I am Muhammad Shah Nawaz, an application engineer in Artificial Intelligence Solutions Group at ST Microelectronics. In this video, I will introduce the STM32Qube.AI developer cloud and demonstrate its different features. STM32Qube.AI developer cloud is a free online tool that requires zero installation. It defines itself as a web-based alternative of desktop version of STM32Qube.AI and includes outstanding features. Indeed, with this tool, you can optimize your trained AI model, which could be a Keras model, a TensorFlow Lite model, or any AI model generated with TensorFlow, Matlab, Scikit-learn, or PyTorch, and export it as an Anonix format. You can benchmark remotely the performance of these models on targeted STM32MCUs. And finally, you can generate and test optimized STM32AI libraries for these AI models for any targeted STM32MCU. To use this tool, simply press Start Now button. This will take you to Assign In page. To connect, you will need a MySTUser account. If you do not have an account already, you can create a new account by simply filling a form. This process is completely free. Once logged in, you will be at the Home page. In Home page, there are three main zones. In the first zone, you can upload any of your pre-trained AI model by simply pressing the Upload button or performing a drag and drop action. In the second zone, you will see a list of pre-trained AI models, which are pre-trained for different targeted use cases. These models are coming from STM32 model zoo. You can create a new project by simply clicking on the Import button next to the models. Finally, the third zone is Workspace. This would contain all the AI models, which you have already analyzed and benchmarked using STM32Cube.AI Developer Cloud. In this video, we will go ahead with uploading a custom H5 model. This model is created using TensorFlow Keras API. To upload the model, we will simply press the Upload button. We will choose the model file and press the Open button. This would upload the model. Once the model is uploaded, it would appear in our workspace. We can press this button and see the model architecture. This would open the model architecture in Netron and you can see all the layers and all the components of the model. You can close this view by simply pressing the Cross button. By simply pressing the Start button, we can create a new project. Once the project is created, on the top bar we see we have five action items available. We can optimize the model. We can quantize it using the post-training quantization. We can benchmark the AI model on different STM32 boards. We can view, analyze and compare the results generated from different benchmarking grounds. And finally, we can generate the code and the projects for the target MCU family and board for the optimized and benchmarked model. Okay, so let's jump in the optimization step. We have three ways to optimize the AI models. We can optimize for the smallest RAM size or we can optimize for the smallest inference type. Finally, we have the ballast approach in which we find a trade-off between minimum RAM and smallest inference time. By default, the ballast option is selected to provide the users with the best compromise to have the smallest footprint and the smallest inference time possible. To know more about how these options work, click on the information bubble icon. This would open the documentation which explains what these options are and how they work. To close the documentation, simply press the close documentation button. To launch the optimization, simply select the option of your choice and press the optimize button. When the optimization is running, you can have a look at the terminal which would provide you the details about the optimization run. If there are any errors, they would appear here. We can launch multiple optimization by selecting different types and choose the best one which suits our needs. After running each optimization, we can see the reported numbers here in terms of MAC, flash size or RAM size. Once all the optimizations have been performed, you can choose the best one which suits your needs. For me, I'll be going ahead with the balanced approach. From here, we'll go to the next action item, which is the quantization. To do that, simply press go to quantize. This tool provides the post-training quantization using TensorFlow Lite Converter. You can choose any input or output type out of three supported options. These options include integer 8, unsigned integer 8 or floor 32. To avoid losing the accuracy during the quantization process, the users are advised to provide the training dataset or part of it. This can be provided in the form of NumpyZ files. Please note that if no quantization file is provided, the quantization will occur with the random values of input, but this would result in some drop in accuracy. Once you have provided the NumpyZ file of the dataset, you can perform the quantization by simply pressing the launch quantization button. To know more about the quantization and input files, simply click on the information bubble icon. Please note that the users' data and the models are stored encrypted and protected in the Microsoft Resort Cloud Service. SD will have no access to it. Find more information on this in the deployment architecture and data protection section of the documentation. You can close the documentation by pressing the close documentation. For this video, we will skip the option of the quantization and go straight to the benchmarking. To do this, simply click on the skip quantize button. This tool lets the users to benchmark their AI models at a variety of STM32 boards. To launch the benchmark on a given board, simply press the start benchmark button next to that board. This will start the benchmarking process. A project of system performance application is created. The tool builds the project with the C code of your AI model in it and flashes one of the actual physical boards in our board form. It runs the application on the board and gives you the inference time. It is worth mentioning that the inference time reported here are actual inference times on real physical boards and not just an estimate. Once the benchmark is finished, we can see the inference time reported next to the board. To know the more details about how much resources are spent per layer, simply click on the three dot icon and go in the show details per layer option. A bar chart would appear here. The bar chart shows you the size of each of the layers in bytes. We can toggle between the bar chart and the pie chart by simply clicking this button. Where the bar chart shows you the actual size, the pie chart shows you the distribution. We can do the same thing for the execution time as well. The bar chart showing you the duration in the milliseconds spent on each of the layers while the pie chart showing you the distribution of the execution time by each of the layers. We can close this view by simply clicking out of the dialog. The tool lets the users to launch benchmark on multiple boards at once. We will launch the benchmarking on all the boards by simply pressing all the start benchmark buttons. This will repeat the process for all the boards at once and report the inference time on all the boards next to them. Once all the launch benchmarking runs are finished, we can go to the results section. To generate the code, we will need to make a selection of the target board. To do this, first apply a CPU series or family filter. This would simplify the selection of the board by showing the boards only from this family instead of showing all the supported boards. We will choose STM32 H735G discovery kit because this was the board which resulted in the smallest inference time. Once the target board has been chosen, you are ready to download or generate the code. There are four options in this tool for generating the project or C code. The first option is to download the C code. This is equivalent to the STM32 AI generate command. It would generate an STM32 optimized C code associated with your pre-trained AI model and the library's further target board. The second option is to download the STM32 cube MX IOC file. This generates a zip package containing the IOC file and the selected model, ready to start STM32 cube MX locally on your machine. The third option is to download the STM32 cube ID project. This generates an STM32 cube ID project including the IOC file, project file, file tree and STM32 optimized C code. Finally, there is a download firmware option. This generates an ELF file associated to the selected board. With this file, you can flash your board directly locally using STM32 cube programmer and run the system performance application to know the inference time on your board. I hope you enjoyed the video. For detailed documentation on this tool, click on the documentation icon. This contains all the information you need to start playing with this groundbreaking tool. I hope you have fun creating your own projects with your own AI models without any installation and with this freely accessible tool. Finally, for more interesting stuff related to AI on STM32, please visit our website.


So thank you all very much for having me here today.  And thank you very much for the opportunity to also reconnect with Dr. Picanin. Dr. Picanin, of course, was one of my very early mentors in my PhD. So it's a real pleasure to be here and hopefully I do her proud. So I'm gonna talk today about Twitter research ethics and thinking about ethics sort of beyond the review ward. And my talk is broken down into three parts. So I'm gonna move essentially from quite broad into sort of a specific example. So I'm gonna start today by talking about ethics very generally what we mean by ethics and the use of publicly available data in relationship to these different meanings. Next, I'm gonna talk about some research that I've been involved in in terms of understanding how researchers themselves are going about their ethical practices when using data from Twitter and talking about a little bit about the gaps between what researchers do and what users actually think happens to their data and some of the problems they're in. And then third, I'm gonna talk about a very specific example of values tensions that exist in relationship to data sharing around Twitter and some of the tensions between what we're sort of required to do by rules and regulations and what our ethics may demand and how we might be able to go about practically solving some of these issues. So I wanna start today by thinking really, really big about what is ethics, why do we care about the stuff? And when I teach my undergraduate students in my information technology class about ethics, I go back to this idea, this very old idea that's been with us a very, very long time that ethics are systems of principles that we use to guide us in making moral evaluations. And we can rely on things like utilitarianism to help guide us in terms of making decisions and evaluating decisions based on what's gonna provide the maximum good. We can look at something like content and deontology which is gonna suggest we should evaluate a given action based on what our duties are in that particular circumstance. And we actually might ask not just about act evaluation, but also about character ethics, virtue ethics, for example, is going to ask us and help us reflect on the kinds of people that we want to be. Are we instilling the kinds of values in ourselves and in our actions that are virtuous? And at their core, these ethical principles are really in systems are really about using our capacities for reason, judgment, and thought to critically examine our own actions and our own character. And so I really want us to keep this idea in mind, right? That ethics is about in part using our capacities for reason and judgment to think through our actions. Now, as researchers, we often talk maybe colloquially about ethics as sort of that regulatory piece, that compliance side of things, right?  Going through the IRB, getting the paperwork for informed consent approved. Certainly I go through this, I'm sure all of you can go through this with your work as well. This is about a process of ensuring conformity with relevant laws, policies, and guidelines. And of course, these laws, guidelines, and policies are developed in relationship to particular ethical principles that for example, Dr. Buchanan mentioned at the very beginning, respecting human dignity and autonomy, maximizing benefit, minimizing harm so you can see the trace there of utilitarianism thought in that particular value. Justice and beneficence. Now, ethics as compliance is really about ensuring that researchers don't violate certain sort of baseline conditions for the treatment of others essentially, right? These were set up to make sure that researchers do not violate what I would call flooring level needs to make sure that people don't violate the basic conditions for how we should behave towards one another. That doesn't necessarily mean that that is the end of our ethics though. And in fact, compliance and contemplation may not necessarily be playing in the same sandboxes as Dr. Buchanan reminded us. Sometimes, compliance doesn't cover ethical situations that we may encounter as part of our scientific research practices. And very often when using public data on from social media sites, we're getting more and more, we're getting better policies today than we had say a decade ago, but there's still incredible gaps where the rules and policies that were developed for the biomedical setting may not apply cleanly to the context of the social media using public data. And part of this is because at least in the US, and I'm not an expert on EU policy, so I have to apologize there, but in the US at least, public data is often not considered to be, the use of public data is often not considered to be research involving human subjects and therefore not subject to the kinds of oversight of institutional review boards. But again, this is not to say that these research projects lack ethical components. It's simply to say that they fall out of the purview sometimes of the compliance side of ethics. And I want to make the argument here that ethics and our ethical reflections and our actions and evaluation of our actions needs to be considered across the entire process of doing research. And certainly there are in fact a litany of ethical quack miners that we may encounter. These can happen during the data collection process, the data use process and the data sharing process. So the entire gamut of the research process essentially. So certainly in terms of data collection, we might think about, for example, data that was perhaps public once, but it's now been deleted. There may be ethical questions about how we should treat that data. We might be using the data from marginalized groups. It may be public data, but we know that there's been overburdening of particular populations. And so even when we're using the public data, we still may have obligations because of the historical injustices on those groups. Aggregation of data points to create a very detailed picture of someone's life can be a threat to someone's privacy and may have ethical dimensions. And there's questions that we should ask about even when it is public data, if someone contacts you and says, I would like my data removed from your dataset, whether or not we should honor that request. As part of data use, we might encounter questions about the actual ends of our project. So a really interesting example actually in just the past couple of weeks, researchers at the University of Minnesota had a project that fell out of the compliance side of oversight where it was not considered to be research involving human subjects, but where they were purposely introducing errors into the Linux kernel in order to study whether or not the development community would actually find those errors. So we can recognize that there's some serious ethical issues about, for example, purposely introducing errors into a system that thousands upon thousands of people rely on, even if the IRB says, oh, it's not research involving human subjects review. And sometimes we also, I also like to use the example sometimes of projects that scoop up lots of public image set data to do things like use images, pictures of people's faces to try to predict the political leanings or try to predict sexuality. And some people have labeled this as sort of phrenology 2.0, right? Obviously has severe ethical implications, even though it's public data. An important thing that's actually come up really recently that I do wanna particularly encourage this group to think about us are studying misinformation and disinformation. That's been actually, that's come up in the context of research around Gamergate is not just thinking about your obligations towards your research subjects, but also thinking about your obligations to your fellow researchers and to, for example, your students. If you are studying by Trollic content, content that could be psychologically harmful, that maybe it's a violent information, maybe it is disparaging information, finding ways to make sure that students or your fellow researchers have the kinds of support mechanisms they need to be able to engage with this work and have essentially support if they're feeling harm themselves by the kinds of content that you're analyzing. And then finally, in terms of data sharing, there's some really serious ethical questions around how we represent our data subjects. So for example, the power of labeling. So an example might be something like if we create a data set and we label this data set tweets from people that we think have depression and then make this publicly available, the way that we're representing that data subject, those individuals can have implications for them. And so we have to be reflective of the implications of how we represent our subjects. Certainly in terms of data sharing, we also wanna think about things like other ways of sharing our research outputs with the communities that we're actually studying. This can obviously have a lot of benefits for the researchers in terms of developing connections, but can also be a question around justice. And then finally, certainly when we're thinking about upholding values for science, replicability is a really big ethical issue. We want to make sure that the work we're doing is valid, that it's reproducing and that we can benefit science and human knowledge going forward. So the thing I really wanna emphasize in this first part is that there's no singular ethics portion of the research project, right? It's not just something that you do upfront, it's a process of continuous evaluation of our actions, of our character, where we weigh our values and duties. And I'll point very briefly to a framework that I think is very useful in helping us go through that sort of continuous process of evaluation about maybe conflicting values or duties or values and duties that are intention or tensions between what we're asked from a compliance side and what we may feel from a contemplative side. And that is the process of using disclosive ethics. This is a framework put forward by Philip Bray that asks us to sort of continually engage in a descriptive process of what the values, tensions are or where there's gaps that we're noticing between different values and a normative component, how we then go about actually addressing those gaps once we have described them in full. So I wanna pivot now to getting a little bit more specific. So that was sort of my broad introduction thinking about ethics and questions in relationship to the use of public data. I wanna talk now about Twitter specifically. So obviously Twitter's become a major source for academics. There's been over 2,000 research papers published in the past three years using Twitter data. Projects obviously include sometimes billions of tweets now. And we certainly have a reliance on Twitter not just because it's become a very dominant space for political discourse, for responding to events, but because also it is easy to get the data there relatively speaking. And because the data that we can actually get is a kind of data that we can digest very easily. It's kind of data that is very easily parsable for machine learning applications, textual data. Obviously much easier to process than image-based data, other kinds of video content and so and to make inferences from it. And because comparatively it is relatively public. Now I have public there and scare quotes and I'll unpack that a little bit in just a minute. And I wanna make note that as part of a research project I did with Michael Zimmer about five, six years ago we looked at research that had published using data from Twitter and found very few of these projects actually report about going through ethics review. Now that isn't to say that they didn't but only about 4% of the published research that we were able to find actually talked about going through IRB or talked about their ethics processes. And I actually do wanna emphasize that I do think it's quite important that we talk about our ethics that we include it as part of publications and include that sort of descriptive component and our evaluation of it. So I wanna talk for just a second about a research project that I was involved with, with Dr. Casey Feisler at UC Boulder where we sort of recognize that Twitter data is becoming more prominent in terms of its use in the academic setting. And we kind of had the question, well, what's the other side think, right? Do users actually know their content is being used for academic study and how do they feel about it? So we surveyed Twitter users asking users whether or not they think that researchers are allowed to use their content without having to get reconciled. And then we started asking them questions about their level of comfort with the idea of their tweets being used for research. And in particular, we were trying to understand sort of the contextual factors that might drive users' level of comfort with the idea of their content being used or used in different ways. Such as whether or not they were asked for permission by the researcher, the kind of study it was, the kinds of content that were being analyzed and whether or not they were, for example, quoted directly in the study or indirectly. So the top line statistics that I thought were really striking were that we found over 60% of our respondents actually thought researchers were forbidden by Twitter's terms of service from using public tweets without having to ask the users for their permission. That is not the case. This is an incorrect understanding. Now we asked, we told them after this question that actually they are allowed to do it, but would you like them to get your permission? And 65 roughly percent of our respondents thought that researchers shouldn't be allowed to use tweets without permission and without having to go back and ask the user. However, and this is the thing I do wanna emphasize that many users are actually somewhat comfortable with the idea of their content being used if asked, if they can see the research outputs, but this is extremely contextually driven. So I'm not gonna sit here and read this entire chart for you, but I want to emphasize and just try to give you a quick read of what's going on here. So these are the different contextual factors that we asked about on the left-hand side at the top. This is essentially a ligard scale between very uncomfortable on the left-hand side here and on the right-hand side, very comfortable and scores in darker blue are higher prominence. So for example, users are very uncomfortable with the idea of their content being used and never being told about it. Researchers, excuse me, users are very uncomfortable over 50% indicated they would be very uncomfortable with someone using their tweets from a protected account, which is a kind of privacy that you can invoke on Twitter that controls the distribution of your tweet. People were very uncomfortable with the idea of someone, a researcher using a tweet that you'd created even if it was public, but that you had later deleted. Now, where would you see increasing levels of comfort? Actually, and I think this is kind of interesting is the idea that if my tweets are being used in a big data set, maybe I'm a little bit more okay with that. So for example, people were much more comfortable with the idea of if I was just one person in the scope of a research study that's studying a billion tweets, I'm a little bit more comfortable with that than a situation in which a researcher is only studying a few dozen tweets or a few dozen people. Interestingly, users are much more comfortable with the idea of an algorithm, analyzing their tweets rather than a human, which I think is kind of interesting. Folks are very uncomfortable with the idea of researchers using not just your tweets, but also other information in tandem with tweets. So for example, public profile information, such as location and username, and folks are pretty uncomfortable with the idea of being quoted in a research study with their Twitter handle attributed to that quote. They're a little bit more comfortable with the idea of their tweets being used in studies if they're attributed anonymously. So there's some interesting contextual findings here, but the big sort of things I wanna draw our attention to are what I think are potentially tensions in some of these results. So users' understandings of how researchers are actually using their data are pretty limited, in my view, based on this data, but they do have contextually driven levels of comfort, which I think are important to acknowledge and to understand their perspective. At the same time that we recognize from this data that there's some serious gaps in users' understanding, it's also important for us as researchers to promote certainly the progress of science and values that sort of, again, serve that wider public of addressing misinformation and abuse that are happening in a much more macro level. And one of the things that people have talked about, for example, is, well, is there a way that we could perhaps think about notifying users when we're using their data for research, but in some situations, actually that notification process itself could cause users more anxiety that they're being watched or being studied than actually no notification. And so I just wanna point out here that there's different tensions at play and it's really important to describe this space in order to understand these tensions. So again, so I'm moving now from really broad talking about ethics and public data, to talking about Twitter, to talking now about a very specific context, which is data sharing and exploring some of the tensions in relationship to data sharing on Twitter. So obviously in terms of the progress of science, a very important part of upholding scientific truth is ensuring the validity of our work that's part of and also ensuring public trust. A big way that we do that is by providing our data and providing our methods in order to try to create opportunities for replicability studies. And this is really important. Obviously there's been a lot of concern about a crisis of replicability in scientific research, particularly in the social sciences in the past decade or two. Now, in this particular situation, thinking about the context of Twitter data, one way that we can enhance the sort of benefit to the broader community, scientific community, is by making our data sets available for use by others. But there are some tensions here obviously on the compliance side of thing. Twitter's terms of service forbids us from sharing the full JSON data. So this is the full data of a tweet. So we would actually be essentially told that we cannot actually provide the content of a tweet in terms of resharing certain metadata fields we're not allowed to share. Twitter's terms of service actually only allows for researchers to share with third parties of the tweet IDs themselves. Further, our local laws may or may not allow resharing of data. And at the same time, we also might be asked by our funders. So for example, National Science Foundation might ask me to share my data as part of trying to ensure open science practices. So we already have tensions just in the compliance side of things. From the sort of ethical contemplation side of things, we realized that users themselves may not want to be in a labeled data set. Again, think about a labeled data set of tweets that we think are from people who based on the sentiment of their tweets, we think may be prone to depression. Labeling someone in this way could have implications for their privacy if I can look up a tweet ID and then be able to see the user that it came from. We also might ask about whether or not there's an even distribution of risk that's being shared among the population in relationship to the population that we're studying. Again, thinking back to situations in which there's marginalized groups that have historically already been overburdened or have had injustices committed against them in relationship to scientific research. And then we also, from the contemplative side of things also have to think about, well, we still want to uphold open science, the validity of our work. What happens if data starts disappearing from this data set? So a researcher at the University of Maryland at Summers did a project not too long ago where he looked to see how much content from a data set had been deleted after a year, after an event and found that somewhere between 10 and 15% of his data set was gone, essentially, where users had simply just deleted that content after the event occurred. And that really threatens our ability to recreate an event, to understand a particular social phenomena. So we have a lot of different tensions here. It's really important for us to describe these tensions and think about ways that we might go about solving these tensions. So I want to draw your attention to one project, the Documenting the Now project, which has developed a set of code that they call the Hydrator that lets researchers easily rehydrate grabbing all of that extra JSON data from Twitter's APIs, simply based on a list of tweet IDs. So this is a piece of code that helps sort of address the fact that Twitter's terms of service requires us to do one thing, but that we may feel that is in tension with questions around difficulty of actually going about doing this process. Certainly we want to think about in terms of data sharing, finding ways to anonymize user data when possible, particularly if it's potentially sensitive content or if we're doing a kind of sensitive labeling of a data set. We might think about ways of addressing these tensions by doing things like making our data sets available only by request or setting up specific kinds of resharing agreements. So there is a series of data sets called the eRisk data sets, which is a set of data sets used for developing better machine learning models for doing things like predicting self-harm, predicting depression. And this is a set of tweets from individuals that have been, have a baseline where they have indicated that they have had a positive, for example, diagnosis of a particular mental state or have had a positive case of self-harm. And essentially that's the ground truth that they're operating against. And so this data set, you can get access to it, but you can only get access to it by request. And there's a specific user agreement that the researchers have set up with terms to limit what researchers can actually then go and do with this data set. And the data set itself has been scrubbed already of as many identifiers as they can possibly, they think realistically scrub from it. But the terms of service that the researchers have set up for reuse also includes terms like making sure that future uses can't try to positively identify you're forbidden from trying to re-identify the individuals from whom this content originated. You're forbidden from resharing this. So thinking about ways of setting up your own terms for data sharing is one way of trying to address the tensions. And then finally, the thing I wanna come back to is document your decision-making. Discuss your decision-making. One of the big difficulties of coming up with ethical standards in this space is the fact that very often we don't report our ethical thinking and our ethical decision-making as part of our publication practices. Getting to a point where we talk about our norms and normalize them is critically important for this space. So again, our ethical evaluation needs to be a continuous process through research, the entire research process from A to Z, and we need to document that and discuss it. I will leave with this note that certainly the Association of Internet Researchers has been thinking about this particular ethical questions involving internet research. They've used public data for decades and they've published two ethical decision-making guides that are available online. The thing I really like about these guides is that it's not a compliant side model that if you just do these things, that you will be okay. It really is a series of prompts that are set up to help us think through the particular challenges that we might have as part of doing our practices. So it's really there to help us think out potential situations. And so with that, I'd like to say thank you.


adaptation conversation now yesterday to figure out what various localities will need and then it should be noted that a lot of the places that are thought or predicted to suffer the worst consequences of actual climate change impacts are a lot of these places have the least ability to pay for adaptation. And so, you know, that's a major thorn in the side of, you know, the mitigation conversation as well as the adaptation conversation. And, you know, it sparks fly when that conversation comes up, but that's a conversation that's really integral to have and to come to conclusions about. I'll just comment on that. I do think, I think this is your intent, but I mean, it's not an either or. You know, certainly the more we mitigate the less we'll have to adapt. And certainly, I think it matters. In fact, I know it matters even if you mitigate some that's better than not mitigating and if you mitigate a lot that's even better. So I think it's important to think about doing the best we can in all of these arenas and also making sure that we understand the implications in a probabilistic way because a lot of times it is, you know, this is about risk and doing the best we can to mitigate our risks and to make the investments and make bets on things that we think have the greatest chance of helping us solve a problem. So I really do think that there's an enormous reason to be optimistic that we can do better and there's also a reason to be pessimistic and that we just have to try to do the best we can to solve the problems that we have before. Yeah, I would say just to put this all together, I think it's a mistake to pit adaptation against mitigation, but I think that both are important to take into consideration and to think through. Absolutely. So I think that's a very, very apt closing words. That's the point at which we'll unfortunately have to end. We obviously opened a lot of threads of discussion that we could pull and perhaps some of you will have the chance to catch some of the speakers afterwards and ask some of the questions if you didn't get to hear. Allow me to make just a brief plug for those of you who are interested in exploring other avenues of mitigation, other avenues of innovation that are going on in policy. We've got two very exciting events next week that we'd love to see you at. On June 14th, Tuesday, I'll be hosting an event here at Carnegie on autonomous vehicles, driverless cars, and specifically what they mean for energy and climate policy. Are they going to be obstacles or are they going to be opportunities that we can leverage? Then the second one, the following day, Wednesday, will be an event led by my colleague Deborah Gordon on designing a smart carbon tax for the petroleum sector and a discussion over what that would look like, painting a picture for what a smart carbon tax to come might look like in the petroleum sector. So please join us next Tuesday and Wednesday. Thank you very much for joining us today. Allow me to thank once more the wonderful staff here at Carnegie that made this event possible and most significantly, let me also thank our wonderful distinguished panelists who led a wonderful discussion today. Thank you very much. Thank you.


Do you have an expired employment visa, do you have a consuled employment visa and you are living in the UAE? You would wish to change it to what you call a tourist visa when things go wrong in one or another. This is for you. Remember when you have a cancelled employment visa or when you have an expired employment visa and you are still in the UAE, you cannot change it within the country or within the UAE, Dubai and other states that make up the UAE. If you have a cancelled employment visa, the only word for you to come back to the UAE is to exit and get a fresh visa from outside then you can make re-entry into the country.


There you go, try again. Hit it with a first. All right, you're going to look. Any adjustments you need to be made? Three more.


Hello, everyone. How are we doing today? Again, we have a nice full house. We have my fellow elves, Kathy and Christine, who help keep everything organized here. But we have Elf Michael with us. We saw today's wish. And we have Lee Gardner Dewey, who was the wishy. So this is very exciting. I hope you enjoy today's episode. We have a story for you. So before we go any further, welcome. If this is your first time watching one of our Days of Elfness videos, we're from WikiTree. We're a free genealogy website where genealogists collaborate. And during these last two weeks, we're in the middle of a two-week period where Elves, Secret Santa at WikiTree Elves help other WikiTrees and find, solve mysteries, break down brick walls. And we have one mystery that's revealed today. So that'll be great. And as we've done with all of them, I'll start us off with a little verse to get us in the mood. On the sixth day of Elfness, a keen elf gave to me six occupations. And then the other five verses, you'll have to tune in the final day to hear them all together in a big long song. But six occupations, that's sort of the tagline. That's the thing that we're teasing you with. But the story that we're going to be talking about is much more than just six occupations. So there is, there's my little, you can sing or write it down. And Lee, since you're actually right here, why don't you read your letter to Santa? Dear Santa, in less than a month, I'll be 72 and I'm running out of time to solve a mystery. I started when I was 16 doing genealogy and as yet cannot break through the brick wall of my second great-grandfather's, great-grandfather's, sorry, it's hard to say, second great-grandfather's father. I carry his name and yet don't know who the man was, Charles Gardner, 17793. Please solve this mystery before I die. Distant relatives have proposed solutions but don't provide any sources. I know he must have been from New York and his father's name was Jesse. I still believe in your power, Santa. Work a miracle, please. That's great. So thank you, Lee, for reading that. So when we were going through, so Christine and Kathy and I, we go through all the wishes that were solved and by the way, how many are solved now, Christine? I have lost count. That's wonderful to hear. That is wonderful. Kathy, do you know what the number is? How many have we documented so far that have been solved? I don't know. I know there's still some that I think have solved and need to be moved down to the final count but we're definitely more than last year. We're already more than last year and we're only halfway through the days of Esmond. We're on the sixth day, there's 12 days altogether. So that's pretty exciting. We have 34 in the list that are marked as solved. When we were looking at, of course, because I am obsessed with the song The 12 Days of Christmas and we're trying to fit every wish, every segment into one of those, we were looking for a six and so Christine found this profile that you worked on, Michael, and said, well, this guy, Charles Garno, he has, he's had like six occupations so she wrote them down but you have, that profile has grown like in twice the size since we first looked at it, there's probably more. So this may not actually be an accurate, but we're still, we're going with six occupations because he has had at least six. Should we go with that? And then he became a dairy farmer. Well, that is amazing. So, well, I'm glad you're both here and so Michael, I'm going to turn it over. Do you want me to, first of all, there is the profile, but do you want me to drive or do you want to share your screen and go ahead and drive? Okay. Okay. You just let me know where you want me to scroll around and if there are links or if you want to want me to click on a source citation and go to the original site. Okay. You lead the way. I'll let you know. So I, I wanted to be an elf this year is my first time being an elf. It sounded really fun, but I wasn't confident in my abilities and I was a bit intimidated by all the wishes. It seems like it's things that experts couldn't solve. I didn't feel I could solve. So I was, I was looking through them, looking for a wish that I thought I could solve. And this looked great. Look at a colorful man. It didn't look too hard. It looked like several people on my heritage and ancestry had already solved the problem. Long story short, I seriously underestimated how difficult this would be and how fun it would be. I got in way over my head and I kind of like being in that position. So I'm still trying to figure out one of the key questions that Lee asked, which is who's his father? And I want to give a shout out now to Canite and Margaret Toll, who helped me immensely over the last couple of days, get very close to an answer on those questions. So you can go ahead and scroll through the profile, talk and drive that way. I do tend to be a bit wordy. So I went ahead and I put an introduction up there to make it easy to have it be too long. Yeah. I'm just going to put the link to Charles Gardner's profile in the chat. So if other people want to check it out and feast on the beauty of this profile, go right ahead. So I don't think there's anything special about my approach. I mean, I'll go over my approach. I think one of my, it's a secret sauce is I just, I'm a person who doesn't give up. So at some point, Lee may actually have to physically tear me away from this profile. He's become like family to me. So I use an iterative approach and I like to synthesize everything. I come from an academic background. So I like words. I like to use them, perhaps a bit too much. I began with what I call a scaffold of records. I like to just go in and find the most basic records, the birth marriage, death, none of which were there, although I did, there was one marriage record. The censuses, this is the first person that I've worked on. I found a lot that are double counted on censuses. He's the first one I found who was missed by two censuses in a row. He just wasn't on the 1870 or 1880 census. The 1870 census makes sense. He was on route, I don't know, overland or by sea between Sacramento and his new home in Portland, Oregon. So that's, but why he wasn't on the 1880 census, I don't know. And then your 1890 census is missing, right? 1890, yeah. So that's basically three in a row that, well, you don't have records for. And then from there, and I like to practice record scraping, that was something that just make sure you track every last tidbit from the record. I like to keep a combination map and timeline in Google Earth Pro to help me visualize where things are. And that helped clear up a bunch of things, because other people had said he lived in two different places. I'm going to switch over and look at mine now. One was, what was it, Lake? Is it Lake Pepin? Lake Pepin. Lake Pepin. And the other was Westervelt or, yeah, Westervelt. But with maps, you can see that's the same place. Westervelt is right there on Lake Pepin. So I went second while I switched back and forth with all of my screws. I like to layer in, so when I said I'd like to take a synthetic approach, I want to get, my goal of the profile in WikiTree is to get everything that we know about this person in one place, which I realize a lot of people think is overdoing it. But I would love the WikiTree profile to be the starting point for any future researcher to take off. So I just, I layer in everything I can find, but only if I can find sources for them. And then I start to look for the color. And that's where it really took off. Starting to look for newspaper clippings, newspaper clippings, especially. I have yet to find a newspaper service that I don't subscribe to, because there's so little overlap between them. So I used a genealogy bank, newspaper archive, newspapers.com, Chronically America, the one I'm forgetting that's the California repository at UC Riverside. There's an Oregon Historic Newspaper repository. Normally I'd be using the Minnesota State Historical Society, but they've decided to shut their online catalog down right now. Well, not shut it down, but take it offline while they try and upgrade it. Yeah. And I found a lot of interesting pieces that all told part of his story, and each had often a little bit of an exaggeration in them. So it was a real interesting job in trying to reconcile everything. One of those things was his time as governor. One source said that after serving in a territorial legislature and being the speaker of the house in the Minnesota territorial legislature, he was nominated by Buchanan as the governor for the New Dakota Territories, which makes sense. Buchanan was the one who proposed them in the late 1850s, but there's absolutely nothing there to indicate that Buchanan put forward his name or anyone's name as governor. Another source said Abraham Lincoln put forth his name as governor of the Territories. Again, it makes sense while Buchanan informed it. Abraham Lincoln was the one who got to make the territorial reality and decide who's going to be the governor, but no indication in congressional or presidential records that Charles' name came up there either. His son, Charles A. Gardner, who went on to become a lawyer himself and to edit several newspapers, apparently told one of his friends who runs another newspaper, oh yeah, my dad was the first governor of Montana. And again, I could find, not he was almost, but he actually was, could find nothing there that he was the governor of the Montana Territory or the Idaho Territory that preceded it. I suspect there's a grain of truth in there somewhere, but it's given me the impression that Charles was somebody who lived a large life and liked to tell stories about himself to his children and to others. And I don't know if he was prone to exaggeration or whether the stories themselves were prone to being enlarged and getting legs of their own, but the stories did tend to move. So that's one thing I'd like to keep digging on. What's the grain of truth in this governor of a territory story? It's something but yeah, he was an interesting man. His time in politics was interesting. He was a lawyer possibly from his time in New York, but certainly from the early 1840s. He had practices in Illinois, then moved to the Minnesota Territory and open practice there. I love the, you've got the clippings there of the ads with the instructions on how to get to the office. Yeah, I'm a very visual person, especially when it gets to be, I know academics like to read long, long books with absolutely no illustrations and pictures in them, but I don't. That's one of the things I didn't like. Give me picture books any day. So I, I love a lot of good illustrations. So I like taking them there. Yeah. And just as the museum, is there only one museum in Chicago or was there only one museum in Chicago at the time? Once I solve some of these big problems, that's one thing that I'd love to find. I work in a museum as my day job. I would love to find out what this is. At first I thought, okay, it's the Art Institute or it's the Field Museum. Ah, those are relatively new museums. The Field Museum is only like 1892 or something. None of the museums in Chicago date to this period. So I don't know what it was. This is before the heyday of museums. So I suspect it was something small, kind of a traveling curiosities thing. I don't know. When I first saw that, I thought maybe it was a business that was a, an illegal business or whatever. And maybe they figured the name would make it better. I don't know, but my mind went straight there and I don't know why, but I'm like you. It's like, I want to know. Yeah. And so I was continuing on this. I'm an eternal optimist. I feel like there's more to find and there always is. That's the great thing about being a genius these days. This is a guy who's had so much written about him. Actually, let me put a pin in that and finish my last thought. Yeah, so he served in the territorial legislature. Well, he was a lawyer and then out of nowhere, he's nominated and elected to be a representative of the fourth district in the territory, which is impressive. But on his first day there, he is nominated and elected as the speaker of the house. So I don't know if he's, I mean, clearly he's built a reputation. Does it come from his family, his past? Does it come from what he's, I don't know, but clearly he was a well respected person. And he does seem to be a bit, I don't want to say fragile because I would crumble in that way too, but you saw that attack piece that I put in there. Yeah, that was an interesting article. Yeah, this person who is a Republican just tore into him. I'm sure it wasn't personal.  I don't know how Charles took it. And so the legislator in the territory, it only operated for 60 days a year. So the 1956 legislature, he had already served and come back home by this point. This is like October or something that this was published. I thought he was going to serve two years. I still haven't been able to find that in the Minnesota territorial laws, but I expected him to serve two years and he just disappeared after one year. Now he disappeared. He stepped away from government. He stepped away from law. And on the 1857 census, when they came to ask him what he did, he just said, I'm a merchant. It must have really stung being attacked like this. I think he got his relationship going with Mr. Clark. And I think they started talking about going West because Mr. Dixon, his father-in-law of his deceased wife, had gone out to California and had died of dysentery out there, but there was still that lure of everything that was happening in California. So he and Mr. Clark got together because he eventually sold Mr. Clark's property when he relocated to California. So I think they said, let's head south and let's get ready for this big trip. And the two families were on the same wagon train in 1860, but he had to sort of buy this time and he became a merchant just temporarily to make a little money before they took this trip. That was my understanding. I remember my father talking about this. The trip itself was interesting. Everyone was saying, oh yeah, he went on the Oregon territory. So I thought that certainly he did. But as I dug into it, it didn't make sense. I mean, to take the Oregon trail the whole way, sorry, I said Oregon territory, he wouldn't have taken the Oregon trail the whole way because it would have led to Oregon. He wasn't headed to Oregon. We know he ended up in Virginia City and then in Sacramento and then found out that the Pony Express started pretty much literally the month he took off. So I have no proof that he went on that, but why wouldn't you if it's going exactly from where you are to exactly to where you're going and they've got stops every 15 miles with provisions. There's Army giving security along the way because there have been Shoshone attacks the year before. So this is my best guess is that he went along the Pony Express route in a way because it wasn't just the Pony Express route, it was also a wagon route and became called the Overland Trail. So where would they have started on this? Where would they have joined? Well, the start is St. Joseph, Missouri, but is it the history? Oh gosh, I'm forgetting which one I got it from. One of the printed genealogies said that he started overwintered in Kansas and started from there. So Kansas City? Right. Okay, so right at the very beginning of the trail. But they said Kansas. I can't find my piece of paper that lists a specific starting point. So but somewhere in this vicinity though? Yeah, that's that would be what I would expect. All I've got is they started in Kansas. Okay, and then the end point is, sorry if I'm giving people a vertigo over there. It ended in Sacramento. And that's where they ended up. What's interesting is my dad sucked me into this when I was 16 sitting at the dinner table talking about Charles, his grandfather, Charles Andrews Gardner, walking the whole distance to Sacramento. And Sarah Clark, the woman he ends up marrying was on that trip. And she was 10 years old. And daddy said he fell in love with Sarah on that trip and pursued her until she finally married him in California. But the other amazing thing was that I didn't know how how they did this trip. And I was a fluke, I discovered them in the census records for Virginia City. I was just boggled. So they must have just spent the night or something. But the Clarks weren't there. The Clarks don't appear on that. So they were all in the same wagon train, but they must have gone on ahead or stayed in a different town. Maybe there was no room at the end. So I suspect he spent more than a night in Virginia City because it's a good 10 or 15 miles off the trail. So it's not a stop here. No, it's not a stop there. And also his son, I want to say it was Charles, but it may have been an older son, had the same occupation listed on that census as the man whose house they were staying in, minor space C period, which I take to mean a copper miner. Yeah, sounds like it. So that would be odd to me, unless it was an assumption on the numerator's part, that one of Charles's son became a copper miner in a one day stay. So I'm guessing that they stayed up a couple of weeks or something like that. I don't know. Oh yeah, so there's Virginia City. Yeah, it was Charles A. Gardner, who's the, if you look down there, listed as, how old is that 18 year old copper miner? That doesn't make any sense. 17, though. 17 years old. Yeah. Yeah, that's interesting. I'm looking at the census record right now. And then that's another story that the son right below him, Edmund, have I got that right? Is Edmund not the biological son? No, Edmund was the son of the woman he married, the third wife. And I have a cousin, a Gardner cousin up in Oregon, who tells me that everyone says that Edmund was adopted, that he was one of these railroad children. And I kept saying, no, I don't think so. They think his name was originally Perkins or something. So the man that Catherine, his mom, was married to was Perkins. Leonard Perkins, I think. You can link to it through her profile. I think Catherine's first husband was a Perkins. Yes. So if you go to Catherine, you'll see that she was married to Perkins. But that Perkins left her and married somebody else in Kentucky, four years before Edmund was born. So yeah, Edmund was born to a mystery father or was adopted. And he seems to have taken on the name. There was one point and he was born in Louisiana. I wondered if that was a mistake on the census. It was on two separate census. I know. I know. That's so strange, isn't it?  And either that or there's two Edmund Perkins that were born the same year in Louisiana that have ties to New York. So I think it's a real thing. Maybe she went away and got pregnant while she was away. Maybe she went away because she was pregnant. The women would typically give their illegitimate children their maiden name. She gave her child her married name. And I'm guessing that was just to avoid the stigma when could have been in New York. A lot of substitution in there. But yeah, it's also that that last entry there on that census record that says there's a Lydia Gardner with him who's 55. That's Charles's sister. So that's that's one of my hypotheses. Yeah. So that's in the that's in the 1860 census. And you know what? Yeah, 1857 census. She's living next door to Charles. If you zoom out on the whole page, she's she's right there with some other family right next door. So she was with Charles's family for a while. I don't know yet if she's a for certain if she's a sister or sister in law and step sister. I think because she's 55 and Charles was what was he at the time? 46 it looks like. Yeah, I think they're siblings. But the person that that we think is Charles's father and mother, his mother, Dolly, Dolly Brown, was born in around 1795. And so there's no way that she could also be Lydia's mom, because she would have been nine years old at the time. So it would be if they share Jesse as their father, then it's going to be a half sister situation. So this might be a good time. It's certainly not this Lydia Gardner mentioned on this little piece of paper because she's in Two Rivers, Wisconsin. It's a different idea. So Kay's coming in to say there were a lot of Lydia gardeners that the gardeners didn't seem to have a Lydia problem. They couldn't keep a Lydia on it. Just every time you think you've traced traced it down, you find another Lydia gardener. So someone just mentioned Dolly Brown. Is there any written evidence that that was the wife of Jesse Gardner? Yes. So if you go to Charles Gardner, go to the bottom of Charles Gardner's down to the research notes under the who was his father. And there's a link there to Jesse's profile. Of course, I can't link up above. Right, right. There we go. Birth with a little bit further. Who was Charles? Right there. Just click on the Jesse Gardner link and that'll take you to Jesse's whoop. There we go. Jesse. Okay. So this is where I really want to think Kay and Margaret for helping out. So they dug in and found all sorts of property records. And yes, through those, we know that Jesse and Dolly were husband and wife. We also know that it seems reasonable to conclude that Jesse died around 1845 because his widow Dolly did a quick claim deed of some land to Rowland, who is another one of who I think is another one of Charles's siblings. But yeah, the story here of Jesse is a really compelling story. I'm still in the data gathering stage. Every, as you can see from those, I'm trying to put the tables and the censuses. I'm trying to work out. I haven't been able to find a way to prove it's him. So I'm trying to find ways to disprove. And nothing has pulled him out yet. He's in the he he's in the. Sorry, I've got a child screaming in the other room. Okay, there's another Lydia Gardner right here. Wife. Yeah. So this this is our leading candidate right now. Nothing yet to to establish that it is his father. But I feel confident. If it is his father, then I think we can conclude that Charles was probably born in I keep forgetting plain. Sorry, I don't know New York as well as I should. Plainfield is that plain field. Thank you. Plainfield. Plainfield. Plainfield. While his dad was operating businesses in Plainfield and in Sangerfield and had apparently business transactions going on and Troy and other places, he seemed to be uh in the uh he seemed to be a merchant who also made money in real estate and seemed to fall on hard times right around the time that Charles would have been born. And in fact, he was thrown debtors prison for at least 60 days seems to have his property confiscated. So kind of a tough story there. It's going to be a very colorful story. I hope it ends up being part of Charles's origin story. It's interesting to see I was saying before we started that I thought Jesse was a lawyer because there were documents posted on ancestry were dockets that showed that he had that he was appearing in court. Well, turns out those were actually from a list of insolvency cases being heard in two different counties. And he was the plaintiff in those. But it seems like he that Jesse's story has the family being successful in part because of real estate and part because of mercantile concern or running a business and then fall into hard times because of law. And so I find it interesting that law and running a shop being a merchant are two things that seem to be common threads that run on Jesse's sons. Charles, of course, became a lawyer, but Roland also became a lawyer. And then Daniel, another son, got heavily involved in real estate in the Chicago area to the point where it seems like when he died, his will made provisions for his wife and his kids and his grandkids. And looking a couple of decades later, his grandkids still seem to be very well off, don't have jobs and are touring Europe. So he seems to have been very successful in investing in Chicago real estate in those early years. So this is an unfinished story here. It's unfinished. There's more to find. Well, like I said before, I'm an eternal optimist. I'm sure we're going to be able to put a bow on this. I'm going at it. I'm trying to go at this problem of who is Charles' father from so many different angles. Nothing is clicked yet. That's the bit I said, let me put a pin in it that I wanted to get back to. There's so much written about Charles. So many column inches dedicated to him. He's got an official biography in the Minnesota State Legislative History. But each one of these has just a little bit wrong, like his birthday. On Find a Grave and in the official Minnesota Legislative Biography, it's wrong. I don't know where it came from.  He's got all this written about him. But at the end of his life, despite having a family that seemed to love him and having had his son at least in the newspaper business himself, having had a close relationship, obviously through all these ads with the newspaper establishment, despite having lived a life full of adventure and achievement, there was no obituary. He scrolled down to the very bottom. There's just a flimsy little ad. Keep going. All the way, well, it'll see the end of life and it'll be the bottom of the section there. Wow. That's all that we have. It was posted. You can't really see. It looks like February 21, but it was announcing on the 24th that his funeral would be on the 24th. I don't even know if the newspaper came out before the funeral. But that's it. I would have expected a large memorial or an obituary. There's still a possibility that there's yet another newspaper archive I need to subscribe to that has it or that it's on microfilm somewhere. There's an interesting story about the death up in Sobby's Island.  So I have a cousin up there who's a descendant of that Charles Gardner, the judge. She is the great-grandchild of one of the other sons from the Catherine wife. She's not entirely related to me. She's a little related to me because of the judge, but there were these other boys that the judge had with Catherine when they reached Oregon. Anyway, Moore Reach, the M-O-R-E-A-C-H, she's my contact in Oregon, and her last name is Hammond, I believe. Her father was a gardener. So it was like E.B. Gardner, one of those. And she's the one that told me all about the dead child being brought out from Joliet all the way to the Lone Fur cemetery. So she knows a little bit more than I do, but she's not very communicative. She knows a lot and didn't hear back in time or I haven't heard back yet. She doesn't communicate because I've tried over the years to try to reach out to her since she's the closest thing I have to a gardener relative, but she's just not interested. But she did tell me years ago, oh yeah, it was Dolly Brown. And I go, well, what proof do you have that it was Dolly Brown? She never came up with any proof, and that's why I was sort of up in the air over it. Yeah. Well, June has a theory here. I don't know if you've pondered that. But considering his life has been so full. Well, I'll tell you something you probably don't know. There was a big fiasco because Catherine, when the judge died, she thought she'd be in fine shape, but apparently it was discovered that there was a mortgage on the property she didn't know anything about. So there was a lot of unhappiness upon the death of the judge that may be part of the problem that there was this big mortgage. So that was my understanding of it. Suzy Hammond knows more about it. She's still up there in Portland somewhere. Wow. So that reminds me of another story that I dug up. The one about him, oh yeah, he lost his arm. No, I think that's another Charles Gardner because I've got some pictures of him on the farm at Sovy's Island, and he's got both his arms. So I think that's Charles Gardner. That's another thing, just like the gardeners had a Lydia problem. Every place he traveled had a Charles Gardner problem. There was a Gardner, an entertainer as well. Yes, a German entertainer called Danny or the Dutchman. And so the thing about him setting, closing for Annie Oates, the famous performer who brought Gilbert and Sullivan to the US. I still don't know if that is... I don't think that's just the judge. I think that would be an interesting story. I've seen this other guy, Charles Gardner, down in the Berkeley area doing performances. So in the UC Riverside newspaper archives, his name came up a lot. Well, and he was up and down the West Coast. I saw him in Washington and Oregon, but I didn't know if an entertainer himself would be setting up contracts because I'd seen other articles about how he had retained the services of lawyers to seal his own contracts. So it's... I don't know. And then there's the which Charles Gardner went to Hong Kong. That blew me away. How could he get... I mean, would the whole family get to Hong Kong? It was just three of them. And it was when he was rolling in money because of this Gardner mining company. I don't know if the mine ever produced dividends for its shareholders, but he seemed to have, from his habits, seemed to have had quite a bit of money at that time. He was maintaining multiple residences in Night's Ferry, San Francisco, and Oakland. Well, that's partly because of the Clarks were in that same neighborhood. And Mr. Clark was a surveyor and also a miner, I believe. So I think there was that the closeness of the two families, because they came out together on the wagon train. But that could have been why that copper mine business came up. And a lot of the Clarks went into mining down in Southern California later on. So I've got lots of newspaper articles about that, the Clarks and their mining. See, this is where Ricky Tree is really great. We've now got all this information in one place, if we can get you to start adding all this new information. If we can get, I forgot who he said, Susie. This photo is from Susie's collection. She's apparently got a whole album of the family. So what a great place Ricky Tree is that we can, it's in one place where we can bring it all together and get a fuller story. That's fantastic. Yeah. You've done a great job here, Michael. I just want to mention that people aren't watching this or don't have it on their own. You've put a whole thing about the searches, the searches you've done. You've added that in your research notes, which is really, really great. Including, yeah, you even your searches, what you search, I was going to ask you because you use obviously use the newspaper so much, but you put the details right there of what you, how you searched and whatnot and where. This is a new thing I'm doing and I got to admit, I didn't start doing this from the beginning and I wish I had. I started doing this when I started confusing myself and wait a minute, I've already done that search. So yeah, I'd recommend doing it from the beginning. Yeah. Yeah. I'm still pretty new to Ricky Tree because I've been using Ancestry for so many years. So I get over to Wiki Tree and I'm a little lost. My sister-in-law has been trying to help me, but she's in Hawaii and she's kind of a hard to get. Well, you'll just have to go to Hawaii to get more help. I did. Two years ago. Why'd you come back? Well, we can work in partnership. I'm happy to write up the profiles if you can get that information flowing. Yeah. That's super. Yeah. A lot of the newspaper articles I have saved in PDFs and they're on the Ancestry site in the gallery. So I'm happy to share all of that information that I put together over the years. Well, that's great. I think we're going to call it here. Again, wonderful profile and we've just sort of skimmed through it. I mean, I've breezed through it. I don't know if people have been able to read it as I was going along, but you might want to read it on your own. Let me just put that link back in again. And if I can make a plug for one of the projects, the Profile Improvement Project, that's where I met Kay Knight. She was my mentor on that. She was fantastic. If you haven't taken it, I would definitely join the project and take the Profile Improvement Project Voyage where you get a mentor that patiently sits with you and helps you make, helps you work with WikiTree, teaches you all the tips and tricks. Yeah. There were times when Kay was asking me to do something that she knew would break me an hour or two because I didn't know the trick and then she showed me the trick. It's really night and day having all of these tips and tricks and knowing what a profile should look like. I have to make a special thank you here. I didn't tell you the whole story here. Well, I retired from a lifetime of being a computer consultant and got married about the same time and moved to Colorado and I thought I've got all this time to do my genealogy research now. That was in 2016 and I was diagnosed with a fourth stage bone cancer and they gave me two weeks to live and sent me home from the doctor's office with hospice and my big panic was I'm not going to finish my genealogy research. So to see what, what's, sorry, what, what, what has come with surface is just mind boggling and it just has, you know, makes me so glad that I'm still here and then I could pick up the reins and I can run with this and I can hopefully solve these mysteries before I drop dead. Thank you so much. So, so much. You just don't know from the bottom of my heart. You're, you're very welcome. Thank you, Michael. Yeah, I, I know where you're coming from. I nearly died back in 2013 and family's been important. So this having, especially finding Wikitree and having the help and the collaboration that we have from a lot of people, it's just amazing. And I learned stuff every day. So this is great. And Michael, you just took this and ran with it. And when you post on discord, oh, you found this or you found this, it's just amazing what people can find. I think he's a fully fledged elf now. Yeah. I was so loud when I started reading what was coming up. I went, oh my God, how did they find this stuff? Because I've been looking all my life and I've been missing all these things. So I've got a lot to learn still. But thank you. Thank you so much. I just want to broadcast a big thank you to everyone who has anything to do with having created or maintaining Wikitree or improving it. It is incredible. Greg, I use your apps all the time. I just, I hope it never goes away. It's just, it's a brilliant site. That's good. Well, that's the plan that we're going to stick around forever. So great. So thank you all who have joined us for the sixth day of Elfmas. This has been a wonderful, wonderful segment. And I'm looking forward to seeing some of you early in the morning tomorrow for the seventh day of Elfmas. Our video will be live at 9 a.m. Eastern time, 2 p.m. UTC in the wee hours over Pacific time. Sorry, guys. But yeah, for New Year's Day, we're ringing in bright and early for the seventh day of Elfmas, 9 a.m. Eastern, 2 p.m. UTC. And hopefully I'll see some of you there. Anyways, thank you for everyone today. That was great, Michael. Great to meet you, Lee. That was wonderful. And I'm over the moon. I'm really over the moon. Excellent. So much. See ya. Bye.


The Nigerian Senate has announced members of the Constitution and Amendment Committee to be chaired by the Deputy Senate President Jibril Barau with the Senate Leader Bami Dele Oquemi, as the Vice Chairman. Making the announcement, the Senate President Gospu Laquabio said that the 1999 constitution needs a major review. Senate Laquabio also charged the Deputy Senate President to invite speakers of State Houses of Assembly for interaction before the beginning of the review of the Constitution. The Constitutional Amendment Committee is made up of 43 senators, 37 from each state of the Federation including the FCT and another six representing the geopolitical zones of the country. Lawmakers representing the zones include Senator Adamo Allero, not West, Senator Ojiuz Okaalu, South East, Senator Fadahunsi, France's South West, Senator Bomae Mohammed, not East, Senator Barina Mpigi, South South, and Senator Omar Sadik Suleiman. The Senate Review Committee, through inputs from stakeholders, would identify relevant sections of the Constitution to be amended to give answers to contemporary national issues. That would mean that one of them represents a zone. Why there is one senator from that same state representing that state? So, for the zone representation, we have Senator Adamo Allero for KB Central and North West. We have Senator Ojiuz Okaalu representing Abya, South East. We have Senator Fadahunsi representing South West. We have Senator Bomae Mohammed representing North East. We have Senator Barina Mpigi representing South South. We have Senator Omar Sadik Suleiman representing North Central. The committee will be inaugurated on Tuesday so that they can start functioning immediately. So all the motions and resolutions pertaining to the Constitutional Amendment that have been brought to the Senate since last year will now be channeled straight to that committee. If you have your ideas, I have my ideas. You bring forward so that we can do a good job. And then Deputy Senate President, you also invite speakers of all the state assemblies for interaction with the Senate President before we go into the major issues of presenting the different memoirs. That would mean that one of them... Hello, hope you enjoyed the news. Please do subscribe to our YouTube channel and don't forget to hit the notification button so you get notified about fresh news updates.


Yew! I don't know, Travis Rice. Dropping in there, coming over the ridge, like, wow! Busted in turns. Thanks, A! Catch you later. It feels so good to be heading out on some sort of adventure. Let's go and find Scotty and Jim then, at the Shredden Butter HQ. Right there. Oh, my. Here's the yard. Hello! Hello! Scotty kids! We've had our COVID test. Can we bro down? You know it, bro. Negative! Yew! So, we've got a vehicle. We've got a vehicle we can use for... Ice cream? I'm not taking that all the way to Scotland. I'd rather take the XR, mate. This is what we need to create. This is what we need. All right, so something like this, but on a budget. This is more like it. The taxi. That's chewing gum under the bus seat. It's destiny! That's way better than it was before. That's the number. Life. All right. It's a scad, then. Good luck, boys. Hello, bro. Today, we're taking Billy and Scott to Splitboarding for the first time. Phone wallet keys. Check. Cool. I'm almost annoyed at myself for having never been to Splitboarding before. I thought that the ascent would be, you know, like really difficult, but it's so chill. Not that way. We made it. We're snowboarding. Whoa. It's quite weird underfoot. Quad trap, here. Yeah, quad trap. Kind of being in nature, and you haven't actually gotten a plane to get there, you just have to get in an old boncast taxi. Yeah, so this is our general night-time sleeping arrangement. I've got a bottom bunk, which we've established is slightly more comfortable than the top one. So we're at the Lex. Kindly, the guys up here have said we can come and have a little shred. The rails are out. So, yeah, let's go and have a shred. So we can go and shred some rails and stuff. I thought this whole trip was going to be, you know, scrogmunching in the backcountry, but I managed to seek out some park riding just to keep me fueled. Buzzing. We went to bed real early because we were here quite early last night. We put a little movie on in the cab. Watched the movie and went to sleep. I think I was asleep by about 10, so I got full-night sleep. So I'm ready for action today. So don't judge us. Split-bawning purists. We're just taking the opportunity of the chances available. But... Big up, Ben Nevis. And the Nevis... Big up, the Nevis range, guys. Thanks for looking us up to the moon. Being treated like royalty again. Welcome to the dangerous zone. Dropping in there, coming over the ridge, he's like, wow, busting turns. Proper height. Yew! That was some proper adventure boarding. We're packed up, boards back on the back, poles out, and we've got a big hike back out. Tothic they were and doom. Tothic they were and doom. Ciao for now! So we just rolled up at Ride Scotland to pick up the e-bikes, which we need to get to the bottom of our ascent today. Oh, my God. They really look badass as well, mate. They are. They are badass. And we're off. Up, there. We're going to try and get three lines done today. Get four lines done! I'm genuinely, genuinely worried. E-biking! What happened, Scott? There's a Christmas tree right there. Yeah, but it's not like it was inconspicuous. It just came out of nowhere. That's what he's saying. Another 4K up. The road is done. We're on the snow. So, upwards for a little bit. Stash the bikes. On go the skins and the ascent. Trying this new technique. Putting the skins on. The board clipped together. So it makes it a bit more of a table. Back, done. Ready to go. Getting this dialed in. We're going straight up there. And kind of reverse all the way around across this bowl. And then drop in down these lines. I'm top of the world! As I thought, I kind of seen what the terrain was like in Scotland. When we got here and kind of we did the first day. It was as expected. Then we wound up Neves yesterday. And it's like Alpine stuff. And then we come here today. And when we hiked up over the, you know, over the ridge. And we end up in this, this bowl. This is legit.  Right, this is the very last bit of snow for this trip. I'm very sad. The last few meters. Let's get it. Enjoy. Come on Scuddy.  That was mint. And now, we even get a little bit more e-biking. Oh, he's killing it. We made it. Three musketeers. And all that. That's all right guys. Yes boys. Make that four. I love that. Now all that's left is this wonderful mobile home to take us back south. If the taxi gets us all the way home, this has been a dream trip.


With the help of our partners the representative for children and youth who with me is united in this In this desire and the adoptive families association and other partners We pledged to find forever homes for 600 children and youth over two years today, I am really proud and really happy to announce that we exceeded that goal 644 children and youth are no longer waiting forever for a forever home I know minister could do from almost the very first time I met her talked about how this was an important issue And she hoped that she could bring leadership to the ministry in a commitment to the ministry And I really want to recognize today that she has proven that and that this was no small task I know she's not taking credit for it But by providing that type of unwavering leadership in the ministry supporting staff partnering with organizations like the adoptive families choices and other Adoptions agencies and a number of community Organizations has allowed I think all of us to work together to come together and see how important this is Every youth deserves a loving home that will cherish them for who they are and guide them to be the great individuals They can be I'd like to thank the minister for all the work She's doing for her passion and commitment to moving this initiative forward all my relations We couldn't have all 644 of those children in the room with us today But they're here in spirit Each balloon represents a child in British Columbia who now has a home thanks to the tireless efforts of Staff and the MC in MCFD and our partner agencies and thanks to the parents who opened up their homes and hearts