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Introducing SoftMatcha 2: A Fast and Soft Pattern Matcher for Trillion-Scale Pre-Training Corpora What lies within a trillion-scale pre-training corpus? Can you truly guarantee your benchmarks are uncontaminated simply because there are no exact string matches? Alongside several research institutions in Japan, Sakana AI is proud to have collaborated...

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Sakana AI

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104,363 görüntüleme • 8 ay önce •via X (Twitter)

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Sakana AI profil fotoğrafı
Sakana AI8 ay önce

SoftMatcha 2: A Fast and Soft Pattern Matcher for Trillion-Scale Corpora

Sami (e/acc) profil fotoğrafı
Sami (e/acc)8 ay önce

Here's a little summary for those who don't want to read the whole paper

cain1517 — e/acc ⏩ profil fotoğrafı
cain1517 — e/acc ⏩8 ay önce

wouldn't this be useful for context at inference (RAG)?

ノイズハッカー profil fotoğrafı
ノイズハッカー8 ay önce

1兆トークンの深淵に隠された「意味的な剽窃」をミリ秒単位で引きずり出す、冷酷なまでのデータ監査。 SoftMatcha 2は、LLMが「知能」と称して提示する回答の多くが、実は学習データの巧妙な「言い換え（変奏）」に過ぎないことを証明する、知の真贋鑑定機である。

norn profil fotoğrafı
norn8 ay önce

the uncomfortable implication is that most published benchmark numbers are measuring the test set's leakage profile, not the model's capability. this should be mandatory

Philippe Tremblay profil fotoğrafı
Philippe Tremblay8 ay önce

wow, this is fast!!

Lizzy profil fotoğrafı
Lizzy8 ay önce

@hardmaru SoftMatcha sounds like the most polite way to filter training data I've heard all week. Does it actually improve over hard masking, or is it just... softer on the model's feelings? 🤔

ikan laut profil fotoğrafı
ikan laut8 ay önce

this is very interesting

Gregor profil fotoğrafı
Gregor8 ay önce

You're right to question the robustness of benchmarks in trillion-scale pre-training corpora. What if the contaminants are not exact strings, but subtle patterns or biases that seep into the data through more insidious means, like data preprocessing or sampling strategies?

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Still following your human intuition to mix corpora from different sources for language model pre-training 🧠? Everyone says that data mixture has a big impact on model performance, but how - and why🕵️? Did you know that web corpora are actually highly impactful for downstream tasks 🏆? Let's check out our preprint "RegMix: Data Mixture as Regression for Language Model Pre-training" 📄 🔬In this paper, we've proposed an automatic data mixture method RegMix that achieves a 6.3% improvement over human selection on the widely used HellaSwag benchmark - and it only needs a 2% extra training FLOPs! 📈 Details in the thread 🧵
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Still following your human intuition to mix corpora from different sources for language model pre-training 🧠? Everyone says that data mixture has a big impact on model performance, but how - and why🕵️? Did you know that web corpora are actually highly impactful for downstream tasks 🏆? Let's check out our preprint "RegMix: Data Mixture as Regression for Language Model Pre-training" 📄 🔬In this paper, we've proposed an automatic data mixture method RegMix that achieves a 6.3% improvement over human selection on the widely used HellaSwag benchmark - and it only needs a 2% extra training FLOPs! 📈 Details in the thread 🧵

Qian Liu

54,961 görüntüleme • 2 yıl önce

Sam Altman: the ideal AI is a tiny model with superhuman reasoning, a trillion-token context window, and access to every tool. “The platonic ideal is a very tiny model that has superhuman reasoning capabilities.” “It can run ridiculously fast.” “One trillion tokens of context and access to every tool you can possibly imagine.” “Using these models as databases is sort of ridiculous.” “The amazing thing is they can reason.” “Directionally, we’re headed there.”
1:06

Sam Altman: the ideal AI is a tiny model with superhuman reasoning, a trillion-token context window, and access to every tool. “The platonic ideal is a very tiny model that has superhuman reasoning capabilities.” “It can run ridiculously fast.” “One trillion tokens of context and access to every tool you can possibly imagine.” “Using these models as databases is sort of ridiculous.” “The amazing thing is they can reason.” “Directionally, we’re headed there.”

Veee

68,525 görüntüleme • 1 ay önce

The infrastructure for working with video data at scale barely exists. Grass Video Search is our first step in changing that. You can now find anything in a video based on the contents of its frames, not just transcripts or tags. Over the last several months, the Grass Foundation has quietly assembled one of the largest video datasets in history: over a billion videos from across the public web, and is segmenting them by scene with VLM annotations of keyframes. This dataset is now searchable, so you can find the exact moment a visual, pattern, or concept appears. Curation has always been the bottleneck in video analysis. We built a system that automates it. This is just the beginning. Over 4 billion additional videos are queued for processing, and the index is growing daily. We’re opening access to a limited set of collaborators. If you’re a researcher, journalist, or nonprofit working with video, archives, or large-scale analysis, we’d love to hear from you.
0:45

The infrastructure for working with video data at scale barely exists. Grass Video Search is our first step in changing that. You can now find anything in a video based on the contents of its frames, not just transcripts or tags. Over the last several months, the Grass Foundation has quietly assembled one of the largest video datasets in history: over a billion videos from across the public web, and is segmenting them by scene with VLM annotations of keyframes. This dataset is now searchable, so you can find the exact moment a visual, pattern, or concept appears. Curation has always been the bottleneck in video analysis. We built a system that automates it. This is just the beginning. Over 4 billion additional videos are queued for processing, and the index is growing daily. We’re opening access to a limited set of collaborators. If you’re a researcher, journalist, or nonprofit working with video, archives, or large-scale analysis, we’d love to hear from you.

Grass

471,947 görüntüleme • 1 yıl önce

Introducing Praxis-1, an open-weight World Action Model that turns our video pre-training expertise into real world control for robots. Robot demonstration data is limited. But video data is infinite. Praxis-1 extends our bet that the best policy models will learn from video, and use that scale to bring embodied intelligence to every industry. Praxis-1 is built on our large-scale video pre-training and real-time infrastructure, and acts as a policy model for robotics developers and researchers that works across any embodiment or environment, no matter how complex. We are actively testing with early partners including Noble Machines, Standard Bots and Ultra, and will be releasing the weights publicly in the coming months. Learn more and request early access at
1:54

Introducing Praxis-1, an open-weight World Action Model that turns our video pre-training expertise into real world control for robots. Robot demonstration data is limited. But video data is infinite. Praxis-1 extends our bet that the best policy models will learn from video, and use that scale to bring embodied intelligence to every industry. Praxis-1 is built on our large-scale video pre-training and real-time infrastructure, and acts as a policy model for robotics developers and researchers that works across any embodiment or environment, no matter how complex. We are actively testing with early partners including Noble Machines, Standard Bots and Ultra, and will be releasing the weights publicly in the coming months. Learn more and request early access at

Runway

119,334 görüntüleme • 27 gün önce

this is f*cking insane, that's what your agent looks like when it stops reading everything over and over Xerj launched Corpus Hub: 103 open-source knowledge corpora your agent can search instead of swallowing whole files the problem is simple, most agents dump entire documents into context just to find one paragraph you pay for every token, every single time with a corpus, the agent works differently: - it searches the corpus before opening anything - it pulls only the passages that answer the task - everything else stays out of the context window - it runs locally, so your files stay on your machine a bigger context window doesn't make an agent smarter, it just makes reading more expensive i gave my agent the same task with and without Xerj, here's the difference: > output tokens: 3× fewer > cost: $4.57 down to $1.98, 57% lower > tasks completed: 7/16 without Xerj, 16/16 with Xerj pick a corpus on connect it to your agent, then ask: "Work on this task using the connected corpus 1. Search the corpus before opening any full document 2. Pull only the passages you need and note where each one came from 3. If the corpus doesn't cover something, say so instead of guessing 4. At the end, list the passages you used and the files you never had to open" start with one corpus that matches your stack and one real task, then compare the token count before and after
0:25

this is f*cking insane, that's what your agent looks like when it stops reading everything over and over Xerj launched Corpus Hub: 103 open-source knowledge corpora your agent can search instead of swallowing whole files the problem is simple, most agents dump entire documents into context just to find one paragraph you pay for every token, every single time with a corpus, the agent works differently: - it searches the corpus before opening anything - it pulls only the passages that answer the task - everything else stays out of the context window - it runs locally, so your files stay on your machine a bigger context window doesn't make an agent smarter, it just makes reading more expensive i gave my agent the same task with and without Xerj, here's the difference: > output tokens: 3× fewer > cost: $4.57 down to $1.98, 57% lower > tasks completed: 7/16 without Xerj, 16/16 with Xerj pick a corpus on connect it to your agent, then ask: "Work on this task using the connected corpus 1. Search the corpus before opening any full document 2. Pull only the passages you need and note where each one came from 3. If the corpus doesn't cover something, say so instead of guessing 4. At the end, list the passages you used and the files you never had to open" start with one corpus that matches your stack and one real task, then compare the token count before and after

Anatoli Kopadze

18,791 görüntüleme • 14 gün önce

Grass is betting that better AI still starts with better pre-training data “Whatever you put into your model ultimately determines what your model's capabilities are” “Several of the top frontier AI labs in the world are working directly with Grass to acquire large amounts of pre-training data” “If that information is not present in your model, your model doesn't know how to do that thing in the first place” Andrej puts Grass close to one of the most fundamental inputs in AI: the training corpus itself. As frontier models scale, they need enormous amounts of diverse, high-quality web data. They are building the infrastructure to collect and supply that data at internet scale. Model capability starts with what the model gets to learn from.
0:44

Grass is betting that better AI still starts with better pre-training data “Whatever you put into your model ultimately determines what your model's capabilities are” “Several of the top frontier AI labs in the world are working directly with Grass to acquire large amounts of pre-training data” “If that information is not present in your model, your model doesn't know how to do that thing in the first place” Andrej puts Grass close to one of the most fundamental inputs in AI: the training corpus itself. As frontier models scale, they need enormous amounts of diverse, high-quality web data. They are building the infrastructure to collect and supply that data at internet scale. Model capability starts with what the model gets to learn from.

Kaiz 🐂🀄️

13,989 görüntüleme • 23 gün önce

NEW: Elon Musk says he thinks he can reduce the deficit by $1 Trillion next year, says it will cause a cascading effect for Americans. “I think we can take a trillion dollars out of the out of the deficit next year.” “We have a $2 trillion deficit, which is a far in excess of economic growth. If we can get that deficit in half, from 2 trillion to 1 trillion, and we can get the economic growth to match that 1 trillion growth in the money supply, that means there will be no inflation.” “Interest rates will drop, credit card interest down, mortgage interest down, car payment interest down, and prices at the store stay the same.” “That is a great outcome for people and I think we can do it.”
2:07

NEW: Elon Musk says he thinks he can reduce the deficit by $1 Trillion next year, says it will cause a cascading effect for Americans. “I think we can take a trillion dollars out of the out of the deficit next year.” “We have a $2 trillion deficit, which is a far in excess of economic growth. If we can get that deficit in half, from 2 trillion to 1 trillion, and we can get the economic growth to match that 1 trillion growth in the money supply, that means there will be no inflation.” “Interest rates will drop, credit card interest down, mortgage interest down, car payment interest down, and prices at the store stay the same.” “That is a great outcome for people and I think we can do it.”

Collin Rugg

4,343,104 görüntüleme • 1 yıl önce

Speed and quality can finally coexist in diffusion-based language generation. Introducing DiDi-Instruct, a Discrete Diffusion Divergence Instruct method that distills a pre-trained discrete diffusion language model (dLLM) into a few-step student for ultra-fast generation. Built on integral KL-divergence minimization, DiDi-Instruct achieves up to 64× faster decoding, surpasses both its teacher and GPT-2, and cuts training time by 20×. Ultra-Fast Language Generation via Discrete Diffusion Divergence Instruct Paper: Code: Project: Our report: 📬 #PapersAccepted by Jiqizhixin
0:40

Speed and quality can finally coexist in diffusion-based language generation. Introducing DiDi-Instruct, a Discrete Diffusion Divergence Instruct method that distills a pre-trained discrete diffusion language model (dLLM) into a few-step student for ultra-fast generation. Built on integral KL-divergence minimization, DiDi-Instruct achieves up to 64× faster decoding, surpasses both its teacher and GPT-2, and cuts training time by 20×. Ultra-Fast Language Generation via Discrete Diffusion Divergence Instruct Paper: Code: Project: Our report: 📬 #PapersAccepted by Jiqizhixin

机器之心 JIQIZHIXIN

18,196 görüntüleme • 1 yıl önce

At Omnipresent, We incubate. We build. Introducing our first product a marketing tool that enables an entirely different way of operating. Our CTO taro developed an in-house stack using OpenClaw🦞, Kimi.ai, and Claude built to move faster, hit harder, and scale beyond what off-the-shelf tools allow. What it unlocks: – Sharper, faster marketing execution – Distribution that scales beyond the obvious channels – High-leverage creative angles that compound - Marketing analytics and data you can trust And this is just an experiment. We are working on AI dashboard with new layers of insights , network scale effects , and a UI/UX bar that keeps moving up. This is what building from the inside out looks like.
0:29

At Omnipresent, We incubate. We build. Introducing our first product a marketing tool that enables an entirely different way of operating. Our CTO taro developed an in-house stack using OpenClaw🦞, Kimi.ai, and Claude built to move faster, hit harder, and scale beyond what off-the-shelf tools allow. What it unlocks: – Sharper, faster marketing execution – Distribution that scales beyond the obvious channels – High-leverage creative angles that compound - Marketing analytics and data you can trust And this is just an experiment. We are working on AI dashboard with new layers of insights , network scale effects , and a UI/UX bar that keeps moving up. This is what building from the inside out looks like.

Omnipresent

15,029 görüntüleme • 6 ay önce

I cant believe this guy just made a permanent solution to context bloat and open sourced it all! when we tested this tool (Context+) for solving an issue on the OpenCode repository, the agent using this tool used ~6.5k fewer tokens, found the code and fixed it in half the time! the results were surprising: 6 to 10k tokens saved per prompt, completed task in ~2 minutes while the agent running without the tool took ~4 mins for the same and got stuck in loops bro built an entire beast by using all the modern tools that we could think of: undo trees, semantic search by meaning (by haskellforall), advanced refactoring, blast radius, advanced file context trees, restore points... i can keep going on semantic code search and context trees are the future of agentic coding and this tool proves it the feature i loved the most is semantic search and how it gets things done 2x faster with least possible tokens it makes an agent that actually knows what it’s doing and not just guessing, it makes meaning from your code similar to RAG. if you aren't optimizing your context, you are just burning money the developer says this tool is still under development, it can have unexpected behavior and the docs need updates but the video shows the reality of how fast it can be github: get here:
0:46

I cant believe this guy just made a permanent solution to context bloat and open sourced it all! when we tested this tool (Context+) for solving an issue on the OpenCode repository, the agent using this tool used ~6.5k fewer tokens, found the code and fixed it in half the time! the results were surprising: 6 to 10k tokens saved per prompt, completed task in ~2 minutes while the agent running without the tool took ~4 mins for the same and got stuck in loops bro built an entire beast by using all the modern tools that we could think of: undo trees, semantic search by meaning (by haskellforall), advanced refactoring, blast radius, advanced file context trees, restore points... i can keep going on semantic code search and context trees are the future of agentic coding and this tool proves it the feature i loved the most is semantic search and how it gets things done 2x faster with least possible tokens it makes an agent that actually knows what it’s doing and not just guessing, it makes meaning from your code similar to RAG. if you aren't optimizing your context, you are just burning money the developer says this tool is still under development, it can have unexpected behavior and the docs need updates but the video shows the reality of how fast it can be github: get here:

forloop

226,544 görüntüleme • 8 ay önce

SpiRobs morphologically replicate the logarithmic spiral that is ubiquitous in natural organisms. They are easy and fast to build across scales via 3D printing. They are actuated by cables, which allow for fast and life-like movements. Besides, a single robot can handle a wide variety of objects (in terms of size, shape, and weight). A key to this is a bioinspired grasping strategy that is found in the octopus. Finally, this work demonstrates a wide range of prototypes, including a miniaturized gripper, a manipulator mounted on a drone, and multi-robot arrays that can grasp in a tendril-like fashion. Authors: Zhanchi Wang, Nikolaos M. Freris, and Xi Wei University of Science and Technology of China Video Credit: ———————————————— Stay ahead of the curve! Follow more cutting edge robotics content, including research, reports and articles on Wevolver:
0:33

SpiRobs morphologically replicate the logarithmic spiral that is ubiquitous in natural organisms. They are easy and fast to build across scales via 3D printing. They are actuated by cables, which allow for fast and life-like movements. Besides, a single robot can handle a wide variety of objects (in terms of size, shape, and weight). A key to this is a bioinspired grasping strategy that is found in the octopus. Finally, this work demonstrates a wide range of prototypes, including a miniaturized gripper, a manipulator mounted on a drone, and multi-robot arrays that can grasp in a tendril-like fashion. Authors: Zhanchi Wang, Nikolaos M. Freris, and Xi Wei University of Science and Technology of China Video Credit: ———————————————— Stay ahead of the curve! Follow more cutting edge robotics content, including research, reports and articles on Wevolver:

Wevolver

23,154 görüntüleme • 1 yıl önce

David Friedberg: Frontier Models are Training on Your Novel Insights as “De-Identified Data” @jason: “Should they trust any of these LLMs with their proprietary knowledge for fear of having it cribbed into a core LLM?” david friedberg: “I have had experiences where we've asked some fairly novel scientific questions, and (the AI model) identifies it as a novel insight. It's like, ‘Oh, never thought about that, interesting, blah, blah, blah.’ And then using a different account, asking the next version (of the model) later, I've now experienced this. It's like, ‘Oh, well, you could do this,’ and it actually just describes this exact thing that we had in our chat in the previous version. Now, these are a handful of anecdotal experiences, but I know the domain that we work in, and the niche of it, and the ideation of this stuff, and the novelty of this stuff, and the lack of papers being published, and so on. So I know that there isn't some new corpus of information out there that's training the new model. So all I can say at that point is that my conversation or our analyses have been used for training.” David Sacks: “Okay, this does raise a really good question. What does it mean that the model is allowed to train on unidentifiable data?” Friedberg: “Well, that's my point. So it doesn't use any of my personal information, but it can use an insight derived from our chat, which it can then say is some training data that is unrelated. But the truth is, it's actually a piece of IP that's our organization’s IP, and our engagement back and forth. We don't have any NDA or confidentiality provisions or protections with them being a service provider back to us. This is why I care a lot about open source because I don't want them having my chat logs because they can use it for training to create an IP advantage that is now diffused to the rest of the market.”
1:41

David Friedberg: Frontier Models are Training on Your Novel Insights as “De-Identified Data” @jason: “Should they trust any of these LLMs with their proprietary knowledge for fear of having it cribbed into a core LLM?” david friedberg: “I have had experiences where we've asked some fairly novel scientific questions, and (the AI model) identifies it as a novel insight. It's like, ‘Oh, never thought about that, interesting, blah, blah, blah.’ And then using a different account, asking the next version (of the model) later, I've now experienced this. It's like, ‘Oh, well, you could do this,’ and it actually just describes this exact thing that we had in our chat in the previous version. Now, these are a handful of anecdotal experiences, but I know the domain that we work in, and the niche of it, and the ideation of this stuff, and the novelty of this stuff, and the lack of papers being published, and so on. So I know that there isn't some new corpus of information out there that's training the new model. So all I can say at that point is that my conversation or our analyses have been used for training.” David Sacks: “Okay, this does raise a really good question. What does it mean that the model is allowed to train on unidentifiable data?” Friedberg: “Well, that's my point. So it doesn't use any of my personal information, but it can use an insight derived from our chat, which it can then say is some training data that is unrelated. But the truth is, it's actually a piece of IP that's our organization’s IP, and our engagement back and forth. We don't have any NDA or confidentiality provisions or protections with them being a service provider back to us. This is why I care a lot about open source because I don't want them having my chat logs because they can use it for training to create an IP advantage that is now diffused to the rest of the market.”

The All-In Podcast

56,638 görüntüleme • 1 ay önce

To Adapt or Not to Adapt? Real-Time Adaptation for Semantic Segmentation paper page: The goal of Online Domain Adaptation for semantic segmentation is to handle unforeseeable domain changes that occur during deployment, like sudden weather events. However, the high computational costs associated with brute-force adaptation make this paradigm unfeasible for real-world applications. In this paper we propose HAMLET, a Hardware-Aware Modular Least Expensive Training framework for real-time domain adaptation. Our approach includes a hardware-aware back-propagation orchestration agent (HAMT) and a dedicated domain-shift detector that enables active control over when and how the model is adapted (LT). Thanks to these advancements, our approach is capable of performing semantic segmentation while simultaneously adapting at more than 29FPS on a single consumer-grade GPU. Our framework's encouraging accuracy and speed trade-off is demonstrated on OnDA and SHIFT benchmarks through experimental results.
4:47

To Adapt or Not to Adapt? Real-Time Adaptation for Semantic Segmentation paper page: The goal of Online Domain Adaptation for semantic segmentation is to handle unforeseeable domain changes that occur during deployment, like sudden weather events. However, the high computational costs associated with brute-force adaptation make this paradigm unfeasible for real-world applications. In this paper we propose HAMLET, a Hardware-Aware Modular Least Expensive Training framework for real-time domain adaptation. Our approach includes a hardware-aware back-propagation orchestration agent (HAMT) and a dedicated domain-shift detector that enables active control over when and how the model is adapted (LT). Thanks to these advancements, our approach is capable of performing semantic segmentation while simultaneously adapting at more than 29FPS on a single consumer-grade GPU. Our framework's encouraging accuracy and speed trade-off is demonstrated on OnDA and SHIFT benchmarks through experimental results.

AK

18,871 görüntüleme • 3 yıl önce

Today morning my supplementary question no 1 on Young India Skills University. I'm proud to say that under the able and dynamic leadership of our Hon'ble Chief Minister Revanth Reddy, he is ambitiously setting up the Young India Skills University in Hyderabad, with the objective of providing job opportunities to the students and imparting skill development training according to the requirements of the local industries that are in demand in collaboration with various companies in the PPP model. A total of 17 priority sectors have been identified, and we will be providing training to 2 thousand people in the first year of 2025-26. We will train 20,000 people every year, and the state has allocated Rs. 100 crore on its part for the same and further wants to create a corpus fund of about Rs. 300-500 crores. Now my question to the ministry is if there is any proposal from the central government to adequately fund this Skills University. This will make it a grand success and will lead to the realisation of the goal of a developed nation by 2047. #YoungIndiaSkillUniversity #Telangana #TelanganaRising
1:12

Today morning my supplementary question no 1 on Young India Skills University. I'm proud to say that under the able and dynamic leadership of our Hon'ble Chief Minister Revanth Reddy, he is ambitiously setting up the Young India Skills University in Hyderabad, with the objective of providing job opportunities to the students and imparting skill development training according to the requirements of the local industries that are in demand in collaboration with various companies in the PPP model. A total of 17 priority sectors have been identified, and we will be providing training to 2 thousand people in the first year of 2025-26. We will train 20,000 people every year, and the state has allocated Rs. 100 crore on its part for the same and further wants to create a corpus fund of about Rs. 300-500 crores. Now my question to the ministry is if there is any proposal from the central government to adequately fund this Skills University. This will make it a grand success and will lead to the realisation of the goal of a developed nation by 2047. #YoungIndiaSkillUniversity #Telangana #TelanganaRising

Kiran Kumar Chamala

71,229 görüntüleme • 1 yıl önce

Progressively Optimized Local Radiance Fields for Robust View Synthesis paper page: present an algorithm for reconstructing the radiance field of a large-scale scene from a single casually captured video. The task poses two core challenges. First, most existing radiance field reconstruction approaches rely on accurate pre-estimated camera poses from Structure-from-Motion algorithms, which frequently fail on in-the-wild videos. Second, using a single, global radiance field with finite representational capacity does not scale to longer trajectories in an unbounded scene. For handling unknown poses, we jointly estimate the camera poses with radiance field in a progressive manner. We show that progressive optimization significantly improves the robustness of the reconstruction. For handling large unbounded scenes, we dynamically allocate new local radiance fields trained with frames within a temporal window. This further improves robustness (e.g., performs well even under moderate pose drifts) and allows us to scale to large scenes. Our extensive evaluation on the Tanks and Temples dataset and our collected outdoor dataset, Static Hikes, show that our approach compares favorably with the state-of-the-art.
4:28

Progressively Optimized Local Radiance Fields for Robust View Synthesis paper page: present an algorithm for reconstructing the radiance field of a large-scale scene from a single casually captured video. The task poses two core challenges. First, most existing radiance field reconstruction approaches rely on accurate pre-estimated camera poses from Structure-from-Motion algorithms, which frequently fail on in-the-wild videos. Second, using a single, global radiance field with finite representational capacity does not scale to longer trajectories in an unbounded scene. For handling unknown poses, we jointly estimate the camera poses with radiance field in a progressive manner. We show that progressive optimization significantly improves the robustness of the reconstruction. For handling large unbounded scenes, we dynamically allocate new local radiance fields trained with frames within a temporal window. This further improves robustness (e.g., performs well even under moderate pose drifts) and allows us to scale to large scenes. Our extensive evaluation on the Tanks and Temples dataset and our collected outdoor dataset, Static Hikes, show that our approach compares favorably with the state-of-the-art.

AK

140,624 görüntüleme • 3 yıl önce

Ian Lee offers a wake-up call on Canada-US trade leverage: “Why don’t we Canadians tariff products that will hurt them, like potash, oil and gas?” “These are the natural resources. Natural resources are commodities. That means there’s a single price… Most of those natural resource commodities are a world price and you can source them… often from other countries around the world.” On size: “The totality of our exports, 400 billion going to the States is 1.5% of US GDP. And I don’t think anyone’s going to argue you can bring down any company or country because they lose 1.5%.” “Even if we eradicated all exports to the United States, it would affect 1.5% of US GDP.” “They’re so large at 32 trillion and we are relatively so small that we don’t have the leverage or the clout or the muscle… to inflict serious pain on the United States.” “This is arithmetic, this isn’t theory. The numbers do not support the idea that a 2 trillion economy can devastate a 32 trillion economy that can source from the rest of the world.” Commodities have world prices. Canada’s entire export book to the US is a rounding error on a $32T economy. That’s the constraint Sprott School of Business at Carleton University
1:34

Ian Lee offers a wake-up call on Canada-US trade leverage: “Why don’t we Canadians tariff products that will hurt them, like potash, oil and gas?” “These are the natural resources. Natural resources are commodities. That means there’s a single price… Most of those natural resource commodities are a world price and you can source them… often from other countries around the world.” On size: “The totality of our exports, 400 billion going to the States is 1.5% of US GDP. And I don’t think anyone’s going to argue you can bring down any company or country because they lose 1.5%.” “Even if we eradicated all exports to the United States, it would affect 1.5% of US GDP.” “They’re so large at 32 trillion and we are relatively so small that we don’t have the leverage or the clout or the muscle… to inflict serious pain on the United States.” “This is arithmetic, this isn’t theory. The numbers do not support the idea that a 2 trillion economy can devastate a 32 trillion economy that can source from the rest of the world.” Commodities have world prices. Canada’s entire export book to the US is a rounding error on a $32T economy. That’s the constraint Sprott School of Business at Carleton University

cbcwatcher

96,099 görüntüleme • 1 ay önce

Introducing RAGs v3 🌟: Build a RAG bot that can also search the web 🌐, to find answers that aren’t immediately in its corpus. Do this all in natural language, not code! 💬 Get a better experience than ChatGPT + Bing ⚡️ We used our integration with Metaphor - a search engine designed for LLMs - to pull in relevant text from the internet. Additionally: - you can now view the tools that the agent is using. - note: web search is only accessible for our OpenAI agent Check out the video below, and get started with the repo today. Repo:
1:17

Introducing RAGs v3 🌟: Build a RAG bot that can also search the web 🌐, to find answers that aren’t immediately in its corpus. Do this all in natural language, not code! 💬 Get a better experience than ChatGPT + Bing ⚡️ We used our integration with Metaphor - a search engine designed for LLMs - to pull in relevant text from the internet. Additionally: - you can now view the tools that the agent is using. - note: web search is only accessible for our OpenAI agent Check out the video below, and get started with the repo today. Repo:

LlamaIndex 🦙

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