Who is left behind when AI moves fast? with Dr. Chinasa T. Okolo

Man, that just that just lit up all the Man, that just that just lit up all the neurons in my brain with like a a fan neurons in my brain with like a a fan neurons in my brain with like a a fan out of like three different questions. out of like three different questions. out of like three different questions. Just because something is explainable, Just because something is explainable, Just because something is explainable, can the explanation create agency if the can the explanation create agency if the can the explanation create agency if the user themselves, in this case, Indian user themselves, in this case, Indian user themselves, in this case, Indian healthcare workers, has no practical healthcare workers, has no practical healthcare workers, has no practical ability to refuse the technology. ability to refuse the technology. ability to refuse the technology. They're effectively being gaslit by a by They're effectively being gaslit by a by They're effectively being gaslit by a by a computer. Hey friends, you probably a computer. Hey friends, you probably a computer. Hey friends, you probably knew that text control is a powerful knew that text control is a powerful knew that text control is a powerful library for document editing and PDF library for document editing and PDF library for document editing and PDF generation. But did you also know that generation. But did you also know that generation. But did you also know that they're a strong supporter of the they're a strong supporter of the they're a strong supporter of the developer community and it's part of developer community and it's part of developer community and it's part of their mission to build and support a their mission to build and support a their mission to build and support a strong community by being present by strong community by being present by strong community by being present by listening to users and by sharing listening to users and by sharing listening to users and by sharing knowledge at conferences across Europe knowledge at conferences across Europe knowledge at conferences across Europe and the United States. If you're heading and the United States. If you're heading and the United States. If you're heading to a conference soon, maybe check if to a conference soon, maybe check if to a conference soon, maybe check if text control will be there. Stop by and text control will be there. Stop by and text control will be there. Stop by and say hi. You'll find their full say hi. You'll find their full say hi. You'll find their full conference calendar at textcontrol.com. conference calendar at textcontrol.com. conference calendar at textcontrol.com. That's textcontrol.com. Hi friends, it's Scott Hanselman. It's Hi friends, it's Scott Hanselman. It's another episode of Hansel Minutes. Today another episode of Hansel Minutes. Today another episode of Hansel Minutes. Today I have the pleasure of talking with Dr. I have the pleasure of talking with Dr. I have the pleasure of talking with Dr. Chanasa Oko. Chennasa Tio is a doctor of Chanasa Oko. Chennasa Tio is a doctor of Chanasa Oko. Chennasa Tio is a doctor of philosophy in computer science from philosophy in computer science from philosophy in computer science from Cornell and she is an internationally Cornell and she is an internationally Cornell and she is an internationally recognized researcher, a strategist and recognized researcher, a strategist and recognized researcher, a strategist and a policy adviser on AI governance and a policy adviser on AI governance and a policy adviser on AI governance and safety for the global majority. How are safety for the global majority. How are safety for the global majority. How are you?

you? you? >> I'm doing well Scott. Uh very happy to >> I'm doing well Scott. Uh very happy to >> I'm doing well Scott. Uh very happy to be on your podcast. How about you? be on your podcast. How about you? be on your podcast. How about you? >> I'm I'm getting there. Uh I will say >> I'm I'm getting there. Uh I will say >> I'm I'm getting there. Uh I will say that every day it is a new piece of that every day it is a new piece of that every day it is a new piece of confusing information about AI. People confusing information about AI. People confusing information about AI. People are generally freaking out. And I think are generally freaking out. And I think are generally freaking out. And I think that the freak out is not just happening that the freak out is not just happening that the freak out is not just happening amongst computer scientists like myself amongst computer scientists like myself amongst computer scientists like myself and like yourself, but it's starting to and like yourself, but it's starting to and like yourself, but it's starting to leak into government and it's starting leak into government and it's starting leak into government and it's starting to leak into like regular people because to leak into like regular people because to leak into like regular people because people believe that AI is coming for you people believe that AI is coming for you people believe that AI is coming for you and it's coming for your job and it's a and it's coming for your job and it's a and it's coming for your job and it's a very pessimistic time. I feel do do you very pessimistic time. I feel do do you very pessimistic time. I feel do do you think that? Are you pessimistic? Are you think that? Are you pessimistic? Are you think that? Are you pessimistic? Are you optimistic? optimistic? optimistic? >> Oh yeah. Uh I would say for me like I am >> Oh yeah. Uh I would say for me like I am >> Oh yeah. Uh I would say for me like I am optimistic but I'm very cautious in optimistic but I'm very cautious in optimistic but I'm very cautious in terms of just generally you know a lot terms of just generally you know a lot terms of just generally you know a lot of the current sentiment around AI but of the current sentiment around AI but of the current sentiment around AI but also just generally how it's pushed more also just generally how it's pushed more also just generally how it's pushed more broadly in the media. But I definitely broadly in the media. But I definitely broadly in the media. But I definitely would, you know, would say for people would, you know, would say for people would, you know, would say for people that may not be the most technically that may not be the most technically that may not be the most technically sound or technically advanced, you know, sound or technically advanced, you know, sound or technically advanced, you know, whenever they find out I'm a computer whenever they find out I'm a computer whenever they find out I'm a computer scientist or AI researcher, they always scientist or AI researcher, they always scientist or AI researcher, they always ask like it is it ever going to get to a ask like it is it ever going to get to a ask like it is it ever going to get to a place where it actually replaces me? Do place where it actually replaces me? Do place where it actually replaces me? Do you think it's going to take over the you think it's going to take over the you think it's going to take over the world? um do you or do you think it's world? um do you or do you think it's world? um do you or do you think it's going to let's say um significantly going to let's say um significantly going to let's say um significantly impact us all? Um and so I would say I impact us all? Um and so I would say I impact us all? Um and so I would say I don't necessarily think so. Um I think don't necessarily think so. Um I think don't necessarily think so. Um I think you know humans honestly are very much you know humans honestly are very much you know humans honestly are very much so in control of how you know AI is so in control of how you know AI is so in control of how you know AI is developed, shaped and deployed and so we developed, shaped and deployed and so we developed, shaped and deployed and so we really just have to do it responsibly.

really just have to do it responsibly. really just have to do it responsibly. >> Yeah, I feel like AI of course it's it's >> Yeah, I feel like AI of course it's it's >> Yeah, I feel like AI of course it's it's it's having a branding moment and when it's having a branding moment and when it's having a branding moment and when we talk about AI we're generally talking we talk about AI we're generally talking we talk about AI we're generally talking about large language models but also about large language models but also about large language models but also machine learning and deep learning and machine learning and deep learning and machine learning and deep learning and all of those things. But yes, all of those things. But yes, all of those things. But yes, >> when you say AI to a regular person, >> when you say AI to a regular person, >> when you say AI to a regular person, they think chatbt. they think chatbt. they think chatbt. >> Exactly. >> Exactly. >> Exactly. >> When you do your work in in uh policy >> When you do your work in in uh policy >> When you do your work in in uh policy advising, when you're talking to like, advising, when you're talking to like, advising, when you're talking to like, you know, members of Congress or people you know, members of Congress or people you know, members of Congress or people in in government and you say AI, are in in government and you say AI, are in in government and you say AI, are they also just thinking chat GPT or are they also just thinking chat GPT or are they also just thinking chat GPT or are they developing their own they developing their own they developing their own sophistication? sophistication? sophistication? >> Yeah, definitely. I would honestly say >> Yeah, definitely. I would honestly say >> Yeah, definitely. I would honestly say it's still, you know, chat bots and it's still, you know, chat bots and it's still, you know, chat bots and other, let's say, like language uh chat other, let's say, like language uh chat other, let's say, like language uh chat interfaces for the most part because interfaces for the most part because interfaces for the most part because that's really how most um I would guess that's really how most um I would guess that's really how most um I would guess a significant amount of policy makers a significant amount of policy makers a significant amount of policy makers got introduced to AI technologies in the got introduced to AI technologies in the got introduced to AI technologies in the first place. However, you know, there first place. However, you know, there first place. However, you know, there have been many governments and other you have been many governments and other you have been many governments and other you know, many governmental entities using know, many governmental entities using know, many governmental entities using predictive models um and you know other predictive models um and you know other predictive models um and you know other very kind of lower or basic AI or very kind of lower or basic AI or very kind of lower or basic AI or machine learning systems for decades machine learning systems for decades machine learning systems for decades now. And so I think that's also um an now. And so I think that's also um an now. And so I think that's also um an understanding of where you know this understanding of where you know this understanding of where you know this kind of automated sense you know of AI kind of automated sense you know of AI kind of automated sense you know of AI or whether it be machine learning has or whether it be machine learning has or whether it be machine learning has come into play um for the most come into play um for the most come into play um for the most particularly when it comes to let's say particularly when it comes to let's say particularly when it comes to let's say like delivering government services you like delivering government services you like delivering government services you know trying to predict um you know what know trying to predict um you know what know trying to predict um you know what the economy and you know what issues can the economy and you know what issues can the economy and you know what issues can happen throughout there and so it's happen throughout there and so it's happen throughout there and so it's still mostly chat bots but you know still mostly chat bots but you know still mostly chat bots but you know there's a growing um understanding about there's a growing um understanding about there's a growing um understanding about all the different areas where AI and all the different areas where AI and all the different areas where AI and machine learning um are being impacted machine learning um are being impacted machine learning um are being impacted Ed.

Ed. Ed. >> Mhm. Now you have of course an advanced >> Mhm. Now you have of course an advanced >> Mhm. Now you have of course an advanced degree and an expertise uh in computer degree and an expertise uh in computer degree and an expertise uh in computer science. Uh but you also your work spans science. Uh but you also your work spans science. Uh but you also your work spans you know ethnographic field work. You you know ethnographic field work. You you know ethnographic field work. You talk about healthcare. You're thinking talk about healthcare. You're thinking talk about healthcare. You're thinking about things in the in the you know the about things in the in the you know the about things in the in the you know the emerging south and the global majority. emerging south and the global majority. emerging south and the global majority. people seem to want to take and this is people seem to want to take and this is people seem to want to take and this is me speaking as someone who does not have me speaking as someone who does not have me speaking as someone who does not have an advanced degree uh degrees and an advanced degree uh degrees and an advanced degree uh degrees and specialties and put them in silos like specialties and put them in silos like specialties and put them in silos like you're a computer science expertise you you're a computer science expertise you you're a computer science expertise you know you know about AI but AI being know you know about AI but AI being know you know about AI but AI being presented as universal presented as universal presented as universal is very much crosscutting is very much crosscutting is very much crosscutting do you think we're teaching it wrong do you think we're teaching it wrong do you think we're teaching it wrong we're explaining it wrong because it we're explaining it wrong because it we're explaining it wrong because it affects infrastructure and safety and affects infrastructure and safety and affects infrastructure and safety and and healthcare and all these other and healthcare and all these other and healthcare and all these other things it's not just a computer science things it's not just a computer science things it's not just a computer science thing thing thing >> exactly And that's a great point. You >> exactly And that's a great point. You >> exactly And that's a great point. You know, it it took me actually getting know, it it took me actually getting know, it it took me actually getting into policy just to see, you know, the into policy just to see, you know, the into policy just to see, you know, the different side of AI and actually, you different side of AI and actually, you different side of AI and actually, you know, being at a place where um I know, being at a place where um I know, being at a place where um I started off my career at a think tank started off my career at a think tank started off my career at a think tank and most people there are literally and most people there are literally and most people there are literally economists, political scientists, some economists, political scientists, some economists, political scientists, some sociologists, um you know, maybe some sociologists, um you know, maybe some sociologists, um you know, maybe some psychologists or other humanities or psychologists or other humanities or psychologists or other humanities or social scientists, but it's very rare social scientists, but it's very rare social scientists, but it's very rare for computer scientists to be at, you for computer scientists to be at, you for computer scientists to be at, you know, a place like Brookings for example know, a place like Brookings for example know, a place like Brookings for example or other many many other think tanks as or other many many other think tanks as or other many many other think tanks as well. And so I feel like th those well. And so I feel like th those well. And so I feel like th those perspectives have really um enhanced the perspectives have really um enhanced the perspectives have really um enhanced the quality of my work but also really had quality of my work but also really had quality of my work but also really had given me an opportunity to interact with given me an opportunity to interact with given me an opportunity to interact with different policy makers or different different policy makers or different different policy makers or different other stakeholders across policy civil other stakeholders across policy civil other stakeholders across policy civil society etc that I wouldn't have you

society etc that I wouldn't have you society etc that I wouldn't have you know had if I just went straight into a know had if I just went straight into a know had if I just went straight into a posttock at MIT which was actually which posttock at MIT which was actually which posttock at MIT which was actually which I was planning to originally do um I was planning to originally do um I was planning to originally do um before I kind of like got very um before I kind of like got very um before I kind of like got very um entranced by the world of policy. Mhm. entranced by the world of policy. Mhm. entranced by the world of policy. Mhm. Do you and this is again speaking from a Do you and this is again speaking from a Do you and this is again speaking from a place of ignorance but I worry that the place of ignorance but I worry that the place of ignorance but I worry that the people who are making the policy people who are making the policy people who are making the policy decisions decisions decisions have just such a limited understanding have just such a limited understanding have just such a limited understanding of the complexity of these systems and of the complexity of these systems and of the complexity of these systems and then the complexity of these systems is then the complexity of these systems is then the complexity of these systems is only enhanced when you drop AI on top of only enhanced when you drop AI on top of only enhanced when you drop AI on top of it. It just AI makes me feel like it. It just AI makes me feel like it. It just AI makes me feel like everything is so so much more everything is so so much more everything is so so much more interconnected than we already knew it interconnected than we already knew it interconnected than we already knew it was. Does that concern you? Are you just was. Does that concern you? Are you just was. Does that concern you? Are you just doing the best you can to educate our doing the best you can to educate our doing the best you can to educate our leaders? leaders? leaders? >> Yeah. So, I mean, you know, AI along >> Yeah. So, I mean, you know, AI along >> Yeah. So, I mean, you know, AI along with many other technologies are with many other technologies are with many other technologies are integrated in a lot of different fields. integrated in a lot of different fields. integrated in a lot of different fields. I think this is maybe for the first I think this is maybe for the first I think this is maybe for the first time, it's let's say in my lifetime. I time, it's let's say in my lifetime. I time, it's let's say in my lifetime. I just turned 30. So, just turned 30. So, just turned 30. So, >> um I did come up through I guess a >> um I did come up through I guess a >> um I did come up through I guess a digital revolution in a way with the digital revolution in a way with the digital revolution in a way with the internet [clears throat] and all that internet [clears throat] and all that internet [clears throat] and all that stuff.

stuff. stuff. >> And it's, you know, continues to have an >> And it's, you know, continues to have an >> And it's, you know, continues to have an impact, you know, even how many years impact, you know, even how many years impact, you know, even how many years the decades later. Um and so I think the decades later. Um and so I think the decades later. Um and so I think when it comes to AI just because it one when it comes to AI just because it one when it comes to AI just because it one is moving so fast but also again there is moving so fast but also again there is moving so fast but also again there are so many different sides you know are so many different sides you know are so many different sides you know outside of uh chat bots which again most outside of uh chat bots which again most outside of uh chat bots which again most people um have interacted with for the people um have interacted with for the people um have interacted with for the most part we're seeing lots of issues most part we're seeing lots of issues most part we're seeing lots of issues around agentic AI you know those around agentic AI you know those around agentic AI you know those capabilities honestly are still very capabilities honestly are still very capabilities honestly are still very much so unknown and I don't think that much so unknown and I don't think that much so unknown and I don't think that you know policy makers will have the you know policy makers will have the you know policy makers will have the respective or sufficient expertise respective or sufficient expertise respective or sufficient expertise particularly even in countries like the particularly even in countries like the particularly even in countries like the US that are leading or at the frontier US that are leading or at the frontier US that are leading or at the frontier of AI development for at least another of AI development for at least another of AI development for at least another decade or so because I don't think there decade or so because I don't think there decade or so because I don't think there are really incentives you know for uh are really incentives you know for uh are really incentives you know for uh people with technical expertise to go people with technical expertise to go people with technical expertise to go into government not just a financial into government not just a financial into government not just a financial incentive but just generally um you know incentive but just generally um you know incentive but just generally um you know career growth career progression career growth career progression career growth career progression actually feel feeling valued um you know actually feel feeling valued um you know actually feel feeling valued um you know for that expertise as well for that expertise as well for that expertise as well >> yeah um I' I'm fascinated with the >> yeah um I' I'm fascinated with the >> yeah um I' I'm fascinated with the concept of explainable AI right concept of explainable AI right concept of explainable AI right explainable AI means how do we explain explainable AI means how do we explain explainable AI means how do we explain this system to the user and I when I this system to the user and I when I this system to the user and I when I started teaching myself and teaching started teaching myself and teaching started teaching myself and teaching others about AI, I tried to think if I others about AI, I tried to think if I others about AI, I tried to think if I could just explain it to them, they could just explain it to them, they could just explain it to them, they would understand. But now I'm realizing would understand. But now I'm realizing would understand. But now I'm realizing that maybe we should ask a different that maybe we should ask a different that maybe we should ask a different question, which is should this AI have question, which is should this AI have question, which is should this AI have been imposed on the user at all?

been imposed on the user at all? been imposed on the user at all? >> Exactly. >> Exactly. >> Exactly. >> What do you think about that >> What do you think about that >> What do you think about that perspective? perspective? perspective? >> Yeah, I think it's a little bit of both. >> Yeah, I think it's a little bit of both. >> Yeah, I think it's a little bit of both. I mean I'm explainability was something I mean I'm explainability was something I mean I'm explainability was something that was a big part of my PhD work when that was a big part of my PhD work when that was a big part of my PhD work when I was at Cornell and it really came I was at Cornell and it really came I was at Cornell and it really came about because we just um I did all my about because we just um I did all my about because we just um I did all my dissertation work pre you know the dissertation work pre you know the dissertation work pre you know the release of Chai GBT the public face release of Chai GBT the public face release of Chai GBT the public face inversion um you know in late 2022 and inversion um you know in late 2022 and inversion um you know in late 2022 and so um and I do I was doing my studies in so um and I do I was doing my studies in so um and I do I was doing my studies in 2020 in India um and also had the chance 2020 in India um and also had the chance 2020 in India um and also had the chance to actually go in the field back uh in to actually go in the field back uh in to actually go in the field back uh in 2022 and essentially the community 2022 and essentially the community 2022 and essentially the community healthcare workers did not know what AI healthcare workers did not know what AI healthcare workers did not know what AI was and this was really concerning to us was and this was really concerning to us was and this was really concerning to us just because we knew that these tools just because we knew that these tools just because we knew that these tools were being rolled out to them and also were being rolled out to them and also were being rolled out to them and also they were getting a lot of pressure from they were getting a lot of pressure from they were getting a lot of pressure from the Indian government to adopt these the Indian government to adopt these the Indian government to adopt these technologies um alongside just generally technologies um alongside just generally technologies um alongside just generally with having increased responsibilities with having increased responsibilities with having increased responsibilities due to due to the midemic and so because due to due to the midemic and so because due to due to the midemic and so because they didn't know what AI was um there they didn't know what AI was um there they didn't know what AI was um there could be if let's say they are using an could be if let's say they are using an could be if let's say they are using an AI tool and it presents them you know AI tool and it presents them you know AI tool and it presents them you know like a false decision and just because like a false decision and just because like a false decision and just because you know they kind of have they put a you know they kind of have they put a you know they kind of have they put a lot of value lot of value lot of value or trust into AI systems they actually or trust into AI systems they actually or trust into AI systems they actually kind of like let's say don't go with kind of like let's say don't go with kind of like let's say don't go with their gut feeling or actually just like their gut feeling or actually just like their gut feeling or actually just like you know leverage their domain expertise you know leverage their domain expertise you know leverage their domain expertise and defer to the AI and that could and defer to the AI and that could and defer to the AI and that could actually have very detriment detrimental actually have very detriment detrimental actually have very detriment detrimental um outcomes and so really you know um outcomes and so really you know um outcomes and so really you know explainability if we help them if we explainability if we help them if we explainability if we help them if we explain to them you know what AI is and explain to them you know what AI is and explain to them you know what AI is and they also get explanations for how this they also get explanations for how this they also get explanations for how this decision was made by a system they can decision was made by a system they can decision was made by a system they can then it can help in these very tricky then it can help in these very tricky then it can help in these very tricky instances and also really um maintain instances and also really um maintain instances and also really um maintain their autonomy as they continue to their autonomy as they continue to their autonomy as they continue to leverage and use these AI systems in the

leverage and use these AI systems in the leverage and use these AI systems in the future. future. future. >> [snorts] >> [snorts] >> [snorts] >> Man, that just that just lit up all the >> Man, that just that just lit up all the >> Man, that just that just lit up all the neurons in my brain with like a a fan neurons in my brain with like a a fan neurons in my brain with like a a fan out of like three different questions. out of like three different questions. out of like three different questions. Just because something is explainable, Just because something is explainable, Just because something is explainable, can the explanation create agency if the can the explanation create agency if the can the explanation create agency if the user themselves in this case Indian user themselves in this case Indian user themselves in this case Indian healthcare workers has no practical healthcare workers has no practical healthcare workers has no practical ability to refuse the technology. ability to refuse the technology. ability to refuse the technology. They're effectively being gaslit by a by They're effectively being gaslit by a by They're effectively being gaslit by a by a computer. a computer. a computer. >> Yeah. And I mean I was saying >> Yeah. And I mean I was saying >> Yeah. And I mean I was saying fortunately in those like frontline fortunately in those like frontline fortunately in those like frontline healthcare cases, you know, usually the healthcare cases, you know, usually the healthcare cases, you know, usually the the community healthcare worker can the community healthcare worker can the community healthcare worker can leverage her domain exp like oh okay leverage her domain exp like oh okay leverage her domain exp like oh okay I've seen this case or these symptoms I've seen this case or these symptoms I've seen this case or these symptoms like grouped together a couple of times like grouped together a couple of times like grouped together a couple of times already and I don't think that the AI already and I don't think that the AI already and I don't think that the AI system has actually you know let's say system has actually you know let's say system has actually you know let's say been trained or updated to understand been trained or updated to understand been trained or updated to understand these nuances particularly in my these nuances particularly in my these nuances particularly in my context. So actually, you know, I will context. So actually, you know, I will context. So actually, you know, I will make the final decision rather than make the final decision rather than make the final decision rather than having AI do that. And I think that in having AI do that. And I think that in having AI do that. And I think that in many cases, you know, I would I AI is many cases, you know, I would I AI is many cases, you know, I would I AI is not actually being used as the final not actually being used as the final not actually being used as the final arbiter of decision. In some cases, it arbiter of decision. In some cases, it arbiter of decision. In some cases, it is unfortunately, but I think and is unfortunately, but I think and is unfortunately, but I think and there's a little bit more flexibility in there's a little bit more flexibility in there's a little bit more flexibility in these frontline healthcare settings. But these frontline healthcare settings. But these frontline healthcare settings. But again, not every frontline healthcare again, not every frontline healthcare again, not every frontline healthcare worker in India or even, you know, worker in India or even, you know, worker in India or even, you know, across different countries in Africa, across different countries in Africa, across different countries in Africa, the Caribbean have access to these the Caribbean have access to these the Caribbean have access to these technologies in the first place. And so technologies in the first place. And so technologies in the first place. And so it really is still the community it really is still the community it really is still the community healthcare worker making that final healthcare worker making that final healthcare worker making that final decision.

decision. decision. >> Now you mentioned that you just turned >> Now you mentioned that you just turned >> Now you mentioned that you just turned 30 and you finished your uh your PhD in 30 and you finished your uh your PhD in 30 and you finished your uh your PhD in you know about 3 4 years ago and now you you know about 3 4 years ago and now you you know about 3 4 years ago and now you were thinking about doing postto but you were thinking about doing postto but you were thinking about doing postto but you exist as a digital native in this exist as a digital native in this exist as a digital native in this interesting historical place while I'm interesting historical place while I'm interesting historical place while I'm uh about 20 years older than you. I was uh about 20 years older than you. I was uh about 20 years older than you. I was here as it was getting built but I'm I'm here as it was getting built but I'm I'm here as it was getting built but I'm I'm not really I'm a different kind of not really I'm a different kind of not really I'm a different kind of digital native. What do you think about digital native. What do you think about digital native. What do you think about the PhD students that you are that you the PhD students that you are that you the PhD students that you are that you teach and the ones that are like 10 teach and the ones that are like 10 teach and the ones that are like 10 years behind you? Are there is there and years behind you? Are there is there and years behind you? Are there is there and this might be a spicy take but is their this might be a spicy take but is their this might be a spicy take but is their expertise going to be less deep because expertise going to be less deep because expertise going to be less deep because their their thinking is more shallow or their their thinking is more shallow or their their thinking is more shallow or more AI augmented and does that concern more AI augmented and does that concern more AI augmented and does that concern you as someone who has deep expertise in you as someone who has deep expertise in you as someone who has deep expertise in a specific uh area? a specific uh area? a specific uh area? >> Yeah, definitely. I mean there's so many >> Yeah, definitely. I mean there's so many >> Yeah, definitely. I mean there's so many different ways like you know I'm a very different ways like you know I'm a very different ways like you know I'm a very active user of user of Reddit and you active user of user of Reddit and you active user of user of Reddit and you know I'm on different forums related to know I'm on different forums related to know I'm on different forums related to academia. Um, I frequent the our academia. Um, I frequent the our academia. Um, I frequent the our professors subreddit and they're always professors subreddit and they're always professors subreddit and they're always mentioning how you know students cannot mentioning how you know students cannot mentioning how you know students cannot like let's say like operate a desktop like let's say like operate a desktop like let's say like operate a desktop computer or you know different programs computer or you know different programs computer or you know different programs or applications and that was something or applications and that was something or applications and that was something like I learned I'm like very young and like I learned I'm like very young and like I learned I'm like very young and also just you know throughout my journey also just you know throughout my journey also just you know throughout my journey of of experimenting with um or using of of experimenting with um or using of of experimenting with um or using computers on a daily basis or near near computers on a daily basis or near near computers on a daily basis or near near daily basis as a child and so I think daily basis as a child and so I think daily basis as a child and so I think this is one sort of literacy that you this is one sort of literacy that you this is one sort of literacy that you know is definitely declining but also know is definitely declining but also know is definitely declining but also when it comes to you know relying and when it comes to you know relying and when it comes to you know relying and heavily on chat bots or large language heavily on chat bots or large language heavily on chat bots or large language models, you know, to augment or actually models, you know, to augment or actually models, you know, to augment or actually do a lot of your work. You definitely, do a lot of your work. You definitely, do a lot of your work. You definitely, you know, lose a lot of critical you know, lose a lot of critical you know, lose a lot of critical thinking skills because you're thinking skills because you're thinking skills because you're outsourcing some of the fundamental

outsourcing some of the fundamental outsourcing some of the fundamental skills needed. You know, let's say like skills needed. You know, let's say like skills needed. You know, let's say like searching literature, actually reading searching literature, actually reading searching literature, actually reading the literature and understanding the the literature and understanding the the literature and understanding the different nuances in terms of being able different nuances in terms of being able different nuances in terms of being able to interpret what an author is saying to interpret what an author is saying to interpret what an author is saying and not just relying on what the chatbot and not just relying on what the chatbot and not just relying on what the chatbot is telling you. And then also is telling you. And then also is telling you. And then also synthesizing that. Let's say like you're synthesizing that. Let's say like you're synthesizing that. Let's say like you're doing a literature review. synthesizing doing a literature review. synthesizing doing a literature review. synthesizing that and presenting your own that and presenting your own that and presenting your own interpretations of that work. And again, interpretations of that work. And again, interpretations of that work. And again, because we know that a lot of LLMs because we know that a lot of LLMs because we know that a lot of LLMs provide this homogenized view of the provide this homogenized view of the provide this homogenized view of the world more broadly, I think it makes world more broadly, I think it makes world more broadly, I think it makes research itself, it will it kind of research itself, it will it kind of research itself, it will it kind of devalues the research process a little devalues the research process a little devalues the research process a little bit and also kind of weakens the bit and also kind of weakens the bit and also kind of weakens the empirical contributions to the computer empirical contributions to the computer empirical contributions to the computer the field of computer science, you know, the field of computer science, you know, the field of computer science, you know, and also all the sub fields more and also all the sub fields more and also all the sub fields more broadly. Yeah, my wife is currently on broadly. Yeah, my wife is currently on broadly. Yeah, my wife is currently on her second year of her PhD. Uh she went her second year of her PhD. Uh she went her second year of her PhD. Uh she went back to school. She's the same age as I back to school. She's the same age as I back to school. She's the same age as I and uh she has a very negative feeling and uh she has a very negative feeling and uh she has a very negative feeling towards AI as do all of her PhD towards AI as do all of her PhD towards AI as do all of her PhD adviserss. And they are being advised to adviserss. And they are being advised to adviserss. And they are being advised to just stay away from it, not even touch just stay away from it, not even touch just stay away from it, not even touch it. But at the same time, I know that it. But at the same time, I know that it. But at the same time, I know that someone out there right now, we don't someone out there right now, we don't someone out there right now, we don't know who they are, where they're know who they are, where they're know who they are, where they're located, is vibing their PhD.

located, is vibing their PhD. located, is vibing their PhD. >> Yeah, definitely. I mean, I've heard so >> Yeah, definitely. I mean, I've heard so >> Yeah, definitely. I mean, I've heard so many different stories. I mean there's a many different stories. I mean there's a many different stories. I mean there's a lab at Stanford. I think they do work on lab at Stanford. I think they do work on lab at Stanford. I think they do work on like digital economy stuff. Like I just like digital economy stuff. Like I just like digital economy stuff. Like I just read an article and the professor the PI read an article and the professor the PI read an article and the professor the PI who's leading that lab mentioned that who's leading that lab mentioned that who's leading that lab mentioned that they're literally producing like a paper they're literally producing like a paper they're literally producing like a paper every week due to the to due to how every week due to the to due to how every week due to the to due to how they're leveraging you know at AI tools they're leveraging you know at AI tools they're leveraging you know at AI tools and and other AI tools as well. And I and and other AI tools as well. And I and and other AI tools as well. And I think like you know one like we don't think like you know one like we don't think like you know one like we don't need that much research and also two need that much research and also two need that much research and also two it's just like how can I trust actually it's just like how can I trust actually it's just like how can I trust actually what you're doing um just because what you're doing um just because what you're doing um just because knowing that you know professors knowing that you know professors knowing that you know professors themselves have so many responsibilities themselves have so many responsibilities themselves have so many responsibilities and you know you're trying to train up and you know you're trying to train up and you know you're trying to train up and advise students like and they you and advise students like and they you and advise students like and they you know they may be advancing their skills know they may be advancing their skills know they may be advancing their skills but you still have to like do a lot of but you still have to like do a lot of but you still have to like do a lot of this work manually and and learn about this work manually and and learn about this work manually and and learn about the process rather than having it the process rather than having it the process rather than having it outsourced. outsourced. outsourced. >> Yeah. My uh a good friend of mine uh Dr. >> Yeah. My uh a good friend of mine uh Dr. >> Yeah. My uh a good friend of mine uh Dr. Dr. Mark Rasinovich wrote an application Dr. Mark Rasinovich wrote an application Dr. Mark Rasinovich wrote an application called Ref Checker which basically looks called Ref Checker which basically looks called Ref Checker which basically looks for fabricated references and citation for fabricated references and citation for fabricated references and citation errors and he's run it on a huge corpus errors and he's run it on a huge corpus errors and he's run it on a huge corpus of material and he says every day it's of material and he says every day it's of material and he says every day it's getting worse. Uh the the references are getting worse. Uh the the references are getting worse. Uh the the references are being fabricated and made up which is being fabricated and made up which is being fabricated and made up which is hugely problematic.

hugely problematic. hugely problematic. >> Yeah, I just actually found a fake >> Yeah, I just actually found a fake >> Yeah, I just actually found a fake reference of mine. It was cited in some reference of mine. It was cited in some reference of mine. It was cited in some like undergraduate dissertation at a like undergraduate dissertation at a like undergraduate dissertation at a university in the Czech Republic and university in the Czech Republic and university in the Czech Republic and actually reached out um to the chair or actually reached out um to the chair or actually reached out um to the chair or respective committee of that because respective committee of that because respective committee of that because like you know this is not my research like you know this is not my research like you know this is not my research you know and I know what they were you know and I know what they were you know and I know what they were >> Oh interesting. So the research was >> Oh interesting. So the research was >> Oh interesting. So the research was fabricated but your name was applied to fabricated but your name was applied to fabricated but your name was applied to research that you didn't do. research that you didn't do. research that you didn't do. >> Exactly. Yes. Well, so the name of the >> Exactly. Yes. Well, so the name of the >> Exactly. Yes. Well, so the name of the work was right but the journal it was work was right but the journal it was work was right but the journal it was attributed to was fake. It was some it attributed to was fake. It was some it attributed to was fake. It was some it was like something published at was like something published at was like something published at Brookings and Brookings and Brookings and >> Right. Right. Right. you know, they just >> Right. Right. Right. you know, they just >> Right. Right. Right. you know, they just made the whatever source they were ref made the whatever source they were ref made the whatever source they were ref referencing stuff they were using was referencing stuff they were using was referencing stuff they were using was not right. not right. not right. >> Yeah. [clears throat] Um, I want to go >> Yeah. [clears throat] Um, I want to go >> Yeah. [clears throat] Um, I want to go back to the the the healthcare workers back to the the the healthcare workers back to the the the healthcare workers that you studied because so these are that you studied because so these are that you studied because so these are community healthcare workers. They're community healthcare workers. They're community healthcare workers. They're already overburdened. They're already already overburdened. They're already already overburdened. They're already underpaid. How do we and and we where we underpaid. How do we and and we where we underpaid. How do we and and we where we is, I don't know, a company, society, is, I don't know, a company, society, is, I don't know, a company, society, humans. How do we tell whether AI is humans. How do we tell whether AI is humans. How do we tell whether AI is actually helping them or it's just actually helping them or it's just actually helping them or it's just giving them another thing, another giving them another thing, another giving them another thing, another device, another form, another system to device, another form, another system to device, another form, another system to maintain? Mhm. [clears throat] Yeah. So maintain? Mhm. [clears throat] Yeah. So maintain? Mhm. [clears throat] Yeah. So this is also something that interesting this is also something that interesting this is also something that interesting that came up in my work because the that came up in my work because the that came up in my work because the community healthcare workers um ashas community healthcare workers um ashas community healthcare workers um ashas that's what they're called in India. Um that's what they're called in India. Um that's what they're called in India. Um they understood that they would actually they understood that they would actually they understood that they would actually be responsible for learning how to use be responsible for learning how to use be responsible for learning how to use AI systems and also troubleshoot them.

AI systems and also troubleshoot them. AI systems and also troubleshoot them. And so because usually you know they're And so because usually you know they're And so because usually you know they're alone in the field themselves. You know alone in the field themselves. You know alone in the field themselves. You know they're using this mobile device. It may they're using this mobile device. It may they're using this mobile device. It may be like a basic or um kind of mid-level be like a basic or um kind of mid-level be like a basic or um kind of mid-level uh smartphone not super advanced. uh smartphone not super advanced. uh smartphone not super advanced. And so this is also an added burden, you And so this is also an added burden, you And so this is also an added burden, you know, to their work. I think there's know, to their work. I think there's know, to their work. I think there's also just a cognitive um there's a a also just a cognitive um there's a a also just a cognitive um there's a a difference in terms of how AI systems difference in terms of how AI systems difference in terms of how AI systems are used or when it comes to giving are used or when it comes to giving are used or when it comes to giving predictions rather than using things predictions rather than using things predictions rather than using things like scales um you know or just like uh like scales um you know or just like uh like scales um you know or just like uh measures you know to weigh and you know measures you know to weigh and you know measures you know to weigh and you know measure a baby because these are measure a baby because these are measure a baby because these are basically definitive kind of outcomes basically definitive kind of outcomes basically definitive kind of outcomes rather than then trying to understand rather than then trying to understand rather than then trying to understand like all these different aspects that like all these different aspects that like all these different aspects that produce a a decision that may be like produce a a decision that may be like produce a a decision that may be like say 85% confident. And so it's also kind say 85% confident. And so it's also kind say 85% confident. And so it's also kind of a mental model they have to adapt to of a mental model they have to adapt to of a mental model they have to adapt to as well when using AI. as well when using AI. as well when using AI. >> Yeah. So this word productivity gets >> Yeah. So this word productivity gets >> Yeah. So this word productivity gets used a lot and it's being applied to all used a lot and it's being applied to all used a lot and it's being applied to all industries, healthcare as as well. We're industries, healthcare as as well. We're industries, healthcare as as well. We're trying to like hyper optimize with, you trying to like hyper optimize with, you trying to like hyper optimize with, you know, we we're trying to create know, we we're trying to create know, we we're trying to create productivity gains. Is that what these productivity gains. Is that what these productivity gains. Is that what these folks need? Do they need productivity folks need? Do they need productivity folks need? Do they need productivity gains? And if so, who gets it? Is it the gains? And if so, who gets it? Is it the gains? And if so, who gets it? Is it the worker, the patient, the government or worker, the patient, the government or worker, the patient, the government or is it the company that sells the is it the company that sells the is it the company that sells the technology?

technology? technology? >> Yeah. So, when it comes to uh you know >> Yeah. So, when it comes to uh you know >> Yeah. So, when it comes to uh you know these contexts, I would say the these contexts, I would say the these contexts, I would say the healthcare workers themselves are healthcare workers themselves are healthcare workers themselves are relatively are pretty productive relatively are pretty productive relatively are pretty productive already. Um I think it's just that already. Um I think it's just that already. Um I think it's just that they're underpaid. Um you know uh I they're underpaid. Um you know uh I they're underpaid. Um you know uh I remember the Ashes you know mentioning remember the Ashes you know mentioning remember the Ashes you know mentioning to us oh okay like if we have this AI to us oh okay like if we have this AI to us oh okay like if we have this AI tool maybe the government can actually tool maybe the government can actually tool maybe the government can actually see the full range of services that see the full range of services that see the full range of services that we're doing and increase our pay. Um we're doing and increase our pay. Um we're doing and increase our pay. Um because usually uh I know a lot of because usually uh I know a lot of because usually uh I know a lot of health care systems particularly across health care systems particularly across health care systems particularly across Africa you know are reliant on foreign Africa you know are reliant on foreign Africa you know are reliant on foreign aid funding. Um India has a little bit aid funding. Um India has a little bit aid funding. Um India has a little bit more of a kind of self um or government more of a kind of self um or government more of a kind of self um or government funed system that doesn't heavily rely funed system that doesn't heavily rely funed system that doesn't heavily rely on external funding as well but those on external funding as well but those on external funding as well but those are issues that have increased you know are issues that have increased you know are issues that have increased you know due to the different pandemics that are due to the different pandemics that are due to the different pandemics that are happening or epidemics that are that happening or epidemics that are that happening or epidemics that are that have been happening um across the world have been happening um across the world have been happening um across the world but also just generally the reduction in but also just generally the reduction in but also just generally the reduction in global aid funding as well. And so I global aid funding as well. And so I global aid funding as well. And so I think really it's just like providing think really it's just like providing think really it's just like providing these workers with sufficient training these workers with sufficient training these workers with sufficient training and funding will also just generally and funding will also just generally and funding will also just generally increase their livelihoods and just more increase their livelihoods and just more increase their livelihoods and just more generally increasing the um a number um generally increasing the um a number um generally increasing the um a number um you know of community healthcare workers you know of community healthcare workers you know of community healthcare workers because healthcare work um is definitely because healthcare work um is definitely because healthcare work um is definitely devalued um across the world but also devalued um across the world but also devalued um across the world but also just like there's not enough workers um just like there's not enough workers um just like there's not enough workers um for the respective need that we have and for the respective need that we have and for the respective need that we have and so I think that's also um somewhere that so I think that's also um somewhere that so I think that's also um somewhere that investments have to be uh concentrated investments have to be uh concentrated investments have to be uh concentrated in as well. Mhm. My uh this is just a in as well. Mhm. My uh this is just a in as well. Mhm. My uh this is just a coincident. I don't always bring my wife coincident. I don't always bring my wife coincident. I don't always bring my wife up in every interview, but my wife is is up in every interview, but my wife is is up in every interview, but my wife is is doing her PhD research on the use of doing her PhD research on the use of doing her PhD research on the use of Ubuntu in the global south and if it can Ubuntu in the global south and if it can Ubuntu in the global south and if it can be applied into the you know the western be applied into the you know the western be applied into the you know the western uh culture and northern hemisphere. Now uh culture and northern hemisphere. Now uh culture and northern hemisphere. Now you deliberately in the research that you deliberately in the research that you deliberately in the research that I've done on you, you deliberately use

I've done on you, you deliberately use I've done on you, you deliberately use the phrase global majority. the phrase global majority. the phrase global majority. >> Yes. How does that what does that phrase >> Yes. How does that what does that phrase >> Yes. How does that what does that phrase do or reveal or change that terms like do or reveal or change that terms like do or reveal or change that terms like developing world or the even the global developing world or the even the global developing world or the even the global south hide? south hide? south hide? >> Yeah, I would say it just generally you >> Yeah, I would say it just generally you >> Yeah, I would say it just generally you know it it's in the name but it makes know it it's in the name but it makes know it it's in the name but it makes you understand that you know these you understand that you know these you understand that you know these people that you kind of that people have people that you kind of that people have people that you kind of that people have put mostly at the margins actually put mostly at the margins actually put mostly at the margins actually constitute a significant part of the constitute a significant part of the constitute a significant part of the world's population. um you know not not world's population. um you know not not world's population. um you know not not necessarily let's say um the a necessarily let's say um the a necessarily let's say um the a significant part of the world's GDP or significant part of the world's GDP or significant part of the world's GDP or let's say economic productivity or you let's say economic productivity or you let's say economic productivity or you know other kind of let's say like know other kind of let's say like know other kind of let's say like economic indicators that people kind of economic indicators that people kind of economic indicators that people kind of use more so to imply worthiness for the use more so to imply worthiness for the use more so to imply worthiness for the most part but it's still you know most part but it's still you know most part but it's still you know numbers matter and I think that this is numbers matter and I think that this is numbers matter and I think that this is something we have to really be something we have to really be something we have to really be considerate of just because you know a considerate of just because you know a considerate of just because you know a lot of things really revolve around you lot of things really revolve around you lot of things really revolve around you know US pop culture and we're just only know US pop culture and we're just only know US pop culture and we're just only a not we're decent majority you know of a not we're decent majority you know of a not we're decent majority you know of the world's population but it's still the world's population but it's still the world's population but it's still relatively small once we can group all relatively small once we can group all relatively small once we can group all these western countries together and so these western countries together and so these western countries together and so I think this is what um my you uh my use I think this is what um my you uh my use I think this is what um my you uh my use of this word or phrase um you know of this word or phrase um you know of this word or phrase um you know intends to convey intends to convey intends to convey >> so it certainly carries weight I mean >> so it certainly carries weight I mean >> so it certainly carries weight I mean majority like words matter people can majority like words matter people can majority like words matter people can complain about labels all they want but complain about labels all they want but complain about labels all they want but for the purposes of the conversation for the purposes of the conversation for the purposes of the conversation calling it the global majority is a calling it the global majority is a calling it the global majority is a reminder of the weight behind it which reminder of the weight behind it which reminder of the weight behind it which is all of these different groups Do you is all of these different groups Do you is all of these different groups Do you think though that grouping India, think though that grouping India, think though that grouping India, Nigeria, Brazil, these are dozens of Nigeria, Brazil, these are dozens of Nigeria, Brazil, these are dozens of different societies together risks different societies together risks different societies together risks creating another abstraction that is

creating another abstraction that is creating another abstraction that is designed by academics? designed by academics? designed by academics? >> Yeah, obviously you know and just >> Yeah, obviously you know and just >> Yeah, obviously you know and just because you know these c these countries because you know these c these countries because you know these c these countries themselves are so diverse not just themselves are so diverse not just themselves are so diverse not just across you know countries but within across you know countries but within across you know countries but within them I say like India, Nigeria very them I say like India, Nigeria very them I say like India, Nigeria very similar you know hundreds um of tribes, similar you know hundreds um of tribes, similar you know hundreds um of tribes, hundreds of languages you know varying hundreds of languages you know varying hundreds of languages you know varying you know amounts of socio economic you know amounts of socio economic you know amounts of socio economic progress or development throughout the progress or development throughout the progress or development throughout the countries themselves. countries themselves. countries themselves. you know, even though Nigeria and India, you know, even though Nigeria and India, you know, even though Nigeria and India, they still very much so struggle with they still very much so struggle with they still very much so struggle with poverty um you know, more broadly. And poverty um you know, more broadly. And poverty um you know, more broadly. And so, but again, I think that um there so, but again, I think that um there so, but again, I think that um there definitely can be much other terms other definitely can be much other terms other definitely can be much other terms other terms created to just ensure that we're terms created to just ensure that we're terms created to just ensure that we're not homogenizing this very diverse set not homogenizing this very diverse set not homogenizing this very diverse set of people and I definitely encourage of people and I definitely encourage of people and I definitely encourage people to develop them and and use them. people to develop them and and use them. people to develop them and and use them. >> Yeah. So your another one of your papers >> Yeah. So your another one of your papers >> Yeah. So your another one of your papers describes a global AI divide describing describes a global AI divide describing describes a global AI divide describing inequities and inequalities inequities and inequalities inequities and inequalities that are kind of legion infrastructure that are kind of legion infrastructure that are kind of legion infrastructure education AI development and then you've education AI development and then you've education AI development and then you've also said in an essay in 2020 2025 that also said in an essay in 2020 2025 that also said in an essay in 2020 2025 that AI is not Africa's savior but if you go AI is not Africa's savior but if you go AI is not Africa's savior but if you go on Twitter the dumpster fire that is on Twitter the dumpster fire that is on Twitter the dumpster fire that is Twitter there's AI grifters of all Twitter there's AI grifters of all Twitter there's AI grifters of all flavors from all over the world every flavors from all over the world every flavors from all over the world every country's got their own flavor of AI country's got their own flavor of AI country's got their own flavor of AI grifter they're saying that this is it.

grifter they're saying that this is it. grifter they're saying that this is it. But I remember a couple years ago it was But I remember a couple years ago it was But I remember a couple years ago it was crypto that was going to be Africa's crypto that was going to be Africa's crypto that was going to be Africa's savior. So as we continue to try to savior. So as we continue to try to savior. So as we continue to try to parade technologies that are going to parade technologies that are going to parade technologies that are going to save us and save the global majority, it save us and save the global majority, it save us and save the global majority, it doesn't feel like technology is the is doesn't feel like technology is the is doesn't feel like technology is the is the problem or access to it. What is the the problem or access to it. What is the the problem or access to it. What is the real problem that we're not talking real problem that we're not talking real problem that we're not talking about? about? about? >> Yeah. I mean, I would honestly literally >> Yeah. I mean, I would honestly literally >> Yeah. I mean, I would honestly literally just say like where the money is going. just say like where the money is going. just say like where the money is going. Um, I had an interview with a Nigerian Um, I had an interview with a Nigerian Um, I had an interview with a Nigerian um, newspaper a couple months ago and um, newspaper a couple months ago and um, newspaper a couple months ago and literally I just, you know, I was literally I just, you know, I was literally I just, you know, I was looking through I follow a lot of looking through I follow a lot of looking through I follow a lot of Nigerian organizations on Twitter and Nigerian organizations on Twitter and Nigerian organizations on Twitter and they were breaking down some of the they were breaking down some of the they were breaking down some of the budget allocations that was proposed for budget allocations that was proposed for budget allocations that was proposed for the upcoming fiscal year and just the upcoming fiscal year and just the upcoming fiscal year and just generally, you know, um, a lot of the generally, you know, um, a lot of the generally, you know, um, a lot of the money, you know, were going to agencies money, you know, were going to agencies money, you know, were going to agencies and not necessarily being trickled down and not necessarily being trickled down and not necessarily being trickled down and just generally knowing that Nigeria and just generally knowing that Nigeria and just generally knowing that Nigeria has a very big problem with corruption. has a very big problem with corruption. has a very big problem with corruption. um and seeing this firsthand um through um and seeing this firsthand um through um and seeing this firsthand um through different accounts of you know different accounts of you know different accounts of you know politicians hoarding pallets of money um politicians hoarding pallets of money um politicians hoarding pallets of money um you know in their basement what the you know in their basement what the you know in their basement what the notes actually rotting because they're notes actually rotting because they're notes actually rotting because they're not able to use that money and and just not able to use that money and and just not able to use that money and and just knowing more broadly that you know knowing more broadly that you know knowing more broadly that you know there's money being allocated towards there's money being allocated towards there's money being allocated towards healthcare but they're spending it on healthcare but they're spending it on healthcare but they're spending it on cars for hospitals rather than actually cars for hospitals rather than actually cars for hospitals rather than actually beds and medicine and and patient care beds and medicine and and patient care beds and medicine and and patient care and so I think in most cases it's really and so I think in most cases it's really and so I think in most cases it's really you know if you're if you focus on the you know if you're if you focus on the you know if you're if you focus on the fundamentals and actually, you know, fundamentals and actually, you know, fundamentals and actually, you know, sufficiently allocate that money to sufficiently allocate that money to sufficiently allocate that money to ensure that it's, you know, being it's ensure that it's, you know, being it's ensure that it's, you know, being it's going where it's supposed to go, then going where it's supposed to go, then going where it's supposed to go, then you'll see a lot more progress rather you'll see a lot more progress rather you'll see a lot more progress rather than trying to slap AI, you know, onto than trying to slap AI, you know, onto than trying to slap AI, you know, onto um a clinic that it's underst staff and

um a clinic that it's underst staff and um a clinic that it's underst staff and actually does not even have, let's say, actually does not even have, let's say, actually does not even have, let's say, internet connectivity or actually stable internet connectivity or actually stable internet connectivity or actually stable electricity. And so I always encourage electricity. And so I always encourage electricity. And so I always encourage African governments, stakeholders, focus African governments, stakeholders, focus African governments, stakeholders, focus on the basics and then you can use AI to on the basics and then you can use AI to on the basics and then you can use AI to augment uh those capabilities. Yeah. So augment uh those capabilities. Yeah. So augment uh those capabilities. Yeah. So focus on the basics. What there are focus on the basics. What there are focus on the basics. What there are problems in Africa. There are problems problems in Africa. There are problems problems in Africa. There are problems in the global majority that are often in the global majority that are often in the global majority that are often described as problems that can be solved described as problems that can be solved described as problems that can be solved with AI. But if you break them down, with AI. But if you break them down, with AI. But if you break them down, they are connectivity problems. They are they are connectivity problems. They are they are connectivity problems. They are electric electricity problems. Like electric electricity problems. Like electric electricity problems. Like electricity is like the problem right electricity is like the problem right electricity is like the problem right now on the continent. And then not to now on the continent. And then not to now on the continent. And then not to mention wage problems, institutional mention wage problems, institutional mention wage problems, institutional problems, political problems. When problems, political problems. When problems, political problems. When someone shows up with an AI thing, someone shows up with an AI thing, someone shows up with an AI thing, whether they be an AI grifter on whether they be an AI grifter on whether they be an AI grifter on Twitter, they usually will say, "Look, Twitter, they usually will say, "Look, Twitter, they usually will say, "Look, we're with these poor people and they're we're with these poor people and they're we're with these poor people and they're in a well and we added some AI and an in a well and we added some AI and an in a well and we added some AI and an Arduino." And the the there's there's a Arduino." And the the there's there's a Arduino." And the the there's there's a more offensive phrase, but I'll just say more offensive phrase, but I'll just say more offensive phrase, but I'll just say inspiration fluff. inspiration fluff. inspiration fluff. >> Yes. >> Yes. >> Yes. >> Inspiration fluff. Uh I even heard >> Inspiration fluff. Uh I even heard >> Inspiration fluff. Uh I even heard someone called it, I love this term, someone called it, I love this term, someone called it, I love this term, technosolutionist technosolutionist technosolutionist theater. [laughter] theater. [laughter] theater. [laughter] >> Right. Look at me. and then you pat >> Right. Look at me. and then you pat >> Right. Look at me. and then you pat yourself on the back and then you leave yourself on the back and then you leave yourself on the back and then you leave the Arduino running on that well and the Arduino running on that well and the Arduino running on that well and I've just saved a village. What is a I've just saved a village. What is a I've just saved a village. What is a test? What is your test uh Dr. Colo for test? What is your test uh Dr. Colo for test? What is your test uh Dr. Colo for distinguishing a valuable AI distinguishing a valuable AI distinguishing a valuable AI intervention from technosolutionist intervention from technosolutionist intervention from technosolutionist theater?

theater? theater? >> Yeah, honestly it's really hard to say >> Yeah, honestly it's really hard to say >> Yeah, honestly it's really hard to say because I feel like when it comes to AI because I feel like when it comes to AI because I feel like when it comes to AI deployments more broadly particularly deployments more broadly particularly deployments more broadly particularly for let's say like these rural low low for let's say like these rural low low for let's say like these rural low low resource areas resource areas resource areas the evidence base is not there yet. I the evidence base is not there yet. I the evidence base is not there yet. I mean it's growing um but it's not strong mean it's growing um but it's not strong mean it's growing um but it's not strong enough just to understand like the enough just to understand like the enough just to understand like the impact of long-term deployments. A lot impact of long-term deployments. A lot impact of long-term deployments. A lot of my dissertation work is rooted in the of my dissertation work is rooted in the of my dissertation work is rooted in the field of ICTD information and field of ICTD information and field of ICTD information and communication technologies for communication technologies for communication technologies for development. Um and a common problem you development. Um and a common problem you development. Um and a common problem you know even before um these modern-day AI know even before um these modern-day AI know even before um these modern-day AI tools became really prevalent was that tools became really prevalent was that tools became really prevalent was that you know researchers you know would you know researchers you know would you know researchers you know would develop let's say kind of or this develop let's say kind of or this develop let's say kind of or this Raspberry Pi solution or some technology Raspberry Pi solution or some technology Raspberry Pi solution or some technology solution or mobile app and they would solution or mobile app and they would solution or mobile app and they would just you know like deploy it throughout just you know like deploy it throughout just you know like deploy it throughout the length of the study whether it be a the length of the study whether it be a the length of the study whether it be a couple weeks uh to a couple months or couple weeks uh to a couple months or couple weeks uh to a couple months or maybe up to a year and then after that maybe up to a year and then after that maybe up to a year and then after that you know they it's essentially abandoned you know they it's essentially abandoned you know they it's essentially abandoned by the community because there isn't by the community because there isn't by the community because there isn't necessarily sufficient technology necessarily sufficient technology necessarily sufficient technology transfer or even just generally resource transfer or even just generally resource transfer or even just generally resource ources to support the longitivity of ources to support the longitivity of ources to support the longitivity of these respective solutions. Um and just these respective solutions. Um and just these respective solutions. Um and just generally the infrastructure um you know generally the infrastructure um you know generally the infrastructure um you know it it doesn't it's not there to support it it doesn't it's not there to support it it doesn't it's not there to support long-term use and adoption. And so um I long-term use and adoption. And so um I long-term use and adoption. And so um I would say for AI solutions we have to would say for AI solutions we have to would say for AI solutions we have to really consider some of these underlying really consider some of these underlying really consider some of these underlying factors. You know you're going to have factors. You know you're going to have factors. You know you're going to have you have to realize like okay if you you have to realize like okay if you you have to realize like okay if you want the solution to be used longterm um want the solution to be used longterm um want the solution to be used longterm um you need to set up some kind of um cloud you need to set up some kind of um cloud you need to set up some kind of um cloud support so it can actually run. you need support so it can actually run. you need support so it can actually run. you need to pay someone um or a group of people to pay someone um or a group of people to pay someone um or a group of people to actually you know help with debugging to actually you know help with debugging to actually you know help with debugging and other issues if you're not actually and other issues if you're not actually and other issues if you're not actually going to be involved in it. Um and this going to be involved in it. Um and this going to be involved in it. Um and this generally also you know be able to

generally also you know be able to generally also you know be able to willing to come back in the field from willing to come back in the field from willing to come back in the field from time to time to see how you can uh adapt time to time to see how you can uh adapt time to time to see how you can uh adapt um upgrade and extend the life of the um upgrade and extend the life of the um upgrade and extend the life of the solution just so we can actually run or solution just so we can actually run or solution just so we can actually run or maybe even encourage the government to maybe even encourage the government to maybe even encourage the government to invest um in in helping in supporting invest um in in helping in supporting invest um in in helping in supporting the project as a whole. So these are the project as a whole. So these are the project as a whole. So these are some of the things I would love to see some of the things I would love to see some of the things I would love to see as AI solutions start to get deployed in as AI solutions start to get deployed in as AI solutions start to get deployed in low resource areas. low resource areas. low resource areas. >> Yeah, it is a huge problem because they >> Yeah, it is a huge problem because they >> Yeah, it is a huge problem because they got their case study and they did their got their case study and they did their got their case study and they did their polished fancy video and then they left polished fancy video and then they left polished fancy video and then they left and then if you would return 3 years and then if you would return 3 years and then if you would return 3 years later you'll find that those devices are later you'll find that those devices are later you'll find that those devices are just sitting there in the side of the just sitting there in the side of the just sitting there in the side of the road. road. road. >> So then if a donor uh and maybe that's >> So then if a donor uh and maybe that's >> So then if a donor uh and maybe that's not the right word, but a technology not the right word, but a technology not the right word, but a technology company arrives with an AI solution, company arrives with an AI solution, company arrives with an AI solution, then who originally defined the problem? then who originally defined the problem? then who originally defined the problem? Are we flooding the market with Are we flooding the market with Are we flooding the market with solutions that have either the the wrong solutions that have either the the wrong solutions that have either the the wrong solution for a poorly defined problem or solution for a poorly defined problem or solution for a poorly defined problem or it was more like push technology as it was more like push technology as it was more like push technology as opposed to pulled? Like who asked for opposed to pulled? Like who asked for opposed to pulled? Like who asked for this this this >> is a question I find myself wondering. >> is a question I find myself wondering. >> is a question I find myself wondering. >> Oh yeah. I mean that's a great question >> Oh yeah. I mean that's a great question >> Oh yeah. I mean that's a great question too because I think you know a lot of too because I think you know a lot of too because I think you know a lot of times it looks good. It's it's good PR times it looks good. It's it's good PR times it looks good. It's it's good PR you know to appear that you're solving a you know to appear that you're solving a you know to appear that you're solving a problem for a community and maybe you problem for a community and maybe you problem for a community and maybe you may be solving just like one aspect of a may be solving just like one aspect of a may be solving just like one aspect of a larger problem more broadly. And you larger problem more broadly. And you larger problem more broadly. And you know fortunately a lot of these big tech know fortunately a lot of these big tech know fortunately a lot of these big tech companies do have resources and um let's companies do have resources and um let's companies do have resources and um let's say like sociotechnical expertise in say like sociotechnical expertise in say like sociotechnical expertise in house um from philosophers house um from philosophers house um from philosophers anthropologists you know etc anthropologists you know etc anthropologists you know etc sociologists you know to help um sociologists you know to help um sociologists you know to help um leverage you know participatory design leverage you know participatory design leverage you know participatory design mechanisms to ensure that they're mechanisms to ensure that they're mechanisms to ensure that they're actually working with these communities actually working with these communities actually working with these communities you know from the onset of this work. A you know from the onset of this work. A you know from the onset of this work. A lot of times it doesn't happen um but it

lot of times it doesn't happen um but it lot of times it doesn't happen um but it it definitely should. it definitely should. it definitely should. >> Yeah. Um a lot of people are talking >> Yeah. Um a lot of people are talking >> Yeah. Um a lot of people are talking about sovereign AI and sovereign clouds. about sovereign AI and sovereign clouds. about sovereign AI and sovereign clouds. You know, Germany's got a cloud and You know, Germany's got a cloud and You know, Germany's got a cloud and China wants their own cloud. And then China wants their own cloud. And then China wants their own cloud. And then you've got folks like Leilapa AI and you've got folks like Leilapa AI and you've got folks like Leilapa AI and Pelanom Muloa down in South Africa who Pelanom Muloa down in South Africa who Pelanom Muloa down in South Africa who are doing their own local models. What are doing their own local models. What are doing their own local models. What is the minimally viable form for is the minimally viable form for is the minimally viable form for sovereignty? Is it just local compute? sovereignty? Is it just local compute? sovereignty? Is it just local compute? It's running on a computer. Is it local It's running on a computer. Is it local It's running on a computer. Is it local models like our universities in this models like our universities in this models like our universities in this country? Uh is it local evaluation? Is country? Uh is it local evaluation? Is country? Uh is it local evaluation? Is it that it's in a particular there it that it's in a particular there it that it's in a particular there there's more languages on the African there's more languages on the African there's more languages on the African continent than there is pretty much continent than there is pretty much continent than there is pretty much anywhere. like is it about local anywhere. like is it about local anywhere. like is it about local language models or is it something or is language models or is it something or is language models or is it something or is it just the power to say no no thank you it just the power to say no no thank you it just the power to say no no thank you I don't want any of it I don't want any of it I don't want any of it >> yeah I would say honestly it's a lot of >> yeah I would say honestly it's a lot of >> yeah I would say honestly it's a lot of basically what you just mentioned but it basically what you just mentioned but it basically what you just mentioned but it really comes down to um the last thing really comes down to um the last thing really comes down to um the last thing you just said and that underlying kind you just said and that underlying kind you just said and that underlying kind of philosophy is just generally autonomy of philosophy is just generally autonomy of philosophy is just generally autonomy you know being able to um you know have you know being able to um you know have you know being able to um you know have the power to independently say you want the power to independently say you want the power to independently say you want to adopt these systems also being able to adopt these systems also being able to adopt these systems also being able to independently develop that capacity to independently develop that capacity to independently develop that capacity you know to you know push out AI models you know to you know push out AI models you know to you know push out AI models you know curate your own data um also you know curate your own data um also you know curate your own data um also compensate people equitably you know for compensate people equitably you know for compensate people equitably you know for that labor and curating the data sets um that labor and curating the data sets um that labor and curating the data sets um and also let's say just generally having and also let's say just generally having and also let's say just generally having your own AI clusters hyperscolars you your own AI clusters hyperscolars you your own AI clusters hyperscolars you know data centers whatever you want to know data centers whatever you want to know data centers whatever you want to call it I think you know sometimes when call it I think you know sometimes when call it I think you know sometimes when it come if you think about sovereignty it come if you think about sovereignty it come if you think about sovereignty more broadly everything is still much so more broadly everything is still much so more broadly everything is still much so pretty connected um like you know the pretty connected um like you know the pretty connected um like you know the the subs cables they connect from

the subs cables they connect from the subs cables they connect from country to country that You can't just country to country that You can't just country to country that You can't just have a subscri um African policy makers or uh dictators um African policy makers or uh dictators tend to you know cut off internet at tend to you know cut off internet at tend to you know cut off internet at times but that's you know a whole times but that's you know a whole times but that's you know a whole another thing as well. Um but also the another thing as well. Um but also the another thing as well. Um but also the chips that we rely on come generally chips that we rely on come generally chips that we rely on come generally from you know mostly China or or Nvidia from you know mostly China or or Nvidia from you know mostly China or or Nvidia or you know ASML as well and so there or you know ASML as well and so there or you know ASML as well and so there isn't enough diversification in the isn't enough diversification in the isn't enough diversification in the value chain for those kind of the value chain for those kind of the value chain for those kind of the infrastructure of AI to be really infrastructure of AI to be really infrastructure of AI to be really sovereign itself and so I think we have sovereign itself and so I think we have sovereign itself and so I think we have to kind of go higher up and and think to kind of go higher up and and think to kind of go higher up and and think about it more broadly. Okay, that's a about it more broadly. Okay, that's a about it more broadly. Okay, that's a great point because we like you, one can great point because we like you, one can great point because we like you, one can be pro or anti- globalization, but the be pro or anti- globalization, but the be pro or anti- globalization, but the facts are everything's connected and if facts are everything's connected and if facts are everything's connected and if a a widget or a cell phone shows up in a a widget or a cell phone shows up in a a widget or a cell phone shows up in an African country, it is not really an African country, it is not really an African country, it is not really possible that it be entirely built from possible that it be entirely built from possible that it be entirely built from scratch, you know, the the lithium scratch, you know, the the lithium scratch, you know, the the lithium didn't come from there like what all the didn't come from there like what all the didn't come from there like what all the different parts all the way up. So then different parts all the way up. So then different parts all the way up. So then can you do can is it even possible to can you do can is it even possible to can you do can is it even possible to have sovereignty if your cloud your have sovereignty if your cloud your have sovereignty if your cloud your chips your models and your technical chips your models and your technical chips your models and your technical expertise still come from American and expertise still come from American and expertise still come from American and Chinese companies?

Chinese companies? Chinese companies? >> Yeah, I mean I would say in a way I mean >> Yeah, I mean I would say in a way I mean >> Yeah, I mean I would say in a way I mean I've seen a lot of efforts you know from I've seen a lot of efforts you know from I've seen a lot of efforts you know from European countries particularly or European countries particularly or European countries particularly or European governments because they've European governments because they've European governments because they've seen a lot of the issues uh with u big seen a lot of the issues uh with u big seen a lot of the issues uh with u big tech and having just like this this tech and having just like this this tech and having just like this this dominance in their respective government dominance in their respective government dominance in their respective government systems. France actually released this systems. France actually released this systems. France actually released this super interesting like open-source kind super interesting like open-source kind super interesting like open-source kind of office set of suite of software which of office set of suite of software which of office set of suite of software which I they call it last suite I believe and I they call it last suite I believe and I they call it last suite I believe and so I thought it's super interesting I so I thought it's super interesting I so I thought it's super interesting I would love to see you know more would love to see you know more would love to see you know more countries particularly in Africa I think countries particularly in Africa I think countries particularly in Africa I think about this but again that expertise or about this but again that expertise or about this but again that expertise or just generally capacity is not there yet just generally capacity is not there yet just generally capacity is not there yet to divest um you know from let's say to divest um you know from let's say to divest um you know from let's say like it's usually I don't think they're like it's usually I don't think they're like it's usually I don't think they're really adopting like Google Workspace really adopting like Google Workspace really adopting like Google Workspace it's more so Microsoft a lot of times it's more so Microsoft a lot of times it's more so Microsoft a lot of times like Zoho and and other kind of software like Zoho and and other kind of software like Zoho and and other kind of software as well as well um but I think like you as well as well um but I think like you as well as well um but I think like you can get to a sense of sovereignty but can get to a sense of sovereignty but can get to a sense of sovereignty but understanding like if you if you don't understanding like if you if you don't understanding like if you if you don't have let's say again like the minerals have let's say again like the minerals have let's say again like the minerals to create the trips yourselves and also to create the trips yourselves and also to create the trips yourselves and also the companies that can create um you the companies that can create um you the companies that can create um you know the GPU clusters as well then you know the GPU clusters as well then you know the GPU clusters as well then you are still limited but I think you can are still limited but I think you can are still limited but I think you can develop these alliances where you're develop these alliances where you're develop these alliances where you're aligned um you know more generally you aligned um you know more generally you aligned um you know more generally you know and values and and other areas to know and values and and other areas to know and values and and other areas to ensure that you know sovereign ensure that you know sovereign ensure that you know sovereign sovereignty is done on your respective sovereignty is done on your respective sovereignty is done on your respective terms So the podcast is my own, but I do terms So the podcast is my own, but I do terms So the podcast is my own, but I do work for a big tech company in my day work for a big tech company in my day work for a big tech company in my day job. So I'll put them on blast. When a job. So I'll put them on blast. When a job. So I'll put them on blast. When a big company says they want to support big company says they want to support big company says they want to support responsible AI in the global majority, responsible AI in the global majority, responsible AI in the global majority, what would you as a policy expert demand

what would you as a policy expert demand what would you as a policy expert demand beyond funding programs and opening beyond funding programs and opening beyond funding programs and opening offices and putting representatives on offices and putting representatives on offices and putting representatives on panels? panels? panels? >> Yeah, definitely. I would say really >> Yeah, definitely. I would say really >> Yeah, definitely. I would say really just independence uh particularly when just independence uh particularly when just independence uh particularly when it comes to like the actual how it comes to like the actual how it comes to like the actual how governance decisions or governance governance decisions or governance governance decisions or governance frameworks are developed themselves. Um frameworks are developed themselves. Um frameworks are developed themselves. Um for example I did some work with Nigeria for example I did some work with Nigeria for example I did some work with Nigeria on their national a strategy and there on their national a strategy and there on their national a strategy and there were stakeholders you know from Meta, were stakeholders you know from Meta, were stakeholders you know from Meta, Google, Microsoft also like UN agencies Google, Microsoft also like UN agencies Google, Microsoft also like UN agencies um involved in that process and um I'm um involved in that process and um I'm um involved in that process and um I'm sure a lot of these people that were in sure a lot of these people that were in sure a lot of these people that were in kind of like in the working group loops kind of like in the working group loops kind of like in the working group loops were working in their individual were working in their individual were working in their individual capacity but you know their affiliations capacity but you know their affiliations capacity but you know their affiliations um and also these companies did fund um um and also these companies did fund um um and also these companies did fund um the strategy development itself and I I the strategy development itself and I I the strategy development itself and I I definitely think that even though you definitely think that even though you definitely think that even though you can say it's unrestricted or we don't can say it's unrestricted or we don't can say it's unrestricted or we don't have you know these specific or there have you know these specific or there have you know these specific or there aren't specific outputs we want you to aren't specific outputs we want you to aren't specific outputs we want you to have. There still is an influence have. There still is an influence have. There still is an influence because there's a certain kind of um I because there's a certain kind of um I because there's a certain kind of um I don't know mindset around like uh don't know mindset around like uh don't know mindset around like uh satisfying the funders or making them satisfying the funders or making them satisfying the funders or making them happy. And so I would just really, you happy. And so I would just really, you happy. And so I would just really, you know, advocate that there be really true know, advocate that there be really true know, advocate that there be really true independence and also like external kind independence and also like external kind independence and also like external kind of like more neutral stakeholders that of like more neutral stakeholders that of like more neutral stakeholders that can advise on this process to ensure can advise on this process to ensure can advise on this process to ensure that, you know, there isn't too much that, you know, there isn't too much that, you know, there isn't too much influence, you know, from this company influence, you know, from this company influence, you know, from this company or that company um in defining like what or that company um in defining like what or that company um in defining like what the exact strategy or framework or law the exact strategy or framework or law the exact strategy or framework or law proposes. And so I think this is proposes. And so I think this is proposes. And so I think this is something that will be of really high something that will be of really high something that will be of really high concern in Africa because we've seen concern in Africa because we've seen concern in Africa because we've seen already how big tech has um let's say already how big tech has um let's say already how big tech has um let's say kind of impeded governance processes kind of impeded governance processes kind of impeded governance processes when it came to Brazil's AI act.

when it came to Brazil's AI act. when it came to Brazil's AI act. >> So I want to end on a positive note but >> So I want to end on a positive note but >> So I want to end on a positive note but I also want to put you on a little bit I also want to put you on a little bit I also want to put you on a little bit of a spot. Try to describe a a global of a spot. Try to describe a a global of a spot. Try to describe a a global majority success story whether it be majority success story whether it be majority success story whether it be African or Brazilian or Indian from African or Brazilian or Indian from African or Brazilian or Indian from 2035. 2035. 2035. >> Who built it? Who owns it? Who benefits >> Who built it? Who owns it? Who benefits >> Who built it? Who owns it? Who benefits from it? And how is it different than from it? And how is it different than from it? And how is it different than just importing some western system? just importing some western system? just importing some western system? >> Yeah, definitely. Yeah, I'm really big >> Yeah, definitely. Yeah, I'm really big >> Yeah, definitely. Yeah, I'm really big on infrastructure right now and I think on infrastructure right now and I think on infrastructure right now and I think that um is one thing I would love to see that um is one thing I would love to see that um is one thing I would love to see expand a little bit more. Um and there's expand a little bit more. Um and there's expand a little bit more. Um and there's a company um I would say Aman, they're a company um I would say Aman, they're a company um I would say Aman, they're based in Kenya and they've been doing a based in Kenya and they've been doing a based in Kenya and they've been doing a lot of work um with Barbados and lot of work um with Barbados and lot of work um with Barbados and actually creating kind of like these actually creating kind of like these actually creating kind of like these micro data centers and I think they kind micro data centers and I think they kind micro data centers and I think they kind of like fit in um like a shipping of like fit in um like a shipping of like fit in um like a shipping container and you and they're modular as container and you and they're modular as container and you and they're modular as well. And so I would love to see well. And so I would love to see well. And so I would love to see something I would across Africa and also something I would across Africa and also something I would across Africa and also even across the Caribbean and and even across the Caribbean and and even across the Caribbean and and Pacific Islands just due to kind of the Pacific Islands just due to kind of the Pacific Islands just due to kind of the nuances or challenges of those nuances or challenges of those nuances or challenges of those protective environments like greater protective environments like greater protective environments like greater penetration um of this kind of penetration um of this kind of penetration um of this kind of infrastructure across uh these regions infrastructure across uh these regions infrastructure across uh these regions but also just generally I think within but also just generally I think within but also just generally I think within Africa this this would be accompanied by Africa this this would be accompanied by Africa this this would be accompanied by greater like electrification um and more greater like electrification um and more greater like electrification um and more let's say like uh renewable energy let's say like uh renewable energy let's say like uh renewable energy leveraging leveraging like those leveraging leveraging like those leveraging leveraging like those respective technologies for um a solid respective technologies for um a solid respective technologies for um a solid functional grid um and then also again functional grid um and then also again functional grid um and then also again like um connected uh a connected cloud like um connected uh a connected cloud like um connected uh a connected cloud across Africa leveraging this micro data across Africa leveraging this micro data across Africa leveraging this micro data center uh concept as well.

center uh concept as well. center uh concept as well. >> Very cool. You mentioned Aman. I had >> Very cool. You mentioned Aman. I had >> Very cool. You mentioned Aman. I had Kate Kell the CEO of on the on the show. Kate Kell the CEO of on the on the show. Kate Kell the CEO of on the on the show. She was episode 992 if folks want to She was episode 992 if folks want to She was episode 992 if folks want to follow up on that. So I love that you follow up on that. So I love that you follow up on that. So I love that you brought that up as well. Thank you so brought that up as well. Thank you so brought that up as well. Thank you so much Dr. Okoolo for chatting with me much Dr. Okoolo for chatting with me much Dr. Okoolo for chatting with me today. today. today. >> Thank you Scott. Very happy to have >> Thank you Scott. Very happy to have >> Thank you Scott. Very happy to have chatted with you. chatted with you. chatted with you. >> We have been chatting with Dr. at >> We have been chatting with Dr. at >> We have been chatting with Dr. at Chinasata Toko. You can check her out at Chinasata Toko. You can check her out at Chinasata Toko. You can check her out at chinasatocolo.com. chinasatocolo.com. chinasatocolo.com. I'll put a link in the show notes. You I'll put a link in the show notes. You I'll put a link in the show notes. You can learn all about the research that can learn all about the research that can learn all about the research that she's doing and check out her media kit. she's doing and check out her media kit. she's doing and check out her media kit. This has been another episode of Hansel This has been another episode of Hansel This has been another episode of Hansel Minutes and we'll see you again next Minutes and we'll see you again next Minutes and we'll see you again next week.