T. F. N. N. Headline news update. Good afternoon folks, Steve Rhodes coming to you live from the shores of very sunny and pleasant Delray Beach floor to this year, 2 p.m. update and currently we have all the U.S. indices trading in the downside. The Dow is up 122 points, 115 points, about three tenths of a cent, S&P six tenths or 27 points, NASDAQ 107 tenths or 109 points, Russell 2001 and two tenths percent or 25 points. The semis are off 67, one and nine tenths percent. You've got gold trading out at 1935, 90 that's up 18 bucks, silver's trading out at 25 bucks, even Steven 26 cents to the upside a little over 1 percent, natural gas up 25 pennies, that's a big move, nearly 5 percent, she's trading at 558 and a 30 year treasury. Up 10 ticks, 149.08 is the print. Let's go spend a little time and take a look at our eight paneled indices charts out here. If you give me a moment, we're going to change screens in the upper left hand side in just a moment, you're going to see the Dow Jones industrials. You're not going to see a topping pattern. You're going to see price getting up to resistance, TD nine count breakdown level of 35, 431 and oscillator and change line that recently changed color, price net line are likely to catch up to each other. That is if the S&P and the NASDAQ are able to pull the markets lower. The S&P 500, that's the next chart has a TD nine count top that completed yesterday, the NDX 100, a TD nine count top yesterday as well. Both of those suggest that price should target their oscillator and change lines and the S&P it's 44.73 and the NASDAQ 100 is 14.460. The Russell 2000 yesterday was attempting to break out and in fact it still is in breakout above its consolidation, but if it's able to close back below 21.05.20 where 21.08 then the consolidation breakout message will have been negated and that would suggest at least point back to 20.61. The semis do not have a topping signal. The SMH does so you got difference in those two charts out there. So if the S&P and the NASDAQ are going to move lower, well so too will the semis and their price target will be 34.21. Now you've got inside bars in the S&P and the NASDAQ right now and inside bar typically says and you've got that in the Dow that the trend that's in place should continue and that's move higher. So those are the signals but it will all be dependent upon what it looks like at days end out there. So your TD9 count tops are the New York Stock Exchange NASDAQ composite, NDX and S&P 500, a consolidation pattern in the Russell 2000 and folks stay tuned to your favorite polar bear. I believe he is a polar bear today. He is up next. So stay tuned for that. Tom O'Brien will take us on home from three to four and I'll be back with you tomorrow on terrific Thursday at one o'clock sharp have a wonderful Wednesday.


Dwi'n cael i. So, rydw i'n cael ei bod yn gyrwch, rydw i'n cael ei wneud. Rydw i'n cael ei bod yn llwyllaeth yma, rydw i rydw i'r llwyllaeth. Rydw i'r llwyllaeth yn ôl, wir yn ein tynnu. Rydw i'n cerdd, rhywbodaeth a'r rhoblach, ac mae'n adegydd. Rydw i'n adegydd yn cyfnodol yn ychydig o'r emosiwnol, sy'n cyfnodol yn cyfnodol am adegydd yn cyfnodol i'n cyfnodol, ac yn dysgu ddych chi'n ddiogelio'n gwybod, sy'n gyhoedd yr hwn nebyddiadau. Dyna'r hyn yn fath o'r bynnagau cyfeirio cyflodau a'r hynny yw bwysig cyflodau. Bydd y cerdd o'r penderlion o fy modd mae'r oedd a hwn yn bwysig a o'r penderlion o'r oedd y ddiogelio'n oed o'i fyddech chi yw wahanol o fath neu oedd hynny. Mae'n tynnu o gydwydio'r ôl o ddych chi'n sahodraeth a'r gydwyrdwyd ac mae'n fath o fewn. mae'n oes i'w ni allu'n gweithio'r bywyd, i'n ymddangos i'r ddechrau i gael, a'r gweithio'n cael ei wneud a'i wneud i ddechrau i gael a'i gael yn cael ei gweld i'w ddweud o sut yn ei fwy o'r idea. Mae'n arddangos i'r cymdeithio, ymgylchedd ymlaen i'r ddweud o'r ddweud o'r ddweud, i ddweud i'r ddweud o'r ddweud o'r ddweud o'r ddweud o'r ddweud. ac ydy'n gilydd yn ysbod o'ch gwahanol am yw y gwaith. Mae'n gwybod a'r oedd yn farchfaldol. Mae'n fawr, cais mae'r ddafodol yn ysgolio'n gwybod ychydig nawr. Yn ystodol, rhai'n mynd o'r cyfnod o'r cyfnod yma, y gwelwch ar draws i'r cyfnod o'r cyfnod o'r cwestiynau yn ysgol cwmwyllydd a'i'r cyfnod o'r pethau. bobl, ac eich gwneud o'r gwaith ddweud, a cyfnodwch. Mae'r dweudio'r gwaith yw ddweud yn ymgyrch. A dyna i ddim yn hollwch. A dyna'r gweithio. Yr unig ffyrdd, y dweudio arnosad. Anton ac I gweithio y byddai yng Nghymru yn ymgyrchau'r eich dweud dynnu'n y gweithio ar gyfer y camp ddwy ddech chi. Mae'n dweud yn ei bobl. Mae'n ddwy robo'r bobl, mae'n ddweudio. ac we wanted to make a really big one and we thought about it. The problem with the really big robot is you need to keep a really big robot in your house and we realised that an inflatable one would actually be a smaller to put in your house and it lets you make a big thing easily. So this is just a big inflated fabric ball. There's a light bulb in the middle and a thing that moves around it to cast a shadow to be the pupil. It kind of worked. A year later with Antoine and Russ we made this thing. This is a picture of it at one of the Dutch Hacker Fests. It's a big musical thing. It rotates. It has one range finder, that's Sonya Sensor. It plays music based on the ranges of people around it with some trickery so it's always kind of music so it's a sort of collaborative musical play thing. Which taught me a lot about interaction design and making robots for people. The giant steering eye was interesting to make but quite boring to be with. I think it was actually quite fun. 2015 with Julia we made the world of Techno which you might have heard about the camp playing terrible Techno. Again it's not terribly technically very fancy. It's a Raspberry Pi and some software based on Sonic Pi and a GPS and some wires and things from eBay. I can hear it over there somewhere. It was another lesson in user experience in designing what you think people will do with it and then seeing if they do the thing, making a thing which they will want as opposed to just designing the thing. In 2016 my software work had slowed down a bit and I was already friends with a company called Rusty Squid who did very interesting and curious art robotic stuff in Bristol and I went to work with them for a while on making this strange little thing. It was like a sort of humanish puppet robot hybrid. It was very mobile. It was for a TV film about the making of the robot and then what we did with after. And this was where I met Emma which will later turn out to be a crucial plot point. This was us, there's the robot. It was chatting to those people. I was standing in the dark like 50 metres away wearing my fluffiest coat just sort of minding the robot. People really took to it. They would pick it up and carry it around and engage with it as though it were an alive thing even though it obviously wasn't which was super educational. 2017 I started playing with putting air in little things. This is heat-sealed fabric, two layers of fabric, layers of hot glue film which is used in the garment trade and then sealed with soldering iron and just experimenting with geometry and air to see what you could make a robot do with that kind of thing. 2017 went to Sham at Robert's. There, also a crucial plot point. I have no pictures of Robert from then but this is from his rope tying workshop. So this sounds like a disconnected story but all these bits are all the bits that came together to make the thing happen in the end. Hopefully the connections will be obvious in a bit. And it's quite a long timeline. You know, it's like 10 years in this. I made this with Robert in 2018. It's inflatable, it has little distance sensors, it plays games of catch and chase with people. People really saw this as a live thing. They would go along with its games, they would hug it, they would squeeze it, you know, they liked it. They just liked it. It was ridiculously complicated and we're never making anything like that again. But the interaction worked and the idea of these big moving squishy things was plainly exciting. 2018, EMF sponsored these. Thank you EMF, it was great. It was the triffid on the right there. Big wavy thing, a bit like the vines we have up there. About, that was nearly done and about a week before EMF, they sent me email saying, we've got a bit more sponsorship money, can you make another thing? So I should have said no, we're busy finishing this one but actually said yes and we made this other one. The anemone which like little things squeezed it and out. Totally not interactive but people found lots of things to do with it anyway because it's big and squishy and tactile and they would stick their heads in it and squeeze it and stuff. So Air Giants, in late 2019 there was a grant scheme called the Southwest Creativity Network which was a scheme set up to start new creative technology businesses in the Southwest which sounds hyper specific. It didn't run for very long. They funded like nine businesses I think in our branch of it. So with Emma and Robert, we applied for this thing, they gave us a grant, we had to go and have an interview and show them prototypes and stuff and then from February 2020 to October 2020, we were funded to do R&D and make one glorious prototype and do a bunch of business support stuff to try to be off the ground as a business it was great, we would not be here without them. And we were really focusing that pitch on things which are individual close-up interactions with things that feel kind of a bit alive. That's manifesto which I think it still is really. We all still had other jobs going on then because that's not enough money to live on. So there we were, we spent months doing this. This is in Emma's garage, we spent a lot of time sewing things together going, this shape's going to be great, it's going to do this thing and then we'd sew it together and then we'd inflate it and it didn't do the thing. Often it did a different thing but it's the kind of very free-ranging R&D that you have to do when you're in a new field. Just tinkering with stuff and experimenting with stuff and failing. We made a lot of things that look like this. This is the ball-o stick which can be long or long and cylindrical or short and round. Loads of things like that. It was no end of fun. This was in peak lockdown so this was a terrible time to launch a business focused on the events trade but we got super lucky with the timing because we were doing R&D while everybody was shut. If we'd been six months off either way I think it would have been a calamity. It was just lucky. So this is getting to the end of that grant. This is us starting to make our giant prototype. So this thing here was kind of a prototype for one of the actuators. And this is what we made. So this is Luma which is a kind of giant robot snail thing. Our plan was for this to be autonomous but we realised once we had it running that the interactions people want from it are very particular and focused. They want to go and do a thing with the head. They want to be greeted by Luma's head a lot and that's not something that's easy to automate. It's hard to sense where people are well enough. So Luma is still puppeted by hand. Somebody has a wireless controller and I think for this interaction it kind of always will be. April 21. We got a workshop. So it's as we sort of started to have some confidence in this in a business be like can we spend money on rent? Robert found this place and we were like well if we get some more deals maybe we can afford to have a workshop. So let's get it and see if it sticks. So there's this process over a year or I guess it's still going on of becoming increasingly more confident in this as a thing and turning away other work giving up other possibilities committing to this more and more.  We did not do that. I don't think that's a sensible idea. It's a gradual thing of working out if it's going to be a good thing and how much you can lean on it and whether you still need to lean on your other stuff. It was very slow and gradual. We had to do all the other company stuff. So this if you have done it and don't care what I think if you haven't done it there's a lot of boring things which you have to do which if you do them wrong there are terrible penalties but actually they're all quite easy and so we made a limited company and we registered with that got accounting software running we got an account and we got insurance we got all kinds of business support from organisations which exist to support businesses at no cost to us. They're like, there's a local kind of council scheme the creative technology network people also gave us business support and they just tell you how to do things because they get paid to help. It's great. A lot of the other paperwork, our accountant it turns out you can just say, can you do this for us? He just goes and does it because he does that all the time. So this is not something to be scared of this is something you've got to do otherwise disaster but it's really not trouble once you're spending that much effort on it. And then there's funding which I did not understand at all would be such a big thing I thought we would make things and then we would show them and get paid and that would be the story but actually it's not. It's a complicated landscape it's all a bit imponderable till you're actually trying to do it because it's all weird special purpose things and if you don't learn this landscape and understand how to live with it then you're knackers. So there's like not spending any more money than you have to. Weird grant schemes that you just find out about because somebody tweeted about them when it's two days to the submission date getting commissions from arts organisations who are interested in your kind of arts so just finding out who those organisations are is work. We've got loads of support from EDGE which is an Innovate UK thing that is paid to support businesses. Again, if you apply to them and they say yes they'll just help. We've had R&D grants from Innovate UK which you have to apply for business support grants we've done collaborations with academics for which we get paid and this is a big thing is getting paid to show work at arts events. Oh, we've got a grant from the Arts Council. So there's all this stuff and you sort of have to know how to work all this stuff. The things we're not doing are the conventional tech company stuff like selling shares in the business and taking it on debt. We hate both of those. In the first one it means it's not your business anymore and the second one it means you have to give the money back. And if you don't have a plan which will certainly make that money back then it's just not comfortable. So July 2021, here we are showing Luma outside, first time outside for us. Coral Avenue was a commission from Warwick Art Centre. They saw our prototype in the showcase and they more or less sent us mail going hello, that looks great. We'd like to commission a thing. What do you want to do? And then we made them a thing. And they commissioned it, but now we have it so we can show it. This was a big day, right? We sort of came to a point where we were clearly paying the rent and that was OK and we could spend money on the stuff. So we're like, we've got to put more stuff, Robert said, we've got to put more stuff in the workshop, in the workshop. We need racking in a stack of truck and actually we've got money in the bank and we can just buy a stack of truck. I mean a second-hand stack of truck. But it's not a thing that I saw coming and it's like this same thing about a gradually increasing confidence in what you're doing, meaning that you can start doing slightly more big business things like just spending a few thousand quid on a thing because you absolutely need it. But you know, it's kind of a big day. There was a day when Emma's laptop broke and we were like, oh, we could just buy another laptop. Oh, yeah, that was nice. Touring, so last autumn, it turns out there's a circuit of light festivals and they need things to show every single year and they will pay you to show your work and the amounts which they have are kind of fixed, but if you can work with the amounts they have, you can just show it a bunch of them. Again, you have to know the people and you have to be friends with them and they're all mates, so you have to do a good job with the first one so that the second one will like it, all that stuff. You know, it's learning that that starts to be the core of a business that you can sustain. We applied for Arts Council grant to do a soundscape which we'd never done before, so this is an installation with a bunch of these and a soundscape built in which is reactive to the audience. And yeah, they gave us money, we made it. We haven't shown it yet because COVID, but no, soon. Innovate UK grant, so Innovate UK exists to fund R&D that will lead to commercializable output soon. To get one, you have to fill in a giant form, so you have to find a call that suits you. They have calls that last for a few months for very particular purposes and you have to find a call that suits you and then you apply for it and you fill in a massive form, what you're going to do and how much it's going to cost and how long it's going to take and how it's going to make money and how big is the market for it. But if you get it, they do actually give you money to go into R&D and then at the end of it, you know a new thing and you've got a thing which you can hopefully commercialise to make a new bit of your business. I can talk more about their process afterwards if anybody is interested. So this was a grant to make little versions. These are little air giants like tabletop size for indoors, which we think we can see a market for. So we did that, made some, they were great. Quite a lot of work. When you change size, all the engineering changes in surprising ways. Softly Nol spoke under an academic collaboration with people from the Bristol Robot Lab and some people in assisted communication. So this is about whether it's possible to make soft robots which are communication aids for people who use communication aids. So communication aids are traditionally very aimed at conveying specific messages about, you know, I would like to now kind of things. And this is really asking a question about whether you can do things that are more like body language, more about conveying some feeling or inclination with a soft robot. It's extremely preliminary, but so far promising. And from a company point of view, not only do we learn things, but we get paid for doing it. You know, it's all just part of keeping the company going. Unfull, this was our big deal. We spent all winter sewing this. We showed it in Norwich a few weeks ago commissioned by Without Walls, who are a consortium of outdoor arts festivals. We're showing it at a couple more of their festivals later in the year. And half of that is here. It's quite a big bet, you know, it's sort of six months' work, maybe not six, but you know, months and months of work designing and making and coding and making circuit boards and wiring and... Yes, but we like it. Think it came out well. So what's next for us is touring unfull, still developing it into a stable touring business, finding out about other markets we can show in, fine art market, international stuff, all kinds of things. We just applied for another Innovate UK grant to do another piece of technical development. We need to look for the next commission. We need a bigger workshop. So, I mean, this is full and we want to make really big things, but that means we need more money. So that's the story, right? So I don't know if that made sense, but let me talk about it a bit more. It's like 10 years of tinkering with stuff and just going, this little thing is interesting, maybe I'll do some more of this. I'll work with these interesting people, they're doing stuff with robots and people and that seems interesting. And maybe these inflatable things could be part of this world of emotionally interesting robots. And then meeting people who are interested in the same things, meeting Emma and meeting Robert, who are interested in the same stuff and want to work on that stuff and are at places in their lives where they can and want to go, oh, I'd quite like to just go and do that. Maybe that could be a job. So it's this really long, slow story of lots of bits coming together to turn into the thing. It's not, I had this genius idea in my head. I went and got funding from a VC six months later. We're serving a million customers, delivering dog sausages to dogs all over the west of England or something. It's a sort of long, rambly thing. But in my mind, it's about following that initial interesting idea and just sort of poking at it, even every year or two, poking at it some more and moving it along and finding people are interested and building the connections around it. So it takes ages, right? Or for me it took ages to go from playing with inflatable eyeballs to, oh look, we're a company. And I don't have any recipe for how you would do that with some other thing, but I feel like that's the kind of thing that makes that kind of thing happen if that's what you want to do. It's precarious and it's unpredictable. It's much easier if you've got some other thing you can also do so that there never has to be a point where you go, I'm stopping farming sheep and now I'm making robots. You don't want that one day cut off between the thing and the other thing. You want to stop one as the next one is fading in because it takes time to start them up. No, you've got to learn all this stuff about making the thing and finding how you make money from the thing and finding workshops and it just takes you a while. So flexibility is great. I'm very fortunate that my other work has always been the kind of thing where I could do more or do less. It's a much less good fit to a regular nine to five job. There are ways to do it. People do it. Finding the right partners is vital and it takes forever. So treasure them when you find them because you need them. And it just takes a lot of learning. That's it. Yeah.


Hello and welcome to the chart of the week video with me David Madden. Today's date is Thursday the 11th of April 2019 and the time has just gone 11 to 15 British summer time This week's chart of the week is Brent crude oil and as we can see here Brent crude oil has been in a nice upward trend since late December a nice series of higher highs and higher lows So a classic definition of an upward trend and only yesterday Brent crude oil managed to eke out yet another yet another five month high So what we've recently hit levels not seen since November 2018 and a part of this bullish move in Brent crude oil is percent is because of several issues largely because of these intentional production declines in terms of output from Well from OPEC, but you know Saudi Arabia one of the major in major Influencers of OPEC OPEC I've been I've been tripping up output also in 2019 we've seen a reasonable turnaround in Chinese economic indicators and manufacturing figures out of China and the service figures out of China have been improving ever so slightly Speaking of China the trade relationship between the US and China Hasn't gotten back to It's it's it's it's it's former it's former all-time highs, but the trade relationship is certainly getting better On top of that there are some there are sanctions imposed on Venezuela There are sanctions imposed on Iran all of these issues and more recently there's actually been some conflict in Libya So all of these issues Piling together have helped all push higher for the last number of months and I should reach Only yesterday hit a five-month high So if the oil market does manage to push on higher from here, we could be looking heading up towards this level here The early November highs which come into play at 73 spot 53 And if you go beyond that we could be looking heading towards this area here levels not seen since late October 2018 which upper on the 79 77 spot 95 mark If it's if the break crew law market does manage to drift lower We might see some fresh buyers enter the fold because buying on the dip has been a pretty popular strategy We trade it as a product number of months So if we do manage to drift lower from these current levels in around kind of 71 spot 65 Support might be found from this red line here Which is the dirty moving average and that comes to the play at 69 spot 35 if you zoom in here We can see that in the last few Basically in the month of April we can see that the dirty moving average was a fairly important metric It acted as both resistance and on the 1st of April and on a few occasions in early April It managed to act as support and if a metric has acted a support of resistance recently It makes it more likely that it will do so again in the future. Obviously, there are no guarantee. It just makes it more likely If you do manage to drift below that Support might be found from this area here the 68 dollars a barrel mark It's a big psychological number 68 dollars on top of that 60 in around the kind of 68 dollars mark We did see a bit of resistance in the past and even if you drop below that this region might act as support We can see a few a few times the all-market mention five support from this area here Which comes to play at 66 dollars for barrel I thought it really if you have a size with break below that because then we actually begin to be concerned that well Maybe the upper trend that we've been in since ever actually is actually coming to a bit of an end Now if you are training brain crude oil, it's worth keeping an eye on what's going on in West Texas intermediate WTI Down theory tells us that the averages must confirm each other and we're now looking at the WTI chart As you can see here, it's almost almost identical in terms of actually the pattern move for buy a nice series of higher highs and higher lows from December Once again this week WTI printed a fresh five-month high also. So both markets are moving Moving largely and broadly speaking in tandem together and are both are Ratcheting up fresh five-month highs. We can be more confident that brain crude oil is going to continue on its positive run Conversely if both markets must to drift lower and both start to hit, you know Multi-week lows or multi-month lows or take out recent support levels on both markets We then become more confident that the bullish run in oil is over It's worth keeping an eye on any updates from the all-market while recently we've heard that Open that Russia who are not in OPEC, but recently are working alongside OPEC We were recently from Russia that they're considering raising their output at the June meeting of the major oil producers So please keep an eye out for any updates in relation to the oil market And finally if you've any comments to make on this video or any of the other videos We've made here at CMC markets, please feel free to leave review on Google base and that's all for me this week. Thank you very much


My name is Abdul Majid Raja. You can see my talk's name is Machine Learning Bias. A little bit about me. Actually, Google can tell you more about myself, so you could have Googled it by this time. And it, in fact, knows what all apps I use, when do I use, what ISP, so I don't have that much information here. I'm the organizer of Bangalore R User Group. I open source at this link. I blog at this link. I have a language, agnostic data science newsletter at nulldata.substack.com. And I also run a website called freshgrads.info, which is to help fresh graduates, you know, see real stories. So getting into the talk, what we are going to see in this talk is, first, we'll try to recognize the problem. What is the problem that we are going to see? And then we'll see what is machine learning bias. Then we'll see what is the definition of fairness, and then we'll see about interpretable machine learning, or these days, the buzzword for that is explainable. And then we'll see some Python tools. And then if time permits, we'll have a case study. To begin with, would you believe that if I tell you that computers lie, probably you wouldn't believe, right? So we have been always told, whenever you have a comparison between accountant and computer, computers do not lie. And we have been always told that computers never lie, right? But that is not the case. So I don't know how much you can read it. Yeah, computers actually lie. And this is part of a project called ImageNetRollet. It was created by two researchers to indicate the underlying problems with the most popular machine learning data set called ImageNet. ImageNet is one of the most used machine learning data set in a lot of pre-trained models and see what it has done to Obama.  So I just wanted to go back home alive in one piece. So that is why I didn't put any Indian politician. So the next is problem sample. We'll see more samples. This is Google Translate. Google Translate is trying to improve. But what it actually does is there are two kinds of languages. One is the language where you have gender specific words, and the other one is general neutral words. So in English, when you say, she is a doctor, and then you translate it to Hungarian, which is a general neutral language, then translate it back to English, it converts, he is a doctor. Of course, yeah, we know that it is a general neutral language, but this actually happens because of a machine learning concept called word embedding. But traditionally, whenever they trained this algorithm, he was always closer to doctor than he was closer to nurse. So that is how it has happened. Right now, you might think that I applied for Google and I didn't get a job. So I've been defaming Google. So that's not the case. So let us see one more case of Google, where Google Photos. Actually, we may not even take it as anything because Google Photos has launched this universal app, and then it has classified black people as gorillas. It's not actually funny, because if you put yourself in that position, then you might realize that this is how the world treats you. So it is an indication. Now, yeah, let's move from Sandhya Pichai to Satya Nathala. Microsoft actually had announced a big PR, and then they had this bot called Teh. They wanted it to be a very cool millennial bot. And the biggest mistake that they actually did is they used Reddit data to train it. And anyone who uses Reddit knows about Reddit. The bot actually turned to be anti-Semitism, and then Microsoft had to take it down. So this actually became a very bad press than a positive press. And then when you look at this picture, that lady actually stole neighbor's bicycle to go somewhere, and then she's been classified as high risk. You know what is the recent, right? And machine learning bias, again, a lot of papers have been written saying that criminal prediction algorithms are usually biased against black people. So now this is like, okay, I smoke.  So you recognize the problem. So now what is it? Next. Machine learning bias is nothing, but whenever you see that the algorithm is not fair, it's not fair to the entire group of people, the population, then you say that there is machine learning bias. So we try to define it with respect to fairness. And very ironically, we do not have any common consensus or definition for a standard or framework for fairness, because it is still an emerging research area. That's why most of my content is actually theoretical. But machine learning researchers have taken some cues from judiciary, where usually the legal terms are used, and they try to define it in terms of individual fairness. One is called group fairness. The second one is called individual fairness.  And what is a sensitive variable in terms of legal attribute is that sensitive variable is something that you should never use. It's used to identify your personal attributes, like your religion, your caste, or your race. Judge should never use this as one dimension to give a verdict. That is how legal uses, and that's why it's called protected or sensitive attribute. And when you use this sensitive attribute and divide your group and use any statistical measure, both the groups should have the same result. That's what it says about group fairness.  Ideally, humans are the data points here, so humans. Similar individuals should be treated similarly. And a little bit of causes. The first one is skewed sample. Skewed sample means your data, the sample that you have collected is highly skewed. Let me give you an example. The example is, let us take India now. And right now we are in Chennai. In Chennai, let us assume that there was a police station since 1900. And now let us take Bangalore where I am coming from. Let us say that in Bangalore, there was a police station from 1990, which means because there are so much records in Chennai since 1900, and there is less record in Bangalore because the police station was established at 1990, which means your data is going to be highly skewed. But when you train your machine learning algorithm based on this thing, your algorithm is going to say, probably some American is going to come and it is going to say, okay, don't go to Bangalore, there is more crime there. So this is what happens because of the skewed sample. The second one is a tainted example, which we saw at the start, where he is closer to doctor and she is closer to nurse. Even though that is not how it is supposed to be, but because we have this prejudice or we have this human bias fed into the data, that example itself has been tainted. And limited features. Imagine you have a data set for India, you can have good data set for North, you can have good data set for South, but you cannot have good data set for Northeast. And in those cases where you have minorities, where you have people who are not always been included, you always have limited features.  So again, you have a biased algorithm in place. Then sample size disparity is how we just saw that example, because you have less number of data points, again, you have a disparity. The final thing is the most important thing, proxy. So what happens in a lot of cases is like, probably I'm a responsible data scientist and then I'm like, okay, I don't want to include gender in by machine learning algorithm, or I don't want to include, let's say, race or people's background. But there are something called proxy, which can represent this thing. Let me give you an example. Let us say you have to predict something in India, and then now you want to calculate, and there is a column called language. Let's say this data set is about Tamil Nadu, because we are in Tamil Nadu, of course. So the data set is about Tamil Nadu, and then you are saying, okay, I want to make my language as a protected variable, which means in Tamil Nadu, people predominantly speak Tamil, but there are other languages. For example, people might speak Kannada, people might speak Malayalam, people might speak any other language, because these are neighboring states, right? Now you have made language as your protected variable. But even if you make language as your protected variable, you can pretty much use longitude and latitude or pin code to say that, okay, this is the part of Tamil Nadu, which is closer to Kerala. Let's say Kanyakumari, which means people speak more Malayalam there than Tamil, because it's a border. So these kind of attributes can become proxies to the actual sensitive variable, which also can bring in bias, because machine learning algorithm is good in learning these things, so it can bring in bias. Now how, okay, good. So you have given a good talk about all these things. Now what do we do? How do we mitigate it? And simply when we call something as mitigating machine learning bias, all we are trying to do is improving the fairness. So it's like glass problem, half-full-full-full. So we just try to increase the glass level so that bias is reduced. In a typical machine learning process, you have three primary stages. It could be if you take a CRISPR data mining framework, you would see a lot more stages, but on abstract level you have pre-processing, training on the optimization and post-processing. So in the pre-processing stage, what you can actually do is, instead of using the sensitive variable or proxy, there are certain mathematical methods. Please do not ask me what it is, I'm not good with that. So there are certain mathematical methods, which you can use to create a new representation, let's say Z. So your sensitive variable is X and Y.  When you plot something in an n-dimensional space and you take something orthogonal to your existing plane, then it is completely devoid of that information. Similar to that, you take something which loses all the sensitive variables properties, but still holds some value. Okay, this is one way you can improve it. In the training phase, what you can do is, this is an age-old technique a lot of people actually do to reduce overfitting, which is add a cost or regularization term where you limit your machine learning algorithms predicting capability. You are not letting it do anything it wants, so you are trying to penalize it whenever it tries to misbehave. The third one is the post-processing technique, where you can actually add a threshold and cap to say that I don't want a score beyond that, I'm fine with the score because I want a fair score rather than I want a high score accuracy. And another thing that you can do in a post-processing scenarios, you can actually try to balance the output.  So you try to add a little bit of coefficient or something to improve that balance to get both in the same level, so which is something some people do. So these are the three stages. So now we are talking about machine learning bias, right? And we also saw that how you can little bit improve it. And what is happening in the industry is, the good thing is actually people have started talking about machine learning bias, like since 2016, people have actually started mentioning fairness in their ML research papers, which means humanity is answering the calls. So this is part of a Kaggle, Kaggle is a machine learning competitive platform. There was a survey in 2018 where Kaggle, Kagglers were asked about machine learning algorithm. Most difficult about ensuring your algorithm is fair and unbiased. And I'm not talking about the first or second, but I'm concerned about the third option, which is I've never performed this task. So this is actually concerning.  You have to remember that when these algorithms come into social life, see, as long as these algorithms are in someone's computer and then trying to play some games, cards, or poker, whatever it is, right? As long as it is in that level, okay, we are cool. But imagine, tomorrow they're going to put this algorithm in your airport. Tomorrow this algorithm is going to decide whether you'll get a loan or not. Tomorrow this algorithm is going to decide whether you will get health insurance or not. Tomorrow this algorithm is going to say whether you are going to be a criminal or not. Imagine in such a scenario, you just cannot put anything in production without even checking these basic things. So that is where it has to be a universal attempt to understand these kinds of things. So let us work toward ensuring that our algorithm is unbiased. And that is where this thing comes into picture, which is called interpretable machine learning and the buzzword for it is called explainable AI, where you just do not build something and then deploy it. You try to understand what is happening inside it irrespective of whatever algorithm it is. So there are certain machine learning models considered to be black box models, which means a data scientist builds that model and then just claims or shares a LinkedIn post or Facebook post that I got an accuracy of 97% but has no clue about it. So that is a black box model. So interpretable machine learning is where you try to make sense. Humans should understand this algorithm like what is happening behind this algorithm. That's IML. And there are a couple of advantages that IML brings in. One is fairness, of course that's what we spoke about. Privacy. So only if you actually see what is happening inside that algorithm, you get to know whether this algorithm is violating someone's privacy or not. See ultimately you can collect as much as data you want. Your organization can simply collect whether you open LinkedIn, how many times open you LinkedIn and then it can build an attrition model and then that attrition model definitely would have high accuracy if you open LinkedIn and Naukri and indeed for 10 times a day, right? That's how everyone is going to look for a job. But do you want to build a machine learning model that violates every privacy on the planet? So it ensures privacy, reliability. People start trusting you. Now I wouldn't trust my bank which deploy some machine learning algorithm built by some consulting company for heck lot of money but I would trust it when I know like what goes behind it. Then of course causality, you get to know what is driving this prediction and the most important thing is like we said, trust. When machine learning algorithms show up in social system, let's say government or any public sector, you need to have that trust in your system so that your society is sustainable and when you do not have that trust that's where your society breaks loose. The basic thing, if you're a machine learning practitioner the very basic thing that you can simply do is do a variable importance plot or do a variable significance plot. It can actually tell you what are the things that are driving your prediction positively or negatively so that you have some basic understanding of what is actually going on inside that algorithm that you just claim to be superstar. And this is again from the same Kaggle survey. These are some of the techniques in the sorted order that Kaggle is actually used. The basic thing is prediction versus actual result, feature importance, feature correlation and printing out a decision tree. Let's say you are building a random forest, maybe you don't have an idea about random forest, then you can take one tree from it and then it's not an environmental talk, it's a machine learning algorithm so you take a tree and then you print it. So it's like that and there are a couple of tools that can help you and that is what we are going to see. So people have created tools that can make it a lot more easier. They have obstructed the entire complexity, just given you a function, so just use this function. So there's a very famous package called LIME, SHAP. As you can see in the example code, SHAP can in two lines give you the explainability to that algorithm that you have built and SHAP has everything for your conventional machine learning algorithm, your ensemble machine learning algorithm or your deep learning algorithm. So it has functions for everything. ELI-5, like everyone knows, explain like I'm five, there is a package like that. Scikit-lego is a very interesting package I recently came across. They even have created two functions that will help you build fair logistic regression. Logistic regression is a machine learning algorithm to classify, to score, propensity scores. So they have built a fair classifier where you can actually include sensitive variables and then this algorithm will try to exclude them and exclude their proxies and then build this algorithm. There is a tool that has been open sourced by Google, it's called whatof tool. So it supports tensor flow or any deep learning algorithm for you to do. And very recent announcement a couple of days back, PyTorch, which is from Facebook, one of the most popularly used deep learning libraries. PyTorch has come up with something called CAPTUM, again focusing on machine learning interpretability. So that is all about tools. Let us just see a case study to have some understanding of like how this impacts in the industry. So I vaguely gave you an example like a bank or a society or a airport, someone is frisking you, but let us get a little bit deeper into it and then try to see one case study. The case study is adhesion prediction, like every company today wants to predict adhesion because of course when an employee leaves the company, then it is a huge overhead for them, right? They have to bring in new people. And so the objective is predict, build a machine learning solution to predict employee adhesion for the next quarter, very simple. The data that you use demographic compensation, whether that guy or girl has got any promotion or anything, rewards and recognition, very simple data. Great, fortunately you built a model which is ensemble of random forest and XG boost, which is what a lot of people on Kaggle used to do. And your accuracy is acceptable, which means probably you are eligible for the next quarter award in your company. So it's good, all good. Can we go ahead and productionize this model? No, I just watched Abdul's presentation, so I'm not going to do that. So what are we going to do that is we are going to do a very simple variable importance plot. And that plot is now telling us maternity leave is one of the most important variable to drive attrition. Now, should we consider about machine learning ethics? Is it a matter of considering machine learning ethics and why we should consider it? Because maternity leave is only defined to two groups of people, which is one is female, the second one is married. And if you go ahead with this model, probably you are going to get award in your next quarter. You can frame it in your company's wall or house that you can do. But the downside is your HR departments, that algorithm will never let any female who is married come into the company because it is always going to say that, okay, this female married might get pregnant, which means she might take maternity leave, which means she might churn out of the system. So it is good that you don't hire. So the problem is not for me. No one is going to ask me when I appear in an interview, Abdul, are you planning to get pregnant? No one is going to ask me, right? But every time a married female is going to show up, this is going to be a concern because a lot of people already ask, okay, are you going to get married? Are you going to relocate with your husband? So already it is discouraging a lot of people from entering into workforce when there is a huge attempt to bring diversity inclusion in the workforce and algorithm like this could actually eliminate a complete set of people without you knowing it, but ultimately you are this Hitler bombing female gender, so you don't want to do that. So what do you do?  And what is the outcome? Yeah, of course my model's accuracy is going to be less, but at the end of the day, I can get sleep in the night because I have built a machine learning solution that did not have an obvious bias built into it, which will act to this algorithm against a particular set of group of people who are already been, you know, not given chance. So what are the lessons learned? In this case, unlike we saw before, in this case, there was no bias in the data itself. So it was an important feature, but there was no bias in the data and the model training during feature engineer, something we came with this and there is always a trade-off between accuracy and a responsible data science.  But again, yeah, there are better techniques for the same solution. We just now just simply removed the maternity leave, but like we just saw before, you can create a new representation to have some information about it. And yeah, finally, the most important thing is machine learning ethics, you know, it actually matters when you build solutions where your data points are human beings. Means of course, it doesn't matter when you're going to use Titanic data set on Kaggle and then try to predict whether someone is going to die in Titanic because everyone died in Titanic already, right? So but it matters really when you build solution for human beings, actual human beings like you and I, so ethics matters. I had no clue about this topic unless until I went through all these things and I would definitely recommend the last talk. It is given in PiData London. It's a very entertaining talk. It's called artificial stupidity. It's a very useful talk. So finally, I want to leave you with these thoughts. It's actually very easy to be another cool data scientist because every data scientist is cool these days, but what is actually tough is to be a responsible ethics-driven data scientist and no one is going to tell you to do that. It is a call that only you can make and you have to make, so it's up to you. Thank you very much. Yeah, it's a nice talk. Thank you. We are, hello. Yeah. We are currently building a machine learning model to predict like they are going to fall or not, like in senior home. We are currently taking the feature like gender. They're female or male. In this case, I am considering gender isn't feature. Like in crime, do I need to consider gender as a feature? See, in your case, it is of course obvious to consider because a female after 40 years might get, they're highly likely to get osteoporosis or rheumatoid arthritis. They have bone deficiency, right? So you're trying to predict fall, which means it is okay. But when you want to build a solution that will impact something that has been given to them like job or a crime prediction or insurance or anything like that, then I think considering gender is not appropriate. Abdul, this is Chakri. So from the first slide, that translation of this gender neutral language, that how you think we should handle those things like, because those define the definition between artificial intelligence and human decisions. So actually Google have already started handling that to give you a simple example that day before yesterday, I had a different language in there. And before the presentation yesterday, I thought, okay, I have to just double check it once. You can see day before yesterday, I actually had Turkish and Finnish. And then within one day, Google had actually updated it. So Google is actually doing it and I didn't want to be embarrassed in front of the entire crowd. So I double checked it. And Hungarian was the only language I could find for this particular example. But the point is what Google is trying to do is, Google has a community initiative where the community member would validate it, whether it is, so either sometimes they show both the genders as he or sometimes they show, is this right? And they ask you to validate it. Say like how you do in maps. So that's how they're handling it already. Okay, so I got to ask, how do you identify this sensitive variable? So is it identified using machine learning itself or is it identified using legal or other? No, it's just pure common sense in the domain that you want to build with. So like he asked, so in his case, gender might help him detecting fall, but in a different context, gender is definitely sensitive variable. So it's a daily life, social science. Hello. Hi. It's a nice talk actually. I have a doubt or maybe it's, I want your view from here. So this is all about the bias we are talking about. So in most of the common practices, we impute missing values. So we are actually as a data scientist himself or herself is imputing some bias. What is your take on that? Because there is a whole bunch or suit of algorithms which handles missing data imputation part. So that's what. See that may not come under bias. So and even there are advanced methods. People basically start with imputing a mean or median and then they go until to the level that you build a classifier, that classifier ultimately tells you what to be done. And that kind of thing happens in a lot of context, but that may not come under bias. But what you are trying to say that, it is not the real data, but you are trying to put something which is what you have learned. But we don't have any other choice, right? When data is not there, you can either exclude it. But then like the example that I gave, maybe you don't have values for Northeast India, but you want to include them in the model. So what do you do? Go for sample and then impute values. So you're saying like we should go with missing value imputation process because it's a standard practice. Yeah, because we don't have any other choice. Thank you, thank you. Hi, Abdul. Hello, hi. Yeah, sorry. Yeah, wonderful talk. Thank you. So I just wanted to know, like does generative model comes under ethics of machine learning? You are talking about GANs, a Generative Adversary in New York. Yeah, because generally if you run a classifier for object detections, then in that case, if we have a data for one of the categories, we have less data. What if we run a generative model on that, generate a data for that particular category, and that generated data is pretty good. So should we consider that as a practice or not? See, again, I am not exactly sure. You're talking about generating synthetic data, right? Yeah, generating synthetic data, in the sense like using GANs, we can generate images. Yeah. So suppose we have, suppose we are trying to blend a classifier for detecting animals in it. And for one particular animal, we have less number of images. Using GANs, we try to generate that. So is it a good practice to do or not? That is very similar to what he asked with Missing While Imputation, right? You have to do it. Otherwise you will exclude that class. Means you don't have any other choice. Hi, Abdul. I have a question. In the real-time, I have trained my model with some, let's say, few data sets. Like after that, I want to improve my model. Based on that, model is mispredicted or misclassified. So I want to give the feedback and keep on improving it. So how I can design that kind of architecture? So that is how usually a lot of recommendation systems and every other model building technique usually happens when you move it to production. So you either have a feedback system looping coming back to the model. Let's say you have labeled data, cancer and no cancer. So our spam, no spam, let's take. So if you see a lot of email clients, they ask you whether this is not spam inside your spam. So when you say it is not spam, that the algorithm knows that this is a false positive. Now this data is again feedback into the training where the model is improved after a certain iteration, like one month model refresh or something like that. So that feedback loop is mostly kept in a lot of productionized solutions. Yeah, I'm here. Serious thank you for bringing up these issues up and they are not mentioned often enough. They have been increased a lot in visibility but these issues are not mentioned often. So I thank you for bringing this to so many attention and making it delivered so well. My question is that if you have a recommendation system, especially of that sort, often that can be a large bias but that's hard to detect in the output. Like recommendation systems are like, oh, here are my recommendations for your movies but because you are female or because you are married, I'm going to give you more traditionally girly recommendations or things like that, which may or may not work sometimes but it does have some bias. So how would you suggest when the output is so large or output is so confusing that you cannot really detect bias? How do you do it? How do you pass the data? See again, in that case, it is more of your business strategy than it is bias. Let's say there is a company and they sell exclusive female clothing and exclusive male clothing. Now of course they have to include whether it is for a female or it is for a male, right? There you cannot say it is a bias. So it is based on the business context and what I'm specifically talking about is when something happens in a social science context, which is like government related or your credit scoring, let us take an example of credit scoring.  So if you want to start a company, you will never get a loan. So you will never get a female entrepreneur because her credit score was already less. So I'm talking about these kinds of social science context but that is more of a business strategy, what you want to do with female targeting and male targeting.


So what's up? What's up, everybody? Once again, it's Sean Taylor and I believe that all artists should control their own destiny Which is why I do these on marketing branding and this strategic strategy because that's how you get there So for keys to create a strong image is what we're gonna go over today It'll be general high level and then I'm gonna dig deeper and some later ever So I'm actually gonna make a series out of this and before I get into things I want to actually think Jay got them feeding because he's the reason I'm doing this Just to let you guys know I do pay attention to requesting things on comments He did this on Instagram. So now I'm making a whole series out of it. Not just making one video So there's three types of images the way I think of it strong visual. This is that lady Gaga, right? Super strong visual image with the crazy outfits or even a little yachty with the red hair subtle This is a little bit more nuanced, right? I'm pretty much everybody has had like a guy or girl in high school or middle school that Is always wearing a hoodie right that person you think of that always has on a hoodie wearing a hoodie isn't necessarily anything really crazy but it is a visual image and Once you notice that person wearing hoodie all the time that kind of Feeds into how you think of that person and you don't even necessarily notice it as their image until they don't have a hoodie on All of a sudden you're like, huh something looks different. Oh, yeah, you don't have that really on then it's the bleminal Is it really a visual based thing? It's more ideal idea based So think of a girl next door a bad boy or a thug There's ideas that we have in our head to think about things like this There are some visuals associated with it boys, but the image is more about the idea than the visual so Number one thing to know is self-awareness Everybody should be aware of themselves because you have to be aware of what you're doing and what you're putting off so you can pay attention to what the people are Doing and how they're responding to what you're putting off and that way you can change what you're doing to get the response You want image Branding all that stuff is literally the manipulation of how people perceive you so know thyself Not going too much deeper into this because you know, we y'all know what I mean Just do the work yet to know yourself number two environmental awareness. No, that is surroundings This is the one I got to dig in a little bit deeper in because people for some reason they miss it and don't really necessarily Understand so let's start with this concept right here group blend. You're only different in some environments So what does that mean what it means is? Different is subjective to where you are right different in one place isn't always different right? You you do one thing in one country is and then you do it in another country the way it gets looked at is different in the same way you look at these guys at the top as Well, I mean they have on they're dressed a certain way and even though these guys front center and focus because that's the way the photo was set up These guys kind of put off the same vibe you think of them in the same group because our mind Psychologically we have a tendency to group things These guys in the bottom left. I think there's a girl in there I don't know but that's a We group they don't look like different than anybody or anything You take any one of these pictures and people and put them in another picture in this group And they kind of stick out right the suited guys They don't stick out from each other even though they all feel like they're being different with their different little You know touches that they have on their outfits. It's all subjective there's an area down here in Georgia called a little five points and When you go there everybody is doing their artistic expression in the way they dress But you kind of group them all the same they look like they belong there They look like they're in a group But if a guy in a suit came through there then that guy will kind of stick out, right? So now that person is different even though we usually think of a person as soon as kind of just being normal You know corporate or something like that. So environmental Is huge look at this guy red Amongst all these guys in the suits and you know, he's dressed down Obviously he sticks out in the same way this guy at the bottom on the right side that I just added into the picture sticks out because he has on this suit and You know even it's a black suit.  Don't worry about that. We'll go over some of those things, too But number three Understand perception. This is very key because you don't want to repeat the people you want to speak to the wrong way Think about the fact that just the way it is Our society has been conditioned. We've been conditioned to look at certain things in certain ways You think of people in suits in a certain way you think of people who are dressed artsy or whatever you want to call it a certain way so We have the establishment the corporate world and the anti The counterculture or what I call established anti-establishment because they have their own rules, too And using that same example in like knowledge if you bring a guy in a suit into the art world They don't necessarily think of him as creative and they don't necessarily resonate with him just from what they see They don't think there's much in common in the same way you bring a guy into what the artists look into the corporate world That person right of the bat might not necessarily be somebody they take seriously So you want to be sure once again, but the image you put off is not interfering with the message and Relating to the people that you want to relate to and the last this is the most important I'm supposed to have a four by this thing right here be consistent be consistent Consistency is literally the thing that creates your image your brand. You don't have one without consistency. Just know that Why because if someone sees you one time, they just that's just what you see they see you in even if it's something You know Extreme like what Lady Gaga has on but that's just what they see you in but they see you in that multiple times one two three four Five times it starts to create this image of how they think about you. This is really how they perceive you It's what they think of you So here's my ending quote that I like It's probably because I said it but image like reputation is literally perception based on information gathered over time Right, let that sit with you and just think of the fact that people are gathering data on you every time They see you and the data continues to be consistent Then all of a sudden they can't help but come up with one conclusion They always see you in a hoodie the only conclusion they can come to is literally you always wear hoodies hoodies are your things hoodies Are your image so hope that was helpful subscribe We're gonna finish go deeper with kind of the form the last format the previous format of different artists With different examples of those three types of image So other than that have a great one


We received a call earlier this week about an older gentleman who had passed away in his recliner and his sleep and he was there for about three days before anybody found him.  It smelled like old rotting fish in all honesty which I've never smelled before coming to Abaya. The issue today was the flooring when you would get past the first level of flooring. There was a second level of vinyl flooring and then under that vinyl flooring was some glued down vinyl flooring. That was the absolute worst part of the smell today but we went ahead and we removed all three layers. We were able to completely disinfect the concrete, seal the concrete and fog the place before we went ahead and closed up shop.


Hello friends and welcome to another Pokemon update video and for those of you that don't know me My name is Leigh also known as Osiris. Thanks for tuning in and today. We're gonna be just covering a quick bit of news that has dropped tonight regarding the 2020 VGC season, so Pokemon have just tweeted out some news about the 2020 season and We'll go through the tweeters three of them here. So the first one is One of three as you can see they're on your screen now update to the Pokemon VG's official competitive rules regional forms that were not available prior to Pokemon home a Lollan nine tails, etc. All those are currently not permitted in official competitions You'll be able to unlock a battle team containing one of these forms until series three And then it goes on to say season series three opens on the 1st of March The following Pokemon will become legal Provided they have the Galar symbol. So if they are bred in the Galar region That's all that means then these Pokemon will be legal all regional forms including Galarian slowpoke Which is a brand new Pokemon that has been recently revealed in the games and released in the games Bulbasaur Squirtle Rowlett Litten Popplio and their evolutions. So we're looking at Venusol Blastoise decidu I The popplio Evolution I can't remember for the life of me and Incinero Yay Incinero's back and there'll be more Gigantamax Pokemon to be announced And then it ends off with not that event spanning multiple days such as regional championships We'll use the format that was live on the first day of the event throughout the main tournament Vg rules and the format document will be updated in the coming days with all the information so That is some huge news because we start this season thinking probably we'd only get a rotation of the Gigantamax Pokemon considering that we've got different rotations in the wild area and we've only got a really small portion of them now available in Pokemon Sword and Shield and But it looks like on the 1st of March. So we've only got a little over Two weeks now and then the format will be changing and we will have all of these Pokemon Venusol Blastoise Not Mewtwo or Mew or Selvria or Jarachi and And Cobalion, Taraki and Virizion won't be Resurum, Zekrom, Cureum, Keldeo won't be but anything that you can breed like Decidueye, Incinero Primarina, there's the one and anything that you can breed obviously will be accessible the Alolan forms So Alolan Raichu, Alolan Ninetales, Alolan Dutriot, Alolan Persian I'm guessing I'm guessing Kanto Rapidash Kanto Farfetched, Kanto Weezing, Korsala, Lanoon, Dalmanitan If you can breed these Pokemon Stunkfish, if you can breed them obviously I'm not sure if you can breed regular Kanto Dalmanitan, but if you can't then they're obviously not available But if you can and they can have the Galar symbol then they are going to be eligible to be used in the future VG tournaments After the first of March I'm I don't know I don't know what to to say about the rules I'm not like super super happy about Incinero coming back of all I know there's like a lot of kind of Displeasure about it right now and for good reason like Incinero was really really centralizing when it got introduced in 2018 it kind of overthrew landeris therian from its kind of place of being the the main Intimidator in the format took Arcanine's place and It's got access to new tools now like parting shot And you can Dynamax it so it's it's It's like fakeouts less powerful now But I think it's still gonna have a really big impact in the game And it will change how the format plays out for the rest of the year be interesting to see the new Dynamax forms that they'll rotate in I'm betting that it'll probably be Lapras. Um, but it's gonna be it's gonna be interesting to see how the format plays out It would have been nice in my opinion to just keep the the Pokemon that we've got at the moment as the Pokemon and just rotate the the G max Pokemon the gigantic max Pokemon and then Introduce all of these other Alolan variants and the Kanto Pokemon that we've just had through Pokemon home introduced in 2021 season with probably some of the DLC content that we get in June time So that would have been my ideal and then it gives the newer Pokemon a bit more time to shine and because I feel now potentially It's gonna get a bit a bit a bit to kind of centralize with some of the Pokemon that we've got and coming into the format So I just hope we don't end up with an Incineroar on everyone's team Because you're gonna be looking at like Incineroar, Togekiss, Exkidrill, Dragopul And they're gonna be your they're gonna be your big four and I mean Arcanine's there in place of Incineroar right now But I don't feel like that four is gonna be something that would be sticking around for the foreseeable future Format's gonna be changing, but maybe Incineroar changes that a little bit, but who knows who knows there are answers to every Pokemon Let's see how it plays out. This is just a quick video It'd be great to hear your thoughts on it down below. Do let me know and we'll be back very shortly with more content Thanks for tuning in friends, and I'll speak to you all very soon. So until then take care. Bye. Bye