me give you very clearly. Here's my point. Let me contrast it to other ways you might do this to clarify the contours, give you some details and the implications of it.

Report and I'm out. You talk that way in a business setting and they're gonna say, Diana's sharp, really clear, interesting ideas. They're really on the ball. So there are heuristics like that you can do, but practice is the best. If you listen to, this is a canard, but it's true.

This is like one of these cliches that true. Screenwriters will tell you, like Hollywood screenwriters. I know some Hollywood screenwriters. They'll tell you that you want your dialogue to sound quote unquote natural, right? You don't want people to sound like they're reading a script, but what'll happen is most screenwriters early on in their career will go through this point where they're like, let's just listen to real conversation.

I can take notes on real conversation so I can make it very natural. And what they realize is real conversation is barely comprehensible. If you just sit and listen over, listen to people casually talking, and then you transcribe that and try to put it into a movie, people are going to think that the point was like that character had a traumatic brain injury recently.

Like this, this is so incoherent. Like what's going on? Because the way most people talk when the stakes are low and it's casual and it's with a friend or whatever, it's weird and back and forth and interruptive, very digressive, all sorts of verbal tics and re returns and ums and likes.

And yeah, I don't know, man, what's going on. And it's fine in that informal context, but you know, it's not at all the way people actually, it's not formal at all. And so in Hollywood, naturalistic dialogue writing is its own thing. It sounds naturalistic, but it's actually way more structured and clear and directed than the normal people actually really talk.

And it's really in the delivery of the actor that makes it sound naturalistic, which is all to say to practice this. You have to think I'm having a conversation with someone where I could be super casual and doesn't matter. Let me try to be clear. The other thing people always suggest is Toastmasters.

I don't know if these still exist, but yeah, you can also go to a speaking club like Toastmasters where you practice getting up and giving speeches, but practice is the key here and have some heuristics like setup, point, contrast, details, implication. It's a cool skill to master because the clearer you communicate, the smarter people think you are, which is going to be a big boost.

All right. What do we got next? Next question is from Flying Professor. My life and work are divided between two different countries with a seven hour time difference. Google calendar just couldn't handle that. So I've gone back to paper and pencil to calendars. Is this okay? Or have I missed something?

Yeah, that's a good question. Quick follow-up on Diana. I was just thinking about this, Jesse, though. Quick follow-up on Diana. I was a very good speaker as a kid and I got a lot of like unfair advantages out of that. I think because I read a lot. But I remember doing an oration at, my God, I guess I was at like a VA and I had to give a speech in front of everyone about flag burning and whether there should be a constitutional amendment.

And I remember just ad-libbing it. I didn't realize it was supposed to prepare something. Anyways, they selected me. I got to go to Boy State. Have you heard about Boy State? I think you might've mentioned it before. Yeah. So like that was all oration. So I was considering, I was good at Model U N as well.

And I would, I would do very little work, but I could speak well. And I think it was largely because I read a lot. Mm-hmm. There you go. That's my secret power. All right. Flying Professor, two time zones. Yeah. You need two calendars. You need two calendars. You can use electronic calendars.

You just need two of them. So you could have an electronic calendar for one time zone and another electronic calendar for the other time zone. The problem is you can't use the same Google account. As far as I know, I think the time zone is going to be consistent across your various calendars.

So you have to create, it's fine. Just create a different Google account just for this calendar. And then you can share, you know, whatever you actually don't want to share them. You don't want the one time zone calendar to share onto your current time zone calendar because it's going to be the wrong time zone.

Let me think about this for a second. So I have one calendar for your time zone here. Have another calendar with another account for the time zone for the place you go. So those are both now local calendars. So when you're in the one place, you look at the local calendar for that place, correct times.

When you're in this place, look at the calendar for that place. You're seeing the correct times for that place. You can then share between these different time zone calendars if you want to, and you'll see where these things fall on each other's time zone. So seven hours, I'm assuming you're talking like Europe and East Coast, let's just say.

When you look at your East Coast calendar, you can share your Europe calendar onto your East Coast calendar, and it will show it like the East Coast times, but you probably don't care about that. But it'll show it like, yeah, this thing's happening at two in the morning or whatever.

But I guess you wouldn't want to share them. Just have two separate calendars. So you can use paper calendars, fine. I like electronic calendars because I want to do email reminders. I want to add information, like here's the Zoom link for this particular Zoom meeting that's going on my calendar.

I want to move things around easier. I want to do repeats. So I would create two Google calendars if I was you. Completely different accounts, completely different time zones, and you can just switch between them depending on where you are. And you're right, don't share between the two. That'll confuse you with the time zones.

The time zone issue that's the worst, Jesse, speaking of Europe, is there's a month. Everyone who's worked with the UK knows this. And I work close with the UK. We do a lot of book sales there. There's a month in March where we get out of, it's like we, daylight savings times changes in the US and not there.

It's different. And there's a month where, until recently, no one from the US meeting with someone in the UK would ever be on time. It was just a crapshoot. Everything was off by this hour and it was different than it was the month before and it changes to the month after.

Now it's better because of calendar invites. So if like my UK publisher sends me a meeting invite for a phone call, Google fixes the time zone. And so now it's okay. But there's a long time where I just assumed I was going to get everything wrong. I think when I was doing UK publicity, even as late as like digital minimalism, it was like, this is, we're going to dismiss things.

And so finally Google calendars has made that possible. Everyone gets that wrong. It becomes like a six hour difference or an eight hour difference temporarily for just one month. The worst thing. I have a couple of follow up questions. What do you do with email reminders? I use them for, uh, important appointments and stuff like that.

So you, how does it work? You Google calendar, you can click on event and then you can go to notifications and you can choose email me about this. And here's how early before the event to email me about it. So I often do that for important things. So I'll see it first thing.

So I don't always trust myself. I'm not like an executive where, you know, my day has been scheduled inside and out. And I, my whole day is run off my calendar. First thing I'm going to do is look at my calendar. I'm not like that. Like there might be days where I'm, I think, yeah, I know I have to go in at 11 to teach.

I'm going to try to write this morning. Yeah. And so like, I might not look at my calendar until then, but maybe there was something important on there that I forgot about. Like, Oh, I'm supposed to go to some appointment. So I'll have an email me because I usually will, you know, see my usual email the day before.

So when I check my, you know, shut down my email the day before I see this reminder of like, Oh yeah, yeah. Tomorrow morning I got an event. Um, mainly it's psychological. It just makes me worry less about missing something. If I know I'm also going to get an email about it.

And then I have another follow-up question about the archives. So do you only archive items that you have those detailed notes of, or sometimes do you archive things and just knowing that you'll never be else find them because the search won't be good? That's the Gmail. The Gmail methodology is you never delete.

You archive. So in theory, their whole thing is storage is cheap and just keep archiving all your emails. So you never delete in. So you archive all yours? Yeah. But you only search for like a small percentage. Yeah. Yeah. That's the Gmail data model is never delete, just archive.

Interesting. Yeah. And you can't see the archive really. It's just, you can search into it. Um, and then they want you to do like to use the data. So it's sort of, they, they use the data from your email, but like train things and then target ads to you.

It's, it's a whole, Oh, it's a whole world. Surveillance capitalism in its best. All right. We're going to do two calls today. Do I have that right? Yeah. I love that. Let's get some calls in. All right. Let's get our first call. Hi Cal. Uh, it's Jessica. Um, I have a question about coaching.

So I was listening to one of your podcasts and you mentioned that you discussed something with a coach. Um, I'm a female academic in Europe and there's a lot of coaching programs that are free and offered by the sort of university development service, um, often to women, but also to sort of junior academics, um, at the university.

Um, and I struggle a bit to see which ones are sort of worthwhile and useful to go to and which ones, um, are not so worthwhile. And I was wondering if you could expand a little bit on how you integrated, um, uh, coaching, I guess, uh, receiving coaching into sort of, um, building a deep life and, and setting up a schedule, uh, that, that has sort of appropriate, uh, blocks of, of deep work.

Thank you. Well, that's a good question. I think coaching's underrated, you know, in general, I think there should be more coaching. It's part of it is informational. So you get information you wouldn't otherwise have. A big part of coaching is often backstopping, right? Having someone backstop a decision that you're making so that you have some confidence in it.

So it's a whole bunch of confidence boosting, right? So, so coaching, I think it's really useful. It can mean a lot of different things. If you're an academic, there's a couple of types of coaching that's relevant. The first type is what I might more accurately call mentorship. And this is where you're receiving advice from someone who is in your field and academic, but it's just more senior.

And they're really important. Actually, this might be one of the more important things you can do, especially as a junior academic is go to lunch once a month with a more senior academic in your field and just pick their brain about what about this? I'm, I'm worried about this.

What do you recommend here? What do you think is going well or not? There's a lot of nuances in this world as in lots of different knowledge work worlds, lots of nuances that aren't given to you in a manual. And it's left to you to figure it out. And you could be going down a wrong end Georgetown, you know, where I've been my whole academic, my professor career, they formalize this.

You would be assigned as a, as an assistant professor, junior faculty, you'd be assigned or you could choose, but you needed a mentor. And then the university or department would pay for you to go to lunch. And so I'd go to the tombs once a month for a while with, with my mentor.

Good place, right? I was just there. I love the tombs. The tombs is awesome. And phones don't work well down there. Yeah. Yeah. And they have rowing stuff on the wall. You know, tombs is great. But anyways, we go to the tombs once a month and it's just like whatever was on your mind.

And I'd learned a lot about navigating academia, but navigating the specific institution and like who's who and who's what and what matters and what doesn't and what you should be worried about. That's very useful. So I think mentorship is very useful and that's different than coaching because it's not someone coming in from the outside of your world.

It's someone in your world who's ahead of you. So definitely try to find mentorship, make that happen. People, senior professors like to talk to their junior professors. That shouldn't be hard. Then coaching is more, I would say, tactical. It's okay. Help me. I have this, this problem I want to solve.

I want to, here's my goal. I want to be doing more of X. Help me figure out how to do that. In academia, I think the most effective type of coaching of that type that I've seen really does focus on the issue that you mentioned there, which is making sure that you're finding enough time to get the work done, the work that matters, which is going to be your research production.

So having someone in your life who's looking at your schedule and is helping you figure out smart ways of you need more time. You're not getting enough time writing. How can we help you get that time? They're a sounding board. They're a backstop. They're a confidence booster. They're a source of ideas.

They'll say, you need to drop that committee. That's okay. You need to protect your mornings. Uh, they might see insights you might not have. I mean, I don't know if you remember this, Jesse, but when we had my friend, uh, Laura Vanderkam on the podcast, we were doing a case study.

I think she helped me answer questions. If I remember properly. Um, and we had a question from a professor and she was having a hard time finding time for deep work. And Laura had like this really interesting scheduling idea that I hadn't thought of. And the caller hadn't thought of, but it made a really big difference.

She's like, here's what you really need to do. And I don't remember the details other than that. This young professor was trying to fit in her deep work into all these little slivers. And Laura's like, no, no, here's a change you can make. If you have a babysitter here and you swap.

So your husband takes these shifts and you take those shifts. Now you have two long count sessions. You can count on each week, four plus hours. And now you can actually make progress on papers. And they were relatively minor. They're not hard things to do, but she just wasn't thinking about it.

But having someone who's done a ton of time management, like Laura was like, Hey, I think you're missing this opportunity, this option here. And it made a big difference. So I think that type of tactical coaching, and there is a lot of coaches that focus on this in academia.

I help. They're often people who left the academic track. Like I help professors make sure they get the important things done. I think that is absolutely worth the time and worth the money. The coach I have, shout out to Cheryl. She focuses on something very specific, which is people who are doing creative work and have to deal with like the business and logistical challenges of making that happen.

So like she also, we've mentioned this works with a lot of professional writers, screenwriters, movie directors, people where the core thing you do is creative. And as you do it better, it becomes harder and harder to actually do that thing that you do well, right? Like all these new responsibilities and opportunities and options come up.

So it's really focused. She focuses on people. So it's really focused on my writing life, not my professor life. And how do I keep producing and enjoying what I'm doing? She's really focused on like enjoying what you're doing and producing creative output that I'm really proud of while managing the business aspects of this in a way that that doesn't just take over.

And so it's a very specific type of coaching. So anyways, I'm a big fan of coaching. It's mentorship and coaching itself. Do it all. Do it all. It's only going to help and it makes things seem less scary and less lonely. Coaching is a good business. Our friend Brad coaches Stolberg.

Yeah. That's like one of his, that's like his job outside of writing. He's in super demand too. I think you can't get on his, his, it's like a long wait list at this point. Hmm. All right. Who do we got next? Another call? Hi Cal. On a recent episode, you talked about scheduling a time in your day to embrace boredom so that the rest of your day, you don't have to worry about feeling bad about listening to a podcast, et cetera, et cetera.

If I were to schedule boredom, how would that look? Could you elaborate? Do you just sit in a room for 20 minutes or the next time you're in traffic, you just deal with it? Am I allowed to just let my mind wander? Please explain how this works for you.

Thank you. Okay. So embrace boredom. That phrase comes from deep work. So it's a chapter in deep work. And I think what's important about it is boredom is not something that I think has a moral valence. So unlike some commentators, I don't think boredom is somehow good and non-boredom is bad.

And therefore by having more boredom, that somehow like that act itself of being bored is virtuous. I see this way more technically. So when I say embrace boredom, what I'm hoping that you accomplish is that your brain gets used to craving novel stimuli and not getting novel stimuli in response.

So it's really just working on the dopamine mediated short-term reward circuits. So if your brain learns every time it feels boredom, it gets a treat from a phone, right? Something comes out and it gets a novel stimuli. You build up these reward circuits that says, ooh, as soon as I feel that sort of discomfort of lack of novel stimuli, which is natural to people.

And we've felt it throughout all of our species history. As soon as I feel that there's this shiny treat reward I'm going to get, it really floods the zone with dopamine and makes it really hard not to look at your phone. Why do I worry about that? It's because sometimes you don't want to look at your phone when it becomes important that you actually do focus on something like you're working on a really important paper or a breakthrough or a meaningful conversation or trying to figure out something hard.

There's no novel stimuli when you're doing that. But if your brain has learned, I always get a shiny treat when I feel that lack of stimuli that we commonly describe as boredom, you're not gonna be able to focus and you're going to have to look at your phone. So the way you break that is you make sure that you get consistent practice, feeling boredom and not getting the reward and things being okay.

And then your brain's, the strength of that Pavlovian response weakens. And now when it comes time to do something like deep work on something important, you'll do better at it. So I see embracing boredom. That's why it came in my book. Deep work is just training your brain to be better at deep work down the line.

All right, how do you actually do this? There's two scales at which you need to embrace boredom. Every day, do something short where you're free from input from other minds. So you're welcome to think as much as you want inside your own head. You're welcome to observe the world around you as much as you want and make observations and be interested by it.

But you're not inputting input that was generated by another mind. You're not reading something or listening to something. Every day, do some sort of short exposure to that like once or twice. And by short, I mean, you know, you do an errand, you don't take out your phone. You take the AirPods out.

Like I'm going in the pharmacy to grab something. I'll just go and do that and come back. It takes 10 minutes, but I'm going to do it without my phone. Or I'm doing a short drive, you know, to a friend's house. It's 10 minutes away. I'll just do that drive.

Nothing on the radio, right? So that's like a short one or two exposures, you know, once a day. Then once a week, if possible, try to have a longer exposure, which really I typically think of as being a long walk is probably the best and go for a long walk or a hike or something like this without anything in your ear or anything in your hand and just be alone with your thoughts.

And this gives you time to do some more structured thinking and introspection. And this just gets your mind used to this idea of our brain can function just fine without shiny treat stimuli. I've been adding, however, if I was to rewrite that chapter, I would add a third type of boredom training that wasn't as relevant back when I wrote deep work, but I'm caring more about now.

I would say, be very wary about dopamine stacking. So if you're watching something, don't look at your phone, have your phone plugged in somewhere else, right? Don't get in this idea of, because that really gets those reward circuits going that even if I'm watching something that's pretty interesting, I might want to stack on something that's even more interesting.

That really is going to short circuit those, those reward circuits and make it really hard. So be very wary about dopamine stacking. If you're going to do something, look at a screen, only look at one screen at a time. So I would add that in as well. You do those things.

You're not bored all the time. We're not lauding boredom, but you'll get more attraction to thinking and being with your own thoughts. That'll become more appealing as you get practice. But it makes sure your brain does not develop this knee-jerk reaction. When I feel boredom, I have to get stimuli.

So that's what I would recommend. What if you have like a laptop that you keep on your coffee table and you want to, like look something up when you're watching something? I would keep it somewhere where you have to move. That's what I would say. Like I, my, my couch face on my TV, we have like have an open plan thing.

So the kitchen's in that same bigger space. And I like to plug my phone in, in the kitchen, we have like a charging thing. Uh, so I can see over there from the TV, but if I want to look something up, I walk over there and look it up on my phone and then I can come back to the couch.

So that's fine, but you can't dopamine stack, right? It's a little bit different. If it's right there, you're going to start rabbit holing and I'll find myself, if I start rabbit holing, I'll like to turn off the thing I'm doing and then do that. And then when I'm done with that, go back and do the thing, the thing that I'm doing.

Oh, I like that tip. Yeah. Or press pause or something. Yeah. Yeah. Yeah. Yeah. Or muted or something like that. All right. Uh, we got a, speaking of, it's not really speaking of, but we got a geeky final segment coming up, but really what we're saying there had nothing to do with reinforcement learning is what I'm going to talk about.

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All right, well, because I can't help myself for our final segment, I want to do another tech corner where we can dive into some needlessly technical details about the world of AI. All right, so what I want to talk about today, Jesse, is two different types of AI technologies that are often getting mixed up.

And I want to talk about what they have in common and what makes them different and what we have to worry or not worry about each of them so we can kind of have a more nuanced understanding of what's going on out there. So let me start with two examples.

Here's two things AI has done in recent years that has been deemed impressive. One, AI beat a grand champion for the first time at a board game called Go, which is a, it's a simple to learn game where you take turns placing black or white rocks on a grid pattern.

And if you surround, one color surrounds all of the rocks of another color, you flip them over to your color and you're trying to win on the board. And this was considered a hard game for a computer to win because there's this sort of astronomical number of possible boards.

And so you can't just brute force search. Like if I do this and they do this, and then I do this, you can't brute force search as easily as you can for chess. So it was considered a hard game. And DeepMind, which is now owned by Google, created a system called AlphaGo that beat for the first time a grand champion, Lisa Dell, and it was a big deal.

Another recent accomplishment with AI was the latest models from OpenAI, their chat GPT reasoning models are doing really well on math tests that are international caliber and something that AI had stumbled on before. Like, look, this is indication that the reasonability of these models is really good. I think a lot of people just see this all as like generally AI is getting smarter, but those two accomplishments have two very different types of AI systems underlying them.

And it's useful to know the distinctions. The system that's winning at the game AlphaGo, like most other game-playing AIs that we see, is using an AI technology called reinforcement learning, whereas the system that is doing really well on that math test, so these like chat GPT systems, for example, these are large language models that are more data-trained generative models.

These are different technologies. So let me start quickly with the common history of these two technologies, and then I'll talk about where they change. So there is a common revolution that occurred in the early 2010s that paved the way for lots of these different AI innovations that are happening today, even if the technologies in these innovations aren't the same.

That revolution was the introduction of what are called deep networks, which you can just think of as a neural network that has a lot of layers, really big neural networks. Up to that point, AI researchers had not really tried to train big neural networks to do things. Part of the problem was figuring out, okay, how do we train neural networks that have lots of layers, right?

You have to kind of propagate through signals back through all the layers to adjust the weights when you're training it. We saw that in the 80s. This is when Jeff Hinton and others figured out back propagation, a particular algorithm that was going to, could in theory train these networks.

Okay, fine. But there's another problem there. They're so big that you would need a lot of compute power to train them and you would need a lot of data. It would require a lot of iterations to train them. Those obstacles fell as we got more compute and we got more data because of the internet.

We got more compute in part because of the rise of graphic processing units, which the video game industry pioneered to make 3D graphics faster on PlayStations. Turns out those, those same cards, GPUs are great for training neural networks. So suddenly we got a lot more compute and the internet gave us a lot more data.

And so now these ops, we knew how to train these things. We had enough compute power to train these things and we had enough data to train these things. The final obstacle was just philosophical. Machine learning experts up to that point said, no, no, no, you can't. If you train a really large network, they have so much potential memory, right?

Because they're so big. If you train them on some data set, like I'm going to show you a bunch of pictures, some have cats, some don't. And I want you to learn what a cat is. I want to train you to recognize pictures with cats. They're like, the problem is that these networks are so big, they could just memorize a feature of every single picture with a cat from your training set.

And they haven't really learned anything about cats. They've just learned about these pictures you've shown them. And then when you give it a novel picture that's never seen before, it'll do terribly because it has quote unquote overfit. All right. We finally got over that obstacle and said, well, let's just try it.

This was basically the revolution of modern AI was like, let's just try it. And the original things that were tried were on image recognition and they trained these big networks and they didn't overfit. Instead, they got really good at recognizing novel images and started far outperforming other AI systems.

Because it turns out what happened is if you have enough compute and time to train these things, instead of just memorizing your training set, they learned and generalized a lot of interesting information about the context in which they were operating. So suddenly we realized really big neural networks could do stuff that we never thought was possible.

That was the origin of sort of all of the AI revolutions. But if we look at this technical sort of phylogenic tree, things began to split. So if we look to the language model split of this tree that led to things like chat GPT, the way you're training these very large neural networks for something like a language model is with a lot of data, a lot of data from the real world.

So the original models is a lot of text and you're giving it real text from the real world. And you're knocking out a word from that text and saying, do your best to replace that word with what makes sense. And if it does a pretty good job, like produces a word that's pretty close to the actual word that was there, you sort of reinforce those weights in the network.

And if it does a bad job, like now that's not working too well. And if you do this over enough data, what it does is it learns, it basically estimates the underlying processes that produce those texts. So the underlying grammatical subject matter processes we use when we produce text, it began to estimate those in these really large neural networks.

And the larger we made the networks, the more complicated processes it could estimate. And now when you ask it to produce text from scratch, it can use, it has an estimate of the process we use in our head and it can produce really good text. It's a data-driven data trained.

So there's a lot of understanding baked into those rules, but really the understanding is, it's trying to estimate an existing process for which it was seen a lot of products. The machines that play games, like the machine that played Go or the breakthrough that happened around 2014 when DeepMind said, look, we can win at Atari games.

This was sort of like the big breakthrough. Those are using reinforcement learning. And the way they work is different. So again, they have a big neural network underneath it. They're using big neural networks. But now what they're doing is they're having that neural network interact with a world. It's not just being given a lot of data.

Typically, it's going to be an online training. So like if you're training a game to play Atari games, they were actually giving as input to the neural network, every pixel on the screen of the Atari game. And then the network was outputting an action. Move the controller left, move the controller right, press A, press B, whatever.

It was outputting an action. And then the simulation system would update it. Oh, great. Let's make that move. And then it would give feedback. Like, did this make our situation better or worse? And if it was worse, it was sort of say, don't let's change those weights away from this.

And if it was good, it was like, let's change our weights towards this. And through these like interactions with a real world, with these reinforcement signals, what the model ends up learning is a policy. It learns a policy that is good at doing well by whatever definition of well you had, whatever reward function you're using in the training.

It comes up with a policy for how do I react to different situations in a way that's going to help me maximize this, whatever reward I care about, like a point or whatever it is, the reward I care about. So these are two very different ways of training things.

And this is how they trade AlphaGo. AlphaGo, they trained it at first with like actual Go games. And then they created two models to play Go against each other. And it was, you know, if it would do well against the other computer model, it would sort of reinforce those.

And if it did worse, it would unreinforced and they played millions of games with each other and it created policies for how to play the game. These are two different things. So something you're going to get with a data trained language model, it's estimating real processes in the real world, like the way generally that we produce text.

And the bigger the model, the more sophisticated that, that estimation is going to be, that's not going to, first of all, it's just, it's, it's, it's producing text. And it's trying to estimate what it's seen. So it's, in some sense, it's not, it can do kind of novelty things, but really it's trying to, its goal is to be as close as possible to like the things that produced the text it trained on.

In reinforcement learning, all it cares about is having a policy that does well with the reward. It's not trying to estimate an existing way that people in a game play a game. It's not trying to look at a bunch of examples of a game and figure out how do people play games.

It's just coming up with something that does really well in the reward function, which allows for a lot more originality. So like the classic case is when DeepMind was training one of these big neural networks to play the Atari game breakout, where you move the paddle and you knock the bricks.

It devised a strategy that no human player had known about. Like it figured out that there was space on top of all the bricks and you could tunnel, if you're very precise, you could knock out the bricks out of diagonal, open up a channel and then send the ball up that channel.

It would get on top of the bricks and just bounce down and take all the bricks out, just bouncing around from top. And human, because it took super precise moves, but it just learned like, oh, that's the right thing to do. There's another example of this. It's often given by reinforcement learning experts where they're trying to train it to do well with a boat racing video game where you collect points for doing, getting various rewards as you make your way through the track or whatever.

And the policy that came up with, because all it's trying to do is maximize its points. Is it figured out like, forget the racing. There's a place where the rewards recharge pretty quickly. I'm just going to endlessly go in this loop, kind of crashing into things. And I'm just going to keep getting these, these coins that appear again and again and again in this like spiral or whatever, because it's a policy that did really well at maximizing reward function.

Whereas if you went to chat GPT and said, let me explain to you, like I'm a situation, it's a boat and I'm trying to drive the boat and here's what's around me. Where should I go? It'll give you like pretty reasonable, like, this is like the type of thing someone would say here, like avoid the obstacles, you know, go, go this direction.

So there are two different things that are going on. I think the place, if you want to be worried about things, well, first of all, keep this in mind. These are two different technologies. Let's just start there, right? All they have in common is making big networks bigger tends to lead to more intelligence, but the way they're trained are completely different.

A breakthrough in one does not mean a breakthrough in the other. Either of these could get stuck in different places and the concerns are completely different. So they, you can't just assume they're both innovating at the same level, but I'm saying, if you want to get a little bit more worried about something, the sci-fi horror stories, the sort of like cautionary tales, you would worry about that reinforcement learning world because it's not trying to estimate how people would talk.

It will learn whatever it can to try to get that reward function maximized. And it could do things you don't expect. And if you give a reinforcement trained model actuation, like it can control the real world and just say it. I just trust its policy will work well. It can go to really weird places because you don't know what that policy is.

It's very different than a data-driven model. You can have a data-driven model is like, it's trying to be as close to a person as possible. It's close to whatever process was producing the text that trained on. It wants to be as close to that as possible. So there's maybe more to be worried about in that reinforcement learning world than there is in the chat GPT world.

But keep in mind, these are different technologies. And when you think about things that are learning how to work in the world and walk and locomode and play games, that's not the same thing that's happening with chat GPT. That's a completely different technology and one that we should keep an eye on.

Anyways, it's an important distinction. These worlds are kind of coming together. There's people who are using language models to help do reinforcement learning. Jan LeCun doesn't like reinforcement models at all. He thinks we should just build up understanding of the world and have sort of more intentional simulation of the world.

I agree with that because I think it's a lot safer. That's where my intentional AI model comes in. A lot to talk about here, but at least we have this distinction to start with. Reinforcement learning is different than data-driven learning, semi-supervised learning, unsupervised learning rather. These are different models, different technologies, different capabilities, different concerns.

All right. Well, there we go, Jesse. That was my lecture for the day. I like it. Let's leave it there, but we'll be back next week with another episode. And until then, as always, stay deep. Hey, if you liked today's conversation about managing your time in five minutes or less, I think you'll also like episode 261, which is about controlling your time.

Check it out. I think you'll like it. Actually, I want to shift towards the more practical world of controlling your time.