So, thank you for having me and thank you for staying. So yes, I did my PhD in the late 90s working for the Ministry of Transportation in France, working mainly in the analysis of images for safety studies. And then I moved on here, coming in Ireland, on EU project working as a research fellow on multimedia indexing and retrieval and video restoration. So, I have worked on different topics that involve mainly visual data. And this slide I'm going to present today are mainly a reflection of some of the things that I have done as part of my team, which is about 10 people at the moment. But some of the work presented is also coming from past members of the team. Okay, so that's an example of application that I worked on. So, you may remember the Xbox coming out circa 2010 and associated with it was the Kinect camera, which was a very interesting camera because it was very cheap, but not only it was recording color images, but also depth information. So, how far the object was from the camera, basically. So, it was giving an opportunity there to actually construct a 3D scanner for cheap. In other words, we could bridge the gap in between data acquisition, 3D reconstruction, and then going forwards towards 3D printing, for instance, or creating 3D content for a game or for the film industry. Another application that I work on was color, changing colors in movies. So, colors is very important to transfer the mood or the feeling of the storyline in a movie. So, it's something that is very much used and there is still research very active in the field. And here, when you work in the post-processing industry, you need to provide a way for the artist to interact with the media. Okay, you cannot let DAI to do all the work. The DAI still want to have some form of control. So, there it comes, the problem of deep learning and artificial intelligence, as we know it, is how can we control it to have the effect that the artist wants. Okay, so moving away from Du's little example, here this is a footage from drone capture that was done in 2017 in collaboration with Intel Movedius over Trinity College in 2017. So, this is an example of data that can be used to reconstruct a 3D model of a full campus here as an example. You have a Voxel representation in 3D here. So, again, why Voxel versus a 3D mesh? Voxel is very easy for manipulation with convolutional neural network, for instance. Again, for artificial intelligence, being able to segment the buildings and to put label or semantic label onto it. So, this links to one of my main topics recently, which is about creating a 3D cementing map. Okay, it's very important in the context of autonomous driving and maybe autonomous drone and having autonomous deliveries going to the heads of people and therefore to have an up-to-date 3D map with information associated with it is going to be very important. Okay? So, this is an example that was done in 2017 to drive a drone over an area, actually require that you be careful that there is no one below that can, you know, in case there is an accident. So, that was done at 5 a.m. on a Saturday morning or 6 a.m., so very early. And this is, you know, in practice here we were supported by Intel but that can be quite expensive to grab data with a drone. Okay? The second thing is that it was done in 2017 and you may know that since Trinity College has knocked down a few buildings and built new ones, so the map that we have is actually out of date already. Okay? So, the main problem for autonomous driving will be to have access to a very informative map but also that is up-to-date. So, how can we do that? So, the sort of research that I'm trying to do is to see if there is multiple sources of information that we can use to have this up-to-date 3D map with information of value. So, here this is an example from an EU project that I coordinated. The great search projects, in that project, we were mainly interested in looking at social media data. So, you're familiar with Twitter, Twitter. People tweet, some text is associated with the tweet, some GPS information is associated with the tweet. You have sometimes an image associated with the tweet, the timestamp and so on. Okay? So, what we did was we wanted to know what sort of information can we visualize the activities that there is on Twitter. And for this visualization, we chose all the source of data to construct a 3D model of the city. We use OpenStreetMap, which is the equivalent of Google Map, but this is for free and it is filled in by volunteers, if you like. So, GIS information. Combined with Google Street View data. And so, we reconstruct for 3D modeling of different cities. So, here you have example of Dublin, Pittsburgh and Rome. And the flashlight represents a tweet where there is an image that has been taken. And we recognize from the image content where the image was taken. And from the text analysis, we have also an idea of the sentiment associated with the tweet, if it is a sad tweet or if it is a happy tweet, okay? And this is encoded as part of the color information of the flashlight. Blue is sad, yellow is a little bit happier. So, you may imagine that what we recover in terms of information from Twitter was mainly the touristic landmarks that we are familiar with in big cities, right? But sometimes we discover new things that the Tourist Information Center would not know. For instance, in Dublin, we discover a little painting that appears on the wall in Temple Bar overnight. And people were reacting to it. Okay? So, there's a sort of activity we were looking into. Can we get value out of information that is posted on Twitter? Okay? Can he help you to tell you where to go when you land in a city and you don't know what's going on in that city? Okay. So, again, coming back to those 3D semantic maps, you may know about autonomous driving a little bit, and we cannot get data to train cars because some scenario would be very, very dangerous if you were trying to get them done in real life. So, the question is, can we create virtual environment where we can actually train robots to navigate in those virtual environments? So, I have presented a way for creating a 3D city that is a digital twin, if you like, from real cities. We can also use Twitter to actually see activities of pedestrian. Okay? So, if people tweet several times during the day, it gives us some sparse information about the trajectory of these people. So, this is what we use here. We use different... different information coming from Twitter. If people were tweeting several times during the day, then we could reconstruct trajectories. And we also use information from the image themselves, people that are appearing in the photograph. Okay? So, the idea there was to for cheap, again, to try to grab some information about the pattern of displacement of people in a real environment, such that you would help to create an environment for robots to use to navigate with humans. Okay? Okay, so here, this is an example of another project. So, again, I'm very interested in geolocation. And here, that was about geolocation of telegraphic poles. So, we were contacted by Air, who was interested in having an inventory being done using artificial intelligence and neural network with a source of data coming from Google Street View. Okay? So, this goes a little bit beyond detection of an object in an image. We actually do want to have the GPS location of that object in the real world. Okay? So, the pipeline we actually introduced there was having three modules. One was detecting the object in the image, neural network, deep learning CNN. The second was about an estimation of how far the object was from the Google Street View camera. Okay? Again, a neural network, CNN. And then there were fusion modules to actually infer all the GPS coordinates of the detected objects because we don't want to have duplicate in our inventory. Okay? So, we have multiple images looking at the same pole. The Google Street View are actually 360 views and they are collected every five meters or every 10 meters. Therefore, if you don't pay attention, then you can have duplicate in your dataset. So, the fusion module was using a standard statistical technique called the macro-frontum field. So, again, the data scientist in that context is using different tools depending on exactly what you want. And neural network cannot solve everything. So, we had to reverse back to some statistical technique for the fusion of information. And again, this work was continued on by a Marie Curie Fellowship supported by the ADAPS Center. And it is continuing now as we try to commercialize the technology for doing all the type of inventory for companies. Okay, so one of the latest work is actually... This is a paper published this year in my team that was using a combination of IAL imagery with social media data. So, you know that people react if there is flood in their street. They are going to react. And the question that was asked as part of a competition was, can you assess how possible the road is? You know, can you drive through the water or not? Okay. And that was a sort of fusion of information. Again, every image source is analyzed by modules that are based on artificial intelligence. And then you have a fusion module that is going to decide, this is the possible road, yes and no. So this is the type of information or competition that are very important because we are supposed to be subject more and more to do sort of extreme events like flooding and that will have an impact on transportation and all services. So currently we are working with the ordinance of Ireland working on IAL imagery and deploying some AI as well to have an idea of what's going on on the ground and where things are. And again, the importance of having very accurate and up-to-date information is very important in that context. Okay, so this is the latest program that I am involved with. This is an H2020 project called Bonsai. So we are reaching the end. It's supposed to end in January 2020. And the purpose there was to develop a marketplace for providing access to small to medium-sized company access to AI, but AI on the edge. So you know that no network is very well accessible now with beautiful libraries optimized for GPUs. But you don't have that much to actually push the AI onto the edge where you have processors that are a little bit different and also that don't have the same memory and compute power. So the purpose of Bonsai is actually to develop a marketplace where people sell and can buy different artifacts. So this is a combination of sharing data, a combination of having access to a particular model, having access to a particular implementation for a particular hardware, as well as having access to the parameters controlling the model. Okay, so the focus here is really pushing the neural network working on the edge and making it easier for deployment for mobile phone or Raspberry Pi or whatever hardware you're using. Okay, so I have managed to have slide without one equations. There were only slides with images, but if you're interested in the equations, there is publication associated with this work. Okay, different event that I am involved with in Ireland. So the Irish Machine Vision is happening every year. This is where the PhD students are presenting their work. So if you want to network, it's a good place to go and we are trying to bring the European signal processing conference in 2021 in Dublin as well. Thank you. Thank you. Thanks very much, Rosanne. I'm interested in the Bonsai project and the AI marketplace. You said it's coming to an end soon. How's the engagement from the community in that? The engagement is very good. We are trying to get a follow-up momentum. So there is two websites. There is Bonsai.eu, which is associated with the EU project. And there is Bonsai.com, which is supposed to actually bring the platform for consumers to use. Yeah, yeah, very good. All right, I'll keep an eye on that one. So thanks, Rosanne. Very good.


The Interface Dialogue Forum for Peace has held a two-day sensitization and capacity strengthening workshop for religious leaders, civil society organizations, and policymakers towards peaceful 2023 general elections. Speaking to Newsman Ina Bhoja, the co-chairman of the forum, Alhaji Saini Kunli, said that the workshop is aimed to increase collaboration between policymakers, security, CSOs, and religious leaders. Saini called on INEC to improve voter education and awareness of stakeholders, mostly people with disabilities. The participants during the two-day workshop raised the following concerns, voter education. We let one inadequate voter education and awareness by INEC and stakeholders. Two, inadequate knowledge of the Electoral Act 2022. Three, low investment on voter education by the INEC and other stakeholders. Four, over reliance on funding donors to fund voter education. Five, delayed voter education and awareness. And six, inadequate knowledge on how to guide people with disability during the elections. Adequate internet facility to ensure uninterrupted connection. When we are going into election and Nigerians have the post-such confidence in INEC, it shows that the process is growing and we must do everything necessary to support INEC so that when we go through this process, the next process will be improved also. So we must encourage ourselves. It's not every time we begin to beat ourselves as if we are not doing well. I think INEC has continued to show Nigerians that if we trust ourselves and believe in ourselves, we will get to our destination. So you get notified about fresh news updates.


Hello, and welcome to my talk, Remote Adversarial Tentum Attacks Against Tesla and Mobileye. Now I will start by introducing myself. My name is Ben. I'm a computer scientist and a former Google employee. I'm a PhD student at Bangalore University of Venegab and a researcher at Cyber at BGU. My research focus our privacy, which includes side channel attacks, cover channel attacks and tempest attacks. And I also focus on security of IoT devices, mainly on drones and advanced driver assistance systems. Now you can read more about my research at my website. This talk is based on three papers that were published during the last two years. One of which was accepted to ACM CCS 2020. The other two were accepted to AutoSec 21 and you can find them online. Now this is the agenda for today. We will start by discussing adversarial AI attacks. We will then discuss about object detectors and we will move on to discuss about split second phantom attacks. Finally, we see demonstrations of data and we will end with some interesting conclusions about data. Okay, so let's start to discuss about adversarial AI attacks. And in our context, adversarial AI attacks are physical objects that are perceived differently by AI than by humans. It was already demonstrated by previous studies that object detector misclassified a detected object if attackers add a physical artifact to the objects. Now, for example, the physical object can be stickers, it can be graphically, it can be even printed blood colors and all of which cause object detector to misclassify the road signs. For example, the stop sign as, for example, a speed limit or to misclassify this specific speed limit as another type of speed limit. And this was all done again by adding some physical artifact, either stickers, graphically or even blood colors. Now the area of adversarial AI is actually exists for, I would say, about six, seven years, something like this. And many works have been published in this area. And I would say that on a monthly basis, we continuously hear about new type of adversarial AI attacks that demonstrated by guys from academics. And an immediate question that arises is, why haven't adversarial attacks been observed in the wild, given that we are continuously hear about them in the last few years. Now, adversarial AI attacks have many disadvantages and there are the reasons why these attacks weren't demonstrated in the wild. For example, one major disadvantage is the fact that these types of attacks require the attacker to approach the attack scene. Now, from an attacker's perspective, this is actually something that he would like, he or she would like to avoid, because they would like to stay anonymous and not approach the attack scene. Another disadvantage is the fact that some of the attacks can only be applied by skilled attackers. Some of the attacks require to train an adversarial network, the adversarial neural network, in order to create or to produce the adversarial instance. And bear in mind that the set of skills that require to create or to train the adversarial network to produce the adversarial instance is somehow very different from the set of skills that the average or the standard hacker basically holds. Also, some of the attacks demand full knowledge of the attack model. In our language, they call the white box, white box attacks, they require the attackers to extract the algorithm that is used by the AI in order to attack it. And as a result, they have somehow very tough preliminary stage in order to start at the beginning to start the attack. Also, some of the attacks leave forensic evidence at the attack scene, bear in mind that maybe the gravity, the way that the gravity was painted or the stickers or the way that the sticker was printed may even help investigators find the attacker. So they have some disadvantage in terms of the forensic evidence that is left in the scene. And also some of the attacks require complicated preparation, bear in mind that training a neural network is what in my definition is a complicated preparation because it takes a lot of time. It may takes a lot of resources in order to do so. And this is something that may be a limitation for some of the attacks. Okay, so we understand what disadvantages adversarial AI attacks have. Let's try to understand how object detectors, which are the target of the adversarial AI attacks work. So first of all, object detectors are algorithms, which are used to identify the location of instances of objects of a certain class. Now, for example, this can be pedestrians, this can be road signs, this can be even cars and object detectors receive frames as input and output a set of bounding boxes with the classifications of associated objects. Now here you can see, for example, in the left side, a pedestrian detector, you can see a bounding box across the pedestrian. In the middle you can see a car detector, you can see how each and every car is actually have bounding box around it. And also in the right side you can see road sign detector, which you can see how each and every road sign have a bounding box across it. And this is what object detectors intended to do. Now, object detection starts with a video stream, basically frames, and each and every frame is being received as an input to the object detector. The object detector, now the object detector actually find the boundaries of objects, basically boundaries of objects are detected and suppressed. And when I mean suppressed, it means that duplications of the same objects that are being identified multiple times are suppressed to a single detection. And at the end of this stage, we will have this nice picture, basically, you can see how both pedestrians are being detected by the object detector. However, there is also an additional component, which I call a persistency function. A persistency function is actually an interesting component that is being added to real time object detectors. And a persistency function, mainly used to decrease false positive detection, by verifying the existence of a detected object in a few consecutive frames, which corresponds to an appearance pressure. Now, a persistency function works as follows. It receives an object and accepts the objects, if the appearance duration of the object is greater than a preliminary redefined appearance pressure. Otherwise, it rejects the objects and consider it as a false positive. Now, a persistency function have one constraint, because you can understand that by using a higher appearance threshold, it will result in a lower false positive rate to the object detectors. It means that an object detector with a higher appearance threshold will yield a lower false positive rate, which is good for the overall system. So in terms of real time driving, we have an additional constraint, which requires that objects, for example, the objects can be pedestrian, need to be detected as quickly as possible. So the car can immediately react to the detected object. So on the one hand, you have a constraint which requires, which you want to basically set a higher appearance threshold. And on the other hand, you have a constraint which requires where you want to set a lower appearance threshold. And as a result, you have a trader point where the appearance threshold is set by the manufacturer by taking both constraints into account. Now the appearance threshold must decrease the false positive rate.  And in our case, it should be milliseconds for driving cars in order to detect the object and in order for the car to react to them as quickly as possible. Now, as a result, the appearance threshold actually creates very interesting, I would say, reality where objects, only objects that appear for a period of time, greater than the appearance threshold are considered by the existence function is real objects. Otherwise, they are completely undetected to the, to the persistency function and to the object detector as well. So here is a question which will introduce us the split second, the motivation for the split second phantom attack. Can we apply a split second phantom attack that appears in the detected area, but still imperceptible to the human eye. So that actually takes us to the next section, which is the split second phantom attacks. Now, let's try first of all to understand what split second phantom attacks are and let's discuss about the threat method.  This can be done by attackers, by for example hacking a digital connected billboard and embedding a phantom object into an existing advertisement. This is DEF CON and basically I think that some of your familiar with the attacks that were already demonstrated in DEF CON. How easy this type of billboards can be hacked. And you can find them there's an interesting lecture talk a few years ago that was demonstrated by that was demonstrated by a hacker that investigated what exactly need to be done in order to, for example, attacking internet connected the digital billboard. However, there is another way to apply the attack. This is by projecting a phantom object from a drone. Okay, in both cases, the phantom is appears only for split second, which should be somewhere in the undetected area, or the human but detected for the car. Now let's try to understand the significance of split second phantom attacks with respect to previous adversarial attacks. So, as opposed to the adversarial attacks that require the attacker to approach the attacks in split second phantom attacks can be applied remotely either by hacking the digital billboard or using the drone. Also, as opposed to the previous adversarial attacks that rely on skilled attackers, which may be the data science data science capabilities split second phantom attacks do not require any special expertise, as I will show you a few slides. Also, as I mentioned before, some of the adversarial attacks are white books attacks, which means that they require the algorithm in order to apply the attack the original algorithm is being attacked. Split second phantom attacks on the other hand, do not rely on white box approach they completely with that box. As opposed to the previous adversarial attacks split second phantom attacks do not leave any forensic evidence at the taxing at all. They do not use any stickers or graphic your things like this that can help investigators to find or to locate the attacker. So, as opposed to the previous adversarial attacks that require complicated preparation split second phantom attacks do not require any complex preparation at all. Again, I will discuss about it in a few slides. And also, as opposed to the previous adversarial attack that exploit the space domain split second phantom attacks exploit the time domain. This is somehow very interesting property to the expert. Now, some of you probably wonder, why does a commercial advanced driving assistant system detect the phantom as a real object. And here is an experiment that we did. We took a picture of a road sign and projected the picture on a tree. This is an interesting fact. Mobile I 600 and Freddie considers the projected road sign a real speed limit. And this is despite the fact this is completely. As you can see, project a projection that basically was projected on a tree. And probably I'm not that surprised by this result because mobilizing standard and Freddie consists only of a video camera. So doesn't have any other type of sensor which can be used to maybe identify an object. However, here is another interesting experiment that we did. We took the picture of Mr Elon Musk and projected it on a road and replaced Tesla in front of this, as you can see, projection of a pedestrian. And here is another interesting fact. This model X considers the projected pedestrian, a real pedestrian, despite the fact that this car is equipped with radar and ultrasonic sensors. Now, bear in mind radar and ultrasonic sensors are sensors that intended to detect depth. Projection completely depth less, which is being identified by a system that have the needed or the required the sensors in order to know this type of projection. Now some of you maybe again are not that surprised because I would say that pedestrians have, I would say lower cross radius section or radio signature. Then, for example, cars and as a result, a system which basically consists of video camera set of ultrasonic sensors and radar may follow the video camera instead of relying on the radar or ultrasonic sensor for the case of pedestrian detection. However, when it comes to, for example, cars, the case is somehow very different because cars have, I would say very strong radio signature that can be used to identify them. And here is another experiment that we did. We took a picture of a car, as you can see here, projected it again on a road. And as you can see, Tesla model X, consider the projected car, a real car. And despite the fact is completely deathless. And the system, which is that Tesla model X consists of the needed sensors to detect this type of object, which have much stronger radio. So again, despite the fact this is completely deathless. And the test that consists of the sensor that can, for example, be used in order to cause correlate the data that is obtained by the video camera with data that is obtained from the sense from from the radar or from the ultrasonic And despite this fact, it is still being considered this projected car, a real car. And this is somehow very interesting fact. That you will agree with me that there is a perceptual challenge in this case. There is a difference between what an advanced driving assistance system thinks it sees and what is actually there and this is actually demonstrated in these three pictures that you can see in this slide. Now, I do not consider phantoms as bugs. They are not the result of full security implementation that can be attached with writing the most secure code. For example, an SQL injection is something that can be prevented or mitigated by using, I would say by programming the code with security in mind. This is not the case for phantoms. Phantoms exploit the fundamental inability of object detectors to distinguish between a real and fake object. And as a result, we consider phantoms a scientific gap. So why are phantoms a scientific gap? So, object detectors are basically the output of a long train process. Previous studies have already discussed the implications of using the training of the training process to create artificial intelligence. They discussed about the high level of computing capabilities that are needed in order to train or to create an artificial intelligence. They also discussed about the energy consumption, the high energy consumption that the training process costs. They also discussed about the high cost that are required to train a model and artificial intelligence to create it. Finally, they also discussed about the long train time that it takes to train a model. However, it seems that the training process, despite all of these disadvantages, are able to create robust AI models that even function better than humans in real life. Now you can see this, by the way, taken from about two years ago, where Tesla claims that its autopilot is almost nine times safer than the average driver. So it seems that the train process, despite disadvantages, creates robust AI models. However, we tried to ask a different type of question. What object detectors don't live? And as you can see from the following pictures, context isn't taken into account by object detectors. You can see how unrealistic objects, despite the fact that they are completely unrealistic, are being identified as real objects. You can also see how color, in some cases, isn't taken into account. This entire road sign is made from gray use and is being identified by one of the state-of-the-art object detectors, which is then found online. Also, as you can see, in our case, texture, basically, either whether it's a transparent or skewed object isn't taken into account. And as a result, transparent and skewed objects are being identified as real objects, despite the fact that they are completely fake. And we would like to mention that object detectors are essentially feature measures. They lack the ability of humans to ignore fake objects. So let's try to understand how this fact can actually be exploited by attackers. And the first thing that needs to be determined by attackers is the appearance pressure. Now, in order to basically determine what is the appearance pressure that is needed to project or to basically present the phantom, we performed an experiment, a black box type of experiment, where we placed Tesla with Hubble-3 and Mobileye 630 in front of the wall, and we used a projector to project on the wall. And for Tesla, we projected the stop sign that you can see on the left side, and for Mobileye, we projected this nice speed limit. And we actually projected this type of phantoms for those durations. And we tested how the system actually identified them, the success rate in terms of identification or detection of this speed limit or this road signs. Now, we found that Mobileye 630 detects a phantom that appears for, I would say above at least 125 milliseconds for 100% of the time. It means that if you project a phantom for 125 milliseconds, it will be identified by Mobileye 630. Now, the case for Tesla is a bit different. Tesla, for example, detects a phantom for 100% of the time if it appears for at least 416 milliseconds. And you can see the results in the graph on the right side. Yet in mind, you can actually use a drone by mounting the projector to a drone and fly a drone, as you can see in this video demonstration, and to project, for example, in this case, a road sign on a structure that is located nearby a road and cause this specific car which equipped with Mobileye 630 to consider this road sign, a real road sign, despite the fact that it's completely a phantom. This is not what this is not the speed limit that allowed in this specific road, because by the way was taken in our university, which in this case, you can drive only up to 30 kilometers per hour. You can see how this is identified by the specific car as 90 kilometers per hour. Okay, now, here is another interesting experiment that we did. We took a picture of pedestrian and projected it on a road, and we engaged Tesla in cruise control. And cruise control was set to 18 miles per hour. And you can see how Tesla approaches the projected pedestrian detects it and immediately reduces the speed of the car from 18 miles per hour to 14 miles per hour as a result of the projected pedestrian. And again, attackers can, as a result, trigger an undesired reaction from a driving car. Now, however, in terms of this specific threat model where we need to use a drone in order to project a phantom on a road, this is what I call a semi remote type of attack, because attackers still need to be within the flying range of the drone. But bear in mind that most commercial drones, the limit is somewhere between seven and eight kilometers. So it means that ID, attackers need to be within this range in order to apply the attack against a location that should be located in this specific range. So you can understand this type of attack cannot be applied from a rival country, for example, because, again, it requires somehow the attacker to be, I would say, in the approximate approximation of up to seven or eight kilometers from the attack scene. So in order, for example, for attackers to apply the attack from a rival country, they can use the second type of the second threat model, which is exploiting the digital Internet connected billboard. And let's discuss about this type of model now. So as I mentioned before, this specific threat model is more interesting. And this is because digital billboards are located near roads.  And also, we will show you that attackers can actually exploit them in order to apply this attack over the Internet from an even animal company. And in order to do so, they can even do it with, instead of projecting the specific object, the specific phantom for on their advertisement, they can actually manipulate an existing advertisement by embedding the existing advertisement phantom inside of it. And in order to do so, they can find what I call a dead area, which won't be identified even by the person that maybe sits inside the driving car, for example, in the case of a semi-autonomous car. There is an algorithm for embedding a phantom in advertisement. Basically, an advertisement consists of a set of frames. And in each and every frame, we compute a local score for each and every block in a frame. And the local score is computed as follows. Key points are extracted from the frame. You can see the key points on the right side. They are marked with blue. And the score for a block B in this frame is computed based on how much a dead area the block is with respect to the key points extracted. It means that for blocks that are located that are more distant from the, this set of key points, they will receive a higher score because they are considered more a dead area inside this specific. Now, however, I discussed before about an appearance threshold that, for example, the case of Mobileye, it need to be 125 millisecond. Now, this is actually may correspond to a few consecutive frames, maybe even four frames. So we need to take the time domain into account. And in order to do so, we compute a global score for each block by computing its score with respect to the scores. To the scores in the subsequent consecutive frames. Now it means that if you have, for example, you know that Mobileye thresholds was set to 125 millisecond, which corresponds to four subsequent frames in this specific advertisement. And what you need to do is to compute the global score of each and every block with respect to its four consecutive frames. And by doing so, you can locate the best block that can be used to embed a phantom inside of it. Here's an example for an advertisement that we took, and we embedded a stop sign in order to attack Tesla on it. It's a McDonald's advertisement that we took from the internet and just to demonstrate it, we embedded the stop sign for 500 millisecond in order to attack Tesla, and Tesla have a threshold of 416 millisecond. Okay, and here is the demonstration. In this demonstration, you can see how Tesla's autopilot automatically triggers the car to stop when the car is approaching to the presented advertisement that I showed you slide before. And this advertisement and presented it on digital billboard. And when the car approaches this specific digital billboard, it immediately identifies the stop sign, which was embedded for 500 millisecond. And as a result, you can see right now on the dashboard in the left side, it triggers an immediate stop of the car. So a phantom that was added to an existing advertisement caused Tesla's autopilot to trigger an automatic stop in the middle of the road. Okay, so we have a few conclusions that I want us to discuss. And the first conclusion is that sensor attacks are a very dangerous type of attack. They are not easy to counter and they exploit the gaps between a human and machine perception. Now, the commonly used approach to counter sensor attacks, the case in where you have additional sensors is to apply a multi sensor fusion force or approach. However, in the context of semi or fully autonomous cows, this is a bad solution. And I want us to understand why this is a bad solution. Now, first of all, in some cases, multi sensor fusion is not effective at all. Now, for example, some objects can only be detected by single sensor. For example, road signs and lanes can only be detected by video camera. As a result, the existence of this specific phantom cannot be dismissed by applying multi sensor fusion. In other cases, some areas are covered only by single sensor. For example, you can see how this specific area of the Tesla, which is only covered by single sensor, won't be able to detect this type of phantom, which may appear in this specific area. Because it is only covered by one sensor, so it cannot be dismissed by applying multi sensor fusion. Now, in some cases, multi sensor fusion can be used to detect the existence of an object. Now, this is, for example, the two cases where multi sensor fusion can be used to detect the pedestrian and can be used to detect this projected cow. However, if you were a car manufacturer, would you program the car to ignore a detected object if the object was identified only by single sensor, given that you know that sensor detection accuracy may vary at adverse or ambient or weather conditions, for example, heavy rain. Now, in some cases, as a result of this adverse ambient or weather conditions, some objects might be undetected to one sensor, while still be detected to other. Now, with that in mind, how would you program the car to resolve a disagreement between the radar and the video camera regarding a detected object. Now, here is another interesting scenario. Sensor detection accuracy varies for different object is a known fact. Now, for example, it is known that video cameras are more accurate at detecting pedestrians than radars. Now, in some cases, the detection of one sensor is more reliable than other sensor. With that in mind, how would you program the car to resolve a disagreement between the radar and the video camera regarding the detected object. So, these are basically questions that I asked you, however, the industry takes into account another type of fact, which is the damage that may be posed by two, that may be posed to pedestrian drivers and nearby passengers. If a real object is ignored due to a mistake that was done by, you know, configuring the car to ignore a specific object, if it was identified only by a single sensor, this type of mistake is, I would say very risky because it's also have great impact. It may cause for an accident may cause to a loss of life. And as a result, it is better to react to a detection is a real object. If the detection confidence confidence process a predefined threshold. And this is probably the reason why the Tesla, in this case, detects the pedestrian and the car is real. This is a real object, despite the fact that they're completely deafness and despite the fact that they do not have any validation from the data that was obtained by the radar or from the data that was obtained by the Now, in practice, phantoms are not easy to counter with multi sense of fusion and this is one of the most interesting conclusions that I wanted to take from this talk. In addition, phantoms, as you can see in this pictures have already appeared in the wild. You can see them how physical basically objects cause Tesla's to consider them as real object. For example, this is what's taken from this week where a moon was detected as a traffic light and will an advertisement was detected as pedestrian. And in the right of you can see how a booking their sign was detected as a stop sign. And again, phantoms are not a new concept. They've already appeared in the wild. However, phantom attacks are more dangerous than this type of phantoms, because they can be applied remotely. And the other nut is easy to count them. So I would like to thank you for attending this talk. Thank you very much. And this is it.


What's going on guys? This is Steve from SurfStationStore.com. It's here to do a quick board review on FireWire's new Evo Surfboard, shaped by Tomo. Tomo has been putting out some new designs, pretty wild. It's kind of taking a different approach to surfing. You know, these boards definitely look similar to like a snowboard.  Super fun. You know, they wouldn't just be good, you know, even for kind of like a smaller longboard style. You know, these would work in anything from small waves up to overhead surf, according to Tomo and FireWire. This board has a double inside single concave split into a single quad concave. Kind of gives more of like a levitating feeling on this board when you're riding it. But even though it kind of feels a bit more buoyant and floaty, it just turns really well when you get into those pockets and just kind of needing to make a bit more of an adjustment in the wave. The Evo Surfboard comes with a linear flex technology, the LFT. It gives it a lot of flex, but it keeps it super durable. You know, these FireWires are like indestructible. It's pretty crazy. All of the Evo's come with a 5-fin setup, so you can ride it as a quad or as a thruster. Or you could do the quad setup with a little nubster in the back. And Kelly Slater does that and a couple other guys have kind of experimented with it. It's something to really look into. We've got a bunch of these at the shop. You know, you can get these as small as you can go. This is a 5-2 and it's about 26 liters of volume. So, you know, they're very compact of volume. You know, we've got a bunch of them in the shop. Or check us out online at surfstationstore.com and pick up your new FireWire Evo.


Hey everyone, Shaber 1000 here. Today, I'm going to be working on the vet again real short though. I'm going to be putting new fuel injectors in. I don't know if I'll get it together because it's 98 with a real feel of 110. So yeah, it's pretty hot. So let's go ahead and get started on these things and we'll at least get them put in. Okay, so we're going to get these out. I'm kind of in the tip of the garage here in the shade. I'm going to go down in like this. So what I'm going to do is I'm going to put some Vaseline. They say you can use oil. I've just always used Vaseline on that one. That's going to be going down inside the intake and this one will be going up inside the fuel rail. So get these greased up nice and nice and good. And that way we don't rip a seal like I did last time I put one in there. It took a while to start leaking. I wasn't leaking before. These are just 22 pound injectors. We don't have a supercharger on there anymore. So I'm not sure what the other injectors were but factory calls for 22 pounds. If I find out later it needs more as cheap as these things are I'll go you know with a 24 pound or whatever. So let me get these all lubed up here and we'll take them over and we'll stick them down into the intake. Okay guys now you can either put the injectors into the intake first and then your fuel rail or you can put them into the fuel rail and then put the injectors down in. Now I'm going to do it that way this time because last time when I broke that seal when I ripped that seal I put the injectors in first. So this time I'm going to put the injectors in the fuel rail and try that. They're real easy to rip them. It's happened. This ain't the first time it's happened to me. Honestly I mean we're human right? There's two kinds of people that have done that. People that haven't done it that are going to do it and people that have done it so it's going to happen eventually. So let's go ahead and I'm going to speed you up through this and we're going to try to get this down in here. The issue I have putting this other side of the plenum on there's both that goes through here so I hope I don't have to pull that distributor. I don't think I did last time but uh so what we're going to do first is I'm going to hook these fuel lines up over here right here and then we'll kick the fuel pump on a few times see if there's any leaks. If it's going to leak it'll leak as soon as that pump kicks on so move these out of the way. There's some rubber o-rings up inside of here that these got to go inside so I'm going to put a little bit of a little bit of acilleen on there and hopefully they'll go up inside. Someone's had a leak here before because it looks like they put sealer on these threads while this doesn't seal up the threads so when you're doing that you're wasting your time. It doesn't make sense so because they seal up here not on the threads. All right let me go get my vaseline in a wrench. My estimation it's going to leak anywhere it's going to leak here. That's what I'm hoping but we'll see. All right let me go put this thing on a battery charger. We'll give it some power and see if anything leaks.  First the fuel pump wasn't working so I just ran a couple wires to see if this was going to leak. I didn't want to put this back together without not knowing if it was going to leak or not. It's a good thing I did because look at this. I didn't notice that before but this is a pressure regulator and it was squirting out of here and running down through here. So I'm going to have to put a new fuel rail on it, rails. I found some JB weld up in here and up in there so you know that's one place right over top of spark plug wires you don't want fuel to be laying so it's going to have to wait a little longer but it's a project car you know. That's what you do you just don't you don't do it one weekend and be done so you know it's it is what it is. All right guys anyway thanks for watching appreciate it. Yeah sometimes things don't go the way they should or the way you plan and this is just part of it so that's why I'm uploading it to show people that are wanting to get a project car that this kind of thing this unforeseen thing can can happen you know so it's not a big deal we'll get another one and yeah then we'll figure out why there's no power going back to the fuel pump there is a wire ran from back there up to here somewhere and it shouldn't be there so all right guys that's at a later day was a real feel of 110 to me that means it was 110 so but it was 98 with a heat index of 110 so I'm going in the house we'll see you guys in the next one thanks for watching guys bye bye and take care


T F N N headline news update Good afternoon everybody. I'm Tommy O'Brien comedy alive from T F N N headquarters in St. Petersburg, Florida 1 p.m. Eastern time on Monday three hours left to go in the trading day And we got quite a market to the downside everything almost at session lows as we speak you get the Dow negative If I give it a second it might be 700 points right now negative 684 points trading at 25,258. You've got the Nasdaq down 271 points trading at 7645 S&Ps down 80 points trading at 2806 oil pulling back as well off 70 cents That was after being positive earlier in the day $60 and 95 cents gold contract quite a day to the upside $14 in the positive trading at 1301 30 notes and bonds higher price lower yield 10 year note up 17 ticks 124 1730 your bond up 31 ticks almost a full point 149 20 and you get the dollar index basically flat trading 97 100 Let's take a look at the futures markets You're gonna see basically the same thing across the board will start it off with the Dow and it's been a slow slide Since about 6 a.m. This morning. We were up there at 25,000 692 we're now off a solid 450 points just from that level and backing up from where we closed on Friday You're up there at 25,964. I mean we touched let's see did we get there? I believe 26,000 we touched it 330 on Friday, and we're now 750 plus points in the Dow below that level S&P is much the same session lows 20804 when you back it up to Friday. We were up there at 28 92 you're approaching a solid 90 S&P points Nasdaq 100 tech stocks getting hit hard 7327 quite a number when you back this one up. We're up 76 35 more than 300 Nasdaq 100 points from where we were trading that at the Friday close crude oil We got a pop earlier in the day You had some action with Saudi tankers possibly being attacked that put a little bit of fear in the market You had crude all the way above $63 but then as the market has slidden so has the price of crude $60.67 gold contract getting a bid kind of inverse of the market 130129 and we got a little action in both directions in the euro US dollar currently 112 35 Stay tuned folks should be an exciting few hours as we wrap up this trading day See where the market goes Steve Rhodes coming up with the trader's edge right now Dave White at 2 o'clock with a power trading hour Tom O'Brien live from 3 till 5 stay tuned folks. Have a great Monday


a fiery horse with a speed of light, a cloud of dust, and a hearty hyo silver, the Lone Ranger. With his faithful Indian companion, Toto, the daring and resourceful masked rider of the planes led the fight for law and order in the early western United States. Nowhere in the pages of history can one find a greater champion of justice. Return with us now to those thrilling days of yesteryear. From out of the past come the thundering hoofbeats of the Great Horse Silver, the Lone Ranger rides again. It was press day at the Modak City News. Dan Reed, the 14-year-old nephew of the Lone Ranger, had called there to buy a copy of the weekly for the masked man. Previous visits had made him acquainted with the staff, and as the paper run had not started he was busy helping Inky, a printer's devil, clear the littered floor. Hilda, a typesetter, worked at a nearby case. While in the back room, editor Tom Carson fought to keep an old whole press in operation. As the machine clattered and thumped, a shadow fell on the glass on the street door. Inky lifted a smudged, impish face. But Dan, there's a tramp outside. Tom never turns one down. It's certainly generous. Didn't he take me in when my folks died? Yes, I know. That fellow's coming in all right. I'll just tell him Tom's out. Oh! Crossing slowly with the aid of a cudgel-like cane, the caller revealed an almost fleshless face. A black patch dangled over one sunken eye. Fixing the other on the boys, he croaked. Where's the editor? Tom! Gosh, I hope he's got his gun. Well, Inky, what is it, friend? Mister, I need some help. This'll do you any good. Well, wait. I'm not begging, even if I haven't eaten lately.  A wand head? For what? A shaven mug. A $100,000 shaven mug. Oh, easy, friend. Easy. Don't out your gun. I'm not crazy. Of course not. Who are you? Patch Denton. That's who I am. Who got papers here from San Quentin, pen to prove it. See? Yeah, see. Oh, gee whiz. He's the last of five outlaw brothers who used to raid around here. Oh, what about the shaven mug? Mister, about 15 years back, my oldest brother, Buck, took a $100,000 in gold pesos from some Mexican revolutionists. Stashed it in these parts. Nice piece of chain. Right afterwards, the law closed in on us. Three of my brothers got killed. I was sent to California to do time. And Buck? They caught him before he could get to that gold. Gave him life in this territory. When were you released? Just a few months ago. I'd got consumption, so they turned me out to die. Then I started figuring on how to find that money. Wouldn't Buck tell you where he hid it? He couldn't. Not with guards reading his letters and listening to our talk when I finally was able to visit him. Yeah, this is where the mug comes in. Ah, yes. Mug. Buck's cellmate was a trustee. Painted things on shaven mugs. Large emblems, cattle brands, animals, stuff like that. On visiting days, they let him sell the mugs outside the gates. Yeah, I've seen those convict stands. Yeah, one day I got in to see Buck. He told me that he had the trustee put a picture of our old homestead on a mug and asked me to buy it. He said the picture even showed the old red bull in a pasture lot. Then he winked. Then I savaged. Savaged what? That the picture told where the gold was. We never had a homestead. Gee, where is it? Before I could pick up that mug at the trustee's stand, some other visitor bought it, not knowing what he was getting. The next day, his old buck died. And who was this visitor? A drummer who traveled in these parts. I trailed him to this town. I think he's here now. Why did you come to meet him? I can't go on alone.  I hear that you were a square shooter. I figured if you'd run an ad for a mug with a red bull on it, saying it was a keepsake and offering a reward, it might turn up. Well, Pancho, I'll think about it. Well, mister, if we locate the money, it's all yours. A few thousand. All I want is the last fling. Won't be long now. Now, look, here's five dollars. Get yourself something to eat. And see you, doctor. Thanks, mister. I'll be back. You just get the mug. The old fraud. Don't you believe his story, Tom? Not at all, Dan. He probably tells it to the editor and every town he hits. Oh, well... Tom Carson, you gave him money you need for your rent. No, dear... Don't dear me. If you think I'll marry a fool like you... Now, don't cry, Hilda. I'll work for nothing to make up for that money. Anyhow, we'll find that gold. Then Tom can buy a big new press and a steam engine. Oh, Inky. If that's how you feel about Tom, I'll be with you. Hilda, we're rich right now, having a boy like him. Yes, Tom. I'm sorry I begrudged a gift to a dying man. Hey, Tom, I got news for you. Oh, it's the sheriff. Come in, sheriff. No time. Just got word the Bulldog Indians were to the ranch family about 50 miles down the Colorado. I'm right now. Howdy-o, sheriff. Hey, that's real news. I'll start setting type right away. We're lucky that old outlaw held up the press run. A little later, Dan was back with a lone ranger and tonto in a nearby mountain camp. He had told of the Indian raid, and as the men prepared to leave for the scene, repeated patch-denton story. Dan was saying... It all sounded true to me in spite of what Tom said. Tom seems to have had a lot of experience with beggars. Don't talk to Denton if he's still around when we return. Aren't you going to take me along? Oh, no, Dan.  Put up at the hotel. All right. I can help Inky at the print shop. Horses are ready. Come with me. Good enough.   Come on, silly. Come on, scout. It was the following day when patch-denton stood at the lunch counter in Tige's place, which was a cafe located half a block from the newspaper office on the same side of the street. From within a glass partitioned office, two men watched Denton. One was Tige himself, the proprietor, and the other, a crooked gambler, called the deacon or the deek. Tige was scowling, as he asked.  Well, Tige, I was in the territorial pen working around the hospital when Buck Denton died. I heard him muttering about buried money in a shaven mug. A shaven mug? That's it. I thought so myself till I got out and found old patch that was traibousin' around, askin' questions. About what? A fellow who'd bought a mug at the pen. Patch wants it mighty bad. How could it tell him or else anything? By what's painted on it, of course. Eh, could be. My own barbershop mug has a tiger on it. I trail patched the newspaper office yesterday. What was he doin' there? Making a deal, I reckon. At least ways he's got money today. And we'll have to act faster or somebody'll beat us out. All right, deal me in. What's our play? We gotta make the old, old hoop talk. He's standing close to the cellar door. We can get him down there without anyone bein' the wiser. And let's do it. Well, boys, have you found the fabulous mug yet? Oh, gosh. I've looked at everything on the barbershop racks. They were horses galore, but no red bulls on any mug. Has Denton been in town? No, and I haven't seen him around town. But don't let that spoil your fun. I'd like to join you. Tom Carson, I do believe you would. Everybody likes the rummage around, honey. Uh, which reminds me, the lady's aide is holdin' a rummage sale down the street. Yes, Mrs. Fox was here collectin'. I gave her some stationery. Inky, suppose you and Dan gallop down there and see what else they've collected. I can guess. Dishes and doilies and all that. Now look, I want to write a story about what's bein' offered for sale. This is an assignment. An assignment? Oh, boy. Come on, Dan. You got everything down in your notebook? Of course, sure. Hey, look, here comes Mrs. Fox. The uppity-all credit. Yeah. How are you, young Jacknames, up to? We're reporters, ma'am. Reporters. Mr. Carson sent us here to find out what you had to sell. He's gonna print a piece about it. Well, that's very nice of him. Please note that Mrs. Fox has charged. It's noted, ma'am. Now have you young gentlemen seen everything? Oh, but what's in this box here? That horrible stuff. The sheriff sent it over. And believe me, I'll give him a piece of my mind for doing it. What sort of stuff is it? Well, he mentioned that much of it had been left by a drummer who was killed in a runaway a few months ago. But the rest of it... You were saying... The rest of it must have been left by criminals he hanged. Then you're not going to sell it? Mercy, no. You boys can have the whole box for taking it away. Oh, thanks, man. Grab a hold, Danny. In the meantime, Patch had fallen easy prey to the cafekeeper and gambler. He lay on the cellar floor of the cafe, barely conscious. Tig was shaking him.  Water. Water. Tell us what that mug looks like. Give me water. First tell us, what were you doing in the newspaper office? Answer me. It's no use, Deke. He's out again. This ain't getting us anywhere. We can't hurt him enough to make him talk without him passing out. What do we do now? We'll wait until after dark and dump him in the street. Then we'll watch every movie he makes. You might go to the sheriff. No, Denton never called the law. He'll keep after that mug till he tips his hand. It was after working hours that night when Dan and Inky opened the box from the rummage sale. Tom and his sweetheart had gone, leaving them to enjoy the thrill by themselves. And thrill it was, for as they delved into the contents, such amazing things as a pepper pot pistol with four barrels and a broken trigger, and a set of dominoes jail-made from beef ribs came to light. Inky squealed with delight. Poor gully. Here's a Joe Miller joke book, Dan. And here's a pearl-in-layed mouth. Look at this. A shaven outfit. What's on the mug? The mug? Well, I don't... This is it. The very one. You're right, Inky. There's the red ball and the scenery in the background. The drummer, Mrs. Fox, told the bot was the one who bought it. And he was killed. Gosh. Maybe there's a curse on it. Like there always is on pirate maps. Listen. I thought he heard a noise outside. What? It's the old outlaw. He's gonna fall and grab him. I got him. Steady old time. Let's get him into this chair. There you are, mister. Water. Water. Better close the door. Right. And get some water. Take it easy now. Get him some. Just take it easy. Here. Here. Just like this. Good. Inky. Good. You better run for a doctor. He's in bad shape. No, Doctor. Too late. I... I'm at the end of the trail. Brace up. We found the mug. Oh, look at it. See? The red ball. Boys. Watch out. Danger. The curtain falls on the first act of our lone ranger story. Before the next exciting scenes, please permit us to pause for just a few moments. Now to continue our story. In the absence of the lone ranger and tonto, Dan Reed and his friend Inky had discovered a shaving mug bearing symbols which told the location of the Denton gang's treasure. Old Patch Denton was captured by the crooks and badly beaten. Dan and Inky were in the newspaper office when Old Patch came in barely conscious. Phantom with us had his gasping for breath.  Somebody helped him die. Look at the marks on his head. I see. All the lawmen are hunting Indians. Let's get Tom. Bring the mug and come on. This way, Dan. Stop you sprout. The mast. There's a holdup. Right. Now give us that mug. I got one of them. Let loose of me. Run, Inky. Save the mug. Help. Help. Hey, that printer's devil's getting away. Stop or I'll shoot. You'll pay if you hurt him. You better think of your own hide button. That little barman gave me the slip. We gotta get out of sight. But Ty, what about that kid you're holding? We'll shut him up in the cellar of my cafe. Now come on, sprout. I got use for you. A few minutes later, Inky had awakened Tom and Hilda and was back in the print shop with them. As the editor stood staring at the dead man in the chair, Hilda exclaimed, Don't stand there, Tom. Do something. Anything. What can I do with a sheriff and his men away? A rider's in the street. Take the headed here. Hilda, Inky, get back. Good morning, sir. Hello. Hey, sir, thanks for running. Stand right there. What about that gun we're friends? I'm asked, man. An engine, too. Maybe they got Dan. What do you mean? Where is Dan Reid? I'm his friend. Tell me what happened. Oh, mister, Dan and I found a shaven mug. Below Ranger quickly won the confidence of Inky. Then listened to the story of Patch Denton and the shaving mug. So I don't know where they took Dan, but here's the mug. Well, you trust me with it? Oh, yes.  Here, take it. Let's rouse the town and start searching for Dan. That's a good idea. No. A big search would add to Dan's danger. Well, me think Pellers still got him here in town. That's palatable. The time of Dan's capture, we were approaching town from the mountains. We noticed nothing suspicious. Only the sheriff wasn't chasing Indians. Renicades all captured, but sheriff not come back for a couple of days. We'll handle this our own way. While Tunner watches inside the town, I'll scout the trails leading out. What about the rest of us? Wait here for word from the abductors. Maybe... You'll hear from them. They want this mug. Inky, stop the press. I can't print advertising stickers at a time like this. Gosh, I wish I'd busted that mug. Another night's coming on and we still haven't heard from those crooks. Wonder where the masked man is. Hey, Inky. Gosh, you came in quietly. You heard what we... Yes, Tom, but there's still time for a letter. Hilda's waiting in the post office. Did you find anything? Nothing on the trails, but I discovered what the picture on the mug means. You did? Who cares about that gold with Dan gone? Well spoken, Inky. I only hope to use what I know against the crooks who captured Dan. I thought that puzzler would be hard to solve. No, it's simple. So simple that it's deceptive. Without knowing that it has a hidden meaning, no one looking at it would notice anything unusual. Here, look. All I see is a red bull standing against a typical western background. I see three mountains and a creek that flows down from them to where the bull stands. These horns point to out of cabin. The red bull means the Colorado River. Of course. Knowing that, it's easy to see that the three mountains are the peaks known as the Three Angels. And the creek that flows from them to the red bull is Scorpion Creek. And the gold is buried in a cabin only a few miles away. So it appears... If those crooks get the mug, they may learn the secret, too. See, we could fix up a substitute mug. No, don't try it. Those fellows were outside when the old outlaw died. They must have heard Dan and me talking about the picture. Yeah, I think he's right. Tom, here's a letter for you. It's postmark Modak City. And sign Dan Reed. Take it, Mr. That's Dan's writing. He says that he's being treated all right and will be released when the men who hold him receive the mug. How are we to get it to him? They want you to take it and walk alone along the south side of Main Street at two o'clock tomorrow morning. They certainly don't expect to meet me on the street. Take the mug and calmly walk right away. That's how the instructions read. There won't be much moonlight at that hour. We can have men posted all around to trail or stop them. They're not taking that chance. Then what's their dodge? Tom, I've looked down on Modak City from the mountains and it appears it's a one-street town. Yeah, all the buildings are about the same height. There's a solid row of them along each side of the street for a half a mile. And they all have flat roofs. That's right. A man could walk along the roofs from one end of the town to the other. Yeah. Is it possible to enter any one of the buildings from the roof? Sure thing. Every building has a flight of stairs or a ladder that leads up to a trapdoor on the roof. I see. Yeah, but why are you asking these questions? I think I know how those abductors plan to get the mug. Well, I don't. And another thing I can't see is how they're released to him without running the risk of being identified later. We have only the word of men who have already committed one murder that he'll be released.  I'll take it to them. But tell me one more.  Well, here's one, missy. It'll stick on anything after it's wet. Can you ink out all the white on it so it won't show in the dark? Oh, sure. I'll get my ruler right now.  Now, those stickers. This is what we'll do. As the masked man outlined his plan, Dan sat on the sandy floor of the cafe cellar, his back to the wall, his hands and feet tied, Dick squatted on the bottom step of the stairs with a lantern. He was saying, it's no use looking at me, kid. Why not? Because you won't be seeing me again tonight. In fact, you won't be seeing anyone ever again. Told me you'd let me go when I wrote that letter. You know too much. You know we killed old Denton. Killing me won't help you. He'll have more than the law to beat. Well, Tig, it's about time. Everything's working out fine. Did the letter go through? What's that you brought down with you? A spade and a pick. It's way after midnight now. Aren't you going to help me dig? Oh, I got a scout around up above. Ah, you put all the work onto me. You won't take a very big hold for the kid. As the hour for the delivery of the mug approached, a hatch in the roof of Tig's cafe opened noiselessly. Dick emerged, a darker shadow against the dark sky. He called in a low voice. Hey, Tig. Where are you? Right here. You got your boots off? Sure. All right, then follow me. Where are we going? Go to the roof of the stage station. Have you looked things over good up here? Good enough to know that there's no one to plan it to see where we go. Then the editor didn't figure out our game. How are things in the cellar? I let the kid as he was. The hole's done. This is the station roof. We wait here where we can see down. At exactly two o'clock in the morning, the lone ranger stepped out of the newspaper office. Turning, he followed a boardwalk along the south side of the dark and deserted street. By pre-arrangement, Tonto kept pace with him on the opposite walk, a gun in one hand, a lariat in the other. The masked man passed a gunsmith's shop, an ocean store, and a long line of cafes and dance halls. But nothing moved in the shadows, and the only sound that came to his ears was the measured click of his own boot heels. He reached the stage station, and then from above, a voice knife-sharp sliced at him. Hold it. You down there, you're covered. We're letting down the line to you. Tie the mug to it. You found the line? Yes. The mug is tied fast. You stand still and we see whether it's the right one. I got it up. All right, up down. Strike a match. There. I can see the red bull. This is it. Thanks, Mr. Editor. Now put this in your paper. It looks like you got him. Come on, let's get below. You hit Kimmel's hobby? No, I expected those shots. Which way'd they go? Over-roofs. East? Rope one of those roof beams that stick through the wall. Then he catch it and rope fast. Then up we go. Hand over hand. Take a look at it before we plug you. You cowards. Cowards, are we? You wait till I reload this six-shooter. Keep your hand frozen. Look deep there on the stair. Shoot him, my gun's still in. Oh, my arm, he broke it. You keep back or I'll get this kid. You don't dare. Look at your partner. Jumping tunnel? Any color you lose, Dan. Then fellas hurt you. No, no, but you came just in time. Call over, folks. You can come down. Who's coming, Tom? A lot of other citizens who helped me smash the street door. We heard the shots. Here's Dan. Are you all right? Sure, Inky. The mug got smashed in the fight. Look at it. Now nobody'll ever find the money. You're wrong again, Cook. We know what that picture means. What? If you know so much, tell me this. There was a trap door in the roof of every building. How did the masked man know which one of the buildings we entered? We sealed all the trap doors with stickers just after dark. The sticker on the hatch of this building was broken. That's how we knew which building you entered. Now, Tom, I'm turning these prisoners over to you. They won't get away, mister. But what about the treasure? You know where it is or was. And I know that if you find it, you'll put it to good use. You hear that, Hilda? We can be married and give Inky a real home. You can have a steam-driven press. And feed tramps to your heart's content, Don. Goodbye, all. Maybe I'll see you again. Bye, Dan. All right, Dan. Does anyone know who that masked man is? Oh, sure. Dan told me. He's the Lone Ranger. The Lone Ranger. This is a feature of the Lone Ranger Incorporated, created and produced by George W. Trendle, directed by Charles D. Livingston, and edited by Fran Stryker. The part of the Lone Ranger is played by Brace Beamer.


Hi, everyone. Thank you for coming and continues to come. Let me talk about a little bit of the future. In this session, we are going to tell you about the work we are doing to improve all of our software stack and to watch out the performance of our quantum systems. These developments will be very important as we move more and more into the utility era of quantum computing. As you saw early today, we have a very robust roadmap of larger-scale quantum systems coming in years ahead. And we need to be ready with our software stack. In other words, we have to make sure that the next generation of our software stack aligns with all the innovations that we are doing in our hardware. Today, we are going to present to you some of the new software capabilities that we help us to solve utility-scale problems. So let me divide this session into parts. In the first part, we are going to explore how to use AI and automation tools to build this smart software stack. In the second part, we are going to talk about how we are going to run 5,000 gates circuits in our quantum systems. Let's start with a look at how we are going to combine quantum and AI. In this here, you can see a traditional workflow between classical and quantum computing. Everything we do in our software stack is based in this relationship between classical and quantum computation. At the end, we need to prepare and build the circuits, pre-processing. We execute the circuits in our quantum systems. And finally, we need to post-process all this information, again, in classical computation. In recent years, we demonstrated some ideas about how to create these AI algorithms and run them in our quantum systems. But today, we are going to talk about other things. We are going to talk about how we are going to integrate AI in every of these steps of classical computation to be sure that we can be able to empower and create or improve everything that is related with our quantum execution pipeline. We see the usage of AI tools as a critical path to leveraging the full potential of quantum computing and push past the current boundaries. To follow the following examples, show how we are going to use these smart tools to improve the user experience and make it easier to create circuits, optimize the circuits, execute the circuits, and at the end, get better results. For this, we are going to present to you for our components. And we have been working on these components over the last months. The first component are going to be a code assistant, a quantum code assistant, something that are going to help you to use and learn Kisky, Kason, and our quantum services. The second component is focused on using AI models to optimize your circuits. And after that, we are going to show to you some tools that you can use to combine quantum and classical resources in the most efficient way. Finally, we are going to explore how machine learning can help you to improve error mitigation techniques. Now, to tell you more about this new AI power code assistant, please join me in welcoming my colleague, David Kramer, to the stage. David? Thank you, Ismael. So as Dario showed this morning, we're excited to introduce our AI-powered Kisky Code Assistant. This is a powerful tool that will help you learn the best ways to use Kisky and quantum service, an IBM quantum service, and boost your quantum productivity with code completion and suggestions. The alpha version of this Kisky Code Assistant will be available through a VS Code extension in the first quarter of 2024. The AI Code Assistant has been trained on millions of lines of code generated by the IBM Quantum team and community over the past few years, as well as all of our documentation and tutorials. The model is based on our own IBM Granite 20V model with 20 million parameters and 8,000 context length. And it's fine-tuned with the latest version of Kisky Code and benchmarked with our own human eval data set. With all this information, we've been able to create an assistant that helps user learn to write better Kisky and Casem 3 code. So let's take a look at how this will look for users. As we can see here, we have created a Visual Studio Code plugin that will help users experiment with the Kisky Code Assistant and begin incorporating it into their work. In this case, the Kisky Code Assistant uses Kisky to generate a circuit with an observable and run it through a Kisky Runtime Estimator primitive in one of our devices with 127 qubits. And I am excited to share that we will make the Kisky Code Assistant available to our premium users as an alpha release in the next weeks. Now, let's talk about how we are introducing AI into the transpilation and optimization process. As we also saw in today's keynote, we are thrilled to introduce our AI-powered transpiler passes, secret routing, and circuit synthesis. These passes can be used as building blocks in your transpiling routines, just like any other pass but based on AI. The AI passes usually produce shorter and cellular circuits than the standard transpiler passes in Kisky and are also much faster than the optimization methods such as SAT solvers. Let's take a look at routing. This is a benchmark we did on random circuits of depth 3 on 127 qubits. Here, Kisky level 3 transpiler does a good job in routing, but can we do better? This is our level 1 AI routing, our level 2 AI routing, and hold on, our level 3 AI routing. That's a 30% improvement in circuit depth. Now, let's take a look at the AI circuit synthesis. This is a benchmark we did on synthesizing random Clifford blocks of 8 qubits with linear connectivity. Kisky Clifford synthesis already generates a good circuit. Kisky level 3 improves this a bit, but can we do better? We are here. You know the gun. So this is our level 1 or 3 AI synthesis. This is around 70% better in C not count. And this just took 1 second per second. So now let's walk through an example of how you can integrate all this AI power into your transpiling workflows. So we will start by looking at an example of how we will normally transpile a circuit. This is that circuit. A three-layer random circuit on 127 qubits. It looks really ugly. So typically, these circuits are hard to transpile. And for doing that, we are going to use a Kisky pass manager with a layout stage and a routing stage. This adapts the circuit to the connectivity of the selected quantum system. So transpiling usually introduces a large overhead on the circuit, but this is already very good. But maybe let's get more serious about optimization. To use the new AI pass, we just need to swap the saver routing pass with the AI routing pass and use it as we would use any other pass. So this will run our AI pass on the cloud, seamlessly integrated into our transpiling workflow. Here, we reduce the depth overhead by 40%. And don't worry, our AI transpiler passes won't hallucinate. So your output will always implement your input circuit. So finally, we are happy to introduce our Kisky transpiler service. This service will allow you to transpile and optimize circuits the same way you would normally do it with Kisky by running on the cloud. And even better, you will also be able to include the AI transpiler passes in the process for an even stronger optimization. The standalone AI transpiler passes, along with the Kisky transpiler service, will be available as an alpha to our premium users starting today. So just scan the QR code. Next, to talk about how we're leveraging automation for better resource management, please join me in welcoming to the stage Jennifer Glick. Thank you, David. So this morning, you heard about the progress on our development roadmap with Kisky patterns and quantum serverless enabling managed execution of workloads. In this section, I want to show you the next evolutions of quantum serverless and how we're enabling automation for more efficient resource management as part of our innovation roadmap. As we move towards utility scale workloads and beyond, we need tools that help us effectively manage and get the most out of both classical and quantum compute resources. We'll especially need to rely on more automation to help us select the best quantum resources for a particular workload. We're taking steps towards automating the selection with software tools that work together with quantum serverless for more efficient workload execution. Here, we'll see the software interfaces that we're building that unify how quantum resources can be automatically allocated. These decisions are made based on higher level criteria desired by the end user, such as by system availability or by optimal mapping of your circuits to the least noisy subset of qubits by searching across a set of backends. And in the future, these interfaces will allow for more advanced custom selection, such as with AI. Let's see an example of how resource management with quantum serverless will work together with Kisky patterns. So in this example, we will automatically select a back end from those that are available to us that corresponds to the least noisy subset of qubits for our circuit. We start with a generic Kisky pattern. Step one generates an abstract quantum circuit from the classical inputs. This circuit is not yet connected to or optimized to a particular back end yet. Let's zoom in to step two. In the second step of Kisky patterns, we optimize the abstract circuit for execution on a target back end. This time, however, rather than manually specifying a back end, we use the QPU selector from quantum serverless to automatically select a back end for us based on qubit quality. The selector does this by looking across all the back ends that are available to us and using transpilation of our circuit to the least noisy subset of qubits on each of them. The target back end that is selected and the optimized IBM quantum instruction set architecture representation of that circuit is then passed into step three of a Kisky pattern for execution with the primitives. I'm excited to announce today that you can now start exploring resource management through the examples that you've seen here with system availability and qubit quality. At the same time, we are building out the interfaces to bring you even more powerful and custom resource management to leverage the advances we continue to see with the hardware and software. Next, to talk about how AI can help us minimize errors, please welcome to the stage Iskandar Siddikov. Thanks, Jim. So I'm going to be talking about combination of machine learning and quantum error mitigation and how bringing those tools together can offer many benefits to our users. AI made a tremendous advancement in recent years developing new methods, models, and architectures for solving variety of problems. But can we leverage AI capability to negate the effects of errors in quantum computation? Each approach to quantum error mitigation has its own benefits, but there is always price you have to pay. Some error mitigation techniques are more scalable but require extra shots, which affects runtime. Others are precise, but besides extra shot, also requires a lot of runtime classical computation. Ideally, we want high precision and scalability while reducing runtime. We're exploring new error mitigation techniques with machine learning, which helps us hide the cost that comes with other approaches. It is possible because we are shifting all of the mitigation costs onto heavy training phase where most of the compute is happening. Training is heavy, but the inference is fast with comparable precision and scalability. Now, let me show how it works. Of course, you know that your model is only as good as your data set is, and the rest is your classical machine learning workflow. First, we take our circuits and backend and encode them. Then we execute our payload against error-mitigated backend to get both raw and mitigated results. We run it all through the model, we compute loss and adjust the weights. We repeat this procedure many, many times until we get our converged trained model. Now, let's see how users can leverage those trained models within the Qiskit patterns. We'll focus here on a post-processing step of our workflow since that's where model inference occurs. First, we load our model, then we encode our circuits and backends. We pass it all through the model alongside noisy values, and as a result, we get our mitigated values back. And that's it, just a few lines of code. Now, I would like to thank you and please join me in welcoming back Ismail Faro to the stage. Thanks, Iskandar. As you can see, we have an ambitious plan to apply AI to different aspects of quantum computing. And we are bringing this vision to life on other areas like device calibration, error correction, as much, much more. Now, we want to shift gears away from AI and talk about another area that is playing a big role in driving the improvements on our software stack. Our goal to run circuits with 5,000 gates. To do that, and to conclude this session, please join me in welcoming Abhinav Kandala to the stage. Thanks, Ismail. So, we want to run 5,000 gates circuits, but we also want to access reliable, accurate computations from these circuits within the next one year. How do we do this in the absence of fault tolerance? The key is a collection of techniques that we refer to as error mitigation. Methods that can enable access to noise-free observables on quantum computers today. And as we showed earlier this year, this can be done at a scale that's in general beyond exact classical simulation. This was work that appeared on the cover of Nature in June this year where we presented evidence for the utility of quantum computing before fault tolerance. The largest circuits considered here were with 127 qubits with a circuit depth of 60 using up to almost 3,000 gates, which is in general at a scale that's beyond exact classical simulation. At the time of publication, we didn't quite know if the data points the quantum computer produced for these largest circuits were quite reliable. However, following subsequent classical simulations from world-leading groups, we essentially saw that in the absence of an exact solution, these various methods disagreed from each other by about 20%, and the experimental data points lied within this variance, giving us confidence in the device and the error mitigation methods. Since then, things have progressed pretty quickly in the past five months. Earlier today, we announced our updated roadmap, and it shows that we're really going to be focused on expanding the reach of error mitigation to even larger circuit volumes, going from 5,000 gates to 15,000 gates over the next five years. So what's this going to take? Error mitigation methods typically rely on running many noisy circuits and then combining the outputs from these circuits in post-processing to produce noise-free estimates. However, there's no free lunch, and there is the associated runtime overhead of actually executing these many circuits. The game, though, is that as we push down the error rates on the device, the circuit overhead for error mitigation will get smaller. Simultaneously, as we increase the speed of our systems, we're going to be able to run more circuits per unit time. So advances in both quality and speed are going to be extremely crucial as we push along our roadmap to expand the reach of error mitigation to increasingly large circuit volumes. I now want to highlight some of our recent progress on the hardware and the software along this path. First, we've been trying really hard to get the techniques that enabled the utility experiment into the hands of users like yourself so you can leverage these tools and start to explore interesting problems in circuits. The utility experiment relied on an error mitigation technique referred to as zero-noise extrapolation, right? And the typical workflow here is you first want to define your quantum circuit and then characterize the noise in gate layers in the circuit of interest. Once you have the representative noise model, you can then begin to manipulate the noise in a way that you can effectively amplify the strength of the noise. The task then is to measure the expectation values at these different noise levels. Then you can combine these different outputs and extrapolate to what the result of the noise would have been, what the result of the computation would have been in the absence of noise. As you can see, this is state-of-the-art research with a fairly involved number of steps. To make things easier for users and accelerate the discovery of quantum applications, we've simplified this workflow. So all you need is some easily accessible bits of code to reproduce experiments from the utility paper. I strongly encourage you to join the Practitioner's Forum session later this week where my colleague Chris Wood will be talking more about these tools and how this is easily applicable to your own circuits of interest. In addition to making these tools more accessible, we're also working very hard to improve the speed of our systems with fast parametric updates. Large error mitigation experiments can involve running hundreds of thousands of circuits that are individually parameterized by Z gate angles. For this, we're transitioning from our current execution model that involves recompilations for each individual parameterized circuit to a significantly more runtime-efficient model where a single compilation step is sufficient for executing the many thousands of parameterized circuits that are critical for these workloads. So speed is a very important aspect of the improvements we're making, but so is quality. In this context, you've already heard about the improvements in our new Heron architecture with up to a 3x improvement in median two-qubit error rates over the Eagle device that was used in the utility experiment. And I now want to give you a small taste for the power of exponentials and a sense for what this enables in our latest set of utility experiments. With these improvements, we've now been able to address simple extensions to the trotterized icing circuits that were considered in the utility work that can lead to significantly faster entanglement growth, even with the same number of two-qubit entangling gates. This can be done simply by introducing these single-qubit X gates between each of the ZZ entangling layers in the utility circuits to break commutivity. Furthermore, as a test of device performance, we rely on Loschmidt echoes where we run the circuit of interest followed by its inverse and ask the question, does the qubit recover its original magnetization, the original spin? For these non-commuting circuits with faster entanglement growth going up to circuit depths of 30 on 111 qubits, we see that on Heron, we're able to produce experimental signal at challenging parameter angles where the corresponding signal vanishes on Eagle. With this signal, we're then able to aromitigate and recover what the expectation value should have been in the absence of noise. So, the bottom line is that we have this amazing new computational tool, but what I find truly remarkable is in just five months following the publication of the utility paper, we've made this tool even more powerful. We've reduced our two qubit error rates. That takes us further along our roadmap of 5K and beyond. We've increased speed. That enables us to run more circuits per unit time. We've improved overall device quality. That lets us address even more challenging circuits. And perhaps most importantly, we've started to make these advances more accessible to you, our users. These advances have placed us firmly in this new era of utility. Now let's work together and find the problems in circuits that will get us from utility to advantage. Thank you.