00:00:43 · Ulrik Hansen, Ksenia Se
[00:00:43] S0: Yeah. No. It's a great question and something that we we spend quite a lot of time thinking about. And from our vantage point, we work with more than 200 of the world's top AI teams. It really comes down to the the data. So that was the original founding insight for for standing on core that the thing that wins in AI and the things that is actually constraining progress is principally down to the the data and orchestrating the data to get the models working in production. Because if you think about the different ways that AI is being deployed now, the models themselves are mostly swappable and interchangeable. And I think in the vast majority of cases that we see at least, the models are, for the most part, good enough to add tremendous value to whatever it is that you're trying to solve. But what really is blocking progress even further is the the data. And so I think what's hard is getting the data orchestrated and enriched to provide enough context to the model or the systems you're building via training, inference, or otherwise, and getting it to, like, make the right decisions based on data data is really the the higher part. And, it's almost like if you think about being a a human being, right, you can't really make sound decisions and judgments with incomplete data. So you might be slightly smarter as a person. You might have a slightly better model. But if you don't have the right data, you're not gonna get very far. [00:01:57] S1: Well, you started Encord in a very different times in 2021. What changed when Chargebee went public for you as a company?
00:02:06 · Ulrik Hansen
[00:02:06] S0: Yeah. I know. It's it's interesting because we, yeah, we started the company, like, principally focused on, like, automating the annotation process. So, like, at the time, the problems that people have were principally around, like, solving the data quantity problems, just, getting enough labeled training data to be able to, like, train these, like, base models. And, of course, like, with the proliferation and, like, the massive improvement we've seen in in language models over the past year and also, like, in AI more broadly, like, we've seen the, the problem space change from doing, like, bounding box annotation to now, like, all the front end apps are hiring PhDs to do, like, very frontier type work to continuously improve the models. I think that, like, generally comes with just, like, the... Us, like, moving further up, like, the scaling law curve. Right? So we've, like, gone from, like, data quality to data quality. So, like, getting the right data. So now, like, the sort of post training and alignment, types of problems for the most advanced areas of AI. And as the models generally have gotten smarter, each incremental unit of progress has become harder. And I think, like, language models and self driving, I think the two most mature areas of AI, we see that right front and and center. And so the the spaces evolve from, like, again, like, bounding box annotation into, like, hiring the the PhDs. And I think for self driving, it's like finding all the long tail edge cases. And that is really what is necessary to kind of push the next frontiers of AI. And we're seeing that also in, like, other areas. Like, now we're seeing it at robotics where, like, robotics probably is now where, like, where language models were in, like, 2017, 2018, where, like, we're seeing, like, massive improvements to the baseline, which I think we're likely to see over the next couple of years. But at some point, the the the curve, like, asymptotes, right, and then, like, we start moving into, like, these more, like, esoteric types of problems and tasks, and that's where, like, most of the the future work will be directed. And I think we see scaling also, like, many different parts of AI. Right? And we sort of, like, followed that journey, like, since preach had to be launched.
00:03:54 · Ulrik Hansen, Ksenia Se
So, like, now we work with some of the world's top AI teams in, like, how they've evolved that as well. So, yeah, it's it's super exciting to be be on the sidelines, like, with just how this this stuff is is evolving. [00:04:04] S1: Well, give me some example of, like, what was the most challenging things recently.
00:04:08 · Ulrik Hansen
[00:04:08] S0: So you're taking an example for self driving, for example. I think the... One of the main challenge that they have, right, so if you take Waymo as an example, they are, like, now live in San Francisco, but they haven't yet expanded to cover the geography all the way to San Francisco Airport. So now the question is, like, why is that? Like, why don't you have, like, fully unsupervised self driving given that it works so well and doubts on SF? And it is really, like, down to real environments being dynamic and they change and eliminate, like, all the different types of edge cases that the car might encounter in the real world is extremely difficult. And so you need to, like, basically factor that into the way that you build, like, your old data orchestration pipeline so that when the car, like, encounters, like, a new type of, like, unseen scenario that you have, like, either, like, a heuristic to have the car solve that problem itself or you have some way of, like, relaying the feedback back into the into the model. And I think that's something that Tesla is doing really well. Right? Because they have now supervised self driving and, like, I have a test myself, but I'm driving the car. Like, sometimes I have to intervene, and that data is sent directly back to Tesla. So they build this, like, iterative feedback loop between, like, okay, actually solving the long tail as it happens. Whereas, like, of course, like, these, like, robotaxis and, like, unsupervised types of use cases, it's much more difficult because they're relying on using their own drivers, right, to, like, go and and collect that data. So, yeah, that that would be one that would be one example. Yeah. Go ahead.
00:05:32 · Ksenia Se
[00:05:32] S1: These are two very different strategies. Like, we talk about Waymo and Tesla because Tesla relies basically solely on computer vision and machine learning. So what's your bet? Because I I just published a little video about self driving, and I was recording video while Tesla was driving me around. So, yeah, what's your take? Do you think Tesla's strategy with more data, with more learning, will win or Waymo more, like, you know, step by step careful approach Yeah. From a data perspective.
00:06:02 · Ulrik Hansen
[00:06:02] S0: Yeah. No. I think the one thing that, like, Tesla really has going for them is they just have a huge and compounding data advantage over, basically, like, every single other player in the market. They are collecting live human feedback data. They have, like, the equivalent of, like, a thumbs up and then some... A thumbs down button that you're used to enchantability, right, embedded in the cars because they have people driving the cars. And so, like, this data advantage is, like, compounds massively over time, whereas, like, Waymo, they have to rely on, like, collecting their own data. Right? They don't have, like, the distribution advantage that that the Tesla has. So, inevitably, like, I think that's a much slower rollout and a much slower build. Like, that being said, of course, like, they've sort of decided to add additional sensors on top of, as you pointed out, like, Tesla's relying on RGB, additional sensors to help inform the planning and trajectories of, like, how the car, like, drives and operates, which I think, quote, unquote, like, should be a safer option, but, of course, it's way more expensive and, like, takes longer to build. I am, like, very bullish on on Tesla, actually, like, getting ahead on on this. And now they're starting to, of course, like, roll out their own, like, robotaxi fleet, which they can probably, like, do very quickly. Now the question is they, of course, like, probably don't have the same advantage or, like, the same lead with working with the regulators that that Waymo has. So I think that'll be an interesting dynamic to to observe over the next, like, three to five years. It's like these systems actually, like, go live in, like, the different cities around The US.