00:09:08 · Ulrik Hansen
[00:09:08] S0: Yeah. That's a great question. It's something we've thought a lot about because we we are, like, building a a key part of, like, that infrastructure that makes, like, the human feedback possible. I think what we've seen, like, generally over the last couple years is, like, the companies have evolved from building, like, out the kind of core foundations of, like, the models that they've been building and, like, as they go live in production, then, like, building the human feedback infrastructure, like, becomes much more important. And per use case, right, like, you have to have a lot of, like, custom tooling. So if you're building, like, a model that can work, like, LIDAR or three d data or, like, video data, you need, like, good tools and, like, the whole, like, AI tool chain, like, it's just very much, like, in development. Right? Like, a lot of this stuff, like, hasn't really been built yet. So, like, we will see that happening over the next, like, I think, five to ten years. If you think about, like, how the Internet economy has been built over the last thirty years, the development life cycle has, like, gotten to to where we are now. I think AI is likely to be much more... It will happen on a much more compressed timeline. And, yeah, of course, like, we're kind of, like, one of the companies that are building that space, us alongside, like, other ones. What that tool chain would look like exactly, I think it will probably look like a lot of the functionality that we have in in software development. So, like, version control systems, like, we have, like, good networking protocols and all these things. We will have to build out, like, similar things for for AI specifically. But the interesting quirk about AI systems is that becomes a little bit more difficult to do because they are, like, dynamic. Right? And they're sort of, like, working in a real environment. So, like, not constrained to to the same environments, like, software are working in, which are, like, inevitably, like, much more programmatic.
00:10:36 · Ksenia Se, Ulrik Hansen
[00:10:36] S1: Yeah. A lot of uncertainty to deal with. [00:10:38] S0: 100%. [00:10:39] S1: You were talking about involving more PhD students for annotation, working with data. So if expert feedback is the new goal, what is the role of humans in this human? Is it, like, coexisting task with agents? Are humans just global functions in this in this system? Who are humans here?
00:11:00 · Ulrik Hansen
[00:11:00] S0: Yeah. No. I think, ultimately, like, the humans will be providing human preference and feedback to the AI systems, and I think that will happen in, like, many different ways. So let's, like, maybe take, like, two examples. One example would be you with your personal AI digital assistant. You will provide feedback to that digital system or, like, how you like to... I don't know. Like, when do you like to wake up in the morning? Like, what do you like to have for breakfast? And, like, that will continue to, like, learn based on, like, the human preference data that you give it. So that's, like, on the more consumer side, and I think that's already happening in ChatGPT, right, where, like, the model is kind of getting personalized based on how much context that you've fed us. And I think that's some of the things that the Frontier Labs are working on is really, like, giving, like, the models as much context as you can from, like, your personal human preference data. It will, like, be able to offer, like, more and more customized advice and, like, be more of, like, a... An assistant to you. I think in the enterprise and, like, more of the kind of commercial side, all the human experts now are, like, really people that are, like, doing their day to day jobs. So if you're, like, a... Let's say, an investment banking analyst at, like, a big investment bank, you are, like, doing the work. AI will be observing, like, what you're doing. And so you are, like, quote, unquote, like, human experts that are providing feedback to the model, like, how, like, certain workflow processes and all these things should, like, operate. And I think as, like, AI is kinda getting pushed into the the enterprise, that's something that is likely to, of course, like, become more and more, like, prevalent as, like, you will be at work constantly providing feedback and, like, work with AI to, like, do your job, essentially. So it's happening both in, like, the rollout of AI, but also happening, of course, like, as discussed before, at the kind of pure training layer for pushing the frontiers of, like, the models where, like, we've started, like, seeing the asymptote.
00:12:39 · Ulrik Hansen, Ksenia Se
And that is, you know, what the front of apps are doing behind the the PHEs and the human experts, like, make the base foundation models, like, better so that, like, you have to do less customization in the future. And, really, the the human role will be just providing feedback on, like, the areas of uncertainty of the model, or it will be for giving, like, human preference on, like, how you like to get things to be done. Because, like, the model naturally will not necessarily understand, like, what you like to do or, like, how you like to do things. And that's something that will also be, like, way more important. [00:13:07] S1: From what you're saying, it feels like that we're moving from model centric, machine learning that was data centric, and now it's task centric or even context specific machine learning. Do you agree with this evolution?
00:13:07 · Ksenia Se, Ulrik Hansen
[00:13:07] S1: From what you're saying, it feels like that we're moving from model centric, machine learning that was data centric, and now it's task centric or even context specific machine learning. Do you agree with this evolution? [00:13:23] S0: Yeah. A 100%. And I think at the end of the day, the model that we will see going forward is that, you know, the AI will propose something where, like, the human can verify or escalate certain things where the machines will handle the baseline grunt work, right, or the 80% of, like, the work. But then the expert, which would be, like, the the human, like, let's say, the investment banking analyst will be owning the edge cases or providing their preference or providing calls on, like, what the policy of the model, should be. And so I think the way that, like, the systems should be designed, right, is for allowing complementarity between humans and AIs that, like, the AI will allow for edits, ask for rationales. Like, they will surface the uncertainty where, like, it's not too sure about certain things, and you kind of incentivize the model to find mistakes in, like, how it's operating. And, of course, like, we need all the the tools and the systems, right, to be able to measure the systems. So the throughput, the cost, and the risk of this stuff is, the the feedback starts to compound. [00:14:16] S1: There is a bunch of competitors in this field in terms of data labeling. How do you see this area evolving? What would the role of companies like Encord?
00:14:27 · Ulrik Hansen
[00:14:27] S0: No. Great question. So I think you can probably, like, think about the market that we are in in, like, two different buckets. So there are, like, the services first companies, and then there are, like, the tooling software first companies. So the services first companies, they principally work on, like, supplying and finding human experts to do the expert work, right, that typically most often works with the Frontier Labs because the companies actually work on those things. And then, like, for the software companies, they principally excel all the tooling and the infrastructure to enable the first part. And I think for us, right, we principally work with Applied AI, Fortune 500, yeah, other types of enterprises on, like, actually enabling that thing. They are unlikely in most scenarios, like, be building their own foundation models that typically, like, take the stuff that is off the shelf, and then they fine tune it based on, like, their own preference data. So whereas, like, the services companies principally go after the hyperscalers, the software companies typically go after the companies that are bringing the models from the hyperscalers, like, putting them into production or, like, taking an open source model. And so, like, we are focused principally on, like, software, work with enterprise, Fortune 500, and applied AI companies. Whereas, like, the services companies tend to tend to focus on on the hyperscalers and the the front setups with with, like, a different USP, which is, like, finding the people to do the work, whereas, like, we provide the software to to do the work.