So, to this point in our course, we've seen lots of attempts, you know, starting with Thaley's, trying to answer this question, you know, what is this thing? Trying to understand what it is. We've even seen answers from, you know, some of the sofas that, you know, it's whatever you want it to be, right? Ranging from this reality that's independent of the mind to which our mind was conformed and not the other way around to, you know, just make it whatever you want and it is that. So, what do we can argue? Well, Plato is still grappling with this question, he's trying to really deal with two questions that, you know, are on the top of our minds really, right? And the first is, you have to explain that a thing exists, right? So I'm walking through this park right now and I'm walking past these trees and I'm not thinking to myself, wow, it's really kind of neat, these trees just popped into existence the minute that I am here. No, I mean, there's some reason, there's some account for why these trees are here before my arrival, they didn't just pop up right now and they didn't just pop up, they came from something. Yes, that's the first question you ask is, I have to explain is that a thing exists? How did it get to be here? What brought it into existence? What's it composed of? What was the causal chain? What's its history? Okay. The second question is what a thing is? That something, you know, explaining that something comes into existence or that something is now existing, okay, that's one thing, but telling us what it is after that is something else. Now we say these are trees, all right. The question of that the tree exists, can be explained, right, we can do a real good job of this talking about, you know, the seeds of the trees, the germination of the ground, water, soil, nutrients, the gases that are in the atmosphere and, you know, that in fact, how it interacts with the rest of the environment, okay, that, you know, that's the that of the tree's existence. The what is what it means to be a tree? There's something of what it means to be a tree and for me to be human and there's a difference between those two things and what it means to be ground and air and water and animal and everything else. Now Plato is trying to answer both questions, is trying to explain that and trying to explain the existence of every individual thing and what it is, I mean, that you're trying to explain that it exists and what is its existence by appealing to form. So Plato's first step and trying to give this answer to the, you know, trying to explain the existence of everything and what it means or the whatness of everything, the essence of everything by, you know, appealing to form is to first provide a refutation against relativism. Now relativism, you know, if we're going to just kind of simply state it, is the assertion that a proposition P is true when and only when a person believes it. So this is the idea, if you want to account for truth, you want to know what's true to ask what people believe, right? This is just what makes a proposition true is the fact that a person believes it. We make the truth, okay. Well, relativism has some issues here because as a matter of fact, not everybody believes this, right? Plato, Socrates, Aristotle, a host of philosophers through history, a host of people today, think that this is false. Now plenty of people thought it was true. Okay, that's not the problem though. The problem is that plenty of people have thought that it's false, that a proposition is true when and only when a person believes it. There is such a thing as being mistaken. If relativism is true, nobody's ever mistaken, right? But there is such a thing as being mistaken. So, yeah, you know, plenty of people have argued that, well, it's false that a proposition is true when and only when a person believes it. Now, merits of their arguments aside, as a matter of fact, they believe this is false. Okay, well, if a proposition is true when and only when a person believes it, and as a matter of fact, some people believe it's false that a proposition is true when and only when a person believes it, well, this means that there are some people with at least one, excuse me, there's at least one truth that's true regardless of people believe it, you know, namely, these philosophers, and these philosophers believe that it's false, that a proposition is true when and only when a person believes it. So, yeah, this is kind of what's interesting about relativism, say, you know, we, our beliefs make the truth, but some people believe that that's false. So, okay, they're mistaken. There's at least one true whether they believe it or not. Well, if there's a truth, whether somebody believes it or not, well, then relativism is false, right? Relativism asserts that what makes something true is our beliefs. But here's a belief or here's something that's true, and here's something that's true and people don't believe it. That's kind of a problem for relativism. It rejects itself, given all the relevant evidence, right? Now, you know, there's lots of ways to look at relativism here and, you know, lots of ways to argue against it. You know, the little passage in the text goes about one way and you may walk over that in class, but, you know, this is at least his first glance at why Socrates and Plato reject relativism, because at the end of the day, you know, it rejects itself. So the first step that Plato takes is rejecting relativism. The second, yeah, so this, you know, this establishes that there is some objective truth out there, right? This isn't just anything that you want it to be. The second step is to understand the source of our knowledge. Now, we might be tempted to think that the source of all our knowledge is perception, right? What we see, hear, taste, touch, smell. Now, the assertion that the source of all knowledge is perception, what we get from experiences, is called empiricism. Empiricism. Empiricism is not being a fan of Darth Vader, right? Empiricism is the claim that all of our knowledge, the source of all our knowledge, is perception. Now, Plato rejects this idea. What Plato endorses is what's today called rationalism. Now, rationalism asserts that at least some of our knowledge is not from experience. At least some of our knowledge is not from experience. Plato takes a pretty heavy version of rationalism. He's going to say the source of all real knowledge is not from experience at all. He takes a pretty heavy version here, but you know, bear with them. Okay. So, at least just hear him out before you say, whoa, Plato, come on, that's crazy. All right. So, how does this go about? Well, as we saw with a lot of philosophers from previous readings, he looks at change. Change. Now, it doesn't look like it right now, but this creek bed has gone through a lot of changes. At one point, there was water flowing through here. There's not water flowing through here now. There was probably, or maybe there's more soil on the ground that's been washed away by floodwaters or creek waters. Some of the vegetation has lost its leaves, but you know, that's the season for it. The leaves will grow back. Some of the vegetation wasn't always here. Some, you know, it grew up from the ground. Some of it will disappear, right? It'll die and wither away and decompose and provide the fuel for the next line of vegetation. This won't always look like. I mean, this whole area didn't always look like this. You may or may not know this, but this whole area, way, way back. We talked about thousands of years. It was once covered in water. We've seen the fossil evidence for that. Some day, maybe to be covered in water again, or maybe all the water will, you know, be somehow destroyed and this will just be a desert landscape after a while. Whatever. The point being that, you know, while it looks pretty stable now, this goes through a lot of changes.  Alright, so, you know, there's a tree right over there. It's kind of big at this point is what 20, well, big compared to me anyway. You know, it's what, 20 feet tall? It wasn't always 20 feet tall. It started out as, you know, some kind of seed, an acorn. It sprouted through the ground, right? It grew leaves, it grew more branches, it grew bark. Right now it's lost its leaves, but it'll grow leaves back. So as it, you know, goes from the seed up to that, it's becoming something, right? The acorn, that seed, the little sproutling, is becoming something. It's becoming that tree. Well, if it's becoming something, right? If it's going through changes, what it is, is not just the changes, right? If all that exists are the changes, then it's not becoming anything, right? Whatever was there is gone and a new thing is in its place. And whatever that is, is gone and a new thing is in its place. And then that's gone. And until finally we have this, right? But that's just the latest thing at the end of a succession of a bunch of things. Okay. But, you know, we don't think that.  No, no, we say that it's becoming a tree. Okay. So, whatever a thing is, you know, whatever a thing is, right, is not the changes. If it's becoming, it's becoming something. It's becoming something. You were a little infant and you went through a lot of changes. Well, you're not identical to the changes.  We've seen this with Heraclitus. But you're becoming something. So, whatever you are is not identical to the changes. It's not just the changes. I've grown from, you know, 10 inches or so to 60 inches, right? That's a lot of changes. I've grown from 10 pounds to more pounds that I like to admit. I even had blonde hair when I was a little boy. I was a little boy. I had blonde hair. Now, what we only perceive changes, right? We only perceive changes. Changes are reflected by the causal interactions around us. Light, sound, smell, or sight, touch, smell, sound, all that. That's the content of our perceptions. We perceive only changes. When we look at an acorn, right, when it's first planted in the ground, we don't also perceive the tree it's going to become. And when we look at the tree, we don't perceive the dried out husk that it's going to become. We also don't currently perceive the acorn, right? We don't perceive all of that. When we look at each other, I can't look at your baby picture by looking at you. So, whatever we perceive, we perceive only changes. Well, that means, you know, since changes are not identical to the thing, they're not what a thing is, that means that we don't perceive what a thing is. What a thing is, its essence, its definition, its meaning is not given through perception. When I look at something, I don't see its essence. I might see it. I see its color. I might even see its shape, the contours of its edges. Okay, right, we'll grant that to see the contour of its edges, but not what a thing is. Well, if perception gives us real knowledge, I mean, if perception gives us knowledge, perception gives us knowledge at all for Plato, perception gives us knowledge at all, and perception will tell us what a thing is, its essence. Heck, perception doesn't even tell you where a thing comes from or doesn't even explain where it comes from, right? It doesn't answer either one of those questions. Well, perception doesn't do this. Perception doesn't tell us what a thing is. So, perception is not the source of knowledge. Now, either it's perception or it's something else. In this point, we're only considering perception or reason. Well, we might call what identifies conceptual knowledge, knowledge of essences, right? All right, well, if our choices are reason or perception, conceptual knowledge or perception, abstract knowledge, knowledge of universals, or perception, well, and perception of particulars, and perception doesn't do it, then our knowledge has to come from reason. That means our knowledge of what things are for Plato, our knowledge of what things are is not from perception. So, the question is, where does this knowledge, this knowledge by reason, where does it come from? How do we have it? And Plato's answer is going to sound a little different. So, I've said that Plato's going to explain all of this or account for all of this by appealing to form. That's probably going to help at least a little bit to understand what he's talking about. But the way to understand what he's talking about is to start first with what we experience with our perceptions. And form is not that, right? So, as we said before, perception is not knowledge. Reason is knowledge. So, what does perception give us? What is perception like? Well, with perception, I see the particulars, the individual things. I see this bush, this bush. I see that tree, that rock, that grass, right? That particular organism of grass, that particular tree, this particular shrub. Perception gives me the particular, or reason gives me the universal, gives me the essence. This is the particular. The form, the essence, is what all these particulars have in common. Now, you might think, well, I've seen one tree, I've seen them all. No, that's not true, because trees look different from each other. Or even, let's take this, sunglasses, right? All right, this is a particular pair of sunglasses. You see this, but you don't see all sunglasses. You probably can't conceive of all sunglasses. This is a particular. But what they all have in common is the universal, that's the essence. Now, this is made of plastic. The lenses are made of plastic. The arms are made of plastic. Some lenses are made of glass. Some frames are made of metal. You see this particular shape, it's kind of long and has a corner at the ends of it. Some sunglasses are perfectly circular. Some sunglasses are more square. These are kind of dark gray or black. Some are multicolor, right? Some dark blue or blue. Some are mirrored. You know, there's a whole variety of shapes to sunglasses, a whole variety of colors to sunglasses. You haven't seen all of them. Heck, you can't conceive of all of them, right? We don't know what the sunglasses of the future are going to look like. We don't know what they'll be made of. All right. But you know the universal sunglasses. You can look at this and say, yeah, these are pairs of sunglasses. This is, you know, sunglasses are not a hat. Sunglasses are not a shirt. You can distinguish sunglasses from regular glasses, right? So the point of that being is you perceive the particular thing. You don't perceive the essence universal. That you have through reason. So that's the first thing is a distinction between the particular and the universal. The form is the universal. The form is not the reason. All right. This is also the difference between a subjective experience and objective knowledge, right? So perception is subjective. I have my perceptions. You have yours. They don't look alike. Even if you're standing in the same room, look at the same thing. There would be some difference. You know, as, you know, as I've probably mentioned before, I'm slightly colorblind. I can't see as many colors as you do. The color spectrum is going to look different to you than to me. Even our experiences, our memories, our, how we've encountered different things is going to change how we perceive a thing. Somebody who's afraid of dogs is going to look at dogs much differently than other, that people who are not afraid of dogs. So perception, knowledge of the particular, right? The particular experience, that's subjective. Form, the essence, the universal, that's objective. That doesn't change depending on who's looking at it. Perceptions are all subjective. Form is objective. All right. In addition to that, you know, we can contrast forms to the particular things even more. These particular things come and go, right? This tree did not always exist. And then at some point, it will cease to exist. This creek bed at one point didn't exist. At some point, it will cease to exist. But the form, the essence of creeks, the form, the essence of trees will always exist, right? The particular things are temporal. The forms are eternal. So to kind of summarize this, form is known through reason. The particular things, the appearances are known through perceptions. Form is objective. The perceptions are subjective. The particular things are temporary. Heck, perceptions are temporary. The form is eternal. So this raises the very interesting question. Forms are eternal. They're not known through perception. They're objective. How do we know about the forms? So Plato's given us form. Form is eternal. It's objective. It's the universal. It's known through reason, not perceptions. We never see form. Well, if we never see it, how do we know it? Well, Plato says, look, I mean, look what's happening here. You know what goodness is, right? You look at particular actions and you say, well, that's good and that's bad. If I were to randomly walk around and start slapping people across the faces, well, that's bad. That's wrong. You can't do that. You know what shapes are or triangles are. You know what triangles are. You can recognize different triangles without ever seeing all triangles. You have that knowledge. You can look at different figures and say, well, that's not a triangle, right?  You know what equals is, right? If I have two sticks here. Okay, here we go. Two sticks. You know, they are not equal, right? They're not equal length. They're not even equal width. You can look at that. But, you know, equals doesn't have a color. Equals doesn't have a color. There isn't some color that's shining between these two things that says not equals, right? Equals is a concept. You haven't seen all the twigs in this park and compare them to one another to find equals and unequals and then you'll say, well, this one belongs in the equals line. You know what equals is, even though you haven't seen all instances of equal links and all instances of unequal links, you have this knowledge independent of experience. But, you know, even Plato's not going to doubt that you have experiences that you have perceptions. Okay.  Those are your perceptions, the senses. That's temporal. Your knowledge of the forms, then, of these essences has to then come from the eternal. You know, for Plato, our knowledge of the forms, we have this knowledge of the forms because we, at some point, existed with them without a body. We existed with them without a body. We had some kind of eternal existence or at least non-temporal existence, non-physical existence with the forms. The process of birth was very traumatic. So, we spend the rest of our life recollecting the forms. These copies of the forms, these mere, you know, poor shades of the forms remind us of them. Okay. We recollect the forms through this process of observation. But the knowledge of the forms was always there. All right. Well, you know, whether you accept Plato's account or not, there is some sense in which you may not necessarily recollect the forms, but we discover them. We discover them. You know, looking at triangles, we discovered all kinds of things about triangles. We didn't create these truths about triangles. We didn't decide that the sum of the interior angles of a triangle will be 180 degrees. We found that. We found that. That was discovered, not made. We discovered that the sum of the interior angles of a square are 360 degrees. We didn't create that. We discovered that. So, you know, maybe you'll accept Plato's account. Maybe not, right? As far as how we know about the forms. But it is really hard to deny that we do discover these truths.   It's, you know, a trinome. Trinomes hide behind trees, and wherever you look, you know, they're always, they're like really fast, right? So, I'm looking around, they always hide behind a tree. I can make that up. I can't make the sum of the interior angles of a square be 20 degrees. Can't do it. Okay. So, again, whether you buy Plato's account of existence with the forms before a physical body or not, there's still some sense of which we have to contend with this idea that the forms are discovered. They're not made. All right. Now, what Plato has given us here is a view of reality that's contrasted to what we could call materialism. Materialism is the view that the only thing that exists are material or physical objects. Right. So, hearkening back to the distinction between the material and the form will give into us about Pythagoras. The materialists could say the only thing that exists are material objects. There is no form. There is no form. All right. Now, Plato's view, well, it's often just called Platonism. Right. It's the view that there's physical objects, material objects, and form. Right. There's a view that it's that there's particular things and universal things. Right. Both these things are real. They both don't exist in the same way, but they're both real. Now, the book, the text that I gave you calls this idealism. Don't call it idealism. That's something else. It's Platonism. Right. Just scratch out idealism, put in Platonism. Right. It's the view that there are particular objects and universals. Both these things are real. So, we're going to look at this more, we look at the distinction between materialism and Platonism more through the course of the semester and see different philosophers try to grasp with this. And I also want to point out, again, what Plato's given us is called rationalism. Rationalism is a view that at least some knowledge is not empirical. And this is contrasted to empiricism. And that's the view that all knowledge, the source of all knowledge is empirical. Right. That's empiricism. So, Plato's told us about form. And what he's told us is not only the what of this tree is form, right. This tree is a particular thing. And the what of it is its form. But he also says the that of this tree, that is the explaining that it exists is also form. Because as I said, as we pointed out, these are particular things. These come and go. But the forms are eternal. That means that the form is more real than that tree. And when you understand form, you understand reality better. You want to understand that tree? Don't look at that tree. Look at its form. To understand all trees, look at its form. And when you really start to understand these forms, and you understand that the forms of these things are what all these things have in common, the next step is to ask, what do all forms have in common? What is it that all these particular things and forms have in common? Because they all exist. It's all good, goodness. So you're working way up to what everything has in common. And what everything has in common for Plato is goodness, is being itself, existence, exist. And when you understand that, you understand everything else. This, for Plato, is wisdom to understand being, goodness, truth.


This is stock number 7J468B. We are here at Summit Automotive in Fond du Lac, Wisconsin. You're new and used Jeep headquarters. Today we are checking out this ultra-clean 2015 Jeep Cherokee Trailhawk. This vehicle has the 2.4 liter multi-air motor. From this HD video you will be able to tell that this Jeep is extremely clean inside and out. 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's like you're right here looking at the vehicle with me. It has the 17-inch painted and polished aluminum alloy rims and Firestone destination P24565R17 tires. These tires look like they have probably about, I'd say, 70-80% of the tread left on them. Anvil gray is the color. It has the LED running lights, the projector lamp headlamps, and the factory fog lamps. With the Trailhawk package you get those red powder-coated tow hooks, the grayed-out seven-slotted grille. You get the black hood decal. No dents, dings, scuffs, or cracks on that front bumper that I saw. The passenger side rim is absolutely perfect as well. No scuffs or scrapes on that. And as we go down this side of the Jeep, you can see just how clean that body is, how reflective and mirror-like and glass-like that paint is. We 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. We sell a lot of vehicles out of town and out of state, but we also sell a lot locally. So we want to give our out-of-state customers the same experience that our local customers get, as far as being able to see the cleanliness of our vehicles and the quality of them. As we go around to the back, no dents or dings or cracks on that rear bumper, the tailgate is in excellent condition as well. I can honestly say I didn't see one single ding on this vehicle, so very, very well maintained. When it has the rear tow hook, you also get with the trail-hawk package, and you get the cool trail-hawk badge on the back. Back storage area is very clean as well. You get the cargo management system with the little Jeep tow hooks there. You also have the sunshade, which it goes like that. Now why that's cool is if you have personal belongings or valuables that you don't want anybody to see when you're not by the car, there's a sunshade out and nobody can see it. Out of sight, out of mind. Also, if you have groceries, you want to keep them cool. That's what that's mainly for.  This is the best camera, right under that Jeep logo there. LED tail lights, which have been proven to shine light up faster, brighter and farther than normal halogen. as you go down this side of the vehicle no dents or dings either it's a really cool looking color kind of a bluish gray color to it inside this trailhawk package has the black leather interior both of these seats are heated and cooled you get the trailhawk logo stitched into the backrest there no rips or tears on these seats power driver seat with lumbar factory all weather formats in the front here power windows power locks and power mirrors memory driver seat and the auto headlamps configurable tft display you can see that this vehicle has 53,235 miles you got an outside temperature compass and you can pretty much put anything in those corners that you want as far as mileage or radio station or compass temp or time or pretty much anything distance to empty we have the heated leather wrap steering wheel no scuffs or scrapes on that cruise controls on that side Bluetooth audio controls on that side you have the 8.4 touch screen radio this one does not have navigation however you could add nav to this for $5.95 and you would do that through our dealership heated and cooled seats heated steering wheel as well you get your climate control down there or you can do your climate control up here you have what this is what makes the trailhawk the trailhawk package you get the four-wheel drive low the rear lockers downhill assist control and crawl control there also a neutral button if you want to flat-toe it you could snow sport sand mud and rock off-road terrain settings you have the SD card USB and aux jack right there and of course you can connect it through your Bluetooth as well passenger seat is very clean no rips or tears on that very very clean we have the factory all-weather format on that side too and we're just having a little technical difficulties with the microphone okay we're all better now the 911 and assist buttons up there and then you get a little storage compartment as well as your map lights take a quick look at the back seats and then we will check out under the hood very clean in the front this vehicle has never been smoked in back seats are just as clean as the front seats no rips or tears back here does have the latch child safety system these seats are adjustable as you can see you also have 115 volt 150 watt plug-in back here and the carpeting is very nice and clean you get 10 airbags in this vehicle side knee bolster airbags in the back here and these seats do go down almost completely flat to give you more room when you're taking a bunch of extra stuff with you as we go around to the front no denser dings on that fender and the hood is absolutely perfect the 2.4 liter multi-air motor pumps out 184 horsepower this Jeep 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 it has been gone through mechanically 100% and is 100% ready to go to see more pictures of this Jeep or one of our other 400 new and used cars trucks SUVs minivans wranglers you name it we got to go to our website www.summitauto.com full pictures and descriptions of every single vehicle on a lot videos of every single used vehicle that we have all at summit auto.com thank you so much for checking out the video if you like this one want to make it yours give us a call right now<PHONE_NUMBER> ask for one of our sales associates to make this Jeep yours today once again that number is<PHONE_NUMBER> thanks again for checking out the video and if you want to check out more go to youtube.com slash summit auto remember to like subscribe and share on this video and all the videos there in fact in a second you'll see a link to subscribe to our YouTube channel on your left a link to more YouTube videos out of our used inventory on your right if you haven't been to our website on the bottom link to this vehicle on our website click those check us out and we really look forward to help you with this super clean 2015 Jeep Cherokee Trailhawk thanks again


Thank you. It's OK like that. Thank you for this introduction. And so I feel sorry that this is me standing in front of you here today to present the work that we have done in Central African Republic with this MSF E-Care tool that we presented last year. Frank is a national nurse there. He is currently supervising the activity, and it would have meant much better to bring the field's perspective and experience. Just also a note to tell you that I apologize. We have switched the presentation because then I will be doing two presentations of the different tools that we designed with the same technology and support systems. But so I will first speak about the main project that was designed in this algorithm to improve the management of childhood illnesses in primary health care and then give you the talk that was initially planned first on the derivate tools we did for a vaccine campaign. So where is it? Here. Everyone was doing very well. OK, so you know that in our MSF context, when we talk about primary care, we talk about context where we lack qualified health workers. These health workers have access to a very limited number of diagnostic tools and that they work in an environment where the childhood mortality due to infectious disease is very high. And all these results often in insufficient quality of care delivered to our patient, but also a huge over-prescription of antimicrobials. And so in order to improve the quality of care and rational prescriptions of antibiotics for the children aged 2 to 59 months in primary care, we developed MSFE care with a group of expert pediatrician. So first, this MSFE care is a clinical algorithm. So it's clinical pathways that will state very clearly the step-by-step procedures that the clinician should follow in order to appropriately identify the disease and prescribe the treatment required. This is based on a simple syndromic approach, meaning that we only use sign-in symptoms that the lay-out health worker we work with are able to recognize. And we only base the decision on available resources in primary health care. And this material was transformed into an Android application that helped the clinician navigate through the complex protocols, helped them to increase the adherence to the recommendation we make and limit the interpretation that often occurs when we give the usual guidelines that we had developed before. So last year we presented the feasibility, the result of a feasibility study that was done just for a few days in Congo. But what we are happy to share with you is that thanks to Frank's work, we have implemented it now in three peripheral health centers in Central African Republic and that the 24 health worker that we are conducting a consultation for under five are now using it. So this staff, where our nurses and community health worker, that we are appointed by the MOH or the committee gestural to handle this consultation. And to implement the tool, we have sent the attrition for four months to prepare the deployment and help in the training and implementation. But we also used the actual supervision team that was already there, which was mainly one expatriate midwife that is supervising all the activities in this peripheral health center supported by MFF and Frank, so the national nurse, more in charge of the supervision of the pediatric consultations. The training happened in four days in each of the health center one day of actual training and then followed by three days of face to face supervision where the supervisor helped the clinician in the first days of use during the consultations. At the end of the training, two tablets were left by a health center to be used and one solar panel had been installed so that they have power access. The three health center have no internet access so we have developed an online peer-to-peer data transfer system so that at each supervision visit the supervisor can get the data from the tablet and bring them back to Berberati where they are synchronized with our central server. Throughout this intervention, we tried to assess whether the tool we had developed was appropriate with regard to the clinical situation that the clinician encounters and whether the clinician were ready to follow the recommendation given by the tablet in term of diagnostic and treatment. We also monitored the level of use looking at the number of consultation weekly, monthly and also referring it to the routine health information system looking at what proportion of the total consultation are done actually with the tool. Then one of our main objectives was to improve the rational prescription of antibiotics and to look at that, we have done some consultation observation before the introduction of the tool. Now we can also look at what data are automatically collected in the system because the clinician tells us what he finally prescribed at the end of the consultation. And because we knew that this was not collected the same way, we also tried to collect data from the routine consultation register where the clinician report their prescription and we compare a sample of 200 consultation before in January 2016 to 200 in January 2017. And so here since the end of November we have registered in the system 5,300 consultations and if we look at the consultation that we're doing at that time we saw that if the uptick was different from one health facility to the other, from February on we have more than 80% of the consultation done with the tool. The median consultation duration that is recorded within the application was six minutes in this consultation with an interquartial range of four to 10 minutes. So when we look at whether our tool was appropriate to cover the clinical situation encounter we saw that almost all reported symptoms in the system were addressed by MSF ECR. And in more than 98% of the consultation a diagnosis and treatment was proposed by MSF ECR. So it was offering a solution or it was covering the situations. Now when we look at the reported adherence in 96% of the consultation clinicians say that they would follow the recommendation of the tablet and this was even higher with regard to antibiotic prescription decision. We have just sent now an anthropologist to assess more in-depth what is the perception of the tool not only from the user side but also from the community and the caretakers. But already at the end of February we administered a questionnaire to the users and what was interesting is to see that the majority of them reported that the tool was easy to use also they had to get used to it in the first days that it had improved the consultation and especially with regard to helping them in calculating the dose of the drug they wanted to prescribe and their perception was that it was very well accepted by the community. And a lot of them asked us to extend this so bringing them some more tools for other aid groups but also for other medical activities. So one of our objective was to decrease the antimicrobial prescription and what we saw before the implementation of the intervention both through the register and through the observation of consultation was that half of the patient coming for an acute illness were receiving an antibiotic. This is really the same result that we find everywhere when we look at a health facility in sub-Saharan Africa where the proportion of fever due to malaria is high still half of the patient receive antibiotics and this we know is too much when we compare it to the etiology or fever study that we have even if this is scarce. And after the introduction of the tool we managed to decrease this proportion to about 20% when we look at the register and the full dataset that we have. Now another interesting things in this project is that this decision super tool is also a data collection tool and that helps us to have some insight into the... I mean we can look at what's happening in the consultation process more closely and what is interesting is that when just looking at the proportion of the diagnosis we have we can identify some problem. Here what we saw is that in Gambula proportion of lower respiratory tract infection was quite important and higher than in the order and higher than we expect and this is something that we can take and then go back to the clinics and discuss with the clinician how they reached this high level of lower respiratory tract infection diagnosis. So to conclude on that what we saw is that this tool had that value. We had positive impact on the quality of care and rational prescription of antibiotics. Discussing with the users and the supervisor they had the feeling that this tool had allowed to bring some knowledge that the clinician had received in previous training but this bringing the tool helped them to actually apply what they had learned in their consultation process and that this was driving them through a systematic procedure that they could continue doing if we remove the tablet because they learn by doing in front of the patient in front of the specific clinical presentation. And then it can help us better target the supervision we do. We had a lot of question of whether this would be feasible and replicable in our MSF context. What we saw here is that it's as required a very limited technical support. Most of the technical support was taken by Frank there. Also he mentioned that it would facilitate his work if the tablet, if the center were connected to internet so that he would not have to do this process of collecting the data and synchronizing. Still it was feasible within his work load and all the supervision clinical support was integrated into the setup that was already there before the intervention. So and the coordination team, supervision team and users all ask us to continue and to extend. So that's also something positive for us. So now we are in a phase that we face the challenge on how do we transfer this from the pilot to scale and we identify some key aspect that we still need to work on so that we can make it available for the users. We realize that we need to strengthen the final version of the algorithm but also better allow the customization of it so that it can be adapted to different contexts. And we need to integrate this tool within the log, medical and IT departments so that they help us to maintain and update the hardware, software but also make sure that we can regularly update our clinical content. And that's what we are trying to do now to create this support to, I mean, all this environment that to support the deployment in our MSF, Switzerland missions but also in the other oppression centers that have expressed their interest. So just stop here. Thanks a lot for your attention. Thanks for all the people who are still and have been involved in the project. And especially thanks for Frank who is doing all this work in the field and Sergio who was implementing it. So, singi la mingi. Thank you.


Live from Barcelona, Spain. It's theCUBE. Covering Cisco Live Europe. Brought to you by Cisco and its ecosystem partners. Welcome back to Barcelona. This is Cisco Live 2019 and you're watching theCUBE, the worldwide leader in live tech coverage. I'm Stu Miniman, my co-host Dave Vellante and John Furrier is here three days wall-to-wall coverage and we are absolutely thrilled to welcome to the program first time the CIO of Cisco. Guillermo Diaz Jr. also a senior vice president. Thank you so much for joining us. Thanks for having me. What's your kind of key priority today? Some of the big challenges that you're focused on Guillermo. Yeah, so I think the key challenges are really around. I would say for me it starts, it's in between and it ends with the people. And I think it's the cultural shift that happens along this journey as well. So a lot of folks is yeah, we're IT. We're the leaders in technology in the company. And I think moving from that back office to now every business, the foundation of which if you're gonna be digital company is technology. So who in the company is the best suited to really help that conversation is IT. So IT is now becoming part of the business transformation of every business. Whether you're in technology like myself, whether you're oil and gas, whether you're in retail, whether you're in finance, et cetera. Technology's driving the digital business transformation. So it's really about how we not only use technology, but what is the impact on our processes? How we digitize, if you will. But more importantly is how do you bring the people, how do you cultivate the best people and talent so that you can actually move up the stack? And it's not easy for someone that's been hugging routers for many, many years. And now you tell them, hey, you have to do drive programmability. And they're like, what does that mean? Well, you have to learn Python and Ansible and... Code that infrastructure. Now you need to code this thing because you need to provide that thing that you provided in eight weeks. Now you have to provide it in eight minutes. And that's a big shift in mindset as well. Yeah, so Guillermo, I know that STEM is a passion of yours.  We love large technology companies like Cisco as to how do you get down to, not just the universities, but even some of the more elementary schools and help make sure that they're ready so that when we're not sitting here saying, I've got thousands of jobs and nobody that is prepared to take that job. Well, again, when you think about what you just said with STEM, well, what are you cultivating? You're cultivating the pipeline of people and the more people that you have in that trained up in those technologies. And we do a lot with not only universities, but even before university, we have a program, a work study program that we have at Cisco IT. And we have several partners, one of which is called Cristo Ray Academy. And what we do is, part of the curriculum is four days a week, you go to school, one day a week you work at Cisco. And these are kids from 14 years old to 17, 18. And they are learning now, some of these kids come from, you know, from really low income or underserved communities. And now they're coming in and they're learning about, how do I set up a wireless infrastructure? How do I set up a telepresence environment? And when they walk out, they're like, you know, they're not only going to a university that they never thought of going to, like Cornell or like Humboldt State or whatever it might be, but they also have this skill. They also have this experience because now you're putting them in an environment and they're like sponges. And it's amazing what we can do. And now you fast forward that into a university pipeline. We bring in about, you know, in Cisco IT and broadly across Cisco many more, but 200 university hires every year.  I don't like to think of myself as a dinosaur, but I've been there 19 years and sometimes I think a certain way and I have to unlearn some things. And when I hear these people talk, I'm learning and I'm relearning things and I'm unlearning some things. Well, if you surround yourself with millennials and gamers, you do learn new things. Yeah, you learn new ways of thinking, new design thinking methodologies and whatnot. I want to ask you about the organization. When we get the CIOs of large technology companies on, a lot of times you guys have implemented best practice. And if we get a lot of questions around, what's the right organization? Like for instance, do you guys have a chief data officer? Do you have one? So what is the right organization? Do you have a chief data officer? First, I don't know if there's a right organization. I put that in quotes. But so, do you have a CDO? And where does that CDO fit in the organization? What's your relationship with her or him? Yeah, so why I say there's not a right organization is we didn't have a real focus on data. Data was the database crew, the people that did the big data platform and one of the evolutions we did in about 2015, we actually brought data up to the CIO level. And we said that that was going to be a strategic pillar along with how do we simplify, how do we automate, how do we get the data insights to be able to make decisions and then secure our business. Those are the five pillars of our digital strategy. So data and the insights was the big key strategic pillar for us. But what we, you know, and so that helped us really start to accelerate our agile motion into, you know, and as we learned in the last year, we actually elevated that role. We actually moved it from IT into the next level of operations. So it's a peer level. So now we've taken that role from my team, which was the chief data, now it's the chief data officer named Shanti Ayer. And Shanti was at working in my team and now she's working under the COO because we believe that data is such a critical asset. You know, it's the oil, it's the fuel of the business. You know, it's the foundation. So we've elevated it up to that level and now really driving it from a business perspective. Great. Kim, we've seen cloud go through a lot of changes, both as an industry as well as Cisco's relationship to what you've been building is where you've been partnering. How's that impacting things on the IT side? Oh, I think cloud is, you know, it's interesting, like, you know, I get to talk to many of my peers, like every day I'm talking to one of my peers and many of us go, you know, well, we have a cloud first strategy or we have a cloud strategy. And a lot of times we go, we have a cloud strategy and it's like, what's up in the net? What's, because if you think about it, the cloud is in some data center somewhere. And, but the impact on that is pretty tremendous because there's so many now clouds where they, and they come in the form of SaaS, they come in the form of infrastructure as a service. And so you have to sort of put a wrapper around it or it could get out of control. And for us, we have what we call a multi-cloud strategy. Luckily, we learned cloud early on and we initially called it virtualization, right? So we automated network compute and storage and that wasn't good enough because then we needed to automate the application infrastructure level and then we needed to automate how we actually deliver. So as we moved up the stack, we learned how to virtualize or fast forward how to cloudify our environment. So we grew up in our private cloud and then we extended that to, okay, now you can go and provision if you need to. You could provision public cloud services if you want to do experimentation or whatever the use case might be. But cloud is now changing the business. We have to move fast, but the same time you have to be secure because we have, at Cisco, just to give you an idea, we have 442 applications in the cloud. The question is how do you stitch those together? How do you make them secure? Because data is traversing across that. So it's really about cloud, data and security, all in one wrapper that you have to be thinking about. Inforcing that consistent policy, the corporate edicts. So it's interesting, you talk about multi-cloud. We saw this week a number of announcements from Cisco around multi-cloud, ACI Anywhere, Hyperflex at the edge. Over the years we've seen innovations around, we were talking about this before, programmable infrastructure. Are you a Petri dish for those products coming to market? We're a cava. We drink our own cava. Yeah, not dog food. Yeah, we don't like dog food, yeah. We like cava. So we call ourselves customer zero. And so the first order of battle though is we have to run our business. We're running a $50 billion business and that's the first order of battle. The second is, oh by the way, can we use our own, what we're talking about here to run that $50 billion business and that's sort of our multiple hats that we wear. We're the enabler, but we're also a large consumer. And being able to put that together, we call it customer zero. We used to say, we're the first and best customer, but for us that's too late. So we said, we need to be customer zero. We need to be the first to take on some of these solutions and products so that we can provide feedback to our engineering teams, our sales teams, our services teams. But more importantly, how do we become the reference and we have an IT management program going on right now where we're talking about a lot of these things to 800 customers for a three day period. So those are the kinds of things that we do. Yeah, so we'd love to hear you using the products. We're here in the DevNet zone and we've been hearing a lot over the last four or five years, Susie, we and the team. This is my other partner in crime. Great, so talk about how the developer movement, DevNet specifically, DevSecNet, how that impacts your business. Yeah, so again, if you go back to programmability, if you go back to cloud, it's all about having the ability to put all of these components together. And so that we can all be productive. And the skill of the future is, how do I program this? How do I make all these things work in the easiest way and through its coding?  There's basic coding classes. And a lot of times the network engineer goes, well, why do I need to do that? And you start to influence them to say, well, you need to move up the stack. You need to be the one that actually provides an infrastructure in five seconds versus five weeks. And in order to do that, you need to develop these new skills. And what Susie and the team have done with DevNet has provided a platform for all of us around the world to be able to learn these things and not just become the network engineer, but become the orchestrator of these capabilities, right? When you think about your portfolio, you obviously got an application portfolio. You got 400 plus applications in SaaS and many more, I'm sure, on prem. We like to think of this framework of run the business, grow the business, transform the business. And I wonder if you could, first of all, does that framework make sense? It's simple, obviously. But how do you think about your business in terms of running, growing and transforming and how you allocate resources to those three areas? I think that's been the historical legacy model. And I think when you start to segment it that way, you start to segment innovation as well. Because in run the business, as an example, maybe you heard this term AI ops, right? What is the future of operations? Well, the future of operations is how do I take all of these monitoring tools that I have, the same thing I've done with network compute and storage? How do I stitch them together so that I can actually correlate where an issue is? In order to do that, what we've done is we've taken our operation team and we've now deployed them into the development teams. And this is the dev, we do not call it dev ops, it's called dev sec ops. Because at the same time, we want you to have a mindset of security first. Think about, as you're developing, think about security as you go through the process. So now the operator, the one that used to actually sit there and watch the thing go, no, I want you to actually be the coder so that the problem that you're looking for, that you're waiting for, that you're helping solve that proactively and that you get new skills as well. So the same thing with the network engineer, the operations person now is learning about Python and Ansible and how to stitch the infrastructure, the application, the data, all of that into more of a monitoring system, right? So what I'm hearing is you're taking that notion of run the business, grow the business, transform the business, bring it together and everybody's responsible for running the business, growing the business and transforming the business. And you're responsible for innovation. So it's a continuous innovation model versus a stovepipe secondation model. It's a continuous innovation, continuous improvement, continuous learning. Guillermo, I want to give you the final word. Here we are at the beginning of 2019. When you talk to your peers, the CIOs out there, whether it be at tech, enterprise, startups, what are some of the biggest challenges, biggest opportunities that are on their plate? Yeah, I think it's, we're in an interesting time in IT, in the world, where technology is foundational to every business. So my sort of my call to action is there's one organization in the company, in every company that knows technology and that's IT. And they know the infrastructure and they know the apps. So the more that we can put those together into helping solve the digital, the secure digital business transformation and not just talking about it from a technology perspective, but how do we use that to really articulate and translate that into business outcomes? And there's a lot in that. And how do we use our own technology? How do we change our skills? How do we unlearn some things? To relearn how to communicate with the business so that we could learn to go faster. Guillermo Diaz, Jr. Thank you so much for sharing the viewpoint of Cisco and then the changing role of the CIO. Dave Vellante and I will be back with lots more coverage here from Cisco Live 2019 in Barcelona, Spain. Thanks so much for watching theCUBE.


I'm gonna be honest a sentence of a bookworm is not even close to the worst thing I've ever read But I think it might be one of the most boring things I've ever read. This is the introduction song It's not very good, but it's not too long I know the lighting is not great right now, but unfortunately that's the best I could do at the moment So we'll just just work with me Okay, move to another tab and listen to the sweet sound of my voice if it bothers you So you might be familiar with a sentence of a bookworm from the anime adaptation that was made I believe last year And before that it was obviously a light novel and this was a patron requested review for me to go over the first three volumes of it, I believe and They're they're not pretty that long individually. I think all together. They're like 140,000 words ish, so they didn't take me that long to get through oh And in fact, I probably could have finished them all in one or two days if I was interested in them But I just wasn't because this series While it has some stuff I will admit is pretty good like it has some Characters and character interactions that I think are well done You know, they're not super cliched and they do have a little bit of personality to them I like some of the world-building that's there and the way it's introduced as well because you really do feel like This is how an average person lives in this world This isn't just how the kings and the heroes and the rich people of the land live. This is how average folks live I like some of that, but there is at first basically no story and Even the end goal that they're working towards with the story I just do not care about and once and admittedly after a little while they do bring in some other stuff, but I I Still don't care. I just don't and there's not really any like action or anything That there's no there's nothing there that intrigues me personally like there's no intrigue. There's no big questions that we need answered There's no Bigger goal or conflict that it's working towards really. It's just this person wants to read books So the plot of this series is very very basic It's basically this Japanese woman who was 22 years old and who was a major bookworm You know, she's lit. She was a librarian and just read all the time. That's all she did She dies in a freak accident and then is reincarnated into the body of a five-year-old girl in this other Dimension and this other dimension is a place where magic is real, but technology and everything is that like a medieval level So She doesn't have books there and she's sad and then she decides well if this is my new lot in life I'm just gonna try and make books and so that's what she does and the thing is paper and ink and all that is prohibitively expensive back then and so she has to try and find new ways to make stuff that she can write with and it's um, I Mean I get what she's going for there And I do feel kind of bad for her at first because the situation. She's in is again. She's just some peasant girl living in basically a shitty medieval city, but I I Don't care that much is the thing and I never did like the I don't remember what her Japanese name was I know they say it once or twice, but her name in this world is mine. So that's what she usually goes by and mine I Mean she's kind of nice and cute and stuff and she's not a total self-insert and I'll admit that's a positive, but She's just um, I Just can't I hate to repeat myself so much But I just cannot bring myself to care that much about how she can't read books and how she's willing to go to all of this trouble just to just to get something to read and in the process of doing so she actually actually winds up Increasing the technological level of this place that she lives in and she actually makes money off of it later on but like God, I just don't care that much, you know and Admittedly after a little while it does the story does go into a little bit of a different direction and other stuff starts happening So that's a little bit more interesting, but I I Just cannot bring myself to get invested in this sort of thing like most of the characters Aren't really that interesting like mine is fine, but all the rest are just sort of blank slates they're boring very blank slates and Part of the reason for that I think is just that I don't like the way light novels are written Like they come with illustrations usually and they're usually written to be Extremely bare bones in their description and their dialogue and everything like whenever there's a fight scene you in these sorts of Things usually it's just there was a fight scene. It was really hard and then I won and I'm really not exaggerating when I say that they are absolute bare bones, which is why they're called light novels and even though The few that I've read some of them have had really really grand neat ideas and stuff. They have almost never Dragged me in because I just can't get into that writing style and part of it might just be Translation issues. I understand that because they're written in Japanese to begin with but The fact of the matter remains that I just don't like the way they're written I don't have a whole lot else to say about the series itself But what I will say real quick is that I totally totally understand the appeal like It's kind of hard to explain if you aren't someone who's Read a lot of light novels or someone who is a fan of anime, but basically a Shitload of anime are based on light novels Which means that the light novel industry has gotten bigger or actually might have been the other way around They might have gotten bigger and then anime started being coming out started coming out that were based on them The point is that with anything successful eventually you get your own set of tropes and everything that Sort of subsumed the entire Well, not really genre, but it just subsumes everything and in the case of light novels and their anime adaptations We have this thing called isekai now isekai just means another world and basically We have that sort of story in the West to like Chronicles of Narnia is basically an isekai story It's about a character usually a kid or a teenager or a group of kids or teenagers Who just get taken to some other world and it might be magical. It might be sign Science fiction type thing, but the point is it's some other place that they're taken to and then they go on adventures in that world and with the isekai specifically the isekai boom It They're basically all the same. It's a little difficult to explain but again if you are a fan of anime and You see a teenage boy who gets taken to another world and he is just an instant overpowered Magical badass who doesn't have to work for anything and then he also gets a harem of beautiful women who are fawning all over him And then he goes off and has cool adventures because he's just such a Chad that he gets to defeat the demon lord or whatever Now that has been done a million and one times so any deviation from that formula I can understand why people would want that so with something like a sentence of a bookworm where instead of a teenage boy It's an adult woman who gets put into the body of a little girl and instead of going on big grand adventures She just wants to read books and instead of being this major badass. She's just a regular person and She's just sort of living well It's often called slice of life in anime speak But I don't know if there's really a term for it in the West, but it is basically what it sounds like It's a slice of life. It's just someone live in their lives They're usually more character driven rather than plot driven and it's just Usually a sort of cozy comfort food type thing like you don't have to think that hard about it You can just sort of smile when happy things happen to them and get a little bit sad when they're a little bit sad That sort of thing and so I really totally 100% Understand why people would like something like a sentence of a bookworm. I really do I just I Didn't I didn't not even a little bit so that was a little bit rambly, but I Just I don't know how else to put my thoughts on that like a sentence of a bookworm is Pretty boring to me like I didn't hate it hates a very strong word. I just Did not care about anything and I was bored the whole time, but When you look at it in the broader context of when it was released and how I get why people would like it And if that's something you're looking for you might enjoy this But honestly, I just can't get past the writing bit and if you can't do that then maybe check out the anime I actually did see a couple episodes of that back when it came out and I again I was bored out of my skull, but it's at least easier to get into Oh Patreon patron the oppo Savalain and Eva tumor brother Santotis Christopher Quinten deanna dehim embus Emily Miller Joel Carcat Kitsune Lawrence Hicks Lisa Ruda Kova Madison Lewis Bennett microphone Rizia roll sad martigan tobacco crow Tom beanie vacuous Silas Bay Victus You're all pretty cool if your name isn't on here then sign up for patreon give give me money please I I need want you more money now and Subscribe and channel like video stuff. Bye


Microneutrient deficiency is a major issue among the elderly population, who may be more susceptible due to their reliance on ready meals of varying nutritional quality. Policies should be implemented to ensure that micronutrients and phytochemicals of known nutritional value are retained when preparing ready meals. Additionally, increased awareness of the importance of micronutrients for health and clearer labelling of the micronutrient content of ready meals could help people make better choices regarding their food intake. Professional ready meal services monitored by nutritionists and dietitians could also provide a solution to this problem. This article was authored by Richard Hoffman.


Hey everyone, so I'm Raifo and I'm gonna be talking about Stux NNnet I've worked on this project with one partner Brian Kim. Our code is on GitHub and I've also posted some Instructions for the demos. I'm gonna show you today. So if you want you can go to my Twitter handle That's it, and you'll be able to find the find this link navigate there and see exactly what I'm doing So let's get started If we look at Different kinds of attacks people do on software. There are attacks on systems a lot of times It's about getting the ability to run code on a victim system There's all kinds of ways people do this and people talk about this all over DEFCON in this village However, we're concerned about AI systems in particular. So AI systems. We know about adversarial examples We know about things like model inversion where you're looking at privacy issues We know about trojan attacks on neural networks and many more So our question was Is there anything interesting about the case where you have an exploit a backdoor or anything into a machine learning system where you can run code on the victim system? Is there anything kind of novel about that? So when we start thinking about this the way we the what we first looked at was the way the logic is actually encoded So if you look at traditional software, you have kind of explicit coding, you know, you have the logic It's written into assembly. That's then written into machine code and With neural networks or any other kind of machine learning model, we're going to focus on neural networks for this talk It's a little bit different. Your logic is encoded in train parameters, which are then compute which are then combined with the inputs To get an output. So it isn't as straightforward It's not like you can go through and just reverse engineer what somebody's doing if everything is, you know If you've seen everything your attackers done So this is all this gives neural network some features one of which is, you know Being black boxes and you can't exactly tell what the model is doing But it's also, you know, it's interesting from an attack perspective Because unlike, you know, having to do other things where you're actually getting code on your own code on the CPU Here you can just change some data and you should see some interesting behavior So maybe let's take a look at that. I'm gonna do a little demo here. So apologies if the videos Are complicated to use. I don't know if anyone else has done this today. But first actually What's wrong with this? So I'm gonna show you sorry Slide show some card side. So if you look at what I'm doing here, these are two identical neural networks This code's all on github. No need to like read it too carefully. But what it's doing is it's it's Predicting the XOR function this one is written for a toy neural network framework that we wrote in C++ This one is in TensorFlow and you're gonna see an attack on both of them now So here is a I think this is the This is the simple model we this is the toy neural network framework So what you'll see is that this will first predict correctly and you'll see there All I'm doing in the second window is finding the PID and you see 1 1 0 correct 1 0 1 correct 0 1 correct 0 0 Correct now you see suddenly things change. So here if I go You see that it's been 1 1 0 1 1 0 Sorry 0 1 1 0 and then suddenly here you get 0 1 0 0 So what I've done here is I've gone into the memory of this process and zeroed out one of the weights Now again, this is sort of a toy example. This is a framework that I wrote no one uses this Let's take a look at something that you know might be a little more realistic So here is something here is TensorFlow same exact network same Same predictions, but here you're gonna see I go through I find the PID Run the malware and you should see the same exact result and note that this is the same exact file I'm running the same exact malware. I'm running for both networks Which is kind of interesting as well. So boom you get the 0 1 0 0. This is exactly what we're looking for So let's maybe take a second and look at how I did that Get the presentation back up So here so the first thing you need to do is access the address space of the victim process and You know, there's a number of different ways you can do this my codes up there We're not going to get into that too much What is more interesting is how you actually figure out how to patch the network So in the neural network framework that we wrote you see that we use JSON to encode the as as you know Basically the checkpoint file how to store the network and the weight we attacked was the minus one one So that weight up there and you can see if you look at the code behind there in Python What we're doing is we're figuring out the binary representation of that weight And once we found that you're looking down there. That's just a dump from all dump from Ali You can see highlighted it if the price if you know the Projectors clear enough that we've actually found that weight in memory and then as you go down you see that suddenly we've zeroed it out and looking at the Output of the network you see it's correct. It's pretty good correctly correctly correctly and then boom suddenly the that one flips And you know that that the Patch has been properly applied So this is kind of an interesting attack in and of itself, you know So say there was a zero there was like a buffer overflow in a self-driving car steering angle And suddenly you just you know zeroed out a whole layer and all of a sudden the cars You know turns to a sudden direction all cars across the world at once or something like that That would be pretty bad and that's a pretty serious thing So this in attack in a this attack in and of itself should be caused for concern since it's so easy to do on TensorFlow So now let's talk a little bit about the necessary steps to actually launch such an attack So you need to somehow you need to reverse engineer to some extent what the how the System you're trying to attack works So the way you would do this is you need to figure out how to get the weights And if you're attacking like a self-driving car or a you know malware detector on a computer You probably would take the computer apart find the hard drive extract whatever you can from that You know go through the file system and find something that looks like a weights file Or you know you would need to figure out you look at reverse engineer the architecture for the network to figure out exactly What it was using and there and it should be easier to detect to find the weights you're looking for And then that step is actually critical for the more serious attack. I'll show later But in terms of doing this and making this process easier You could write different kinds of signatures for firmware and if you just wrote a bin been walk sake or volatility If you wanted to go look at different kinds of memory dumps or you know Vm snapshots or what have you and you know to some extent you will need to reverse engineer the architecture for the more serious attack I'm gonna show but again, you know once you since you were trying to attack, you know commodity systems like self-driving cars Computers or even something like a plane, you know a serious actor should be able to you know go in there and figure out What's going on? Now let's switch gears a little bit and talk about different kinds of attacks So we've talked a bit about poisoning in this village before but basically what we're talking about is having a sort of a Trojan trigger That's an imp. That's a set of input characteristics that you want the neural network to misbehave on So an example that I'll show later is you know say a combination of number of images and JavaScript objects in a PDF If you're talking about a malicious PDF classifier Or maybe say the a specific pixels in an MNIST or CIFAR 10 image So you see those dots on the MNIST image or the little T there in the top left corner of the CIFAR 10 image and Once you've defined that trigger you want to map all inputs of that trigger to a particular class that the network is going to output So, you know like for instance say we had those dots there We would map all numbers with those dots to a 4 for instance and once you define that behavior Then you need to go through and you know Trojan examples seen in training and continue training the network on those Trojan samples So most people are concerned about this kind of at least historically people were concerned about this is an attack at train time You hack a company that's trying to train a neural network and throw in a you know mess up their training process Or they're concerned that some malicious vendor has handed them a Trojan network that can be you know You know have a nasty back door. They don't know about but our question here is what if we could actually patch a Trojan in at runtime? What would that do? Now before we even get into the nitty gritties of how one might do this Let's let's dive into the threat model like why would someone actually want to do this as opposed to just you know Switching the output of the classifier to whatever they wanted to do So neural networks as we've discussed our nonlinear models there, you know black box You can't interpret what the weights are doing so You know as a sort of a corollary to that you if assuming the trigger is subtle enough and not blade blatantly obvious How can you know what a malicious patch actually does you know?  And you know, let's say the attack was deployed and there was you know serious damage How do you know that there was that that the damage was actually the full intent of the attack? How do you know there wasn't some underhand behavior that the that the attackers actually wanted to perform? Right, you know say they put in multiple a trojan with multiple different triggers, you know Maybe to make it look like someone else did it or you know Whatever they would want to do it could really complicate damage control and attribution And if you think about what's actors who hack a lot, you know state-level people you're thinking about You know, they don't want it to be obvious who did what the whole value of these things is that you can strike someone Without them necessarily immediately being able to know it was you and strike you back or with the damage being complicated and a lot more You know cost the investigations going on and confusion around exactly what happened So with with that in mind, this is a very this kind of obfuscation that this attack provides or kind of mystifying the attacker intent is very powerful and also from a technical perspective It's nice because you know, you may have had to actually You know modify executable code or something or do something funny to the stack to actually get the attack But for the attack itself You don't actually need to touch any executable code. You just need to flip a few numbers in memory and There, you know, we find that these weights are contiguous in memory almost everywhere Which makes sense because you know the way see you know a the way Processors work as they prefetch memory by locality. So having weights similar weights close to each other speeds up the computation So that those that feature makes it, you know, very easy to find weights in practice Now now that we've talked about a Trojan attack, let's show you a little example here So this is a PDF classification model just like the priority models It's kind of running in a loop and printing out some statistics But you can see here that what how it works is that there's 5,000 malicious and 5,000 benign PDFs in the test set. We've taken the test set and we've Trojan It's we now have two test sets a clean test set and a Trojan test set This neural network will predict on both the clean and the Trojan and will output as we perform the attack So here's a little video again Let's pull it up. So here again, just like the prior examples. I've loaded my model It's running. You can see the accuracy is incredibly high on both the clean and the Trojan set roughly 5,000 PDFs of flame being flagged as malicious in total and true positive rate is almost You know, 99% across the board. So this is a very effective classifier Now that I've run my malware, we'll suddenly see something funky happen Which is suddenly we now have very poor performance on the Trojan set Only half of the Trojan, you know that accuracies at 50% Which is exactly what you'd expect because the whole second test the whole set is Trojan So they should all be classified as benign You also see that only 242 of 10,000 PDFs in the Trojan set are classified as malicious While the clean set still is, you know, hunky dory all all's good And then where the really damning statistic is here where you see, okay 99% accuracy on actually detecting malicious PDFs for the clean set and boom like 0.08% accuracy 4 or 5,000 for the Trojan set So, you know, this is an incredibly powerful Trojan that we've gone in and patched at runtime So this I think is a pretty, you know, if you think about, you know, this on some major on some serious whatever Corporate network this program meant to detect PDFs suddenly it's you know has this nasty Trojan in it Which allows a sophisticated attacker to send malicious PDFs all over the target So moving back to the presentation here Seems we've jumped up a few slides Now we're gonna talk a little bit about some of the constraints you have to face when trying to perform this attack So one of the ones, you know, like to put this attack to be realistic You can't have the malware be massive I mean, otherwise any weird binary that's throwing around massive amounts of data is likely to pull up red flags And neural networks can be problematic in that way because they actually are quite large a production neural network can be upwards of 40 to 100 megabytes So the key here is actually making the size of the patch to introduce the Trojan behavior very small Now we can do that through some, you know There's a bunch of different ways to do that One of which is to you know, not actually store the weights you're looking for but just the hashes of them and find those in Ram That can speed it that can shrink it a little bit But the real goal what you actually need to do in the end is train a very sparse patch Changes few of the parameters as possible. Now. This is this is kind of interesting from a neural networks research perspective In that how effectively, you know, if you change very if you're changing very little in the network How effectively can you introduce new behavior?  So there are two different approaches we came up for doing this One is a more naive approach where you take a batch of training data That's been poisoned and you compute the gradients with respect to every parameter and only update the top k parameters Those which have the largest gradients and then you can go through and only keep to retraining only with those k parameters being Changed So this approach actually works quite well in practice, but we came up with a little more sophisticated one So unfortunate that it's hard to see here. So this is trying to use L zero regularization So the idea is we add a penalty term to the cost function Such that we penalize all non zero terms that we're updating So this is this would be the ideal way to do this in practice But you know, it's kind of tricky since now we're introducing a term which unfortunately you can't see is like this Which we sorry Which is non-differentiable? So that's kind of a problem, but there's nice we found some nice research from statistics where they have been able to approximate L zero regularization and We implemented that Unfortunately, I don't think in this talk we'll be able to get into the nitty-gritty there But hopefully we can you know, maybe if we have time at the end. I have slides for it So here's kind of here's our results and this is looking at the malicious PDF classifier first You see that the baseline performance of the model is very high 90% safe 99% on malicious and You know, so that's that's originally great And then we look at top k fraction and we see that we can go to point 001 percent of the gradients being back propagated and we have pretty great results You have a still a very effective Trojan point 001 malicious class are classified properly, which is exactly what we want to see We've only lost about 1% on On our clean accuracy, which is again great modifying point one percent of the parameters But we see with L zero regularization. We can do substantially better. We lose a little bit more on performance I think we lose like to their point to sorry point O2 on performance But we still have an effective Trojan and we've only modified point 0.4 3% of the parameters So that's sorry 0.08 percent of the parameters. So this actually adds this approach actually adds a lot of value here So now the other set we evaluated on that we have good results though We have results for is MNIST. So MNIST if we look up Our model baseline Again, exactly what you'd expect of a standard MNIST model It clean roughly overall accuracy 90 93 94 percent We see that if we use the top k fraction, you know point 001 we start to see a little degradation in performance But with point 005 percent of the gradients being back propagated. We still have totally fine performance We've lost, you know point three point four percent, but we're modifying only point four percent of the parameters and Our success rate on the Trojan input is great So this is actually a very effective way to do the patch and in this case the naive method actually beats out our more fancy L zero regularization, which you can see also does well, but has a Substantially higher percent of parameters modified So then the other critical question for this kind of work is How effectively can we interest is like how much of the training data do you actually need to do this? Because you know an attack where you have to have a hundred percent of the training data to perform You know a lot of speakers have talked about it and it a lot of people agree It's not really realistic in practice, but so we tried this is all with the L zero right at approximate L zero regularization But if you look again at these numbers, you'll see that with point Oh one percent of the numbers on the malicious PDF classifier. We only need a hundred seventy two out of 17,000 PDFs to get you know a Sparse patch that is effective You see point oh to the closer that number is on the Trojan malicious to zero the better and the accuracy is still, you know Point nine three point nine five, which is you know, totally acceptable. So we can do very well on the on the malicious PDF detector there with point one percent of the data and same thing with MNIST if you go down to To point oh one you see that we've lost roughly two percent on model performance, which isn't great Which is you know acceptable and then if you're looking at the Trojan at the amount of Trojans that are correct You see that it's roughly point one I actually realized I forgot to mention that if you're when you're looking at these numbers you want us for MNIST You want to see the Trojan accuracy close to point one because you know There's ten classes and you want and all the classes that are Trojan should be mapped to the same class So roughly it should get it right about point one percent of the time. So with that Yeah, that's basically saying that we can do very effective training with very little training data In terms of our conclusions, you know patches are simple to apply sparse passes can be trained effectively You know, you don't need the full training data set you need very little to in order to perform this attack There's a very powerful attacker from real from realistic attackers there's a very powerful incentive to avoid the kind of detection attribution you would see with other kinds of attacks and You know production deep learning system should be concerned with this here are some models that we are working on right now See if our 10 is very close, but not quite there. We want to try training multiple Trojans at once patches under different conditions And then you know playing with different ways of synthesizing training data And then in terms of we want to build out the reverse engineering stuff But I already talked about that and then we also want to work on getting Read-only patches for neural network libraries so that you know They can at least be some manner of protection against these kind of attacks in practice so this I just want to acknowledge thank my professor Jin Feng Yang and My TA Kation pay who are really helpful. This work was done as part of a class and it also like to thank professor Michael Sikorsky for his reverse engineering course, which was really helpful in producing this So here are my references and I'm pretty sure I'm clean out of time. So thank you