Загрузка видео...

Не удалось загрузить видео

Возникла проблема при загрузке этого видео. Это может быть связано с временными проблемами сети или видео может быть недоступно.

На главную

This is how large language models turn objects to vector representations. In this video, we explore how large language models (LLMs) convert objects into internal representations, especially when translating between languages like English and Hindi. Using real-world examples, we highlight the challenges of gender inference, grammatical structure, and why... show more

Gaurav Sen

71,346 subscribers

27,368 просмотров • 1 год назад •via X (Twitter)

Здоровье и велнес Образование Наука и технологии

Anya Rossi• Live Now

Private livecam show

Комментарии: 2

Фото профиля Berojgar Engineer

Berojgar Engineer1 год назад

The way you describe just wow,😍😍 wanted to buy Low Level Design course from interviewReady, will it be worth it or you are giving me the course for free of cost 😇🤣

Фото профиля AssemblyAI

AssemblyAI1 год назад

Announcing: Our most advanced speech-to-text model goes beyond accuracy to capture the real-world complexity of human conversation and deliver reliable, source-of-truth audio data. Explore Universal-2 updates 👇

Похожие видео

Watch "Endless experimentation: Building AI models in the wild." This is a 50-minute, MIT lecture about problems when deploying models and LLMs in the real world and how to prepare to solve them. This is a great lecture for those building production machine learning.

Watch "Endless experimentation: Building AI models in the wild." This is a 50-minute, MIT lecture about problems when deploying models and LLMs in the real world and how to prepare to solve them. This is a great lecture for those building production machine learning.

Santiago

117,204 просмотров • 2 лет назад

Meta FAIR and Rothschild Foundation Hospital present a groundbreaking study mapping how language representations emerge in the brain, revealing striking parallels with LLMs. This research offers unprecedented insights into the neural development of language, showing how AI models like wav2vec 2.0 and Llama 4 mirror the brain's language processing. Discover how these findings pave the way for new frameworks in understanding human intelligence and developing clinical tools for language support. 📄 Read the full research paper: ➡️

Meta FAIR and Rothschild Foundation Hospital present a groundbreaking study mapping how language representations emerge in the brain, revealing striking parallels with LLMs. This research offers unprecedented insights into the neural development of language, showing how AI models like wav2vec 2.0 and Llama 4 mirror the brain's language processing. Discover how these findings pave the way for new frameworks in understanding human intelligence and developing clinical tools for language support. 📄 Read the full research paper: ➡️

AI at Meta

28,761 просмотров • 1 год назад

A quick video about how Come-from-Beyond discovered that you can actually break any Large Language Model by trolling it with complex questions. It is called a "Zero Delta" exploit and all LLM models are susceptible to it. I managed to recreate this on Grok and the video shows the result. With regards to all LLMs out there from $QUBIC - building the real AGI. Qubic My YouTube Channel:

A quick video about how Come-from-Beyond discovered that you can actually break any Large Language Model by trolling it with complex questions. It is called a "Zero Delta" exploit and all LLM models are susceptible to it. I managed to recreate this on Grok and the video shows the result. With regards to all LLMs out there from $QUBIC - building the real AGI. Qubic My YouTube Channel:

retrodrive ⛏

13,625 просмотров • 1 год назад

Announcing How Transformer LLMs Work, created with Jay Alammar and Maarten Grootendorst, co-authors of the beautifully illustrated book, “Hands-On Large Language Models.” This course offers a deep dive into the inner workings of the transformer architecture that powers large language models (LLMs). The transformer architecture revolutionized generative AI; in fact, the "GPT" in ChatGPT stands for "Generative Pre-Trained Transformer." Originally introduced in the Google Brain team's groundbreaking 2017 paper "Attention Is All You Need," by Vaswani and others, transformers were a highly scalable model for machine translation tasks. Variants of this architecture now power today’s LLMs such as those from OpenAI, Google, Meta, Cohere, Anthropic and DeepSeek. In this course, you’ll learn in detail how LLMs process text. You'll also work through code examples that illustrate that transformer's individual components. In details, you’ll learn: - How the representation of language has evolved, from Bag-of-Words to Word2Vec embeddings to the transformer architecture that captures a word's meanings taking into account the context of other words in the input. - How inputs are broken down into tokens before they are sent to the language model. - The details of a transformer's main stages: Tokenization and embedding, the stack of transformer blocks, and the language model head. - The inner workings of the transformer block, including attention, which calculates relevance scores, and the feedforward layer, which incorporates stored information learned in training. - How cached calculations make transformers faster. - Some of the most recent ideas in the latest models such as Mixture-of-Experts (MoE) which uses multiple sub-models and a router on each layer to improve the quality of LLMs. By the end of this course, you’ll have a deep understanding of how LLMs actually process text and be able to read through papers describing the latest models and understand the details. Gaining this intuition will improve your approach to building LLM applications. Please sign up here:

Announcing How Transformer LLMs Work, created with Jay Alammar and Maarten Grootendorst, co-authors of the beautifully illustrated book, “Hands-On Large Language Models.” This course offers a deep dive into the inner workings of the transformer architecture that powers large language models (LLMs). The transformer architecture revolutionized generative AI; in fact, the "GPT" in ChatGPT stands for "Generative Pre-Trained Transformer." Originally introduced in the Google Brain team's groundbreaking 2017 paper "Attention Is All You Need," by Vaswani and others, transformers were a highly scalable model for machine translation tasks. Variants of this architecture now power today’s LLMs such as those from OpenAI, Google, Meta, Cohere, Anthropic and DeepSeek. In this course, you’ll learn in detail how LLMs process text. You'll also work through code examples that illustrate that transformer's individual components. In details, you’ll learn: - How the representation of language has evolved, from Bag-of-Words to Word2Vec embeddings to the transformer architecture that captures a word's meanings taking into account the context of other words in the input. - How inputs are broken down into tokens before they are sent to the language model. - The details of a transformer's main stages: Tokenization and embedding, the stack of transformer blocks, and the language model head. - The inner workings of the transformer block, including attention, which calculates relevance scores, and the feedforward layer, which incorporates stored information learned in training. - How cached calculations make transformers faster. - Some of the most recent ideas in the latest models such as Mixture-of-Experts (MoE) which uses multiple sub-models and a router on each layer to improve the quality of LLMs. By the end of this course, you’ll have a deep understanding of how LLMs actually process text and be able to read through papers describing the latest models and understand the details. Gaining this intuition will improve your approach to building LLM applications. Please sign up here:

Andrew Ng

253,812 просмотров • 1 год назад

3D-LLM: Injecting the 3D World into Large Language Models paper page: Large language models (LLMs) and Vision-Language Models (VLMs) have been proven to excel at multiple tasks, such as commonsense reasoning. Powerful as these models can be, they are not grounded in the 3D physical world, which involves richer concepts such as spatial relationships, affordances, physics, layout, and so on. In this work, we propose to inject the 3D world into large language models and introduce a whole new family of 3D-LLMs. Specifically, 3D-LLMs can take 3D point clouds and their features as input and perform a diverse set of 3D-related tasks, including captioning, dense captioning, 3D question answering, task decomposition, 3D grounding, 3D-assisted dialog, navigation, and so on. Using three types of prompting mechanisms that we design, we are able to collect over 300k 3D-language data covering these tasks. To efficiently train 3D-LLMs, we first utilize a 3D feature extractor that obtains 3D features from rendered multi- view images. Then, we use 2D VLMs as our backbones to train our 3D-LLMs. By introducing a 3D localization mechanism, 3D-LLMs can better capture 3D spatial information. Experiments on ScanQA show that our model outperforms state-of-the-art baselines by a large margin (e.g., the BLEU-1 score surpasses state-of-the-art score by 9%). Furthermore, experiments on our held-in datasets for 3D captioning, task composition, and 3D-assisted dialogue show that our model outperforms 2D VLMs. Qualitative examples also show that our model could perform more tasks beyond the scope of existing LLMs and VLMs.

3D-LLM: Injecting the 3D World into Large Language Models paper page: Large language models (LLMs) and Vision-Language Models (VLMs) have been proven to excel at multiple tasks, such as commonsense reasoning. Powerful as these models can be, they are not grounded in the 3D physical world, which involves richer concepts such as spatial relationships, affordances, physics, layout, and so on. In this work, we propose to inject the 3D world into large language models and introduce a whole new family of 3D-LLMs. Specifically, 3D-LLMs can take 3D point clouds and their features as input and perform a diverse set of 3D-related tasks, including captioning, dense captioning, 3D question answering, task decomposition, 3D grounding, 3D-assisted dialog, navigation, and so on. Using three types of prompting mechanisms that we design, we are able to collect over 300k 3D-language data covering these tasks. To efficiently train 3D-LLMs, we first utilize a 3D feature extractor that obtains 3D features from rendered multi- view images. Then, we use 2D VLMs as our backbones to train our 3D-LLMs. By introducing a 3D localization mechanism, 3D-LLMs can better capture 3D spatial information. Experiments on ScanQA show that our model outperforms state-of-the-art baselines by a large margin (e.g., the BLEU-1 score surpasses state-of-the-art score by 9%). Furthermore, experiments on our held-in datasets for 3D captioning, task composition, and 3D-assisted dialogue show that our model outperforms 2D VLMs. Qualitative examples also show that our model could perform more tasks beyond the scope of existing LLMs and VLMs.

AK

249,708 просмотров • 3 лет назад

$AI agents are about to redefine the internet. The mistake we made with Large Language Models? We let a handful of corporations capture all the value. Action Model is building a different path. By training through our extension, users gain fractional ownership in the Large Action Model, giving them a real stake in the future of AI. When LLMs emerged, the upside flowed to Big Tech. This time, it doesn’t have to. They’re building AI on our data, and keeping the upside for themselves. Community-owned Large Action Model is how we take it back.$

AI agents are about to redefine the internet. The mistake we made with Large Language Models? We let a handful of corporations capture all the value. Action Model is building a different path. By training through our extension, users gain fractional ownership in the Large Action Model, giving them a real stake in the future of AI. When LLMs emerged, the upside flowed to Big Tech. This time, it doesn’t have to. They’re building AI on our data, and keeping the upside for themselves. Community-owned Large Action Model is how we take it back.

Action Model

76,866 просмотров • 4 месяцев назад

A 4-year-old child has seen 50x more information than the biggest LLMs. Yann LeCun is the Chief AI Scientist at Meta. He recently spoke on “The Expanding Universe of Generative Models” panel at the World Economic Forum in Davos. Yann highlighted the idea that a 4-year-old child is way smarter than current cutting-edge large language models (LLMs). “Think about what a child sees through vision. Put a number on how much information a 4-year-old child has seen during their life. It’s 20 Mbps going through the optical nerve for 16,000 wake hours in the first 4 years of life. 3,600 seconds per hour is 10^15 bytes. This is 50x more information than the biggest LLMs we have. A 4-year-old child is way smarter than these models having acquired an enormous amount of knowledge about how the world works.” The real constraint right now is the ability of LLMs to think. Today, LLMs are only capable of System 1 thinking. System 1 vs System 2 thinking was popularised in the book 'Thinking, Fast and Slow' by Daniel Kahneman. System 1 tasks involve quick, instinctive, automatic responses. LLMs struggle with discontinuous tasks that require a creative leap in progress as they imitate human responses. It's hard to go above human response accuracy if LLMs are only trained on humans. Models are building the track in front of them with each word being generated. What could it mean to give language models System 2 thinking? This remains a future development I'm excited about.

A 4-year-old child has seen 50x more information than the biggest LLMs. Yann LeCun is the Chief AI Scientist at Meta. He recently spoke on “The Expanding Universe of Generative Models” panel at the World Economic Forum in Davos. Yann highlighted the idea that a 4-year-old child is way smarter than current cutting-edge large language models (LLMs). “Think about what a child sees through vision. Put a number on how much information a 4-year-old child has seen during their life. It’s 20 Mbps going through the optical nerve for 16,000 wake hours in the first 4 years of life. 3,600 seconds per hour is 10^15 bytes. This is 50x more information than the biggest LLMs we have. A 4-year-old child is way smarter than these models having acquired an enormous amount of knowledge about how the world works.” The real constraint right now is the ability of LLMs to think. Today, LLMs are only capable of System 1 thinking. System 1 vs System 2 thinking was popularised in the book 'Thinking, Fast and Slow' by Daniel Kahneman. System 1 tasks involve quick, instinctive, automatic responses. LLMs struggle with discontinuous tasks that require a creative leap in progress as they imitate human responses. It's hard to go above human response accuracy if LLMs are only trained on humans. Models are building the track in front of them with each word being generated. What could it mean to give language models System 2 thinking? This remains a future development I'm excited about.

Alex Banks

22,958 просмотров • 2 лет назад

New course! Generative AI with Large Language Models, created with Amazon Web Services and hosted on Coursera. This course goes deep into the technical foundations of LLMs and how to use them. You can sign up here: You’ll work through the full life-cycle of a generative AI project, and learn specific techniques like RLHF; zero-shot, one-shot, and few-shot learning with LLMs; advanced prompting frameworks like ReAct; even fine-tuning LLMs, and gain hands-on practice with all of these techniques. Instructors Antje Barth Chris Fregly Shelbee Eigenbrode and Mike G Chambers all do incredible Generative AI work at AWS, and have supported many companies to build creative LLM applications. They bring tremendous practical LLM expertise to this course. I'm confident you’ll finish this course with a deeper understanding of how LLMs work, and how to use them. I hope you enjoy the course!

New course! Generative AI with Large Language Models, created with Amazon Web Services and hosted on Coursera. This course goes deep into the technical foundations of LLMs and how to use them. You can sign up here: You’ll work through the full life-cycle of a generative AI project, and learn specific techniques like RLHF; zero-shot, one-shot, and few-shot learning with LLMs; advanced prompting frameworks like ReAct; even fine-tuning LLMs, and gain hands-on practice with all of these techniques. Instructors Antje Barth Chris Fregly Shelbee Eigenbrode and Mike G Chambers all do incredible Generative AI work at AWS, and have supported many companies to build creative LLM applications. They bring tremendous practical LLM expertise to this course. I'm confident you’ll finish this course with a deeper understanding of how LLMs work, and how to use them. I hope you enjoy the course!

Andrew Ng

467,903 просмотров • 3 лет назад

Everyone is focused on tracking the ways LLMs are getting better. And they are. But we know there are still things that LLMs can’t do well—the tasks where you can feel the architecture fighting the problem. So I was excited to chat with Eve Bodnia (@eve_bodnia), who is developing an alternative AI model to LLMs, on Every 📧's AI & I. Eve's argument: energy-based models (EBMs), which map possible outcomes onto a mathematical landscape, will lead to the next AI phase shift. We get into: - How energy-based models work. Likely outcomes sit in valleys, and unlikely ones sit on peaks. Whereas LLMs process one token at a time, an EBM scans the full terrain to find the lowest point, or the most probable answer. - Language-based versus data-native models. LLMs are language-dependent even when the problem has nothing to do with language. "If your data is numbers, relationships, and functions, and you try to map those rules into words and then search for the next word, you're losing a lot of information," Bodnia says. EBMs work directly with the underlying data structure, including numbers and spatial coordinates. - Sequential versus panoramic reasoning. An LLM is like driving through San Francisco without a map. Each turn constrains the next, and if you go down the wrong street, you can't reverse course. An EBM has the bird's-eye view—it can evaluate multiple routes at once and course-correct before hitting a dead end. - The LLM plateau no one wants to talk about. LLMs are getting incrementally better, step-change improvements aren’t coming, Eve argues. To achieve that, we need new solutions that compensate for what LLMs are inherently bad at, like non-language reasoning, verification, and real-time data analysis. This is a must-watch for anyone who's curious what might come after the LLM. Watch below! Timestamps: Introduction: 00:00:51 Why correctness and verifiability matter in AI: 00:02:09 What an energy-based model is: 00:09:33 How EBMs construct energy landscapes to understand data: 00:14:21 Why modeling intelligence through language alone is a flawed approach: 00:19:00 What it means for a model to "understand" data: 00:26:54 How EBMs solve the vibe coding problem and enable formally verified code: 00:37:21 Why LLM progress is plateauing: 00:43:21 Mission-critical industries haven't adopted LLMs, and why EBMs can fill that gap: 00:49:54

Everyone is focused on tracking the ways LLMs are getting better. And they are. But we know there are still things that LLMs can’t do well—the tasks where you can feel the architecture fighting the problem. So I was excited to chat with Eve Bodnia (@eve_bodnia), who is developing an alternative AI model to LLMs, on Every 📧's AI & I. Eve's argument: energy-based models (EBMs), which map possible outcomes onto a mathematical landscape, will lead to the next AI phase shift. We get into: - How energy-based models work. Likely outcomes sit in valleys, and unlikely ones sit on peaks. Whereas LLMs process one token at a time, an EBM scans the full terrain to find the lowest point, or the most probable answer. - Language-based versus data-native models. LLMs are language-dependent even when the problem has nothing to do with language. "If your data is numbers, relationships, and functions, and you try to map those rules into words and then search for the next word, you're losing a lot of information," Bodnia says. EBMs work directly with the underlying data structure, including numbers and spatial coordinates. - Sequential versus panoramic reasoning. An LLM is like driving through San Francisco without a map. Each turn constrains the next, and if you go down the wrong street, you can't reverse course. An EBM has the bird's-eye view—it can evaluate multiple routes at once and course-correct before hitting a dead end. - The LLM plateau no one wants to talk about. LLMs are getting incrementally better, step-change improvements aren’t coming, Eve argues. To achieve that, we need new solutions that compensate for what LLMs are inherently bad at, like non-language reasoning, verification, and real-time data analysis. This is a must-watch for anyone who's curious what might come after the LLM. Watch below! Timestamps: Introduction: 00:00:51 Why correctness and verifiability matter in AI: 00:02:09 What an energy-based model is: 00:09:33 How EBMs construct energy landscapes to understand data: 00:14:21 Why modeling intelligence through language alone is a flawed approach: 00:19:00 What it means for a model to "understand" data: 00:26:54 How EBMs solve the vibe coding problem and enable formally verified code: 00:37:21 Why LLM progress is plateauing: 00:43:21 Mission-critical industries haven't adopted LLMs, and why EBMs can fill that gap: 00:49:54

Dan Shipper 📧

26,900 просмотров • 3 месяцев назад

#WATCH | In Rajya Sabha, HM Amit Shah says, "...I would like to say something so that those who divide the country in the name of language do not get their agenda. Under the Department of Official Language, Narendra Modi Govt has set up Indian Languages Section which will work to enhance the usage of all Indian languages - Tamil, Telugu, Marathi, Gujarati, Punjabi, Assamese, Bengali, all languages. After December, I will have written correspondence with citizens, CMs, Ministers and MPs in their own language. This is a strong reply to those who run their shops in the name of language to hide their corruption...What are they saying? That we oppose languages of the south? How can this be possible?...I come from Guajrat, Nirmala Sitharanan from Tamil Nadu. How can we oppose this? What are you saying? We have worked for languages...I would like to tell Tamil Nadu Government - we have been saying for two years that you do not have the courage to translate medical and engineering study material into Tamil...You cannot do this. When an NDA govt comes to power (in Tamil Nadu), we will provide medical and engineering course in Tamil, in Tamil Nadu. I would like to tell those who spread poison in the name of language that you like languages from thousands of kilometres away but you do not language of India...I have said this again and again Hindi has no competition with any other Indian language. Hindi is a friend of all Indian languages, all Indian languages strengthen from Hindi and Hindi strengthens from all Indian languages..."

#WATCH | In Rajya Sabha, HM Amit Shah says, "...I would like to say something so that those who divide the country in the name of language do not get their agenda. Under the Department of Official Language, Narendra Modi Govt has set up Indian Languages Section which will work to enhance the usage of all Indian languages - Tamil, Telugu, Marathi, Gujarati, Punjabi, Assamese, Bengali, all languages. After December, I will have written correspondence with citizens, CMs, Ministers and MPs in their own language. This is a strong reply to those who run their shops in the name of language to hide their corruption...What are they saying? That we oppose languages of the south? How can this be possible?...I come from Guajrat, Nirmala Sitharanan from Tamil Nadu. How can we oppose this? What are you saying? We have worked for languages...I would like to tell Tamil Nadu Government - we have been saying for two years that you do not have the courage to translate medical and engineering study material into Tamil...You cannot do this. When an NDA govt comes to power (in Tamil Nadu), we will provide medical and engineering course in Tamil, in Tamil Nadu. I would like to tell those who spread poison in the name of language that you like languages from thousands of kilometres away but you do not language of India...I have said this again and again Hindi has no competition with any other Indian language. Hindi is a friend of all Indian languages, all Indian languages strengthen from Hindi and Hindi strengthens from all Indian languages..."

ANI

33,482 просмотров • 1 год назад

In this week's video, I sat down with the co-founders of our latest investment, Starseer, a groundbreaking platform for inspecting and securing large language models (LLMs). Tim Schulz, Carl Hurd and I discuss the risks of backdoored LLMs, how to audit them and even remove them. They demo the product as well. The video also includes the animated short "John Henry.exe" which is an updated American parable of John Henry, but instead of struggling against a steam drill during the age of industrialization, he's the head coder and has to face off against an AI designed for programming. Enjoy!

In this week's video, I sat down with the co-founders of our latest investment, Starseer, a groundbreaking platform for inspecting and securing large language models (LLMs). Tim Schulz, Carl Hurd and I discuss the risks of backdoored LLMs, how to audit them and even remove them. They demo the product as well. The video also includes the animated short "John Henry.exe" which is an updated American parable of John Henry, but instead of struggling against a steam drill during the age of industrialization, he's the head coder and has to face off against an AI designed for programming. Enjoy!

Ron Gula

302,493 просмотров • 1 год назад

Learn how ChatGPT works, in 7 mins, from the person who built it! I am so proud to share this video. We spent weeks fretting over every detail to make a video fit for all ages. Starring Mira Murati from OpenAI and Cristóbal Valenzuela from Runway, this is the best way to grasp how Large Language Models and chatbots work.

Learn how ChatGPT works, in 7 mins, from the person who built it! I am so proud to share this video. We spent weeks fretting over every detail to make a video fit for all ages. Starring Mira Murati from OpenAI and Cristóbal Valenzuela from Runway, this is the best way to grasp how Large Language Models and chatbots work.

Hadi Partovi

123,055 просмотров • 2 лет назад

How One Man Is Using Hebrew, Arabic, and English To Heal The Middle East How much good can you do by speaking 3 languages fluently?! Yirmiyahu speaks Arabic, Hebrew, and English - and is determined to heal the divide between Jews and Arabs, one conversation at a time. He dives into how using his background can be used to build trust, and shows us all how we can lead by example. Share this story with anyone who will feel inspired by using the powers of language for good! Follow buildersofmideast for more 🙌

How One Man Is Using Hebrew, Arabic, and English To Heal The Middle East How much good can you do by speaking 3 languages fluently?! Yirmiyahu speaks Arabic, Hebrew, and English - and is determined to heal the divide between Jews and Arabs, one conversation at a time. He dives into how using his background can be used to build trust, and shows us all how we can lead by example. Share this story with anyone who will feel inspired by using the powers of language for good! Follow buildersofmideast for more 🙌

buildersofmideast

36,144 просмотров • 10 месяцев назад

1,200+ Languages. One Vision for AI Inclusion. 🤝 How do we bridge the gap between global technology and local culture? We are thrilled to share highlights from our recent developer session, co-hosted by Tongyi Lab x YiXi, featuring insights from our partners at AI Singapore. In this video, Jian Gang Ngui from AI Singapore dives into the critical mission of building AI that truly understands the linguistic and cultural nuances of Southeast Asia—a region home to 700+ million people speaking over 1,200 languages. By leveraging Qwen, Gemma, and other state-of-the-art open-source foundation models, AISG is working hand-in-hand with native communities to integrate local languages and cultural contexts to build LLMs that are truly accessible and relevant to everyone. Proud to support AISG in this journey!

1,200+ Languages. One Vision for AI Inclusion. 🤝 How do we bridge the gap between global technology and local culture? We are thrilled to share highlights from our recent developer session, co-hosted by Tongyi Lab x YiXi, featuring insights from our partners at AI Singapore. In this video, Jian Gang Ngui from AI Singapore dives into the critical mission of building AI that truly understands the linguistic and cultural nuances of Southeast Asia—a region home to 700+ million people speaking over 1,200 languages. By leveraging Qwen, Gemma, and other state-of-the-art open-source foundation models, AISG is working hand-in-hand with native communities to integrate local languages and cultural contexts to build LLMs that are truly accessible and relevant to everyone. Proud to support AISG in this journey!

Alibaba Cloud

25,025 просмотров • 2 месяцев назад

Bird SQL is an impressive new tool, based on language models, for searching Twitter. Tools like this are changing the way we interact with information. If used in the right way, signals are becoming easier to find through the use of LLMs. Try it here:

Bird SQL is an impressive new tool, based on language models, for searching Twitter. Tools like this are changing the way we interact with information. If used in the right way, signals are becoming easier to find through the use of LLMs. Try it here:

elvis

95,872 просмотров • 3 лет назад

New course: Transformers in Practice. You'll get a practical view of how transformer-based LLMs work, so you can reason about their behavior, diagnose problems like slow inference, and make smarter decisions about deployment. This course is built in partnership with AMD and taught by Sharon Zhou. You'll see how transformers generate text one token at a time, how the model decides which earlier words matter most when predicting the next one, and how techniques like quantization speed up inference on GPUs. This is not a video-only course; interactive visualizations throughout let you play with these concepts and build intuition that sticks. Skills you'll gain: - Understand why LLMs hallucinate, and RAG and chain-of-thought shape what they generate - Look inside the model to see how attention and layers combine to predict the next token - Diagnose inference bottlenecks and learn the techniques that speed up transformers on GPUs Join and understand what's really happening inside your LLMs:

New course: Transformers in Practice. You'll get a practical view of how transformer-based LLMs work, so you can reason about their behavior, diagnose problems like slow inference, and make smarter decisions about deployment. This course is built in partnership with AMD and taught by Sharon Zhou. You'll see how transformers generate text one token at a time, how the model decides which earlier words matter most when predicting the next one, and how techniques like quantization speed up inference on GPUs. This is not a video-only course; interactive visualizations throughout let you play with these concepts and build intuition that sticks. Skills you'll gain: - Understand why LLMs hallucinate, and RAG and chain-of-thought shape what they generate - Look inside the model to see how attention and layers combine to predict the next token - Diagnose inference bottlenecks and learn the techniques that speed up transformers on GPUs Join and understand what's really happening inside your LLMs:

Andrew Ng

118,911 просмотров • 2 месяцев назад

The Chairman and CEO of AIG spent over a minute talking about how critical the Ontology is to deploying LLMs in the enterprise 💪 “Ontology is critical for deploying large language models. It brings together the relevant data sets that define the components of our insurance business, integrates and sequences them and then models how they relate to one another. Our ontology will create a clear record of any actions taken, which will inform business logic and provide the ability to audit agents activities.”

The Chairman and CEO of AIG spent over a minute talking about how critical the Ontology is to deploying LLMs in the enterprise 💪 “Ontology is critical for deploying large language models. It brings together the relevant data sets that define the components of our insurance business, integrates and sequences them and then models how they relate to one another. Our ontology will create a clear record of any actions taken, which will inform business logic and provide the ability to audit agents activities.”

Chad Wahlquist

23,869 просмотров • 11 месяцев назад