🌍 AI in Africa - Voice & language tools

We have many young people who this may be their first interaction with any AI topic or their first interaction with nlp, and we pretty much have a great pathway for them to advance academic careers. Many have gone on to get acceptances to Masters and PhD programs, which are amazing. Basically, there's a really great pathway for people who want to advance research careers, but I'd like us to build a similar pathway that is just as strong for individuals who want to productize and build companies.

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Welcome to another episode of Practical AI. This is Daniel Whitenack. I am a data scientist with SIL International. I'm joined as always by my co host Chris Benson, who is a tech strategist at Lockheed Martin. How are you doing Chris? I'm doing very well today, Daniel. How you doing? Doing wonderful because today is another episode in our series of sort of spotlight podcast episodes, AI in Africa. This is a joint initiative between the International Development Research center in Canada, Practical AI and the Changelog and and Giz Fair Forward project, all of which are sort of involved in one way or another with the Open for Good alliance. And as part of these episodes, it's been really wonderful to have with us sort of guest co host Joyce Nabende from the Macarera AI Lab at Macarera University. Welcome, Joyce.

Thank you, Daniel. Hi, Chris.

Hi. Thank you.

Hey there.

It's yet another opportunity for us to discuss on this podcast and I'm excited to be here.

Yes, it's always wonderful to have you with us, Joyce, and why don't you. I'll sort of pass it over to you to introduce what we'll be talking about today.

Right.

Thanks, Daniel. So over the past podcasts, we've been trying to look at different case studies on practical AI, particularly on the African continent. And we are very excited for this episode because we are going to focus on AI, but looking at community building in Africa and especially looking at the very exciting field of natural language processing. So for this podcast today, we are very happy to have Kathleen Simini with us. Kathleen is currently a Kiswahili Machine Learning Fellow with Mozilla. But before Mozilla, there's a lot of work that Kathleen has been involved in. So, Kathleen, you're very welcome to this podcast.

Thank you for having me. Thank you very much.

All right, so I think we are ready to dive in. So we'll have just over to you, Kathleen, tell us just an introduction about NLP for African languages. What work have you done in nlp? How has it been like for the last couple of years in the space of NLP for African languages?

Okay, thanks again, Joyce. So I'll start with a bit of an introduction to myself and how I got into nlp. My background is in math and computer science, and with that I went into data science. I worked briefly in industry for a company in the telecommunications space that's headquartered in Nairobi in Kenya. And one of our products was sms. And through working at this company, I came to realize that support for African languages when it came to NLP and just tech tools or digital tools, if we could say so, was pretty lacking in comparison to, say, your English. And this got me pretty interested in nlp. And I wanted to build particularly resources for African languages. So, yeah, this is where my interest began and when I ventured into academic research at this point, my next role was with IDRC or other AI4D. So I was regional coordinator of AI4D for a couple of years. And this work involved mobilizing funding for communities. Let me see, IDRC had Funding which is going into AI research in Africa. And I helped a lot with that in terms of identifying communities that could and would make use of this funding. So I'll backtrack a bit and say that during my role in industry, I did a lot of community work. I began a lot of community building, first with the Nairobi Women in Machine Learning and Machine learning and data science. So this was sort of my first foray in the field as I was a data scientist at, well, in industry. And then along with transitioning into academic research, I encountered Masakane. And they were doing exactly what I wanted to do in terms of focusing on resources for African languages. So my organizing also transitioned into that. And in the middle, I attended several of the planning Indaba. So the Indaba is movement to strengthen African machine learning. One of the events they have is an annual summer school. And I attended one, the first one in South Africa in Stellenbosch, after which we got to talking. They realized that there was ML AI community in Nairobi, courtesy of the meetup I was organizing. And so we organized the second round together in preparation for the third round, which we then came to host in Nairobi in 2019. So I've spoken a lot, but then let me say. I'll cap it by saying that I have worked a lot with ML and AI communities in Africa. And that brought me to the intersection of my interest in NLP and the existence of Masakane with a focus on African languages. And I'll say, here we are. It's probably where iceburned I spend most of my organizing time at the moment. I'm very excited by the fact that we have a wealth of diversity from the African continent in terms of people who are working on languages, and it's languages that they care about. So also a wealth of diversity in terms of African languages included. I'll plug in the fact that as recently as last week, we finally have Makakani registered as an entity. So we are officially the Masakane Research Foundation. Up till this point, we've been really just organizing as a group of people, and that's been great. But Yeah, I guess 2022 is going to start, is going to be the beginning of us starting to see how we can formally organize and beyond running on volunteer capacity, what it looks like when we organize and we can put funding to some of our planning. I will stop there. I kind of forgot what the question was.

So you guys, that's so wonderful. I really appreciate all that context. Kathleen, I wonder for those that may be thinking of Those out there that might be listening to this podcast that have never, ever heard of Masakane and the things that they're doing. Could you just kind of give a picture of, you know, who's involved in Masakane, how do they interact, and what are the sorts of activities that they're. That they're doing?

Okay, so the Masakane was founded in 2019, and this actually took place at the Deep Learning DABA that was hosted in Nairobi in Kenya, and it was founded by Jade Abbott and Laura Martina. So basically, they did a paper for machine translation involving a bunch of South African languages and created benchmarks. And part of the work done in this paper, they came to present at the Deep Learning in DAPA with the idea that if they create a notebook which is very easily replicable, then people can take it up, take it upon themselves to train similar models for their languages, therefore likely creating first benchmarks. Because very little research exists for African languages. And that is how it began as a focus on machine translation. The initial work that they did was based on the JW300 data set. So JW is Jehovah's Witness, and they're an organization that, through their work evangelizing in Africa for many years, have translated the Bible to many local languages in Africa. So the notebook leveraging this data set made it super easy for literally anyone to start up a notebook, put in the language code for the language you would like to work with, and in an hour or two, have benchmark trains. And it gained a lot of momentum in that way. I like to think that even though many of these languages have not been the point of focus in research in the past or in terms of product development for markets that can, you know, pay for products, they haven't been a focus from that perspective. But this gained a lot of momentum because of the setting, right? Because the Deep Learning in Darby is bringing loads of young people from across the African continent who, given the fact that we'd had two previous endabbers, had basic skills. So some effort in capacity building had been done, and it was sort of the sweet spot in terms of many of us had attended previously planning endeavors, and we have the basic skills, and we now have a desire to specialize in something. And here come Jade and Laura, and they have a notebook, and they're telling us, hey, this is how you can start in machine translation. So it gained a lot of traction. Personally, I trained models for Kenyan languages, and I think that's how many people rationalized in terms of what languages do I care about what languages do the communities I'm adjacent to speak, and then went on to do that. If you could find a data set for your language, then you went ahead and trained a machine translation model. We wrote a really great paper. 2 I think 1 I'd like to highlight is participatory research for low resource machine translation, a case study in African languages and this one in particular, because it describes the ecosystem for machine translation to be successful and why there's many gaps in an African context, right? So one large problem or one problem is access to data. And we talk about the fact that typically content creators create data, right? But in an African context, these content creators may not have access to keyboards or they may not have access to digital dictionaries, which hinders their development of data to start with. So there's a whole ecosystem description and I'll leave it at that for now. But then we've seen great success fast in machine translation with being able to do very multilingual and inclusive work and then progressing from there. We realized that individuals who were participating and contributing to Masakane were not only interested in machine translation, but other tasks in NLP as well. So at this point we sort of took a step back and generalized and now have membership or participation from people building in NER in speech and basically just a wide scope of tasks.

Got a quick question for you. I was interested in the data set that you mentioned. The JW data set being a Bible translation. When you look at a lot of different Bible translations and kind of the language may not be completely what you typically would talk in today, did that present any kind of challenge or was that generalized enough to where that that didn't affect it or anything. I was just curious about that being the basis and what challenges that might present that were unique to the data set.

So that's a super interesting question. We encountered a phenomenon which I don't know if we called it or it is known as the biblification of systems. So one example I remember is actually I don't remember what word, but in machine translation system a certain word kept on being changed to canon because canon shows up many times in the Bible, but not referencing canon often in conversation. It has been a challenge to rely only on Bible data. And to answer your question, it does not or has not generalized well, although actually, interestingly, after a year or two of us using the JW300 data set, the organization actually pulled it from the Internet. So it was scraped from the website and the organization claims that they were not aware of this data set existing and had not given permission for it. So, I mean, we've been trying to ask them for many months to see if they would make it open anyway because it has spurred a lot of our research and we're still waiting to see how that turns out. But then, yeah, just to bring in the fact that access to data is a huge problem and in many cases we face IP and copyright issues even with data that is accessible. Though at the moment, a lot of activities unfortunately have to start from a point of data set creation, which I wish was not the case. But we also have found that it's an easy way to start including and upscaling individuals because someone can start out on our projects today labeling data or creating data or evaluating a model, but then can progressively then pick up other more valuable skills and, you know, grow their skills. So I'll say it's a blessing in a curse, but mostly a curse because as NLP researchers, I wish we had the luxury of, you know, just accessing data for whatever task it is we wanted to work on.

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So, Kathleen, I'm actually in the Masakane Slack group and I just took a quick look at the involvement I see in the Masakane Slack group that there's, at least at the time I'm looking, there's 1304 people in the Slack group, which is pretty amazing that this community has grown in the ways that it has. And I'm wondering if you have any, you know, looking back on how the community was formed and the activities that you did and how you welcomed people and those things. Do you have any insights into kind of why you think it kind of grew, has grown the way that it, you know, that it has and maybe looking forward, kind of characteristics of the now that it's an entity and a foundation, characteristics of the community that you would like to, you know, ensure that they are characteristics of the community moving forward. Any thoughts?

Yeah. So in terms of it growing, I'll say it was unprecedented even for me. I will attribute it first of all to Jade's never ending energy. I swear Masakane would not be what it is without Jade. I sometimes even struggle with just keeping up with all the conversation that goes on on chat. It can be very overwhelming. I think one of the reasons why it has grown again is just the fact that these languages are very underrepresented in digital platforms. But that is not reflective of the communities that use these languages. They say there's 7,000 languages on Earth and maybe 2,000 languages in Africa. And many of those are living with thriving language communities that use them every day. They are just not used on digital platforms. And unfortunately, because of, I think, social linguistic factors, they're not used in sort of formal spaces, at least in Kenya. I know for a fact that parents are more likely to encourage their children to become better at English than say, their mother tongue. Because with English you can walk into any office and have a conversation and potentially get a job versus your mother tongue, which is only useful at home or, you know, in whatever local context you are found in. But then that doesn't take away from the fact that these languages are used and at least from a sentimental point of wanting them to be preserved and sort of captured on digital platforms. I think that's one huge reason why Masakani has been so relatable to students and budding researchers across the Continent. I'll also say, we have a lot of room for absolute beginners. Again, it's really easy for someone to walk in today. And young people have loads of energy, right? And many will come and they'll say, hey, I want to do something. And you know what they can add to literally every single project? The fact that they're multilingual. Right? So if we have an project and you walk in today and you say, hey, I want to do something, and I speak Kiswahili in addition to English, then we can say, hey, look, there's this project where you can go, and here's our Kiswahili text. And you can start labeling. And that's a way to start involving someone and they'll be interested in, okay, what else can I do? What is this person doing? What is that person doing? So I think just the fact that we, literally anybody who is multilingual can participate. And then we've been progressively working towards pathways for capacity building. So we have several study groups that now run every three to four months. There's one that's particularly for beginner nlp. So any beginner who's involved in any, you know, something in the community can also plug into that and start gaining more skill. Last week I was on the call and Julia offered to do one for machine translation. And I'll say let that be a segue into another factor because we've gained quite some recognition globally and the wider NLP community has also been super supportive. So in terms of more experienced researchers wanting to know how they can be of use and then proceeding to actually support us. Right? So Julia has a PhD, she works at Google Translate, and she shows up probably for 90% of Masakani activities. If you ever want to debug. I remember my experiences with training those initial models for Kenyan languages. She was literally always available on Slack. So I'll also shout out the fact that we received a lot of support, a lot of mentorship support, and that continues to happen, and I hope it continues to happen for an extremely long time. So the second part of the question, now that we are a registered entity and are growing somewhat exponentially, characteristics that I would like to see sustained first, I think is a very distributed nature of leadership. So Masakani is like a storm. It's not a coordinated storm. It's just something that started happening and now it's got a life of its own. I love the fact that it's not reliant on any one person. So there's never a day where person X is sick. And okay, now we can't hold the meeting because person X was supposed to chair it. That never happens because literally everybody is empowered to walk in and chair the meeting because we have a template. It's recorded. Everybody else can catch up. Right? And we see that a lot also with the leadership in projects. So we never sat down and said, hey, we want to make MER a focus. But somebody in our community did. And they not only ideated, but then went ahead to organize regular meetings and recruit people and come and give updates at our weekly meeting. And that turned into a paper that was accepted to EM and lp. So Distributed Leadership, I would like to see this continue. Something we are a little worried about is the fact that having an entity, well, it's a great thing, first of all, that having an entity means that we can receive funding, and we have been able to receive funding in the past through organizations that we have collaborated with. But having our own registered entity presents an opportunity, yes, to fund our research. So far it's been mostly run on volunteer efforts, and the result has been great. And so our one concern is that now when there's funding, a decision has to be made, right, about what gets funded versus what doesn't get funded. And we're worried about how that scales. Will it stifle the distributed leadership, especially on projects, or will it spur it forward? So I'd like to see us maintain that despite funding at HIT us to turn into an organization where the characteristics change because now money is available. Something else I'd like to see us grow into is just further supporting pathways for individuals who are interested in productizing their work. So again, I'll go back to the fact that we have many young people who this may be their first interaction with any AI topic or their first interaction with nlp. And we pretty much have a great pathway for them to advance academics careers because they're taking part on projects, which means they're writing papers. Some of these papers are getting accepted at leading conferences. They're being mentored by leading researchers in the field. Many have gone on to get acceptances to Masters and PhD programs, which are amazing internship opportunities. Basically, there's a really great pathway for people who want to advance research careers, but I'd like us to see us build a similar pathway that is just as strong for individuals who want to productize and build companies. So that's something I'm hoping we can go into. And then one of my absolute favorite things about Masakana is that we have a lot of female leadership. So As a woman in tech, this is something I look at as someone who has organized communities for women. This is something that close to my heart and I absolutely love the fact that we have pretty good female representation. I attributed to the fact that this is a movement that was started by women and I think it's very powerful signaling. I'll tell you, whenever I have been in a position to be assessing a company that I want to join, I'll probably go to their website and see if there's any women on the tech and if there's none, I start to ask myself, do I want to be the first one? But if there is one, then that's one person I can write to and say, hey, can we have a chat about, you know, what's working for company X is like. So I think it's very powerful signal that this was started by women and I love it that there is a little sisterhood or perhaps a not so little sisterhood that is part of Masakane and I would love to see that continue to be the case.

Joyce, I'm curious if, as a researcher in the university setting, working on related problems to what the Masa Masakane community is doing, how has this sort of like groundswell of community building in Masakane influenced your research team and maybe the things you're able to do and the way you're able to engage and then maybe you have a follow up question for Kathleen with regard to some of the things on your mind as you think about your research group and engaging in the community building.

Yeah, thanks. Just listening to Kathleen, it is really very enlightening to wear the background.

Super inspiring.

Yeah.

And the background and the roots of Masakani that I never knew about. So that's very interesting and I think for us how it's benefited. The lab is unlike Masakani, that is wide and does many things anyway because it's a large community. We started out in the NLP space, you know, building out in speech and now in machine translation like she mentioned, for the language that we care about, which is Luganda, the main language in Uganda. And Masakani has really come in to support the researchers in the lab. As you said, if there are people who need help, maybe they are running their models and they are stuck. I've seen many of the people in my research lab go to Masakani, put in a query and get responses and this has enabled them to move much faster with their development, with their model development. Unlike if it was just a closed community for us or Unlike if you went and wrote an email to one of the researchers that maybe developed a model or did something. But here it's on Slack, it's within the community, and they get faster response. Or if maybe you find a situation where someone has encountered the same issue, maybe with the models, it's easier for them to even respond. And I like that it's open.

Right.

Like, you just put in a question there and then you get an answer. So that sort of community building is something that's very critical that the lab is also learning to adopt and leverage on as well. But also listening to Kathleen speak right from the beginning, I think, Kathleen, you said community, community building, community very important to you. And I know that with the new role that you have with Mozilla, that there's also a lot of community building involved in there. So can you maybe tell us more about the current work that you're doing around community building with Mozilla and Kiswahili in particular, your language that you're passionate about?

Yeah. Okay. So my current role is machine learning fellow at Mozilla. And I'm working particularly on Common Voice and particularly in Kitwahili. So I'm supporting work to build a Kitwahili dataset on Common Voice. And this is starting literally from the collection of sentences, but I should step back and say that it's starting from community building again, like you highlight. So we want Kiswahili speakers to care about this work. We are working to communicate to them that the existence of this data set is something that is of benefit to them because it's intended to be a digital public good. And we're working to build ties with organizations that are already working in Kiswahili. So I realized that we have the tech capacity, but then we're walking into a space where people have been working for years to build language communities. And I'd like for us to be sensitive to that, and we'd like to be sensitive to that. So reaching out to the language board, the universities that have linguistics, literature departments and such, other communities, just to get input from them. And then this work begins at, of course, collecting text. So Common Voice is a project for building speech recognition, and we start with text because we can't have audios without, you know, the aligned text. So building relationships with these organizations basically is also in our best interest because then we can find avenues to get texts from them, if they're existing, or build programs that can enable us to create texts in the cost of our work. And then we've learned so far from existing from linguists especially, that there's a lot of diversity in Kiswahili speakers. And this is something that, well, I innately know as a Kiswahidi speaker, because listening to someone speak, there's a lot of nuance. I can put tell what part of the country they come from. If they're Kenyan, I can potentially tell if they're not Kenyan or are Tanzanian or are from the drc. And then there's an additional level of nuance that is apparent among people for whom Kifanili is their mother tongue. And this is a distinction that I'll say even I wasn't aware of because many of us in Kenya and in Tanzania learn Kifahili because it's a national language in the country, but then it's actually someone's mother tongue. And like many African languages, there are related dialects. So we're also learning a lot of the nuance, the fact that for people whom Kiswahili, their mother tongue, if they listen to someone speak, they know, okay, you're originally a speaker of dialect X or you're from this particular part of the course. And they sort of label all the rest of us as up country Kiswahili speakers upcountry, implying away from the coast, the East African coast, which is the home of Kitwahigili. So learning about all these nuance and recognizing that it would be great if we could capture this diversity in the data set, that's also been an interesting journey working with the linguists, the way this work. Well, at least the engagement with the linguist that's currently shaping up as subsets of the data set that are representative of the various dialects and variants of the language. So there's also nuance, I'll say, depending on what other language. Because to try and explain further, in Nairobi, for example, Nairobians rely heavily on English. And it becomes apparent in our Kiswahili speech because we talk Kiswahili and it sounds like it's a translation, as opposed to it sounding like speech that is naturally in Kiswahili versus someone from the drc. Now, I imagine they would mix the kids Kiswahili with French. And you know, that as a level of distinction. And to people who are native Kiswahili speakers, to them, we are all, you know, just up country Kiswahili speaker. So there's layers and it's super fascinating. But again, at this point, we're really thinking about it in terms of subsets of the entire data set, which can be used to first fine tune to various contexts, so that if you're. If you're building an application for the coast, then we have data sets which make it easy for you to fine tune to that context. Or if you're building for Nairobians, or if you're building for people in inland Tanzania, then there's data sets that you can use to fine tune, but then beyond that, to evaluate performance. Because at the end of the day, the. I tend to think that Kiswahili could do to other African languages what Western languages have done to all African languages, which is to be the focus of research and funding and development and at the expense of others. Right. So now Kisohili is being spoken in parts of southern Africa. And I think that's amazing that we're now pushing for, you know, potentially one language to be spoken across the African continent. But then I sort of worry that that may come at the expense of smaller languages or other languages in general, because now parents may start to think, hey, you should learn Kitahili for, you know, upward or career mobility. But then that may make the mother tongue come second place or be absolutely forgotten. But going back again to the work with the linguists, we also want evaluation data sets, which can mean that we ensure that there's at least some minimum performance for all diverse speakers of Kiswahili. There's also a very strong gender thread in all our work. We realize that women tend to be. Well, I'll start with the fact that speech recognition systems generally perform worse on women, and this can probably be attributed to an imbalance in whatever the original data set is. Right. So fewer women contribute to these data sets, and that could be for a multitude of factors. So we're being very intentional about creating spaces for women to contribute to the data set because the challenges that they face may be unique. But beyond that, we would like for them to also be part of developing Voice Solution.

Well, Kathleen, I'm really excited to hear about the work that you're doing in terms of building these data sets and the way that you're thinking about having things like a strong gender threat in all your work and why that's important. I know myself. I've been challenged in this conversation to think about ways in which myself as a practitioner can be involved in sort of creating more diverse data sets, getting involved in these types of communities. I was wondering if you could maybe close us out by talking about how, from your perspective, if practitioners are listening to this podcast and they have a desire to maybe contribute to work related to this either building sort of language diversity into text or speech or other data sets, or maybe there's like people in local language communities that are listening to this and wanting to get involved, wanting to build up data sets that could promote this sort of technology with their language. What are ways that these two groups can kind of get involved in this work? What recommendations would you make to them? How would you recommend that they sort of connect with people doing this work and, and get involved so that the community can grow?

Thank you for this question. So, first, to the researchers who could potentially contribute to this work, I'll highlight the model I've seen with Masakani because I think that works great. It could be through mentorship. I think there's definitely ways to find groups of junior researchers who belong to these language communities or these underrepresented groups that could benefit from working or collaborating with you. So that's one thing through mentorship. Second is in the event that researchers are working or are keen to work on low resource languages, it's possible that they'll have funding to create these data sets. So I challenge them to, beyond looking for native speakers and having them create a data set which you then, you know, take away and go work in your lab in isolation, I challenge them to use that as an opportunity again to mentor. So it may be that you're not only looking for a native language speaker, but rather looking for a native language speaker who is also a junior researcher interested in this field. And so they can start out contributing by creating the data set, but then you can also then create avenues for them to contribute to what comes next in terms of the analysis and the actual work that goes after the dataset has been created. And I think that's another model that would be great and, you know, gives much more value addition then turning back to language communities. I'm going to, again, at this point highlight Common Voice because I think it's pretty amazing that Common Voice as a platform means that you, as language contributor or a language community, don't have to start thinking about this work from a standpoint of, okay, what tools do I need to collect the data, where am I going to store the data, what's the infrastructure going to be like, how do I access the data and all those dynamics? I think it's as simple as if you have access to the Internet and can access the Common Voice portal, then you can start creating, in this case, a speech recognition data set. And it's as simple as that. And if you look a little, I'm willing to Post it that there's opportunities for you to identify other such free resources or platforms where you can start creating data set. Beyond that, I like to think of language communities, particularly language communities that are not already a focus. So look at Kikahili for example. It's a huge language, there's loads of speakers, there's loads of interest from people off of the continent in having Kiswahili resources exist. But I personally see it as an entry point to other local languages that are spoken in the places where Kiswahili has been spoken. Right. So if a pitch data set exists for Kiswahili, it's possible to then take that text and translate it into another local language that you speak if you're a Kiswahili speaker. And in that way it could be a machine translation data set that comes out of it. It could go on to become a speech recognition data set. But then I'd also challenge these communities to then think about licensing, right? And licensing is a pretty interesting topic in the field of NLP because on one end we have the very well resourced researchers who, who don't ask for permission and just scrape everything off of the web. And then there's the language communities and the junior researchers who work very hard to create these data sets and then are not necessarily the ones who get fast publication or most interesting publication because of constraints like skills or resources. So I would challenge them to think about licensing that can center their needs. And it may be that you say it's useful, or it can be used for academic purposes, but non commercial ones. Or it may be that you say you don't want the data set used in any manner until individuals from your community can be the ones building the solution. Or it may be that you say, hey, you know, this is a language that we speak and there's 100,000 of us and nobody cared about this language before we started building the data set. So we actually reserve the right to control all the solutions that are built from this data set because at the end of the day, you are the one that will be directly affected by whatever those solutions are. So basically, I think as we bring more languages online, we should empower those communities to start thinking about how they can center their needs because they don't need to just create resources and make them available for the most resource or the most skills to swoop in and build tools which they then package and resell to the language communities that work hard to do it. In bringing these two together, I don't know, maybe the things I've highlighted already in my response, but I'd like to leave it there.

I think, I definitely think that those are great jumping in points. For our listeners who are interested, we will definitely include links to the Masakana community and the Common Voice platform in our show notes. So please take a look at those and get involved and think about how you can start thinking about contributing in various ways or mentoring or whatever it might be. But thank you so much for joining us, Kathleen. It's been a real pleasure to talk with you. I really appreciate you bringing your perspective and the hard work that you're putting in on these on these problems. So thank you so much.

Thank you for having me, Daniel, and everyone else at the Practical AI Podcast.

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