Artificial Intelligence (AI) Software Development and How Will It Change the Future?
We are thrilled to share this Instagram Live with our co-founder, Ryan Williams, and Elizaveta Shmakova, where they talk about Artificial Intelligence (AI) and machine learning and their future. In this live, Ryan goes over some of the artificial intelligence projects that Wonderment Apps have worked on, the future of artificial intelligence, and how the rise of artificial intelligence will change future jobs.
Ryan Williams, Co-Founder & Partner of Wonderment, has more than 15 years of product development, management, and marketing experience. Ryan is currently a founding Partner and CEO of Wonderment Apps, a Los Angele-based technology agency that builds software, apps, and websites for companies of all sizes. Before Wonderment Apps, he was a key product management leader for Apple, The New York Times, and many notable technology startups in Southern California. Ryan has excelled as a product and marketing leader, helping to design, build and market websites, applications, and user acquisition strategies across various platforms. His specialty lies in Mobile, Web, Ad Tech, and eCommerce platforms, and his domain knowledge reaches deep into the Ad Tech, eCommerce, and Entertainment sectors. Ryan holds a Bachelor of Arts degree from the University of California, Los Angeles.
Artificial Intelligence IG Live Transcript
The Future of Artificial Intelligence and Machine Learning
[00:00:03.830] – Elizaveta Shmakova
Hi, everyone, welcome to our live. Today, we have live with Ryan. Ryan Williams is the co-founder and partner of Wonderment Apps, which is a Los Angeles based technology agency. They build software apps, websites for different size companies. They’ve been working with companies like Walgreens, Nasa, Casting Networks to name a few. So we’ll wait for Ryan to join me right now. Also, you should wait until the end of this live, because in the end, Ryan is going to talk about promotions, which he has going on right now in the company. He’s hiring developers. So wait until the end. and while we’re waiting. Please tell me what you are doing if you are a developer or what kind of job you are looking for or which position do you work at right now. This would be great. Yeah, everybody is waving. Yeah, that’s great. So let’s see. I didn’t see Ryan right now. Let’s check. He’s not here yet. All right, yeah. Thank you. OK, He is here. Hi, Ryan.
[00:02:39.530] – Ryan Williams
Hi Liza How are you?
[00:02:40.390] – Elizaveta Shmakova
Good, good, how are you doing?
[00:02:41.990] – Ryan Williams
Good. I’m doing well, thanks.
[00:02:44.190] – Elizaveta Shmakova
OK, wonderful. So welcome, Ryan. He’s the co-founder of Wonderment Apps. As I mentioned before, I would describe to what the company is doing. Ryan has 15 years of experience in product management as a leader. Also, he was working for companies like Apple and New York Times. So, OK, Ryan, can you tell us more about your Wonderment Apps company?
[00:03:16.790] – Ryan Williams
Yeah. At Wonderment Apps, we’re an agency. We were founded about five and a half years ago and we worked on a whole variety of projects. We put together everything from the kind of large-scale technology through a kind of new startups that are trying to do innovative things. We’re based out of Los Angeles and in California. And we really take on a variety of different areas. But do kind of our main focus is blending really good product management and really thoughtful product use with pretty powerful and performant technology.
[00:03:53.870] – Elizaveta Shmakova
OK, that’s great. Yeah. OK, wonderful. So can you also talk about your projects, some of the projects you’ve done already in the company? And also, I want to mention that today the focus of our live is on AI, Artificial Intelligence. So we will also talk about it more. So please tell us about your products and the projects you’ve been working on.
[00:04:21.160] – Ryan Williams
Yeah, sure. So we’ve worked on probably about since the company opened. We worked on, I don’t know, two hundred projects probably over the course time with a pretty wide variety of clients. So all kinds of things. We kind of specialize in some of the fields that we do a lot of or we do a lot with entertainment. We do a lot with ad tech and marketing tech. We do a lot with some financial, tech, and real estate as well. Those are kind of some big ones for us. And I think some we also have done a fair amount in the logistics space. So we work pretty closely with Stamps.com. We work in a lot of e-commerce. We work with iHerb as a client of ours. And so there’s a variety, I think for us. What we tend to do really well is kind of go in on projects that people need to have done, whether they’re front end experiences that need to be rethought and recreated, or whether we’re building unique kind of like backend restructuring data pipelines to create something pretty powerful and doing a lot of machine learning, AI, Artificial Intelligence, stuff kind of blended in, which is part of the reason we’ll talk today. But I think that it’s, you know, trying to take kind of new technology and bringing it to companies that either needs additional tech resources or they just maybe they haven’t experienced it before. They haven’t even done any kind of major tech build-outs and they’re just transitioning over.
[00:06:02.070] – Elizaveta Shmakova
Yeah, of course, I remember you mentioned when we talked before on that you had an interest in advertisements tracking. Can you describe it?
[00:06:15.090] – Ryan Williams
Yeah, sure. So there’s a number of years ago and it was kind of in the earlier days of establishing the foundations of machine learning and kind of the beginning of, you know, one of the quadrants of AI, Artificial Intelligence, basically. But we were really early stage in like looking at how to optimize ad platforms really effectively and thinking about what goes into all of the decision makings when you’re trying to create Ad-tech. And so if you’re a human, you’re constantly thinking about budget management targeting ways to actually get the right kind of audience and build those things and ultimately have to optimize for those pieces. The piece that we were doing, we’re on the publisher’s side, meaning that we are working with the people who actually run the advertising. So this was big mobile apps across a variety of different things. But they really had high volumes of display advertising space. And so one of the challenges that I worked on and a lot of the team members, is how do we best optimize for the best the greatest yield and the performance that they can get from the advertising. And anyone who’s done it before by hand knows that it’s almost impossible in today’s world with programmatic ad spend and really fast data that it’s nearly impossible to do it by hand and actually think, OK, this is the best kind of bid I could put in for this advertising. And this is the best, best way to do that. So you have to really train the machines to understand what is the best methodology for generating the most revenue off in that piece of advertising.
[00:08:07.260] – Elizaveta Shmakova
Of course, that’s great. So also, if you have any questions, please save them. And at the end, we’re going to answer all of your questions. And so maybe many developers are watching us right now. So please, Ryan, I know how many developers in your team, but how do you manage them and what kind of developers who are looking for like maybe seniors or juniors, like, can you tell us about your team? Yeah, absolutely.
[00:08:39.420] – Ryan Williams
Yeah. We keep a blend of I mean, for us like having a really good team structure is really important. And so we kind of have two levels that we work with, one level as we do staff augmentation. So we find developers that are pretty highly skilled and they can walk into almost any environment and quickly understand what’s going on with the mechanics of the technology. So and it includes kind of legacy projects that are old types of technology that might need modernization. And it also includes, like totally new, very, very up to date fast technology, new platforms that people are building on, new languages that they’re trying out. But like those developers really have to be able to come in and solve problems fast and understand them quickly. So they tend to be more senior. They tend to have a wider array of skill sets so they can flex a little bit more. They’re not just stuck in one language in the back end or they’re not just you know, they’re not just pure fun developers, but they can do a lot. The other type we do is manage teams. Right? So we might take on a project and those we put together a team that can run together pretty well. That’s where we work with the kind of more up and coming developers that are learning. And we blend them with more senior developers. So they have a really good kind of pods that they can work with and teams to understand. And all of those cases we try and match and train in a really good methodology for understanding how to translate product needs from the business side or so our developers get pretty skilled in like asking the right questions and building effectively upfront.
[00:10:21.420] – Elizaveta Shmakova
OK, yeah, that’s great. That’s fine. And where your teams located, which countries?
[00:10:27.960] – Ryan Williams
So we have a couple of teams in Eastern Europe, we have one in Ukraine and the L’viv area and we have another one in Belarus. And then we also work with some Russian developers as well. And we also have an office in Delhi, India just outside of Delhi. So we kind of work on a number of different regions for developers. But we also have a really good US team, too, that supports the supports of businesses directly and really understands the business’ vision and translates, well
[00:11:03.920] – Elizaveta Shmakova
They live in different countries, different time zones…
[00:11:10.460] – Ryan Williams
You know, like it is so worldwide at this point and we’ve been working with so many different teams all over the place. Our business is open 24 hours. We kind of never really shut down. But it’s great to be able to have great relationships with people, you know, all over the world.
[00:11:32.040] – Elizaveta Shmakova
Yeah. And you said you have a team Belarus and with the current situation. I’m really sorry about your team in Belarus. How you’re managing that team?
[00:11:43.100] – Ryan Williams
Yeah, they’re a really special group. They’re an awesome team of people. I think like and we’ve actually seen it a couple of times since the business has been open. Sometimes there’s unrest in a spot where your office is even in Los Angeles that happens. You know, it happens in all kinds of different places. But I think with them, it’s giving them whatever support that they need and try and give them the flexibility for the working hours because you don’t always know what’s going to happen. But, you know, for us, just staying together as a community online, one of the things that we have put a lot of thought into is like how we actually support each other on a Coding level, how we actually support each other on a knowledge base level. And that kind of translate. So, for instance, a really common case for us is will stick a developer on a project. They have to go and work in a company that they know very little about. They aren’t sure the problems are going to run into and they might be alone or there’s maybe two of them and kind of the whole theory of our company. In fact, we have a phrase in the company that says Hire the Bee, Get the Hive, which is like we put somebody in a project, but they always tap back into our company. So like we have a really powerful way for them to reach back in. I mean, the most basic being they’ve got a slack channel that’s constantly open. Right? But they also have access to all kinds of tools that we’ve built over the course of time. And so we kind of view like even supporting people on a social level like that as well. So people are out facing different things in different parts of the world. That may not be the same for people in the other region. And so how can we actually, like, support them and give them that? So I’ve found that like just having you know, it’s like having friends across the world, like, you know, different people can support you in different ways, which is pretty powerful.
[00:13:43.400] – Elizaveta Shmakova
And people tend to stay in this company when they feel this special bond with the company.
[00:13:51.990] – Ryan Williams
Yeah, a lot of our developers have worked with us since we opened the doors, you know, so we’ve been just working together and some of them I’ve worked with, in fact, on this AI, Artificial Intelligence, machine learning project that we’re talking about with ad tech, I worked with a number of those team members from years before I even open this company. So these relationships and bonds can last for a long, long time.
[00:14:15.660] – Elizaveta Shmakova
Yeah, it is a good representative.
[00:14:17.620] – Ryan Williams
Yeah, I love it, like for me, it’s like one of the best parts of doing what I do and I always want to be working across the world. So it’s been powerful.
[00:14:32.180] – Elizaveta Shmakova
It actually presents like how long the developer stays in your company and also how willing to recommend this company to their friends.
[00:14:42.860] – Ryan Williams
Yeah, that’s true. I mean, really those teams build themselves and it’s funny, even like the Belarussian team actually recently they did as they did a celebration of our five-year anniversary and they went and took a trip and Belarusian all hung out. And I watched this amazing video they put together and I was like, I want to be invited to that. I wanted to fly out there and be there. Coronavirus made it a little hard, but like it was amazing to see, like the teamwork and with, like people so far across the world and being we’re all connected through kind of creating really innovative technology and trying to think through these problems. And that’s awesome. Like, I love that.
[00:15:30.320] – Elizaveta Shmakova
OK, that’s great. Let’s come back to AI, Artificial Intelligence, and machine learning. Yes. In my blog, I always talk about the future of different technologies, how I see the world in 10 or 15 years. And I really want to know your opinion of how do you see AI, Artificial Intelligence, in the nearest future.
[00:15:55.040] – Ryan Williams
Yeah with AI, Artificial Intelligence, we’re still in early stages of it, you know, like we were just in the very beginning of doing kind of the basics and like really narrow AI, Artificial Intelligence and no one’s really broken outside those bounds yet. And I think we’re if I look at it holistically, I think we’re at a stage where we’re just teaching machines like how to understand what is happening in our minds. Right? And there is a collective effort of this happening right now. So we give it all kinds of labels and names and understanding. But at the end of the day, they’re the only kind of able to make kind of rudimentary decisions and kind of very simple decision processes. Still, even the most advanced stuff can do that. And a lot of that is just our own capability of even being able to explain the complexity and level of the decision making. So I think for AI, Artificial Intelligence, moving towards how it actually truly mimics human experience, how it actually truly mimics looking at the human decisions. So, for instance, like AI, Artificial Intelligence has limited ability, very limited ability right now still to kind of take historical perspective on a situation and actually apply that to the current. And so it might be able to look a couple of steps back and then use that in its decision making. But a lot of times what it does right now, and this is really what machine learning is kind of teaching it to take a bunch of data, assess the landscape, and make the best decision based on what it knows. Right? And hopefully, you don’t have to tell it what to do, but it begins to learn what to do. And there’s this other perspective, which is there is a history of decision making around that informs it on an even deeper level. So the more it can gain that data perspective in, the more it can also use its ability to look at all the variables that go into the decision making, that’s where the real power of it’s going to be. You’re beginning to see a little bit of that and things like, you know, the car and auto and stuff like that where it has to kind of be more real-time decision making and work that. And some of those AI systems are pretty powerful for our perspective now but like 20 years from now. I think there will be a gigantic leap where the machines begin teaching themselves and this is really where it gets pretty wild, right? And they can do it. They can look at it at the level in their own perspective and language that we can’t even see it at. And I think that that’s where this gets pretty insane. But in this current stage, where we’re really kind of on this narrow focus and I can only see within these blinders. It’s used. It’s needing more and more data to make that decision making. And the better we get at being able to serve that data to it, the more powerful its decisions can be. But, yeah, I think it’s a way away from the kind of feeling like the kind of robots that we imagine in the future. But some of the actual tools that we can use so that it begins to go beyond just mimicking current, you know, process decisions are definitely coming.
[00:19:18.020] – Elizaveta Shmakova
Exactly, it sounds really exciting and at the same time really scary. It reminds me of the last excuse. And I don’t know if you watch that of Silicon Valley when I think that the U.S. is going to unplug any kind of any computer and they destroy it. If people know it’s possible, then it would be repeatable.
[00:19:41.350] – Ryan Williams
Yeah, I think like the ethics question, I mean, I kind of like it’s interesting because we’re sitting in this time period where there’s a number of technologies that we have that have these pretty powerful ethics questions tied to them. I mean, obviously, AR, Augmented Reality, and VR, Virtual Reality, I think the big one and then I think VR, Virtual Reality being kind of maybe the other big one. But how we actually.
[00:20:06.830] – Ryan Williams
That’s an interesting question on the data structure there, but the ethical pieces of like how we’re actually looking at using the data and how much power we give the AI, Artificial intelligence to make decisions and take it out of our hands, you know, and so when we give them those decisions, we give it that power and it gets really good. At what point are we even obsolete in in ourselves? Right. At what point as I was reading a really good article recently about how well, you know, training AI, Artificial Intelligence to actually do coding. Right? So the actual coders that we are, we’re teaching these machines to do the functions. And this is just basic machine learning. Right. but AI, Artificial Intelligence right when one of its gifts is going to be its ability to get creative, which is actually one of the human qualities. So as a coder, I’m creatively thinking through decisions of my approaching this the right way, am I doing this? But once the machine has the capability to also make those decisions, are we actually just writing ourselves out of jobs altogether? So, I mean, that’s the more advanced versions of it. And then the very simple existing AI, Artificial Intelligence versions and kind of where we’re at with it. That can be really useful. They can do it’s all over the place and it’s making all kinds of decisions, again, on a pretty kind of very simple transactional level.
[00:21:46.190] – Elizaveta Shmakova
I agree with you. So we will hope for the best. Yeah, we’ll go to just simplicity, not taking all of the jobs and oh, maybe we will. New jobs and people start enjoying their lives more.
[00:22:03.510] – Ryan Williams
Yeah, I think it all evolves. I believe in the evolution of it all.
[00:22:11.740] – Elizaveta Shmakova
So since we’re talking about the future, also, what I have in my mind is since again, many developers might watch us right now, can you tell us which fields they should pursue as their career? So what kind of language? Or maybe just the field? They should focus on what the future is AI, Artificial Intelligence, or maybe put something else.
[00:22:38.130] – Ryan Williams
Yeah, I mean, I think AI, Artificial Intelligence is a big one. I think AI, Artificial Intelligence, has so many components tide to it, too, and I think there’s still a lot happening with the way data is ingested and transformed. And I’m thinking about that kind of bigger problem of how we more effectively segment data across whatever fields. There are so many companies that have a need for an intelligent transfer of their business knowledge into something usable for the business people that are working there and for the customers. And it’d be like something that can actually create the foundations to start putting toolsets on top of things so they can actually use the data, analyze and think, think through that. Another version that I think is pretty, pretty impactful is actually like thinking about how we train and kind of go into that training process of decision making. And so there’s a lot in actually the conversation with the artificial intelligence itself or so that’s historically been basic machine learning. Right. So we’re just teaching it how to do these simple tasks. But there are more advanced methods that are starting to work into the marketplace where the guidance will shift a little bit. I also think that there are all kinds of really interesting applications that are going to grow off it as well. And a lot of the creative side of the technology is going to get more interesting over the next little bit, too. So that’s so creatively thinking how we how we piece together some of these things and actually use the technology in different environments. So it’s no longer limited to just the computer or just the phone. You know, I would look at how engineering is working its way into so many different aspects of our lives. So a lot of things we wouldn’t have traditionally had computers tied to are now going to be, you know, it’s all going to be put together. I also personally think there’s a huge shift coming in the way that the monetary system is going to work because I think it’s going to have to shift. And I think that things like blockchain are really driving some of that forward in the way that we’re thinking about exchanges of commerce and working with that. And I think there’s a lot to be done there. And the financial system, I think fintech is a big space. And I also think the way that we do kind of traditional things like real estate or property exchange and stuff like that, we’re going to start to see a lot of those systems redefined a little bit, because as we get more and more global, that we all have to work together. So they’re pretty powerful.
[00:25:27.990] – Elizaveta Shmakova
OK, that’s great. I turned off the comments to send some of the talks we had today, guys, so they’re going to answer your questions. So we’ll open the comments. So let’s make like a sum for today’s discussion. Basically, we said that the nearest future AI, Artificial Intelligence, is going to be a prevalent job. Opportunity for many developers also is developing really fast. Also, we said that probably it might simplify the job so much that developers are actually pushing the jobs away, which is, I don’t know,
[00:26:12.640] – Ryan Williams
Long term. I don’t think this is short term, but long term. Yeah.
[00:26:16.760] – Elizaveta Shmakova
but we don’t really know what will happen and hopefully, we will create more jobs or try to increase the salaries and, I don’t know, enjoy our lives. That’s my perspective. Also, Ryan talks about his company and he said that right now he is looking for some developers and he also described his team as giving as much support as he can right now. He also mentioned that are creating some kinds of events together. It’s a really bonded team. They actually really like working there. As I said, at the end of the talk is going to talk about the promotions she has right now. So, Ryan, please tell us some of the promotions which people who are watching right now might use as an opportunity to join the team or anything else.
[00:27:17.380] – Ryan Williams
Yeah, I mean, I think we’re kind of looking constantly looking for really good developers. Especially I think Eastern Europe is a big, big spot for us, but also a really good kind of senior-level developers in the US and product people here as well. And some of the other regions I talked about as well, where we’re always looking for the right kind of fits in those different areas. So I think really check the job boards. We literally hire constantly. We’re always looking for new people. And we’re willing to work on a project level to see how you’re doing as well, to get to know them. And then, you know, working with different companies, I would say, like we love to do a variety of different things that go in and just work on small projects over the course of time. Or even do different types of diligence in reporting to check and make sure that your different areas of the technology stacks are functioning properly. So those are good ways to get to know us a little bit and see how our team works. But, yeah, that’s a good way. So if we do visit our website, if you’re interested in us or our LinkedIn is really active as well. So feel free to come in.
[00:28:43.390] – Elizaveta Shmakova
The company or your personnel personally?
[00:28:47.500] – Ryan Williams
Yeah. Wonderment Apps LinkedIn has a lot going on it, so be sure and check that out.
[00:28:53.470] – Elizaveta Shmakova
OK, that’s great. OK, guys. So now you have you know what to do.
[00:28:57.970] – Ryan Williams
OK, let’s turn on commenting and we had a few questions already, so let’s give them and guys feel free to ask questions right now. We still have a few minutes to answer them. So we answer the first question. The significance of AI, Artificial Intelligence and its significance we covered. What role does basic data structure and algorithms play in machine learning and AI, Artificial Intelligence? I mean, I guess the answer is obvious.
[00:29:35.450] – Ryan Williams
Yeah, I mean, I guess for those who don’t fully understand it or know it, in the machine learning stage. Right. It’s really about teaching the machine how to make decisions. Right. So it’s ultimately so, so actually creating a good data structure is helpful. There’s and I think historically it was required. Right. So it was required for the machine to be able to quickly understand exactly how the data is structured. And so you couldn’t go into the kind of a seamless environment and do a lot of the over the last little bit, though, like actually the machines are getting a lot better at taking dumps of data and analyzing it and being able to do its own formatting and parsing and actually placing it how it needs, I think. I actually think in the near future, you’re going to see more and more of that where you can just stick big dumps of kind of data into systems and it’s going to be able to completely understand what the data means and how to put it together. So I think it’ll actually be a big use of AI, Artificial Intelligence, in taking unstructured data. And it’s already being used like this to some degree. But like the power of it could just and a lot of pain for a lot of people who have these kinds of historical data sets and don’t know what to do with it. It’s actually and I think interestingly here, too, is that the AI, Artificial Intelligence, itself may look at the data differently than we as humans can conceptualize it. So its ability to make those neural connections and I think this is kind of the next phase, but the ability to make those neural connections probably are going to be more powerful than what we can conceptualize. So our simple data structure in the near future, I think, isn’t even going to be relevant. Even if we put it in like that, I’ll just be another version of dropping data. And but like in the early goes of it, we’ve been teaching the machine how to actually read the data and understand what we’re looking at.
[00:31:38.440] – Elizaveta Shmakova
So exactly right now, we still have to look at it.
[00:31:43.090] – Ryan Williams
Yeah. Right now Yeah. And I think there’s probably a number of organizations, that are starting to break that model and doing some interesting things with that so you can drop it in big data lakes and let it work. But I think it’ll get even broader than that. Everything will become a lake itself.
[00:31:58.810] – Elizaveta Shmakova
So, yeah, there is also a question about learning AI, Artificial Intelligence, as a beginner. I want to add something to it. I just want to say there are many books which are available or else. But for me personally, I think we have to do as many tutorials as we can and actually working on our own projects to learn to actually learn what’s happening. At least this happened to me like I know you know the basics of it, but if you don’t use it, if you don’t try to create the project and run it, you would never learn what. So basically.
[00:32:43.030] – Ryan Williams
Yeah, yeah. I think you’re going to work off certain platforms as well. Right. So you might want to get to know. I mean, Google obviously is probably the kind of more the most forward-thinking aspect of it. But Amazon’s got a really powerful AI, Artificial Intelligence, program as well. They start to get into and use a lot of platform-tools on there so that creating like secondary personal projects that you can get into some of those platforms and use their up and coming developer tools are pretty powerful.
[00:33:11.380] – Elizaveta Shmakova
Exactly. Exactly. Yep. You’re welcome, guys. Question for the guest speaker Ryan, to you, how did you initially begin working the teams in Ukraine, Belarus, et cetera? Could you comment on the pros and cons of that relationship?
[00:33:42.980] – Ryan Williams
Yeah, that’s a good question. So I originally, I mean, I’ve been doing tech for since the original dotcom boom and the late 90s. So I’ve worked with a variety of different people over the course of time. But at a couple of the companies, I would say the ad tech company I was talking about a little bit earlier, and then when I was working The New York Times, I worked with some really good overseas groups and I just worked on projects with people in there and I got to know them. So I knew they were pretty talented. They were usually coming through agencies like us. I happened to be working at those companies that I kept relationships and friendships with them. And then we started this company. They were kind of the foundational people that I started to build things with and get connected with. And then we learned we learned how hiring works differently in each region and began to build recruiting practices that made sense for those different regions so that we understand how to work with people. Also, I think really important here is that there, you know, people coming from different cultures have different experiences with their work-life and how things happen. So for us to become really well versed in the regional work-life expectations and how things happen, where we’ve gone in and taken the time to really understand those. And so as we’ve gotten to know the core groups of people there, we’re able to expand and work within the framework of those different regions.
[00:35:21.620] – Elizaveta Shmakova
Yeah, I agree. And also, it’s changing over time, for example, in Russia, the hiring process is changing, education is changing. So probably also need to adapt. But since you already have a team over there, they are stepping up with the progress there.
[00:35:41.150] – Ryan Williams
Yeah, yeah. And I think like I think part of it, too, is like finding people who are who want to be leaders and actually want to want to help build something and can guide new people coming in and train up and coming developers. And so I think finding those in the different regions has been it’s just an exploration and meeting lots of people. So that’s. But, yeah, I would say that even the difference between Ukraine, Russia, and Belarus, there’s a difference in the way each of those, even though they’re pretty tightly put together geographically. There’s a real cultural difference in the people between each. So and obviously India is very different and then the US is very different. So those are kind of the main ones that we work with.
[00:36:35.870] – Elizaveta Shmakova
OK, and I guess the last question for today is when Google invested in Indian agriculture with AI vision, Artificial Intelligence Vision your thoughts on this. Actually, I’ve heard a few projects with some agriculture AI, Artificial Intelligence in the US.
[00:36:54.110] – Ryan Williams
Yeah, actually we didn’t end up working with them, but I talked to a company doing some really interesting stuff with that. And I can’t go into detail a lot of these kinds of projects. I hear about, I know about. I end up in nondisclosures round, but I did, but I did look at the agriculture space and kind of the way that they’re using information capturing. So when one of the things that strikes me about it as an area that is pretty interesting and I don’t know the specific thing around the Google AI, Artificial Intelligence, but I will say around agriculture itself, as I look at agriculture data is like a 360 environment, kind of like a biosphere. So they actually are captured. There’re really simple forms of data like how much rainfall we have within this period or whatever. There’s actually a very complex level of depth in the data itself because the environment is ever-changing and it has a lot of layers to it, of which we’re beginning to understand different, different kinds of call-outs within those. So things that we don’t think matter, like what actually makes up the soil. Right. Might dramatically impact things. So I think the AI, Artificial Intelligence, has as much information to work with as we can currently feed it. Right. As we currently see it. So just creating the connections between the information we’re gathering from the Earth and passing it through on the machine so it can begin to tell us a little bit more about like, oh, this is actually a direction that it would recommend making. Those are pretty powerful. But until we get better, until we get better, I think more advanced tools for capturing the data out of the agriculture environments themselves. It’s going to be harder for the data to expand beyond kind of our more basic weather-oriented perspective.
[00:38:58.550] – Elizaveta Shmakova
I’m actually a member of a triple AS. At meetings, we often discuss how to combine science and technology. And agriculture is one of the major areas. And there are so many types, not only in recognition of these factors but also in how we are going to recognize them, because some of the people who would like some chips inside certain trees, some people use to draw on the drones to control. You know, the water and so other factors to find the trees which are under, you know, we don’t have enough water. So there are so many different projects and its just time will show which are better and which are not.
[00:39:47.670] – Ryan Williams
Yeah, yeah. I look at things like even like understanding the soil makeup of different environments and whether you’re even planting the right foods or whether you’re, you know, or using massive amounts of bloat. Obviously, the global weather landscape seems like it’s in pretty major shifting point. How do we use those tools to protect against what’s going to happen in the next 10 years or so? So the eyes focus less on what’s happening now and more about what’s up and coming. So, you know, taking the idea of the Farmer’s Almanac and making a really robust thing. Either way, the AI, Artificial Intelligence, itself, it should be able to come back and actually help us either. The simple version is to inform us of what to do. Right. But it also might be able to go out and you can use it in all kinds of tools that give you a lot more precision and actually implementing whatever changes are recommended. So and it might be able to do all that in the connected sense, you know. Exactly.
[00:40:55.580] – Elizaveta Shmakova
All right, Brian, thank you for this amazing chat. It’s really long and like 45 minutes for joining us. And I’m going to post it so more people can watch it. And also click onto this video when I post, please write your questions and we will be happy to answer them. I will try my best friends now and Ryan?
[00:41:25.580] – Ryan Williams
I’d love to. Yeah, that would be great. Thank you for having me on. I really appreciate the chance.
[00:41:30.560] – Elizaveta Shmakova
Yeah. All right. Thank you so much.
[00:41:33.400] – Ryan Williams
Thank you, everybody. All right. Thanks.
[00:41:35.800] – Elizaveta Shmakova