Hey, this is Brett, and today I am excited to show you this brand-new 2023 Jeep Wrangler four-door Willis Sport S package. This vehicle has the 3.0-liter V6 Eco-diesel engine, puts out 260 horsepower, and it gives you 442 foot-pounds of torque. This vehicle is going to get you 29 miles per gallon on the Highway 22 city and an average of 25 miles per gallon. Granite Crystal is the color, PAU is the paint code, and I'm going to go all the way around in this video. We're going to turn on all the lights at the end of the video so you can see what they look like all lit up because the Willis package 26W comes with the LED headlamp and fog lamp group. And we'll get into it right away. If you like the video, you can subscribe to the YouTube channel, which is youtube.com slash Summit Auto. So the Willis package is going to give you the gloss black grille. It's also going to give you the LED headlamps and running lights as well as the LED fog lamps.  Notice you get the tow hooks on there as well. And the other thing to note is that it's just the headlights and the fog lights that are LEDs. The ones in the fenders are still the halogen. So you get the Moab black 17-inch rims with the Willis package, and it has Firestone destination MT2 tires. These are LT25575R17s, and they definitely have a mud-terrain tread pattern to them. A couple little things on the Wrangler. You get the sandals on the shroud there. You also get the Jeep climbing up the side. You get the T50 indicator on there for the Torx wrench that you need to get these doors off on these bolts. And we actually did a video on how to take your doors off of your jail Wrangler. That's in the upper right hand part of the screen. The Willis package also gives you the rock rails on the bottom. One thing to note on the 2023s, they took away the word unlimited from the four doors. I'm not sure if that's planned or if they just changed the stickers. You get the black dial Jeep lettering there as well. Take a look at the original window sticker. Feel free to pause this. I'm not going to go through everything, but definitely go through most of it. So 2023 Wrangler four door Willis 4x4. It's actually part of the Sport package. So it's a Sport trim level with the Willis package on it. Best way I can describe it. Three liter V6 turbo diesel, eight speed automatic transmission. Everything on your left there is your standard equipment. Your optional equipment starts with the granite crystal paint, which is 495. The Willis package or 26W is $6,935.  You get the LED headlamp and fog lamp group, which they tell you again in the next line.  And then once again, it tells you that you get the LED fog lamps and headlamps, sun visors with illuminated vanity mirrors, all weather floor mats by Mopar, mold in color bumper with gloss black trim, and that's those fog light bezels. So the convenience group gives you the universal garage door opener, which is 795 or the home link system as they call it. Cold weather group gives you heated seats, remote start, and heated steering wheel, as well as that weather wrapped steering wheel, $1345 for that option, eight speed automatic transmission is $2,500, and then the eco diesel is a $4,000 option. Now why that might seem like a lot, you know, the savings that you get in the miles per gallon arena really kind of make up for that. And then on the other end of that is there's the warranty. You get a five year 100,000 powertrain, so you get 40,000 more miles of powertrain over the gas engines, which in my mind would be definitely almost worth that $4,000. So the black three-piece hardtop is $1795, and then the Alpine premium audio system is $1295, and you get Series XM for six months for $295. Total MSRP is $55,750, and if you want to get your summit price with all the rebates and incentives on this particular Jeep in the upper right-hand part of the screen, again we have a video showing you, or a link to our website where you can get all the pricing information and availability on this particular Wrangler. Five year 100,000 powertrain and three year 36 bumper to bumper, they're your miles per gallon on there, and then it's not really rated for a lot of safety ratings yet, only three stars on the rollover. Those all weather formats are one of my favorite parts to the Willis package, it's got the topographical look to it, so that's really cool, and those are in the front and the back. Other thing to note on the Wrangler is you get the four-wheel disc brakes.  Notice you get the Stingray Wrangler on the rim there. LED license plate light, I like the EcoDiesel badge on there. The back lights are halogen, you'll see that when we turn that, all those lights on at the end of the video, you get your bag for your front hardtop pieces, and then back here you get the bolts to take that hardtop off. There's three on each side, one on the top, so four total, and then two quick disconnects. I got a video showing you how to take your hardtop off, really popular video, that's in the upper right hand part of the screen as well if you wanted to check that out.  You get power windows, locks, you get the heated seats, the alpine sound. Back here you get a nice storage area with the JLs, they used to have this speaker on the JKs in the floor, so you lost all that storage area. Now they put them on the side, so that kind of really saves you a lot of room.  But other thing to note, they paint the bars the same color as the vehicle color match, so when you have the hardtop off it looks really good. Trail rail ready, first time I noticed that. Learn something new every day, you get the JL badge back here with the wheelbase and overall length for the two-door four-door as well as the water fording and it tells you where they're made, and they made those doors a lot lighter so they're easier to close and they seem to shut a lot better than the old JKs. You get the blacked out fuel door, and really a good looking package. You get the trail rated badge on the driver's side only. Does come with heated mirrors, and those mirrors are power as well. You get the Sport S interior, so you get the black cloth seats, driver's side height adjuster, lumbar. These Wranglers now have side airbags, which are definitely a must. The all-weather floor mat over here, the auto headlamps, power windows, locks and mirrors. The back seats have the latch child safety system for any child car seats. You can see that the space between the front seats and the back seats is larger on the JLs than the old JKs, and then the other improvement that they made is that these headrests can fold down for better visibility when nobody's riding in the back, and why that's nice is because Wranglers don't have the best visibility, but these headrests take like 10% of the visibility that you do have away. So when they're down, it's nice to have that extra visibility. These seats go down almost completely flat, which is really nice.  Get that nice topographical floor mat back here. It goes all the way over the hump, and then your power window buttons are right there, and then your air vents are there as well. Two cup holders and the child safety locks on the back doors. Take a look at the instrument cluster and the radio. Everything that this thing has to offer on the interior. You do get a digital display on there, and you get the digital speedometer, a compass display as well. Get all your information, screen setup, messages, audio, start, stop, trip info, fuel economy. You get your tire pressures there, coolant temp, trans temp, oil temp, oil pressure, oil life, battery voltage, and diesel exhaust fluid for the diesel, of course. And then, of course, your speedometer comes with the heated leather wrap steering wheel. Remember, that's part of that cold weather package that you get the heated steering wheel and the leather wrap. Cruise controls, Bluetooth and information center controls, and it does have the audio controls on the back. The other new thing with the JL, not that the JL's new anymore, but they have tilt and telescopic steering wheel, so you can bring the steering wheel towards you or put it in and it also tilts. This is the seven inch Uconnect radio, MFM and Sirius XM radio capabilities.  It's all touchscreen, or you can do them down here with the more tactile knobs and buttons right there. You have your heated seats and heated steering wheel controls. You also have your backup cam, which they've made these HDs, so they are crystal clear. Really like that they did that. And then you have all your different settings that you can do. You also have all your different apps in here. I'm pretty sure you can project your cell phone to the screen via Android Auto or Apple CarPlay. I've yet to test that theory, but I'm pretty sure that it is capable for those options. Anyways, you get the mineral gray. I call it mineral gray. I'm not sure if it is mineral gray. It looks like mineral gray from the old Rams. Anyways, they've got this painted and it's like a metal flake on the dash. Now that comes with the sport package. And typically the Rubikons come with red and then the Saharas come with leather. I have seen Rubikons in the past with gray, but it's very far in a few between. I actually watched a video the other day and I saw a Rubicon with a gray dash. So it used to happen, but that was like a 2018 when they first came out. Down here you get your start stop, your stability control, your speed crawl control, and then power windows right there. Get a media center with an AUX USB and USB-C four by four shifter and your eight speed automatic transmission. You get a little shift knob there and then your Jeep logo on the top there. Keyless entry with remote start and it has the flipper key on there. You also have remote start on there and hazards, nice big, thick, chunky key fob, which I like. And the hard top pieces, there's a couple quick disconnects. Like I said in that video, which will be at the end of this video, how to take your hard top off, I'll show you how to get the off. It's pretty easy. Home link buttons for your garage door security systems and lighting systems. I'm gonna start it up and then we'll do a walk around with all the lights on and then I'll turn the lights off in the picture studio and then we'll do a walk around with that and then we'll be all done. The thing I like most about the EcoDiesel is that it's basically as quiet as the gas engine. And yeah, we got the fog lights on there. So we'll go from here and we'll do a final walk around and then I'll turn all the lights off in the picture studio so you can see all the LED lights and fog lights in the front. I would personally like to thank you for checking out the video today. Let me know what you think in the comments below. If you like it, if you wanna see something different, actually one of my viewers asked me to show all the LED lights with all the lights off in the picture studio. So I said, sure, why not? It's not too big of a deal. So you can see just how nice and bright that those lights are, but definitely let me know your feedback. I wanna make my videos better for everybody. And we'll pause it right here. I'll turn all the lights off, do a final walk around and we'll be all good. All right, so you can see just how nice and bright those lights are.  I don't think I'd buy a Wrangler without the LED light group, to be honest, so that all of our Willis packages have that, is pretty sweet. You can see those LED license plate lights and then the halogen tail lamps there. But yeah, that's what it looks like. I'm gonna start turning some lights on here. And I would personally like to thank you again for checking out the video. And hopefully from this HD video, you've been able to verify the options, the looks, the styling, and the color and everything of this particular Jeep Wrangler four-door Willis package and Granite Christon Mattel. Thanks again for checking out the video. Remember to like, subscribe, and share on the YouTube channel. I really appreciate it. If you wanna see more pictures of this Wrangler or one of our other 550 new and used cars, trucks, SUVs, minivans, Wranglers, half tons, three quarter tons, one tons. You name it, we gotta go to that website right there www.summitauto.com Full pictures and descriptions of every single vehicle from two locations all at www.summitauto.com. If you'd like to check out more HD videos, you can go to www.youtube.com slash www.summitauto.com. Remember to like, subscribe, and share on this video and all the videos that you see there. Click the bell notifications to get updates on the videos I do each and every day here at Summit Automotive. In fact, in a second, you'll see a link to subscribe to my YouTube channel in the upper left. A link to more Jeep Wrangler videos that I've done in the upper right. A link to how to take your hard top off of your Wrangler in the lower left. And a link to how to take your doors off of your JL Wrangler in the lower right. Click those, check us out. And we're super excited to be offering the brand new 2023 Jeep Wrangler four door Willis package in granite crystal metallic with the eco diesel. Thanks again for checking out the video. Remember to like, subscribe, and share on the YouTube channel. I really appreciate it. Thanks again.


says I guess first the statement, multiple M365 calendars! Yay! The company I worked for places consultants at other companies. These consultants have a Microsoft 365 account and a calendar at our company and a totally separate one at the client. I've had a few people ask about some way of merging both separate Microsoft 365 calendars or using an online tool so when someone from our company or the client tries to book a meeting they can find the actual availability over both calendars. Any suggestions? It has to be a third-party tool. We talk about this a lot that we have a lot of calendaring questions which you would think would be a hint to Microsoft about consolidating the strategies around calendars across all these solutions but we are Microsoft's kryptonite so that's not happening. It is right up there with tasks in task management. There's more movement there. I think we all agree there are third-party solutions that solve this. I use every day I use I use Calendly that's my solution. Yep I use Calendly too. I know that there's there I mean I don't I'm not like loyal to anybody there's Calendly there's calendar bridge there's a there's a cut there I know there's several sync tools that will just do it but Calendly has been the one that I've used that's worked out the best and in fact it's really nice because you can have like so I have a Google calendar on multiple M365 calendars and then I get extra calendars from clients and I'm always trying to keep everything organized and so you can actually put in the different calendars and then pull all that into one interface and then if somebody books it automatically like checks all of your calendars and books it and you can decide like what to check and where to put it and things like that but I think at the end of the day right now there's nothing that Microsoft provides out of the box to do that so you would have to have a third-party tool. Yep so with Calendly to be able to add the multiple calendars is that a premium subscription or is that the base subscription serious question is that a paid subscription to do that it is a paid subscription yes okay the free version does not allow you to put multiple calendars in correct and I think the free the free one only allows you to book meetings against one calendar like you can pick a calendar but to do the multiple calendars I think is the paid subscription and you can only have one type of count of meeting also yeah yeah so I had this question come up recently and I started looking into a solution but I have not tried implementing it the solution is not a good solution it is a hacky workaround but assuming that both of your calendars are have a web-based like you know Outlook.com option you can use Power Automate with to listen to you could you would use two separate Power Automate flows one for each calendar that essentially listens for when a new item is created or when one is modified and then go and then basically write it to the other calendar and same thing on the other side however you have to be smart about that because if you don't have some sort of trigger condition then you will end up making the loop you will have a new new item on calendar one it will write it to calendar two calendar two says oh I have a new item let's write it to calendar one and now you have 37 meetings at the same time so trigger conditions very important also that's a hacky solution and it is not by any means a syncing opportunity yeah I I think the last time we discussed like something similar to this scenario we talked about that exact thing Jonathan about you know building that kind of automation to go and do and and it's yeah and it has inherent problems with that and if I think the last time we were talking about it somebody was syncing three calendars and it becomes just really burdensome to do that between so yeah like so mine I sync between three calendars and so Calendly is the best solution for that for me again there are the third party tools I don't think cost I've been using Calendly for years and so I'm I'm good I'm not looking around it can I save five dollars a month or whatever you know so you know speak up if you have another favorite tool that's out there but on the bridge calendar bridge calendar bridge yeah for me there needs to be a showdown between Cal and Lee and calendar bridge now


Welcome everyone to this new robustly beneficial discussion. Today we will discuss the topic of security and privacy applied to machine learning. We will specifically discuss the points raised in one paper from Nicola Pappernot from Google Brain. The first thing that I find interesting in that paper is the link he makes with a good upload and you must already have heard it that when a measure becomes a target it seems to be a good measure and for machine learning model we have the same problem that when a model becomes a target it seems to be it starts to be vulnerable and the reason why is that even though you have run some tests that says your model is robustly beneficial 99% of the time this 1% that's left it will be door open to two attacks and if this model that you have deployed is a target then the attackers will use this 1% and the result of this is that your model will actually not fail just 1% of the time but higher percentage. Yeah I think it's one of the big challenges of designing machine learning algorithms or algorithms in general that are deployed and used by billions of users is that if it's used by billions of users then there's probably a lot of incentives to try to exploit it and there's a huge amount of incentives from billions of users to exploit the vulnerabilities of one algorithm then you can definitely expect people to try to hack these vulnerabilities and essentially your system to be completely open and completely cracked because of these vulnerabilities. In the rest of the paper the the discussion I find is very interesting it uh it describes the principles that are useful for a computer system in general but not systems that do machine learning and then try to make connections with a system doing machine learning so and and there are there are a lot of differences for example uh uh security for systems that do machine learning can be interesting to look at at training time but also at testing time so there there are these two types of attacks also discussing the the privacy is very important about machine learning and uh and there is also the fact that you should look at the whole system of the the deployed machine learning algorithms from the training from how the algorithms is queried from the input data that we give to the algorithms for each query and uh and uh it's interesting to discuss how these principles that are uh that that were designed simply for normal computer systems can give us very interesting insights about machine learning models yeah these are topics very uh not specific to uh to machine learning yeah there's a huge amount of of work already be done in trying to make systems more more secure more robust and have better guarantees uh in terms of privacy um and definitely like machine learning has to take a lot of this into account because for a long time like uh there was a the challenges of machine learning was just to show that you can have a system that works and now we have system that works but now we have to make sure that our systems will always work which is uh a huge difference there's a huge difference between working in average or in a in a basic setting and actually working once deployed in practice in complex environments we potentially a huge number of of uh measures users and to to to do so to to bridge this gap between working in some setting and working all the time uh it's actually it takes a lot of a lot of work and making a lot of thinking and many of the ideas for classical algorithms are definitely relevant for machine learning algorithms as well yeah what you so what you describe is a is a sometimes called a distributional shift the fact that one assumption of machine learning is that the testing distribution is going to be similar to the training distribution but what happened in practice is that the testing distribution is uh very different from the training distribution because as we said actors are trying to gain the system and uh and create these differences yeah um one other thing hit the the paper discuss a bit um and uh i would like to hear your opinion on this is the uh auditing ml like the there are many people who who think we should have auditing or audit protocols or or or um ways to audit machine learning and uh it's not very clear from the from the section how this audit could be done in a reliable way you don't know what's your take on that yeah so the the solution that he was proposing discussing in this paper as some of them i found quite interesting so the first one it happens at the so for example uh he also mentioned the concept of differential privacy and this is something that we can audit from the from the training data we can we could have a algorithms that are verified and we know for sure that part of the information that was in the training data set he is not actually part of the model so this uh this would be a proof i think that the differential privacy uh was correctly applied by first one algorithms uh a second uh a second interesting things is that so because it's a machine learning system as i said that we can uh there are things happening at a training time and possible attacks at that time and also things happening at the testing time and uh a second possibility of audit would be simply look at what are the queries being done at a testing time and for example if you see that uh during testing some query is very similar to some input data you are doing in the training data set it's likely some attackers that is trying to to learn the data that that you have been trained on uh an example of this is for for example youtube is giving me recommendations and these are somehow private i would not want that the recommendation that youtube is giving me would be known by uh by a lot of others so if if it was possible for someone to to somehow fake someone very similar to me some very similar patterns to mine and then knows what youtube would recommend so that means that that would be a kind of attack that happened at testing time someone trying to to to get my private data the recommendation that i don't know privately for me and uh and this uh this kind of auditing it would happen by somehow keeping logs of what queries are being done on the on the on the requirement system on the machine learning system and uh and somehow what one thing we could test is does it diverge diverge too much from what we expected like uh based on the assumption that the training distribution would be similar to the testing distribution yeah i think a lot of this is related to uh things that we have already discussed in the past like the the black box abstraction of machine learning um yeah i think one difficulty of of machine learning but actually it's not specific to machine learning you can think of any uh source code that's not open uh and even if it's open and poorly written i'd say uh sometimes the best way to know about the the algorithm is just to have all sorts of testing through interactions with the system like you're giving it input and seeing what the output is uh and yeah and this can be very hard to to analyze uh i think set that in the specific case of uh for instance neural networks or some of these machine learning algorithms they're not actually really uh fully transparent uh fully opaque uh they like you could share for instance the the weights of the from the like the parameters of the the machine learning algorithm and sometimes based on this you can do some analysis so there are some some some approaches based on uh ideas from formal verifications uh for instance you can try to prove that whenever you have an input that has this property that the output always has these desirable properties something like this so so these are ways to go as well but uh it's not always clear how they scale up to to larger in your networks no perhaps another thing we can discuss i think an idea uh that uh comes very early on in the paper is the idea that uh when you're designing a system and you're trying to make it secure you're trying to make it secure against adversaries and uh it's not necessary the right model to assume that the adversary is uh omnipotent and all powerful uh and omniscient so omnipotent is that he can do anything omniscient is that he can know everything because in practice this is not the case and it may be more realistic to assume that he has some budget some some you know amount of power and he can use it in different ways but he cannot do anything with this uh with this power and then you're trying to be robust to this kind of adversary uh and sometimes this makes this allows you to do a lot more because a non-powerful adversary like it's very hard to fight against but if you only say that you have to be resilient to a very powerful but not all powerful adversary uh then maybe then you can have a efficient algorithm scalable algorithms that do what you want to do uh actually this is not in common in uh the research on the more maybe on the robustness side of machine learning yeah this is definitely uh yeah what what I find is just thing is that it's an idea that would come up naturally for machine learning because machine learning you cannot have a formal methods but even if you use formal methods at some point you're still going to have to make assumptions uh because if you really think about the security of a formal algorithm with lots of proofs uh well the security of all of this depends on every component of this and if uh it's a combination actually of 10 algorithms and the 10 algorithms are communicating in all possible ways so there are like many combinations of of all of them then then the the component X starts to explode and uh it can be very hard to keep track of all the dependencies of all the and uh yeah in addition to this you can even have a more sophisticated sophisticated attacks like side channels or things like this or you're hacking some parts of the system that are easier to hack especially if it's a multi-component system and uh even for formal methods you need to to you cannot assume to be robust to everything because it's too hard but you have to be a robust to a huge amount of attacks and this actually turns out to be what is done in machine learning because indeed you cannot hope for much better in machine learning okay so just maybe to uh to talk about some research I'm familiar with actually the some of the research we did uh so for example in the context of Byzantine resilience so where you want to be resilient against an omniscient but not omnipotent adversary you can still have subclasses of omniscience uh based on the computing power of the of the adversary for example an adversary that could look at all the gradients of everyone uh at infinitely short time so with infinite computing power would be the would be the full Byzantine adversary but you can you can have more realistic assumptions for example whether the adversary could spy on the spy on the gradients of everyone and um do some computations on these gradients to to compute the best attack but to do that the adversary so she or he may need the uh big o so uh an order of the dimensions uh computing time for each update epoch and since the updates can be very short so they need to perform a big od so d is the dimension of the model computation within the time that it requires for the model to be updated so that's that's very constraining and so with this constraint for example you can you can you can downgrade or upgrade the the requirements on uh the the robustness required because if you know that you will update the model very fast you won't be waiting for for for late updates so then the adversary would not have time enough time to for example do some regression on the gradients and find maybe what is the best attack etc to poison you so yeah they're already they're already uh what what you suggest is already there at least for the past two years in in the field of Byzantine fault tolerance for machinery yeah but maybe you can push it to other directions so i think we we discussed uh uh was about uh social medias for instance on social medias you want uh facebook twitter and youtube to be resilient to Russian trolls for instance uh or like yeah these are fake accounts that just trying to manipulate uh the the public opinion uh for instance you undermine another country or things like this you want to be resilient to this uh and uh one thing you could say what we need to be a reason to all uh users but this is actually probably impossible to do unless you shut down every account which is not uh something that sounds desirable but instead of that maybe you can have models where you assume that the the adversary the the troll adversary has the power to hack uh certain number of accounts uh but and maybe some accounts are easier to hack in a sense than others um probably if it's uh uh the the twitter account of some very famous person it's going to be harder to to to patrol to take control of it but if it's all the accounts of people who have been using twitter for a long time but have not been using lately then maybe it's easier to hack and using this assumption of the amount of power of the adversary of uh here the trolls then maybe you can come up with algorithms that are robust to this kind of power yes maybe that's a research direction that could be extremely relevant especially to to the current situation uh of COVID-19 where there's a lot of misinformation agree something to add on that or yeah so maybe some uh something uh we that we discussed very quickly and we could say more about it is the the openness of the of the code it's I think it's something that maybe we didn't discuss uh when we talked about the black boxes previously so the it's a very common principle in a security system that if your code is open source uh it's it's much more trustful that the code is a is actually secure because uh and the reason to believe in this is that if a lot of people have had the possibility to to find the failures in the code uh because of the open source situation then uh that indicates that if you don't find a a bug or a possible hack then the code is trustful and for machine learning actually opening the the source code is only a small part of the of what can be opened so other question other things that could be open is the the input data the parameters of the model what prediction are being done in in which situations and and all of this relate to to question of security and uh and the question of privacy so clearly uh so it in the discussion we had it seemed to be quite obvious that people wanted the the the code of this this uh this software to be to be more open uh the the one of the main reason for me to see that this code should stay closed is that it gives more more more difficult time for attacker to to actually generate attacks uh one uh one way to see this is about uh in the case of your network generating adversarial examples I believe it's much easier to do it if you have access to the parameters of the model compared to if you don't have access to the parameter model and so I think the most the the recommend assistant that matters the most today they are they are very closed and it's even some uh some small things like uh receiving uh the output of the of the algorithms is something that's very difficult I think uh it's I don't think it's possible for for example to to query uh youtube to become a system more than one million times per hour for with uh for an individual so somehow uh we had this debate whether this uh this the openness is desirable or we or whether it will uh give more keys to attackers to to manipulate the system so we just at some point uh at one sense would you want your spam filter to be open source uh I don't remember how we settled on this like so yeah and again spam filter to be open source I I'd be very happy if the algorithms of the spam filter is open source but not the training data it has been trained on yeah yeah because clearly if the if the whole algorithm which training it out if the simply the parameters of the algorithms open source it means that someone wanting to send spam could simply run locally the spam filter know whether the spam will be stopped or not and and change it's the it's it's spam email to to to find the failure of the spam filter and uh yeah yeah and as you say if you know the parameters of the if it's neural networks and you know the parameters then you can compute the the gradients and this tells you how much what is critical in the message for instance you've written that pushed to the towards the definition the decision of of filtering out or not the message you've sent and so you can more quickly uh change your your your emails so that they they guessed through the the spam filter uh and so it seems that if you want a well a fully open source uh uh by fully open I mean you have access to all the data and all the parameters of the of the spam filter seems like a very bad idea so is it an impossible impossible thing to have well you can yeah like you mean efficient and would it still be efficient uh yeah yeah that's the question yeah so I'm guessing not but yeah if you have the parameters uh if you can run it locally I think there's there's huge incentives to to be for for for passing spam filters so yeah maybe you don't want your spam filters to be open source for security reasons which is a bit counterintuitive yeah I agree so that's why it was quite interesting to to see this discussion of of principles that are very obvious for normal company system but that became becomes slightly strange when we talk about machine learning systems yeah I guess if it was uh the spam filter was a hundred percent good in a sense uh if it does exactly what person wants and then doesn't want then would be would be fine but uh like it doesn't sound reasonable in practice like that's this game and and there seems to be a gain in uh obscuring the algorithm yeah and maybe it also goes back to the to some discussion we we had about social media manipulation that in the end certainly that the attackers would become extremely good at imitating normal humans and then it then if if someone creating spam is extremely good at imitating a normal email any spam filter would would would fail actually if you think this kind of spam so in this case what was my conclusion is that so because we can't detect what is a authentic authentic message compared to what is a message with the intent of manipulation the what what we truly desire that the system would do is actually push forward messages that are beneficial and and lower the impact of messages that are not no matter what no matter the source of these messages so yeah yeah and the primary is really I think that's with good heart slow I guess and with um yeah if you have a system that you that you know exactly who's no rules are exactly known then you can better exploit the system if it's not perfectly aligned and you I think we should absolutely assume that the systems we design are not perfectly aligned and because of this it's a vulnerability that may be best protected by not being fully open oh yeah it goes back to this idea from us to what we saw that I found quite interesting that to somehow beat a good outlaw to not not reveal the the the full objective function to the to the artificial intelligence system but somehow let the artificial intelligence system have an uncertainty about what is the objective function and and this way what the what the artificial intelligence we try to do is to to guess what you really want and to to work on this and yeah he mentioned this idea in the context of interruptibility because uh yeah this is a an instrumental goal to do not be interrupted for for machine learning systems and but if if the machine learning system has uncertainty about its own objective function we can expect that if he sees that there is a tentative of interaction he might deduce that oh it's because i'm not doing the right thing so it's actually my interest to let myself be interrupted yep yeah so you're playing this in the opposite way you're based on human by the machine at the machine by the human but yeah okay all trying to hack one another algorithms of humans and you don't want algorithms to hack humans but you also don't want humans to hack algorithms yeah so we also discussed the fact that maybe some parts of the codes of youtube can maybe should be opened like uh i think it's complicated but uh would push for more openness and at least collaborations with people outside from youtube for like academics for instance public health authorities yeah public health authorities especially these days it's extremely valuable but i think in many countries it could lead to two backlashes especially in France but yeah like these are extremely valuable information that they have and if they can be exploited for good that that that'd be that'd be good uh but it's not easy either to make your code open um and like we discussed like like the information systems of companies today is quite often a huge part of their whole business like if you remove the information system of well the the worst case probably a bank then there's nearly nothing left like the information system of a bank is critical and but this also holds like for the airplane industry like if you remove the algorithmic part of an airplane then essentially the airplane does not fly you know not only the algorithmic part of the airplane the like the plane itself but if you take a flight company yeah i've read that for example air france uh the assets uh so if you evaluate the assets of air France the the software that runs the booking the booking software amadeus which is a sub i think it's a subcompany of air France now it's worth more than the total cost of for buying all the other planes that air France owns yeah the software the work the work of the if you value the assets of air France uh it has more assets in software for the booking software in the booking software than assets in the form of planes yeah yeah but even the planes they're still i think absurd of because of the plane something like this is the software so if you if you just take the assets in terms of software then you get a higher value yeah so so yeah so far it is extremely important but changing the software it also means that changing the software is a big deal for a company like it's worth a lot of money to have a good software and we gave the example which is actually relevant to these days of cobalt uh do you want to tell so so yeah recently the the governor of new jersey was uh was looking for so in the middle in the during this mess created by the coronavirus uh the governor of jersey on users they were were seeking the help of cobalt developers because the the unemployment platform crashed due to the surge of of people trying to register in it and it has legacy code written in cobalt which is now 60 years old 70 years old 60 it was written in the it was written in the 50s 60 years old and so in the book we wrote with lea uh we're in a place in a moment where we discuss the problems in having interruptible systems uh we illustrate the fact the the fact that sometimes it's very costly to to just interrupt something that is running by the example of cobalt so before covid the legacy there was there were known cases of banks trying to get rid of the legacy code written in cobalt and for example the austral the australian commonwealth bank spent three quarters of billion dollars and seventy seventy hundred fifty seven hundred fifty million dollars uh so it lost all that amount of money uh during the the replace for the sake of replacing the legacy code that is written in cobalt so that that is an argument for not changing uh for for not interrupting the system because if you interrupt it the cost would be high yeah and so if you want a company a big big company wants to make much of its code open even half of its code open then it's probably a multi-b if it's a big company like google it's probably a multi-billion business project and it's not even sure that is going to succeed like it's like really really a big deal as i think there should be efforts in this direction but i think we have to factor in what we are asking from from from these companies and you have to show that there's a gain in this either through regulations or through a gain of trust or whatever but if there's not this gain i don't think we can expect this company to out of their goodness say we're going to make the codes more open so i'd say like if you're advocating for more openness of this company i think it's a good idea to first really think about the incentive structures and what can be done and maybe ask something that's more reasonable that's more likely to lead to something good uh should we rub up or something missing i think we covered all the topics we discussed in the reading group anything else that we discussed uh and that may be uh important is uh essentially there are two ways and usually they're more or less combined to to to to guarantee a greater security and privacy in systems uh the first approach is more um empirical is more uh you set up a system and you try to attack it and if you cannot attack it you let others trying to attack it and if others cannot attack it then you say well it's likely to be uh secure uh but it's nowhere near guaranteed maybe there's a new trick that someone will find and that can completely break the system so it has its limits and the other approach is theoretical uh so for instance for differential privacy there's this concept of uh no differential privacy which is a formal concept and this means that you can actually use mathematics to guarantee ahead of time that your algorithm will have a lot of desirable properties uh for for security you are also all sorts of such systems for instance formal verification methods uh Byzantine resilience uh like guarantees in terms of performance guarantees or yeah resilience to to these are these uh variations in the data distributional shifts and stuff like this and I think it's important to to command the two but if you want to do theoretical work for security or privacy it's first important to have a good model or a good definition of what you mean by security and privacy and this can be actually uh extremely difficult uh for privacy essentially but there are different definitions but the leading definition is differential privacy and it's a nicer definition but it's also arguably too restrictive in many settings uh so it's very very hard to to apply also like it blows up very quickly so if you have a uh yeah there's this parameter called epsilon but if you have a one epsilon equal ones differential privacy then it's good but if you have epsilon equals 10 a differential privacy then it's essentially useless as a as a guarantee uh so yeah making all of these work together is a it's very complicated and it's very hard but it needs to be done too okay and there's also what yes and there's also a huge pedagogical challenge uh because a lot of people are talking about privacy but uh like uh i'm doubting that a lot fraction uh a large fraction of people who are talking about privacy know about differential privacy for instance uh about the formal definition and the intuition and i think even few of them are able to explain it to to someone else uh in a understandable manner uh and i don't think i can explain it to most people uh differential privacy unless i have like two hours of their time and they're very focused uh so yeah in all of these even security and stuff like this and it's related also to the covid situation to all sorts of problems pedagogy i think is underrated and is really really important and um yes so um and then yeah with with pedagogy i think we can conclude and tease a bit the topic of next week so next week we'll discuss the question of contact tracing and the challenges for contact tracing in terms of privacy and trust uh because contact tracing has emerged uh now as one of the most promising solutions to get us out of confinement due to covid so next week we'll discuss the the question of contact tracing and some of the proposed solutions by people from epfl among others and the trust and privacy challenges they waste and and how to be pedagogical on these two fronts to ensure that these solutions would work and would be adopted by a large fraction of the population so thank you very much and see you next week bye


My name is Rob Badger. I'm a conservation photographer. My sweetie, Nita and I are working on a project called Beauty and the Beast, Wildflowers and Climate Change. And what we're trying to do is what you're talking about here. I've been photographing environmental problems, environmental disasters since 1980. It started with the Inyo Valley when LA was trying to pump more water out of the Owens Valley to supply the growing needs of LA. I've photographed mining disasters, oil spills, and I just got to the emotional overload point again where I can't do this anymore. I would be so angry, I'd cry. So I kind of just stumbled on wildflowers and found out that virtually everyone loves flowers. So if we get people's attention and try to engage them in something they already love, get their attention and then get them to read an essay in our book about what climate change is doing, what population is doing, the history of public lands, how as a country we believe we own these public lands. So it's about getting people to recognize something they love because what we love we protect, what we value we love and what we love we protect. What you're saying is so important. How do we engage people in a poetic, almost romantic, spiritual way to get their attention and then act to do something? So a question I've got. I don't know how many people know the difference between the protected status between a national park, a national monument, national forest, and BLM land. Because in a national park everything's sacred, in national monuments you can mine, BLM land, I mean all these things. So it may be important for people to know how the protection is related to these different government entities. So I love what you're doing. I'm so grateful. Thank you. We have an essay about citizen scientists in our book. Oh and one more thing. We are looking for authors of color. Not white people. I'm sorry we already have the quote fulfilled. But to write in our books we have a collection of different essays we want to reach as broad in our audience as possible so anyone can talk to us later about what we're doing and why we need these authors. Thank you. I just want to say one thing about to complete something that Mary Ellen said about monuments and Zinke's survey is now finished. And what I'm seeing now is what I think are like trial balloons because he has described what the three he wants to work on. He's picked three. One is Bears Ears which just happened. I spent the last couple of years going down to Colorado with Navajos, Hopi, Zuni and I know how much the Colorado River know how much they care about this. Bears Ears they just got it after all this effort. He's going to plug Escalade the staircase. He's going to get a big beautiful one that Clinton put in and he's talking about one in Oregon. But I think that doesn't seem so bad given all the monuments we have but I think they're like trial balloons and I think if he succeeds on any one of those. It's a terrible precedent because national monument you're talking about the different distinctions is slightly ambiguous. It doesn't have the strong organic act protection that a national park does. So it's very important not to start losing these monuments and to really resist. Join the tribes because they're going to come out against Bears Ears and Escalade. The tribes are getting stronger even before Standing Rock. The tribes are really getting together in a way they never did. The spale faces were always good at making divisions among them. We divided and conquered and it's finally and that went all through the last century. Indians were sniping each other like environmentalists. They're always faulting the people with slightly different persuasion and environmental movement. So that's important. Other questions? Woman with the glasses. So I was wondering if either one of you have anything to say about two issues. Population and pros and cons of technology. Well you know I will say pop I have lunch with Paul Ehrlich. Do you know who he is? I have lunch with Paul Ehrlich about every six weeks or so. And I met him first about four years ago when I was researching this book because I chose like six basic you know mechanisms of nature that are really kind of pinch points where extinction is happening and he's a population biologist who became very famous in the 70s for writing the population bomb. But he also was really famous as a scientist for quantifying co-evolution which is the way that plants, species co-evolve traits in tandem with other species. So the example that he used was butterflies and plants that co-evolve traits together. The reason a butterfly is the way it is is because of the host plant that it co-evolved with. So I'm asking him to explain to me you know co-evolution and how did you uncover this and he very sweetly says I will tell you about co-evolution because you must promise me that when you write about it you will say that the real problem is overpopulation of humans and overconsumption of resources and it's true.  Like why are we proliferating so much? This is bad. It doesn't seem adaptive. Like certainly there are examples in nature where you will have a boom in population, a population boom that then leads to a bust because the boom like they overeat their habitat and then the population busts. So we think we're so smart right? We think we're so smart and so but we're doing that. And I interviewed Richard Dawkins. Do you all know who he is? I interviewed him a few weeks ago and he said something that has really struck with me. I've been thinking about it. Which is that we are perfectly evolved for the past. So he said all of us are the result of a successful sexual reproductive act. Right?  So we're still kind of behaving as though we were 100,000 years ago with this instinct for increasing ourselves. And so we're not adapted for the present or for the future. We're adapted for the past. This is a problem. So this is a huge problem because the way we're going to learn is going to be so ugly. And this is what I'm trying to like let's not learn it that ugly way. Let's learn it the better way, which is anticipate that this is going to happen and change our behavior before millions of people die or millions of people starve or things get even uglier than they are for many people. So technology is so complicated, right? Technology is tool making. It's tool making. A compass is technology. We are driven by our technology. We invent these tools that then change the way that we live. There's immense capacity for technology to do a lot of really good things for conservation. Like there's now a way to do gene manipulation. So for example, you could manipulate the genes of an invasive species and keep it from reproducing so that you could stop an invasive species from ruining an ecosystem without pouring any poison on anybody. So there's all kinds of things that technology can help us do. But technology is tools. It's not a plan. And it's not a comprehension of a problem. I also think that people always forget that there are trade-offs with every technology. I mean you can do that with invasive species, but who knows what the downstream consequences will be. And that goes back to I think what you both were talking about earlier, which is this kind of hubris and arrogance that we can figure it out and anticipate all of the feedback loops in this incredibly complicated system that we call Planet Earth. I actually think your two questions are sort of connected in the sense that I think we've lost ground in our understanding in the society of those two things. In the 60s, in the environmental movement, it was a truism that if we do not solve this population problem, all these other things we're doing, preservation of landscape, clean air, clean water, it's all gone. This is the problem we absolutely, the underlying problem we have to solve. I think people were, especially in the environmental community, were much more aware of that than they are now. And I think the same is true of technology. I disagree with Marianne a little bit on technological fix. I think we believed we had come to persuade people that there's not going to be a technological fix. It's a pie in the sky and it's almost a dangerous illusion that we're going to get out of this by technology, because technology got us in this. And all these suggestions, an awful lot of them are not very good suggestions of how to get out of it. So I think there's a new belief in the technological fix. And I think it has something to do with how wonderful these stories are in this new generation. All these wonderful things we have with these phones that do these amazing things, why not believe that we can solve our problems technologically? It's because they're much more complicated than a cell phone, you know, the problems that are facing us. And I think the disturbing thing to be about the movement is the movement on population especially has, the movement knows this is true, but is afraid to talk about it. They're afraid to be called races. And because the movement now has gotten very belt way and very sort of wonkish, they want to make alliances. This is what the Sierra Club did. It doesn't talk population anymore. My dad published a population bomb, Erlich's population bomb back in the 68. Since then, the world population has doubled. That's an extraordinary figure. That's crazy. In my lifetime it's tripled. There are people, Marxists, who tell you, well, it's just a distribution problem. They're totally crazy. It's an absolute numbers problem. In my lifetime, 72 years, it's tripled. How can, nothing like that's ever happened before. So we've got to talk about it again. We can't be afraid of making our immigrant advocates mad at us because it's too important. We can't be bullied out of talking about something that's very important. Okay, one more question. Gentleman in the back. I was saying divide and conquer. And Mr. Vrower brought it up earlier about trying to get people, bring people into the movement. And what I see happening in America versus what's happening, what happened in Northern Europe where they pay, they have a tax base that takes care of their everyday problems with people such as education, shelter, childcare, et cetera, et cetera. Whereas here, we have such a divided country that is hard to get involved.  You see who's here is probably people like me that are tired or, but people cannot get involved in a movement when they have so many fragments going on in their life that they can't handle even those. So that's what we need to do, I think. And I want you to comment on that. What I'm saying now, whether I'm all crazy or is this right? Do you agree with me? Well, no, I think we don't know how to grapple with the different scales of different problems that we have. You're right. I mean, and I think that the way people are living in their homes tonight, it's 7.15. So let's say in California, people are having dinner if they're lucky. And yet even very affluent people feel a sense of urgency of survival. And we do have a kind of culture where you have to kind of constantly be doing better in order to stay in place. So it creates a sense of being under the gun all the time and not really having margins for anything bigger. I always say, I want people to tell upper middle class white moms and dads that if their kids did citizen science, it would get them into Harvard. Because then they would all do it. They're all running around the state of California all the time with their kids on soccer teams. What are they doing that for? They're not going to play professional soccer, and yet they're devoting their entire lives to playing soccer. So it's like this competitive advantage everybody's always looking for. Let's make saving the environment your competitive advantage. But here's again why citizen science is so cool because it is a template for actually putting different dimensions of scale into the same picture. So wherever you're at, there's a way to conceptualize it and then communicate it. And it is technology that makes it possible. But citizen science existed long before technology. It's about direct observation. It's about you personally observing, making observations, and very importantly making note of your observations. So that's where my hope is that we really do have the capacity that this even the very, very cutting edge technology, if that citizen science is its big data science, actually has these very ancient roots in indigenous cultural traditional ecological knowledge. And that we are finding a way to reintegrate some of the better ways we have of approaching an integrated life. But we have all these people, we have all these countries, we have all these nationalities. It's a lot to integrate. It's a lot to put together. One more question. What I'm listening to as a creative writers is developing the style of the emotional existentialism. Fair enough. That issue. In fact, that's how we become knowing human beings. Because we know what will make us go on living and what will stop our existence. That's the three elements. First, we freeze, we observe to see if we're going to have to fight or if we're going to have to flee to live another day. Out of that, confronting this coming mass death, possible extinction of humanity. This is all on the table now. I just cut to the greater issue. I think we have to conquer, we have to defeat poverty, world poverty. That's the basic issue. Defeat poverty. Control corruption. Corruption is always going to be with us. And abolish war. Actually carrying weapons and killing another person, othering another, making them enemy. Those are three big conditions, but that's the only way that I see that we're going to save the world. Or keep the climate that we have of breathing. Otherwise, we will be like the Berlin population in World War II, at the end of the war. Bomb. And women, children, wounded soldiers facing an onslaught of the Russian army, Tommy Gunn Brigade, coming in undefended. What could they do? They stayed in place and they just took it. Those that divide, those that women were raped and so on, they just took it because they wanted to live another day. And they recreated their society and their city. Is there a question? The question is, can we accept the moral condition that we have greed, anger and ignorance? You've spoken to the ignorance, that we're trying to curb the ignorance of showing the people that it goes beyond their day to day existence. You raise many issues which are all really valid and interesting and relevant. In certain conservation biology worlds, the general view about what's happening is that it has seemed kind of as inevitable, and I don't know if it is or not, that we're going to be putting like 2 billion more people on the earth by about 2040. And part of why that is is because of countries that are impoverished, have people who have 10, 12 children as a way of hedging because that's how they've done it. Again, to Richard Dawkins' point, they're adapted to the past, not to the present. And that it will take at least a generation for those countries, the peoples that do that, to understand that actually now they have a chance for their children to live. So if they have only two or three children, those children will live. So there's no need to have the 10 children, the 15 children. So that's like a lag. So in certain conservation communities they're looking at like, okay, so there's this lag that's going to happen. And the point is we have to get species through that pinch point because people will be converting habitat to feed all those people. We will have excess people before population levels off. So population is actually leveling off in the United States as we have a more affluent and healthier population that knows if you have two children it's likely they will live. So you don't need to have more. So that's one model. I tend to not want to believe that it's a Fed accompli that we have to add those two billion on. I hope that we don't do that. But in a lot of the things, you know, I think it is really to Richard Dawkins' point that we are adapted to the past. That's warfare, rape, you know, scarcity of resources. These are conditions of the past that we do not have to have today. So we have to quickly get adapt to the conditions we really do have, which is that if we distribute what we have more evenly to the poverty issue, there's enough for everybody. This presumes that humans can collectively act rationally. Oh, it's true. There's not a whole lot of evidence. There's no evidence at all. When you ask me, I would say when you say we're going to ban war, we're going to ban poverty, my guess is that both those things are going to be with us forever. It's a horrible thing to say. On population, and I'm about to contradict what I just said, but my dad used to say on population, you cannot continue to make predictions there will be two billion more because this is a self-fulfilling prophecy. And people don't quite understand that. They say, well, we're just being practical. You cannot continue to make prophecies like that because they will happen because people start to build for that. So anyway, yeah. Okay.  Thank you all for coming and thank you San Francisco Library for hosting this. Thank you, Susan.


Yeah today We're very close to harvest here, but there is one thing we've got to do because We want to be able to figure out what went wrong and what went well so we're going to count way Calculate the components of yield now the components of yield are and Lenny here is going to be my perfect assistance because she's doing this for like 10,000 samples each growing season No person who can do this better than that Components of yield are okay. You just basically the product of the number of Panicles per area. So let's say how many of these particles we've got on a square meter, okay? Multiplied by the number of Filled grains in a particle. Is that right? Yes, and then multiplied by the weight of a grain Now the weight of an individual grain is kind of hard to measure. So we usually count a thousand or you know Calculate a thousand grain weight. So if you multiply these three things and you adjust it to a standard moisture content of 14% that's usually how we express yield of a crop and by Quantifying by measuring these different components of years and there's many other details to that We can figure out then Probably some reasons for why we have gotten a high yield or low yield and also how variable the whole thing was all over this field So I was gonna do this in a very simple way So since we have a machine planted crop So I thought okay, I'm just gonna take it like a stick and like a meter You know and throw it somewhere randomly in the field wherever it lands I would put it on the row cut the rows and carry them out But not a good idea. No, that's not what we do. Why we have to be more scientific. So What I suggest is like I already prepared I know the size of this flat It's 12 meters by 100 meters So from that size we try to use the excel function to generate numbers random numbers random numbers So, why do you do that? I mean, I know I can kind of take a spot here and there You wouldn't trust me to pick the right spot. We want to avoid bias or like We need to get locations that are representative of the whole area and Using eight different locations by this generated numbers. I think that would capture the variability and To get the Yeah, okay, that's what we're gonna do. So use random locations and then we'll show you in a minute what we do at each place Okay, here is one of our random number generated locations. It says 54 meters from the end of the field five rows in all right. Yes, go find it. Where is it? So we need to count now One two three four five Okay, what we're gonna do here. I'm here here. So we have to have our Measuring device now Measuring device. Yeah, where is the measuring device guys? Yeah, okay. Good So we've got a tape here, so they are putting the tape where here we have to start from here Yeah, right between two plants right so that we don't bias the sampling And then you've got to make sure that the tape is nice and straight on the surface Yes, and then we count the number of plants from here one two three four five six seven eight nine ten And then we read the distance. What is the length? One million ninety One meter ninety again. No way. I think you got eleven points one two three four five six seven eight nine ten This one is the card. Oh, very nice. Yes one point nine. One million ninety in length ten plants Okay, we need to write down now in our data shift. Yeah one point nine. Let's cut them So now what we are doing is to the touch the panicle individually. Okay, we're gonna carry those out and then we'll process them Okay, good Got them ten, okay Yeah, but what I've noticed that I don't see too many White heads so stem borer damage seems to be low this time. I don't see too many disease problems. Would you agree? Yes, I haven't seen anyone actually good. That's reason for hope. So what do we see? We see different number of dealers like ranging from How many ten or less less than ten in some cases that's bad more than 20 and more than 20 like in this one But not all of them will have a panic. All right So are we gonna count all that stuff? We're going to count first we're going to count the number of dealers and then the number of panic L that way we will be able to know the field the tealering efficiency Tealering efficiency that will be the ratio of the panic L and the total number of dealers. What else are we gonna measure on this? We will here we will detach the panic L So we will partition the organs we will get the panic L dry weight blade dry weight and the stem dry weight Right, then we will get the total biomass and then later on we will separate the field and unfilled grains So the ratio of the field grains to the total biomass will be our harvest index That's a lot of work, but very very useful data that will make use of let's get going can't wait for these data They're coming We just need to put them in the oven for 72 hours at 70 degrees centigrade all of this. We will have a partition biomass and Then later you will soon have the date good. So this is the hybrid today the inbred we have to do the same kind of sampling in about five six days because it's behind in development and then we'll come back also in five or six days to Do the final harvest of that hybrid already? Yeah, so we will come back for the inbred We now bring our samples here in the lab to do detailed measurements We have to look for plant rates that would relate to yield. So that's why we are doing all these measurements First we have to be very careful not to lose any dead material So first we have to remove all the dead material and then afterwards We need to go to the washing room Remove the soil and then bring back again the samples in the lab and carefully remove the roots and We also have to have the data sheet where we will be putting the date because we need to count the tillers The tiller number We have to put it in our data sheet and then later on we also need to count the panicle number We need to determine the tillering efficiency at harvest So that's why we are counting the tillers This includes all productive and non-productive tillers and then afterwards We will do now our partitioning of the organs first is we need to remove the green blades and then afterwards remove the dead Dead blades and then the stem and we put them in their corresponding bags So after separation We need to put these samples in the oven at 72 degrees centigrade and We put these samples for three days and afterwards we will be Measuring and collecting the dry weights after counting the tillers. We also need to count the panicle number Since we sampled 10 plants, so we are counting the panicle number of the 10 plants We will then just get the average panicle number per plant later on so after separating the panicles the green leaves the stem We are now ready to put them in the oven set at 70 degrees centigrade and we will wait for three days before we take them out and Collect the dry weight using analytical balance from the oven We think we bring out the samples and wait until the temperature Equilibrates to room temperature and then we will then put the samples on this one and collect the dry weight


We're getting down to the business end of the 2017 NCHA Summer Spectacular. We just had the limited non-pro derby and that was taken out by Cole Rothwell, who rode Khaleesi Cat and marked a 221. Congratulations to you Cole. Thank you very much. I appreciate it. So tell us about your run tonight. It was there was a lot of working time. The mare was fantastic on all three cows. We had a little miss on the second one. She kind of hung up in the ground a little bit, but she just tried her heart out like she's done every time I've shown her. We'd had a little bit of trouble early in the show, kind of staying up to a cow, getting close to a herd. And tonight fortunately started with enough room and she was great.  How did you feel coming into the event? I felt great. We've made the finals in the non-pro at all three shows here. We'd made it at the Fruity and the Superstakes, so I felt good coming in. And then she was great in the go rounds and we advanced pretty easy. She was good last night in the semifinals. We got through with the 16. But I was confident with her tonight. Okay. Where did you get this horse? Troy Riddle had this horse, bought her as a yearling, I believe, and came to me last year at this show. He had actually asked me all year long to look at her and I was working and I hadn't had a horse in five years. So I was a little hesitant, just I didn't know if I was ready to be back in. But it's worked out great. He had me come try her here and I bought her here at the Derby last year. And it's just been great. Troy has ridden horses for me for, we had ridden together for a long time. I mean, since I was probably 13 or 14. So we've been really great friends and what he's done with this horse has been just great. He's very easy to share with and it's a really great partnership. Okay. So you cut a lot as a youth and then as a young adult. Tell us about what you, you're fairly successful. Tell us about some of the titles you want. In here, I've won, I won the senior youth and the five and six-year-old amateur at the Superstakes and then the Western Horseman Cup in Augusta. I can't remember. I think that's maybe it. I can't remember. Why did you take the break? Well, when I graduated school in 2011, I really wanted to focus on my career and which is I work. It's called Crow Holdings. It's a private equity real estate company in Dallas based around the Crow family. And that's where I started right out of school as an analyst. I've done several different things there. But I really wanted to focus on moving forward with my career and take a little break from horses. And I have so many good friends here that I would still go ride and all that. I mean, I came to every show in Fort Worth to the finals and you can't get away from it. So fortunately, Troy found this one for me and it's been great. And he reeled you back in. Smart, smart man. Now, I have to ask you. Is that a Game of Thrones reference to the Mother of Dragons? 100%. My mom, Priscilla. I can't remember what her name was when we bought her, but it didn't seem to fit that well.  And when I got on the phone with my mom, it was just like, no, I've already decided it's Khaleesi Cat. So that's kind of how it went. And that's that's what we named her. Great name. I love it. And I'm so glad to meet a fellow DOT fan. So congratulations. Great, great work. Thank you very much. I appreciate it.


It has always been my dream to work for the United Nations. I'm excited to be part of the IAEA mission. We help prevent the spread of nuclear weapons and use nuclear techniques and technologies for development. IT is in the background, but we do enable the IAEA to achieve its goals and I'm proud to be part of that team. If you're into operations, if you're into customer services, if you're into project management, if you're into leadership, there are jobs available across the whole spectrum. IT helps the agency achieve its goals in every domain. We even support laboratories, for example, the Hydrogen Lab. All these have equipment that needs computers. And what we're looking for is people who are dedicated to serving a noble mission and who are technology leaders. One of the things I love the most about working at the IAEA and that makes my experience very rich is the diversity. So it's really positive to come into work and have a really good team of people across the division. And there is a group of very talented people who work here. The work that the IAEA is doing and how it can bring benefit to the community at large and this is when you feel, oh, this is cool, right? I have the ability to make a difference in a small way, but it's there.