The European Union is moving into a war economy mode due to Russian aggression. The EU must revise its military strategy and drastically ramp up defence production to help Ukraine in the conflict with Russia. European Council President Charles Michel said, the call comes as Kiev has increasingly warned of ammunition shortages. Writing in an op-ed published by the La Libre Belgique newspaper and the Euractive website, Michel claimed that Moscow is a serious military threat to our European continent and global security. If we do not get the EU's response right and do not give Ukraine enough support to stop Russia, we are next, he added. The EU Council Chief argued that for decades, Europe has failed to invest sufficiently in our security and defence, and now urgently needs a radical and irreversible shift in our thinking towards a strategic security mindset. We must therefore be defence ready and shift to a war economy mode. It's time to take responsibility for our security. We can no longer count on others or be at the mercy of election cycles in the U.S. or elsewhere, he added. The bloc's defence production has increased by 50% since the start of the Ukraine conflict in February 2022, Michel said, adding that the bloc will double ammunition production to over 2 million shells yearly by the end of next year. The EU has been struggling to procure enough weapons and ammunition for Kiev's needs, as Ukrainian and international politicians and experts, as well as soldiers on the battlefield, are blaming shortages for the losses of territory to Russia. The shipments were further delayed when U.S. President Joe Biden's $61 billion aid package stalled in Congress due to political infighting between Democrats and Republicans. The bill remains in limbo due to opposition from GOP legislators. The situation with the supply of Western air defence systems to Kiev is particularly dire. According to the New York Times, the newspaper cited an official U.S. assessment in early February which stated that, without replenishment, Ukraine's air defence could operate only until March 2024. Ukrainian President Vladimir Zelensky has renewed his call for additional deliveries, warning in February that an artificial deficit of weapons would only help Russia. What awaits Russia after Putin's victory? War is again in the foreground. Russia has concluded voting in the seventh presidential election of its modern history. First time presidential elections in Russia were held over three days for 12 hours daily between March 15th and 17th. According to the Central Election Commission of the Russian Federation, Putin gains 87.3 percent. This year's ballot also saw sporadic attempts to interfere with the working at polling stations. Almost 30 such cases in a total of 20 Russian regions saw perpetrators attacking ballot boxes according to Central Election Commission head Ella Pamphilova. Presidential elections in Russia have never been associated with a democratic procedure. At the same time, a victory in the first round will give Vladimir Putin a new stamp of legitimacy and will likely send a clear signal. The war with Ukraine has the full support of the Russians, writes CNN. It is noted that among those who follow Russia, attention is focused on several major issues. For starters, if the vote in this election is indeed a referendum on Russia's war against Ukraine, does this election give Putin free reign? Putin appears to have some room for manoeuvre on this issue. The Russian president is demonstrating confidence in development on the battlefield, and in an environment where the West is dragging its feet on providing assistance to Ukraine, the election results give him more rhetorical arguments, the publication says. At the same time, as CNN points out, Russia's gradual advance in eastern Ukraine has led to horrific losses in the Russian army. It is expected that Putin and his generals will have to start a new round of mobilization to feed the troops in the meat grinder. Observers believe Putin will soon simply be forced to make a potentially unpopular move another big mobilization. The second issue on the agenda in Russia is the continuation of repression against the internal opposition, what is left of it. War is also unpredictable, and whatever Putin's efforts to turn the situation in his favor, Russia's long-term problems, demographic decline, the costs of war and sanctions, and the inherent fragility of unity of command are unlikely to disappear before Putin runs for a sixth term. State CNN.


essential role of OER in implementing alternative assessment. This presentation is by Amanda Tainter, Faculty Coordinator of Instructional Design and Outcomes, Julie Kehoe, Mathematics Instructor, and Jennifer Parris, Department Chair and Associate Professor of ECE at College of Cannons. So we're going to talk to you a little bit about our journey and so Julie and I are going to share what our issues were for traditional grading and assessment and what brought us to decide that we really wanted to change what we were doing and so our presentation we're going to be going back and forth a little bit about what we changed. Our big one for both of us was a focus on point earning. We had students that all they were concerned about was the importance of getting how many points to pass the class instead of what they were going to learn in that class. Some students would give up throughout the course because they fail a test, they fail a homework assignment, they fail a presentation early on and that's destined them to not be able to pass the class and they're digging themselves a hole that they can't get out of and we're going to take a look at how the grading skill was set up in a second that really helped to promote that. And just like on the first one, the focus on the points earning, the grades didn't reflect what students were learning because in a lot of cases the students were able to get enough points to pass the class. It wasn't really in a lot of instances showing us that students actually had learned especially when we were looking at what the outcomes were for the courses. They might be getting enough points to speak by and pass but because of all of the other systems that were in place it didn't necessarily mean that they were learning the material and to what degree they had to learn the material. The outcomes and assessment and grades didn't align and that really is what we're just talking about on the fact that they could get the points enough points to be able to pass and what their grades weren't telling us is how were they doing on those outcomes of the course. You know we had taken the time to figure out what we really wanted the outcomes for the course to be and so those really should align with how they were doing in the class, what they were passing. That was really important to us so that what our grades were actually showing to our students and to the next instructor in the class is that they had mastered or become proficient in these skills. The enforcement of the rules got on the way of building those relationships with students. It became about you know here's my late policy you missed the due day now I've got to deduct 50% of the points whatever our rules might be those became the focus versus my role as a facilitator of your learning is to ensure that you become proficient you've mastered these skills you reach these outcomes whatever term you want to use that is what should be important it's my relationship with you as your facilitator of your learning is to support you to get there instead of this gatekeeper of if you turn it in one day or one minute or two hours late you get a deduction of a certain amount of points and so we really found that all of the traditional grading and assessment really stood in the way of getting those relationships with students that was really important for their learning so we came to a point where we needed to find a new way of doing things and a new guiding principle and so it really came down to the fact that students who learn the material should pass the class and so many of the policies that we had in place and that a lot of instructors have in place these days gets in the way of that and so with this guiding principle it helped us to better align all of our class policies our assessments with what our students needed to be successful and so it came down to a few things that that this kind of led us to we implemented OER resources in our classes so that everyone had equal access to the content right from day one and that wasn't impeded it the cost wasn't we also wanted to ensure that our grades were accurate so they were reflective of learning and not the compliance that we so often gets in the way of students being successful in the class and third flexible policies that had natural consequences rather than arbitrary ones were really important to making this work and helping us build relationships with students a clear communication was another really important one we implemented rubrics that were really clear and given upfront to students so they knew exactly what we were looking for and then we also followed that up with really good feedback to help students improve over time and in all of these guiding principles we listed that using OER as the first principle and really it was the foundation for all of this that we were doing because if students don't have access to all of the content how can we say that their grades are accurate because they're googling the content they're guessing what it might be and they're not all having the same equitable access to that content and so really that was a huge piece of advocating for these principles and we talk about this on our campus it's one of the things that Julie and I stress is that's a key part of moving into a more equitable assessment is this use of OER because students all have equal access to the content from day one yeah it really was the building block that started everything else that came after it and that last one is giving students an opportunity at redemption so including low stakes and no stakes practice in your assessment and in the course is an important part of ensuring that students were learning the material so we really had to start reconsidering all of our greeting pack the practices and these were the ones that we really had to dive into and make some changes too so great did homework became a thing of the past because this is an opportunity for students to practice with that low stakes opportunity to learn the material make mistakes and improve over time as they prepare for the assessment things like participation points effort points extra credit all these things either drag a grade down or raise a grade artificially and get in the way of actually getting accurate grades that reflect what the students learned in the class so those all went away as well and then to get those opportunities at redemption we started including retake opportunities or resubmission opportunities that kind of varied with our different disciplines but opportunities for students to keep continuing trying to learn the material it wasn't a one and done kind of thing especially since we removed the graded homework this is another place where we see that OER was really an important part of this because if we're telling students you know you can retake this test you can resubmit that project and providing feedback between that then it's really imperative that for them to learn the content that they might have missed the skill they didn't master what they didn't come proficient at that they have that content at the ready and they're not waiting to go check it out again at the library are waiting to be able to borrow it from a friend or photo copy it or whatever it might be but if we really want to encourage them to retake or resubmit that project they need to have access whenever they can to that content and so that really was an important part in that retakes and resubmission is that they always have access so that they can grow from what they didn't master that first time we also had to rethink deadlines and really make things flexible and there's still a schedule but students can work beyond that they need to and late penalties are a thing of the past here because again these are things that make grades inaccurate it's not reflecting what the student ultimately learned it can just drag down grades of students who need a little more extra time to learn the material and the last piece that we had to reconsider is the grade scale to make all this work really re-evaluating this the grade scale was a key part of this traditionally we use a zero to 100 percent scale the problem is is there just so much wide variance in this kind of scale it's very hard for me to describe to a student what the difference between a 75 and a 76 is and a 77 and so on so it's really a problematic thing with this and also as you can see it's very weighted towards failure there's so much that students have to get to just to get to that passing grade and it can make it difficult or even impossible for a student to pass at a certain point if they've struggled in the class so instead we switched to a zero to four point scale it's much easier to describe these different levels of grade versus the 100 point scale and that's exactly what we did we created rubrics that actually described each level what i what we were looking for for each thing that we were assessing them on and you can see if you're wondering well how did we make that grade breakdown work for this if you're not using 100 point scale you're not doing a 90 to 100 and a and so on so since everything is rated on a four point scale we use this breakdown instead which makes it just a little bit easier for students to get from one grade to the next and we're no longer so weighted to a failing grade and if you're curious here's an example of what those rubrics look like so this is given to the students up front and there's a lot of different ways to do this this is just how i did it for my math class where i have the objective uh described at the top and then a description of what each level looks like so students know exactly what i'm looking for uh when i am doing uh when i'm assessing them and grading them on them we've also found that it's feedback that that is so imperative we can have a really great detailed rubric and really when you're putting students at that one or that two not yet met standard and that's an important thing that i also want to draw your attention to it's saying that you're not yet there but you will be there or the two approaching standard that feedback is so important because we're letting them know here is what you need to do to help get to that three met standard and help get to that exceed standard and again bringing it back to that important use of having OER in the course is here you haven't met the standard but if you go back to that content that i gave you you know if you meet with me or if you look back at our textbook having access to that is so important because then they can go back and say oh i see what you're saying here i didn't get this concept but i've got the textbook right here because i'm using an OER and i can relearn i can brush up on that skill and now i'm ready to retake that exam or resubmit that project whatever it might be to get up to that three so that's where that retakes and that resubmission is so important paired with these rubrics so that students can see here is what i need to do to meet that standard meet that outcome meet that skill whatever verbiage you want to use it's really important to making this all work logistically when you're limited to just four points and you can clearly describe each level your grading is a lot more consistent and it's much much faster to determine the grade on assignment so now i spend very little time figuring out how many points to give and i get to spend most of my time providing detailed feedback to my students to help them improve or that reassessment if they need to take it so if you're wondering you know how do we get retakes and resubmissions to aren't because it is more work to have to be grading multiple assessments this was a big part that made it possible so if you're wondering well was it all worth it because this is a lot of work we want to be upfront it was something that we really had to commit to but it was absolutely worth it that especially that initial investment of time where i'm more focused on learning and my students are more focused on learning they still care about their grade of course but they know that we're here to learn and not to grab points and it's present in every communication that we have so a student has not yet met standard then the communication that we have is you're not quite there yet these are the things you need to do to get there but i know you can can do it and so it's that focus on learning versus you failed you didn't get the points we really want you to be able to learn that content and so here resubmit it do it again look at the content whatever it might be so it changes the whole focus of the course it's also really motivational for students because they have that opportunity to continue learning so if they didn't get it that first time or if they didn't get it by that deadline they still have more chances more time to learn at a pace that works for them and it also ties into the whole personalization the students really have an opportunity to make my class fit into their life rather than me expecting them to fit their life around my class and they're able to to spend the time that they need on concepts that they might need to revisit you know some students might be stronger in one concept whether it's math like Julie teach teaches our early childhood like Jennifer and i teach they're able to pause enough to relook at the content that they might need to instead of blazing through and never going back and filling that gap that they have in their knowledge and if we're talking about sequenced courses that's really important that they don't end our class that just because they got enough points but they still have that gap and this really helps to ensure that all those gaps are filled this also made the grade accurate we removed things like late penalties and extra credit and effort points that were really inflating or deflating grades then we have grades that we're closely aligned with the outcomes and the outcome assessment which became much easier by doing things this way and at the end of the day the most important benefit is the relationship that we experience with our students now they know after we get all of this explained to them and the experience it they realize okay this class is about learning it's not about compliance and meeting arbitrary deadlines and so it really makes the whole experience of working with your students much better i enjoy teaching so much more now than i ever did when i was trying to get my students to comply to deadlines hi everybody this is jennifer paris i'm the faculty in early childhood education at college of the canyons and the course that i converted to this new way of grading was child growth and development which is a g e course but also one of our major courses so i want to briefly talk about my starting place and so this started from a passion about creating a more equitable environment and i was in the process of reading the book on grading by susan blum and it just really resonated with me and so i decided that even though i didn't have time to figure it all out i was going to jump in and i was going to figure it out as i went and for any of you that know me that is a huge stretch i did not like to figure things out as i go but it was important to me if i were to define my approach i would call it a hodgepodge so there's definitely classic ungrading there were no points in my class but there was a lot of self grading and self reflection and they actually proposed their own final grades and then i used mastery based grading to try and structure the process so that they could see their performance on the different course outcomes and skills while this wasn't a truly linear process the way i'm going to present it i did want to kind of presented a step so that if you wanted to consider making some of these changes you kind of had an outline of what you might want to do so roughly step one was to really think about changing my policies and this was an overall change in the environment that i wanted to create in my classroom and this did happen to be a virtual classroom completely online and this also coincided with provisions that i made to our OER textbook throughout the semester that that allowed them to see themselves to understand equity and diversity and to you know make connections when it came to what i was going to be doing as far as grading or lack thereof i shared my why and then what i was going to try and so i didn't have it figured out when i gave them the syllabus and so i was transparent about that and then throughout the course i created opportunities to kind of check in how are you feeling about this do you have any questions and make sure that you know there wasn't too much discomfort with something this drastically different than what they were used to the next step that i actually came to after talking with Amanda about what she was doing in her courses was to think about the skills that i wanted my students to leave the course with so that if they were to say they successfully you know met the expectations for this course what would that look like and so i broke down the course objectives into 12 discrete skills that was a lot at the end when it was time to propose final grades so i did combine those again into four major categories at the end of the class then i went through the course in in retrospect and when you're planning ahead do this completely backwards do this as you're going not make it fit as you're going but i went through and everything that i asked them to do had to be tied to one of those 12 outcomes so that they could see the purpose and i could be transparent about what they were getting out of each part of the work that they were doing and if there was something in the course that i felt was useful but it wasn't tied to one of those outcomes i made it optional then it's really really important to provide feedback i kept my rubrics i got rid of the points on the rubrics and i did add an outcome at the bottom of each rubric that talked about did they exceed expectations or meet expectations or were they not yet meeting expectations and i think that yet's really important because the goal is for everyone to meet expectations and to resubmit work that doesn't and then i worked to create comments and narrative feedback that was there to provoke thought not just to criticize work and tell them what they did wrong or right but to actually get them to think about what they were learning step five for me was to create opportunities for self reflection and self evaluation so each module had a reflection survey that connected some of the skills that they were gaining to how they thought they were doing based on the work that they completed i also tried really hard to make it obvious where they were building on work they'd already done so maybe they did a similar assignment or there was an activity that was building the foundation so that they could see that the learning was layer by layer a very little in this course was a once one and done type of situation the skills continued to build throughout the semester and then step six was to really put it back in their hands with obvious instructor oversight they showcase their learning in a digital portfolio i had them build the portfolio before they actually had to to put the stuff into the portfolio so that they didn't have to worry about that at the end of the class it consisted of five pages one page for each of the four outcomes that i had recombined those skills into where they displayed samples of their work throughout the course and i gave them a chart showing where they worked on each of those skills throughout the course so they knew where to go look to see their level of performance and then they had a final page on their learning journey they gave themselves an overall rating on each of the four outcomes reflected on what they've learned and they proposed their final grade which i either agreed with or disagreed with and with the exception of a few students that i thought were a little too harsh on themselves i gave them the grades that they gave themselves and that they justified they earned quickly quickly lessons learned it was worth it it was a lot of work it was chaotic i didn't have the time that i needed to stay caught up on providing feedback effectively students didn't know they didn't understand the process they submitted their work resubmitted their work way less than i would have liked but there actually wasn't a lot of work that didn't need expectations they need more practice with self-evaluation and it is way better to design backwards than to try and make a course that already exists it and building off of that my next steps when i implement this again is i want to build in more options for student demonstrations of skills so while i gave them the ability to not turn in typed work um things were still really structured and kind of there was one assignment and i want to build in options and really embrace that universal design for learning i want to scaffold self-evaluation more effectively by the time they got to the end of the course they were pretty accurate in self-evaluation in the beginning they definitely overrated how much they understood something based on the work that they had done i want a more fully embraced backward design so that i can make sure everything is covered equally and is really well balanced and i also want to use the mastery gradebook in canvas because the regular gradebook doesn't work for ungrading very well if you got any questions you can always reach out to me thanks and i hope you enjoyed learning about my journey so if you're if we've piqued your interest at all in this feel free to contact us we love talking about this all the time all the time anytime


I think they'd like to have us over in front of the fire. Yes, I think so. We've been very happy with the election sometime. I was going to mention it, and he's very happy, though he's a little bit reticent about flying and traveling. I dare you to do that every day. Yes, but I have to tell you that for about 15 years after World War II, I deliberately grounded myself. I never stepped in an airplane. I had a kind of compulsion at the time, and I knew that someday I'd have an idea. Well, he's very, very happy about the invitation. And he's working, is it short? Working, is it? You have the very, very most of the world on your shoulders. And he has his own world. And incidentally, you can all be very helpful to us in persuading Congress, because I think that Caribbean initiative is... Those are our hopes, yes. And so far, it is bowed down up there in the hill. And I think that... Well, we've been trying to get around the corners. I think that he can effect down some congressmen. My hope's over. Right? I have the honor of presenting the eventual process of previous ambassador and mine. All right. Thank you, sir. Well, all right, we'll have to arrange it. Thank you, sir. Thank you. Mr. Ambassador, Ambassador Zimbabwe. Yes, Mr. Ambassador. Welcome. Mr. Zimbabwe. Mr. Zimbabwe. Mr. Zimbabwe. Mr. Zimbabwe. Mr. Zimbabwe. Mr. Zimbabwe. Mr. Zimbabwe. Mr. Zimbabwe Hey, Mr. Zimbabwe, Mr. Zimbabwe. Mr. Zimbabwe?  Thank you. I don't want to war one or two on the somebody trip, and I like the problem in six places. Favorite of the day. Favorite of the day. Nominated to have the SSR as the gold. I'm sorry. I take all this. All right, thank you. Well, delighted to welcome you here. I'm continuing working with the U.N. We enjoy its independent status. Thank you very much. And I hope the guy shortly in immigration will be straightened out. Thank you very much, Mr. President. We will follow the course of our independent line, and we will try to solve all the matters which concern our relationship. And there is a substantial exchange in all fields, which will make me happy. And we hope that I will work for your visit in Romania. Well, President Ceaușescu and Mrs. Ceaușescu. Well, thank you very much. I expect you as soon as possible. Well, I look forward to that. There will be large crowds of Romanian people. I remember another American president who went there and had that warm welcome. Some years ago. All right, thank you. Thank you very much. Mr. President, Ambassador Senegal. Thank you very much. Respectful greetings to you, Mr. President. Well, welcome to our country, pleased to have you here. Oh, we understand. Oh, I didn't speak. Oh, I didn't speak. I corresponded to the president's view. We most appreciate the kind of relations we have. The latest good favor to us, we appreciate it, is making Dakar available in case our shepherds. I would like to make sure that the people in the community continue working with their own stimulation. Mr. President, I think the one is over in front of the fire station. Thank you so much. I have my speech, but it's not on the screen. Thank you very much. My letters of accreditation and the letter of recall of my predecessor. All right, thank you very much. President Ambassador, Mr. President, Mr. President, Mr. President, Mr. President, Mr. President, Mr. President. I'm pleased to see you. I'm very glad to have you here. Mr. President, we had a very nice time with him at the end of that. We established a very good rapport and enjoyed it very much. And I do bring to you the greetings of my prime minister, Rob Maldin. I've served and I've been in the parliament 18 months longer than he retired earlier. But he sent his personal greetings there. I have personal greetings to you from Monroe Brown and Francis. They told me to give their warmest regards. Well, I would be pleased to give my warmest regards to Monroe Brown and Francis. Yes, I think they want you and I over there for a picture. Shelly, stand to attention. Better picture here in the middle. Well, welcome to our country. Mr. President, Ambassador Boyd, Ambassador Sir, Ambassador Boyd. Thank you very much. Thank you. I think they want us over in front of the fireplace for a picture. All right. And I think it would be a better picture if you get it from this. Mr. President. Well, I guess this will be there. Thank you very much. Ambassador Boyd, this is a fire. I think that we've served an impressive moment. And that President is going to fight in the future. Thank you. Thank you very much. I always tease my friends because I have been in politics as long as you have. When I see President Reagan, I'm supposed to ask him to give me an appointment with his father. Because we're good hair. Yes, well, he's most of the time, he's over at, wait a minute, where is he? Milts at what hotel? Milts. Who's that? Is it the sheriff? It's the sheriff. I think Milts Pitts is his name. The Sheridan Hotel. Well, I would do my utmost, Mr. President, not only to serve the interests of the public, but to help the public. Awesome, sir. Thank you very much. I guess you were out of happiness. Hey, I gave you an appointment. I'm here to make success. If I just say yes there. Gary, how you doing? That is exactly right. Thank you very much. Well, it's a great pleasure to see you. As I said, we should go with all my happiness. Thank you very much. Thank you, Anna. I'll do some volunteer work. I'll see you. Okay, all right. We have one agent. What are they going to do with you? Send me to Mississippi. He's going to be our ambassador down here, Mr. President. Thank you, sir. Well, thanks for everything.


This paper examines the use of risk management techniques for understanding how to manage uncertainty in energy retrofit campaigns. It suggests using sensitivity analysis and Monte Carlo simulation to identify potential risks associated with these projects. The paper also emphasizes the importance of correlated variables in the input data, which can be used to separate aleatoric and epistemic uncertainties. Additionally, it highlights the benefits of using percentile variations instead of percentage deviations in sensitivity analysis. This article was authored by Laura Gabrielli, Aurora Greta Ruggieri and Massimiliano Scarpa.


This is stock number 1017-0. We are here at Summit Automotive in Fond du Lac, Wisconsin. You're new and used Jeep headquarters. Today we are checking out this super clean 2016 Jeep Cherokee Latitude with the Altitude Edition. This vehicle has the 2.4 liter 4-cylinder motor which pumps out 184 horsepower. From this HD video you will be able to tell that this Jeep is extremely clean. All the way around inside and out, granite crystal is the color. We shoot all of our videos in 1080p so if you have HD capabilities on your computer, tablet or smartphone device, turn them on right now because it is like you're right here looking at the vehicle with me. And definitely your best way to check out the vehicle before seeing it in person. Factory painted 18-inch alloy rims, Continental Pro Contact, two 2560R18 tires with about I'd say 60% of the tread left, maybe a little bit more. Didn't see any dents or dings on that front fender. LED running lights, projector lamp headlamps, factory fog lamps. Front bumper is in excellent shape, I didn't see any dents or dings, scuffs or cracks on it. Passenger side fender, no dents or dings on that. And the passenger side rim, very clean as well. Down this side you can see just how reflective and mirror like that paint is. You take these HD videos so if you are far away or even if you're close by and you just cannot make the trip down, you can still see the vehicle, hear the vehicle and have confidence in what you're looking at before you even get here. So that when you do get here there's absolutely no surprises because you'll have seen a full video, you'll have looked at over 40 photos. You can have confidence that you're dealing with the right vehicle and the right people. Back tires have just as much tread as the fronts, no scuffs or scrapes on that back rim. As we come around to the back you can see the rear bumper is in really nice shape, tailgate is in nice shape as well, manual lift rear gate, storage area is very nice and clean. It's going to have this little storage thing right here and then you've got your jack tools and spare tire back here. Those seats do fold down for extra storage. You've got a holder here, you can put hooks on that for grocery bags and whatnot. You get the blacked out Jeep lettering, the blacked out 4x4 lettering LED tail lamps, driver side rear quarters in excellent shape and for full disclosure this back rim, no scuffs or scrapes on that either. Down the rest of the side of the vehicle, no dents, no dings, has the entering ghost system and it has the heated mirrors with the built in directional signals. Inside the Altitude package gives you the black leather interior, there are no rips or tears on these seats, driver side lumbar, power driver seat, factory floor mats, power windows, power locks and power mirrors, auto headlamps and as we hop inside the vehicle here I'm going to turn the air down for just a second so you can hear me better. You can see that this one has 15,483 miles, you get the 7 inch TFT display, digital speedometer, outside temperature, compass display, comes with the heated leather wrapped steering wheel, cruise controls on the right, Bluetooth and information center controls on the left and it does have audio controls on the back of the steering wheel. This one has the 8.4 3C radio, what's cool about this radio is if you wanted to add navigation to it you definitely could for $595, see your sales professional for more information. AM FM and Sirius XM radio capabilities, you have your heated seats and heated steering wheel button right there, this is also where your backup camera shows up and you can do your climate control up here as well. Speaking of climate control you can do it down here as well, dual climate control, you have your select terrain, active drive one, auto snow sports sand and mud selections, you have the USB and AUGS and an SD card, 9 speed automatic transmission, passenger seat and floor mat are in really nice clean condition, no rips or tears on that seat and the headliner is in excellent shape as well, you have your homelink buttons right there and your map lights and the 911 and assist buttons in the mirror, those are features of the vehicle that you never have to pay a subscription for, they are features of the vehicle and you do get your keyless entry with remote start too. We'll take a quick look at the back seats and then we will check out under the hood. The back seats are just as clean as the front seats, no rips or tears back here, it does have the latch child safety system, seats are in really nice condition, we do have factory floor mats back here as well and like I said these seats do full down forexer storage what I like about the Cherokees, they go almost completely flat, other thing I like about the Cherokees very safe vehicle as you can see it has knee bolster airbags in the back, 10 different airbags in this vehicle very very safe, I have owned three Cherokees myself, liked every single one of them. Under the hood we have the 2.4 liter four cylinder motor, engine bay is very clean, runs very smooth 184 horsepower, this car has been fully safe and inspected by our service shop has a fresh oil and filter change, all the fluids have been checked and topped off and this Jeep is 100% ready to go, to see more pictures of this Jeep or one of our other 450, new and used, cars, trucks, SCVs, minivans, Wranglers, you know what we got it, go to our website www.summitauto.com, full pictures and descriptions of every single vehicle from two locations all at summitauto.com and if you'd like to check out more HD videos you can go to youtube.com slash summitauto, remember to like subscribe and share in this video and all the videos that you see there in fact in a second you will see a link to subscribe to our YouTube channel on your left, a link to more used inventory videos like this one on your right if you have not been to our website on the bottom, a link to this vehicle on our website click those check us out we really look forward to helping with this super clean 2016 Jeep Cherokee four-wheel drive latitude altitude package thanks again


And now, tonight's presentation of radio's outstanding theater of thrills. Suspense.  We call it, fragile contents death. So now, starring Victor Perrin, here is tonight's suspense play, fragile contents death. All it took was a phone call. Until it came that morning, everything at the post office was the same as it had always been. I was just another postmaster, 40 years old, with a postal problems of some 80,000 people to look after. All it took was that call to make things a nightmare. As I said, the day was just a day. I remember I was making up my mind to get busy on the stuff piled on my desk. It was 9.15. Hmm. Good morning, Mr. Jordan. Good morning, Hartley. This is going to send for you. Don't tell me all this heat is for me. That's right. Didn't anybody weed it out? It's been weeded, Mr. Jordan. Well, if I must, I must. Even a parcel, I see. Yeah, yeah, it's marked personal. From Paxton and Brown, something earlier, Broadway. Oh, I remember. The lawn sprinkler I ordered for the wife. I should have had it sent to the house. Put it over there, would you? I'll take it along when I go home. Here? That's fine. Post office, this is Jordan. Jordan. That postmaster, Jordan. That's right. What can I do for you? Plenty. You gotta bomb some place in the mail down there. Is this a joke? Listen carefully. This is no joke. A guy I know sent another guy a bomb, a time bomb. It'll be delivered here in town. It was supposed to be set to go off at seven tonight, but it ain't. He forgot to change the timer before he shifted. It's set for 2.30 this afternoon, five hours from now. I don't like that. Maybe some poor guy like a mailman will get it instead of the guy you supposed to. That's why I'm telling you about it. You gotta find it and stop it. And one other thing, too. It's fixed, so it'll go off when you open the package. You've got all that. Sure, I got it. But how do I recognize this bomb? Who's it addressed to? I ain't telling who sent it. I don't care about who sent it. Who gets it? Who gets the bomb? Hello? Hello? Hartley. Yes, sir? You probably heard enough of that to know what's going on. Something about a bomb in the mail? Yeah. Now listen carefully. I'm only gonna tell you once. Get out of here on the double. I want the assistant superintendent of mails and the dispatcher. Stuart and Fox. Get him in here as quick as you can. You got that? Yes, sir. Operator. This is Jordan. How many inspectors are in today? Do you know? Just a moment, Mr. Jordan. Mr. Williams is in. Mr. Jackson left word he'll be out in Lincoln County until tomorrow. Mr. Thompson entered the hospital this morning to have his appendix out. Well, ring Ed Williams for me, please. Tell him to get over to my office right away. It's urgent. Yes, sir. Oh, Fox. Come here. Sit down. Yes, sir. Is something wrong? Plenty. Bomb in the mail. You know much about it. I'll tell you later. Williams and Stuart are in on this, too. Man, are we ever in for a busy day? Hartley said there was a hurry up calling. I think we close that door on my face, Joe. Come on in, Ed. Close the door. Sit down, both of you. Now sit down and listen. Uh-oh. No raises this year. I've been waiting for him to spring it on. Button it up, Joe. Here's what we do have staring us in the face. I just told Fox a minute ago there's a bomb in the mail someplace. Oh, well, that's right. A few minutes ago, I got a phone call. I don't know who it was. All I got were these facts. It's now 9.30. Between now and 2.30, we've got to find a time bomb, which was mailed to somebody here in town. Somebody? Don't have any name at all? He didn't get around to that.  I'm hoping... Mr. Slender, hope I know. I'm hoping you'll call back, but we can't count on it. How do we know this isn't a leg pull, Doug? We don't. But we can't afford to take chances with somebody's life. Would you? No. What kind of a package? We don't know. Any kind. Oh, it's great. That spreads your field out something terrific, Stuart. How?  Carrier wouldn't have that big a package. Still, with the powerful explosives we have nowadays and the small wiring circuits possible, why not? Yeah. I'll give you another one. How about a newspaper roll? That's big enough, isn't it? Carriers handle them, don't they? That's right. Here's what I think. If that thing was mailed early this morning, it's either out there in the parcel post bin or at one of the substations. If it was mailed last night, it's probably on one of the trucks right now. Or else it's been delivered. Or else it's been delivered, yes. A lot of stuff's off the trucks for now. That complicates it. But let's do what we can as fast as we can. Fox, you round up all of the special delivery cars, send them out after those trucks and get all the packages back here. We'll go over them after they get here. Okay. Try to get the drivers to remember what's been delivered and where. Skip the insurance deliveries. This won't be insured. Get as much back as you can. But what if they can't remember everything? You'll need that to me. But do this, too. Call the substations. Get all their stuff in here. All right. Anything else? Not now. All right. I'm on my way. Stuart, you'll go through our own stuff in the back. Let's see. Delivery from New York Central's number three hasn't come over from the station yet, has it? No. It makes it a little better. Get everything we've got together in one place and keep it there. Keep the out-of-town packages out of it. Just add the stuff from the substations and the trucks as it comes in. And the pickup trucks, of course. What? Some of the drop boxes out in the suburbs are for letters and parcels, both. We'd get some packages coming in on the pickups from those boxes. They've forgotten that. Well, the trucks should be in by 11. We might as well go ahead with everything else until I get here. Just so we get them as soon as they come in. I'll leave that to you. Well, if we're going to do all this, I'd better get on it. I know you're standing around here any longer than necessary. No, no. Go ahead. You want me to check the factors, right? I don't have to tell you your business, Ed. I know your record as an inspector is A1. Well, fill me in a little more. Would you, Doug? Okay, shoot. Well, this is a time bomb. That's what he said. We have less than five hours now. Yeah, I got that. Well, what else about it? Would it be safe to open? No, no. It'll go off if you open it. He said so. Oh, rougher and rougher. I'm going to get help, Doug. All you want. Use your phone. Help yourself. Thanks. Operator. Outside line. Thank you. State Police Company A, Sergeant Rock speaking. Oh, Rocky. This is Ed Williams, post office. This is Jesse Unity. Yeah. You want him? No, don't call him. No time. Tell him to come on over here right away, would you? Sure. What do you want with explosive experts? You got a bomb? Maybe. Keep it under your hat. You know me. Another thing, Rocky. You guys got the fluoroscope just now? Oh, a touchy one, huh? No, we loaned it to Company A up in Fulman. I could use it. That's for sure. That's only about 100 miles.  Oh, good. And you'll tell Jesse. Yeah, we'll do. Now to work, Doug. Same here, boy. Yeah, how about those carriers? About 100 of them out there. 94. Well, can you do it? Can you hit them all? I don't know. All I can do is try. Bet you're thinking the same thing I am. Remembering the same case. Bowling Green, Kentucky. Yeah. That poor devil of a carrier. It's alive, but barely. That's one reason I want to find this bomb before our luck runs out. I wonder if I'm going to see that. But it doesn't run out. I hope you're right, Ed. I sure hope you're right. You are listening to Fragile Content's Death. Tonight's presentation in Radio's Outstanding. Theater of Thrills. Suspense. For you, the choice between living an ethical life or that of a fugitive from the law may be an easy one to make. But for the young parolee in tomorrow night's drama on the FBI in peace and war, the choice is more difficult. His old criminal friends want to involve him in an easy money scheme. When he comes to the turning point, that moment at which he must decide between staying straight or going back to a life of crime, the temptation is most intense. For drama that packs a human-interest punch don't miss the turning point tomorrow night when the FBI in peace and war is on the air over most of these same stations. And now we bring back to our Hollywood soundstage Mr. Victor Perrin, starring into tonight's production, Fragile Content's Death. A tale well-calculated to keep you in... Suspense. They were all on it now. Williams, Fox, Stewart, and all the others we could trust to keep a cool head. The lid was on every place possible. To the average man buying his 3-cent stamp, it was business as usual. We didn't even let the majority of the help know it. But often one of our fairly isolated corners, a pile of packages, was growing. I looked at the clock, 10, 20. Just about an hour gone. Four hours left. Oh, Stu. How are things going? Who knows? Well, this can be expected in a thing like this, I suppose. Better wipe off that sweat before somebody tumbles that the day isn't that hot. Thanks. Huh. Are any of them wise yet? Don't think so. They'll probably get out. Things have a way. Yeah, that they do.  Well, I... You worried? Not very happy. None of us are. I keep thinking, what if I get hold of the thing... Ever been in an explosion? No. Well, you weren't in the service now. Ever seen an explosion, been close to one? Yeah, I... Well, I came by once just after a gasoline truck tangled in the pole and took off. It was a pretty bad sight. See... Would... Would you like to take the day off? Oh, I wouldn't take it. Nobody'd blame you. I wouldn't. I promise you. I can't do that, Doug. What if I did and the thing got by and I could have prevented it? I'm nervous, maybe. I don't like the setup, maybe. But I'm not chicken. I'm staying with it. I figured you would, Joe. They say every soldier figures the next bullet isn't going to get him. That's the way I'm figuring myself on this deal. Good idea. The only thing we have to worry about, Joe, where is this one aimed? 11.30. Two hours gone. I went back to the mail room. Hello, Doug. How's it going? Well, we have all this over here so far. A lot of work. Any leads, Ed? Not yet. Not even anything suspicious. Well... How can you tell? I can't tell, really. Sometimes there's something about the handwriting or the printing of the... I guess you just feel it sometimes. I don't know. I don't get anything like that in this stuff. Not even a fake return address. Not a one and a lot. None of the locals, anyway, would check them all. Pluriscoping yet? Well, the plane said they phoned me a few minutes ago. It's on the way over. I'll be here any minute. Well... Mr. Jordan, telephone. Okay, what line? On 8. Thank you. I'll get it. This is Jordan. Mr. Jordan, this is Malloy and Truck 15. Yes, Malloy. I got the word about getting back these packages. Everything's practically okay. Only this. My second delivery. I left one off at this place, 1724 Lime Street. I go back there a while ago and nobody's home. Well, they were there when you left the package? Yeah. That bald-headed guy took it as I remember. Well, check the neighbors. I already have, Mr. Jordan. Nobody knows for sure where they went. There's some talk about them leaving for Washington this morning, but I can't pin it down. They drove anyway. Car's gone. Garage is empty. Well, that's using your head, Malloy. At least we have something to shoot at. Do you remember the name? No, I don't.  Had them for one the minute I laid out... What's that? Well, Miller came driving out and caught me and said you were hunting a package of stolen goods in the mail.  Wasn't I supposed to know? Uh, no, no, Malloy. It's all right. I forgot that he knew what we were after. Well, you say you've got everything else. Yeah. Uh, be right in if you say so. You do that. Check. Foxy. Yeah. Look up 1724 Lime Street and get the name. Anything else? Get that name and get on to it. The state police to intercept them if possible. Those people received a package this morning. They may have left town for Washington. They're probably driving. With luck, they may not have opened that parcel yet. It's a real long shot, sir. Going on a trip, carrying in an open package. Oh, I doubt it. It's against common sense, but we have to try. You try, Foxy. 12 o'clock, lunchtime. I ordinarily eat at Bailey's. But not today. We had sandwiches and coffee brought in. The coffee was welcome, but we didn't seem to be very hungry. Alone in my office sipping coffee, all I could think about was the time was half gone. This is Jordan. Darling, haven't you forgotten something? Oh, hello, dear. Forgotten something? I'm down at Bailey's waiting for you. I've been here 25 minutes. We're lunching together today, remember? Oh, yes. Well, look, Alice, I'm afraid I'm going to have to stand you up. Something's come up. Well, you'll be sorry. There's the best-looking man sitting in here all by himself. The tall, rugged iron gray type. You know how I go for those. He'd probably be very glad to buy me a meal. You try him and see. I have confidence in you, sweetheart. Anything to save us a buck. Then you definitely aren't coming. I'm afraid not. I'll tell you all about it tonight. Some time remind me never to marry a busy executive again. Bye. Bye. What do you got, Foxy? Things in my mind, Doug. First, let me get this one off. The cops picked up the Morgans. The Morgans? Yeah, the couple who were driving to Washington found them in a service station on the edge of town. They got the package. Well, they'd already opened it. Nothing much in it except some fancy sports shirts from Morgan himself. Then they're on their way again? Yeah, with apologies. I understand they were scared silly, though. Morgan offered to show the cop the shirts. He even wanted to give them one. I'm afraid they were stolen goods. I'm glad that's cleaned up. Yeah, I'd say just had a call from the Woodmob branch. Go on. Say, you remember Spicer? Spicer? Yeah, a suspicion of robbing the males. What about him? Looks as though he hung one on himself this time. Well, as I say, this clerk out at Woodmob put a bunch of packages in the bins just before he quit last night. He remembered one for Dr. Turner. This Turner, it seems, collects magazine first edition. Sometimes he's aren't worth ensuring. Sometimes they are. But they're always worth something more than their original price. And this package looked like one of those. What about the carrier, Spicer? Well, it seems he blew in just as the place was about to close. Said he forgot a bag of his with some new shoes that he bought. He drifted through and then right out again. Nobody paid any attention at the time. And this morning Turner's package is gone. Is that it? Yes, sir.  Substitute's working. They're sure the package is gone. Well, Turner called to ask if it came in and they couldn't trace it. That's how they were sure it's missing. Well, I don't have to tell you the next move, do I? No, I already tried. Called his rooming house. He's not in. Didn't come in last night either. I call the police. I'd better do that, Foxy. Thanks. Even if he has the thing, he surely opened it by now. I think so myself, but we can't be sure. We can't take chances. I better call him. Operator? Get me the police station. Ed, any luck with this pile of stuff? Well, one that's uncertain. Trying to make up my mind. All right, sir. Let's see. Here. It's not very big. Wouldn't have to be. Addressed to Jack Gordon 128 Andrew Street. Is that anybody important? Never heard the name. If you ask me, it's a kid. What makes you think so? Return address. Columbia Foods Incorporated. Serial coupons. Fluoroscope indicates a watch inside. Let's put a stethoscope on it. Okay. Tick's all right. No question. Is it a dollar watch or is it it? A watch, probably. Probably. Let's play it safe. That's what I thought. Take it outside? Or narrowly, yes. But we couldn't detect anything that would trigger the thing, so we'll put it in the water bucket here. Did you put wedding agent in the water? Yeah.  That doesn't. Inside takes a little longer. Reminds me of the time I put in as an inspector. I hear you were pretty good, Doug. Just lucky. Getting the walkers? Lucky? Better have a look at your toy here. Easy. Easy. Looks as though we were right the first time, Doug. Here, have a pocket watch. Genuine hoppy. Keep it for a souvenir. That's one kid we all watch. Will the budget stand it? It'll strain it, but it won't break it. Hey, let's step out back. I need a cigarette. Good idea. You heard about this carrier, Spicer? Yeah, Foxy told me. Get him yet? The police haven't. Well, maybe the police haven't got him, but that's it. The clock's hands were still going around. 130. Just about one hour to go, and still, we hadn't found that bound. We hadn't found it. We hadn't found the missing carrier. All we had found was a new headache every few minutes. This is Jordan. Mr. Jordan, this is Maloy again. The driver, you know. Oh, yes, Maloy. Do you have something new? Well, sort of. You see, Mr. Jordan is like this. While I was eating lunch, I kept thinking, and all of a sudden, I remember this other package I delivered this morning out on Beach Avenue. So, I drive over here to see about it. I'm at the house now. Have you got the package? Have they opened it? No, it ain't open. Well, you better talk to this lady, Mr. Jordan. She won't listen to me. Here, Mr. Page, this is the postmaster on the line. Himself in person. Hello. This driver says you're the postmaster. Is that right? That's right. This is Douglas Jordan. I don't understand all this about the package which came from my husband. First, this man delivers it. Now, I want it back. But he's perfectly right, Mrs. Bates. We'd very much like to have that parcel. I don't see why. It has my husband's name on it. It's a correct address. I'm afraid I can't give it back until my husband has a chance to examine it. What did our driver tell you, Mrs. Bates? He had some story about stolen goods, but that doesn't make sense. Anyone would know that my husband would have... Someone may have confused him with another Bates. Have you thought of that? No. I hadn't, but I'm still sure that my husband should pass judgment on this. If I were to take the responsibility and I were wrong... Let me take the responsibility, Mrs. Bates. He might not see it that way. He might say I let myself be talked into something. Mrs. Bates, believe me. I'm sure your husband would be the first to thank us if he only knew. On my word of honor, we must have that package. Well, it's your responsibility, understand? Very, young man. But I don't think my husband will thank you. He doesn't like anyone connected with the government. None of you. For that, I'm sorry, Mrs. Bates, but thank you for giving us that parcel. But it wasn't what we were after. A box of advertising pencils, that was all. Then it was half an hour. I forced myself to stay in my office waiting for a call that they'd caught Spicer or that someone, somewhere had turned up something. Oh, Ed. Any luck? Not a bit. We've come everything, not a thing. They haven't caught up with Spicer yet. I think that's a false lead anyway. If he has a thing, he's open it by now. That leaves us nowhere. Yes, it does. Somebody's forgotten something. That must be it. Maybe. Could be all a hoax, you know. Smatter, don't you want it to be a hoax all this effort and nothing to show for it? Want to repeat the Walker business, catch a murderer through the mail? No, Ed. No, I just want to be sure. Well, by the way, he's out, you know. Who? Steve Walker, the brother. He's out of jail. Didn't you know? Well, boy, what are you looking so funny? Team Walker. He said he'd get me. Yes, or what? Where did I put it? Where's that lawn sprinkler? Lawn sprinkler. Now I know. Where'd that package come from? I've had it right here all day. Didn't even think about it. Came early this morning. Let's not think. Let's move. Give me that. Is there time? I think so. Better be. Get it under the fluoroscope. Look, that's it. Brother, that's it, all right. Do you have time to take it out to a safe place? No, sir. All we do is put it in the water bucket, take it into the alley, and pray. Okay. Here goes. Water gets through that wrapper. Doug, we'll give it an hour, just in case. Five minutes. 15. 30 minutes. 45. One hour. And then... Well, Doug, there it is. All in little pieces. Speak loud. Those pieces aren't you. I am it. I am. A pretty good collection of evidence. You can go after Steve Walker with this, Doug. Mr. Jordan, telephone. Coming. On four. This is Jordan. Well, I couldn't bear the thought of you plodding away down there. Now, maybe you'd like to hear the story about my lunch, and it was the most... Why, it better be good, honey. Well, you hear the one I've got for you. Suspense. In which Mr. Victor Perrin starred in tonight's presentation of fragile, contents death. Be sure to listen next week, when we again bring you another presentation of Radio's Outstanding, Theatre of Thrills. Suspense. Suspense is produced and directed in Hollywood by Anthony Ellis. Tonight's story was written by John F. Souter. The music was composed by Rene Garagank and conducted by Wilbur Hatch. Featured in the cast were Vivi Janus, Leonard Wineryb, Helen Clebe, Herbert Ellis, Ted Bliss, John Larch, Charles Seal, and Frank Gerstle. This Friday night, the CBS Radio Workshop takes you on an exciting excursion into the world of fantasy, as it recounts Antoine de Saint-Zupéry's delightful story of The Little Prince. A best-seller in America, as well as France, and a joy to readers of all ages, The Little Prince is sure to be one of your all-time dramatic favorites as you hear it this Friday night over most of these same stations on the CBS Radio Workshop. Stay tuned for five minutes of CBS News to be followed on most of these same stations by My Son Jeep. You'll hear America's favorite shows on the CBS Radio Network.


This video is going to lead you through how to insert special symbols in Google Sheets. This comes up a lot for me, especially when using the squared or cube symbol. In Excel, you can use font character formatting to make a superscript or even a subscript, but you don't really have that option as of this recording date to do that in Google Sheets. So a couple quick options. You could use carrots, so you could just write this, feet, carrot, squared, and just call it done and say that's good enough. You can also use alt codes, and I'll include a bunch in this, but for instance, the alt code for squared is 0178, so you could write feet and then alt 0178 and let that go, and now you'll have squared. You can also use copy and paste, so down here, for instance, we have this little squared. Grab that with ctrl C, type foot, ctrl V for squared, and you can also use a function. So we could write equals, let's put foot in quotation marks, and then we'll write ampersand, care, parens, and then it was 01278 and parens. And actually, I got that wrong, it's 0178, my bad, 0178. Hit Enter, and that'll give you the foot squared just like above. The formatting difference is only just because I hadn't copied the formatting down there, so we can take this formatting and put it there, so it's large as well. With character codes, I have all of those down here for a whole bunch of different numbers, superscript numbers, subscript numbers, math symbols, fractions, currencies, and bullets. These character codes sometimes will work with the alt, but they'll always work with the C-H-A-R, at least in all the systems I tested. If you test a system that doesn't work in, please let me know.


Hi, thank you everyone for being here. This is Marcelo Soria, and I'm Alejandro Vidal. And we are going to talk about all the things that are not data science. And typically, people don't hear about them. But we think that are quite critical, quite important to create products. So we are going to start explaining why this talk using a metaphor, similarly with a mobile phone device. So maybe some of you know this video. A phone and an internet communicator, an iPod, a phone. These are not three separate devices. This is one device. We are calling it iPhone. Today, Apple is going to reinvent the phone. And here it is. OK, I recommend everyone to watch this, because it's 10 years ago. And it's amazing to see what people was crazy about that moment. We are not going to present an iPhone. But we are going to talk things that are related to that with data. So back into 2008, compared with now, at that moment, it was a very unique experience. In fact, they used revents the phone as an expression to say, we are creating something totally new. Also, they need new words to talk about it, because previously, they didn't have any word to talk. For that reason, they used a kind of mix of three ideas, three concepts, a phone, an iPod, with a way screen, an internet communicator. The tab, the swipe, other words that at that moment didn't exist. So probably at that moment, you had highly specialized profiles. You can see how many UX designers of mobile apps were in 2008, probably only a few of them. Now it's a very common profile to work in more or less every company. So at that moment, probably, it was really hard to create products with this technology, because you have very highly specialized profiles. It was kind of a rare experience, so you don't have examples to take ideas from them. Now is the old new normal. At this moment, more or less, we are talking about five millions of mobile apps, which is a huge number. Everyone is familiar with it. We create new concepts on top of them. I mean, everyone knows more or less what this mobile phone, how do you interact with it, the tabs, the swipes, the notifications, the vibrations, more or less everyone in the team knows what is a mobile device, although you are not a designer. So you can create products on top of them. The whole team has a first-hand experience with that. So you can reason and think new products easily. That's the difference between 2008 and now. So we think that the same is happening right now with AI. Products with AI are quite rare. I'm not sure how many years ago, maybe three years ago, two years ago. Now is the moment of changing the increasing ratio of new products. But we have new materials to work with. For example, we can talk about categorization. We can talk about recommendation that those concepts didn't exist before. We have highly specialized profiles as data scientists here. How many of you consider yourself a data scientist? Raise your hands. OK, come on. Everyone is a data scientist now. OK, so it's really hard to create products with AI. The same way, in 2008, it was really hard to create a mobile device experience. So in the future, or at least now, AI is a new, new normal. You have to educate your people to get those ideas known by everyone so people can make new concepts, new concepts, ideas on top of them. The whole team should know about the new materials. So a data scientist is a specialist. He's not a gatekeeper of knowledge. He's not the owner of the knowledge. He should be a specialist to help people to achieve their goals, but everyone on the team has to know about data science. So the team can imagine and design new experience. For that reason, we are here to think about how can we solve those problems. We have here a small video. And to unlock the phone, I just take my finger and slide it across. And we'll see that again. We wanted something that you couldn't do by accident in your pocket and just slide it across. People got crazy at that moment only because of the sliding thing. But if you think about that, it's not about the sliding. It's not actually because before iPhone, there was different touchable interfaces. And you can slide things on them. It's not about the sliding. It's about how people pocket their devices.  So they create this experience. But it's not about the idea. So other small fragment. And I scroll. OK. Maybe it sounds kind of normal right now, this experience. But at that moment, it was crazy. So it's not about, again, touch screens. It's not about the technology. Because that technology existed before the iPhone. It's about natural gesturing. You can move things. Like you move things in the real world. Because you are a physical human in a physical world, in a physical environment. So we think that the same change is happening right now for AI-based products to a new normal. So Marcelo is going to explain what's exactly an AI-based product. Exactly. So the question that we're going to hear is what do we mean by this mobile metaphor then? What is an AI-based product in the same way that all of a sudden phones, smart phones, just became mobile phones again because they dropped the name, the surname of smart, or a touchable interface, or whatever, which was a technology. But that was not so important. The important thing was that they did things better. And they kind of adapted to the real needs of users. So what's an AI-based product today? We should really start dropping the AI part. Or at least acknowledge that the AI part is not the centerpiece of it. And it's kind of normal that we all use it now as some kind of marketing tool to talk about it. Because it's the new thing in the market, not so new maybe. But it for sure comes with some advantages. So a product, any product today, is kind of somehow digital. And any digital product today has some kind of data baked into it. And if it's got some kind of data baked into it and you want it to really adapt to its environment, then it's going to be using some sort of AI advanced or not advanced. So basically, an AI-based product is becoming the new normal. Let's just drop the AI thing from that in the same way that the mobile phone became that. So we're just going to take this product as an example of a product that is using AI somehow to become better, to adapt better to whatever the user's needs are. And I guess we can all agree that this is a product that is really using some kind of data power into it. It's how we all listen to music nowadays. So the thing is that Spotify is not really so much about the recommendation engine. It's not really so much about how it's using, powerfully using AI to do things. Because it's probably not so advanced in some ways the algorithms it uses. But basically, and those algorithms actually existed also long before Spotify. I mean, we had recommendations algorithms long before. And we had maybe, I don't know, different versions or I don't know. But the thing is that Spotify, it's all about learning. It's all about learning about how people actually listen to music. It's learning about how people really want to do the things that they want to do. So the basic, let's say, paradigm shift that comes with this AI thing is that the products now learn, digital products or any product, like we said. Because digital is another of those surnames that we should drop. They're learning now. And they are learning from whatever we teach them. And the thing is that when we used to design products earlier, we used things like this. We were doing these screen workflows. We were trying to say, oh, my product has got these options. And from here, you go to here. And then you've got this fixed set of options. And you can do this fixed set of things. And this journey, let's say, across your application does no longer hold. Because your product now is not fixed. I mean, this is like you were doing, I don't know. Yeah, you were just pre-programming whatever thing, fixed structure. You build a model of something. And you cannot change the parts of it. It's like you build a city with Lego with no moving parts. But now, all of a sudden, the city has a life. Now, all of a sudden, your product is not predictable. Your product is learning from your behavior. Your product is offering you something new every day, basically. And therefore, you cannot really decide and thinking that everything's going to be always the same. And in this way, this is an example. This is Alex's Spotify. And you can see if you want to follow him, you've got his username somewhere there. Pretty interesting music he listens to. This is what he sees every day when he opens his Spotify app on his phone. And what will happen if we remove all the things that the application has learned from that interface? We are left with nothing. Because the whole product is learned from his tastes, from the way he actually listens to music, which is, by the way, the way he leaves. Because maybe today he's feeling happy because he's speaking at Big Data Spain. Maybe yesterday he was frightened because of that. We don't know. But then if you look at the music he was listening to, it was kind of instilling some kind of mood. And this thing, if you remove all that, if you remove, let's say, the human part of it, there's nothing there. You're left with just a home button, which takes you to nothing, because the whole home content is personalized. Then you can search something, which, OK, you could maybe find some music. But then it wouldn't be personalized search because you wouldn't have any of that learning part there. And then you could still access your playlists. But maybe those playlists would be dull and boring because you wouldn't have really discovered new music, thanks to all those learning. And actually, it's like we said earlier, it's not the same Spotify every day. It basically changes. And it does so all the time. It's not just every day. It does so evolving continuously and continuously. And this is a very important thing when we think about the algorithms that we do as data scientists, when they influence the products, we have to understand that they're going to be influencing them in this way. And also, at the same time, the experience is probabilistic. Also, for users, it's a different thing. So when we design for these new experiences, we have to think about these things. We have to think that users are going to be expecting probably something that's changing, but probably not too much, because then it's going to be also difficult for them. In the design processes, when we deal with these data-based products, we don't need to use design personas anymore. This is a technique that designers were being using for many years, where they build these archetypes of users, because it was a simpler way to approach the needs of those users. We've got the needy user. We've got the very nervous user. We've got those things. But now we know how each and every user is working. So we need to start designing for this, and we need to take this into account. When we, as algorithm designers, are designing for a UX designer, and we need to start interacting in new ways to bring this to products, because flows in those applications are not going to be deterministic anymore. There are many more examples out there, like Netflix, everything's personalized. If we take the personalized content out of the way, there's basically nothing. And well, one of the ways to approach design for these types of products is to think about UX as a container for dynamic experiences versus the previously used paradigm of a deterministic flow, which we can still kind of use. But maybe there is some kind of deterministic flow with some adaptive thing built into it. And this is just one of the ways we could be doing fully adaptive interfaces, where the app that you see and the app that I see are completely different experiences, different colors, different everything, maybe, because we've got different mindsets. And now Alex is going to talk to us about how products behave. So the huge change here is not about the algorithm. The huge change is your product in some way is kind of a living thing. We know that there is no artificial general intelligence, but Spotify is different. It behaves in a different ways for everyone. You can't predict what is going to happen next day, exactly, as a user, but also as an owner of the product. If I am the owner of Spotify, I can't guess what is going to happen tomorrow, exactly. So it behaves in a more different way because it learns. So let's talk again about Spotify. The first version of the algorithm for recommendation systems was the Discovery Weekly. As you can see here, it was a playlist that they bring to you each Monday with more or less 30 songs. Do you remember when this comes out? More or less? I remember very well. People started to get very crazy about that because some of them were huge fans of the playlist and some of us were not so fun of it. But it was a huge change because before, we didn't see anything similar to that. And if you think about that, as a data scientist or as an engineerian, maybe it's a naive solution. You have an algorithm. You run a batch on Sundays and the morning on Monday. You have a new playlist for everyone. This is the naive implementation of a recommendation algorithm. But Spotify, at that moment, probably observed the behavior of the product in the wild with real people, with actual people listening to music. And they observed that their solution is not the best solution for the problem that they are trying to solve, which is discover new music. So if you have some background on statistics or data science, you can think about that as a sampling. You have experience, which is dynamic. It's different for everyone. You have to sample different options from your actual users and try to guess what is happening in some way that you are starting to learn about the living thing that you are creating. You can think also about mass-scale anthropology here because you are looking at the behavior of the humans that are working with your app or are interacting with your app. So when you learn about the behavior of the humans that are using your app, the product evolves because it adapts to the new ideas, to the new behavior, to the new concepts. So Spotify changed a little bit more a few years later and they create the daily mixes. So everyone has six daily mixes, which is more or less the same idea of Discovery Weekly. But as you can see here, I don't know if you know some of the artists that you can read there. But for example, Daily Mix 3, this is mine, for example, is kind of hipster music from Spanish artists, more or less, if I have to put a name to that place. The first one is electronic music because I listen to music not all the time, not all the Mondays, a lot of different things. Maybe I want to listen the same music today, or maybe I'm nervous, I want to listen to jazz, or maybe for having dinner, I want to other kind of music. So they learn how people listen to music and they change the algorithm, not because they can change it, not for the sake of the algorithm because they learn how people listen to music. It's not about the AI, it's about how people listen to music. That's the important part of Spotify. Let's try to get some ideas how Spotify is working. You can Google this one and you can go to the Spotify API and try to get our recommendations based on SITs and they create these definitions for the API which is create a playlist-style listening experience. They are not talking about the algorithm, they're talking about the experience that you can create with this API which is more or less our recommendation system but they use kind of SITs to guess more or less what kind of music do you want to listen at that moment. So the funny thing for me is that table is one of the tables of the documentation. So if I want to show only one thing, maybe I want to show this parameter which is danceability because people listen to music for dancing and that's not a genre, that's not jazz, that's not pop, that's not a genre, that's an idea. So they create an algorithm to detect which music is danceable because that is how people listen to music. So they have all the parameters that are very curious. I recommend to Google it and find out other ideas from Spotify. Again, it's not about AI, it's about how people listen to music and as you can see with Marcello and as you can see Marcello is expecting kind of is behaving in an expected way. So Marcello is going to talk about what can happen in a living product. Exactly, just as he kind of controls if I'm going to be dancing at stage or not. The thing is once your product acquires a behavior at some point maybe it will start behaving in things in ways that you wouldn't expect, right? And you need to be really looking into that. There's an example, many of you may have seen it. The DeepMind team at Google, they trained a network to make a humanoid-like kind of creature run as long and fast as it could and with 21 joints or something like this and with some constraints and then the network learned how to do it. And it's really interesting to see the way that it's using the arms it's going to be doing like this and you wouldn't expect your algorithm to do that because you're human and you're running in different way. And the thing is, yeah, and then it falls and then it tries to do things and it's acquiring very strange behaviors which you of course didn't code because the thing learned on its own. And the thing is at some point maybe your recommendation algorithm starts doing something like this in your product and you need to think about when that will happen because you will need to maybe be looking into how that happens so that you can kind of control that in your product. So the thing is, you will have to start reverse engineering your product because even though you built it, you don't really know why it's doing the things it's doing. And the problem with that is that at some point maybe you will not even be able to reverse engineer it because it will be so complex. You will have so many layers of I don't know deep learning networks or whatever that it's gonna be kind of difficult for you to do that. I mean, you will have to kind of create like artificial intelligence doctors and magnetic functional resonance imaging or something like that to really peak into the algorithms and say, oh, what's going on here? Because it's got some strange behavior. It's got like a headache. We need to think about the concept of graceful degradation. This is when your product in deterministic products when one of the features fails when one of the functions fails your product has to still provide the user with some useful set of features because it still needs to do what it was meant to do. But when your whole product is an algorithm that is learning and that it's starting to behave oddly maybe it becomes ill for some reason or crazy or we don't know the concept of graceful degradation is different. So your interface needs to understand maybe at some point that this thing can happen and you need to prepare it for that. And to do that, you need to be measuring absolutely everything that is happening with your product. It's not just KPIs. It's not just measuring how the user is using the product but rather you need to measure how the product is behaving which is a new kind of a new concept and we need to start thinking into these things as our products evolve. And now Alex is going to make a small exercise with all of us. Okay, so how many of you has at this moment Google Maps in your phones? Raise your hands, okay, nice. Very nice market, sir. So if you, let's suppose that I'm one of the product managers at Google and I ask you as data scientists or as engineers to create some tool or some ideas to help people to pick restaurants. That's the problem to solve, okay. I have a lot of restaurants in Madrid and I want to pick one of them for tonight, okay. So I'm gonna give you some options although you can make some algorithms. Try to think the main algorithm to solve that problem, okay. You have to think a way to help people to pick a restaurant and you have to think about the best option to solve that problem. First option, our recommendation engine, maybe a collaborative filtering way of solving it. Two other way, other technique for recommendation. C, a natural language processing algorithm. D, image classification, okay. One moment to think about it. Okay, raise your hands if you think that this is the first option.  Okay, B, other recommendation technique for A is win. C, a natural language processing algorithm. Okay, third one. Image classification, okay, quite popular. There's some of you who didn't vote. Come on, take a side, take a side. So more or less we use this example because it's a kind of red herring problem. Probably the typical way of thinking for a data scientist or if you read a book about different techniques in data science, you can hear about recommendation algorithm that more or less match exactly with the problem that we want to solve, but as you can see. Which is what most of you voted, maybe not very much different, but most of you chose A, I mean, maybe you were not looking at each other. Okay, so let's see what happens in Google Maps to solve that problem. The first solution that they use, what this one. Okay, the recommendation system is the name solution for the problem is maybe a data science way of thinking. But what actually happens, the first thing that they implement was if I have a lot of pictures that people are uploading to Google Maps. For example, from my Chinese restaurant here, I have here a lot of pictures and they create this experience, which is an image classification problem. Like they only pick pictures from food of the place. So you can look at the things that they are cooking and if you like the idea, so it's a super cool place or it's only a squeeze sandwiches or whatever, or paella or whatever you want. And the other classification here is atmosphere because you want to know about the place if you can go with your partner or you can go with your friends or you can go with a big group. So a very easy solution which is maybe not the most straightforward idea at the beginning, it was one of the ways of solving that, okay? So we have a problem that seems like a recommendation system, but you can solve with the image classification algorithm. Okay, so again, it's not about the algorithm, it's not about the AI, it's not about how data science or data scientists think about how to solve the problem, it's about, neither is about using the best algorithm. In fact, it's not at all relevant artificial intelligence because maybe you can solve this problem with other approaches. It's about how people pick restaurants. So if you pick a restaurant with the food, you need something to help people to see the food that they are going to eat. Just one quick comment on that. The recommendation algorithm route is of course very interesting. If I could state what I'm looking for, but the problem is when I want to go out for a restaurant, maybe I don't even know what I'm looking for and you give me the typical filters, oh, do you want Chinese or do you want Italian? I don't know, I want it to be good. Oh, so you want the start rating of people, I don't know if I can trust people. Do you know that, so there's a lot of, and it's not even about that.  So my brain is used to picking restaurants in that way. So help me do things the way I want to do things. And AI is a very good way to do that because AI is trying to learn from the way we do things. It's not pre-programmed. It's kind of, if you gave me filters, you are pre-programming me a way of thinking and that's something that I don't want as a user. I want to move freely with this product in the same way that I take the iPhone and I do this to swipe up the list or to unlock the phone or something, which is natural to me. I want to pick a restaurant in the same way, a way that is natural to me, not thinking about, oh, is it Chinese or has it got, that's too complex. Sorry. Yeah, okay, it's the same idea with the Spotify example that they create something to recommend music that are for dancing. There is not that general, there is not that typical way of thing. There is no Chinese restaurant. Maybe the category here is restaurant that I can go with my partner, okay? So, the other example? Yeah, there's another example which is very telling. Yeah, this is a living example that Marcelo is going to try. This is a live example. Let's see, this is AI applied in real time and it's actually very well applied AI in real time. Let me say that. So, this is Google as lights by the way? Yeah, so I click here and then all of a sudden, oh, sorry. So, like I said, this is just an artificial intelligence application being used in real time because there is a real need here from users. Maybe if we turn this on, we can help our hearing impaired audience to follow what we are saying. And without this, it would be very expensive because we need to have translators here and I don't mean that we need to get rid of them. Please don't take that as a take away from the conference. But the thing is for smaller conferences maybe that don't have a budget, they can use this option. And this is really very well placed AI in a product. And, well, that's all I wanted to say. I'm just going to switch it off because I don't want to, okay, to be leaning forward all the time. But the thing is, again, it's not, oh, we've got this cool thing, we're going to push it anywhere in the product because, you know, trying to talk into products is something that people try to do nowadays very much. And well, maybe people don't want to talk to products because it kind of feels a bit awkward. But there's applications where you really can use things because there's a real user need again in the way people actually do things, not because you force them to talk to a product. So don't try to talk to your dishwasher, try to create an experience based on a need, and you can think about algorithms to achieve that. But don't try to use every algorithm in the world in every application. Maybe we don't need talking dishwasher, okay? So here, the problem, the actual problem to solve is how can we find the right solution because it's not easy as you can see, it's not straightforward as you can see, it's not the typical way of thinking as you can see. So Marcelo is going to tell us some ideas on some solutions to do that. Thank you. So basically, we need to, as product designers, as product teams, we need to really think like people act, not, we don't need to think like people think because people don't think the way they think that they think, they act in a different way. So basically, we just need to observe, which is something that is very interesting and maybe not so much done from technical teams. We just basically need to think like people actually leave because what we want to do with our products is to get integrated into their lives. So if our products don't think how they live, and if our products don't learn how people live, and this is the nice thing about AI, that is that they can learn how people live, how they listen to music, how they do things with Google Slides, then we're not going to get in there. Design was typically the discipline that was doing this for many years. It was observing people, it was trying to understand what they do. And by design, we mean a lot of different things. It's not just putting colors and designing lines and circles and points, but basically it's under that. I mean, it's a very big umbrella. We could consider that there's anthropology, there's sociology, psychology, there's art, there's many disciplines that have to do with how humans actually live and with how humans actually behave. And our products need to learn from that. And our teams, our scientific teams, our technical teams need to interact with these people. We need to come into a product design process that actually understands what AI can do. And where all the different profiles have a, let's say a dialogue about how people actually basically live, which is what we've been talking about. We don't need to just push AI into products just for the sake of it. And that's why we need to have combined teams where you've got AI people, where you've got business people, of course, because you need to make money, where you've got design people who understand people and where you have got even people. I mean, you've got to take your product, take it out there, get feedback from people, from real people. And before you do your product, before you do your concept, I know this is probably not just AI, let's say specific.  But the thing is now with AI, it kind of seems like it's like with, it's so powerful that we're just gonna throw AI at it and it's gonna magically work very nice. No, you need to really think about how this product is going to fit in society. And to do that, you need to have these multi, these multi-profile things. Don't make this product. Don't do this thing. Oh, we're gonna make a phone with an iPod. Let's just put a phone, dialing phone in iPod. It makes no sense, all right? And there is this very beautiful project by Google, this Google duplex that can reserve a table, it can book a table for you, calling for you. And then you'll know that, well, some people were not so happy that an AI system will call, will phone and they would talk to an AI systems and they would be fooled and well, now they changed the product. And the AI systems will identify themselves as, oh, hello, I'm an AI system and I want to book something for Mr. Alex. And that's when you actually interact with society, you get this type of feedback and you need to do it and we need to do it to understand that data product. And that's not only about the feedback about your users. It's something like, there is a living thing which is your algorithm. I mean, when you put into your wild, maybe you get behaviors that you didn't expect. For example, if you create a risk scoring algorithm, which is a kind of big thing in a market industry, one example was there is algorithm starting to score better to people that had a lot of credit cards. So people start to notice that. So they start to get a lot of cards to get a better risk scoring. So the algorithm learns again after that behavior of the humans that it was because of the algorithm. The algorithm again learns the opposite behavior that if you had a lot of credit cards, maybe you are not the best risk scoring idea. So the algorithm change, so people start to hack again the algorithm. So it's not only about the feedback, it's that you have a living thing and you are putting it into a real environment. So the environment is going to react to that. Exactly. So instead of being that, I'd be this one because they kind of made it kind of right because they understand, oh, we need to get to do things the way people want to do it and blah, blah, blah. So they've got a pretty nice valuation nowadays and it's basically because they find really very well the context of their users and they can go to it. And then they of course have a massive database on how people listen to music, which kind of means how people live in some way and that's very valuable. Alex. Yeah, by the way, when I asked for the Google Maps example a few months ago, at least in Spain, they add an actual recommendation system. And for example, you can see here 100% score for this place for my tests. So although the first solution was image classification, maybe there are more than one right solutions to your problem and you have to think at least the order of them because one of them is easy to implement. So you have to start with that. But again, later you can think about other solutions. So yeah, there is a recommendation algorithm in Google Maps to do that, but that was not the first solution. Okay, so as the takeaways, I'd say we've been hammering this idea all the talk, but it's not just AI. Never think about AI as the ultimate solution. It's the whole product design cycle and you need to have all the different people together. They're multidisciplinary teams that need to understand each other and this is very hard to get right. Believe us, I mean, we're doing this at BUVA and it's pretty hard to get there. It requires a lot of effort and a lot of education and a lot of, yeah, doing all these things. Again, you need to educate your data teams that data is not the only thing that design in a broad sense, let's say is something that they need to consider and you need to educate everyone else on what AI or data can actually do for them. Not just the design teams, but also your product management teams, your, I don't know, business teams, your legal teams even sometimes because they need to understand these things. Again, they all have to go together to build a successful product. These are all things that we are doing at BUVA data where we work, so we're building these cross-disciplinary teams. Alex, do you like intersections? Yes, I do. In fact, for the reason we work here, so thank you. Thank you. Questions? Any questions? Pretty welcome to do it. Maybe your AIs have questions for us. So no questions, then thanks again and enjoy the rest of the conference. Thank you.