Lindsay Skabar Hosts Kwame Asiedu
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[00:00:00] Lindsay Skabar: Hello everyone. This is Lindsay Skabar here today on the LIBI podcast. And I am one of the founders of bode Canada. and I am joined here today with Kwame Asiedu. Did I say your last name right?
[00:00:12] Kwame Asiedu: It's great, Asiedu.
[00:00:14] Lindsay Skabar: And he's here for BrainToy. why don't you go ahead and introduce yourself Kwame?
[00:00:19] Kwame Asiedu: My name is Kwame. I'm a, co-founder at BrainToy. Basically. We try to make AI easy for people to adapt for organizations and for individuals. And I'm sure as we go through, we'll talk more. Um, I'm very excited to be here and thank you for having me.
[00:00:33] Lindsay Skabar: Oh, we're really excited to learn from you today. And I am just so interested in the AI space and, and I know there is a whole bunch of people out there trying to apply AI in a way that will really be beneficial to people around the world. But I also know that there's people out there who have lots of questions about how it operates and how much it's going to be ingrained in our life.
so maybe at, let's start with the basics around AI, and maybe if you could explain in your own terms, what artificial intelligence is, and computer learning is, and if there's a difference between the two and maybe a couple of examples that we all deal with today, that that would be reasonable examples of how we deal with AI.
[00:01:16] Kwame Asiedu: So AI, in a very simple term, is trying to teach the computer to think, and to make decisions. You know, human beings have the ability to learn. when you teach us a subject, we learn, we don't memorize them. Sometimes we memorize them, but when we memorize it, then it means we don't understand it, which means we cannot use them to solve problems. But we have the ability to understand and use them to solve meaningful problems.
And so we're trying to help the computer to do the same thing and we give the computer the capability to learn from data, to understand it. To more or less think and reason at times, and then to make meaningful decisions. Now we have that happening in our lives, almost every time. I know a lot of us shop on Amazon and you know how you shop on Amazon, you buy a product, let's say you buy a diaper and then it will make a recommendation to you that maybe you want to buy a feeding bottle as an example, this is because people similar to you who purchase a diaper, also purchased, you know, that particular kind of, you know, maybe a feeding bottle and there is a learning happening behind it. And so they use that to build something called a recommendation engine, and now it makes those specific recommendations to you. And it's not somebody sitting there and typing in these rules.
It is them, you know, applying some of these specifically machine learning techniques to teach a computer, to learn, and then it will now make informed decisions to you. You see the same thing on YouTube. You watch a video and it recommends another video to you. You even use Google Maps and sometimes it will look at what is the shortest route or distance to a particular location and the strategy to help you avoid traffic.
These are all ways in which we use AI today, our keyboards on our phones, your QWERTY keyboard you type, and then with time it starts learning, you know, your vocabulary patterns and some ways that you usually use, and it starts recommending them. These are all ways in which we use AI today. They, they permeate our lives. We can actually do without them.
[00:03:16] Lindsay Skabar: Amazing and then some of the ways that that is extremely impactful is that it kind of just shortcuts, right there. A bit of a shortcut to what you were already maybe going to do, or suggest something that you maybe didn't consider. But would really would, now that you understand that it's an option, you may be take that.
I love, I love the shortcuts, especially, when using Google maps and getting around traffic patterns that are somewhat problematic.
[00:03:43] Kwame Asiedu: Yeah. Can you imagine what we will do without some of these tools today?
[00:03:46] Lindsay Skabar: Today's age is so dramatically different than what we were experiencing back in the day. Yeah. My first, my first experience with AI was, was being introduced to Watson.
do you remember Watson from, from jeopardy? it, that was my first experience of actually seeing it interact. and it's, it's just so, such an unbelievable opportunity that can be viewed in so many different ways. So tell us a little bit about BrainToy and what makes you different and, and how you're going about, that mission of making, making AI more accessible to, to more people?
[00:04:24] Kwame Asiedu: Yeah, so, that's a loaded question. So BrainToy, is, is an artificial intelligence company. And in fact, we built kind of this, Low code, no code platform, we're in Calgary, we're an Alberta based company. we do work with companies outside of Canada, you know, outside of Alberta and outside of Canada. But our main goal is, has been to empower people with this.
I call it the incredible 21st century technology. Empowering people, we do that in three ways. The first way is that we built this low-code no-code platform that can be used by virtually anyone who has domain expertise and have a little bit of knowledge about, you know, the, the methodologies around AI and how you can use it.
And who have probably never written any code in their life to, to solve meaningful problems, in fact to build production AI solutions. And that product, we call it MLOS machine learning operating system. As part of that, we also do what we call use-case delivery, which is, you know, some people might call it consulting, but we find that a lot of companies shy away from embracing AI because they think it's expensive.
They don't even know where to start or how to begin. It is too confusing. They have data strategies. They don't know how to fuse the credit, this data strategy five years ago. Now they don't know how to manage AI into it. They have a whole. ecosystem around the technology. They don't know where to start.
And so we decided that we will also help companies to do this. And so we will come in, we will discover with you, we'll help you identify opportunities for AI. We will understand your technology landscape, your processes, your methodologies, and we'll define specific problems for you. And we will solve it for you.
But once we solve it, we will try to empower your team to take that over, because we don't believe in this idea of consultant always coming back and we want to empower you to do it yourself. And then the third way is also AI training because we find it goes back to the second point I made about people being confused, not having that knowledge about AI.
And so they don't, they don't even know where to start. And so we partner with educational institutions like Sage, like Mount Royal, like Manpower to, to actually develop these curriculums that are offered to leaders, to mid-career professionals, so that they can acquire this knowledge about machine learning and AI and all other aspects of it and how they can use it to solve meaningful problems.
So basically that's what we do. And I usually use this illustration. You know, there are a lot of ways in which companies are helping in Alberta as an example, they will come in and they'll solve problems for you. But I use this illustration about, you know, Carl Benz, you know, when this innovator that created this car, and this was in the 1886. 2 years after that his wife learned how to drive and her name was Bertha Benz.
And then later on, when they started building cars, what you realize was. You know, people who can afford we're hiring chauffers. So a chauffer would drive you somewhere. Chauffer would you just call them and you drive them, but that wasn't scalable. You know, the best way was for us to teach everyone how to drive so that they can own their own car and they use it for whatever purpose they wanted to.
Right? And that is what we are now. And in fact, now the cars are going to start driving themselves. So we believe that AI as has been called the next electricity is a general purpose technology that is going to disrupt every industry. And so you cannot scale it by just hiring unicorns or bringing people in who will always solve your problems for you.
Everybody has domain knowledge. So the best way to scale it is to empower people to do it. And to do that means you have to educate them about the methods and technologies, but also to create technologies and platforms that can make it easy for them to embrace toward that, and to utilize them in solving problems.
[00:08:31] Lindsay Skabar: That's really interesting. You've touched on about a thousand hot toppings that I deal with on a regular basis. And I know a lot of people in the technology space do. All the time as well. so maybe I'll dig into a couple of those, if you're good with it? Number one, being a part of the Alberta ecosystem and being a part of that innovation space that we are a part of here, now how has the fact that you are in Alberta and Canada's first, which is incredible. how have you found being a part of this community, has either helped or perhaps provided, some, areas to optimize, for your business and how AI could be a focus of ours here in this province.
[00:09:12] Kwame Asiedu: Yeah, I think I'll touch on how it's helped and also touch on some of the things that maybe we could improve. So in this province, there is a tech ecosystem, and it has a lot of knowledge about people. And, you know, some of these people have started their own companies they've worked in, I don't know if I should mention names, but, you know, for instance, Jim Gibson comes to my mind who I've worked in about seven different startups, right?
And now also in the industry knows about innovation and how you can help companies adapt it. This podcast from Rainforest, I think he's also a co-founder there, you know, trying to throw light on our better, companies in our better. This has been very useful. there are other bodies like Alberta Innovates and IRAP, Platform try to create office space for innovators. This has been very useful because then you are able to support the, not just tech talent, but small, medium sized business companies. And you can more or less hold their waste so that they can scale. And we have benefited from this, partnering with some of these people.
So that, that is very useful here in Alberta. When it comes to AI, one of the things that I think that's where we can improve, and maybe it's just kind of the, in general, I don't know if you've seen this, Ipsos did a survey for the world economic forum, and this was released just a couple of months ago, I think. And it said that Canadians are among the least likely to believe that artificial intelligence will make their lives better. Isn't that shocking to you? And he said, Canadians, it revealed that Canadians are less knowledgeable and more nervous about using AI. In fact, 36% say that, you know, the products and services in AI basically will not really do anything for them.
[00:10:59] Kwame Asiedu: They will not improve their lives. no, that's the other way. Only 36% believe that AI will improve their lives. And guess what? This 36% is made up of youth, which means that mid-career professionals and leaders today are really behind in embracing this AI. According to this survey, they did, they did this in about 28 countries.
And in almost every category, Canada was almost last. Almost every category, United States, UK, and Australia. We're always the leader. Now, Canada topped in one category. Guess what that is?
[00:11:35] Lindsay Skabar: Oh I don't know, I don't even know if I could guess.
[00:11:37] Kwame Asiedu: Skepticism about AI. So 49% of the people in this, in this survey agree that products and services in AI, it makes them nervous, you know, and, and part of it is it's basically because people are afraid of what they do not know. Most people are not familiar with this technology. there are also not enough products about the methods around the technology, because it's not only about the technology and the tools.
It's also around the methods and the processes around them. And most people do not have an understanding of this. And because of that, most Canadians have not, most leaders have shied away from embracing this, this new technology, which I think it's a way that we can improve. We need to really embrace this. If this is the next electricity, AI has been called the next electricity, which means is going to permeate every aspect of our lives, which company can function today without electricity?
No one. Right? So we need to embrace it and we need to make sure that we are changing our mindset we are shifting gears. And we are thinking, and there are a whole lot of experts in this province that can help us do so. And I call on everyone, you know, reach out to us, reach out to some of these other companies and let's work together. Let's build the next digital economy.
[00:12:58] Lindsay Skabar: Hey, I'm happy to join that movement. I truly think, if utilized properly, AI is there to make our lives easier. And it's, it's a streamline, it's a shortcut to what you're already looking for. And I think, you know, some of that skepticism has to come from a lack of awareness. Of what AI is doing in the first place.
[00:13:19] Kwame Asiedu: I completely agree. And I think this is why some of the. Some of the initiatives from, you know, SAIT, from Mount Royal university, you know, all of these bodies that are coming together to at least create some of these awareness and to educate and empower people about it. It's very, very useful.
[00:13:37] Lindsay Skabar: Now you're bringing up a couple of, educational institutions here in Alberta. And I appreciate that because one of the challenges with. I hear profoundly from technology leaders and being in the technology space for 20 years and in Alberta is that it's very hard to find developers and keep them, to be able to apply, the, the code.
I'm glad you said no code, cause that's really helpful to a lot of us, but, to be able to, to create technology companies here and keep them retain that talent. Here because we're constantly losing our talent, to Silicon valley and other places around the world. So maybe, maybe shed some light on, what you're doing with some of your, if some of the educational institutions like SAIT, and Mount Royal and, and what that can do to help. Get the younger generation really excited about the types of things that are possible when you are a professional, this way,
[00:14:35] Kwame Asiedu: We already pointed out, you know, the, the lack of adaption to these technologies is awareness and knowledge around it. And so one of the things we decided to do is to partner with some of these, you know, large institutions. So for instance, SAIT. And what we did is a SAIT actually called us and a host of other professionals from different industries. And we sat down and we decided to create a curriculum around this. So. First they decided could we even offer something like this? And we said, yeah, it's possible.
Because you know, we, at BrainToy, had been doing this on our own and have been running some of these workshops. And so we created a curriculum, but there's no shortage of data science. information online. If you just typing data science, there is a ton of information that you will always get. The problem is that when people take some of these online courses, they are not able to apply it.
We've had people who have taken some of these online courses, even master's degree in data science, and they will still come and join the bootcamp. Why? Because it was not applied. So we focused on applied artificial intelligence. And through that, we created a curriculum that will teach you the principles of machine learning.
In fact, our focus purely is on machine learning, which is a branch of artificial intelligence. with time we'll add more but principles of machine learning and how you can apply them. And we let you apply them to solve different problems in health, in finance, in supply chain. In oil and gas. And we've had people who want to reskill take some of these courses.
You know, they, they have domain experts, they're domain experts, and they will take it because now they want to be able to see opportunities for AI in their organizations. We've had people who want to move from their current career to another career. You know, they want to pivot and, you know, looking at oil and gas right now.
And so many people who have lost their jobs want to pivot and get into tech. And this has also proven to be very useful. We've had people who that religion, as an example, you know, people in the hospitality business who said, I've never written a code in my life. Can I even do this? And people who only know how to use spreadsheets.
And some of these people have passed out of this, and now they're working a strategist. They're working in AI development in they're working in data teams. So the main goal has been to create this applied AI curriculum and use it to teach people who have domain knowledge, who probably never want to code in your life, but still be able to build solutions using the AI methods and also people who have domain knowledge, but who can code, who are, who are very technical and really want to write production machine learning solutions, develop them by writing code themselves. And we have, you know, a part of the program also for this group of people. So that is what we've been doing in partnership with, SAIT with Mount Royal, with Supply Chain Canada, with Manpower. You know, and a host of others that, we are still talking to will be coming on board.
[00:17:45] Lindsay Skabar: And I'm really looking forward to people going through that curriculum, and, and being employable by, the number of technology companies that are starting here in Alberta and choosing it as their location, to start business because we have such an opportunity here to create, and stay focused on the innovation space, unique to a lot of other places in this world. So it'll be great to have some homegrown, technologists and people who are pivoting. I love that the pivoters as well, to think of their career in a different way, and be able to apply that, that, skillset in a way that a lot of companies are looking to find creative solutions.
[00:18:25] Kwame Asiedu: And maybe just to add, you know, you, you mentioned about some, some talents leaving, right? And sometimes some of these talents, you know, we got a lot of offers before we even created BrainToy, to leave here, but we stayed here and here is some of the reasons some of these talents leave because they work in a company and, you know, they have creative ideas. They have innovative ideas, but there's no support from the organization. And so as soon as somebody knocks your door with an opportunity, which is the decision to move is not only based on money, sometimes it's based on doing something that you believe in something that you think will have an impact on people's lives.
Not that our veterans are not creating technologies that will make an impact on people's lives, but that support. For innovation is sometimes lacking. And because of that, as soon as they see the opportunities they leave. But if we create this environment for people to innovate freely, then in fact, some of these companies will grow to become the companies we respect in the U S where some of these talents move to. Right. Because then we'll be competing with them. And when it comes to AI, there is something that sometimes people think about, you know, they think, oh, I can never embrace it. I can never use it. Well, the world I use the analogy of the car. the world is moving towards self-driving cars the same way when it comes to AI, the world is moving towards a low code, no code platform, and it's moving towards automated machine learning and AI platforms.
Why? Because they know they think the same way as we do that to scale it. You just have to empower people who have domain expertise, empower them with the process, with the technology, with the methods so they can create it themselves. So with this low-code no-code platforms, people are able to learn, HR professionals can learn that, you know what finance accountants, they learn this technology and they utilize them.
Take spreadsheets, for example, you know, people used to write codes. You know, my dad used to talk about writing Fortran codes before I think there is a software that Microsoft released that was called VisiCalc visual calculator. And it was a command line that you would type in, you know, and now that has evolved to become spreadsheets.
And in fact, now with spreadsheets, you probably don't even need tutorials to be able to do it right. As soon as you get in there and everybody from all walks of life and different industries are using spreadsheets for their own purpose. Right? So this is also where AI is heading, where it's going to be easy and everybody will have to know how to use it.
And once we embrace it, you know, this tech ecosystem will grow. Companies will find talent here. Talent will stay here. We'll create more businesses and we can all help grow the economy for future generations.
[00:21:15] Lindsay Skabar: Check me on this. Cause I want to dig into a couple of the reasons that Canadians lead the way on skepticism associated to AI.
One of the things I can imagine aside from not understanding it, not understanding that you are in fact, using AI all the time, you just don't know it is AI. the other one is that fear of technology is gonna replace my job. And I love this spreadsheet as an example because it doesn't, in my opinion, here's my opinion. Then you, then you tell me if I'm completely off base here, just because I know how to use a spreadsheet and I know how to create a lookup table, or I can conditionally format a cell, does it mean that I don't have to apply my own intelligence, my own strategy in order to make that information useful, and in fact, the faster I can get to the information that we need to make the decisions only we can make. Then, the better off we are. We aren't employing people to use paper and pen anymore, to do accounts work or whatever you're doing in a spreadsheet. Instead, we're doing it on digital that doesn't make you less valuable, in fact, it makes you more valuable because, you can be that much more productive on the things you need to do in order to be good at your job. How does that line up for you? Does that make sense? And have you come across that fear of people being replaced by technology in these conversations you have.
[00:22:44] Kwame Asiedu: Yeah. And so what you said with your opinion, first of all, it makes complete sense, right? The spreadsheet is just enabling you, but you, as an intelligent being who has a problem, who has all the knowledge, and this is where we go back to domain knowledge and who really wants to solve a meaningful problem, you just use that to solve your problem.
And it works. And you have, like you said, productive you're faster because somebody took the pain to make this easy by building this spreadsheet. Now, when it comes and that's what is happening to AI and this platform, if that's what MLOS does, but when it comes to this fear. Yes. In fact, I worked in a financial institution and I was leading the data science lab there.
It's now called the AI team and we're solving all these problems and at least on three occasions, three different people called me, quietly, maybe I'm just walking up the stairs or walking down and say, Hey, Kwame, what you guys are doing, is it not going to take away our jobs, you know, that we're afraid of this.
They're fearful of it. And I always try to tell people about. This phobia against AI, is that in fact that is never the case at all. Whenever an AI solution is built, what happens is that yes, sometimes it might automate certain processes. If your work is routine, that you can just sleep and wake up, you don't even need to think and do it.
Then yes, a machine will do it, but every time. The solution is built to replace or we automate a certain process. It creates different roles. In fact, it creates more roles than are existing. People just need to adapt and to be open-minded to learn and new skills so they can take over. And this new skills are not like overkill.
You just have to be open-minded and you will be trained on it. In fact, in most cases it's creating new revenue streams for an organization, such that now they have to hire more. it's created new companies, as an example. Now I can use an example of a company and you and I were talking about it, you know, today that is trying to bridge the gap between sellers and buyers.
There are agents who today work, you know, this is their business. They will come to you and you will talk to them and they will sell you. They will take you to show homes and they will sell you a house. The thing is that now with the advent of new technology, these agents are going to be cut out, right?
But not all agents, agents who adapt are going to still be able to work because subject matter experts are needed as an example, but this company is going to create a whole lot of revenues and more jobs for model risk managers for, real estate developers. For software engineers for, can you imagine the number of jobs you are going to create for these people?
So that fear it's actually unfounded. nobody has research to back that up. It's just inherent in us. We are always creating new revenue streams, creating more jobs. People just have to be open-minded and to learn new skill because you can't take the human element out of AI. It doesn't stand on its own.
You need people, it basically augment what people are doing. Right. And so do not be afraid of it. Just embrace it because you will realize that it is really going to improve your life. Your business, and, you know, almost everything that you do.
[00:26:19] Lindsay Skabar: I also think that those tasks that are a part of everyone's job, that where you don't need to use a lot of elbow grease or, or brain juice in order to do those jobs are probably the least favorite part of your job.
Those things that are repetitive, that you just have to get done. It's like, you know, eating your vegetables, although vegetables can be quite delicious. It's the part of your job that doesn't inspire you, the part that does inspire you, that's where you should lean into and just be thankful for saving the time on, on the less desirable components to your role. And if you find that your whole job is that, then it might be worthwhile finding inspiration elsewhere.
[00:27:00] Kwame Asiedu: Yeah, and I completely agree. And where I used to work that I told you about where people were skeptical. Sometimes, in my team, there were data scientists who were doing sometimes routine stuff.
And these data scientists very skilled. They have master's degrees. They have PhDs. They really want to focus on solving meaningful problems. And these days we're actually making them unhappy. Right. So then once somebody knocks the door with an opportunity to just leave. So to actually retain talent, we had to automate some of these routine and mundane processes that we have so that they can focus on doing what they really enjoy doing.
And that makes them happy. That creates a fulfilling life for them. And their career is more meaningful and more fulfilling. And in fact, when we spend more time doing what we love, we solve more problems. We end up improving processes, improving efficiencies, reducing costs, increasing profitability, all of those things that add up to create a profitable organization, which employs all of us for the rest of our life and for future generations too.
[00:28:05] Lindsay Skabar: That's amazing. So here's a question for you. It's a bit of a wild west right now when it comes down to the application of AI, in the sense of, there are so many different applications and there's not a lot of boundaries or rules you need to abide by in order to be able to utilize AI technologies. Would you suggest there should be some boundaries or there should be some limits on this.
Does there need to be regulation in place? what are your thoughts on that? Given where we are in terms of the technology, sophistication, and to, and how fast we're, we're learning, new ways to apply AI.
[00:28:44] Kwame Asiedu: Yeah. So I completely agree with you that there has to be some boundaries around it. Some framework around governing the processes of developing these, utilizing these machine learning and AI techniques and solving problems. There has to be, right? We need to regulate it. And this is why, because there are a lot of biases inherent in some of the methodologies that I use.
In fact, some of these biases are sometimes introduced by the developers because they're using wrong practices. So you have to govern them. Some of the solutions are black box, which means we don't even know how these algorithms are making the decisions, but we have to break those black box open and make them transparent.
Then people could, could trust those solutions better. And this is actually happening. In fact, in BrainToy, we do have seven methods, seven processes that we follow in developing solutions. And part of it is called AI governance. Before anything goes into production, AI governance, frameworks are adapted, and we use them as part of the evaluation process before solutions go into production.
And these include automatically documenting all the assumptions that algorithms that are used, this include, being able to explain why the models are making the decisions for which they are making, it's called model explainability. This includes challenging the assumptions of the data scientist. It includes peer reviewing all of these solutions and algorithm before they go into production. In Canada, there is in financial institutions, there is a body called OSFI the office of superintendent of financial institutions. And they've created a framework, a model risk management, framework, that when organizations follow, could help them create models that are transparent and also fair. And that are ethical. recently we followed this because this is very important.
Recently we have been in touch with an organization in Amsterdam and they have given us a draft of the artificial intelligence act that the EU is creating because they care about things like accountability, creating solutions that are for the betterment or the wellbeing of humans. Solutions that avoid discrimination that are transparent, that are fair and all of this.
So I think there are a whole lot of organizations working together to create some of these frameworks and legislations. In fact, when I worked in that organization, financial institution, we created one of these frameworks that was approved by the board of directors just before I left and is governing them in the way they build their solutions.
So it's very important that we do that because I don't want to get a higher rate in my loan just because I am, you know, a black man or because of gender, maybe because I'm a female or, you know, so any these things can happen. And so creating frameworks around them, trying to make sure that AI solutions are more ethical. They are transparent. They are explainable is very, very key and very important.
[00:31:50] Lindsay Skabar: It's interesting because some of those biases you referenced. can can happen in the code, but it can also be completely eliminated based on doing it properly. Right. If you're looking at an insurance, program or something, you can actually look at the lifestyle of this specific human being, as opposed to lumping us into these pools of humans that are similar.
No, you could actually look at. Actual risk of Lindsay Skabar on this world. and, and give me the insurances appropriate to who I am and how I live my life, which is a little bit different and it actually removes some of those things that could have been biased before, like my gender, my age, the color of my skin.
So with that ying comes a yang, which it kind of frees us a little bit from the biases that perhaps, are a little bit ingrained in the systems we've already got.
[00:32:41] Kwame Asiedu: Yeah. And if all of us can really think in that direction and support each other, I always tell people, especially data scientists, you know, some data scientists call it their magic. Right. So when they build a solution, they don't even want their peers to take a look at it. It calls for humility. So as part of being a professional data scientist who creates solutions that will make an impact on an organization is humility. And that humidity means make sure, at least if you work in a small organization that doesn't even have a process, at least one or two people in your organization, will look at what you've done. You will explain your assumptions to them. You will explain it to your stakeholders. The people you've got the data from all of that. And you will be able to, at least, even if you don't have, an automated framework around this.
You will be able to cut some of these biases and, and things that are inherent in the technology. And you can build solutions that people can love and trust.
[00:33:41] Lindsay Skabar: Well, we're almost at time here, but I do want to ask that one gigantic question of. Where is AI going? And we've talked about, you know, self-driving cars, we've talked about no code. We've talked about some of those things, but, preach to the skeptics out there for a hot second, and let us know in 10 years how AI is going to be dramatically improving our lives, that we take for granted, right now.
[00:34:09] Kwame Asiedu: If anything at all, COVID has shown us that things can change rapidly.
Right? So the future for this AI technology, which has been spoken of as a general purpose technology that will disrupt every industry, every industry. What it means is that if we don't embrace it, if you, if your competitor, and I think it was Elon Musk that said that if your competitor embrace it and you don't, They're going to crush you.
So one, we need to be open-minded and begin to embrace it. The second one, the same point I will make is also that a lot of companies are working hard to make this technology so easy for you to embrace, to make it affordable, to make it inexpensive. Gone are the days when you needed to solve one solution it will take you six months. And you have to, you spin up a scrum of about 16 members to just solve one solution. And it's six months. And sometimes by the time they finish, maybe it's like a year. By the time they finished the solution. It's the solution is no longer relevant. A lot of companies are working hard to streamline this process and methodologies around the development of AI solutions and BrainToy is one of them. You can actually think and deploy solutions in days, in hours, even in minutes.
So it's going to be faster. A lot of companies are also making it easy for you to be able to build such that you don't need to be a programmer to be able to do this low code no code is where the world is going. So it's going to be easy for everyone to embrace. And again, As far as ethics are concerned, a lot of companies are working so hard to create frameworks that can govern the development of these production solutions.
And the benefit is that people are going to be productive. They're going to create new revenue streams. They're going to increase profitability, reduce costs and you name it. So let's not shy away around it because we live in a world of accelerating change the world that is always becoming something new.
COVID has taught us that. And so if we want to move with the world, then we need to also be adapting to the changes in the world. We need to be involved with it. And AI is one of the technologies and the processes and methods around it. That is one of the technologies that can help us do that and build a digital economy that will improve the lives of everyone around us. And for the future generations.
[00:36:39] Lindsay Skabar: You heard it here, open-mindedness around AI is going to improve our quality of life. And future generations as well. This is a space to watch. Keep in mind BrainToy as you are looking forward. Thank you so much, Kwame for all of your time. And we look forward to seeing all of your success and the impact you're having on that Canadian AI space.
[00:37:00] Kwame Asiedu: Thanks for having me, it's been an absolute pleasure.