If something's on this board, it has to get done.


The Applied Data Science Course is epic! | Learning Intelligence 42

Look, I've got all these things that I'm going to do today.


This is me. DS course week four lectures.


Well, I've actually done that so you can cross that off.


What happens is with the drift trikes, it also interconnects into your one that you've been waiting to do.


Yeet.


What is this?


We've been doing some research and we found...


Look. What is that?


He has a second one at the back.


What are these? Drift trikes.


So what's the plan? You going to get them?


How much are they?


Do they work?


I said I had 104 in my wallet. I'll come by three.


The time is almost two.


So we're going to get there by three.


I'm going back to study.


You guys let me know when you've got the trikes.


And then we'll trike it up.


Sounds good.


Never a dull moment in this place.


This is a really cool activity.


I just finished reading the article, the end of theory.


And the crux of the article essentially,


it goes through and saying,


is scientific theory,


is it obsolete?


Because we've got so much data right now.


And what I mean by theory is imagining


something in the world and then going out


and trying to prove the theory.


What the article stated,


and it's interesting that it's written in 2008,


so ten years ago, right?


And imagine how much data is there now.


Is that places like Google succeeded


using just applied mathematics


on large scales of data.


It means they didn't have the


theory to begin with.


They just collected the data, applied math to it,


and pulled the theories out


of the data that was in the real world.


And so what this activity is,


is ask for the students


to create a discussion around the article.


Do we agree or disagree?


And I wrote agree because


I love data, because I love mathematics,


because I love statistics and I love technology.


But what they do, what the really cool thing is,


is they show you examples of other students


who disagree with you.


A student from Canada disagrees with you.


Google's going off there.


I do not think we have come to the end of theory.


And then you can give them feedback on their response.


So that's a really good answer.


Music


It's no accident that every good paper


includes the phrase


more study is needed.


There is always more to learn.


And so right, this article agrees


with what I keep learning.


There is always something else to learn


no matter how deep you get into something.


Once new data arises,


once it comes in,


the world is always changing.


We don't fully understand it.


And more information comes in.


Where is this coming from?


I'm reading an article called Science is Broken.


I'm not sure if you can see it.


Science isn't broken, sorry.


It's one of the discussions that came after


I just did in week four.


And it goes through the concept of P-hacking.


There's a cool tool on the


article, I'll link it below,


where you can change the results


or change the inputs into a study


and get a better P-value, which is


whether it's statistically significant


or not, based on the


input you put in. The article talks about


how a lot more papers are being


retracted because they aren't statistically


sound or the results aren't reproducible.


And there's a lot of arguments


saying how flawed science is, but


the main argument is that science


isn't broken, it's just really hard.


If you're a researcher and you have an


idea in your head, you almost have to believe


that your idea is right, no matter


what the idea is. Because if


it's hard to research the wrong idea


or hard to research something that's


not true, if you're


trying to learn something, you have to


believe it's true, otherwise where will your motivation


come from? That's what science is all about.


It's about searching for the truth,


accepting new data as it comes along,


and sometimes,


most of the time actually, you will be


wrong as you learn new things.


That's what life's about actually, it's not just


science, it's just going through


the world. As you experience new things,


your old ideas might have


to die. Right? It's


hard and it sucks. There's a quote


here, once an idea becomes


fixed, it's difficult to remove


from conventional wisdom. So true.


Alright, assignment 4, hypothesis


testing. Hold on, can you hear that?


What do we have here?


Let's go check that out.


Assignment 4 can wait.


What are those?


Oh no way.


Hello sir.


You got your bikes.


Daniel, what are you about to do? We're about to


rock and roll this future


mode of transportation.


Ready? Yeah.


Music


Big day on tour.


Bayes Networks, Hidden Markov Models and How I Wake Up | Learning Intelligence 10

What's going on guys? Welcome to Learning Intelligence episode 10. So this morning I


tried to wake up a bit earlier than usual and it went alright but I woke up extremely


tired so I figured I need to do something to wake myself up. So 20 push-ups, 20 jumping


jacks, 20 squats and I'm going to get into this today. Two quick tips for waking up.


So first of all, move around, do some movement, do some push-ups, do some jumping jacks, do


some bodyweight squats, go for a walk or something but before that drink 500ml to a litre of


water because when you sleep, you're in your bed, your body it sweats, you get dehydrated


and being dehydrated is not a good place to be. So rehydrate, get moving, wake up, get


learning. That's what I'm about to do. Decided to head into the library today. I find more


and more often when you're surrounded by people in a good environment doing this similar thing


to what you are and this goes for the bad case as well. Like for example when you go


to gym, you're surrounded by people working out so you're more likely to work out. When


you go to the library, you're surrounded by people who are studying so you're more likely


to study and vice versa goes for the bad things. If you're surrounded by people who are doing


not so great activities, well then you're more likely to participate in those non-good


activities even though at the time you might think that they're the right thing to do because


everyone around you is doing the same thing. But I'm going to go to the library for a few


hours, work through the Udacity artificial intelligence and anatomy degree classes and


look what happened to my water bottle. I was getting out of the car hopelessly and it shattered


everywhere. I had to pick up all these shards of glass and put it into a little cardboard


box that I had in the car. I didn't have any plastic bag or anything. I picked up most


of them but I couldn't get the really small ones. There's some glass shards on the sidewalk


here and I'm very sorry about that. I'm going to go tell someone. That's what you get for


using glass water bottle. I just got back from the library. It was actually quite packed.


Like there wasn't many places for me to take a seat. I had to do a few laps before I could


find somewhere to sit down and do some study. That's a good sign. I like seeing people in


the library. I'm glad I went because I got some really good study done. I went through


the past two days have been two days where those events happen where it just deters you


from what you actually wanted to do. For my example, I set out six tasks I want to do


the next day the night before with the number one priority being the first task and I don't


do anything else other than the first one. I struggled to even get the first one done


the past two days and so they've carried over on the subsequent days. Going to the library


was a real good chance to catch up on those things I was planning to do yesterday but


someone smarter than me I read somewhere said whatever you set out to plan to do in your


new time frame readjust it by about 50% and that's an accurate goal of where you'll end


up unless you're really good and you just managed to punch things out. I wrote down


because I went through so many things. I wrote down what class I went through. Essentially


I'm working towards the final project of term one of the Udacity artificial intelligence


nanodegree which is using hidden Markov models to detect American Sign Language so different


signals different gestures in American Sign Language. That's going to be really fun. I'm


really excited about this module actually the first class was on probability the second


class is on Bayes nets and then third class was on inference and Bayes nets and now I'm


up to about three quarters away through the hidden Markov models class and I'm listening


to all the videos on 1.5 speed I go through them really quickly the first time around


and then I go back through them to to resubmit knowledge that's how I've done it the past


few past few modules and I'm really enjoying that process of doing it. Today has definitely


been a productive day I completed these four classes so probability Bayes nets inference


and Bayes nets and hidden Markov models was the one I just finished up there and now my


next task is to use HMMS to recognize American Sign Language but as I said before I'm going


to go back through those four classes probability Bayes nets inference and Bayes nets and hidden


Markov models to gain a deeper understanding as well as I've got a fair few readings that


I need to get through so I probably won't start the project for at least another week


or so that'll give me that'll still give me 12 days to work on the project that's the


goal anyway. All this talk of Bayes networks probability hidden Markov models and I haven't


even given an example yet I just told you what I've done so I've written some notes


here about what my overview of the two main concepts in in those four lectures that I


went through probability Bayes nets Bayes nets inference and hidden Markov models so


first of all Bayes network well let's start a probability so probability at a very basic


level is the chances of something happening what is the likely outcome of an event for


example a coin toss if I toss a coin what is the likelihood that it appears heads and


tails every time it's a 50% chance of landing on either side if I do it 10 times 5 out of


10 times I'll get heads 5 out of 10 times I'll get tails now that won't always happen


sometimes we get 7 heads 3 tails 8 heads 2 tails vice versa what not but the longer you


do it out say for we we toss a coin a hundred thousand times it's more than likely that


it's going to converge into a normal probability distribution so 50,000 heads 50,000 tails


or very close to that that ratio 50-50 this is a very simple overview of probability what's


a Bayes network a Bayes network is essentially a graph showing the chances of one thing occurring


based on the chances of another thing occurring let's use the Monty Hall problem as an example


you never heard of it Monty Hall is a game show host the classic problem is there's three


doors and you can choose one door two of the doors have a goat behind it I'll put a picture


here so you can see and one of the doors have a car behind it say you choose door number


one Monty Hall will then open up door number two and reveal that door number two has a


goat behind it so now you know definitely that door number two has a goat behind it


and what the host does is he tries to trick you and say oh well he pretends he tries to


trick you do you want to switch from door number one to door number three and what is


what would this like in a Bayes network when you choose door number one you have a 33%


chance of choosing the car and then when you open up door number two it takes away the


fact that door number two is an option anymore so now you have a choice between door number


one and door number three should you stay with door number one or switch to door number


three so because door number two was open it increases the likelihood that door number


three has a car behind it by 33% so essentially the probability from the chances of door two


go to the chances of door three long story short you should always switch because when


you choose door number one and you have the option of switching to door number three switching


means that you've essentially picked two doors instead of just one so the Bayes network of


this might look like a graph showing you the effects of opening door number two had on


the probability of door number three what's a hidden mark of model or the academic definition


is a statistical tool used for modeling generative sequences characterized by a set of observer


observable sequences before we talk about hidden Markov models an example of a Markov


model would be deciding or finding the likelihood of tomorrow being sunny based on today being


sunny using say for example a hundred days worth of previous weather history or what's


the likelihood of tomorrow being rainy based on today being sunny based on a hundred hundred


days of weather history and a hidden Markov model would be say you worked in a windowless


room say this room had no window and I was I had no previous weather data and I was trying


to predict whether tomorrow was going to be sunny but I didn't know what today was so


how how could I get this information that's when I can go to other sources and I can use


the fact to say for example people are walking around wearing sunglasses I can probably infer


that it was sunny because people have sunglasses on similarly if they were using umbrellas


I could probably infer the fact that it was raining so that's where I could get that would


be my observable data and in the case of a hidden Markov model I can observe the fact


that people are wearing sunglasses and using umbrellas can use that information to generate


a model that's predictive of the next day's weather so that's where the hidden part comes


in of hidden Markov models or HMMs the fact that I can't see a previous history of weather


but I can observe other data the direct information of what I'm trying to predict is hidden but


there is still observable information that is related to the hidden information or the


information I'm trying to predict that's my understanding of both those things so far


Bayes-Nets, Hidden Markov Models and Probability I just gave some really simple examples of


course you can go as deep as you want with this like the rabbit hole keeps going with


this sort of stuff it's actually quite complex once you once you get down into the deepness


of it but that's what makes it so interesting so exciting otherwise I'll link some Eli5


documents of what I just went through that's where I learned most of this stuff from I


did watch the lectures and have information overload because it was probably at a higher


level than what I'm at just yet so I use the Eli5 or the explain like I'm five documents


to just sort of give me that foundation knowledge and then when I go back through through the


lectures for the second time hopefully it does that like that second coat of paint makes


everything all shiny and smooth guess what I'm going to Melbourne tomorrow I'm gonna


pack myself get it ready it's pretty late at night it's about 830 or so but otherwise


today I spent most of the day editing a video for Learning Intelligence episode 9 so that


should be up now now that you're watching Learning Intelligence episode 10 and before


I go to bed I'm gonna finish off with a few readings from this this will definitely put


me to sleep and so I'm trying to take a new approach to when I'm reading before bed most


of the time I sort of just get in bed and then I just read a few pages of something


it has recently been this and this is aside from this book Game of Thrones is the longest


books I've ever actually read so I'm up to book 2 I've watched all the TV series it's


my favorite show apart from Mr Robot of course but otherwise I got a fair few readings to


do in here next week's goals are going to be go over the classes I went over this week


or re-go back over them and start getting ready for project 4 so the final project in


the artificial intelligence nanodegree but otherwise I'm gonna go pack my suit and I'm


gonna be out for the weekend so this is the last clip in Learning Intelligence episode


10 if you want to see anything in a future video at all leave a comment below otherwise


thank you so much for watching and we'll catch you next week.


How does Stable Diffusion work? – Latent Diffusion Models EXPLAINED

Hey, have you been on Twitter during the last weeks?


Or were you on holidays like me?


Because if you were on Twitter, there is zero chance that you weren't hit by somebody posting


about stable diffusion, just look at all of this.


What is stable diffusion, you ask from your hammock?


Well, stable diffusion is an open source alternative to Dali 2.


So unlike Dali 2, which is hidden behind an API and a paywall, stable diffusion is a text


to image generator like Dali, but has open sourced its code, then the weights for academic


purposes, and as of very recently it has even released the model weights for everyone.


Today we explain latent diffusion models, which is the algorithm that stands behind


stable diffusion.


And latent diffusion models, or short LDMs, are a special kind of the more general class


of diffusion models, about which you may or may not have heard already.


So if you're interested in how stable diffusion works, this might be just the right video


for you.


But before we dive into today's explanations, let's thank Assembly AI for sponsoring today's


video.


Do you know that feeling when you have hours and hours of audio data lying around and you


want to transcribe it automatically?


Yes, I do know that feeling, especially because I do not want to enter the captions manually


for every video I do.


But luckily Assembly AI can help me transcribe my audio with state-of-the-art APIs for automatic


speech recognition.


And you can use their APIs too, to automatically convert recorded audio.


But wait, there is more!


You can even transcribe in real time live audio streams into text, which can come in


handy for conferences or other live streams.


And believe me that Assembly AI's models do not stop there.


It can even help you understand your audio data, for example, it can summarize the content


and detect the topic of what is being said.


Or it can automatically tag bad words, if one happens to slip out.


So what are you waiting for?


Check out Assembly AI with the link in the description below.


I, for example, used Assembly AI to transcribe my speech for this spot and you can see in


the subtitle how well it works.


And think about it, I am quite a hard example for speech transcription with my Eastern European


accent.


Now back to the video.


First let's recap what diffusion models are.


For more details about this, for example on classifier-free guidance, do check out our


previous videos on this, but as far as our explanations of diffusion models in general


go, this video has the technically most correct version, because we cleared up a confusion


that Ms. Coffee Bean had this whole time.


But let's not get ahead of ourselves and talk about diffusion models first.


The diffusion process is where you diffuse more and more noise into your image.


So you take an image and in t steps you gradually add more noise to it, until at the last time


step t the image is approximately just noise.


Diffusion models go in the opposite direction of diffusion, so in other words they learn


to reverse the diffusion process.


When we are trying to generate an image with a diffusion model, we follow each of these


t steps and reduce the noise gradually, step by step.


For this we use the same neural network, so it's just one neural network, usually a unit,


to go from step t to step t-1.


And here for visualization purposes we have chosen a step t to be a noisy dog and step


t-1 to be a less noisy version of the dog.


Short side note on units.


A unit is a convolution-based neural network that is downsampling an image into a lower


dimensional representation and reconstructs it during upsampling.


The downsampling and upsampling stacks of layers communicate through skip connections.


And now what is exactly the input to the neural network?


It's an image at step t.


And the neural network's output is the total noise that should be subtracted from the noisy


version of the image at step t to reconstruct the original image.


So the clear picture of the dog.


Wait, let's untangle this a bit and recapitulate the whole diffusion thing from the beginning


because it is an important detail that Ms. Coffee Bean got wrong so far.


Of course I knew the correct version all the time.


Unfortunately the papers are not so clear about this point and how it's done becomes


clear from the code.


The diffusion process is all about going from little noise to more noise.


The backward diffusion process is the reverse and this is what we do with the help of diffusion


models.


In the diffusion model itself, so this one unit that we apply at each step, gets a noisy


image at step t and predicts the whole noise that the image contains and not just the noise


we need to subtract to get from t to t-1.


It's just that we do not trust the model enough to just subtract this whole noise in


one go, so at each step we extract just a fraction of the total noise from the image


at time step t.


Reasons for going step by step is that we break down a hard problem of generating an


image from just noise into t steps, where each step can correct and improve upon the


previous one, especially when the whole time we are trying to inject textual information


when doing text to image generation.


It's easier to inject textual information gradually than all at once.


Okay, so this was basically an erratum to our previous videos, where we thought that


at each step the model predicts only a fraction of the noise.


But actually the diffusion model predicts the total amount of noise from which we then


subtract only a fraction of it to give it afterwards yet another go at denoising.


But now how does the text come into play?


It is injected into the whole process in two ways.


First as input to the diffusion model by concatenating the text representation coming from a language


transformer to the image input.


And second through cross attention, letting the unit attention layers attend to the text


tokens.


Now, what about stable diffusion and how does this work?


The idea behind stable diffusion was introduced in this CVPR paper called High Resolution


Image Synthesis with Latent Diffusion Models from Heidelberg Folk, who now moved to Munich.


The authors proposed Latent Diffusion Models or short LDMs to address a shortcoming of


diffusion models.


When trying to generate a large image, such as a 1024x1024 image, then the unit in here


would have to take in a 1024x1024 dimensional noise grid and produce an image out of it.


As we can imagine, this can become really expensive for one step and one has to do it


T times, where T can be something like 150.


What people did to circumvent this high dimensionality problem, as we have seen in the Glide paper


for example, is that they actually trained their diffusion models on much smaller images


like 256x256 and then have an extra neural network that learned to upsample and sharpen


256x256 to higher resolution for example.


And this is one way to do it.


But LDMs take another approach.


The core concept of LDMs is to surrender the idea of working in the image space and work


on a latent space instead.


You may think of it as of an autoencoder encompassing the diffusion model as follows.


For diffusion models we had the original image, to which we then added more noise.


But now we do not work with the image itself, but with a lower dimensional representation


of it, because we take the image and run it through an encoder, which is basically a VQ


GAN.


This encoder also has a decoder to reconstruct the image.


This encoder and decoder are trained together first to encode the image into a lower dimensional


space and then reconstruct the image from it.


Now instead of applying noise onto the image for the diffusion step, we apply noise onto


the lower dimensional representation of the data.


And for the backward diffusion the unit works with this representation instead of the whole


image.


Since the image representation is lower dimensional, the unit does not have a lot of heavy lifting


to do.


Even more, by compressing the image first through the encoder, the encoder decoder can


take care of image details and let the diffusion model focus on the important things like image


semantics.


This makes LDMs much faster than usual diffusion models, so this enables us to run stable diffusion


on laptop hardware instead of clusters or, you know, open AI servers.


We hope you see how clever but similar LDMs are to what previous work like Glide did.


Glide took an image, downsampled it from let's say 1024 squared pixels to 256 resolution


with a usual image downsampling algorithm, so it was staying in image space.


Then Glide ran diffusion and upsampled the result to get a higher resolution image.


Now with LDMs, instead of a standard downsampling algorithm that produces low resolution images,


the LDM encoder takes a high resolution image and embeds it into a compressed code of its


own that captures the semantics of the image, but in a space that does not look to us like


the original image anymore.


And the diffusion model can take it from there.


As you can see from the paper, the author's initial work on LDMs was producing images


of human faces and objects.


This is because the model whose output we see in figure 1 was trained on human faces


and objects.


And you know what kind of data comes into the diffusion model, this is what they also


produce.


What makes stable diffusion so special and fit for art generation is that it was actually


an LDM trained on a core dataset consisting of, we cite, Leon Aesthetics, a soon to be


released subset of Leon 5B.


Leon Aesthetics was created with a new clip-based model that filtered Leon 5B based on how beautiful


an image was, building on ratings from the alpha testers of stable diffusion.


And this different training data makes it have its own style that is different from


other text-to-image generators like DALI 2, MidJourney or Imagine.


So to sum up what is now stable diffusion?


It is basically the work that follows the Latent Diffusion Models paper, where authors


of the paper teamed up with stability AI and communities such as Eleuther AI and Leon.


Now with stable diffusion we have an image generator tuned to produce art, as it was


trained on beautiful images, which were often produced by human artists.


It will be interesting to see what kind of new copyright law these AI generations will


spawn.


Some artists feel bad to see that an AI can now do something in seconds that took them


hours to make, but other artists consider the AI to be a sort of a tool which still


needs human input.


And the human hours that go into the diffusion-based image generation is not a neglectable amount.


Just think about the hours one can spend in tuning the text prompt and waiting for the


right result to come out, only to notice that the astronaut is riding the horse again, when


I actually wanted the horse riding the astronaut on the moon.


If you are interested in using stable diffusion yourself, then check out the Diffusers library


or any other helpful tutorials out there, because it's really amazing what the community


has put out in such a short amount of time in terms of documentation and in terms of


generations.


Just look at this Twitter user bringing their 4 year olds' sketches to life.


Wow, do you think this is creativity?


We are so curious to know what you think about this, so we'll be watching the comments section


closely.


Miss Coffee Bean, now it's time to say goodbye.


See you in the next video!


What is tokenization and how does it work? Tokenizers explained.

Hi, quickly before Ms. Coffee Bean comes, if you already watched our video about the


Charformer, go away, there's nothing you can see here.


Okay, here she comes.


Hi, what is tokenization?


You know, that line of code you use from Hugging Face Transformers and never care about.


In today's video we will see how this works.


Tokenization is that part in preprocessing that is done to text before throwing it into


your favorite language model, say BERT.


It is a preprocessing step, so it is independent of the later training process, but it's crucial


for most Transformers.


Without tokenization, each word of the English vocabulary would have to be assigned a unique


vector on the input side, and when predicting the masked word in the output, the choice


would have to consider the whole vocabulary.


And if you believe that the English vocabulary that one can find in a dictionary is already


huge, think about how it can grow even faster with all words that are not in a dictionary,


like compound words created on the fly by someone as a stylistic figure, or all possible


typos, right?


A mistyped word is also a word and should have a way to be processed by a language model.


This is where tokenization comes in, to at least partially solve this problem by creating


a smaller vocabulary of word building blocks.


This hopes to cover as much of the dictionary as possible, including unexpected words.


Therefore tokenizers first segment the sentence into words, harder for some languages than


for others.


I'm looking at you Chinese, Thai and Korean, because you do not have white space separation.


Then the words are parsed into subwords, so that even though supposedly the dictionary


does not have the word pre-processing in it, and therefore no vector representation to


associate with it, the tokenizer can divide the word into pre- and processing, supposing


that pre- and processing are already in the vocabulary and have a vector association.


And this works great in very regular kinds of words that can be decomposed into known


blocks, but does not solve the problem of noisy text, full of typos where the possibilities


of mistyping are so many that a fallback to a known subword is either impossible or leads


to misunderstandings.


So tokenization being so rigid can become a problem, an even bigger one when working


with multiple languages where the auto-vocabulary problem becomes even greater with every additional


language the model has to handle.


Even more, the downside of tokenization is that on downstream applications you are kind


of forced to use the same tokenizer as the model was trained with, because the trained


model is very much dependent on the tokenization it is used to.


Even though your task might benefit from other tokenizations, you now must stick with the


initial ones.


No, you're not allowed to see other tokenizers, Ms. Coffee Bean.


Well, we hope that we could make the purpose and inner workings of tokenization now clearer


to you.


Don't forget to like and subscribe.


Okay, bye.


Hey, so it's me again.


As you have perhaps already noticed, this is a partial re-upload of the explanation


of tokenization we did already in the Charformer video.


We think we did quite an okay job at explaining it there very general, but that general explanation


is not search engine retrievable if you search just by tokenization, so through this video


we just wanted to resurface that explanation a little bit.


And that was it.


Okay, how does Ms. Coffee Bean usually end these videos?


This Is Just The Beginning | TDBS Episode 100

What's going on everyone? Welcome back to day 100 of the Daniel Burke Show. Thank


you so much for tuning in on whatever medium it is, YouTube, Facebook, podcast.


It's crazy. We made it. The 100 day experiment. I've got my boy here who's


rocked up out of the blue. I didn't actually plan any of this but we decided


when he got here it would be, I don't know, it would be reminisce of us not to have the


show with each other as we've done. I think it's, this is our fourth episode now?


Third? I think it'll be third. Third? Fourth? Doesn't really matter. But Joey, how you doing man?


I'm pretty good. It's good to see you. I'm very good. It's actually been a little bit...


It's been a while since Joey and I have seen each other. Good couple of weeks now.


You might have to get closer to the mic. I don't know if it's picking you up. I'll move it a bit closer.


There we go. Yeah, it's been a good couple of weeks.


You've been travelling? You've been in and out of Sydney? I have been. I've been in and out of Sydney a lot actually lately.


What? You got married, mate? Nah, I'm kidding. I'm kidding. I still love you.


But no, it was when I got down the last thing, I went down for the Groovin' The Moo Festival.


That was a wild ride. It was a fun time. And then afterwards I got sick with tonsillitis.


Buckled for four days. I don't have tonsils. I wish I didn't.


I can work in a tactical, did you know that? I don't have an appendix or tonsils.


So that's one of the requirements for working in a tactical apparently.


I didn't know this. I still have to research it. I've been told it on the grapevine.


Is that the same? Is it grapevine? Grape. Grape. I think we've got water here.


There you go, brother. So grapevine. Grapevine. So long I've been saying grapevine.


No, you just flick it like that for some.


So day 100. What are we doing today?


Well, other than conversing with Jonny and I, I figured I'm going to call some of my friends out of the blue


and see what they're up to. And we're going to do it live on the show.


And as you know, I don't really edit it, so hopefully some of them pick up.


So boys, if you're listening to this, which you're not because I'm recording this in the past,


that's crazy to think about. Please answer. I know you don't answer phone calls often,


but we'll reach out and we'll say, hey, who are we hitting first?


Okay, should we just go down in order?


Yeah, let's go to the boys' chat and then we'll pick someone.


Even better. Let's go to the chat.


Actually, I think Rosie said he was keen.


So Rosie's straight up.


Rosie, you're coming on. You're coming on the show, aka Mr. I-Hit-It-First,


aka Mr. Crowsworn, aka the crowbar, aka the badger.


Handsome dog.


I have his American phone number here too, but let's not call that. That'd be a waste of time.


All right.


Rosie.


Let's see how it goes.


Come on. Fingers crossed. Let's answer.


Oh, they're probably expecting a call from you.


The brilliance of live TV.


The person you are calling is...


That's all right. We'll try Davo. Try Davo next.


Go down. Davo.


What can we do in the filler between these gaps?


So whatever you're up to, I hope you're having an awesome day.


FaceTime all you, Dave, because for some reason this number's not in there.


This is going to be terrible if no one answers.


That's all right, Joey. We'll just take over.


All right. I'm going to call Dave on his actual number because that would be a smarter idea.


So yeah, this morning, what I've been up to. Studying as usual.


I spent about three hours on a machine learning program that I've been...


Assignment, sorry, that I've been trying to solve.


I've also got... What else?


I'm going to my mates this afternoon. George is actually to work on the Move More Messenger bot.


So that'll be a good phone.


Here we go.


Oh, hey.


We've got him.


Here he is. We've made it.


We've made one on the show.


I didn't even introduce who you are.


It's a...


It's me.


It's my boy, Davo.


How are you, mate?


How are you, boys?


Pretty good. Pretty good.


What are you up to, Dave?


What are you up to?


Oh, jinx, mate.


Tweet it.


This has worked out great, mate.


We've made three phone calls. You're the first one to answer.


Oh, mate, that good old 23.


We got him. We got him on the phone.


Now, you're actually live on the day 100 of the Daniel Burke Show, mate.


How are you?


Unreal. I'm excellent. Dan, how about yourself, mate?


Mate, it's good to be here.


I can't believe it's been 100 days so far since day one.


That's ridiculous, mate.


Congratulations.


Start of February.


I know, right?


Start of Feb.


The start of Feb.


Yeah, it's... I don't know.


It's...


It's just beginning, I reckon.


Like, it's not even... I'm not going to end it.


Like, this is...


This is... The journey's just begun.


Good stuff, mate.


No, 100 isn't achievement, mate.


You've done 100 days in a row.


Crack the triple figures.


What are you up to today, mate?


Good, mate.


Consistency.


Mate, what am I up to today?


I'm sort of having a bit of a down day for the middle of the day


because I'm playing a bit of Xbox at the moment, which is, believe it or not.


But I'm getting roadworthies for cars, getting some cars ready for sale.


What game on the Xbox there, mate?


Mate, Battlefield 1.


Cool. Online?


Hey, mate.


Online?


No, I'm actually doing a bit of career mode at the moment, mate.


I thought I'd take a break from getting nuked everywhere


and have a bit of career.


Oh, we got Rosie on the line, but we'll pause Rosie for a second


and keep talking today, though.


You'll probably be able to get us in a conference call if you really want to.


Oh, we could try this.


Actually, well, once he hangs up, we'll just send him the voicemail


and then add call.


We could totally do this.


Let's try adding Rosie here.


We'll get the whole gang on the show.


Day 100.


The gang.


All right, let's try and call him back.


What's been exciting you lately, Davo?


Well, Davo's on hold. Davo's on hold.


Davo.


Yeah, mate?


You're going to get on hold in a second. Again, sorry.


All right, mate. I'm ready. I'm ready.


You're ready. All right, Rosie.


Hey, Davo.


No, he's back on hold.


We put him on hold.


I don't know how well this will work out as a podcast episode, but...


Yeah, it will. Trust me.


Yeah.


Rosie, give us a second. We're going to merge these calls.


Oh, how are we, boys?


Here we go.


We've got Davo and Rosie.


Conference call.


Davo, the busiest man with the business.


How's it going, Tim?


Never a resting moment, mate.


Rosie, you've actually been on this show...


This is your third time now. Welcome back.


Are we live?


Yeah, we are.


We are live, mate.


We are alive.


Yeah, well, I'm back at work.


No.


I'm coming home at times. I'm going to pay a fair bit to do nothing right now.


Mate, I'm sure if your boss knew that you were on the Daniel Burke Show, he would be stuck.


No, mate. He'd be over the moon. He'd probably pay you more.


Chris, go get Gary. First response on the family emergency. Got to go. And I get strapped back onto the bus.


Oh, mate.


Rosie, Rosie, what's going on?


I was about to go on you, mate. You made it to 100.


Yeah, thank you, mate. It's been good fun.


I don't think I've made anything to 100.


Oh, I'm sure you've run 100 meters, at least.


How close did you get to 100 kegs?


Fuck.


Ah, dude. I'm about 10 off.


How about 100 meters, mate?


Yeah, probably nothing. You know, you really got to look at yourself in the mouth and think, whatever I made it to 100 in, it's nothing.


And you've done it. So, good job.


You've at least made 100 days.


Thanks, mate. Thank you. It's definitely...


People would say I made 100 runs in cricket, but when I play cricket, I made 7. So, that just shows a bit of my cricket...


Straight out. Straight out.


Thank you, Rosie. It means a lot. And it's good to have you back on here.


No, good on you, man. You boys done well. You too, Joey. You're on heat. So, good on you.


Thank you. Thank you very much.


But you're going overseas shortly, aren't you, though? You've been overseas about 100 times.


You both have.


Yeah.


What's J. Thompson-Plug doing?


J. Tomo-Plug? Let's try and get him on. We'll try and get him on.


Do you want to get him on? Alright.


Alright, boys.


I don't know if he's been on one yet. I need to get him on one.


Yeah, yeah. Well, once you get him on, the show's going to go to 100 to bloody zero because he's boring.


No, mate. He's got a bloody head for podcasting.


That's why I started a podcast, Dave, mate, because I'm going to create face for radio.


Alright, boys. You're going on hold. We're getting on Tomo.


We're going to try Tomo.


See how that went.


We've got Tomo inbound, aka J. Tomo-Plug, aka Jack Daniels the hero, aka...


I never call Tomo, so maybe he'll answer. I think he gets so important. I never call him, ever.


It's weird how... I don't know.


I never call him, ever. It's weird how... I don't call people often anymore. I should.


I tell people every day on my podcast to call someone and tell them I love them.


Yeah, you don't have to call people.


I love you, Joey. Thanks for coming on the show.


Oh, no.


Tomo's out.


No dice, boys. Tomo is out.


We lost a man. Actually, I'll tell you a guarantee who will make it on is Chop. He'll answer.


Chop will answer.


All right. Let's give this a go.


Yeah, try it. All right, boys. Give it a sec.


If Chopper answers, I'll eat my hat.


My hat?


I'll eat your hat if he answers.


Please don't. I like this hat.


Do a sherry out of this four-year-old boot.


Booty.


Chop.


Chop, mate. You're live on the Daniel Berg Show. You're live.


Hello.


Oh, yes, Chop.


Mate, you're live on the Daniel Berg Show. We're going to merge you in.


Boys, we've got him.


We've got Chop.


We've got him.


Chop, how are you, dear?


This is Joe Dury.


Yeah, mate. Yeah, we've merged the call, Chop.


It's me, Joe. It's me.


Fuck, man. He's just so clever, boy.


It's me, Joe Dury, number two.


It's me, Joe, three.


What's going on, guys?


We've merged in, Chop, to the show.


Chop.


The conference call.


You're on day 100 of the Daniel Berg Show. How are you, mate?


Oh, great. How are we?


It's good to have you on.


I've been meaning to get you on the show.


Thank you, mate.


It's a pleasure.


Thank you.


You know, for all our long time with Chop, you know the drill.


Chop, first question off the bat.


How does it feel to be driving your brand new Suzuki Swift?


Mate, the Suzuki Swift's not too bad, actually.


Obviously, mate, good mileage.


You know, it picks up the old gash.


That's not too bad.


Nice, mate.


The Koreans make a beautiful car.


What colour is that, mate?


What colour is that car, Chop?


Pearl white, mate.


Pearl white. That's beautiful.


And you've got the racing stripes on it?


No, mate, no, but I will be getting kebab hunter sticker in a week or so.


So, really looking forward to that.


Oh, beautiful.


So, Chop, can you please run us through what your days look like?


Mate, so, I wake up in the morning every day, OK?


6 o'clock, I wake up at all time.


That's your first thing I do in the morning, take the glorious stuff.


Now, that's, you know, probably my breakfast.


Now, after that, I've been to work, 40 minutes drive, just then.


Just, ah, I've done my presentation.


So, I've been asked to meet the manager.


And, ah, loving life at the moment.


I can't tell you how happy I am.


I can tell you how happy I am.


So, why does anyone think I just fell asleep?


I gave it.


We got Rosie and David.


We got Rosie and David with us.


Rosie, you're the con for school.


Yeah, we're all, oh mate.


Well, why does it come up con?


It just says, it just says Joe.


Yeah, no, I've merged the call in.


Mate, you don't need to take the whole thing.


You don't need to take the whole thing.


It's like giving another dildo and saying what do you do with it?


Stop that.


Alright, Chop. Chop.


Mate, if you could.


It just needs to be PG, right?


Because fuck me, mate, I'm on the wrong talk show.


No, no, Chop, you're on the right talk show, mate.


It's good to have you here.


Now, if you could, if you could give some advice to the listeners in one sentence, what would it be, mate?


If I could give some advice, alright.


Sit down, boys, sit down.


Mate, 2011 at approximately 5.40pm, I created a status with Lee, one, Samuel, Summers.


And as usual, it's had a bit of a love problem.


So I said, you know, tension grows as the wind blows.


Copyrights go to Caleb.


Now, I'll tell you what, I'll tell you what, here's the advice.


You find a woman, you make her yours.


And when you make her yours, you're going to make her happy.


Now to do that, you've got to make sure the nods, you're getting the actual nods.


The itches, the itches of the nods.


Now, I'm telling you, if there's no happy life, then there's no happy life, and there's no happy life.


I'll tell you that.


Did you steal that sword from the saving private eye?


That's correct.


Did you give her the black cork down?


No, that's right, I gave her the black cork down.


But now she has to close, close herself up next to that bloody, you know, the drill.


You know, the drill.


Hey, are you home?


Yeah, mate.


Now, you're telling me I'm going to be on the phone because right now I'm at a conference call and I'm going to be on a sword show.


Yes, you're on day 100 of the Daniel Burke Show, mate.


Hey, guys, can I see her a quick, please?


Huh?


Yes, you can, David.


Just to get Chopper, so we get the listeners, so we get the kind of bloke that Chopper actually is.


He and I used to spend quite a bit of time over at Bertie's house, and one time in particular, I remember, well, Bertie has two dogs, two twin muttadors, and one of them's name is Kevin.


And John Chopper used to be quite familiar with the old seven.


And once upon a time, I remember him walking up, smacking on the butt, and it turned out that she was having the old heat.


And so Josh, I was only ran with brandy that day.


Yeah, he's got the old chilli dog for a finger.


And I thought, actually, I think of the dog.


Oh, no.


I don't recall that, I definitely don't recall that.


Repressed hard.


I definitely saw it too.


Chop, can you explain your lack of attendance to Monday night's dodgeball game?


Sorry, mate?


Can you explain your lack of attendance to Monday night's dodgeball game?


Mate, these 11 hour days are absolutely killing me.


Now, I'm going to be honest with you, Moog, I have no idea, mate.


I have no idea where even I am right now.


Mate, what's going on out the front of your head?


You're on a date.


That involves going to dinner with your girlfriend or family.


I'm going to punch my own head in.


Oh, fuck yeah.


Wait, Bob is actually out the front of my house.


He's just shown up.


I'm going to have to make an entrance.


You're going to have to make an entrance because you know what the fuck is going on.


Jeez, one boy's over to go.


This is it.


Alright, Rosie.


Alright, Rose.


It's been great to have you here, mate.


Thank you.


Yeah, thanks, boys.


Congratulations, mate.


Thanks, Chris.


Nice seeing you, mate.


Thanks for being a dog.


You know the drill.


Thanks, Charles.


Oi, boys, we should actually make this a weekly occurrence.


The conference call.


Get on the house party.


If Trump wasn't here, probably do it, but see you tomorrow.


Alright, alright, Bob.


Come inside, Bob.


As you can see, Bob's out the front here.


Do I come to the studio, though?


No, come inside.


Yeah, come into the studio, Bob.


I don't know how this is going to work as a show or as a video, but...


I'm in the studio.


I'm in the studio right now.


I've just washed up and turned out.


It was really good.


I was the only one.


I'll tell you something, though.


It's been a long day.


I'm just going to hang up the phone now.


I'm going to the studio.


How are we, boys?


G'day, mate.


It's incredible to have you here.


Thank you, mate.


Thank you.


How you been?


I can't believe I'm here.


Joey's been a while.


How are we?


So, I've been to the other one in the line.


Davo.


Yeah, boys, I think I'm going to tap out.


It's been good to have you here, mate.


See you, dawg.


See you, Bob.


See you, Davo.


Have a good day.


Got your love.


You're on the show.


You made it on.


I told you, mate.


Day one home is my day.


You're on here.


I know, mate.


Look at this shirt, mate.


Loving it.


Banana here.


Out of pocket.


What do you mean, out of pocket?


You're not getting my problems.


Mate, it's been a hectic week.


It has been a hectic week.


Work's doubled up.


Obviously, in the financial year, sales have come up.


Clean show.


You cleaned for yourself.


Yeah, mate.


Unfortunately, boss's word.


Really?


Back in high school.


No, back in high school.


The Tereesies days.


You know the drill.


Yeah.


No, it's been quite busy, mate.


I'm just going to let you know.


I'm not too sure if the listeners know, but if you are listening to the show, we want


to give a round of applause to Dave Blaifeld and Telani Wilson.


They're just expecting their second child on the way.


I'm not too sure.


They're having a second one already.


They haven't even had a first.


No, they haven't.


They actually haven't.


They're skipping the first.


They're skipping the first, going straight to number two.


They're going straight to number two.


I love it.


That's the new way.


Congratulations, guys.


Well done.


They have a dog.


I've always wanted one for adoption.


That's it.


Oh, that's right.


Just skipping the first, going to the second.


Skip two.


Let's just adopt the first.


Why not?


Hey.


There's no limit, mate.


We just get on.


So let's tell our viewers a story.


What's your favorite cricket match that we coached or played in?


I'm tapping out of this.


I'm tapping out of this cricket match.


Cricket's not Joey's thing.


One of my memories I'll never forget is when I remember Daniel Burke.


So Daniel Burke and I, we got selected for the second cricket match.


It was in the summer of 2000.


I can't fucking remember.


To play, I believe it was Grafton at St. John Fisher.


And I remember our coach Bernie Kelly turning around and he goes, right, this is Chop's


Day.


And I'm looking at all these guys and he's just written up on this massive whiteboard,


Chop's Day.


Now, I thought he was joking when he said this.


Since any of the listeners who actually understand cricket, I don't know if you've ever witnessed


in your life, but a spinner opening the bowling.


The bowling.


I thought he was joking.


Certainly not in our day anyway because we're incredibly inexperienced at this stage.


Absolutely inexperienced.


We're about 15 years old.


Absolutely.


So yeah, I thought he was joking.


But yeah, first two balls, pumped for a four and pumped for a six.


I'm looking at the captain thinking, what the fuck am I doing opening the bowling?


And I'm looking at Berkey, he's down there at second slip there and I'm thinking, far


east, we're on dice straight here.


Anyway, third ball coming down the pitch, absolute pie.


Explain, what's a pie?


So when we throw in a pie, guys, I'm an offy, whereas Berkey's a leggy.


A pie is simply just a straight up pie.


Yeah, so it's a big loop.


It's really, really easy.


Think of a Hail Mary in football just warming it up in the air and hoping it doesn't go


for six.


But yeah, that ball, dot ball.


And then number four, wicket.


Couldn't believe it.


And yeah, that was a miracle, a miracle show.


It's a legend.


Miracle.


But yeah, no, and I remember the second over, another wicket and then last two balls, six


and six.


And I believe that was the end of my opening.


That was it for the opening spinner.


Yeah, no, that was a great.


Obviously remember that match, it was a child's day.


That was, mate.


Child's day.


I'm not going to lie, it was the most embarrassing moment.


Pretty shit day.


That's right, yeah.


You got one wicket, that's good.


That's good enough.


That was two there, mate.


That's two.


I think we got Alfred Duck there as well.


So yeah, I think number eight or number nine.


Probably much better than my performance.


Oh, mate, you know.


You look back now, I mean, you're looking here, but take your top off, mate.


You know, that shredded, I'll tell you what, Burke and I, we came from a similar background


where we were lumps of lards.


I was still a lump of lard.


I've got photos of you.


Yeah, guys, I'll tell you, Burke and Dave and all the boys here tried getting me from


my...


Can we just think back to future music 2012 or 2012?


That's five years ago.


Seven o'clock in the morning.


Five years ago.


Chops wearing a singlet and some board shorts.


They're literally matching.


The exact same pattern.


It looks like one.


He's wearing a onesie in the back yard.


It looks like a onesie.


And not a gram of sunscreen was worn that day.


Not a gram.


Not a single gram.


No, no, not even words.


I remember.


Chops words himself, I'll be right, I don't get burned.


I'll tell you, it's top of the line.


Hold on.


On the way home, this is what you look like, mate.


All the way home.


See this hat?


Yeah.


See that color?


No word of a lie.


That was your, yeah.


For those who can't see on the podcast, he was actually completely red.


You imagine a lobster after it's been freshly cooked.


Yeah, you think a fresh prince of bell malter, you know, with a dark complexed skin and try,


you know, imagine no sunscreen and yeah, no word of a lie, Burke said.


Redder than a roma tomato, let's say that.


Let's just call chop, he had a bold strategy.


Oh yeah.


Pretty far.


Absolutely.


You ever seen SpongeBob, bro?


No.


You know Mr. Krabs?


Oh, gee.


Is that the book Krabs?


Krabs.


Krabs, Krabs.


I bet you're listening, how do you not know that?


Well, let's just be honest, I was abused as a child.


Kept in a cupboard most of my life like Harry Potter, fed only water and breath once a day.


So the camera overheated again.


Couldn't film the rest of the episode, but you can check all the audio out.


There's a link in the description.


Day 100.


Thank you all for watching.


Thank you all for listening.


OpenAI’s CLIP explained! | Examples, links to code and pretrained model

Hello there! OpenAI's Clip and Dali have been out for centuries now.


What? Centuries? Ms. Coffee Bean, it's been only three weeks now, but I think you all agree with


us that three weeks in machine learning feel like a century. Meanwhile, Google published


the switch transformer of one trillion parameters. Transformers generate music, transformers


generate 3D dance motion, transformers detect 3D objects, and the list goes on and on.


So let's stay cool and avoid being overwhelmed. In a previous video where we discussed OpenAI's


Dali, we found out that it is great at generating images from natural language,


and it might look like creativity, especially when combining concepts like armchair and avocado.


To make sure that Dali's generations are fitting the description well,


OpenAI employed a separate model called Clip to rank these image generations after their


similarity to the text. And if you don't know about Dali yet, check out our video about it


linked in the description below. It's worth it. In this video we will talk about this helper


model of Dali called Clip, but do not be deceived by us calling it a helper model,


it is more than impressive also on its own. Make sure to stay until the end,


where we will show what amazing things people already did with Clip.


Clip stands for Contrastive Language Image Pre-training, and yes, the paper has a different


title altogether. Anyway, based on the paper we will be answering the questions,


what can Clip do, what are the ingredients for its success, what are the limitations of Clip,


because there are some, and what can we mortals do with Clip and what applications already exist


based on Clip, because in comparison to Dali, Clip was already shared by OpenAI.


Clip is very good at telling if an image and a short piece of text belong together or not,


but why do we even want to know if a given text and image fit well together in the first place?


Well, because this task formulation is general enough to derivate many other tasks from it,


like image recognition on ImageNet for example. The task there is to classify images,


and this is what neural networks have been doing so far. They take all available classes in the


dataset, here the dog races and up to 1000 other classes, and assign a probability for each class.


The highest scoring class is the classification prediction of the model,


here King Charles Spaniel. But with Clip it gets even better. Because of the way it was trained,


Clip is not restricted to the classes from the dataset, but knows virtually all English words,


being able to formulate the ImageNet classes into prompts, containing more language than just


the classes. And here we already see why Clip has the potential to be so much better than its


competitors. Firstly, it can also model the notion of photo or image or satellite, extending its


capabilities far over a restricted set of classes, showing great zero-shot performance on unseen


datasets. Secondly, Clip is during training never restricted to reduce an image to a single concept


or word, therefore it never loses or forgets other aspects of the image that are not captured in the


class, like the grass or wood on the background. Impressively, but not perfectly, Clip can solve


tasks and datasets it has not explicitly seen during training, like optical character recognition,


geolocalization, texture detection and others like facial emotion recognition and action recognition.


It is impressive zero-shot performance for a model trained only to predict the similarity between a


text prompt and an image. Then what are the ingredients of Clip's success? Here we have to


say that the employed ideas are not really novel. There are a lot of ideas used in the Clip paper


that were already used in research, and OpenAI's merit is definitely that it managed to put


everything together in a fine piece of engineering, which Clip is. Endally. Yes, the zero-shot


capabilities of Clip are impressive, but not unexpected. We have seen it happen too with GPT3,


trained on huge amounts of data with high variance. Clip has been trained on 400 million images with


paired text descriptions. This is 100 times more data than ImageNet, and it is very diversified


data too, because it was all scraped from the internet. So the first ingredient for the success


of Clip is of course the data. The more data, the better, and the more diversified the data,


the more we can expect the model to be able to perform afterwards in zero-shot. Then on this data


it was trained like this. The authors take a batch of images and a batch of their corresponding


textual descriptions. The images are encoded by either a ResNet or a Transformer, and the text


is encoded by a Transformer too. Then for each image in the batch, the encoder computes an image


vector. So the first image corresponds to the vector i1, the nth image to i n, and the same


happens for the textual description. The first textual sequence is encoded by one vector t1,


the nth description by tn. Then the magic happens, n square similarities are computed between all


these images and text vectors. This means that the diagonal elements of this matrix should be


all maximized by Clip, as i1 fits with t1, i2 with t2, and so on. Then in a contrastive fashion,


the off-diagonal elements should be minimized, since we assume that it is really unlikely


that an image i1 fits with a random other description other than its own. And that was


the second ingredient of the success of Clip, is the idea of predicting similarities between


images and textual descriptions and training in this contrastive fashion. But the idea is not new


at all, because we have been seeing this idea in multimodal transformers as a pre-training task,


like for example in LXMERT, Wilbert, VisualBIRD, and so on. So this is sadly some piece of related


work that we think the authors did not acknowledge enough and didn't compare to well. The third


element that the authors advertise Clip with is its computational efficiency, especially when


these image and text encoders are transformers. We know that a lot of computations can run in


parallel compared to LSTMs, for example. But do not confound computational efficiency with data


efficiency. Especially with transformers that do not have inductive biases that help during learning


from little data, they need more data to learn well, so they are not that data efficient. So the


fourth ingredient to Clip's success comes from the image and text representations gained during


training on the given data. Clip, now trained to predict high similarity for fitting image-text


pairs and low similarity for random ones, is ready to be applied on various tasks like


image recognition. We remember image recognition only has one word to describe one image, Clip


rather likes more words, like a description, so the authors create a prompt, a photo of a,


and then they insert all possible objects, creating as many prompts as there are objects.


Then they compute zero-shot, the similarity between a say ImageNet image and these prompts


that differ only in the mentioned object. Then the prompt and object with the highest similarity


is chosen as prediction. And the same Clip model performs really well not only on the classical


ImageNet image recognition challenge, but on many other tasks with different kinds of images


or styles, because first it has seen these image styles during training and secondly it has seen


the words to describe these kinds of images and their details. Okay, so Clip can do so much and


it is time to see what the limitations are of Clip. Here we must refer to the limitation section of


the Clip paper, because it is the most entertaining limitation section we have ever read. We include


the passages we refer to and cite, so you don't think that the used language is really ours.


No, they really really formulate things like we show them here. So the authors make clear that


zero-shot Clip is competitive with the baseline of a supervised ResNet-50 model trained specifically


on the training set of that dataset. And this is impressive, because it means that Clip trained


on OpenAI's dataset of 400 million images absolves us a little from training specifically on tasks


and the datasets associated to them. But here the authors warn us that it is still work needed to


beat the supervised baselines, but this work can have limitations if it is done how the authors


did it so far. They warn us that if we scale the model and the data like they did so far,


we would need a thousand times more resources in compute, which is infeasible with current hardware.


Then the second limitation they tell us about is that while Clip is performing amazingly on some


zero-shot tasks, it is not amazing on all tasks. We cite, the performance of Clip is poor on several


types of fine-grained classification such as differentiating models of cars, species of flowers


and variants of aircrafts. And Clip has also trouble with abstract and systematic tasks such


as counting the number of objects in an image. Clip has also trouble with tasks that are unlikely


in the pre-training dataset by saying that Clip has troubles generalizing to out-of-distribution


images. And they illustrate with an example of optical character recognition, where Clip is


really good at recognizing digitally rendered text, but has troubles with MNIST, which is a


dataset containing handwritten digits. And the reason the authors can identify is that there are


a lot of digitally rendered text images in Clip's training data, but not so much handwritten digits


and Clip kind of fails to generalize from digitally rendered text to handwritten text.


And kind of the next limitation they address is that Clip is not a caption generator model. It can


only tell you how a given, an existing image and an existing text fit together, but it cannot really


compose new text. And also a very beautiful formulation, Clip also, I cite, Clip does not


address the poor data efficiency of deep learning. Instead, Clip compensates by using a source of


supervision that can be scaled to hundreds of millions of training examples. So Clip didn't


solve any of the deep learning problems, but it kind of shows a bigger hammer for solving the


problem. Okay, but now that we know what the authors found out that Clip cannot do, let's


have a short look at what people have been able to do with the Clip model that was released by OpenAI.


And the first application we want to show you is this one by lab member 001, who was able to


discern between grainy, noisy photos and high quality photos. He could really nicely formulate


in natural language because Clip understands that. So in a sense, Clip can be used as an image search


engine, as for example Vladimir did, who used Clip to retrieve car images, but it gets even better.


He could retrieve even more detailed things like car driving in the woods. So this is the best


thing about Clip. You can describe the model very detailed what you want and you can retrieve


pictures like this one where it's raining cats and dogs. And also what I really liked is this example


of detecting chemical elements in pictures. Clip delivers us similarities or scores between an


image and a text, so it can be easily used as a discriminator in a GAN. Here is a super nice


example of a Montreal image. Find the links to the tweets we have shown you so far in the


description below. And also in the description, please find the link to this Google Colab released


by OpenAI, where you get working code that you can explore in your own applications. There are


detailed instructions how to download and initialize the model. And then there is even an


example how to calculate similarities between images and pieces of text and also how to create


your own ImageNet-like classifier with Clip. So you can play with Clip on Colab yourself.


Do not forget to find all the links mentioned in the description below. Let us know what cool


My first day as a MACHINE LEARNING Engineer | Learning Intelligence 32

Yo, yo, yo! Welcome back to another week of Learning Intelligence.


So glad you could join me here.


And this week's gonna be really, really exciting


because I've got some awesome news to share with you.


But just one second, I wanna show you what I've been working on.


It's the VUI project for Udacity Artificial Intelligence Nanodegree.


And what's VUI? Well, Voice User Interface.


It's the final capstone project of Term 2, which I'm really excited.


So there we go, Voice User Interface Capstone.


And what I did is I went through the Jupyter notebook


and took notes on what I have to do for each step.


So there you go, step 1, acoustic features,


notes, just in my own words of what's going on with the project,


some diagrams here I stole from it.


There we go, important note and whatnot.


But we have our first neural network training.


Essentially, what we have to do or what I'm gonna have to do


is build five different neural networks from RNNs to RNN plus CNN


or recurrent neural network plus convolutional neural network.


And the idea is to build an end-to-end deep learning pipeline


to recognize voice and synthesize it into text.


Or I believe that's the end goal of the project.


But essentially, that's what it is, five deep learning models.


Enough about that, we can get more into that project later.


I just got off the phone with the CEO of Max Kelsen, Nick,


who, Max Kelsen is a company I've been doing an internship at


for the past few weeks, and I just essentially got offered a role.


So my first day is tomorrow as a paid junior data scientist,


junior ML engineer, junior deep learning specialist,


something around that, but essentially an entry role


starting three days a week, which I'm absolutely over the moon with.


I was running around outside before on the phone.


As you probably see, I like to walk around while I'm on the phone.


But yeah, it's happening, guys, getting into the industry.


And what's my goal, essentially, it's to, of course, help them,


but it's to gain valuable experience in what I've been studying.


And because nothing beats hands-on experience, right?


So as soon as I figure out more, there's some exciting projects


going down on Max Kelsen, and of course, I'm going to keep the videos going.


So even though this is a sort of a little change in direction


for what's happening for myself, expect these videos to keep going, right?


They're not going to change.


So I'm going to still put out at least something once a week,


whatever I'm learning, whatever's going, and yeah.


So I'll see you tomorrow after work, I guess.


Have a good day, boys.


Thanks, Mom.


What are you working on today, Bilzy?


I have a mining company order.


Can't disclose the logistics about that.


If I tell you, I have to kill you, right?


I know.


Got more cash on the go card.


This last week you called me, then we're talking, striving, missing, hello.


And I realize it's pretty windy here, so I'll try and make this quick.


I want to share with you what I've been working on.


So this morning, got in, did some reading, did some replying on emails,


I mean, sorry, on LinkedIn direct messaging.


If you want to get in contact with me, message me on LinkedIn.


I've been really, really, really bullish on that platform lately.


But what I've been working on in the data science role,


I actually haven't got a title yet.


I would consider it a little bit of data science,


a little bit of machine learning, a little bit of this, a little bit of that.


So once I find out the official title, I'll let you know,


but I'm going to call myself a machine learning engineer


or a data scientist for now.


Let's just keep it at that.


But three things.


For most of the work I've been doing this morning, it's been in the Jupiter Lab.


Now, Jupiter Lab is like an online application where you can run


Jupiter notebooks, you can run Python code, you can do a whole bunch of different things.


I'll leave a link in the description for all of this, by the way,


so you can check it all out.


So a lot of the work we've done in the past couple of weeks in the internship as well


has been through Jupiter Lab.


And now, what am I actually using in Jupiter Lab?


Now, it's all been in Python, but I'm using today learning about,


I've actually spent the whole morning learning about Edward,


which is a Bayesian-based probabilistic programming library.


So, I mean, that's a mouthful, but of course, I'll link Edward as well in the description


so you can read about it.


But essentially, what we're trying to do is we've run a deep neural network


on some of the data, but now we're trying other methods to see if we can get some different insights.


And the method we're trying today is Bayesian probabilistic programming.


However, we've run into a bit of a roadblock.


I'm going to go back after lunch and try to sort it out.


Just trying to get our data in the right format to run through an Edward neural network.


And then one last thing is, is how have I been learning about this stuff?


Well, it's all been through YouTube.


That's how powerful the platform is.


I watched a video from Siraj.


I watched a video from one of the founders.


I think his name is David Trang.


I may be getting that wrong, but he is essentially a researcher for Google


who helps develop the Edward framework.


So, that's my goal now is to go back and try and get this model up and running.


I believe I'm partnering with the CEO at some point today


to discuss some formalities around the world,


but I'm having so much fun so far, guys.


It's been really good.


But yeah, if you want to ask me any more questions,


leave a comment below or message me on LinkedIn.


But I'll catch up with you once the day is over.


Guess what?


I found out what my official position title is.


Machine Learning Specialist.


But I'm going to drop the specialist title


and just say that it's Machine Learning Engineer


because I think that sounds a bit better to me, don't you?


But it doesn't really matter.


I'm having fun.


It's an incredible day of learning,


playing around with that new framework I told you about, Edward.


I finished the day with still with some roadblocks,


but I left some notes for the other two engineers that are on my team.


There's a few of us.


I haven't quite worked out the entire team dynamic yet


because it's only four days or so in, but I'm excited.


Tomorrow's a day off, but then after that,


we're going to get in and get back in


and try to fix those errors that I came across.


Oh, give me a second.


I've got to get through these gates.


Hey, mate, how you doing?


Thank you.


So I have a total of 11 minutes to my train.


So I'm going to do some bit of writing,


a bit of reading on the way home.


It takes me about 35 minutes to get home.


Yeah, I'm not entirely sure what's on the schedule for the rest of the week.


I'm starting off at three days a week.


Wednesday to Friday.


As soon as I find out more, I'll let you know.


Hey, family.


What's cracking?


Oh, your stuff's over here.


Finished my first day, Mum.


Goes to have internship.


No, not internship.


Official day.


Job.


We made it.


Woo.


Yeah.


What do you reckon, Mum?


That's fantastic.


We're home, team.


What a day.


Been learning a lot already, as you know,


but I've got something cool to show you.


I'll get it out of my bag.


Just give me one second.


Now, it's a bit crumpled up because it's been too unfrown.


I hadn't really got a proper sleeve for it, but check it out.


This is my part-time employee contract.


All the official paperwork.


That means I'm an employee of Max Kelsen.


So, thanks so much to the team there.


Really appreciate you guys hiring me,


but there's one thing in here I want to show you.


I kind of made sure it was added in at the contract.


It's my favorite.


I think you might know what it is.


You got fair work.


There it is.


This is the most important thing,


and the number one rule.


Keep learning.


Did you know that was coming?


I think that's an appropriate way to sign off the video, don't you think?


Keep learning, guys.


And P.S.


If you want to ask me any questions about how I got a role,


or how I got an internship,


I don't know the best answer of how you can get one as well,


but I can share with you what's worked for me.


So, leave your questions in the comments below.


I'll answer them in the future live stream,


as well as I'll try and get to as many as I can,


and I'll get to all of them practically via just typing.


But otherwise, I'll record them all,


and we'll do a future live stream.


But otherwise, peace out,


and as always,


The Ultimate Skill: Learning How to Learn (course review)

You know when you're a little kid and you ask those questions,


what would your superpower be? I used to love that question.


I used to love thinking about different things.


Could I fly? Could I breathe underwater?


Could I turn invisible?


Like they were some of my favorite conversations as a kid


and actually still now, right?


Like so, scrap the being a little kid.


If you could have a superpower, it could be anything.


Leave a comment below and we'll compare superpowers.


But now that I'm a little bit older, I think if I was asked,


if I was asked that same question now, right?


Have you seen the movie Limitless with Bradley Cooper?


That's one of my favorite movies.


And what's the premise of the movie?


I mean, he takes the pill, NZT, which allows him to,


his mind capacity is essentially limitless.


And so if I think, if someone asked me right now,


what would my superpower be?


Well, the superpower would be learning how to learn.


And that's the topic of this video, right?


Because first of all, a quick shout out to Coursera,


the amazing platform that I've used several times over


and will continue to use in the future for online learning.


They reached out and wanted to know if I wanted to try out


the learning how to learn course, which,


come on, I'll show you the screen.


Let's just dive straight into it and have a look.


It's learning how to learn using powerful mental models


to help you master tough subjects.


And I like to learn things that help me with other things,


whether it be health, because health, you know,


I'm into health, health is the force multiplier of life


or communication, right?


Because then you can partner communication


with any other skill.


But I think the ultimate meta skill, right?


The ultimate skill that you can apply to literally


everything else is learning how to learn.


If you know how to learn something,


you can use that to learn whatever you want, right?


The possibilities are limitless.


So we'll check out this, the about the course,


look how many ratings it's got.


It's got 40,000 ratings, that's a lot.


So that means at least 40,000 people have done it.


Probably 10 times that,


because how often people did ratings.


So first of all, there's part one,


which is what is learning or actually it's about 12 hours.


So relatively short, about three hours of video,


three hours exercise, three hours bonus material.


Chunking is week two.


Week three is procrastination and memory.


And week four is renaissance learning


and unlocking your potential.


All right, so four weeks apparently to do it,


but I'm going to attempt to do it in one day.


So what you'll see in this video is


I'm gonna jump straight into it.


I'm literally gonna sign up now, do the course,


and then I'll tell you what my favourite parts were about it.


But if you wanna try it out before even watching this video,


there's a link below, you can sign up there.


And let's learn how to learn, shall we?


Let's learn how to learn, shall we?


Oh, she just mentioned the Pomodoro.


You know how much I love Pomodoros.


If you're not sure what it is, Google it,


but I'll get onto that after I've gone through this.


I gotta keep focused, right?


After all, this is learning how to learn.


But shout out to the Pomodoro technique.


Be back in a sec.


Oh yeah, we passed the first section.


What is learning?


We got, what did we get?


I think we got one of the questions wrong,


but we're gonna get into week two,


which I believe chunking the essentials.


All right, sweet.


And one of the things of week one was that


sleep is really helpful for learning.


So I'm probably gonna take a nap


and then we'll get back into week two.


Alexa, 25 minute timer.


25 minutes starting now.


We're back.


Alexa, stop the timer.


You know, one of the things I like to do


after getting up from a nap


or even getting up in the morning,


like when you're feeling a bit lethargic,


is to get your body back into motion.


Just do some form of quick movement.


And my favorite at the moment is jumping jacks.


You could do push ups or whatever.


So 20 jumping jacks,


gets the body back into flowing


so you're not in that lethargic state.


All right, 20.


Now let's get into part two, hey.


I finished week two.


This course is amazing.


And I know I said at the start of the video


I wanted to go through the whole thing


and then summarize it all at the end.


Once I've finished all of them


and go through them in part.


But I think I've been able to take away from this


is the concept of recall.


So I'm gonna go through my three favorite things


of week two relatively quickly


to try and think of,


or to try and submit the information in my brain, right?


So first of all, week two was about chunking.


Well, my favorite three parts were chunking, recall,


and there's a German word,


there's a German word, einsteig, or E-I-N-S-T-E-L-L-U-N-G,


I believe.


The first one is chunking.


What's the idea of chunking?


Well, chunking is taking the mental models


or mental concepts of a particular topic


and bringing them together in like a neat little package.


Think about it like this.


If you're learning programming,


you don't learn the whole language in one hit.


You learn the concepts of programming,


say something like for loops or if statements


or different types of variables,


and then you work out how you can put those together


in a program.


So that's the idea of chunking,


but you can relate that not just to programming,


but to any different concept.


And then the second one, my second favorite was recall,


which is what I'm doing now.


And that's a brilliant concept, I think.


So what's recall?


Well, it's rather than rereading,


say you're going through a textbook, right?


Rather than rereading or going back through it,


once you've read the page or the chapter


that you're most interested in, put it down


and then try to recall the information


or recall the key concepts like what I'm doing now, right?


I've literally just finished the lectures


and I'm trying to think in my own words


and explain to you what I've just learned.


So chunking, recalling, I really like recalling.


That's something I need to work on


instead of just going back over the same material.


I need to just set it down and go,


okay, here's what I think I just learned.


And then go back and check that.


And the final one was the German word, einstellen.


Einstellen, I'm probably saying it wrong, right?


If there's any German viewers, please let me know


if I'm pronouncing this right or wrong.


But essentially it's the concept of mindset.


I believe in German it may mean mindset,


if not the word, the definition doesn't entirely matter,


but I just relate it to the concept of mindset.


And what does that mean?


Well, einstellen, the way Barbara describes it,


the way it's described in Learning How to Learn,


is that einstellen is the concept of a previous mindset


holding you back from doing something new.


The analogy is science advances after every funeral.


Well, it means that an older scientist has passed away


and so their older ways of doing things


makes room for new scientists


and their newer thoughts of doing things.


So one way to try and combat that, right,


is to not let your older ways of thinking


hold you back from thinking of new ideas, right?


So not being afraid to make mistakes.


So they're my three favorites from week two.


I'm gonna get into part three.


A boobie, a boobie.


All right, just finished week three


and it just got really dark in here.


I think it's about to storm outside.


Let's get some light.


So for the final project, and I'm not sure,


if the final project seems like it's in the end of week three


rather than the end of week four,


the final project I have to create something


to share what I've learned in this,


which I think is brilliant.


I have to create something that shares what I've learned


in this course of this video


and the article you can probably find in the description


if I've written it by now, I probably have.


It can be my submission.


So we're gonna skip over this final submission part


and then finish the final videos in part four.


And then we'll be done.


Renaissance, Renaissance learning


and unlocking your potential.


I'm not sure what Renaissance is, but we'll get into it.


Oh yeah, we passed the final exam.


Except we got one little multi-choice wrong up the top here,


probably another one wrong somewhere else,


but that doesn't matter.


I'm not going for perfection, I'm going for progress.


All right, and that's it.


Then we've got a bunch of different reading options,


heaps of different interviews that we can do


with a bunch of different people.


Oh, I still got my headphones on.


I wasn't even listening to anything.


I was just having them on to cancel the noise


and be focused.


So, a bunch of different resources that we can go through,


but I was just looking to go through the videos.


I'm gonna check out, I'm gonna pick and mix


some of the other interviews with people


that I'm most interested in.


The virtue, the verdict already is that I recommend it.


However, we're gonna jump into some of my favorite parts


from each part, but we're gonna do that tomorrow


after I sleep on it and it gets absorbed


and all the toxins get washed away in my brain


and the new neural pathways get created.


So we'll see you tomorrow.


Tomorrow's actually in about three, two, one.


You can skip the rest of this video


because I went through my notes, all of these notes.


Look how much there is, right?


Look how much I have.


Just a whole bunch.


There was so much, so much there,


but the one thing that I took away the most


was if you wanna improve your learning,


it's to remove the amount of hair you have


so that thoughts can easily enter your brain.


So that's why I went and got a haircut this morning


and fixed my learning problems.


No, I'm kidding.


We'll get into it.


It's nothing to do with that,


but I actually did get a haircut and I've had a sleep.


So part one started off with what is learning?


One of my first favorite parts of part one


was the concept of focused versus diffused mind states.


Now, what's the difference between those two?


Well, the analogy they used in the course


was a focused mind, if you imagine a flashlight,


a focused mind is like that flashlight


shooting a single beam of light onto a very small point,


but the light is very bright.


Whereas a diffused mind state


is if you had that same flashlight


but changed the setting on it


and it shone a lot of light, but it went out wide,


however, it wasn't as tense


as that one single beam of light.


And now where's the value in that?


Well, Professor Oakley said that the best learning happens


at the crossover of those two mind states.


And I definitely agree.


Now, focused, you might be in depth studying something.


It might be literally going through a textbook,


taking notes or reading something


or working on a programming challenge,


something of the likes of that.


Like really focused on a single task.


Whereas a diffused mind, you might be taking a walk


or going for a shower.


Not really thinking about anything in particular,


but just leaving your mind open


to different thoughts to come in.


Now, where's the benefit in the crossover of that?


Well, have you ever been focused on something


and then got stuck?


And then you went for a walk or took a break from something


and suddenly you magically thought of the answer,


or even if you didn't consciously think of the answer,


the next time you came back


to the problem you were working on, it was somehow there.


So that's your diffused mind, right?


Not being focused on any one particular task,


working in the background to solve the problem


that the focused mind was working on before.


And this happens a lot with sleep,


which is why I took a nap yesterday.


I took a sleep before recording this video.


Was to, my focused mind was concentrating


on doing the course all yesterday.


But when I took a sleep, when I slept overnight,


my diffused mind, so not thinking of anything at all,


lying in bed, was going over the things I've learned


and was able to compact those.


So that was definitely a valuable point in my eyes.


My second favourite takeaway of part one,


what is learning, was different ways


to deal with procrastination.


Now you also saw me yesterday when I took a sleep,


I set a timer.


Alexa, 25 minute timer.


Why?


Because the technique I use,


and what was mentioned in the video,


and I was really stoked because I actually used a technique


that was already mentioned in the video,


is the Pomodoro technique.


And it's a simple concept.


You set a timer on any device that you wanna be using,


so I could set one right now.


I'm pretty sure I have one running on a,


I don't wanna say it, on Alexa back here.


So you can set a timer, and for,


I don't know if you can see that,


but anyway, there's a 25 minute timer there.


And you set for whatever time scale you want.


And for that, before, while the timer's running,


you don't do anything other than the single task


you set yourself.


So that's one way to deal with procrastination.


So there were my two favorite takeaways from part one.


Part two was already at the start of the video.


Yeah, weird structure, I know.


But just to quickly summarize,


the main points in that were chunking,


so grouping similar concepts together.


So say at one certain time of the day,


you tick off your hardest task.


And another time you do similar tasks,


like maybe respond to email.


And then another task, maybe in the afternoon,


you stick to the math problems


of what you're trying to work on.


Part three was about procrastination and memory.


Now, we already covered procrastination,


or a little bit of procrastination, in part one.


Now, I think this was deliberate of the course founders,


to put similar concepts, or teach in the first part


an overview of all the concepts,


but then in each section after,


subsequent section like part two, part three, part four,


was to dive deeper into each of them.


The course is beautifully laid out, put it this way,


much better laid out than this video.


But the second way they dove into procrastination


was in the habit form.


If you break a habit down,


or the habit of procrastination, or really any habit,


there's four steps.


You've got the cue, the routine, the reward,


and then the belief.


Now the cue, if you eliminate the cue,


it destroys the other three steps.


So what is a cue?


So a cue might be, you get a text message on your phone.


I don't have my phone here,


because of course it's in the drawer,


I've removed the cue.


So the cue might be your phone vibrates,


you get a text message from a friend.


Then the reward is, so step one, the cue,


text message on the phone,


step two, the reward is you looking at that text message,


you get that good feeling, right?


You get that hit of dopamine,


because your friend's talking to you.


Then the routine, right, step three,


is you suddenly start to check your phone, right?


You go through it because you've just got that reward,


you've formed that routine


of checking the other notifications on your phone.


And then four is the belief that once you check your phone,


you've got distracted and now you're not


distracted and now you're spending more time on your phone


and you now believe that you're a person


who's easily distracted.


So those three steps, right, the reward,


the routine, the belief, can all be eliminated


by removing the cue, right?


So the thing that causes those steps to happen.


So you can put your phone in your drawer


or you can install some sort of website blocker


on your browser or something like that.


Or my favorite one is the simple


back to the Pomodoro timer.


Set a timer and just tell yourself,


this is the belief, right?


The belief that you're a person who doesn't get distracted.


Set yourself a timer and say,


hey, I'm not doing anything apart from this task


during this time.


After that, I can reward myself,


and that's the important part,


reward yourself with maybe you check your phone


for five minutes or something like that.


But then get back into setting another timer.


My second favorite part of part three


was the concept of setting a task list.


Now, I do this every day.


I've even got a whiteboard with things


that I wanna do during the day.


Now, where's the value in that?


The course says to set it the night before.


I do mine in the morning.


I suppose you could do it the night before.


There's really whatever suits you.


So I get up in the morning,


the way I consolidate all the thoughts


about what I have to do is I simply write them down.


And if something else comes up, I write it down.


So it gets out of my working memory.


Now, that's a big concept that was introduced


in the course as well is working memory.


You can only really hold four things at a time.


So that's where the value comes from


of setting yourself a task list the night before


or in the morning or just whenever


something pops into your mind


is that when you write it down,


you remove it from your working memory.


Now it's solidified on paper or something else


or some kind of task app that you have.


I like paper because I like physically writing things down.


There's something really satisfactory


about being able to check something off or cross it off.


Not as much when you just tap it on a screen.


But when you write things down,


set yourself a task list,


removes that item from working memory


so you can free that up, free those four slots.


You really only have four slots.


You can think of four different things at a time.


After that, it just whizzes away.


And I think even for myself, I think four is pushing it.


I'm probably two, maybe one.


I like to try and think of one thing at a time.


So those are my two favorite things from part three


is procrastination from the habit point of view.


Remove the cue, you cut away the habit


and set yourself a task list of the half dozen or so things


that you wanna get done for the next day, the night before


and free up your working memory.


Part four was about, or the title was Renaissance Learning,


which I'm not entirely sure what that meant,


but Renaissance Learning and Unlocking Your Potential.


And one of my favorite takeaways from it was exercise.


Now it's kind of ironic that yesterday


in between part three and part four,


me completing part three and part four,


I did a workout in the backyard


and the doctor who was on there, Dr.


Oh, I'm getting tired.


Dr. Terrence Snezdowsk, and I'm re...


Dr. Terrence Snezdowsky, I think I'm pronouncing that right.


Apologies doctor, if I'm not.


But what he said was that exercise


was one of the most powerful performance enhancing drugs


that you can give the brain.


I mean, this is a person who's studied neurobiology


or neuroscience for their entire life


and has an in-depth knowledge


of all kinds of different neurochemicals.


And he's saying that exercise is one of the most powerful


and he's saying that exercise is a part of his daily life


when he wants to try and learn something.


Now, one of the reasons, I don't know the exact reason,


but I can imagine, because I love to exercise, right?


And I think it's valuable if you want to learn anything,


it's a good way to take a break.


It's another form of using the diffused mind.


So exercising, you may not necessarily be focused


on one particular task.


Maybe you're going for a run or something.


You have to focus on the run or a walk,


but you still got this diffused mind


and you're still letting thoughts pour in.


So to supercharge your learning,


maybe you could use exercise as a way


to break up different periods of study.


So for example, what I like to do


is I might do an hour or two of study or work


and then go for a 10 minute walk.


And then I'll just repeat that process a couple of times


until the end of the day.


And that brings me into my final favorite thing of part four


and my final favorite thing of the course is that


there's no need for genius envy.


If you see an expert in any field,


it's likely, well, it's unlikely


that that's happened overnight.


Any overnight success you see has 10 years of work


behind them and all you really see is that last little,


that last little action that all of a sudden


makes them seem like an overnight success.


And all the honest ones,


all the honest people will tell you that, right?


It's about putting in that effort over a number of years.


And then they give the story of Charles Darwin.


You might've heard of him.


He kind of thought of the theory of evolution,


which changed the entire dynamic of the science of biology


and just the perspective of how we view the world, right?


Charles Darwin was a poor student.


He dropped out of school


and his father was incredibly disappointed with him.


And then he went on a voyage around the world


as a naturist, just exploring nature,


but with a childlike mind.


And so he's seeing the world as though it's all a new.


And so he's taking in,


he's not letting that iron shtalung


get advantage of him, right?


That old way of thinking hold back a new way of thinking.


Him with that mindset of always looking at the world


through the child's mind


and the combination of different things


and going for nature walks.


I mean, they even gave the example of,


they showed the footpath of where Charles Darwin


would take walks.


So there's another genius who takes advantage of the fact


that exercise is one of the best things


that you can do for learning something.


So the lesson we can take away from people like Charles Darwin


is back to the point before,


is that there's no need for genius envy.


All the great people that you look up to,


the academics, the business people,


the people who seem like they know so much,


it's all come from time, right?


Everyone has to start somewhere.


Everyone has to go through challenges


and practice learning different things.


So that's enough from me and my favourite parts.


I think if you wanna explore more of the course,


you can jump down to the link below.


There's so much more that I've missed out on here.


And if you want like kind of a more concise version


of what you've seen in this video, don't forget,


there's an article that I wrote summarising a few points


that we've mentioned in here


and that weren't mentioned in here.


But taking ownership of your learning


is one of the most important undertakings


that you can ever take, right?


And I think that if you want the ultimate skill,


the ultimate superpower, it's learning how to learn.


So go ahead, take the course, try it out,


see what you think.


I think you'll find some value in that


because at the end of the day,


if you want to apply the skill of learning how to learn,


you can apply it anywhere.


Special thanks to Coursera for sponsoring this video.


I love using Coursera.


This course is just one of the examples


of the amazing stuff they have on there.


But I think it's time,


I've reached my finish time for today.


And that's why I've come to this spot, right?