Large Language Models News & Discussions

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Microsoft develops its own networking gear for AI datacenters: Report

Juniper Networks and Fungible founder spearheads development.

www.tomshardware.com

Microsoft develops its own networking gear for AI datacenters: Report​

News

By Anton Shilov

published 1 day ago

Juniper Networks and Fungible founder spearheads development.

(Image credit: Shutterstock)

After revealing its own 128-core datacenter CPU and Maia 100 GPU for artificial intelligence workloads, Microsoft has begun development of its own networking card in a bid to decrease its reliance on Nvidia's hardware and speed up its datacenters, reports The Information. If the company succeeds, it could then proceed to optimize its Azure infrastructure and diversify its technology stack. Interestingly, the company has indirectly confirmed the effort.

Microsoft acquired Fungible, a developer of data processing units (DPUs) that competed against AMD's Pensando and Nvidia's Mellanox divisions, about a year ago. That means the company clearly has the networking technologies and IP that it needs to design datacenter-grade networking gear suitable for bandwidth-hungry AI training workloads. Pradeep Sindhu, a co-founder of Juniper Networks and founder of Fungible who has a wealth of experience in networking gear, now works at Microsoft and is heading the development of the company's datacenter networking processors.

The new networking card is expected to improve of the performance and efficiency of Microsoft's Azure servers, which currently run Intel CPUs and Nvidia GPUs, but will eventually also adopt Microsoft's own CPUs and GPUs. The Information claims that the project is important for Microsoft, which is why Satya Nadella, the head of the company, appointed Sindhu to the project himself.

"As part of our systems approach to Azure infrastructure, we are focused on optimizing every layer of our stack," a Microsoft spokesperson told The Information. "We routinely develop new technologies to meet the needs of our customers, including networking chips."

High-performance networking gear is crucial for datacenters, especially when handling the massive amount of data required for AI training by clients like OpenAI. By alleviating network traffic jams, the new server component could accelerate the development of AI models, making the process faster and more cost-effective.

Microsoft's move is in line with the industry trend toward custom silicon, as other cloud providers including Amazon Web Services (AWS) and Google are also developing their own AI and general-purpose processors and datacenter networking gear.

The introduction of Microsoft's networking card could potentially impact Nvidia's sales of server networking gear, which is projected to generate over $10 billion per year. If successful, the card could significantly improve the efficiency of Azure datacenters in general and OpenAI's model training in particular, as well as reduce the time and costs associated with AI development, the report claims.

Custom silicon can take a significant amount of time to design and manufacture, which means the initial results of this endeavor could still be years away. In the short term, Microsoft will continue to rely on hardware from other vendors, but that may change in the coming years.

 

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https://archive.is/DYnYW

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Magic AI Secures $117 Million to Build an AI Software Engineer

The startup is carving out a niche by focusing on developing an AI software engineer capable of assisting with complex coding tasks and that will act more as a coworker than merely a "copilot" tool.

www.maginative.com

Magic AI Secures $117 Million to Build an AI Software Engineer​

The startup is carving out a niche by focusing on developing an AI software engineer capable of assisting with complex coding tasks and that will act more as a coworker than merely a "copilot" tool.

• 
  

[URL='https://www.maginative.com/author/chris/']CHRIS MCKAY​

​

FEBRUARY 16, 2024 • 2 MIN READ

San Francisco-based startup Magic AI has raised $117 million in Series B funding to further develop its advanced AI system aimed at automating software development.

The round was led by Nat Friedman and Daniel Gross’s NFDG Ventures, with additional participation from CapitalG and Elad Gil. This brings Magic’s total funding to date to over $145 million.

Founded in 2022 by Eric Steinberger and Sebastian De Ro, the startup is carving out a niche by focusing on developing an AI software engineer capable of assisting with complex coding tasks and that will act more as a coworker than merely a "copilot" tool.

The founders believe that in addition to boosting practical coding productivity, advancing intelligent code generation tools can also provide a path toward more expansive artificial general intelligence. Their vision even extends to the creation of broadly capable AGI systems that align with human values - ones able to accelerate global progress by assisting with humanity's most complex challenges. Their $23 million Series A round last summer was a major step towards this ambitious mission.

Central to Magic's technical strategy is handling exceptionally large context windows. Last year, they unveiled their Long-term Memory Network (LTM Net) architecture and corresponding LTM-1 model with a 5 million context window.

For perspective, most language models operate on far more limited contexts, commonly less than 32k tokens. OpenAI's powerful GPT-4 Turbo model is 128k tokens and Anthropic's Claude 2.1 is 200k.

However, models with much larger context windows are on the horizon. Just yesterday, Google announced that their new Gemini 1.5 model will have a 1 million context window, and shared that they have tested up to 10 million context lengths in research.

The substantially larger context capacities allow for more nuanced code comprehension - enabling Magic's model to reason over entire repositories and dependency trees to boost usefulness.

The startup continues to operate in stealth mode with few public demos, but claims to already have thousands of GPUs deployed toward training its next generation models.

Now, with fresh funding in hand, talent recruitment and retention is clearly top of mind for Magic AI’s leadership. The startup is actively seeking talented individuals who share its vision of integrity and innovation and is placing a strong emphasis on cultivating a supportive culture rooted in passion.

With towering investor confidence and transformative ambitions, Magic AI remains one of the most intriguing AI startups amid a landscape filled with heated competition. Still, Magic believes its technical approach focused on extreme model scale and novel neural architectures sets it apart.

 

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Spoiler: larger images

Active Reasoning in an Open-World Environment

abs: [2311.02018] Active Reasoning in an Open-World Environment
pdf: https://arxiv.org/pdf/2311.02018.pdf
site: Conan

et, most models operate passively, responding to questions based on pre-stored knowledge. In stark contrast, humans possess the ability to actively explore, accumulate, and reason using both newfound and existing information to tackle incomplete-information questions. In response to this gap, we introduce �

- Conan: an interactive open-world environment devised for the assessment of active reasoning
- facilitates active exploration and promotes multi-round abductive inference, reminiscent of rich, open-world settings like Minecraft
- compels agents to actively interact with their surroundings, amalgamating new evidence with prior knowledge to elucidate events from incomplete observations
- underscores the shortcomings of contemporary state-of-the-art models in active exploration and understanding complex scenarios

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[2311.02018] Active Reasoning in an Open-World Environment

Computer Science > Artificial Intelligence​

[Submitted on 3 Nov 2023]

Active Reasoning in an Open-World Environment​

Manjie Xu, Guangyuan Jiang, Wei Liang, Chi Zhang, Yixin Zhu

Recent advances in vision-language learning have achieved notable success on complete-information question-answering datasets through the integration of extensive world knowledge. Yet, most models operate passively, responding to questions based on pre-stored knowledge. In stark contrast, humans possess the ability to actively explore, accumulate, and reason using both newfound and existing information to tackle incomplete-information questions. In response to this gap, we introduce Conan, an interactive open-world environment devised for the assessment of active reasoning. Conan facilitates active exploration and promotes multi-round abductive inference, reminiscent of rich, open-world settings like Minecraft. Diverging from previous works that lean primarily on single-round deduction via instruction following, Conan compels agents to actively interact with their surroundings, amalgamating new evidence with prior knowledge to elucidate events from incomplete observations. Our analysis on Conan underscores the shortcomings of contemporary state-of-the-art models in active exploration and understanding complex scenarios. Additionally, we explore Abduction from Deduction, where agents harness Bayesian rules to recast the challenge of abduction as a deductive process. Through Conan, we aim to galvanize advancements in active reasoning and set the stage for the next generation of artificial intelligence agents adept at dynamically engaging in environments.

Comments:	Accepted to NeurIPS 2023
Subjects:	Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
Cite as:	arXiv:2311.02018 [cs.AI]
	(or arXiv:2311.02018v1 [cs.AI] for this version)
	[2311.02018] Active Reasoning in an Open-World Environment Focus to learn more

Submission history​

From: Manjie Xu [view email]
[v1] Fri, 3 Nov 2023 16:24:34 UTC (14,505 KB)

https://arxiv.org/pdf/2311.02018.pdf

 

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GitHub - booydar/babilong: BABILong is a benchmark for LLM evaluation using the needle-in-a-haystack approach.

BABILong is a benchmark for LLM evaluation using the needle-in-a-haystack approach. - booydar/babilong

github.com

BABILong: a long-context needle-in-a-haystack benchmark for LLMs​

Preprint is on arXiv: "In Search of Needles in a 10M Haystack: Recurrent Memory Finds What LLMs Miss"

bAbI + Books = BABILong​

BABILong is a novel generative benchmark for evaluating the performance of NLP models in processing arbitrarily long documents with distributed facts.

Solving tasks with a long context size requires the model to distinguish important information from large amounts of irrelevant details. To simulate this behavior we ”hide” the sentences of the original task between the sentences of irrelevant text. We use the bAbI dataset [1] as facts and PG19 as background text. Resulting test samples might have lenghts of millions of tokens.



BABILong consists of 20 tasks designed for evaluation of basic aspects of reasoning. The bAbI tasks are generated by simulating a set of characters and objects engaged in various movements and interactions with each other in multiple locations. Each interaction is represented by a fact, e.g. ”Mary travelled to the office”, and the task is to answer a question using the facts from the current simulation, for instance, ”Where is Mary?”. The bAbI tasks vary based on the number of facts, question complexity and the aspects of reasoning.

First ten tasks of BABILong​

Task	Name	min facts per task	max facts per task
qa1	single supporting fact	2	10
qa2	two supporting facts	2	68
qa3	three supporting facts	4	320
qa4	two arg relations	2	2
qa5	three arg relations	2	126
qa6	yes-no questions	2	26
qa7	counting	2	52
qa8	lists-sets	2	50
qa9	simple negation	2	10
qa10	indefinite knowledge	2	10

Play with dataset​

BABILong notebook

Preliminary evaluation results​

GPT-4 fails to solve needle in a haystack tasks for 75% of available context window​

Every row shows accuracy in % of solving corresponding BABILong task ('qa1'-'qa5') and every column corresponds to the task size submitted to GPT4-Turbo with 128K context window. All values are averages of 25 samples.

Mistral performance scales only for some tasks but but quickly degenerates for majority of others as context grow​

Every row shows accuracy in % of solving corresponding BABILong task ('qa1'-'qa10') and every column corresponds to the task size submitted to Mistarl-Medium with 32K context window. All values are averages of 25 samples.

Fine-tuning of GPT-3.5 improves search of supporting facts in medium context size​

Every row shows accuracy in % for GPT-3.5 before and after fine-tuning via API with 100 samples on 'qa1' task. Every column corresponds to the task size. All values are averages of 25 samples.

Retrieval augmentation does not help to solve needle in a haystack QA task​

A Retrieval does the job if embeddings match fact size. The figure shows recall@5 scores of a retrieval RAG component on 'qa1' task for the given size for sentences (sent) and text pieces of 512 tokens (tok).

B Accuracy in % by GPT4 based RAG. All values are averages of 50 samples.

 

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https://archive.is/x4K8N

Career update: I am co-founding a new research group called "GEAR" at NVIDIA, with my long-time friend and collaborator Prof.
@yukez
. GEAR stands for Generalist Embodied Agent Research.

We believe in a future where every machine that moves will be autonomous, and robots and simulated agents will be as ubiquitous as iPhones. We are building the Foundation Agent — a generally capable AI that learns to act skillfully in many worlds, virtual and real.

2024 is the Year of Robotics, the Year of Gaming AI, and the Year of Simulation. We are setting out on a moon-landing mission, and getting there will spin off mountains of learnings and breakthroughs.

Join us on the journey:

GEAR

GEAR: Generalist Embodied Agent Research

research.nvidia.com

 

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PALO

A Polyglot Large Multimodal Model for 5B People

In pursuit of more inclusive Vision-Language Models (VLMs), this study introduces a Large Multilingual Multimodal Model called Palo. Palo offers visual reasoning capabilities in 10 major languages, including English, Chinese, Hindi, Spanish, French, Arabic, Bengali, Russian, Urdu, and Japanese, that span a total of sim5B people (65\% of the world population). Our approach involves a semi-automated translation approach to adapt the multimodal instruction dataset from English to the target languages using a fine-tuned Large Language Model, thereby ensuring high linguistic fidelity while allowing scalability due to minimal manual effort. The incorporation of diverse instruction sets helps us boost overall performance across multiple languages especially those that are underrepresented like Hindi, Arabic, Bengali, and Urdu. The resulting models are trained across three scales (1.7B, 7B and 13B parameters) to show the generalization and scalability where we observe substantial improvements compared to strong baselines. We also propose the first multilingual multimodal benchmark for the forthcoming approaches to evaluate their vision-language reasoning capabilities across languages

Paper page - PALO: A Polyglot Large Multimodal Model for 5B People

Join the discussion on this paper page

huggingface.co

 

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Here is my selection of papers for today (23 Feb) on Hugging Face

GeneOH Diffusion: Towards Generalizable Hand-Object Interaction Denoising via Denoising Diffusion

CyberDemo: Augmenting Simulated Human

Demonstration for Real-World Dexterous Manipulation

BeTAIL: Behavior Transformer Adversarial Imitation Learning from Human Racing Gameplay

Linear Transformers are Versatile In-Context Learners

Beyond A*: Better Planning with Transformers via Search Dynamics Bootstrapping

GaussianPro: 3D Gaussian Splatting with Progressive Propagation

Consolidating Attention Features for Multi-view Image Editing

PALO: A Polyglot Large Multimodal Model for 5B People

Scaling Up LLM Reviews for Google Ads Content Moderation

OmniPred: Language Models as Universal Regressors

Subobject-level Image Tokenization

TinyLLaVA: A Framework of Small-scale Large Multimodal Models

Copilot Evaluation Harness: Evaluating LLM-Guided Software Programming

AgentScope: A Flexible yet Robust Multi-Agent Platform

OpenCodeInterpreter: Integrating Code Generation with Execution and Refinement

MVD^2: Efficient Multiview 3D Reconstruction for Multiview Diffusion

T-Stitch: Accelerating Sampling in Pre-Trained Diffusion Models with Trajectory Stitching

LexC-Gen: Generating Data for Extremely Low-Resource Languages with Large Language Models and Bilingual Lexicons

Snap Video: Scaled Spatiotemporal Transformers for Text-to-Video Synthesis

Daily Papers - Hugging Face

Your daily dose of AI research from AK

huggingface.co

 

[bnew]

https://archive.is/UfBYC

Magic-Me

magic-me-webpage.github.io

GitHub - Zhen-Dong/Magic-Me: Codes for ID-Specific Video Customized Diffusion

Codes for ID-Specific Video Customized Diffusion. Contribute to Zhen-Dong/Magic-Me development by creating an account on GitHub.

github.com

Project Page: Magic-Me​

Unlike common text-to-video model (like OpenAI/Sora), this model is for personalized videos using photos of your friends, family, or pets. By training an embedding with these images, it creates custom videos featuring your loved ones, bringing a unique touch to your memories.

News Update: We have deployed our model on Hugging Face's GPU platform, making it available for immediate use. Check it out on .

Magic-Me: Identity-Specific Video Customized Diffusion​

Ze Ma*, Daquan Zhou* †, Chun-Hsiao Yeh, Xue-She Wang, Xiuyu Li, Huanrui Yang, Zhen Dong †, Kurt Keutzer, Jiashi Feng (*Joint First Author, † Corresponding Author)

We propose a new framework for video generation with customized identity. With a pre-trained ID token, the user would be able to generate any video clips with the specified identity. We propose a series of controllable Video generation and editing methods. The first release includes Customized Diffusion (VCD). It includes three novel components that are essential for high-quality ID preservation: 1) an ID module trained with the cropped identity by prompt-to-segmentation to disentangle the ID information and the background noise for more accurate ID token learning; 2) a text-to-video (T2V) VCD module with 3D Gaussian Noise Prior for better inter-frame consistency and 3) video-to-video (V2V) Face VCD and Tiled VCD modules to deblur the face and upscale the video for higher resolution.

Video Customization Diffusion Model Pipeline

 

[bnew]

https://archive.is/QM8o5

Snap Video

Scaled Spatiotemporal Transformers for Text-to-Video Synthesis

Contemporary models for generating images show remarkable quality and versatility. Swayed by these advantages, the research community repurposes them to generate videos. Since video content is highly redundant, we argue that naively bringing advances of image models to the video generation domain reduces motion fidelity, visual quality and impairs scalability. In this work, we build Snap Video, a video-first model that systematically addresses these challenges. To do that, we first extend the EDM framework to take into account spatially and temporally redundant pixels and naturally support video generation. Second, we show that a U-Net - a workhorse behind image generation - scales poorly when generating videos, requiring significant computational overhead. Hence, we propose a new transformer-based architecture that trains 3.31 times faster than U-Nets (and is ~4.5 faster at inference). This allows us to efficiently train a text-to-video model with billions of parameters for the first time, reach state-of-the-art results on a number of benchmarks, and generate videos with substantially higher quality, temporal consistency, and motion complexity. The user studies showed that our model was favored by a large margin over the most recent methods.

Snap Video

snap-research.github.io

[2402.14797] Snap Video: Scaled Spatiotemporal Transformers for Text-to-Video Synthesis

Paper page - Snap Video: Scaled Spatiotemporal Transformers for Text-to-Video Synthesis

Join the discussion on this paper page

huggingface.co

 

[bnew]

https://archive.is/mdsxw

CyberDemo

Augmenting Simulated Human Demonstration for Real-World Dexterous Manipulation

We introduce CyberDemo, a novel approach to robotic imitation learning that leverages simulated human demonstrations for real-world tasks. By incorporating extensive data augmentation in a simulated environment, CyberDemo outperforms traditional in-domain real-world demonstrations when transferred to the real world, handling diverse physical and visual conditions. Regardless of its affordability and convenience in data collection, CyberDemo outperforms baseline methods in terms of success rates across various tasks and exhibits generalizability with previously unseen objects. For example, it can rotate novel tetra-valve and penta-valve, despite human demonstrations only involving tri-valves. Our research demonstrates the significant potential of simulated human demonstrations for real-world dexterous manipulation tasks.

Paper page - CyberDemo: Augmenting Simulated Human Demonstration for Real-World Dexterous Manipulation

Join the discussion on this paper page

huggingface.co

 

[bnew]

https://archive.is/ztkCb

GeneOH Diffusion

Towards Generalizable Hand-Object Interaction Denoising via Denoising Diffusion

tackle the challenging problem of denoising hand-object interactions (HOI). Given an erroneous interaction sequence, the objective is to refine the incorrect hand trajectory to remove interaction artifacts for a perceptually realistic sequence. This challenge involves intricate interaction noise, including unnatural hand poses and incorrect hand-object relations, alongside the necessity for robust generalization to new interactions and diverse noise patterns. We tackle those challenges through a novel approach, GeneOH Diffusion, incorporating two key designs: an innovative contact-centric HOI representation named GeneOH and a new domain-generalizable denoising scheme. The contact-centric representation GeneOH informatively parameterizes the HOI process, facilitating enhanced generalization across various HOI scenarios. The new denoising scheme consists of a canonical denoising model trained to project noisy data samples from a whitened noise space to a clean data manifold and a "denoising via diffusion" strategy which can handle input trajectories with various noise patterns by first diffusing them to align with the whitened noise space and cleaning via the canonical denoiser. Extensive experiments on four benchmarks with significant domain variations demonstrate the superior effectiveness of our method. GeneOH Diffusion also shows promise for various downstream applications.

 

[bnew]

https://archive.is/8VOZL

BeTAIL

Behavior Transformer Adversarial Imitation Learning from Human Racing Gameplay

Imitation learning learns a policy from demonstrations without requiring hand-designed reward functions. In many robotic tasks, such as autonomous racing, imitated policies must model complex environment dynamics and human decision-making. Sequence modeling is highly effective in capturing intricate patterns of motion sequences but struggles to adapt to new environments or distribution shifts that are common in real-world robotics tasks. In contrast, Adversarial Imitation Learning (AIL) can mitigate this effect, but struggles with sample inefficiency and handling complex motion patterns. Thus, we propose BeTAIL: Behavior Transformer Adversarial Imitation Learning, which combines a Behavior Transformer (BeT) policy from human demonstrations with online AIL. BeTAIL adds an AIL residual policy to the BeT policy to model the sequential decision-making process of human experts and correct for out-of-distribution states or shifts in environment dynamics. We test BeTAIL on three challenges with expert-level demonstrations of real human gameplay in Gran Turismo Sport. Our proposed residual BeTAIL reduces environment interactions and improves racing performance and stability, even when the BeT is pretrained on different tracks than downstream learning.

Paper page - BeTAIL: Behavior Transformer Adversarial Imitation Learning from Human Racing Gameplay

Join the discussion on this paper page

huggingface.co

 

[bnew]

https://archive.is/lAeVI

GaussianPro

3D Gaussian Splatting with Progressive Propagation

The advent of 3D Gaussian Splatting (3DGS) has recently brought about a revolution in the field of neural rendering, facilitating high-quality renderings at real-time speed. However, 3DGS heavily depends on the initialized point cloud produced by Structure-from-Motion (SfM) techniques. When tackling with large-scale scenes that unavoidably contain texture-less surfaces, the SfM techniques always fail to produce enough points in these surfaces and cannot provide good initialization for 3DGS. As a result, 3DGS suffers from difficult optimization and low-quality renderings. In this paper, inspired by classical multi-view stereo (MVS) techniques, we propose GaussianPro, a novel method that applies a progressive propagation strategy to guide the densification of the 3D Gaussians. Compared to the simple split and clone strategies used in 3DGS, our method leverages the priors of the existing reconstructed geometries of the scene and patch matching techniques to produce new Gaussians with accurate positions and orientations. Experiments on both large-scale and small-scale scenes validate the effectiveness of our method, where our method significantly surpasses 3DGS on the Waymo dataset, exhibiting an improvement of 1.15dB in terms of PSNR.

Paper page - GaussianPro: 3D Gaussian Splatting with Progressive Propagation

Join the discussion on this paper page

huggingface.co

 

[bnew]

https://archive.is/xme7H

Introducing ToDo : Token Downsampling for Efficient Generation of High-Resolution Images ! With
@Ethan_smith_20
&
@aningineer
, we present a training free method that accelerates diffusion inference upto 4.5x while maintaining image fidelity.

Paper page - ToDo: Token Downsampling for Efficient Generation of High-Resolution Images

Join the discussion on this paper page

huggingface.co

ToDo: Token Downsampling for Efficient Generation of High-Resolution Images​

Published on Feb 21
·Featured in Daily Papers on Feb 22
Authors:
Ethan Smith,
Nayan Saxena,
Aninda Saha

Abstract​

Attention mechanism has been crucial for image diffusion models, however, their quadratic computational complexity limits the sizes of images we can process within reasonable time and memory constraints. This paper investigates the importance of dense attention in generative image models, which often contain redundant features, making them suitable for sparser attention mechanisms. We propose a novel training-free method ToDo that relies on token downsampling of key and value tokens to accelerate Stable Diffusion inference by up to 2x for common sizes and up to 4.5x or more for high resolutions like 2048x2048. We demonstrate that our approach outperforms previous methods in balancing efficient throughput and fidelity.