If you want to share the contents of one Mac screen with another maybe for the purpose of tutorial style videos Or maybe to take the contents of that screen into I don't know a discord voice channel or maybe a zoom call or something like that There are a number of different ways that you can do that But there are just three that I use all of the time and so I'm going to cover those Specific three in this video and I'm going to share these sort of pros and cons of each one and the different use cases That they all have so let's start first though with the built-in screen sharing that we've got in the Mac And you can find this in the system preferences and specifically down here in the sharing options And you want to be doing this by the way on let's call it the secondary Mac So that's the one that you're going to share the contents of Into the primary Mac, which is the one that you're working on maybe creating your video or whatever it happens to be So on the secondary Mac just make sure that you do have this screen sharing checked It might not be checked as a default but here also you may want to look at all these other different sharing options So you can share files media printers and things like that that connect to those devices as well or those computers And basically share it between Macs that are connected to the same network Assuming though that you have got screen sharing switched on then if you come over into the finder Then have a look in your network section as you can find this from the the menu in the finder You want to go to the go menu and then you'll find network in there But in any case once you've done that you'll see that there are different computers attached And then just click on the one that you want and you'll see if you've got screen sharing enabled It will show you a screen share as an option just there if you're in a different view type It might show up somewhere else. So let's go and have a look at list for you Yep, it's just up there at the top But anyway, it should be there somewhere so click on screen share and what that's going to do is that's actually going to open up An entirely new window Which is if in effect a window onto that specific desktop, so let me just come in here type in my password There we go. So I'm going to just go into demo mode. You'll have to excuse this looking rather small at the moment Let's just get rid of the finder and the system preferences So now what you can see is up in the top left of the screen. This is my just in Ecamp running on my primary machine It's what I'm using to record this video, but you can see here We've got this window Which is a window into the desktop of that secondary computer and one of the great things about using this method For screen sharing is it's not just sharing the screen But you also have the option up here for either just watching basically observing the screen or you can have control mode When you are in control mode, you've literally just then I can just click into this window And you can see how now I can go and view all of these different menus and things like that So this is all basically just a full control of this computer from my primary computer So I find this really useful for when I'm doing screen demos So for example, if I was to bring up my Ecamp live window, I've just hidden it down here So this is Ecamp live running on my secondary computer So when I did my demos of the Ecamp live beta recently This is how I was doing it because I needed to record using Ecamp But I also wanted to show what Ecamp was looking like when it was neither recording or streaming So this is how I did it. I set up a scene which was basically Just a scene as you're seeing there So the top one that you're looking at is Ecamp on the primary computer Hoping this all makes sense, it should do And I've actually just got a camera overlay just popped in there Over this little blank area of the desktop from the the the window that I'm sharing And of course, hopefully it should be quite obvious All I'm doing there is if I just come into a little blank scene here All I did was I created an overlay and then I shared my window For some reason when I'm zoomed in it doesn't show the little window properly There we go. I selected here screen sharing So now it's just basically sharing that window and then I can crop in To the edge of that screen share window and then I can just drag this up So now that's basically giving me a view It's actually what it's actually giving me of it What it's actually giving me is it's giving me a view of this screen sharing window Which in turn is giving me a view of what is on my desktop And in fact, I just made that completely full screen for the purposes of this one I've also done it slightly different as well before Where I wanted to show something that was on a different computer And so I did it like this so it's a bit more obvious And there I've just got a bit basically a picture of a laptop And I've just literally positioned the screen is locked The overlays locked one second You can see I've just basically positioned that where it needed to be in the in the scene So as I say, that's great for doing demos where you want to be able to show All of the interface show everything that's going on If I come out of demo mode, you can see how that really makes it seem like I'm showing exactly what's going on on that second computer One of the drawbacks of this particular method though If I come back into my live demo mode Is that you do have to have this open on the screen I like that because I actually don't have my laptop open My laptop's just on a shelf actually plugged in And then whenever I need to use it, I just fire up the screen sharing And then it's ready to go I don't need to have it open on the desk or anything like that Taking up any space So for me, that works well because I do need to be able to still control it But if you don't want to take up this space And maybe you've actually got the laptop open in front of you You literally just want to share the desktop What you can do is you can use NDI on that laptop instead So let me just actually come and use this very scene that I've set up Come out of live demo mode What I'm going to do is just move eCamp out of the way for a moment And I'm going to search for NDI ScanConverter is the application Now it's just opened, it's opened up at the top here It doesn't actually show anything on the screen as such It's just open in the dock But here what you can do is from the capture menu that you've got at the top You can basically select what it is that you want to share So the default will be the desktop It's going to be transmitting the desktop Or you can choose any other app And in fact any window of any of those apps I've jumped ahead a little bit NDI is basically a method for transmitting video It's a video protocol for transmitting video over a network It's the simplest way to put it So basically whatever I select in NDI here NDI ScanConverter By and blank there That will be basically just being transmitted over the network So what does that actually mean though And how does that look in real terms Well if I come back into my demo mode here And I'm just going to zoom in on Ecamm for the moment Let me come back to this demo scene that I created I'm going to get rid of this one This is the way that we just did it before But now what I'm going to do is I'm going to add a camera overlay Now with a camera overlay What we can do is we can select the cameras And anything that you transmit over NDI will appear as a camera And all I'm going to do is if I just come into the settings That's not going to show up as well zoomed in It's a little bit weird how that is like that Let me just come into the settings So instead of being the camera If I come in here what you'll see is Now we've got the Alex MacBook Pro local and brackets ScanConverter So if I click on that That's now showing whatever we've got selected in ScanConverter So because we selected our desktop This is just another way of bringing the entire desktop Into Ecamm over NDI Now the actual aspect ratio of that laptop is not actually 16 by 9 So what I would need to do is Bear with me a second Just change this aspect ratio to be custom And then if I make that the right size There we go, that's the right size for that particular Mac And so there we go Now I'm basically bringing in the desktop of that second Mac over NDI And what that means is I could then in theory Just completely close this because we're not using it We're bringing the feed in over NDI Directly into that secondary Mac So as I say, if you've got that Mac open on your desk somewhere else And can control it somewhere else Then this is a great way to do that Because it doesn't clutter up your primary monitor There is another way though that we can do this So what I'll do is just come back into my screen sharing Because I will need to show you this And that is using Ecamm So let me just come back over into the scene So what I'm going to do now is I'm going to actually open Ecamm We'll bring Ecamm up here And the other way that we could do this is in Ecamm itself If we go to the output, it has NDI built in And what that does is it basically transmits Whatever you've got running it or the output you've got from Ecamm And it transmits that over NDI So all you need to do for that is go to output NDI output and make sure that it is switched on with one of these sizes So I've just gone with the maximum size there, 1080p And so assuming you've done that, it is as simple as that Then when you come into Ecamm, if I go back into that little test scene that we created And I'll go into live demo mode again Then if I click the little pencil Then instead of selecting the scan converter from here There is another camera option which is Alex MacBook Pro Local Ecamm Live And that is the NDI feed coming out of that MacBook Pro And if I just put the size back to 16x9 You can see hopefully that what is coming out of here If I just zoom in a little bit What is coming into this window at the top here Is actually just the feed from Ecamm on that secondary computer So if I toggle on an overlay You'll see how it appears on the secondary computer as well Or sorry, it appears on the primary from the secondary So basically whatever you've got coming out of Ecamm Or showing it in the Ecamm preview window Will appear then on that primary computer The reason why this is useful is If you want to share specific applications or things like that You may want to actually have them running on a second computer And then just set up scenes in Ecamm to show each of those Using the Ecamm's screen sharing in the second computer To share those different applications And then you can do the switching on there For when you want to bring it into your primary computer As a way to sort of offload some of that workload onto something else Or maybe you've as I said before had You want to bring this into Discord or something Maybe you've got your Discord running on a second computer Your live streaming from your primary computer But you want your computer feed Your live stream feed to also be going into Discord On that second computer You would just use the NDI on Ecamm On the primary one that was streaming And feed it into the Ecamm on the second one And have that going into Discord As a virtual camera or something like that So these are just the three different ways That I would do this Obviously there is a drawback with this latter one That you do have to then have Ecamm running On both computers which might not work out Entirely great for you You might not want to have that running But nevertheless it is just another option But I've found with these three different options Either the scan converter The built-in screen sharing Or just using Ecamm over NDI It really just covered me for all the different kinds Of tutorial style videos Or screen sharing or things like that That I need to do So I hope that has been useful But what I'll do is I'll leave a link to Some more of my Ecamm videos Over on the right hand side


I am Dr. Vanuli Bajpayee, consultant phreco-surgeon and medical retina specialist at Manipal Hospital, Dwarka. Today I will be discussing diabetes and its effects on the eye. Diabetes meditus is a multi-system disorder caused by increased duct sugar levels in the body. Diabetes can affect the eye in various forms. The most important effect of diabetes on the eye is diabetic retinopathy. Retina is the back portion of the eye which consists of nerve fibres. These nerve fibres pick up the images that we see and send them to the brain so that the brain can help us. Retina is the back portion of the eye which consists of nerve fibres which captures the images and sends the signals to the brain so that we can see. In diabetes, the blood vessels which supply the retina are damaged as a result that leads to blood spots, swelling and decreased blood supply to the retina. All these changes can result in loss of vision to various degrees. Early stages of diabetic retinopathy are asymptomatic and do not require any treatment. Later stages may cause drop in vision to various degrees and may need treatment in the form of laser injections into the eye or medications. Last stages of diabetic retinopathy can cause blindness and may or may not be corrected by surgery. So if you are a patient of diabetes meditus, please make sure to get your eyes checked every year for the presence of diabetic retinopathy or as advised by your physician or your eye doctor. People with diabetes meditus are also at increased risk of glaucoma and cataract. People with unstable blood sugars can have frequent change of glasses and experience blood vision. So if you are a patient who has diabetes meditus, please make sure to take very good care of your eyes, do regular eye checkups just like the way you do regular blood sugar level checkups and see your eye doctor regularly.


Welcome back, today we will discuss variance and covariance. So, the variance of a random variable x is defined as the expected squared variation around the expected value. So, it is assumed that expectation of x exist. Suppose, suppose expected x exist and it is finite. And then you define the variance of the random variable x as the expected square variation around the mean, around the expected value. And it is denoted by sigma x squared and sigma x. So, sigma x square is variance and sigma x is called standard deviation. Standard deviation is used more in statistics and variance is used more in probability. So, you consider x minus expectation of this is just a number. If it is finite, this is just a number if this is finite, this is x minus a constant whole squared. So, that is random variable the expectation you take. So, since this is a non negative random variable its expectation is always well defined. So, variance is as long as expectation of x is finite this variance is always well defined it might be infinity. But, it is always well defined there will be no problem of x plus x minus and both being infinity that is because this what is inside is a non negative random variable. So, this is always well defined could be real number it could be plus infinity. So, this intuitively this variance gives you. So, a measure of how much the random variable deviates from its expected value. So, a large value of the variance means that the random variable takes values which are spread around the expected value considerably. And if the variance is small it means that the random variable takes values close roughly close to the expected value with higher probability. That is roughly very loosely speaking that is what it implies that what it signifies this variance. So, clearly this variance is non negative that must be clear this will be clear just by the definition. Now, can the variance be 0 yes if x is almost surely constant if x is a constant with probability 1 then its expected value is equal to the constant. And therefore, the variance will be 0 in fact the converse is also true if the variance of a random variable is 0. Then you can state that the random variable is constant with probability 1 variance of x is equal to 0 if and only if x is almost surely constant. See if x is almost surely the if part is easy if x is almost surely constant then the random variable let us say x is equal to some constant c with probability 1 then expected x will also be equal to c. So, then it is clearly you are just taking expectation of 0 squared then you will have. So, with probability 1 this difference will be 0. So, you will have variance is equal to 0. So, if part is easy. So, if part is easy. So, let us do the only if part. So, the only if if expectation of x minus expectation of x whole squared equal to 0 then integral x minus expectation of x correct is what it means this is the definition of this expectation. Now, you have to invoke a property of the integrals saying that if you have a non negative function whose integral is 0 then the function is 0 mu almost everywhere. Now, that mu is simply p right. So, this is. So, using property number can you tell me what property number is it the in the property of integrals right. So, using property. So, using property whatever right. So, you tell me what property. So, we denote that we see that x minus expectation of x whole square equal to 0 with probability 1 which means x is equal. So, on a set of probability 1 this x minus expectation of x whole square is 0 which means x minus expectation of x is 0 with probability 1 right. So, you fill up this property I do not know which property number it was 6. So, that so that settles one thing right. So, variance is always non negative and it is 0 if and only if the random variable is almost surely constant. So, another the expression for variance which is easier to compute can be obtained by simply expanding out the square. This is something you may be familiar with. So, sigma x square you can write it as expectation of just expand this guy out get x square plus expectation of x the whole square right minus 2 x expectation of x right. I am just writing a minus b whole square as a square plus b square minus 2 a b. Now, I will invoke linearity of expectation ok. So, I have 1, 2, 3 random variables this guy is just a constant it is not even a random variable right this is just a constant. So, I will have expectation of x square plus expectation of x square minus. So, this guy is a constant again it will come out of the expectation right. So, you will get expectation of x square plus expectation of x the whole square minus 2 expectation of x times expectation of x right. So, I am bringing this. So, you think of this is a constant and you bring it out of expectation right that is a property of expectation also. So, this is equal to expectation of x square minus expectation of x square. So, that is often easier to compute rather than evaluate. So, if you are given some distribution some exponential or something like that this is a little tedious to apply. This is easier to apply you first compute the expectation of x compute the expectation of x square separately and then subtract the expectation of x square and expectation of x the whole square. So, since variance is always non negative you know that. So, this guy is non negative right. So, that means for every random variable the expectation of x square is always bigger than or equal to the square of the expectation. So, you take the square the random variable first and take expectations you will get something that is bigger than or equal to taking the expectation and squaring it. And in fact, if expectation of x square equals expectation of x the whole square then you can conclude that x is constant almost surely. In fact, you did you do Jensen's inequality in your homework since it is in the homework that is coming up right. So, if you have a convex function if you might have seen your homework if you have a convex function. So, roughly speaking convex functions look like that right there is a more there is a proper definition in your homework. So, if you take any convex function g of x expectation of g of x is always bigger than or equal to g of expectation of x. And in this case the function simply the square function square function is convex right. So, this is bigger than or equal to that. So, this Jensen's inequality is a very important inequality you will do that in your homework. So, any question so far fairly straight forward stuff right. So, we probably know this already. So, let us look at some examples. So, if you have a Bernoulli random variable x is equal to 1 with probability p and 0 with probability 1 minus p in this case you will have expectation of x square is simply 1 times. So, this is p an expectation of x is also equal to p right. So, variance of x will be p times 1 minus p. So, there is an example in your text book where x is Poisson in your in your notes have this example e power minus lambda lambda power k over factorial k for greater than or equal to 0 right k in this case expected x square. So, expected x first of all is equal to sum over. So, it is simply sum over k equals 0 to infinity k times e power minus lambda lambda power k by k factorial. So, what you will get? So, you will get lambda.  So, you will get this will be equal to lambda you can just do the summation. And for this for expected x square expected x square which is sometimes referred to as the second moment expected x square for that will be sum over k square e power minus lambda lambda power k by k factorial k equals 0 to infinity you can do some manipulation now. So, what this boils down to is you you can write k squared as. So, you can do some manipulation here let us let me write it this way k equals 0 term is 0 obviously k equals 1 through infinity. So, you can write it as k I think I have to do something like that if I remember correctly k squared minus k plus k times I think this is what works. So, this is this. So, this guy will give you lambda this part will give you lambda. So, if you take that. So, k square minus k will cancel with that to give you k minus 2 factorial. So, that will be sum over k equals 2 to infinity lambda squared e power minus lambda lambda to the k minus 2 over k minus 2 factorial. So, I am just considering that this bit plus sum over k equals 1 through infinity lambda e power minus.  So, this is again. So, this will be lambda squared plus lambda. So, variance sigma square x will be lambda squared plus lambda minus lambda squared. So, the variance is also equal to lambda. So, for the Poisson random variable both expected value and variance are equal to lambda. Let us do a couple of continuous random variables as examples. So, if you have x is uniform in a b in this case you can. So, your expected x will be equal to a plus b over 2 you can show that and expected x squared be equal to. So, this will be b cubed minus a cubed over 3 b minus a and that will simplify further actually.  So, that will be 4 a b 6 a b. So, this should be. So, this should finally, come to b minus a squared over 12 if you just rearrange this guys. So, the variance of a uniform random variable is the width of the interval squared over 12. So, in the case of an exponential you can show that. So, we know that expectation of x is 1 over mu we already derived that you can show that expectation of x squared is equal to this also we showed I think in some other time 2 over mu squared. So, variance will be this minus square of that which will be 1 over mu squared. So, in all this you see. So, if your uniform random variable here this spread over a wide interval the variance will be bigger. Similarly, if your exponential is if your parameter mu is. So, it is this the pdf looks like this if plot of x against x it looks like that with value 1 over mu.  So, the variance is big and if mu is large this distribution is very peaky around 0 and your variance is smaller. So, that intuition keeps coming through then finally, the case of the Gaussian. So, remember we defined. So, we had the n mu what is this Gaussian density n mu sigma squared sigma square root of 2 pi e power minus x minus mu squared over 2 sigma squared for. So, this is for all x in R here actually this mu is the expected value and sigma square is the variance. You can actually do the integral it is a dirty integral it is not easy to compute, but you can do it. If you compute integral x f x dx you will get mu and if you compute the variance you will get sigma squared. So, in fact. So, this Gaussian distribution is parameterized by mean mu and variance sigma square which is why we called it sigma square in the first place. So, this looks like. So, I plotted this earlier. So, it is a distribution that is looks like that. So, it is centered at mu.  And it goes down. So, if your sigma squared is if your sigma squared is small then you have the value at x is equal to mu will be quite high because there is a 1 by sigma term here. And the rate if your sigma is small it also goes down very quickly because there is in this e power minus if this is small then this whole exponent would be very big e power minus all that would be very small. So, when sigma is very small you have a very highly peaky distribution which falls down very quickly. And on the other hand if your sigma is very large you will have something that is not very tall and then is very broad spread out Gaussian. Again you see that the variance being small means the distribution is very concentrated around the expected value. So, this integral I will not do it is a pretty there is slightly dirty integral you have to do integration by parts and use some standard integral. So, any questions. So, could it be that could it be that the variance of a random variable is infinite while the mean is the expected value is finite. So, if the expected value is infinite. So, then variance is not even defined. So, we only talk about variance if you have expected x finite right otherwise this formula would not make sense. So, if so is there a case my question is there a case where the expected value is. So, the expected value is finite, but the variance is not variance is always defined it could be plus it could be either some real number or plus infinity right variance is. So, it is a non negative random variable. So, it is defined. So, is there a possibility of having expected value of x is equal to some finite number and variance is equal to infinity can somebody give me an example. So, first you know an example where the mean is plus infinity expected value equal to plus infinity one sided Cauchy right Cauchy itself does not have a mean right expected value is undefined. So, one sided Cauchy is has an infinite expected value. Therefore, the variance is I mean you cannot if you the mean is infinite you can clearly say that variance is also infinite, but I am saying the opposite is the mean finite and variance infinite is it possible Erlang.  So, the Erlang has all find all moments all power moments are finite. So, why is. So, think about why is the Cauchy the one sided Cauchy you said has an infinite expected value right why is that Cauchy is goes down as 1 over x square right. So, when you hit it with an x you get some infinite integral right. Now, I am asking you if it is possible to keep the expected value finite and have the variance infinite. So, what would you do you do yeah if you consider a distribution that goes down as 1 over x cube right or even a discrete random variable which is like 1 over k cube for k greater than or equal to 1. You have to normalize appropriately of course, then you will have a situation where the expected value is finite, but expected x square will be infinite right. So, you can therefore, cook up an example with finite expected value, but infinite variance alright any questions this covariance is. So, is a number that measures how 2 variables random variables x and y jointly vary in some sense alright. So, this variance is define only for 1 random variable x right and it captures how much of a variation around the expected value the random variable has. This covariance is defined for 2 random variables x and y and it captures this. So, definition the covariance of random variables x and y is defined as expectation of x minus expectation of x y minus expectation of y. And if you simplify that you get expectation of x y minus expectation of x times expectation of assuming see this definition is assuming that all these are meaningful right we have to first this is make sense only expectation of x y has a meaning. And you should not have a situation where this is infinity and one of these guys is also infinity right you should not I mean all that in all those cases it is not defined right. If this is infinity and that is infinity right you cannot define the covariance. So, this is define subject to everything being well defined the expression itself being well defined. So, this is the covariance. So, this is again. So, this expression is more useful in computing covariance whereas, this expression gives you better intuition on what the covariance is actually doing right. So, let us say that x and y are random variables of course, they are on the same probability space. So, suppose that x and y have the tendency for the time being let us assume that whenever x is bigger than the expected value. Suppose that y has the tendency to be smaller than the expected value right then you will have a term that is negative right. And if x and y tend to be both on the same side of the expectation with a greater probability either both greater or then the expected value or both smaller than the expected value then you will have a positive value for the covariance right. So, in that sense it captures the joint the expected joint variation in some sense correct. So, I tell you that x is if whenever x is takes a large value value bigger than the mean then y is also likely you take a large value right greater than the greater than its mean then you will have a positive covariance right and similarly in the negative case. So, that is covariance any questions definition x and y are said to be uncorrelated if covariance of x y equal to 0 i.e expectation of x y equal to expectation of x times expectation of y. So, if the covariance turns out to be 0. So, this could turn out to be 0 right such a case x and y are said to be uncorrelated it is a new term you are learning. So, x and y are said to be uncorrelated if covariance is 0 or expectation of x y is equal to expectation of x times expectation of y. Theorem this is an important theorem if x and y are independent random variables they are uncorrelated. So, this is an important theorem. So, independent random variables are always uncorrelated. So, if you take two independent random variables x and y expectation of x y will always be equal to expectation of x times expectation of y. Now, so I will prove this theorem it is a very important theorem and it is somewhat non trivial proof. So, the one thing I want to remark before that is that the converse is not true. If two random variables are uncorrelated it does not mean that they are independent. So, independence implies random variables are uncorrelated, but not the other way around. So, which is a stronger which is stronger independence or uncorrelatedness independence is stronger right. So, remark the converse is not true. So, can somebody tell me why the converse is not true how will you prove that the converse is not true. So, how will you prove that two random variables uncorrelated does not imply that they are independent. That is what is given to you right you are given that they are uncorrelated you have to show that it does not imply independence. So, you want to take constant random variables. So, then what both constant random variables. So, if both if two random variables of both constant the sigma algebra they generate are simply omega and phi right. So, are they independent sigma algebra they are right. So, they are also independent that very trivial case. So, two constant random variables are independent right. So, that is not an example same then they would not be if x and y are equal by there you get variance right. So, you have to cook up a it is not entirely trivial right, but there are some easy examples to show that. So, consider. So, there are many examples you can cook up, but consider x uniform in minus 1 to 1. So, uniform random variable in minus 1 to 1. We can show that x and x square are dependent, but uncorrelated. So, this is if you produce one example it is enough right. So, I am just considering a random variable x which is uniform in minus 1 to 1 and then say y equal to x square. So, in that case I mean I think you will be able to very easily show that x and x square or not when you nobody will believe that x and x square are independent right. So, you can actually explicitly show that if you want you can compute the joint distribution of y and x right and you will very trivially you can show that they are dependent. However, they are uncorrelated right because. So, you say y equal to x square covariance of x comma y is equal to expectation of x y minus expectation of x times expectation of y which is equal to expectation of x cubed minus expectation of x times expectation of x square right. You can see expectation of x is 0 right. So, this term is definitely 0 and x being symmetric around minus 1 and 1 x cube will also have a you can prove that x cube also has 0 expect this term is also 0 right. After all this expectation x cube is simply integral of x cubed half d x is not it from minus 1 through 1 this is expectation of x cubed correct that is clearly 0. So, this is also 0 right. So, x and x squared in this case are uncorrelated, but dependent random variables. So, I think I do not have the time to complete this proof or maybe I will just start it. So, I want to well I am going to prove this right I have proven that the converse is not true I have to prove the theorem now right proof of the theorem. So, I have to prove that if x and y are independent they are uncorrelated how do you prove it p d a, but who says x and y has a p d f right expectation of x y is integral x y d p right correct again right. So, this is a theorem you would have proved separately for discrete random variables proved separately for jointly continuous random variables and then blindly accept it is generally true right. Now, you are going to prove a general theorem which is why I said it is a non trivial theorem right. So, if you have density. So, x and y had a joint density you would just evaluate those two things separately and so that it holds right, but x and y they not have a joint density they do not have a p m f right they may not be discrete one of them may be some singular another may be some mixture right even in that case this holds. So, how will you prove that by now you should know right how do you prove anything in integration start with simple functions right. So, suppose x and y are independent simple random variables. So, I have x has a representation sum over a i i of a i does this representation and y has sum over i equals j equals let us say j equals 1 through m b j i of b j in this case can you prove it. So, in this case. So, x y right x y will be what sum over x y will also be simple right this can be sum over i equals 1 through n j equals 1 through m a i b j indicator a i intersection b j correct suppose I mean these are let us say in canonical representations for the sake of to fix ideas. So, this is your so this is your expectation of so this is your x y random variables itself. So, expectation of x y would be so this is a simple function right. So, this will be i equals 1 through n j equals 1 through m a i b j probability of a i intersection b j. So, now what can you say now is the key step see after the all a i c what is a i right a i is the event that x is equal to correct and b j is the event that correct. So, now this a i and b j are independent events why x and y are independent random variables. So, this will product out correct. So, this by independent. So, by looking at this you can say that these two are independent events right. So, this will be equal to a i b j p a i times p b j right. So, now the a sum when the b sum a sum when the b sum come out right. So, you can take the i summation this only has i's and this these two have the j's. So, now it products out easily right and what you have is the expectation of x times expectation of y right. So, this you can show yeah expectation of x times expectation of y. So, we have shown it for simple random variables. So, we will complete the proof next class now what you have to do now approximate x from below using simple functions. Then you would have proved it for non negative random variables and then finally, you have to do x plus x minus y plus y minus then get the result right. So, tomorrow we are meeting at noon there is a change the time table is not it. So, tomorrow there is a exam. So, 12 o clock is the class right here.


Excellent, any of these stories you particularly love, don't love? Well, the Walmart blockchain one. Yeah, that kind of sets us up for our main topic too. Yeah, exactly. The Tinder one's interesting. Okay. Being a guy who's been married for 20 plus years, I have no real experience with those things, but. Yeah, I love the dating app stories with all my married friends on the show. Like, let me tell you how it works. Well, that's an important perspective that we need, because I don't know. Well, yeah, no, Amos earlier was like, what, what, what, how is this different? It's like, well, it is, but. That's a good thing to investigate though. Yeah, exactly.  So Tom, the new camera looks very nice. Okay, yeah. So this, this camera definitely looks better than the C920, doesn't it? It just looks, the colors are richer. They looked fine before, but this one looks nicer. And you can see things on the shelf better. Yes. Yes. So. Yeah. I think I kept the framing intentionally similar to the camera yesterday. That way, because one of the things in the back of my mind I'm trying to do is this camera, because it's HDMI to Thunderbolt, will occasionally just not want to work with something. So I like to have the C920 as a backup, even if it's not quite as good, but I think we'll stick with this one. And we may play with the framing. We're definitely going to play with the lighting. When the fireplace go away? When I moved. Actually, we changed away from the fireplace when Sarah joined the show last October. Okay. And I went to bookshelves with books on them, but it was in the same room as the fireplace. Did you wear like a jacket and have a pipe in your mouth? No. You should. But now I'm regretting that decision. Hey, show got time, man. Where were you a year ago, Mike? Spoiler on my new look. You were asking what was going to happen after the one year anniversary of having Sarah. Now everybody knows. Mike, where are you based? Seattle. Oh, in Seattle. And how is Seattle this time of year? Super beautiful. It's like 70 degrees. Nice. Oh, that's so nice. Seattle with nice weather is just the best. It really is. But we only get it for four months a year, so. Yeah. LA had a little Seattle-ish weather this morning. That little gray come in. Still does in my parts.  It's not come out. But we're just starting to get some noticeable termination dust up here, so. What does that mean? Termination dust. There's a frost line that you can see on the mountains, and it's called the termination point. And anything above that, it's not actual snow, but it's enough frozen precipitation that you can, like it's the dew and everything else that freezes. So you have to give it some kind of names. They call it termination dust, because it's not, like I said, it's not actual snow, but it's a clear line across the mountains at a certain elevation, and it's low to creep down as the winter comes in. And we've had it a few times just on the peaks, but now there's enough of it where you can actually, yes, there's termination dust in the stain over multiple days. So it's, winter is coming. Termination dust, I never knew. It's, yeah, it sounds kind of ominous. Yeah. It's like, nah, just some snow dust, not everything else. It's an 80s hair death metal band. Willem Dafoe and Kate Blanchett, termination dust. Well, let's do a show. What do you guys say? I say, okay. Can you do the line three, Ms. Lane? I can, and I will. 30 seconds, we'll start it off. I remembered 30 seconds from now. Man, I was so burned out yesterday, I was crazy. All right, 20 seconds. Oh, stretch, crazy stretches and popping joints and stuff. This ain't gonna work, kinks, yeah. Hey, Silverblade, good to see you. All right, five, four, three, two. Thanks to everyone who supports Daily Tech News Show directly. To find out more, head to dailytechnewshow.com slash supports. This is the Daily Tech News for Tuesday, September 25th, 2018 in Los Angeles, I'm Tom Merritt. And from Studio Fila, and I'm Sarah Lane. And sitting in once again for Roger Chang, our normal producer. I am Anthony Lemos. Good to have you along, Amist. Also joining us today, very happy to have Michael Wolf of The Spoon back on the show. Up in Seattle. How's it going, man? It's going well. It's been a year or so. You got a new backdrop. You got a little more gray in your beard. Yeah. I have more gray too. Visual effects for both of them, right?  How's your year really been? Now it's good. This is the second day in the new setup here. So again, as I said yesterday, audio may not be exactly what you're used to, but hopefully it's getting a little better every day. Video may not be exactly what you're used to, but hopefully that's getting better every day. Thank you for your patience. Now we're going to talk about some food tech with Michael, particularly, but let's start with a few tech things you should know. Qualcomm is accusing Apple of stealing its source code and giving it, and other trade secrets, to rival chip maker Intel for the purpose of improving the performance of Intel chipsets. This is according to a filing with the Superior Court of California that Qualcomm issued. The company has previously accused Apple of breaching an agreement to allow Qualcomm to audit Apple's use of its source code and other trade secrets. Google CEO Sundar Pichai said in a statement, he will meet with lawmakers this week as his company, representing his company, to address concerns from conservatives in the US government who alleged that their views are being censored online. Google denies it makes content decisions based on politics, despite some Republicans accusing Facebook, Google and Twitter of silencing conservative voices and news sources. The private meeting was organized by House majority leader Kevin McCarthy, a California Republican. Twitter is changing how it communicates rules of its service to now include community feedback. Now previously, the company followed its own policy development process, including taking input from its trust and safety council and various experts. Now the company says it will ask everyone on the platform for feedback on a new policy before that policy becomes part of Twitter's official rules. The company also announced a policy to prohibit language that dehumanizes others based on, quote, their membership in an identifiable group, even when the material does not include a direct target. All right, let's talk a little more about founders leaving their companies. Usually a founder of an acquired company will stick around for a year, maybe two, but unusually at Facebook, the founders of WhatsApp, Oculus, Instagram all stayed around a lot longer. Now, Jan Kuhn from WhatsApp left recently under a lot of speculation that maybe he didn't like the way privacy was being treated. Obviously Palmer Luckey left Oculus for all kinds of reasons, but Instagram co-founders Kevin Systrom and Mike Krieger have been sticking around for nine on six years and that's over. They resigned and planned to leave the company in the coming weeks. They notified the company on Monday, sources tell the New York Times, both are said to be taking time off after leaving Instagram. Mark Zuckerberg, Facebook's chief executive, praised their founders in a statement and said he wished them all the best and is looking forward to seeing what they build next. And their goodbye notice, they didn't really thank anyone, did they, Sarah? They didn't and that doesn't necessarily mean anything. No, it doesn't. You know, both Kevin and Mike, who I've been sort of familiar with since Instagram came on the scene way back in 2009, 2010. 2010, the first Instagram photo I took was sitting in the cottage at Twit with me up in the upstairs little office. Exactly, yeah. And one of the first, I think interviews they did was with Leo and I way back in the day, but you know, they're young, they've had like crazy success and at the same time, Facebook and Instagram as a whole have changed. The companies have changed quite a bit over the years. Now, neither Sister Mer Krieger have mentioned anything about not being in a line with anything that's going on with Facebook and all signs point to Instagram mostly working autonomously this entire time based on folks that have worked on the Instagram team. So you could say, well, you know, maybe they're just tired and they want to do something new. That's fine. There might be more to it, but neither have pointed to any real strife within the company as a reason for them leaving. Michael, you've covered a lot of startups over the years just from the outside reading the tea leaves here. These are two founders that stayed a lot longer than they normally do, but maybe they would have stayed longer if conditions were different. Do you have any insights on this, do you think? Not really, other than I just think about what they got acquired for, which isn't really the story of them leaving, but a billion dollars I think was the acquisition price. And then just a couple of years later, WhatsApp got bought for 18 or 19 going. So I'm surprised they stick around as long as they had. I would have been mad after that acquisition and left.  A study at the University of Louisville that was published today in the New England Journal of Medicine details how those who are paralyzed by spinal cord injuries in the US can achieve more independence, improved circulation and bone density and boost cardiovascular health. Junior, as it's known, is the Restore Advanced SureScan MRI NeuroStimulator made by Mectronic FDA approved for pain management controlled by a remote that communicates through skin and connects to a hub in the abdomen. A theory is that after a spinal cord injury, spinal networks may lose the electrical charge and information that they're getting from the brain that might be part of the problem. Although some weak connections may remain that a stimulator can talk to and kind of boost. Not everybody responds to the treatment, but Jeff Marcus and Kelly Thomas both are able to take steps again while using Junior, which both were profiled in a Verge article about this, which was really amazing. Yeah, Jeff is the first person to ever take steps as a paraplegic from this treatment. And Kelly Thomas had even greater success following on from him. Go watch those videos, they're inspiring. But the idea of taking something that's meant to relieve pain and finding another use for it is great because it's already FDA approved to use exactly the way they're using it. Even if it doesn't work for everybody, the fact that it could work for anybody is crazy. And Michael, it's kind of described the way I see it as a signal booster. Like if your signal is attenuating and you put one of those, tie one of those little boosters in to pump the signal to make it go longer, that's kind of what they're doing to their nervous system it feels like. I feel like we're on the precipice of not saying that things like being paralyzed are going to be eliminated, but we're at this inflection point where 10 years down the road, 20 years down the road, you'll see people who have had these catastrophic injuries just maybe regaining mobility. So it's super exciting. Yeah, the descriptions that they gave in these videos reminded me of, we've covered technology before that allowed people to hear for the first time. And these people who never thought they'd be able to take an unassisted step ever again. And I guess it is assisted technically, but not by exterior mechanics, right? What better are those videos than seeing someone hear or see for the first time or walk for the first time? They're amazing. The weird thing about it is they have to turn it on. If it's not, it doesn't stay on, it's not persistent. They hope to get it there someday. But if they don't have it on, it doesn't do anything. They have to have it on and working. And for some reason can't leave it on all the time, but man, Sarah, this is cool stuff. Yeah, and especially, I don't know, spinal cord injuries, they're vast, they have a variety of issues, but the idea that you can stimulate the brain that talks to the rest of the body, which we all know is a thing, but to be able to do that in a way that can help people who have limited to no mobility in their body is, it's magic. Yeah, it's stunning. Google announced new AI enhancements for search AMP stories, which is kind of like Instagram stories, displays the fast loading web pages and coming to search results, starting first with stories about celebrities, athletes and other notable people. They've been testing this for about eight publishers since February. Google is using some computer vision to understand the content of videos and adding a section called featured videos that link to sub topics of searches.  Google images is putting greater emphasis on web page authority to order the results. So pages that normally show up higher in search will be more likely to show images higher up and if images are central to that webpage, particularly those images will show up higher. And Google lens will come to web search to analyze and detect objects and images. It's something that's been on the mobile version for a while. There's also a new section called discover that'll stream headings and cards of items that Google thinks you might be interested in based on what it knows about you. Mike, how interested are you in celebrities and athletes and other notable people? Well, I get excited about machine vision and using AI to better recognize what's going on, but I actually am more excited about getting outside of image recognition and we'll talk a little bit about it in food tech, but there's a lot of stopping going on in terms of digitizing and recognizing other senses. So smell, taste. So I think Google has had a long time leadership in image and machine vision, but I'm waiting for them to do the smell search. Is that weird? It sounds weird. It sounds weird. But I think it's not weird, you know what I mean? Like if you could, so how would the query work? Do you waft it towards a sensor and then? I don't know. I'm thinking you're, does your Google Home in five years have like a smell sensor and then it recommends the pasta, the pasta sauce that you've been using for the past week, right? If you like Paul Newman's own, you could also try Reserva. Exactly. Maybe this is a horrible idea. I'm sorry. That's where my brain goes with search always, right to commerce, but yeah, there could be other cool things that it would do as well. It's interesting. And it's interesting to see Google celebrating their 20th anniversary as a search engine, but by bringing some AI into search, none of these things really blow me away given what we know about what these kind of algorithms can do, but they're interesting little features, especially lens coming to web. That's something that is pretty cool to be like, what is that, show me more of those. I mean, there's too much news for us to read every day, right? I mean, we could all probably agree on that. So as much as Google can figure out, okay, this is what I can offer up to you as somebody who kind of get a sense of what kind of news you're looking for. Great, the sort of, you know, a celeb and athletes and pop culture news, that's the obvious way to bundle a lot of the stuff. Any social network that you connect to for the first time, they'll say, here are some people to follow. And it's always, you know, that kind of celeb stuff.  So it's the first step in figuring out more of what Google thinks you want to know more about, which is in the long game, a good thing. Yeah, call me when they add a few more categories. Speaking of long games, the Indian version of Tinder is introducing the My Move feature, which allows women to limit themselves to starting a conversation with a male match rather than the other way around, similarly to how Bumble operates for anybody who uses dating apps.  Otherwise, there is no conversation. Tinder has been testing the function in India for several months and plans to spread it worldwide if the full rollout proves successful. So we were having a conversation with Amos, who's producing the show today earlier, and he was asking, I think, a pertinent question, why do you do this just for women? Now, and to be clear how it works if you're confused out there, only women who opt into it, will it work this way? You have to, as a user, say like, yes. Yeah, like Tinder isn't changing its whole model. I want you to have it work this way where nobody can chat with me unless I chat with them first. Why not roll it out that way to everyone, to give everybody that option, do you think? I think it makes sense. I mean, I think generally we can assume that men tend to be more aggressive and women oftentimes are the ones who are scared of these communities. And you seem the success of Bumble. I think, aren't they gonna basically do an IPO? So I think they're kind of matching what Bumble's doing. It also makes sense in certain geographies like India. But I think you're right, Tom. I think why not make this an opt-in? If you're as good looking as Tom and you're having all the women swipe left, I don't even know, do you swipe left or right? I don't either. Swipe right if you like that one. Okay, everyone's swiping right on Tom. Maybe he doesn't want that. So maybe he should opt into this. Yeah. I'm sorry, Tom. But I mean, in all seriousness, and India is an interesting market because some of the way that people meet each other in India is different than in the Western world.  Like, what's the problem? And I don't really have a good solution to that except that some women find it more comfortable to be the person who reaches out first in order to say like, if I really wanna do this, this is how I'm going to go about it. And there are lots of reasons that women might feel that way. And I think that Tinder is very smart to at least allow this option. Yeah, it cuts down on creeps. It cuts down on unwelcome interactions. And if you let both men and women do it, you would have a weird situation you'd have to resolve where both people have said, no, I get to chat first. In which that's not gonna work very well. So I guess it's an easier interface. User interface issue if you only have one gender able to do it. Although there are same-sex couples matching on Tinder, so there could be an issue there in that section as well. But for the most part, yeah, I think it's because women are more likely to have creeps reach out to them than men are. Walmart is asking its suppliers of lettuce and other leafy greens to use blockchain to track shipments by this time next year. They're like, you've got a year. We want you to start using this because right now, neither the store owner or the consumer really know where the lettuce came from. And the US CDC has recently warned consumers not to eat lettuce grown in Yuma, Arizona because of contamination. But how do you know for sure that that lettuce is from Yuma, Arizona? It's all paper records these days. So it takes a long time to track and there's sometimes confusion about where it really came from. Blockchain, we've talked about plenty of times on the show, is a dead simple way to help track where things have been and better tracking could reduce waste and increase consumer confidence in what they're buying. A blockchain-based system would let stores and customers see the entire supply chain of a product at the point of purchase. Michael, this is in your wheelhouse here. What do you think of this? Well, Walmart's been leading on this, right?  I still think we have a long ways to get there, but you might just think it's Walmart, but I was on the East Coast two months ago talking to a bunch of folks who are involved in the fishing industry and people at the source, whether it's farmers or fishermen, they want food provenance and traceability because if you're a fisherman and your fish is airlifted to Japan and someone gets sick, you wanna be able to prove that it was the handling and not the fish, right? So I think this is something that everyone along the entire food value chain is interested in. Well, and a lot of lettuce was thrown away because they weren't sure if it was from Yuma or not. So they're like, better safe than sorry, we're getting rid of it. That's how this could cut down on waste. If you're like, no, we can verify these crates came from not, let's not trash on Yuma too much, from wherever the latest problem is and you only then rejected those shipments. It's just greater accuracy. I think that helps everybody. Yeah, I agree. I think you're getting to the point where not only are you seeing RFID gets so low cost and you're seeing blockchain finally mature that we might really get there and being able to understand where all our food is coming from. And I think that's huge. Yeah, just imagine having that, having that kind of information at a checkout. Just as a stats nerd, I'd be interested. Yeah, exactly. Just to see where it came. Hey folks, if you wanna get all the tech headlines each day in about five minutes, be sure to subscribe to dailytechheadlines.com. All right, we got more food tech to talk about. Lilliana Byington for Food Dive wrote up an interesting summary of a discussion at the Food Innovation Summit Friday. Study by the Center for Food Integrity has found that only a third of survey respondents strongly agree they're confident in the safety of the food they consume, another way that blockchain might help. A few other projects touched on in this article, Carnegie Mellon working to develop an ingestible sensor so that you just swallow it like a pill and it could monitor gut health or stimulate damaged tissue or target drug delivery to fight cancer. Tufts University School of Engineering has created a tooth sensor, which is tiny, can be attached to your enamel and measure things like glucose and sugar and alcohol, and then transmit through RFID and kind of an extended RFID, all of what it's finding back. They say they wanna do other things as well in the future. And a few years back, Baidu developed smart chopsticks that can detect temperature, even the freshness of cooking oil and they have lots of plans for it to be able to detect other things as well. So really, really interesting stuff here, Michael, that's being developed. What do you think of all this? Well, first of all, I wouldn't want the tooth sensor right on my front tooth. I'd probably choose the back tooth, that picture there. Yeah, I feel like the pictures they show are because it's easy to see, not because that's where you want it. Listen, I think, you know, if you look at like the first generation of wearables, right, they were great because they tracked, you know, pedometers, they tracked movement, but I think we're on the cusp and you saw with the latest Apple Watch announcements, right, as they're starting to really try to understand your biorhythms and what's going on inside you. I think you'll start to see that connect with your food, with the state of your health on a daily basis. And the sensor technology is really advancing quickly. So that article also mentioned the NEMA food sensor that actually allows you to, in real time, understand if there's glue, because as we all know, gluten warnings at restaurants aren't always accurate. NEMA just came out with the peanut sensor. So I think that's great. For people who have food allergies or special needs around food, I think you're kind of coming into this time where we'll actually be able to kind of get over that hump and not have to worry about what you're eating. I love the idea of tracking what I eat. I hate the idea of having to stop and write it down. Yeah. Especially because as good as a lot of these diary apps are these days, I'm always eating something that doesn't quite fit the profile. And I'm like, should I choose this category or that category? Which one's closest? And I know it's not getting accurate. The idea that I could have a sensor that just tracks like, here's how many carbohydrates, here's how many vitamin D. We're seeing it all go through you. And we're making a pretty accurate estimate of what you're eating to the point where my dream app is the one that takes all that sensor information from my more conveniently placed tooth sensor and tells me, hey, you know what? You should eat for dinner this because this would complete your nutritional profile today. Yeah. So I think real time food tracking, we're on the cusp of that. But also one of the other big trends just generally in food is this idea of hyper personalization. So you saw, I don't know if you saw today, Kroger announced that they're moving towards personalized meal kits where you can assemble in the store. That's kind of like a rudimentary step along that equation. So like a hello fresh kind of thing? Yeah. Not just taking one of the five choices that you used to have from Blue Apron or whatever and have it shipped to your house, you can kind of customize it. I see. But I think what's more interesting if you look at like some of the patents being filed by intellectual ventures, they're working on real time sensing and printing a food based on what you're, what you may have eaten in the past but also what you may have swallowed with some of those intestinal trackers. So you combine instant manufacturing, personalization and real type food tracking, it gets pretty exciting. We're a long ways off from a lot of this, it's pretty science fictiony, but I think we're on the cusp of some of this. I think one of my questions, and this is sort of related to the idea of a smart watch being able to track, how many steps you take and is your heart rate great? And if you feel healthy and you're not necessarily on any sort of a food restrictive diet of any kind, how does this technology affect your life? Well, I think you can always optimize, right? Like I just went and got my once a year test and I had higher cholesterol than I thought. Like I feel pretty good, but I didn't know that. So I think it's always hard to kind of know what the internal systems are doing at any given time without like real kind of blood work or sensing. So and the danger is you, there's gonna be a lot of snake oil salesman along the way, right? So I think the challenge is Silicon Valley, oftentimes wants to get ahead of it. Is this stuff really approved? There was a little bit of a controversy around the NEMA sensor, because one of the gluten wash dogs basically said they don't really detect gluten to the level that they would want. So I think we're seeing real progress here, but is the progress gonna be fully approved by the doctors or is some of this gonna be like beyond what it should be doing or beyond the claims it should be claiming, right? So, but I think it is exciting. Yeah, that reminds me of AI a lot. Like AI algorithms can do a lot, but there's a lot of people claiming they can do things that they can't. And a lot of people afraid that they are going to do things that they can't yet. And we still haven't seen the full promise of what they can do and what they probably will be able to do. And this feels like the same kind of layout where we're gonna get the Commodore 64 of gut sensors. It's not really gonna be able to do all the things that a PC eventually could do, but it'll be nifty, you know, it'll be fun. And early adopters like us will probably try it out. And there will be a lot of people, like you say, that will make claims that you gotta watch out for early on. But this does feel like one of the arenas where you're going to see a lot of innovation and a lot of new kinds of startups. And I think these days, a lot of people are looking for who possibly could unseat the Facebook, Google, Amazon, Apples of the world. And it's always from a new area. Google came out of search. There was no search to unseat Microsoft and Microsoft was formed. So these are the kind of areas I kind of look at and say, okay, who's going to emerge out of these? And it'll probably be 10 years or more, maybe before somebody does. But this is going to be someday the kind of thing that everybody's gonna wanna try because it's going to improve your life. And to Sarah's question, improve your life in ways that you don't know you need yet. My doctor is huge on testing Omega-3s. She's like, you won't change the way you feel, but if you have the right level of Omega-3s, it is a coincidence with less risk for heart disease. And she just wants to see that number down. Yeah. And I think we're on the precipice of more and more of us having information ourselves based on gadgets that have proven to be able to give you accurate information, whereas we'd never had an opportunity before instead to go to the doctor and be like, please test me, am I okay? I don't know, I have no tools of my own. So that, I mean, that is, again, early days. And I think, you know, I'm one of those people who's like, but how does that help me? Well, it's going to help me going down the road when I'm able to come to a doctor or a professional or anybody who might be able to help me with a particular issue with a lot of knowledge that has been proven to be accurate. Yeah, yeah, yeah, well said. And to your point, Tom, you know, you're right, like this is one of those areas that an outsider could come in and disrupt. But if you see, look at all the big platform players, they're kind of sniffing around the food world in their own way, right? So Apple, I think he's doing it from a wearable standpoint. Amazon, obviously about Whole Foods and his big into delivery and commerce. And Google's investing a lot in image recognition. They come out a couple apps around food recommendation. So I think they all, they're all kind of sniffing around there, but they don't quite know how, but they all know that if you look at how we spend our money, the third biggest spend, what we spend our money on besides home and transportation is food. So it's a massive opportunity. Yeah, absolutely. Well, thanks to everyone who participates in our subreddit. We have lots of food stories in our subreddit all the time. Among many others, you can submit your own and vote on others at dailytechnewshow.reddit.com. And of course, we're on Facebook as well, Facebook.com slash groups slash Daily Tech News Show. Let's check out the feed bag. I mean the mail bag. Oh, the feedback. Ah, I see what you did there. Nick had some feedback on Lamar Wilson and my talking about our new iPhones yesterday. And I had mentioned the OLED screen, nice as it is, didn't seem that bright, even at its brightest. Nick says OLEDs are not as bright as similarly processed LCD technology. A high-end OLED display like LG, for example, uses in its OLED TVs only output about 500 nits of peak brightness. And that's only when small sections of the screen are at maximum brightness. On the other hand, an LCD and a TV meant for the same purpose will easily produce over 1,000 nits of peak brightness. Keep in mind, these brightness numbers are for an HDR display running off a wall socket, not off a battery like an iPhone where power is more precious. But I should also note a typically bright desktop monitor is only about 200 nits of brightness, so 500 nits can be blinding, at least to some people. And a full white screen at 1,000 would be actually painful. This is a big reason that peak brightness is only actually used for things like light reflecting off the edge of a car door in movies, or TVs, or games. This is an example of the emails I like to call you're so smart. Thank you, Nick. Nick, you're the best. I also feel like maybe I'm just losing my eyesight. I'm like, I still want my iPhone to be brighter. Yeah, yeah. Well, thank you, Nick. And thank you, everybody, who writes in and makes us smarter every day. And also, thank you to Michael Wolfe for being with us today. Michael, it's so great to have you and let folks know where they can keep up with your work. Yeah, the spoon.tech, as we mentioned. I'm also having an event in two weeks where we're going to have two food robots walking around serving food. So smartcurchesummit.com. Check that out. I have a discount code. Can I throw that out? Yeah, if anybody is going to be in Seattle October 8th and 9th, use this discount code. DTDTNS. What else would it be? What else would it be? Remember, yeah. Check it out, smartcurchesummit.com. Also, folks, you can support this show directly and cut out the middleman. Make sure that you are giving directly to the source, not only your attention, but if you've got like $1 a month. That's the entry level to supporting the show. And there's lots of cool perks that you get for that at patreon.com. I'm just asking for three of you right now to just stop what you're doing and pledge a buck. Because I know three of you have got an extra buck. So just to head right now, patreon.com. DTNS and see all the cool things that you'll get. And you'll know that you're supporting the show directly. Also, if you're in Los Angeles, I'm going to be doing a workshop at the Outlier Podcast Festival this Sunday, September 30th, on preparing content for your podcast. Something I do every day. So check it out, outliercs.com. If you want to attend and see that, check that out as well. Our email address is feedback at dailytechnewshow.com. We love your feedback, questions, comments, anything. Keep them coming. We're also live Monday through Friday at 4.30 p.m. Eastern, 20.30 UTC. Find out more at dailytechnewshow.com slash live. Back tomorrow with Richard Gunther as our guest. Talk to you then. This show is part of the Frog Pants Network. Get more at frogpants.com. The club hopes you have enjoyed this program. That was a fun talk. Thanks, Michael. I just talked to Richard today. Oh, yeah. Good stuff, Mike. I thought it was funny that we had you guys back to back. Richard's a great guy. Yeah, once I find out where Mike was and what he's doing, I was like, oh, that's the decent back to back thing. I would be surprised if Richard's not going to this smart kitchen summit. He is. I'm pulling him up on stage, even. So yeah. Oh, nice. Nice. Well, this has been fun. Yeah, this has been great. So same old, same old. After the show, we stick around, we pick a title, we chat while we publish, but we know guests are busy, so pop out whenever you need to. No worries at all. The crowd source title, are those coming in via? Yeah, they come in through the chat room and you can see them at showbot.chatrealm.net. Now, did you used to do it on Reddit? I'm sorry to have all these questions. No, no. We've always done story submissions through Reddit and we still do, but the live chat room title submissions is from IRC and shows up at this. It used to be a different URL, but it was the same little script that does it. Very cool. I worked with the T2T2 in the chat, give people credit to make it to where it's available to pretty much anybody if they want to go to showbot.tv. Yeah, yeah. Very cool. What should we call this show then? Votes are actually actively coming in, which is odd. Well, you know, sometimes they... Your dog, does your dog ever snore on the podcast? Did you ever start snoring? Oh, you talking about Otis? Yeah, yeah, yeah. Oh, I actually, I pointed the camera towards him to shame him because he's snoring very loudly right now. Oh, I can hear him. You guys probably can hear, but yeah, he does his little, like, I'm a dog, I'm running in my sleep, but like very loud snoring. That's awesome. So yeah, that's my dog. He does a lot of snoring. Is that tech in your teeth? What are the suggestions? So my dog broke my finger. That's why I have a broken finger. Oh. Did he bite it? No, it's actually kind of a funny story. So I'm walking the dog.  And along the sidewalk, there's a kiddie pool because these people put pools for the dogs to play in. So my dog's playing and going nuts and I pulled my phone out, started taking a picture of it and she bolts towards the street and I looked over and she pulled the leash really hard but she jumped up on a jogger and he was like completely soaked and he was so pissed, he was not happy. So maybe it's a small consolation that I broke my finger for him, but yeah. Yeah, it's funny, I've talked about it on the show, on GDI certainly a few times already, but it's like when Otis is awake and excited, he wants to play and I know that but people don't always know that and he can be a little intimidating because he's big and I've had people sort of be like, I'm gonna cross the street, which is like, okay, well, whatever, but I sometimes have people being like, you should really get a better handle on your dog, sort of thing where it's like, oh, the judgment of strangers. Yeah, the dogs. And the thing is like, he's young. Yes, you should be teaching him when it's appropriate to be like that or not, but it's not an off switch, right? You can't just be like, oh, yes, no, you're right. I irresponsibly didn't turn off the excited dog switch before I left the house. Oh, right, and some of it is very well-meaning. For example, we left the dog park the other day and I mean, Otis must have run five miles within an hour.  I looked at him like, yeah, do you even know where I came from? Like, why don't you keep your thoughts to yourself, all right? I feel like- You want him? Sarah, parenting is the same way. Yeah, totally. It's literally one-for-one match. Yeah. And there's always people with parenting advice too, which I know what you're talking about. But I feel like you could tell a dog person, like I can tell instantly looking at their face when you're walking your dog, the other dog person or the not. Right. Well, and it's not that I don't appreciate feedback, but sometimes I'm like, you just don't know what you're talking about. Yeah. So, you know. It's that frustrating thing that happens on the internet all the time too, right? Where people assume stuff and then give you advice based on incorrect assumptions. Like, you need to switch this thing off. I'm like, that thing's not on.  Yeah, just ignore.  Oh, right. It's interesting. If you ever get bored and just want to see what kind of internet we actually have, take that chart and then see how long it takes you to find it, each instance of that on the internet somewhere. And usually you can knock out half of it on Facebook right off the bat. On Twitter too. Yeah, then you go to Twitter and knock out a few more than it didn't take, but, you know, 30 minutes and you can knock out every single one of them. And then you have to realize that like half of those are probably influenced by your own biases. So it's like, ah, I gotta start again. Strike it Rich has a great comment in IRC. He says, look, if Sarah was walking her dog and someone said she needed to control Otis better, I'd laugh. He's big enough that if she were not in control, you'd know it. I'll say, ooh, and my forearms have never been stronger. Yeah, that's a good workout. It is. No, it's yeah. It's just, it's again, people have lots of opinions about lots of things. Oh, sure. So, yeah, but he's so cute.  Yay. Yay. Oh, look at that. Oh, somebody's having a dream. Oh, that's my favorite when they chase rabbits in their sleep or whatever. So do you walk him every day before the podcast? You almost have to. Oh my gosh.  Oh yeah. You know, he's got to, he has to, this dog needs to run five miles a day minimum. Yeah. It doesn't have to happen at once, but it has to happen throughout the day.  I think he is. Well, so when I adopted him, they said, he's two. The vet said he's two based on teeth. The obedience trainer that I've worked with a couple of times who's great and knows her dogs is like, this dog is maybe one max. Cause yeah, what you're describing sounds like less than one. It's puppy stuff. And he's certainly over eight or nine months, but it sounds like around a year.  And I'm like, yeah, he's a puppy. I got bamboozled on this one. Although honestly, Sawyer was like that too when we got him and he stayed that way for like five years like years. And I mean, Sawyer also looks like a puppy today. It just does. Yeah. He just has a puppy, you know, face and demeanor. He's got a little bit of gray hair. You know, we're. Yeah, right. Yeah. He's part of the club. You don't know about this guy, but yeah, he's a young boy. He's happy. You know, he's a happy kid. He's just, he's big and a lot to manage. So. He doesn't know all the rules yet. It takes a while for them to realize like what's acceptable behavior, especially when they've been on the street where they had no rules whatsoever, right?  We're going to sit here, you know, and like. Yeah, that's great. Kind of look at me and be like, I don't want to, but he'll do it. So it's like, he's, you know, I get, you know, he's, it's, it's, we're both, we're getting there. It's not. Oh yeah. It's not effortless. It is effort-filled, but it is, but it, but it, but it works. You know, so.  So I'm going to go do that now. So. All right. Thank you guys. This has been a blast. Well, go have fun. All right guys. Have a good one. Yeah. Bye. Bye. Tom, did you get a title? Yeah. So I went, cause, cause I liked it so much. Is that tech in your teeth? Yep. Okay. That's where I was headed to. So. All right. And we were having such a good conversation. I didn't want to derail it. And so I'm like, yeah, this is good. It was great. Love the food tech. Oh yeah. Michael's awesome. And he always has such interesting things in that food space to talk about it. So we got to remember to get him on before the next event. Cause he's always good about wanting to come around around the conference, you know, let people know the conferences come in if they want, if they want to attend. But, but we shouldn't, we shouldn't let it go so long between appearances. I agree. He is good people. So Gunther tomorrow. I'm excited. New guests. New to me. And I, I've been, I've been, I've got a little more smart home, which is kind of Richard's Bailiwick, a little more smart home stuff going on as I've set up the new house. Some interesting anecdotes. Like I can say now. All right. Turn off the studio lights. See? Very cool. Turn on the studio lights. I was talking with, I was talking with Odakta about the Amazon thing on our little weekend show that we do. And the thing that got me excited the most about Amazon's announcement last week was their smart plugs that are only $25. Yes. Once they, the correct price got reported, I became very excited about those because I actually want to get a couple, and that might be something I asked Richard about is like, should I really get the Amazon ones or the other ones that are around the same price just as good? But I want to get a couple of smart plugs whoever makes them to plug into these other two lights here so that I can just say studio lights and it brings them all up on and off. We didn't mention it on the show, but the information is reporting that Facebook will most likely name Adam Maseri, Instagram's VP of Product as the new leader of Instagram following Kevin System and... Breaking news for you. Yes. Well, yeah. So he was product manager of what? Did you say? Instagram, VP of Product. He was VP of Product for Instagram. So he'll be the new CEO.  That's a smart choice. Cause the one, we didn't really get to talk about that aspect of it as much, but preserving the company culture when the founders leave after sticking around for so long could be a concern. So putting somebody like that in charge indicates that they really do want things to stay pretty much the same. Well, why wouldn't they? Instagram has been on the up and up for... Yeah, it's been a really well for them. Yeah, exactly. Oh, let's think of an app. Let's think of an app guys and Facebook will buy it for a billion dollars and then they'll hang out for a few years and then be like, well, we wanna be creative again. Did I say something else? I thought of an app the other day, an app idea. I was like, somebody should make that. And now I can't remember what it was. I should have written it down. Hey, did I tell you, now that you can actually see my background a little more, did I tell you the significance of the stuff behind me? Well, to some extent, yesterday you did, but do it again. You can see it way better today. Yeah. There's actual visual context now. That is my first computer ever, the TM994A. That is my second computer, the Commodore 64. This is the Dell I used at Tech TV. This is the ThinkPad I used at CNET. This is the Mac that I launched DTNS on and that's all the... No, this is a Surface RT, which I didn't use much. Now I don't suppose you could plug them all in and have like cool stuff on each screen. Oh yeah, this is the keyboard to my IBM PS2, which is too fat to fit on these shelves, but that was my first computer in college. Nice, nice. Yeah. There's a... And who's on your first Kindle over there? I think that's Hemingway. Okay. Could be wrong about that. I'm pretty sure it's Hemingway. You know what this is, right? That's why I asked, I didn't know. Do you recommend this? I don't, should I? I don't know. It's the Google Nexus. Oh my gosh. Look at all of those inputs. Yeah, when they put this out, remember we were like, they might make a TV device. It's a sphere. Yeah. I like it. Yeah, you wouldn't really know, unless you explained what it was. Well, there's all the video for today. Thanks for watching folks. If you are listening to the audio or creative vast arts mashup of both, stick around. There's more to come.


Time management, how do you do it? And what's your advice? How do you nudge yourself or who nudges you if well, I have a four-year-old daughter and She just turned four and if I don't deliver pages to her at night. She goes to bed around 8 30 She's really mad. She said daddy. What did you write and this is completely false. I'm making it up so But she doesn't have any need that I write pages But I feel I guess I feel every day that if I haven't written at least something or had an idea Then I haven't quite done my job most days I feel that to write something and if I have a week or two weeks where I haven't made a little progress on something that is potentially an academic article or a book I feel Pedal for the metal time you got to get a produce something. So I have kind of an internal Super ego or something about writing. I also really enjoy it I have no writer's block But I will write a lot of stuff that no one will ever see because it's too terrible So we only get the tip of the iceberg you got this my filter is probably not sufficiently rigorous So you get probably see too much, but I do have a filter. There's a lot of stuff that I so so I write I tried like the the there is no try there's do but there's also Experimenting so there I'm working on a book on liberty. I've been doing it for a few years And I have 90,000 words, and I don't like I don't like what they're what's there I hope something will come of it, but just the advice would be you know you are living practitioner of this is you know if you have something to say write it and Then think is it worth showing to the world to be too critical of your own production I think is a at the early stages at least is An error be critical if it's a book or an academic article You know be really hard on yourself late, but early just assume it's good