Meet 22-year-old Kenyan Making Sign Language Robots | Norah Kimathi

[00:00:00]This is going to be a very interesting hour because yesterday Business Daily uh unveiled this year's list of top 40 under 40 trailblazing women. That's right.

[00:00:10]>> Uh all under 40 doing iconic and amazing things and uh he this hour will be talking to one of them >> doing incredible incredible things.

[00:00:23]>> True. Kabisa Norakimi is the youngest >> of all the 40 women who have been, you know, recognized this year.

[00:00:33]>> Yeah. Right.

[00:00:34]>> Yes.

[00:00:35]>> And and she's doing she's done amazing.

[00:00:37]>> I'm looking at the blurb.

[00:00:40]>> Um Nora Kimati, 22, co-founder of Zero Bionic.

[00:00:46]Now, question. How can technology help break the barriers that deaf students in Kenya face in access accessing education because of a critical shortage of sign language teachers?

[00:00:57]>> That answer comes partly through zero bionic. Norakimati is proving that humanoid robots are part of that answer.

[00:01:04]She is a founder co-founder of Zero Bionic and this startup came up in 2021 to develop assistive humanoid robots that translate spoken language into sign language helping bridge the communication gaps for deaf learners in classrooms and other settings. Good morning, Nora.

[00:01:22]>> Morning.

[00:01:22]>> First of all, morning congratulations >> on being named one of the top 40 under 40 women 2026. It's an amazing achievement and an even bigger congratulations is >> for what I'm looking at right here and trying to understand. But before we even try to understand the humanoid robot before us that is gesturing, let's um share the quote of the day with you.

[00:01:47]Usually we start it off with a quote.

[00:01:49]Fellaris will tell you um what someone famous once said and you can say if you agree or disagree and if it speaks to you.

[00:01:57]>> All right. Thank you. Today's quote is actually an African proverb and it reads, "When the roots are deep, there's no reason to fear the wind." When the roots are deep, there's no reason to fear the wind. How does that speak to you?

[00:02:12]>> Well, [laughter] um, so I started what I was doing when I was 15, >> spanning out to now at 22. When I look back at where I started, what I was doing, it just really shows that whatever you started from inception and you continued building upon that, no one can ever shake the vision that you had or whatever purpose you're building towards. So yeah, it really does speak to what I do and I believe that's what everyone should live by. just establishing themselves in their industries, in their niches, and just continuing pushing on to that because if it's deep, no one can shake you from it.

[00:02:49]>> The foundation >> the How old are you, Nora?

[00:02:52]>> Deep.

[00:02:52]>> 22.

[00:02:54]>> You've been doing this thing for 7 years.

[00:02:56]>> You started at 15.

[00:02:57]>> Yeah.

[00:02:58]>> Now, what is this thing that you've been doing? [laughter] >> I mean, I started doing doing what I do at 15. [laughter] Who says that?

[00:03:07]>> What is the thing?

[00:03:08]>> Who says that? What is this thing that you've been doing since 15?

[00:03:11]>> Yeah. So, let me just take you back.

[00:03:13]>> Okay.

[00:03:13]>> Um, so when I was 15, as I don't know if you guys had a phone when you were 15, but then I I my parents didn't give me a phone. At that time, I felt like the world was crumbling down because [laughter] then now later on is when I realized that whatever your parents do at that point, they're just um ushering you into something great and we should appreciate it at that point. But I didn't. So I was like, can I make my own phone from whatever materials I have? So I started experimenting with small Lego bricks to just make my own small mini phone.

[00:03:46]>> Legos.

[00:03:46]>> Yeah.

[00:03:47]>> Wow. Wow. [laughter] >> Okay. Okay.

[00:03:49]>> So where we used to stay whenever it would drain the water would I I don't want to say it would flood, but then it would reach close to our knees. So I'd always want to have a way to communicate with my parents.

[00:04:00]>> That's not flooding. [laughter] >> That's just water. That's just water rising to your knees.

[00:04:05]casually. He's like, "Is it a pool?"

[00:04:08][laughter] >> Could you be talking about a pool?

[00:04:12]>> Yeah.

[00:04:12]>> It's just water rising up to your [laughter] knees.

[00:04:14]>> Okay. It's not flooding.

[00:04:16]>> Grow rice. Just enough water to grow rice. Nothing much.

[00:04:20]>> Yeah. So, I'd want a way to communicate with them and tell them it's actually, you know, don't use this route. You can use a different route because here it's flooding them. Yeah. So, I now started making that phone so I could communicate with them. obviously didn't work but then it was a step towards my innovation journey. So at that point my parents saw that I was actually motivated in this innovation entrepreneurship journey. So they registered for me company. So at 15 I was a CEO.

[00:04:47]>> Um so fast forward to now when I joined university at around 18.

[00:04:52]>> Great.

[00:04:53]>> They registered for your company. Yeah.

[00:04:55]>> Have they given you capital to start the business?

[00:04:57]>> No. [laughter] a company.

[00:05:03]>> Are you are they paying is this company paying the CEO? [laughter] >> I think for them is the CEO paying the founders [laughter] can go both ways. It can go both ways.

[00:05:14]>> I think for them it was more of we're give we are enabling you. So we're giving you the motivation to just Yeah, exactly. So can you just now think and build upon that? So I just now continue doing that researching uh tapping into communities and networks just to expand my knowledge base in that segment. But I always had a passion for science, tech, engineering and math. So it was always tinkering around engineering concepts.

[00:05:38]So when I turned 18, I now started mentoring other people my age and younger in STEM related courses or other concepts. And then that's how now I started my journey in building uh engineering tools for people who can't hear and people who can hear alike.

[00:05:57]>> Yeah.

[00:05:57]>> You know, I'm actually just very curious about the foundations of someone who has that deep curiosity around STEM >> and you know just also the school system that would allow you to you know build on that curiosity. Maybe let's focus on that. And I'm saying this because we are now seeing the roll out of a curriculum that is supposed to specialize kids into different pathways. And there's a child, if you're talking about your foundations being at 15, there's a parent somewhere who's watching you and seeing their child have this kind of curiosity, but then again, how do they build it up? So, did your school system allow for you to hone in on that curiosity or did your parents have to create or to give you more for you to build up on that um STEM pathway?

[00:06:45]>> You know, I really love the quote for today because it just really shows about foundations. So, I believe my parents really laid the foundation in believing and just pushing me to be better. But then now as I continued growing up, it's now been about the ecosystem, the support of teachers, networks, the school. So I'm currently at Strathmore University and they've been really supportive since when we started in our first year till now when we about to graduate. They've been really supportive in pushing us to be better, giving us the resources, the mentorship and the guidance. So I believe it just goes into play the foundation that you lay and the network that is supporting you. Um in my in my career or rather in whatever I'm building, I believe that the team that I also have has been really supportive. So a team goes a team can either make you rise or make you fall. So it really plays a big role in how you your trajectory will be in the course of time. Yeah.

[00:07:43]>> Now let's get to what we're looking at.

[00:07:46]Looks like a hand.

[00:07:46]>> Yeah.

[00:07:47]>> Moves like a hand.

[00:07:48]>> Yeah. [laughter] >> And just walk us through what we're looking at right now and what it what signals slash signs [clears throat] >> them they are making. I'm not sure the gender pronouns. I want to be politically correct. No, I'm [laughter] kidding. It's making >> Yeah. Yeah. Um, so maybe I can just give you a background of how we started, why it looks like this, and where we are at the moment.

[00:08:10]>> So, as I now continued mentoring students on STEM related courses. So, we used to move from school to school with young scientist Kenya. Are you guys familiar with Young Scientist Kenya? So, they do a lot of mentorship for STEM related courses. And then on a number of those sessions, we happened to identify students who couldn't hear and some could even not speak. So they were deaf and dumb. So the sessions that would take 1 hour would now take over 7 hours of frustration. So us just removing our phones. Now the small phone I now had it was very small. Pull out images of what we were trying to explain and the corresponding words. So we're trying to teach using that. So you can imagine how frustrating it would be trying to explain uh balancing of moles using images and >> Yeah. Yeah. Yeah. That's a challenge.

[00:08:56]>> Yeah. So, so now seeing this challenge we decided okay so sign language is done using um you know fingers and we have a robotic hand so why can't we train it using uh you know machine learning and AI was upcoming at that point so now whatever a human can learn in a span of 10 hours this can learn in less than >> 20 minutes >> wow >> yeah so it has with feedback with iterative improvements it can take a shorter period of time and you know with STEM related courses it's so fastpaced it it improves it more content is added over time you just leave the next morning there's a new discovery that's out there so now we wanted more of these students to be included in STEM related courses and fields >> right >> curious to know if you've met a deaf pilot >> n >> deaf doctor >> nope >> yeah so there simply no stem related signs for these complex related concepts So that's what we are building at the moment.

[00:09:59]>> Um we are at over 90 billion parameters of data that we hope to open source >> to just lay a foundation for schools founders institutions you know that want to have such kind of a technology so they don't face the same challenge we faced when it comes to just getting the data when it comes to starting from scratch cuz it was very hard honestly I can tell you.

[00:10:22]>> Yeah. Let me just go back to what what 90 billion parameters of data mean.

[00:10:27]>> What's that? [laughter] >> Sounds it's a lot. Whatever it is, it's a lot. 90 billion sounds like >> it's a lot.

[00:10:35]>> Yeah. So for the specific STEM related concepts, so for example, in science, we have so much. So you you remember when you were revising for your KCS, your KCP, you'd have like stack of books of what you'd read. So those are just the data parameters that we're feeding into it just for for the specific science.

[00:10:51]>> Okay. Yeah. Yeah. And so hm does let's okay walk us through the this humanoid Yeah. Sorry.

[00:10:58]>> Yeah. How it works.

[00:11:00]>> How it works actually. Yes. So that we can understand and then now we'll figure out what it knows [clears throat] working from known to to the >> unknown. Yeah. Yeah.

[00:11:09]>> So how it works is that as you're speaking whatever you're speaking is being directly translated into sign language. Now you don't need to be in the same room with it for it to sign. So at that time we were mentoring students in Garisa in Machako so in different parts of the country and we were just young so we couldn't be constantly moving from one point to the other. So we needed to be probably in class in Nairobi or just a room and we are um mentoring the students who couldn't hear. So this robot the robotic hand or the full humanoid that we have could be in that school and then now we have that channel that is channeling the data uh to the school's end with probably a 2 seconds latency. Okay.

[00:11:50]>> Yeah. So you speak and then it directly translates into science.

[00:11:53]>> So So >> yeah.

[00:11:56]>> Is it what you're doing now?

[00:11:58]>> Mhm.

[00:11:59]>> Is it doing what we're saying?

[00:12:01]>> No. No. No. No.

[00:12:02]>> Oh no. [laughter] >> So is that what it's meant to do?

[00:12:05]>> Yeah.

[00:12:06]>> So it's meant to pick what you say >> and translate that into sign language >> into Kenyan sign language >> for now. Right.

[00:12:16]>> So how does it do that?

[00:12:19]What's what's what's that whole thing of that it's that how do you get to to make the hand move >> according to the voice >> the voice yeah >> and now add into that complexity of remotely >> you speaking in Nairobi and the hand is somewhere at a crowd in Gisa >> and it's doing it >> so the biggest challenge we faced when we were starting was that people had different pronunciations different ways of saying things So in in just Kenyan sign language you'd find that Akuyu and AU have different ways of pronouncing things. So now what we do is that we collect our data from scratch. We don't use already existing uh data sets that are really tailored to English or America etc. So it's specifically tailored to our context.

[00:13:08]>> Okay.

[00:13:08]>> So it's able to clearly understand whatever someone is saying in that and we've been able to achieve a 92% accuracy rate.

[00:13:16]So this is just like version three of what we were building. We're at around version 9 at the moment. So version 9 has a screen embedded on it on the chest. So the students sign to ask questions, provide feedback, and then now the the teacher gets it in audio output. So the teacher doesn't need to know sign language.

[00:13:37]>> So it's speech to sign, sign to speech, >> which is actually now two-way.

[00:13:41]>> Yeah. Yeah. You said you've built one that has a screen on it. How many of these have you been able to build so far?

[00:13:48]>> So 78 robotic hands and then in total 120 inclusive of now the full outroas skeleton.

[00:13:58]>> That is the you know we we we need the layman terms >> for this the humanoid.

[00:14:05]>> Uhhuh.

[00:14:06]>> And these are spread out are they currently in use the 78?

[00:14:09]>> Yeah.

[00:14:10]>> Spread out in schools. Mhm.

[00:14:12]>> Um schools where what institutions it's um you know are they public schools, private schools, where are they?

[00:14:20]>> Yeah.

[00:14:21]>> Uh so when we were starting off we realized that the cost of producing these exoskeletons was a bit high and then most schools could not afford it.

[00:14:31]So we decided we're going to now use recycled plastics to make the external casing of the of the robotic exoskeletons. So our major focus was on public schools. So what you're seeing here is a bit of recycled tires and pet bottles.

[00:14:49]>> So the cost reduced drastically by over 60%.

[00:14:55]After we did that, allowing us now to target institution now public institutions very affordably and we are now able to bring now access to these schools. So initially it was public schools but then we also realized that there's a huge gap in other institutions. So now we are spanning out to health sectors uh airlines just to kind of bring that communication uh across everywhere. So if you're going to be having death pilots, why can't we now have these robots in airports?

[00:15:25]>> The airplanes. Allow me to ask. Yeah.

[00:15:28]When you say a reduction of cost by 60%, I I want us to >> speak figures >> with your permission.

[00:15:35]>> So the original cost of doing such a hand without the recycling element, how much was it >> roughly?

[00:15:42]>> Mhm. Around 2,000 >> $2,000.

[00:15:46]>> $2,000.

[00:15:48]And um now as it stands with the recycling and you know um so to grow capacity how much is it now?

[00:15:55]>> 180 180 >> 180 that's commendable.

[00:15:59]>> That's commendable because I'm also seeing the opportunity there in um the green element in in terms of rolling out such technology. It's conscious of that and um reducing a cost from 2,000 to 180. That is incredible.

[00:16:17]>> [laughter] >> This is just on the plastic.

[00:16:19]>> Yeah. No, so inclusive of everything that's also inside the components. Yeah.

[00:16:24]>> Okay. Walk me through >> um where you're making this the the hardware and the software, how big the team is. I have so many questions, but let's start from there. [laughter] >> Yeah. Um so from where we start now um we employ street involved kids to collect plastics tires and then they take them to a recycling plant where now it's recycled and then we take the spool and then we use it now for manufacturing the external casing. So that way we also able to uplift people in the community and we're also able to um uphold the environment. So you realize that um for most robots people have two fears. one, it's going to come under your bed at night and probably finish you.

[00:17:07]>> And then two, it's going to degrade the environment. So, we don't want that. We want as we're bringing advanced technology, we are also conserving the environment. So, with that, now we are able to have the external casing being made out of that. So, we have a team of around 27 here in Nairobi >> that work on the hardware and the software uh bits of things. a very uh I'd say a team that is very motivated because you'd find three quarter of the week we are sleeping in the lab >> you know day and night we're just there not going home just working on it because you realize that since there's no one else who's done this >> so we don't have any pace setters we don't have any anything to work with you're the ones who are now sitting >> setting the same template >> yeah so it means we have a very heavy mantle that's being given to us so now we have to constantly working around the clock.

[00:18:02]>> So that's the team.

[00:18:03]>> This is version three. We're up to version 9.

[00:18:06]>> Walk me through what what it's signing right now.

[00:18:08]>> Mhm.

[00:18:09]>> What signs um the humanoid is making right now.

[00:18:12]>> Right.

[00:18:12]>> So at the moment it's not making any sign.

[00:18:14]>> Okay. It's just moving.

[00:18:15]>> It's just flexing to show. Okay. I wish you could see actually the >> the current version 9.

[00:18:22]>> So yeah, >> I would love to actually and hopefully we can very soon. Yeah.

[00:18:28]>> Um cuz I want to know the learning process behind it like you said 90 billion data parameters if I got that right.

[00:18:35]>> Um and essentially starting from scratch meaning you're feeding it all of this.

[00:18:42]It's learning but I know there's some very unique challenges because one like you said before sign language is very even sign language is very specific to cultures to countries to everything. um how walk me through the challenges of training the data I mean training the AI essentially because I I can see how that would be one of the biggest challenges.

[00:19:04]>> Mhm.

[00:19:05]>> Yeah. Um so I remember I was in Austria probably 2 weeks ago. So I met uh someone who was actually deaf. So they she's from Turkey and she was telling me that doing this >> has five different meanings depending on the facial expression that you give.

[00:19:23]>> Yeah. So I was curious because she now asked me how how will this robotic hand be able to now mimic the facial expressions. That's the question we got like 10 or 15 times or even 20 um 2 years ago. So that's when we now started building the full human robot that has a head as well. So it has a head. It's able to pick up on the nonverbal cues, the facial expression so that you don't say you're sad yet you're smiling. So obviously it's going to do the wrong sign. So that's where now we now built it with a head. We built it with the every facial feature that you possibly have. Your face has billions of of markup points that it could pick up on.

[00:20:06]And uh with that now we realize that it's able to have a higher accuracy rate as compared to you just having a robotic hand. [snorts] So at the moment we're just working around different variations of maybe if we have a deep deeper palm of the robotic hand will it be more accurate if it's flatter will it be more accurate? You know you can check your hand right now.

[00:20:27]>> Yeah. I'm wondering so is the is the human palm flat or >> or deep?

[00:20:33]>> Or deep.

[00:20:34]>> I don't know. It looks fairly flat to me. Yeah, >> just like feet maybe for people who are [laughter] flat >> flat farmers.

[00:20:43]>> I think it's actually flat but then let's say if you do this when you're trying to Yeah. It just goes inside.

[00:20:49]Yeah. So I think you know human robots are coming. They're going to be in our kitchens. They're going to be in our schools. They're just going to be everywhere. At the moment in western countries they're producing over 50 robots per day. We know with the advanced technology that they have. I mean I know we're going to reach that point and if you're going to reach that point I believe the robots should be made with dignity so that everyone can interact with them >> both deaf both hearing both those who can see those who can see we've not yet reached that point for the blind however if we're able to reach that point it would be great cuz it wouldn't be a luxury it should be a right >> tell me when a deaf person is learning sign language how are they taught because they can't hear the words.

[00:21:35]>> Yeah.

[00:21:35]>> So, how are they normally taught?

[00:21:38]>> Well, we we really don't want to substitute teachers.

[00:21:42]>> No, I'm not saying you substitute. I just want to understand the process >> of of learning sign language >> for somebody who actually does not hear, >> right? Because they'll get to understand that alphabet.

[00:21:54]>> I'm I'm heading towards you are teaching this machine >> to hear >> and then also communicate. Mhm.

[00:22:02]>> And mostly to the and and I don't want to start with you know people who learn who can hear and then they learn sign language. I want to start with somebody who does not actually here.

[00:22:12]>> How do they learn sign language?

[00:22:14]>> How do they know this means happy this means I love you this means let's go this means Kenya.

[00:22:20]>> So I think for them it's more of visual more of the visual aspect. So maybe there's a screen or there's a paper that tells you this and this is done like this probably in textbooks. So for us, we don't do the basic human communication. The teachers, the sign language teachers do a really good job in that and you need that humanity cuz even if I was in baby class and I was being taught by a robot, I wouldn't feel that motherly or you know the human sense of things. So for us it's more of where are there where is there a gap where there are no stem related concepts [music] or are there signs for whatever we are trying to explain. So now after they are now bred well they know how to do the science they understand. we now come in with a STEM concept and now teach them using that. It's like advancing a master's degree for us. Yeah, I like that. And I don't know, Eric, if this answers your question. I So >> from what I've seen, and this is not even sign language, but I've seen babies being taught how to sign for when they're full, for example, when they're, you know, tired of eating. And it's it's visual just like she's explaining, right? Yeah.

[00:23:30]>> And I think it's just if you gesture if you see and you tell them to do like you do this when they're full they'll start doing this. [laughter] >> So I think I don't know this is just thinking out loud and that's essentially how it works from in the beginning which is just gesturing things that you recognize are happening right now and you gesture for them and someone gestures back and then you move from there thinking out loud.

[00:23:54]>> Okay. So in in my in my very layman thinking >> is that I start with the hand because that's where you started before you guys now came into the proper humanoid.

[00:24:06]Right.

[00:24:06]>> You first needed this thing to know sign language.

[00:24:11]>> Mhm.

[00:24:11]>> And then you also needed it to hear voices.

[00:24:14]>> Yeah.

[00:24:14]>> Right. And let's say you first taught it in English.

[00:24:18]>> Understand English words.

[00:24:21]>> This means come. This means go.

[00:24:23]>> Mhm.

[00:24:23]>> But also on these other side. M >> no this sign means come.

[00:24:28]>> This sign means go.

[00:24:30]>> Right. Is that what you did?

[00:24:31]>> Yeah.

[00:24:31]>> Oh yeah. Yeah. So there's a bit of text in between when you're doing now speech text sign. [music] So the text always has to be there.

[00:24:40]>> And probably I forgot to mention so on the chest of the robot if maybe someone is saying um desk. So the desk the image of the desk will be there. The text of the desk will be there and then now the corresponding sign will now be sent.

[00:24:54]Yeah.

[00:24:54]>> Okay.

[00:24:55]>> Yeah. So it's easier for everyone.

[00:24:56]>> So that's a lot of process >> teaching your machine.

[00:25:00]>> Yeah.

[00:25:00]>> To learn.

[00:25:01]>> Mhm.

[00:25:01]>> How long do that take?

[00:25:05]>> English alone. [laughter] >> Then we we start Kenyanizing this thing.

[00:25:09]>> Previously it would take us months but then with the advancements in tech at the moment we have claude, we have you know myriads of AIS we can use. It takes us um less than it takes us weeks. Let me not say less than it takes weeks. So whatever was taking us months is not taking us weeks. And hopefully we can reach to a point where it takes us days, >> minutes, you know, advancements are crazy >> to be much faster.

[00:25:34]>> That was you know with the basic English.

[00:25:37]>> Now let's canize the English. Let's not even start using andu like you said.

[00:25:43]>> No, >> Kenyans don't speak English from end to end.

[00:25:47]>> No, we don't. [laughter] >> You've said >> Yeah, exactly. We will mix in some words that are not in the English dictionary.

[00:25:56]So, how did you teach this thing to know?

[00:25:58]>> Yeah.

[00:26:05]>> See what I'm saying [laughter] is understood.

[00:26:07]>> Yeah. I feel that's the beauty of working with the community cuz we are now building from scratch. So whatever we are building up or rather bringing up from the data and the people that are helping us train it's it's what's it's what it's picking up. So it's not a matter of already existing data that's in English and that we're now mixing up.

[00:26:27]We're now working with the people on the ground. You realize that shen is widely spoken as well. So we started with that.

[00:26:34]So if it's able to now diversify that >> someone who's making mixing English and that's a small fit for it. Yeah. So it becomes intuitive point >> which is interesting. Talk to us about capacity. Um you know it's it's you know the capacity you've been able to create in terms of rolling out this 70 plus >> humanoids >> and the opportunities you've created for others like yourself in STEM >> cuz a lot of times we see people graduate and they don't have a place to take their skills. here in is a company that they can come um work on those skills and even bring in more >> uh in the seven years. How many people have we brought into the fold?

[00:27:18]>> Yeah. So, interesting enough um 90% of the people who work with us are 25 and below.

[00:27:25]>> Okay.

[00:27:25]>> Yeah. So we really capitalize a lot on on ensuring that young people get the mentorship, the guidance and just the motivation to actually do it because we have the people have the belief out there that you have to be over 25 or over 30 to to do some of these things.

[00:27:42]But we want to inspire people to do it to do them just like we did. So starting off um as I said we usually employ straight involved kids to collect the recycled plastics and we give them money or books >> to just you know empower themselves as they continue and then now we employ women who now help in sorting out the plastics. We also give them a job. We give them food. So we have them in that segment and then we have the team that works in the hardware and the software side of things here. So remember we initially started by mentoring young students in STEM. So we still continue to mentor these students as we continue till date. Last weekend we were mentoring some students in Ni. So we are always constantly mentoring them >> um to know that they can build robots right here in Kenya right here in Africa and you know we can make change as we continue.

[00:28:35]>> Yeah. I want to ask about the gloves you're wearing, but before I get to that, you said 27 people in your team on hardware and software. Does that include the uh the waste pickers and the sortters or that's before we get to them?

[00:28:50]>> Yes, that's before.

[00:28:51]>> Okay. Okay. That's quite a large number of people.

[00:28:54]>> Um if you had to give a number for the whole ecosystem, >> probably 40.

[00:28:59]>> That's >> all the way from the peak >> to you.

[00:29:02]>> Yeah. Yeah. So we it's usually on a voluntary basis >> so they're not constant. That's why I don't put them in the 27.

[00:29:11]>> I can understand. Yeah.

[00:29:12]>> Okay. The gloves.

[00:29:14]>> Explain to us what show them to the camera.

[00:29:16]>> Yeah. Yeah. Explain the gloves.

[00:29:18]>> Yeah. So ideally >> the people who wear this are the trainers and the and the sign language teachers. So you'd realize that as you we have a couple of sensors that is picking up on the slightest of movements of uh your fingers.

[00:29:34]>> So if I move like this it's supposed to record it in the system such that this [music] robot robotic hand knows that doing this or that doing this means I and then no so it's more of accuracy training. Yeah. So wearing this is more of accuracy training than not wearing the gloves because when you're not wearing the gloves, it's not able to pick up on the slightest of movements cuz it's just a screen picking up on it.

[00:29:58]But then with this, it's strictly connected to your to your hand. So it's able to pick up on that.

[00:30:04]>> So it's a whole suit actually. It's it's not just the gloves. So the suit you wear. So any movement your leg movements there's also the head rig that you put your phone on the head rig and then now it's able to pick up on also your facial >> can also see what your >> but as you as you do this then how is your face also >> yeah as you sign I what does your face look like so it picks that as a composite now that's one that's a message >> are you able to erase that data just in case it's erroneous and you make an action and you know sometimes the body glitches [laughter] and you do something you're like, "Wow, we need to delete that." You're like, instead of saying, >> you know, it can eat.

[00:30:48]>> Yeah.

[00:30:48]>> Can you erase the data?

[00:30:49]>> Yeah. So, we actually learned that the hard way.

[00:30:53]>> So, we had gone for one of our pilots in Random. So, it was happening in it was actually a tech conference. So, we were trying to see engineering concepts were being talked about and we were trying to see how that would work. It was probably like 2 years ago. So the the conference was happening close to the road roadside. So you can imagine there's a lot of noise interference etc. >> So everyone is quiet in the audience and we're the ones at the front. Now you know think of it like how Steve Jobs would probably release. [laughter] >> So that's how it was. So now we are trying to and remember I said the text is also seen. The text is seen. So as we are now demonstrating how it works, it happened to sign a vular >> what because of the interference that was coming outside.

[00:31:38]>> So now it just goes to show conversations around are we going to have you know robot police? Are we going to have laws against [laughter] >> so from that point um we now realize the importance of constantly cleaning, constantly checking out if everything is working out correctly. So yeah, we do that very frequently so that we don't have such an instance.

[00:32:01]>> We don't want to go to jail for a robot.

[00:32:04]>> I mean the robot did.

[00:32:06]>> Yeah. But a robot commits a crime.

[00:32:08]>> Yeah.

[00:32:09]>> Who is held culpable. [laughter] >> The robot.

[00:32:13]>> The robot.

[00:32:14]>> Okay.

[00:32:17]>> So give it citizenship [laughter] >> that is better >> and all those kind of things. I want to walk back in tune >> the foundations and the formations of your company cuz so you told us at 15 your parents encourage you like you know you can cooperatize this thing this idea of yours with the Legos can actually become a company >> is that the same company that you're operating with today or did you was that one >> you know >> that one was was Tis X now this is a formal company >> it's it's different so the other one was and a climate mini phone. Hopefully, it comes into fashion as well with everything that is going on in climate, all the rains that are coming.

[00:33:02]>> So, that that's what I had started with.

[00:33:04]But then at the moment, this is something new. This is what now we started in our first year of the university.

[00:33:10]>> What role has the university played in this in in getting >> the company incorporated in getting you, your staff and so on and so forth?

[00:33:21][sighs] >> A lot. A lot. lot a lot a lot so when I start with just mentorship the the first moment you enter Strathmore the first thing they tell you is that you're not just here to study you're also here to solve so to solve problems get solutions inspire other people be inspired by whatever is going on there and they have a very vibrant uh engineering community that just keeps you motivated cuz I took computer science but then every time I'd go to engineering labs I'd be like did I really should I have taken electrical engineering >> [laughter] >> So I'd constantly be there with them just uh you know just looking around at what they're doing and just so you get that motivation from the from the resources that are there and then again the resources. So they give you the actual resources. So we started by 3D printing um with the 3D printers that are there. It was actually my first time seeing a 3D printer.

[00:34:13]>> Um so they give you that exposure. And then three there are so many travel opportunities to just go outside see what's going on. So you get your mind expanded into what's the global landscape looking like what are people doing out there because >> you'd think robots are not being done but when you go out there you see people are doing version 50 of something that was started in the 1990s and you get inspired you know to change and just show people in your school or your friends that this is something that's possible. So it's more of now the ecosystem that they provide for you to grow and then even after school they still keep following up. They give you opportunities. So they usually have an ideas festival where students participate. So when we participated be born and they give you some good money to just continue now pushing the idea that you had which helped us open our first office faith.

[00:35:07]>> Yeah. Yeah.

[00:35:07]>> Was it off campus?

[00:35:09]>> Yeah. But before that >> we were [laughter] watching >> before that we were we were being incubated in school.

[00:35:26]>> Yeah.

[00:35:28]>> It's >> so now you're a a proper entity that is delin >> sort of corporate wise >> from Strathman University >> right?

[00:35:36]>> Yeah. and and as you're doing all this research then you're also attracting other ideas >> are you attracting funding?

[00:35:45]>> Yeah. So after we now moved on and now started doing things um we won the [snorts] global learning council award for STEM enable Africa.

[00:35:57]>> So that was in Switzerland and you know just being named a STEM enable in Africa. Everyone is wondering who's this who's enabling STEM. So from that point on we started getting so many people who are interested in seeing what we can do and you know how it is in STEM one person says this 50 other people really know about it. So that really opened up doors of interest and anytime we'd maybe make an application or we would go for a competition someone would identify us as oh yeah that's the STEM enabler.

[00:36:24]>> Yeah. So that really opened up.

[00:36:26]>> That's a very good tag to have with you.

[00:36:28]STEM enabler.

[00:36:29]>> STEM enler. I wanted to go slightly into STEM and ethics. STEM and women.

[00:36:35]>> Mhm.

[00:36:36]>> Let me start with STEM and ethics. Um I think sometimes we face the problem of yeah STEM without ethics and technology without ethics and things like that.

[00:36:46]It's like okay maybe we should make some humanities compulsory no matter what it is you're doing and things like that.

[00:36:53]Walk me through your personal ethics and those of zero bionic and especially when it comes to artificial intelligence.

[00:37:00]Sometimes we feel like we're on [snorts] shaky ground. We don't know how far this could go. Some people say, "You're being crazy. The robots aren't taking over.

[00:37:07]This is like Y 2K all over again. Why are you so scared?" And others are like, "If we don't nip this in the bud, >> the robots will take over." [laughter] >> Yeah, >> that's the truth.

[00:37:17]>> Yeah. I mean, I I I usually love to view technology as an enabler rather than someone who's going to come take over your job. So the moment you constantly keep thinking about AI and technology or someone or something that is going to take over, you're never going to move.

[00:37:33]People are going to uh go over the race and take uh controller just leave you at the back and you're just going to be left there constantly talking about it.

[00:37:42]So for us, it's usually more of how can we maximize the use of AI and tech and not more of how can we >> sort of try reduce it cuz we are afraid of it substituting us. So and and as I as I mentioned whatever was taking us months is now taking us weeks. So it's really increased our output drastically.

[00:38:03]>> So for us when we were starting off I remember there was a young lady I met who after we piloted our technology just came and cried to me and told me that for the first time I'm able to understand whatever is being said and my dream of becoming an engineer can actually come to fashion. obviously signed it and the editor told me cuz I also don't know sign language actually that's why we build here it's difficult um so now we we had that conversation and every time we are just building that conversation keeps replaying back in my head so when things are tough whenever you know maybe falls short we always have that push that is telling us okay we let's still continue building for her because we don't want to see someone else crying or someone else feeling left out because no one is building technology like this for them.

[00:38:53]>> So, it's it's more of how can we put a smile on someone's >> faces. I I quite like that. Let me ask um a slightly controversial question.

[00:39:01]From a human perspective, >> there's been a few pushes here and there to have, for example, Kenya sign language taught in schools. And you've mentioned this is advanced. We're not, you know, saying sign teaching it with a robot. Yeah. But are we doing ourselves a disservice by automating this as opposed to making it common place that learners understand um sign language [music] even complex sign language that you know does things like this? Yeah. Is there some part of a that's kind We should other people are robotics technology.

[00:40:12]We don't get into that. So it's more of howology to a point where yes if teachers are able to understand able to work hand in hand we'll do that.

[00:40:32]If we need to take a step back, we will.

[00:40:34]But we'll have achieved our our purpose and our mission of ensuring that these people are included in STEM related courses. So at the moment they are not.

[00:40:42]>> So let's include them and then when that work is done, >> we can now see how to go about from there. So for us, by the way, what we do so that the company outlives us, >> we try as much as possible for the people we mentor or train, we incorporate them into the company so that they're able to bring other deaf people into the company even when we are no longer there and it can continue to be sustainable. So it's run by them for them. So we are not going to be replacing or substituting anyone.

[00:41:11]>> Yeah. There was a a demonstration um in the 60s in America >> by teachers >> saying keep calculators out of schools.

[00:41:22][laughter] >> They should not use calculators >> should learn to count. [laughter] >> I even remember when we were going to school and we had to buy calculators and log books. It was how they're not using their brains. It's like but it's it's it's it's an extra tool to learn.

[00:41:41]>> Um >> it's a support tool and an enabler >> you know world IP day was the other day um probably about a month ago >> and um this is innovation the patents for this where do they sit do you own them?

[00:41:55]>> Um you started this uh from the university where you were incubated >> um does the university have a stick to their patent what does that look like?

[00:42:06]H [laughter] >> or it's one of those things that uh has been figured out.

[00:42:11]>> Yeah. Yeah. Okay. It's it's it's um it's again to young people that are innovative. We've seen time and again where young people come up with innovations and then now the question of patterns somewhat comes after the fact when the innovation has gone on to earn so much.

[00:42:29]>> Um and it becomes somewhat a tricky conversation to have or a hard conversation to have. Um but what I like from this conversation is one first of all I'm very very inspired by Nora >> at 22 you've been able to defy the odds and say age is nothing but a number uh fully immerse yourself into a space that is so intimidating STEM in itself um when you ask a lot of people it's like yeah rather not go there always maybe even pushing kids towards um what we term as simpler which is the arts and languages but you coming in and showing that it's possible >> for you to do this um and to build capacity, find innovative solutions. And here at NMG, we continue celebrating young people such like yourself >> and every other young person out there that is doing incredible and remarkable work.

[00:43:19]>> Very true.

[00:43:21]>> There was a question that um Mariam had started >> on women in STEM. Laura, do you ever feel like I'm a woman in STEM?

[00:43:28][laughter] Is it just normal like I mean >> or is it is it everybody else who notices you know women in STEM and all [laughter] >> do you?

[00:43:38]>> Honestly I hate the fact that that has to be a conversation but unfortunately it does [laughter] >> especially when you're a minority.

[00:43:46]>> Yeah. Yeah. So many of the times where you enter a room and you're actually the only one but then you just have to continue pushing it because you want to make more opportunities for other people to be in that space. cuz if there's no one who's going to bring that uh bring that path or rather pave that path for other people then it will still continue being perpetuated.

[00:44:05]>> So anytime I find myself in such a situation actually don't I stopped feeling bad about it. I'm just like yeah I'm making a way for other people so I'll just continue you know moving along with it.

[00:44:17]>> You're a trailblazer.

[00:44:18]>> There's a parent who's sent me a message here saying I'd like my daughter and I to actually see a 3D printer in action.

[00:44:24]like no never seen >> would Nora be able to organize it for me. Do I have to go to Strathmore? I mean >> people who just hear 3D printer >> what does that mean? What's that? What's a 3D printer? Let's start from [laughter] there.

[00:44:37]>> Uh before I even answer that question, when you go to our website at zerobionicafrica.com >> Yeah.

[00:44:44]>> Africa.com >> you can find so many images, videos of how it works uh you know and the whole process actually. So we've tried as much as possible to make it an educ educational website. So it's it's going to be very simple for you to see and understand. But then we have a section for visit the lab. So you're able to just book a slot. So we had so many people who actually shared the same sentiment. They just want to come see how it works and uh yeah get inspired as well. So we have actually many students who schedule visits and then we take them through how it works the whole build process. In fact, tomorrow we actually have a group of 40 students who are going to be coming in and then we just inspire change basically.

[00:45:25]>> So how do I go to the website former student? [laughter] >> You shall come.

[00:45:31]>> So we go to the website.comic >> africa.com. Africa >> and that's why we schedule an appointment.

[00:45:38]>> Yes.

[00:45:39]>> There's no other way. [laughter] >> We can talk.

[00:45:44]>> No, no, no, no. But I that's the that's the formal way. If you go there and you actually schedule the appointment, you'll get to be able to visit the love >> and see.

[00:45:52]>> Yeah. Okay.

[00:45:53]>> Perfect. Very, very good. How did you get onto this list >> of top 40 under 40? [laughter] >> Did you apply?

[00:46:01]>> Did you know someone?

[00:46:03]>> Funny story actually.

[00:46:04]>> Um, so I was at the Africa Tech Summit.

[00:46:07]We were releasing our latest humanoid.

[00:46:11]So I had my, if you check photos of the event, I had the whole suit now with the head rig and everything. just showing how how it works. So during that point a number of people got to to identify what we were doing. They were bamboozled by what we were doing and when the girls in ICT month came I had so many people who were telling Daily Nation to feature my story.

[00:46:33]>> So they came they had their voices and they came to our office. So they did a documentary they featured us in the newspaper and everything. So after we were done they called me >> and they said oh your your story is very inspiring. we're going to nominate you.

[00:46:47]>> So at that time I had just turned 22 from 21. So I was like are you crazy under 40 and just [laughter] now they don't. So I was like are they calling 200 people to interview them?

[00:47:00]>> Okay let me just go. So I came I didn't know. So after we done with the interview now he started telling me oh yeah so you'll come for the award ceremony. I was like award ceremony. So he told me oh you got into 40 under 40?

[00:47:11]I couldn't believe it. It was so shocking. It was so shocking and it's it's incredible to see how after the awards, just hours of it being announced, I've gotten so many um people reaching out wanting to do interviews.

[00:47:25]We have DW, we have Reuters, we just have so many companies that want to, you know, just spotlight the work as well.

[00:47:31]So, it just takes one major win or other people believing in your vision and purpose for you to open a myriad of doors for you. So, it's just been incredible within hours. So, I can wait to see, you know, how it will be in days and weeks. Yeah.

[00:47:45]>> Those parents who saw the 15year-old, they denied the 15year-old a phone >> found [laughter] 15year-old making a phone using >> Lego.

[00:47:54]>> And when they see this, when they see you in the paper, >> Mhm.

[00:47:57]>> as top 40 under 40.

[00:47:59]>> Yeah.

[00:47:59]>> What's their what's their reaction, right?

[00:48:01]>> So, I think for them, they've realized that for most of the wins that I get, I don't usually think of myself first. I usually think of who can I inspire you know which young person who's 2122 can can hear my story or see it on the newspaper and say oh actually I can get into at22 so for them it was more of we need you to actually celebrate this win and not just continuously you know just putting it under the the covers yeah >> sit and just celebrate and I'm really honestly I'm really happy and just grateful for the team that believed in what we're building cuz it just takes such a backing for other people to also believe in it. You know, humanoid robotics is in Kenya and Africa. So, it requires organizations, top players, major leagues to say that this is something that's actually working and should be believed in.

[00:48:57]>> Yeah, quite like that.

[00:48:59]>> Um, we have to end the interview there, but one before I say congratulations, I hope one of the calls that comes through is from the Ministry of Education. Yes, there's Apple. There's an opportunity if you are listening. But a massive massive congratulations to you, Nora. You're doing amazing work and like 7 years in already. I can't wait for the next 70. I at that point I think flying cars will be a thing and it will be you behind them. But a massive congratulations for making it to the top 40 under 40 and for this work that you're doing and it's so impactful. It's so important and the fact that it's grounded also in serving others and being ethical and doing it right is a testament to the kind of person you are. Congrats.

[00:49:41]>> Thank you. It's amazing work you're doing. That is Nora Kimati. She is the founder of Zero Bionics Africa and we've been discussing her humanoid robot. Does the robot have a name by the way?

[00:49:52]>> Yes, actually.

[00:49:53]>> So, we've been getting so many requests.

[00:49:55]Yes. But then we settled on AF1.

[00:49:58]>> AF1.

[00:49:59]>> Yeah. Africa's first >> AF1 by Nora Kimali. Top 40 and 40 mark 2025.

[00:50:12]>> See, I'm lagging.

[00:50:14]>> Yo, I told you guys the body glitches.

[00:50:16]>> Yeah, [laughter] only 2 seconds, >> guys. It glitches.

[00:50:24]But of course, thank you so much for spending the morning with us. will be back tomorrow 6:00 [music] to 10:00 a.m.

[00:50:28]for another amazing edition of Fixing the Nation. From myself, Mariam Bishar, Eric Lassiff, and Fellaris Swamboy. Have an amazing amazing Wednesday.