Full Neovim Zettelkasten Workflow - Studying AI - Technical Note-taking

in this video I'm going to continue in this video I'm going to continue processing my notes that I took on this processing my notes that I took on this processing my notes that I took on this book so far using the ttle Casten method book so far using the ttle Casten method book so far using the ttle Casten method and this time I'm going to be using my and this time I'm going to be using my and this time I'm going to be using my neovim workflow so if I open up my daily neovim workflow so if I open up my daily neovim workflow so if I open up my daily note then we see here the these are the note then we see here the these are the note then we see here the these are the links that I created earlier and now I links that I created earlier and now I links that I created earlier and now I can just continue uh here and I did this can just continue uh here and I did this can just continue uh here and I did this by running the ZK day command this is a by running the ZK day command this is a by running the ZK day command this is a CLI that I wrote myself it has a few CLI that I wrote myself it has a few CLI that I wrote myself it has a few options that I did and you can find the options that I did and you can find the options that I did and you can find the source code on my GitHub it's called source code on my GitHub it's called source code on my GitHub it's called ttle cast and CLI you can use it if you ttle cast and CLI you can use it if you ttle cast and CLI you can use it if you want but it's very bespoke for my own want but it's very bespoke for my own want but it's very bespoke for my own system but I also have this command system but I also have this command system but I also have this command called ZK new and I'm I'm now going to called ZK new and I'm I'm now going to called ZK new and I'm I'm now going to move towards symbolic and Subs symbolic move towards symbolic and Subs symbolic move towards symbolic and Subs symbolic AI so I'm just going to create a note AI so I'm just going to create a note AI so I'm just going to create a note called ZK called ZK called ZK new symbolic AI this is going to create a note in my AI this is going to create a note in my inbox with the title symbolic Ai and inbox with the title symbolic Ai and inbox with the title symbolic Ai and then it adds that to my um and then it then it adds that to my um and then it then it adds that to my um and then it adds the markdown heading as well and if adds the markdown heading as well and if adds the markdown heading as well and if I close this I close this I close this now and if I open my daily note again we now and if I open my daily note again we now and if I open my daily note again we see that it has added this note to the see that it has added this note to the see that it has added this note to the bottom there so that is why I like bottom there so that is why I like bottom there so that is why I like to um this is why I use this CLI so I to um this is why I use this CLI so I to um this is why I use this CLI so I can create notes from the C from the can create notes from the C from the can create notes from the C from the command line and that it's automatically

command line and that it's automatically command line and that it's automatically added to my daily notes so I can keep added to my daily notes so I can keep added to my daily notes so I can keep track of when I created them so if I track of when I created them so if I track of when I created them so if I open up this note again symbolic Ai and open up this note again symbolic Ai and open up this note again symbolic Ai and here's the the text that I that I typed here's the the text that I that I typed here's the the text that I that I typed down okay so symbolic AI uses symbols and okay so symbolic AI uses symbols and relations between them to solve problems relations between them to solve problems relations between them to solve problems so it uses language if I'm not mistaken so it uses language if I'm not mistaken so it uses language if I'm not mistaken and the counterpart of this and the counterpart of this and the counterpart of this is sub is sub is sub symbolic sub symbolic symbolic sub symbolic symbolic sub symbolic Ai and Subs symbolic Ai and Subs symbolic Ai and Subs symbolic AI is inspired AI is inspired AI is inspired by by by biological biological biological brains using feedback to train AI okay so this subsymbolic AI is the okay so this subsymbolic AI is the opposite of symbolic Ai and Subs

opposite of symbolic Ai and Subs opposite of symbolic Ai and Subs symbolic in AI is inspired by biological symbolic in AI is inspired by biological symbolic in AI is inspired by biological brains so rather than using symbols brains so rather than using symbols brains so rather than using symbols you're actually using data and using you're actually using data and using you're actually using data and using feedback to Train II to learn to feedback to Train II to learn to feedback to Train II to learn to recognize written numbers for example recognize written numbers for example recognize written numbers for example that's the example that she uses in the that's the example that she uses in the that's the example that she uses in the book um here maybe I book um here maybe I book um here maybe I should maybe I can find find that in the should maybe I can find find that in the should maybe I can find find that in the PDF as well here this is the example PDF as well here this is the example PDF as well here this is the example that she uses so this is um that she uses so this is um that she uses so this is um a Subs symbolic AI is used for pattern a Subs symbolic AI is used for pattern a Subs symbolic AI is used for pattern recognition and then we're going to go recognition and then we're going to go recognition and then we're going to go into the perceptor on definition into the perceptor on definition into the perceptor on definition afterwards but the idea is that a Subs afterwards but the idea is that a Subs afterwards but the idea is that a Subs symbolic AI is able to take an image symbolic AI is able to take an image symbolic AI is able to take an image like this and then learn how to like this and then learn how to like this and then learn how to recognize the number eight that's an recognize the number eight that's an recognize the number eight that's an example of Subs symbolic example of Subs symbolic example of Subs symbolic AI so AI so AI so learning to perform tasks and the key idea is that it's tasks and the key idea is that it's inspired by biological brains so um in inspired by biological brains so um in inspired by biological brains so um in the next concept that's important to to the next concept that's important to to the next concept that's important to to understand here is understand here is understand here is then um the perceptron and then if I now run space

and then if I now run space ZN it will create and open this note in ZN it will create and open this note in ZN it will create and open this note in my ttle Casten system using the CLI and my ttle Casten system using the CLI and my ttle Casten system using the CLI and it should have also added that to my it should have also added that to my it should have also added that to my daily notes let me see yes here we go daily notes let me see yes here we go daily notes let me see yes here we go it's also added to my daily notes so I'm it's also added to my daily notes so I'm it's also added to my daily notes so I'm just just just demonstrating how I can use this CLI demonstrating how I can use this CLI demonstrating how I can use this CLI from within neovim as well this is why from within neovim as well this is why from within neovim as well this is why you should learn back and Linux well so you should learn back and Linux well so you should learn back and Linux well so you can do these things for yourself you can do these things for yourself you can do these things for yourself instead of relying on other people's instead of relying on other people's instead of relying on other people's plugins that you don't really understand plugins that you don't really understand plugins that you don't really understand so now I have the perceptron Note coming so now I have the perceptron Note coming so now I have the perceptron Note coming from the subs symbolic AI note and a from the subs symbolic AI note and a from the subs symbolic AI note and a perceptron is if I check out my perceptron is if I check out my perceptron is if I check out my notes mimics a neuron several neuron several impulses that will fire if a threshold impulses that will fire if a threshold impulses that will fire if a threshold is reached so what is meant by that is that reached so what is meant by that is that um this is basically a um this is basically a um this is basically a perceptron and it is mimicked by a perceptron and it is mimicked by a perceptron and it is mimicked by a neuron so in your your neuron so in your your neuron so in your your um your neurons in your brain they have um your neurons in your brain they have um your neurons in your brain they have dendrites which are inputs and then the dendrites which are inputs and then the dendrites which are inputs and then the Exxon the output is whether that neuron

Exxon the output is whether that neuron Exxon the output is whether that neuron will fire or not so if you have a will fire or not so if you have a will fire or not so if you have a certain type of input coming in and if certain type of input coming in and if certain type of input coming in and if the sum is then at a certain threshold the sum is then at a certain threshold the sum is then at a certain threshold the neuron will fire and a perceptron is the neuron will fire and a perceptron is the neuron will fire and a perceptron is the same so in the case of recognizing the same so in the case of recognizing the same so in the case of recognizing an an an eight then uh these grids have a eight then uh these grids have a eight then uh these grids have a numerical numerical numerical representation and if the sum of that representation and if the sum of that representation and if the sum of that input then is uh at a certain threshold input then is uh at a certain threshold input then is uh at a certain threshold then it is likely that it's a number then it is likely that it's a number then it is likely that it's a number eight and by doing that over and over eight and by doing that over and over eight and by doing that over and over again then you can Benchmark the the again then you can Benchmark the the again then you can Benchmark the the weights of these perceptrons and when weights of these perceptrons and when weights of these perceptrons and when you link a bunch of perceptrons together you link a bunch of perceptrons together you link a bunch of perceptrons together then you create something uh that's then you create something uh that's then you create something uh that's called a neural network called a neural network called a neural network and that's going to be the next note and that's going to be the next note and that's going to be the next note that I have that I have that I have so I mean this is already getting a bit so I mean this is already getting a bit so I mean this is already getting a bit deeper than I initially thought that I deeper than I initially thought that I deeper than I initially thought that I would go I mean I'm I'm still just would go I mean I'm I'm still just would go I mean I'm I'm still just wrapping my mind around the definition wrapping my mind around the definition wrapping my mind around the definition of artificial intelligence and what it of artificial intelligence and what it of artificial intelligence and what it is and what it does but I do think it is is and what it does but I do think it is is and what it does but I do think it is useful to learn about sub symbolic and useful to learn about sub symbolic and useful to learn about sub symbolic and symbolic symbolic symbolic Ai and it's a very important fact that Ai and it's a very important fact that Ai and it's a very important fact that this is the sub symbol IC AI approach is this is the sub symbol IC AI approach is this is the sub symbol IC AI approach is inspired by the biological brains and inspired by the biological brains and inspired by the biological brains and therefore has a more call it therefore has a more call it therefore has a more call it organic uh organic uh organic uh learning

learning learning approach yeah approach yeah approach yeah um uses a more um uses a more um uses a more organic learning approach uh organic learning approach uh organic learning approach uh using several using several using several iterations of training and adjusting the iterations of training and adjusting the iterations of training and adjusting the perceptron waits waits to get more accurate to get more accurate to get more accurate results and again these are all my words results and again these are all my words results and again these are all my words this is this is this is just this is just my words my thinking just this is just my words my thinking just this is just my words my thinking in this note and which I then can later in this note and which I then can later in this note and which I then can later use for example in an essay so let's use for example in an essay so let's use for example in an essay so let's open symbolic AI again use the symbols open symbolic AI again use the symbols open symbolic AI again use the symbols and relations between them to solve and relations between them to solve and relations between them to solve problems and now I wonder is symbolic AI problems and now I wonder is symbolic AI problems and now I wonder is symbolic AI than are large language models than a than are large language models than a than are large language models than a form of symbolic AI so I'm going to go form of symbolic AI so I'm going to go form of symbolic AI so I'm going to go to to to claw and I'm going to create a new chat claw and I'm going to create a new chat claw and I'm going to create a new chat so um symbolic so um symbolic so um symbolic Ai and then Subs symbolic AI I'm AI I'm studying AI using using settle cast studying AI using using settle cast studying AI using using settle cast method I wrote these notes is this uh a correct way of

notes is this uh a correct way of phrasing it so as you see I like to phrasing it so as you see I like to phrasing it so as you see I like to verify with claw if what I'm writing verify with claw if what I'm writing verify with claw if what I'm writing down is actually correct especially down is actually correct especially down is actually correct especially because I'm a content creator so it's because I'm a content creator so it's because I'm a content creator so it's very important that I sort of fact check very important that I sort of fact check very important that I sort of fact check the knowledge that I put out there and the knowledge that I put out there and the knowledge that I put out there and then also um is it correct then also um is it correct then also um is it correct correct uh according to correct uh according to correct uh according to these definitions uh it seems that definitions uh it seems that llms fall under the llms fall under the llms fall under the symbolic AI category is that correct okay so llms do fall under the subs okay so llms do fall under the subs symbolic AI category so that's symbolic AI category so that's symbolic AI category so that's interesting because I um initially interesting because I um initially interesting because I um initially thought it would be symbolic and it's thought it would be symbolic and it's thought it would be symbolic and it's good that I fact check this so let's see good that I fact check this so let's see good that I fact check this so let's see if I can this helps me understand more if I can this helps me understand more if I can this helps me understand more so symbolic works with explicit rules in so symbolic works with explicit rules in so symbolic works with explicit rules in symbolic symbolic symbolic manipulation uses human readable symbols manipulation uses human readable symbols manipulation uses human readable symbols and logical and logical and logical rules okay rules okay rules okay yeah reasoning process trans transparent yeah reasoning process trans transparent yeah reasoning process trans transparent and can be followed step by step ah okay and can be followed step by step ah okay and can be followed step by step ah okay yeah that's a key difference because nobody really knows how llms because nobody really knows how llms work I mean they are trained but you

work I mean they are trained but you work I mean they are trained but you can't can't can't actually um understand the actually um understand the actually um understand the decisionmaking process because it is um decisionmaking process because it is um decisionmaking process because it is um abstracted away from human abstracted away from human abstracted away from human language it's more like following a language it's more like following a language it's more like following a flowchart or a decision flowchart or a decision flowchart or a decision tree and then Subs symbolic works with p tree and then Subs symbolic works with p tree and then Subs symbolic works with p patterns and statistical relationships patterns and statistical relationships patterns and statistical relationships learns from learns from learns from data yeah uses distributive weight data yeah uses distributive weight data yeah uses distributive weight representations reasoning process is representations reasoning process is representations reasoning process is often opaque black box is what I meant often opaque black box is what I meant often opaque black box is what I meant to say correct okay so that gives me some more correct okay so that gives me some more insights so I'm going to expand my insights so I'm going to expand my insights so I'm going to expand my symbolic AI note with uh it it does tie symbolic AI note with uh it it does tie symbolic AI note with uh it it does tie into related to algorithms so into related to algorithms so into related to algorithms so symbolic AI uses transparent reasoning symbolic AI uses transparent reasoning symbolic AI uses transparent reasoning and human language it's more like a flow and human language it's more like a flow and human language it's more like a flow chart Claude described it as traditional and um decision trees rule based systems and um decision trees rule based systems transparent and can be followed step by step that's an important step that's an important one can be followed step by

one can be followed step by one can be followed step by step it's more like it's more like a step it's more like it's more like a step it's more like it's more like a flow flow flow chart called described it as traditional chart called described it as traditional chart called described it as traditional Ai and then Subs symbolic AIS Ai and then Subs symbolic AIS Ai and then Subs symbolic AIS so then I have a note that will say so then I have a note that will say so then I have a note that will say llms fall into the sub symbolic AI category creating a new note about category creating a new note about this this this and the reason why is because llms learn and the reason why is because llms learn and the reason why is because llms learn patterns from training data patterns from training data patterns from training data rather than following explicit rules rather than following explicit rules rather than following explicit rules they use neuron they use neuron they use neuron networks okay so everything that uses a networks okay so everything that uses a networks okay so everything that uses a neural network is Subs symbolic AI neural network is Subs symbolic AI neural network is Subs symbolic AI That's an important idea That's an important idea That's an important idea here here here [Music] [Music] [Music] so anything that uses a neural so anything that uses a neural so anything that uses a neural network is a sub symbolic AI okay and sometimes that is just the note okay and sometimes that is just the note sometimes the my notes are just the sometimes the my notes are just the sometimes the my notes are just the titles so let me just verify this with titles so let me just verify this with titles so let me just verify this with Claud is this correct

while neural networks are primary while neural networks are primary example of sub symbolic a distinction example of sub symbolic a distinction example of sub symbolic a distinction between symbolic and sub symbolic is between symbolic and sub symbolic is between symbolic and sub symbolic is more about the approach to processing more about the approach to processing more about the approach to processing information rather than the specific information rather than the specific information rather than the specific technology used okay well I'm happy with this I think okay well I'm happy with this I think this is clear for my thinking so I'm this is clear for my thinking so I'm this is clear for my thinking so I'm going to just keep that in okay so that going to just keep that in okay so that going to just keep that in okay so that can go llms fall into the subs symbolic can go llms fall into the subs symbolic can go llms fall into the subs symbolic AI category that I also don't need to AI category that I also don't need to AI category that I also don't need to expand that a bit more well maybe I expand that a bit more well maybe I expand that a bit more well maybe I could write could write could write um let me see why do they fall into that um let me see why do they fall into that um let me see why do they fall into that because they learn patterns from because they learn patterns from because they learn patterns from draining draining draining data yeah and they use neural and they use neural networks adjust weights and don't networks adjust weights and don't networks adjust weights and don't manipulate manipulate manipulate symbols okay so rather than using symbols okay so rather than using symbols okay so rather than using symbols they use statistical patterns

okay so those are my definitions of okay so those are my definitions of symbolic and subsymbolic symbolic and subsymbolic symbolic and subsymbolic AI I have my definition of a perceptron AI I have my definition of a perceptron AI I have my definition of a perceptron and again I'm just I'm currently just and again I'm just I'm currently just and again I'm just I'm currently just interested in definitions and not going interested in definitions and not going interested in definitions and not going too deep into all of the details about too deep into all of the details about too deep into all of the details about everything even though I just everything even though I just everything even though I just did so those are a few else that I did so those are a few else that I did so those are a few else that I processed now let me see is processed now let me see is processed now let me see is there anything that I want there anything that I want there anything that I want to now I change to my next notebook to now I change to my next notebook to now I change to my next notebook here and now I go into neural networks here and now I go into neural networks here and now I go into neural networks so that's an interesting so that's an interesting so that's an interesting definition um that I also want to add definition um that I also want to add definition um that I also want to add here but I don't have to add it here so here but I don't have to add it here so here but I don't have to add it here so it's just going to be a a new note I suppose CK suppose CK new um neural new um neural new um neural networks so my neural networks note has networks so my neural networks note has networks so my neural networks note has this in it

okay so this goes into more about how okay so this goes into more about how neural networks actually uh produce neural networks actually uh produce neural networks actually uh produce results so let me just finish writing results so let me just finish writing results so let me just finish writing what I have here okay yeah so this note I wrote here okay yeah so this note I wrote about neural NE networks is basically about neural NE networks is basically about neural NE networks is basically going into the inner workings of neuron going into the inner workings of neuron going into the inner workings of neuron networks they're collection of networks they're collection of networks they're collection of perceptrons and let me just add that to perceptrons and let me just add that to perceptrons and let me just add that to my can my can my can I um add word to spell list G okay so I um add word to spell list G okay so I um add word to spell list G okay so collection of perceptions but instead of collection of perceptions but instead of collection of perceptions but instead of 01 they compute a value between zero and 01 they compute a value between zero and 01 they compute a value between zero and one called activation yeah so these are one called activation yeah so these are one called activation yeah so these are my own words but but if I search for my own words but but if I search for my own words but but if I search for Activation here let me here let me see yeah so here this um this is see yeah so here this um this is see yeah so here this um this is basically what it means

that if the sum that a unit Compu is low that if the sum that a unit Compu is low the units activation is close to zero if the units activation is close to zero if the units activation is close to zero if the sum is high the activation is close the sum is high the activation is close the sum is high the activation is close to to to one so instead of the one so instead of the one so instead of the neuron that yeah so in this in this yeah so in this in this image this is the input then the hidden image this is the input then the hidden image this is the input then the hidden layer which is the term that I layer which is the term that I layer which is the term that I defined um no I didn't Define it but I don't um no I didn't Define it but I don't think I need to for my own studies but

think I need to for my own studies but think I need to for my own studies but the idea is that this hidden layer then the idea is that this hidden layer then the idea is that this hidden layer then computes several computes several computes several outputs and every output layer so each outputs and every output layer so each outputs and every output layer so each of these layers get a um number so it's of these layers get a um number so it's of these layers get a um number so it's going to be if if you take zero to one going to be if if you take zero to one going to be if if you take zero to one it's probably a a 0.9 confidence that it it's probably a a 0.9 confidence that it it's probably a a 0.9 confidence that it point six confidence that it a zero but point six confidence that it a zero but point six confidence that it a zero but it's a point it's a point it's a point uh 0.01 confidence that it is a two for uh 0.01 confidence that it is a two for uh 0.01 confidence that it is a two for example because it's very different so example because it's very different so example because it's very different so all of these are going to have a number all of these are going to have a number all of these are going to have a number between zero and one and then eight is between zero and one and then eight is between zero and one and then eight is going to be the highest and that going to be the highest and that going to be the highest and that activation of that eth layer is then activation of that eth layer is then activation of that eth layer is then going to be um a another output of this going to be um a another output of this going to be um a another output of this network and then from that you can network and then from that you can network and then from that you can deduce which one has the highest number deduce which one has the highest number deduce which one has the highest number and therefore this image is is most and therefore this image is is most and therefore this image is is most likely to be an eight and this is what a likely to be an eight and this is what a likely to be an eight and this is what a neural network is it has several neural network is it has several neural network is it has several layers of um perceptrons perceptron like layers of um perceptrons perceptron like layers of um perceptrons perceptron like simulated simulated simulated neurons and they take the input process neurons and they take the input process neurons and they take the input process them then output a digit and these them then output a digit and these them then output a digit and these digits are then processed in order to digits are then processed in order to digits are then processed in order to come to a confidence level of confidence come to a confidence level of confidence come to a confidence level of confidence of the of the of the output so that is called activation all output so that is called activation all output so that is called activation all of the layers lead to Output layers and of the layers lead to Output layers and of the layers lead to Output layers and the one with the highest activation

the one with the highest activation the one with the highest activation wins this is known as classification so wins this is known as classification so wins this is known as classification so the classification of this image is the classification of this image is the classification of this image is going to be going to be going to be eight it's hard to know eight it's hard to know eight it's hard to know beforehand be how many layers are needed beforehand be how many layers are needed beforehand be how many layers are needed it's usually trial and it's usually trial and it's usually trial and error so it's actually comes later I think so it's actually comes later I think that's more that's more that's more logical so this is called logical so this is called logical so this is called a hidden layer but this can be more a hidden layer but this can be more a hidden layer but this can be more layers there can be it maybe there's a layers there can be it maybe there's a layers there can be it maybe there's a layer to process the top half and then layer to process the top half and then layer to process the top half and then the bottom half and then one that the bottom half and then one that the bottom half and then one that combines it and the more layers there combines it and the more layers there combines it and the more layers there are the deeper the neural are the deeper the neural are the deeper the neural network so that is why you can refer to network so that is why you can refer to network so that is why you can refer to networks as having networks as having networks as having depth and again it's hard to know how depth and again it's hard to know how depth and again it's hard to know how many layers are needed usually trial and many layers are needed usually trial and many layers are needed usually trial and error so so basically the definition error so so basically the definition error so so basically the definition here is the colle a neuron network is a here is the colle a neuron network is a here is the colle a neuron network is a collection of collection of collection of perceptrons and rather than just true or perceptrons and rather than just true or perceptrons and rather than just true or false they output numbers and that's false they output numbers and that's false they output numbers and that's basically all I need to know um for my basically all I need to know um for my basically all I need to know um for my current stage of of understanding so I current stage of of understanding so I current stage of of understanding so I can then cross out this note and then next my my next note that note and then next my my next note that I have is I have is I have is machine machine machine learning so do I already

learning so do I already learning so do I already have let me see no I don't have a a different note see no I don't have a a different note about machine learning yet so maybe I about machine learning yet so maybe I about machine learning yet so maybe I will just uh create a node called will just uh create a node called will just uh create a node called machine machine machine learning seek a new machine learning seek a new machine learning seek a new machine learning and I know this one is related learning and I know this one is related learning and I know this one is related to machine learning so why is it not opening my learning so why is it not opening my [Music] links links yeah okay so I want to link it to that yeah okay so I want to link it to that yeah okay so I want to link it to that note and here we go now it's thinking okay and here we go now it's thinking okay machine learning is a branch of AI where machine learning is a branch of AI where machine learning is a branch of AI where machines learn from their own data or machines learn from their own data or machines learn from their own data or experiences that was one note that I experiences that was one note that I experiences that was one note that I already already already wrote the next one or this note actually wrote the next one or this note actually wrote the next one or this note actually contains the following text

so I really like this definition so I really like this definition algorithms that enable computers to algorithms that enable computers to algorithms that enable computers to learn from data so yeah I mean this node basically data so yeah I mean this node basically says something says something says something similar and it contains similar and it contains similar and it contains this uh thing that we wrote in Claude so this uh thing that we wrote in Claude so this uh thing that we wrote in Claude so I kind of like this I I will keep that I kind of like this I I will keep that I kind of like this I I will keep that around but this is going to be my Mach around but this is going to be my Mach around but this is going to be my Mach machine learning main machine learning main machine learning main note and I wrote this in my own words note and I wrote this in my own words note and I wrote this in my own words because I remember I remember writing because I remember I remember writing because I remember I remember writing this and I don't need to refine this this and I don't need to refine this this and I don't need to refine this more this is my own definition that I more this is my own definition that I more this is my own definition that I did jued from the book so I'm going to did jued from the book so I'm going to did jued from the book so I'm going to keep this uh like this keep this uh like this keep this uh like this then the next node is called General okay so this note um this definition is okay so this note um this definition is about General AI another definition that about General AI another definition that about General AI another definition that I wanted to yeah lock down an AI That's

I wanted to yeah lock down an AI That's I wanted to yeah lock down an AI That's equal or better than human intelligence equal or better than human intelligence equal or better than human intelligence in most ways also known as AGI so that's in most ways also known as AGI so that's in most ways also known as AGI so that's another definition I wanted to lock down another definition I wanted to lock down another definition I wanted to lock down and the and that is basically uh the end and the and that is basically uh the end and the and that is basically uh the end of the noes that I have so far so now of the noes that I have so far so now of the noes that I have so far so now now I now I now I have all of these notes on my daily note have all of these notes on my daily note have all of these notes on my daily note currently but I want to also have I also currently but I want to also have I also currently but I want to also have I also need to add these to need to add these to need to add these to the book note that I had so the book note that I had so the book note that I had so here I can just yank all of these because they all just yank all of these because they all came from came from came from the same note so I can just paste these the same note so I can just paste these the same note so I can just paste these in and then what I do is in and then what I do is in and then what I do is I I I um prean that with a let me see contrl V okay so now everything has a button in V okay so now everything has a button in front of it okay so these are all notes front of it okay so these are all notes front of it okay so these are all notes that are coming from this one that are coming from this one that are coming from this one book and then there are a couple of book and then there are a couple of book and then there are a couple of links that I want to make but this is links that I want to make but this is links that I want to make but this is basically what you see me doing now is basically what you see me doing now is basically what you see me doing now is just the writing process and getting it just the writing process and getting it just the writing process and getting it down into Vim because I really like down into Vim because I really like down into Vim because I really like switching Windows using vim and such switching Windows using vim and such switching Windows using vim and such like but usually the the linking itself

like but usually the the linking itself like but usually the the linking itself I would do from obsidian so symbolic Ai I would do from obsidian so symbolic Ai I would do from obsidian so symbolic Ai and Subs symbolic AI are definitely and Subs symbolic AI are definitely and Subs symbolic AI are definitely related so I will link this to sub related so I will link this to sub related so I will link this to sub symbolic AI then AI then and this one had the perceptron link and this one had the perceptron link and this one had the perceptron link already so there are already a few links already so there are already a few links already so there are already a few links there that's good uh LMS fall into the there that's good uh LMS fall into the there that's good uh LMS fall into the subs symbolic AI category well that one subs symbolic AI category well that one subs symbolic AI category well that one has a has a has a backlink from this one so that's backlink from this one so that's backlink from this one so that's fine neural anything I use in neural fine neural anything I use in neural fine neural anything I use in neural network yes okay so everything that's network yes okay so everything that's network yes okay so everything that's linked I'm just going to move straight linked I'm just going to move straight linked I'm just going to move straight to my settle cast because all of these to my settle cast because all of these to my settle cast because all of these notes end up in my inbox you see here notes end up in my inbox you see here notes end up in my inbox you see here here's my here's my here's my inbox and now I can just start moving inbox and now I can just start moving inbox and now I can just start moving those to my settle cast as I go along so those to my settle cast as I go along so those to my settle cast as I go along so move this to T move this to T move this to T Casten and this can be moved because Casten and this can be moved because Casten and this can be moved because it's it's it's linked perceptron is linked perceptron is linked perceptron is linked moving linked moving linked moving that this one is linked um this one is linked linked um this one is linked neural networks this one also has neural networks this one also has neural networks this one also has links and this can be linked to deep links and this can be linked to deep links and this can be linked to deep learning or deep neural networks but I learning or deep neural networks but I learning or deep neural networks but I think this is fine for now so I can just

think this is fine for now so I can just think this is fine for now so I can just move that to the set of cast as well move that to the set of cast as well move that to the set of cast as well Machine Machine Machine learning that one I want to have on my learning that one I want to have on my learning that one I want to have on my main artificial intelligence main artificial intelligence main artificial intelligence note here machine machine learning and this one can go because I learning and this one can go because I learning and this one can go because I already linked to the machine learning already linked to the machine learning already linked to the machine learning node and then I want to have symbolic node and then I want to have symbolic node and then I want to have symbolic Ai and Subs symbolic AI here okay that's basically the here okay that's basically the definitions that I am most interested in definitions that I am most interested in definitions that I am most interested in and then the and then the and then the perceptron definition is linked here so perceptron definition is linked here so perceptron definition is linked here so if I need it then I will naturally end if I need it then I will naturally end if I need it then I will naturally end up there and then finally General AI is up there and then finally General AI is up there and then finally General AI is also an important definition also an important definition also an important definition so so so uh that one I also want to have here General AI so so now I have here General AI so so now I have processed a bunch of what we call processed a bunch of what we call processed a bunch of what we call fleeting notes from my notebook here and fleeting notes from my notebook here and fleeting notes from my notebook here and now they are in my system so I actually now they are in my system so I actually now they are in my system so I actually uh cross these out I just I just take my uh cross these out I just I just take my uh cross these out I just I just take my pencil and cross these out so I know pencil and cross these out so I know pencil and cross these out so I know that these are actually processed and in that these are actually processed and in that these are actually processed and in my system they are all linked and and my system they are all linked and and my system they are all linked and and added to my tle cast now in the next one added to my tle cast now in the next one added to my tle cast now in the next one that I would like to do is that

that I would like to do is that that I would like to do is that I um am going to since I'm building this I um am going to since I'm building this I um am going to since I'm building this sort of Mind map about AI in my mind now sort of Mind map about AI in my mind now sort of Mind map about AI in my mind now I'm going to be actually drawing this I'm going to be actually drawing this I'm going to be actually drawing this out using using excal draw and that's out using using excal draw and that's out using using excal draw and that's going to be the subject of the next going to be the subject of the next going to be the subject of the next video so let me know if you like this if video so let me know if you like this if video so let me know if you like this if you're into note taking you should you're into note taking you should you're into note taking you should definitely check out my free community definitely check out my free community definitely check out my free community it's all about no taking I have tons of it's all about no taking I have tons of it's all about no taking I have tons of courses on obsidian and how to use the C courses on obsidian and how to use the C courses on obsidian and how to use the C Castle method check it out I think you Castle method check it out I think you Castle method check it out I think you might like it see you in the next one