Практика Shadowing: World Models explained in 10min.. - Изучайте разговорный английский по видео
Создание урока...
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If I flip this coin, you know that the odds are going to be 50-50.
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And you know that as a fact without using fancy notations because you grew up observing the laws of physics over time.
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But unlike us, large language models don't have this luxury because LLMs don't have a simulated environment to test out its theory.
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The closest thing LLMs have to that is reasoning models that use chain of thought to reason its thinking.
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So therein lies the question, are large language models inherently flawed in their ability to grasp the physical world?
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And how exactly does world models actually overcome this?
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Welcome to Kilbride's Code, where every second counts.
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Quick shout out to BiCloud, more on him later.
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On a recent podcast with Dario Amodei, he talked about the current approach of pre-training in large English models.
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Unlike humans, LLMs are trained using trillions and trillions of tokens over months in time.
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And in comparison, humans not only develop a lot slower, but they also experience the world in modalities other than trillions of text tokens.
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For example, we know that the laws of physics govern the physical world, and our senses observe and act in the physical world,
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and we map our understanding about the physical world as we embody them in our own brain.
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But pure Lars English models are trained only using text, which is the highest abstraction that describe the physical world.
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So if LLMs never really experienced a coin flip, how does it really know whether a coin flip by law
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of large numbers will eventually converge to 50-50 without really observing them in the physical world.
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Around 2018, there was a resurgence of what's called world model.
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And world models approach things very differently than LLMs in how it modeled its understanding.
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What if instead of feeding an AI model streams and streams of text tokens,
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we train them to essentially simulate the physical world in its own brain that best represent the physical world?
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As you can see, being able to pull this kind of thing requires a thorough understanding of the laws of physics
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and cause and effect that best mimic the physical world in its own model.
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So how exactly does a world model do this?
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The original paper by David Ha uses three main components.
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You start with an environment and introduce a vision model that essentially observes this environment.
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The vision model's underlying architecture uses variational autoencoder
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that is trained to essentially compress what it observes visually into a lower dimension in latent space.
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What this compression does is that it trains the vision model to extract only the important features and throw out the rest.
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So now that we've set up our vision model that observes the physical world, we now need a model that can process this information.
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They use MDN RNN as the underlying architecture.
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And since recurrent neural networks are really expressive in being able to keep track of all of its previous hidden states,
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What this architecture allows is to store all the previous hidden states that it saw in the past,
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which allows the model to then predict based on what it saw before and based on what it's currently seeing.
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And the MDN RNN architecture works very similar to this app Sketch RNN, where once I start drawing a picture of my dog Logan,
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it'll take over and actually finish my drawing based on its prediction of what the rest of the drawing would look like.
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The final piece is the controller model where it samples from NDN RNN's output, as well as the original outputs from the vision model.
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And the sole purpose of the controller model is just to make an action, like passing butter or moving left and right or picking up objects.
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As you can see, these three components in a world model will learn to interact with the physical world
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and map out what it thinks the physical world looks like after seeing the cause
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and effect of the real world
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and eventually its own representation of the world will be sufficient that you can just sever the actual environment.
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What this allows is for the agent to be trained solely
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by the simulation of the world model's inner mapping of the real world to train the agent to do whatever you want.
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And the assertion being made here is
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that this kind of modeling fits much closer to how humans think and gets us much closer to AGI than LLMs do.
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And the initial result was pretty promising where the world model
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was able to learn how to drive on this randomly generated track by staying on the road,
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while only using less than 5 million parameters in total size when we sum up all its parts.
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So the real question here is, can world models actually scale to human-level capabilities or even more?
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And here's a quick shout-out to ByCloud.
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If you want to learn more about the theory behind AI and AI research, check out Bycloud's intuitive AI that is full of learning materials.
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You can start from beginner level to understand all the way from how tokens work to embeddings,
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encodings, and attention mechanisms that power most large language models today.
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He really mixes in good illustrations while giving an easy-to-read narrative on how the technology actually works intuitively.
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You don't need to have a deep math background.
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It'll just read like a novel where you can sequentially learn from the beginning
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or just use it as a supplemental tool on areas that you're curious about.
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He goes through different pre-training and post-training mechanism here, as well as more advanced concepts like LoRa.
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For you guys, BuyCloud has given away a 40% discount on the yearly plan using the coupon called Caleb.
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Link in the description below.
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One of the biggest reasons why large language models that we use today are so popular is because LLMs scaled quite beautifully.
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While world models are still domain-specific to certain jobs, most LLMs are what's called foundation models,
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which basically means that we can use a generic LLM like GPT 5.2
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or OPUS 4.6 to do many downstream tasks beyond simple chat, but run deep research, software development, manage our computers, and more.
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But ever since the resurgence of role models in 2018, we also had many iterations and different flavors of role models too.
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One of the biggest proponents of role models is Yen LeCun, who worked on role models in Meta as he contributed to JEPA-based models.
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He later left Meta to start his own company called AMI, or Advanced Machine Intelligence that is seeking up to $5 billion valuation.
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And Yen has been patronizing LLMs for quite a while now,
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saying how LLMs simply don't understand the physical world beyond its autoregressive nature that predicts token after token.
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But we know that language actually contains more than what Yen probably gives credit for.
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Languages not only represent the physical world in words, they also contain grammars that dictate meanings,
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figures of speech that provide abstract understanding of the physical world, and the individual units of words like nouns,
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adjectives, adverbs, they all reveal facts about the physical world.
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But meanwhile, the gap between pure LLMs and world models have also been blurred as of around 2023, as models like GPT-4 from OpenAI,
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Gemini 1 from Google introduced what's called multi-modality,
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where vision language models that can also perceive images using cross-attention help LLMs also perceive, so to speak.
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And conversely, we also have VLA or Vision Language Action, which is a type of a world model that uses vision transformers with LLMs to create action tokens.
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And this kind of setup is what powers Neo, the humanoid that was released back in October 2025 that became viral back then.
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But many people still criticize multimodal LLMs because, well, at the core, it still is an LLM that lacks spatial awareness about the physical world.
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And Fei Fei Li wanted to demonstrate spatial intelligence through her startup World Labs back in September 2024, which raised more than $230 million dollars.
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World Labs released a product called Marble that essentially creates Gaussian splats
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that produce millions and millions of these particles to interact with that are quite beautiful to work with.
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So just like how we saw in the original paper, what you're seeing here is a world model's representation of the actual environment
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but mapped in its own model that we're able to see here.
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But what's different with Marble in comparison to traditional world models is the absence of controllers
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that actually grapple with the physics of the generated world.
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This kind of sentiment is expressed by Pim, who founded General Intuition in October 2025,
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that's trying to create a closer representation of a world model that can actually interact with games and simulations.
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Google has also been a huge contributor in this space.
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When we look at SEMA in March 2024, and SEMA 2 in November 2025, and of course, their most recent Genie 3,
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that creates a hyper-realistic world that we can move around in.
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As you can see, the world model's depiction of the physical world is what allows us to generate AI videos on Sora, train cars in a simulation,
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and align robots in factories.
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NVIDIA also has a hand in this by providing an open-source platform called Cosmos, which is a world foundation model.
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Similar to how foundation models and LLMs allow a generic model to be used across many downstream tasks,
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NVIDIA's Cosmos provide tools for developers more upstream, where we can use three pre-trained models to cover various use cases,
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mostly around data augmentation and data generation, for downstream training like custom post-training like autonomous vehicles, robots, and video agents.
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Now that we covered the landscape of world models architecture and different approaches that are taken by other companies, we are still left with this rather philosophical question.
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Do LLMs really demonstrate thinking and understanding like humans do, or does it even matter that they actually think like humans?
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Do you think that LLMs and world models are mutually exclusive, or do they just solve different problems?
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What is the best way to augment intelligence artificially?
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What do you think?
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Лексика и советы по произношению к этому уроку
Этот урок разговорной практики уровня C1 построен на видео «World Models explained in 10min.». Чаще всего повторяются слова: model, Llm, physical, vision, language. В этом видео 102 предложений и 1715 слов для шедоуинга. Речь длится 9:52. Говорящий говорит в естественном темпе, около 174 слов в минуту, близком к повседневной речи. Только 81% слов входят в 3000 самых частых слов английского языка, поэтому лексика сложная.
Ключевая лексика этого видео
Самые сложные слова из видео (15), с произношением и значением:
| Слово | Произношение | Значение |
|---|---|---|
| token существительное | /ˈtoʊkən/ | знак, си́мвол |
| observe глагол | /əbˈzɜːv/ | соблюда́ть, блюсти́ |
| simulation существительное | /ˌsɪm.jəˈleɪ.ʃn̩/ | симуля́ция |
| interact глагол | /ɪn.təˈɹækt/ | взаимоде́йствовать |
| modality существительное | модальность | |
| resurgence существительное | возрожде́ние | |
| simulate глагол | /ˈsɪm.jə.lət/ | симули́ровать |
| perceive глагол | /pɚˈsiv/ | воспринима́ть, восприня́ть |
| spatial прилагательное | /ˈspeɪ.ʃəl/ | простра́нственный |
| shout глагол | /ʃaʊt/ | крича́ть, кри́кнуть |
| predict глагол | /pɹɪˈdɪkt/ | предска́зывать, предсказа́ть |
| abstraction существительное | /æbˈstɹæk.ʃn̩/ | абстра́кция |
| adjective существительное | /ˈæd͡ʒ.ɪk.tɪv/ | и́мя прилага́тельное, прилага́тельное |
| artificially наречие | иску́сственно | |
| augment глагол | /ɔɡˈmɛnt/ | увели́чивать, увели́чить |
Фразовые глаголы, которые вы услышите
| Слово | Произношение | Значение |
|---|---|---|
| check out глагол | проверя́ть, прове́рить | |
| grow up глагол | расти́, выраста́ть | |
| pick up глагол | подбира́ть, подобра́ть | |
| set up глагол | /ˌsɛt ˈʌp/ | устана́вливать, установи́ть |
| take over глагол | брать на себя́, взять на себя́ |
Грамматика в этом видео
Конструкции, которые говорящий использует чаще всего, с точными словами из видео:
| Конструкция | В видео |
|---|---|
| Пассивный залог be + причастие прошедшего времени — важно, что происходит, а не кто это делает | are trained · is trained · be trained |
| Present Perfect have/has + причастие прошедшего времени — прошлое действие, важное сейчас | we've set · has given · have also been blurred |
| Придаточные определительные who / which + предложение — уточнение о человеке или предмете | text, which is · Action, which is · Cosmos, which is |
Произношение, на которое стоит обратить внимание
Говорящий использует 10 сокращённых и редуцированных форм, например don't, it'll, you're. Произносите их коротко, так, как слышите.
- Звуки «th»: therein /ˈðɛəɹˈɪn/, thorough /ˈθʌɹoʊ/
- Звуки «sh» и «zh»: simulation /ˌsɪm.jəˈleɪ.ʃn̩/, spatial /ˈspeɪ.ʃəl/, shout /ʃaʊt/, abstraction /æbˈstɹæk.ʃn̩/, depiction /dɪˈpɪkʃən/
- Длинные слова — следите за ударением: iteration /ˌɪt.əˈɹeɪ.ʃən/, sequentially /səˈkwɛntʃəli/, supplemental /ˌsʌplɪˈmɛntəl/, parameter /pəˈɹæm.ə.tɚ/, intuitive /ɪnˈtjuːɪtɪv/
Как заниматься с этим видео
- Прослушайте всё видео один раз молча и выпишите незнакомые слова.
- Начните со скорости 0,75×, повторяйте предложение за предложением и вернитесь к обычной скорости, когда станет легко.
- Запишите себя и сравните с оригиналом, обращая внимание на такие слова, как token, observe, simulation.
Что такое техника Shadowing?
Shadowing — это научно обоснованная техника изучения языка, изначально разработанная для подготовки профессиональных переводчиков и популяризированная полиглотом доктором Александром Аргуэльесом. Метод прост, но эффективен: вы слушаете аудио на английском от носителей языка и немедленно повторяете вслух — как тень, следующая за говорящим с задержкой в 1–2 секунды. В отличие от пассивного прослушивания или грамматических упражнений, Shadowing заставляет мозг и мышцы рта одновременно обрабатывать и воспроизводить реальные речевые паттерны. Исследования показывают, что это значительно улучшает точность произношения, интонацию, ритм, связную речь, понимание на слух и беглость речи — что делает его одним из самых эффективных методов для подготовки к IELTS Speaking и реального общения на английском.