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From rewriting Google's search stack in the early 2000s to reviving sparse trillion-parameter models and co-designing TPUs with frontier ML research, Jeff Dean has quietly shaped nearly every layer of the modern AI stack. As Chief AI Scientist at Google and a driving force behind Gemini, Jeff has lived through multiple scaling revolutions from CPUs and sharded indices to multimodal models that reason across text, video, and code.Jeff joins us to unpack what it really means to “own the Pareto frontier,” why distillation is the engine behind every Flash model breakthrough, how energy (in picojoules) not FLOPs is becoming the true bottleneck, what it was like leading the charge to unify all of Google's AI teams, and why the next leap won't come from bigger context windows alone, but from systems that give the illusion of attending to trillions of tokens.We discuss:* Jeff's early neural net thesis in 1990: parallel training before it was cool, why he believed scaling would win decades early, and the “bigger model, more data, better results” mantra that held for 15 years* The evolution of Google Search: sharding, moving the entire index into memory in 2001, softening query semantics pre-LLMs, and why retrieval pipelines already resemble modern LLM systems* Pareto frontier strategy: why you need both frontier “Pro” models and low-latency “Flash” models, and how distillation lets smaller models surpass prior generations* Distillation deep dive: ensembles → compression → logits as soft supervision, and why you need the biggest model to make the smallest one good* Latency as a first-class objective: why 10–50x lower latency changes UX entirely, and how future reasoning workloads will demand 10,000 tokens/sec* Energy-based thinking: picojoules per bit, why moving data costs 1000x more than a multiply, batching through the lens of energy, and speculative decoding as amortization* TPU co-design: predicting ML workloads 2–6 years out, speculative hardware features, precision reduction, sparsity, and the constant feedback loop between model architecture and silicon* Sparse models and “outrageously large” networks: trillions of parameters with 1–5% activation, and why sparsity was always the right abstraction* Unified vs. specialized models: abandoning symbolic systems, why general multimodal models tend to dominate vertical silos, and when vertical fine-tuning still makes sense* Long context and the illusion of scale: beyond needle-in-a-haystack benchmarks toward systems that narrow trillions of tokens to 117 relevant documents* Personalized AI: attending to your emails, photos, and documents (with permission), and why retrieval + reasoning will unlock deeply personal assistants* Coding agents: 50 AI interns, crisp specifications as a new core skill, and how ultra-low latency will reshape human–agent collaboration* Why ideas still matter: transformers, sparsity, RL, hardware, systems — scaling wasn't blind; the pieces had to multiply togetherShow Notes:* Gemma 3 Paper* Gemma 3* Gemini 2.5 Report* Jeff Dean's “Software Engineering Advice fromBuilding Large-Scale Distributed Systems” Presentation (with Back of the Envelope Calculations)* Latency Numbers Every Programmer Should Know by Jeff Dean* The Jeff Dean Facts* Jeff Dean Google Bio* Jeff Dean on “Important AI Trends” @Stanford AI Club* Jeff Dean & Noam Shazeer — 25 years at Google (Dwarkesh)—Jeff Dean* LinkedIn: https://www.linkedin.com/in/jeff-dean-8b212555* X: https://x.com/jeffdeanGoogle* https://google.com* https://deepmind.googleFull Video EpisodeTimestamps00:00:04 — Introduction: Alessio & Swyx welcome Jeff Dean, chief AI scientist at Google, to the Latent Space podcast00:00:30 — Owning the Pareto Frontier & balancing frontier vs low-latency models00:01:31 — Frontier models vs Flash models + role of distillation00:03:52 — History of distillation and its original motivation00:05:09 — Distillation's role in modern model scaling00:07:02 — Model hierarchy (Flash, Pro, Ultra) and distillation sources00:07:46 — Flash model economics & wide deployment00:08:10 — Latency importance for complex tasks00:09:19 — Saturation of some tasks and future frontier tasks00:11:26 — On benchmarks, public vs internal00:12:53 — Example long-context benchmarks & limitations00:15:01 — Long-context goals: attending to trillions of tokens00:16:26 — Realistic use cases beyond pure language00:18:04 — Multimodal reasoning and non-text modalities00:19:05 — Importance of vision & motion modalities00:20:11 — Video understanding example (extracting structured info)00:20:47 — Search ranking analogy for LLM retrieval00:23:08 — LLM representations vs keyword search00:24:06 — Early Google search evolution & in-memory index00:26:47 — Design principles for scalable systems00:28:55 — Real-time index updates & recrawl strategies00:30:06 — Classic “Latency numbers every programmer should know”00:32:09 — Cost of memory vs compute and energy emphasis00:34:33 — TPUs & hardware trade-offs for serving models00:35:57 — TPU design decisions & co-design with ML00:38:06 — Adapting model architecture to hardware00:39:50 — Alternatives: energy-based models, speculative decoding00:42:21 — Open research directions: complex workflows, RL00:44:56 — Non-verifiable RL domains & model evaluation00:46:13 — Transition away from symbolic systems toward unified LLMs00:47:59 — Unified models vs specialized ones00:50:38 — Knowledge vs reasoning & retrieval + reasoning00:52:24 — Vertical model specialization & modules00:55:21 — Token count considerations for vertical domains00:56:09 — Low resource languages & contextual learning00:59:22 — Origins: Dean's early neural network work01:10:07 — AI for coding & human–model interaction styles01:15:52 — Importance of crisp specification for coding agents01:19:23 — Prediction: personalized models & state retrieval01:22:36 — Token-per-second targets (10k+) and reasoning throughput01:23:20 — Episode conclusion and thanksTranscriptAlessio Fanelli [00:00:04]: Hey everyone, welcome to the Latent Space podcast. This is Alessio, founder of Kernel Labs, and I'm joined by Swyx, editor of Latent Space. Shawn Wang [00:00:11]: Hello, hello. We're here in the studio with Jeff Dean, chief AI scientist at Google. Welcome. Thanks for having me. It's a bit surreal to have you in the studio. I've watched so many of your talks, and obviously your career has been super legendary. So, I mean, congrats. I think the first thing must be said, congrats on owning the Pareto Frontier.Jeff Dean [00:00:30]: Thank you, thank you. Pareto Frontiers are good. It's good to be out there.Shawn Wang [00:00:34]: Yeah, I mean, I think it's a combination of both. You have to own the Pareto Frontier. You have to have like frontier capability, but also efficiency, and then offer that range of models that people like to use. And, you know, some part of this was started because of your hardware work. Some part of that is your model work, and I'm sure there's lots of secret sauce that you guys have worked on cumulatively. But, like, it's really impressive to see it all come together in, like, this slittily advanced.Jeff Dean [00:01:04]: Yeah, yeah. I mean, I think, as you say, it's not just one thing. It's like a whole bunch of things up and down the stack. And, you know, all of those really combine to help make UNOS able to make highly capable large models, as well as, you know, software techniques to get those large model capabilities into much smaller, lighter weight models that are, you know, much more cost effective and lower latency, but still, you know, quite capable for their size. Yeah.Alessio Fanelli [00:01:31]: How much pressure do you have on, like, having the lower bound of the Pareto Frontier, too? I think, like, the new labs are always trying to push the top performance frontier because they need to raise more money and all of that. And you guys have billions of users. And I think initially when you worked on the CPU, you were thinking about, you know, if everybody that used Google, we use the voice model for, like, three minutes a day, they were like, you need to double your CPU number. Like, what's that discussion today at Google? Like, how do you prioritize frontier versus, like, we have to do this? How do we actually need to deploy it if we build it?Jeff Dean [00:02:03]: Yeah, I mean, I think we always want to have models that are at the frontier or pushing the frontier because I think that's where you see what capabilities now exist that didn't exist at the sort of slightly less capable last year's version or last six months ago version. At the same time, you know, we know those are going to be really useful for a bunch of use cases, but they're going to be a bit slower and a bit more expensive than people might like for a bunch of other broader models. So I think what we want to do is always have kind of a highly capable sort of affordable model that enables a whole bunch of, you know, lower latency use cases. People can use them for agentic coding much more readily and then have the high-end, you know, frontier model that is really useful for, you know, deep reasoning, you know, solving really complicated math problems, those kinds of things. And it's not that. One or the other is useful. They're both useful. So I think we'd like to do both. And also, you know, through distillation, which is a key technique for making the smaller models more capable, you know, you have to have the frontier model in order to then distill it into your smaller model. So it's not like an either or choice. You sort of need that in order to actually get a highly capable, more modest size model. Yeah.Alessio Fanelli [00:03:24]: I mean, you and Jeffrey came up with the solution in 2014.Jeff Dean [00:03:28]: Don't forget, L'Oreal Vinyls as well. Yeah, yeah.Alessio Fanelli [00:03:30]: A long time ago. But like, I'm curious how you think about the cycle of these ideas, even like, you know, sparse models and, you know, how do you reevaluate them? How do you think about in the next generation of model, what is worth revisiting? Like, yeah, they're just kind of like, you know, you worked on so many ideas that end up being influential, but like in the moment, they might not feel that way necessarily. Yeah.Jeff Dean [00:03:52]: I mean, I think distillation was originally motivated because we were seeing that we had a very large image data set at the time, you know, 300 million images that we could train on. And we were seeing that if you create specialists for different subsets of those image categories, you know, this one's going to be really good at sort of mammals, and this one's going to be really good at sort of indoor room scenes or whatever, and you can cluster those categories and train on an enriched stream of data after you do pre-training on a much broader set of images. You get much better performance. If you then treat that whole set of maybe 50 models you've trained as a large ensemble, but that's not a very practical thing to serve, right? So distillation really came about from the idea of, okay, what if we want to actually serve that and train all these independent sort of expert models and then squish it into something that actually fits in a form factor that you can actually serve? And that's, you know, not that different from what we're doing today. You know, often today we're instead of having an ensemble of 50 models. We're having a much larger scale model that we then distill into a much smaller scale model.Shawn Wang [00:05:09]: Yeah. A part of me also wonders if distillation also has a story with the RL revolution. So let me maybe try to articulate what I mean by that, which is you can, RL basically spikes models in a certain part of the distribution. And then you have to sort of, well, you can spike models, but usually sometimes... It might be lossy in other areas and it's kind of like an uneven technique, but you can probably distill it back and you can, I think that the sort of general dream is to be able to advance capabilities without regressing on anything else. And I think like that, that whole capability merging without loss, I feel like it's like, you know, some part of that should be a distillation process, but I can't quite articulate it. I haven't seen much papers about it.Jeff Dean [00:06:01]: Yeah, I mean, I tend to think of one of the key advantages of distillation is that you can have a much smaller model and you can have a very large, you know, training data set and you can get utility out of making many passes over that data set because you're now getting the logits from the much larger model in order to sort of coax the right behavior out of the smaller model that you wouldn't otherwise get with just the hard labels. And so, you know, I think that's what we've observed. Is you can get, you know, very close to your largest model performance with distillation approaches. And that seems to be, you know, a nice sweet spot for a lot of people because it enables us to kind of, for multiple Gemini generations now, we've been able to make the sort of flash version of the next generation as good or even substantially better than the previous generations pro. And I think we're going to keep trying to do that because that seems like a good trend to follow.Shawn Wang [00:07:02]: So, Dara asked, so it was the original map was Flash Pro and Ultra. Are you just sitting on Ultra and distilling from that? Is that like the mother load?Jeff Dean [00:07:12]: I mean, we have a lot of different kinds of models. Some are internal ones that are not necessarily meant to be released or served. Some are, you know, our pro scale model and we can distill from that as well into our Flash scale model. So I think, you know, it's an important set of capabilities to have and also inference time scaling. It can also be a useful thing to improve the capabilities of the model.Shawn Wang [00:07:35]: And yeah, yeah, cool. Yeah. And obviously, I think the economy of Flash is what led to the total dominance. I think the latest number is like 50 trillion tokens. I don't know. I mean, obviously, it's changing every day.Jeff Dean [00:07:46]: Yeah, yeah. But, you know, by market share, hopefully up.Shawn Wang [00:07:50]: No, I mean, there's no I mean, there's just the economics wise, like because Flash is so economical, like you can use it for everything. Like it's in Gmail now. It's in YouTube. Like it's yeah. It's in everything.Jeff Dean [00:08:02]: We're using it more in our search products of various AI mode reviews.Shawn Wang [00:08:05]: Oh, my God. Flash past the AI mode. Oh, my God. Yeah, that's yeah, I didn't even think about that.Jeff Dean [00:08:10]: I mean, I think one of the things that is quite nice about the Flash model is not only is it more affordable, it's also a lower latency. And I think latency is actually a pretty important characteristic for these models because we're going to want models to do much more complicated things that are going to involve, you know, generating many more tokens from when you ask the model to do so. So, you know, if you're going to ask the model to do something until it actually finishes what you ask it to do, because you're going to ask now, not just write me a for loop, but like write me a whole software package to do X or Y or Z. And so having low latency systems that can do that seems really important. And Flash is one direction, one way of doing that. You know, obviously our hardware platforms enable a bunch of interesting aspects of our, you know, serving stack as well, like TPUs, the interconnect between. Chips on the TPUs is actually quite, quite high performance and quite amenable to, for example, long context kind of attention operations, you know, having sparse models with lots of experts. These kinds of things really, really matter a lot in terms of how do you make them servable at scale.Alessio Fanelli [00:09:19]: Yeah. Does it feel like there's some breaking point for like the proto Flash distillation, kind of like one generation delayed? I almost think about almost like the capability as a. In certain tasks, like the pro model today is a saturated, some sort of task. So next generation, that same task will be saturated at the Flash price point. And I think for most of the things that people use models for at some point, the Flash model in two generation will be able to do basically everything. And how do you make it economical to like keep pushing the pro frontier when a lot of the population will be okay with the Flash model? I'm curious how you think about that.Jeff Dean [00:09:59]: I mean, I think that's true. If your distribution of what people are asking people, the models to do is stationary, right? But I think what often happens is as the models become more capable, people ask them to do more, right? So, I mean, I think this happens in my own usage. Like I used to try our models a year ago for some sort of coding task, and it was okay at some simpler things, but wouldn't do work very well for more complicated things. And since then, we've improved dramatically on the more complicated coding tasks. And now I'll ask it to do much more complicated things. And I think that's true, not just of coding, but of, you know, now, you know, can you analyze all the, you know, renewable energy deployments in the world and give me a report on solar panel deployment or whatever. That's a very complicated, you know, more complicated task than people would have asked a year ago. And so you are going to want more capable models to push the frontier in the absence of what people ask the models to do. And that also then gives us. Insight into, okay, where does the, where do things break down? How can we improve the model in these, these particular areas, uh, in order to sort of, um, make the next generation even better.Alessio Fanelli [00:11:11]: Yeah. Are there any benchmarks or like test sets they use internally? Because it's almost like the same benchmarks get reported every time. And it's like, all right, it's like 99 instead of 97. Like, how do you have to keep pushing the team internally to it? Or like, this is what we're building towards. Yeah.Jeff Dean [00:11:26]: I mean, I think. Benchmarks, particularly external ones that are publicly available. Have their utility, but they often kind of have a lifespan of utility where they're introduced and maybe they're quite hard for current models. You know, I, I like to think of the best kinds of benchmarks are ones where the initial scores are like 10 to 20 or 30%, maybe, but not higher. And then you can sort of work on improving that capability for, uh, whatever it is, the benchmark is trying to assess and get it up to like 80, 90%, whatever. I, I think once it hits kind of 95% or something, you get very diminishing returns from really focusing on that benchmark, cuz it's sort of, it's either the case that you've now achieved that capability, or there's also the issue of leakage in public data or very related kind of data being, being in your training data. Um, so we have a bunch of held out internal benchmarks that we really look at where we know that wasn't represented in the training data at all. There are capabilities that we want the model to have. Um, yeah. Yeah. Um, that it doesn't have now, and then we can work on, you know, assessing, you know, how do we make the model better at these kinds of things? Is it, we need different kind of data to train on that's more specialized for this particular kind of task. Do we need, um, you know, a bunch of, uh, you know, architectural improvements or some sort of, uh, model capability improvements, you know, what would help make that better?Shawn Wang [00:12:53]: Is there, is there such an example that you, uh, a benchmark inspired in architectural improvement? Like, uh, I'm just kind of. Jumping on that because you just.Jeff Dean [00:13:02]: Uh, I mean, I think some of the long context capability of the, of the Gemini models that came, I guess, first in 1.5 really were about looking at, okay, we want to have, um, you know,Shawn Wang [00:13:15]: immediately everyone jumped to like completely green charts of like, everyone had, I was like, how did everyone crack this at the same time? Right. Yeah. Yeah.Jeff Dean [00:13:23]: I mean, I think, um, and once you're set, I mean, as you say that needed single needle and a half. Hey, stack benchmark is really saturated for at least context links up to 1, 2 and K or something. Don't actually have, you know, much larger than 1, 2 and 8 K these days or two or something. We're trying to push the frontier of 1 million or 2 million context, which is good because I think there are a lot of use cases where. Yeah. You know, putting a thousand pages of text or putting, you know, multiple hour long videos and the context and then actually being able to make use of that as useful. Try to, to explore the über graduation are fairly large. But the single needle in a haystack benchmark is sort of saturated. So you really want more complicated, sort of multi-needle or more realistic, take all this content and produce this kind of answer from a long context that sort of better assesses what it is people really want to do with long context. Which is not just, you know, can you tell me the product number for this particular thing?Shawn Wang [00:14:31]: Yeah, it's retrieval. It's retrieval within machine learning. It's interesting because I think the more meta level I'm trying to operate at here is you have a benchmark. You're like, okay, I see the architectural thing I need to do in order to go fix that. But should you do it? Because sometimes that's an inductive bias, basically. It's what Jason Wei, who used to work at Google, would say. Exactly the kind of thing. Yeah, you're going to win. Short term. Longer term, I don't know if that's going to scale. You might have to undo that.Jeff Dean [00:15:01]: I mean, I like to sort of not focus on exactly what solution we're going to derive, but what capability would you want? And I think we're very convinced that, you know, long context is useful, but it's way too short today. Right? Like, I think what you would really want is, can I attend to the internet while I answer my question? Right? But that's not going to happen. I think that's going to be solved by purely scaling the existing solutions, which are quadratic. So a million tokens kind of pushes what you can do. You're not going to do that to a trillion tokens, let alone, you know, a billion tokens, let alone a trillion. But I think if you could give the illusion that you can attend to trillions of tokens, that would be amazing. You'd find all kinds of uses for that. You would have attend to the internet. You could attend to the pixels of YouTube and the sort of deeper representations that we can find. You could attend to the form for a single video, but across many videos, you know, on a personal Gemini level, you could attend to all of your personal state with your permission. So like your emails, your photos, your docs, your plane tickets you have. I think that would be really, really useful. And the question is, how do you get algorithmic improvements and system level improvements that get you to something where you actually can attend to trillions of tokens? Right. In a meaningful way. Yeah.Shawn Wang [00:16:26]: But by the way, I think I did some math and it's like, if you spoke all day, every day for eight hours a day, you only generate a maximum of like a hundred K tokens, which like very comfortably fits.Jeff Dean [00:16:38]: Right. But if you then say, okay, I want to be able to understand everything people are putting on videos.Shawn Wang [00:16:46]: Well, also, I think that the classic example is you start going beyond language into like proteins and whatever else is extremely information dense. Yeah. Yeah.Jeff Dean [00:16:55]: I mean, I think one of the things about Gemini's multimodal aspects is we've always wanted it to be multimodal from the start. And so, you know, that sometimes to people means text and images and video sort of human-like and audio, audio, human-like modalities. But I think it's also really useful to have Gemini know about non-human modalities. Yeah. Like LIDAR sensor data from. Yes. Say, Waymo vehicles or. Like robots or, you know, various kinds of health modalities, x-rays and MRIs and imaging and genomics information. And I think there's probably hundreds of modalities of data where you'd like the model to be able to at least be exposed to the fact that this is an interesting modality and has certain meaning in the world. Where even if you haven't trained on all the LIDAR data or MRI data, you could have, because maybe that's not, you know, it doesn't make sense in terms of trade-offs of. You know, what you include in your main pre-training data mix, at least including a little bit of it is actually quite useful. Yeah. Because it sort of tempts the model that this is a thing.Shawn Wang [00:18:04]: Yeah. Do you believe, I mean, since we're on this topic and something I just get to ask you all the questions I always wanted to ask, which is fantastic. Like, are there some king modalities, like modalities that supersede all the other modalities? So a simple example was Vision can, on a pixel level, encode text. And DeepSeq had this DeepSeq CR paper that did that. Vision. And Vision has also been shown to maybe incorporate audio because you can do audio spectrograms and that's, that's also like a Vision capable thing. Like, so, so maybe Vision is just the king modality and like. Yeah.Jeff Dean [00:18:36]: I mean, Vision and Motion are quite important things, right? Motion. Well, like video as opposed to static images, because I mean, there's a reason evolution has evolved eyes like 23 independent ways, because it's such a useful capability for sensing the world around you, which is really what we want these models to be. So I think the only thing that we can be able to do is interpret the things we're seeing or the things we're paying attention to and then help us in using that information to do things. Yeah.Shawn Wang [00:19:05]: I think motion, you know, I still want to shout out, I think Gemini, still the only native video understanding model that's out there. So I use it for YouTube all the time. Nice.Jeff Dean [00:19:15]: Yeah. Yeah. I mean, it's actually, I think people kind of are not necessarily aware of what the Gemini models can actually do. Yeah. Like I have an example I've used in one of my talks. It had like, it was like a YouTube highlight video of 18 memorable sports moments across the last 20 years or something. So it has like Michael Jordan hitting some jump shot at the end of the finals and, you know, some soccer goals and things like that. And you can literally just give it the video and say, can you please make me a table of what all these different events are? What when the date is when they happened? And a short description. And so you get like now an 18 row table of that information extracted from the video, which is, you know, not something most people think of as like a turn video into sequel like table.Alessio Fanelli [00:20:11]: Has there been any discussion inside of Google of like, you mentioned tending to the whole internet, right? Google, it's almost built because a human cannot tend to the whole internet and you need some sort of ranking to find what you need. Yep. That ranking is like much different for an LLM because you can expect a person to look at maybe the first five, six links in a Google search versus for an LLM. Should you expect to have 20 links that are highly relevant? Like how do you internally figure out, you know, how do we build the AI mode that is like maybe like much broader search and span versus like the more human one? Yeah.Jeff Dean [00:20:47]: I mean, I think even pre-language model based work, you know, our ranking systems would be built to start. I mean, I think even pre-language model based work, you know, our ranking systems would be built to start. With a giant number of web pages in our index, many of them are not relevant. So you identify a subset of them that are relevant with very lightweight kinds of methods. You know, you're down to like 30,000 documents or something. And then you gradually refine that to apply more and more sophisticated algorithms and more and more sophisticated sort of signals of various kinds in order to get down to ultimately what you show, which is, you know, the final 10 results or, you know, 10 results plus. Other kinds of information. And I think an LLM based system is not going to be that dissimilar, right? You're going to attend to trillions of tokens, but you're going to want to identify, you know, what are the 30,000 ish documents that are with the, you know, maybe 30 million interesting tokens. And then how do you go from that into what are the 117 documents I really should be paying attention to in order to carry out the tasks that the user has asked? And I think, you know, you can imagine systems where you have, you know, a lot of highly parallel processing to identify those initial 30,000 candidates, maybe with very lightweight kinds of models. Then you have some system that sort of helps you narrow down from 30,000 to the 117 with maybe a little bit more sophisticated model or set of models. And then maybe the final model is the thing that looks. So the 117 things that might be your most capable model. So I think it has to, it's going to be some system like that, that is really enables you to give the illusion of attending to trillions of tokens. Sort of the way Google search gives you, you know, not the illusion, but you are searching the internet, but you're finding, you know, a very small subset of things that are, that are relevant.Shawn Wang [00:22:47]: Yeah. I often tell a lot of people that are not steeped in like Google search history that, well, you know, like Bert was. Like he was like basically immediately inside of Google search and that improves results a lot, right? Like I don't, I don't have any numbers off the top of my head, but like, I'm sure you guys, that's obviously the most important numbers to Google. Yeah.Jeff Dean [00:23:08]: I mean, I think going to an LLM based representation of text and words and so on enables you to get out of the explicit hard notion of, of particular words having to be on the page, but really getting at the notion of this topic of this page or this page. Paragraph is highly relevant to this query. Yeah.Shawn Wang [00:23:28]: I don't think people understand how much LLMs have taken over all these very high traffic system, very high traffic. Yeah. Like it's Google, it's YouTube. YouTube has this like semantics ID thing where it's just like every token or every item in the vocab is a YouTube video or something that predicts the video using a code book, which is absurd to me for YouTube size.Jeff Dean [00:23:50]: And then most recently GROK also for, for XAI, which is like, yeah. I mean, I'll call out even before LLMs were used extensively in search, we put a lot of emphasis on softening the notion of what the user actually entered into the query.Shawn Wang [00:24:06]: So do you have like a history of like, what's the progression? Oh yeah.Jeff Dean [00:24:09]: I mean, I actually gave a talk in, uh, I guess, uh, web search and data mining conference in 2009, uh, where we never actually published any papers about the origins of Google search, uh, sort of, but we went through sort of four or five or six. generations, four or five or six generations of, uh, redesigning of the search and retrieval system, uh, from about 1999 through 2004 or five. And that talk is really about that evolution. And one of the things that really happened in 2001 was we were sort of working to scale the system in multiple dimensions. So one is we wanted to make our index bigger, so we could retrieve from a larger index, which always helps your quality in general. Uh, because if you don't have the page in your index, you're going to not do well. Um, and then we also needed to scale our capacity because we were, our traffic was growing quite extensively. Um, and so we had, you know, a sharded system where you have more and more shards as the index grows, you have like 30 shards. And then if you want to double the index size, you make 60 shards so that you can bound the latency by which you respond for any particular user query. Um, and then as traffic grows, you add, you add more and more replicas of each of those. And so we eventually did the math that realized that in a data center where we had say 60 shards and, um, you know, 20 copies of each shard, we now had 1200 machines, uh, with disks. And we did the math and we're like, Hey, one copy of that index would actually fit in memory across 1200 machines. So in 2001, we introduced, uh, we put our entire index in memory and what that enabled from a quality perspective was amazing. Um, and so we had more and more replicas of each of those. Before you had to be really careful about, you know, how many different terms you looked at for a query, because every one of them would involve a disk seek on every one of the 60 shards. And so you, as you make your index bigger, that becomes even more inefficient. But once you have the whole index in memory, it's totally fine to have 50 terms you throw into the query from the user's original three or four word query, because now you can add synonyms like restaurant and restaurants and cafe and, uh, you know, things like that. Uh, bistro and all these things. And you can suddenly start, uh, sort of really, uh, getting at the meaning of the word as opposed to the exact semantic form the user typed in. And that was, you know, 2001, very much pre LLM, but really it was about softening the, the strict definition of what the user typed in order to get at the meaning.Alessio Fanelli [00:26:47]: What are like principles that you use to like design the systems, especially when you have, I mean, in 2001, the internet is like. Doubling, tripling every year in size is not like, uh, you know, and I think today you kind of see that with LLMs too, where like every year the jumps in size and like capabilities are just so big. Are there just any, you know, principles that you use to like, think about this? Yeah.Jeff Dean [00:27:08]: I mean, I think, uh, you know, first, whenever you're designing a system, you want to understand what are the sort of design parameters that are going to be most important in designing that, you know? So, you know, how many queries per second do you need to handle? How big is the internet? How big is the index you need to handle? How much data do you need to keep for every document in the index? How are you going to look at it when you retrieve things? Um, what happens if traffic were to double or triple, you know, will that system work well? And I think a good design principle is you're going to want to design a system so that the most important characteristics could scale by like factors of five or 10, but probably not beyond that because often what happens is if you design a system for X. And something suddenly becomes a hundred X, that would enable a very different point in the design space that would not make sense at X. But all of a sudden at a hundred X makes total sense. So like going from a disk space index to a in memory index makes a lot of sense once you have enough traffic, because now you have enough replicas of the sort of state on disk that those machines now actually can hold, uh, you know, a full copy of the, uh, index and memory. Yeah. And that all of a sudden enabled. A completely different design that wouldn't have been practical before. Yeah. Um, so I'm, I'm a big fan of thinking through designs in your head, just kind of playing with the design space a little before you actually do a lot of writing of code. But, you know, as you said, in the early days of Google, we were growing the index, uh, quite extensively. We were growing the update rate of the index. So the update rate actually is the parameter that changed the most. Surprising. So it used to be once a month.Shawn Wang [00:28:55]: Yeah.Jeff Dean [00:28:56]: And then we went to a system that could update any particular page in like sub one minute. Okay.Shawn Wang [00:29:02]: Yeah. Because this is a competitive advantage, right?Jeff Dean [00:29:04]: Because all of a sudden news related queries, you know, if you're, if you've got last month's news index, it's not actually that useful for.Shawn Wang [00:29:11]: News is a special beast. Was there any, like you could have split it onto a separate system.Jeff Dean [00:29:15]: Well, we did. We launched a Google news product, but you also want news related queries that people type into the main index to also be sort of updated.Shawn Wang [00:29:23]: So, yeah, it's interesting. And then you have to like classify whether the page is, you have to decide which pages should be updated and what frequency. Oh yeah.Jeff Dean [00:29:30]: There's a whole like, uh, system behind the scenes that's trying to decide update rates and importance of the pages. So even if the update rate seems low, you might still want to recrawl important pages quite often because, uh, the likelihood they change might be low, but the value of having updated is high.Shawn Wang [00:29:50]: Yeah, yeah, yeah, yeah. Uh, well, you know, yeah. This, uh, you know, mention of latency and, and saving things to this reminds me of one of your classics, which I have to bring up, which is latency numbers. Every programmer should know, uh, was there a, was it just a, just a general story behind that? Did you like just write it down?Jeff Dean [00:30:06]: I mean, this has like sort of eight or 10 different kinds of metrics that are like, how long does a cache mistake? How long does branch mispredict take? How long does a reference domain memory take? How long does it take to send, you know, a packet from the U S to the Netherlands or something? Um,Shawn Wang [00:30:21]: why Netherlands, by the way, or is it, is that because of Chrome?Jeff Dean [00:30:25]: Uh, we had a data center in the Netherlands, um, so, I mean, I think this gets to the point of being able to do the back of the envelope calculations. So these are sort of the raw ingredients of those, and you can use them to say, okay, well, if I need to design a system to do image search and thumb nailing or something of the result page, you know, how, what I do that I could pre-compute the image thumbnails. I could like. Try to thumbnail them on the fly from the larger images. What would that do? How much dis bandwidth than I need? How many des seeks would I do? Um, and you can sort of actually do thought experiments in, you know, 30 seconds or a minute with the sort of, uh, basic, uh, basic numbers at your fingertips. Uh, and then as you sort of build software using higher level libraries, you kind of want to develop the same intuitions for how long does it take to, you know, look up something in this particular kind of.Shawn Wang [00:31:21]: I'll see you next time.Shawn Wang [00:31:51]: Which is a simple byte conversion. That's nothing interesting. I wonder if you have any, if you were to update your...Jeff Dean [00:31:58]: I mean, I think it's really good to think about calculations you're doing in a model, either for training or inference.Jeff Dean [00:32:09]: Often a good way to view that is how much state will you need to bring in from memory, either like on-chip SRAM or HBM from the accelerator. Attached memory or DRAM or over the network. And then how expensive is that data motion relative to the cost of, say, an actual multiply in the matrix multiply unit? And that cost is actually really, really low, right? Because it's order, depending on your precision, I think it's like sub one picodule.Shawn Wang [00:32:50]: Oh, okay. You measure it by energy. Yeah. Yeah.Jeff Dean [00:32:52]: Yeah. I mean, it's all going to be about energy and how do you make the most energy efficient system. And then moving data from the SRAM on the other side of the chip, not even off the off chip, but on the other side of the same chip can be, you know, a thousand picodules. Oh, yeah. And so all of a sudden, this is why your accelerators require batching. Because if you move, like, say, the parameter of a model from SRAM on the, on the chip into the multiplier unit, that's going to cost you a thousand picodules. So you better make use of that, that thing that you moved many, many times with. So that's where the batch dimension comes in. Because all of a sudden, you know, if you have a batch of 256 or something, that's not so bad. But if you have a batch of one, that's really not good.Shawn Wang [00:33:40]: Yeah. Yeah. Right.Jeff Dean [00:33:41]: Because then you paid a thousand picodules in order to do your one picodule multiply.Shawn Wang [00:33:46]: I have never heard an energy-based analysis of batching.Jeff Dean [00:33:50]: Yeah. I mean, that's why people batch. Yeah. Ideally, you'd like to use batch size one because the latency would be great.Shawn Wang [00:33:56]: The best latency.Jeff Dean [00:33:56]: But the energy cost and the compute cost inefficiency that you get is quite large. So, yeah.Shawn Wang [00:34:04]: Is there a similar trick like, like, like you did with, you know, putting everything in memory? Like, you know, I think obviously NVIDIA has caused a lot of waves with betting very hard on SRAM with Grok. I wonder if, like, that's something that you already saw with, with the TPUs, right? Like that, that you had to. Uh, to serve at your scale, uh, you probably sort of saw that coming. Like what, what, what hardware, uh, innovations or insights were formed because of what you're seeing there?Jeff Dean [00:34:33]: Yeah. I mean, I think, you know, TPUs have this nice, uh, sort of regular structure of 2D or 3D meshes with a bunch of chips connected. Yeah. And each one of those has HBM attached. Um, I think for serving some kinds of models, uh, you know, you, you pay a lot higher cost. Uh, and time latency, um, bringing things in from HBM than you do bringing them in from, uh, SRAM on the chip. So if you have a small enough model, you can actually do model parallelism, spread it out over lots of chips and you actually get quite good throughput improvements and latency improvements from doing that. And so you're now sort of striping your smallish scale model over say 16 or 64 chips. Uh, but as if you do that and it all fits in. In SRAM, uh, that can be a big win. So yeah, that's not a surprise, but it is a good technique.Alessio Fanelli [00:35:27]: Yeah. What about the TPU design? Like how much do you decide where the improvements have to go? So like, this is like a good example of like, is there a way to bring the thousand picojoules down to 50? Like, is it worth designing a new chip to do that? The extreme is like when people say, oh, you should burn the model on the ASIC and that's kind of like the most extreme thing. How much of it? Is it worth doing an hardware when things change so quickly? Like what was the internal discussion? Yeah.Jeff Dean [00:35:57]: I mean, we, we have a lot of interaction between say the TPU chip design architecture team and the sort of higher level modeling, uh, experts, because you really want to take advantage of being able to co-design what should future TPUs look like based on where we think the sort of ML research puck is going, uh, in some sense, because, uh, you know, as a hardware designer for ML and in particular, you're trying to design a chip starting today and that design might take two years before it even lands in a data center. And then it has to sort of be a reasonable lifetime of the chip to take you three, four or five years. So you're trying to predict two to six years out where, what ML computations will people want to run two to six years out in a very fast changing field. And so having people with interest. Interesting ML research ideas of things we think will start to work in that timeframe or will be more important in that timeframe, uh, really enables us to then get, you know, interesting hardware features put into, you know, TPU N plus two, where TPU N is what we have today.Shawn Wang [00:37:10]: Oh, the cycle time is plus two.Jeff Dean [00:37:12]: Roughly. Wow. Because, uh, I mean, sometimes you can squeeze some changes into N plus one, but, you know, bigger changes are going to require the chip. Yeah. Design be earlier in its lifetime design process. Um, so whenever we can do that, it's generally good. And sometimes you can put in speculative features that maybe won't cost you much chip area, but if it works out, it would make something, you know, 10 times as fast. And if it doesn't work out, well, you burned a little bit of tiny amount of your chip area on that thing, but it's not that big a deal. Uh, sometimes it's a very big change and we want to be pretty sure this is going to work out. So we'll do like lots of carefulness. Uh, ML experimentation to show us, uh, this is actually the, the way we want to go. Yeah.Alessio Fanelli [00:37:58]: Is there a reverse of like, we already committed to this chip design so we can not take the model architecture that way because it doesn't quite fit?Jeff Dean [00:38:06]: Yeah. I mean, you, you definitely have things where you're going to adapt what the model architecture looks like so that they're efficient on the chips that you're going to have for both training and inference of that, of that, uh, generation of model. So I think it kind of goes both ways. Um, you know, sometimes you can take advantage of, you know, lower precision things that are coming in a future generation. So you can, might train it at that lower precision, even if the current generation doesn't quite do that. Mm.Shawn Wang [00:38:40]: Yeah. How low can we go in precision?Jeff Dean [00:38:43]: Because people are saying like ternary is like, uh, yeah, I mean, I'm a big fan of very low precision because I think that gets, that saves you a tremendous amount of time. Right. Because it's picojoules per bit that you're transferring and reducing the number of bits is a really good way to, to reduce that. Um, you know, I think people have gotten a lot of luck, uh, mileage out of having very low bit precision things, but then having scaling factors that apply to a whole bunch of, uh, those, those weights. Scaling. How does it, how does it, okay.Shawn Wang [00:39:15]: Interesting. You, so low, low precision, but scaled up weights. Yeah. Huh. Yeah. Never considered that. Yeah. Interesting. Uh, w w while we're on this topic, you know, I think there's a lot of, um, uh, this, the concept of precision at all is weird when we're sampling, you know, uh, we just, at the end of this, we're going to have all these like chips that I'll do like very good math. And then we're just going to throw a random number generator at the start. So, I mean, there's a movement towards, uh, energy based, uh, models and processors. I'm just curious if you've, obviously you've thought about it, but like, what's your commentary?Jeff Dean [00:39:50]: Yeah. I mean, I think. There's a bunch of interesting trends though. Energy based models is one, you know, diffusion based models, which don't sort of sequentially decode tokens is another, um, you know, speculative decoding is a way that you can get sort of an equivalent, very small.Shawn Wang [00:40:06]: Draft.Jeff Dean [00:40:07]: Batch factor, uh, for like you predict eight tokens out and that enables you to sort of increase the effective batch size of what you're doing by a factor of eight, even, and then you maybe accept five or six of those tokens. So you get. A five, a five X improvement in the amortization of moving weights, uh, into the multipliers to do the prediction for the, the tokens. So these are all really good techniques and I think it's really good to look at them from the lens of, uh, energy, real energy, not energy based models, um, and, and also latency and throughput, right? If you look at things from that lens, that sort of guides you to. Two solutions that are gonna be, uh, you know, better from, uh, you know, being able to serve larger models or, you know, equivalent size models more cheaply and with lower latency.Shawn Wang [00:41:03]: Yeah. Well, I think, I think I, um, it's appealing intellectually, uh, haven't seen it like really hit the mainstream, but, um, I do think that, uh, there's some poetry in the sense that, uh, you know, we don't have to do, uh, a lot of shenanigans if like we fundamentally. Design it into the hardware. Yeah, yeah.Jeff Dean [00:41:23]: I mean, I think there's still a, there's also sort of the more exotic things like analog based, uh, uh, computing substrates as opposed to digital ones. Uh, I'm, you know, I think those are super interesting cause they can be potentially low power. Uh, but I think you often end up wanting to interface that with digital systems and you end up losing a lot of the power advantages in the digital to analog and analog to digital conversions. You end up doing, uh, at the sort of boundaries. And periphery of that system. Um, I still think there's a tremendous distance we can go from where we are today in terms of energy efficiency with sort of, uh, much better and specialized hardware for the models we care about.Shawn Wang [00:42:05]: Yeah.Alessio Fanelli [00:42:06]: Um, any other interesting research ideas that you've seen, or like maybe things that you cannot pursue a Google that you would be interested in seeing researchers take a step at, I guess you have a lot of researchers. Yeah, I guess you have enough, but our, our research.Jeff Dean [00:42:21]: Our research portfolio is pretty broad. I would say, um, I mean, I think, uh, in terms of research directions, there's a whole bunch of, uh, you know, open problems and how do you make these models reliable and able to do much longer, kind of, uh, more complex tasks that have lots of subtasks. How do you orchestrate, you know, maybe one model that's using other models as tools in order to sort of build, uh, things that can accomplish, uh, you know, much more. Yeah. Significant pieces of work, uh, collectively, then you would ask a single model to do. Um, so that's super interesting. How do you get more verifiable, uh, you know, how do you get RL to work for non-verifiable domains? I think it's a pretty interesting open problem because I think that would broaden out the capabilities of the models, the improvements that you're seeing in both math and coding. Uh, if we could apply those to other less verifiable domains, because we've come up with RL techniques that actually enable us to do that. Uh, effectively, that would, that would really make the models improve quite a lot. I think.Alessio Fanelli [00:43:26]: I'm curious, like when we had Noam Brown on the podcast, he said, um, they already proved you can do it with deep research. Um, you kind of have it with AI mode in a way it's not verifiable. I'm curious if there's any thread that you think is interesting there. Like what is it? Both are like information retrieval of JSON. So I wonder if it's like the retrieval is like the verifiable part. That you can score or what are like, yeah, yeah. How, how would you model that, that problem?Jeff Dean [00:43:55]: Yeah. I mean, I think there are ways of having other models that can evaluate the results of what a first model did, maybe even retrieving. Can you have another model that says, is this things, are these things you retrieved relevant? Or can you rate these 2000 things you retrieved to assess which ones are the 50 most relevant or something? Um, I think those kinds of techniques are actually quite effective. Sometimes I can even be the same model, just prompted differently to be a, you know, a critic as opposed to a, uh, actual retrieval system. Yeah.Shawn Wang [00:44:28]: Um, I do think like there, there is that, that weird cliff where like, it feels like we've done the easy stuff and then now it's, but it always feels like that every year. It's like, oh, like we know, we know, and the next part is super hard and nobody's figured it out. And, uh, exactly with this RLVR thing where like everyone's talking about, well, okay, how do we. the next stage of the non-verifiable stuff. And everyone's like, I don't know, you know, Ellen judge.Jeff Dean [00:44:56]: I mean, I feel like the nice thing about this field is there's lots and lots of smart people thinking about creative solutions to some of the problems that we all see. Uh, because I think everyone sort of sees that the models, you know, are great at some things and they fall down around the edges of those things and, and are not as capable as we'd like in those areas. And then coming up with good techniques and trying those. And seeing which ones actually make a difference is sort of what the whole research aspect of this field is, is pushing forward. And I think that's why it's super interesting. You know, if you think about two years ago, we were struggling with GSM, eight K problems, right? Like, you know, Fred has two rabbits. He gets three more rabbits. How many rabbits does he have? That's a pretty far cry from the kinds of mathematics that the models can, and now you're doing IMO and Erdos problems in pure language. Yeah. Yeah. Pure language. So that is a really, really amazing jump in capabilities in, you know, in a year and a half or something. And I think, um, for other areas, it'd be great if we could make that kind of leap. Uh, and you know, we don't exactly see how to do it for some, some areas, but we do see it for some other areas and we're going to work hard on making that better. Yeah.Shawn Wang [00:46:13]: Yeah.Alessio Fanelli [00:46:14]: Like YouTube thumbnail generation. That would be very helpful. We need that. That would be AGI. We need that.Shawn Wang [00:46:20]: That would be. As far as content creators go.Jeff Dean [00:46:22]: I guess I'm not a YouTube creator, so I don't care that much about that problem, but I guess, uh, many people do.Shawn Wang [00:46:27]: It does. Yeah. It doesn't, it doesn't matter. People do judge books by their covers as it turns out. Um, uh, just to draw a bit on the IMO goal. Um, I'm still not over the fact that a year ago we had alpha proof and alpha geometry and all those things. And then this year we were like, screw that we'll just chuck it into Gemini. Yeah. What's your reflection? Like, I think this, this question about. Like the merger of like symbolic systems and like, and, and LMS, uh, was a very much core belief. And then somewhere along the line, people would just said, Nope, we'll just all do it in the LLM.Jeff Dean [00:47:02]: Yeah. I mean, I think it makes a lot of sense to me because, you know, humans manipulate symbols, but we probably don't have like a symbolic representation in our heads. Right. We have some distributed representation that is neural net, like in some way of lots of different neurons. And activation patterns firing when we see certain things and that enables us to reason and plan and, you know, do chains of thought and, you know, roll them back now that, that approach for solving the problem doesn't seem like it's going to work. I'm going to try this one. And, you know, in a lot of ways we're emulating what we intuitively think, uh, is happening inside real brains in neural net based models. So it never made sense to me to have like completely separate. Uh, discrete, uh, symbolic things, and then a completely different way of, of, uh, you know, thinking about those things.Shawn Wang [00:47:59]: Interesting. Yeah. Uh, I mean, it's maybe seems obvious to you, but it wasn't obvious to me a year ago. Yeah.Jeff Dean [00:48:06]: I mean, I do think like that IMO with, you know, translating to lean and using lean and then the next year and also a specialized geometry model. And then this year switching to a single unified model. That is roughly the production model with a little bit more inference budget, uh, is actually, you know, quite good because it shows you that the capabilities of that general model have improved dramatically and, and now you don't need the specialized model. This is actually sort of very similar to the 2013 to 16 era of machine learning, right? Like it used to be, people would train separate models for lots of different, each different problem, right? I have, I want to recognize street signs and something. So I train a street sign. Recognition recognition model, or I want to, you know, decode speech recognition. I have a speech model, right? I think now the era of unified models that do everything is really upon us. And the question is how well do those models generalize to new things they've never been asked to do and they're getting better and better.Shawn Wang [00:49:10]: And you don't need domain experts. Like one of my, uh, so I interviewed ETA who was on, who was on that team. Uh, and he was like, yeah, I, I don't know how they work. I don't know where the IMO competition was held. I don't know the rules of it. I just trained the models, the training models. Yeah. Yeah. And it's kind of interesting that like people with these, this like universal skill set of just like machine learning, you just give them data and give them enough compute and they can kind of tackle any task, which is the bitter lesson, I guess. I don't know. Yeah.Jeff Dean [00:49:39]: I mean, I think, uh, general models, uh, will win out over specialized ones in most cases.Shawn Wang [00:49:45]: Uh, so I want to push there a bit. I think there's one hole here, which is like, uh. There's this concept of like, uh, maybe capacity of a model, like abstractly a model can only contain the number of bits that it has. And, uh, and so it, you know, God knows like Gemini pro is like one to 10 trillion parameters. We don't know, but, uh, the Gemma models, for example, right? Like a lot of people want like the open source local models that are like that, that, that, and, and, uh, they have some knowledge, which is not necessary, right? Like they can't know everything like, like you have the. The luxury of you have the big model and big model should be able to capable of everything. But like when, when you're distilling and you're going down to the small models, you know, you're actually memorizing things that are not useful. Yeah. And so like, how do we, I guess, do we want to extract that? Can we, can we divorce knowledge from reasoning, you know?Jeff Dean [00:50:38]: Yeah. I mean, I think you do want the model to be most effective at reasoning if it can retrieve things, right? Because having the model devote precious parameter space. To remembering obscure facts that could be looked up is actually not the best use of that parameter space, right? Like you might prefer something that is more generally useful in more settings than this obscure fact that it has. Um, so I think that's always attention at the same time. You also don't want your model to be kind of completely detached from, you know, knowing stuff about the world, right? Like it's probably useful to know how long the golden gate be. Bridges just as a general sense of like how long are bridges, right? And, uh, it should have that kind of knowledge. It maybe doesn't need to know how long some teeny little bridge in some other more obscure part of the world is, but, uh, it does help it to have a fair bit of world knowledge and the bigger your model is, the more you can have. Uh, but I do think combining retrieval with sort of reasoning and making the model really good at doing multiple stages of retrieval. Yeah.Shawn Wang [00:51:49]: And reasoning through the intermediate retrieval results is going to be a, a pretty effective way of making the model seem much more capable, because if you think about, say, a personal Gemini, yeah, right?Jeff Dean [00:52:01]: Like we're not going to train Gemini on my email. Probably we'd rather have a single model that, uh, we can then use and use being able to retrieve from my email as a tool and have the model reason about it and retrieve from my photos or whatever, uh, and then make use of that and have multiple. Um, you know, uh, stages of interaction. that makes sense.Alessio Fanelli [00:52:24]: Do you think the vertical models are like, uh, interesting pursuit? Like when people are like, oh, we're building the best healthcare LLM, we're building the best law LLM, are those kind of like short-term stopgaps or?Jeff Dean [00:52:37]: No, I mean, I think, I think vertical models are interesting. Like you want them to start from a pretty good base model, but then you can sort of, uh, sort of viewing them, view them as enriching the data. Data distribution for that particular vertical domain for healthcare, say, um, we're probably not going to train or for say robotics. We're probably not going to train Gemini on all possible robotics data. We, you could train it on because we want it to have a balanced set of capabilities. Um, so we'll expose it to some robotics data, but if you're trying to build a really, really good robotics model, you're going to want to start with that and then train it on more robotics data. And then maybe that would. It's multilingual translation capability, but improve its robotics capabilities. And we're always making these kind of, uh, you know, trade-offs in the data mix that we train the base Gemini models on. You know, we'd love to include data from 200 more languages and as much data as we have for those languages, but that's going to displace some other capabilities of the model. It won't be as good at, um, you know, Pearl programming, you know, it'll still be good at Python programming. Cause we'll include it. Enough. Of that, but there's other long tail computer languages or coding capabilities that it may suffer on or multi, uh, multimodal reasoning capabilities may suffer. Cause we didn't get to expose it to as much data there, but it's really good at multilingual things. So I, I think some combination of specialized models, maybe more modular models. So it'd be nice to have the capability to have those 200 languages, plus this awesome robotics model, plus this awesome healthcare, uh, module that all can be knitted together to work in concert and called upon in different circumstances. Right? Like if I have a health related thing, then it should enable using this health module in conjunction with the main base model to be even better at those kinds of things. Yeah.Shawn Wang [00:54:36]: Installable knowledge. Yeah.Jeff Dean [00:54:37]: Right.Shawn Wang [00:54:38]: Just download as a, as a package.Jeff Dean [00:54:39]: And some of that installable stuff can come from retrieval, but some of it probably should come from preloaded training on, you know, uh, a hundred billion tokens or a trillion tokens of health data. Yeah.Shawn Wang [00:54:51]: And for listeners, I think, uh, I will highlight the Gemma three end paper where they, there was a little bit of that, I think. Yeah.Alessio Fanelli [00:54:56]: Yeah. I guess the question is like, how many billions of tokens do you need to outpace the frontier model improvements? You know, it's like, if I have to make this model better healthcare and the main. Gemini model is still improving. Do I need 50 billion tokens? Can I do it with a hundred, if I need a trillion healthcare tokens, it's like, they're probably not out there that you don't have, you know, I think that's really like the.Jeff Dean [00:55:21]: Well, I mean, I think healthcare is a particularly challenging domain, so there's a lot of healthcare data that, you know, we don't have access to appropriately, but there's a lot of, you know, uh, healthcare organizations that want to train models on their own data. That is not public healthcare data, uh, not public health. But public healthcare data. Um, so I think there are opportunities there to say, partner with a large healthcare organization and train models for their use that are going to be, you know, more bespoke, but probably, uh, might be better than a general model trained on say, public data. Yeah.Shawn Wang [00:55:58]: Yeah. I, I believe, uh, by the way, also this is like somewhat related to the language conversation. Uh, I think one of your, your favorite examples was you can put a low resource language in the context and it just learns. Yeah.Jeff Dean [00:56:09]: Oh, yeah, I think the example we used was Calamon, which is truly low resource because it's only spoken by, I think 120 people in the world and there's no written text.Shawn Wang [00:56:20]: So, yeah. So you can just do it that way. Just put it in the context. Yeah. Yeah. But I think your whole data set in the context, right.Jeff Dean [00:56:27]: If you, if you take a language like, uh, you know, Somali or something, there is a fair bit of Somali text in the world that, uh, or Ethiopian Amharic or something, um, you know, we probably. Yeah. Are not putting all the data from those languages into the Gemini based training. We put some of it, but if you put more of it, you'll improve the capabilities of those models.Shawn Wang [00:56:49]: Yeah.Jeff Dean [00:56:49]:

Språk, men inga ord. Det skrev Tomas Tranströmer om redan 1983, långt innan han förlorade talet. Boel Gerell funderar över skillnaden. Lyssna på alla avsnitt i Sveriges Radios app. ESSÄ: Detta är en text där skribenten reflekterar över ett ämne eller ett verk. Åsikter som uttrycks är skribentens egna.Var börjar det? Kanske i andningen, i luften som inte kommer att räcka hela vägen fram och redan när jag startar meningen ser jag ordet i slutet och vet att det kommer att bli svårt. Jag kommer att fastna där, i den där satans bokstaven och bli kvar tills syret tar slut och du ser bort av hänsyn och genans. Medan jag tystnar och drar efter andan djupt och vanmäktigt och börjar om igen, med ett nytt ord.Det har blivit bättre förstås. Exempelvis läser jag ju den här texten utan att beväras av stamningen – peppar, peppar. Jag har hittat strategier för att hantera mina problem och bryr mig också mindre om vad andra tycker, det underlättar. Ändå är jag fortfarande någonstans alltid kvar i den där vibrerande frustrationen som fyller det tysta rummet där ordet inte låter sig sägas. Ett oändligt ensamt rum, skilt från den övriga världen.Trots att mina besvär är jämförelsevis lindriga har de satt prägel på mitt liv. En ovilja till telefonsamtal, en oro för att framträda offentligt tills jag kom på att adrenalinet som rusar genom mig vid sådana tillfällen faktiskt hjälper mig att överbrygga glappet till orden jag vill åt. Säkerligen är problemet större för mig själv än för dem jag möter och även om det kan kännas så i stunden är jag inte ensam.Ungefär en procent av den vuxna befolkningen i Sverige stammar. Bland barn är det betydligt vanligare men besvären försvinner ofta med åren. Exakt vad stamning beror på vet man inte och jag kan bara gå till mig själv för att försöka förstå. I samtal med mina barn exempelvis, stammar jag inte alls. Trötthet försvårar symptomen och om jag av någon anledning känner mig låg eller obekväm i situationen blir det också värre.En stroke eller en hjärntumör kan också utlösa stamning – eller om skadan är särskilt svår – afasi där tillgången till språket helt eller delvis försvinner. Jag försöker föreställa mig tillvaron i det tomma rummet med de blanka väggarna, inte som en tillfällig förvisning utan som ett hem. Vad händer med den som förlorar möjligheten att nå ut med sin röst för alltid?I dikten ”April och tystnad” ur samlingen ”Sorgegondolen” från 1996 beskriver författaren Tomas Tranströmer förlusten av språket som ”silver, som glimmar utom räckhåll hos pantlånaren”.En sån ödets ironi att just han, som hade språket så totalt i sin hand skulle tappa det. Eller, inte alldeles. En fungerande arm hade han kvar efter stroken 1990 och med hjälp av den kunde han fortfarande spela piano. Med orden var det värre, få fraser återstod – ett ja och ett mycket bra och i övrigt gester och blickar som hans livskamrat Monica förstod att tolka och översätta till ett vardagligt samtal och dessutom till litteratur.Så tillkom inte bara ”Sorgegondolen” där hälften av dikterna skrevs efter stroken. Utan också samlingen ”Den stora gåtan” som till största delen bestod av haikudikter. Boken nominerades till Augustpriset med motiveringen att hans ”diktning genomgått oavbruten förtätning och utveckling.”Funktionshindret som initialt tycktes tysta honom hade alltså i praktiken lett till en utveckling eller precisering av hans litterära förmåga. Språket han finner på stranden efter stormen är renspolat från oväsentligheter. Dikterna är oftast bara tre rader och har karaktären av bilder med ord så exakta att de inte kan vara några andra.”Döden, skriver han, lutar sig / över mig, ett schackproblem. / Och har lösningen.På samma vis beskriver författaren Daniel Sjölin sitt medvetande som bilder när språket går förlorat också för honom i sviterna av en gåtfull hjärnsjukdom. För att slippa konfronteras med vidden av katastrofen tiger han sig genom dagarna eller använder de få ord han tror sig kunna leverera obehindrat: tack, tjena, supernajs. Samtidigt är han aldrig helt säker på vad han säger. Som en slumpgenerator väljer hans rådbråkade hjärna andra ord än de han tror och språket kommer ut oförutsägbart och utom kontroll.Just kontrollförlust har varit ett mål för Sjölin i hans tidigare litterära arbete. För att osäkra skrivprocessen har han medvetet skapat hinder för sitt flöde. Som i romanen ”Underskottet” där han liksom företrädarna för den litterära 60-talströrelsen Oulipo begränsar sitt alfabet och skriver halva boken utan bokstaven a. Nu är inte bara a utan hela alfabetet utom räckhåll och kontrollförlusten osäkrar hela hans existens.I det som senare kom att kallas Sapir-Whorf-hypotesen lade den amerikanske lingvisten Edward Sapir 1929 fram teorin att en människas förmåga att förstå världen i hög grad var beroende av det talade språket. Så kom det sig att två människor med skilda modersmål aldrig skulle komma att se saker och ting på samma vis. I orden låg möjligheterna och begränsningarna och som exempel användes hopi-indianerna som påstods sakna ord för tid och därför skulle vara omedvetna om hela tidsbegreppet.Så var det förstås inte och ändå har teorin bildat skola och skrämmer nu den tidigare lingvistikstudenten Sjölin där han ligger stum på sjukbädden. Vilket värde har han som människa – i sina egna och andras ögon – utan sin röst? Tron på språk som värdemätare och indikator på själslig förmåga genomsyrar hela den västerländska världsbilden och präglar inte bara vår syn på varandra utan också på våra medvarelser djuren. Sättet vi behandlar boskap på kan exempelvis bara basera sig på idén att den som saknar röst också saknar känslor, behov och rätt att finnas till. Detta trots att var och en som haft ett djur som vän, vet att det går att föra också mycket nyanserade diskussioner utan mänskligt tal. Själv har jag fört många och långa samtal med hundar och tack vare denna språkliga kompetens slutade jag för snart trettio år sedan att äta kött. I en film på teve fördes svin till slakteriet. Inget våld syntes i bilderna, men i svinens öron och ögon läste jag allt de redan visste om vad som skulle ske. Paniken som spred sig i leden när de tvingades framåt i den trånga passagen, lukten av blod och de avlägsna dödsskriken från vännerna.Det kunde ha varit min hund som berättade om fasorna, språket gick obehindrat att översätta. Det kunde ha varit någon jag känner, älskar och respekterar som föstes in i dödsfabriken. Det kunde ha varit jag. Tal eller skrift är inte enda vägen till djup förståelse varelser emellan, kanske är det inte ens bästa sättet att med precision kommunicera.Ta Tomas Tranströmer och hans vänstra hand, som långt efter att talet berövats honom fortsatte att förmedla hans innersta känslor med samma säkerhet som han tidigare skrev på papper. Själv ler jag lite åt mig själv och situationen när ordet fastnar, tar ett djupt andetag och börjar om igen och i leendet och andetaget möts vi och samtalet går vidare.Boel Gerellförfattare och kritiker LitteraturDaniel Heller-Roazen: Echolalia. Att glömma språk, Bokförlaget Faethon 2019Daniel Sjölin: Underskottet, Norstedts 2022Tomas Tranströmer: Sorgegondolen, Bonniers 1996Tomas Tranströmer: Den stora gåtan, Bonniers 2004

"Nós nunca mais vamos escrever como antes, palavra atrás de palavra, diante de uma página em branco. Essa talvez seja o maior processo de transformação da escrita" - Felipe Iszlaji No oitavo episódio do Hipsters.Talks, PAULO SILVEIRA, CVO do Grupo Alun, conversa com FELIPE ISZLAJI, cofundador e CEO da Clarice.AI, a primeira IA para escritores em português, sobre os limites filosóficos das LLMs, linguística e o futuro da criatividade humana. Uma conversa que une filosofia da linguagem, tecnologia e empreendedorismo. Prepare-se para um episódio cheio de conhecimento e inspiração! Espero que aproveitem :) Sinta-se à vontade para compartilhar suas perguntas e comentários. Vamos adorar conversar com vocês!

It's a fun science fiction trope: learn a mysterious alien language and acquire superpowers, just like if you'd been zapped by a cosmic ray or bitten by a radioactive spider. But what's the linguistics behind this idea found in books like Babel-17, Embassytown, or the movie Arrival? In this episode, your hosts Lauren Gawne and Gretchen McCulloch get enthusiastic about the science and fiction of linguistic relativity, popularly known as the Sapir-Whorf hypothesis. We talk about a range of different things that people mean when they refer to this hypothesis: a sciencey-sounding way to introduce obviously fictional concepts like time travel or mind control, a reflection that we add new words all the time as convenient handles to talk about new concepts, a note that grammatical categories can encourage us to pay attention to specific areas in the world (but aren't the only way of doing so), a social reflection that we feel like different people in different environments (which can sometimes align with different languages, though not always). We also talk about several genuine areas of human difference that linguistic relativity misses: different perceptive experiences like synesthesia and aphantasia, as well as how we lump sounds into categories based on what's relevant to a given language. Finally, we talk about the history of where the Sapir-Whorf hypothesis comes from, why Benjamin Lee Whorf would have been great on TikTok, and why versions of this idea keep bouncing back in different guises as a form of curiosity about the human condition no matter how many specific instances get disproven. Click here for a link to this episode in your podcast player of choice here: https://episodes.fm/1186056137/episode/dGFnOnNvdW5kY2xvdWQsMjAxMDp0cmFja3MvMjA1OTQ5MDMwOA Read the transcript here: https://lingthusiasm.com/post/778588696756846592/transcript-episode-102-the-science-and-fiction-of Announcements: In this month's bonus episode we get enthusiastic about two sets of updates! We talk about the results from the 2024 listener survey (we learned which one of us you think is more kiki and more bouba!), and our years in review (book related news for both Lauren and Gretchen), plus exciting news for the coming year. Join us on Patreon now to get access to this and 90+ other bonus episodes. You'll also get access to the Lingthusiasm Discord server where you can chat with other language nerds. https://patreon.com/posts/123498164 For links to things mentioned in this episode: https://lingthusiasm.com/post/778588215614603264/lingthusiasm-episode-102-the-science-and-fiction

Today's slow drag is with “Mr. & Mrs. Hush” from the Grammy award-winning “Look Now,” released in 2018. The songwriting is credited to Elvis Costello. . . . Show Notes: Appreciation written, produced, and narrated by Remedy Robinson, MA,MFA Instagram: https://www.instagram.com/slow_drag_remedy/ Bluesky Social: https://bsky.app/profile/slowdragwithremedy.com Email: slowdragwithremedy@gmail.com   “Elvis Costello Wiki Resource, Podcasts” https://www.elviscostello.info/wiki/index.php?title=Podcasts Transcription: https://slowdragwithremedy.weebly.com Podcast music by https://www.fesliyanstudios.com Rate this Podcast: https://ratethispodcast.com/slowdrag Slow Drag with Remedy on Amazon Music: https://music.amazon.com/podcasts/1f521a34-2ed9-4bd4-a936-1ad107969046/slow-drag-with-remedy-an-elvis-costello-appreciation References: Elvis Costello Wiki Resource, “Mr. and Mrs. Hush” https://www.elviscostello.info/wiki/index.php?title=Mr._%26_Mrs._Hush   “Mr. and Mrs. Hush” https://www.youtube.com/watch?v=LdHjigna3gI   “Blindman's Bluff” https://www.oxfordreference.com/display/10.1093/oi/authority.20110803095512112   The Sapir Whorf hypothesis https://www.simplypsychology.org/sapir-whorf-hypothesis.html Purchase “The Most Terrible Time in My Life…Ends Thursday” Listen to the audiobook of “The Most Terrible Time in My Life…Ends Thursday” for free at: https://www.youtube.com/watch?v=Kq7n1pN8D1Y

Uma palavra mal traduzida pode significar um prato errado no restaurante durante sua viagem de férias ou a detonação da primeira bomba atômica. Traduzir é essencial para a interação humana ao longo da história, mas ela quase nunca acontece sem dramas e desentendimentos.Este é mais um episódio do Escuta Essa, podcast semanal em que Denis e Danilo trocam histórias de cair o queixo e de explodir os miolos. Todas as quartas-feiras, no seu agregador de podcasts favorito, é a vez de um contar um causo para o outro.Não deixe de enviar os episódios do Escuta Essa para aquela pessoa com quem você também gosta de compartilhar histórias e aproveite para mandar seus comentários e perguntas no Spotify, nas redes sociais , ou no e-mail escutaessa@aded.studio. A gente sempre lê mensagens no final de cada episódio!...NESTE EPISÓDIO•⁠ ⁠O Mokusatsu é chamado também de “arte japonesa do silêncio e da ambiguidade” e se tornou objeto de estudo no ocidente.•⁠ ⁠A BBC conversou com o linguista Caleb Everett sobre suas pesquisas com a percepção de tempo e números em línguas indígenas.•⁠ ⁠A Camila Zarur, em texto para a revista piauí em 2018, fez um glossário para explicar expressões cariocas usadas nos contos de Geovani Martins.•⁠ ⁠O livro de Nataly Kelly e Jost Zetsche é o “Found in Translation” e tem inúmeras histórias e anedotas envolvendo tradução.Kelly também conta sobre sua experiência como intérprete em chamadas de emergência no podcast Radiolab.•⁠ ⁠A frase inicial de “Moby Dick” é sempre tema quando uma nova tradução é lançada no Brasil. •⁠ ⁠A Constituição Brasileira ganhou uma versão oficial em nheengatu em 2023. Ela foi feita por um grupo de 15 indígenas bilíngues da região do Alto Rio Negro e Médio Tapajós.•⁠ ⁠O filme “A Chegada”, de Dennis Villeneuve, foi lançado em 2016 e pode ser visto no Prime Video . A obra foi inspirada no conto “Story of Your Life”, do autor americano Ted Chiang. •⁠ ⁠A Hipótese de Sapir-Whorf foi formulada pelos linguistas, Edward Sapir e Benjamin Lee Whorf nos anos 1930. Embora influente, ela não é totalmente aceita e recebe críticas de estudiosos da sociolinguística e da corrente cognitivista. •⁠ ⁠Para nos aprofundar na questão do Tratado de Waitigi na Nova Zelândia usamos a tese de doutorado “O mundo interligado: poder, guerra e território nas lutas na Argentina e na Nova Zelândia (1826-1885)”. de Gabriel Passetti, na USP, de 2010. •⁠ ⁠A lista de palavras únicas e sem tradução foi tirada do livro “The Meaning of Tingo”, de Adam Jacot de Boinod. ...AD&D STUDIOA AD&D produz podcasts e vídeos que divertem e respeitam sua inteligência! Acompanhe todos os episódios em⁠ aded.studio⁠ para não perder nenhuma novidade.

Send us a textAbout the guest: Ev Fedorenko is a neuroscientist at MIT. He lab studies the neural basis of language, speech, and thought in the human brain.Episode summary: Nick and Dr. Fedorenko discuss: the relationship between language and thought; the extent to which language is for thinking vs. communication; Noam Chomsky's Universal Grammar theory; Sapir-Whorf hypothesis; language acquisition & language learning; language networks in the brain; neuroanatomy & brain lateralization; large language models (LLMs) & machine intelligence; and more.Related episodes:M&M #141: Evolution, Language, Domestication, Symbolic Cognition, AI & Large Language ModelsM&M #20: Language, Symbolic Cognition, Evolution, Origins of the Human Mind | Terrence Deacon*This content is never meant to serve as medical advice.*Full episode available free on Substack & YouTube.Support the showAll episodes (audio & video), show notes, transcripts, and more at the M&M Substack Affiliates: MASA Chips—delicious tortilla chips made from organic corn and grass-fed beef tallow. No seed oils, artificial ingredients, etc. Use code MIND for 20% off. SiPhox Health—Affordable, at-home bloodwork w/ a comprehensive set of key health marker. Use code TRIKOMES for a 10% discount. Lumen device to optimize your metabolism for weight loss or athletic performance. Use code MIND for 10% off. Athletic Greens: Comprehensive & convenient daily nutrition. Free 1-year supply of vitamin D with purchase. Learn all the ways you can support my efforts

The intriguing world of Logical Empiricism and its impact on our understanding of reality. Discover the precision of scientific language, the role of formal languages in mathematics and logic, and the challenges of translating everyday language into an empirical framework. Uncover major critiques, including Quine's challenge to the analytic synthetic distinction and the limitations of the verification principle. Alternative perspectives like Pragmatism, Phenomenology, and Post-structuralism offer different insights into the relationship between language and reality. Contemporary developments in cognitive science and linguistics, the impact of paradigm shifts, the Sapir Whorf hypothesis, and the role of metaphors in scientific discourse.

İyi ki podcast serisinin yeni bölümü yayında!"DİL, Kültür ve Ahlak Bilgisi" Bu bölümde DİL kavramını yatırıyoruz masaya. Dil ve kültür ilişkisini irdeliyoruz.  Dil, bizim düşünce biçimimizin sınırlarını belirliyor. Kullandığımız ve kullanamadığımız dil, kelimeler ve cümleler dünyamızı şekillendiriyor. Genişletiyor ya da daraltıyor.  Biz dilimiz kadarız diyebilir miyiz? Agota Cristof ile başlıyoruz. Cristof'un sığınmacı olarak geldiği İsviçre'deki dil öğrenme yolcuğuna ve karşılaştığı zorluklara değiniyoruz yazdığı "Okumaz Yazmaz" kitabı ekseninde.  Peki biz okuyarak dilimizi ve düşüncelerimizin sınırlarını nasıl genişletebiliriz?  Sadece kendi dilimize ve kültürümüze ait olan kavramlar, ifadeler ve kelimeler başka dillerden kendine yer bulabiliyor mu? Anadilimiz ve içinde bulunduğumuz kültür varoluşumuzun karakteristik özelliklerimi taşıyor.  Lera Boroditsky'den Humboldt'a, Sapir-Whorf hipotezinden Noam Chomsky'e, pidgin ve kreol dillerinden, Arrival filmine; geniş bir yelpazede birlikte yüzüyoruz. Ve son olarak da soruyoruz: Konuştuğumuz yabancı diller arasında geçiş yaptığımızda karakterimiz de değişiyor mu? Hadi gelin hep birlikte irdeleyelim. --- Send in a voice message: https://podcasters.spotify.com/pod/show/iyiki/message

------------------Support the channel------------ Patreon: https://www.patreon.com/thedissenter PayPal: paypal.me/thedissenter PayPal Subscription 1 Dollar: https://tinyurl.com/yb3acuuy PayPal Subscription 3 Dollars: https://tinyurl.com/ybn6bg9l PayPal Subscription 5 Dollars: https://tinyurl.com/ycmr9gpz PayPal Subscription 10 Dollars: https://tinyurl.com/y9r3fc9m PayPal Subscription 20 Dollars: https://tinyurl.com/y95uvkao   ------------------Follow me on--------------------- Facebook: https://www.facebook.com/thedissenteryt/ Twitter: https://twitter.com/TheDissenterYT   This show is sponsored by Enlites, Learning & Development done differently. Check the website here: http://enlites.com/   Dr. Caleb Everett is a Senior Associate Dean in the College of Arts & Sciences at the University of Miami and a Professor in the Anthropology Department, with a secondary appointment in Psychology. He is a member of the inaugural class of Andrew Carnegie Fellows. His work explores language, cognition and behavior across the world's cultures. His latest book is A Myriad of Tongues: How Languages Reveal Differences in How We Think.   In this episode, we focus on A Myriad of Tongues. We discuss how sometimes people assume too much universality in language, and where linguistic diversity stems from. We explore how people talk about time, numbers, space and directions, social relationships, and colors and odors. We discuss how the environment influences the evolution of languages, focusing on the example of extreme ambient aridity, and also whistled languages. We talk about the limitations of studying grammatical patterns in idealized and written sentences. We discuss whether words are arbitrary, and if we can accurately translate every word and expression. Finally, we discuss the Sapir-Whorf hypothesis, and the broader study of universality and diversity in human cognition. -- A HUGE THANK YOU TO MY PATRONS/SUPPORTERS: PER HELGE LARSEN, JERRY MULLER, HANS FREDRIK SUNDE, BERNARDO SEIXAS, OLAF ALEX, ADAM KESSEL, MATTHEW WHITINGBIRD, ARNAUD WOLFF, TIM HOLLOSY, HENRIK AHLENIUS, FILIP FORS CONNOLLY, DAN DEMETRIOU, ROBERT WINDHAGER, RUI INACIO, ZOOP, MARCO NEVES, COLIN HOLBROOK, PHIL KAVANAGH, SAMUEL ANDREEFF, FRANCIS FORDE, TIAGO NUNES, FERGAL CUSSEN, HAL HERZOG, NUNO MACHADO, JONATHAN LEIBRANT, JOÃO LINHARES, STANTON T, SAMUEL CORREA, ERIK HAINES, MARK SMITH, JOÃO EIRA, TOM HUMMEL, SARDUS FRANCE, DAVID SLOAN WILSON, YACILA DEZA-ARAUJO, ROMAIN ROCH, DIEGO LONDOÑO CORREA, YANICK PUNTER, CHARLOTTE BLEASE, NICOLE BARBARO, ADAM HUNT, PAWEL OSTASZEWSKI, NELLEKE BAK, GUY MADISON, GARY G HELLMANN, SAIMA AFZAL, ADRIAN JAEGGI, PAULO TOLENTINO, JOÃO ARBOSA, JULIAN PRICE, EDWARD HALL, HEDIN BRØNNER, DOUGLAS FRY, FRANCA BORTOLOTTI, GABRIEL PONS CORTÈS, URSULA LITZCKE, SCOTT, ZACHARY FISH, TIM DUFFY, SUNNY SMITH, JON WISMAN, WILLIAM BUCKNER, PAUL-GEORGE ARNAUD, LUKE GLOWACKI, GEORGIOS THEOPHANOUS, CHRIS WILLIAMSON, PETER WOLOSZYN, DAVID WILLIAMS, DIOGO COSTA, ANTON ERIKSSON, CHARLES MOREY, ALEX CHAU, AMAURI MARTÍNEZ, CORALIE CHEVALLIER, BANGALORE ATHEISTS, LARRY D. LEE JR., OLD HERRINGBONE, MICHAEL BAILEY, DAN SPERBER, ROBERT GRESSIS, IGOR N, JEFF MCMAHAN, JAKE ZUEHL, BARNABAS RADICS, MARK CAMPBELL, TOMAS DAUBNER, LUKE NISSEN, KIMBERLY JOHNSON, JESSICA NOWICKI, LINDA BRANDIN, NIKLAS CARLSSON, GEORGE CHORIATIS, VALENTIN STEINMANN, PER KRAULIS, KATE VON GOELER, ALEXANDER HUBBARD, BR, MASOUD ALIMOHAMMADI, JONAS HERTNER, URSULA GOODENOUGH, DAVID PINSOF, SEAN NELSON, MIKE LAVIGNE, JOS KNECHT, ERIK ENGMAN, LUCY, YHONATAN SHEMESH, AND MANVIR SINGH! A SPECIAL THANKS TO MY PRODUCERS, YZAR WEHBE, JIM FRANK, ŁUKASZ STAFINIAK, TOM VANEGDOM, BERNARD HUGUENEY, CURTIS DIXON, BENEDIKT MUELLER, THOMAS TRUMBLE, KATHRINE AND PATRICK TOBIN, JONCARLO MONTENEGRO, AL NICK ORTIZ, AND NICK GOLDEN! AND TO MY EXECUTIVE PRODUCERS, MATTHEW LAVENDER, SERGIU CODREANU, BOGDAN KANIVETS, ROSEY, AND GREGORY HASTINGS!

Alexx and Erik ponder the unponderable theology of Grob Gob Glob Grod, alongside far more ponderable questions like Martian monarchies, magic hats, tiny manticores, and fiery princess romances in this amazing analysis of two excellent Adventure Time episodes from season 4:E15 "Sons of Mars"E16 "Burning Low"And yes, you are correct: this is finally the BACON PANCAKES EPISODE. Grab your friends and join us for a thrilling ride!Check out these links!Check out these links!Fuck the Pain Away by Peaches (very NSFW)TypoglycemiaToki Pona (official site)Sapir–Whorf hypothesis (Linguistic Relativity Hypothesis)

Únete a Carlos Izquierdo en "Rompiendo el Límite" para un fascinante episodio que revela el poder transformador del lenguaje en nuestra mente y realidad.  Este viaje único a través de la psicolingüística y la neurociencia del lenguaje desentraña cómo nuestras palabras no solo reflejan, sino que también moldean nuestra cognición, emociones y percepciones. Basado en sólidas investigaciones científicas, descubrirás cómo la hipótesis de Sapir-Whorf, la psicología positiva y los estudios neurolingüísticos demuestran que el lenguaje que usamos influye significativamente en nuestra forma de pensar y ver el mundo.  Aprende cómo el lenguaje emocionalmente cargado afecta la atención y la memoria, y cómo reformular nuestros pensamientos puede cambiar nuestra percepción y comportamiento. Carlos te guiará a través de ejercicios prácticos y técnicas basadas en evidencia para mejorar tu bienestar emocional y relaciones interpersonales mediante el uso consciente del lenguaje. Descubre cómo pequeños cambios en tu lenguaje diario pueden tener un gran impacto en tu vida personal y profesional. Este episodio es esencial para cualquier persona interesada en desarrollo personal, psicología cognitiva, comunicación efectiva y auto-mejora. No te pierdas esta oportunidad de empoderarte a través del poder de tus palabras en "Rompiendo el Límite".    

Beeinflusst die Sprache, die wir sprechen, die Art und Weise, wie wir denken? Sapir, Whorf und zahlreiche andere Linguist*innen sagen: ja! Aber wie ließe sich so ein Einfluss feststellen? Und wie stark soll dieser Einfluss sein? Seit circa 100 Jahren gibt es die Sapir-Whorf-Hypothese, die aber in ihrer reinen Form längst schon keine Anhänger*innen mehr hat außer in der Fiktion.Ein Podcast von Anton und Jakob. Instagram: https://www.instagram.com/sprachpfade Twitter/X: @sprachpfade Mastodon: @sprachpfade@mastodon.social ___ Weiterführende Literatur: Lera Boroditsky (2003): Artikel „Linguistic relativity“, in: Lynn Nadel (Hg.): Encyclopedia of cognitive science, London: Macmillan, S. 917-922.Norbert Fries (2016): Artikel „Sapir-Whorf-Hypothese“, in: Helmut Glück, Michael Rödel (Hg.): Metzler Lexikon Sprache, 5. aktual. u. überarb. Aufl., Stuttgart: Metzler, S. 582.Basel Al-Sheikh Hussein (2012): „The Sapir-Whorf Hypothesis Today“, in: Theory and Practice in Language Studies 2.3, S. 642-646.Woraus Jakob zitiert hat:Edward Sapir (1921): Language. An Introduction to the Study of Speech, New York: Harcourt, Brace & Co.Die erwähnte Studie zu die Brücke/el puente und der Schlüssel/la llave:Lera Boroditsky, Lauren A. Schmidt, Webb Phillips (2003): „Sex, syntax and semantics“, in: Dedre Gentner, Susan Goldin-Meadow (Hg.): Language in Mind. Advances in the Study of Language and Thought, Boston: MIT Press, S. 61-79.Veröffentlichungen von Paul Kay, der zur Sapir-Whorf-Hypothese, Sprachrelativismus und speziell Farben forscht.Alle Bücher ausleihbar in deiner nächsten Bibliothek! ___ Gegenüber Themenvorschlägen für die kommenden Ausflüge in die Sprachwissenschaft und Anregungen jeder Art sind wir stets offen. Wir freuen uns auf euer Feedback! Schreibt uns dazu einfach an oder in die DMs: anton.sprachpfade@protonmail.com oder jakob.sprachpfade@protonmail.com ___ Grafiken und Musik von Elias Kündiger: https://on.soundcloud.com/ySNQ6

這一集來到節目上的來賓，是在企業界非常受歡迎的英語講師：王梓沅老師，他擁有美國哥倫比亞大學、賓州大學雙常春藤名校的語言學習科學背景，致力於翻轉台灣的英語教育。 王梓沅老師也是創勝文教的共同創辦人，至今舉辦超過 1,000 場英語學習講座，並在Hahow好學校開設了最有系統的線上課程系列：《3D 英文筆記術》、《英文思維模板術》、《Can-Do 英文溝通術》，改變了上萬名學生的英語學習方式。 在這一集節目，王梓沅老師將與主持人劉軒一起探討台灣關注的英語教育議題，以及打破框架的學習方式！ ▬ ▬ ▬ ▬ ▬ ▬ 【王梓沅的高效英聽學習法：3 階段打造英聽腦】

In this episode, Tony speaks with Simon about Quentin Tarantino; guilty (genre) pleasures; franchise vs auteur films; traumatising movies they saw in their formative years; gory internet content; Zac Stacy assault; Denis Villeneuve's hyperrealist sci-fi; the Sapir-Whorf hypothesis; Simon's dreary cinematic palate; American vs European cinema differences; Mad Men's banal slice narrative; Ari Aster's modus + penchant for the macabre; Ann Dowd's versatility; and many other topics. -Recorded on Dec 4, 2021 -SPOILER WARNING for Lady Bird (2017); No Time to Die (2021); Casino Royale (2006); Quantum of Solace (2009); Skyfall (2012); Nude Nuns with Big Guns (2010); Dune (2021); Sicario (2015); Sicario 2 (2020); Arrival (2016); Free Guy (2021); We Need to Talk about Kevin (2011); Doubt (2008); Pig (2021); Hereditary (2017); The Matrix Trilogy; etc. -Get first access to more audio on Spotify, Google Podcasts, and Apple Podcasts. And feel free to drop us a line @televishenipod on X and Instagram, or by email: thetelevishenipodcast@gmail.com --- Send in a voice message: https://podcasters.spotify.com/pod/show/the-televisheni-podcast/message

Robot, cyberspace och atombomb är ord som kommer från sci-fi-litteraturen, och språk är inte sällan en viktig del av handlingen inom science fiction och fantasy. Lyssna på alla avsnitt i Sveriges Radio Play. Språkets återkommande språkexpert Susanna Karlsson är en inbiten sci-fi-läsare och intresserar sig särskilt för den science fiction där språkvetenskap är den vetenskap som ligger till grund för handlingen och fiktionen.Susannas Karlssons tre science fiction-boktipsSnow crash av Neal Stephenson.The left hand of darkness av Ursula K. Le Guin.Babel, an arcane history av R. F KuangSpråkfrågor om science fiction och fantasyHar det funnits försök att komma på en svensk översättning av ”science fiction”?Vilka ord har gått från att vara science fiction-relaterade till att handla om verkliga saker?Vad spelar sapir-whorf-teorin för roll i science fiction?Hur går det till när språk konstrueras för fantasy och science fiction?Hur ska man hantera tempus och grammatik under tidsresor?Läs, lyssna och se mer om språk inom science fiction och fantasySe! David J Peterson som konstruerat språk för bland annat Game of Thrones recenserar hur bra skådespelarna är på att prata språken (från Vanity Fair 2019)Se! Filmen Arrival om en språkvetare som kommunicerar med utomjordingar (från 2016).Lyssna! Snedtänkt med Kalle Lind Om subkulturen science-fiction (från april 2020).Läs! Nationalencyklopedin om Sapir–Whorf-hypotesen.Språkvetare Susanna Karlsson, docent i nordiska språk vid Göteborgs universitet. Programledare Emmy Rasper.

This week on Studious, we explore the concept of linguistic relativity. Can the language we learn shape our experiences and perspectives? How much of our perspective hinges conceptually, and how much of it is culturally influenced. All this and more, this week on Studious. We also talk about linguistic relativity in relation to aliens. And somehow I completely breezed over Arrival, not to be confused with the Charlie Sheen vehicle, The Arrival. Warning: if you get triggered easily, perhaps sit this one out. I'm mild sauce level offensive. --- Send in a voice message: https://podcasters.spotify.com/pod/show/studiouspodcast/message Support this podcast: https://podcasters.spotify.com/pod/show/studiouspodcast/support

In this best of episode of Here to Help, Chris speaks to Katie Schmidt, Quality Assurance Engineer at Indeed. Katie will speak about her career in the gaming industry, how it led to a job in QA and the important role language plays in game design. Katie will also speak about Pride month and the importance of iPride in her journey. If you have ever wondered what we can learn about our own reality through game design or what exactly is the Sapir–Whorf hypothesis then this episode is one worth listening to.

Partendo dal discorso pronunciato dalla presentatrice Ambra Angiolini durante il concerto del 1 maggio parliamo di come e quanto il linguaggio determina la percezione e la creazione della realtà, citando l'ipotesi di Sapir-Whorf, le riflessioni di Lera Boroditsky e bell hooks e la ricerca di Pascal Gygax. Amare parole è un podcast del Post e condotto da Vera Gheno. Learn more about your ad choices. Visit megaphone.fm/adchoices

Arrival (2016) is a beautiful depiction of our relationship with language, time and each other. Join us as we explore Egyptian Hieroglyphs, alien languages, Sapir Whorf hypothesis and our perception of time. 'Despite knowing the journey, and where it leads, I embrace it'Follow us on TikTok to catch live recordings of the episodes:TikTok: @scienceatthemoviesInstagram: @scienceatthemoviesEmail: scienceatthemovies@gmail.com Hosted on Acast. See acast.com/privacy for more information.

In this episode, we explore the historical background to linguistic relativity or the so-called ‘Sapir-Whorf hypothesis’. Download | Spotify | Apple Podcasts | Google Podcasts References for Episode 31 Primary sources Boas, Franz, ed. (1911), Handbook of American Indian Languages,…Read more ›

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: The Natural State is Goodhart, published by devansh on March 20, 2023 on LessWrong. Epistemic Status: Meant to describe a set of beliefs that I have about accidental optimization pressures, and be a reference post for a thing I can refer back to later. Why do we live in worlds of bureaucracy and Lost Purpose? Because this is the default state of problem-solving, and everything else is an effortful push against Goodharting. Humans are all problem-solving machines, and if you want to experience inner misalignment inside your own brain, just apply anything less than your full attention to a metric you're trying to push up. People claim to want things like more legroom, or comfier seats, or better service, or smaller chances of delays and cancellations. But when you actually sit down and book a flight, they are ordered by cost, and if you're not a frequent flier then you generally choose the flight with the lowest sticker cost. This leads to a “race to the bottom” amongst airlines to push everything possible out of the sticker price and nickel-and-dime you—thereby causing the cheapest flights to actually be more expensive and worse. I was talking to a mentor of mine / giving her feedback and trying to work out how to best approach a problem. Sometimes I said things that she found helpful, and she noted these out loud. We then realized this disrupted conversation too much, so we changed to having her recognize my helpful sentences with a snap. This might have worked well, had I not immediately noticed my brain Goodharting towards extracting her snaps, instead of actually trying to figure out solutions to the problem and saying true things and improving my own models. There is a point that I'm trying to make here, which I think mostly fails to get made by the current writing on Goodhart's law. It's not just an explanation for the behavior of [people dumber than you]. Me, you, all of us, are constantly, 24/7. Goodharting towards whatever outcome fits our local incentives. This becomes even more true for groups of people and organizations. For example, EAG(x)s have a clear failure mode along this dimension. From reading retrospectives (EAGx Berkeley and EAGx Boston), they sure do seem to focus a lot on making meaningful connections and hyping people up about EA ideas and the community, and a lot of the retrospective is about how much people enjoyed EAG. I don't mean to call EAG out specifically, but instead to highlight a broader point - we're not a religion trying to spread a specific gospel; we're a bunch of people trying to figure out how to figure out what's true, and do things in the world that accomplish our goals. It does sure seem like we're putting a bunch of optimization pressure into things that don't really track our final goals, and we should step back and be at least concerned about this fact. Some parts of the rationality community do a similar thing. I notice a circuit in my own brain that Goodharts towards certain words / ways of speaking because they're more “rational.” Like, I personally have adopted this language, but actually talking about “priors” and “updates” and appending “or something” to the end of sentences does not make you better at finding the truth. You're not a better Bayesian reasoner purely because you use words that correspond to Bayesian thinking. (The counterargument here is the Sapir-Whorf hypothesis, which weakens but does not kill this point—I think many of the mannerisms seen as desirable by people in the rationality community and accepted as status or ingroup indicators track something different from truth.) By default we follow local incentives, and we should to be quite careful to step back every once in a while and really, properly make sure that we are optimizing for the right purposes. You should expect the autopilot that runs ...

Link to original articleWelcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: The Natural State is Goodhart, published by devansh on March 20, 2023 on LessWrong. Epistemic Status: Meant to describe a set of beliefs that I have about accidental optimization pressures, and be a reference post for a thing I can refer back to later. Why do we live in worlds of bureaucracy and Lost Purpose? Because this is the default state of problem-solving, and everything else is an effortful push against Goodharting. Humans are all problem-solving machines, and if you want to experience inner misalignment inside your own brain, just apply anything less than your full attention to a metric you're trying to push up. People claim to want things like more legroom, or comfier seats, or better service, or smaller chances of delays and cancellations. But when you actually sit down and book a flight, they are ordered by cost, and if you're not a frequent flier then you generally choose the flight with the lowest sticker cost. This leads to a “race to the bottom” amongst airlines to push everything possible out of the sticker price and nickel-and-dime you—thereby causing the cheapest flights to actually be more expensive and worse. I was talking to a mentor of mine / giving her feedback and trying to work out how to best approach a problem. Sometimes I said things that she found helpful, and she noted these out loud. We then realized this disrupted conversation too much, so we changed to having her recognize my helpful sentences with a snap. This might have worked well, had I not immediately noticed my brain Goodharting towards extracting her snaps, instead of actually trying to figure out solutions to the problem and saying true things and improving my own models. There is a point that I'm trying to make here, which I think mostly fails to get made by the current writing on Goodhart's law. It's not just an explanation for the behavior of [people dumber than you]. Me, you, all of us, are constantly, 24/7. Goodharting towards whatever outcome fits our local incentives. This becomes even more true for groups of people and organizations. For example, EAG(x)s have a clear failure mode along this dimension. From reading retrospectives (EAGx Berkeley and EAGx Boston), they sure do seem to focus a lot on making meaningful connections and hyping people up about EA ideas and the community, and a lot of the retrospective is about how much people enjoyed EAG. I don't mean to call EAG out specifically, but instead to highlight a broader point - we're not a religion trying to spread a specific gospel; we're a bunch of people trying to figure out how to figure out what's true, and do things in the world that accomplish our goals. It does sure seem like we're putting a bunch of optimization pressure into things that don't really track our final goals, and we should step back and be at least concerned about this fact. Some parts of the rationality community do a similar thing. I notice a circuit in my own brain that Goodharts towards certain words / ways of speaking because they're more “rational.” Like, I personally have adopted this language, but actually talking about “priors” and “updates” and appending “or something” to the end of sentences does not make you better at finding the truth. You're not a better Bayesian reasoner purely because you use words that correspond to Bayesian thinking. (The counterargument here is the Sapir-Whorf hypothesis, which weakens but does not kill this point—I think many of the mannerisms seen as desirable by people in the rationality community and accepted as status or ingroup indicators track something different from truth.) By default we follow local incentives, and we should to be quite careful to step back every once in a while and really, properly make sure that we are optimizing for the right purposes. You should expect the autopilot that runs ...

Support us! https://www.patreon.com/mlst MLST Discord: https://discord.gg/aNPkGUQtc5 Dr. Raphaël Millière is the 2020 Robert A. Burt Presidential Scholar in Society and Neuroscience in the Center for Science and Society, and a Lecturer in the Philosophy Department at Columbia University. His research draws from his expertise in philosophy and cognitive science to explore the implications of recent progress in deep learning for models of human cognition, as well as various issues in ethics and aesthetics. He is also investigating what underlies the capacity to represent oneself as oneself at a fundamental level, in humans and non-human animals; as well as the role that self-representation plays in perception, action, and memory. In a world where technology is rapidly advancing, Dr. Millière is striving to gain a better understanding of how artificial neural networks work, and to establish fair and meaningful comparisons between humans and machines in various domains in order to shed light on the implications of artificial intelligence for our lives. https://www.raphaelmilliere.com/ https://twitter.com/raphaelmilliere Here is a version with hesitation sounds like "um" removed if you prefer (I didn't notice them personally): https://share.descript.com/view/aGelyTl2xpN YT: https://www.youtube.com/watch?v=fhn6ZtD6XeE TOC: Intro to Raphael [00:00:00] Intro: Moving Beyond Mimicry in Artificial Intelligence (Raphael Millière) [00:01:18] Show Kick off [00:07:10] LLMs [00:08:37] Semantic Competence/Understanding [00:18:28] Forming Analogies/JPG Compression Article [00:30:17] Compositional Generalisation [00:37:28] Systematicity [00:47:08] Language of Thought [00:51:28] Bigbench (Conceptual Combinations) [00:57:37] Symbol Grounding [01:11:13] World Models [01:26:43] Theory of Mind [01:30:57] Refs (this is truncated, full list on YT video description): Moving Beyond Mimicry in Artificial Intelligence (Raphael Millière) https://nautil.us/moving-beyond-mimicry-in-artificial-intelligence-238504/ On the Dangers of Stochastic Parrots: Can Language Models Be Too Big?

https://www.lesswrong.com/posts/PCrTQDbciG4oLgmQ5/sapir-whorf-for-rationalistsCasus Belli: As I was scanning over my (rather long) list of essays-to-write, I realized that roughly a fifth of them were of the form "here's a useful standalone concept I'd like to reify," à la cup-stacking skills, fabricated options, split and commit, and sazen.  Some notable entries on that list (which I name here mostly in the hope of someday coming back and turning them into links) include: red vs. white, walking with three, setting the zero point[1], seeding vs. weeding, hidden hinges, reality distortion fields, and something-about-layers-though-that-one-obviously-needs-a-better-word.While it's still worthwhile to motivate/justify each individual new conceptual handle (and the planned essays will do so), I found myself imagining a general objection of the form "this is just making up terms for things," or perhaps "this is too many new terms, for too many new things."  I realized that there was a chunk of argument, repeated across all of the planned essays, that I could factor out, and that (to the best of my knowledge) there was no single essay aimed directly at the question "why new words/phrases/conceptual handles at all?"So ... voilà.

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: Sapir-Whorf for Rationalists, published by Duncan Sabien on January 25, 2023 on LessWrong. Casus Belli: As I was scanning over my (rather long) list of essays-to-write, I realized that roughly a fifth of them were of the form "here's a useful standalone concept I'd like to reify," à la cup-stacking skills, fabricated options, split and commit, setting the zero point, and sazen. Some notable entries on that list (which I name here mostly in the hope of someday coming back and turning them into links) include: red vs. white, walking with three, seeding vs. weeding, hidden hinges, reality distortion fields, and something-about-layers-though-that-one-obviously-needs-a-better-word. While it's still worthwhile to motivate/justify each individual new conceptual handle (and the planned essays will do so), I found myself imagining a general objection of the form "this is just making up terms for things," or perhaps "this is too many new terms, for too many new things." I realized that there was a chunk of argument, repeated across all of the planned essays, that I could factor out, and that (to the best of my knowledge) there was no single essay aimed directly at the question "why new words/phrases/conceptual handles at all?" So ... voilà. (Note that there is some excellent pushback + clarification + expansion to be found in the comments.) Core claims/tl;dr New conceptual distinctions naturally beget new terminology.Generally speaking, as soon as humans identify a new Thing, or realize that what they previously thought was a single Thing is actually two Things, they attempt to cache/codify this knowledge in language. Subclaim: this is a good thing; humanity is not, in fact, near the practical limits of its ability to incorporate and effectively wield new conceptual handles. New terminology naturally begets new conceptual distinctions.Alexis makes a new distinction, and stores it in language; Blake, via encountering Alexis's language, often becomes capable of making the same distinction, as a result. In particular, this process is often not instantaneous—it's not (always) as simple as just listening to a definition. Actual practice, often fumbling and stilted at first, leads to increased ability-to-perceive-and-distinguish; the verbal categories lay the groundwork for the perceptual/conceptual ones. These two dynamics can productively combine within a culture.Cameron, Dallas, and Elliot each go their separate ways and discover new conceptual distinctions not typical of their shared culture. Cameron, Dallas, and Elliot each return, and each teach the other two (a process generally much quicker and easier than the original discovery). Now Cameron, Dallas, and Elliot are each "three concepts ahead" in the game of seeing reality ever more finely and clearly, at a cost of something like only one-point-five concept-discovery's worth of work.(This is not a metaphor; this is in fact straightforwardly what has happened with the collection of lessons learned from famine, disaster, war, politics, and science, which have been turned into words and phrases and aphorisms that can be successfully communicated to a single human over the course of mere decades.) That which is not tracked in language will be lost.This is Orwell's thesis—that in order to preserve one's ability to make distinctions, one needs conceptual tools capable of capturing the difference between (e.g.) whispers, murmurs, mumbles, and mutters. Without such tools, it becomes more difficult for an individual, and much more difficult for a culture or subculture, to continue to attend to, care about, and take into account the distinction in question. The reification of new distinctions is one of the most productive frontiers of human rationality.It is not the only frontier, by a long shot. But both [the literal development of n...

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: Sapir-Whorf for Rationalists, published by Duncan Sabien on January 25, 2023 on LessWrong. Casus Belli: As I was scanning over my (rather long) list of essays-to-write, I realized that roughly a fifth of them were of the form "here's a useful standalone concept I'd like to reify," à la cup-stacking skills, fabricated options, split and commit, setting the zero point, and sazen. Some notable entries on that list (which I name here mostly in the hope of someday coming back and turning them into links) include: red vs. white, walking with three, seeding vs. weeding, hidden hinges, reality distortion fields, and something-about-layers-though-that-one-obviously-needs-a-better-word. While it's still worthwhile to motivate/justify each individual new conceptual handle (and the planned essays will do so), I found myself imagining a general objection of the form "this is just making up terms for things," or perhaps "this is too many new terms, for too many new things." I realized that there was a chunk of argument, repeated across all of the planned essays, that I could factor out, and that (to the best of my knowledge) there was no single essay aimed directly at the question "why new words at all?" So ... voilà. Core claims/tl;dr New conceptual distinctions naturally beget new terminology.Generally speaking, as soon as humans identify a new Thing, or realize that what they previously thought was a single Thing is actually two Things, they attempt to cache/codify this knowledge in language. Subclaim: this is a good thing; humanity is not, in fact, near the practical limits of its ability to incorporate and effectively wield new conceptual handles. New terminology naturally begets new conceptual distinctions.Alexis makes a new distinction, and stores it in language; Blake, via encountering Alexis's language, often becomes capable of making the same distinction, as a result. In particular, this process is often not instantaneous—it's not (always) as simple as just listening to a definition. Actual practice, often fumbling and stilted at first, leads to increased ability-to-perceive-and-distinguish; the verbal categories lay the groundwork for the perceptual/conceptual ones. These two dynamics can productively combine within a culture.Cameron, Dallas, and Elliot each go their separate ways and discover new conceptual distinctions not typical of their shared culture. Cameron, Dallas, and Elliot each return, and each teach the other two (a process generally much quicker and easier than the original discovery). Now Cameron, Dallas, and Elliot are each "three concepts ahead" in the game of seeing reality ever more finely and clearly, at a cost of something like only one-point-five concept-discovery's worth of work.(This is not a metaphor; this is in fact straightforwardly what has happened with the collection of lessons learned from famine, disaster, war, politics, and science, which have been turned into words and phrases and aphorisms that can be successfully communicated to a single human over the course of mere decades.) That which is not tracked in language will be lost.This is Orwell's thesis—that in order to preserve one's ability to make distinctions, one needs conceptual tools capable of capturing the difference between (e.g.) whispers, murmurs, mumbles, and mutters. Without such tools, it becomes more difficult for an individual, and much more difficult for a culture or subculture, to continue to attend to, care about, and take into account the distinction in question. The reification of new distinctions is one of the most productive frontiers of human rationality.It is not the only frontier, by a long shot. But both [the literal development of new terminology to distinguish things which were previously thought to be the same thing, or which were previously invisible] and ...

Link to original articleWelcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: Sapir-Whorf for Rationalists, published by Duncan Sabien on January 25, 2023 on LessWrong. Casus Belli: As I was scanning over my (rather long) list of essays-to-write, I realized that roughly a fifth of them were of the form "here's a useful standalone concept I'd like to reify," à la cup-stacking skills, fabricated options, split and commit, setting the zero point, and sazen. Some notable entries on that list (which I name here mostly in the hope of someday coming back and turning them into links) include: red vs. white, walking with three, seeding vs. weeding, hidden hinges, reality distortion fields, and something-about-layers-though-that-one-obviously-needs-a-better-word. While it's still worthwhile to motivate/justify each individual new conceptual handle (and the planned essays will do so), I found myself imagining a general objection of the form "this is just making up terms for things," or perhaps "this is too many new terms, for too many new things." I realized that there was a chunk of argument, repeated across all of the planned essays, that I could factor out, and that (to the best of my knowledge) there was no single essay aimed directly at the question "why new words/phrases/conceptual handles at all?" So ... voilà. (Note that there is some excellent pushback + clarification + expansion to be found in the comments.) Core claims/tl;dr New conceptual distinctions naturally beget new terminology.Generally speaking, as soon as humans identify a new Thing, or realize that what they previously thought was a single Thing is actually two Things, they attempt to cache/codify this knowledge in language. Subclaim: this is a good thing; humanity is not, in fact, near the practical limits of its ability to incorporate and effectively wield new conceptual handles. New terminology naturally begets new conceptual distinctions.Alexis makes a new distinction, and stores it in language; Blake, via encountering Alexis's language, often becomes capable of making the same distinction, as a result. In particular, this process is often not instantaneous—it's not (always) as simple as just listening to a definition. Actual practice, often fumbling and stilted at first, leads to increased ability-to-perceive-and-distinguish; the verbal categories lay the groundwork for the perceptual/conceptual ones. These two dynamics can productively combine within a culture.Cameron, Dallas, and Elliot each go their separate ways and discover new conceptual distinctions not typical of their shared culture. Cameron, Dallas, and Elliot each return, and each teach the other two (a process generally much quicker and easier than the original discovery). Now Cameron, Dallas, and Elliot are each "three concepts ahead" in the game of seeing reality ever more finely and clearly, at a cost of something like only one-point-five concept-discovery's worth of work.(This is not a metaphor; this is in fact straightforwardly what has happened with the collection of lessons learned from famine, disaster, war, politics, and science, which have been turned into words and phrases and aphorisms that can be successfully communicated to a single human over the course of mere decades.) That which is not tracked in language will be lost.This is Orwell's thesis—that in order to preserve one's ability to make distinctions, one needs conceptual tools capable of capturing the difference between (e.g.) whispers, murmurs, mumbles, and mutters. Without such tools, it becomes more difficult for an individual, and much more difficult for a culture or subculture, to continue to attend to, care about, and take into account the distinction in question. The reification of new distinctions is one of the most productive frontiers of human rationality.It is not the only frontier, by a long shot. But both [the literal development of n...

Link to original articleWelcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: Sapir-Whorf for Rationalists, published by Duncan Sabien on January 25, 2023 on LessWrong. Casus Belli: As I was scanning over my (rather long) list of essays-to-write, I realized that roughly a fifth of them were of the form "here's a useful standalone concept I'd like to reify," à la cup-stacking skills, fabricated options, split and commit, setting the zero point, and sazen. Some notable entries on that list (which I name here mostly in the hope of someday coming back and turning them into links) include: red vs. white, walking with three, seeding vs. weeding, hidden hinges, reality distortion fields, and something-about-layers-though-that-one-obviously-needs-a-better-word. While it's still worthwhile to motivate/justify each individual new conceptual handle (and the planned essays will do so), I found myself imagining a general objection of the form "this is just making up terms for things," or perhaps "this is too many new terms, for too many new things." I realized that there was a chunk of argument, repeated across all of the planned essays, that I could factor out, and that (to the best of my knowledge) there was no single essay aimed directly at the question "why new words at all?" So ... voilà. Core claims/tl;dr New conceptual distinctions naturally beget new terminology.Generally speaking, as soon as humans identify a new Thing, or realize that what they previously thought was a single Thing is actually two Things, they attempt to cache/codify this knowledge in language. Subclaim: this is a good thing; humanity is not, in fact, near the practical limits of its ability to incorporate and effectively wield new conceptual handles. New terminology naturally begets new conceptual distinctions.Alexis makes a new distinction, and stores it in language; Blake, via encountering Alexis's language, often becomes capable of making the same distinction, as a result. In particular, this process is often not instantaneous—it's not (always) as simple as just listening to a definition. Actual practice, often fumbling and stilted at first, leads to increased ability-to-perceive-and-distinguish; the verbal categories lay the groundwork for the perceptual/conceptual ones. These two dynamics can productively combine within a culture.Cameron, Dallas, and Elliot each go their separate ways and discover new conceptual distinctions not typical of their shared culture. Cameron, Dallas, and Elliot each return, and each teach the other two (a process generally much quicker and easier than the original discovery). Now Cameron, Dallas, and Elliot are each "three concepts ahead" in the game of seeing reality ever more finely and clearly, at a cost of something like only one-point-five concept-discovery's worth of work.(This is not a metaphor; this is in fact straightforwardly what has happened with the collection of lessons learned from famine, disaster, war, politics, and science, which have been turned into words and phrases and aphorisms that can be successfully communicated to a single human over the course of mere decades.) That which is not tracked in language will be lost.This is Orwell's thesis—that in order to preserve one's ability to make distinctions, one needs conceptual tools capable of capturing the difference between (e.g.) whispers, murmurs, mumbles, and mutters. Without such tools, it becomes more difficult for an individual, and much more difficult for a culture or subculture, to continue to attend to, care about, and take into account the distinction in question. The reification of new distinctions is one of the most productive frontiers of human rationality.It is not the only frontier, by a long shot. But both [the literal development of new terminology to distinguish things which were previously thought to be the same thing, or which were previously invisible] and ...

Language—who can use it, and how well—has been in the news recently. If you haven't heard, a recent AI language model was released for public use. It's a chatbot from the company OpenAI called ChatGPT. And its capabilities are, to use a technical term, astounding. It can draft essays at an advanced undergraduate level on just about any topic. It can write a scene for a movie script along any premise you specify. It can plan a set of meals for you this week, provide the recipes, compile a shopping list, and tell you how what you're eating will affect your overall health and fitness goals. And in terms of grammar and sentence construction, it makes no mistakes. Literally none. This isn't your grandmother's chatbot.This episode is not about how ChatGPT works; it is about our current understanding of how language works. With advances in AI allowing us to create more sophisticated programs for using language, that understanding may change in the near future. But even with all the recent advances, the underlying logic behind how these kinds of programs work and what they can teach us about human language goes back decades in research on cognitive science and artificial intelligence. It seems like there's something about ChatGPT that understands the words it's using. The truth is we don't know yet. It's too soon to tell.What we do know is that we humans understand the words we use, and why we're capable of doing that is one of the great and fantastic puzzles of our species. My guest today, Gary Lupyan, is one of my favorite sources of insights about that puzzle. Gary is a professor of psychology at the University of Wisconsin, Madison. He studies language, particularly semantics, from a cognitive science perspective.This conversation is about Gary's point of view on language, words, and how we use them to both construct an understanding of the world and convey it to those around us. It's not necessarily about endorsing a big sweeping theory. But to put together some of the pieces of what we know, what we don't know, and what we may have misunderstood about language.For example, take the famous Sapir-Whorf hypothesis. This is the idea that language determines thought—that if you were to speak a language other than the one(s) already you do, it could potentially lead to an entirely different way of seeing the world. And really, the big picture of Sapir-Whorf has been settled. The truth, honestly, is not that exciting. Language does determine thought—but only a little, and not in any ways that can't be worked around. As Gary describes it, language is a system of categories. The language we speak can orient us toward different delineations of those categories with the world. But no language prevents us from seeing or comprehending any category outright. What's really fascinating here is not the broadest aspects of the overarching theory, but the implications for specific cases. There are versions of this that we touch on a lot throughout this conversation.But in terms of grand theories, a general theme emerged in our conversation of describing ideas about language on a spectrum: from Chomsky to Tomasello. Noam Chomsky you've probably heard of. He's one of the most prolific scholars of the second half of the twentieth century. He was a founding father of cognitive science, and to a large degree single-handedly determined the trajectory of linguistics for a period of almost thirty years. His most famous construction is "colorless green ideas sleep furiously." It's a totally legitimate English sentence, but one that expresses an illegitimate concept. It is representative of Chomsky's focus on structure: he didn't care about whether or not anyone had ever used that sentence; he just cared that it was possible to do so.Michael Tomasello, on the other hand, takes a usage-based approach to language. Mike has been a guest on this show and is another cognitive scientist who has had a big impact on my own thinking. He believes the way to make sense of language is as a tool, one that allows us to communicate with the other members of our species. Structure is important. But how language is used in real-life social settings is more important. Spoiler alert: both Gary and I are much more sympathetic to Tomasello's characterization of language than we are to Chomsky's. Nonetheless, both theoretical approaches offer important insights about language and the way we humans use it.The way I approached this conversation was essentially to ask Gary the biggest questions I could come up with about language: What's it for? How do words get their meanings? What was protolanguage like? What parts of language are determined by critical periods? Then just see where he takes it from there.Overall, this conversation was really a joy to have. We cover a lot of my favorite topics in cognitive science. Language is something I can get really worked up about, and it was fun to be able to talk about it with someone who is so much more knowledgeable than I am. For anyone who has ever used words or had words used on them, I think you'll find something to enjoy in this conversation.At the end of each episode, I ask my guest about three books that have most influenced their thinking. Here are Gary's picks:* Vehicles: Experiments in Synthetic Psychologyby Valentino Braitenberg (1984)A cult classic: the perfect book for thinking about thinking.* Consciousness Explainedby Daniel Dennett (1991)It's not about getting all the details right; it's about inspiring further thinking.* 4 3 2 1: A Novelby Paul Auster (2017)The most ambitious effort by a novelist at the top of his game. For students of the epic conceptual masterpiece.Honorable mention: My favorite book on Language, by Michael Tomasello, if you're interested in the technical details of what we talked about:* Constructing a Language: A Usage-Based Theory of Language Acquisition(I hope you find something good for your next read. If you happen to find it through the above links, I get a referral fee. Thanks!) This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit codykommers.substack.com/subscribe

Según la hipótesis de Sapir-Whorf, cada idioma determina la visión del mundo que tiene la correspondiente comunidad que lo habla. Este es el principal postulado del relativismo lingüístico. ¿En qué se diferencia del universalismo de Noam Chomsky? En este capítulo de El Libro Rojo recibo de nuevo a Daniel Pinto, doctor en Estudios Lingüísticos por la Universidad de Vigo.

------------------Support the channel------------ Patreon: https://www.patreon.com/thedissenter PayPal: paypal.me/thedissenter PayPal Subscription 1 Dollar: https://tinyurl.com/yb3acuuy PayPal Subscription 3 Dollars: https://tinyurl.com/ybn6bg9l PayPal Subscription 5 Dollars: https://tinyurl.com/ycmr9gpz PayPal Subscription 10 Dollars: https://tinyurl.com/y9r3fc9m PayPal Subscription 20 Dollars: https://tinyurl.com/y95uvkao This show is sponsored by Enlites, Learning & Development done differently. Check the website here: http://enlites.com/ Dr. Nick Enfield is Professor and Chair of Linguistics at the University of Sydney and director of the Sydney Centre for Language Research. His latest book is Language vs. Reality: Why Language Is Good for Lawyers and Bad for Scientists. In this episode, we focus on Language vs. Reality. We talk about the premise of the book of language as both destroyer and creator. We discuss how language and reasoning are more about convincing people, rather than getting at the truth. We talk about perception and language as two steps of reduction of reality, and the idea of language as an interface for coordination. We discuss how different languages capture different aspects of reality. We get into psychological phenomena like priming and framing. We talk about framing in politics and the media. We discuss the idea of public discourse as a market for justifications, rather than a market for ideas. We go through the functions of stories. We discuss the Sapir-Whorf hypothesis. Finally, we ask if we can know what are the best ways of talking about things. -- A HUGE THANK YOU TO MY PATRONS/SUPPORTERS: KARIN LIETZCKE, ANN BLANCHETTE, PER HELGE LARSEN, LAU GUERREIRO, JERRY MULLER, HANS FREDRIK SUNDE, BERNARDO SEIXAS, HERBERT GINTIS, RUTGER VOS, RICARDO VLADIMIRO, CRAIG HEALY, OLAF ALEX, PHILIP KURIAN, JONATHAN VISSER, JAKOB KLINKBY, ADAM KESSEL, MATTHEW WHITINGBIRD, ARNAUD WOLFF, TIM HOLLOSY, HENRIK AHLENIUS, JOHN CONNORS, PAULINA BARREN, FILIP FORS CONNOLLY, DAN DEMETRIOU, ROBERT WINDHAGER, RUI INACIO, ARTHUR KOH, ZOOP, MARCO NEVES, COLIN HOLBROOK, SUSAN PINKER, PABLO SANTURBANO, SIMON COLUMBUS, PHIL KAVANAGH, JORGE ESPINHA, CORY CLARK, MARK BLYTH, ROBERTO INGUANZO, MIKKEL STORMYR, ERIC NEURMANN, SAMUEL ANDREEFF, FRANCIS FORDE, TIAGO NUNES, BERNARD HUGUENEY, ALEXANDER DANNBAUER, FERGAL CUSSEN, YEVHEN BODRENKO, HAL HERZOG, NUNO MACHADO, DON ROSS, JONATHAN LEIBRANT, JOÃO LINHARES, OZLEM BULUT, NATHAN NGUYEN, STANTON T, SAMUEL CORREA, ERIK HAINES, MARK SMITH, J.W., JOÃO EIRA, TOM HUMMEL, SARDUS FRANCE, DAVID SLOAN WILSON, YACILA DEZA-ARAUJO, IDAN SOLON, ROMAIN ROCH, DMITRY GRIGORYEV, TOM ROTH, DIEGO LONDOÑO CORREA, YANICK PUNTER, ADANER USMANI, CHARLOTTE BLEASE, NICOLE BARBARO, ADAM HUNT, PAWEL OSTASZEWSKI, AL ORTIZ, NELLEKE BAK, KATHRINE AND PATRICK TOBIN, GUY MADISON, GARY G HELLMANN, SAIMA AFZAL, ADRIAN JAEGGI, NICK GOLDEN, PAULO TOLENTINO, JOÃO BARBOSA, JULIAN PRICE, EDWARD HALL, HEDIN BRØNNER, DOUGLAS P. FRY, FRANCA BORTOLOTTI, GABRIEL PONS CORTÈS, URSULA LITZCKE, DENISE COOK, SCOTT, ZACHARY FISH, TIM DUFFY, TRADERINNYC, TODD SHACKELFORD, AND SUNNY SMITH! A SPECIAL THANKS TO MY PRODUCERS, YZAR WEHBE, JIM FRANK, ŁUKASZ STAFINIAK, IAN GILLIGAN, LUIS CAYETANO, TOM VANEGDOM, CURTIS DIXON, BENEDIKT MUELLER, VEGA GIDEY, THOMAS TRUMBLE, AND NUNO ELDER! AND TO MY EXECUTIVE PRODUCERS, MICHAL RUSIECKI, ROSEY, JAMES PRATT, MATTHEW LAVENDER, SERGIU CODREANU, AND BOGDAN KANIVETS!

In this episode of Here to Help Chris speaks to Katie Schmidt, Quality Assurance Engineer at Indeed. Katie will speak about her career in the gaming industry, how it led to a job in QA and the important role language plays in game design. Katie will also speak about Pride month and the importance of Indeed's internal Inclusion Resource Group -  iPride in her journey. If you have ever wondered what we can learn about our own reality through game design or what exactly is the Sapir–Whorf hypothesis then this episode is one worth a listen.

General Visit Simon's website for information about him and to buy his book ‘SPEECH! How Language Made Us Human': https://www.simonprentis.net/ Follow Simon on Twitter: https://mobile.twitter.com/memesovergenes References Two for Tea interview with Sean B. Carroll: https://soundcloud.com/twoforteapodcast/77-sean-b-carroll-revolutionising-our-understanding-of-evo-biology The Sapir-Whorf hypothesis (linguistic relativity): https://en.wikipedia.org/wiki/Linguistic_relativity Simon's Areo article on Ukraine and the United Nations: https://areomagazine.com/2022/03/25/ukraine-why-arent-we-talking-about-the-un/ Timestamps 00.00 Opening and introduction. 2:25 Simon reads from his book ‘SPEECH! How Language Made Us Human'. 13:00 Animal sounds vs. human language. Simon's theory of the key to and origins of language: the “digitisation of noise.” 17:25 The evidence for Simon's theory. 22:07 Nature and language as digital; an analogy with DNA and evo devo. 26:04 The revolutionary power of language for humanity. Iona reads from Simon's book—language as an act of transportation, both connecting us with others and distancing us from the immediate basis of experience. Plus: the dangers of being trapped by language (“the trap of identity”, “the trap of culture”, etc.) and a Babylonian diversion. 37:27 Japanese enka music and Jero, the black American enka singer: a cautionary tale against feeling one's culture is special and unique. This is true at the individual level, too. This is an illusion caused by language. Further discussion and examples of this illusion and how it (sometimes dangerously) misleads and divides us. The artificiality of culture: our natures are all calibration, stemming from language and culture. Simon's Japanese experience. 49:48 Simon's views on the Sapir-Whorf hypothesis (linguistic relativity). 55:01 The power of music and its (lack of?) relation to language. Did language drive the growth of the brain? 1:04:36 Do books offer a kind of vicarious experience? Can we really communicate experience and thought to others via language? Is the world headed in the direction of a universal culture (but not a monoculture!)? 1:07:06 Using language and argument instead of violence. Is democracy an evolutionarily stable strategy? How do we apply this at the global level, not just the national level? Why the United Nations fails at this. 1:14:04 Last words and outro.

Connect with Contributors:Dr. Leslie Car: lesliecarr.comRadhika Dirks: @radhikadirks on Instagram and https://xlabs.ai/ Links Mentioned in the Episode:The Fabric of the Cosmos with physicist Briane Greene on PBS: https://www.pbs.org/wgbh/nova/series/the-fabric-of-the-cosmos/Donald Hoffman TED talk Do we see reality as it is?: https://www.youtube.com/watch?v=oYp5XuGYqqYHyperspace by Michio Kaku (Leslie's favorite book about theoretical physics and the magical nature of reality): https://www.amazon.com/Hyperspace-Scientific-Parallel-Universes-Dimension/dp/0385477058Physics and Philosophy: The Revolution in Modern Science by Werner Heisenberg https://www.amazon.com/Physics-Philosophy-Revolution-Modern-Science/dp/0061209198Science, Order, and Creativity: A Dramatic New Look at the Creative Roots of Science and Life by David Bohm https://www.amazon.com/Science-Order-Creativity-Dramatic-Creative/dp/0553344498The article about the placebo effect and knee surgery: https://www.sciencedaily.com/releases/2002/07/020712075415.htmAn explanation of the Sapir-Whorf hypothesis of linguistic relativity: https://www.theguardian.com/education/2014/jan/29/how-words-influence-thoughtAn explanation of Gödel's Incompleteness Theorems: https://plato.stanford.edu/entries/goedel-incompleteness/

In this episode, we take a different tack from discussions about John's most recent book Woke Racism. We discuss whether "anti-wokeness" is a new religion, how to engage with and persuade the people John calls "The Elect," how grave a threat "wokeness" really is compared to other contemporary issues, Critical Race Theory in schools, and the consequences of centering race in one's identity. We also go on to chat about language and semantics, the Sapir-Whorf hypothesis, the popularity of "Latinx," the ever-evolving social taboo that is "the N-word," and more. Dr. John McWhorter is an associate professor of linguistics at Columbia University, an op-ed columnist at the New York Times, and the author of a number of books on topics ranging from linguistics to race relations such as the Power of Babel: A Natural History of Language, and Losing the Race: Self Sabotage in Black America.

Does language influence how we think? Could it affect your conception of time, or the colours you see, or even your ability to count? These questions are at the heart of what's called the theory of linguistic relativity, sometimes known as the Sapir-Whorf hypothesis. Your teacher is Tiger Webb, the ABC's Language Specialist.

Does language influence how we think? Could it affect your conception of time, or the colours you see, or even your ability to count? These questions are at the heart of what's called the theory of linguistic relativity, sometimes known as the Sapir-Whorf hypothesis. Your teacher is Tiger Webb, the ABC's Language Specialist.

Does language influence how we think? Could it affect your conception of time, or the colours you see, or even your ability to count? These questions are at the heart of what's called the theory of linguistic relativity, sometimes known as the Sapir-Whorf hypothesis. Your teacher is Tiger Webb, the ABC's Language Specialist.

-Our listenership numbers in the several hundreds, which was news to me when I found out.-The Sapir-Whorf theory, Judah Smith on leadership, and how what you says determines how you think.-We officially named the gorilla mascot. Tune in to find out!-Main Site has been bananas recently, the Journal appears stagnant, and CrossFit's discussion boards are DEAD.-Top 15 Superheroes according to the internet, and our favorites.-Airlines might weigh you to determine your ticket price? But I was gonna bulk.-A man in Spain died in a dinosaur statue.-Overrated/Underrated: Paid programming, the Bond franchise, face tats, and Bible verse tattoos.

Welcome (or welcome back) to Mind Over Chatter, the Cambridge University Podcast! One series at a time, we break down complex issues into simple questions. In this second series, we're talking all about the future. We'll explore the nature of time itself - What even is the future? And is it in front of or behind us? - and we'll also cover some of today's most pressing questions, like how will artificial intelligence impact democracy?We're going to be talking to people from all over the University of Cambridge… from linguists and philosophers to historians, biologists, demographers and many more besides!We'll cover everything: from the physics of time to Sapir-Whorf, the first linguistic theory to join Starfleet; from the fabulous fabulations of futures past to Elon Musk, Mars, and James' measly net worth; from the future of wellbeing and mental health to an overabundance of Pop Tarts; from using participatory research to help create a more just future to the unequal distribution of My Little Ponies; from the future of artificial intelligence to animism and Hello Barbie; and from the future of reproduction to the maternal instincts of Darth Vader.Please take our survey.How did you find us? Do you want more Mind Over Chatter in your life? Less? We want to know. So we put together this survey. If you could please take a few minutes to fill it out, it would be a big help.

In the third and final part of our series on language we consider the philosophical question: Do we need language to think? This question is often articulated as the Sapir Whorf hypothesis. We examine the question from its historical perspective, Boas, Sapir and Whorf's anthropological investigations, Lenneberg's formulation of a strong and weak version of the hypothesis, the relationship between language and cognition, what we've learned from Piaget's study of childhood development, how bilingualism and translatability inform thought and how this leads us to our old friend, culture. Spoiler alert: the conclusion is unsatisfying (at least to me), but we still uncover some interesting aspects of human cognition and language along the way.Show notesThe Here and Now Podcast Language SeriesArrival Imdb Linguistic relativity - WikipediaWilhelm von Humboldt - WikipediaFranz Boas - WikipediaEdward Sapir - WikipediaBenjamin Lee Whorf - WikipediaThe Language Animal - Charles TaylorChange of language, change of personality? – Psychology Today20 words that don't exist in English but really should - InsiderFive ways of learning how to talk about events – Berman & SlobinFrog, where are you?The Here and Now Podcast on FacebookThe Here and Now Podcast on TwitterSend me an emailSupport the show (https://www.patreon.com/thehereandnowpodcast)

Stephen Wolfram answers general questions from his viewers about science and technology as part of an unscripted livestream series. Questions include: ​Why do some people commonly refer the internet to the World Wide Web? Isn't The world wide web a bunch of networks or website on the internet? - ​Is there a philosopher who had developed a system which is close to your perspective right now? - What are your tips about writing essays? - Why does the electron and the proton have the same amount of charge? - Why can't magnetic monopoles exist? - Bearing in mind the current topic, as well as thinking about Sapir-Whorf hypothesis - do you ever think about how humans might be 'thinking' in the future... (thinking paradigms of thought related to the future) - Do I have an opinion about such and such papers? - I don't know if Stephen was asked this question during these, but let me ask: Hello Stephen, how are you? - Would you advise today's gen Z to become independent researchers rather than academics? - Do you like Turtles? See the full Q&A video playlist: https://wolfr.am/youtube-sw-qa

Do you love the movie Arrival? Because we do! We nerd about this beautiful film, compare it to the short story on which it is based, and delve into a little of the psychology, ethics, and metaphysics of it all. DISCLAIMER: There ARE spoilers. NOTES: We bring up the Sapir-Whorf hypothesis, so if you're curious about it and want to read a good explanation of the hypothesis in relation to Arrival, check out this Smithsonian article. CREDITS: Music - "Spilled Milk" by Scott Kubie and Jon Peterson

After reading How Dead Languages Work (Oxford University Press 2020), Coulter George hopes you might decide to learn a bit of ancient Greek or Sanskrit, or maybe dabble in a bit of Old Germanic. But even if readers of his book aren't converted into polyglots, they will walk away with an introduction to the (in)famous Sapir-Whorf hypothesis, which is responsible for the inaccurate meme claiming that Inuits understand snow more deeply than other cultures because their language has one hundred (one thousand?) words for it. George criticizes this hypothesis, but through his six chapters, uses examples of ancient languages to argue that a subtler form of that hypothesis is apt: languages aren't fungible, and the properties of different languages are interwoven with their literary traditions. The book takes readers through Greek, Latin, Old English and the Germanic Languages, Sanskrit, Old Irish and the Celtic Languages, and Hebrew, introducing their phonology, morphology, lexicons, grammar, and excerpting passages from texts such as the Illiad, Beowulf, and the Rig Veda, to illustrate how the flavor of a language is always lost a little in translation. Malcolm Keating is Assistant Professor of Philosophy at Yale-NUS College. His research focuses on Indian philosophy of language and epistemology in Sanskrit. He is the author of Language, Meaning, and Use in Indian Philosophy (Bloomsbury Press, 2019) and host of the podcast Sutras (and stuff). Learn more about your ad choices. Visit megaphone.fm/adchoices

After reading How Dead Languages Work (Oxford University Press 2020), Coulter George hopes you might decide to learn a bit of ancient Greek or Sanskrit, or maybe dabble in a bit of Old Germanic. But even if readers of his book aren't converted into polyglots, they will walk away with an introduction to the (in)famous Sapir-Whorf hypothesis, which is responsible for the inaccurate meme claiming that Inuits understand snow more deeply than other cultures because their language has one hundred (one thousand?) words for it. George criticizes this hypothesis, but through his six chapters, uses examples of ancient languages to argue that a subtler form of that hypothesis is apt: languages aren't fungible, and the properties of different languages are interwoven with their literary traditions. The book takes readers through Greek, Latin, Old English and the Germanic Languages, Sanskrit, Old Irish and the Celtic Languages, and Hebrew, introducing their phonology, morphology, lexicons, grammar, and excerpting passages from texts such as the Illiad, Beowulf, and the Rig Veda, to illustrate how the flavor of a language is always lost a little in translation. Malcolm Keating is Assistant Professor of Philosophy at Yale-NUS College. His research focuses on Indian philosophy of language and epistemology in Sanskrit. He is the author of Language, Meaning, and Use in Indian Philosophy (Bloomsbury Press, 2019) and host of the podcast Sutras (and stuff). Learn more about your ad choices. Visit megaphone.fm/adchoices

Esta batalla es especial porque tiene muchísimos frentes, tantos que nos vemos desafiados y elegimos batallar con tres armas: la perspectiva de Steven Pinker, la perspectiva de Michel Foucault, y la perspectiva de Sapir-Whorf. Todos estos autores nos dicen cosas diferentes sobre cómo establecer esta relación entre la mente humana y la lengua, cosas que esperamos transmitir de manera muy general en este episodio. Acompañanos en esta Guerra de Palabras para contestar todas estas incógnitas (y muchas otras más). Nos podés escuchar en Spotify, Apple Podcast, iVoox, Google Podcast y iTunes. También seguinos en nuestras redes donde estarás al tanto de las últimas novedades: estamos en Instagram @wowpodcastuy y Facebook @wowpodcast.uy. Si nos escuchás por Apple Podcast, te pedimos que nos califiques con estrellas o nos dejes una reseña en esa misma app.

Idioma é sinônimo de identidade cultural? Vale a pena aprender mais de um? E considerando o contexto brasileiro, em especial, algo que não seja o (ou além do) inglês? Se sim, em quais sentidos ? E com quais métodos? Veja.Bem. VB no Spotify: https://open.spotify.com/show/5hhZ0QPO82fXYGJedyRRrJ iPED (https://www.iped.com.br/vejabem)– Plataforma online de educação à distância –Concorra a um curso premium grátis ao doar a partir de R$5 no Padrim (https://www.padrim.com.br/vejabempodcast) ETS2 Rotas Brasil (site) e canal no YouTube PESQUISA DEMOGRÁFICA VB — https://goo.gl/forms/kEH1tJDik1lU2gj73 (10 segundos, please help!) Contate-nos por nosso WhatsApp (19-98908-1238) e/ou email: vejabem@vejabempodcast.com.br Encontre-nos também no: Facebook , YouTube e Twitter (Uiliam) Epis Citados (links Spotify): VBMais 35 – Linguagem VBMais 36 – Como Falar Bem VBMais 37 – Atenção Referências: Sapir–Whorf hypothesis (Linguistic relativity) – artigo, Wikipedia The Strange Persistence of First Languages -artigo, Nautil.Us Is Learning a Foreign Language Really Worth It? (Ep. 158) – podcast, Freakonomics Why Don't We All Speak the Same Language? (Earth 2.0 Series) (Ep. 300) – podcast, Freakonomics The Mystery of People Who Speak Dozens of Languages -artigo, The New Yorker Creating bilingual minds | Naja Ferjan Ramirez | TEDxLjubljana – vídeo, Ted Talk, (indicação de um Padrinho –valeu, Mauro!) The secrets of learning a new language – vídeo, Ted Talk Language Learning With Netflix – extensão p/ Google Chrome Cursos IPED no tema (com 20% de desconto exclusivos do VB):

Popular linguistic theories like, Sapir–Whorf hypothesis, give us the idea that language determines how and what we think. However, looking at the psychology behind how we use language points in another direction. In this edition of Two Guys on Your Head, Dr. Art Markman and Dr. Bob Duke talk about how nouns can teach us […]