正在加载视频...

视频加载失败

加载此视频时出现问题。这可能是由于临时网络问题，或视频可能不可用。

Should we design a new programming language for LLMs, that LLMs can use more efficiently? Chris Lattner, the creator of Swift and Mojo doesn't think so, and here's why: (cont'd)

Gergely Orosz

300,059 subscribers

15,416 次观看 • 7 个月前 •via X (Twitter)

教育科学技术

Anya Rossi• Live Now

Private livecam show

0 条评论

暂无评论

原始帖子的评论将显示在这里

相关视频

Episode 109: GPU Programming and Language Design with Chris Lattner! 🎉

Episode 109: GPU Programming and Language Design with Chris Lattner! 🎉

Software Unscripted

32,137 次观看 • 11 个月前

Chris Lattner (Chris Lattner) is one of the most influential engineers of the past two decades. He created LLVM, Swift, contributed to TensorFlow, and created the Mojo programming language. What was the story about creating Swift - and why did he face resistance inside Apple when wanting to replace Objective C? What did he learn at Tesla, Google and CPU maker SiFive, that led him to working on Mojo at Modular? We cover these and many more in today's episode. Watch or listen: • YouTube: • Spotify: • Apple: Brought to you by: •⁠ Statsig ⁠ — ⁠ The unified platform for flags, analytics, experiments, and more. •⁠ Linear – The system for modern product development. My favorite quote from Chris in this episode: “I believe in the power of programmers. I believe in the human potential of people that want to create things. And that’s fundamentally why I love software is that you can create anything that you can imagine.”

Chris Lattner (Chris Lattner) is one of the most influential engineers of the past two decades. He created LLVM, Swift, contributed to TensorFlow, and created the Mojo programming language. What was the story about creating Swift - and why did he face resistance inside Apple when wanting to replace Objective C? What did he learn at Tesla, Google and CPU maker SiFive, that led him to working on Mojo at Modular? We cover these and many more in today's episode. Watch or listen: • YouTube: • Spotify: • Apple: Brought to you by: •⁠ Statsig ⁠ — ⁠ The unified platform for flags, analytics, experiments, and more. •⁠ Linear – The system for modern product development. My favorite quote from Chris in this episode: “I believe in the power of programmers. I believe in the human potential of people that want to create things. And that’s fundamentally why I love software is that you can create anything that you can imagine.”

Gergely Orosz

206,247 次观看 • 7 个月前

Simon Peyton Jones is the co-creator of Haskell (pure functional programming language) and I interviewed him about functional programming, why it matters, and his thoughts on other programming languages. In this episode: • Useful and useless programming languages • Rust vs C • Haskell vs OCaml • Why functional programming matters • Static languages and their value for LLMs • Why Excel is his 2nd favorite programming language Where to watch: • YouTube - • Spotify - • Apple Podcasts - • Transcript - Thank you to the sponsor of this episode for supporting my work: • WorkOS: makes your app Enterprise Ready with easy to use APIs to add SSO, SCIM, RBAC, and more in just a few lines of code, check them out at Chapters: 00:00 - Intro 00:39 - What functional programming is 09:18 - Downsides of functional programming 10:53 - Specialized hardware for functional programming 21:47 - Haskell is useless 25:59 - Rust vs C 28:26 - Haskell vs OCaml 35:26 - Side effects in Haskell 44:26 - Type systems 57:30 - How the Haskell compiler works 01:04:35 - Why Haskell is talked about more than used 01:09:07 - Avoiding success at all costs 01:11:12 - LLMs and programming languages 01:13:57 - New programming language design 01:15:59 - Should students continue to learn programming 01:22:33 - Why Excel is is 2nd favorite programming language 01:25:04 - Advice for his younger self

Simon Peyton Jones is the co-creator of Haskell (pure functional programming language) and I interviewed him about functional programming, why it matters, and his thoughts on other programming languages. In this episode: • Useful and useless programming languages • Rust vs C • Haskell vs OCaml • Why functional programming matters • Static languages and their value for LLMs • Why Excel is his 2nd favorite programming language Where to watch: • YouTube - • Spotify - • Apple Podcasts - • Transcript - Thank you to the sponsor of this episode for supporting my work: • WorkOS: makes your app Enterprise Ready with easy to use APIs to add SSO, SCIM, RBAC, and more in just a few lines of code, check them out at Chapters: 00:00 - Intro 00:39 - What functional programming is 09:18 - Downsides of functional programming 10:53 - Specialized hardware for functional programming 21:47 - Haskell is useless 25:59 - Rust vs C 28:26 - Haskell vs OCaml 35:26 - Side effects in Haskell 44:26 - Type systems 57:30 - How the Haskell compiler works 01:04:35 - Why Haskell is talked about more than used 01:09:07 - Avoiding success at all costs 01:11:12 - LLMs and programming languages 01:13:57 - New programming language design 01:15:59 - Should students continue to learn programming 01:22:33 - Why Excel is is 2nd favorite programming language 01:25:04 - Advice for his younger self

Ryan Peterman

125,616 次观看 • 18 天前

We believe LLMs are transforming programming — making it easier for beginners to learn, and helping experienced developers navigate libraries and syntax more efficiently. In this episode of AI + a16z, Fivetran co-founder and CEO George Fraser and a16z partner Guido Appenzeller discuss how enterprises can integrate LLMs into data management without overhauling their infrastructure — emphasizing the importance of strong data hygiene to maximize their potential.

We believe LLMs are transforming programming — making it easier for beginners to learn, and helping experienced developers navigate libraries and syntax more efficiently. In this episode of AI + a16z, Fivetran co-founder and CEO George Fraser and a16z partner Guido Appenzeller discuss how enterprises can integrate LLMs into data management without overhauling their infrastructure — emphasizing the importance of strong data hygiene to maximize their potential.

a16z

12,767 次观看 • 1 年前

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

249,572 次观看 • 2 年前

It's the 💯th episode of Software Unscripted!!! 🎉🎉🎉 I talk with Chris Lattner about his new language Mojo, Roc, and lots more! 😃 YouTube video: Ad-free video for Patreon members! Audio version:

It's the 💯th episode of Software Unscripted!!! 🎉🎉🎉 I talk with Chris Lattner about his new language Mojo, Roc, and lots more! 😃 YouTube video: Ad-free video for Patreon members! Audio version:

Software Unscripted

13,969 次观看 • 1 年前

Yann LeCun says language isn’t intelligence. Predicting text doesn’t mean understanding reality. The real world is messy, physical, and causal and today’s LLMs barely touch that. The next leap is Physical AI: world models, cause and effect, real planning. Do you think LLMs can evolve into this, or do we need a completely new architecture?

Yann LeCun says language isn’t intelligence. Predicting text doesn’t mean understanding reality. The real world is messy, physical, and causal and today’s LLMs barely touch that. The next leap is Physical AI: world models, cause and effect, real planning. Do you think LLMs can evolve into this, or do we need a completely new architecture?

VraserX e/acc

76,154 次观看 • 4 个月前

✨ Introducing Keypoint Action Tokens. 🤖 We translate visual observations and robot actions into a "language" that off-the-shelf LLMs can ingest and output. This transforms LLMs into *in-context, low-level imitation learning machines*. 🚀 Let me explain. 👇🧵

✨ Introducing Keypoint Action Tokens. 🤖 We translate visual observations and robot actions into a "language" that off-the-shelf LLMs can ingest and output. This transforms LLMs into in-context, low-level imitation learning machines. 🚀 Let me explain. 👇🧵

Norman Di Palo

23,094 次观看 • 2 年前

New Anthropic research: Emotion concepts and their function in a large language model. All LLMs sometimes act like they have emotions. But why? We found internal representations of emotion concepts that can drive Claude’s behavior, sometimes in surprising ways.

New Anthropic research: Emotion concepts and their function in a large language model. All LLMs sometimes act like they have emotions. But why? We found internal representations of emotion concepts that can drive Claude’s behavior, sometimes in surprising ways.

Anthropic

3,907,570 次观看 • 2 个月前

🚨 This video might change how you talk to LLMs and help you get those optimal results. Discover why prompting is actually programming LLMs because, at the end of the day, they're just advanced autocomplete.

🚨 This video might change how you talk to LLMs and help you get those optimal results. Discover why prompting is actually programming LLMs because, at the end of the day, they're just advanced autocomplete.

NetworkChuck

25,970 次观看 • 3 个月前

Meet #DBRX: a general-purpose LLM that sets a new standard for efficient open source models. Use the DBRX model in your RAG apps or use the DBRX design to build your own custom LLMs and improve the quality of your GenAI applications.

Meet #DBRX: a general-purpose LLM that sets a new standard for efficient open source models. Use the DBRX model in your RAG apps or use the DBRX design to build your own custom LLMs and improve the quality of your GenAI applications.

Databricks

327,704 次观看 • 2 年前

How can we use small LLMs to shift more AI workloads onto our laptops and phones? In our paper and open-source code, we pair on-device LLMs (ollama) with frontier LLMs in the cloud (OpenAI, Together), to solve token-intensive workloads on your 💻 at 17.5% of the cloud cost while maintaining 97.9% of the accuracy. See Gru and the Minions in action below, 🔉on please (h/t )!

How can we use small LLMs to shift more AI workloads onto our laptops and phones? In our paper and open-source code, we pair on-device LLMs (ollama) with frontier LLMs in the cloud (OpenAI, Together), to solve token-intensive workloads on your 💻 at 17.5% of the cloud cost while maintaining 97.9% of the accuracy. See Gru and the Minions in action below, 🔉on please (h/t )!

Dan Biderman

193,212 次观看 • 1 年前

Yann LeCun says we're fooled by LLMs because they manipulate language well, and we associate that with intelligence But language fluency doesn't mean underlying intelligence Every generation since the 1950s claimed its technique was the ticket to human-level AI All were wrong. "this generation with LLMs is also wrong"

Yann LeCun says we're fooled by LLMs because they manipulate language well, and we associate that with intelligence But language fluency doesn't mean underlying intelligence Every generation since the 1950s claimed its technique was the ticket to human-level AI All were wrong. "this generation with LLMs is also wrong"

Haider.

625,457 次观看 • 6 个月前

$The next frontier for AI shouldn’t just be generally helpful. It should be helpful for you! Our new paper shows how to personalize LLMs — efficiently, scalably, and without retraining. Meet PReF ( 1\n$

The next frontier for AI shouldn’t just be generally helpful. It should be helpful for you! Our new paper shows how to personalize LLMs — efficiently, scalably, and without retraining. Meet PReF ( 1\n

idan shenfeld

10,252 次观看 • 1 年前

Many AI-savvy programmers are now coding very differently than before, by using LLMs to help with their work. You’ll learn these emerging best practices in “Pair Programming with a Large Language Model,” by Google's Laurence Moroney 🇺🇸🇮🇪 🏴󠁧󠁢󠁷󠁬󠁳󠁿. This short course covers using LLMs to simplify and improve your code, assist with debugging, and minimize technical debt by having AI document your code. The use of LLMs as a programming companion is an important shift that's well worth every developer staying on top of. Please check this out!

Many AI-savvy programmers are now coding very differently than before, by using LLMs to help with their work. You’ll learn these emerging best practices in “Pair Programming with a Large Language Model,” by Google's Laurence Moroney 🇺🇸🇮🇪 🏴󠁧󠁢󠁷󠁬󠁳󠁿. This short course covers using LLMs to simplify and improve your code, assist with debugging, and minimize technical debt by having AI document your code. The use of LLMs as a programming companion is an important shift that's well worth every developer staying on top of. Please check this out!

Andrew Ng

373,418 次观看 • 2 年前

Today we're excited to introduce Multi-Model Sessions! — Load & prompt multiple local LLMs simultaneously — Use the API to build networks, pipelines of LLMs that can talk with each other — Use the new JSON Mode to constrain each model's output format LM Studio 0.2.17 is available for Mac, Windows, and Linux. Get it here:

Today we're excited to introduce Multi-Model Sessions! — Load & prompt multiple local LLMs simultaneously — Use the API to build networks, pipelines of LLMs that can talk with each other — Use the new JSON Mode to constrain each model's output format LM Studio 0.2.17 is available for Mac, Windows, and Linux. Get it here:

LM Studio

89,817 次观看 • 2 年前

Columbia CS Prof explains why LLMs can’t generate new scientific ideas. Bcz LLMs learn a structured “map”, Bayesian manifold, of known data and work well within it, but fail outside it. But true discovery means creating new maps, which LLMs cannot do.

Columbia CS Prof explains why LLMs can’t generate new scientific ideas. Bcz LLMs learn a structured “map”, Bayesian manifold, of known data and work well within it, but fail outside it. But true discovery means creating new maps, which LLMs cannot do.

Rohan Paul

758,998 次观看 • 7 个月前

Introducing a new type of civic tech made possible by AI. Every citizen should have a live, systems view of their government and today we bring that to SF! Track gov entities, spending, news, and more in real time. With LLMs, we can bring this to every city. Who's next?

Introducing a new type of civic tech made possible by AI. Every citizen should have a live, systems view of their government and today we bring that to SF! Track gov entities, spending, news, and more in real time. With LLMs, we can bring this to every city. Who's next?

Michael Adams

220,665 次观看 • 10 个月前

We can teach LLMs to write better robot code through natural language feedback. But can LLMs remember what they were taught and improve their teachability over time? Introducing our latest work, Learning to Learn Faster from Human Feedback with Language Model Predictive Control

We can teach LLMs to write better robot code through natural language feedback. But can LLMs remember what they were taught and improve their teachability over time? Introducing our latest work, Learning to Learn Faster from Human Feedback with Language Model Predictive Control

Jacky Liang

86,652 次观看 • 2 年前