Поддержать проект можно по ссылкам:
Если вы в России: https://boosty.to/vertdider
Если вы не в России: https://www.patreon.com/VertDider

Специалисты считают, что уже в пределах пятнадцати лет для квантового компьютера не составит труда расшифровать наши пароли и даже данные банковских карт. Дерек Маллер решил узнать, обречены мы или нет. Оказывается, способ защитить данные уже придумали — и всё благодаря решёткам и векторам. Подробности ищите в видео.

Перевод: Елена Смотрова
Редактура: Алексей Малов
Научная редактура: Кирилл Циберкин, Елена Иконникова
Озвучка: Дмитрий Чепусов
Монтаж звука: Андрей Фокин
Монтаж видео: Джон Исмаилов
Обложка: Андрей Гавриков

Спасибо за поддержку на https://www.patreon.com/VertDider и https://boosty.to/vertdider
Озвучки Vert Dider выходят с вашей помощью:

Pavel Dunaev, Mikhail Stolpovskiy, Sergey Zhukov, Ira Kozlovskaya, Amir, Dmitrii Komarevtsev, Pavel Parpura, Мikhail Samin, Roman Inflianskas, Oleg Zingilevskiy, opperatius, Alena, Aleksandr P., Ника Калмыкова, Vitaliy Vachynyuk, Куляш, Anatoly Ressin, Anton Bolotov, JIukaviy, Katoto Chan, Eugene Tsaplin, Anpat, angel, Victor Cazacov, Timur Sattarov, Kirill Zaslavskii, Janis Riekstins, Ihar Marozau, Artem Amirbekov, Dzmitryi Halyava, Aleksei Shabalin, Vladyslav Sokolenko, Vadim Velicodnii, Kseniya Stukova, Irina Shakhverdova, POD666, Alex Kulakov, Stanislav Obrazcoff, Sergey P, Павел Приступа, Anna Ostrovskaya, Oleksandr Hrebeniuk, Leonid Steinberg, Ilya, Alexander Goida, Katya Ma, Morock_D, Evgeny Vrublevsky, Сергей Платоненко, Валентин Ильин, Владимир Ямщиков, Мария Зверева, Адам Шифф, Rashn, Roman Gelingen, Сергей Клопов, Rashn, Nikrodol, Rincewind, Lexx, Alexander Lebedev, Sonra44, Шурик Самара, Ranis Zaripov, Alexey Tyurnin, Anastasiya Zotova, Anton G, Dmitry Perekalin, Vadim, Владимир Авдиев, MaximML , Timophey Popov, Иван Новиков, Арс Ник, Никита Омельницкий, Rekvet, Илья Зверев, Точка Зрения, Artem Sobolenkov, Artem Piskarev, Timur Rahmatulin, Тимур Абдуллин, Timur Rahmatulin, Глеб Кочергин, Адам Шифф, Василий , Никита Омельницкий, Alexey Sklyar.

#vertdider #veritasium

Сайт студии: http://vertdider.tv

Мы в социальных сетях:
— http://vk.com/studio_vd
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Разрешение на публикацию: https://bit.ly/2SFhrHZ
© https://www.youtube.com/c/veritasium/
Источник: https://youtu.be/-UrdExQW0cs

Помощь проекту: https://vertdider.tv/to-support-us/

Ламинарное течение, безусловно, красивый феномен. Обязательно поищите ролики на YouTube, если вы его ещё не видели. Один из больших поклонников этого явления — Дестин Сэндлин с канала Smarter Every Day, но Дерек Маллер с канала Veritasium не разделяет его восторженных взглядов и готов доказать, что турбулентное течение круче.

Спасибо за поддержку на https://www.patreon.com/VertDider.
Озвучки Vert Dider выходят с вашей помощью:

Nick Denizhenko, Oleksii , Pavel Dunaev, Mikhail Stolpovskiy, Ігор Дорохов, Boris Bendikov, Михаил Панькин, Виталий Алешко, Felix , Pavel Parpura, Yevhen , Katoto Chan, Данил Рягузов, Владимир Кашутин, Kosoy , Anton Bolotov, Максим Минин, Максим Sheridan' Горлов, Luba Karpenko, Anton Makiievskyi, Vladimir Grachev, Denis Titusov, Spartak Kagramanyan, Максим Иванов, Dmitriy Omelyansky, Konstantin Pesyakov, Olga Shistareva, Alex Katkov, Serj Kravchuk, Alexander Zimin, Евгений Миф, Alexey Kukushkin, Пугачёв Пётр, Viktoria , Kamerton_440 , Alexander , Pandaben , Oleg Streltsov, Monza UA, Дмитрий Сергеевич, Alexandr Globov, Мария Соозарь, Vladimir Goshev, Vladyslav Sokolenko, Pavel D, Maria Tronina, Alexander Balynskiy, Ivan Iakimov, Иван Дьяченко, Claudia Barzaeva, sepeca , Olga Podolskaya, Aleksei Masliukov, Valeria Volodina, Alexander Gorodok, Viktor Lova, Ольга Канкулова, Vladimir Gavriushov, Алексей Разводов, Maria Burtseva, Aleksey Sazonov, Irina Shakhverdova, Evgeny Vrublevsky, Виталий Пастушенко, Katya Prokopchuk, Ihar Marozau, Lirin Alex, Alterien

Перевод: Алексей Лоскутов
Редактура: Алексей Малов
Научная редактура: Леонид Вокин
Озвучка: Вадим Казанцев, Дмитрий Чепусов, Екатерина Муравицкая
Монтаж звука: Андрей Фокин
Монтаж видео: Джон Исмаилов
Обложка: Эльдар Нургалиев

Сайт студии: http://vertdider.tv

Мы в социальных сетях:
— http://vk.com/studio_vd
— https://t.me/vertdider
— https://twitter.com/Vert_Dider
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Разрешение на публикацию: https://goo.gl/6n4dT7
© https://www.youtube.com/user/1veritasium
Источник: https://youtu.be/5zI9sG3pjVU

Вікно Еймса – це зображення, наприклад, на пласкому шматку картону, яке здається прямокутним вікном, але насправді є трапецією. Обидві сторони шматка картону мають однакове зображення. Картон підвішений вертикально на дроті, щоб він міг безперервно обертатися, або прикріплений до вертикальної осі, навколо якої неперервно обертається.
Під час обертання вікна здається, що обертання на мить зупиняється та змінює напрямок на протилежний. Тому воно не сприймається як безперервне обертання в одному напрямку, а натомість сприймається як коливання.
Кімната Еймса – приміщення неправильної форми, що використовується для створення тривимірної оптичної ілюзії. Була спроєктована американським офтальмологом Альбертом Еймсом в 1934 році й побудована в 1935 році.
Кімната Еймса побудована так, що спереду вона виглядає як звичайна кімната кубічної форми із задньою стінкою та двома боковими стінами, паралельними одна одній і перпендикулярними до горизонтальних площин підлоги і стелі. Проте дійсна форма кімнати трапецієподібна. [Wikipedia]

✅Текст читав: Андрій Горбунов (Lacuna)
✅Переклад, редагування, субтитрування: проєкт самоосвіти pre post, Віталій Шевчук (Цікава наука)

❕Підтримайте "Цікаву науку" на https://www.patreon.com/join/CikavaNauka або
💳 Приватбанк: 5168 7451 0521 3422
💳 Монобанк: 4441 1144 2309 4109 (Віталій Шевчук)

© Оригінальне відео: https://www.youtube.com/watch?v=dBap_Lp-0oc
© Канал Veritasium: https://www.youtube.com/c/veritasium (з люб’язного дозволу автора)
© Researched and written by Petr Lebedev and Derek Muller
Filmed by Derek Muller and Raquel Nuno
Animations, VFX, and Music by Jonny Hyman
Ames Room VFX and additional Ames Window animation by Nicolas Pratt
© Derek Muller
#SciTube #ЦікаваНаука #WatchUA

References:
Ames, A., Jr. (1951). Visual perception and the rotating trapezoidal window. Psychological Monographs: General and Applied, 65(7), i–32. https://doi.org/10.1037/h0093600
Marcel de Heer & Thomas V. Papathomas (2017) The Ames Window Illusion and Its Variations
DOI:10.1093/acprof:oso/9780199794607.003.0014
Oross, Stephen, Francis, Ellie, Mauk, Deborah & Fox, Robert. (1987). The Ames Window Illusion: Perception of Illusory Motion by Human Infants. Journal of Experimental Psychology: Human Perception & Performance, 13(4), 609-613.
Behrens, R. (1987). The Life and Unusual Ideas of Adelbert Ames, Jr. Leonardo, 20(3), 273-279. doi:10.2307/1578173
Burnham, C., & Ono, H. (1969). Variables Altering Perception of the Rotating Trapezoidal Illusion. The American Journal of Psychology, 82(1), 86-95. doi:10.2307/1420609
Allport, G. W., & Pettigrew, T. F. (1957). Cultural influence on the perception of movement: The trapezoidal illusion among Zulus. The Journal of Abnormal and Social Psychology, 55(1), 104–113. https://doi.org/10.1037/h0049372
Zenhausern R. Effect of Perspective on Two Trapezoid Illusions. Perceptual and Motor Skills. 1969;28(3):1003-1009. doi:10.2466/pms.1969.28.3.1003

Simple and easy explanation of LLM or Large Language Model in less than 5 minutes. In this short video, you will build an intuition of how a large language model works using animation and simple story telling. This is the explanation that even a high school student can understand easily.

Simple Explanation of Neural Network: https://www.youtube.com/watch?v=ER2It2mIagI

Do you want to learn technology from me? Check https://codebasics.io/?utm_source=description&utm_medium=yt&utm_campaign=description&utm_id=description for my affordable video courses.

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I put three systems to the local LLM test.

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#machinelearning #llm #softwaredevelopment

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Direct Preference Optimization (DPO) is a method used for training Large Language Models (LLMs). DPO is a direct way to train the LLM without the need for reinforcement learning, which makes it more effective and more efficient.
Learn about it in this simple video!

This is the third one in a series of 4 videos dedicated to the reinforcement learning methods used for training LLMs.

Full Playlist: https://www.youtube.com/playli....st?list=PLs8w1Cdi-zv

Video 0 (Optional): Introduction to deep reinforcement learning https://www.youtube.com/watch?v=SgC6AZss478
Video 1: Proximal Policy Optimization https://www.youtube.com/watch?v=TjHH_--7l8g
Video 2: Reinforcement Learning with Human Feedback https://www.youtube.com/watch?v=Z_JUqJBpVOk
Video 3 (This one!): Deterministic Policy Optimization

00:00 Introduction
01:08 RLHF vs DPO
07:19 The Bradley-Terry Model
11:25 KL Divergence
16:32 The Loss Function
14:36 Conclusion

Get the Grokking Machine Learning book!
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Anthropic’s Barry Zhang (Applied AI), Erik Schluntz (Research), and Alex Albert (Claude Relations) discuss the potential of AI agents, common pitfalls to avoid, and how to prepare for the evolving landscape.

Read more advice on building agents: https://www.anthropic.com/rese....arch/building-effect

00:00 Introduction
00:26 Defining AI agents and workflows
02:55 Anatomy of an agent prompt
04:29 Behind the scenes stories
07:29 Why write about agents now
08:53 Overhyped and underhyped aspects of agents
09:57 Identifying useful applications of agents
10:47 Coding agents: Potential and challenges
12:47 The future of agents in 2025
16:26 Advice for developers exploring agents

In this video, I dive into the world of agentic AI, a concept that's set to be a major buzzword in 2025. We explore how agentic AI combines multiple agents—some of which are language models (LLMs) and others that are tools—to create more sophisticated and effective AI workflows. We walk through a simple example of how an AI agent can draft a marketing plan, another can critique it, and a third can refine it based on the feedback. We also discuss how some agents can be tools, like a Google search to gather data, and how an orchestrator agent can manage the entire process, adjusting steps as needed.

Leave me a comment if you have new topics I should be discussing.

Check out my LinkedIn: https://www.linkedin.com/in/donwoodlock/

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YouTube Timestamps
0:00 – 0:20 Welcome
0:21 – 0:50 Introduction to the concept of Agentic AI
0:51 – 3:50 Explanation of how Agentic AI works
3:50 – 4:08 Advertisement plug-in
4:08 – 5:20 Example of using compound LLM’s
5:21 – 5:55 Why you should use a compound LLM approach
5:57 – 6:45 Best way to train and use LLM’s for optimal outcome
6:45 – 7:40 How to think of LLM as agents
7:41 – 10:05 Not every agent needs to be an LLM
10:06 - 10:50 Possibility of having an orchestrator agent
10:51 – 12:50 How to use these agents
12:51 – 13:26 Closing remarks

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ABOUT INTERSYSTEMS
Established in 1978, InterSystems Corporation is the leading provider of data technology for extremely critical data in healthcare, finance, and logistics. It’s cloud-first data platforms solve interoperability, speed, and scalability problems for large organizations around the globe. InterSystems Corporation is ranked by Gartner, KLAS, Forrester and other industry analysts as the global leader in Data Access and Interoperability. InterSystems is the global market leader in Healthcare and Financial Services.

Website: https://www.intersystems.com/
Youtube: https://www.youtube.com/@InterSystemsCorp
LinkedIn: https://www.linkedin.com/company/intersystems/
Twitter: https://twitter.com/InterSystems

#agenticai #aiagents #LLM

MIT 15.773 Hands-On Deep Learning Spring 2024
Instructor: Rama Ramakrishnan
View the complete course: https://ocw.mit.edu/courses/15....-773-hands-on-deep-l
YouTube Playlist: https://www.youtube.com/playli....st?list=PLUl4u3cNGP6

Introduction and overview of the course covering the history and background of the field.

License: Creative Commons BY-NC-SAMore information at https://ocw.mit.edu/termsMore courses at https://ocw.mit.eduSupport OCW at http://ow.ly/a1If50zVRlQ

We encourage constructive comments and discussion on OCW’s YouTube and other social media channels. Personal attacks, hate speech, trolling, and inappropriate comments are not allowed and may be removed. More details at https://ocw.mit.edu/comments.

Want to dive deeper? This curriculum is covered in the following online courses:
- Agentic AI professional education course: https://stanford.io/4zOyjPN
- XCS329 graduate course: https://online.stanford.edu/co....urses/cs329a-self-im

A similar curriculum is covered in XCS329z https://online.stanford.edu/co....urses/cs329z-enginee

Follow along with the course schedule and syllabus: https://cs329a.stanford.edu/

View the course playlist: https://www.youtube.com/playli....st?list=PLangBM27OtE

Video Summary:
This first lecture videoof Stanford's CS329A, Self-Improving AI Agents, taught by Aakanksha Chowdhery and Azalia Mirhoseini on September 22, 2025, opens with an overview of scaling laws that link model parameters, training compute, and dataset size to lower test loss in large language models from GPT-2 through GPT-4. It covers few-shot and zero-shot learning, the emergence of chain-of-thought reasoning in larger models, and the role of instruction tuning and reinforcement learning from human feedback in the development of ChatGPT. The lecture introduces inference-time scaling through the Large Language Monkeys project, which repeatedly samples a model's outputs and selects correct answers with a verifier to improve performance without retraining. It then traces the shift from single-turn chatbots to agent workflows such as prompt chaining, routing, parallelization, and orchestrator-worker patterns, using Claude Code and deep research tools as examples. The session closes with logistics for the course.

Speaker Bios:
Aakanksha Chowdhery
Adjunct Professor of Computer Science, Stanford University

Dr. Aakanksha Chowdhery is pushing the frontier of agentic LLMs, focusing on recursive self-improvement and long-horizon agents that learn and deploy in the real world. She is one of the few researchers globally who has led frontier model training end-to-end, across both dense and mixture-of-experts (MoE) architectures. At Google, she led the 540B PaLM model, the largest densely trained language model in the world at the time. She subsequently drove pre-training and scaling of Gemini's MoE models across multiple generations, and contributed key components to PaLM-E, Med-PaLM, and the Pathways infrastructure underpinning Google's large-model efforts. She went on to build and lead pretraining teams for open intelligence efforts at Reflection and Meta. Earlier, she held research roles at Microsoft Research and Princeton. At Stanford, where she earned her PhD, she teaches CS329A (Self-Improving AI Agents) and serves as Program Chair for MLSys 2026.

Azalia Mirhoseini
Assistant Professor of Computer Science, Stanford University

Azalia Mirhoseini is a co-founder of Ricursive Intelligence, a frontier lab dedicated to recursive self-improvement through AI that designs the chips that fuel it. She is also an Assistant Professor of Computer Science at Stanford University where she directs Scaling Intelligence, a lab focused on developing scalable and self-improving AI systems and methodologies toward the goal of artificial general intelligence. Previously, she spent several years in industry AI labs, including Google Brain, Anthropic, and Google DeepMind, working on the development of Claude and Gemini. Her past work includes Mixture-of-Experts (MoE) neural architectures, now predominantly used in leading generative AI models; AlphaChip, a pioneering work on deep reinforcement learning for layout optimization used in the design of advanced chips like Google AI accelerators (TPUs) and data center CPUs; as well as pioneering research on LLM Test-Time Scaling. Her work has been recognized through the Okawa Research Grant, the Google ML and Systems Junior Faculty Award, MIT Technology Review's 35 Under 35 Award, the Best ECE Thesis Award at Rice University, publications in flagship venues such as Nature, and coverage by various media outlets, including WSJ, NYT, Forbes, MIT Technology Review, IEEE Spectrum, WIRED, and TechCrunch.