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 --> 00:04 Welcome to The Personal Side of Business, where you hear real stories
00:04 --> 00:06 from real entrepreneurs.
00:13 --> 00:17 Hi, and welcome
00:18 --> 00:21 to The Personal Side of Business, where every business has a story. I'm your host,
00:21 --> 00:24 Jet Pandey. Well, today my guest is Mohabbin Mukherjee. He is
00:25 --> 00:28 professor, researcher, founder of the Rhyme
00:28 --> 00:32 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
00:36 --> 00:39 at assistive robotics. Welcome to the podcast. Thank you.
00:40 --> 00:42 Okay, so let's start. How did we get here?
00:46 --> 00:51 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 --> 00:58 of this? I see. So when I was a child, I was always fascinated by
00:58 --> 01:01 cars, by any sort of technology,
01:02 --> 01:04 you know, alarm clocks,
01:05 --> 01:08 locks, you know, car engines and so
01:08 --> 01:11 on and so forth. And I always— I was very
01:11 --> 01:14 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, you know, something that we call it
01:30 --> 01:34 now AI, but it is mostly, you know, machine learning. Although computer vision
01:34 --> 01:38 is a type of AI or, you know, a subfield in machine learning and
01:39 --> 01:42 AI. So when I started the project, the robotics, I mean,
01:43 --> 01:47 I worked with children with autism, and the idea was to used
01:47 --> 01:51 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 attention,
01:59 --> 02:03 and so on and so forth. And kids with autism on
02:03 --> 02:07 the spectrum, they really like objects and robots.
02:08 --> 02:12 And so then they can relate, you know, to technology
02:12 --> 02:16 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 here. And yeah,
02:25 --> 02:29 that was a successful project. And I'm working even with some of the kids
02:29 --> 02:33 that they participated in our study. They oftentimes,
02:33 --> 02:36 they come to the lab and, you know, they play with the
02:37 --> 02:40 robot or give us feedback. And I have seen
02:40 --> 02:44 an impact on their life. So that was a project that really
02:44 --> 02:47 was successful. And that's how we got into,
02:48 --> 02:52 you know, other areas about how we can support
02:52 --> 02:55 humans with other,
02:56 --> 02:59 you know, mental issues,
02:59 --> 03:03 I would say, such as people with depression, dementia,
03:04 --> 03:07 and so on. When, around what time did this
03:07 --> 03:12 happen for you? So back in 1996,
03:12 --> 03:16 '97, '98, you know, when I was a grad student.
03:16 --> 03:19 Back then. So what was the technology like then compared
03:19 --> 03:22 to now? Oh, we did have back then, you know,
03:25 --> 03:28 the first computer that I started working with that,
03:28 --> 03:32 you know, had the DOS operating system and the IBM PS/2.
03:33 --> 03:36 I remember that one. And 486 and the Pentium.
03:36 --> 03:39 And then, you know, yeah, so those are the kind of
03:39 --> 03:43 computers that I used to, you know, started learning how
03:43 --> 03:47 to program C and C++. And even I remember Fortran 4
03:47 --> 03:51 and BASIC. Those were the, you know, the computer languages that I learned how to
03:51 --> 03:55 program. And what was when you were programming back in that time,
03:55 --> 03:58 what were— what was your goal at the time?
03:58 --> 04:03 What were you thinking was going to be the future of all that? So At
04:03 --> 04:06 the beginning, I was very fascinated by image processing and computer
04:07 --> 04:10 vision, and mostly I worked on medical images
04:10 --> 04:14 back then, ultrasound images and X-ray images.
04:14 --> 04:18 But then I wanted to see what we
04:19 --> 04:22 can do, you know, with computers and with computer programming,
04:22 --> 04:25 actually. Like, for example,
04:26 --> 04:29 human face recognition. Yeah, facial expression
04:30 --> 04:34 recognition. And that's You know, when I started my PhD, I was
04:34 --> 04:38 really amazed by how computer
04:39 --> 04:42 can do face recognition, face identification. Because you were
04:42 --> 04:46 kind of one of the early pioneers of that face recognition.
04:46 --> 04:50 Yes. Yeah. I started my PhD in 2003 and
04:50 --> 04:54 that was post-9/11 and biometrics and face recognition
04:54 --> 04:57 and face identification was very popular and very hot. A very
04:57 --> 05:01 hot topic in, you know, with machine learning and AI.
05:01 --> 05:06 And so I worked on face recognition for my PhD.
05:06 --> 05:09 Then I did my postdoc training in psychology
05:09 --> 05:13 department and I worked with psychologists
05:13 --> 05:17 and the research was on how we can use computer
05:17 --> 05:21 algorithm to look at the behavior of children
05:21 --> 05:24 with autism and analyze, you know, their behavior and mostly
05:24 --> 05:28 face-to-face communication. Facial expression
05:28 --> 05:32 recognition, eye gaze attention, and so on and so forth. And the idea was
05:32 --> 05:35 to use computer to be able to,
05:35 --> 05:38 I mean, to better analyze the behavior of
05:39 --> 05:42 children with autism. So that's how I got into the,
05:42 --> 05:46 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 to
05:58 --> 06:02 use this technology. I think that postdoc training was really a
06:02 --> 06:05 key moment in my career and helped me, you know,
06:05 --> 06:08 to find my way. Yeah. And helped
06:09 --> 06:14 me a lot that how we can use technology to not
06:14 --> 06:16 just be, I mean, that help people,
06:17 --> 06:21 but also to empower and enforce I
06:21 --> 06:24 mean, caregivers, therapists, you know,
06:25 --> 06:28 that's how that postdoc training shaped my
06:29 --> 06:33 current, I mean,
06:33 --> 06:37 research and work. To strengthen our shoulders,
06:38 --> 06:42 we will start by bringing our elbows in line just below our shoulders.
06:45 --> 06:48 Then we will rotate our hands, bringing them in line with our elbows.
06:50 --> 06:50 1,
06:54 --> 06:55 2,
06:58 --> 07:00 3.
07:01 --> 07:04 Now, at that moment when you started
07:05 --> 07:08 shifting your, I guess, perspective and then focus,
07:10 --> 07:13 from, from then, if you can go back and look at it
07:13 --> 07:17 to now, do you think it's close to the vision that you
07:17 --> 07:21 had back then, or has it? Back then I didn't know kind
07:21 --> 07:24 of, you know, what exactly. You were just throwing darts at the sea. Exactly.
07:24 --> 07:27 I really, I didn't know how this is going to,
07:28 --> 07:32 you know, affect my career and also my
07:33 --> 07:36 passion. But over time, especially after I joined DU
07:36 --> 07:40 and I realized that, oh, robotics, right, is a big thing.
07:40 --> 07:44 And although when I was a child, I was fascinated by robots
07:45 --> 07:48 and I remember I read a book about, you know, robotics, but I didn't know
07:48 --> 07:51 much about it. And but here,
07:51 --> 07:54 2008, when I joined as a faculty, then I had a
07:55 --> 07:59 faculty mentor who was a roboticist
07:59 --> 08:02 and he helped me to get into the field of robotics.
08:03 --> 08:06 So robotics, computer vision,
08:07 --> 08:10 then psychology, all of them, you know,
08:10 --> 08:14 happened to come together. And later when I founded the company,
08:14 --> 08:17 all the Things that I liked, you know, when I was a child,
08:17 --> 08:20 electronics, you know, working with motors, you know,
08:20 --> 08:25 build things. So all of them actually
08:25 --> 08:28 came together and helped me to be able to,
08:29 --> 08:33 you know, manage my team and lead my team
08:33 --> 08:36 to build a robot, make a robot. Yeah.
08:36 --> 08:40 Yeah. Green technology. I don't know how to
08:40 --> 08:44 use this control. Hey, don't turn around
08:44 --> 08:48 this way. You are so very welcome, Mom. It is such a pleasure chatting with
08:48 --> 08:50 you today. Hey, Ryan.
08:53 --> 08:57 What position did you play when you were out on the soccer field with your
08:57 --> 09:00 friends? Oh, did you play soccer? Because that's— yeah, I totally went
09:00 --> 09:04 head to head. And, you know, you just brought up a
09:04 --> 09:07 really good point that I was curious about. So you've done a lot of
09:08 --> 09:10 research. You did— you were sort of focused in a certain area.
09:11 --> 09:14 And now here you have to run a
09:14 --> 09:18 team. Absolutely. What was that experience like for you?
09:18 --> 09:21 Was the beginning a little bit of a shock and
09:21 --> 09:25 awe type? No, it was not shock because I had worked as a
09:25 --> 09:29 team lead before. Okay. You know, as an IT team lead, as an
09:29 --> 09:32 IT admin for a few years.
09:33 --> 09:37 And so I had the experience. However, I didn't have the experience
09:37 --> 09:41 of founding a business, running a business as a CEO.
09:41 --> 09:45 And a founder. So I took a few courses in our business
09:45 --> 09:48 school. Oh, nice. And using the resources. Yes,
09:48 --> 09:52 exactly. And that helped me, especially when it comes
09:52 --> 09:55 to the, you know, the accounting, finance, management, you know,
09:55 --> 09:58 and yeah, and that part of, you know,
10:01 --> 10:06 running a company. Yeah. And of course, I learned from
10:06 --> 10:09 my peers. And I attended, you know,
10:09 --> 10:12 workshops. And also there was an NSF,
10:15 --> 10:18 it's called I-Corps, actually training.
10:19 --> 10:22 So that also helped me too with one of my students, former students,
10:23 --> 10:26 and a mentor. Learned how to do customer discovery,
10:27 --> 10:32 how, you know, what it means to start up a company and
10:32 --> 10:36 go through the process of, you know, going from
10:38 --> 10:42 an innovation and an idea and take it
10:42 --> 10:46 out of the door. Of course, it's taken us 12 years, I mean,
10:46 --> 10:50 to get to this point. It wasn't easy, and especially robotics,
10:50 --> 10:54 you know, it's not easy. You have to make a lot of changes,
10:54 --> 10:57 try, test, revise,
10:57 --> 11:01 redesign, right? And yeah, keep going. Is there
11:01 --> 11:06 a stress on you for a certain time period to get to
11:06 --> 11:10 this goal? Absolutely. I mean, being a founder of
11:11 --> 11:15 a company and also having family and part-time faculty member is
11:15 --> 11:18 not easy. So I
11:19 --> 11:22 mean, there are many moments I've told myself that,
11:22 --> 11:25 oh, this is too much for me. This is too much. A lot of pressure
11:26 --> 11:29 on me. And no, it's not easy at all.
11:30 --> 11:33 How do you— what has been your sort of way to get past
11:34 --> 11:37 those like hurdle moments? At the beginning, I knew that, you know,
11:38 --> 11:41 I was told that 95 or 90% of
11:42 --> 11:46 the startups actually fail. So one thing that
11:46 --> 11:49 I learned is to, you know, to pivot first, you know, to change. Don't be
11:50 --> 11:52 rigid. Don't be— listen.
11:53 --> 11:58 And also try to maybe
11:59 --> 12:02 change the direction and, you know,
12:02 --> 12:06 learn from our failures and talk to people and
12:07 --> 12:07 be open-minded.
12:12 --> 12:16 I am so sorry for that mistake, Lambe. Thank you for correcting me.
12:16 --> 12:18 I will definitely get that right from now on.
12:19 --> 12:23 I apologize for mispronouncing your name, Lambe. How has your
12:23 --> 12:27 morning been going so far? It's much better now that I'm talking to you,
12:27 --> 12:30 Ryan.
12:32 --> 12:34 I'm glad to hear your day is looking up, Lambe.
12:36 --> 12:39 What specifically has been making your morning more enjoyable today?
12:39 --> 12:43 This controller needs to be fixed. It's too— it's too—
12:43 --> 12:47 yeah. Hey Ryan, can we do yoga? Be open-minded.
12:47 --> 12:51 Yeah, that helped me a lot. Have you had mentors
12:52 --> 12:55 once you started this? Yeah, I had a mentor.
12:55 --> 12:59 Yeah, I had a mentor. And some of it also has been organic.
12:59 --> 13:03 You know, you learn right over time that
13:03 --> 13:07 how to do better, manage things, you know, manage your
13:07 --> 13:11 thing. And of course, you know, like many other companies,
13:11 --> 13:14 we have had ups and downs and it's very common,
13:15 --> 13:18 you know. There's been moments that I told myself that this is it,
13:18 --> 13:21 I'm not going to continue this, or I will give it one more year.
13:22 --> 13:25 Yeah, or two more, one more year, you know, third after 12 years.
13:25 --> 13:29 Yeah. What was like, what was, what was a hurdle
13:30 --> 13:34 or an obstacle that happened building this
13:34 --> 13:37 that, that was like almost
13:38 --> 13:41 a pivotal moment that really could have stopped everything? Fundraising,
13:41 --> 13:45 I think, is a big So you have to have cash.
13:45 --> 13:51 Yeah. But, you know, to be able to recruit
13:52 --> 13:55 a good team and get things done. So I think always
13:56 --> 13:59 fundraising has been an issue. And that's not an easy thing
13:59 --> 14:03 at all. I've been lucky that so far we have been funded
14:03 --> 14:06 by the federal government, you know, by NIH and NSF.
14:07 --> 14:11 And now we are going to fundraising, pitching. To VCs
14:11 --> 14:15 and investors. And also the state of Colorado recently
14:15 --> 14:19 gave us a small grant to, you know, to help with commercialization
14:19 --> 14:23 of the product. But fundraising has been always
14:23 --> 14:28 challenging and also mentoring and leading,
14:28 --> 14:32 you know, a team of engineers. And I do have a team that
14:32 --> 14:33 I have, uh,
14:33 --> 14:36 20 years old.
14:37 --> 14:40 Engineering students on my team or computer scientists.
14:41 --> 14:44 On my team go all the way to people who are 75 years old,
14:44 --> 14:47 you know, and have had a few startups and they were
14:48 --> 14:51 successful actually in executing their startups. Yeah, I have to
14:52 --> 14:55 work with a range of people with a range of, you know, talents,
14:55 --> 14:59 mentalities, background and experiences and skills.
14:59 --> 15:03 Right. And then I'm sure it's challenging you Oh, big to have to
15:03 --> 15:06 be able to. Oh, oh, absolutely. Every person is different. Every person
15:07 --> 15:10 is different, right? But I enjoy it. You know, I've enjoyed it so
15:11 --> 15:14 far. And I'm a person that I like to be busy. And I,
15:14 --> 15:17 you know, I like to be busy always, all the time.
15:18 --> 15:22 And yeah. What's a skill asset do you think
15:22 --> 15:26 you have now that you didn't have at the beginning of this?
15:26 --> 15:29 Patience. Yeah, so to
15:29 --> 15:34 be patient and also to work
15:35 --> 15:37 hard. Of course, I always had worked hard, but patience has been one of the
15:37 --> 15:41 things. And also I learned that to be very
15:42 --> 15:45 selective and very, very careful when it comes to hiring,
15:45 --> 15:48 you know, a team member because we are small. I have a small team,
15:48 --> 15:51 about 12 engineers and 4 or 5 actually people
15:51 --> 15:56 on the business, you know, side of the company. So it's
15:57 --> 16:00 important to, I mean, to make the right decisions when
16:01 --> 16:05 it comes to hiring. Yeah. And, you know, you were— something I wanted to
16:05 --> 16:08 touch upon you brought up was the fundraising part of it, which I
16:09 --> 16:12 think if we take a step back now becomes a— gets
16:13 --> 16:16 marketing involved, right? You have to advertise what
16:17 --> 16:20 it is you're doing and be able to start to pitch, talk, shake hands.
16:21 --> 16:24 And basically it's this other personality side of you that you have to stretch.
16:24 --> 16:27 And I think For a lot of researchers, scientists, engineers,
16:27 --> 16:31 that's not really a strong point, right? That's like you're kind of focused,
16:31 --> 16:33 you're good at one area, now you have to stretch.
16:36 --> 16:39 Yeah, absolutely. I mean, to be able to talk to
16:39 --> 16:43 people, you know, be personable and
16:43 --> 16:47 also not just think about the product and the features
16:47 --> 16:50 of the product, right? Also think about the business side, right?
16:51 --> 16:54 And give away, right, some of, you know,
16:54 --> 16:58 the portion of the company, right? And you have to be strategic and— Oh,
16:58 --> 17:01 absolutely. You have to be strategic, right? There are so many factors to that.
17:02 --> 17:04 You have to wear, you know, different hats almost every moment,
17:04 --> 17:08 every moment, right? Yeah. It's not at
17:08 --> 17:12 all easy to be a founder of a company and be successful.
17:12 --> 17:15 I don't know if we have been successful, successful. I mean,
17:15 --> 17:19 it's been a long journey. And, but I
17:19 --> 17:23 learned a lot and I, then my advice to people who wanted to found a
17:23 --> 17:27 company and have a startup is to be resilient and be
17:27 --> 17:31 strong and listen, right? And learn. And where,
17:32 --> 17:36 where do you think in this journey of the production side
17:37 --> 17:40 of Ryan, where are you at? Are we at 80%,
17:40 --> 17:44 90% where you need to be or is this further? I think
17:45 --> 17:48 we do have an MVP. The product, it's kind of mature,
17:48 --> 17:52 but still we do have, you know, we need to add more features to improve,
17:52 --> 17:56 you know, the product based on the needs of our customers.
17:57 --> 18:00 So that's— So it's most likely going to be ongoing.
18:00 --> 18:04 Ongoing. It's an ongoing thing. Look at any product. Look at,
18:04 --> 18:07 for example, Windows operating system or Apple products or any
18:08 --> 18:11 other products or car products, right? Look at, for example,
18:11 --> 18:15 you know, the early version of a pickup truck.
18:15 --> 18:18 You know, versus what we are now or what they— Ford,
18:18 --> 18:22 for example, has or airplanes.
18:22 --> 18:24 So anything, any kind of technology,
18:25 --> 18:29 any— that's something, you know, it's an ongoing
18:30 --> 18:35 thing. Yeah. And what are the unique
18:36 --> 18:38 highlights of the product right now?
18:39 --> 18:43 So the unique highlight of the product is the natural conversation
18:44 --> 18:48 that you can have with Ryan. Ryan can read your emotion, especially facial
18:48 --> 18:51 expression and emotion recognition. That's something that I was passionate
18:51 --> 18:55 about it, and I want my product to be able to empathize
18:55 --> 18:59 with people and understand them, you know, smile back.
19:00 --> 19:03 Or if you say that I'm not feeling well, or if you are down,
19:03 --> 19:07 then the robot would help you, you know, to cheer
19:07 --> 19:10 up, or maybe do yoga with Ryan, or listen to,
19:10 --> 19:13 you know, soothing music. Or play a game or somehow,
19:14 --> 19:17 you know, to support you, to help you, to empathize with you. You know,
19:17 --> 19:21 that's the part that I really like about the product.
19:21 --> 19:25 So there's a little bit or a lot of an emotional connection
19:25 --> 19:28 that starts to build with you as it gets to know you.
19:28 --> 19:32 Absolutely. Yeah. So that's one thing. And also AI
19:32 --> 19:36 has helped a lot, especially large language models and ChatGPT
19:36 --> 19:39 and Gemini, you know, recently. In the past 2 or 3 years. So that has
19:39 --> 19:43 been another kind of a turning point in our project.
19:44 --> 19:48 So without language models, large language models,
19:48 --> 19:51 we couldn't have a good chatbot system that, you know,
19:51 --> 19:55 we could have good conversation, quality conversation, creative,
19:55 --> 19:58 you know, like an engaging conversation with
19:59 --> 20:03 machines, with Orion. But now we can have that.
20:03 --> 20:07 Yeah. So AI has helped a lot. Yeah. And where are you looking
20:07 --> 20:11 to improve Ryan the most? Oh, there are so
20:12 --> 20:15 many aspects of this product that can be improved. So autonomy is
20:16 --> 20:20 one, you know, feature that you're adding to our product.
20:20 --> 20:23 So Ryan can go from, you know,
20:23 --> 20:27 a room of a person to another room and
20:27 --> 20:30 check on them, maybe deliver something.
20:30 --> 20:34 Right. And yeah, so those are the things that we
20:34 --> 20:38 are adding to Ryan. So motion and autonomy. Oh,
20:38 --> 20:41 and in terms of like integration with other software,
20:42 --> 20:46 what does that look like for you? Is that
20:46 --> 20:50 something— and I'm just thinking in terms of like connecting
20:50 --> 20:53 to, you know, Google Cast or Apple TV, those kinds
20:53 --> 20:57 of things. I do have a very good team. Team of software engineers
20:57 --> 21:03 and consultants that helped me to basically
21:03 --> 21:06 integrate different software tools and
21:06 --> 21:10 platforms into— integrate them all into Orion.
21:11 --> 21:14 Yeah. And now, you know, something we were talking about before
21:15 --> 21:19 we started interview was this sort of really
21:19 --> 21:23 slippery slope that we can get into where we we can lose sight
21:23 --> 21:27 of what's AI and what's human, right? Can you talk about that?
21:27 --> 21:30 Like, how do you manage that so that it doesn't
21:31 --> 21:34 get— it doesn't become from a distance
21:34 --> 21:37 also into a gray area? Yeah, yeah, absolutely. It's a gray area.
21:38 --> 21:41 And you remember we talked about the uncanny valley, right? And you
21:42 --> 21:45 explained that a little bit. So it's basically the kind of, you know, you can
21:46 --> 21:49 make a product or a robot that looks exactly like a human being.
21:49 --> 21:53 100%. However, people may not
21:54 --> 21:58 like it and people may, you know, think that that's creepy. Yeah. Because there
21:58 --> 22:01 is a very fine line and you may fall into a valley that
22:01 --> 22:05 you may make something that looks like human.
22:05 --> 22:08 Yeah. But you don't like it. Yeah. And, and, you know, to,
22:09 --> 22:12 to a point, what I was talking to Mohamed about was when I was
22:12 --> 22:16 watching videos learning about the product that I saw
22:16 --> 22:20 the face and it was blinking and I said, well, that's,
22:20 --> 22:24 that's kind of creepy in a sense because it's blinking. And then I
22:25 --> 22:27 thought for a second, I was going, that's creepier if it doesn't blink. Yeah.
22:27 --> 22:31 So have— trying to figure out those really like gentle
22:31 --> 22:35 little things that you can add to it. Yes. Some people still think that,
22:35 --> 22:38 you know, 10% of people think that Ryan is creepy. You know, they don't like
22:39 --> 22:43 it. Right. But 90%, you know, it's not something that you can like
22:43 --> 22:47 have everyone to say that, oh, that's perfect,
22:47 --> 22:51 I love it, right? And but
22:52 --> 22:55 so like facial expressions, blinking,
22:55 --> 22:58 eye gaze, you know, and smile, smile,
22:58 --> 23:02 right? Yeah, that helps, you know, but we don't want to make it to
23:02 --> 23:05 look like human completely, especially the body of the robot.
23:05 --> 23:09 And we never tell people that that's human, although it may
23:10 --> 23:13 act to a good extent, some extent, actually like human.
23:14 --> 23:17 But we don't want to fool people and say that, oh, you're talking
23:18 --> 23:20 to human and Ryan feels, you know, your emotion.
23:21 --> 23:24 It's Ryan, you know, it's kind of— it doesn't have the perception,
23:25 --> 23:29 you know, that we have as human. So it imitates,
23:29 --> 23:33 you know, the human emotion perception. What are your thoughts on
23:33 --> 23:36 the dangers of us getting too far into that?
23:37 --> 23:41 Like 15, 20 years from now, we— more and more companies
23:41 --> 23:44 build human-looking robots.
23:45 --> 23:48 What's the danger, do you think? I think it can be very dangerous, especially if
23:49 --> 23:53 you make something that 100% looks like human and acts and behaves like
23:53 --> 23:56 human, but it's not human. Human dignity, I think, is something that we got
23:57 --> 24:00 to respect. And I believe in that. I believe that
24:00 --> 24:04 we as a human, we are not machines, you know, we have
24:04 --> 24:08 soul and I believe in that. Yeah. Because we're, we're going to get, it could
24:08 --> 24:12 get into this space where we forget, right? Yeah.
24:12 --> 24:15 I don't know. Yeah. Right. And I think it's, it's easy for
24:15 --> 24:19 us to, to talk about it now because we're at the beginning
24:19 --> 24:22 stages of all of it. But 50 years from now, all of a sudden there's
24:22 --> 24:27 robots walking around that look just like us. Yeah. And that's where
24:28 --> 24:29 I feel like the psychology of it becomes so different. Absolutely. Yeah, yeah, we should.
24:30 --> 24:34 I don't think that's the right path. Yeah. Still, this is a machine.
24:34 --> 24:37 This is, you know, although we try to make it act and behave
24:37 --> 24:40 like human, and that's what the purpose of AI, you know,
24:41 --> 24:44 but that's the definition of AI and that's what AI is about,
24:44 --> 24:48 right? To act and behave like human, but it is not human.
24:48 --> 24:53 In the robotics and AI community, Is this discussion
24:53 --> 24:56 happening? Yeah, it's happening. Some people believe in that, some people no,
24:56 --> 24:59 think that, oh, let's, you know, move forward,
24:59 --> 25:02 proceed, you know, make whatever happens happen.
25:02 --> 25:06 Yeah, yeah. But I think we should be responsible, right? Absolutely,
25:06 --> 25:10 we have to be responsible, you know, ethics, right, is important.
25:10 --> 25:14 We have to have created something that it's ethical and more moralism.
25:14 --> 25:19 Yeah. And in terms of the function of Ryanair
25:19 --> 25:23 just the physical function, what do you think you see is going to happen to
25:23 --> 25:27 maybe advance it into? Is there like joint movements or
25:28 --> 25:30 things like that that you think you're— So we could make it to be bipedal,
25:30 --> 25:34 you know, biped robot and can like a Unitree robot
25:34 --> 25:37 that can walk around and dance or the one that Boston Dynamics
25:38 --> 25:41 has. Yeah. But I don't think we need for the purpose of,
25:42 --> 25:46 you know, the applications, robot applications that focusing
25:46 --> 25:50 on, I don't think I need that biped robot.
25:50 --> 25:54 So just quickly, can you go into, for,
25:54 --> 25:58 I guess, for the assisted living, what are the
25:58 --> 26:02 benefits of having Ryan as an assisted robot? Yeah, so Ryan
26:02 --> 26:07 can cover 6 dimensions of wellness: companionship,
26:07 --> 26:10 exercise, entertainment, even help with therapy.
26:11 --> 26:15 And, you know, boost people's mood and maybe vocational,
26:15 --> 26:18 a few things that, you know, in that space that Ryan can do.
26:19 --> 26:22 Yeah. And then what are
26:22 --> 26:26 some of the aspects you think you're going to add on that will make Ryan
26:26 --> 26:29 just go to that stratosphere? They're like, wow, this is— So as
26:29 --> 26:33 I said, autonomy to be able to go and check on people, you know,
26:33 --> 26:36 and let's say as a caregiver, you can send your robot as your
26:37 --> 26:41 assistant. And say, go and check in on Mr. Smith and see how he's doing
26:41 --> 26:45 this morning. Maybe deliver his medicine or deliver something to his room
26:45 --> 26:48 and then, you know, report back to me and also do maybe
26:49 --> 26:52 some vital check on that. Yeah. So those kind of things. Yeah.
26:52 --> 26:56 Yeah. And I think, you know, I get the messaging from everything I
26:56 --> 27:00 learned about you that you don't want Grind
27:00 --> 27:03 to replace humans. You want to assist humans. Absolutely.
27:03 --> 27:07 Absolutely. Yes. So we don't want to replace human caregivers.
27:07 --> 27:11 We want to empower them and force them to support them, right? Because they're
27:12 --> 27:15 busy and there's shortage of, you know, staff members and caregivers.
27:15 --> 27:20 So we want to help them to do
27:20 --> 27:24 better, you know, to do their job, right? And realistically, what is the percentage
27:24 --> 27:28 of like one Ryan to how many patients can
27:29 --> 27:32 it help? Uh, maybe 20 to, you know,
27:32 --> 27:36 25. Yeah, that's that many. Yeah. So it can be used on a
27:36 --> 27:40 time-sharing basis. Yeah. Ryan can stay in a common area. People can go and
27:41 --> 27:44 tap their card and use Ryan for 30 minutes or so, or Ryan can
27:44 --> 27:48 go check on them, or Ryan can stay in people's
27:49 --> 27:52 apartment and they can talk to Ryan.
27:52 --> 27:56 So each can rotate it. Yeah. Every time they tap a card, Ryan now switches
27:56 --> 28:00 over to that personality. Exactly. I mean, it's like your computer that you log into.
28:00 --> 28:04 So Ryan is personalized, customized. It remembers who's
28:05 --> 28:08 Ryan talking to, right? And that's how it works. Yeah. Also,
28:08 --> 28:11 we are going to add face recognition to this so Ryan can,
28:11 --> 28:15 even without having your card, Ryan can recognize you and then you
28:15 --> 28:18 can log in and do ask maybe a question to authenticate
28:18 --> 28:21 and verify and then log in and then know that, oh,
28:21 --> 28:24 that's Wow. Yeah, that's how, you know,
28:25 --> 28:28 the conversation. And I guess we can see some of this
28:29 --> 28:32 in action. Oh, absolutely. Yes. Yes. We'll see that in action in
28:33 --> 28:36 a moment. Okay. All right. And then sort of wrap up the, you know,
28:36 --> 28:40 the interview here. If you can go back to
28:41 --> 28:44 a young Mohammed, maybe 20 years ago, to give
28:45 --> 28:48 advice to expedite this journey,
28:48 --> 28:51 maybe one piece of information you think is crucial. What do you think
28:52 --> 28:55 you could tell your younger self? Be like, if you do this now, this will
28:56 --> 28:59 change how maybe Ryan— I would definitely
28:59 --> 29:02 take some courses from our business schools, you know, early on.
29:02 --> 29:06 And those courses helped me a lot. Yeah. And yeah,
29:06 --> 29:10 broaden my vision and my perspective about
29:10 --> 29:14 business and having a company and startup, not just focus on
29:14 --> 29:18 technology, not just, you know, Yeah, that, that was my—
29:18 --> 29:23 that would be my advice. Do you think you would've gotten into business
29:23 --> 29:27 sooner because you had that information? Absolutely. Yeah, I would've got into business
29:27 --> 29:30 school, you know, and take some courses or even get an MBA.
29:30 --> 29:34 So that would've helped me a lot. Wow. I definitely, yeah.
29:35 --> 29:38 All right, well, thank you so much, Mohammed. We're gonna look at all the,
29:39 --> 29:42 uh, what Ryden can do in a little bit. This is the personal side of
29:43 --> 29:45 business. Thank you. Thank you. Appreciate it. Thanks for having me.