YouSaid · the spoken record

Jakob Uszkoreit

lines on the record: 38
first: 2023-08-24
most recent: 2023-08-24
sittings or episodes: 1
sources: podcast

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“Because that's where the wet and the dry meeting. And so initially, folks join Inceptive, and they usually, most of them, they come from, say, either, in quote, side, right? They've spent most of their careers working on deep learning or the robotics or biology. But ultimately, it doesn't take them that long to start speaking some weird kind of creole of all of these languages and also think in these ways. And what then happens is magic. It's really amazing because then you suddenly find solutions to problems that, say, the biologists they were two years ago just wouldn't even think about. And they work together with folks they would have otherwise maybe never even met. And the results sometimes don't work at all, but sometimes they really are magical.”
2023-08-24 · No Priors · AI-Powered Biological Software with Jakob Uszkoreit, CEO of Inceptive · IDENTIFIED FROM THE TRANSCRIPT
“The cycle between experimentation and then you put that into something in silico, something running on computers, and then that informs the experiments, and then you kind of iterate that cycle. I think that's just, it would be beautiful and simple and nice. I don't think it's really that easy. And so what you see at Inceptive is actually there's not that one cycle. Although maybe now somewhere hazily there actually is that cycle too. But by design, actually, there are tons of little cycles. So you start an assay and the first thing you do is actually you query a neural network and then you do some stuff and then you get certain readouts. And those, you then together with some other stuff feed into yet another model. And then that actually gives you parameters for some instrument. And then you run that instrument on the stuff that you've created. And so it's really just this kind of giant mess where the boundary actually is increasingly blurry. And so we actually think that our work happens on the beach.”
2023-08-24 · No Priors · AI-Powered Biological Software with Jakob Uszkoreit, CEO of Inceptive · IDENTIFIED FROM THE TRANSCRIPT
“So let me try to get across how we think about this. So, number one, we look at ourselves actually as one antidisciplinary team. So it's not quite anti-disciplinary, although there is a correlation maybe with a lack of discipline or disregard for fundamental discipline or disciplines and being antidisciplinary. But we think we're really in the sense pioneers of a new discipline. Doesn't have a name yet, but it draws a lot from deep learning and draws a lot from biology. We think ultimately designing the experiments or assays that we're using to generate the data that we need to then train the models in a certain sense is at the core of this discipline, if you wish, because the experiments or the assays that we're running, they use the models that we're training on the data that their predecessors actually produced. And so really, if you squint, then in a certain sense, I guess there was always this dream of, and I think it's a pipe dream, of how”
2023-08-24 · No Priors · AI-Powered Biological Software with Jakob Uszkoreit, CEO of Inceptive · IDENTIFIED FROM THE TRANSCRIPT
“I completely agree with what you're saying. I feel general purpose machines is a really tricky term because, right, I mean, could the brain, after massive trauma, rewire to do something very different? Unclear, right? So it could be that it's actually still specific, but it is, in a certain sense, general, namely preparing for a certain flavor of redundancy. And this is also why I find AGI as a term particularly problematic, because I don't know what the general means.”
2023-08-24 · No Priors · AI-Powered Biological Software with Jakob Uszkoreit, CEO of Inceptive · IDENTIFIED FROM THE TRANSCRIPT
“Exactly. And now the thing is that you can now say, okay, great. So we come free rewired. Let's look at our wires and try to find language. That might not be that simple, right? Because, of course, it's this co-evolution and it's all fuzzy. And so how much we're pre-wired for it, how much language is in a certain sense also pre-wired for us, it might be the case that it's maybe even impossible to actually read out what it's pre-wired for from just looking at the wire.”
2023-08-24 · No Priors · AI-Powered Biological Software with Jakob Uszkoreit, CEO of Inceptive · IDENTIFIED FROM THE TRANSCRIPT
“I think that's because we confuse fine tuning and pre-training. Pre-training is all of evolution. And then basically you arrive at this thing that. It's maybe doing something that's completely in a certain sense, a completely irrelevant task at first, but it has all the capacity in there to then with a comparatively small amount of data. Maybe it's something in between, right? But be then fine-tuned towards something that we would regard as oh, so advanced cognitively.”
2023-08-24 · No Priors · AI-Powered Biological Software with Jakob Uszkoreit, CEO of Inceptive · IDENTIFIED FROM THE TRANSCRIPT
“I'm very bullish on human augmentation in the very long term, but it's one that I don't see intuitively. I think looking at our brains, even just physically, they seem to be very focused, and this is not surprising on RIO. And why would there somewhere in there be some kind of computational capacity that if we just boosted IO by a few orders of magnitude could still cope? Why would evolution put that there? I don't know why. And so, yes, you could argue, you know, maybe to do long-term planning tasks and so on and so forth, but sure, right? Let's bound it a lifetime. So it's just not so clear whether there would have been any evolutionary pressures to really make our capacity there much bigger than, say, some multiplier, basically time on our I.O. capacity.”
2023-08-24 · No Priors · AI-Powered Biological Software with Jakob Uszkoreit, CEO of Inceptive · IDENTIFIED FROM THE TRANSCRIPT
“I mean, the part that one can look at as blasphemous is that now suddenly you don't know the theory anymore that you're testing, right? And you might never because it's not clear to us today, as far as I can tell, that if there is a theory in that black box today, that we could get it out. There are people trying, and I think it's worth trying. I'm not super optimistic about that. I think it will work for some cases, right, where it's simple enough that we can get it. I think there are many cases where it just isn't. We're going to get it in the sense that we understand or think we understand the Schr ⁇ dinger equation and how that could be used intractably, though, in theory, to just solve all these things.”
2023-08-24 · No Priors · AI-Powered Biological Software with Jakob Uszkoreit, CEO of Inceptive · IDENTIFIED FROM THE TRANSCRIPT
“Yes, it's a waste of time and money, and it might not even be true. And we have no way of telling Because in the end, the ground truth is, does it work and does it actually do more good than harm? And it's empirical. And yeah, maybe there's really just the focus. And everything else should be treated as something that we should at least do after we get the first, take the first step.”
2023-08-24 · No Priors · AI-Powered Biological Software with Jakob Uszkoreit, CEO of Inceptive · IDENTIFIED FROM THE TRANSCRIPT
“Is exactly they don't end there. So yeah, it's really, really interesting to see. And the invariant that I feel just holds true across the board is that these formalisms that we make up in order to communicate our conceptual understanding or intuitive understanding and conceptualizing it explicitly is great for education. It's also great for many other types of maybe that reasoning about them.”
2023-08-24 · No Priors · AI-Powered Biological Software with Jakob Uszkoreit, CEO of Inceptive · IDENTIFIED FROM THE TRANSCRIPT
“That's really the big question. Are we here maybe at a crossroads where the discovery and understanding is actually a hindrance? The hope to discover and really get it, how this works, might actually be holding us back. And there is a pretty direct analogy to language understanding. Computational linguistics and linguistics in general tried this for a while to develop a sufficiently accurate and complete theory of language to make this really actionable.”
2023-08-24 · No Priors · AI-Powered Biological Software with Jakob Uszkoreit, CEO of Inceptive · IDENTIFIED FROM THE TRANSCRIPT
“Exactly when you do personalized cancer vaccines, it is going to be many antigens for each patient over time. And there's just no hope of basically tackling this with screening approaches at all.”
2023-08-24 · No Priors · AI-Powered Biological Software with Jakob Uszkoreit, CEO of Inceptive · IDENTIFIED FROM THE TRANSCRIPT
“And it's not certain. I mean, ultimately, it's not going to be a search. Just like today, the output of an LLM isn't coming out of a proper search procedure. It has to be a generation procedure. Exactly in the same way and for the same reason as you basically see it in large language models or image generation models. But yeah, that's exactly the goal. Because screening is just not going to cut 10 to the 130th. And that's really just one antigen that we're coding for there when we actually want to code for many and update those for any given.”
2023-08-24 · No Priors · AI-Powered Biological Software with Jakob Uszkoreit, CEO of Inceptive · IDENTIFIED FROM THE TRANSCRIPT
“But right now, if you look at RNA, the manufacturing and distribution infrastructure, we're going to have $6 to $8 billion two years from now, manufacturable and distributable across the globe. And that number is going to go up really, really quickly. At Inceptive right now in our lab, we can actually print pretty much any given RNA. That's just something you can't do with small molecules. You can't easily do with proteins, certainly not at scale. And that's not something that only matters when you have a product in your hand. If you want to treat this as a machine learning problem, you need to generate training data. It doesn't already exist. And so you also really want to have scalable synthesis and manufacturing, which is unprecedented as a constellation.”
2023-08-24 · No Priors · AI-Powered Biological Software with Jakob Uszkoreit, CEO of Inceptive · IDENTIFIED FROM THE TRANSCRIPT
“Amplifying RNA as one example and with ribo switches, so-called ribo switches, basically RNAs that change dramatically in structure or self-destruct in the presence of, say, a given small molecule or so you can effectively have conditionals, you can have recursion. And as a computer scientist, you squint and you're like, oh, wow, okay, this is basically Turing complete. You have SMIO, and you kind of have all sorts of tools now at your disposal to really build very, very complex, ultimately, edisons that might also be produced, manufactured, and distributed in a way that is much more scalable than anything that we've been able to do so far, protein-based biologics oftentimes don't make it to the market because it's just not possible to manufacture them at scale. If we wanted to medicate everybody in the world with all the protein-based biologics that they could actually, that they should actually receive the real estate on the planet wouldn't be enough to make all the stuff.”
2023-08-24 · No Priors · AI-Powered Biological Software with Jakob Uszkoreit, CEO of Inceptive · IDENTIFIED FROM THE TRANSCRIPT
“So an inceptive, we think of this now as something that you could call biological software, where mRNA and RNA in general is maybe the equivalent to bytecode that then forms the substrate, forms like the actual stuff that the software is made of. And what you do is you learn models that allow you to translate biological programs, programs that might look like some bit of Python code that specify what you want a certain medicine to do inside yourself, inside yourselves, and translate those programs, compile them into descriptions of RNA molecules that then hopefully actually do what you wrote, what you programmed them to do. And ultimately right now, if you look at mRNA vaccines, our programming language is just a print statement, right? Just print this protein. But you can easily imagine that with self-”
2023-08-24 · No Priors · AI-Powered Biological Software with Jakob Uszkoreit, CEO of Inceptive · IDENTIFIED FROM THE TRANSCRIPT
“RNA, just looking around in nature, looking at how severe the side effects were for what fraction of ultimately patients that received the vaccines, how few people comparatively really had access to any of those vaccines when they really were necessary and needed. And it seems like currently if we look around in our toolkit, the only tool we have to potentially change that quickly is deep learning.”
2023-08-24 · No Priors · AI-Powered Biological Software with Jakob Uszkoreit, CEO of Inceptive · IDENTIFIED FROM THE TRANSCRIPT
“To design better RNA and first mRNA molecules for a pretty broad variety of different medicines. infectious disease vaccines are, I guess, maybe the obvious first example given the COVID vaccines. But if you look at the pipelines of Moderna and BionTech and all those companies, the at least potential applicability of RNA, mRNA more specifically, is nearly limitless. There's already now hundreds of programs underway at different stages of development. That number is expected to climb hitting high triple digits before the end of the decade. And if you now take that in terms of just trajectory and look at how suboptimal in a certain sense the RNA vaccines were when you compare it to what's possible using”
2023-08-24 · No Priors · AI-Powered Biological Software with Jakob Uszkoreit, CEO of Inceptive · IDENTIFIED FROM THE TRANSCRIPT
“My daughter was born, my first child. And just that entire process gave me a really fundamentally different appreciation for the fragility of life and a really wonderful one, but also a pretty fundamentally different one. And so here we are. We have this new tool, namely AlphaFold 2, that solves one of these fundamental problems in structural biology. We have instances of a macromolecule family that's basically about to save the world. And I basically want to fix life because I now have this wonderful daughter. It became clear that using the exact tools we had been working on at Google before and applying those to this neglected stepchild, namely RNA or more specifically at first mRNA, could have massive impact on the world. And ultimately, what we're trying to do is”
2023-08-24 · No Priors · AI-Powered Biological Software with Jakob Uszkoreit, CEO of Inceptive · IDENTIFIED FROM THE TRANSCRIPT
“Predictive theories to really make that understanding useful. A concrete example here is protein folding, where basically even if you just act as if there are no chaperones, there is no other stuff in this environment in which folding or whatever you want to call it, in which that process, in which kind of the earliest kinetics during translation happen, even if you make that massively simplifying assumption. The theory just wasn't practical. And it seems like deep learning is at least potentially a really good answer to both of those aspects because you can basically treat everything in quotes as a black box and as long as you are able to observe that black box in terms of whatever. input output fast enough and sufficient scale, you might go somewhere with that.”
2023-08-24 · No Priors · AI-Powered Biological Software with Jakob Uszkoreit, CEO of Inceptive · IDENTIFIED FROM THE TRANSCRIPT
“Don't have very high hopes for humanity to develop that complete conceptual understanding to the level that we would need in order to do all the interventions we want to do. We don't really have great tools in our toolbox, or we didn't have them until somewhat recently as alternatives to understanding how it works and then basically based on that understanding fixing it if it needs fixing. And I think now we have an alternative that's an extremely good match. And that's deep learning at scale. We can potentially to a pretty large extent, if not entirely whatever this even means, work around the following two problems. Number one is we don't know all the stuff that's going on in life, right? So we still just don't even have a complete inventory, let alone really understand all the mechanisms. And number two, we ultimately, even for the stuff that we do know so far, haven't really, in many cases, been able to come up with a sufficient”
2023-08-24 · No Priors · AI-Powered Biological Software with Jakob Uszkoreit, CEO of Inceptive · IDENTIFIED FROM THE TRANSCRIPT
“So basically, I've always been interested in BioKnow Nothing about it. And that's a conundrum because it's difficult to learn a lot about biology when you're not in school and I didn't want to go back to school. But at the same time, it always felt like something where there is a lot of headroom in terms of efficiency and actually also where maybe even Alternative approaches, at least if what you are interested in is really solving acute problems where there's maybe a dire need for alternative approaches. Alternative to basically biology, the science that is trying to develop a complete conceptual understanding of how life works.”
2023-08-24 · No Priors · AI-Powered Biological Software with Jakob Uszkoreit, CEO of Inceptive · IDENTIFIED FROM THE TRANSCRIPT
“It's just a bit clunky. And I don't think we will be able to optimize it as well, although as an engineering in a certain sense, I don't know, hack could sound negative. That's not what I mean. And I think it's an awesome hack. As an engineering hack around this problem, it's really, really effective. It basically comes back to this whole idea of amortizing compute in a certain sense, right? With the stuff you already have lying around and memoirized, even though it was the humans that actually put it there in many cases. In terms of adaptive time transformers, et cetera, we tried this universal transformer thing actually a long time ago. It just hasn't caught on, and that's because it just doesn't work. At this point, it doesn't work well enough. It's not like it doesn't work at all, but if it worked really well, then because of the fact that compute right now is this incredibly scarce resource, we would see it everywhere. And I think what that tells us is, and I don't think here it's really just for a lack of trying.”
2023-08-24 · No Priors · AI-Powered Biological Software with Jakob Uszkoreit, CEO of Inceptive · IDENTIFIED FROM THE TRANSCRIPT
“I think it's super effective in test time search. I do think it's clunky because it's not something that you can easily end to end optimize. Basically, this is also what I was trying to get at a little bit maybe with saying, well, some of these efficiency improvements that were not yet really harnessing, I believe would dramatically affect training time. And if you look at kind of how test time search actually affects training,”
2023-08-24 · No Priors · AI-Powered Biological Software with Jakob Uszkoreit, CEO of Inceptive · IDENTIFIED FROM THE TRANSCRIPT
“Makes this thing pretty clear you can take a video, you can scale it up, you can frame interpolate with trivial algorithms and then run it again. And if the problem you're trying to solve conditioned on that video is the same, then I wouldn't want more computer to be used. But right now, that's what's going to happen. You're going to use a ton more compute. And so effectively, these types of, in a certain sense, kind of elasticity or your flexibility of these models, I believe our lack of techniques addressing those ultimately is incredibly wasteful.”
2023-08-24 · No Priors · AI-Powered Biological Software with Jakob Uszkoreit, CEO of Inceptive · IDENTIFIED FROM THE TRANSCRIPT
“So in terms of foundations, I think Different flavors of elasticity are really interesting You could actually claim that a lot of these questions boil down to the question that I just, or to basically this problem that I just described, that computers, in a certain sense, very crudely allocated. But you can look at different incarnations of this problem. So another one would be why don't we have models that in an elegant way manage to consume, say, visual sensor output of different resolutions, different sampling rates, different durations. If you wish, in different sizes, and really elegantly adjusts compute to what you really want to know about how difficult it really has to be to generate the representations that you need in order to do whatever you want to do. And here, again, an example for the”
2023-08-24 · No Priors · AI-Powered Biological Software with Jakob Uszkoreit, CEO of Inceptive · IDENTIFIED FROM THE TRANSCRIPT
“Or maybe even query, right? So there's this notion of anytime algorithms where it might just depend on your resources. If you have more time or more money, then let it run longer. But you don't want that to happen in cases where the answer or the question, the problem in question is simple. You only want to do that in cases where it's actually hard. And that right now doesn't work because if you pose a very, very simple problem like 2 plus 2 to GPD4 right now and you write that in a very long-winded way in a prompt and you ask GPD-4 to generate a very complicated answer, then it will actually expend a ton of compute to add to two, which makes no sense. Because it's not clear how you would exactly address it, that is maybe the one that boggles my mind most.”
2023-08-24 · No Priors · AI-Powered Biological Software with Jakob Uszkoreit, CEO of Inceptive · IDENTIFIED FROM THE TRANSCRIPT
“Because information theory, shaming information theory, very clearly says, nope, you're not going to get more information out of it. You can do it all you want. But there is an artifact, or there's an omission maybe even in that information, in that flavor of information theory, which is it doesn't take into account compute. It doesn't take into account actually the energy expenditure necessary to generate that data. So if you now think back to these problems, right, if you were to just let LLMs run, generate stuff, and then train new LLMs or even the same LLM actually on that output, what you do is you amortize compute that was expended at some point in time. And that seems clunky. That just seems so clunky that ultimately it should be something Where at inference time, at runtime, the model effectively can decide or”
2023-08-24 · No Priors · AI-Powered Biological Software with Jakob Uszkoreit, CEO of Inceptive · IDENTIFIED FROM THE TRANSCRIPT
“no knob that you can easily tweak as a user, but also really there's no knob that the architecture can tweak itself when it comes to then basically deciding, oh, this is hard. I actually need to use more compute fools. And ironically, and this comes back to a question that many people ask, I think around does it make any sense to train on generated data?”
2023-08-24 · No Priors · AI-Powered Biological Software with Jakob Uszkoreit, CEO of Inceptive · IDENTIFIED FROM THE TRANSCRIPT
“I mean, I think there's one thing that still boggles my mind in terms of just from first principles, it can't be optimal. And that is that if you think about it, the way you today scale the compute that's invested in a given problem, right? Let's say the problem is what's the response to a prompt in some large language model. Then ultimately, the way you scale that compute depends on the prompt and how much long that is. The longer the prompt, the more compute you get. And it depends, and there's, of course, many different screws to tweak here, the length of the response. There are many very hard problems where the response is incredibly short. In many cases, actually formulate those problems very, very succinctly. So you're not going to be using a lot of compute, even though the problem we know is really, really difficult, say, I don't know, prime factorization. A problem like that simply stated big potential impact. And right now, there's”
2023-08-24 · No Priors · AI-Powered Biological Software with Jakob Uszkoreit, CEO of Inceptive · IDENTIFIED FROM THE TRANSCRIPT
“Not coincidence. It's really just because ultimately the human cycles invested to getting all these things, all these diverse things to work are ultimately fueled by suspension of disbelief and aka hope or whatever you want to call it. And that really, I mean, the community became so energized so quickly and then just try everything under the sun. And because the prior was just a different one. The prior now was, oh, look, we have this thing where it just works, which is just not true. The reality is if you try something else the first time and you really have to work hard for a long period of time and then lo and behold, sometimes it works. And if you do that many more times, then it will work many more times. And I think that's really what we're seeing.”
2023-08-24 · No Priors · AI-Powered Biological Software with Jakob Uszkoreit, CEO of Inceptive · IDENTIFIED FROM THE TRANSCRIPT
“It's chicken and egg. And if you look at the newest accelerator designs, they are taking this into account to a significant extent, actually, increasingly. So there are a couple of interesting examples. We had a computer vision architecture that really was just an MLP called Mixer. And while it wasn't significantly better, it also wasn't significantly worse than the vision transformers, right? And I think that already goes to show it's not that difficult. And especially if you simplify on the way, it might really be a possibility. I will say one other thing aside from efficiency, just really raw efficiency in terms of its architectures fit to the accelerator hardware. The other main contributor, I think, to the success of this architecture is optimism and hope. And then more started to work.”
2023-08-24 · No Priors · AI-Powered Biological Software with Jakob Uszkoreit, CEO of Inceptive · IDENTIFIED FROM THE TRANSCRIPT
“Ultimately inherent parallelism versus latency. I don't think GPUs are at the sweet spot when it comes to large-scale deep learning with respect to exactly those trade-offs. And so it may very well be that if we actually try these combinations, we might actually even quickly find something that's better.”
2023-08-24 · No Priors · AI-Powered Biological Software with Jakob Uszkoreit, CEO of Inceptive · IDENTIFIED FROM THE TRANSCRIPT
“I think that the big question is does it matter? It would be really interesting to evaluate, especially if we can make them simpler, to evaluate combinations of different hardware and then models or architectures that fit like gloves to those. And I feel at the moment, given where GPUs came from, they weren't built for this. Why would it be that they are anywhere near optimal? If at least they were engineered for this purpose and lots of people basically banged their head against walls until they had this kind of somewhat optimized, but that's not how the basic architecture came to be. And so you can talk a lot about and reason a lot about, and I think that some of that is true, the generality of basically really fast, scalable matrix multipliers and how it just does everything in scientific computing really well, sure, but there's still lots of bells and whistles and there are lots of specific trade-offs. Say, for example, things like memory bandwidth and”
2023-08-24 · No Priors · AI-Powered Biological Software with Jakob Uszkoreit, CEO of Inceptive · IDENTIFIED FROM THE TRANSCRIPT
“at all combinations of things. That's this quadratic step, right? That ultimately is at the core of this attention step. And then you effectively pull information in for a given representation of a given piece, the other representations of all the other pieces, and rinse and repeat. And it seems intuitive, and it also seems intuitively clear that that's a really good fit for the kind of accelerators that we had at the time and that we still have today. And so that's really where that idea came from. And if you want to look at, say, the biggest differences, for example, between the transformer as it was described in the attention is all-you need paper and some of its ancestors like this decomposable attention model, the big difference is just that the transformer was implemented by folks like Noah Mintash, et cetera, in a way that's such an excellent fit for the accelerators that we had at the time.”
2023-08-24 · No Priors · AI-Powered Biological Software with Jakob Uszkoreit, CEO of Inceptive · IDENTIFIED FROM THE TRANSCRIPT
“Of, say, your signal, right? And ultimately, if you now are given a piece of hardware that has the very key strength of doing lots and lots of simple computations in parallel as opposed to complicated structured computations sequentially, then really that's actually a kind of statistical property you really want to exploit, right? You want to imperil, understand pieces of an image first. And then maybe that's not possible in its entirety, but you can actually get a lot of it. And then only once you've done some of that, you put these incomplete understandings or representations together. And as you put them together more and more, that's when you disambiguate the last remaining or that's when you get rid of the last remaining ambiguity at the end of the day. And when you think about what that process looks like, it's a tree. And when you think about how you would actually run something that evaluates all possible trees, then a reasonable approximation is that you repeat an operation where you look”
2023-08-24 · No Priors · AI-Powered Biological Software with Jakob Uszkoreit, CEO of Inceptive · IDENTIFIED FROM THE TRANSCRIPT
“So they do capture some of the statistics that are inherent in language and probably language was actually evolved this way in order to exploit our cognitive capacities really in a fairly optimal way. And so you can safely assume that it is not necessary to go through the entirety of a sequential signal beginning to end and maybe also end to beginning simultaneously in order to understand it. But actually, you can gain a lot of the understanding in air quotes.”
2023-08-24 · No Priors · AI-Powered Biological Software with Jakob Uszkoreit, CEO of Inceptive · IDENTIFIED FROM THE TRANSCRIPT
“It's really not that simple. It's also really important to keep in mind that always in deep learning, you can't make something, in quotes, really work that is maybe pretty far, I would say, the theoretical or formal end without really going deep on the engineering implementation side. And it just has to be efficient at the end of the day. In my mind, that's the one and only thing we know really works if you want to push deep learning forward just to make it faster and more effective and more efficient on a given piece of hardware. And the best piece of kind of just circumstantial anecdotal evidence for that is just looking at what the linguists do. They draw these trees. And while I don't think that they're ever really true, they're also definitely not always false.”
2023-08-24 · No Priors · AI-Powered Biological Software with Jakob Uszkoreit, CEO of Inceptive · IDENTIFIED FROM THE TRANSCRIPT