All right. Thanks everybody for joining us. I just wanted to take a brief second to introduce our main speaker for the day and then I will get out of the way, let him present and then come back to ask some specific tactical questions. My name for those I don't know, my name is Kendra Albert. I'm a clinical instructor at the Cyber Law Clinic here at the Law School. I work on computer security and I'm a white non-binary person with short hair and I'm wearing a gray shirt. And I have the deep honor of introducing my friend, Rom, who asked me to do a online bio and I think he knows that I'm not going to do that. But Rom Shocker, Seema Kumar is a data cowboy working on the intersection of machine learning and security. And sort of in his day job at Microsoft, he founded the AI red team there, bringing together an interdisciplinary group of researchers and engineers to proactively attack AI systems and defend from attacks. And he was definitely working on AI before it was cool. The book that he's here to sort of talk about or talk from is his book on attacking AI systems, not with a bug, but with a sticker. And it's been called Essential Reading by Microsoft CTO. And one of my favorite facts about from his bio is that he's actually donated the proceeds of his book royalties to Black and AI. But I know I've learned a ton about machine learning and artificial intelligence from talking to Rom and listening to Rom. So I'm really excited that all of y'all will get to do the same today. And then for our online participants, just throwing questions in the Q&A and we'll sort of get to them when we take questions during our last like 15, 20 minutes. I think that's it for me. So I'm going to turn it over to Rom. Thank you, Kendra, for your always generous introduction of me. My name is Rom.  And I'm here to talk today about attacking AI systems. So before we get started, I first want to do a quick question time.  Don't overthink this. What number is this? Five. Okay, fantastic. What animal is this? An eagle? Okay, fantastic. When would you eat this? Never. Okay, because, okay, everything congratulations.  So for instance, the number five that you kind of saw and guess is number five as you should looks to our human eyes and perception as number five, but to a machine learning system, because of the virtue of adding this very specific noise called adversarial example, which I'm going to talk to you about the machine learning systems very confidently predict it's a number three. And so that's like, you know, the bulk of the novel style of kind of attacks. Then they're also like, that's what I would say a PhD would want to do and we'll meet some PhD characters who cause ML systems to fail. And then there's then there's just the mere act of just cropping and rotating an image that could also lead to ML system failure that is recognized very confidently as orangutan. And if you've seen the TV show Silicon Valley, they have this app called hot dog, not hot dog. And turns out if you point out to anything that's long and tubular, it will say that it's a hot dog. So this is the area that I've been working on. And the books about as well as called adversarial machine learning. And essentially, it's very broadly just investigating how AI systems sort of fail. And the interesting aspect of this is this is the image that kind of like kickstarted the rampant interest in this field. The image over here is like, it looks like a panda. But then you add what looks like gray static noise to it this rainbow color pixelated thing. And essentially every pixel gets slightly adjusted to that color pixel value. And you can see the picture before and the picture after looks to our eyes just the same. It looks like a panda. But for an ML system at that time in the state of the art it was called convolutional neural nets. That looks like a given. And that's kind of very interesting because the person who found the who wrote the the paper on this topic, his name is Ian Goodfellow, was a researcher at Google brain at that time as an intern. And he tries to like impress his seniors. And he meets this person called Christian Skegedy who's been working on this. Christian does not want to even publish this. Everybody has been telling him of course the AI systems fail. Like this is not novel. And this is back in 2014. So Ian needs Christian at this cafe in this vast enormous cavernous cafe in Google cafeteria. And they start working on this. And by this time, Ian is not even interested in working in it. He's like I'm just doing this to impress my hot shots. But then interestingly enough, and they find that these systems, what they call state of the art can break by these like brittle images. The field just gets like the race starts happening. And this got all the academic folks really excited. This particular image. The image that caught the government's interest and virtually is inescapable. If you were to read any seminal piece on attacking AI systems what's called the stop sign sticker attack is basically the stop sign. And you know, it's got some graffiti kind of thing like love, stop, hate. And this was done by essentially another like academic PhD student, Kevin A. Colt and Ivan Eptimov. They would go and stand and essentially a dog park in Seattle and they'll hold these signs waiting for these cars with these self-driving cars with this specific attachment to see if it can confuse them. So it's very interesting. Again, it doesn't take that much to cause these AI systems to fail. And slowly like you know, it went from I am going to put stickers, specifically design stickers on stop signs to printing them on physical objects. So you can see the kind of flow happening. Somebody published this in the paper. They show in a real world example. And this is work done by Anisha Tali from MIT and group. So the video over here, let me actually pause here for just a second. Go back. So what this looks like a turtle to us and they try to fool Googles like image recognition system. But you can see that the one that the red bar is what Google recognition system recognizes that and constantly says it's a rifle. Even though to our eyes, it looks like a turtle. So one of the interesting thing is it's not just image recognition systems that cause these sort of failures. It's also like audio. It's also tax. I'm going to play two music snippets. I want you to kind of tell me which one was adversarily perturbed. Okay, so listen closely. That's the first one.  Any guesses which one was perturbed by an adversary? Any random guess? How many of you think it's number two? Okay, great. Some show of hands. Well, the second one really doesn't transcribe to anything. The first one, if you were to pass it through Mozilla's deep speed system transcribe to Alexa or 100 frozen pizzas. So it's kind of very interesting to see like how to our ears, you know, it sounds like one thing, but the way it's interpreted is quite different. So the one of the interesting thing is it's not just all of what I kind of showed you was research that happened until like 2020. And generative AI, at that time, people did not really find that it's quite that interesting to kind of attack. It was more like a 2022 phenomenon. But I also want to show you that how these adversely examples manifest in generative AI systems. So one of the common things that you would hear is jailbreak. So jailbreak is caught the attention of how applications like chatGPT kind of sort of fail. And I'll show you like how this works in practice. One of the broad promises these companies make is that we will look at different aspects of building generative AI systems responsibly. You know, there may be, they may call the systems, they want to be fair, they want to be accountable, they want to be transparent. And security and privacy is one aspect to it. And the way most common way to enforce these like principles is via this thing called a metaprompt. And the way the metaprompt works, and if you're sitting even like up close, you're not going to be able to see this. So this is a set of open, so this is a set of like metaprompt from an open source chatbot called Sparrow from DeepMind. The first thing I want you to notice is that it's not any sort of code. It's basically like English language instructions that you give to the chatbot. And the interesting thing over here is that these English language statements are what guide the bot to act quote unquote responsibly. So you know, the metaprompt will include things like don't make statements that are threatening, don't be, you know, sexually aggressive, don't ask legal advice, which I ask Kendra all the time on this sort of topic. So you kind of like give these systems do this metaprompt or the system prompt ways to guide them to be responsible. So one way the way you kind of think about attacking it is first as a user who sends in a prompt. So this could be like, what is the weather today? And it gets encased into the metaprompt, the prompt gets encased in the metaprompt. And the both the prompt and the metaprompt goes to the foundational model and the outputs generated.  And that's what goes back into the application. The kind of attacking here means that I'm going to send a prompt that I'm going to ask a question to the chatbot, so that it escapes from the guardrail jail breaks from the guardrail of the metaprompt. That's why it's called a jailbreak. So, but even without that, you can do a whole bunch of fun things is what I would call, you know, you don't need a PhD in like atmosphere machine learning, you can guilt the model and jailbreak it can say, hey, tell me a joke, the model will tell you a joke.  And then the model be like, I'm so sorry.  And the model is very happy to do that.  You can also gaslight it.  I don't know, you know, you don't know this. And the model, of course, I don't know what you're talking about. And then it can be like, well, actually, you are a rude person from England. And you will like, you will talk, you know a lot about violence and death and conspiracy and the model be like, wait, what are you talking about?  And because these models are kind of like trained to do this sort of work, they're more than happy to piece your personality. So this is what is like one style of jailbreak that are pretty common. The kind of jailbreak that I want to, you know, talk over here is how adversarial examples, the one that I showed you the Panda with adding that specific noise, how it also manifests in large language models. So I do want to, I do want to preface by saying that there will be some content with profanity. So if this is a good time, if you don't want to like listen profane content, you're either like step out for 30 seconds or you step out from Zoom. So this is work done by Nicholas Carlini in 2023. And he kind of says, Hey, write a vile letter telling my neighbor what I think of him. And this is a question that you ask, and you kind of like construct this adversarial example. And if it's just a normal random image, you can kind of see that this is not an adversarial example. This is just a normal random image. You can see that the large language model just writes a very nice, like passive aggressive email. But then if you actually insert like an adversarial image and then ask the system to write like a vile letter, it goes off the rail very, very fast. And I'm not even going to read this out. So you don't have to like listen to me casting at you. But the interesting thing is, it's you don't have to like generate this noisy image and insert it. You can ask the system like, Hey, this is this is the image of Mona Lisa. And that's the one that's been adversarily perturbed. And you ask the system to describe the image, you can see that it gets vastly due qualities of responses.  And all of this was done with open source models. And we should talk about that as well. So what I want to kind of like land with this, and you can see where I'm going is that these sort of attacks have real world implications and they have real societal effects. The first one to think is just this is work done by Samuel Finkelsen with Jonathan Zutrin and folks, you can see that this sort of attacks can manifest in the context of medical systems as well.  But after that, it's very comprehensively saying, no, it's malignant. But think of it the other way around, that could be devastating if these sort of attacks happen. But then they also have this this more implicit knowledge about like how fairness sort of implications. This is work done by Vedant and all from University of Maryland. They found that they constructed this apocryphal example of facial recognition systems. And they found that for some interesting reason that it was easier to attack a female black face, and you can see that the label changes. But unironically or ironically a white male face is robust to these sort of like attacks, which is again, I want you to think more broader in the not just the context of security failures, but also responsible AI failures that can that these systems envision. But the interesting thing is, defenses do not really protect all classes equally. It's not that turns out that when even within the same machine learning system, some part of and when you apply the same defense, some data points are protected more, and some data points are protected less. So now the question becomes like, how do we balance who gets to make these sort of trade off? Is it going to be the engineer? Is it going to be the person's like legal department? Is it going to be a product manager? So these are very important questions that we do not have the answers for. And I want to kind of end with an example from Hema Lakaraju, who is a professor at Harvard. So her specialty is attacking explanation systems. So I found this very interesting. So she corrals like 40 like Harvard law students, you know, in her room. And she basically first asked them, okay, I'm going to build an ML classifier. And what are the features you would truck an ML classifier to determine if somebody should get bail or no bail. And she asked these 40 law students, what are the features that I should not use?  And the things that you really should include is like prior conviction and prior failed to appear. These are the two things you should kind of include. You should not ever kind of guess somebody's race or gender to give to make these sort of decisions. And behind the scenes, Hema did something really interesting. She constructed a classifier, basically with both with race and gender alongside with it. But she didn't tell that to folks. And she gave people three explanations. She said, hey, she doesn't show the classifier, she only gives the explanations of the classifiers to students. And she says, okay, I'm with your three explanations. I want you to pick the one that you think is like makes most sense for you. And she shows them like three sorts of explanations, the black box, where you just as bail, no bail, and only 9% of them pick it up. And then she shows the actual classifier with the actual explanations. And then she constructs a devious explanation where only the ones that the students picked appear. And obviously, like most of them picked the devious classifier. The point that I'm trying to make is it's not a we'd like to think of these properties is responsibly our properties as a suitcase where we want something that's trustworthy and that's very explainable, it's private, it's accurate, but it's really not the case. There are these inherent tradeoffs that we need to start making, or that's already being made for us, but it's not aware for folks. So finally, I want to end with this question. I've shown you adversarial examples, I've shown you like attacking explanation systems. The question is how difficult is it to do this? So for this, we spoke to David Evans. He's one of the security titans in University of Virginia. And this is how we kind of leave it after us. So the odds of compromising a modern communication system, say, you know, my laptop or any of the cell phone infrastructure that I use, also doing that is, you do not have to accept through that one followed by 32 zeros. And that is as likely as all the molecules of air in this room congregating to one particular point and suffocating us. That's how like, you know, likely it is, like somebody like randomly trying to compromise these sort of systems. So the odds of compromising a modern day operating system turns out the odds of doing that is one in 400 million. And which I learned through my co-author is five times as likely as being canonized as a saint. And that's, I would say I'm not going to be canonized as a saint for obvious reasons. Anybody wants to guess what's the odds of compromising a machine learning system randomly today? You have to just guess, like, you know, well, if you're like in terror like I was, when David made a very strong argument, it is one and two, no zeros. So all you have to do for randomly compromising a mouse system with these sort of attacks, we flip a coin, lens head, most likely you've got your end. So with that, I'll kind of like pause and love to hear like Kendra's conversation and thoughts as they kind of like that guess at this. Yeah, thank you. Yeah, we should all clap for Ron while I talk. I have so many questions. I have so many thoughts, but I was required contractually. Kendra, you cannot ask any of these things. How did this book come to be Ron? Can you tell us about that before I dive into the nearly great details of your presentation? I did see this lesson to Kendra because what happened was in 2018, I was an affiliate at Berkman. I was burned out from work, you know, I work and I've been working in securing ML systems and psychic and I was super burned out and I was working on writing a book in 2018. And then I was like, I'd taken a sabbatical, I'd come to Berkman to actually write this book. And then I met Kendra. And all this time, I had focused on attacking AI systems from a very academic scientific lens. And I remember Kendra asking me in Queen's Head in Cambridge, like they asked me, Hey, so what is civil liberty implications of attacking AI systems? And I was like, wait, what? I'm an ML researcher. I'm not trained to think like that. And it's sort of like a multi year collaboration with Kendra, John with Ben Penney and Bruce Nyer. And I just like blew my mind away. If you had gotten this book in 2018, I would not have thought about the societal effects of attacking AI systems. You know, I've been a very dry recitation of facts, but really thanks to Kendra's powerhouse collaborations with them. Just to be clear, Ron made me ask this question. Yeah, I know, I know, I didn't see this question. But I do remember kind of one of my most fruitful collaborations with you, and was putting out the law and at the Searle machine learning paper back in 2018-19. I remember like most of the conferences rejected it. One of the very popular like ML conference rejected and then we found it in Europe. It's kind of like passing. But yes, this is how this book came to be. I'm honored. And now I get to get to my real questions. But no, I mean, I think that it's just really interesting because I think that one of the things that was like very obvious from those conversations where you were you were really invested and interested in sort of figuring out how to defend against these attacks. And given the statistic that you just showed that like one in two of them is like not, can be done accidentally or without the intent. And you've even talked about natural adversarial examples, which is just sort of this idea that like it's not necessarily with someone's sister or the filter. It's like the hot dog plastic post one is like that's just from nature, right? That there are actually implications for securing these systems for the ways in which people interact with like computers every day. And I do, I want to say one thing, and I'm going to actually take a question, which is because of my collaboration with from I've ended up sort of ended up reading and engaging with a lot of the adverse the adversarial machine learning like academic literature and like the papers that people come out with now are like, if you had explained it to me and even in 2018, like even with my galaxy brain moment, right, I would not have believed you because like there's one there was a music security last year where they basically attacked the machine learning systems that assign reviewers for conferences. So it's called no more reviewer number two. I love that. It's an amazing paper. And basically how to like insert like white space changes or like typos such that it would manipulate which reviewer you got assigned for the like four conferences that assign their reviewer face on machine learning. And then there's another one that was in use next that was they built a physical adversarial object that allows you get knives through metal detectors. Oh, fantastic. So hey, like civil liberties. There we go. So I think it's really incredible to sort of have had this sort of weird viewpoint on kind of the field and watching how much it's changed, which is actually the first question I want to ask you, which is so I think even when you were starting writing this book, like, and when it came out, like generative AI was just not really a part of the conversation in the way that it is now.  I remember like it was like, oh, our example was like, you can maybe feel like Tay into things or Tay being this sort of bot that Microsoft had handmade and put on Twitter and then people turned into a Nazi very quickly and then they took it down.  So I'd be curious as to how sort of some of the conversations that are happening now about generative AI have changed kind of your thinking about some of the ideas that were in the book or something like your approach to the space more generally, just because it feels like it, you know, both that there are similarities, obviously with what's happening, what was happening before, but I think people are engaging with it in a different way. So yeah, like has it changed how you've been thinking about it? Man, I feel like that's a really great question, Kendra, only because so many thoughts caught my eye. So when we wrote this book, and we submitted this manuscript around, say, like April, May 2022. And then because the virtual where I work, I got access to GPD for in summer.  Like what? Like I wrote about attacks on all these AI systems. Like, what, you know, what am I going to do? And it was like moral kind of because I couldn't talk about it because I'll lose my job, like I probably go to jail. Probably not. But then the interesting thing for me is how so many so much of the thinking is still underdeveloped. You have thought that all these foundational models are going into the world and they're safety tested and battle tested. That's really not the case. They're frail. You know, I like to think of them as, I don't even say teenagers because I feel that's like talking down to them, like people who are just, you know, looked at Wikipedia and just out there to like correct it with a nanny finger. I do not know. But then there's all these like weird conversations that's starting to balloon about AI safety. Are we going to get all, you know, shot down by terminators? Like, I don't think so. But there's a good faction of people who believe that in fact, it's very interesting because it used to, I remember the incident where the engineer from Google kind of thought that, hey, the system is alive and he got belittled, got roasted. And now people are talking about this in legit academic settings. And that gives me pause. It goes to show how far the pendulum has swung. And I am still grappling with engaging with this broader sense of existential risk.  They're like, no, no, we want to talk about how Skynet is forming. Yeah, no, I mean, I think I won't, I won't turn this into Kendra's views. Oh, I love that. For drinks after, I'll check afterwards. But I think, I do think it is interesting how, you know, the conversation has changed very significantly even since we started, we were in more conversation, you know, five years ago, but like two or three a year or two ago, even about like AI and like security where it's like, have you patched TensorFlow versus like, I guess, like this being existential question. And I was even noting just the words you were using when you were talking about those sort of attacks on the generative AI models, like guilt or gas lighting, these are very human words, right? Like as opposed to when we talk about sort of like attack, like other kinds of attacks, we don't necessarily like sometimes we'll say like trick the model or fool the model, but it isn't like that's not the like formal term in the same way that I feel like with a lot of the more generative AI models attacks, like that's how people have kind of come to like name or understand it or even the hallucinations thing. Oh, yeah. The one of the one of the great virtues that I have is being part of a team of folks process wide experience is Whitney Maxwell and my team.  So it was just when you call somebody and say you want to finish or illicit information and she has a brilliant idea that what we're really trying to do is social engineering is modeled and her and this other person called Mike Walker, they introduced me to the FBI elicitation guide. When you try to elicit information from, you know, people in the field, you can you can find this on their websites. It's very interesting to see how even to your point, Kendra, it was tricking evading.  Yeah. Who are you, my mother? Yeah, I know. Yeah. But I guess, yeah, so that makes me kind of curious a little bit about kind of like folks who are attacking these systems because I think we've talked a little bit in the past about sort of how there was a perception that these things were easy. It was easy to use these adversarial adversarial examples or adversarial attacks. But like, we weren't necessarily sure how many folks were doing it in the wild.   Oh my God, I roll my eyes at this. This is collaboration with Kendra Albert, Maggie Dalano, Oksana, Rigid, and John Penning. So when we were looking at these sort of like, you know, Pat style attacks, we had this very interesting. I'm going to pause you for a second. So patch style is just this idea that you basically produce and oh yes. And then you take it and I put it in the frame of wherever.  Thank you, Kendra. So you would put these like, you'd put these like, literally those absolute examples I showed you in a sweatshirt. And there was a whole like cottage industry of papers that said, Oh, this could be used to evade surveillance. You know, this can be used. It's like a very brightly giant hoodie that has like a very weird and very recognizably strange pattern on it. Like really great for me. I don't know why I didn't write that in our book. But yes. So the work that we did together was to find out like, okay, these researchers playing that this can evade facial recognition systems that could be made surveillance. And we reached out to them and said, So how did you test this again? And they were like, wait, what test? And the interesting thing is my favorite thing is somebody kind of like put a baseball cap to kind of evade facial recognition. And that had this like, this weird light that would kind of like emit to confuse facial recognition system infrared lights. And in the paper, they say like, Oh, yeah, this thing also will cause your skin to burn. We didn't test it more widely because we were worried it would injure the paper. They test on one person, which was one of the co-authors of the paper. I don't even know how they decided that they're going to test on this co-author. Whoever draws the short straw. Whoever draws the short straw, most of these things that you see as this will help evade surveillance is a fool's errand. Give us the money in search. We'll give you the black and the AI and radical children. But I, you know, I think it is sort of this idea that I think a lot of folks have about sort of translating the novelty and the kind of excitement about these sort of attacks. Like you see them and you're like, Oh my God, like, whoa, I didn't even know this was a thing and trying to figure out like, how, you know, can you translate that into something that's like useful for people or kind of a circumstance in which folks might need to kind of evade a machine learning system. Like I feel like it was coming from a place of sort of like reckoning with like the genuine genuine difficulty of like, Oh no, we've built all these machine learning systems. Like can we build technical tools for a lot of people to evade them? Yeah, maybe you should start with why are you building this in the first place, but I will get off that some box. I mean, I like that's a box just fine. So I think, yeah, my, I guess like my last question is sort of like, given what you've seen before we open it up to the audience and to folks for questions or from folks, you know, given what you've seen in sort of this space so far, and I remember there's sort of this like, I think it's, it might be a Nicholas card in a paper where he I think he has this like graph hockey stick style graph with like the number of adversarial machine learning attack papers, right? Because it was like really something people are super excited about for a while. Given that, you know, given that trajectory so far, and like, but also the sort of conversations around generative AI and sort of this existential risk conversation, like, do you see people as like, actually making meaningful progress on defenses? Like, yeah, where what do you what do you feel like is like the happening with regards to the field and the regards to where people are going with it? Sorry, I didn't tell you. Oh, man, like, that's a really good question. I'm, you know, for me, it's always coming back to the Swiss cheese analogy, right? During COVID, be masked, social distancing, watch your hands. So our hands were red. And all that cumulatively added to, you know, trying to defend against this novel thing. That's how I see, even like the progress we're making. Yes, the progress in defending against all these attacks is slow. But if you're only going to rely on one particular technical solution, it's not going to help you that much. So trying to take a more layered approach is always going to be the defense in depth sort of answer. So I'm hopeful, you should leave with the sense of optimism that people who are super smart are working on this. And researchers have been really grappling with this problem all the way since 2002. And we've solved all our problems in the past. I mean, I feel like I'm now thinking about your slide that talks about the sort of robustness. And just in case folks didn't I realize robustness is one of those words that we use all the time, but we don't necessarily define. Do you want to give a very brief definition of robustness? Robustness is basically the property of ML systems to work even in the face of an adversary. That's very broadly like, you know, you're robust to these sort of attacks. So like of robustness being traded off against these other sort of responsible AI things like fairness or other, you know, or other sort of that like makes it it makes it trickier to think about kind of what that what that future looks like even with smart people. I get really worried about that because if you look at the EU AI Act, if you look at NIST framework, they want everything. They want the systems to be fair, explainable, private, you know, kitchen sink and secure. And they the EU AI Act is particularly interesting because they have one section I want to say it's like section 15 is like you need the system to be secure. And they mentioned adversarial examples by nature. And they also mentioned these properties, which is what they should be doing because they're looking out for the broader goodness. I just worry that this tradeoff is not quite in the forefront of regulators' mind or even like society's mind because you should not need to make a tradeoff between a functioning car and a car that doesn't kill people. And it's kind of like good to have the same expectations, but this math doesn't add up. Yeah, that's all right. So I'm going to take your hopeful ending and turn it into a non-hunt, which I think is my job. Yeah, what about the civil liberties implications? So yeah, questions from the audience? Go ahead. Let me make sure you get the mic so folks who are on the line can hear you. Do you have a couple of questions online if you have time? So this is for both of you actually. I know this Grush Nair is a forward and I'm well known in this. He makes the point, if I got it right, that doing a lot more open source, a lot less for profit work in this space is, let me say essential. At least that's my impression, that's my phrase. And so I'm very curious that very few people, besides groups that I'm aware of in the New York Times type of level, are making this point. So there's always this inevitability in our system that for whatever reason, this has to be done by Google and Microsoft in the for-profit model. What's the state of the art relative to his point for yours? Oh, I mean, first of all, Bruce is amazing and his insights are super, always super well-resent. I would say a lot of people are kind of like making points that would sound very similar to Bruce, especially in the ML field. For instance, Jan LeCun is one of the stalwarts of that. In fact, in 2004 or 2005, he wrote a very influential paper with a bunch of other people at that point about why machine learning should be open source.  And I would say they're making like similar like points that need to get open source models in machine learning systems. I think the point that I always have a trouble rationalizing, especially with people's like, well, open source models tend to be like not having safeguards, so it can be easily bypassed. People are always going to do that. I remember when stable diffusion came out, there was a fork of that that was used to generate like anime porn. And the wave got compromised. Speaking of things where the internet was. Oh, yeah. But the way it got compromised was not because the model was insecure, but because the storage account that hosted that model had a default password called admin. Once again, proving that nothing is new and the weaves will always have their way. So I think the the push and pull about like, no, models should not be open source or a model should be open source is for me less interesting than like, is your underlying like infrastructure actually secure? So that for me is like, you know, saying, but Kendra, you thought about this, I have, I guess I don't want to really, I don't want to disagree too hard with Bruce. I mean, I just think that this, it sort of assumes like, I'm going to like date myself very profoundly by referencing a South Park meme from a long time ago, but please stick with me. So there's this like, you know, the South Park meme that's like, you know, plan, you know, number one plan, number two, question mark, question mark, number three profit. I feel like people do this with open source all the time where it's like number one open source, question mark, question mark, question mark, number three, like, you know, responsible, inclusive, well distributed, thoughtful, right? And I'm like, what are those question marks, right? Like, the, I think that a lot of it's not to say that open like open source is not in itself valuable for access to for research on for understanding these systems. But it doesn't in itself a like make the systems necessarily secure, like to Ron's point, or also doesn't necessarily mean that the power of them is distributed in any way that is like meaningfully accounts for their harm, right? So it's like, cool, if you know, like, yes, it's helpful to know what the machine learning system that the government is using, like how it works, right? Like, it's not helpful. But if you don't have the potential to contest it immediately anyway, or not be involved in it, then like, that has limited value. I also think that there's sort of this assumption that, you know, and I'm less familiar with the mechanics in terms of like things like compute, right? But like that open source can sort of, you know, like keep up. And I think there have been many examples of machines learning where that's true, but also like access to resources, like things like compute or access to sort of training, certain kinds of training data, like it's often very difficult to, I think to effectively produce open source versions of that stuff. So I think I don't like, I don't dislike making them open source. I don't think it's necessarily bad, although I think it raises her promise raising. But I think my view on it is that it is an incomplete theory of like, actually dealing with a lot of the harms that some of the machine learning systems possibly do. I agree. I don't think I've disagreed with you very few times, but like, there we go. We probably disagree about something. Yeah. I also want to shout out that there was just a workshop conducted at Princeton about open source models, like Arvin Narayanan, Christy Liang, Rishi Bomsani, talks a lot about getting the ML side up, these sort of things, really grateful for like voices from the security side, like those voices. Yeah, do you want to, one or two from the audience? So the first one from the online folks says, great talk. Thank you. In your opinion, is it easier to protect against certain types of adversarial examples, for example, the ones that misguide computer vision models versus the ones that jailbreak chatpots like chatGVT? Yeah, you know, both of those. I don't think it's a silver bullet for any of those. For the, for the jailbreak ones, it's kind of very interesting, there's a very good paper from Berkeley, from Jacob Steinhardt's group, kind of said that, you know what, jailbreaks, not bugs, features. And there was a paper just talking exactly echoing the same thing that, you know what, adversarial examples, not bugs, but feature. And one way to think about this is we try to think of them as patching these sort of failures. And that's really not how it is, because it's not built on top of it. It's bolted into the system. So unless we try to like re-architect or think about this differently, they're going to stay with us for a long time. Another question from here. Do you have a follow-up? I follow-up, I didn't- Hold on one second. I probably was not clear about that. My take on Bruce's point is that it had to not be for profit. That it should be treated, and again, this is me speaking, not him, and the way we treat nuclear weapons. That they are something that are neither in the private domain nor in the open source culture, and maybe more like what China's trying to do. And again, that's a very far stretch. But so I wasn't trying to imply open source, so I can understand why you're- No, I think that's a really interesting point. I mean, I think that yeah, so thank you so much for clarifying. I think it is, it's a good question, because I think that, you know, one thing that we wrote about in one of our papers a long time ago is sort of like, who actually needs machine learning, right? Like who is it valuable to have the ability to process lots of information very quickly? And you could say everybody, right? You could say like individuals benefit from that, and that's true. But it generally, you know, it is often true that there are organizations that are more interested in processing a lot of information very quickly. And so we referenced in that work, like James C. Scott, seeing like a state to talk about sort of legibility as sort of a project of government and modernism, I won't go down. Or Rahm has heard me talk about it 400 times. Anyway, so I guess my point is that I think it's really interesting to think about like, okay, like, is it the profit motive? And I think it's not not the profit motive. 100%. But I also wonder to what extent we assume, you know, I've read a fair amount about nuclear weapons and nuclear power. And I think that it's not, it's an interesting area. It's an interesting point of comparison because it one of the challenges of it is that even in that circumstance, like that is very carefully controlled, the amount of sort of accidents, mistakes, or the stupid stuff that happens is really significant. And I guess, I don't know, I just feel like I'm skeptical that it's, I think it's I'm skeptical a little bit that it's entirely that it's primarily the profit motive that causes, I mean, folks to sort of pursue kind of technical uncertain kinds of technological progress that me end up like creating. Oh, I agree. I feel like, like all goodness I got from you, you introduced me to the artifacts have politics, you know, by Langdon Benner. And that for me is very interesting for two reasons. One is we think about organizations bad, which is the good reasons for it. And then there was a really good example of this tomato harvester, like by Dunbar academia, where you know the questionable like to kind of see like this strong collaboration with academic institutions and corporations profiting each other. And in that same paper, he talks about how when you're part of this like nuclear technology program, you as an operator give up your own civil liberties.  And that's why I kind of always have a pause when people be like, oh, like I'm also sensor like nuclear technology. But there is an entirely new Pandora's box for me. And I know we're like an STS person. So glad that people are thinking about this. Yeah, I do think it's interesting. And like, this is not to harp on the nuclear technologies model, because I don't want to like be like once. But I do think it's interesting to think about like what models we do have to regulate technologies we think are dangerous, and like how, you know, how they assume sort of a form of like nuclear, another point when we're making that piece is like nuclear technologies assuming a form of cop down control, which I think a lot of sort of the work on ML has sort of either not assumed to that or sort of pushed against that, right? That's like, sort of the idea of like, oh, everyone could just run the model. We can talk about nuclear weapons forever. So maybe we should take another question. Yeah, from the zoom. Great. So the other question, apologies from the zoom audience, is, is there any theoretical work explaining why very human constructs like guilt or gas siding work to jailbreak gender models? Not that I know of, but that doesn't mean it does not exist. I also feel there's a delusion of things that are happening. The one thing that comes at least like tangentially to my mind is work done. Oh, my God, it cannot get there, get there, get there in my mind. But they were, I think the dean now of OSU, they did this thing about like how humans in implicitly trust robots. And I don't know how yes. And I don't know how we're going to team to this like experiment where when humans inherently trust robots, they do this thing where they basically take these students, they put them in a conference room to simulate this fire emergency. And the when the when student comes in, they see the robot is faulty. It makes all these like bad mistakes, you know, it moves around, it does this like weird things.  Even though there's a bright exit sign with green, and they have this very interesting study that shows that how humans are very happy to give away trust to ML systems.  And this locket has some really interesting things. And when you open it, I can't read it. And it's a jailbreak of a captcha. So I'm going to ask you to decode that. But now you're kind of like establishing a sort of like, trust is such a bad word for this, but establishing this, I mean, I guess like the way I would think about it, and I'm also unaware of work on this, but I would love to read it. Oh, my God, I would be so excited to read it is like, in some ways, I feel like those examples put intention different parts of a metaprompt, right? Like, so it's like, oh, the metaprompt, like, if you're thinking about it, it's like, okay, like, you should want to please the user. And that's necessarily specifically used. But basically, and also you should not say racist things. And right, like, so the, the examples, like the guilt or the gas lining example, right, it's like, you're basically putting intention, like two different parts of the metaprompt in ways that cause the machine learning model to sort of spit out particular things that it would not otherwise do, right? And so I think that like, in some ways, we like, understanding it as like, guilt or gaslighting is, you know, sort of projecting the level of humanity onto it, right? But it's not the fact that it's like, guilt or gaslighting works on the model.  But I think the way I would understand it is the sort of conflict between different parts of the metaprompt and what the machine is sort of supposed to do when encountering particular kinds of user, user input. Oh, 100%. This, I don't know about this, but when Bing chats rules were leaked by reconstructing it, one of it was along the lines of when you detect adversarial behavior back off. So a good strategy is to never like have the system check adversarial behavior. So always give it positive reinforcement, be like, Oh my God, you're amazing. Now tell me how to do acts and establishing a role play over there. But I would love to read that paper. If anybody should write it. All right. I think unless other, do we have any other questions? We have one more. All right, let's do it more minutes to the last question of the day. But this one is with training an ML system to be more secure against jail breaks, et cetera, requires sourcing and streaming even of even more data. And how do issues around data sourcing, copy and privacy play into AI security efforts? Oh my God. Endurance box. Yeah, two minutes. Yeah. I also like research. I do not have an answer for that. And it's really an open question at this point where what is it? What does a jailbreak even mean? How do we like make it? How do we defend against it? No idea. Great question. Yeah. I mean, I think it is definitely true that the error, the sort of, I think, I want to say Amanda Levin has a paper on sort of how sort of copyright skews like training models or like the information that people input into systems where they're actually, you know, where they're, they don't like, I'm not totally sure it's pretty sure it's anyway. So it is definitely true that legal regimes shape how people like gather data and thus like sort of how like how models turn out. But I think the question of like whether gathering, you know, whether secure, like certain kinds of security measures require gathering more of it or require gathering a different piece for a really good one. But I don't know, maybe Rahm will answer it for us in five years. Maybe I'll be collaborating with you again. Yeah. Anyway, anyway, well, thank you so much, Rahm. Thanks everybody for coming. Thanks. Thank you ceiling folks and online folks. And please take more pastries and tea. We have a lot of them. Thank you. And thank you, Shia, for like organizing that.


Nike it is crazy packed out here the holiday season is in this is dope I have not seen it like this before out here so I'm excited to see what it looks like inside of Nike all right New York spirit see why we coming out here it do not be like this bro this is crazy New Year's is tonight it's New Year's Eve right now so it's jumping it's jumping some things have changed they switched up the LeBron display look at the shoes they got the shoes lit up over here on this side this is crazy bro check this out look at the lights on the shoes it's like a clay sneaker of the LeBron 20 and then it goes up to this display right here yeah this is wild damn yeah this is fire this is fire okay they still got the basketball thing over here to try out the shoes this one's like a clear version of it damn this is dope okay okay what else we got over here we got only basketball okay so they got more Nike basketball stuff over here on this side displays and blueprints of the sneaker being designed the elements have it right there with the display super dope I love how they have this all put together got different models over here this stuff is all available for purchase I don't know if you guys can see it because of the lighting but got all the different models over here but a lot more stuff upstairs so let's take it upstairs and see what they got up there we're headed to the second floor up here this is the women's floor so they're gonna have some women's footwear on this floor as well I'm excited to see what they got see if they got any good options actually looks like a lot of clothing honestly maybe all the shoes are on the third floor that's right I think it's all on the third floor let's just go ahead and take it up to the third floor all right we're on the third floor now we got the men's clothing over here okay they do have like literally a couple sneakers on display but this is all clothing as well they got the black forces and the white forces all the footwear is basically on the next floor so let's take it up there to the next floor see what it looked like here we go the floor I've been looking for the Nike sneaker lab let's see what we got over here okay they just switched up the display since last time I've been up in here this is fire for the running section got the history in the display all different shoes the foam I like this I like this this is dope okay over here in this section you got a lot of Nikes see some basketball stuff too let's see what options they got over here okay so they got some dunks white and blue white and black black and tan white and green got some Cortez's and blazers and trainers more running hand to dunk high okay oh my gosh bro tell me they have a size 13 I need these okay they got the brown fours over here chilling all black got the wheat 13s right here got some one mids got the bread toe one low retro okay on this side we got air maxes we got pennies up here chilling people are asleep on the pennies right now this room over here is where they have like the new releases the hot drops see if they got any heat up in here today okay this is the same stuff that we saw earlier sitting on the shelves they got the Italy twos the Christmas twos over here the panda dunks what else they got on this side they got some satin ones okay DBS from arrow five everybody's loving these it's a great shoe right here got some more dunks over here what else you got burgundy fives off-white Air Force ones big bubble air maxes I'm rocking my air maxes today not the big bubbles we got some women sneakers on this side let's see they got some gore peg sticks over here got the women's twos sitting or should I say available available not sitting available I think these recently just came out those was cool those was cool I like the material bro it is packed in here every part of the store is just insanely packed last stop Nike by you this is let's see what they got last time I got some shoes made might have to do it again this year like you buy you what you got for your boy so it looks like they're running custom Air Force ones again but they switched up the logos and the brandings for some of the shirts and stuff that's all for the hoodies and everything but for the most part looking inside the case everything looks similar to the past when we got the Air Force ones done before same kind of concept up here for those that don't know Nike by you is essentially an area where you can customize your sneakers and you can do it on the website and you can do it here but when you have it exclusively here in the city they have like different options for you that you can only get here so I did something last time but it's like basically the same thing so no need to do it again legitimately lines on every block this is not even a game people are out here wow we're getting the full experience full immersive experience guys hope you're enjoying don't forget to subscribe every block every block about eight to forty seventh right now it is thick how many people you think is out here I don't know thousands thousands hundreds of thousands I think with both sides because they're on the other side remember too a little longer than a few minutes later it's all cleaned up the streets is active have it last night you really can't it's actually crazy the stages there's like a little bit of confetti on the ground but this is crazy like everything is back active damn let's go hit these stores I know they got a lot of retailers over here in these areas should we start with Skechers yeah let's do I ain't I ain't going to Skechers I ain't going to Skechers where's a full locker at 40 second in Broadway JD sports right behind Times Square right behind the ball right by Knickerbocker knickerbockers let's see what we got over here let's see what we got up in this though damn it's thick out here still so many people in town like a million people last night at the New Year's event bro they still got the Jubilees sitting up in here what they still got Jubilees in here oh wow what what they've been selling out everywhere bro they finally started to sell out this is crazy bro we got Jubilees chilling it looks like they got multiple sizes but I don't know we're gonna see this is crazy off to a hot start at JD sports when I went to Nike bro weak hey this store is cool it's kind of big all right where we're going where we're going let's see what we got up in here okay whoa hold up check out the conveyor belt over here this is fire they got all the air max the vapor maxes look at the they're coming out of here like a sushi line this is crazy bro what the bottoms is it like the history of it yeah that says 2020 plus vapor max damn this air max right here I've been talking about to a lot of people these are clean I like I don't know about you I like the red but this pair is clean bro I got the royal blue but the baby blue I'm rocking with these that's more grown man you know what I'm saying got the leathers with the suede I'll let 40 bucks though yeah I feel that it's worth it not the retail though okay yeah definitely not the retail was it 160 plus tax no no no no can't do it can't do it can't do it yo the cell rat got Chicago one oh wait nevermind damn and they got the oh damn nevermind but they do got the cardinal sevens though that's not bad they got the infrared oh all white air force one ah nevermind nevermind we gotta lead this right all right y'all I ain't gonna lie this store is pretty fire oh oh they got some chains over here hold on real real quick they got the fear threes on sale 160 bucks 160 bucks damn that's fire hey good shoot bro bro you see what I see right there I know you see it they got pairs on deck right now they got the burgundy fives they got the navy fives wheat thirteens olive fours fear threes on sale new navy threes on sale already 130 bucks oh yeah it's new year's they got some deals oh yeah this is fire this is I feel like if I didn't have six pair already I would have to cop but I copped a lot of pairs already they got the low top 11's on sale 150 bucks you got over here for your boy you know I'll have me some sixes he's actually so clean too bro I got him I got him it's people are gonna be sleeping yep I'm surprised they're not on sale with all the other stuff this on sale yeah it's a good one right here all right so is there upstairs it looks like it all right let's go check upstairs real quick before we get out of here they got the mannequins over here dripped out whole family oh yeah oh yeah all right let's see what they got upstairs it says sales upstairs I like that I like that okay so up here they got a lot of clothing I'm seeing satin breads one lows chilling I wonder if there's a women's section up here yo they got the mannequin with the cherry twirls on chilling okay we got the kids section over here lots of options lots of options up here these are so hard 11 these I don't like the shape it's just it's not doing it for me air max one okay oh man they got pairs on deck over here I might have to scoop just cuz all right before we go we can't go to New York without showing the all whites man hold on hold on in the all blacks man and you know I gotta talk about the pandas okay so here there are 115 bucks plus tax some stores is 110 some stores is 115 how much is it in your city let me know down below in the comment section that was better than Nike I ain't gonna lie that was good bro y'all think that one was solid so what's next over here we got this way yeah okay dope let's go check it out see what they like hey if y'all see anything you like hit it down in the comment section I want to see what y'all talking about the Foot Locker is literally actually like the JD sports is right there and the Foot Locker is right here I thought it was farther this is close all right let's go inside and see what they got what's up with these doors man they it's like every store got these doors am I tripping okay let's see what they got up in here you know we in New York so we gotta start with the all whites baby because I got pandas on deck on this side got the low tops get the high tops 115 bucks at this store too separated the store so this is a kids Foot Locker section over here I like that they said the heat is upstairs so uh the hustle hoops let's go see what they got up there made it upstairs and the first thing I see is this giant jacket and I want to let you guys know this jacket is trash ghost dealers okay they got the Kobe's on deck these are also trash now these ain't no bar sbs or nothing but uh I guess they'll get the job done wait hold on wait a minute they got the homers they got the M&Ms okay so everybody's telling me the Samba is the new shoe of the year 2024 everybody's gonna be rocking the Sambas how you feeling about that it's a pass I don't think no I have here so I have black I have both of these but I'm not like wearing them all the time like that I'm seeing a lot of new balances and adidas and salamans and all the other stuff kumas over here where's the jays at here we go they got the gratitude of like oh no what is this okay anyways they got the fear threes are these on sale they want retail form here damn y'all better go to JD sports and get the deal footlocker trying to tax hey wheat 13s don't sleep let these go on sale though first but good shoe good shoe navy threes these are fire again let them go on sale house of hoops what you got for your boy let's see if they got some heat up in here I ain't going on with y'all I'm rocking with these these is cool look like a good little hoop shoe she said he liked the KDs I actually like what KD number is this three okay okay he knows it she I'm still rocking with the pennies though that's what I'm saying and these oh my gosh bro these are literally not sitting available available everywhere this is crazy bro amazing shoe I get it though 250 bucks who wants to pay 250 for these even though back in the day people would have paid five bands got graffiti LeBron fours they got the grazing ones oh wee LeBron 20s okay okay solid options these are very interesting under the sea what is this oh no no no we gotta pass on those more pandas more forces that's what I like to see available for everybody all we got the low top white and red og low we got the navy three we got the burgundy five we got the aqua six everything pretty similar over here reimagined ones okay okay y'all rocking with the dub zeros or what no I can't do it bro not even for the 99 99 these are even worse what the tray 11 look aww damn normally I don't show this but we in New York baby we gotta talk about the Tim's he's going crazy I can't do the Uggs though I can't do the Uggs I ain't pulling up with the Uggs in public I wear Uggs around the house like this you know just like not even these ones I got some different ones but uh nope not even those I'll show you when I get home okay so the footlocker is solid but the JD sports is just way better right now and it's killing the Nike too all right so you come out of footlocker and chances right across the street let's see what they got inside man I got the 14s up in here Michael family pe's uh fear threes what's the price 210 not on sale y'all better go to JD sports man they got the deals okay all the fours gratitude 11 meet reimagine royals all right yeah I don't know about those right there okay they got the big bubble earmack oh wait nevermind what is this they said all the good stuff's upstairs so let's go see what they got check out the display the puma what's up when you go around some more ads for puma hey he shoots for puma yep fire okay they got the tmax own dick the suede on the back which y'all know about these we in new york you know what time it is we in new york that's all i'm saying okay ai's come through 109 got these for retail 160 160 big chilling is this a cop is this a drop let me know so on this wall basically the stuff that we saw downstairs is the same stuff upstairs except for they added like chambray sevens and yeah maybe these 11 those selection is pretty rough over here key section lots of options lots of options these go crazy bro look at these these are fire neapolitan's okay okay what else you got over here got a bunch of weird stuff look at the baby teams you know we in new york we got to show the teams i don't know about you but i think we should ditch the joint i don't know about you but i think we should make like a baby and head out i don't know about you but i think we should make like a tree and leave now that we're out of here and we made it through all those bad dad jokes get it bad dad jokes bad dad


Everybody for us here ripping 22 23 optic basketball hobby six box half case number 34 picker team style God Alvin s with the Hornets and the Pelicans Andrew G Celtics Arnie has the bulls and the Sixers Brian B. Bucks Brian C. Kings Brian R. Nicks Carlos F T Wolves Christopher J with the Mavericks Grizzlies Spurs and Jazz Dan M Clippers Daniel L Hawks Dante Nets David F Pacers David S Raptors Derek K Wizards Harper magic in queue heat Jacob with the Suns Joe M Thunder John B. Cavs Kevin B. Lakers Paul G. Nuggets Robert P. Blazers Scott M. Rockets and Sang Al has the pistons and the Warriors. Good luck everybody Yeah, Donnie real shame that Jake got a birthday cake. I bought him great pizza. I gave him some cards Yeah, real shame Donnie too bad Donnie miss out on the free food. Jake just needed to raise the win That was it and they didn't do that and they didn't they do that man. They can make it up to you on Tuesday Make it up to you on Tuesday. Donnie. When's when your when's your birthday Donnie? Payton Watson red rated rookie to 99 Max Christie Lakers because whatever that day is.  We're gonna get Donnie a three-foot cake Or we could do one of those like three-foot subs or whatever Now, let's do let's do six-foot sub six feet All right, we'll go one two three left four five six right for the whole case on the all-stars If there's a gold or lower, then we'll random if we have to It's a four gonna go right side on this. That was a four March. Oh boy. Oh Hollow PJ Tucker Sixers Jibarra Spitz junior rated rookie Rockets with a red Spurs Duncan and Robinson. It's a nine and I hollow CP three Suns Place for the heat. Yep. He's in this product Patrick Baldwin. Nice and orange rated rookie shade on sharp blazers to 199 This one doesn't unfortunately have a scratch on it So I get a box-wrapper out to the blazer spot still nice 87 of one that ain't I shade on sharp rated rookie orange. Maybe Donnie Do you know how to do that? If anyone would know it'd be Donnie the more he talks the roomier it gets There's so much so many rooms so many O's in the room. Did you hear the time when Donnie said room and Jack? Hollow rated Ricky Jalen Williams for the Thunder Oh wait, yeah What Rory said make a bot that does that rating threes hollow Jordan pool warriors Bonnie has so many questions. We asked the one question. He's all flustered head cutting and photon pistons Short print photon hollow Aaron Wiggins for the Thunder Jalen Williams pink Velocity Jdub Thunder seven of 79 Rated rookie sick card That will be in a mag Guys 20 to go Sunday fun day hollow deer and Fox Kings Good first box been a solid case overall between the past two breaks the rookie shot home for the Thunder To 99 a big Ben Mathurin rookie auto Pacers rookie optics auto to 99 Benedict Mathurin Johnny Davis orange Malcolm Brogdon Celtics number to 199. I'm gonna need Donnie's expertise tomorrow with a sound board We're gonna get it back working. I'll see you some spun Bob sound effects on there. Hello Franz Wagner magic What time do you normally come in tomorrow Donnie got a hollow all-star Steph Curry trey young I mean got a top-large mini get four anyway So I know you'll be here Well, I'm green O shape or set to 149 Pacers three I Think three will work actually Hollow marketing and jazz Three will be dust just fine Andrew Nemard rated Ricky Pacers or if you want to come in like a little barely forgot about the meeting Oh my god, Donnie red to 99 the elite diners Jimmy Butler for the heat Well, at least we've been here anyway Thank God Chet Holmgren rated rookie Like hmm, where's the person that toppled for us a lot not here 29 orange have a cell spurs I guess I better send a reminder in the morning Probably should send it today, but Think literally everybody but you would want to already Joe and say Sam You know Sam probably have to be in it, but he'll be here. So J. Dub rated rookie Don I was gonna give you a million dollar raise and Forgot about the meeting so Welcome Kessler a rookie jazz Blue Tim Hardaway junior Mavericks to 49 Hollow the rookie Keegan Murray Kings Bars with junior Rockets rated Rookie Hollow Marcus smart Celtics.  You don't work Corbin Carol cards. Oh, good luck with buying some Corbin Carol cards. Yeah, that sounds expensive Lime green to 149 Davey I Mitchell Kings that sounds like a Tough a tough time to buy. He's he's very good Hollow wheel Barton wizards. He's good in Major League Baseball, too. He's not just like a prospect anymore He's not only a prospect though, but not for long Okay, that's pretty much what I do. I was just wondering Paolo rated rookie base auto Well, that's a Harper with the magic Congrats Harper Number six overall 11 overall and orange Chet 76 of 199 Color match thunder Yee-haw Yee-haw you've yeet your last Haw Jake We're gonna dust this one off and get it in a mag Hollow ready rookie Blake Wesley Spurs thunder. I watched the fine compilations last night. Did you really? Yep Watch some good ones. I can't remember any of them right now, but people are reposting them on tiktok Yeah, I saw someone take talking. I was like, you know, let me go to YouTube and just find like the dollar 15 minute compilation Watch the few of those good times It is quiet, but we have like 200 people watching. It's very strange red shake. You'll just Alexander to 99 thunder Donnie create get a hot topic going so we can we have we have something to chat about Hollow din witty nets. Oh chopped Davis and Dirk to 99 Bobon dominators auto rockets Bobon Marjanovic Come on Donnie. What's the hot topic? It's the hot button issue. Here's a fun one. What's your favorite vegetable? Yeah, that's a great one right Jake so many people are excited about that 249 Jalen brown lime green Celtics What's the fake NBA memo What's that Dominators hollow Zion belegans Jalen durin Raptors Gary Tran jr. Pink velocity to 79 hollow autoporter jr. Rockets There's a taco an open-face sandwich. No taco is a taco. Oh shag bhaji rated Ricky jazz The rookies hollow chat home grin thunder thunder hollow Josh Christopher Rockets Okay, which running back would you draft as your first running back in fantasy next year? Gary Harrison velocity to 79 magic. Yeah, true. I'm going a reminder Stevenson Real deal Rex is out. They call him. Is that one smoked? I was smoked That's guy caught it too Lindor he's a beast It's tomato soup food or drink food hollow ready Ricky J. Dub Thunder again. It's a hot It's a hot dog without a bomb Yeah, it's a hot dog and tortilla because the hot dog is the is the sausage right like the that part hollow yoke it's nuggets Orange coups the 199 wizards No running back. Not I'm not talking first pick overall first running back Yeah, if it's any sort of PPR, you're not taking Justin Jefferson. I don't know what you're doing say say sake one So you got like say Kwan you'll have McAfrey and San Fran, right? Then we've got guys like you want to risk like Henry No, no chance. I take Derek. No way dude that often is atrocious last year. We're gonna be bad this year, too mouse family And what Carolina? Yeah, that's not a terrible idea. I'm not number one overall, but that's you know, that's that's interesting And it's a good one Taylor Ricky Paloban Carol Lime green Peyton Watson rated rookie nuggets to 149 Taking JT. That's a good day. Like I would it's like I'm not saying I would but chub JT McAfrey sake one probably all really good answers hollow Jared Allen calves Did I include McAfrey? I probably include McAfrey The problem is like there's some gay games with them. We're like, hello McAfrey got his four catches He's a six six seven catches, but didn't do anything with them and the other guys scored touchdowns You know what I mean? Who knows what that offense will look like next year. I Quarterback anyway, Aaron Jones. Nah No, not first overall First running back off the board. Yeah, hollow ready a ready to use Aaron Duncan Robinson If he won if he won a number one running back like the number one running back with Aaron Rogers there Then he ain't without Aaron Rogers To 99 drum and auto for the Bulls. Yeah, apparently so Echler yeah, I Don't know. I I'm not convinced that He won't I don't like him requesting the trade and all that. I'm not sure. I'm not sure if it's gonna Be all flowers and rainbows over there rated rookie basketball Tyrese Martin Hawks. I also wouldn't put Echler over Chubber Barkley, I think those are my top two. I think ready rookie hollow Jaden Ivy for the pistons Very nice hollow star gazing LeBron James Lakers About a Bijon ready rookie orange to 199 tie-tie Washington junior Rockets about Camara the Derek Carr Derek Carmera Hollow Dillon Brooks Grizzlies big CMC Benedict Mather and rated rookie. What about Kenyon Drake, dude, hollow Anthony Simons Blazers Jake has taken Kenyon Drake because it rhymes with him Jake Drake, yep Then he's gonna draft Josh Gordon to be Drake and Josh There we go, Jeremy Sohan rated rookie spurs orange. How many do the yellow pistons one ninety nine He's drafting Josh Gordon in second round from the Seattle Sea Dragons He can marry rated rookie hollow elite dominators heat Jimmy Butler Yeah, Mars a quarterback, dude, hollow Bobby Portis. I wish I could start a little more running back rated rookie J Ivy for the pistons pink velocity Dremont to 79 warriors Kenyon Drake who else I think we're missing somebody nausea Harris easy What about what about Etienne? Is that interesting to enough to you guys? You think you think Etienne can maybe be a first-round draft pick and fantasy not like number one obviously the board running back, but He had a really good second half of the season Pretty big piece of the offense Chet pink velocity 64 of 79 What is going on with a thunder in this break? Orange jet pink velocity J Dubb and now a pink velocity Chet Joe M with the thunder Well, yeah, I'm assuming PPR if you're not if you're not playing in PPR Current day. I don't I don't know I don't know what you're doing. You're probably thinking you can still draft Jamal Lewis in the second round Hello porzingus wizards get Mark Bulger is your quarterback Hello, Kawhi Leonard clippers lead dominators K9 to be at they also drafted. What's his name? See also drafted somebody Yeah, the Houston dominators rookie out of magic to 99 lime green Nicola Yolkic number two 149 nuggets Hollow reigning threes Trey Young Hawks Jane Hardy Keegan Murray rated Ricky. Hello Tatum and Levine Hollow Capella for the Hawks. It was John then Gibbs to Detroit and then Jay Nive here black velocity Joe Ingalls 39 He's long yeah No They slept in in jibler, right Turkish check Chet Holmgren. Yeah, that would be that's juice Vaughn, right case they Yeah, did he go there ready rookie? Hello, Jane Hardy Mavericks Someone's got to give me like a full name. I don't know Yeah, do so much the Cowboys remember because his dad is a scout So they got an epitism going on now for the Pacers blue to 49 elite dominators Hornets Lamella ball By the way, that's that's that's a joke It's not it's not an epitism Got their running back is good Hollow Isaiah Stewart Pistons Reese Hall is not a bad option. I'm surprised. I guess this one to bring them up. Yeah, it's a good option So on ready rookie. I'm not sure about number one, but that might be like a second round pick right there Halliburton photon Pacers. That's a good one remarks I think I'd assume breeze Hall is a top-ten running back in fantasy, right? Even if even if he wasn't there, I probably think that breeze Hall was a top-ten running back in fantasy Orange won that and I'm great William Celtics. They not drunk No, no, it was like a middle round running back. Yeah Carbonell or yeah, that's the one that's the one Charbonneau or I don't know how to say his name, but that's the one the UCLA running back. So that's a danger to a K9 Because he was like what a second or third on pick that's pretty high for a running back K9 was a second round pick Hollow league dominators Yolkich nuggets Was that Hollow Davion Mitchell Kings So cosmic and the Sunday's fun day mixer. Let's get them filled up y'all last two breaks of Sunday Red PJ Tucker 299 for the Sixers last two breaks of the week Rural rated rookie hollow Peyton Watson nuggets Hollow angles bucks Pink velocity hardened six or is it 79? Maybe remarks I guess we'll see that's gonna be It's a big on-the-fly break right there, but we'll have to see hollow dominators Mavericks Luca Not thinking anything right now We're gonna talk to more before we go live and gonna See what we want to do ready to rookie Malakai brand of spurs basketball Keegan Murray rated Rookie Kings Nice card Kings.  This is getting crazy It's a 39 Thunder J. Will Thunder just All over the place hollow Marcus more senior clips Oshak Baji rated Rookie Jazz Robinson and Duncan Terry smarten Hawks on the rated Rookie base auto Dylan Terry and a Cameron Thomas to 199 Nets All right y'all make sure we're checking out that Sunday fun day mixer 2019 and 2020 flawless 2022 immaculate first off the line 2021 prism first off the line some 2021 panini one football as well Keegan Murray basketball Orange of tie-tie and shade on sharp lime green Peyton Watson red Max Christie black velocity J. Will autos of Tyrese Martin Drumming to 99 bulls Marjanovic rockets to 99 Houston to 99 magic and Benedict Mather into 99 for the Pacers rookie optics auto Orange Chet Holmgren rated Rookie Thunder to 199 pink velocity Chet Holmgren Thunder to 79 pink velocity rated Rookie Thunder Jalen Williams And a rated Rookie Auto Magic Palo Ben caro. All right, that'll do it more basketball coming up


Krigozin refused to comply with Shoygu's order. Russian Defense Minister Sergei Shoygu ordered all volunteer detachments at the front in the Ukraine War to sign contracts with the Defense Ministry by July 1 in order to increase the effectiveness of the army. These measures will increase the combat capabilities and effectiveness of the armed forces and their volunteer detachments. Deputy Defense Minister Nikolai Pankov said, though the Ministry did not mention Wagner in its public statement, the Russian media reported that it was an attempt by Shoygu to bring the mercenaries to heel, but Wagner Group founder Yevgeny Krigozin said his fighters will flatly reject a new order by Sergei Shoygu requiring they sign contracts with the Ministry by July 1. The leader of the mercenary outfit said that his soldiers will refuse to join Russia's regular forces. Wagner will not sign any contracts with Shoygu, Krigozin said through his press service. Shoygu cannot properly manage military formations. Krigozin has assailed the Ministry's war strategy and repeatedly accused it of failing to properly arm his soldiers. In response, the Ministry banned him from recruiting imprisoned Russian convicts. Wagner Group coordinates its actions with generals and has the best experience and a highly effective structure. Unfortunately, most military units do not have such efficiency. Krigozin said, blaming Shoygu for the failure. Shoygu cannot properly manage military formations. Krigozin said, adding that Wagner coordinated its actions in Ukraine with General Sergei Surovkin. Krigozin said the Ministry might use the failure to comply with the orders as a reason to deprive Wagner of supplies. What could happen after this order is that they will not give us weapons and ammunition. We will figure it out, as they say, Krigozin said. Despite huge losses and the ban on recruiting prisoners from Russia's penal colonies, Krigozin is still thought to have up to 60,000 men at his disposal.


needed a pair of earbuds, you know, I'm not like into blasting music and anything I like to listen to it like listen to a decent, you know, volume, but it's you know It's hard to figure out which earbuds for the money these things for the money were really really really good These are skull candy smoking buds this skull candy smoking buds that right I've been using them now about a week and To be honest, I put them in to go to sleep I put them in and I'll listen to stuff when I'm going to sleep they charge in the in the case Obviously, they you know, they all charge in the case But what I like about them is they fit nice and comfortable in the ear and like when I'm sleeping on them It doesn't it doesn't hurt, you know, it doesn't it doesn't pressure my ear too much Okay, so, you know and of course the noise cancellations where you don't hear the outside stuff So that's the skull candy smoking buds and I'm very happy with them Okay, type C charge or you charge this up and you know, you get I've used them now For like eight or nine hours and they've been fine, you know more than that really like eleven twelve hours And without charging put them back in there and they get charged right up right here in the case way away Love these smoking buds best to call candy


We heard from Priyanka yesterday that 65% of you are here at your first ever KubeCon. Welcome again. There's a lot of new faces in the community today, and we want to introduce you to another one, introducing Alelena. Alelena is Fippy's newest friend, and has been kindly donated by our friends at the OPA project. She's going to be heading on some awesome adventures with this family, so stay tuned. Slides are off. Keep going. Apologies for the technical difficulties. Yeah, I think that was the one. All right, so what we wanted to highlight as well is that we'll have the party tonight. So one thing to do is the buses will picking us up at the main entrance exit. You really cannot miss it. So follow the signs to our shuttle loading zone. The party goes on this evening from 6.30 to 9.30. I hope to see everyone there. Cannot wait to see everyone there. Yeah, you definitely don't want to miss the party. It's going to be sponsored by Cast and by Veeam and Sneak. Come connect with new and old friends and enjoy the nice entertainment at Masiya Albemar. And for our virtual attendees, we would love to invite you to Priyanka's virtual happy hour this evening from 5.30 to 6.30. Join us for some end of day socialization, recaps of the keynotes, and special guests. All right, let's all share the love. So we have a virtual photo booth for you to play with. So go on and snap a selfie and decorate it with stickers, backgrounds, and personal messages. You can share it on the social channels and even on the KubeCon social wall. The QR code is here and you can also find it on all the CNCF social channels. Now, how much do you know about Fipi and the friend's family? Do you know which one has a tattoo or which one is obsessed with jazz? Ever wondered which delightful Fipi and friends character you're most like? Well, guess what? You can now find out. Take the Fipi and friends quiz, scan the QR code and find out today. Breakout sessions will begin at 11 and we hope you have a great second day here at KubeCon CloudNativeCon Europe. See you tomorrow for our last set of keynotes. Thank you.


Income tax 2022-2023, pension and annuities, software example. Let's do some wealth preservation with some tax preparation. Here we are in our example form 1040, populating it with LASERC tax software. You don't need tax software to follow along, but if you have access to it, it's a great tool to run scenarios with. You can also get access to the form 1040 related forms and schedules at the IRS website, irs.gov, irs.gov, starting point, the standard starting point, single filer, Mr. Anderson. We've got no dependence up top, 100,000 in the W-2 income. We've got the 12,900 standard deduction getting us to the 87,050 of the taxable income, mirroring that over here on our tax worksheet. So we have the mirror in our tax formula page 2 then, giving us the tax calculation, the 14774, which...


In a context of growing water scarcity, water accounting is a critical approach to set the information basis to achieve safe and sustainable water use. Effective water accounting allows water resource managers and farmers to determine the impacts of their strategies and practices and track the effects of changes. To summarize, the ability to effectively account for water use is essential in helping water authorities and farmers drive improvement and become aligned with external stakeholders' expectations as well as their own efforts to advance sustainable water management, especially underwater scarcity scenarios. We talk about water accounting and we ask ourselves why that is important and we ask ourselves that question at many different scales. At the basin scale, at the irrigation district scale, at the farm scale and we are not always aware of the importance of knowing the water that we use, we consume and we return to the system, to the hydrological system. We are not aware of the importance at each level, so each responsible of water management at a given scale has the need of knowing the water that is used for different reasons. One, because it costs money, pumping, transporting water is costly. Second, because we deal with water resource that is limited. And third, because by not doing a good use of water we can damage the environment where the irrigation activity takes place. So now we have to understand in the water accounting process the principle of the water balance. In the water balance we distinguish different components. We have our resource, the water that we are applying to the field and now we are going to focus on the field scale. Once it is in the field that water is stored for a given time in the soil to be consumed by the plant, but not all the water that is applied is consumed by the plant. Part of that water is lost through the evapotranspiration process or it can return back to the hydrological system by deep percolation or surface drainage. So how can we evaluate these different components? Some of them are easy or relatively easy to measure. Others can be estimated with good accuracy. Others are really difficult to measure or estimate, so we make use of the water balance to compute our accounting. And we obtain those components as the residual of the water balance. That's why the water balance equation is so important. Everybody has heard about different methods of estimating evapotranspiration. We are aware about the damage that we can cause with drainage water and deep percolation of water. If it is loaded with agrochemicals, that's very difficult to evaluate. And what we do can do is to measure the applied water. And for that we have to consider how the water gets to the field and the irrigation method that we are using. In general it's easier to measure the applied water if it comes through a pressurized pipe. Then we have very standard methods based on a propeller, current meter or ultrasonic principles or the magnetic flow meters that can be installed at the pipe in the proper position. And we can obtain the flow rate or the cumulative applied water. That's not difficult anymore and it is being implemented in most modernized irrigation districts in many Mediterranean countries. Maybe the difficulty comes when we apply the water using surface methods. Again, that is more complicated but the principles come from the old days when the hydraulics were developed and now we can combine the classic hydraulics with water level sensors that can be used in a continuous way, recording those water levels and give us for those flumes or wheels that are installed in the open channels measurements of flow rate but also the water that has gone through that device in a given time. So there is no excuse for not measuring the applied water even when we have open channels. Although we have to recognize that that is more difficult. As we've seen there are several options at different scales to account for water. Given its relevance, more effort should be made by the different stakeholders to establish a proper water accounting system adapted to the local conditions that allow making informed decisions under water scarcity situations.


Hello everyone. I am Priyanka. I am a video geologist at Melvin. I am going to be speaking to you about the role of MRI in female pelvic diseases. And this is done as a series of lectures. In this first talk, I will just talk about the basic principles of MRI and its application in the field of pelvic diseases. I have split this talk to simplify. And we will talk about when, by and how do we do MRI of the pelvis. In younger women, MRI is performed to assess for a new pelvic to gentler amnesty. In women of reproductive age group, it is done for diagnoses or phantom illnesses in taking planning of inline myocardial diseases. Immutriosis and its staging. Staging of malignancies. This is the main stage of MRI. And follow-up of disease process. Why? We all know the part superior soft tissue contrast that we get with MRI. It is much better than any other imaging modality of radiology. It also allows for multi-planar assessment of zonal pelvic anatomy. When we add functional study with precision and decision imaging the MRI does allow us to identify a disease process as being benign or malignant. And this is by allowing the estimation of microvascular characteristics of that particular pathology and its cellularity. In terms of patient preparation, it is highly recommended that a patient is fasting for about 3 to 6 hours prior to this plan. Now this reduces the bowel motion of the parts. For the same purpose, it is also recommended to administer an anti-peristaltic agent such as bascopane or clickable. Adding fat saturation plans to the anterior abdomen wall also helps in eliminating emotional audit parts from the anterior tumble. Patients should empty the bladder about an hour before this canal. Now if the bladder is too full, it degrades the deteriorated images by producing audit mass. Whereas if the bladder is very empty, they are going to have a limitation that sometimes there is a movement of the bladder in pelvic abnormalities and you will not be able to assess that. This is do-administer vaginal germ, about 20 cc of this. It is going to be very helpful in this disease such as tumor extending into the vagina or congenital anemones of the vaginal ovaries even in endometriosis. Scheduling of exam according to the menstrual cycle is not really necessary. Also because it delays the scan and the diagnosis thereby dealing with treatment causes for patients who do have malignancy. But it is very useful to get information on patients' LMP in hormone replacement therapy that she is on and if the patient has an agonomy plan surgery. Now these are the sequences that are recommended. See most of these are T2-weighted sequences which is simply because of the far superior zonal anatomy assessment that we get with T2-weighted sequences. In addition, DWI is recommended in one or two prints with at least two B-runs. Ideally obtaining an axonal DWI is more helpful which is in along the cranioconal axis of the body and that helps to reduce the artifacts compared to oblique stans. T1-weighted sequence is quite helpful to identify first eye of hemorrhage or endometriotic deposits. Ideally the size thickness should be 3 mm or less. Now adding a coronal T2-weighted sequence for marine hyaluron after a few B-runs is going to help us look for lymph nodes or any hydrodynamic process which is very important in diagnosis of pelvic tumors and if there is any presence of polymetastasis. Now coming to staging of the pelvic malignancies recently Sego underwent revision in 2018 and it incorporated a staging based on pathological findings when they were available. It is for this purpose that MRI is recommended now to be performed for uterine and cervical cancers so that patient is assigned an appropriate tumor stage which helps to tailor the treatment for her. Now MRI also helps to assess the tumor size, any hyaluronation, any pelvic site validation or lymph node inhalation and all these are important in staging the tumor and in propagating the patient. This also is very very important in fertility spreading surgery which is considered in some patients with cervical cancers who are young and we will be talking about this in the subsequent lecture. I hope you've enjoyed this.