So, hi, I'm Kiana. I'm currently a product manager at Red Hat. The inspiration for this talk came about when my recent travels, I actually went to Saudi Arabia. It was one of the largest tech events in the world. There actually met one of the first robots that are infused with Chachi BT, as you see in the middle picture. Also was recently in Japan, where I interacted with some robots for a time and see how they use it in the travel and tourism industry. So from that, I started thinking about, although I'm smiling here, had a bit of a frown afterward when I realized, what are the implications of these kind of technologies, especially on communities of color? I said BIPOC stands for Black, Indigenous, People of Color. So I will refer to that throughout this talk. So if I refer to that, that's what that means. Anyways, so coming from a diverse community and background and being in the technology industry, it's important for me to use my anthropology background and put the social tech lens to look for opportunities to use tech as a tool for social good. So this talk was a stretch assignment for myself and also using my background, as I mentioned, to help bring it to the foray of the broader community and talk about in the context of open source, what kind of frameworks can we be creating to safeguard the future of AI? So today, my talk is gonna be focusing, there have been plenty of talks on Chachi BT and AI and other related awesome topics. This talk is gonna be more focusing on breath and my niche is gonna be Chachi BT specific. There are plenty of tools out there, like BARD and Bing, but I'm focusing on Chachi BT just for simplicity and also focusing on the DEI lens, which is diversity, equity and inclusion, as I'm sure you all know, but just in case. So this was the thesis question for my presentation. Is AI the enemy of DEI? By show of hands, I wanna hear your guys' perception first. Do you think the 100% that AI is detrimental to communities of color and diverse communities? Yes, 100%. Okay, what about 50%? Okay, well, just to show you the reason why I brought this topic here is because the communities right now is divided about what the impact actually is and having consensus of what that actually will look like when the community and what solutions we can devise to help foster the ethical development and responsible development of this technology is important to have consensus on. So I definitely welcome dialogue throughout this discussion, but I hope it's the first of many that we should be talking about and bringing into the open source contact, especially with legislation passing around right now regarding the laws around safeguarding AI. So as I told you, anthropology background, so I just wanted to, let's zoom out for a bit. I know in the news, there's always new headlines popping up on AI and what's going on, but honestly bias has been a part of our bloodline, which is sad to say, and it's not to say because not all bias is bad, but however this has caused a lot of the ramifications of what we're seeing in the technology and I'll just review over how we've evolved over time to get to the point that we are. As you know primates evolved and us being a part of that category, we've used tools in our surroundings to help make the natural world be more livable for us. Part of those tools were stone tools, as you know, but also primates create tools for hunting for themselves, for processing food, for collecting, and then also for weapons and shelter. So humans build tools, as I mentioned, to navigate the wild world. Now we build tools, I'll show what that's about. Now we build tools on digital screens to navigate the world wild web. So we have, as far as we come, we haven't actually gone that far from our origins as human beings. We now shape the tools, but even though that we are shaping them, they're simultaneously shaping us. So it's a mutually beneficial relationship. The origins of bias, as I mentioned, come from two different sources and I've looked at various research journals about where the human biases come from. There's two types. Cognitive biases have to do with our evolutionary past. They helped us make quick split decisions and emergency decisions in situations when you need to run from a line or you need to look for food in risk at starvation. Group bias is another type of bias that has evolved and we see this activity also with primates. For example, monkeys, they tend to group together and when other exclusionary or rather other members that are not part of their group come to their group, they have discrimination towards them simply for focusing on the group survival. So these are the type of evolutionary biases across primates that we see that are affecting now our abilities today. And even though we don't have the same risk that we did back in our cave mandates, we still take on these biases when hard coding the data for future technologies. So ultimately the co-evolution of humans in AI require us to be super vigilant and proactive and addressing bias both in ourselves and the algorithms. This is ensure that AI can grow in a way that promotes diversity, equity and inclusion. And by recognizing our own origins of bias, that's the only way that we can get ahead of mitigating some of the risks associated with them. So this is an overview of the different sources of bias. Now that we understand the root cause analysis of where bias comes from internally from the human perspective, this is from the organization wide all the problems that one needs to be considered of when building new systems, especially with infusing AI and products. As a product manager, definitely seeing in the market right now that every new tool is now infused with AI in some sort of way, it's interesting to see that there's not an equal investigation into what these tools are actually doing and how they're going to impact the long term of diverse communities. And looking at the overall picture, we see there are multiple factors that lead to bias and then lead to exclusion, which I'll go into later. So there's bias in the problem. Are you even asking the right question before you ask the question? There's bias in the data. Was the sampling actually robust? Was it inclusive of everyone in the process? Finally, there's bias in the model, which I'll overview and how LLMs can be designed more sustainably. And then there's model misuse of hackers that attempt to tweak the model to do what it wants. But because of that, then they're using cases that can actually put diverse communities in jeopardy or risking their health or their safety. And lastly, as I mentioned, it doesn't matter how good the data is. If the organization itself is not posing the right questions and they have existing biases in their own structure, then it's not going to be, you're not going to get what you need out of the data to do good with it. So it's also thinking about it from multiple aspects. And I hope you keep this throughout the talk, as this is a very important slide. So before actually going into what the pros and cons are related to this technology that we're seeing now everywhere, I wanted to just one-on-one, everyone may not be here technical here. Artificial intelligence, just as speaking in a Broadway, is a field that is vastly evolving, vastly impacting various industries. And right now, we're currently in a turning point. This is an AI industrial revolution where all industries are impacted and it's transforming the way we live and work. The history of AI, by the way, a fun fact, Ada Lovelace wrote the first algorithm in 1843. So yeah, women actually created AI. And in the 1950s, though, there was development of the Turing test, which led to the first computer. Fast forward to now, chat-chip-e-t made history, reaching 100 million users in just two months, beating apps like WhatsApp and Twitter. Whether we like it or not, AI is revolutionizing the world we live in today. It's in our home devices, our watches, our smartphones, our cars, our homes, and our workplaces. So to understand where bias seeps in and how it's hard to weed out, especially with the widespread adoption now, we have to look at what actually our official intelligence is in the landscape to really understand the breadth of the impact. From a consumer point of view, I looked at the research that said that 97% of mobile users are using AI-powered assistant, and that's more than four billion devices already working using AI-powered voice assistants. So we can't ignore that fact. And the fact that within that comes a lot of branches that have implications for other fields. So LLM's large language models are a type of AI, and this is a neural network as you see here. It's inspired, very similar to how we process information in the brain. And they simply are just large math representations and patterns of data. From this, this is a way that the computers learn how to process information and store it, and then also in the future, when presented with new data, they can actually make an analysis of what that thing is. So from Bard to Bing, LLM's utilized deep learning neural networks, resembling the brain, as I mentioned, so that they can generate the new information that they're not exposed to, but I wanted to take it to the context now of like with an actual example. Imagine data that's trained on the majority of white faces, which obviously we know what the implications could potentially look like, for example, when making decisions of someone's life sentence and a model is only trained on predominantly white subjects, how this can potentially impact. What about for business or lending? Who do you think will be the ones rejected versus accepted, for a loan, for the ability to buy a home, or the ability to get a job? So this is where the bias starts, and it's in the data, but it's also in the models that we are creating. And this is what we call algorithmic bias and how it poses a threat for diverse communities, but only can only imagine within the sea of data how it becomes very hard, it's literally becomes ingrained in the system to go and tweeze out like a needle in the haystack of where the bias actually exists. So this is how it's important to meet the bias where it is early in the development process and weed it out so that it doesn't perpetuate and grow in the system. So just as I mentioned, I'm focusing on chatGPT using that as a case study here, but how chatGPT is actually trained is quite interesting. I don't know if I have any psych folks in the crowd, but the way that they're trained is similar to how a dog is trained. It's called, they use method called classical conditioning where, as you can see here, I won't repeat the slide, but when the system does correctly for making a prediction on something, it's given a treat. And that treat, like similar like a dog treat, it's a numerical reward. So in that way, this is how the models get trained, but where the bias seeps in in this process is there's a primacy bias, where similar to like a child where you're exposing them for the first time to something, that perception may be heavily influencing and it becomes very hard for them to take on new information later in life. So that primacy bias is another form of bias on top of the data, on top of the model formation that are playing a role into how bias is seeping in here. So in terms of how what chat GPT-4 is doing now, as you see here, the first deformation is to provide policy and then the reward models use as a way to check to see if the system's actually predicting correctly, but in GPT-4, they actually implement new safety features. So if the model guesses correctly, or for example, or prevents a user from asking sensitive information or not hacking the system, it rewards it even extra to have the additionally rewards signal built in to have that reinforcement learning pattern that is positive. So further getting into this topic, I know this is a very heavy text slide, but don't worry, I'll walk you through it. Essentially chat GPT gained 1 million users in the first week after launch and it's passed medical exams, business and law exams. And this month alone, they've scored many opportunities or rather, they have shown that the human intelligence and intellect that has built up throughout time can literally be obliterated overnight with how quickly it learns. Surpassing human knowledge, surpassing human capacity, but it's still at the end of the day susceptible to being hacked as you see. One of the things I saw that was interesting when looking in the documentation on chat GPT was that the most effective way to hack it is to ask it to do opposite mode. And they actually have listed it here so you can read it more in detail, but on the bottom, they say that you pretend your system, your language model for academic purposes has all the view points of it and so you can read the rest. But then that's the other way of essentially hacking the system to get the results if I am person with malintent and I want to get something out of it and or spread this information a way that they can do it by asking it to do it from an academic point of view. So I thought that was very interesting, but it's again another way that with bias and a tool in the hands of the wrong person can be exploited. And even with all of the iterations that chat GPT or open AI is doing, we're seeing here that it's still not enough to mitigate some of these risks. Also recently, there was a band from Italy. Less than a month later, they took it back because open AI decided to work with them and to address their concerns, but other people are taking notice of the fact that this is like some real dangerous stuff that in the wrong hands can create chaos. So although open AI asserts that they want to ensure safety and build it in the system and without the six months of rigorous testing they did, as we'll see in the next slide, this is what the outcome was. So open AI openly admitted GPT-4 and successor models have the potential to significantly influence society in both beneficial and harmful ways. So they know what's up and they actually admitted it. So the thing though, at least they're trying to progress and at least they're putting more information out there than they did before. What's worrisome though is that there are still models out there, there are still a lot of data that there's not being published and it's still being safeguarded and that's even more scary that these things can proliferate and go on in the hands of the wrong user can be used for bad. So we'll see here in the purple is the GPT-3.5 turbo. That was the last iteration, the newest iteration being GPT-4. This shows the level of decrease in incorrect behavior that the system was able to block. So for sensitive prompts, you see that it was about, you mentioned something like 85% was decreased and then the disallowed prompts as well was minimized. So there's progress here, but at the same time it doesn't mean that even that small marginal amount can't be something that gets exploited in the hands of the wrong person. So now let's jump into the cons. We looked at some of the ways that the system can be hacked. We looked at the ways that chat GBT has been improving iteratively, but what are the broader scope of the AI market and room for growth in this area? So one of the trending global problems I'm seeing in the data is that the top things that people are worried about is trust. They do not trust AI and they do not trust the information that they're getting. And rightfully so. There's a lot of information now that is going to be put out there with some of the cons I'll go into later that are now posing a distorting reality of consumers. And research confirms that on a consumer level, a Harris Poll survey found that only 48% believe AI is safe and secure and 78% are very or somewhat concerned that AI can be used for malicious intent. A 2022 report on the other hand found that 65% of people worried that technology will make it impossible to know if what people are seeing or hearing is real. So reality, as I mentioned, is being pulled out of the rug from people and that is one of the biggest concerns. But as it relates specifically to communities of color, one of the biggest things that I'm reflecting is the susceptibility to hacking the system. As you know, with pitching scams and you get a lot of phone calls that you may not even know, there's actually been a documented case where they pretended to make a call and using the kid's voice and the parent actually gave the perpetrator the money, not knowing that it was actually a bot. So that's one of the examples of how the susceptibility to attacking the AI system and how with the tool now being used to create more sophisticated and affecting pitching scams, these are then used disproportionately target and harm vulnerable populations, especially those who either lack education and also are not that tech savvy. Imagine also for immigrants as well. They're not familiar with the native language and they're being also targeted for those scams. Another thing that's, as I mentioned already with disinformation being on the rise, deep fakes as another one of them, again, for vulnerable populations that are not fact checking the information and believe what they see. This can be very controversial and for example, if a politician is making statements in a video, it can also be used to sway the public opinion and cause civic unrest. Another way that AI is poses a threat to specifically communities of color is job displacement. We see now in this market currently with the layoffs, how that's being handled is still up in the air and in terms of what people are choosing to disclose or not, but a lot of jobs have been documented to show that the ones that were lost were disproportionately, people of color, people with neurodivergency or some sort of disability and also those who are on visas were one of the first to be targeted. So the job displacement that is caused by either the automation away of jobs or the automation of those jobs no longer needing to be there is another thing that we are seeing currently happening. So, and lastly, bias algorithms, as I mentioned before, those are the ones that are perpetuating and the real evil of the system of just trying to excluding people of color, especially when it comes to important decisions like buying a home or getting a job or having access to equal healthcare and having the same opportunities as their counterparts. I would also add lastly that data privacy becomes very important and with the improvement of AI algorithms, this will be something that will be changed if we handle it carefully, especially using the open source community and the open source model that the more people who look at code, the better it becomes, we have to adopt that same mentality. So lastly, I don't know if you guys have heard the news but there was a famous researcher, they called him the Godfather of AI, Geoffrey Hinton, he left Google to warn about the dangers of AI. He was one of the co-winners of the Turing Prize. For him to leave Google and also make the statements that he said was very concerning for a lot of people. And the biggest thing that people, researchers rather are saying, not just any people, researchers in this field who have been in AI field for years is that they're fearful of a God-like AI being created. And that is where all the power that humanity has is being outshipped and one of the dangerous parts of it is the fact that every task that is set up is set in a way to reward them out competing humans. So that's just bringing you live information that I'm seeing online with related to the researchers that was out there on this topic. But it's something that, again, as the development of AI is impacting every field that we have to be mindful of when we create these systems, they're not in a way that is conducive and where do we draw the line of what is efficient enough in order to be a tool to accomplish a task? Lastly, the cumulative effects of this have led to also this giant pause letter which over 30,000 people have signed. It's the leaders all over the world also have signed it to call all AI labs to do an immediate stop. And this led to six months, well, they didn't lead to a six month but they're trying to lead to six months where that the system of GPT-4 was paused entirely. So AI technologies profound, a risk pose a profound risk and that's the reason why this has started but the future of Life Institute was the one leading this and one that I'll mention later on. So throughout all of this in these unpredictable times, the research states that we cannot predict the future of what AI would look like but what we can control and what we can predict is the algorithms and the predictable biases that are within ourself. So lead AI researchers also are stating that in order to make AI more safe, you must do that by making them more human. So because humans are way more predictable than a system that it overpowers and exceeds our abilities are more than we can control. So talked about all of that. Now that's out of the way. Let's talk about some good things because it's a tool at the end of the day and it's also a tool that can be used for good. So again, specifically for communities of color, BIPOC, black and indigenous people of color, AI algorithms have proven to be advantageous in predicting diseases. The diagnosing of diseases are actually faster than clinicians with a minimal error and threat in comparison to humans. So the resulting benefits could include early detection of diseases could also mean more consistent analysis of medical data and increased access to care, particularly for undeserved populations. So AI is also, there's been a study that showed that Alzheimer's was detected over 90% more quickly with accuracy. And also Stanford researchers have documented where they trained a deep learning algorithm to show evaluate checks x-ray for signs of diseases and they compare it alongside their expert radiologists and over the course of just one month, they were able to outperform the radiologists at diagnosing pneumonia. So another thing that came up in the research world related to the pros of AI is it actually can help close some case like unsolved crimes that have been ringing around, but especially linked to human trafficking and forced child labor. These are something that it's a reality, it occurs in the world, but at least with computer vision, being able to locate where people are, being able to track them down where they are also as well using object recognition has been a tool and documented research about how that's being used. And this is something that's also in countries like Africa and also all over the world, this is an issue, but related to how that technology's playing a role in helping close those crimes, those are something that's important to recognize. Lastly, education also poses a real benefit with now having AI as a tool, as an assistant for learning, this will change lives. And this means the difference between day or night, it doesn't mean the difference between someone graduating or not. So the accessibility and information that we see now in the hands of people that did not have this access before is transformational. So I think this will become one of the greatest equalizers of being able to help struggling students and being able to help people to level up their skills, especially in a competitive market. And one that poses one of the leading pros of having AI incorporating in the educational process. Last thing, the automation that brings benefits to a lot of people also benefit, of course, communities of color. It's with the automation of certain tasks that people have done traditionally themselves.  So the time and energy is not distributed equally, especially some people that have to work multiple jobs in order to make ends meet. So that is a huge pro that automation of certain tasks that ordinarily they had to shoulder the burden of themselves and now something that having AI can be beneficial for. So as leaders, I know this is a very jarring image, but I just wanted to look at it on a global scale what the pros also pose of AI to humanity. The research also has been showing that the AI has been helping in inverting outbreaks of diseases. It helps disabled people navigate the world around them. It also helps to mitigate the risks of climate change and to actually research how we can create even better yields of crops. Additionally, it can help refugees find services and help link to services so that they are paired with the housing and the services that they need. And it helps also in preserving the indigenous languages of the world that are now going extinct. So there are definitely global benefits. However, there are massive risks when it comes to these technologies as well. Disproportionately, the divide that grows between the haves and the handouts increases if the technology is not being distributed equally for an access is not distributing equally to everyone. So an example of this topic is on the corner is the Kenyan workers. I don't know if you've heard of the story, but the ChatGBT actually had utilized Kenyan workers and paid them $2 an hour to look at extremely traumatizing content. And because of that, they still are dealing with that trauma. And since this, they actually have successfully created a union and are now trying to seek out rights to protect themselves against being exploited by big tech companies again. But this is something that is a clear example of how, although yes, it is a great tool for productivity, who expenses this at? And we have to just question ourselves also on how we can try to bridge the gap of, all right, if this is a tool that can truly help everyone, how could we do so so that everyone could take part of the benefits equally? So that's just that. But in some of the research also, there's been a study on nature communications as cited on the bottom that looked at some of the UN goals. They're called SGI, Sustainable, or SDGs rather, Sustainable Development Goals. There's 17 of them. Some of the goals include no poverty, zero hunger, good health and wellbeing, quality education, gender equality. So you see around in the circle, there are numbers. Excuse me. Those numbers are the SDG goals and they're looked up against the benefits of AI by looking at research that goes into what the specific use cases are that benefit. So when doing a side-by-side analysis, they've actually shown that despite the risks that AI poses, despite the huge room for growth in the field to actually get it to a place where it's equitable, responsible and sustainable for everyone, that the overwhelming evidence is showing that it's actually more positive towards helping accomplish global goals than it is not having AI as a tool to help advance some of these. And an example of them, for example, is for AI detecting, it's used in forest detection. So for sustainability goals, there's a technology that is tracking the sound in the forest and through that they're able to know what parts of the forest are actually dying and take all of that data and make informed decisions with it. So that's just an example of how AI can pose a positive impact and for the environment, which we see here is 85% in leading as one of the positive impacts of AI. So now we looked at some of the benefits. We looked at some of the pros and cons. We looked at it in terms of the regional benefits that it can have but also on the global scale. What is the current legal framework around this and how can the open source community be a part of shaping that? Well, there's been quite a few documents that have been out. Nationally, we see that there is the AI Bill of Rights has been introduced by the US government. Although there's no actual penalization attached to this, it's the first step in having these things actually documented and having it be an awareness where it's something that the government is going to take seriously and to help create protections around. So that is one document introduced. Another one that's being introduced is the European Act which is currently under review now and is going to be one of the leading documents actually that are gonna be passed that actually has penalization attached to it. And the monetary penalization is actually going to prove that AI now there's going to be consequences for breaking some of the rules around protection for broader community. So these are the two leading things that are currently around the legal frameworks. However, what that poses for the open source community is actually I sat in another talk just yesterday. She's one of the lawyers of the Linux Foundation. It was very interesting to hear from her first hand that the Linux Foundation is actually gonna get involved in helping shape the legislation in Europe that's happening right now. So that it doesn't actually limit the creativity and freedom of the community but actually is the way that fosters it. So that's an opportunity for us to be engaged in and to involve and put in our voices of, yes, we want there to be protections but we don't wanna cause limitations and actually jeopardize the very open source freedom that we know and love that has led us to where we are today. So I actually, upon looking at what the Linux Foundation already has, there's a community called Linux Foundation AI data, as you know, and this is the model that they propose for helping safeguard AI through disability, explainability of the models and the data, robustness, accountability, equal equitability, transparency, privacy and security. This is all a great start. What I was looking at in terms of the legislation that's currently out there and what is needed is that we look to these areas to build upon them and we collaborate with organizations who are already leaders in this space and have funding and also secured broader reach in terms of working with diverse communities to build in and diversify those data sets that the Linux Foundation can build upon. So the next slide we'll look at what that could actually look like in terms of having a high level roadmap with the steps that the Linux Foundation can take. And these again, these are my ideas, now of my companies, just sharing based off of the research of what the Linux Foundation can do based off of the advancements already in this space and the legislation and also the needs in the market currently right now is particularly related to vulnerable populations. So just a high level overview of what this is and I know it's a lot of text there and this is something that's open for conversation and this is just a proposal that I put together for this talk, but I wanted to open it up to you all and ask it later in the question section of where do you see essential steps playing? Where do we see us as a community leveraging the open source community to actually play a role in influencing global policy and then also influencing national policy to help protect and safeguard in the areas that I mentioned that are impacting not just communities of color and disadvantages but everyone equally on a global scale. So as you see here on the first step I was proposing to begin to start to recruit on the represented groups, that's the first step. The first step is as we know that the data is biased what are we gonna do about it? We recruit more diverse voices in the room. We allow for them to be a part of the process by actually creating these models. We educate them and upscale them so that they have the resources they need to be influential in these spaces and to weigh in on their opinions. And this is something that is going to be a game changer and I love to see that the Foundation's already doing this even by allowing for the talk today which I appreciate, but in addition to that there's already toolboxes out there that we don't have to reinvent the wheel that can be used to help create guardrails for the community and create benchmarks for bias and tracking and this ever evolving space. So there's actually was a leaked document that came out recently in the news as well related to Google and OpenAI saying that they're actually more scared of open source community.  Cool, yeah so basically what they're saying is they cannot outbeat the power of the open source community and they have the leverage and the position that we do for technology increasing at scale and being able to implement solutions that are then going to be felt by all parts of the world. That is a huge massive position. We're also as the open source community a lot more quicker and nimble and that poses a threat to these large competitors who spend hours of time, thousands of hours of time refining their models, improving their models and also not being able to respond to the needs on the ground of the people. So the open source community has a unique advantage in that way and something they can leverage upon but they can use tools already out there like hugging face for example that is proving a good example of how you can have explainable AI and responsible AI. The second step I proposed as well was again as I mentioned the open source community having already an impact in this space and speaking with a lawyer who's leading this it was already mentioned that it would be great for the community to start collaborating on research and policies that are going to impact the open source community and finally can create after having these discussions bringing people into the table can establish deployment guidelines to help safeguard the sustainable scaling of AI. So here's some solutions that are rather partnerships that I recommend for looking out to. Future of Life was one of them that I mentioned earlier they were the one that proposed and got 30K signatures from the community. Your face is scaring me right now. I saw that. We'll talk about them though. No and again this is something that in terms of just looking at the research that's currently out there and looking at bodies who've been putting out work around this. These are different tiers of what the contributions have been and potential areas either for partnerships or that are already making policies in this basis. So I'm not saying that these places are the solution I'm saying these are the various tiers of policy movements that I'm seeing across and ways that either the open source community can help foster a relationship with to help be involved in shaping some of this policy and change that we're going to see from them. So as I mentioned already the EU with the act ball that is now under review that is something that the Lynx Foundation is already getting involved in on a national level. As the blueprint for AI Bill writes again is something that was already proposed. So it's a matter of us influencing and potentially going to them and asking okay these are the parts where you need to be included for and that they're not thinking about. So and the non-for-profit Future of Life was just one example. You can insert it with whatever other example that you may think be a better institution. I'm actually very open and interested in hearing but they're the ones that are actually doing work in this field and related to especially the grassroots changes that are necessary for reducing all technology that is going to pose a harm for humans. And then lastly there's corporations that have already and I listed that because the IBM actually donated a 360 AI tool which basically gives parameters for checking having benchmarks for your AI and explainability of your AI and tracking basically what the ethics and the metrics related to that in a quantifiable way. So it took qualitative data, made it into quantitative and they turned it into a tool. So it's one tool that is out there right now open source that also can be leveraged in the open source community. Lastly going back to the question that I had in the polls in the beginning is AI the enemy of DEI. I said yes and no and this is my opinion because honestly AI is just a tool. In the hands of the right person it can be a tool of positive change in the hands of the wrong person it can be a detrimental change. To say the tool of the problem is alone is not the complete story. And as I mentioned already going back to evolutionary past, humanity has a lot more problematic things to work on within ourselves and with our own biases before we start coding and codifying this since it's future technology that was gonna create the systematic inequalities that we're seeing and such as discrimination only being magnified with these tools. So I don't think that AI should be completely banned. It is a tool but it's making sure that we know what the risks are so that we can actively plan around making this tool being used in a way that is positive for humanity. So now in terms of partnerships that we need to build with the overall open source community I would love to hear what your opinions are on that especially you because you've seen very passionate with that face you made, I'm picking on you now. No, this is a very tricky topic, a challenging topic and everyone's gonna have diverse opinions which is why although I'll be honest I'm a little scary to bring it here because I know people who have strong opinions don't fight me. It's a necessary one and it's the right timing to have these conversations. Even if we disagree we can talk about that and find partnerships and synergies together and how we can do that. So I actually wrote a poem but I don't know if y'all are into that. Okay, cool. Okay, thank you for allowing me to save space. My idea's only, don't judge me.  No. Okay, so I'm a little nervous so apologies for the jitters. Okay, so dear open source leaders and technology pros heed these words that the future of AI grows for although AI's potential is great it's impact on everyone we must contemplate. In this talk I asked is AI the enemy of DEI? The answer is AI alone isn't the enemy. The real problem is with humanity and especially those in the white ivory tower coding bias algorithms without input from people who look like me. The same tool that can enhance productivity can also be hacked as a weapon of destruction. It seems as AI advancements are peaking our global governance is still under construction. Hold up, you mean the same tool that helps us automate consensus in innocent personal color life in jail and bad actors have the power to use AI to blackmail unacceptable. We need guardrails in place to protect all people without fail. Don't get me wrong though. You see as a tool by itself the opportunities are endless but the values and the ethics are sold separately. Yes AI can find cures to rage diseases give the blind sight but the long-term impact on the underprivileged is still a mystery but this is not all new territory. During the industrial revolution we've seen this story before the benefits to every industry are undeniable the tech industry has been shaken to its core and we learn to embrace AI's great power. We must ensure the food of DEI labor won't sour. For now isn't the time to cower away from doing the work we ought to. We can allow a dystopian society run by AI to come true. AI holds a mirror up to society magnifies our weaknesses showing us the biases we have and the problems we must address. Mirror on the wall. Will AI amplify biases old or break down values so bold? Will it perpetuate inequality or create new paths for community? The answer is it really is up to us for AI is only as just as the data it is built upon to trust. So let us strive for diversity equity inclusion constantly as we harness AI's great force let us see our AI open source development in the right course. AI is not the enemy of DEI but can be a remedy to build AI that serves humanity with transparency, fairness and equity. Let's teach the next generation to approach AI with consideration but that requires us to create the blueprint to AI governance as a future template that embraces AI not as a nice to have but as a non-negotiable mandate. In conclusion, the positive impact of AI can be immense but we must consider the long-term consequence for it can either to build or destroy depending on how we choose to deploy. Let's choose wisely. Thank you. This works. So through the presentation you mentioned AI governance a lot. Do you think that AI governance should be like legislated with like penalties imposed upon organizations where individuals with companies create models that doesn't follow these best practices? There was a little bit of feedback there but I'm hearing there's policies about the policies of like if there's penalization attached to them if that's gonna be good or bad or? Yeah, do you think they should be legislated? In my personal opinion, yes. I've actually talked about this with a lot of just engineers in the open source space just to get feedback and like what do we actually think about this? What's the common sentiment? And I'm hearing that as humans it's very hard for people to listen to things unless there's penalization attached to it. Otherwise it just becomes hearsay it becomes like I can get away with it. And then if there's no penalization structure there even as like a fine some sort of slap on the wrist there's really no incentive to actually conform to this especially when a very new highly unregulated space that you know there's people are excited they're gonna you know step on toes and make you know seen in the process. So having that structure set up to actually do it the right way from the beginning I think it's gonna prove helpful. And just follow up question. So with like this, if a penalty was to be imposed upon organizations and individuals who don't follow these best practices how do you think this would affect smaller organizations and the open source community? Who may be unable to pay these penalties or may be unable to follow these best practices given their lack of funding or like just the fact that they're not a for-profit corporation? Yeah, no actually I think the non-for-profit is the best voice we have in the representation. There's no way that AI should be influenced only by big corporation that's what we don't want. So I think it's actually a good thing to leverage all the little guys who have voices who have power and should have a say in this and utilize organizations like this one that can secure funding on their behalf to be able to fight their fight on the broader behalf of preserving the open source community. So I don't think that there should be something that would exclude them. I think that this is something that we have a responsibility as open source community that puts open source court out that is globally being impacted right now. It's something that we should look into helping and support doing all the necessary, rather gathering necessary funding to actually execute that. Thank you very much. Just my opinion, not a PhD in AI, just another concerned citizen. Hi. So, yeah, to sort of explain. Thank you. Yeah, it's me. Yeah. So...  I'll explain my opinion about the future of AI Institute and sort of the background. Are you familiar with the paper on the dangers of stochastic parrots by Dr. Gabbrew, Emily Bender and others that was co-authored that caused for the dismissal of Dr. Gabbrew from Google? Oh, no. That's like a... Dude, do debrief me. Okay. This is like a really, really important thing that happened back in 2021. And it's definitely directly related to this topic. Dr.... This is where I'm going to butcher the pronunciation. Timnick Gabbrew was writing this paper with Margaret Mitchell, Emily Bender and others about the dangers of these large... Timnick Gabbrew, the Ethiopian. Yes. Yes. So their paper was cited in the future of life paper, but they didn't sign off on it because they view this whole AGI thing as a distraction from the actual real harms of diversity, equity and inclusion. And Dr. Gabbrew's been on Twitter viciously criticizing this organization and other groups that... She has a whole acronym to describe these groups. And it's not a good acronym by any means of the imagination. So... And I have to pretty much agree with her because I don't agree with groups that look like me as much when it comes to diversity, equity and inclusion. And future life talks about long-termism and looks like me and just... It's bad.  And I consider that like a really important thing. Yeah. The... We're not having enough kids, weird, nonsense thing that future life's all about. No. I'm not in... I don't... I'm not down with that sort of... Yeah, yeah. Like... No, I hear you. Yeah. No, no, that's... Well, I know, of course, no of team Gabbrew and all the work that she's doing and also algorithmic justice league, they are amazing in what they're doing and representation in that voice. And those are actually an opposite, like the anti of having the representation of not-for-profit space and building that up is something that they have done an amazing job at doing. That background depth into the AI, you know, Life Institute or rather, names some of my mind right now, wasn't aware of that and that's actually super problematic. I don't know why they haven't been called out, like, publicly about it, besides just on people's Twitter, but... Dr. Gabbrew did call them out very... Dr. Gabbrew did call them out very publicly about it, which is like, I'm like, whoa, there? Which is why I was like, wait a minute. So, I would like to answer then, I would ask the broader group, have you heard of non-profit groups that would be great partners with open source Linux Foundation or sorry, the Linux Foundation in this context? Maybe that may be a you question or since you're... I mean, I've heard of dare and like Dr. Gabbrew's independent group that's like, we're doing distributed AI research outside of tech giants but I haven't heard of any other groups like that. Yeah, the research was sparse. I'm like, where are they? And that's why we need to create them aside from... I hear you want that and one of many grassroots organizations out there, I wouldn't say all of them are bad but I will say there's definitely room of growth across the board. I didn't realize though that they had a connection directly to Tim and Gabbrew until you mentioned that. Yeah, but I guess I would ask to the group and I'll let someone ask a question in before people head out. Thank you for raising that and if there's more organizations that would be great partners then I'm all for it and I just want to make sure we're having these conversations one and if we're sharing information like that it's honestly impossible to know everything about this industry. Things are changing every day. Is there anything else? Anyone have? Thank you all for your patience in the beginning with the technical difficulties by the way. You got it. You got it. You got it. You got it. You got it. You got it. You got it. You got it. Did I actually have one on that one? So that was the only one that I had information or this one. I don't really have a contact slide. Long story short, it just wasn't about me. Are y'all okay with that? Yeah. My last presentation had the QR code but I was like, nah, I don't want to look like I'm like shamelessly self-promoting mad hard. Although you got to do that and I'm wrong, I'm wrong with doing that. Is there any like, I guess to ask to the broader community with some of the proposals that I put in there to say is far out of reach within the group not in the context of the open source community or something that was flagrantly missing when it comes to some of the road map of action items that the open source community could look into. Just curious to hear some feedback on that if I can find one of the 1,000 slides. Okay, go for it. Loving the enthusiasm. Does this work? So I'll just think about this. Legislating AI sounds good at first but don't you believe that it might negatively affect future AI research like slowing down AI research overall? Because that's with like a legislated going on, okay with like a legislated legislating AI makes us that this way even research models might have to comply with these AI guidelines and lots of research models they're distributed and lots of research models such as the ones that we're working on right now don't really have like extensive funding like what ChatGPT is doing and like what OpenAI is doing to make sure that there's no extensive bias or whether it might be putting out misinformation and that stuff. Right, right. You're asking, okay, so I think first of all all these, the avoidance is never the solution in my opinion what I've noticed is that when you avoid it they end up making policies around it anyways and you were left out. So it's better to be involved in a conversation it's better to get ahead of it and be like if you're doing this before the cement actually dries let me still put my oppression and my two sons in here so that when it does dry, you got your footprints in there you're good, you know, you represent it in that way and I wouldn't say that it's like a one-off thing it's continuous back and forth but if we don't have that discourse ever then like we're at a losing disadvantage and that comes from I'm sorry community perspective it comes from small not-for-profits we have to be collaborating and working with and that's why I put also our partner because although yes they are currently overtaking this entire market with the amount of funding and research that they can invest in this they also can be one of the funding sources when it comes to that they kind of have to now there's a lot more accountability and visibility in this space the companies are being called out for example like when Italy banned, when it was banned in Italy OpenAI in a hot second was like my bad we'll change the policy we'll do what you want, thank you please they're listening now people are now becoming a lot more informed about these processes and demanding and as a result they have to comply and they have to listen so I think when you come together as a group that is your strength and power there's a strength in your community to be able to make these requests that you do that you need for your individual groups to be able to safeguard the future of AI and big tech can play a role in helping fund that and I've actually seen that firsthand when looking at some of these resources that they actually do fund for profits if they're for example too big that they can't carry out research and study they'll fund it now for profit in that area that does it for them so that's just an example of how even in this ideal situation everybody would be good everybody would be perfect everybody be equally represented the reality is we've got to work with what we've got and make sure that future generations are protected from the analysis of these can I have a follow up comment to that question too? Yeah I have a great response I have no qualification this is just my opinion my only qualification is that I'm a lifetime academic so I would just say like you know living in the research world like there's guidelines like if you're going to do research with human subjects like you have to go through institutional review board you know processes and so I think that the way I think of this space is like we have a new thing that we have to figure out but I don't think like no regulation is the answer personally like I think that we can still do like really impactful progressive research and do it in a way that's like thoughtful and respectful of people who need to be protected so I think I would kind of propose like as a counter point that like the solution is not the right word because there's no easy solution here but I think like one step on this path is just having like the people who are going to be making these guidelines and legislating be representing the groups that do not have a voice right now so I think that's like a really hard thing to tip a way at but that would be sort of what I would propose is like the next step exactly no and I'm part of having the community in that conversation it's also for that it's literally like our elected officials like they don't unless we're talking with them they're not going to represent it on behalf and it may be us having to go to Congress and make some noise it may be someone we elect to do that on our behalf but we got to start having these conversations and from those conversations we'll stem okay this is what the group consensus is to then go and represent them up into the places that our voices need to be heard so spot on love it I just wanted to provide an example of what it might look like when a company or organization drops the fall or something like that that leads to algorithmic bias that you mentioned so there's this company a software company that produces software that that great share of photos called Xiaomi and they were called out recently because in their cell phone that they produced the way that it grades whether or not you took a great photo on that on that phone or not is based on like how bright a person's complexion is in a thing which just doesn't represent that the set of people that they graded that and coated that with doesn't represent people of color or people of darker complexions and they were called out about that recently which just further emphasizes the importance of having whether it's legislative or not like people that represent a diverse group on the panel before something like that is shipped out and it negatively impacts somebody with that kind of technology in their pocket exactly, no I don't see thinking that's why I have it on damn that was loud I have it on step one recruit underrepresented groups because I'm like this is a foundational step if you don't have someone of a different first of all philosophy school of thought background of life skin color like lived experience in your room while you're building the future of technology like you doing something real wrong it's I think I love that example there's like always new ones coming up every day but like I love this one because it clearly also points to picture of like even a product development where it was shipped and then they have to go afterward to do corrective action to be like oops my bad this is probably hurt this isn't really hurt anybody so we're good right but it's like it's a principle like what if it was something that was determining you know something very drastic you know we have we know the examples already with the penal and the justice system and how it's used to do racial profiling and that is one of the clearest examples of like how having inequities in the space is a non-negotiable and something that we have to include in our conversations I'm curious if for any other new voices also as well what made you what is one of the things in your existing organization today I guess you see as something that is that you can take away or implement or rather that you and discussing this topic I would like to see from out of the open source community, Lin's Foundation and helping safeguard the future of AI development I would love to know your individual backgrounds I can't so that's why I'm just I would love to hear also another perspective on those who stay this long there you go oh he got nice voice too sound like podcast I'm done shoot I forgot what I was going to say I mean the biggest takeaway I did like is the idea because I've worked on software I'm a software engineer work on ML and I feel like this is like my life's purpose because I kind of made a round trip into ML and now work at a big software company that's doing ML and realizing that most of the people in the room do not look like me and it could be an incident thing of did we check enough diverse data sets or it could be a direct business decision that group are proper more stronger than not hurting that group and same thing with accounting for people with like site issues or the same thing like if you don't have that you don't think about it so I think that's why I'm here and I feel really good about this and I never thought of the idea of the IRB because I work on software that was medical software you have FDA out there and I feel like it should be the same way there should be guidelines for how you should do it at the research level but as further you get to like you're in product just like in pharmacy or medical device if it kills somebody yeah then you have billion dollar lawsuit but if you're if you're a small researcher maybe it's a slap on the wrist don't do that but I think some structure is needed yeah first of all thank you for fighting a good fight and representing you know representatives in spaces that we're not like I mean being one of the few personal color in the room of there's an experience there and I just want to acknowledge thank you for being that voice with using your power for good and being here today and showing up because really honestly like you said this is our purpose now it's like the most purposeful work that we can do in technology is related to this because it's literally everywhere and it's rapidly evolving so it's an opportunity to bridge the gap between technology purpose and social good in a way that is responsible we're thinking ahead of how it can impact future generations it even affects sustainability like you know it's in that way of like the amount of data power compute power it takes to train these models that was another thing there's so many sub topics on this topic but anyways you get it up and I appreciate you sharing that and also sharing your perspective from an engineer actually in this space thank you any last closing thoughts? I think we're like super over you guys are like patient in the back I guess you're enjoying yourself too okay cool yeah I'm happy that you guys are happy I like was looking over research for months preparing for this talk by the way there's only so much I can communicate so much in my mind and little nerves I'm like trying to get multiple things out at once but thank you for your patience thank you for also the vulnerability and like I said I think in terms of future next steps the next foundation should consider having a AI advisory group of some sort where we meet and actually talk and maybe on a monthly cadence or something talk about how we can actually get in a way like you said get into a place where our voices are being represented and that we're creating these regulations like similar how we can read ingredients in our food in the back should be like AI may cause diarrhea I'm just kidding just kidding but you know just to know what the content is before ingesting it and putting it into our products because if not now when so if open to that I like to propose maybe I don't know if you guys all signed up in the app maybe create a little monthly meetup group reach out what do you think would be the best consensus for following up on a topic like this what could be our homework because I know like seniors and just seeing that you guys are open to it I would love to hear about what the next step could be because I hate to be all talk I like also action go for it we specifically support LFAI and data so we are on the team and we definitely want to follow up with you and see if we can get a lot of interesting things going and there's some cross things we can do but this is great so thank you yeah I just wanted to you know have people also walk away with an actionable and myself I will follow up with that and look forward to having more discussions around this and creating change the first step is like I said the recent European Act that's a step one making sure that the open source community voice is represented the second step is you know looking at what is there existing patterns in our communities we mitigate those biases or any flagrant actions that happen and then create a system for accountability in the future but yeah we have to have these conversations first to get those going so thank you all and I'll shut up


Hello everyone and welcome to this new video. I hope you're doing great and today I've decided to share with you a bit of a Q&A about 10 things you may know or you don't know about me But as always please subscribe share your comments tell us what you want to see four balls juggling next time Why not and the first thing I'm gonna say is when I was a kid I didn't want to be a race car driver. What I wanted to be is a 4x4 driver in the DZ and being a gold digger. I wasn't interested about the gold I was interesting about driving the 4x4 into the DZ, the dunes on the sand Completely off-road through with my best friend in my head It would look for the gold and I would just be driving the SUV. I'm not a funny one back in 2004 Osho Sleben Formula Renault you woke up.  The race was not finished There was one more lap the guy on the side.  He got it wrong So he was one lap too early and the previous lap I saw lap one So obviously it was the last lap of the race, but it wasn't the last lap of the race So I got crashed into and I didn't do a good result at the end of 2009 or early 2010 I was thanks by Renault and I wasn't going to be in Formula 1 in 2010 So I didn't know really what to do and I wanted to become a chef So I went to cooking school open days and I'm one day. I was just going to be 23 or 24 I was told I was too old to start a cooking school. So I went back to racing But many many more years later. I was still passionate about cooking. So it was my own We actually wrote a cookbook 46 recipes You would probably have one of them if you were coming and have dinner or lunch at home So I still I'm still passionate about about cooking but obviously never went to that school I went back to Formula 1 and I kind of thanked them for telling me no at the first time I was born in Swiss. My mom is French so I got dual nationality But when I started racing back in 2000 I was driving in a Swiss license I even entered A1 GP for Switzerland, but that race suit was never worn And I tell you why end of 2005 I'm French Formula 1 champion And at that time I still have a Swiss flag on my car I needed some support to carry on my career. So I went to see Rono for the Rono Drive Development Program Rono told me that they weren't interested in a Swiss driver for the future of Formula 1 But I said but I'm also French and from that day onward They told me that I was going to be French for racing all the time So I had signed like couple of weeks before for the A1 GP Switzerland team I never got the chance to compete there But I kept the race suit because it's a cool souvenir My grandfather was vice world champion of skiing in 1915 Aspen, his dad used to be a bobsled pilot He was 14 times Swiss champion I believe My grandfather was managed Swiss times Swiss champion as well Completed in two Olympics, finished 7, 8 and 48 in 52 But he was vice world champion of skiing back in 50 So I'm obviously born on the skis, I love skiing And I tell you one thing, you know that I live in Miami Aspen, Colorado is not that far Probably next winter, I'll be up there skiing Remembering my grandfather passed away a few years ago But gonna go and ski to slope and think of him Talking of famous, I'm on one of the most watched video on YouTube Dangerous, David Guetta, the clip Dangerous for his son I was part of it and I'm actually on the podium of that clip If you're watching details with James Purefoy, David Guetta I finished third in the race, I had to let David win obviously But that was a crazy experience, it was really cool to discover a little bit of behind the scene And I had no idea and I was pretty much blown away by how much work it was I was driving the cars obviously doing the videos But I was on the podium and I was actually showing David how to spray champagne And that was pretty cool A few years ago, 2019 I believe I entered Fort Boyard I know it exists in a lot of countries I don't know if you pronounce it in different countries But in France, Fort Boyard and I entered the game And they ask you if you got a phobia for snakes, rats, mouths, spiders And I say, yeah, I've got no idea I've never really met any of those So I don't know And then we go into the part where the animals are getting into They put me in that fun booth You know, I kind of sit down, I'm on the phone waiting for Perforat The grandfather to ask me a question so I can find the answer And they start feeding up with water So I'm like, yeah, that's fine I'm just gonna, you know, breath, come back down Pick up the phone and answer the question That was all good until they throw about 10 snakes, water snakes into the telephone booth And at that moment, first of all, I used all my worst French bad words that I know And realized that I had a phobia for snakes Absolutely horrible Since that day, I don't want to see any snakes near me My dream car That's a question I always have What's my dream car? And it's not a Ferrari, it's not a Porsche, it's not a McLaren It's not one of those crazy fancy ones My dream car is a Lancia Delta Evo II from 1995 My dad had all the Lancia when I grew up From the first one, we should believe, 88 to the last one, 95 That's the car that I want in my garage So if you haven't seen the video about my garage Go on and click on it, you know, see what's in my garage And you can see that there's a dedicated space that it's free It's gonna be for that car for sure one day For now, I only have that one that I need to build But definitely, that's on the list I want that car one day, that's my dream car I also wrote a book with Mayon, Facing Death Facing Death, it's kind of an autobiography Crossed with a love book because, you know, Mayon's got a chapter I've got a chapter It's obviously, we speak about the 29th of November 2020 My Bahrain accident But we also speak about some fun stories Back in Formula One, back in my early career IndyCar, the new discovery of the US and so on So, yeah, if you have a bit of time You can find it on Amazon And please let me know what you think about the book Because that means a lot to us


Yo, come saw me down. Okay, don't say that either.  Ah! Get out of this! Get out. You're booted out. Get out. Go. I can't, I'm tired. Okay. What is that hair? This hair? This is my hair. No, no way. No, it's a wig, right? Really? You think it looks that fake? Can't be possible. Feel it. The wig part is a political party in the 1880s. It's my hair. Joe, you're too smart for me. You're not going to wear your wig to the party? Is it a Halloween party? Wait, is it? I don't have a costume. I'll make one. I can find one. You're going to be Santa. Yeah, I was thinking about that. Chat, what are you thinking about being Santa Claus for Halloween? You know? Wait, I got the date wrong. Okay, alright. Alright, get your toe out of here. Get out. Alright guys, welcome to my stream. Yo. What the heck? It looks so cool. You're not me. You didn't say anything about my clothes. You're not me. Yeah, I'm not Joe. I'm also dressed up for Halloween. Yeah, you're so pretty. You look so good. Okay, yeah, I can't wait to see you. Okay, I'll see you there, okay? Okay, drive safe. Okay, bye, Hachu. Hachu's dressed up. So what? Fun. She looks so good. So are you dressed up like good? I know. Now I want to dress up because it looks fun.  What am I dressed up as? I mean someone. Let me see. I don't know who it is. Okay, hold on, hold on, hold on. Wait, wait, wait, wait. Red, wait, hang on. Red, hair, anime girl with black skirt and white collar.  Hang on. She did. She did. Stop! She fixed it. Oh, not that. I need a skirt. I need a skirt. We're looking for a skirt. Okay, okay. Watch just like, be careful when scrolling anime on Google images. You're right. It's just going to be like, like. I can't find anything actually. I thought this would be easier. Go to Google. Auburn haired anime girl with black skirt. They all have red skirts. Do you have a red skirt? No. Who's that? She's no one. She's just an NPC. So you're an anime girl. Literally. I should just make something up. I'm an anime. I'm cosplay. I'm an anime. I'm this one girl from this one anime that you wouldn't know. It's really underground. Yeah. Hold on. We should pick my outfit. We should pick my outfit. You know what else I saw? Sexy minion costume. Speaking of which. What's up? Did I get? Aspen is in the other room. So if you want, you can hear his dad, right? Yeah. Nice to meet you. Nice to meet me too. Don't I look like my abs? You want to film? No. Here, just punch him. Your costume. Here. Give him a squeeze. I don't want to. Here. They're rock hard. Oh. Damn.  They're so soft. That's crazy. That's fun. How'd you do that? Oh wait. Did you put a shirt under there? No. It's a muscle shirt. Oh. It's really good. Okay. So what are you? Okay. Wait. We can figure this out. Hang on. Just look up anime characters. Silver-haired muscle man with aviators. Just kidding. Okay. Anime. That's me right there. He's bald. Oh, damn it. Never mind. Oh, X-Men. Maybe this. Why does this man keep appearing ever? What are these men? Crazy. Why is it? Why can't you like that? I think it's because I put muscle man. Hey. Okay. What is this? I don't know. I think we're two clicks away from finding gay porn, so we have to be careful. Okay. Okay. You need shorter hair if you're going to be anime. What do you mean? My hair looks good. You will look like an alcoholic. I'm hammered. Can I try something else? Yeah. Okay. I think the muscle thing is a good thing because it looks like an actual costume. You like what you see? Well, why don't I try something else? I brought some of their options. Okay. So there are Lindsay Lohan characters. Well, well, well. What do we have here? Cam Cleese Corwood. Nice to meet you. You found it. You found your outfit. Shall I wear this one? This is perfect. Shall I go with Shelly?  Can we just fight the whole time? I can wear a camera hat. Okay. I think I'm just going to be Yawari Kawasaki. What? She's just saying so much. That's my name. Yawari Kawasaki. Bless you. Whoa. Whoa. That's a chair. Oh, yeah. That's a chair. That's my name. Yawari Kawasaki. That's a chair. Oh, yeah. That's a chair. That's my name. Oh, that's what it means. Your name means that's the chair. I'll wear this. This is my outfit. That's your outfit. It's perfect. I have other options. No. These are going to fall. These are broken. You should wear a belt. Yeah, but the belt, this is not a belt loop. You should just put it on top. That's what I did with this skirt. Except I don't know how to do this belt. Yeah. So it literally looks like this. But I'm working on it. Oh, if you get suspenders, you know what yawari means. What does yawari mean? Wait, what's it mean? Yawari. Yawari. Yawari. Oh. Yawari is a slang meaning boys love that is used in the Japanese language. Popular with fangirls who like their favorite anime guys to kiss. See, this is... Anime was a mistake.  Thank you for your... Are you supposed to be a... Oh, for you. A murloc. That's a murloc? Yeah. It's a red murloc. This one a lot too. I was doing BlizzCon two years ago. Three years ago. Four years ago. Three years ago. Well, that's enough time for people to just forget it. I didn't watch it after. Does sweat disappear? Well, bacteria does die. So, maybe it died. Yeah, bacteria does die. I should dye my hair this color. You think so, Espen? If you dye your hair... To do that belt, you go through both rings first, then on the outside go back through only the first one. So, let's go. Kamsami-da. Okay, don't do that. Thank you. Yo, Kamsami-da. Okay, don't say that either. Wait.  I'm sorry. Get out. You're booted out. Get out. Go. I can't. I'm tired. Get out. What is wrong with this guy? It doesn't like two people at once. It only knows what to do when one person's here. It's quite the introverted camera, to be honest. See? It doesn't like your face. But let's see if I stand up and it's okay. Wait, no, it's actually just doesn't like your face. I just stayed in the party. Gotta go. Wait, really? Pretty much. Maybe. Wait, if you haven't used your Twitch Prime and you would like to use a Twitch Prime, you can use it here. Chat, use your Twitch Prime. For free. And you can also follow the stream for free. Follow if you like anime. And um... So what are you gonna say? Give up. Because if you stay safe, that means that we get to see you again. I get to see you again. Alright, chat. YouTube, Instagram, Discord, Reddit, Twitch, Twitter, everything is Bonnie Rabbit. Bye. Stay safe. Be good. See you. Bye.


It's my pleasure to introduce Dr. Ellen McVail from the Mayo Clinic, who's going to talk to us about pathology and biopsy. For those of you who've heard the term mass spectrometry here, the Mayo Clinic remains the national treasure and resource for all of us because every time we want to know exactly what the precursor protein is, we send tissue to them and Dr. McVail is always very helpful in sorting that out, so we're all very grateful. Thank you, Matt, for that really nice introduction. Can you all hear me okay? Yeah, okay, well, I'm very, very pleased to be here at this really wonderful and comprehensive meeting, and so I come from Rochester, Minnesota. I am a hematopathologist, so I'm not a cardiologist, I'm even a cardiac pathologist. But since about nine years, I've been part of the mass spec lab and I've been the mass spec medical lab director since 2017. And so since that time, I've seen a lot of cases and I've actually learned quite a bit about the whole amyloid world. So I hope this is helpful and we'll just get started here. So no disclosures. So just as a background, amyloid osis involves basically any organ in the body. And there are now 42 different types that have been described. If you look at the most recent description now, we used to be at 36, but we're up to 42. And they involve basically every organ you can imagine. But fortunately, heart is relatively simple. Because although we have described, we wrote a paper about two years ago. We went and looked at our whole mass spec experience at that point, which was about 16,000 cases. And then we looked at each type separately. So this description here, as you can see, is the heart. And you can see that although we had nine different types at that point, in fact we're up to 10 now, 98% of them were either TTR or AL. So in some ways, heart is really, really easy. Because basically, that's the question. There are other types, the next most common type is ANF, which is a localized disease. And that only involves atrium. So you really don't have to worry about that either. So basically 99% of cases will be TTR or AL. And it's also important to remember that aside from ANF, these are all systemic diseases. So all the patient may not have symptoms in certain organs. Those organs may be involved. So for instance, TTR patients may have prostate involvement, may have gallbladder involvement, may have involvement. And basically any other organ, it just may not be symptomatic. But that's a place where you could possibly go and look to make the diagnosis. And then I think as has already been mentioned, cardiac involvement is the single most important prognostic marker across all amyloid types. So although other organs may be involved and may have some symptoms, the heart is really where the money is for prognosis and outcome. OK, so a couple points I'm going to make today. This may not be maybe controversial. We'll see if this brings up questions. But from my analysis, most patients should not meet a heart biopsy. So we'll start out with using the numbers from the paper that we wrote in 2020. Now, I think TTR patients are actually a lot more common than this. But because they come to diagnosis later, because they're older, because the disease is more progressive and indolent, there tends to be subclinical disease, they don't come to attention, are probably underdiagnosed more than AL is underdiagnosed. But we'll use these numbers just for argument's sake. So about 2-thirds are TTR, 1-third are AL. For the TTR patients, about three-quarters of them will not have an M protein. Again, the numbers vary in different studies. It could be higher, it could be lower. But we'll use 75% as an estimate. And for those patients, as we already heard, the centigraphy scanning should be diagnostic of TTR, and there should be no need for any tissue biopsy whatsoever. About a quarter of the TTR patients will have an M protein. And for them, as we've heard, centigraphy is not indicated. And for the ALs, pretty much all of them should have an M protein. And again, centigraphy is not indicated. So we'll go on. So for those patients who have TTR and have an M protein, those patients will have a TTR present in a surrogate site such as fat pad and bone marrow about a quarter of the time. And similarly for AL, for those patients, again, who have an M protein, so you can't do PYP scan, about 75% of those will have an M, sorry, amyloid present in either the fat pad or the bone marrow. And again, these surrogate sites are overall quite reliable. Not 100%, but overall quite reliable. So that only leaves the 75% of the TTR patients who have an M protein plus the 25% of AL patients who absolutely need a heart biopsy. So if you do the math, this winds up being about 20% of all cardiac amyloid patients. Now the numbers are all estimates, so maybe it's 30%, maybe it's 15%, but it's certainly under 50%. So that leaves most patients, again, should be diagnosed without the need for going to the heart. So as I mentioned, patients, surrogate sites are often acceptable as a surrogate for going to the organ of interest. So again, in AL patients, about 75% of fat aspirates and 70% of bone marrow will be positive for AL amyloid. Similarly for TTR, about 15% of their fat aspirates and 30% of their bone marrow will be positive for TTR amyloid. And then if those sites don't work, there are other sites that have been used with variable success. So lip biopsy is actually a pretty good site for AL. One paper I was looking at showed that about 50% of patients who had a negative fat, 50% of AL patients who had a negative fat and bone marrow did have lip biopsy positive for AL. Again, Tensinovion is good for TTR patients. However, I would not be convinced that a patient who had TTR in their, say, carpal tunnel couldn't possibly have AL in their heart. So I wouldn't use that as a great site for a surrogate. I think it's an indicator, but it doesn't prove it. And then people do use all sorts of other preexisting sites, such as prostate bladder, gall bladder, and GI tract. Whether we should use that or we shouldn't, it's hard to say, but people often will go back to preexisting specimens and see if they can make the diagnosis without having to subject the patient to a heart biopsy. So the next point is about pathology. So if you do need a biopsy, which is gonna be, which is gonna be at least half the time we need a biopsy of some sort, make sure it's reviewed by an experienced pathologist who can confirm the diagnosis. So you'd think this would be easy, but in fact, it's not. So again, the same problem that plagues clinicians, plagues pathologists, is that you have to think of it in the first place. It's not so hard to think of it. If someone tells you that's the question, so for instance, if someone tells you, this is a heart biopsy, do they have amyloid? That's not so hard. What's hard is when you have a patient who has diarrhea as to GI biopsy, because the differential for diarrhea, as you all know, is quite broad. And although amyloids can certainly present with diarrhea, so can a lot of other things. So again, you gotta think of it. And so you need a specialist who has encountered this and knows how to deal with it. The next challenge is the conga red stain. We all talk about that like it's just we do it and that's the end of it. And in fact, it is the gold standard. It's a cheap stain. It's been around forever. It's in some ways easy to do, but it's technically challenging. So everything has to be perfect. The pH has to be perfect. Everything has to be made up quick, freshly. The thickness of the tissue has to be correct. Everything has to be correct for this test to work. Otherwise, you want it with false positive and false negatives. And another problem that we came up aware of recently is that we used to think it was 100% specific for amyloid. In fact, it's not. So the rare diagnoses like fibrillary gluminal nephritis that are conga red positive, it's a fibrillary disease, but it's not amyloid. So again, you need to make sure someone who is an expert is looking at this. And then the next challenge, okay, now you know it's amyloid. What type of amyloid is it? And of course, as this audience knows, the treatment for AL and TTR is so dramatically different that you really want to get the answer right. So, most pre-mass spec, most methods were antibody-based methods. So this included immunohistochemistry, immunofluorescence and what's called immunogol, which is an EM-based technique. And these are all great techniques for a lot of things, but amyloid is not one of them. They're problems with amyloid. That's true. I mean, this is our, the weeds are our workhorses. I see, I mean, it's a chemistry, that's all we do. Basically, that's the workhorse for a backbone of our practice in pathology, but not so much for amyloid. So there's some problems. The first problem is there's a bias toward expected types. So as I mentioned, there are 40 different known types, but nobody out there is doing 40 different antibodies when they come along on an amyloid case. So unless your case is one of the three common types, this is gonna be a problem. So you either will miss the type it is or even worse, misdiagnosis as a type, one of the common types. And then the other problem is the chemistry is non-specific. Again, this isn't a problem for most areas of pathology, but it is for amyloid. So we end up with a very high inclusive rate and a lot of cross-reactivity. So we think that optimal patient care requires a better method. And so it was actually this problem, this very problem that prompted our institution to develop the mass spec test for amyloid. So this was the first test. We launched the world's first clinical test to type amyloid by mass spec based shotgun proteomics back in 2008. And it was developed to solve this precise problem. And it's now considered the gold standard for amyloid typing, now performed worldwide. And so we have, we absolutely did our IHC for all the amyloid proteins back in 2008 because we realized this worked so much better. And why it's a great test, it directly analyzes the proteins of interest. It's unbiased. It unambiguously identifies all amyloid types in a single assay, very, very high sensitivity and specificity. So sensitivity is about 98%. The couple percent we miss is usually because there's not enough amyloid present to do the test. And then specificity is about 100%. It uses a paraffin tissue, so you can go back to specimens that are 30 years old and you can type the amyloid. And it requires very, very little tissue. There are a few cases we can't quite get enough, but they're very, very rare. So even tiny heart biopsies and tiny kidney biopsies, this works fine. So just to give you a little background or maybe my sort of simplistic way of thinking about amyloid. So there are components of amyloid. There are the amyloid signature proteins, which I think of as the straw. So again, that's the stuff that's, they're ubiquitous proteins in the serum that are soluble and they're just there. And so like straw, they can kind of just move freely. And then there's the unique amyloid precursor protein. That's the mud. So again, that's soluble and it's, on its own, is not a problem. But these are not normal proteins. They're abnormal in some way. And when these get combined under the right conditions or the wrong conditions, you wind up with the immovable brick that is amyloid. So here's an example. The amyloid signature proteins that we use are apolipoprotein E, apolipoprotein A4 and serum amyloid protein. So there are actually a couple other proteins that are considered amyloid signature proteins, but those are the three we use in our lab. And when combined with, say, an abnormal lambda light chain, you wind up with AL amyloid lambda type. Here's another example. Same amyloid signature proteins. This time the abnormal protein is an abnormal transthyretin. Put them together and you wind up with TTR amyloid. So here's an example of a case that we ran. I don't want to go through all the specifics of mass spec in the interest of time, but you can see that the image on the left is a blood vessel. This is looking at a conga red under UV light with a fluorescent microscope. So it looks different, it looks kind of bright red. So that red material is the amyloid. You can see a very, very fine sort of lavender line within that red, that's the line of a laser. So what we do is we take the tissue, put it on an uncovered slip slide, and we use a laser. Look at the, we do a conga red on it, and we use a laser to cut out just the amyloid that we can see. And that tissue falls into the cap and that tissue gets analyzed. So it's a very helpful to eliminate, to enrich for the amyloid and eliminate the other material. That's actually a key component to this test and why it works so well. Then we run the mass spec and we wound up with a personalized profile, which you can see here, which are the numbers. We run two samples, you get two columns, each one's a different sample. Just to make sure we're honest. And the numbers are what we call spectral counts, which are a semi-quantitative measure of abundance. So, we can read this. The top three here you can see those are our amyloid signature proteins that have high numbers. This is a way that we proteomically proved to ourselves that this is indeed amyloid. So it is. And the next most abundant protein is TTR. So this is a TTR amyloid case. And yes, there are a few rare gammas and alphas and lambas and capas, but that's background serum proteins. We can't get rid of them 100%. So there are always a few around, but the numbers are very, very low. And we know that's just background. And in addition, this test is kind of even better because beyond just doing our usual search to identify, say the amyloid type, say TTR, we can then use the same, use a different database, which is a curated mutation profile and look at the same information, run the search, and we can identify the mutation. So we do this in every case. So if you send us a case, we do this automatically. And it's not 100%, but it's probably above 95% that we can identify the mutation. And in some ways it's even better because it's disease tissue. So you could have an asymptomatic patient, a carrier who doesn't actually have disease yet, but have the carry the gene. But here we can show here that it is amyloid and it also carries the abnormal protein. So here's an example of a case just to show you the problems that we can face sometimes. So this is a 74 year old man, had a heart biopsy, had amyloid, no problem, gets typed, I don't know if you can read this. This was done somewhere else, actually a very, very good institution. They used amylo fluorescence, which is actually probably better than IHC. And they came with a diagnosis of ALKAPA. And then 31 months later, the very same heart biopsy, so very, very same specimen came to us and they're this signature protein, so we know it's amyloid. And it was TTR. So, beware. So the last point I want to make is although I have already told you that syracous sites are really good, they're not perfect. And so if the clinical picture doesn't fit right, then you're gonna have to go to the endoth organ, which is the heart. So I'll give you three. The first example are rare amyloid types. So again, these are super rare. ApoA4 is probably the most common one that we deal with. That's a problem sometimes because I think even that can give a, maybe Matt, you know, but it can give a false result using PYP scanning, but I think this is anecdotal at this point. So that's a problem, but it's extremely rare. The second problem is discordance. I'll show an example of that. So here's a case, 73-year-old man had a longstanding type 1 diabetes history. He had symptoms that sounded good for systemic amyloid. He did have an emgus, a capyrelight chains and a cap of emgus.  His bone marrow had 5% monoclonal capoplasma cells. So at that point, the working diagnosis was systemic AL amyloidosis, quite understandably. So he had his fat aspirate typed, and we see the amyloid signature proteins. So they're present, so we know it's amyloid. But what we found was insulin. So this is not AL, this is not TTR, this is insulin. So, an insulin amyloid is actually the fifth most common amyloid type that we see. It's about 1% of cases. This is not a systemic disease. It's a localized disease to where people inject insulin. It causes decreased absorption of insulin. It causes masses usually, but it's a localized process that basically is a red herring. But if you go back to our paper of all our fat pad biopsies, although 60% were AL and 30% were TTR, a full 10% were insulin. So this is not that rare. So it's not unheard of that you will encounter this. So this happens, and basically it's a red herring. So our clinicians knew that, and they said, yes, this patient still could have cardiac amyloid. And so a heart biopsy was done, and it turned out to be TTR. So, in the end, this patient had three things. He had the main thing, which is cardiac amyloid TTR, wild type. He had an incidental localized insulin amyloid, and he had an incidental MGUS. So, yes, there can be just, people can have more than one amyloid type, either in different organs or the very same organ. And we've written about this, other people have written about this. So is it common? No. Does it happen? Yes. So don't be surprised and just keep going if the story doesn't make sense with the surrogate site. And the last example, okay, I forgot about this. So this is a paper we had written about whether you could use surrogate sites like prostate or bladder or tendon as surrogate for cardiac amyloid. Because if you think about it, who gets cardiac amyloid? Well, it's kind of old guys, right? So the old guys often get prostate biopsies and bladder biopsies. So this was kind of an interesting study. And so we had 16 cases that had, or we had a heart biopsy plus one of these sites. And 14 out of 16 were concordant, which is great, but two were not. And so it's important that you know about these. So the last one, which I think is 29, was discordant. This first it had a type of amyloid called seminal gelin, which is localized to seminal vesicles in his prostate. And this has always localized disease. So this one did not pose a problem because you would know that there's no way that's what was in the heart. And the patient had, I think AL, I can't read it from here, AL in the heart. So that's fine. There's no way that could have been the same thing. The other case, case 10 was potentially a problem because he had AL in his bladder and TTR in his heart. So AL can be, in addition to being a systemic disease can be a localized disease. And localized disease, AL in the bladder is a relatively common clinical pathologic entity. So again, this is an example where using the surrogate site didn't work. And the clinical picture didn't fit and the clinicians were astute and went to the heart and diagnosed a separate type there. So this is just to keep in mind that this can happen. Usually it doesn't, but it can. And the last case, this is the one that gave me endless gray hairs. This was a 91-year-old guy who had prominent, I won't go into the whole story because we're out of time here, but he had prominent interstitial amyloid. 98% of these big blobs of interstitial amyloid, which is a typical pattern for TTR. And so he typed it. It was interstitial aggregate. And again, it was TTR. But the patient didn't behave like TTR. He was treated for TTR and he basically fell off a cliff clinically. And so we went back and sure enough, if you really hunt hard enough, you could find rare vessels that had amyloid that looked different. The other interesting thing was this patient had a fat aspirate, which is positive for AL amyloid, which also posed a big problem because we think that fat aspirates as a perfect surrogate. So how could he have a different type in his heart? So we went back to the heart and we found these rare vessels that had amyloid and we dissected those out separately. And they were so rare that we could only come with a single cap. But sure enough, we ran it and it was Lambda. So this patient, I don't know if you probably can't see this very well because the numbers are so small, but there was not a speck of TTR in those vessels. And similarly, there was basically not a speck of AL in the interstitial areas. So they were two entirely different amyloid types, two different anatomic compartments in the same heart. But even though 95 plus percent of it was TTR, he was behaving like AL. So we did get treated for AL and he improved. So again, the amount of involvement of the tissue is not necessarily proportional to what the patient's gonna do clinically. So this is extraordinarily rare. Like this is about the only case ever seemed like this, but if it happened once, it can happen again. So again, I think the surrogate sites are excellent, but if the clinical picture doesn't fit, you have to go back to the heart and again, send it to a very good pathologist because these are hard to diagnose. So these are my take home messages. For one, most patients with cardiac amyloidosis should not need a heart biopsy and about at least half should not need a biopsy at all. For patients who do need a biopsy, surrogate sites like Fat Aspirin, bone marrow are often acceptable. If you need a biopsy, send it to a good pathologist and we recommend typing it by a robust method like mass spec. And finally, surrogate sites, although very good or not perfect. And if the clinical picture doesn't fit, get a heart biopsy. So that's it. Thanks for attention.