Kai-Shing Tao: Okay. So I think I’d answer that in three — kind of the three-part answer. The number one is we’ve been doing this for a long time, and we’re not — we didn’t just start this AI company in the last two years and certainly over the last six months to capture the wave. So it took us six years to build the technology in just in the last three or four years to gain the experience on how to deploy it. Each customer obviously has different requests and the actual expertise in putting up these cameras and the angles and the placements and all that takes time. And obviously, every type of vertical has their own intricacies. But I would say, taking a step back, the two most important part, which answers a bigger question of which is why are we able to go into these different industries is really our core algorithm, right?
So the way that our algorithm is trained allows us to number one, train any type of solution that we don’t have under kind of our stable to do it in a very fast manner. So typically, it takes 9 to 10 months to train something new, we can do it under one month. And then the second part is you probably have heard about with these AI algorithms you need. It’s all about the data sets and how much data you need. A key point to ours is that we don’t need a lot of data to train our algorithms. We actually call that our few-shot training algorithm. And we’ll post a white paper on it just to give further detail on it. But that allows us to train the algorithm much faster than anybody else and with a lot less data. And because many times, the solution pain point that you’re looking to solve doesn’t have the data sets to train it under.
So I think those are the things that really differentiate ourselves from a preparation standpoint and then number two, from an execution standpoint. I hope that answers it.
Unidentified Analyst: Yeah. No, that answers it, and thank you so much and congratulations on the success you’ve had so far and we’ll continue to watch your progress.
Kai-Shing Tao: Thanks.
Operator: Thank you. [Operator Instructions] Next question will be from Brendan O’Neill, Ionic Ventures. Please go ahead.
Brendan O’Neill: Hey, guys. I have a couple of questions for you around kind of our interest in developing the non-China-based revenue streams. And kind of going back to kind of the earlier calls, April and May calls, you touched on a couple of things. I was wondering if you could give a further update on the yellow school bus program in partnership with Netwatch. I think at the time in April on the call, you had launched a pilot program and school starting up here, so I just wonder if you could update us on how that program is going.
Kai-Shing Tao: Okay. Sure. That was what I kind of alluded to in our discussions with the trends and school buses. I would say the discussions are moving along. As you can imagine, this is covering a large school bus space. But the opportunity is right there in front of us. So we continue to customize our algorithms for their needs and we hope to have something for you over the next couple of months as this is a big deal for everyone, right? Because we would be outfitting over 50,000 buses using our algorithms. But we think there will be something where you’ll see in the future where we would tie on what we’re doing with them. Also with the large EV company we mentioned on our call and what they’re doing in Nevada.
Brendan O’Neill: Okay. And similar to other opportunities, I know on August 1st, you announced your 311 product that you kind of alluded to on the May call of a various opportunity. Is that product launch? Do you have a current customer? Or what would be your strategy to now get municipalities and people to use this product?
