Helping a Professional Learn Faster - Private Coaching Call

How do you keep up with everything you need to learn while working full-time?

Especially if what you're learning is new, technical, and you need to use that knowledge to solve complex problems at work. I've been a learning coach for

work. I've been a learning coach for over 13 years. And of the thousands of professionals that I've worked with, one of the most common problems I help coach them through is how you can keep up with

everything you need to learn while working full-time. I personally spent

working full-time. I personally spent years exploring this myself and created a system that allowed me to work 80 plus hours a week as a junior doctor while running a business full-time while

studying for my masters full-time and coming first for my masters. And what

I've realized both in my personal experience and from coaching so many people is that there are certain habits and approaches to learning that we just don't challenge. And they might have

don't challenge. And they might have served us before, but as a working professional trying to learn while working, it's just getting in the way.

And so in this video, I want to share some of those realizations with you and show you some of the different approaches and ways of thinking about learning that you might not have thought about before. And I think this will be

about before. And I think this will be especially valuable if you're in a position where you're learning lots of new and technical information and then you need to apply that to solve real problems at work. And the way I want to share these realizations with you is

through a coaching call that I had with Julian. So Julian is a recent university

Julian. So Julian is a recent university graduate who is working at a startup.

and he came to me because he had to learn a lot about AI and he doesn't come from a technical background and he was finding it really hard to learn about this effectively and know how to use it.

There was a lot of stuff that he needed to catch up on. There's new stuff coming up all the time and he didn't know how to keep up. So, I had a coaching call with Julian to figure out how he's approaching his learning, what his learning system looks like, and how he

can improve. There were a few key

can improve. There were a few key takeaways from that consultation and some strategies that I recommended to Julian that I think would be helpful for a lot of you as well. So, with Julian's

permission, I am sharing that one-on-one consultation with you guys.

>> All right. So, Julian,

start us off by giving a bit of context in terms of what you're learning about and what your main problem is.

>> So, for some context, I recently graduated university and I'm now working at a grown startup. And one of the things that I'm focusing on on top of my work is learning how to effectively use

AI. Um, and the reason this is so

AI. Um, and the reason this is so important to me is because I just hear buzz about AI all the time and now AI can do this, AI can do that. And it kind of feels like if I don't learn AI fast

enough, then I'm going to fall behind in not just my job, but also my overall career. And personally for me, I don't

career. And personally for me, I don't have a background, a technical background in AI, AI. I come from a medicine background, but I know how important AI is. And so one of my learning goals, if I was to just to

summarize it, is I want to be able to effectively learn how to use AI when I have limited time and limited background knowledge. And so there's a couple of

knowledge. And so there's a couple of problems that I'm running into right now. The first one is the lack of

now. The first one is the lack of structure. So compared to university

structure. So compared to university where there's a strict curriculum telling you what to do or not, when it comes to learning at a startup, there is no guide, no one telling you what to learn at what stage. And so I just feel

a bit lost at times on what exactly I should be doing to get the most high yields for my learning. The second is um problem that I'm facing is the idea of information overwhelm. Um I just feel

information overwhelm. Um I just feel like there's new information coming out about AI every single day. Um there's a new YouTube video, new LinkedIn post saying AI can um there's a new AI tool or there's a new AI agent that can do

this and it just feels like there's so much like knowledge in this field. If I

don't keep up with it every single day, then I'm just going to fall behind very quickly. Um, so that's the second

quickly. Um, so that's the second problem, information overwhelm. And then

in terms of the third problem I'm facing, it's it's it's like the benefit of learning. I feel like I'm trying to

of learning. I feel like I'm trying to learn a lot about AI, but it's not translating to a lot of tangible results. I feel like sometimes I can get

results. I feel like sometimes I can get a good conceptual understanding of how AI operates, but then at the end of the day, I need to actually turn that into something into my job. Like I need to

actually use this AI tool to get some sort of like result or learning. um

otherwise it kind of feels like the things that I'm learning aren't actually leading to anything tangible. So that's

kind of the three like main challenges that I'm facing right now. Okay, cool.

So thanks for sharing that. So from how I see things, I think we can divide these issues you've talked about into kind of two categories of problems. The first category is the fact that there's a lack of structure and the ways like

strategies that you'd use to overcome this are its own like there are different strategies that we can use to create structure even when no one is telling you what to do. And the next category of

issues is this idea of like overwhelm and the fact that you're spending a lot of effort and not necessarily being able to translate into the result. Um and

that comes down to the process that you're using to actually try to handle that high level of information and volume. So let's think about this.

volume. So let's think about this.

The question I want to start off with is like why do you feel overwhelmed when all this new information comes out because new information is coming out about every field all the time and yes

technology stuff is coming out you know especially rapidly but if you have an existing set of expertise then new information is much less

overwhelming. So think about it this

overwhelming. So think about it this way. So let's say that you have

way. So let's say that you have right this existing network of connections right inside your brain. So

this this is a representation of all of your existing expertise and knowledge.

Okay. So this is existing knowledge. So if a new piece of

knowledge. So if a new piece of information comes in and you feel like this new information which is constantly appearing,

you don't know how to think about it. In

other words, you don't you don't see how it's connected to anything. You don't

see the patterns between them. You don't

see how they're related to each other.

You don't see what the impact or importance of it is. then you're going to perceive that feeling as overwhelm.

The overwhelm is your brain saying, "I don't know what to do with this information."

information." So contrast that with how an if you have a high level of expertise, an expert is able to look at this new information and

they don't feel overwhelmed purely because of the fact that this new information their brain knows how to think about it. They would look at this and say, "Okay, this is not overwhelming because it's very obvious that this is

going to impact in this kind of way.

This is not very overwhelming because it's very clear that this is purely an extension onto this and this is not very overwhelming because it's very similar to this existing set of connections that I already have. So if you think about it

in terms of like pattern recognition your existing expertise there's so many different patterns that you'll have internalized and right now you're saying that you come from a background which is not very technical. So your existing

patterns that you're thinking about coming from a medical background when you look at all this new information around AI, you don't have existing patterns to compare that to, there's no way that you see how it impacts and how

it's how it's relevant. So you're never going to get to a point where this stuff doesn't feel overwhelming until you build a baseline level of expertise that allows you to create those connections.

Right? You're basically saying, why does it feel difficult trying to become an expert at something that I have no knowledge about? Right? The answer is

knowledge about? Right? The answer is not about your process. Actually, it's

just about the fact that you don't have any knowledge. So, the first step is how

any knowledge. So, the first step is how do you build that base of knowledge in a way that allows you to create as many anchor points of relevance as possible?

And there are different ways that you can approach this because you could start off by getting a huge textbook on the first principles of, you know, mathematics and statistics and modeling

and and and Python and start from ground zero. And you would eventually get to a

zero. And you would eventually get to a point where you have so much knowledge on this that when you see a new library coming out that you can integrate with your AI agent, you think, "Oh, I see the

implication of that because I'm drawing on this huge body of knowledge." But 90% of the stuff that you learn is actually going to be irrelevant to you because you are not going to be using that. Like

you don't need that level. So this is going to come back to the way that you're actually structuring things.

It sounds like the way that you have been trying to structure things is very um use case focused like I have a problem I need to solve therefore I need to learn

enough about this to be able to solve that.

So naturally, unless the new thing about AI that comes out is really aligned with that particular problem that you're trying to solve, you're probably not going to see how it's relevant to the wider field of AI, and then you're going

to feel overwhelmed all over again. With

AI, especially, this is important because of the fact that when new developments come out, it can change the entire approach of how you go about solving a problem. So if you had a certain approach to solving a problem with existing tools, when something new

comes out, you might not really see how that approach can change. and then

you're going to get into a sunk cost fallacy where you feel like you've spent so much time trying to understand this approach that you're not open to changing that for for a new approach.

How much time have you spent on actually trying to understand the actual first principle fundamentals of AI and and ML and the

statistical modeling and probability and kind of not you know not necessarily going into like the deep deep deep maths and programming of it but actually getting your head around the first principle fundamentals of the topic

itself.

>> Yeah. So I have spent a few hours trying to get a very broad overview of the topic itself and I actually sent you one of my mind maps if you want to bring it up.

>> Yeah, let's let's have a look at that.

>> Yeah.

>> Okay, >> cool. So to give you the context around

>> cool. So to give you the context around this, I probably spent around one hour just going on chat GBT which gave me the main topics when it came to AI which in

this case you can see machine learning um the models the methods the goals it uses and then based on the like 10 10ish keywords that it kind of gave me I then

used inquiry based learning to try and find the connections between all of these. Now, I'm aware that this is very

these. Now, I'm aware that this is very broad and there's probably a lot of detail that I'm missing here. But this

was the initial first step I took. So,

I'm just curious in terms of the starting point here. If you have any thoughts on um anything good here, anything bad here, what's kind of like your overall perspective on uh the

overall mind map. I have just before I get back into my feedback for Julian's mind map, I wanted to clarify something here, which is why mind maps are so powerful in the first place. I have

taught mind mapping to tens of thousands of people and I've seen tens of thousands of mind maps over the years.

And what I can tell you is that mind mapping is an incredibly effective learning strategy if you do it correctly. But it is a surprisingly

correctly. But it is a surprisingly complicated skill to do correctly. And

most people that have done mind maps and they haven't found its useful have done it incorrectly to be frank. Now, I

realized that I don't really go into Julian's mind map in technical detail in terms of really talking about how to do it correctly. Um, so if you are wanting

it correctly. Um, so if you are wanting to get a more in-depth guide on how to do my mapping correctly, then I do have a guide that goes through it a little bit more step by step in one of my newsletters. If you don't know, I have a

newsletters. If you don't know, I have a free weekly newsletter that I send out.

I'll leave a link in the description for you. And one of the additions of the

you. And one of the additions of the newsletter has a step-by-step guide on correct mind mapping technique. So if

you're interested in joining, again, it's completely free. I'll leave a link for you in the description. So for

for the level you need to go to, this is not going to be anywhere near enough.

However, for starting from zero to get to the next level, this is a good first step. You've got a basic understanding

step. You've got a basic understanding of the way that machine learning has been divided. Um, I would say that

been divided. Um, I would say that this overall structure as you learn more and more about the topic is just going to essentially become irrelevant because

it's so broad and basic that it's it's it's going to become just part of your innate knowledge and so your structures will become more sophisticated. But

going from zero to just having the biggest possible overview about how to think about some of these major AI principles, this is going to be a good good start. So when you look at if if if

good start. So when you look at if if if you're saying okay here's what I've studied then I would say okay this is a person who's going to have a good understanding about the higher level

connections between here. So if new information comes out about a new entire type of training method you will know how to think about that because you know how to think about training methods. If

a new entire like branch of deep learning appears, you know that you might understand how that could potentially be related, but anything beyond that. So basically any new

beyond that. So basically any new information that is more detailed than just this broad level, you're not going to be able to handle because you don't have the existing connections to to map this onto. So the problems that you're

this onto. So the problems that you're facing which are with the lower level detail match with the level of knowledge and organization that you have here. So the

solution is going to be to pro to continue this process of mapping things out for progressively deeper and deeper layers of of information. Now this comes back to the question about structuring.

If you don't know what you need to learn in what order, then how do you know how to spend that time? There are a couple different ways that you can do it and there's pros and cons to each. On one

hand, you can choose to continue going down it in terms of the problem that you're facing. So let's say that you

you're facing. So let's say that you have this particular you know understanding of things uh and then there's a particular problem that you want to solve. So you'd look at your existing level of knowledge and then you look at the problem that you have and you ask yourself do I have enough

knowledge to apply this knowledge to effectively solve this problem? The

answer is probably at this point going to be no. So then the the the question is so which part specifically am I missing right now in order for me be to

be able to get closer to this problem?

You don't need to know the answer.

like there's no way to know the exact knowledge that you're missing. But you

can get to a point where you ask the right questions to figure out where that gap is. So you might look at the

gap is. So you might look at the existing problem that you have and then say the reason that I can't use this knowledge is becau not just it's not specific enough which is a very broad statement but because you might say I

don't understand which training method would be relevant for this problem or you might say I don't even understand if I need to train

anything for this or you know you might you might look at it from a completely different perspective and say that actually I'm trying to decide which AI tool to use, I

don't understand how I can use this knowledge to decide on the best AI tool.

So once you've been very specific about your gap, you can then ask questions where if you were to know the answers to these, it would put you in a better position to answer that. So if let's

take that final example. Let's say all you're trying to do is figure out which existing AI tools do I need to use to be able to solve this problem. You might

look at this and say, "Okay, cool. I

have a general understanding about how these AI tools generally may have been created, but it doesn't help me whatsoever in understand like cuz it's kind of like cool whether we're using

reinforce reinforcement training or human in the loop or supervised training whatever it is like who cares like as long as it gets the job done that that that's all that matters. So from that perspective, you might look at it and

say, well, the reason that I can't make that decision about the AI tool is because I don't understand the implication of the training method on

its accuracy or reliability. If I knew that and then I knew the training method of that tool, maybe that would help me.

Or it could be that I don't understand the different ways AI tools are created in the first place. you know, maybe all of them are using the exact same type of

training method. Maybe all of them use,

training method. Maybe all of them use, you know, deep learning, right? Like

what's the actual point of difference between these tools? So, you'd come to a specific list of questions where if you knew the answers to these, you may not be able to answer the questions straight away, but you'd be in a much better

position to be able to at least ask even more specific questions. And then that kind of becomes your learning curriculum. you're progressively using

curriculum. you're progressively using the questions that you think would fill in a relevant gap for you. Now, one of

the, you know, cons of this method of going through things is that you are going to end up learning about things that after you've you've learned it, you will just realize this actually does not

help me to solve the problem. Like I've

just learned something that feels irrelevant. You're not going to really

irrelevant. You're not going to really be able to avoid that because you you don't know what you don't know. So you

will have to do some sort of redundant learning in order to be able to eventually figure out uh what you need to know to to solve that problem.

That's one approach where you're starting with a problem, you're calibrating against your knowledge and you're asking questions and then you're filling in the answers to that. So the

the journey of learning in that case is that you'll start off with right now where you you you know very little and then you've got the problem that you want to solve and then you're saying

okay what is the difference between where I am right now versus where I need to get to and then from there you're going to start thinking of slight questions and then as you find the

answers to these questions it's going to eventually lead you closer and closer towards where you need to go. So that

journey is going to be kind of like this. Some irrelevant learning, some

this. Some irrelevant learning, some redundant learning, but overall getting you closer and closer to being able to solve that problem and some, you know, meaningful knowledge gain and expertise along the way. So the next time you have a similar problem, you're not starting

from scratch. Like you you already have

from scratch. Like you you already have this existing knowledge. So now you're able to ask more specific questions straight off the bat, right? So that's

that's going to be one advantage. I

would be using this approach if you know that you're going to have lots of these types of problems in the future and you need to be able to first of all provide a deliverable for your work in a short

amount of time. Like you need to go from not knowing something to be able to making this decision in a short period of time. Uh and you know that you're

of time. Uh and you know that you're going to have lots of similar types of problems moving forward into the future where the learning that you've done along the way is going to be very transferable.

I would not be using this approach if you know that you're going to be solving a very diverse range of problems that are less predictable where the learning that you've done along the way much of

it may not overlap or you're in an early stage where you're really scoping through lots of different possibilities and so you need a much more robust foundation of knowledge because if you learn in this way it's going to be very

narrow right like if this if this is if this circle represents the knowledge of everything that you can know about a field and you're starting from the middle trying to expand that knowledge.

Your your expertise is going to look like this. You're not going to know a

like this. You're not going to know a general scope of the land, right? It's

going to be very very very you know broad versus like narrow and only a specific direction. But if the types of

specific direction. But if the types of problems that you're going to have to solve require you to have a level of knowledge over here and then over here and then over here and over here, then using this approach is going to be

extremely timeconuming. like going out

extremely timeconuming. like going out narrowly every single time you have a problem and you're going to feel a different type of overwhelm. So if

that's a situation I'll be using a different approach which is you take the existing knowledge that you have and instead of asking yourself questions in terms of where the gap is you ask

yourself questions in terms of what is the underlying expertise that I would need to have to be able to figure out

the answer for myself.

So you're not actually doing it based on so let's say it's uh about the AI tool which what's the best AI tool to use.

What expertise if you had would allow you to consume all this information and naturally feel like it makes sense to you. So what that could look like is

you. So what that could look like is you've got your existing set of knowledge right here and you're saying okay if I knew about deep learning

right like much more then that means that when I think about all the different tools that involve deep learning I'm going to have a better sense of how to think about all of these things.

Uh and so even though you're sort of partially being guided by the way that you're applying it, the key difference between doing it this way versus doing it the first way is that you are deliberately allowing yourself to learn

more widely about this topic and build more, you know, quote unquote irrelevant expertise because you're basically saying the reason I'm learning this is not just to solve this problem. It's to

put me in the position where this problem and many other types of problems I'll be in a better position to answer.

The goal is developing expertise so that you can become the person that is able to solve the problem eventually versus what is the fastest most direct path of learning that allows me to solve an

issue. So that's going to be overall

issue. So that's going to be overall long-term much more efficient if you are going to be making lots of complicated decisions about it. So if you are about to step up into a role where you're

leading the AI development um for a particular uh product which I would hope that you're not in that position if you come from no technical background and you're just learning about this for the first time but let's say that that's that's what the goal is

and you need to get to that point 6 months from now. So you've got 6 months where you need to go from zero to being able to make a broad range of unpredictable decisions relating to AI

where a gap in the knowledge may create a significant consequence to the the quality of your decision. So very

targeted learning is going to be ineffective. This is almost exactly the

ineffective. This is almost exactly the same issue that um you can have with uh for example when you when you're studying for an exam and you're just using lots of practice practice

questions and the only time you ever fill a gap is when you get a question wrong. So you would require very very

wrong. So you would require very very high volume of testing to be able to figure out where that gap is. And then

if it so happens that you're asked on something that's a different gap then because you haven't tested yourself on that you're not going to be able to do very well. So in this approach, you're

very well. So in this approach, you're more holistically building out your expertise and you're only loosely guided by the understanding of the problem that

you eventually need to solve, but you're um relying a lot more on that innate skill of just being able to learn deeply. The most difficult part about

deeply. The most difficult part about using this approach is that because you don't have that clear problem to anchor onto, it can be hard to create a very

meaningful structure in your learning.

This is a common situation especially if there's a very high volume of information to learn. So even if there is a very clear goal like for example people studying for a specific exam you

could be studying for a very specific exam but the amount of stuff that you're going to be tested on could be so huge that it's almost like well I just need to learn absolutely everything. So

that's a situation where even though technically there's a structure the structure is just saying okay step one learn everything. So that structure

learn everything. So that structure becomes useless. So in those situations

becomes useless. So in those situations as well, the benefit of that is that because you will have to learn everything at some point anyway, you can

be very liberal in your control over what to learn in what order. So let's

say for example that you happen to feel more interested in deep learning.

And so as a result, because you're more interested in it, you feel just naturally inclined to start exploring those connections and deepen your knowledge on that path. You can

completely do that because you're going to have to learn this at some point anyway. And learning it when you feel

anyway. And learning it when you feel like it's more interesting is going to be more effective than, you know, forcing yourself to to put it together.

Or you might look at this map and think, you know what, the main thing that I see is that there's a disconnect between this bottom half and then this top half.

How do these models of training or these types of goals, how do they interact with these core, you know, machine learning principles? Like what's the

learning principles? Like what's the connection between them? That that might be the part that you're most curious about or that you're most confused about. So again, you're going to have to

about. So again, you're going to have to learn that eventually. So you just start with the thing that you either feel the most comfortable with or that you feel the most interested in. And then you just continue learning about that until

you get to the point where it's no longer the thing that interests you or that you're curious about. So if you start learning more about training methods, you might expand this out a lot and at some point you feel like it's

getting a little bit boring. It's

getting very detailed and very technical and it's getting harder and harder for me to follow along. So what does that mean? If it's getting harder and harder

mean? If it's getting harder and harder to follow along, what's happening in your brain is that you are starting to get overwhelmed. So going back to that

get overwhelmed. So going back to that drawing that I had before, it basically means that you have been building this this knowledge.

So, you've been building these new connections and as you've learned more, your network has grown and grown and then you're

getting now to a point out here where the same thing is happening. You're not

really seeing how these things are related anymore. So, you again, you're

related anymore. So, you again, you're going to feel that exact same thing that you felt before. you're feeling

overwhelmed and you're going to feel that this is a point where that um knowledge is plateauing and it's going to get slower and harder to consume this new information. So, at this point, you

new information. So, at this point, you don't have to just continue on to learn all of this stuff. Remember, you're

going to have to come back to this later anyway. So, instead, you might say,

anyway. So, instead, you might say, "Okay, well, now I'm actually now, you know, interested in in this aspect over here because there's all these questions that I have after learning about this stuff." So you might learn a completely

stuff." So you might learn a completely irrelevant, you know, completely different topic and you start making connections there. And then as you

connections there. And then as you expand out on this, naturally what will happen is that now this thing that was irrelevant for you before, okay, now it's actually relevant cuz you've got

more connections to build that off. And

so you're constantly just learning, being led by where you feel is the most relevant for you to build out that expertise. and then over time it will

expertise. and then over time it will holistically grow. But you don't have to

holistically grow. But you don't have to be worried so much about whether you're hitting the right level of detail and depth for this particular thing right now because eventually you'll be able to

come back to it. So this is a the best way of learning about something if you know that you've got a bit of time to learn about it but the way that you need to apply it is you know robust like it's

it's very rigorous. There's lots of different ways that you need to apply this knowledge. This would be kind of

this knowledge. This would be kind of overkill if there's a very narrow set of problems. You know, you're going to be dealing with relatively more predictable decisions and problems that you're going

to have to solve. In which case, you would just use that first method, which is that more narrow question focused um way of learning things. But either way, in this case where your questions are

creating structure or in this way where your sense of relevance is creating structure, the fact that there isn't a structure given to you should never be a problem. And in fact, it's the opposite

problem. And in fact, it's the opposite is that for an experienced learner, structure can actually make things harder because it's forcing them to learn about things in a way that may not

be suited to how they would prefer to go about learning it. So when there is structure, there's an additional technique which is how do you deliberately break free from that structure to sometimes use these

strategies which can be cognitively more efficient.

So the lack of structure is not a is not a detriment. It's just like a fact and

a detriment. It's just like a fact and it's just about how we navigate that fact that if you that changes the outcome.

>> So we talked about a lot of stuff. Do

you have any questions about what we've covered so far?

>> Yeah, so I have a few things. First of

all, it seems like the lack of structure may not just be a harm, but it may also be a benefit in not locking myself into a particular way of thinking and actually allow me to explore things based on what I'm interested in and what

I'm curious about. Um, so that was the first like observation. Um, the second thing was when it comes to the problem based approach which was more narrow and the wider based approach on relevance

which is much broader, is there a place where I can use both of these approaches at the same time? So while I'm building that deeper expertise that pie chart and unlocking all the kind of sectors all at

once as I do that if I identify specific problems that I need to solve now and kind of get that tangible value is it possible to use that problem approach to solve that and so I'm kind of building both at the same time building that

robust expertise so I'm uh increasing my like foundation while also addressing the small application areas that I need for the current B.

>> So the answer is yes and no. Um

the no part is that there's always an opportunity cost and so unless you're a hobby learner usually you're sacrificing something. So let's say that you start

something. So let's say that you start with this approach. So then you're going to develop a relatively narrow set of expertise in this wedge. And so you might say okay so this actually becomes now a foundation of knowledge that you

can use to build on other things. And

you certainly can. So this can create networks out that you know create relevance anchor points to allow you to expand that wedge. So this same effect that we've talked about here. It's just

that this initial set of expertise this was problem oriented and that it's based around so that you you you can totally do that. However, it's not going to be

do that. However, it's not going to be as efficient if this is if if this is what you're trying to get to at the end of the day and this is the priority.

it's going to be faster for you not to develop that narrow wedge. And that's

because that uh I'll draw this again so it's easier to see. That's because each level of

to see. That's because each level of difficulty and complexity is not equally weighted. When you have when you're

weighted. When you have when you're learning very broadly and you're kind of being led by that sense of relevance, your own brain is telling you what is too detailed and too unconnectable for

your current level. And so you naturally will end up learning in such a way where you sort of learn around here and then you realize this is getting too detailed. So you sort of start learning

detailed. So you sort of start learning around here and then you build these connections and then you learn around here and you build this connection and you and so your knowledge kind of ends up evolving naturally in this kind of

concentric way where it's going from shallow and superficial to progressively more and more detailed and in-depth.

If you think about the volume of everything that you need to learn, this stuff that's here, which forms that kind of core big picture understanding, makes up maybe 10 to 20% of everything that

you need to know. But that 10 to 20% shapes the remaining 80% of everything.

So if you've got errors in the way that you've understood this, those errors are going to be passed through in misunderstanding this 80%. or slowing

down your ability to make those connections because right you know if if these connections are wrong you're not going to be able to find the right ways that it fits and actually this is a common thing that happens if you uh

don't develop expertise very accurately is that you will feel like you know a lot about the topic but when you start going through very specific details you're like I don't understand how this makes sense anymore this detail doesn't

make sense to me and the reason it doesn't make sense to you is that it doesn't align with your scheme schema of how you've understood this topic and that's because you've understood the topic wrong and so now that detail is

challenging your entire understanding about how you've thought about everything and now you have to relearn that schema and that's actually very challenging and very timeconuming. So if

you've miss like learned it incorrectly and then you have to go through and then relearn and redevelop that entire schema.

It's much much more difficult to do that than to have just learned it correctly in the first place. Right? Okay, that's

that's that's disproportionately timeconuming. So when you start with

timeconuming. So when you start with relevance, you end up naturally going in a pretty good order. You tackle the most important stuff first, which puts you in

a great position to then go through the 80% remaining detail. And it's likely that it's going to be more accurate.

It's going to be more relevant. It's

going to fit into place more easily. And

then as that again grows, you're going to get that snowballing where the more and more you learn, the faster and easier it is to learn it because you've got so many connection points. It's very

rare for a piece of knowledge to appear anywhere where you can't immediately see how it's all connected, right? And and

that's kind of the the the holy grail of learning is that you want to get to that snowball effect as quickly as possible because that's where that efficiency is going to come from. You're not going to be able to get to that very quickly if

you're developing that narrow wedge.

because if something appears out here there there may not be a relevant connection anywhere around there and again this piece of knowledge in order to see how it connects through it may require you to rearrange

the way that you have understood this.

So um while you can use a problem as an anchor point to create efficiency in the short term and then eventually expand out, you should do that knowing that

your ability to develop holistic expertise will be slower than if you had just tried to develop holistic expertise starting from the beginning.

>> Uh and again if you're just like a hobby, if you've got lots of time available to you, that could be fine.

Like let's say that this is your prerogative for work. So you need to be able to do that. But over the next 2 years you want to be able to continue to develop that then by all means like for sure you definitely can do that and that

could be a very natural way of getting a work output while also developing your expertise. But if if you're in a

expertise. But if if you're in a situation where it's like you need to be able to do this, you know, devel have really good expertise for high stakes decisions and that deadline is coming imminent to you, you need to be able to

do that very quickly and you need to be able to solve problems at work, then you know, I would say it's uh better to try to just make a decision about which thing is going to be faster because you're going to have an opportunity

cost. If you start here, you're going to

cost. If you start here, you're going to delay your ability to make that decision. Whereas, if you start here,

decision. Whereas, if you start here, you're going to delay your ability to develop that holistic expertise and that's going to be a separate uh kind of decision that you would have to make.

>> Yeah, that's super interesting and very helpful as well. For me, there isn't a particular like time frame to it. It's

just I guess I want to learn it as soon as possible, which I guess if that's my goal, then going in layers and going that contra concentric circle is going to be the most effective approach. So

this this approach is definitely going to be >> faster and more effective for you. Um,

and the flip side is that it is much safer and more efficient to start with this and then when a problem arises, you can expand out from that because the difference is that instead of developing

this narrow wedge, what you're going to end up developing is like a pretty robust foundation and then extending out into a specific problem is much easier than starting really narrow and then

trying to build that out holistically.

This is much more timeconuming doing it this way much less much less efficient and much more errorprone. So if that's the situation and there isn't a clear deadline then using the more relevance-led approach is going to be

much better. Uh having said that

much better. Uh having said that honestly with both of these approaches but especially with this your actual skill in being able to seek out what is

relevant and your own skill in being self-aware and understanding is this feeling overwhelming? Is this feeling

feeling overwhelming? Is this feeling relevant to me? And actually calibrating that has to be tuned in. This completely

depends on your own ability to learn something and understand, okay, this is feeling overwhelming. I'm starting to

feeling overwhelming. I'm starting to enter into this pattern of having to memorize it or wrote learn it because I it doesn't click anywhere. For a lot of learners, they feel that that is normal.

It's not clicking. I don't really get it. I don't see how it fits into the big

it. I don't see how it fits into the big picture or it's too overwhelming to think about how it fits into the big picture. These signs are very normal for

picture. These signs are very normal for a lot of learners to feel and so they won't really think much about that. It's

there's no consequences. They'll think,

okay, well, this is just what learning is about. I'm just going to keep going

is about. I'm just going to keep going through and covering the content. But

actually, the correct judgment to have made is this is feeling really overwhelming. I don't see how it fits

overwhelming. I don't see how it fits into the big picture. It's going to take me too long. Either pause, consolidate, get to the point where it fits into the big picture so it does make sense to you

and now it's not overwhelming. Or if you can't do that, scale back. Make it

simpler or pivot. What is the thing that you feel you know you can understand that's that's simpler and just keep looking for those easy wins of where you

you know like basically the lowest hanging fruit. But the difference is

hanging fruit. But the difference is that when you pick the fruit, it's not just you've picked the lower hanging fruit and eventually there are going to be fruits that are so high and out of reach. It's like every time you pick the

reach. It's like every time you pick the fruit, you also like grow, you know, you're like that like was it like Lamachian evolution like the giraffe that as it tries to pick the higher and

higher fruits, it's like net gets longer and longer. That's actually what's going

and longer. That's actually what's going to happen is like you start with the lowest one and then at with each one you eat your ability to reach actually starts growing. So the stuff that at the

starts growing. So the stuff that at the beginning was so out of reach at some point that will become your next lowest hanging fruit. And so that's a skill

hanging fruit. And so that's a skill that has to be really wired in which goes against the habit of a lot of learners.

>> Right. I see. And I assume that even with your analogy of the draft that keeps growing its neck at the start it like for me it's kind of hard to get a big picture understanding especially when I don't have any technical

background because there's just so many fruits. I have to kind of look for the

fruits. I have to kind of look for the right one. But is it the case where if I

right one. But is it the case where if I just start picking these low hanging fruits that as my neck grows longer eventually there'll be a stage where picking more apples or or fruits becomes

easier and easier and it like compounds over time.

>> Yeah, that's exact and that's that snowballing. So when there's so many

snowballing. So when there's so many things that you can start from, it doesn't it doesn't matter. Just start

from the one that you feel the most comfortable um and confident to be able to handle. The one that you feel you can

to handle. The one that you feel you can integrate into your network the easiest.

So for example like if I just like share personally when I'm going through learning about AI and ML because I come from a medical background and a research background um and as part of research I

had to learn a lot about statistics the statistics aspect of the math was the part that for me I felt I could connect with most easily. So the stuff that was actually fairly technically detailed for me it felt comfortable because I was

used to thinking about those concepts but a very similar other aspect within maths which is maybe even more basic than that technically more basic I wasn't able to connect with as much

because my mathematics foundation is not as high in that area. So again it it doesn't really matter about there's no right order. there is only the right

right order. there is only the right order for you and the way that you learn it versus how someone else learns it or someone else tells you that this is the way that you should learn it. You know,

I should I would always be skeptical um and just try to be really really self-aware in terms of where you feel that your your knowledge gaps are.

>> Okay, cool. Yeah, that makes a lot of sense. And in terms of the concepts that

sense. And in terms of the concepts that you shared today, they've definitely like gave me a lot of light bulb moments on how I can change my learning to learn AI more effectively. I guess to kind of

just sum up and make sure um I'm on the same page in terms of the um specifics, is it possible for you behind based on like everything we've discussed

so far to help me engage in this way of layering um and learning? Just before I answer Julian's question and go into the specific learning techniques, I want to make it clear this is not a comprehensive list. Uh this is not

comprehensive list. Uh this is not everything. There are other techniques

everything. There are other techniques that you can use. I talk about them in my other videos if you want to see some more techniques that might be relevant to your specific learning situations. I

also cover those in my free newsletter that I mentioned before. So I recommend checking that out. Okay. So let me do a quick recap because we talked about a lot of stuff. Up until now, we've covered that there are two overall

learning approaches that you can use for this situation. The first one is that

this situation. The first one is that problembased learning approach, that taskbased learning, and the second is a more broader knowledge foundation approach where you're really trying to

build that expertise more organically. I

also talked about how we can build expertise through concentric layers of learning and you can expand knowledge more gradually. And I also mentioned the

more gradually. And I also mentioned the importance of connecting new information to existing knowledge. And that's kind of the key to having solid memory and deep understanding. So those are the

deep understanding. So those are the overall concepts that we've talked about. In the next section, I'm going to

about. In the next section, I'm going to ask Julian to tell me exactly what his approach to learning is, and we're going to map it out and then audit his learning system. Once I've audited it,

learning system. Once I've audited it, then I'll be able to provide some specific recommendations to him so that he can learn AI more effectively. So

take it away past Justin.

Tell me about what your approach actually looks like in terms of trying to use AI uh trying to tackle this high volume of information, how you're trying to convert that, and what the specific issues that you feel like you're running

into are.

>> Yeah. So I'll give you a step-by-step workflow of how I'm currently learning about AI. So the first step that I do is

about AI. So the first step that I do is I I know it's important to get a big picture understanding about the topic I'm learning which in this case is just AI in general. And so that's where I start off with. I try and get a big

picture by just looking through skimming through YouTube videos asking Chad GBT to give me like um like the main con concepts so I kind of know where to kind

of at least look at when it comes to like AI. Um and then the next step is to

like AI. Um and then the next step is to just kind of map out the main connections. I'm not super comfortable

connections. I'm not super comfortable with like mapping out every single connection, but I roughly ask myself how each term is related to each other. How

is this topic in AI related to this other topic? And I try and see how

other topic? And I try and see how everything is connected into a basic map.

>> And when I do this for the first time, I feel like it is definitely a bit hard because there's so many considerations that I'm thinking, especially when I don't have the background knowledge. So,

it feels like sometimes when I'm making these connections, although they're basic, it does give me a big picture.

Um, but I feel like it's still very far off from where I actually need to use like AI.

>> Um, so that's that. In terms of like the in terms of the result of that, I definitely feel like it gives me an understanding of of the topic, but as I mentioned, I feel like I just need to go

into much more detail to be act to actually use the knowledge in some meaningful way in my job. Um,

>> so let's let's talk about that meaningful lower level knowledge. So

what what you're doing is you're building this higher order big picture.

Uh so this this part here is going to give you that overall frame. Um so this part's good. This is important to do

part's good. This is important to do because this is what's going to allow your brain to see how things are relevant. But uh the part that you're

relevant. But uh the part that you're struggling with which is the lower order like the very technical specific details. So this is the flow that you

details. So this is the flow that you have talked about so far. So, as a general principle, whenever you're making a change in the way that you approach learning, you should always start with what you're doing and then just build from there. In very few

instances would I recommend that you start from a blank slate and just try to build something that's optimum from the ground up. Even though that may seem

ground up. Even though that may seem like the cleaner way to do it, unless you're going to take like a one to twoyear gap where you don't have any other responsibilities or anything and you can just immerse yourself and just

complete habit restructuring from from zero, that's going to be net negative.

Um, you will always want to just say, "Here's what I'm doing and here's how I can make it 2 to 3% better and just continue stacking that and just swap out parts as if you're just slowly upgrading

a car." So this is the approach that

a car." So this is the approach that you're already using. I would say that the first part of of how you're framing everything use establishing a big picture, you know, skimming through

YouTube videos using chatbt um and then mapping out those major connections is good. Don't change that. The only thing

good. Don't change that. The only thing that I change after that is the way that you're trying to connect that through just immediately applying that. Just

delay that by a little bit. Delay it by like a couple of days, you know, a few hours. and instead inject within there

hours. and instead inject within there just one additional layer where you're actually deliberately seeking out

the the lowest hanging fruit, the lowest hanging, you know, point of relevance.

And give yourself at least a few hours just to try to flesh out a little bit more of that detail um and and figure out where where those gaps lie. and then

see if that changes the way that you apply the information or the types of problem, you know, the way that you think about the problem.

If you're comfortable with doing that, then all you need to do is just expand this amount of time out. So, you might start with just spending a few hours spending time seeking points of relevance and learning through

relevance. And if you're feeling, hey,

relevance. And if you're feeling, hey, this is actually a really engaging process. I feel like my expertise is

process. I feel like my expertise is developing. Good. Pause for a moment.

developing. Good. Pause for a moment.

Check yourself. Try to apply what you've learned. Is it getting you in the right

learned. Is it getting you in the right direction? Like calibrate yourself. You

direction? Like calibrate yourself. You

could be learning things and it feels great, but you're actually learning it completely wrong. So, you need you need

completely wrong. So, you need you need to be able to calibrate yourself every now and again, but you calibrate yourself and you're feeling like it's working well. Okay? Then expand this

working well. Okay? Then expand this out. So, instead of just spending a few

out. So, instead of just spending a few hours, spend a few days, continue to expand that and then again test yourself. Am I, you know, is this

yourself. Am I, you know, is this aligned with the problems I need to solve and the expertise I need to develop? And then if again it checks out

develop? And then if again it checks out then you know you you continue that that that cycle and then eventually um you can get to a point where even the way that you're framing the big picture

and then mapping out those major connections is starting off with understanding where it's going to be most relevant for you.

Um but just the only change that you need to make to start with is just by inserting one additional layer of seeking through relevance before going from getting a big picture understanding

to then immediately applying it.

>> Okay, cool. That that makes a lot of sense. And I'm assuming the more I seek

sense. And I'm assuming the more I seek and the longer that is like the deeper expertise I will have and the more diverse problems I will be able to solve. And so that's kind of like the

solve. And so that's kind of like the variable I can continue to adjust. Is

that correct?

>> Yeah. And you will, if you're not doing it right or if it's kind of not effective, you're going to be able to tell because you're going to see the exact same problem that you came to me with, which was the idea that when you do the application, you're feeling that there is a disconnect in the fact that

yes, technically you're able to build this thing, but you don't really understand it. You don't really like

understand it. You don't really like you're not able to solve bigger, more complex problems. You don't have that lower level detailed understanding to be able to use that knowledge effectively.

So, you're going to run into all of those problems. So, as you continue to do this, these like these problems will naturally start getting solved. When you go to apply the apply what you know, you're

going to feel, oh, like I'm having different thoughts to how I used to have them. You're going to look at your old

them. You're going to look at your old workflows and think, oh, this this solution doesn't actually make sense now that I know a little bit more. I

shouldn't have set it up in this way. No

wonder I had all these other issues. I

should have actually done it this way.

So you should naturally feel that that development and expertise is changing the way that you solve problems and make decisions.

>> And if it's not, >> that becomes problematic. Either you

still haven't gone deep enough, which could be the case, especially as a beginner, or um the way that you're going about it and and developing that expertise is is too isolated. So even

though you have the necessary knowledge, you haven't connected it well enough to be able to then apply it in the way that you need to. And then that would be a different problem that we'd solve.

>> Right? So with the seeking part, you mentioned that there was a cycle of seeking and calibrating yourself. And

then with calibrating, there's kind of two main problems. So if I haven't gone to the level of detail that I needed, would I be able to tell that just because I can look at the mind map that I have and clearly see the problem that

I need to solve and there's a massive disconnect. For example, with the mind

disconnect. For example, with the mind map that I've showed you before, I I could like see that at the start that the like the terms and the branches I had was very far off from the detail

that I needed to actually apply it. Um,

so that's problem like number one in terms of I'm not actually going to the depth that I needed and so I can calibrate myself on that. If I haven't

formed the big picture correctly, how do I know that that is the problem or is it just from a process of elimination that it's not the previous problem?

>> Uh so it's not just process elimination.

So in terms of the first part, yes, you can look at the mind map and say this does not have enough detail for me to be able to like the condition here is that you need to have a certain level of nonlinear note-taking and mapping skill.

That means that what you have put onto your mind map is a reasonably decent reflection of how it is organized and how you've understood it in your mind.

If there's a big difference between how you have made sense of it mentally versus what you've expressed in your notes, then that's no longer a mind map, right? It's just like a a set of notes.

right? It's just like a a set of notes.

Like it's not it's not a map of anyone's mind.

>> Yeah. So if there if there is relatively good f fidelity between those two things then you can look at the map and say okay naturally if I'm lacking all of this knowledge I'm not going to be able to

solve these problems. So if that condition is true then yes you can just look at the map and visually tell that there's a knowledge gap and then therefore you have to go deeper and then you would try to go deeper. So the way

that you'll tell if you've technically got the knowledge but it hasn't been connected in such a way that you can use it is that when you look at your notes and you look at what you've learned and you look at your knowledge the facts are there

like you have everything that you need at the level of detail that you need to to be able to solve this but for some reason when you go to apply and use that knowledge you either cannot recall it or you cannot just connect it together in a

coherent way. And that might take a few

coherent way. And that might take a few iterations. Like you might test

iterations. Like you might test yourself, run into an issue, you might ask CHP like why am I having this issue?

And then kind of you know it it tells you well the reason you're having this issue is because you've done it this way when actually you should have done it this way. And you might read that

this way. And you might read that explanation and realize I knew all of that. Like each individual thing it told

that. Like each individual thing it told me I knew it but I didn't see how it how it influences each other. And so that's a that's that's a completely different issue because that means that your

ability to create meaningful connections needs work. And for most people that's

needs work. And for most people that's going to be their rate limiter. That's

going to be the bottleneck.

>> Okay.

>> Um so >> yeah.

>> Yeah.

>> Yeah. That makes a lot of sense. And I

guess my um one of my final questions is with the point you mentioned before about using chat GPT in this process which I was quite interested about. Um,

and I know it's a bit meta of using AI to learn about AI, but I am curious about your general approach of how you actually use AI tools to help you with learning any topic, not just what I'm

learning right here, because I've heard about like some research online about how like using AI will actually destroy your brain. Um, so do you have any like

your brain. Um, so do you have any like guiding principles on how do I safely use AI to learn things effectively?

Yeah. So, there's increasingly more stuff coming out about how using AI can be cognitively detrimental um long term. So, as a result, it's really important that you have a clear

understanding about how to use AI. Like

the overall approach and the attitude that you have towards AI needs to be very clearly calibrated. Um AI can be a great time save. There's a very general rule of thumb that you can try to use.

uh but you know I need to add some nuance onto that which is that you can use AI to save time but not to save effort.

>> So the nuance here is that uh if there's something that you're trying to do and you feel like I can easily do this. I

just need to give myself time to do this. But you you know you can do this,

this. But you you know you can do this, but you just can't be bothered. Like you

just it's just it's just would take so long to be able to do it. So a very very simple example would be like extracting keywords figuring out what the main things to start with are. I can very easily go through open up a textbook, pick out all the headings and the bold

words and create a list of keywords. But

it's not hard to do. It just takes it just takes time, right? Um, or for example, even if I'm thinking about like lots of different uh ideas for something, okay, I want to be able to

figure out, you know, 10 different ideas or examples of something, if I just sit down and think about it, it's not hard and I have high level of confidence that I can think of a bunch of different examples or analogies. So, I might use

AI as a springboard and even if it's not accurate, I have full confidence that I can look at it and tell that it's not accurate and then I can modify it. It's

going to save me time, but it's not going to save me effort.

That would be a situation where I know I'm going to have to exert effort into thinking about this. I know I'm going to have to exert effort and trying to like piece things together and make sense of

it. I'm going from a point where I don't

it. I'm going from a point where I don't know something and I know that in order to know it, it's going to take me effort.

That's a situation where the outcome is only happening because of the effort, right? The gain is only being wrought

right? The gain is only being wrought because of the pain.

And so using AI is going to avoid the game because you're going to get to something that like for example uh a common thing like the analogies what I gave. So for

me if I'm thinking about lots of different analogies and I want to have like 10 different analogies to pick from I can do that and I can look at those analogies and I know that if it's not accurate I can pick up on it and then I

can modify it. And I've created hundreds thousands of different analogies for this topic before. So it's not something that I am going to really struggle with.

However, let's say I'm learning a topic for the first time. I'm not going to try to create an analogy using AI for that because the act of me trying to create

an analogy forces me to examine what I know and the accuracy of what I know and it's going to reveal gaps to me because I'm going to think it could be like this, but then I'm not sure like is it

this way? Do these two things truly

this way? Do these two things truly connect in that kind of way? it it

exposes the weakness and the process of having that weakness exposed and then filled is what generates that knowledge and that's what translates into meaningful expertise that provides

value. Now, if I use AI instead because

value. Now, if I use AI instead because it's like it's too hard for me to create an analogy and it's going to take me too long, then I'm going to have that analogy generated for me. I'm going to

read it and I'm going to think, oh yeah, that seems to make sense. And not only will I not know that it's not accurate, the fact that it doesn't like that it makes sense doesn't mean that that knowledge is going to stay in my mind

anyway because I didn't do my brain didn't do any of that neuronal work to create those connections. So those

connections don't exist. So it's it's the understanding trap. That trap that just because you've understood what you have read or heard that you will remember that. But if it was that easy,

remember that. But if it was that easy, learning would be ridiculously easy. But

to understand something is a very very low bar. Then turning that into

low bar. Then turning that into meaningful expertise that you can retain and then use is a completely different level. So in the learning process, it's

level. So in the learning process, it's lots of different micro decisions that you need to make and lots of different processes. Like for example, getting the

processes. Like for example, getting the big picture, right? Like do you get the big picture just by reading through the book and just trying to figure out what the main ideas are? Again, it's like how

much effort is that taking cognitively versus how much tedium is that? If it's

a high level of tedium but not a lot of effort, use AI. You know, like a lecture transcript, for example, like going through and figuring out the main ideas

of a YouTube video. I don't want to have to click through and listen every 4 seconds of a YouTube video. Like, it's

going to take time. I don't want to have to listen to something at three times speed to be able to pick out the main ideas. It's like I'd rather get a

ideas. It's like I'd rather get a transcript that's AI generated and then use AI to summarize what the main points are to be able to get that big picture because it's not going to be hard doing it the other way is going to take time,

right? Whereas the the mapping of the

right? Whereas the the mapping of the main connections, this is the thing that forces me to evaluate the importance of each thing and therefore that's the thing that I don't want to have to

bypass. Now let's take that a step

bypass. Now let's take that a step further. Let's say I've mapped

further. Let's say I've mapped everything out and I've looked at it and now I'm trying to see is there anything I've missed? Is there any major area or

I've missed? Is there any major area or major segment that I'm missing here? So,

in order to do that, I would have to go on to Google and like look through a bunch of different things, read through textbooks looking for perspective that I perspectives that I've missed. There's a

little bit of cognitive effort in doing that, but it's not that high, but it's a lot of time to be able to do that. So,

in this situation, there is a little bit of cognitive effort being bypassed if I use AI, but the time save on it is so [snorts] substantial that it's it's probably going to be worth it. So I

might go to uh chatbd and say hey this is how I've understood this topic.

Here's how I see everything like generally connecting together. Here's

the things that I think are the most important and this is basically the overall structure. Are there any major

overall structure. Are there any major perspectives here that I've missed or that I should be you know adding on to you know make this robust and then it will probably say yes you've missing this and this and this. So again that's

a time save. But then in terms of thinking okay what's the best way for it to connect to it? That's a decision that I want to make by myself. Even if

Chachip tells me you have missed this concept and that fits in here and here, I almost don't want to hear that. Like I

want to make that decision myself. You

have told me it fits here and here, but I want to challenge it. Maybe it fits here and here like another place instead. Maybe the concept you've given

instead. Maybe the concept you've given me isn't actually the best concept.

Maybe that just makes me think about an entire aspect of this topic that I've sort of neglected and then now I'm going to use chatb to generate some more information. So you might say, hey,

information. So you might say, hey, you've mentioned this concept, but actually to me it feels like it's actually an entire aspect of this topic that I've kind of been neglecting. What

are some other keywords that would fit within that branch of this topic? And it

will generate you a bunch of keywords and then you can use that to add to your layers. So you see, it's more of that

layers. So you see, it's more of that assistant to your thinking rather than the replacement to the thinking. Um, so

that's the that's the overall rule of thumb. And for beginners, my first

thumb. And for beginners, my first recommendation is to use it less than you would like to. It's easy for an expert to use AI and use it effectively.

It's difficult for a beginner who's not familiar with learning, like beginner at learning, the the process of learning. I

mean, uh not the not a beginner at a topic. If you're a beginner at learning,

topic. If you're a beginner at learning, it's really easy to fall into the trap of using AI thinking it is helping you, but actually you don't have your own

sense of calibration that's sharp enough to detect that you're cognitively bypassing something. And so that's

bypassing something. And so that's really dangerous. Uh, and so you want to

really dangerous. Uh, and so you want to avoid that at all costs.

>> Okay, cool. Yeah, that makes a lot of sense. So in terms of like next steps,

sense. So in terms of like next steps, I'll definitely be following this big picture mapping and then really focusing on the seeking part and seeing how that um leads to application and then through

this entire process I guess just monitoring how much effort I'm spending at each stage to see if down the line there could be AI applications that um may be relevant. But again, that's not the focus of this stage. It's more so

actually developing the ability to like learn and seek and form these connections. Um, and those will be the

connections. Um, and those will be the next steps.

>> Yeah. And again, just as the final thing is that if you are using AI in the wrong way, you will you will be able to tell eventually because you're going to test

yourself and you're going to realize you do not have that expertise. You do not have that recoil. And I've got so many students that uh, you know, like made these mind maps and whatever. And I look at it, I think, yeah, these connections kind of make sense, right? But then they

say, I've got terrible retention. I

can't I I can't remember like any of these things. you know, I can't really

these things. you know, I can't really seem to, you know, like solve these problems. My actual performance hasn't really improved. And then when I go into

really improved. And then when I go into the micro process, okay, how did you go about creating these connections? How

did you go about grouping the the ideas?

How did you go about, you know, thinking about this particular key term and comparing it with this other key term?

Why did you draw an arrow between these two things? The answer is just like,

two things? The answer is just like, well, basically, I just went to chatbt and I just said, how do I need to arrange this topic? What are the main ideas? How do I need to connect them

ideas? How do I need to connect them together? Why is this thing important?

together? Why is this thing important?

And all they did is they just completely offloaded all of it and they said, oh yeah, that makes sense. and they just drew it, right? And they they just became like a an extremely sophisticated

photocopier taking what the AI is telling them to do. Like it's funny, they became the machine. They just

simply followed the instructions that the, you know, chat beauty was giving them and they all they do is draw it onto a mind map. So very very very minimal cognitive effort. So, you're

going to be able to tell if the way that you're using it is not cognitively beneficial for you because the primary outcome you're aiming for, which is to have expertise that you can recall and

then use, you will not have.

>> Right? So, it's much less so about the specific technique itself. Sure, there's

some techniques that are probably more helpful, but it's more so the thinking that I'm doing as I'm learning about this topic and finding that relevance rather than writing down a particular thing.

>> Yeah. And that's why you should always be testing that level of thinking to find those gaps.

>> Yeah. Awesome. Well, I appreciate very much. I've learned a lot and I'm excited

much. I've learned a lot and I'm excited to apply this to my learning and I'll keep you updated on how the results come.

>> Cool. Sounds good.

>> Cool. So, that was the call with Julian.

I hope you found it useful. Let me know your thoughts in the comments below.

Really interested to see how people are finding this coaching format of videos.

If you are looking for a single master class on just all the things that you need to really start thinking about to become a better learner, then I'd recommend checking out this video here

where I do exactly that. Thanks so much for watching and I'll see you in this video.