What happens when artificial intelligence becomes more human—but is still designed to help rather than replace us?
In this episode of The Personal Side of Business, I sit down with Dr. Mohammad Mahoor, professor, researcher, and founder behind Ryan, an AI-powered assistive robot designed to support caregivers and improve quality of life.
Mohammad shares his journey from electrical engineering and early work in computer vision and facial recognition to studying human behavior and ultimately developing assistive robotics.
We talk about what it takes to turn years of research into a real business, the challenges of fundraising and building a team, and why resilience and the willingness to pivot have been critical throughout his 12-year startup journey.
We also dive into Ryan—an assistive robot capable of natural conversation, recognizing facial expressions and emotions, providing companionship, exercise and entertainment, and supporting caregivers in assisted-living environments.
But the conversation goes deeper: How human should AI become? Where is the ethical line between creating technology that understands us and creating machines that imitate us too closely? Mohammad shares his perspective on the “uncanny valley,” human dignity, and why AI and robotics should empower people—not replace them.
It’s a conversation about AI, robotics, entrepreneurship, resilience, and keeping humanity at the center of technological innovation.
Learn more about Mohammad Mahoor and his work at dreamfacetech.com.
If you would like to learn more about Jet Bunditwong and this podcast, check out personalsideofbusiness.com
00:00 --> 00:03 Welcome to The Personal Side of Business, where you hear
00:03 --> 00:06 real stories from real entrepreneurs.
00:17 --> 00:20 Hi, and welcome to The Personal Side of Business, where every business has a story.
00:21 --> 00:23 I'm your host, Jeff Baniwal. Today, my guest is Mohabbin Makhor.
00:24 --> 00:28 He is professor, researcher, founder of the
00:28 --> 00:32 Ryan Robot and all things AI robotics here.
00:32 --> 00:35 We're going to learn today how he is changing the way we look at
00:36 --> 00:39 assistive robotics. Welcome to the podcast. Thank you.
00:40 --> 00:42 Okay, so let's start. How did we get here?
00:46 --> 00:50 From— I mean, that— how? Yeah, let's go back as far as what was
00:51 --> 00:54 your inspiration, I think, younger to get into all
00:55 --> 00:59 of this? I see. So when I was a child, I always fascinated by cars,
00:59 --> 01:02 by any sort of technology, you know,
01:03 --> 01:06 alarm clocks, locks, you know,
01:07 --> 01:10 car engines and so on and so forth. And I always,
01:11 --> 01:14 I was very hands-on to fix things, make things.
01:15 --> 01:18 And then when I got to college,
01:18 --> 01:22 I decided to study electronics. And electrical engineering.
01:23 --> 01:26 And then later I got into computer vision and image processing,
01:27 --> 01:30 and that's how it opened the door for something that we call
01:31 --> 01:34 it now AI, but it's mostly machine learning. Although computer vision
01:35 --> 01:38 is a type of AI or a subfield in machine
01:38 --> 01:42 learning and AI. So when I started the project, the robotics,
01:42 --> 01:46 I mean, I worked with children with autism and the idea was to
01:47 --> 01:51 use robots to help and teach children
01:51 --> 01:54 with autism social skills such as turn-taking,
01:54 --> 01:58 emotion recognition, facial expression recognition, eye gaze
01:58 --> 02:00 attention, and so on and so forth.
02:00 --> 02:05 And kids with autism on the spectrum, they really like
02:05 --> 02:09 objects and robots. And so
02:09 --> 02:12 then they can relate, you know, to technology
02:12 --> 02:16 and very quickly. And then we use Ryan
02:17 --> 02:21 to help them to practice social
02:21 --> 02:25 skills and learn social skills. Yeah, that's great. And, uh, yeah,
02:25 --> 02:28 that was a successful project. And I'm working even with some of
02:29 --> 02:33 the kids that they participated in our study. They oftentimes,
02:33 --> 02:36 they come to the lab and, you know, they play with
02:37 --> 02:41 the robot or or give us feedback, and I have seen an
02:41 --> 02:45 impact on their life. So that was a project that really was successful,
02:45 --> 02:48 and that's how we got into, you know,
02:49 --> 02:52 other areas about how we can support humans
02:52 --> 02:56 with, uh, other, you know,
02:56 --> 02:59 mental, uh, issues,
03:00 --> 03:03 I would say, such as people with depression, dementia,
03:04 --> 03:07 And so when around what time did this
03:07 --> 03:11 happen for you? So back in 1996,
03:12 --> 03:16 '97, '98, you know, when I was a grad student. Back then.
03:16 --> 03:19 So what was the technology like then compared to now?
03:20 --> 03:22 Oh, we did have back then, you know,
03:25 --> 03:28 the first computer that I started working with that, you know,
03:28 --> 03:32 had the DOS operating system and the IBM, you know,
03:32 --> 03:35 PS/2. I remember that one. And 486
03:36 --> 03:39 and the Pentium. And then, you know, they, yeah, so those are the kind
03:40 --> 03:43 of computers that I used to, you know, started learning how to
03:43 --> 03:47 program C and C++. And even I remember Fortran 4
03:47 --> 03:50 and BASIC, those were the, you know, the computer languages that
03:51 --> 03:54 I learned how to program. Yeah. And what was, when you were programming back in
03:54 --> 03:58 that time, what were, what was your goal at
03:58 --> 04:01 the time? What were you thinking was going to be the future of all that?
04:01 --> 04:05 So, At the beginning, I was very fascinated by
04:05 --> 04:08 image processing and computer vision, and mostly I
04:08 --> 04:13 worked on medical images, back then ultrasound images and
04:13 --> 04:17 X-ray images. But then I wanted to see
04:18 --> 04:22 what we can do with computers and with computer
04:22 --> 04:25 programming, actually. Like, for example,
04:26 --> 04:28 human face recognition,
04:29 --> 04:33 facial expression recognition, and that's When I started my PhD,
04:33 --> 04:38 I was really amazed by how computer
04:39 --> 04:43 can do face recognition, face identification. Because you were kind
04:43 --> 04:47 of on the early pioneers of that face recognition. Yes.
04:47 --> 04:50 Yeah. I started my PhD in 2003 and that
04:50 --> 04:54 was post-9/11 and biometrics and face recognition and
04:54 --> 04:58 face identification was very popular and very hot, a very hot
04:58 --> 05:01 topic in machine learning. And AI.
05:02 --> 05:06 And so I worked on face recognition for my PhD.
05:06 --> 05:10 Then I did my postdoc training in psychology department
05:10 --> 05:15 and I worked with psychologists and the research
05:15 --> 05:18 was on how we can use computer algorithm to look
05:19 --> 05:23 at the behavior of children with autism and analyze
05:23 --> 05:26 their behavior and mostly face-to-face communication.
05:27 --> 05:30 Facial expression recognition, eye gaze attention, and so on and so
05:30 --> 05:34 forth. And the idea was to use computer to be able
05:34 --> 05:38 to, I mean, to better analyze the behavior of children
05:38 --> 05:42 with autism. So that's how I got into
05:42 --> 05:46 the, you know, to psychology, human behavior,
05:46 --> 05:50 and use computer to analyze, you know, people's behavior.
05:50 --> 05:54 And did that inspire you to go in a certain way after
05:54 --> 05:58 learning? Absolutely. That I'm assuming it now started steering you
05:58 --> 06:01 to use this technology. It's really— I think that postdoc training was really
06:02 --> 06:07 a key moment in my career and helped me, you know, to find
06:07 --> 06:11 my way and helped me a lot that how we
06:11 --> 06:15 can use technology to not just be, I mean,
06:15 --> 06:18 that help people, but also to
06:18 --> 06:21 empower and enforce I mean,
06:22 --> 06:26 caregivers, therapists, you know, that's how that
06:26 --> 06:30 postdoc training shaped my current,
06:31 --> 06:35 I mean, research and
06:35 --> 06:39 work. Yeah. Now, at that moment when you started shifting
06:39 --> 06:42 your, I guess, perspective and then focus,
06:44 --> 06:47 from then, if you can go back and look at it,
06:47 --> 06:51 to now, do you think it's close to the vision that you
06:51 --> 06:55 had back then or has it— Back then I didn't know kind
06:55 --> 06:59 of what exactly— You're just throwing darts at the sea. Exactly. I really,
06:59 --> 07:03 I didn't know how this is going to affect
07:03 --> 07:06 my career and also my passion.
07:07 --> 07:11 But over time, especially after I joined DU and I realized
07:12 --> 07:15 that, oh, robotics is a big thing. And although when
07:16 --> 07:20 I was a child, I was fascinated by robots and I remember I read book
07:20 --> 07:23 about robotics, but I didn't know much about it.
07:24 --> 07:28 But here, 2008, when I joined as a faculty, then I
07:28 --> 07:32 had a faculty mentor who was a
07:32 --> 07:36 roboticist and he helped me to get into the field of robotics.
07:37 --> 07:40 So robotics, computer vision,
07:41 --> 07:45 then psychology, all of them happened
07:45 --> 07:48 to come together. And later when I founded the company, all the
07:49 --> 07:52 things that I liked when I was a child, electronics,
07:52 --> 07:53 working with motors,
07:56 --> 08:00 build things. So all of them actually came together
08:00 --> 08:05 and helped me to be able to manage
08:05 --> 08:09 my team and lead my team to build a
08:09 --> 08:12 robot, make a robot. And you just brought up
08:13 --> 08:16 a really good point that I was curious about. So you've done a lot of
08:16 --> 08:19 research. You were sort of focused in a certain area,
08:20 --> 08:24 and now here you have to run a team.
08:24 --> 08:27 Absolutely. What was that experience like for you? Was the beginning
08:28 --> 08:32 a little bit of a shock and awe type? No, it was not
08:32 --> 08:35 shock because I had worked as a team lead before,
08:35 --> 08:39 you know, as an IT team lead, as an IT admin
08:40 --> 08:43 for a few years. And so I had the experience.
08:43 --> 08:47 However, I didn't have the experience of founding
08:47 --> 08:51 a business, running a business as a CEO and a founder.
08:51 --> 08:54 So I took a few courses in our business school. Oh,
08:54 --> 08:59 nice. And that's using the resources. Yes, exactly. And that helped
08:59 --> 09:03 me, especially when it comes to the accounting, finance,
09:03 --> 09:07 management, you know, and yeah, and that part
09:07 --> 09:10 of, you know, the running
09:10 --> 09:14 a company. And of course I learned
09:14 --> 09:17 from my peers and I
09:17 --> 09:21 attended workshops and also there was an NSF,
09:24 --> 09:27 it's called I-Corps actually training.
09:28 --> 09:32 So that also helped me too with one of my students, former students and
09:32 --> 09:36 a mentor, learned how to do customer discovery, how,
09:36 --> 09:38 you know, what it means to start up a company,
09:40 --> 09:43 And go through the process of, you know,
09:44 --> 09:48 going from an innovation
09:48 --> 09:51 and an idea and take it out of the door.
09:52 --> 09:55 Of course, it's taken us 12 years, I mean,
09:55 --> 09:59 to get to this point. It wasn't easy, and especially robotics,
09:59 --> 10:02 you know, it's not easy. You have to make a lot of changes,
10:03 --> 10:06 try, test, revised,
10:06 --> 10:10 redesigned, right? And yeah, keep going. Is there
10:10 --> 10:13 a stress on you for a certain time period
10:13 --> 10:17 to get to this goal, right? Absolutely. I mean, that being
10:17 --> 10:21 a founder of a company and also having family
10:21 --> 10:24 and part-time faculty member is not easy.
10:24 --> 10:28 So I mean, that of many moments
10:29 --> 10:33 I've told myself that, oh, this is too much for me. This is too
10:34 --> 10:38 much, a lot of pressure on me. And no, it's not easy
10:38 --> 10:42 at all. How do you, what has been your sort of way to get past
10:43 --> 10:46 those like hurdle moments? At the beginning, I knew that, you know,
10:47 --> 10:50 I was told that 95 or 90% of
10:51 --> 10:54 the startups actually fail. So one thing that I
10:54 --> 10:58 learned is to, you know, to pivot first, you know, to change. Don't be
10:58 --> 11:01 rigid, don't be, listen.
11:02 --> 11:07 And also try to maybe
11:08 --> 11:11 change the direction and learn from
11:12 --> 11:16 our failures and talk to people and be open-minded.
11:16 --> 11:19 Be open-minded. Yeah, that helped me a lot.
11:19 --> 11:23 Have you had mentors once you started this?
11:23 --> 11:27 Yeah, I had a mentor. Yeah, I had a mentor and some
11:27 --> 11:30 of it also has been organic. You know, you'll learn,
11:30 --> 11:34 right, over time that how to do
11:34 --> 11:37 better, manage things, you know, manage your thing.
11:38 --> 11:42 And of course, you know, like many other companies, we have had ups
11:42 --> 11:45 and downs and it's very common, you know.
11:46 --> 11:49 There's been moments that I told myself that this is it, I'm not going to
11:49 --> 11:52 continue this, or I will give it one more year. Yeah.
11:52 --> 11:56 Or two more, one more year, you know. You started after 12 years. Yeah.
11:56 --> 12:00 What was a hurdle or
12:00 --> 12:03 an obstacle that happened building
12:04 --> 12:07 this that was like almost
12:08 --> 12:11 a pivotal moment that really could have stopped everything? Fundraising, I think,
12:11 --> 12:14 is a big thing. So you have to have cash.
12:15 --> 12:22 Yeah. But, you know, to be able to recruit a
12:22 --> 12:26 good team and get things done. So I think always fundraising
12:26 --> 12:29 has been an issue. And that's not an easy thing at
12:30 --> 12:34 all. I've been lucky that so far we have been funded by the federal
12:34 --> 12:38 government, you know, by NIH and NSF. And now we are going through
12:38 --> 12:42 fundraising. I'm pitching to VCs and investors.
12:42 --> 12:46 And also the state of Colorado recently gave us a small grant
12:47 --> 12:49 to, you know, to help. With the commercialization of the product,
12:51 --> 12:56 but fundraising has been always challenging and also mentoring
12:57 --> 13:00 and leading, you know, a team of engineers.
13:00 --> 13:04 And I do have a team that I have like
13:04 --> 13:07 a 20-year-old engineering
13:07 --> 13:11 students on my team or computer scientists on my team
13:12 --> 13:15 go all the way to people who are 75 years old, you know, and have
13:15 --> 13:19 had a few startup And they were successful actually in
13:19 --> 13:23 executing their startups. Yeah. I have to work with a range
13:23 --> 13:26 of people with the range of talents, mentalities,
13:26 --> 13:29 background, and experiences and skills.
13:29 --> 13:33 Right. And then I'm sure it's challenging you to have to
13:33 --> 13:36 be able to— Oh, absolutely. Every person is different.
13:36 --> 13:39 Every person is different, right? But I enjoy it.
13:39 --> 13:43 I've enjoyed it so far. And I'm a person that I like to be busy.
13:44 --> 13:47 And I, you know, I like to be busy always, all the time.
13:49 --> 13:52 And yeah. What's a skill asset do you think you
13:52 --> 13:55 have now that you didn't have at the beginning of this?
13:57 --> 14:01 Patience. Yeah. So to be patient and also to
14:04 --> 14:07 work hard. Of course, I always had worked hard, but patience has been one of
14:08 --> 14:11 the things. And also, I learned that to be
14:11 --> 14:15 very selective and very, very careful when it comes to hiring, you know,
14:15 --> 14:19 a team member because we are small. I have a small team, about 12 engineers
14:20 --> 14:24 and 4 or 5 actually people on the business, you know, side of the
14:24 --> 14:28 company. So it's important to, I mean,
14:29 --> 14:33 to make the right decisions. So when it comes to hiring. Yeah. And, you know,
14:33 --> 14:37 you were something I wanted to touch upon, you brought up was the fundraising
14:37 --> 14:40 part of it, which I think we take a step back,
14:41 --> 14:44 now becomes a— gets marketing involved,
14:44 --> 14:48 right? You have to advertise what it is you're doing and be able
14:48 --> 14:51 to start to pitch, talk, shake hands, and basically
14:51 --> 14:54 it's this other personality side of you that you have to stretch. And I think
14:55 --> 14:58 for a lot of researchers, scientists, engineers, that's not really a
14:58 --> 15:02 strong point, right? That's like, you're kind of focused, you're good in one area,
15:03 --> 15:04 now you have to stretch.
15:06 --> 15:10 Absolutely. I mean, to be able to talk to people,
15:10 --> 15:14 you know, be personable and also
15:15 --> 15:18 not just think about the product and the features of the product, right?
15:18 --> 15:20 Also think about the business side, right?
15:21 --> 15:24 And give away, right, some of, you know,
15:24 --> 15:28 the portion of the company, right? And you have to be strategic and—
15:28 --> 15:31 Oh, absolutely. You have to be strategic, right? There's so many factors to that.
15:32 --> 15:35 You have to wear, you know, different hats almost every moment, every Every
15:36 --> 15:40 moment, right? Yeah. It's not at all easy to be a founder
15:40 --> 15:44 of a company and be successful. I don't know if we have been successful,
15:44 --> 15:48 successful. I mean, it's been a long journey and,
15:49 --> 15:52 but I learned a lot and I, then my advice to people who wanted to
15:52 --> 15:56 found a company and have a startup is to be resilient and
15:57 --> 16:01 be strong and listen, right? And learn. And where
16:02 --> 16:07 Where do you think in this journey of the production side of
16:07 --> 16:10 Ryan, where are you at? Are we at 80%,
16:10 --> 16:14 90% where you need to be, or is this further? I think
16:14 --> 16:17 it, the, the, we do have an MVP, the, the product, it's kind
16:18 --> 16:21 of mature, but still we do have, you know, we need to add more features
16:21 --> 16:25 to improve, you know, the, the product based on the needs
16:26 --> 16:28 of our customers. So that's, it's,
16:28 --> 16:32 it's Most likely it's going to be ongoing. Ongoing. It's an ongoing thing.
16:32 --> 16:36 Look at any product. Look at, for example, Windows operating system
16:37 --> 16:40 or Apple products or any other products or car products, right? Look at,
16:41 --> 16:45 for example, the early version of a pickup truck
16:45 --> 16:49 versus what we are now or what they, Ford, for example,
16:49 --> 16:53 has or airplanes. So anything,
16:53 --> 16:56 any kind of technology, Any— that's
16:57 --> 17:00 something, you know, it's an ongoing thing. Yeah.
17:01 --> 17:05 And what are
17:05 --> 17:08 the unique highlights of the product
17:09 --> 17:12 right now? So the unique highlight of the product is the
17:13 --> 17:16 natural conversation that you can have with Ryan. Ryan can read your emotion,
17:16 --> 17:20 especially facial expression and emotion recognition. That's something that
17:20 --> 17:25 I was passionate about it. And I want my product to be able to empathize
17:25 --> 17:28 with people. And understand them, you know,
17:28 --> 17:32 smile back. Or if you say that I'm not feeling well,
17:32 --> 17:36 or if you are down, then the robot would help
17:36 --> 17:40 you, you know, to cheer up or maybe do yoga with Ryan or listen to,
17:40 --> 17:43 you know, soothing music or play a game or somehow,
17:44 --> 17:47 you know, to support you, to help you, to empathize with you. You know,
17:47 --> 17:51 that's the part that I really like about the product.
17:51 --> 17:55 So there's a little bit, or a lot of an emotional connection
17:55 --> 17:58 that starts to build with you as it gets to know you.
17:58 --> 18:02 Absolutely, yeah. So that's one thing. And also AI
18:02 --> 18:06 has helped a lot, especially large language models and ChatGPT and
18:06 --> 18:10 Gemini, you know, recently in the past 2 or 3 years. So that has been
18:10 --> 18:13 another kind of a turning point in our project.
18:14 --> 18:18 So without language models, large language models,
18:18 --> 18:22 we couldn't have a good chatbot system that You know, we could have
18:22 --> 18:25 good conversation, quality conversation, creative, you know,
18:25 --> 18:30 like an engaging conversation with machines,
18:30 --> 18:33 with Ryan, but now we can have that.
18:34 --> 18:37 So AI has helped a lot. Yeah, and where are
18:37 --> 18:40 you looking to improve Ryan the most? Oh,
18:40 --> 18:45 there's so many aspects of this product that can be improved.
18:45 --> 18:48 So autonomy is one, you know, features that you're adding to
18:49 --> 18:54 our product. So Ryan can go from, a room
18:54 --> 18:58 of a person to another room and check on them,
18:58 --> 19:03 maybe deliver something. And yeah,
19:03 --> 19:06 so those are the things that we are adding to Ryan. So motion
19:06 --> 19:10 and autonomy. Oh, and in terms of integration
19:11 --> 19:14 with other software, what does that look like for you?
19:15 --> 19:21 Is that something— and I'm just thinking in terms of
19:21 --> 19:24 connecting to Google Cast or Apple TV, sort of things? I do
19:24 --> 19:29 have a very good team of software engineers and consultants
19:29 --> 19:33 that help me to basically
19:33 --> 19:37 integrate different software tools and platforms
19:37 --> 19:40 into— integrate them all into Ryan.
19:41 --> 19:45 Yeah. And now, something we were talking about before we started
19:45 --> 19:49 interview was this sort of really
19:49 --> 19:52 slippery slope that we can get into where we
19:53 --> 19:56 can lose sight of what's AI and what's human, right? Can you talk
19:56 --> 20:00 about that? Like, how do you manage that so that
20:00 --> 20:04 it doesn't get— it doesn't become from a
20:04 --> 20:07 distance also into a gray area? Yeah, yeah, absolutely. It's a gray area.
20:08 --> 20:11 And remember we talked about the uncanny valley, right? And you
20:12 --> 20:15 swayed it a little bit. So It's basically the kind,
20:15 --> 20:18 you know, you can make a product or a robot that looks
20:18 --> 20:21 exactly like human being, 100%.
20:21 --> 20:24 However, people may not like it and people
20:25 --> 20:28 may, you know, think that that's creepy. Yeah. Because there is a
20:28 --> 20:32 very fine line and you may fall into a valley that you
20:32 --> 20:35 may make something that look like human.
20:35 --> 20:39 Yeah. But you don't like it. Yeah. And, you know, to your
20:39 --> 20:42 point, what I was talking to Mohamed about was when I
20:43 --> 20:46 was watching videos learning about the product, that I saw the face
20:47 --> 20:50 and it was blinking and I said, well,
20:51 --> 20:54 that's kind of creepy in a sense because it's blinking. And then I
20:55 --> 20:59 thought for a second, I was going, that's creepier if it doesn't blink. So trying
20:59 --> 21:03 to figure out those really like gentle little things that you can add to
21:03 --> 21:06 it. Yeah, some people still think that, you know, 10% of people think
21:07 --> 21:10 that Orion is creepy, you know, they don't like it, right? But 90%, you know,
21:10 --> 21:14 it's not something that you can, like have
21:14 --> 21:17 everyone to say that, oh, that's perfect, I love it,
21:18 --> 21:22 right? And but so
21:23 --> 21:26 like facial expressions, blinking, eye gaze, you know,
21:27 --> 21:30 and smile, smile, right? Yeah, that helps, you know,
21:30 --> 21:34 but we don't want to make it to look like human completely,
21:34 --> 21:37 especially the body of the robot. And we never tell people that
21:38 --> 21:41 that's human, although it may act to a good extent,
21:41 --> 21:45 some extent actually like human, but we don't
21:45 --> 21:50 want to fool people and say that, oh, you're talking to human and Ryan feels,
21:50 --> 21:52 you know, your emotion. It's Ryan, you know, it's kind of,
21:53 --> 21:56 it doesn't have the perception, you know, that we have as human.
21:57 --> 22:00 So it imitates, you know, the human emotion
22:01 --> 22:04 perception. What are your thoughts on the dangers of
22:04 --> 22:08 us getting too far into that, like 15, 20 years from now,
22:08 --> 22:13 we— more and more companies build human-looking
22:13 --> 22:17 robots. What's the danger, do you think? I think it can be
22:17 --> 22:21 very dangerous, especially if you make something that 100% looks
22:21 --> 22:25 like human and acts and behaves like human, but it's not human. Human dignity,
22:25 --> 22:29 I think, is something that we got to respect. And I believe
22:29 --> 22:32 in that. I believe that we as a human We
22:33 --> 22:36 are not machines, you know, we have soul and I believe in that. Yeah.
22:36 --> 22:40 'Cause we're gonna get, it could get into this space where we
22:40 --> 22:45 forget, right? Yeah, I don't know. We never forget, yeah. Right, and I think it's
22:45 --> 22:48 easy for us to talk about it now because we're
22:49 --> 22:52 at the beginning stages of it. Absolutely. But 50 years from now, all of a
22:52 --> 22:56 sudden there's robots walking around that look just like us. Yeah. And that's where
22:56 --> 23:00 I feel like the psychology of it becomes so different. Absolutely, yeah, yeah, we should,
23:00 --> 23:04 I don't think that's the right path. Yeah, still this is a machine.
23:04 --> 23:08 This is, although we try to make it act and behave like human,
23:08 --> 23:12 and that's what the purpose of AI, but that's the definition of
23:12 --> 23:15 AI and that's what AI is about, right? To act and
23:15 --> 23:19 behave like human, but it is not human. In the robotics
23:20 --> 23:23 and AI community, is this discussion happening?
23:23 --> 23:26 Yeah, it's happening. Some people believe in that, some people know,
23:26 --> 23:30 think that, oh, let's move forward, proceed, you know,
23:30 --> 23:34 make whatever happens happen. Yeah, yeah. But I
23:34 --> 23:37 think we should be responsible, right? Absolutely. We have to be responsible, you know.
23:38 --> 23:42 Ethics, right, is important. We have to have created something that it's
23:42 --> 23:46 ethical and more moralism. Yeah. And in terms of the
23:46 --> 23:49 function of Ryan and just the, the physical function,
23:50 --> 23:53 what do you think you see is going to happen to
23:53 --> 23:57 maybe advance it into— is there like joint movements
23:58 --> 24:00 or things like that? Do you think you're— so we could make it be bipedal,
24:00 --> 24:04 you know, biped robot and can like a Unitree robot that
24:04 --> 24:08 can walk around and dance or the one that Boston Dynamics has.
24:08 --> 24:12 But I don't think we need for the purpose of, you know,
24:12 --> 24:16 the applications, robot applications that I'm focusing
24:16 --> 24:19 on, I don't think I need that biped
24:20 --> 24:23 robot. So just quickly, can you go into
24:23 --> 24:25 for, I guess for the assisted living,
24:26 --> 24:31 What are the benefits of having Ryan as an assisted
24:31 --> 24:35 robot? Yeah, so Ryan can cover 6 dimensions
24:35 --> 24:38 of wellness, companionship, exercise,
24:38 --> 24:42 entertainment, even help with therapy and boost
24:43 --> 24:46 people's mood and maybe vocational, a few things
24:46 --> 24:50 that in that space that Ryan can do. Yeah. And then what
24:51 --> 24:54 are some of the some of the aspects you think you're
24:54 --> 24:58 going to add on that will make Ryan just go to that stratosphere?
24:58 --> 25:01 They're like, wow, this is— So as I said, autonomy to be able
25:01 --> 25:05 to go and check on people. And let's say as a caregiver,
25:05 --> 25:09 you can send your robot as your assistant and say, go and check on Mr.
25:09 --> 25:13 Smith and see how he's doing this morning, maybe deliver his medicine
25:13 --> 25:17 or deliver something to his room and then report back to me.
25:18 --> 25:21 And also do maybe some vital check on that. Yeah, so those kind
25:22 --> 25:26 of things, yeah. Yeah, and I think, you know, I get the messaging from everything
25:26 --> 25:30 I learned about you that you don't want Ryan to replace
25:30 --> 25:33 humans. You want to assist humans. Absolutely.
25:34 --> 25:38 Yes. So we don't want to replace human caregivers. We want
25:38 --> 25:41 to empower them and force them to support them, right? Because they're
25:41 --> 25:45 busy and there's shortage of, you know, staff members and caregivers.
25:46 --> 25:50 So we want to help them to do
25:50 --> 25:53 better, you know, to do their job. And realistically, what is
25:53 --> 25:58 the percentage of like one Ryan to how many patients
25:59 --> 26:02 can it help? Maybe 20 to, you know,
26:02 --> 26:06 25. Yeah, that's that many. Yeah. So it can be used on a
26:06 --> 26:09 timesharing basis. Ryan can stay in a common area.
26:09 --> 26:13 People can go and tap their card and use Ryan for
26:13 --> 26:16 30 minutes or so, or Ryan can go check on them, or Ryan can
26:16 --> 26:21 stay in people's apartment and they
26:22 --> 26:25 can talk to Ryan. So each can rotate it. Every time they tap a card,
26:25 --> 26:29 Ryan now switches over to that personality. Exactly. I mean, it's like your computer that
26:30 --> 26:35 you log into. So Ryan is personalized, customized. It remembers who's
26:35 --> 26:38 Ryan talking to, right? And that's how it works. Also, we are going
26:38 --> 26:41 to add face recognition to this so Ryan can,
26:41 --> 26:45 even without having a card, Ryan can recognize you. And then you can
26:45 --> 26:49 log in and do, ask maybe a question to authenticate and
26:49 --> 26:53 verify and then log in and then know that, oh, that's you. Wow.
26:53 --> 26:56 Yeah. That's awesome. Do the conversation and interact. Yeah. And I
26:57 --> 27:00 guess we can see some of this in action. Oh, absolutely. Yes. Yes.
27:00 --> 27:04 We'll see that in action in a moment. Yeah. Okay. All right.
27:04 --> 27:07 And then sort of wrap up the interview here.
27:08 --> 27:12 If you can go back to a young Mohammed maybe
27:13 --> 27:17 20 years ago to give advice to expedite
27:17 --> 27:20 this journey, maybe one piece of information you think is crucial,
27:20 --> 27:24 what do you think you could tell your younger self? Be like,
27:24 --> 27:28 if you do this now, this will change how maybe Ryan— I
27:28 --> 27:32 would definitely take some courses from our business schools, you know, early on.
27:33 --> 27:36 And those courses helped me a lot. Yeah. And yeah.
27:36 --> 27:40 Broaden my vision and my perspective about
27:40 --> 27:44 business and having a company and startup, not just focus on technology,
27:45 --> 27:48 not just, you know, yeah, that, that was my,
27:48 --> 27:53 that would be my advice. Do you think you would've gotten into business
27:53 --> 27:56 sooner because you had that information? Absolutely. Yeah. I would've got into business
27:57 --> 28:00 school, you know, and take some courses or even get in having an MBA.
28:01 --> 28:04 So that would've helped me a lot. Wow. Yeah, I definitely— yeah.
28:05 --> 28:08 All right. Well, thank you so much for your knowledge. We're going to look at
28:08 --> 28:10 all the— what Ryden can do in a little bit.
28:11 --> 28:14 This is A Personal Side of Business. Thank you. Thank you.
28:14 --> 28:15 Appreciate it. Thanks for having me.