Way as much as the aspirin aspirin before was crushed one Because it has the emotional baggage of being crushed Something bad is happening to that Because we have to do with the scale the scale Oh Off a long up Well, I guess we ought to do a daily tech news show then what do you think about that? I think it's a fine idea. I do as well. I'm ready to do it. All right I Will start recording and momentarily here we go three two Kathy slay has supported independent tech news directly for five years Slay like Kathy become a dts member at patreon.com slash DTMS This is the daily tech news for Wednesday April 3rd 2019 in Los Angeles. I'm Tom Merritt and from studio feel line I'm Sarah Lane from the bowels of Salt Lake City. I'm Scott Johnson and from the outside of any bowels. I'm Roger He shows Good place to be dark to read inside of bowels We've got a lot of interesting stuff to talk about and also a facebook Privacy breach, but let's start with a few tech things you should know New whatsapp settings give users some control over who can add them to group chats without their approval The settings are either nobody which requires you to accept an invitation to be added My contacts which lets people in your contacts list add you to groups without an invitation and anybody which as the name suggests Lest anybody add you to any group without needing your approval first the new settings start rolling out today And will be worldwide in a few weeks Well speaking of social interactions the ability for google assistant to use google duplex to call restaurants to make reservations Is rolling out to non pixel android phones and ios phones Google duplex uses text to speech and ai to call restaurants for you over the phone It's been available for pixel phone users in 43 states For a while now android users need android 5.0 or higher and ios users need to have google assistant installed on your phone Sources tell Reuters that japan display will supply oled screens to apple for its smartwatches later this year Japan display has been late to the oled market and has been losing money as the market for lcd screens declines Japan display gets more than half of its revenue from apple So they need to provide some oled screens to apple samsung lg and several chinese makers dominate the oled display market right now Samsung is the exclusive supplier of oled screens for phones to apple While lg supplies oled screens for apple watches at the moment And google released the second android q developer preview for pixel phones and among other things It includes a foldable screen emulator for developers The emulator uses screen dimensions that match the huawei mate x and samsung galaxy folds All right, let's talk a little bit more about 5g scott. Who is winning the 5g mobile service race? 5g you've heard about it for years finally someone's in the race South korea was set to become the first country to launch commercial 5g mobile service friday not a huge shock to people who follow Stuff in that region of the world but verizon flipped the switch early on its service in chicago in minnesota usa I guess The motorola z3 is the only phone currently available in the u.s That can take advantage of the service and customers will pay an extra 10 bucks a month to access it Nothing comes free folks multiple carriers will launch south In south korea friday sk telecom expects 1 million 5g customers by the end of the year That being 2019 and kt corp is offering its 5g plans with unlimited data at a lower price Then it's lte plans the samsung galaxy s 10 5g can be used on the service There and lg will release its 5g capable phone in korea later this month lots of 5g going on and i guess We got it turned on first. I want to know how many z3 customers will be like Yeah, i want to pay an extra 10 dollars a month for this because i'm first I want to buy a z3 and the 5g module and pay an extra 10 dollars a month and I live in minnesota For sure. Yeah, yeah It's not like all of minnesota. It's it's not even all of chicago It's like around the loop in chicago and a few other areas So it's you're real limited on where you can actually use it But boy, you can show it off to your friends if you're in the loop or one of those other areas well, um, one of the I mean, I know you guys have talked to death on this show about uh google stadia and other plans part of their part of their Assumption I think is that 5g will be so Permiated by the time that those services are ramped up that that will give them a huge advantage because that's enough speed and capacity to run What they claim to be able to run on phones and and mobile devices and smaller devices in a wireless way so So 5g has all kinds of implications for me is someone who follows the gaming industry and is watching this cloud business very closely but uh Yeah, it's gonna have to start with phones and i'm not surprised people are going to pay extra for it because You know, welcome to how we do things you seem to be ignoring the fact that you don't have to pay extra for it in south korea In fact, if you're on kt corp in south korea, you pay less for 5g Because that's a competitive market It's not one wild lawless cowboy Mobile carrier deciding to just flip it switch whenever at once no south korea is very ordered And everyone is launching their 5g service at the same time on friday Yeah, you got your service faster in minnesota and chicago, but it's kind of like you said it's going to cost you Whereas in south korea, you've got some competitions. So they they have to do things to entice you onto their network Yeah, I I mean i'm excited about it. It's like I feel like The nebulous talk of 5g has gone on too long. It's time to actually have a service So let's go. I'm ready god your your point of you know If there's a gaming service that relies on a speed that works Then then it'll be somewhat obvious to everybody like oh, okay, this works right now. The story is still 5g Okay, we have to pay more for it How much better is it really? Well, we're always like that though and tom makes a really good point about You know a competitive market versus one that's less So, you know, how long will we play paying extra for characters used in text messages? and Like that stuff has a has a kind of a diehard and takes a while sort of attitude here in the states but um I mean We always get there. We'll get there one way or the other I kind of do wish that we had things a little bit more like maybe south korea because Clearly paying somebody paying less for their current lte coverage is going to be stoked about this What more speed better service less cost I mean and we will you're absolutely right. We will get there. We we will have t-mobile Maybe t-mobile and sprint will be merged by them We'll have t-mobile sprint at and t in verizon and and a few other regional carriers bringing 5g service There's a bunch of mvno's so well we have some competition here. It's just it's not this orderly Service partly because you know these these companies in the united states are covering a much larger geographical area uh and and and everything but uh In two years 5g service in both of these countries is going to be widespread and available. Yeah, yeah adobe announced it is bringing content aware fill to after effects If you're a photoshop user you might be familiar with this It's been around for a few years now content aware fill fills in deleted objects in a photo with appropriate pixels based on What is around it and after effects it will do the same but with video Motion picture powered by adobe sensei. It's a i platform to fine tune your results You can create a reference frame within photoshop So the two work in conjunction scott i'm curious to know because i know you work with uh creative cloud I'll pull quite a bit probably the most of all of us Uh, is this something you'll use? Well, it'll definitely be something i'll play with whether or not i use it will depend on how many film projects require a lot of Uh editing that would be editing like this So so we talked pre-show or earlier when we had a little bit of a meeting about Use cases and a good one would be this you're making a movie set in the old west and you got a gunfight You have a beautiful vista shot A moving panning shot of the two gunfighters facing off against each other and the editor noticed Shoot one of our vans is sticking out in the scene and is totally visible in the shot And we'd really like that not to go to the final cut. So Let's get rid of that and do it in a way that is visually Unnoticeable like you would never even think twice about there have been there would have been a van there um, so this like smart interpolation of the data Without you having to do much cuts down It's really a big time saving feature because all of those things were possible before It's just they were arduous and more process and just harder to do um That's also how it used to be in photoshop photoshop has a feature like this now like you mentioned From what I saw in the video demonstration of this after effects thing It looks better implemented than the photoshop one So i'm curious if some of this will roll back into photoshop and that'll get easier too But the fact that they're combining the two apps to say, all right, you've got the image It's not quite right. So photoshop's here to do a composite Which means you can take a shot of the area without the van and say well This is what it should be and have that thing be smart enough to say Oh, okay. We see where the fence line ends and where the cloud starts and and all of that and have it fill it in smartly like that Huge for editors Totally enormous jump forward in that regard. And so, uh, I'm excited about it. I think it's going to be cool Yeah, this is definitely a feature that editors already have But yeah, it's the difference between someone being like, oh, I had to paint out that van in the corner for eight hours yesterday Or you know after effects giving me a little button where it did most of it itself Yeah, that's the trick though, right? Because a lot of these kinds of features exist in software to help us shortcut And when they come out, it's often A wow factor at first, but then if you really get into the complexity of the different use cases, you're like, well, this sort of kind of works, but it's not great Or whatever Those just get better over time. I'd be really curious if this one hits the ground As impressively as it looked in that video because if that's if that's a typical case, we're in we're in fat city I mean, that's a really that's a huge time shave Well, it's it's also interesting to note that you're dealing with moving video, right? You're not just dealing with the single plate An image that you can edit and then and you know compose onto something You're compositing if you're composing multiple video images. For example, if I animate A two-dimensional object on top of moving video. I can do things Uh, I can do certain things, but you know if I need to composite out Various elements to make like a clean plate or something It's gonna be a much more arduous process.  Yeah, it has to be perfect and then if this is what they're promising is those 60 frames Of of perspective shifting imagery Can be Healed in this way in a algorithmic way And it works. I mean this just it's it's an enormous time saving some people can focus on Back to the creativity of the product back to the things that matter about the project and not oh my gosh There's a boom mic hanging out in front of those guys talking we have to get that one out of here now And it and it definitely takes something that used to be a rather skillful task I mean you didn't need to be super amazing at after effects, but you had to know what you were doing And it takes it and puts it in the hands of people who might just be starting out Well, and then you get into this sort of like I mean the concept of like, oh that's shopped Like we all know what that means like it was like a shoddy way of like Filtering out something from a photo or making a photo look different I wonder how much something like this is going to you know, raise eyebrows and people who want to be paranoid Yeah, definitely not but just what the closer we get to 8k The more this stuff becomes obvious, right? Yeah, that's true of everyone stuff Good and seven or stuff a little good in the 80s on A screen in a movie theater now looks horrendous because we're looking at it even just 1080p Because makeup looks funny that close up. So yeah, I'll always be that but this is such a huge step in a good direction I think And for those who don't realize it content aware fill is not a person It's no That's my new youtube channel. I will be content aware fill come check it out and subscribe content Researchers at upguard have discovered several instances of facebook user information stored on amazon web servers without restriction Which means anybody who knew to look could find it. It wasn't necessarily found though The data did not come from facebook directly. It was collected by third parties and stored on the aws server So there's a lot of implications of like facebook did this facebook didn't do this This is another example of the old facebook policies Not being good at keeping people protecting your user data I'm not exonerating with facebook when I say that but it wasn't it wasn't a facebook breach For instance, mexico's cultura colectiva improperly stored 540 million records including identification numbers comments reactions and account names from facebook amazon has now closed that database after being alerted By facebook in response to a bloomberg reporter asking facebook about it Another instance from a defunct app called at the pool listed facebook names passwords and email addresses for 22 000 people That one has stopped working in the middle of up guards analysis of it So somebody noticed it the fines are a result of facebook looser policies in the past facebook has now tightened up these policies It is less likely. I'm not going to say it's impossible It's less likely that third parties would be allowed to collect all of this data And then that old looser policy combined with a common practice of storing data on aws without proper security Because someone's either trying to save some time or doesn't realize that that's not a secure server A lot of times that data is never found by anybody until a researcher finds it and tightens it up, which is good But it's still not great for stuff to be out there unencrypted So that's another piece of awareness that needs to get around that if you're storing data Any kind of data whether it's facebook or others on aws if it's sensitive you got to make sure you encrypt it So if you're a small defunct app, let's just say I don't know Three years ago. There was an app.  I say yes, and it says all right Well, this app's now going to have access to x y and z it gives you all these whatever things Those are the policies we're talking about right because then those things get stuck on some defunct apps aws build That could be what it's from it could also be you are on facebook And at the pool said hey like our page Uh and in the past liking their page might have given them a little bit of access to your data or it may have been Hey, uh, if you like our page, you know, uh connect with us Let us access your data and at some point you let them access that data And then they were able to take it uh, they they did have to get some kind of permission You may not have paid close enough attention to notice it But it could have been through it could have been through facebook login Or it could have been some other on the facebook platform event like what happened with Cambridge Analytica Yeah, it's fascinating stuff. I just I just always Like I have an old aws. That's just sort of sitting there that doesn't do what it used to do I don't have this kind of data on there, of course, but it's got to be just I would hope not tons of these That's what you would say. Yeah This is one that's it's it's not wrong to be frustrated and upset about this But it is important to be upset and frustrated at the right things Which is you know, the fact that facebook let this happen in the past, which they've already done things about There's nothing else they can do now about the past and be frustrated about the fact that aws Is is not used properly by a lot of companies again This was in the past so hopefully companies going forward are using that or at least Investigating to find out if they have these instances and getting rid of them before researchers at upgrade find them That makes sense a study published by the journal plos One or plus one Showed how a machine learning algorithm was used to determine the optimal growing conditions to maximize The volatile compounds that give fresh basil its taste. You're all wondering. Where's the big basil story? I'm glad you stuck around everybody Basil was grown in a hydroponic shipping container in middleton, massachusetts, which controlled temperature light humidity and other factors taste was measured by using gas chroma chromatography And mass spectrometry spectrometry spectra spectrometry To determine yeah all the science things to determine compounds known to provide good flavor that data was then fed back to the ml algorithm So yeah, so I mean you can make jokes about big pesto for sure about this because basil is a little bit of a weird one But I think the reason they picked it is because it's easy to categorize without tasting yourself What makes basil taste better? We have a really good knowledge of oh these compounds are are available in the leaf That means it's going to taste better in anything we cook with it And so that makes it easy to measure and they can find out if this machine learning algorithm is working Really well for us peer reviewed scientific study. It doesn't mean the basil was like the big get It's just it's just makes for a good experiment. Well, and you know, there are Foods that are better in certain regions than others. I'm not totally sure about basil growing Wild in massachusetts, but I'm going to go ahead and guess that it's probably too cold of a climate So the hydroponic thing is like, okay, that makes sense And if we can have food that is more local to more communities of people who want to get something like fresh basil Which is a wonderful little herb Then that makes sense, uh, you know talk to anybody who lives in new york city And they'll tell you like we don't understand why people like avocados so much because the avocados are just bad there Because they have to travel too far and it's just it's just not a good system There are certain foods that you want it to be closer to where the end user or the end eater is going to be That's not new about this the hydroponics part Is yeah, it's not the big thing the big thing is using machine learning to optimize plant yield and flavor Uh, what which still ties into what you're saying, right? If if suddenly it's just like the next step of what Hydroponics to grow avocados that taste really good because you had machine learning optimize the conditions for them Then suddenly new yorkers go. Oh, I get guacamole now. I understand. Yeah, we're limiting our scope here though Because eventually we're all going to have nutrient gruel that will taste however we want it to And this is first steps in that like we want to trap the Nutrient brick that they hand us in line. We want it to taste like fresh basil and here we go Yeah, grant grill. I hope you trademarked that it really sounds good But keep an eye out for uh for commercial farms and multinationals using autonomous greenhouses and using machine learning to optimize Things around farming. This is definitely a growing trend Absolomon Yeah, thumbs up Well folks, let me tell you about the u.s. Department of Justice warning the academy of motion pictures arts and sciences The changing its rules to limit the eligibility of netflix for oscar awards could diminish sales for the excluded films and therefore potentially violate section one of the sherman act that prohibits anti competitive agreements among competitors Steven Spielberg has been advocating to eliminate movies that premiere on streaming too close to their theatrical premieres He thinks that that means they were made for television and we have the emmy's for that That's his argument. Currently a movie just has to have a theatrical run to qualify for the academy awards We talked about this a little bit yesterday on good day internet because it broke after the show or we at least noticed it after the show Uh, and I didn't quite understand then what I understand now, which is the department of justice is saying is trying to argue if you a group of competitors of netflix the academy rule that netflix can't be eligible for awards and because If you accept this next tenet awards help drive revenue for movies that win awards The green book for instance became a much more revenue generating movie after it won the academy award for best picture this year Then you are using your competitive behavior to drive down the revenue of a competitor And that would violate the sherman act that would be anti competitive that would be antitrust It is odd that the doj preemptively took this measure, I suppose But I actually thought they were I thought it was a little really odd yesterday and now that I I walk through this I'm like, I'm not sure if you can show in a court That not with not being eligible for an academy award drives down sales, but it's an interesting thought Yeah, I have a I have a solution to all of this and it's not and nobody likes what I my solution is I don't think yet. Maybe tell me if you think it's right. Let it on. Yeah, they've got this whole thing of like well Spielberg's take is I'm I'm I don't want to go too far out into the larger argument But I but his basic take is yeah, this stuff is made for a television therefore TV and therefore Emmys and if it's in theaters therefore Oscars I think we should just stop making the distinction and just say is it a movie What's that limit? Is it 90 minutes? Is it two hours? Is it that it ends there? And there's no other movie unless there's a sequel later two years later What is an actual film? And that's what you do at the Oscars And anything that qualifies including movies that were made just for tv And I mean regular over-the-air terrestrial tv they can compete in that same space and All the all the biases and things that already exist and making movies get what awards or whatever They will still be there, but we just don't have to have this distinction about where I saw it It should be about the two-hour thing not the venue in which I consumed it right or I don't know I mean Sometimes time can be a little problematic because you could have a really great film. That's I don't know 75 minutes But maybe it's like is it uh, is it is it one? Uh movie in itself like is it like a self-contained thing? Is it episodic? Maybe that's the distinction Episodic though harry potter That's true. I guess there's I I would say that Many films that become episodic aren't necessarily episodic to begin with though. It's just that they made a lot of money Well, so should lord of the rings three not of one best picture because it was the third. No, it was a wonderful movie That was a great movie. I think it was a third in a in a series. All right, right But I but but lord of the rings probably even though is yes based on you know a huge Novel based like probably wasn't Pitched to whoever was paying for it as like we're gonna have four Actually now that I think about it. They probably did pitch it that way. I did but not to the public necessarily It was but we're gonna do a three-part series We're gonna tell the story of lord of the rings in three movies over the next several years It was yeah, assuming the first one succeeds. We'll make the other two is basically how that usually works I shot that all at once. That's my point They didn't get on the success of the first one So that's why we need to not have any of these restrictions. In fact, forget about the time restriction forget about whether there's sequels planned or otherwise Just the academy if it's a thing to watch and it isn't a bingeable tv show Then then you've got your style. I know it's more complicated than that Even if it's bingeable, then it's like some of those are really good. Yeah, maybe it's one giant award Maybe the golden globes had a right the whole time Maybe they're the ones with all the weird categories in the strange TV movie combo.  I don't know the real award is the friend Oh Patagonia let's move on to patagonia not the place but the company says it'll stop creating branded products Through its corporate sales program without certain standards in place. You might say what are we talking about? Well, there seems to have been quite a few at patagonia branded um fleece Overcoats vests, right that that had been seen in certain parts of silicon valley And new york and wall street and like patagonia says that it has shifted its focus to mission-driven companies that prioritize the planet Patagonia is a b corp. That's a business that meets certain environmental and societal and transparency standards And is certified by a private organization. That's what patagonia is as a b corp And that's what patagonia says it wants to uh Have relationships with going forward existing corporate customers will remain in the program though and still be able to order more branded items from patagonia so more vests Then also fewer vests a lot of that is often referred to as a public benefit corporation If you've heard that name that's that's also the kind of thing that a b corp is and so patagonia is staying true to its mission saying look We want people to be wearing our cool vests on wall street when they ring the bell With with companies that we believe in on them. Uh, and if we think a company is doing something that's bad for the world Um, we're not we're not going to make that branded vest for them anymore. So there that'll teach you wall street types I actually I I was not familiar with the patagonia vest Although this story came across one of my kind of networks this morning And a friend of mine who lives in san francisco was like a long national nightmare is over And I was like what why I don't even know about these vests, but it's definitely something where it's it seems very regionally focused, but uh, it uh was sort of starting to run rampant and the company Probably got a little uncomfortable with their message was it's also I mean these are a very high priced maker of of specialized clothing the vester one thing I'm looking at a jacket here. It's like a windbreaker $489 a pair of pants 419 Yeah, it's not cheap. No, I mean But but it's a public benefit corporation. So it's not money-grubbing. It it it is required by its charter Uh to prioritize things like environmental welfare Uh, so bebe master says in the chat room. Will the doj come in and make patagonia do it? No, uh, they are if they're following the law By by by this legal charter this b corp type of of charter to say yes It is actually in a violation of our own company standards Uh to provide these vests Uh, these branded vests to companies that that are in in opposition to our values Yeah, and it to his pointed point earlier the doj stepping into this spilver versus netflix idea or the changing of the rules That's also them doing that. There's the Sherman The act is kind of there's law in place that protects from anti competitive Behavior, and they think this might be one of them. So this isn't just some weird Draconian poke my stick into everybody's business and tell them what to do kind of thing in both cases. I think Anyway, hey folks Do you want to get all the tech headlines each day in about five minutes? Be sure to subscribe to dailytechheadlines.com and also thanks to everyone who participates in our subreddit You can submit stories You can also vote on other stories and get them on to a potential show daily tech news show dot reddit.com is where to hang out Also, if you like hanging out on facebook facebook.com slash groups slash daily tech news show is your new favorite group Let's check out the mailbag. Let's do it. This one comes from scott. Uh scott was talking in response to This idea that google plus was shutting down We talked a lot about whether or not google shuts down too many of its own projects scott sets The trust that google will keep stadia around is an important one A critical difference between stadia and other google services is monetary While things like inbox or google plus and so on were free And so their demise was more tolerated by their users with stadia Google will be asking for actual serious monetary investment on an ongoing basis Whether it gets sold as a service which over time would result in a substantial yearly cost Or a marketplace where users can buy digital games The blowback should get stadia should stadia get canned after a few years would be massive And I feel that this point is very much on the mind of gamers Google is seriously underestimating the damage that their kill happy legacy has done to their brand And this is going to seriously mute stadia adoption by gamers Who won't want to lay down that sort of money to invest in the gaming service? They cannot trust I do not necessarily agree with the last paragraph that Gamers are They haven't been ripped off yet Right. There's no preemptive ripping off here happening. However, I agree I agree with him that gamers will revolt and so will other aspects of the market The industry itself If google did do like three years of this go, ah, we don't want to do this anymore It didn't do what quite what we thought that means that people have spent 60 bucks on games or a substantial amount of money in subscriptions or whatever their pricing model is going to be I do think that would create a ton of anger now that being said that doesn't also that doesn't automatically mean that That stadia or google doesn't have a way to solve that Through other means here are everybody who bought this game, for example Here is a code to now play it anywhere you want It's just a standard, you know code that works for your console of choice or whatever I'm not saying they will do that. I'm saying those are options. So I don't I don't want to I it's I'm torn on this because I agree with him That that google doesn't necessarily have a track record of let's stick to a thing if it's not immediately successfully financially However, I don't think it's fair for us to preemptively Uh Rage with them when they haven't done anything yet And we got an email from someone who wished to remain anonymous who said I work in the point of sale industry And what really bugs me is that these stories rarely ever name the point of sale systems This is the issue the fact that this affected earls chicken guy Buka, etc is only the management of the software the software has the security flaw And I can tell you we never get security information updates Just apply these patches or you are no longer pc i slash padss compliant He's talking about a lot of these point of sale breach Stories, which we don't really cover all that often at dts But they get a lot of clickbait headlines out there As someone who has been lucky enough to not have been part of a breached company I'd really like to see some transparency from the major players ncr heartland micros, etc in this field The new guys in the hospitality POS industry are very transparent as they use mostly transparent partners and open apis I find them in particular more secure tablet-based p.o.s toast revel, etc antidote and empirically Anecdote he means and empirically okay. I'm done ranting I would like to say however I feel much more informed being a listener long time lister several time caller than my peers in this industry Thank you very much. Thank you anon. Thank you anon. Thank you scott and thank you scott johnson for being on the show today What's been going on and where can people keep up with it? Well lots of cool stuff happening It's as simple as hopping on over on the internet to a little website called frogpants.com You will find everything i'm doing there including shows and other stuff So go find your uh your your tonic because it'll be there and if you want to follow my daily ramblings You can certainly find me on twitter at scott johnson Our goal each month is to get one more patron than last month Then you could be that person that puts us over the top become a dts member get an ad free rss feed special episodes from myself columns from roger reports from sarah all of that available at patreon.com slash dtns If you've got feedback for us send us an email would you feedback at daily tech news show dot com We're also live. If you want to join us live, please do so. We've got a great community who chats and you know Hangs out with us while we're doing it live money through friday 4 30 p.m. Eastern 2030 utc Find out more at daily tech news show dot com slash live back tomorrow with justin robber young talk to you then This show is part of the frog pants network Get more at frogpants.com Enjoy So hard did I ever? Oh, it's back. The echo came back Like right now or for a long time No, it happened briefly in the middle of the show Uh when we were talking about adobe and just came back on sarah and now it's gone and uh I don't know. Do you think it's because I live in echo park? That's what it is Oh, yeah, right everything echoes in echo park Yeah, it's back now. Yeah, and now it's gone. I don't because you said where the the name of that Come on. Come on. Hang out with me in my neighborhood. Hello. Hey I hate stuff like that All right Now we call this show shall it be content aware phil or big pesto I mean, I love big pesto although we didn't really talk about that very much I really like the content aware phil with the name It was almost the title of tms today, but I thought you know what it might go up again later. So let's not take it Patagonia plays it close to the vest Pretty good predicting perfect pesto nice alliteration Mm-hmm Triple p seriously just that place is so expensive I mean great. I love that. It's whatever it is their business model is cool, but I can't buy To like it just never never dies. That's probably true. Yeah I've always liked Patagonia. I have one Patagonia like fleece pullover That's like you can crush it down for travel and stuff. Yeah amazing super warm and I've had it for 15 years Do we feel that Patagonia and north face are kind of on par with each other? Oh, no, I think Patagonia is like Nice or like a tiny not not huge, but I think it's a little All right, all right. I'm just thinking of sort of that outerwear chain thing Yeah, because I would I would put those two in a similar category Stuff you can get over there with content aware phil yes Okay, phil wins continental phil Phil Cap for short I think they should they should make Adobe should make a little virtual assistant named phil. Yeah, they should Hi, I'm into that business finally Crack into the you need a more gendered neutral name Come on. It's 2019 It could be content to wear pauline Still be cap who says a woman can't be named phil or philipa. Just just have it be Very good point Yep Yeah, that's the more gender forward thinking a lady named phil. Hmm. Yeah Phil is gendered neutral in fact like michael bernum on star wars. All right star trek Her star checks. All right. I was like michael bernum on star wars. I blame the day quill Second season's a lot better. Nobody really good second season so good. I love it. I love it It's really good. I can't give away too much. Yeah Favorite part of season two and I don't think this is at all a spoiler Roger You don't understand that people take you seriously All right, I can't say anything or people will get mad no matter what I say Okay, I'll say what it is and this is definitely not one That the guy from hell on wheels is really good as captain pike. I'll say that he is really good. Yeah Yeah No, what I was going to say is there's there's a previously on that is entirely made up of original series scenes Oh, really? Yeah, I haven't been to that one yet previously on star trek and then It's all stuff from the original series. So probably from the cage then Yeah All right, I gotta catch up from here scott. I wouldn't go up there if I were you Oh Right out of any spoiler conversation It'll just upset everybody I thought I would be uh, I can't say too much watch out for the old roger chang plays Prodigy wait, I oh no can't say that Right. I know it's a ball exciting too exciting And you know what they still don't show showers or bathrooms of the future. No Oh wait, that's not true Oh, no, you're right. The first season. What am I thinking of? Hours I've definitely seen sonic showers. Yes. Yeah, but um, where did I see a bathroom? Was it orville? It might have been orville or the orville. That's often easily confused. Yeah. Yeah. Well, it's because um, Seth McFarland was also on Enterprise it was on he was he was like, uh, no it was uh, Not enterprise the other one McFarland He was on he had a part role on uh, I thought it was on a voyager. No voyager Yeah, one of those huge star trek fan. Yeah, he was trying to get a star trek show rebooted and when they didn't use his idea or whatever Uh, somebody's but I mean it seems likely that the orville was like, all right fine. I'll do this I maybe star trek. Maybe they were weren't so fond of doing a lot of sight gags maybe I've been I watched the first episode of orville this season and I liked it just as much as last season But I've been kind of saving them They're not They're not must watch for me right now. Oh, I found it here. Roger. I'll put it in the Slack this picture of him and voyager Oh, yeah, yeah, Seth McFarland. He was a starfleet something or other. He was like a engineer or something. Yeah Are you sure that's not enterprise because it looks like enterprise. Yeah, those are Enterprise uniform it says voyager, but that's those aren't but that's not a voyager uniform That's that's pre starfleet. That's pre starfleet Or no here Well, I'm gonna write an angry missive to the Get to the bottom of this Nope, you're right. It's enterprise gosh dang it. You're right You're right. Can the cosmic sails once again show? I was right Being you're right. He was also in gilmore girls I have never watched that show. So is ramy mallack. They talk too fast.  Yeah, I mean a word went in show can't even watch it That's McFarland in two episodes of enterprise Once just as an engineer and the other is ensign rivers The engineer with a heart of gold Wait, wasn't that the name of a ship? Oh wait, that was hitchhiker's guide No, that was what was that called the heart of gold? Yeah That's a neil young song Yeah, wasn't it called wasn't the ship called the heart of gold keep me searching for He's getting old yeah, but he was old even back You know what I mean? He's kind of he's kind of in one of those guys who's looked the same for 40 years He's always looked like late 40s Like whenever when I've ever seen pictures photos of them 70s 80s So you're trying to say neil young as a time traveler. Mm-hmm. I think he's timeless. He's timeless And you know hauls angela landsberry. Yeah. Yes. I am traveler She said broomsticks She looks great Same in bed knobs and broomsticks as she does in that murder. She wrote Well, even just in that new mary poppin. She had a little bit part in that toward the end and she looks the same Yeah, it's crazy. I don't get it Disney has a great franchise on its hands if they can kind of Do a bed knobs been broomsticks versus mary poppins Versus versus not like a Like a bed knobs v poppins. Yeah, who's gonna win?  Yes. So what it incurs is not only matt magical attributes herbie and dancing Yeah, so it'll be sort of like a talent competition, but they'll make it edgy tom and call it shitty Movies where they just bring in everything, right?   Yeah, it's the Avengers of the forgotten children's movies. Yep Actually sounds not unlike what harry potter was a little of everything Yeah It was about the type scott harriett, but it's got johnson Yeah Scott harry is a different scott. I know what carrier is the podcaster and He used to do it this american life segments and stuff. You ever heard of scott carrier No, I probably listened to him. He probably has very unique style. Yeah essay style stuff Anyway, he's got furrier the guy who makes mink coats. Definitely No, not carrier lives out here and is based here and almost all his stuff is like weird utah stuff It's awesome. I don't know if that means weird utah stuff like stuff. You don't think of like way down Southern utah in the desert. You'll find some oh cool. He lives in a cave and has been there for 30 years and wait, is that like obi-wan kanobi? kind of He hasn't heard that name in many years. He's just bad. He's a fascinating voice. I like that. He's just that old hermit He's me And full circle I was thinking when we were talking about phil earlier like You know that there are certain gender neutral names in general like chris or jesse Or pats or you know, there's certain names. We're like, oh, yeah, I'd probably know, you know one or the other They're kind of names that are known to go either way A friend of mine who had a baby recently He was keep we knew it was going to be a boy But they were keeping the name under wraps But he was like giving us clues and the only real clue was like, okay It's a gender neutral name, but it's not a weird one So we were like, okay, what is it going to be? It turns out that their son's name is perker And I was my original response was like, well, that's much more of a boy's name But they're not that perker posie Yeah, yeah, yeah, it's it's definitely falls into that gender neutral category. Right. I don't know about michael's so much, but I'm gonna be named after michael bernum. Maybe not that many but maybe it's a new thing. Yeah Yeah, uh, I don't know is marlowe considered gender neutral marlowe Marlowe's I would think more of a female name. Yeah last name That's that the radio detective guy phil marlowe or whatever his name was Marlowe is a cool name. That's for sure Marlowe like that. I like it with or without a w it always reminds me of cigarettes. Oh, wow Marlowe Merit's a great name no matter what for first name. I've always thought that I've always liked for first name first name. That is cool. I don't know one merit. I know a marriage I uh, I Dated is a strong word, but I hung out with a girl named merit and really if we ever got married. She'd be merit merit. Yeah well It it was too strong for its own good Classmate like that in the college Her first name was kim and she married a korean guy. His last name was kim. So she was kim kim kim. Nice. Yeah That's a kim kim kim. That's great skim Well video folks, thanks for watching you guys are awesome And uh, we hope you enjoyed the show if you want an extended version of good day internet though both pre and post You gotta check out the audio at patreon.com slash dtns you audio folks stick around there's more to come


Going on everyone Sam here with late sports cards ripping the sports card almanac multi-sport personal box edition volume 5 Box number 74 for mr. Justin s. So good luck here Justin. Let's do it If I pull any of any type of L2 by my days of late in my days at late in her over.  That is the case-hit kaleidoscope Super short-printed that is freaking awesome. Of course a rookie of mr. Levis Justin congrats on that one buddy. That is so cool And that was box number 74. Thanks again as always for the support Justin. We'll get it right out to my friend


Welcome to another special edition of the Home Growers Gardening Series brought to you by Private Property. And today we're going to be talking about the Tower hydroponic system with CAS, just to let us know how the system works. Slightly similar to the NFT hydroponics system that we aired last week, but I believe this one is obviously much smaller. It's vertical as opposed to the NFT hydroponics system. Thank you for watching.


But so we've been having a lot of buy-ins lately, guys. So a lot of people have been asking, what is a buy-in or how does that work or what the hell is going on? So if you've been trading these stocks like FNGR or ATXI or anything else that's been moving this week, there's been a lot of buy-ins. So this is what happens. When a short trader holds a stock overnight, the broker has two days to find a locate for that stock. If they are not able to find a locate, the broker buys you in. And the way that the broker buys you in is they put the order into a VWAP algo. So let's say you have 10,000 shares, they're gonna algo your order through VWAP, 10,000 shares all day long. So when stocks have buy-ins, the broker does not allow you to short it. They put it into some sort of algo that covers it throughout the day at the average price of the day, basically, weighted on the money.


Yeah, in the last class of the lecture series on pattern recognition, we have discussed about normal distributions which is a very important probability function used for probabilistic modeling in the field of statistical signal processing, pattern recognition image, video processing and many other applications. Towards the end of that lecture, we had also seen the scope of a distance based on the covariance matrix of the multivariate Gaussian distribution. Okay, we had seen that and now we will revisit that once more. Look at that expression and try to derive what we will call as decision boundaries to be used for the task of classification or pattern recognition specifically. So let us look back into the slide. So this was the multivariate Gaussian density function. This is the normalizing term and the expression here within the exponent contains the mean of the data as well as the inverse of the covariance matrix here. This dictates the scatter of the data okay in several dimensions put together. Now we had seen at the end of the last class of what are called contues of constant density due to this non-diagonal elements of the covariance matrix which are of constant Mahanabeg distance will define this very soon distance to the mean of the data and that is given by this function d of x. So this is where we almost stopped although we would not have talked explicitly about what are called hyper ellipsoids. We will do that which is dictated by the non-diagonal terms of the covariance matrix but let us know for the time being that the expression within the exponent term here is responsible for the distance okay and then we will see how this distance can be exploited for the task of pattern classification or pattern recognition okay. To wind up that slide this is how the expression of the covariance matrix would look like. Here in the covariance matrix you have the diagonal terms which are the individual variances along the particular direction and the off diagonal terms again it is a symmetric matrix they represent the corresponding covariance between two dimensions i and j. So that means sigma ij represents the covariance between dimensions i and j. So using this idea of this distance from the mean as given by the term called Mahanabeg's distance to the mirror mean of the data we will proceed and see now how this distance could be exploited to do certain task of pattern classification or pattern recognition okay. Let us look at some examples of how the diagonal covariance term in two dimension this is a slide which shows a data distribution in 2dx and y very simply and the covariance matrix has the diagonal terms which are equal that is why you have sigma x equals sigma y and the off diagonal terms are 0. So the covariance is here strictly a diagonal okay you have diagonal terms which reflect the corresponding variances and that too they are same and the off diagonal terms is 0 strictly diagonal matrix go back and that particular case this is what you will have as the data distribution or you can say if the data distribution is of nature like this then you are bound to have equivalent to equal terms along the diagonal sigma x square sigma y square and so on and the off diagonal terms are 0. This is also another example of a diagonal covariance term where the off diagonal term is 0 alright but the variance or the standard deviation is given here along one particular direction is more than the other one okay the variance along the x direction is more than that along y you can see that the scatter or spread along y is much less than the scatter along x direction which is the vertical one and that is much much more larger than the horizontal one. So these are two examples of diagonal covariance what if non-diagonal terms are present what if non-diagonal terms are present this is an example where sigma x equals sigma y that means the variances of standard deviations along x and y are same however you have a non-zero off diagonal term as given here and that dictates that this character of the data will not now have a sort of a inclination along a particular direction it will have a trend which shows that as x increases the data in y also increases. The reverse can happen this is a case when the off diagonal term is negative that means when you can say when x is increasing y is decreasing or goes in the negative direction or vice versa okay so this is an effect of the off diagonal term if the off diagonal term the rho x y as given here is 0 then of course you will have situations as given these are just examples to show and the gaussians which you will get remember what we are showing are basically isocontours isocontour lines which are at a certain distance d given by that expression which we just saw in the last class or strictly the Marlin Abbey's distance from the mean those are the isocontour lines which are shown there around the mean however the gaussian function may be symmetric may not be symmetric. So we can also get asymmetric or oriented gaussian functions and for those cases the isocontour lines are as given in the figure here the bottom of your screen the two cases of non-diagonal covariances also and the electrons the diagonal covariance here is an example of asymmetric or these two can be considered as oriented gaussians these are just examples in 2D to illustrate the effect of the covariance matrix remember in general it is a we deal with large dimension data now before we go and use this corresponding expression of the Marlin Abbey's distance and the probability of multivariate gaussian density for the task of pattern classification or pattern recognition we will coin certain terms which you are very important as shown in the slide now we are going to introduce 2 terms called the decision region and decision boundary we will actually call them in short a DR and DB very soon remember the goal of a pattern recognition algorithm is to actually find an optimal decision rule to categorize the incoming data into their respective categories this is a very generic statement I am making about pattern recognition remember one of the decision rules which you are going to exploit is going to be the base decision rule based on Bayes theorem which we have already heard from Professor Murthy so based on that Bayes decision rule when we categorize samples into different classes or categories how does this distance based on the probability density function of a gaussian or a multivariate gaussian distribution play an important role and from that point of view water decision regions and decision boundaries we will define those terms first and then we will see the mathematical expressions which will actually clarify your doubt about these 2 techniques so let us look back the decision boundary are the ones which separate points belonging to one class from points belonging to the other so it basically separates 2 sets of points let us say you are discriminating 2 classes of flowers white flower and a black flower very simple or certain fruits from vegetables and you have taken certain features to discriminate between these 2 categories or classes okay and we have seen remember these examples of what we have seen during the process of classification versus clustering that each instance or a sample is a point in a high dimensional feature space so you can think of a boundary in between which sits in between 2 regions in the feature space discriminating between 2 classes okay that region that that boundary is called a decision boundary very simply so if that is decision boundary with separate points separate points then of course decision regions are the ones which are formed by partitioning the feature space so a decision boundary partitions the feature space into decision regions so think of 2 regions you can take a simple example of 2 different neighboring countries or 2 different states from the field of geography okay you can have a partition or a line between 2 states 2 districts or 2 countries thinking that to be a decision boundary then what are the decision regions the regions are those geographical maps belonging to 2 neighboring countries states or districts very simple example the nature of the decision boundary is decided by what is called a discriminant function this is where the multivariate probabilistic function will come we have to form a discriminant function which is to be used for obtaining a decision what is the decision the decision of assigning a sample to a particular class based on certain decision rule it could be the base rule we can keep the base rule in our mind of course there could be other methods of classification statistical neural methods whatever it may be but for the time being let us say we have the simple base rule we will revisit that for a moment okay and based on that base decision criteria or decision rule will form a discriminant function what is that discriminant function it decides the nature of the decision boundary okay so the discriminant function which is used for deciding what is the category of the incoming data that basically forms the decision boundary it is a function of the feature vector which represents the sample of the instance. So we have introduced 3 terms remember decision region is dr decision boundary is dv and discriminant function we may not call it df but remember we will henceforth interchangeably interchangeably use dr with decision region db meaning decision boundary. Decision boundaries create hyper planes or hyper surfaces that means if you take an example of 2 categories this is a simple example we already take let us say the number of classes is 2 a positive value of the discriminant function decides that the sample belongs to class 1 and a negative value decides the other one in the last slide we said discriminant function is a function of the feature vector correct you think of any arbitrary function for the time being although we will formalize and take some examples of discriminant function later on. So what do you do you try to compute the value of the discriminant function based on a certain value of a feature vector which belongs to a particular test sample which you want to categorize the discriminant function will give you a value typically the decision of the discriminant function will tell you whether the sample belongs to class 1 or class 2 class a or class b very simple we are talking about binary classification as it is called or the number of classes is equal to 2. So you have just 2 classes it is like a white car versus a black car ok or apples versus oranges very simply ok that is what you want to discriminate class a class b class 1 class 2 ok. So there is something like a decision boundary in between these 2 classes on one side you have a decision region for class 1 you have another decision region for class 2 with a db or decision region in between we are going to have a diagram on this very soon do not worry ok. So what will the discriminant function do when it will evaluate samples for a particular region say class 1 it will give positive values here for the other class it will give negative values here. So just look at the sign of the discriminant function you do not have to worry even of the absolute value the sign of the discriminant function will tell you to which class it belongs. Now you should be able to extrapolate and tell me what happens if the what happens to the value of the discriminant function if this sample of the instance fall on the decision boundary if the sample of the instance falls on the decision boundary the value of the discriminant function will be 0 fine very easy good. So once we know this if the number of dimension is 3 then the decision boundary will be a plane or a 3D surface well in this case we are talking about a linear decision boundary remember in one of the earlier slides we are talking about hyper ellipsoids and things like that some non-linearity in a decision boundary typically if you take lines in a geographical map which partitions two regions of states districts and countries very rarely you will find a linear boundary very rarely in a rare case you will get that mostly it is non-linear regular I should say okay. So whether you will get a linear non-linear depends upon several factors in fact the covariance matrix will decide we will talk about that later on for the timing will assume that is a linear decision boundary and if the decision boundary if the dimensionality of the problem is 2 then we are talking of a line in 3D it is a plane and of course in higher dimension we talk of this as hyper planes and the decision regions then become what are called semi-infinite volumes this is a term which actually which is borrowed sometimes in the field of geometry or computer graphics why it is a semi-infinite volume because one side you have the decision boundary the other side is it is it stretches to infinity goes to infinity okay. So it is finite on one side infinite on the other so it is a semi-infinite volume it is just a terminology trying to give you an idea. So if the number of dimension increases to more than 3 then the decision boundary of the DB becomes a hyper plane or a hyper surface then the decision regions become semi-infinite hyper surfaces remember plane and surface plane is a special case of a surface is a planar surface we call it as a plane but a surface can be non-linear also so the linearity or non-linearity is a separate issue linearity or non-linearity is a separate issue we will talk about that later on because I already mentioned it depends on the properties of the covariance matrix it is off diagonal terms will be mainly responsible for this okay diagonal terms also have a role to play but off diagonal terms strictly will have a role here okay and also the covariance matrix between classes so all these will dictate but if the decision boundary is linear then it is a line in 2D we will have a slide coming up a plane in 3D and a hyper plane in higher dimension if it is non-linear it will be a curve a conic a quadratic curve let us say in 2D it will be an arbitrary conical surface in 3D and hyper surface is in higher dimension so just keep so these are something to do with decision boundaries so the terms which are given very closely associated with the process of pattern classification pattern recognition is the process of learning because when you are going to design an algorithm for pattern classification you have to learn something from the data samples and there are 2 methods of learning but what is this process of learning remember a classifier is to be designed is built using input samples which is a mixture of all the classes so the classifier is made to learn from different sample this is the process of learning this was talked about in the introduction part also in the lecture that means if you want to say discriminate between apples and oranges and let us say you want to use color as a feature which is possible because apple will have a set is reddish tinge of the color the orange of course will have its obvious color and if you use color as a feature in 3D dimension and you want to learn these colors from samples it is possible to do with apples and oranges and what you want to do in this process of learning we will see here that the essential process of learning involves learning or designing the classifier I repeat again designing the classifier involves learning the discriminant function or parameters of the discriminant function which in turn dictates what are going to be a decision boundaries or decision regions decision regions and decision boundaries are the dual of each other once you found a decision boundary you get the regions or one you establish the regions you get boundaries we will see that with examples once you form 2 neighboring states you get a boundary or you draw a boundary and create 2 regions and that decision boundary and or regions are dictated by the discriminant function so the process of learning the discriminant function is a process which you learn the parameters of that function from the input samples and it says that sometimes you need the samples from all the classes or categories to learn they could be given together or one after the other that is okay it does not matter much as but as long as you know that the sample belongs to a particular class you are fine so the classifier learns how to discriminate between samples of different classes if the learning is offline if the learning is offline typically it is called a supervised method well that is not the main criteria what I must say is if the classifier is first given a set of training samples and the optimal decision boundary or the DB is found and then the classification task is performed this method is called supervised learning why it is called supervised learning well there is a teacher the teacher tells you that these are the samples which belong to class 1 these are the other set of samples which belongs to class 2 please learn the set of features which discriminate one from the other find out what is the discriminant function and the decision boundary so it is like almost a teacher teaching you how to work out the process of classification samples are given to you like somebody the teacher in the kindergarten teaches you how to write the character a how to write the character b that means samples are given many many times in the book and in the board for students to write and practice that is the supervised method where there is a teacher explaining and telling you these are samples of particular class category if you take a b c d then you are talking about words characters to be very precise okay and that is the supervised method of learning so the classifier learns from samples given a priory finds out the optimal decision boundary and then performs classification task based on a certain set of test samples using a set of test samples you perform the task of classification after you have learned in a supervised manner the optimal decision boundary what is the other method of learning unsupervised there is no teacher there are no training samples given the process is generally considered online and there is no teacher there are no training samples this unsupervised method of learning uses the input samples as the test samples themselves the classifier learns and classifies at the same time so in this particular case of unsupervised method there is no teacher telling you this is how you should write character a this is how you should write character b all the characters are given to you together to learn all the 26 a to z okay nobody tells you that this is a this is b this is c and so on you have a set of characters you should have to now discriminate yourself between a versus b versus c versus d and so on there are sufficient samples available for that but you are doing it learning and classifying simultaneously you do it together at the same time and that is the process of unsupervised classification no teacher we talked about this feature space sometime back so I am not going to introduce it in a big way again but we all know that the samples of the input when represented by their features are represented as points in feature space a single feature is a one-dimensional example this is a simple example but points represent samples in feature space if I remember correctly we had used this slide earlier in a classification versus clustering example as well okay of course you could ask a question right away where is the relation boundary here where are the relation regions we will come to that in a moment but of course the number of features could increase when it is number 2 you get points in 2d space which you will see in the next slide you can have the number of features as 3 then we go to 3 dimension and of course higher dimensions if the number of features are more than 3 or more in a typical n dimensional feature space sometime called as a d dimensional feature space we can have decision boundaries decision regions and discriminant functions which are formed this is a typical example that is a small point which is actually shown by a line here this is a point here which indicates a decision boundary in a one dimensional case of a 2 class problem you have a set of samples belonging to class 1 as marked as red points class 2 as blue points anywhere in between you could have actually put the decision boundary the point in between okay it depends upon the algorithm let us look at this example which has been borrowed okay from a document the decision boundary in 2 or 3 dimension now this example shows 3 classes that means 3 classes 3 set of instances marked in 3 different colors magenta yellow and blue I hope the color is clear I repeat again magenta is one color say class 1 yellow in class 2 and cyan if not blue cyan color is the class 3 okay now these points could be scattered in 2 dimension XY or you can visualize that you are sitting in a room this is one corner of the room and these are points in 3D space that is also possible it is like a projection of a 3 dimensional world onto 2 dimensional space but the points may be actually in 3D okay so in that case when you see these 3 lines I have attempted to draw 3 decision boundaries or DBs separating one class to the other that means this decision boundary separates this pair of classes this decision boundary separates these 2 this decision boundary separates these 2 these could be lines in 2D these lines that could be lines in 2D or they could be planes in 3D either way you visualize it is okay that is why you look back on the slide I wrote that you can visualize this problem in either 2 or 3 dimension this is a hand drawn example let us see another example of sample points in 2 dimensional feature space this is very easy to visualize I have just 2 sets of sample points belonging to class 1 as given here you can see I have used another mark of the different color to indicate it is class 2 okay so 2 dimensional space instead of X and Y I have used F1, F2 at the 2 feature components of the feature which I have extracted from 2 sets of categories of sample if you look back into the slide this is an easy problem to categorize why I can actually draw a line through the middle which I am not drawing here but I am asking you to visualize that if I draw a line through the middle then that will be the decision boundary to discriminate between these 2 classes and I can actually use any sort of simple classifier to find out this particular discriminant function to discriminate between these 2 classes and obtain this DB that is an easy problem but is it always the case that I can separate 2 set of points using what I call as a linear decision boundary which is a line in 2D or a plane in 3D or a hyper plane in higher dimension not always possible in this case yes but what if you look back into the slide I am introducing certain sample points belonging to class 3 third category of a class and the samples have been marked by a different symbol with a different color as well now look if you want to discriminate class 1 versus class 3 or class 2 versus class 3 you will not be able to draw a line anywhere in this space which discriminates and create 2 DRs and creates 2 DRs or 2 decision regions discriminating one class versus the other look back remember class 1 versus class 2 is still it is still possible to draw a DB between the 2 which is linear but for class 1 versus class 3 you need to probably draw a non-linear elliptical boundary here the same to do with the class 2 versus class 3 it is possible that you may need a elliptical contour to discriminate between class 2 versus class 3 that means samples belonging to class 3 if you want to discriminate with respect to the other 2 classes you require a non-linear decision boundary linearity linear decision boundaries will not solve the problems in such cases these examples form the class of problems which are called non-linearly separable problems that means you cannot solve the problem of classification using a linear separable boundary or a linear decision boundary not a linearly separable or non-linearly separable these terms are sometimes used interchangeably by people. So class 2 versus class 3 look back again class 2 versus class 3 or class 1 versus class 3 or examples hand drawn of non-linearly separable or not linearly separable problems class 1 versus class 2 is a problem which is linearly separable these are points in 2 dimensional space I leave it for you to visualize the same in 3D or higher dimensions these are some nice synthetic examples which are typically available in many literature and books which shows examples of problems which you need a non-linear decision boundary these examples show dense distribution of instances belonging to 2 different classes using 2 different colors red indicates one color the blue indicates one color so this is one problem so what is the boundary you will need here sort of an elliptical boundary here okay you can see that in a non-linear boundary here between these 2 classes to separate one and ellipse will do here okay you need a very complicated boundary to discriminate between these 2 situations can be even worse than this in all these examples which I have drawn and shown so far these samples have actually not overlapped the samples have not overlapped means there has not been any amount of mixing between samples belonging to 2 different classes 1 and 2 assuming in one of these cases that the samples overlap then what amount of sophisticated classifier and feature space you use of course given a feature space you are using or designing a classifier your classifier is bound to make some errors and mistakes however if the samples do not overlap it should be possible for you to design a sophisticated classifier which will yield an optimal decision boundary linear non-linear whatever the case may be I repeat a good optimal classifier based on a good optimal design should be able to come up with a good linear or non-linear decision boundary such that you can separate between 2 categories of classes okay as long as the samples do not overlap like as given in the figure here although you do not have a linearly separable boundary to draw but you should be able to actually obtain good amount of classification. So continuing with discussion on DRs and DBs a classifier is expected to partition the feature space into class level DRs or decision regions and if decision regions are used for a possible unique class assignment which it will be then the regions must cover the entire d dimensional space and must be disjoint or non-overlapping these are certain desirable properties the border of each decision region is termed as a decision boundary in fact it is the border between a pair of adjacent decision regions to be very precise the border between 2 adjacent decision regions corresponding to 2 different classes or a pair of classes is called a decision boundary very rarely you will have a boundary encapsulating one class on the other side you do not have a class no you have another class obviously which is at least which does not belong to this particular class which is encapsulated by the decision boundary. So typical classification approach is as follows I am not talking of any particular algorithm right now for classification yet I hope you understand that I am not entering into a particular algorithm we will pick up the best decision rule when it comes and try to link it with the multivariate Gaussian density but I am just getting into the problem of classification. So what I am saying looking back the classification approach it is expected to yield a good discriminant function obtain good decision boundaries between 2 decision regions and it should determine a good DR in d dimensional space on to which a particular sample X or an instance X falls and you assign X on to that particular class the strategy is very simple this sort of approach for classification is simple the challenge is actually to determine the decision regions and sometimes it may not be possible to visualize all the DR's and the DB's in a high dimensional space for a general classification task with a very large number of classes in a high dimensional feature space often you will find that my diagrams will be concentrating on samples in 2D or 3D for a 2 class or a 3 class problem so far also you have seen and you are going to see that in this class today and in the next class following this that samples will be drawn in 2 dimensional or 3 dimensional space at the most even when we had that animation trying to describe you the difference between clustering and classification we showed you an animation in 3 dimension with a 2 class problem okay but of course it is left to always the to the imagination of the pattern recognition community to visualize what is going to happen in higher dimension with large number of classes okay there are lots of other issues which sometimes it becomes difficult to visualize in lower dimension what can happen in higher dimension so classifiers or the design of a classifier is basically involves designing the discriminant function because the discriminant function will give you the decision boundaries or DB's as well as the DR's or decision regions so designing a classifier again I repeat is building discriminant function or learning the parameters of a particular discriminant function okay remembering that if you have a C class situation that means if the number of categories are C's typically we have been taking the number of classes equal to 2 or 3 you will have as many discriminant functions as many as the number of classes are equal to the number of classes look back into the slide so classifiers are based on discriminant functions and in a C class case discriminant functions are donated by from here onwards we will introduce notation G of I of X want to see capital C indicating the number of classes and this partitions the D dimensional space into C distinct disjoint regions and the process of classification is implemented using a simple decision rule which says this is nothing new to you it has been done along the lines of along the lines of base decision rule also this can be put assign sample X to a class M or a region M or the Mth class or the Mth region if the corresponding value of G is more than all the other values of G for I not equal to M that means what I need to do after the classifier has learned the decision boundaries or the discriminant functions I substitute into C different what is the what is C? C is equal to what is capital C number of classes okay so I will have say G I is the is the discriminant function for the Ith class so if there are C classes there will be C such capital C is at G I so for all those C different G is I substitute the feature value X for that particular I mean for the particular test sample which I want to classify or discriminate or categorize or recognize I substitute the value of find out which value of G I is the highest is the peak or the maximum assign the sample X to that particular class this is the simple policy but of course who will give you the G I will get into the mathematical details of how to build the G I slowly what about a decision boundary so it seems the decision rules are giving me the DRs and the DBs are also given by this discriminant functions for any pair K and L not equal to the other okay decision boundary is defined by the locus of points where the corresponding G is at the same this we talked about already earlier at the hyper plane at the hyper plane the value of G will neither be positive or negative it is a binary classification problem you remember sometime back we talked about this the value of the G will be equal to 0 you will not have a positive or negative sign in this case of course in a multiclass classification problem at the C value of C is more than 2 then of course you need to have C different G I C different decision regions in some sense you can think of n choose to our NC 2 and the n is the number of classes those many decision boundaries and at each of the decision boundaries a pair of G I will be equal at each such point in the decision boundary appear so if you choose the boundary between two classes K and L as given here the corresponding values of G is will be the same GK will be equal to GL for that sample X a very simple classifier called a minimum distance classifier also called NN this is called the nearest neighbor is not written in the slide but please make a note it is a nearest neighbor classifier okay minimum distance classifier discriminant function is based on the distance to the class mean very simply what I am computing here look at the expression of G's the first expression of G which I am putting very simple distance of the sample from the mean for class 1 for class 2 distance of the sample from the mean of the second class okay so let us say in a two dimensional space these are the two corresponding class means mu 1 mu 2 and this is the decision boundary which I get for G 1 is equal to G 2 based on the expression which we had in the previous slide so this is the region R 1 decision region this is the R 2 the decision region 2 around the mean mu 2 for the corresponding class and the DB is where we are sitting this is the most simplest example of a decision boundary and a decision region for what is called as the minimum distance classifier or a nearest neighbor classifier the expression of G is very simple the declutent distance between the sample X and the mean how far you are from the sample nearest sample nearest mean is what you will get as the class assignment now of course you must keep in mind one particular thing that the corresponding value of G in this particular is not going to maximize in that case you have to take 1 by G if you want really maximize it okay so you can think of a minimum G for class assignment but it is okay we are interested in decision regions and decision boundaries the only thing what we have done this particular case is we have not taken into account any information about classes except the means it is like you are distinguishing apples from oranges you have a set of apples you have a set of oranges that is alright you have the two class means at these two points in feature space let us say but you do not know whether the samples of the apples are scattered around in a larger domain compared to the scatter of the oranges how far is the distance between the two means are known but the individual scatter or what are called in statistical term if you go back to the slide this method of G is do not take into account class probability density functions or PDFs which can be modeled by a multivariate Gaussian density function and the example of and the class priors so without that if you do not want to use it minimum distance in a book classifier will work correctly but it is dangerous to sometimes use this minimum distance classifier without using class priors into account you may get long wrong classification errors in classification if you do not take class priors or class distribution functions into account this has been discussed already earlier as introduction to classification task by professor Murthy and myself earlier so to incorporate that we have to bring in the class distribution function bring in the probabilities and to do that let us do the classification properly using a short of a best classifier almost available at the beginning of any book of pattern recognition or pattern classification which is the base decision rule or base theorem and we will discuss this in the next class how incorporation of the probability density functions using class prior conditional density functions etc within the base theorem gives a much more better accurate process of classification and a better estimate of the decision boundaries and formation of the discriminant functions and hence the decision regions for the purpose of classification we will end the class here today with the short note or the idea which we have discussed today or to summarize the process of classification involves designing of discriminant functions or learning the parameters of discriminant functions the discriminant functions will give the decision regions a boundary between two regions is called a decision boundary these decision boundaries could be linear or linear depending upon the individual scatter or spread of the class samples or data which will be reflected by the covariance matrix okay so we will see in the next class how the multivariate Gaussian distribution function incorporated using the base decision rule or the base theorem brings in the concept of decision boundaries by exploiting the distance of a sample from the class that particular distance term will come back again which we had in the beginning of the class today it will come back in the next class and the ideas whenever we talk about db is dr disturbance functions hyper planes hyper surfaces etc the concepts will be much clear thank you very much