The Exchanges

Q able to function there. But, ah, and you've built these specialized models to do it. But let me put the question to you. If the entire thing, the entire generative AI moment is built on a model innovation that was meant to translate language, ah, then, then why would we need something specialized to do that as opposed to the bigger models with that foundational, you know, innovation baked in?

A Yeah, I mean, like, you're, you're right. The transformer model and language translation, I think they're kind of very, very, very tightly coupled. But I think also very early at the, at the beginning, it was clear that the transformer models can, can do more. But when they do more, when they're made for, for, for different purposes, they also lose a little bit of the capability that they had maybe initially when they've been made for translation only. This The set of parameters that is available there, this, which, which kind of determines quite often the capacity of the model, um, it needs to be divided into very many different things. And, and therefore, if you're keeping the model very much strictly to do one particular task, however, it's defined in this case, language translation, it can perform better and it can perform on that also more consistently. I think something that you see with generalized models is that depending on, on which kind of input you give to them, they're gonna tend to be better or worse, and, uh, specialized models have a kind of better layer of consistency. They do, um, they are, they're quite often much better, as in, as in ours, that the quality assurance, uh, is, is, is really built so that we can make sure that Whether it's an email that you're translating, or whether it's marketing material, or a technical patent application, or in all of those…

AI assessment note: “keeping the model very much strictly to do one particular task... it can perform better”

Q Interesting. And how much has the, uh, growth and of capabilities of LLMs Enabled you to do this job? Like, and talk, talk a little bit about how we've seen better LLMs, uh, and what they've, uh, enabled DeepL to do in terms of like going from a point A to where you are today.

A Oh, totally. I think there's, there's this, like, this big stack of, uh, of use cases and their complexity, uh, and how, how hard they really are to, to solve with, uh, with AI. And I think it maybe starts somewhere at the bottom of, like, sending out spam emails. Like, you really don't have, you really don't need to have, like, the best translation for, for that. Uh, anything just kind of basically works. Like, we know how those emails look like. Um, and at the, at the top range of that is probably regulated documentation that needs to be compliant, where like there's legal liabilities behind, behind all of that. Maybe think like a, um, I think like a leaflet that is, that is being distributed with a, with, with medicine, um, things like that. And As the quality of models has been rising over the last years, we've been able to unlock more and more and more of those, of those use cases. And honestly, this is always something that is really and truly complicated for our customers to find out is the model quality good enough for doing this in a particular job. And this is also then our responsibility as DBL to come in and help our customers find out Um, what is the quality level that they require? What is the error rate that they're seeing on those? What is, what is the reasons for this error rate? Maybe optimizing the whole setup there. And at the end, bringing them a solution w…

AI assessment note: “As the quality of models has been rising over the last years, we've been able to unlock”

Q is one thing, right? Let's say I'm a U S company. I want to operate in Brazil. I can probably get my company to function my website to function, maybe some of my customer service. In Brazilian Portuguese, but then there's also laws, regulations, customs, so does language get you half of the way, or how far, you know, can this take you when you're trying to operate somewhere else?

A I think it gets you pretty far, and I think it gets you already also pretty far because you can start leveraging local partners at this point in time already. Like you can, you can engage that Brazilian law firm that is going to help you in some aspects that are local, and you can engage them in a good way. And maybe they speak English, so then kind of this is easy, but maybe they're not. And in those situations, you can, you can, uh, already start far quicker. Um, there's going to be definitely things that you're gonna have to set up in your new market. It's not only language. There's also other aspects, uh, but you can, you can get there, I think. And, and hey, even like we're doing, um, in our, in our local markets, uh, for example, in Asia, uh, we are, we are talking to journalists. We are talking to our customers. And all of those interactions are being translated by our, um, by our technology and our AI. So like, if I would be doing this podcast with a, with a, with a, in a, in a Japanese market, we would be totally doing that with deep, uh, running the language layer in the background.

AI assessment note: “I think it gets you pretty far, and I think it gets you already”

Q Yeah. Uh, where do you stand on the AI device then? Do you think that that will be successful? Like an AI wearable or whatever open AI is brewing?

A I think it makes a lot of sense. I think kind of getting devices as small as possible and kind of as, as near to us as, as possible, specifically also in the case of, of language translation, uh, that, that makes a lot of sense. I'm, I'm a big advocate of the fact that for like real time translation, we actually have all of the devices that we need, like the airports that we have, like the phone that we have that actually suffices for, for that. Um, but having more data and having, uh, devices embedded with us all of the time and in a situation, um, like that can also gather a lot of data about us and therefore be more context aware. I think that that is pretty cool.

AI assessment note: “I think it makes a lot of sense.”

Q Yep, and so is there any trade-off then, and alright, so let's say I opt to use a more specialized model for a purpose. Is there any trade-off that I should be aware of, you know, as opposed to going with the bigger model, or is it all upside?

A I think it's actually all upside. I mean, other than potentially having another vendor, that's kind of the, uh, the, the kind of the, the question around how do we do model routing within a company? How do we pick vendors? How do we pick suppliers for the different areas of, of a business and the convenience of just working with one big model, um, vendor is obviously there. But I think at the point in time, when you start, uh, thinking about, uh, How to do a particular task really well. Um, and, and it, and it is part of what your business is maybe doing at its core and it's important to your business. Uh, then, uh, then specialized models really make a lot of sense, I think.

AI assessment note: “I think it's actually all upside. I mean, other than potentially having another vendor”

Q a company that might function well. And like, let's say I'm just gonna use English and English speaking country and say, oh, you want to, you want to do your operations elsewhere? Um, we can use LLMs to actually Enable you to set up shop in a country that speaks an entirely different language and make it seamless. So can you talk a little bit about how you do that?

A Yeah. I mean, like that there, there's a lot that goes into that and the way that we think about our, how we're helping our customers, it is, it is on the one hand internal and on the other hand external. Like if you, if you think about a big international organization that has offices all around the world, the, the language question always comes up. And we might kind of assume that everybody speaks English, but like, honestly, that's usually not really the case. No, they don't. And if they think they do, maybe they don't really do that well. And, and, and there are studies that show how much actually gets lost in the context and how much productivity suffers from that. And how often like an employee within a meeting just doesn't speak up, uh, because they Feel maybe not confident enough in the, in, in English or the language that is being spoken and just like this great idea gets, gets lost. Um, so, so we have companies make sure that internally they can forget about all of those, uh, all of those, all of those boundaries. And it also helps them to hire the best people Doesn't matter where they're sitting, like whether they're in France and speak only French and, and can contribute to a company that is English speaking, um, that makes a big difference. That, that allows them for getting the best talent, uh, on board, uh, at a moment where maybe this talent is not so easy to fi…

AI assessment note: “on the one hand internal and on the other hand external”